TOP or Treatment Outcomes Profile (“Perfil de Resultados de Tratamiento”) serves as a tool for monitoring and follow-up of SUD treatments. It is a questionnaire that every user must complete at the beginning of treatment, every three months during treatment, at treatment discharge, and some centers may apply it after discharge. Hence, it is crucial to distinguish between the date of application and the date of admission. Additionally, to characterize each user and their profile in each phase of life, it is necessary to standardize his birth date.

 

1. Relationship of TOP Dataset with C1

#create the first changes into TOP dataset
CONS_TOP %>%
  dplyr::mutate(ano_bd=as.numeric(substr(TABLE,4,7))) %>%
  dplyr::mutate(id_mod=sub("(.{5}).", "\\1*",as.character(ID))) %>%
  dplyr::mutate(id_mod=sub("(.{6}).", "\\1*",as.character(id_mod))) %>% 
  dplyr::select(HASH_KEY, hash_rut_completo, id_mod, ID, ano_bd, everything()) %>%
  dplyr::arrange(desc(ano_bd)) %>% 
  assign("CONS_TOP_df",.,envir = .GlobalEnv)
as.data.frame(cbind("Vars. of TOP"=c(names(CONS_TOP_df), rep("",65)),"Vars. of C1"=names(CONS_C1_df_dup_ENE_2020)))  %>%
    knitr::kable(.,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
                caption="Table 1. Comparison between variables of TOP and C1",
              col.names = c("Vars.\nof TOP", "Vars.\nof C1"),
                 align =rep('c', 6))  %>%
  kable_styling(bootstrap_options = c("striped", "hover"),font_size = 8) %>%
            scroll_box(width = "100%", height = "350px")
Warning in cbind(`Vars. of TOP` = c(names(CONS_TOP_df), rep("", 65)), `Vars.
of C1` = names(CONS_C1_df_dup_ENE_2020)): number of rows of result is not a
multiple of vector length (arg 1)
Table 1. Comparison between variables of TOP and C1
Vars. of TOP Vars. of C1
HASH_KEY row
hash_rut_completo table
id_mod hash_key
ID ano_bd
ano_bd id
row nombre_centro
TABLE tipo_centro
Fecha.Aplicación.TOP region_del_centro
Nombre.Apliacador.del.TOP servicio_de_salud
TOP tipo_de_programa
Etapa.del.Tratamiento tipo_de_plan
Fecha.Nacimiento senda
Edad dias_trat
Sexo nmesesentratamiento
Fecha.de.Ingreso.a.Tratamiento dias_en_senda
Plan.de.Tratamiento n_meses_en_senda
Nombre.del.Centro sexo
Tipo.Centro edad
Sustancia.Principal.1 nombre_usuario
Sustancia.Principal.2 comuna_residencia
Sustancia.Principal.3 origen_de_ingreso
Total.OH pais_nacimiento
Dósis.OH nacionalidad
Total.THC etnia
Dósis.THC estado_conyugal
Total.PBC numero_de_hijos
Dósis.PBC numero_de_hijos_ingreso_tratamiento_residencial
Total.COC parentesco_con_el_jefe_de_hogar
Dósis.COC numero_de_tratamientos_anteriores
Total.BZD fecha_ultimo_tratamiento
Dósis.BZD sustancia_de_inicio
Total.Otra edad_inicio_consumo
Dósis.Otra x_se_trata_de_una_mujer_embarazada
Hurto escolaridad_ultimo_ano_cursado
Robo condicion_ocupacional
Venta.Drogas categoria_ocupacional
Riña rubro_trabaja
Total.VIF con_quien_vive
Otro tipo_de_vivienda
Total.Transgresión tenencia_de_la_vivienda
Salud.Psicológica sustancia_principal
Total.Trabajo otras_sustancias_nº1
Total.Educación otras_sustancias_nº2
Salud.Física otras_sustancias_nº3
Lugar.Vivir frecuencia_de_consumo_sustancia_principal
Vivienda edad_inicio_sustancia_principal
Calidad.Vida via_administracion_sustancia_principal
Región.Centro diagnostico_trs_consumo_sustancia
Comentario diagnostico_trs_psiquiatrico_dsm_iv
diagnostico_trs_psiquiatrico_sub_dsm_iv
x2_diagnostico_trs_psiquiatrico_dsm_iv
x2_diagnostico_trs_psiquiatrico_sub_dsm_iv
x3_diagnostico_trs_psiquiatrico_dsm_iv
x3_diagnostico_trs_psiquiatrico_sub_dsm_iv
diagnostico_trs_psiquiatrico_cie_10
diagnostico_trs_psiquiatrico_sub_cie_10
x2_diagnostico_trs_psiquiatrico_cie_10
x2_diagnostico_trs_psiquiatrico_sub_cie_10
x3_diagnostico_trs_psiquiatrico_cie_10
x3_diagnostico_trs_psiquiatrico_sub_cie_10
diagnostico_trs_fisico
otros_problemas_de_atencion_de_salud_mental
compromiso_biopsicosocial
diagnostico_global_de_necesidades_de_integracion_social
diagnostico_de_necesidades_de_integrac_io_n_social_en_capital_humano
diagnostico_de_necesidades_de_integrac_io_n_social_en_capital_fisico
diagnostico_de_necesidades_de_integrac_io_n_social_en_capital_social
fech_ing
fecha_ingreso_a_convenio_senda
usuario_de_tribunales_tratamiento_drogas
consentimiento_informado
fech_egres
motivodeegreso
tipo_centro_derivacion
evaluacindelprocesoteraputico
eva_consumo
eva_fam
eva_relinterp
eva_ocupacion
eva_sm
eva_fisica
eva_transgnorma
diagnostico_trastorno_psiquiatrico_cie_10_al_egreso
diagnostico_global_de_necesidades_de_integracion_social_1
diagnostico_de_necesidades_de_integrac_io_n_social_en_capital_humano_1
diagnostico_de_necesidades_de_integrac_io_n_social_en_capital_fisico_1
diagnostico_de_necesidades_de_integrac_io_n_social_en_capital_social_1
tiene_menores_de_edad_a_cargo
motivo_de_egreso_alta_administrativa
consorcio
id_centro
ha_estado_embarazada_egreso
identidad_de_genero
discapacidad
hash_rut_completo
opcion_discapacidad
sexo_2
embarazo
tipo_de_plan_2
tipo_de_programa_2
fech_egres_sin_fmt
id_mod
ano_nac
fech_ing_ano
fech_ing_mes
fech_ing_dia
concat
dup_todo
obs
dias_trat_inv
fech_nac
edad_al_ing
edad_al_ing_n_as
edad_al_ing_less15
HASH_KEY edad_ini_cons
hash_rut_completo edad_ini_sus_prin
id_mod dias_trat_alta_temprana
ID motivodeegreso_mod
ano_bd sus_principal
row otras_sus1
TABLE otras_sus2
Fecha.Aplicación.TOP otras_sus3
Nombre.Apliacador.del.TOP sus_ini
TOP estado_conyugal_2
Etapa.del.Tratamiento estatus_ocupacional
Fecha.Nacimiento cat_ocupacional
Edad edad_grupos
Sexo origen_ingreso
Fecha.de.Ingreso.a.Tratamiento escolaridad
Plan.de.Tratamiento via_adm_sus_prin
Nombre.del.Centro freq_cons_sus_prin


As seen in Table 1, the C1 dataset had more variables than the TOPs dataset. One of the variables in both datasets is age, sex, the center of treatment, the region of the center and type of center, primary substances, date of admission to treatment, and type of plan of the treatment. Considering the above, we might replace missing or invalid information of C1 with TOP, specially time-invariant variables.

2. Change Dates of Admission, Application of TOP and Birth

As shown in Table 2, we brought a new format to every date to show first a 4-digit year, 2-digit month, and 2-digit day. This format also matches the format of these variables in the C1 dataset. Additionally, we masked SENDA’s IDs to avoid the eventual identification of users from third parties.


#change dates
CONS_TOP_df %>%
  dplyr::mutate(fech_ing= lubridate::parse_date_time(Fecha.de.Ingreso.a.Tratamiento, c("%d/%m/%Y"),exact=T)) %>% #No parse failures
  #dplyr::select(Fecha.de.Ingreso.a.Tratamiento,fech_ing) %>% head() #To see how it responds to changes. Many null values
  dplyr::mutate(fech_ing_sin_fmt= Fecha.de.Ingreso.a.Tratamiento) %>% #keep this variable for comparison
  dplyr::mutate(fech_ap_top= lubridate::parse_date_time(Fecha.Aplicación.TOP, c("%Y-%m-%d"),exact=T)) %>% #No parse failures
  #dplyr::select(Fecha.Aplicación.TOP,fech_ap_top) %>% head() #To see how it responds to changes.
  dplyr::mutate(fech_nac= lubridate::parse_date_time(str_trim(Fecha.Nacimiento), orders = c("%d/%m/%Y"),exact=T)) %>%
  #dplyr::select(Fecha.Nacimiento,fech_nac) %>% View() #To see how it responds to changes. No failures
  assign("CONS_TOP_df",.,envir = .GlobalEnv) #
#Example of transformations
CONS_TOP_df %>%
  dplyr::select(Fecha.de.Ingreso.a.Tratamiento,fech_ing,Fecha.Aplicación.TOP,fech_ap_top,Fecha.Nacimiento,fech_nac) %>% head() %>%
  knitr::kable(.,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
               caption="Table 2. Example of date of admission to treatment",
              col.names = c("Unformatted Date of Admission","Date of Admission", "Unformatted Date of Application", "Date of Application", "Unformatted Date of Birth","Date of Birth"),
                 align =rep('c', 6))  %>%
  kable_styling(bootstrap_options = c("striped", "hover"),font_size = 10)
Table 2. Example of date of admission to treatment
Unformatted Date of Admission Date of Admission Unformatted Date of Application Date of Application Unformatted Date of Birth Date of Birth
01/06/2015 2015-06-01 2019-04-12 2019-04-12 07/08/1980 1980-08-07
01/06/2015 2015-06-01 2019-01-04 2019-01-04 07/08/1980 1980-08-07
01/06/2015 2015-06-01 2019-06-03 2019-06-03 07/08/1980 1980-08-07
01/06/2015 2015-06-01 2019-08-09 2019-08-09 07/08/1980 1980-08-07
29/07/2015 2015-07-29 2019-02-27 2019-02-27 29/09/1984 1984-09-29
29/07/2015 2015-07-29 2019-04-29 2019-04-29 29/09/1984 1984-09-29

 

In the transformation of the dates of admission, we found a considerable number of missing values (n= 1,121). However, there were 0 missing dates of application of the TOP and 0 missing birth dates (despite that 273 cases could be invalid according to the criteria stated by SENDA’s professionals). The fact of having a few missing values in the date of admission is problematic because treatments are conceived as the unit in the C1 dataset (understood as the combination of the date of admission and user ID). Suppose we do not obtain the remaining dates of admission missed. In that case, it is difficult to understand these profiles in the context of a treatment, and if they change within a treatment or in comparison to previous or following treatments.


#Hay un 1% de casos perdidos
CONS_TOP_df %>%
  dplyr::group_by(is.na(Fecha.de.Ingreso.a.Tratamiento)) %>% 
  dplyr::rename("MISS_DATE_ADM"=`is.na(Fecha.de.Ingreso.a.Tratamiento)`) %>% 
  summarise(n=n()) %>% 
  data.frame() %>%  mutate(perc=n/sum(n)) %>%
  ggplot(aes(x="", y=n, fill=MISS_DATE_ADM))+
  geom_bar(width = 1, stat = "identity") +
  coord_polar("y", start=0) + 
  scale_fill_brewer("Missing Date\nof Admission") + theme_minimal() +
  theme(axis.text.x=element_blank())+
  geom_text(aes(y = n/2 + c(0, cumsum(n)[-length(n)]), 
                label = paste0(formatC(n, format="f", big.mark=",", digits=0), "\n(",scales::percent(perc),")")), size=4) +
  theme(
    axis.title.x = element_blank(),
    axis.title.y = element_blank(),
    panel.border = element_blank(),
    panel.grid=element_blank(),
    axis.ticks = element_blank(),
    plot.title=element_text(size=14, face="bold")
  ) 
`summarise()` ungrouping output (override with `.groups` argument)
Figure 1. Pie Chart of Missing Dates of Admission

Figure 1. Pie Chart of Missing Dates of Admission


We adopted the following guidelines:

  • Replace dates from the cases that share the same information within the TOP dataset (1), or
  • Replace them from the C1 dataset, using the date of application and the stage of application as a reference to determine the closest date of admission (2)


First, we identified applications of the TOP that were applied for the same stage (TOP at Admission) and the same user on the coinciding dates of application. These cases could replace missing dates of admission.

CONS_TOP_df_dup_fech_ing_adm <- dplyr::select(CONS_TOP_df, HASH_KEY, fech_ap_top, fech_ing, TOP)%>% 
  dplyr::filter(!is.na(fech_ing), TOP=="Ingreso") %>% as.data.frame()
CONS_TOP_df_dup_fech_ing <- dplyr::select(CONS_TOP_df, HASH_KEY, fech_ap_top, fech_ing, TOP)%>% 
  dplyr::filter(!is.na(fech_ing)) %>% as.data.frame()
######################################
#nrow(CONS_TOP_df_dup_fech_ing) ##DESPITE HAS MORE ROWS, DOES NOT MAKE A DIFFERENCE IN THE QUANTITY
#nrow(CONS_TOP_df_dup_fech_ing_adm)
######################################
#Join datasets 
dplyr::left_join(CONS_TOP_df,CONS_TOP_df_dup_fech_ing, by = c("HASH_KEY", "fech_ap_top"), suffix = c("", ".y")) %>% #names() #junto con la BD que hice
  dplyr::filter(!is.na(fech_ing.y), is.na(fech_ing)) %>%  #filtro los casos que en que sí tengo fecha de ingreso en el merge, pero no la tengo en la BD.
  dplyr::select(row,ano_bd, HASH_KEY, hash_rut_completo, id_mod, Edad, Sexo, fech_ing, fech_ap_top, TOP, fech_ing.y) %>% #dejo para ver cómo la icorporo
      knitr::kable(.,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
               caption="Table 3. HASHs with missing dates of admission that can be replaced by another case with the same characteristics",
              col.names = c("Row ID","Year of Dataset", "HASH KEY","HASH Key (Alternative)","SENDA's ID", "Year", "Sex", "Date of Admission", "Date of Application","Stage of TOP", "Date of Admission (replacement)" ),
                 align =rep('c', 6))  %>%
  kable_styling(bootstrap_options = c("striped", "hover"),font_size = 8) %>%
            scroll_box(width = "100%", height = "350px")
Table 3. HASHs with missing dates of admission that can be replaced by another case with the same characteristics
Row ID Year of Dataset HASH KEY HASH Key (Alternative) SENDA’s ID Year Sex Date of Admission Date of Application Stage of TOP Date of Admission (replacement)
97,780 2,019 9b74476e88c81cb019a468106d65ac85 NA AUAH1**121972 46 Hombre NA 2019-01-30 Ingreso 2019-01-30
100,959 2,019 2af49dc9cc22575093820d6ba8b5c2ff NA PESE1**041975 44 Hombre NA 2019-03-29 Ingreso 2019-03-21
69,925 2,018 dcbebbdebac062c872b382b9236248ab NA PAJA1**101990 29 Hombre NA 2018-01-15 Ingreso 2018-01-15
72,984 2,018 9567fa91d7d04026200044c3e5ba9ab6 NA CHVI1**111970 48 Hombre NA 2018-03-06 Ingreso 2018-03-08
74,605 2,018 9567fa91d7d04026200044c3e5ba9ab6 NA CHVI1**111970 48 Hombre NA 2018-03-06 Ingreso 2018-03-08
81,986 2,018 2056dffeac7afd78936d6b93d0db682f NA MIBE1**121965 53 Hombre NA 2018-07-18 Ingreso 2018-07-19
81,989 2,018 6cbd28cc3294a9b048f1ddf20ca316ba NA JOSE1**021985 34 Hombre NA 2018-07-20 Ingreso 2018-07-25
85,704 2,018 f6fc670b45c68a78ad1bcf7315478076 NA MAUR1**051984 35 Hombre NA 2018-11-29 Ingreso 2018-12-03
45,066 2,017 f0210e3f219b7c4b85d9767fc5446c73 NA PAAN1**091992 27 Hombre NA 2017-02-06 Ingreso 2017-02-06
45,073 2,017 d4eeee40471666533d3cbbda0901fada NA JOFL1**021997 22 Hombre NA 2017-02-03 Ingreso 2017-02-03
48,456 2,017 180a4959a9bd37b7e43d62c591cbfb97 NA MAVA1**021968 51 Hombre NA 2017-10-02 Tratamiento 2017-10-02
50,747 2,017 c6b57a1ddf9555842bd46216851bf2ee NA LUAV1**121973 45 Hombre NA 2017-05-29 Ingreso 2017-05-29
50,749 2,017 f081ac52232781af3b5024fb7a88acb8 NA YEPO1**111974 44 Hombre NA 2017-05-16 Ingreso 2017-04-13
54,309 2,017 c91ec6bb76b4cf5cb8b60f20944a2208 NA ISQU1**041985 34 Hombre NA 2017-08-14 Ingreso 2017-08-21
57,785 2,017 92be85e2ace88e24a99fe3980bcd5fc0 NA ANVA1**081990 29 Hombre NA 2017-10-11 Ingreso 2017-10-11
21,828 2,016 ec15af2d6521535ae5bdc50459a62f8c NA REMA1**011979 40 Hombre NA 2016-03-23 Ingreso 2016-03-23
24,802 2,016 251f431d762221e44eb55ed45dcc5317 NA ROZE1**051983 36 Hombre NA 2016-11-10 Tratamiento 2016-11-04
31,297 2,016 86ff8826f6dfbfcd99b454e9f7e39a9d NA FESI1**071993 26 Hombre NA 2016-11-28 Ingreso 2016-08-12
2,432 2,015 86ff8826f6dfbfcd99b454e9f7e39a9d NA FESI1**071993 26 Hombre NA 2015-05-27 Tratamiento 2015-05-27
4,362 2,015 a11b9369bb6a8d4cfe5cd73a82d28482 NA DEDI1**111993 25 Hombre NA 2015-06-16 Ingreso 2015-06-17 
# HASHs que pueden ser remplazados con otro caso de las mismas características dentro del top, que no sea de fecha de ingreso,no genere filas duplicadas (row_number) por cada fecha de admisión. Sólo tomará las aplicaciones ingreso. Esas las va a calzar con fechas de ingreso.
CONS_TOP_df_dup_fech_ing_adm <- dplyr::select(CONS_TOP_df, HASH_KEY, fech_ap_top, fech_ing, TOP)%>% 
  dplyr::filter(!is.na(fech_ing), TOP=="Ingreso") %>% dplyr::group_by(HASH_KEY, fech_ap_top) %>% dplyr::mutate(row_leftjoin=row_number()) %>% ungroup()  %>% dplyr::filter(row_leftjoin==1) %>% as.data.frame() #lo que hagho es que si hay más de una fecha de ingreso en cada grupo de hash y fecha de aplicación, la dejo pasar y dejo la primera.

#AQUÍ SÓLO REEMPLAZO LA FECHA DE ADMISION CON UNA FILA QUE COMPARTA LA MISMA FECHA DE APLICACIÓN,DENTRO DE LOS CON TOP AL INGRESO, Y ME QUEDO CON LA FECHA DE INGRESO DE ESA VARIABLE. 
CONS_TOP_df %>%
  dplyr::group_by(HASH_KEY, fech_ap_top) %>% dplyr::mutate(row_leftjoin=row_number()) %>% ungroup() %>%
dplyr::left_join(CONS_TOP_df_dup_fech_ing_adm, by = c("HASH_KEY", "fech_ap_top","row_leftjoin"), suffix = c("", ".y")) %>% #names() #junto con la BD que hice, sólo tomo una.
  dplyr::mutate(OBS= case_when(is.na(fech_ing) & !is.na(fech_ing.y)~ "1.1.Replace miss date admission w TOPs w same stage & user",
                               TRUE ~ "")) %>%
  dplyr::mutate(fech_ing= ifelse(is.na(fech_ing)& !is.na(fech_ing.y),as.Date(as.character(fech_ing.y), format="%Y-%m-%d"), as.Date(as.character(fech_ing), format="%Y-%m-%d"))) %>%
  dplyr::mutate(fech_ing=as.Date(fech_ing)) %>%
  dplyr::mutate(fech_ap_top=as.Date(as.character(fech_ap_top))) %>% #APROVECHO DE NORMALIZAR ESTAS VARIABLES PARA QUE TENGAN LA MISMA ESTRUCTURA
  dplyr::mutate(fech_nac=as.Date(as.character(fech_nac))) %>%
  dplyr::select(-fech_ing.y, -TOP.y, -row_leftjoin) %>%
#PAra ver cómo evoluciona y si hay errores. Haasta el momento sólo hay un NA que parece justificado porque no calzan las fechas de aplicaicón.
#    dplyr::filter(HASH_KEY=="0669c73ad96e50fb9605c167cc40693e"|HASH_KEY=="ee0360d19dd5f300526c624c58090c79"|HASH_KEY=="c91ec6bb76b4cf5cb8b60f209#44a2208"|HASH_KEY=="ee0360d19dd5f300526c624c58090c79"|HASH_KEY=="153b828278ea88dc5ab15039e3e0c882") %>% dplyr::select(HASH_KEY,fech_ing, #fech_ing_sin_fmt )%>% print()
  
  #dplyr::group_by(Edad) %>% summarise(n=n()) %>% View()
 assign("CONS_TOP_df_dup_ENE_2020_prev0",., envir = .GlobalEnv) 
#str(CONS_TOP_df_dup_ENE_2020_prev0) #TENGO PROBLEMAS PARA FORMATEAR LA ECHA DE INGRESO SIN HORAS

Done this, we still found many cases that do not have a date of admission (n= 1,116). Another option would be to replace the date of admission with the date of application, in the event of application. However, this was not possible due to inaccuracy of data, observed in the difference between the date of admission and the date of application of those that have both dates available (Mean=-23.04; Mdn= -8 [Q1= -28, Q3= -1]). This means that TOP at admission was applied some days after being admitted to treatments.

CONS_TOP_df_dup_ENE_2020_prev0 %>% 
  dplyr::filter(!is.na(fech_ing), TOP=="Ingreso") %>% 
  dplyr::mutate(diff_fech_ing_ap=as.numeric(fech_ing-fech_ap_top, units="days")) %>% 
  dplyr::select(diff_fech_ing_ap) %>% 
  ggplot(aes(x=as.numeric(diff_fech_ing_ap))) +
  geom_histogram(color="black", fill="white") +
  theme_classic() +
  labs(x= "Diff. in Dates of Admission & Date of Application of TOP", y ="Frequencies", caption=paste0("no. of cases= ",CONS_TOP_df_dup_ENE_2020_prev0 %>% dplyr::filter(!is.na(fech_ing), TOP=="Ingreso") %>% dplyr::mutate(diff_fech_ing_ap=as.numeric(fech_ing-fech_ap_top, units="days")) %>% summarise(n())))
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Figure 2. Histogram of Differences Between Dates of Admission and Dates of Application of TOP, among those questionnaires applied in the stage of admission

Figure 2. Histogram of Differences Between Dates of Admission and Dates of Application of TOP, among those questionnaires applied in the stage of admission


In Table 4, we may see how many dates could be replaced by the dates in C1. We replaced these dates by selecting the applications corresponding to the stage of admission that had missing data. Then, we paired them with cases in C1 with the same HASH and the same date of admission (in which the date of application of the TOP for admission coincides with the date of admission in C1). Finally, we replaced it with this corresponding date.

  #debiese poner una, HASHs with missing dates of admission that can be replaced by cases in C1 with the same characteristics",
CONS_TOP_df_dup_ENE_2020_prev0 %>% dplyr::filter(TOP=="Ingreso", is.na(fech_ing)) %>% #son aplicación Ingreso, les falta la fecha de ingreso
  dplyr::inner_join(dplyr::select(CONS_C1_df,HASH_KEY, fech_ing) %>% mutate(fech_ing=as.Date(as.character(fech_ing))), by=c("HASH_KEY"="HASH_KEY", "fech_ap_top"="fech_ing"), suffix=c(".TOP",".C1")) %>% as.data.frame() %>%
  dplyr::select(row,ano_bd, HASH_KEY, hash_rut_completo, id_mod, Edad, Sexo, fech_ing,fech_ing_sin_fmt, fech_ap_top, TOP) %>% 
  knitr::kable(.,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
               caption="Table 4, HASHs with missing dates of admission that can be replaced by cases in C1 with the same characteristics",
              col.names = c("Row ID","Year of Dataset", "HASH KEY","HASH Key (Alternative)","SENDA's ID", "Year", "Sex", "Date of Admission", "Unformatted Date of Admission", "Date of Application","Stage of TOP"),
                 align =rep('c', 6))  %>%
  kable_styling(bootstrap_options = c("striped", "hover"),font_size = 9) %>%
      kableExtra::add_footnote( c("Note. The date of application of TOP should replace the missing Date of Admission."), notation = "none") %>%
            scroll_box(width = "100%", height = "350px")
Table 4, HASHs with missing dates of admission that can be replaced by cases in C1 with the same characteristics
Row ID Year of Dataset HASH KEY HASH Key (Alternative) SENDA’s ID Year Sex Date of Admission Unformatted Date of Admission Date of Application Stage of TOP
97,780 2,019 9b74476e88c81cb019a468106d65ac85 NA AUAH1**121972 46 Hombre NA NA 2019-01-30 Ingreso
45,066 2,017 f0210e3f219b7c4b85d9767fc5446c73 NA PAAN1**091992 27 Hombre NA NA 2017-02-06 Ingreso
45,073 2,017 d4eeee40471666533d3cbbda0901fada NA JOFL1**021997 22 Hombre NA NA 2017-02-03 Ingreso
50,747 2,017 c6b57a1ddf9555842bd46216851bf2ee NA LUAV1**121973 45 Hombre NA NA 2017-05-29 Ingreso
57,785 2,017 92be85e2ace88e24a99fe3980bcd5fc0 NA ANVA1**081990 29 Hombre NA NA 2017-10-11 Ingreso
18,495 2,016 2adfa7e2f6d763c1c6c2b5ed35889243 NA JAQU1**021981 38 Hombre NA NA 2016-02-23 Ingreso
21,828 2,016 ec15af2d6521535ae5bdc50459a62f8c NA REMA1**011979 40 Hombre NA NA 2016-03-23 Ingreso
31,476 2,016 978988a1c417f54963eb9f5d84f07c60 NA ANLE1**091960 59 Hombre NA NA 2016-11-22 Ingreso
Note. The date of application of TOP should replace the missing Date of Admission. 
#Traerme casos con la fecha estandarizada en C1, crear una fecha de ingreso C1 también formateada, para ver cambios, luego agrupar los casos por HASH y fecha de ingreso, ver cuáles pueden haber más de un hash o fecha de ingreso, sacar sólo los primeros casos de cada combinación, de ahí sólo seleccionar las columnas de interes y convertirlo en data frame (no tibble)

CONS_C1_df_rownum <-CONS_C1_df %>% mutate(fech_ing_C1=fech_ing) %>%  #Actualización= 04-02-2020, ponerle fecha de ingreso formato Date 
   mutate(fech_ing=as.Date(as.character(fech_ing))) %>%
     mutate(fech_ing_C1=as.Date(as.character(fech_ing_C1))) %>%
  dplyr::group_by(HASH_KEY, fech_ing) %>%  
  dplyr::mutate(row_leftjoin=row_number()) %>% 
  ungroup()  %>% 
  dplyr::filter(row_leftjoin==1) %>%
  dplyr::select(HASH_KEY, fech_ing,row_leftjoin, fech_ing_C1) %>%
  data.frame()
  #agrupo por row porque no me interesa que no me traiga el repetido de la fecha de aplicación del top y el HASH, sólo me interesa que desde C1 no me traiga más de una fila.

#join
#n_veces_dup_hash_fech_top
CONS_TOP_df_dup_ENE_2020_prev0 %>% 
  mutate(fech_ing_na=fech_ing) %>%# solo hecha para visibilizar los que fueron reemplazados
  data.frame() %>%
  dplyr::left_join(CONS_C1_df_rownum, by=c("HASH_KEY"="HASH_KEY", "fech_ap_top"="fech_ing"), suffix=c(".TOP",".C1"))%>% #names()
  dplyr::mutate(OBS=case_when((is.na(fech_ing))&(TOP=="Ingreso")&(!is.na(fech_ing_C1))~glue::glue("{OBS};1.2.Replaced missing dates of admission from C1"),
                              TRUE~ OBS))%>% 
  dplyr::mutate(fech_ing= ifelse((is.na(fech_ing))&(TOP=="Ingreso")&(!is.na(fech_ing_C1)),fech_ing_C1, fech_ing)) %>%
  dplyr::mutate(fech_ing= as.Date(as.numeric(fech_ing), format="%Y-%m-%d")) %>% #tuve q agregarle más días para equipararlo
  #dplyr::select(-fech_ing_C1, -row_leftjoin) %>%
#para probar como SE COMPORTA LA TRANSFORMACIÓN
#  dplyr::filter(HASH_KEY=="0669c73ad96e50fb9605c167cc40693e"|HASH_KEY=="ee0360d19dd5f300526c624c58090c79") %>% dplyr::select(fech_ing, #fech_ing_sin_fmt,fech_ing_C1, fech_ap_top) %>% print()
#    dplyr::filter(HASH_KEY %in% c("9b74476e88c81cb019a468106d65ac85","f0210e3f219b7c4b85d9767fc5446c73","d4eeee40471666533d3cbbda0901fada", "c6b57a1ddf9555842bd46216851bf2ee", "92be85e2ace88e24a99fe3980bcd5fc0", "2adfa7e2f6d763c1c6c2b5ed35889243", "ec15af2d6521535ae5bdc50459a62f8c", "978988a1c417f54963eb9f5d84f07c60"), TOP=="Ingreso", is.na(fech_ing_na)) %>% #c6b57a1ddf9555842bd46216851bf2ee  este no debería incorporarlo, porque debiese ser NA
 # dplyr::select(fech_ing, fech_ing_sin_fmt,fech_ing_C1, fech_ap_top) %>% print()
 # View()
  dplyr::mutate(Edad_al_ing=lubridate::time_length(difftime(as.Date(fech_ing), as.Date(fech_nac)),"years")) %>%
  dplyr::mutate(Edad_al_ing=replace(Edad_al_ing, is.na(Edad), NA)) %>%
  as.data.frame() %>% 
  #dplyr::select(fech_nac,Edad_al_ing, fech_ing)
  #View()
  assign("CONS_TOP_df_dup_ENE_2020_prev1",., envir = .GlobalEnv)


And what about another important event that is present along the C1 dataset? Despite that the C1 dataset had two types of events; unfortunately, no other admission dates could be replaced by the dates in C1. We filtered the applications corresponding to the discharge event that had missing data on the date of admission (n= 103). Then, we paired them with cases in C1 with the same HASH and the same date of discharge (in which the date of application of the TOP for discharge coincides with the date of discharge in C1), ending with 0 cases to match.


Another alternative could be the match by coinciding HASHs and years at the time of admission. However, we require to have valid dates of birth in the TOP dataset to calculate years at a given time. There were 273 dates of birth that might represent invalid values along with the dataset, depending on contextual factors (such as the time in which the user was interviewed). That is why we decided to get the age at the time of application of the TOP as a valid filter of valid dates of birth. This criterion aims to bring consistency over both datasets in terms of admitted ages into programs. In the following table, these cases are presented in-depth.


#CONS_TOP_df_dup_ENE_2020_prev1 %>% dplyr::filter(!is.na(fech_nac)) %>% dplyr::select(fech_nac, Edad) %>% dplyr::filter(fech_nac<"1929-03-19"|fech_nac>"2001-11-01")  %>% nrow()

#El filtro que hice yo no sirvió mucho, los rangos de edad. Ahí lo corregí por la influencia de 1929-03-20
#INVESTIGAR ESTE CASO.

  CONS_TOP_df_dup_ENE_2020_prev1 %>% 
  #dplyr::mutate(fech_nac=lubridate::parse_date_time(stringi::stri_sub(ID,-8,-1),"dmY")) %>% 
  dplyr::mutate(Edad_at_ap=lubridate::time_length(difftime(as.Date(fech_ap_top), as.Date(fech_nac)),"years")) %>% #AGREGADO EN APR 2020.
  dplyr::mutate(Edad_at_ap=replace(Edad_at_ap, is.na(fech_nac), NA)) %>% #AGREGADO EN APR 2020.
  #dplyr::filter(Edad<18|Edad>90) %>%
  dplyr::filter(Edad_at_ap<18|Edad_at_ap>90) %>%
  dplyr::mutate(Edad_at_ap=round(Edad_at_ap,2))%>%
  dplyr::filter(Edad_at_ap<18&Edad_at_ap>16) %>%
  nrow() -> edad_adolescentes_menores_18

  CONS_TOP_df_dup_ENE_2020_prev1 %>% 
  #dplyr::mutate(fech_nac=lubridate::parse_date_time(stringi::stri_sub(ID,-8,-1),"dmY")) %>% 
  dplyr::mutate(Edad_at_ap=lubridate::time_length(difftime(as.Date(fech_ap_top), as.Date(fech_nac)),"years")) %>% #AGREGADO EN APR 2020.
  dplyr::mutate(Edad_at_ap=replace(Edad_at_ap, is.na(fech_nac), NA)) %>% #AGREGADO EN APR 2020.
  #dplyr::filter(Edad<18|Edad>90) %>%
  dplyr::filter(Edad_at_ap<18|Edad_at_ap>90) %>%
  dplyr::mutate(Edad_at_ap=round(Edad_at_ap,2))%>%
  dplyr::filter(Edad_at_ap>17&Edad_at_ap<18) %>%
  nrow() -> edad_adolescentes_17

  
  CONS_TOP_df_dup_ENE_2020_prev1 %>% 
  #dplyr::mutate(fech_nac=lubridate::parse_date_time(stringi::stri_sub(ID,-8,-1),"dmY")) %>% 
  dplyr::mutate(Edad_at_ap=lubridate::time_length(difftime(as.Date(fech_ap_top), as.Date(fech_nac)),"years")) %>% #AGREGADO EN APR 2020.
  dplyr::mutate(Edad_at_ap=replace(Edad_at_ap, is.na(fech_nac), NA)) %>% #AGREGADO EN APR 2020.
  #dplyr::filter(Edad<18|Edad>90) %>%
  dplyr::filter(Edad_at_ap<18|Edad_at_ap>90) %>%
  dplyr::mutate(Edad_at_ap=round(Edad_at_ap,2))%>%
  dplyr::select(row, HASH_KEY, id_mod, fech_nac, ano_bd,Edad,Edad_at_ap,fech_ing) %>%
 knitr::kable(.,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
               caption="Table 5. Cases that have a wrongly calculated age at the time of admission",
              col.names = c("Row ID","HASH KEY","SENDA's ID","Year of Birth","Year of Dataset",  "Age", "Age at Application\nof TOP", "Date of Admission"),
                 align =rep('c', 101))  %>%
   kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 8) %>%
  kableExtra::scroll_box(width = "100%", height = "350px")
Table 5. Cases that have a wrongly calculated age at the time of admission
Row ID HASH KEY SENDA’s ID Year of Birth Year of Dataset Age Age at Application of TOP Date of Admission
101,055 78ff5c983fac3d4a90d88e8bacc6f835 NAPO1**092001 2001-09-29 2,019 18 17.57 2019-04-23
101,728 423ba2e3327b4cd7484855db49112da7 DAAN1**052001 2001-05-26 2,019 18 17.97 2019-04-10
102,249 eec336fc9ba6224cfaaf000137396c18 MISI2**052001 2001-05-26 2,019 18 17.99 2019-03-04
63,144 66e4d31c4c6575527f8ed58455396f17 VICA1**071917 1917-07-15 2,018 102 100.55 2017-07-24
63,145 66e4d31c4c6575527f8ed58455396f17 VICA1**071917 1917-07-15 2,018 102 100.77 2017-07-24
63,146 66e4d31c4c6575527f8ed58455396f17 VICA1**071917 1917-07-15 2,018 102 100.79 2017-07-24
70,181 43a56e32d91d4273b58f0e428c775c61 AYFU2**102000 2000-10-12 2,018 19 17.32 2018-01-09
73,005 443afa5786e830784d4ab13440285948 ITGO2**052000 2000-05-02 2,018 19 17.92 2018-03-06
73,178 5f654d25d1c175a90cad6473bf8e9ab3 FRVI1**032000 2000-03-25 2,018 19 17.94 2018-03-01
73,758 bd6d5aba19a0947917879a749155ef40 RISI1**082000 2000-08-31 2,018 19 17.53 2018-03-02
73,759 bd6d5aba19a0947917879a749155ef40 RISI1**082000 2000-08-31 2,018 19 17.80 2018-03-02
79,541 403b2b7d583202300ec9b1a22f62925f MAVI1**062001 2001-06-07 2,018 18 17.04 2018-06-12
81,178 4107fbd0525d333bd2d720877cfb382c CRLI1**092000 2000-09-08 2,018 19 17.79 2018-06-22
85,888 e754a2cf102fd6447c70a3193bdac42b DANA1**012001 2001-01-17 2,018 18 17.74 2019-07-05
85,889 a6929b6c7987cce64b0ca1b3f3299bea ROCO2**072001 2001-07-18 2,018 18 17.37 2019-07-30
32,056 0d5782868d35d58892a0f938813f7608 EDHO1**052015 2015-05-04 2,017 4 2.41 2015-05-04
32,251 a26b1ac1007562c9e19cd5163a68c2ec KAAN2**072015 2015-07-25 2,017 4 1.50 2015-06-11
32,252 a26b1ac1007562c9e19cd5163a68c2ec KAAN2**072015 2015-07-25 2,017 4 1.54 2015-06-11
32,356 87afef689812b1ad6f576f0e83e5c963 DABU1**032015 2015-03-04 2,017 4 2.07 2015-08-21
32,370 1fcf34aa0db73c4218e224628c6173f9 GUVE1**082015 2015-08-20 2,017 4 2.02 2015-08-20
32,494 504ae873237325dc71ed5cdaf630a7d9 MAAV1**111999 1999-11-27 2,017 19 17.12 2015-09-08
32,788 32c4704bfeef5a559ca9c67197cee23e JOSO1**092015 2015-09-12 2,017 4 1.41 2015-11-27
32,789 32c4704bfeef5a559ca9c67197cee23e JOSO1**092015 2015-09-12 2,017 4 1.41 2015-11-27
32,790 32c4704bfeef5a559ca9c67197cee23e JOSO1**092015 2015-09-12 2,017 4 1.41 2015-11-27
32,810 68a1abebf175673e62b97b65fde63b50 BRHE1**092015 2015-09-28 2,017 4 1.46 2015-12-09
32,811 68a1abebf175673e62b97b65fde63b50 BRHE1**092015 2015-09-28 2,017 4 1.77 2015-12-09
32,812 68a1abebf175673e62b97b65fde63b50 BRHE1**092015 2015-09-28 2,017 4 2.04 2015-12-09
33,195 711029aaa5f478f2c55223d285eb84fe SHMA1**082013 2013-08-08 2,017 6 3.72 2016-01-13
33,196 711029aaa5f478f2c55223d285eb84fe SHMA1**082013 2013-08-08 2,017 6 4.24 2016-01-13
39,006 6d533b6ebd408688d42997b791dd8a75 MARA2**111917 1917-11-04 2,017 102 99.34 2016-10-05
43,329 c6549f1ae6aeab85d62f3020a123806a JUCO1**011999 1999-01-14 2,017 20 17.99 2017-01-10
47,652 61d28f0b3b3ddf072a176767ba7eb1b9 CAOR1**031999 1999-03-30 2,017 20 17.90 2017-01-20
53,007 66e4d31c4c6575527f8ed58455396f17 VICA1**071917 1917-07-15 2,017 102 100.07 2017-07-24
53,008 66e4d31c4c6575527f8ed58455396f17 VICA1**071917 1917-07-15 2,017 102 100.28 2017-07-24
53,543 dc5c110ee6fe3bffe3bc20dc5da80d64 JUMO1**061999 1999-06-16 2,017 20 17.99 2017-07-03
55,926 b24908c527faa1b7bd5a267d5dcabd45 MAHE2**091999 1999-09-21 2,017 20 17.98 2017-09-13
57,815 62c4774350934d685fc8113a1169a47b PAES1**111999 1999-11-01 2,017 20 17.94 2017-11-13
58,166 143e489e004ed5834abf9392f843af12 ALMU1**111999 1999-11-02 2,017 20 17.97 2017-10-25
58,442 9018b21f72370bdeca005ab540d7c7be ALNE1**101999 1999-10-09 2,017 20 17.95 2017-12-14
58,919 02191e68784f4d1cc0cab064f2cc36ce GODE1**091999 1999-09-07 2,017 20 17.98 2018-07-03
8,988 6d672b5d02dada8fd6ad1c3a38a69b7f LUGO2**022015 2015-02-25 2,016 4 0.99 2015-05-06
8,989 6d672b5d02dada8fd6ad1c3a38a69b7f LUGO2**022015 2015-02-25 2,016 4 0.99 2015-05-06
8,990 6d672b5d02dada8fd6ad1c3a38a69b7f LUGO2**022015 2015-02-25 2,016 4 1.36 2015-05-06
8,991 6d672b5d02dada8fd6ad1c3a38a69b7f LUGO2**022015 2015-02-25 2,016 4 1.57 2015-05-06
8,992 6d672b5d02dada8fd6ad1c3a38a69b7f LUGO2**022015 2015-02-25 2,016 4 1.57 2015-05-06
8,993 6d672b5d02dada8fd6ad1c3a38a69b7f LUGO2**022015 2015-02-25 2,016 4 1.57 2015-05-06
9,057 cf1c08352647546c64c5e9db55c3c7d9 JOLU1**052015 2015-05-14 2,016 4 1.00 2015-05-14
9,194 afa9a46f43298983918d3f5a9d821786 ALBR1**052015 2015-05-19 2,016 4 0.67 2015-05-19
9,195 afa9a46f43298983918d3f5a9d821786 ALBR1**052015 2015-05-19 2,016 4 0.71 2015-05-19
9,196 afa9a46f43298983918d3f5a9d821786 ALBR1**052015 2015-05-19 2,016 4 0.97 2015-05-19
9,197 afa9a46f43298983918d3f5a9d821786 ALBR1**052015 2015-05-19 2,016 4 1.25 2015-05-19
9,198 afa9a46f43298983918d3f5a9d821786 ALBR1**052015 2015-05-19 2,016 4 1.36 2015-05-19
9,199 afa9a46f43298983918d3f5a9d821786 ALBR1**052015 2015-05-19 2,016 4 1.57 2015-05-19
9,307 5d8091081085c2019f231071c383dea0 JOGU1**042015 2015-04-22 2,016 4 0.77 2015-05-04
9,308 5d8091081085c2019f231071c383dea0 JOGU1**042015 2015-04-22 2,016 4 0.85 2015-05-04
9,309 5d8091081085c2019f231071c383dea0 JOGU1**042015 2015-04-22 2,016 4 1.11 2015-05-04
9,310 5d8091081085c2019f231071c383dea0 JOGU1**042015 2015-04-22 2,016 4 1.27 2015-05-04
9,311 5d8091081085c2019f231071c383dea0 JOGU1**042015 2015-04-22 2,016 4 1.59 2015-05-04
9,377 0d5782868d35d58892a0f938813f7608 EDHO1**052015 2015-05-04 2,016 4 0.73 2015-05-04
9,378 0d5782868d35d58892a0f938813f7608 EDHO1**052015 2015-05-04 2,016 4 0.99 2015-05-04
9,894 c6cd0322a5d9822fe6a73aa31e8ed67f JUSO1**111999 1999-11-20 2,016 19 16.25 2015-06-15
10,090 18f1041924d48c9440b4b397267a58f2 MAAL1**062015 2015-06-04 2,016 4 0.65 2015-06-22
10,091 18f1041924d48c9440b4b397267a58f2 MAAL1**062015 2015-06-04 2,016 4 0.92 2015-06-22
10,208 9630831102f1ea74e3bd52359c335933 PAVI1**062015 2015-06-30 2,016 4 0.64 2015-06-08
10,209 9630831102f1ea74e3bd52359c335933 PAVI1**062015 2015-06-30 2,016 4 0.86 2015-06-08
10,210 9630831102f1ea74e3bd52359c335933 PAVI1**062015 2015-06-30 2,016 4 1.40 2015-06-08
10,211 9630831102f1ea74e3bd52359c335933 PAVI1**062015 2015-06-30 2,016 4 1.40 2015-06-08
10,498 add12d3ddf9271f589c627bbc79c4579 MICA1**072015 2015-07-20 2,016 4 0.49 2015-07-20
10,499 add12d3ddf9271f589c627bbc79c4579 MICA1**072015 2015-07-20 2,016 4 0.72 2015-07-20
10,500 add12d3ddf9271f589c627bbc79c4579 MICA1**072015 2015-07-20 2,016 4 0.75 2015-07-20
10,619 d2ed9aa7d6555131b46251846e8b8e7b RUCA2**072015 2015-07-27 2,016 4 0.55 2015-07-27
10,620 d2ed9aa7d6555131b46251846e8b8e7b RUCA2**072015 2015-07-27 2,016 4 0.55 2015-07-27
10,673 65f57f0a6a732ea3780954d538a75428 SECA1**032015 2015-03-17 2,016 4 0.85 2015-07-07
10,674 65f57f0a6a732ea3780954d538a75428 SECA1**032015 2015-03-17 2,016 4 0.83 2015-07-07
10,823 70597f2a60f6148d50cf465be1a3edb5 MAVI1**012015 2015-01-06 2,016 4 1.48 2015-07-29
10,824 70597f2a60f6148d50cf465be1a3edb5 MAVI1**012015 2015-01-06 2,016 4 1.82 2015-07-29
10,825 70597f2a60f6148d50cf465be1a3edb5 MAVI1**012015 2015-01-06 2,016 4 1.77 2015-07-29
10,931 a26b1ac1007562c9e19cd5163a68c2ec KAAN2**072015 2015-07-25 2,016 4 0.53 2015-06-11
10,932 a26b1ac1007562c9e19cd5163a68c2ec KAAN2**072015 2015-07-25 2,016 4 0.82 2015-06-11
10,933 a26b1ac1007562c9e19cd5163a68c2ec KAAN2**072015 2015-07-25 2,016 4 1.23 2015-06-11
11,004 b9ea83c2b1fbdb556749399b4efd327f JAAL1**102014 2014-10-22 2,016 5 1.20 2015-05-22
11,099 c93824e1813aef5dcc4f242e999a6473 PAAD1**072014 2014-07-27 2,016 5 1.46 2015-08-05
11,318 d5f73b599c69578e2436b8292eaa8cf2 MAAS2**062015 2015-06-15 2,016 4 0.62 2015-08-01
11,319 d5f73b599c69578e2436b8292eaa8cf2 MAAS2**062015 2015-06-15 2,016 4 0.95 2015-08-01
11,320 d5f73b599c69578e2436b8292eaa8cf2 MAAS2**062015 2015-06-15 2,016 4 1.23 2015-08-01
11,321 d5f73b599c69578e2436b8292eaa8cf2 MAAS2**062015 2015-06-15 2,016 4 1.23 2015-08-01
11,386 48358f8da531b84db0c85f3639a09a04 LUCA1**022015 2015-02-15 2,016 4 0.93 2015-08-25
11,387 48358f8da531b84db0c85f3639a09a04 LUCA1**022015 2015-02-15 2,016 4 0.99 2015-08-25
11,705 87afef689812b1ad6f576f0e83e5c963 DABU1**032015 2015-03-04 2,016 4 0.98 2015-08-21
11,706 87afef689812b1ad6f576f0e83e5c963 DABU1**032015 2015-03-04 2,016 4 1.27 2015-08-21
11,794 d83f8f183a061c8664e76b2a4e629a8e CRPE1**072015 2015-07-20 2,016 4 0.55 2015-08-17
11,822 1fcf34aa0db73c4218e224628c6173f9 GUVE1**082015 2015-08-20 2,016 4 0.69 2015-08-20
11,823 1fcf34aa0db73c4218e224628c6173f9 GUVE1**082015 2015-08-20 2,016 4 0.69 2015-08-20
12,084 78b53d7e099c895d0b0c9b0f73188539 HUPO1**092015 2015-09-08 2,016 4 0.67 2015-09-03
12,085 78b53d7e099c895d0b0c9b0f73188539 HUPO1**092015 2015-09-08 2,016 4 0.77 2015-09-03
12,086 78b53d7e099c895d0b0c9b0f73188539 HUPO1**092015 2015-09-08 2,016 4 1.11 2015-09-03
12,087 78b53d7e099c895d0b0c9b0f73188539 HUPO1**092015 2015-09-08 2,016 4 1.11 2015-09-03
12,405 9d1d56fca162aa59f99988e6cafcc296 JOAR1**092015 2015-09-29 2,016 4 0.35 2015-09-29
12,736 3bb60d41dee218ae5ae3846057d48b95 DACA2**092015 2015-09-10 2,016 4 0.55 2015-09-11
12,737 3bb60d41dee218ae5ae3846057d48b95 DACA2**092015 2015-09-10 2,016 4 0.78 2015-09-11
12,738 3bb60d41dee218ae5ae3846057d48b95 DACA2**092015 2015-09-10 2,016 4 1.09 2015-09-11
12,739 3bb60d41dee218ae5ae3846057d48b95 DACA2**092015 2015-09-10 2,016 4 1.29 2015-09-11
12,800 3d108204060b79bd179ed442cd81c510 VAUR2**082015 2015-08-07 2,016 4 0.57 2015-08-24
13,005 06faceb13defbd9a1bb63e22913ab1bb JALO2**102015 2015-10-06 2,016 4 0.63 2015-10-09
13,006 06faceb13defbd9a1bb63e22913ab1bb JALO2**102015 2015-10-06 2,016 4 0.73 2015-10-09
13,036 1dfa0e65e45414b0dc071b61d1bac1a9 PAGU2**082015 2015-08-11 2,016 4 0.53 2015-09-30
13,037 1dfa0e65e45414b0dc071b61d1bac1a9 PAGU2**082015 2015-08-11 2,016 4 1.19 2015-09-30
13,364 c3b01dd3a677c39d5637e50fedd82639 ANPA2**032001 2001-03-09 2,016 18 15.04 2015-09-08
13,416 bbad2b97c37995c0bd0acc4d7086cd76 FRCA1**012015 2015-01-16 2,016 4 1.11 2015-10-29
13,417 bbad2b97c37995c0bd0acc4d7086cd76 FRCA1**012015 2015-01-16 2,016 4 1.28 2015-10-29
13,418 bbad2b97c37995c0bd0acc4d7086cd76 FRCA1**012015 2015-01-16 2,016 4 1.50 2015-10-29
13,422 4c7781ef7fdf301ebff0b016367c4df4 HEIB1**092015 2015-09-28 2,016 4 0.33 2015-10-01
13,423 4c7781ef7fdf301ebff0b016367c4df4 HEIB1**092015 2015-09-28 2,016 4 0.50 2015-10-01
13,424 4c7781ef7fdf301ebff0b016367c4df4 HEIB1**092015 2015-09-28 2,016 4 0.75 2015-10-01
13,425 4c7781ef7fdf301ebff0b016367c4df4 HEIB1**092015 2015-09-28 2,016 4 0.86 2015-10-01
13,450 f02435c816333b972c7e14ec4cf3a6ad LUPA1**012015 2015-01-10 2,016 4 1.05 2015-10-29
13,451 f02435c816333b972c7e14ec4cf3a6ad LUPA1**012015 2015-01-10 2,016 4 1.47 2015-10-29
13,452 f02435c816333b972c7e14ec4cf3a6ad LUPA1**012015 2015-01-10 2,016 4 1.52 2015-10-29
13,478 1ec3f2c2efb06ba95486d0324984a867 MERO2**022015 2015-02-13 2,016 4 1.43 2015-08-24
13,479 1ec3f2c2efb06ba95486d0324984a867 MERO2**022015 2015-02-13 2,016 4 1.61 2015-08-24
13,480 1ec3f2c2efb06ba95486d0324984a867 MERO2**022015 2015-02-13 2,016 4 1.61 2015-08-24
13,512 17427b747a8e5d8ecdcdec6a781a277b MAZU1**102015 2015-10-21 2,016 4 0.27 2015-10-27
13,534 84959e1b6bdc461afd0af4669917bf52 YERA2**072015 2015-07-13 2,016 4 0.76 2015-10-29
13,535 84959e1b6bdc461afd0af4669917bf52 YERA2**072015 2015-07-13 2,016 4 1.00 2015-10-29
13,536 84959e1b6bdc461afd0af4669917bf52 YERA2**072015 2015-07-13 2,016 4 1.05 2015-10-29
13,800 9be202d4f43568a978050002a518f718 UBSA2**092015 2015-09-01 2,016 4 0.50 2015-09-01
13,801 9be202d4f43568a978050002a518f718 UBSA2**092015 2015-09-01 2,016 4 0.81 2015-09-01
13,802 9be202d4f43568a978050002a518f718 UBSA2**092015 2015-09-01 2,016 4 0.89 2015-09-01
14,020 11e10f01088fec73c3faf6c1e4c262d2 NIGA1**022015 2015-02-12 2,016 4 0.99 2015-11-02
14,021 11e10f01088fec73c3faf6c1e4c262d2 NIGA1**022015 2015-02-12 2,016 4 1.24 2015-11-02
14,022 11e10f01088fec73c3faf6c1e4c262d2 NIGA1**022015 2015-02-12 2,016 4 1.48 2015-11-02
14,023 11e10f01088fec73c3faf6c1e4c262d2 NIGA1**022015 2015-02-12 2,016 4 1.74 2015-11-02
14,093 a664b554d169c5f88b243b8c5f47235d GAPE1**062015 2015-06-22 2,016 4 0.66 2015-11-09
14,094 a664b554d169c5f88b243b8c5f47235d GAPE1**062015 2015-06-22 2,016 4 0.75 2015-11-09
14,188 46923f82f46c14595e0b398ed4248eae PACO2**112015 2015-11-17 2,016 3 0.21 2015-11-17
14,506 a537681987ddbe972f0f090b10a699c3 RESA1**102015 2015-10-08 2,016 4 0.29 2015-10-14
14,507 a537681987ddbe972f0f090b10a699c3 RESA1**102015 2015-10-08 2,016 4 0.38 2015-10-14
15,000 5353f1030f5fe554f789c03b039909fc LUCO1**081999 1999-08-26 2,016 20 16.55 2015-11-12
15,054 32c4704bfeef5a559ca9c67197cee23e JOSO1**092015 2015-09-12 2,016 4 0.40 2015-11-27
15,055 32c4704bfeef5a559ca9c67197cee23e JOSO1**092015 2015-09-12 2,016 4 0.77 2015-11-27
15,056 32c4704bfeef5a559ca9c67197cee23e JOSO1**092015 2015-09-12 2,016 4 1.05 2015-11-27
15,115 ff638671b9e28eaef42b8adb43da72f1 PAPA2**111998 1998-11-15 2,016 20 17.16 2015-11-26
15,116 ff638671b9e28eaef42b8adb43da72f1 PAPA2**111998 1998-11-15 2,016 20 17.16 2015-11-26
15,132 68a1abebf175673e62b97b65fde63b50 BRHE1**092015 2015-09-28 2,016 4 0.33 2015-12-09
15,133 68a1abebf175673e62b97b65fde63b50 BRHE1**092015 2015-09-28 2,016 4 0.82 2015-12-09
15,134 68a1abebf175673e62b97b65fde63b50 BRHE1**092015 2015-09-28 2,016 4 0.93 2015-12-09
15,135 68a1abebf175673e62b97b65fde63b50 BRHE1**092015 2015-09-28 2,016 4 1.21 2015-12-09
15,209 a5b8d096bb62e725e52b61fb28aed343 EDLE1**032015 2015-03-26 2,016 4 0.92 2015-12-01
15,210 a5b8d096bb62e725e52b61fb28aed343 EDLE1**032015 2015-03-26 2,016 4 1.26 2015-12-01
15,211 a5b8d096bb62e725e52b61fb28aed343 EDLE1**032015 2015-03-26 2,016 4 1.57 2015-12-01
15,212 a5b8d096bb62e725e52b61fb28aed343 EDLE1**032015 2015-03-26 2,016 4 1.57 2015-12-01
15,442 940072c88254c0601c2f9af1d52bdfa8 JOCO1**092015 2015-09-29 2,016 4 0.42 2015-12-16
15,443 940072c88254c0601c2f9af1d52bdfa8 JOCO1**092015 2015-09-29 2,016 4 0.51 2015-12-16
15,583 4f9fee24532da33014e17e37a7ba2634 DYBU1**051999 1999-05-20 2,016 20 16.69 2015-12-11
15,626 17977a7b3a5dbf99fa59d284bd7e2fe1 VIRA1**042000 2000-04-17 2,016 19 15.89 2015-12-02
15,754 c4d30a1c4557dc3a71da5f034ba3fc57 JUCU1**112015 2015-11-27 2,016 3 0.10 2015-11-27
16,071 fbd1d4d518cf49e6d79dba77657d10f9 FIGA2**122015 2015-12-16 2,016 3 0.05 2015-12-30
16,072 fbd1d4d518cf49e6d79dba77657d10f9 FIGA2**122015 2015-12-16 2,016 3 0.27 2015-12-30
16,073 fbd1d4d518cf49e6d79dba77657d10f9 FIGA2**122015 2015-12-16 2,016 3 0.73 2015-12-30
16,074 fbd1d4d518cf49e6d79dba77657d10f9 FIGA2**122015 2015-12-16 2,016 3 1.03 2015-12-30
30,381 6d533b6ebd408688d42997b791dd8a75 MARA2**111917 1917-11-04 2,016 102 98.92 2016-10-05
30,382 6d533b6ebd408688d42997b791dd8a75 MARA2**111917 1917-11-04 2,016 102 99.11 2016-10-05
30 6d672b5d02dada8fd6ad1c3a38a69b7f LUGO2**022015 2015-02-25 2,015 4 0.23 2015-05-06
31 6d672b5d02dada8fd6ad1c3a38a69b7f LUGO2**022015 2015-02-25 2,015 4 0.48 2015-05-06
147 cf1c08352647546c64c5e9db55c3c7d9 JOLU1**052015 2015-05-14 2,015 4 0.00 2015-05-14
148 cf1c08352647546c64c5e9db55c3c7d9 JOLU1**052015 2015-05-14 2,015 4 0.25 2015-05-14
208 e23a1738301d1ce325d16cae54a4138d MAMA2**051999 1999-05-04 2,015 20 16.13 2015-05-14
293 c7713fe3e6fa0975435ea391005c5d60 KAJO1**051998 1998-05-05 2,015 21 17.25 2015-05-20
294 c7713fe3e6fa0975435ea391005c5d60 KAJO1**051998 1998-05-05 2,015 21 17.30 2015-05-20
302 7bb2cbe20c6cfefb16c861d7bac904a5 TIHE2**052015 2015-05-04 2,015 4 0.10 2015-05-06
303 7bb2cbe20c6cfefb16c861d7bac904a5 TIHE2**052015 2015-05-04 2,015 4 0.25 2015-05-06
480 afa9a46f43298983918d3f5a9d821786 ALBR1**052015 2015-05-19 2,015 4 0.02 2015-05-19
481 afa9a46f43298983918d3f5a9d821786 ALBR1**052015 2015-05-19 2,015 4 0.02 2015-05-19
657 26228558eb3142a325eb9793b7a7643c GEPI1**052015 2015-05-19 2,015 4 0.29 2015-05-19
658 26228558eb3142a325eb9793b7a7643c GEPI1**052015 2015-05-19 2,015 4 0.29 2015-05-19
659 26228558eb3142a325eb9793b7a7643c GEPI1**052015 2015-05-19 2,015 4 0.29 2015-05-19
683 8e5281ff55f663f7f125e66b1eed5e30 CLNE1**052015 2015-05-23 2,015 4 0.11 2015-05-27
684 8e5281ff55f663f7f125e66b1eed5e30 CLNE1**052015 2015-05-23 2,015 4 0.25 2015-05-27
689 8e3ee020d0e375a5465551855beef0d9 IBFR1**052015 2015-05-27 2,015 4 0.10 2015-05-27
690 8e3ee020d0e375a5465551855beef0d9 IBFR1**052015 2015-05-27 2,015 4 0.21 2015-05-27
747 5d8091081085c2019f231071c383dea0 JOGU1**042015 2015-04-22 2,015 4 0.10 2015-05-04
748 5d8091081085c2019f231071c383dea0 JOGU1**042015 2015-04-22 2,015 4 0.32 2015-05-04
782 71c03223af1ce120cdfb9c0b55b4db53 ROTR1**052015 2015-05-20 2,015 4 0.41 2015-05-04
783 71c03223af1ce120cdfb9c0b55b4db53 ROTR1**052015 2015-05-20 2,015 4 0.40 2015-05-04
906 0d5782868d35d58892a0f938813f7608 EDHO1**052015 2015-05-04 2,015 4 0.57 2015-05-04
907 0d5782868d35d58892a0f938813f7608 EDHO1**052015 2015-05-04 2,015 4 0.57 2015-05-04
927 d062b037a6143d839d0cade6d0e78880 DIOR1**092014 2014-09-18 2,015 5 0.62 2015-05-04
928 d062b037a6143d839d0cade6d0e78880 DIOR1**092014 2014-09-18 2,015 5 1.01 2015-05-04
1,105 9097ba34be47c5e4769cbb380b1a19c3 SOHU2**052015 2015-05-06 2,015 4 0.13 2015-05-08
1,123 a0c21e91c5df0785a42f2800657231db RASE1**052015 2015-05-12 2,015 4 0.10 2015-06-01
1,613 c9c50b35412a1b9a7a1ff05c6291897d CAGO2**022015 2015-02-18 2,015 4 0.30 2015-06-08
1,886 c6cd0322a5d9822fe6a73aa31e8ed67f JUSO1**111999 1999-11-20 2,015 19 15.57 2015-06-15
2,148 943a75043cdf1c7c23e056155cda3b74 CICH2**091997 1997-09-23 2,015 22 17.72 2015-06-01
2,187 8b9ede9e8fda1c93f5864009a1e18767 JUBA1**062015 2015-06-26 2,015 4 0.00 2015-06-26
2,381 18f1041924d48c9440b4b397267a58f2 MAAL1**062015 2015-06-04 2,015 4 0.05 2015-06-22
2,382 18f1041924d48c9440b4b397267a58f2 MAAL1**062015 2015-06-04 2,015 4 0.27 2015-06-22
2,537 9630831102f1ea74e3bd52359c335933 PAVI1**062015 2015-06-30 2,015 4 0.14 2015-06-08
2,538 9630831102f1ea74e3bd52359c335933 PAVI1**062015 2015-06-30 2,015 4 0.14 2015-06-08
2,603 eee917660c6c73d0554e56dfddf8e543 ARSO1**062015 2015-06-03 2,015 4 0.21 2015-06-01
2,604 eee917660c6c73d0554e56dfddf8e543 ARSO1**062015 2015-06-03 2,015 4 0.21 2015-06-01
2,605 eee917660c6c73d0554e56dfddf8e543 ARSO1**062015 2015-06-03 2,015 4 0.21 2015-06-01
2,665 1ccc23513d404cbc37a37b1bc3b7ce77 RASE1**062015 2015-06-01 2,015 4 0.12 2015-06-08
2,666 1ccc23513d404cbc37a37b1bc3b7ce77 RASE1**062015 2015-06-01 2,015 4 0.34 2015-06-08
2,776 dd4b4ceef916a24a277300e57d15847a PEME1**042015 2015-04-22 2,015 4 0.21 2015-07-08
2,823 3694089f54b38b8d0a07079102a1e3dc CLHE1**052015 2015-05-06 2,015 4 0.57 2015-07-09
2,824 3694089f54b38b8d0a07079102a1e3dc CLHE1**052015 2015-05-06 2,015 4 0.40 2015-07-09
2,910 40dca59840a518a1c1419b462180edd2 ITQU1**042015 2015-04-02 2,015 4 0.30 2015-06-18
3,023 add12d3ddf9271f589c627bbc79c4579 MICA1**072015 2015-07-20 2,015 4 0.00 2015-07-20
3,024 add12d3ddf9271f589c627bbc79c4579 MICA1**072015 2015-07-20 2,015 4 0.25 2015-07-20
3,260 d2ed9aa7d6555131b46251846e8b8e7b RUCA2**072015 2015-07-27 2,015 4 0.00 2015-07-27
3,261 d2ed9aa7d6555131b46251846e8b8e7b RUCA2**072015 2015-07-27 2,015 4 0.29 2015-07-27
3,361 00dae568a8572f86a9cc69b7281ed7ee ANEC1**041999 1999-04-18 2,015 20 16.30 2015-07-17
3,362 00dae568a8572f86a9cc69b7281ed7ee ANEC1**041999 1999-04-18 2,015 20 16.47 2015-07-17
3,388 65f57f0a6a732ea3780954d538a75428 SECA1**032015 2015-03-17 2,015 4 0.39 2015-07-07
3,630 78137d8b5d25f99a46ac5e27f740dfa9 MADI2**072015 2015-07-27 2,015 4 0.00 2015-07-27
3,664 70597f2a60f6148d50cf465be1a3edb5 MAVI1**012015 2015-01-06 2,015 4 0.56 2015-07-29
3,665 70597f2a60f6148d50cf465be1a3edb5 MAVI1**012015 2015-01-06 2,015 4 0.73 2015-07-29
3,666 70597f2a60f6148d50cf465be1a3edb5 MAVI1**012015 2015-01-06 2,015 4 0.56 2015-07-29
3,667 70597f2a60f6148d50cf465be1a3edb5 MAVI1**012015 2015-01-06 2,015 4 0.56 2015-07-29
3,876 a26b1ac1007562c9e19cd5163a68c2ec KAAN2**072015 2015-07-25 2,015 4 -0.03 2015-06-11
3,877 a26b1ac1007562c9e19cd5163a68c2ec KAAN2**072015 2015-07-25 2,015 4 -0.03 2015-06-11
3,878 a26b1ac1007562c9e19cd5163a68c2ec KAAN2**072015 2015-07-25 2,015 4 0.28 2015-06-11
3,974 7c7a382fbee4f066ddc8afb03be3c826 EVGA2**062015 2015-06-04 2,015 4 0.21 2015-06-04
3,975 7c7a382fbee4f066ddc8afb03be3c826 EVGA2**062015 2015-06-04 2,015 4 0.21 2015-06-04
3,985 56b3ce1aba672f5ac1f78a849709d430 LUVI1**062015 2015-06-21 2,015 4 0.12 2015-07-09
4,048 b9ea83c2b1fbdb556749399b4efd327f JAAL1**102014 2014-10-22 2,015 5 0.76 2015-05-22
4,049 b9ea83c2b1fbdb556749399b4efd327f JAAL1**102014 2014-10-22 2,015 5 0.85 2015-05-22
4,101 84fca3988c6479194fd5709bf61e0139 MIVI1**052015 2015-05-30 2,015 4 0.24 2015-08-03
4,173 43ff82a0657a99412f9af20ed2e9d79d JORE1**082015 2015-08-04 2,015 4 0.01 2015-08-05
4,174 43ff82a0657a99412f9af20ed2e9d79d JORE1**082015 2015-08-04 2,015 4 0.33 2015-08-05
4,192 21803297911589576d8ddacd79b5d55d CAMA2**082015 2015-08-04 2,015 4 0.01 2015-08-10
4,194 c667fcdcaa8983d2d0d3e805c762543a JUHE1**082015 2015-08-04 2,015 4 0.04 2015-08-04
4,223 c93824e1813aef5dcc4f242e999a6473 PAAD1**072014 2014-07-27 2,015 5 1.02 2015-08-05
4,224 c93824e1813aef5dcc4f242e999a6473 PAAD1**072014 2014-07-27 2,015 5 1.34 2015-08-05
4,449 1ec175566a7db6b94d4caf1b5b60be1e ROFL1**052015 2015-05-22 2,015 4 0.27 2015-08-06
4,466 4ba0607a91d0e839fbd5427c855d30ec DICE1**022014 2014-02-06 2,015 5 1.62 2015-08-17
4,509 572a0c1831cd90fd046f1ebc1d67994a LUGO1**082015 2015-08-10 2,015 4 0.14 2015-08-10
4,510 572a0c1831cd90fd046f1ebc1d67994a LUGO1**082015 2015-08-10 2,015 4 0.30 2015-08-10
4,511 a254177942d2718cb7382ba4d0b5464e PAZA2**082015 2015-08-05 2,015 4 0.04 2015-08-19
4,512 a254177942d2718cb7382ba4d0b5464e PAZA2**082015 2015-08-05 2,015 4 0.23 2015-08-19
4,576 3f77045375df8c1ce7e74ae8c36846ff JACA1**062015 2015-06-16 2,015 4 0.19 2015-08-03
4,588 a27e91c389821d524dd9d317dfb489bf ALVA1**071999 1999-07-14 2,015 20 16.40 2015-08-18
4,589 a27e91c389821d524dd9d317dfb489bf ALVA1**071999 1999-07-14 2,015 20 16.40 2015-08-18
4,637 d5f73b599c69578e2436b8292eaa8cf2 MAAS2**062015 2015-06-15 2,015 4 0.16 2015-08-01
4,759 48358f8da531b84db0c85f3639a09a04 LUCA1**022015 2015-02-15 2,015 4 0.52 2015-08-25
5,213 153b828278ea88dc5ab15039e3e0c882 JONA1**012015 2015-01-21 2,015 4 0.56 2015-08-13
5,214 153b828278ea88dc5ab15039e3e0c882 JONA1**012015 2015-01-21 2,015 4 0.85 2015-08-13
5,234 87afef689812b1ad6f576f0e83e5c963 DABU1**032015 2015-03-04 2,015 4 0.49 2015-08-21
5,235 87afef689812b1ad6f576f0e83e5c963 DABU1**032015 2015-03-04 2,015 4 0.73 2015-08-21
5,336 b05925d3aec79d9c5328ce69353e0fdd UBFU2**082015 2015-08-11 2,015 4 0.00 2015-08-11
5,357 d83f8f183a061c8664e76b2a4e629a8e CRPE1**072015 2015-07-20 2,015 4 0.08 2015-08-17
5,358 d83f8f183a061c8664e76b2a4e629a8e CRPE1**072015 2015-07-20 2,015 4 0.30 2015-08-17
5,359 1cca291c439af8de53eefaad88ffedde DADU2**052015 2015-05-25 2,015 4 0.49 2015-05-25
5,360 f40e50584376f17c5209a446aded88a5 MACA1**082015 2015-08-20 2,015 4 0.02 2015-08-20
5,393 1fcf34aa0db73c4218e224628c6173f9 GUVE1**082015 2015-08-20 2,015 4 0.00 2015-08-20
5,394 1fcf34aa0db73c4218e224628c6173f9 GUVE1**082015 2015-08-20 2,015 4 0.27 2015-08-20
5,567 761faab7fc70227180be6e64b167a957 JOGA1**051998 1998-05-09 2,015 21 17.42 2015-08-18
5,630 82fbe76a90e5ec2d518f1f4b01ed76cc DATA1**092015 2015-09-08 2,015 4 0.01 2015-09-08
5,793 78b53d7e099c895d0b0c9b0f73188539 HUPO1**092015 2015-09-08 2,015 4 -0.07 2015-09-03
5,797 4124ab2d1b1d4b06b9fe7c82f35087c2 HERO2**062015 2015-06-16 2,015 4 0.25 2015-09-03
5,912 f739264a92a4019c5875a5dc2cfa6e9a CEME1**092015 2015-09-02 2,015 4 0.13 2015-09-02
5,980 71a82aae5397aa241fa4dada09813f9f SEPA1**022015 2015-02-14 2,015 4 0.70 2015-08-25
5,981 71a82aae5397aa241fa4dada09813f9f SEPA1**022015 2015-02-14 2,015 4 0.70 2015-08-25
5,982 71a82aae5397aa241fa4dada09813f9f SEPA1**022015 2015-02-14 2,015 4 0.84 2015-08-25
6,239 9d1d56fca162aa59f99988e6cafcc296 JOAR1**092015 2015-09-29 2,015 4 0.02 2015-09-29
6,319 102549a4c11ce58e49578d24fb673216 MAAG2**011998 1998-01-01 2,015 21 17.88 2015-09-28
6,320 102549a4c11ce58e49578d24fb673216 MAAG2**011998 1998-01-01 2,015 21 17.88 2015-09-28
6,606 8f7e43aaea738d130d9cc03b55666295 VESO2**082015 2015-08-22 2,015 4 0.05 2015-09-08
6,607 8f7e43aaea738d130d9cc03b55666295 VESO2**082015 2015-08-22 2,015 4 0.30 2015-09-08
6,614 759bb1cc0dbe203e51d79e5767d5aa26 FECA1**022000 2000-02-17 2,015 19 15.60 2015-08-31
6,615 759bb1cc0dbe203e51d79e5767d5aa26 FECA1**022000 2000-02-17 2,015 19 15.60 2015-08-31
6,623 761faab7fc70227180be6e64b167a957 JOGA1**051998 1998-05-09 2,015 21 17.34 2015-08-13
6,642 5ea1c7ba606efe2df58e49e360312e75 ROZU1**092015 2015-09-22 2,015 4 0.00 2015-09-22
6,683 3bb60d41dee218ae5ae3846057d48b95 DACA2**092015 2015-09-10 2,015 4 0.06 2015-09-11
6,684 3bb60d41dee218ae5ae3846057d48b95 DACA2**092015 2015-09-10 2,015 4 0.06 2015-09-11
6,727 3d108204060b79bd179ed442cd81c510 VAUR2**082015 2015-08-07 2,015 4 0.09 2015-08-24
6,728 3d108204060b79bd179ed442cd81c510 VAUR2**082015 2015-08-07 2,015 4 0.09 2015-08-24
6,762 2b45294e123346196cd1849a73934b5f MAEU2**092015 2015-09-29 2,015 4 0.06 2015-10-01
6,794 b36bc9b83aee187478fa1c1b30435529 ASRO2**082015 2015-08-15 2,015 4 0.21 2015-10-07
6,795 b36bc9b83aee187478fa1c1b30435529 ASRO2**082015 2015-08-15 2,015 4 0.30 2015-10-07
6,901 06faceb13defbd9a1bb63e22913ab1bb JALO2**102015 2015-10-06 2,015 4 0.02 2015-10-09
6,926 1dfa0e65e45414b0dc071b61d1bac1a9 PAGU2**082015 2015-08-11 2,015 4 0.18 2015-09-30
6,927 1dfa0e65e45414b0dc071b61d1bac1a9 PAGU2**082015 2015-08-11 2,015 4 0.18 2015-09-30
7,080 8ce26be094be2c7073d8e0d61084747c ANNE1**102015 2015-10-13 2,015 4 0.00 2015-10-13
7,157 ca640aaf41385bea310d7b73f7cdba25 RARO1**042015 2015-04-09 2,015 4 0.55 2015-08-26
7,181 c3b01dd3a677c39d5637e50fedd82639 ANPA2**032001 2001-03-09 2,015 18 14.64 2015-09-08
7,182 c3b01dd3a677c39d5637e50fedd82639 ANPA2**032001 2001-03-09 2,015 18 14.75 2015-09-08
7,189 2a45cdd1da2cdc927c90e5b193750c31 FRME1**091997 1997-09-12 2,015 22 17.98 2015-08-27
7,231 bbad2b97c37995c0bd0acc4d7086cd76 FRCA1**012015 2015-01-16 2,015 4 0.73 2015-10-29
7,232 0616f30596be9524fbee513d685d2932 ROBE1**092015 2015-09-15 2,015 4 0.12 2015-09-15
7,238 4c7781ef7fdf301ebff0b016367c4df4 HEIB1**092015 2015-09-28 2,015 4 0.10 2015-10-01
7,253 f02435c816333b972c7e14ec4cf3a6ad LUPA1**012015 2015-01-10 2,015 4 0.80 2015-10-29
7,268 1ec3f2c2efb06ba95486d0324984a867 MERO2**022015 2015-02-13 2,015 4 0.71 2015-08-24
7,269 1ec3f2c2efb06ba95486d0324984a867 MERO2**022015 2015-02-13 2,015 4 0.79 2015-08-24
7,296 17427b747a8e5d8ecdcdec6a781a277b MAZU1**102015 2015-10-21 2,015 4 0.02 2015-10-27
7,312 84959e1b6bdc461afd0af4669917bf52 YERA2**072015 2015-07-13 2,015 4 0.38 2015-10-29
7,313 84959e1b6bdc461afd0af4669917bf52 YERA2**072015 2015-07-13 2,015 4 0.30 2015-10-29
7,514 9be202d4f43568a978050002a518f718 UBSA2**092015 2015-09-01 2,015 4 0.10 2015-09-01
7,515 9be202d4f43568a978050002a518f718 UBSA2**092015 2015-09-01 2,015 4 0.10 2015-09-01
7,567 8e572a0351477ce0b449d79e728935c3 ANSE2**102015 2015-10-20 2,015 4 0.01 2015-10-20
7,611 10a7d484048197952a402d58199638f0 ROES2**102015 2015-10-05 2,015 4 0.03 2015-10-15
7,625 7c3f75ebb78cd4400b64cd994f7677a9 ALPA1**032015 2015-03-26 2,015 4 0.60 NA
7,725 11e10f01088fec73c3faf6c1e4c262d2 NIGA1**022015 2015-02-12 2,015 4 0.78 2015-11-02
7,845 2576e8499b6e9b9c0eb33037741df8d6 ALIN1**112015 2015-11-19 2,015 3 0.00 2015-11-19
7,941 9d13f7974119875c68872c9bb1315169 CRGA1**102015 2015-10-22 2,015 4 0.00 2015-10-20
7,957 92d3465484cf355dc4e3127ebcf59720 CRAL1**112015 2015-11-05 2,015 4 0.00 2015-09-07
8,060 a537681987ddbe972f0f090b10a699c3 RESA1**102015 2015-10-08 2,015 4 0.09 2015-10-14
8,074 265be787457ad78a6fe5261ae67f5ac3 SITR2**121998 1998-12-05 2,015 20 16.97 2015-11-24
8,184 99184c090c1d5c6b346ee82ab4b267ff JUAS1**112015 2015-11-14 2,015 3 0.05 2015-11-18
8,185 99184c090c1d5c6b346ee82ab4b267ff JUAS1**112015 2015-11-14 2,015 3 0.05 2015-11-18
8,359 2d81681c6238ee6cf0b4ca1d6a7cf8db RIZA1**102015 2015-10-17 2,015 4 0.18 NA
8,380 5353f1030f5fe554f789c03b039909fc LUCO1**081999 1999-08-26 2,015 20 16.31 2015-11-12
8,393 d558c1ea730f400eb75c21566c96c113 MAMO1**011999 1999-01-11 2,015 20 16.84 2015-11-13
8,401 32c4704bfeef5a559ca9c67197cee23e JOSO1**092015 2015-09-12 2,015 4 0.25 2015-11-27
8,438 be1d9df5df199a1d40f36b1a5b63d4ca JOFA1**102000 2000-10-20 2,015 19 15.13 2015-12-02
8,449 68a1abebf175673e62b97b65fde63b50 BRHE1**092015 2015-09-28 2,015 4 0.20 2015-12-09
8,490 a5b8d096bb62e725e52b61fb28aed343 EDLE1**032015 2015-03-26 2,015 4 0.72 2015-12-01
8,518 95da9c51b0a9acafac6c4da28a96d3fa GURI1**112015 2015-11-08 2,015 4 0.06 2015-12-01
8,521 3e59c66a1aebcb3d9fdfa1d3bbc1a85c ROAL1**012015 2015-01-10 2,015 4 0.92 2015-12-09
8,526 a2593b9c1e36f77aa936f7a1a820d4cd JUGO1**112015 2015-11-12 2,015 3 0.00 2015-11-12
8,616 940072c88254c0601c2f9af1d52bdfa8 JOCO1**092015 2015-09-29 2,015 4 0.22 2015-12-16
8,654 4f9fee24532da33014e17e37a7ba2634 DYBU1**051999 1999-05-20 2,015 20 16.59 2015-12-11
8,669 17977a7b3a5dbf99fa59d284bd7e2fe1 VIRA1**042000 2000-04-17 2,015 19 15.64 2015-12-02
8,717 c4d30a1c4557dc3a71da5f034ba3fc57 JUCU1**112015 2015-11-27 2,015 3 0.00 2015-11-27
8,824 d196f8ff1ac48677345bccd7c76b7fea NICO2**102015 2015-10-07 2,015 4 0.21 2015-12-21


An important number of cases were available in the TOP dataset, permitting replacing invalid ages with the right information. Must note that 31 cases had around 17 years of the 43 cases that were not adults at the time of TOP. The next table presents those HASHs that had other registries with a valid age.


#list of distinct HASHs that have a wrongly assigned age
CONS_TOP_df_dup_ENE_2020_prev1 %>% 
    dplyr::mutate(Edad_at_ap=lubridate::time_length(difftime(as.Date(fech_ap_top), as.Date(fech_nac)),"years")) %>% #AGREGADO EN APR 2020.
  dplyr::mutate(Edad_at_ap=replace(Edad_at_ap, is.na(fech_nac), NA)) %>% #AGREGADO EN APR 2020.
  #dplyr::filter(Edad<18|Edad>90) %>%
  dplyr::filter(Edad_at_ap>=18,Edad_at_ap<=90) %>%
  #dplyr::filter(Edad<18|Edad>90) %>%
#  dplyr::mutate(Edad_al_ing=round(Edad_al_ing,2))%>%
#  dplyr::filter(Edad<18|Edad>90) %>%
   dplyr::select(HASH_KEY,fech_ap_top, Edad_at_ap) %>%
  dplyr::group_by(HASH_KEY,fech_ap_top) %>% 
  mutate(rn = row_number()) %>%
  dplyr::ungroup()%>%
  assign("distinct_hash_correct_age_adm_top",., envir = .GlobalEnv)
#dim(distinct_hash_wrong_age2_adm_top) #288 casos

dates_replace_invalid_value <- CONS_TOP_df_dup_ENE_2020_prev1 %>%
      dplyr::mutate(Edad_at_ap=lubridate::time_length(difftime(as.Date(fech_ap_top), as.Date(fech_nac)),"years")) %>% #AGREGADO EN APR 2020.
      dplyr::mutate(Edad_at_ap=replace(Edad_at_ap, is.na(fech_nac), NA)) %>% #AGREGADO EN APR 2020.
          #dplyr::filter(Edad<18|Edad>90) %>%
      dplyr::group_by(HASH_KEY,Edad_at_ap) %>% 
      dplyr::mutate(rn = row_number()) %>%
      dplyr::ungroup()%>%
      dplyr::left_join(distinct_hash_correct_age_adm_top, by=c("HASH_KEY", "fech_ap_top","rn"), suffix=c("",".corr_age_adm_top")) %>% #mantiene la misma cantidad de filas
      dplyr::arrange(HASH_KEY) %>% #order by hashs 
      #dplyr::filter(Edad<18|Edad>90) %>%
        dplyr::filter(Edad_at_ap<18|Edad_at_ap>90) %>%
        dplyr::filter(!is.na(Edad_at_ap.corr_age_adm_top)) %>% nrow()

#Then, apply these cases to the whole population
CONS_TOP_df_dup_ENE_2020_prev1 %>%
dplyr::mutate(Edad_at_ap=lubridate::time_length(difftime(as.Date(fech_ap_top), as.Date(fech_nac)),"years")) %>% #AGREGADO EN APR 2020.
dplyr::mutate(Edad_at_ap=replace(Edad_at_ap, is.na(fech_nac), NA)) %>% #AGREGADO EN APR 2020.
    #dplyr::filter(Edad<18|Edad>90) %>%
dplyr::group_by(HASH_KEY,Edad_at_ap) %>% 
dplyr::mutate(rn = row_number()) %>%
dplyr::ungroup()%>%
dplyr::left_join(distinct_hash_correct_age_adm_top, by=c("HASH_KEY", "fech_ap_top","rn"), suffix=c("",".corr_age_adm_top")) %>% #mantiene la misma cantidad de filas
dplyr::arrange(HASH_KEY) %>% #order by hashs 
#dplyr::filter(Edad<18|Edad>90) %>%
  dplyr::filter(Edad_at_ap<18|Edad_at_ap>90) %>%
  dplyr::filter(!is.na(Edad_at_ap.corr_age_adm_top)) %>% #nrow() # sólo 1 caso.
  dplyr::select(row, ano_bd, HASH_KEY, id_mod, fech_nac, ano_bd,Edad, Edad_at_ap,Edad_at_ap.corr_age_adm_top,fech_ing, fech_ap_top, everything()) %>%
  dplyr::select(-ID) %>%
 knitr::kable(.,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
               caption="Table 6. Total cases with wrong ages at the time of admission, but their HASH had a valid age along the dataset",
                 align =rep('c', 101))  %>%
   kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 8) %>%
  kableExtra::scroll_box(width = "100%", height = "150px")
Table 6. Total cases with wrong ages at the time of admission, but their HASH had a valid age along the dataset
row ano_bd HASH_KEY id_mod fech_nac Edad Edad_at_ap Edad_at_ap.corr_age_adm_top fech_ing fech_ap_top hash_rut_completo TABLE Fecha.Aplicación.TOP Nombre.Apliacador.del.TOP TOP Etapa.del.Tratamiento Fecha.Nacimiento Sexo Fecha.de.Ingreso.a.Tratamiento Plan.de.Tratamiento Nombre.del.Centro Tipo.Centro Sustancia.Principal.1 Sustancia.Principal.2 Sustancia.Principal.3 Total.OH Dósis.OH Total.THC Dósis.THC Total.PBC Dósis.PBC Total.COC Dósis.COC Total.BZD Dósis.BZD Total.Otra Dósis.Otra Hurto Robo Venta.Drogas Riña Total.VIF Otro Total.Transgresión Salud.Psicológica Total.Trabajo Total.Educación Salud.Física Lugar.Vivir Vivienda Calidad.Vida Región.Centro Comentario fech_ing_sin_fmt OBS fech_ing_na row_leftjoin fech_ing_C1 Edad_al_ing rn
13,479 2,016 1ec3f2c2efb06ba95486d0324984a867 MERO2**022015 2015-02-13 4 1.609856 56.61054 2015-08-24 2016-09-23 NA top2016 2016-09-23 viviana Tratamiento Seguimiento 9 meses 13/02/2015 Mujer 24/08/2015 PG-PAI COSAM El Bosque publico Alcohol NA NA 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 METROPOLITANA no desea realizar llenado 24/08/2015 2015-08-24 NA NA 0.5256674 1


As seen in Table 6, only 1 value could be replaced. We replaced SENDA ID, age, ID and date of birth to this case, to get a much cleaner dataset of valid cases.

One observation that comes from these transformations, is that many of TOPs applications that had missing values in one value, had missing values in many others more. Possibly, there were duplicated events, and some of them were invalid. If this is true, we only need to discard events with incomplete data instead of replacing them with valid information. But first, we need to check whether this happens frequently or not. This has to be contrasted in the following stages of data preparation.


#PARA OBTENER LAS EDADES DE APLICACION VALIDAS POR FECHA DE APLICACION DEL TOP
CONS_TOP_df_dup_ENE_2020_prev1 %>% 
    dplyr::mutate(Edad_at_ap=lubridate::time_length(difftime(as.Date(fech_ap_top), as.Date(fech_nac)),"years")) %>% #AGREGADO EN APR 2020.
  dplyr::mutate(Edad_at_ap=replace(Edad_at_ap, is.na(fech_nac), NA)) %>% #AGREGADO EN APR 2020.
  #dplyr::filter(Edad<18|Edad>90) %>%
  dplyr::filter(Edad_at_ap>=18,Edad_at_ap<=90) %>%
  #dplyr::filter(Edad<18|Edad>90) %>%
#  dplyr::mutate(Edad_al_ing=round(Edad_al_ing,2))%>%
#  dplyr::filter(Edad<18|Edad>90) %>%
   #dplyr::select(HASH_KEY,fech_ap_top, Edad_at_ap) %>%
  dplyr::group_by(HASH_KEY,fech_ap_top) %>% 
  mutate(rn = row_number()) %>%
  dplyr::ungroup()%>%
  assign("distinct_hash_correct_age_adm_top2",., envir = .GlobalEnv)
#dim(distinct_hash_wrong_age2_adm_top) #288 casos

CONS_TOP_df_dup_ENE_2020_prev1 %>%
      dplyr::mutate(Edad_at_ap=lubridate::time_length(difftime(as.Date(fech_ap_top), as.Date(fech_nac)),"years")) %>% #AGREGADO EN APR 2020.
      dplyr::mutate(Edad_at_ap=replace(Edad_at_ap, is.na(fech_nac), NA)) %>% #AGREGADO EN APR 2020.
          #dplyr::filter(Edad<18|Edad>90) %>%
      dplyr::group_by(HASH_KEY,Edad_at_ap) %>% 
      dplyr::mutate(rn = row_number()) %>%
      dplyr::ungroup()%>%
      dplyr::left_join(distinct_hash_correct_age_adm_top2, by=c("HASH_KEY", "fech_ap_top","rn"), suffix=c("",".corr_age_adm_top")) %>% 
#Join datasets 
    dplyr::mutate(OBS=case_when((Edad_at_ap<18 & !is.na(Edad_at_ap.corr_age_adm_top))|(Edad_at_ap>90 & !is.na(Edad_at_ap.corr_age_adm_top))~glue::glue("{OBS};1.3.Replaced invalid age at TOP application"),
                                TRUE ~ OBS))%>% 
    dplyr::mutate(OBS=case_when(Edad_at_ap<18|Edad_at_ap>90~glue::glue("{OBS};1.3.Invalid Age at the Time of Application of TOP"),
                                TRUE ~ OBS))%>% 
    dplyr::mutate(ID= ifelse((Edad_at_ap<18 & !is.na(Edad_at_ap.corr_age_adm_top))|(Edad_at_ap>90 & !is.na(Edad_at_ap.corr_age_adm_top)),ID.corr_age_adm_top, ID)) %>%
    dplyr::mutate(id_mod=ifelse((Edad_at_ap<18 & !is.na(Edad_at_ap.corr_age_adm_top))|(Edad_at_ap>90 & !is.na(Edad_at_ap.corr_age_adm_top)),id_mod.corr_age_adm_top,id_mod)) %>% 
    dplyr::mutate(fech_nac=ifelse((Edad_at_ap<18 & !is.na(Edad_at_ap.corr_age_adm_top))|(Edad_at_ap>90 & !is.na(Edad_at_ap.corr_age_adm_top)),fech_nac.corr_age_adm_top,fech_nac))%>%
    dplyr::mutate(Edad=ifelse((Edad_at_ap<18 & !is.na(Edad_at_ap.corr_age_adm_top))|(Edad_at_ap>90 & !is.na(Edad_at_ap.corr_age_adm_top)),Edad.corr_age_adm_top,Edad))%>%      
    dplyr::mutate(fech_nac=as.Date(fech_nac)) %>%
    dplyr::mutate(Edad_at_ap=lubridate::time_length(difftime(as.Date(fech_ap_top), as.Date(fech_nac)),"years")) %>% #AGREGADO EN APR 2020.
    dplyr::mutate(Edad_at_ap=replace(Edad_at_ap, is.na(fech_nac), NA)) %>% #AGREGADO EN APR 2020.
    dplyr::select(-ends_with(".corr_age_adm_top")) %>%
    dplyr::select(-rn,-TABLE,-fech_ing_na, -row_leftjoin, -fech_ing_C1) %>%


##un resumen   
     #dplyr::group_by(Edad) %>% summarise(n=n())  #baja de 273 a 227
#PARA REVISAR
#      dplyr::filter(HASH_KEY %in% as.character(as.vector(unlist(as.data.table(unlist(distinct_hash_wrong_age2_top)))))) %>% # select hashs of wrongly assigned ages. Hay               algunos que no los va a encontrar porque no los tiene, no mas
#        dplyr::select(HASH_KEY, Edad, fech_nac, Edad.y, fech_nac.y, fech_ing, ID, id_mod, ID.y, Edad_al_ing) %>%
#        arrange(HASH_KEY) %>%
#           View() 
#dplyr::select(-id_mod.y, -ID.y, -Edad.y, -fech_nac.y) %>% 
assign("CONS_TOP_df_dup_ENE_2020_prev2",., envir = .GlobalEnv) 
#POR QUÉ ESTE CASO NO QUEDO INCORPORADO  
  #CONS_TOP_df_dup_ENE_2020_prev2 %>% dplyr::filter(!is.na(fech_ing), !is.na(Edad),is.na(Edad_al_ing)) %>% View()
  #CONS_TOP_df_dup_ENE_2020_prev2 %>% dplyr::filter(!is.na(fech_ing), !is.na(Edad),is.na(Edad_al_ing)) %>% 
  #dplyr::select(HASH_KEY, id_mod, Fecha.Aplicación, fech_ing, fech_nac, Edad, Edad_al_ing)
#EL CASO QUEDÓ PERDIDO EN ESTA TRANSFORMACIÓN. INVESTIGAR POR QUÉ, PERO POR MIENTRAS RESOLVERLO MANUALMENTE.
#POR QUÉ ESTE CASO NO QUEDO INCORPORADO  
  #CONS_TOP_df_dup_ENE_2020_prev2 %>% dplyr::filter(!is.na(fech_ing), !is.na(Edad),is.na(Edad_al_ing)) %>% View()
#CONS_TOP_df_dup_ENE_2020_prev2 %>% dplyr::filter(!is.na(fech_ing), !is.na(Edad),is.na(Edad_al_ing)) %>% 
#    dplyr::select(HASH_KEY, id_mod, Fecha.Nacimiento, fech_ing, fech_nac, Edad, Edad_al_ing)
#CONS_TOP_df_dup_ENE_2020_prev2 %>% dplyr::filter(HASH_KEY=="ee0360d19dd5f300526c624c58090c79") %>% 
#    dplyr::select(HASH_KEY, id_mod, Fecha.Nacimiento, fech_ing, fech_nac, Edad, Edad_al_ing)

CONS_TOP_df_dup_ENE_2020_prev2 %>% 
dplyr::select(-TABLE,-fech_ing_na, -row_leftjoin, -fech_ing_C1) %>%
  as.data.frame() %>%
#    dplyr::mutate(Edad_al_ing=lubridate::time_length(difftime(as.Date(fech_ing), as.Date(fech_nac)))/(3*60*60)) %>%
#    dplyr::mutate(Edad_al_ing=replace(Edad_al_ing, is.na(Edad), NA)) %>%
#    dplyr::mutate(fech_nac=ifelse(HASH_KEY=="ee0360d19dd5f300526c624c58090c79",lubridate::as_datetime("1942-08-02"), fech_nac)) %>%
#  dplyr::mutate(fech_nac=as.Date(as.numeric(as.Date(lubridate::ymd(as.character(fech_nac)))))) %>%
#  dplyr::mutate(fech_nac=ifelse(HASH_KEY=="ee0360d19dd5f300526c624c58090c79",lubridate::ymd("1942-08-02"), #lubridate::ymd(as.character(fech_nac)))) %>%
#  dplyr::mutate(fech_nac=as.Date(fech_nac)) %>%
#  dplyr::mutate(Edad_al_ing=ifelse(HASH_KEY=="ee0360d19dd5f300526c624c58090c79",lubridate::time_length(difftime(as.Date(fech_ing), as.Date(fech_nac)),"years"), Edad_al_ing)) %>%
#    dplyr::filter(HASH_KEY=="ee0360d19dd5f300526c624c58090c79"|HASH_KEY=="153b828278ea88dc5ab15039e3e0c882"    ) %>%  #para ver cómo se comporta.
#   dplyr::select(HASH_KEY, id_mod, Fecha.Nacimiento, fech_ing, fech_nac, Edad, Edad_al_ing) %>% View()
assign("CONS_TOP_df_dup_ENE_2020_prev2.2",., envir = .GlobalEnv)

3. Inconsistencies between SENDA ID and HASH Key

    #Take which has a combination of IDs and HASH Keys distinct to the rest
    #With this I may be subestimating the number of cases with a different concatenation.
    #Considering all the distinct combinations, i take what they have a duplicate ID
    #Select the duplicate IDs, it orders and...
    #Filter only cases that has distinct id.
    CONS_TOP_df_dup_ENE_2020_prev2 %>% dplyr::mutate(concat=paste0(ID,"_",HASH_KEY)) %>% 
    dplyr::distinct(concat, .keep_all = TRUE) %>% 
      dplyr::filter(duplicated(ID)) %>% #filter cases that have than one 
  dplyr::arrange(ID) %>%  #filter cases in which there is more than one ID, 
      #despite there is differents combinations of HASH and IDs, and then arrange IDs. This is possible only if a different HASH-Key contains more than one ID or viceversa.
      #take distincts IDs (exclude duplicated repeated IDs)
      dplyr::distinct(ID) %>% 
      assign("ids_more_one_hash_TOP",., envir = .GlobalEnv) # Differently put, take the distints IDs per HASH-Key, of the cases in which there are different combinations                                                      
    # of IDs and hash, and in which subgroup exists duplicated IDs.
    #There are 33 IDs that have more than one HASH key.
    #IMPORTANT: IF THE ID IS DUPLICATED, MIGHT NOT BE REFLECTED IN THIS RESUME IN TERMS OF QUANTITY.

The official individual IDs (RUN) were masked into HASH keys by an informatics professional (For more information about this process, visit the encryption phase). We checked whether they were consistent with each SENDA ID and did not depend on other factors in their identification (eg., individual ID was not well processed), by searching for more than one HASH Key in each SENDA ID. 33 IDs that had more than one HASH key. These 33 IDs affected 183 registries.


    # Then, apply these cases to the whole population. 
    CONS_TOP_df_dup_ENE_2020_prev2 %>%
      dplyr::filter(ID %in% as.character(as.vector(unlist(as.data.table(unlist(ids_more_one_hash_TOP)))))) %>% # Select IDs of cited cases
      dplyr::arrange(ID) %>% #ordeno por ids 
      #183 cases may be affected with this problem
      dplyr::select(row, ano_bd, id_mod, HASH_KEY, hash_rut_completo , Edad, Sexo,fech_ing,Plan.de.Tratamiento, Nombre.del.Centro, Tipo.Centro, Región.Centro, Comentario) %>%
      knitr::kable(.,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
               caption="Table 7. Total registries that each ID have more than one HASH-KEY",
              col.names = c("Row ID","Year of Dataset", "SENDA ID", "HASH KEY", "HASH Key (Alternative)","Year", "Sex", "Date of Admission", "Treatment Plan", "Center", "Type of Center", "Region", "Comment"),
                 align =rep('c', 6))  %>%
  kable_styling(bootstrap_options = c("striped", "hover"),font_size = 8) %>%
            scroll_box(width = "100%", height = "350px")
Table 7. Total registries that each ID have more than one HASH-KEY
Row ID Year of Dataset SENDA ID HASH KEY HASH Key (Alternative) Year Sex Date of Admission Treatment Plan Center Type of Center Region Comment
91,502 2,019 ALCA1**021988 03e477fb3fbca88886a0b4a4e3a23a1e NA 31 Hombre 2018-08-30 PG-PAB CESFAM Boca Sur publico DEL BIO-BIO NA
91,503 2,019 ALCA1**021988 03e477fb3fbca88886a0b4a4e3a23a1e NA 31 Hombre 2018-08-30 PG-PAB CESFAM Boca Sur publico DEL BIO-BIO NA
83,705 2,018 ALCA1**021988 03e477fb3fbca88886a0b4a4e3a23a1e NA 31 Hombre 2018-08-30 PG-PAB CESFAM Boca Sur publico DEL BIO-BIO NA
83,706 2,018 ALCA1**021988 03e477fb3fbca88886a0b4a4e3a23a1e NA 31 Hombre 2018-08-30 PG-PAB CESFAM Boca Sur publico DEL BIO-BIO NA
44,983 2,017 ALCA1**021988 25ac7cdc09cabce1688545ac1453c3c1 NA 31 Hombre 2017-02-21 PG-PAI CT Peulla publico DE LOS LAGOS NA
52,265 2,017 ALDE1**081993 349ebd9b0a0b1597bddac88c1c2831a4 NA 26 Hombre 2017-06-13 PG-PAB CESFAM El Quisco publico DE VALPARAISO NA
52,266 2,017 ALDE1**081993 349ebd9b0a0b1597bddac88c1c2831a4 NA 26 Hombre 2017-06-13 PG-PAB CESFAM El Quisco publico DE VALPARAISO NA
55,225 2,017 ALDE1**081993 e6490438024bc92ae74ae2c3f2b50ac1 NA 26 Hombre 2017-09-01 PG-PAB CESFAM El Quisco publico DE VALPARAISO NA
55,226 2,017 ALDE1**081993 e6490438024bc92ae74ae2c3f2b50ac1 NA 26 Hombre 2017-09-01 PG-PAB CESFAM El Quisco publico DE VALPARAISO NA
55,227 2,017 ALDE1**081993 e6490438024bc92ae74ae2c3f2b50ac1 NA 26 Hombre 2017-09-01 PG-PAB CESFAM El Quisco publico DE VALPARAISO NA
31,442 2,016 ALDE1**081993 e6490438024bc92ae74ae2c3f2b50ac1 NA 26 Hombre 2016-12-09 PG-PAB CESFAM El Quisco publico DE VALPARAISO NA
94,224 2,019 ALMO1**081975 e68d0d83a6d29bd0063ad89b19ac14ee NA 44 Hombre 2018-12-04 PG-PR Comunidad Terapeutica Villamavida privado DEL BIO-BIO NA
85,621 2,018 ALMO1**081975 e68d0d83a6d29bd0063ad89b19ac14ee NA 44 Hombre 2018-12-04 PG-PR Comunidad Terapeutica Villamavida privado DEL BIO-BIO NA
12,674 2,016 ALMO1**081975 df563b42ea36fc66aa5a296e7c34a0a4 NA 44 Hombre 2015-06-18 PG-PAI COSAM La Bandera publico METROPOLITANA NA
6,618 2,015 ALMO1**081975 df563b42ea36fc66aa5a296e7c34a0a4 NA 44 Hombre 2015-06-18 PG-PAI COSAM La Bandera publico METROPOLITANA NA
95,273 2,019 BAHE2**051993 709869d83a4db30a518df175cf9b916a NA 26 Mujer 2019-01-23 M-PR Centro de Tratamiento Residencial Suyai privado DE ATACAMA NA
95,274 2,019 BAHE2**051993 709869d83a4db30a518df175cf9b916a NA 26 Mujer 2019-01-23 M-PR Centro de Tratamiento Residencial Suyai privado DE ATACAMA NA
65,741 2,018 BAHE2**051993 cdd5f1393134a2e63f314c6b93071d9e NA 26 Mujer 2017-10-11 M-PR CT El Buen Samaritano (Mujeres) (CPR Delaia) privado DE COQUIMBO NA
69,627 2,018 BAHE2**051993 709869d83a4db30a518df175cf9b916a NA 26 Mujer 2017-10-11 M-PR Centro de Tratamiento Ayelén privado DE COQUIMBO NA
69,628 2,018 BAHE2**051993 709869d83a4db30a518df175cf9b916a NA 26 Mujer 2017-10-11 M-PR Centro de Tratamiento Ayelén privado DE COQUIMBO NA
69,629 2,018 BAHE2**051993 709869d83a4db30a518df175cf9b916a NA 26 Mujer 2017-10-11 M-PR Centro de Tratamiento Ayelén privado DE COQUIMBO NA
80,622 2,018 BAHE2**051993 709869d83a4db30a518df175cf9b916a NA 26 Mujer 2018-07-11 M-PAI Comunidad Terapeutica Esperanza, Vallenar privado DE ATACAMA NA
80,623 2,018 BAHE2**051993 709869d83a4db30a518df175cf9b916a NA 26 Mujer 2018-07-11 M-PAI Comunidad Terapeutica Esperanza, Vallenar privado DE ATACAMA NA
42,639 2,017 BAHE2**051993 709869d83a4db30a518df175cf9b916a NA 26 Mujer 2017-01-09 PG-PR Comunidad Terapeutica Anawin privado DE ATACAMA NA
42,640 2,017 BAHE2**051993 709869d83a4db30a518df175cf9b916a NA 26 Mujer 2017-01-09 PG-PR Comunidad Terapeutica Anawin privado DE ATACAMA NA
56,705 2,017 BAHE2**051993 cdd5f1393134a2e63f314c6b93071d9e NA 26 Mujer 2017-10-11 M-PR CT El Buen Samaritano (Mujeres) (CPR Delaia) privado DE COQUIMBO NA
53,781 2,017 CABA1**111982 5f8b82f9e556a459fea7c7c9a1e2c6ba NA 36 Hombre 2017-07-20 PG-PR Comunidad Terapeutica Padre Alberto Hurtado privado DE ARICA Y PARINACOTA NA
12,465 2,016 CABA1**111982 5f8b82f9e556a459fea7c7c9a1e2c6ba NA 36 Hombre 2015-09-03 PG-PAI Hospital de Dia de Arica publico DE ARICA Y PARINACOTA NA
12,466 2,016 CABA1**111982 5f8b82f9e556a459fea7c7c9a1e2c6ba NA 36 Hombre 2015-09-03 PG-PAI Hospital de Dia de Arica publico DE ARICA Y PARINACOTA NA
12,467 2,016 CABA1**111982 5f8b82f9e556a459fea7c7c9a1e2c6ba NA 36 Hombre 2015-09-03 PG-PAI Hospital de Dia de Arica publico DE ARICA Y PARINACOTA NA
30,486 2,016 CABA1**111982 5e863eabe503ab0a538991b476939404 NA 36 Hombre 2016-10-06 PG-PAI Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado DE ARICA Y PARINACOTA NA
6,321 2,015 CABA1**111982 5f8b82f9e556a459fea7c7c9a1e2c6ba NA 36 Hombre 2015-09-03 PG-PAI Hospital de Dia de Arica publico DE ARICA Y PARINACOTA NA
6,322 2,015 CABA1**111982 5f8b82f9e556a459fea7c7c9a1e2c6ba NA 36 Hombre 2015-09-03 PG-PAI Hospital de Dia de Arica publico DE ARICA Y PARINACOTA NA
92,127 2,019 CASI1**011993 85431ccd9e5ecbeba44eb7f45f0330ad NA 26 Hombre 2018-10-22 PG-PR Comunidad Terapeutica San Francisco de Asis privado METROPOLITANA NA
105,051 2,019 CASI1**011993 85431ccd9e5ecbeba44eb7f45f0330ad NA 26 Hombre 2019-07-30 PG-PR Complejo Hospitalario San José de Maipo/ Hospital San Jose de Maipo publico METROPOLITANA NA
106,446 2,019 CASI1**011993 d31098d1e5e8a2f314177a9819a7b393 NA 26 Hombre 2019-08-08 PG-PAB CESFAM Chiguayante publico DEL BIO-BIO NA
84,251 2,018 CASI1**011993 85431ccd9e5ecbeba44eb7f45f0330ad NA 26 Hombre 2018-10-22 PG-PR Comunidad Terapeutica San Francisco de Asis privado METROPOLITANA NA
23,347 2,016 CASI1**011993 85431ccd9e5ecbeba44eb7f45f0330ad NA 26 Hombre 2016-05-16 PG-PR Complejo Hospitalario San José de Maipo/ Hospital San Jose de Maipo publico METROPOLITANA NA
104,171 2,019 CAVA2**121997 2c87dde527677b5e51675993af541559 NA 21 Mujer 2019-04-25 M-PAI COSAM Lo Prado publico METROPOLITANA NA
104,172 2,019 CAVA2**121997 2c87dde527677b5e51675993af541559 NA 21 Mujer 2019-04-25 M-PAI COSAM Lo Prado publico METROPOLITANA NA
67,879 2,018 CAVA2**121997 0cec7a200488124671cc4a3ee2d14d3d NA 21 Mujer 2017-11-27 M-PR Comunidad Terapeutica Bellavista (Mujeres) privado METROPOLITANA NA
67,880 2,018 CAVA2**121997 0cec7a200488124671cc4a3ee2d14d3d NA 21 Mujer 2017-11-27 M-PR Comunidad Terapeutica Bellavista (Mujeres) privado METROPOLITANA SE REalizó derivación asistida a COSAM LO PRADO
58,208 2,017 CAVA2**121997 0cec7a200488124671cc4a3ee2d14d3d NA 21 Mujer 2017-11-27 M-PR Comunidad Terapeutica Bellavista (Mujeres) privado METROPOLITANA NA
77,808 2,018 CLPI2**121969 d157e041a1e2e1fc10385e60ecc0d87d NA 49 Mujer 2018-05-30 PG-PAI COSAM Lo Barnechea publico METROPOLITANA NA
38,416 2,017 CLPI2**121969 ecb66b940fe7230e0af1af9d7c7cc8cf NA 49 Mujer 2016-09-29 M-PR Comunidad Terapeutica Crem (Hogares Crem) privado METROPOLITANA NA
38,417 2,017 CLPI2**121969 ecb66b940fe7230e0af1af9d7c7cc8cf NA 49 Mujer 2016-09-29 M-PR Comunidad Terapeutica Crem (Hogares Crem) privado METROPOLITANA NA
38,418 2,017 CLPI2**121969 ecb66b940fe7230e0af1af9d7c7cc8cf NA 49 Mujer 2016-09-29 M-PR Comunidad Terapeutica Crem (Hogares Crem) privado METROPOLITANA NA
38,419 2,017 CLPI2**121969 ecb66b940fe7230e0af1af9d7c7cc8cf NA 49 Mujer 2016-09-29 M-PR Comunidad Terapeutica Crem (Hogares Crem) privado METROPOLITANA NA
29,892 2,016 CLPI2**121969 ecb66b940fe7230e0af1af9d7c7cc8cf NA 49 Mujer 2016-09-29 M-PR Comunidad Terapeutica Crem (Hogares Crem) privado METROPOLITANA NA
18,940 2,016 CRAL1**011980 80e0c1187da0abc82409f95f241e2350 NA 39 Hombre 2016-02-17 PG-PAI COSAM Con Con publico DE VALPARAISO NA
18,941 2,016 CRAL1**011980 80e0c1187da0abc82409f95f241e2350 NA 39 Hombre 2016-02-17 PG-PAI COSAM Con Con publico DE VALPARAISO NA
5,973 2,015 CRAL1**011980 bb59e16f29a42d9478a01ac1f058566e NA 39 Hombre 2015-09-08 PG-PAI COSAM Melipilla publico METROPOLITANA NA
60,708 2,018 CRVA1**091981 01c0bb4b342a90db0e2953edbb1fa034 NA 38 Hombre 2017-03-13 PG-PAB CESFAM El Quisco publico DE VALPARAISO NA
45,464 2,017 CRVA1**091981 01c0bb4b342a90db0e2953edbb1fa034 NA 38 Hombre 2017-03-13 PG-PAB CESFAM El Quisco publico DE VALPARAISO NA
45,465 2,017 CRVA1**091981 01c0bb4b342a90db0e2953edbb1fa034 NA 38 Hombre 2017-03-13 PG-PAB CESFAM El Quisco publico DE VALPARAISO NA
45,466 2,017 CRVA1**091981 01c0bb4b342a90db0e2953edbb1fa034 NA 38 Hombre 2017-03-13 PG-PAB CESFAM El Quisco publico DE VALPARAISO NA
45,467 2,017 CRVA1**091981 01c0bb4b342a90db0e2953edbb1fa034 NA 38 Hombre 2017-03-13 PG-PAB CESFAM El Quisco publico DE VALPARAISO NA
51,597 2,017 CRVA1**091981 c5e3acde9ece03866510277c6804ab37 NA 38 Hombre 2017-05-15 PG-PAI COSAM Estacion Central publico METROPOLITANA NA
78,093 2,018 DACE2**081979 d23e9a7e17afa0907fb418c67cb2bfc0 NA 40 Mujer 2018-04-06 PG-PAI Hospital Graneros publico DEL LIBERTADOR GENERAL BERNARDO OHIGGINS NA
78,094 2,018 DACE2**081979 d23e9a7e17afa0907fb418c67cb2bfc0 NA 40 Mujer 2018-04-06 PG-PAI Hospital Graneros publico DEL LIBERTADOR GENERAL BERNARDO OHIGGINS NA
78,095 2,018 DACE2**081979 d23e9a7e17afa0907fb418c67cb2bfc0 NA 40 Mujer 2018-04-06 PG-PAI Hospital Graneros publico DEL LIBERTADOR GENERAL BERNARDO OHIGGINS NA
78,096 2,018 DACE2**081979 d23e9a7e17afa0907fb418c67cb2bfc0 NA 40 Mujer 2018-04-06 PG-PAI Hospital Graneros publico DEL LIBERTADOR GENERAL BERNARDO OHIGGINS NA
9,345 2,016 DACE2**081979 d064b5c83b804f09489e4dfd020fe2e1 NA 40 Mujer 2015-05-04 PG-PAI Hospital Graneros publico DEL LIBERTADOR GENERAL BERNARDO OHIGGINS NA
864 2,015 DACE2**081979 d064b5c83b804f09489e4dfd020fe2e1 NA 40 Mujer 2015-05-04 PG-PAI Hospital Graneros publico DEL LIBERTADOR GENERAL BERNARDO OHIGGINS NA
90,429 2,019 ELBA2**031985 f47b9efc124bdef08b9c755680538408 NA 34 Mujer 2018-08-14 PG-PAI Unidad de Dependencias CABL 1 publico METROPOLITANA NA
66,933 2,018 ELBA2**031985 f47b9efc124bdef08b9c755680538408 NA 34 Mujer 2017-10-31 PG-PAI Unidad de Dependencias CABL 1 publico METROPOLITANA NA
71,310 2,018 ELBA2**031985 f47b9efc124bdef08b9c755680538408 NA 34 Mujer 2018-02-14 M-PR Comunidad Terapeutica Orion (M-PR) privado METROPOLITANA NA
71,311 2,018 ELBA2**031985 f47b9efc124bdef08b9c755680538408 NA 34 Mujer 2018-02-14 M-PR Comunidad Terapeutica Orion (M-PR) privado METROPOLITANA NA
82,004 2,018 ELBA2**031985 f47b9efc124bdef08b9c755680538408 NA 34 Mujer 2018-08-14 PG-PAI Unidad de Dependencias CABL 1 publico METROPOLITANA NA
82,005 2,018 ELBA2**031985 f47b9efc124bdef08b9c755680538408 NA 34 Mujer 2018-08-14 PG-PAI Unidad de Dependencias CABL 1 publico METROPOLITANA NA
57,611 2,017 ELBA2**031985 f47b9efc124bdef08b9c755680538408 NA 34 Mujer 2017-10-31 PG-PAI Unidad de Dependencias CABL 1 publico METROPOLITANA NA
13,324 2,016 ELBA2**031985 0a5c569599667e08cb0043308f70058a NA 34 Mujer 2015-10-22 PG-PAB COSAM Vitacura publico METROPOLITANA NA
13,325 2,016 ELBA2**031985 0a5c569599667e08cb0043308f70058a NA 34 Mujer 2015-10-22 PG-PAB COSAM Vitacura publico METROPOLITANA NA
13,326 2,016 ELBA2**031985 0a5c569599667e08cb0043308f70058a NA 34 Mujer 2015-10-22 PG-PAB COSAM Vitacura publico METROPOLITANA NA
13,327 2,016 ELBA2**031985 0a5c569599667e08cb0043308f70058a NA 34 Mujer 2015-10-22 PG-PAB COSAM Vitacura publico METROPOLITANA NA
13,328 2,016 ELBA2**031985 0a5c569599667e08cb0043308f70058a NA 34 Mujer 2015-10-22 PG-PAB COSAM Vitacura publico METROPOLITANA NA
7,162 2,015 ELBA2**031985 0a5c569599667e08cb0043308f70058a NA 34 Mujer 2015-10-22 PG-PAB COSAM Vitacura publico METROPOLITANA NA
78,601 2,018 ENRI1**021985 30da8090d1a21cfa15983ba093504bb9 NA 34 Hombre 2018-06-11 PG-PAB CESFAM Monte Patria publico DE COQUIMBO NA
38,290 2,017 ENRI1**021985 5666dcb7e87ff94f0c233544cceb702e NA 34 Hombre 2016-09-13 PG-PR Comunidad Terapeutica Residencial San Paulino de Nola privado DE COQUIMBO NA
38,291 2,017 ENRI1**021985 5666dcb7e87ff94f0c233544cceb702e NA 34 Hombre 2016-09-13 PG-PR Comunidad Terapeutica Residencial San Paulino de Nola privado DE COQUIMBO NA
29,756 2,016 ENRI1**021985 5666dcb7e87ff94f0c233544cceb702e NA 34 Hombre 2016-09-13 PG-PR Comunidad Terapeutica Residencial San Paulino de Nola privado DE COQUIMBO NA
29,757 2,016 ENRI1**021985 5666dcb7e87ff94f0c233544cceb702e NA 34 Hombre 2016-09-13 PG-PR Comunidad Terapeutica Residencial San Paulino de Nola privado DE COQUIMBO NA
87,979 2,019 FRCA1**031989 ae9103ca5fa7cc3ac64074711d825d2a NA 30 Hombre 2018-04-02 PG-PAI Casa Chica Hospital Higueras publico DEL BIO-BIO NA
75,136 2,018 FRCA1**031989 ae9103ca5fa7cc3ac64074711d825d2a NA 30 Hombre 2018-04-02 PG-PAI Casa Chica Hospital Higueras publico DEL BIO-BIO NA
75,137 2,018 FRCA1**031989 ae9103ca5fa7cc3ac64074711d825d2a NA 30 Hombre 2018-04-02 PG-PAI Casa Chica Hospital Higueras publico DEL BIO-BIO NA
75,138 2,018 FRCA1**031989 ae9103ca5fa7cc3ac64074711d825d2a NA 30 Hombre 2018-04-02 PG-PAI Casa Chica Hospital Higueras publico DEL BIO-BIO NA
55,199 2,017 FRCA1**031989 fd436806b9f7ed5cdf23dda329360906 NA 30 Hombre 2017-08-23 PG-PAI PAI Fundacion Casa de La Esperanza Coquimbo (Casa de La Esperanza Coquimbo) privado DE COQUIMBO NA
55,200 2,017 FRCA1**031989 fd436806b9f7ed5cdf23dda329360906 NA 30 Hombre 2017-08-23 PG-PAI PAI Fundacion Casa de La Esperanza Coquimbo (Casa de La Esperanza Coquimbo) privado DE COQUIMBO NA
55,201 2,017 FRCA1**031989 fd436806b9f7ed5cdf23dda329360906 NA 30 Hombre 2017-08-23 PG-PAI PAI Fundacion Casa de La Esperanza Coquimbo (Casa de La Esperanza Coquimbo) privado DE COQUIMBO NA
13,869 2,016 IVGO2**051991 1aa8545cc91d2060c66fcfe7f1df0f70 NA 28 Mujer 2015-10-15 PG-PAI COSAM Macul publico METROPOLITANA NA
16,434 2,016 IVGO2**051991 ca3dcfc02ed5a67095635164ce82a783 NA 28 Mujer 2016-01-19 M-PR Comunidad Terapeutica Crem (Hogares Crem) privado METROPOLITANA NA
58,459 2,017 JOAR1**101981 904aa98b2445a67969c4095ac7b489db NA 38 Hombre 2017-12-11 PG-PAB COSAM Lo Prado publico METROPOLITANA NA
8,692 2,015 JOAR1**101981 51d6c6d1d2948d1c7264532b1dfcc85c NA 38 Hombre 2015-12-10 PG-PAB CESFAM El Quisco publico DE VALPARAISO NA
8,693 2,015 JOAR1**101981 51d6c6d1d2948d1c7264532b1dfcc85c NA 38 Hombre 2015-12-10 PG-PAB CESFAM El Quisco publico DE VALPARAISO NA
86,634 2,019 JOSA1**121979 187778b0c0fa76dd4084166f0870bc3c NA 39 Hombre 2017-10-05 PG-PR Comunidad Terapeutica Manresa privado METROPOLITANA NA
65,754 2,018 JOSA1**121979 187778b0c0fa76dd4084166f0870bc3c NA 39 Hombre 2017-10-05 PG-PR Comunidad Terapeutica Manresa privado METROPOLITANA NA
65,755 2,018 JOSA1**121979 187778b0c0fa76dd4084166f0870bc3c NA 39 Hombre 2017-10-05 PG-PR Comunidad Terapeutica Manresa privado METROPOLITANA NA
65,756 2,018 JOSA1**121979 187778b0c0fa76dd4084166f0870bc3c NA 39 Hombre 2017-10-05 PG-PR Comunidad Terapeutica Manresa privado METROPOLITANA NA
65,757 2,018 JOSA1**121979 187778b0c0fa76dd4084166f0870bc3c NA 39 Hombre 2017-10-05 PG-PR Comunidad Terapeutica Manresa privado METROPOLITANA NA
48,334 2,017 JOSA1**121979 3b0c02f12da06964374c73781e3d5e87 NA 39 Hombre 2017-04-12 PG-PAI COSAM Colina publico METROPOLITANA NA
48,335 2,017 JOSA1**121979 3b0c02f12da06964374c73781e3d5e87 NA 39 Hombre 2017-04-12 PG-PAI COSAM Colina publico METROPOLITANA NA
48,336 2,017 JOSA1**121979 3b0c02f12da06964374c73781e3d5e87 NA 39 Hombre 2017-04-12 PG-PAI COSAM Colina publico METROPOLITANA NA
56,723 2,017 JOSA1**121979 187778b0c0fa76dd4084166f0870bc3c NA 39 Hombre 2017-10-05 PG-PR Comunidad Terapeutica Manresa privado METROPOLITANA NA
69,315 2,018 JOSO1**021963 ba991bc2cee3c621887c98329f905177 NA 56 Hombre 2018-01-22 PG-PAI Unidad de Dependencias CABL 1 publico METROPOLITANA NA
78,633 2,018 JOSO1**021963 b654578dfad0940dd3e04fe919293ac6 NA 56 Hombre 2018-06-12 PG-PAB CESFAM Renaico publico DE LA ARAUCANIA NA
65,990 2,018 JUCA1**051959 4429a2a9d0dd8aa53b45c669a2beea03 NA 60 Hombre 2017-10-03 PG-PAI Fundacion Despertar Caldera (PAI Madre Victoria) privado DE ATACAMA NA
65,991 2,018 JUCA1**051959 4429a2a9d0dd8aa53b45c669a2beea03 NA 60 Hombre 2017-10-03 PG-PAI Fundacion Despertar Caldera (PAI Madre Victoria) privado DE ATACAMA NA
65,992 2,018 JUCA1**051959 4429a2a9d0dd8aa53b45c669a2beea03 NA 60 Hombre 2017-10-03 PG-PAI Fundacion Despertar Caldera (PAI Madre Victoria) privado DE ATACAMA NA
65,993 2,018 JUCA1**051959 4429a2a9d0dd8aa53b45c669a2beea03 NA 60 Hombre 2017-10-03 PG-PAI Fundacion Despertar Caldera (PAI Madre Victoria) privado DE ATACAMA NA
33,389 2,017 JUCA1**051959 d77e9fcd9324ee4dfb090a4897892da8 NA 60 Hombre 2016-02-17 PG-PR Programa Residencial la Roca Valdivia privado DE LOS RIOS NA
56,896 2,017 JUCA1**051959 4429a2a9d0dd8aa53b45c669a2beea03 NA 60 Hombre 2017-10-03 PG-PAI Fundacion Despertar Caldera (PAI Madre Victoria) privado DE ATACAMA NA
18,290 2,016 JUCA1**051959 d77e9fcd9324ee4dfb090a4897892da8 NA 60 Hombre 2016-02-17 PG-PR Programa Residencial la Roca Valdivia privado DE LOS RIOS NA
18,291 2,016 JUCA1**051959 d77e9fcd9324ee4dfb090a4897892da8 NA 60 Hombre 2016-02-17 PG-PR Programa Residencial la Roca Valdivia privado DE LOS RIOS NA
18,292 2,016 JUCA1**051959 d77e9fcd9324ee4dfb090a4897892da8 NA 60 Hombre 2016-02-17 PG-PR Programa Residencial la Roca Valdivia privado DE LOS RIOS NA
106,062 2,019 JUCA1**111979 d7316e24d6b8bb80b1484cb94e699580 NA 39 Hombre 2019-07-26 PG-PAI COSAM Talagante publico METROPOLITANA NA
608 2,015 JUCA1**111979 aca2eab5a84d888e6a4463cbabd4da4d NA 39 Hombre 2015-05-04 PG-PAI COSAM Lo Prado publico METROPOLITANA NA
609 2,015 JUCA1**111979 aca2eab5a84d888e6a4463cbabd4da4d NA 39 Hombre 2015-05-04 PG-PAI COSAM Lo Prado publico METROPOLITANA NA
46,810 2,017 LOPI2**071985 42ab29db4c02e95eeba47d4140eba47a NA 34 Mujer 2017-03-15 PG-PR Comunidad Terapeutica Villamavida privado DEL BIO-BIO NA
46,811 2,017 LOPI2**071985 42ab29db4c02e95eeba47d4140eba47a NA 34 Mujer 2017-03-15 PG-PR Comunidad Terapeutica Villamavida privado DEL BIO-BIO NA
46,812 2,017 LOPI2**071985 42ab29db4c02e95eeba47d4140eba47a NA 34 Mujer 2017-03-15 PG-PR Comunidad Terapeutica Villamavida privado DEL BIO-BIO NA
46,813 2,017 LOPI2**071985 42ab29db4c02e95eeba47d4140eba47a NA 34 Mujer 2017-03-15 PG-PR Comunidad Terapeutica Villamavida privado DEL BIO-BIO NA
29,074 2,016 LOPI2**071985 e51b29ac1edb3668a4da8e7ee49c11ae NA 34 Mujer 2016-09-14 M-PAI Centro de Responsabilidad de Salud Mental del Complejo Asistencial Dr.Victor Rios Ruiz publico DEL BIO-BIO NA
75,383 2,018 LUHE1**011973 0cfe1d9682573288af6adaa2d5500acd NA 46 Hombre 2018-02-13 PG-PAB CESFAM Juan Pablo II publico METROPOLITANA NA
75,384 2,018 LUHE1**011973 0cfe1d9682573288af6adaa2d5500acd NA 46 Hombre 2018-02-13 PG-PAB CESFAM Juan Pablo II publico METROPOLITANA NA
75,385 2,018 LUHE1**011973 0cfe1d9682573288af6adaa2d5500acd NA 46 Hombre 2018-02-13 PG-PAB CESFAM Juan Pablo II publico METROPOLITANA NA
40,610 2,017 LUHE1**011973 46aea7ae7cdabb82bc3e9cfcbf8aa691 NA 46 Hombre 2016-12-09 PG-PAB CESFAM Sagrada Familia publico DEL MAULE NA
40,611 2,017 LUHE1**011973 46aea7ae7cdabb82bc3e9cfcbf8aa691 NA 46 Hombre 2016-12-09 PG-PAB CESFAM Sagrada Familia publico DEL MAULE NA
31,440 2,016 LUHE1**011973 46aea7ae7cdabb82bc3e9cfcbf8aa691 NA 46 Hombre 2016-12-09 PG-PAB CESFAM Sagrada Familia publico DEL MAULE NA
65,331 2,018 LUPA1**041989 b4ca0765e77c9ffede02d3ba7a93c74d NA 30 Hombre 2017-09-27 PG-PR CTR Nehuen (Residencial Melipilla) publico METROPOLITANA NA
65,332 2,018 LUPA1**041989 b4ca0765e77c9ffede02d3ba7a93c74d NA 30 Hombre 2017-09-27 PG-PR CTR Nehuen (Residencial Melipilla) publico METROPOLITANA NA
65,333 2,018 LUPA1**041989 b4ca0765e77c9ffede02d3ba7a93c74d NA 30 Hombre 2017-09-27 PG-PR CTR Nehuen (Residencial Melipilla) publico METROPOLITANA NA
65,334 2,018 LUPA1**041989 b4ca0765e77c9ffede02d3ba7a93c74d NA 30 Hombre 2017-09-27 PG-PR CTR Nehuen (Residencial Melipilla) publico METROPOLITANA NA
65,335 2,018 LUPA1**041989 b4ca0765e77c9ffede02d3ba7a93c74d NA 30 Hombre 2017-09-27 PG-PR CTR Nehuen (Residencial Melipilla) publico METROPOLITANA NA
71,373 2,018 LUPA1**041989 d9e97bb1a2d43c7d31bbaba9ee63674c NA 30 Hombre 2018-02-16 PG-PR Mariano Gonzalez Munoz Prevencion y Salud Mental EIRL. (C.T. Renace San Fernando) privado DEL LIBERTADOR GENERAL BERNARDO OHIGGINS NA
71,374 2,018 LUPA1**041989 d9e97bb1a2d43c7d31bbaba9ee63674c NA 30 Hombre 2018-02-16 PG-PR Mariano Gonzalez Munoz Prevencion y Salud Mental EIRL. (C.T. Renace San Fernando) privado DEL LIBERTADOR GENERAL BERNARDO OHIGGINS NA
56,222 2,017 LUPA1**041989 b4ca0765e77c9ffede02d3ba7a93c74d NA 30 Hombre 2017-09-27 PG-PR CTR Nehuen (Residencial Melipilla) publico METROPOLITANA NA
3,186 2,015 LUPA1**041989 b4ca0765e77c9ffede02d3ba7a93c74d NA 30 Hombre 2015-07-10 PG-PAI COSAM Melipilla publico METROPOLITANA NA
3,187 2,015 LUPA1**041989 b4ca0765e77c9ffede02d3ba7a93c74d NA 30 Hombre 2015-07-10 PG-PAI COSAM Melipilla publico METROPOLITANA NA
3,188 2,015 LUPA1**041989 b4ca0765e77c9ffede02d3ba7a93c74d NA 30 Hombre 2015-07-10 PG-PAI COSAM Melipilla publico METROPOLITANA NA
97,851 2,019 LUSA1**121986 5273bc9e39935d7a754a4269cab031a1 NA 32 Hombre 2019-01-16 PG-PAI CT Peulla publico DE LOS LAGOS NA
102,101 2,019 LUSA1**121986 c31731eb975640eacb799a7c5aa8d805 NA 32 Hombre 2019-05-17 PG-PAI Hospital Nacimiento publico DEL BIO-BIO NA
102,102 2,019 LUSA1**121986 c31731eb975640eacb799a7c5aa8d805 NA 32 Hombre 2019-05-17 PG-PAI Hospital Nacimiento publico DEL BIO-BIO NA
102,003 2,019 MACA1**021995 a2355a41cc63fdb57f298d8ded53a058 NA 24 Hombre 2019-05-08 PG-PAI COSAM Lota publico DEL BIO-BIO NA
57,293 2,017 MACA1**021995 a7c11c7e25336bf797a527c5af3d0565 NA 24 Hombre 2017-10-16 PG-PAI Centro Las Companias Athripan Salir Bien publico DE COQUIMBO Usuario adecuado durante la entrevista.
65,671 2,018 MACA1**101991 85a3eb94a06fb3a230d3074cd7717112 NA 28 Hombre 2017-10-02 PG-PAI Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado DE ARICA Y PARINACOTA NA
66,784 2,018 MACA1**101991 9d7cb8a27e1f6ad49e8e241f10ba1c4b NA 28 Hombre 2017-11-03 PG-PAI COSAM San Bernardo publico METROPOLITANA NA
43,812 2,017 MACA1**101991 85a3eb94a06fb3a230d3074cd7717112 NA 28 Hombre 2017-02-13 PG-PAI Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado DE ARICA Y PARINACOTA NA
43,813 2,017 MACA1**101991 85a3eb94a06fb3a230d3074cd7717112 NA 28 Hombre 2017-02-13 PG-PAI Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado DE ARICA Y PARINACOTA NA
54,367 2,017 MACA1**101991 85a3eb94a06fb3a230d3074cd7717112 NA 28 Hombre 2017-08-02 PG-PAI Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado DE ARICA Y PARINACOTA NA
56,642 2,017 MACA1**101991 85a3eb94a06fb3a230d3074cd7717112 NA 28 Hombre 2017-10-02 PG-PAI Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado DE ARICA Y PARINACOTA NA
87,538 2,019 MICA1**081983 29edc7f842a012b89a6da29ff49ce0ec NA 36 Hombre 2018-03-02 PG-PAI CT Contradiccion privado METROPOLITANA NA
72,883 2,018 MICA1**081983 29edc7f842a012b89a6da29ff49ce0ec NA 36 Hombre 2018-03-02 PG-PAI CT Contradiccion privado METROPOLITANA NA
72,884 2,018 MICA1**081983 29edc7f842a012b89a6da29ff49ce0ec NA 36 Hombre 2018-03-02 PG-PAI CT Contradiccion privado METROPOLITANA NA
72,885 2,018 MICA1**081983 29edc7f842a012b89a6da29ff49ce0ec NA 36 Hombre 2018-03-02 PG-PAI CT Contradiccion privado METROPOLITANA NA
72,886 2,018 MICA1**081983 29edc7f842a012b89a6da29ff49ce0ec NA 36 Hombre 2018-03-02 PG-PAI CT Contradiccion privado METROPOLITANA NA
81,905 2,018 MICA1**081983 dc818e3a974f1a64ab47c22ededfa109 NA 36 Hombre 2018-08-01 PG-PR Comunidad Terapeutica Joven Levantate privado METROPOLITANA NA
65,575 2,018 NIAG1**091996 ea72d4a9ef3baf47fa6921bbd8df61f1 NA 23 Hombre 2017-09-30 PG-PR Comunidad Terapeutica Residencial San Paulino de Nola privado DE COQUIMBO NA
50,114 2,017 NIAG1**091996 d39cec512d14415aff7635bdba2abf86 NA 23 Hombre 2017-05-15 PG-PAI PAI Fundacion Casa de La Esperanza Ovalle (Casa de La Esperanza Ovalle) privado DE COQUIMBO NA
56,543 2,017 NIAG1**091996 ea72d4a9ef3baf47fa6921bbd8df61f1 NA 23 Hombre 2017-09-30 PG-PR Comunidad Terapeutica Residencial San Paulino de Nola privado DE COQUIMBO NA
47,613 2,017 PASA1**061975 047fb2b5ff23cb22ddfc163ee8bd7da9 NA 44 Hombre 2017-04-07 PG-PAI CESFAM No 1 publico METROPOLITANA NA
48,620 2,017 PASA1**061975 2a4d3e18e48a3d8706a14e295fe73e99 NA 44 Hombre 2017-04-10 PG-PAB COSAM Talagante publico METROPOLITANA NA
48,621 2,017 PASA1**061975 2a4d3e18e48a3d8706a14e295fe73e99 NA 44 Hombre 2017-04-10 PG-PAB COSAM Talagante publico METROPOLITANA NA
100,431 2,019 ROAR1**091981 a4d8ac82f0415efcae1cbfb5aed47fdc NA 38 Hombre 2019-04-11 PG-PAI Hospital Salvador, Unidad de Farmacodependencia publico METROPOLITANA NA
100,432 2,019 ROAR1**091981 a4d8ac82f0415efcae1cbfb5aed47fdc NA 38 Hombre 2019-04-11 PG-PAI Hospital Salvador, Unidad de Farmacodependencia publico METROPOLITANA NA
61,513 2,018 ROAR1**091981 4bbb41dd71b487fb07fcb17f8db4ad85 NA 38 Hombre 2017-04-17 PG-PAI CTA Despertar (CT Nuevo Horizonte, Chanaral / Centro Madre Victoria Chanaral) privado DE ATACAMA NA
48,387 2,017 ROAR1**091981 4bbb41dd71b487fb07fcb17f8db4ad85 NA 38 Hombre 2017-04-17 PG-PAI CTA Despertar (CT Nuevo Horizonte, Chanaral / Centro Madre Victoria Chanaral) privado DE ATACAMA NA
48,388 2,017 ROAR1**091981 4bbb41dd71b487fb07fcb17f8db4ad85 NA 38 Hombre 2017-04-17 PG-PAI CTA Despertar (CT Nuevo Horizonte, Chanaral / Centro Madre Victoria Chanaral) privado DE ATACAMA usuario privado de libertad
48,389 2,017 ROAR1**091981 4bbb41dd71b487fb07fcb17f8db4ad85 NA 38 Hombre 2017-04-17 PG-PAI CTA Despertar (CT Nuevo Horizonte, Chanaral / Centro Madre Victoria Chanaral) privado DE ATACAMA NA
42,617 2,017 ROLA1**091981 f25b8516caa5136338e13fbeae80b01f NA 38 Hombre 2017-01-16 PG-PAI COSAM San Bernardo publico METROPOLITANA NA
55,308 2,017 ROLA1**091981 0027e02d3e5f7a17f6c21b28e463c442 NA 38 Hombre 2017-08-08 PG-PAI Comunidad Terapeutica La Roca, La Union (Poblacion Gral.) privado DE LOS RIOS NA
55,309 2,017 ROLA1**091981 0027e02d3e5f7a17f6c21b28e463c442 NA 38 Hombre 2017-08-08 PG-PAI Comunidad Terapeutica La Roca, La Union (Poblacion Gral.) privado DE LOS RIOS No se pudo aplicar instrumento
65,736 2,018 SOOL2**071984 d3306a6154055f6b38f45f7f570e03ba NA 35 Mujer 2017-09-04 PG-PAB CESFAM Recreo publico METROPOLITANA NA
65,737 2,018 SOOL2**071984 d3306a6154055f6b38f45f7f570e03ba NA 35 Mujer 2017-09-04 PG-PAB CESFAM Recreo publico METROPOLITANA NA
74,647 2,018 SOOL2**071984 f4a16ac505ea53624d5f7e1facf7be9b NA 35 Mujer 2018-04-03 PG-PAB Nukemapu privado METROPOLITANA NA
74,648 2,018 SOOL2**071984 f4a16ac505ea53624d5f7e1facf7be9b NA 35 Mujer 2018-04-03 PG-PAB Nukemapu privado METROPOLITANA NA
74,649 2,018 SOOL2**071984 f4a16ac505ea53624d5f7e1facf7be9b NA 35 Mujer 2018-04-03 PG-PAB Nukemapu privado METROPOLITANA NA
74,650 2,018 SOOL2**071984 f4a16ac505ea53624d5f7e1facf7be9b NA 35 Mujer 2018-04-03 PG-PAB Nukemapu privado METROPOLITANA NA
56,700 2,017 SOOL2**071984 d3306a6154055f6b38f45f7f570e03ba NA 35 Mujer 2017-09-04 PG-PAB CESFAM Recreo publico METROPOLITANA NA
74,041 2,018 SOTO2**111992 3126380e5d529e92431497b7e09eaf97 NA 27 Mujer 2018-03-21 PG-PR Comunidad Terapeutica Villamavida privado DEL BIO-BIO NA
21,674 2,016 SOTO2**111992 3126380e5d529e92431497b7e09eaf97 NA 27 Mujer 2016-01-07 PG-PAI CADEM de Chillan publico DE ÑUBLE NA
21,675 2,016 SOTO2**111992 3126380e5d529e92431497b7e09eaf97 NA 27 Mujer 2016-01-07 PG-PAI CADEM de Chillan publico DE ÑUBLE NA
26,239 2,016 SOTO2**111992 592556b93eaaadd30dfde6dcd93f6f71 NA 27 Mujer 2016-07-07 M-PR CT Mirabal privado DEL BIO-BIO NA
26,240 2,016 SOTO2**111992 592556b93eaaadd30dfde6dcd93f6f71 NA 27 Mujer 2016-07-07 M-PR CT Mirabal privado DEL BIO-BIO NA 
    CONS_TOP_df_dup_ENE_2020_prev2 %>%
      dplyr::filter(ID %in% as.character(as.vector(unlist(as.data.table(unlist(ids_more_one_hash_TOP)))))) %>% # Select IDs of cited cases
      dplyr::arrange(ID) %>% #ordeno por ids 
      dplyr::group_by(ID) %>%
      dplyr::distinct(HASH_KEY) %>%
      #183 cases may be affected with this problem
      write.csv2(file =paste0(path, "/Maureen/_8.mismo_ID_mas_de_un_HASH_TOP.csv"))
##33 casos con 66 hashs distintos.
#aCTUALIZACIÓN: estos coinciden con los que consulté por C1. No es necesario volverlos a preguntar.

These inconsistent HASHs have been sent to SENDA professional, who made clear that these cases (every one of them also present in the C1 dataset) corresponded to users with different RUNs, so their HASHs should be different.


4. Define criteria to classify duplicated cases


To define what can be considered a unique event, we identified how many unique combinations of admission dates and HASH keys. We found that only 41.4 percent of the registries are unique combinations. Attending the the questionnaires’ characteristics, it is possible to explore more specific time units (See Table 8).


#create the duplicated dataset, following the recommendation to separate columns
    as.data.table(CONS_TOP_df_dup_ENE_2020_prev2)[, dup_hash_date_adm_top := .N, by = c("HASH_KEY","fech_ing")] %>% ##dim()
      dplyr::group_by(dup_hash_date_adm_top) %>%
      dplyr::summarise(n=n()) %>%
      mutate(perc = round(n / sum(n),2)*100) %>%
      mutate(perc = paste0(perc,"%")) %>%
      dplyr::mutate(unique_cases= formatC(n/dup_hash_date_adm_top, format="f", big.mark=",", digits=0)) %>%
      as.data.frame(.) %>%
    # Duplicated rows
      knitr::kable(.,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
                   caption="Table 8. Frequency and Percentage of Duplicated Values of the combination of HASH-Key & Date of Admission", 
                   col.names= c("Times present in Dataset", " Frequencies", "Percentage", "Unique Cases"),  align =rep('c', 2))  %>%
      kable_styling(bootstrap_options = c("striped", "hover"),font_size = 11) %>%
  scroll_box(width = "100%", height = "350px")
`summarise()` ungrouping output (override with `.groups` argument)
Table 8. Frequency and Percentage of Duplicated Values of the combination of HASH-Key & Date of Admission
Times present in Dataset Frequencies Percentage Unique Cases
1 18,133 17% 18,133
2 21,854 20% 10,927
3 17,739 17% 5,913
4 15,996 15% 3,999
5 12,745 12% 2,549
6 9,348 9% 1,558
7 4,361 4% 623
8 2,520 2% 315
9 1,692 2% 188
10 1,200 1% 120
11 605 1% 55
12 372 0% 31
13 169 0% 13
14 112 0% 8
15 90 0% 6
16 112 0% 7
17 102 0% 6
18 36 0% 2
20 20 0% 1
23 46 0% 2
26 26 0% 1
29 29 0% 1 


    duplicated_rows_concat_TOP2 <- data.frame(duplicated_HASH_date = duplicated(CONS_TOP_df_dup_ENE_2020_prev2[,c("HASH_KEY","fech_ap_top")]), 
                                         row_dup_HASH_date = 1:nrow(CONS_TOP_df_dup_ENE_2020_prev2[,c("HASH_KEY","fech_ap_top")])) #%>%
    as.data.table(CONS_TOP_df_dup_ENE_2020_prev2)[, dup_hash_date_ap := .N, by = c("HASH_KEY","fech_ap_top")] %>% ##dim()
      dplyr::group_by(dup_hash_date_ap) %>%
      dplyr::summarise(n=n()) %>%
      mutate(perc = round(n / sum(n),3)*100) %>%
      mutate(perc = paste0(perc,"%")) %>%
      dplyr::mutate(unique_cases= formatC(n/dup_hash_date_ap, format="f", big.mark=",", digits=0)) %>%
      as.data.frame(.) %>%
      knitr::kable(.,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
                   caption="Table 9. Frequency and Percentage of Duplicated Values of the combination of HASH-Key & Date of Application of TOP", 
                   col.names= c("Times present in Dataset", " Frequencies", "Percentage", "Unique Cases"),  align =rep('c', 2))  %>%
      kable_styling(bootstrap_options = c("striped", "hover"),font_size = 11) %>%
  scroll_box(width = "100%", height = "350px")
`summarise()` ungrouping output (override with `.groups` argument)
Table 9. Frequency and Percentage of Duplicated Values of the combination of HASH-Key & Date of Application of TOP
Times present in Dataset Frequencies Percentage Unique Cases
1 94,515 88.1% 94,515
2 10,466 9.8% 5,233
3 1,590 1.5% 530
4 440 0.4% 110
5 135 0.1% 27
6 54 0.1% 9
7 28 0% 4
9 18 0% 2
10 10 0% 1
11 11 0% 1
12 12 0% 1
14 28 0% 2


One of them is the date of application of the TOP questionnaire. As shown in Table 9, 93.6% of the registries are unique combinations of the date of application of the TOP questionnaire and HASH key.


as.data.table(CONS_TOP_df_dup_ENE_2020_prev2)[, dup_hash_date_ap := .N, by = c("HASH_KEY","fech_ap_top")] %>%
  dplyr::filter(dup_hash_date_ap>1) %>% #Aquí veo quienes aparecen más de una vez
  dplyr::arrange(HASH_KEY,fech_ap_top) %>% 
  dplyr::slice(2000:2200) %>%
dplyr::select(-ID) %>%
  #  dplyr::select(HASH_KEY, hash_rut_completo, id_mod, ano_bd, fech_ap_top, TOP,fech_nac, Edad, Sexo, Plan.de.Tratamiento, Nombre.del.Centro, #Sustancia.Principal.1, Sustancia.Principal.2, Sustancia.Principal.3, starts_with("Total"),starts_with("Dósis")) %>%
      knitr::kable(.,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
                   caption="Table 10. Sample of Duplicated rows of the combination of HASH-Key & Date of Application of TOP", 
                   #col.names= c("Duplicated", " Frequencies", "Percentage"),
                   align =rep('c', 102))  %>%
      kable_styling(bootstrap_options = c("striped", "hover"),font_size = 8) %>%
  scroll_box(width = "100%", height = "350px")
Table 10. Sample of Duplicated rows of the combination of HASH-Key & Date of Application of TOP
HASH_KEY hash_rut_completo id_mod ano_bd row Fecha.Aplicación.TOP Nombre.Apliacador.del.TOP TOP Etapa.del.Tratamiento Fecha.Nacimiento Edad Sexo Fecha.de.Ingreso.a.Tratamiento Plan.de.Tratamiento Nombre.del.Centro Tipo.Centro Sustancia.Principal.1 Sustancia.Principal.2 Sustancia.Principal.3 Total.OH Dósis.OH Total.THC Dósis.THC Total.PBC Dósis.PBC Total.COC Dósis.COC Total.BZD Dósis.BZD Total.Otra Dósis.Otra Hurto Robo Venta.Drogas Riña Total.VIF Otro Total.Transgresión Salud.Psicológica Total.Trabajo Total.Educación Salud.Física Lugar.Vivir Vivienda Calidad.Vida Región.Centro Comentario fech_ing fech_ing_sin_fmt fech_ap_top fech_nac OBS Edad_al_ing Edad_at_ap dup_hash_date_ap
2732cc8376ec74d085b5ecb385fc1509 NA JOBA1**121987 2,019 101,298 2019-04-30 PAOLA MARQUEZ Tratamiento Seguimiento 3 meses 19/12/1987 31 Hombre 22/10/2018 PG-PAI CESFAM Juan Pablo II. Pdre Hurtado publico Alcohol Cocaína Marihuana 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 12 28 0 11 S S 11 METROPOLITANA NA 2018-10-22 22/10/2018 2019-04-30 1987-12-19 30.84189 31.36208 3
2732cc8376ec74d085b5ecb385fc1509 NA JOBA1**121987 2,019 101,299 2019-04-30 PAOLA MARQUEZ Tratamiento Seguimiento 6 meses 19/12/1987 31 Hombre 22/10/2018 PG-PAI CESFAM Juan Pablo II. Pdre Hurtado publico Alcohol Cocaína Marihuana 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 12 28 0 12 S S 12 METROPOLITANA NA 2018-10-22 22/10/2018 2019-04-30 1987-12-19 30.84189 31.36208 3
273814b0cde9539c396bfef41345fa11 NA MAAL2**041977 2,015 8,849 2015-12-23 Nancy Ferrer Ingreso Inicio Tratamiento 26/04/1977 42 Mujer 24/12/2015 M-PAI CT Hogar Crea femenino privado Alcohol Cocaína Marihuana 4 8 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 8 0 0 10 S S 12 METROPOLITANA NA 2015-12-24 24/12/2015 2015-12-23 1977-04-26 38.66119 38.65845 2
273814b0cde9539c396bfef41345fa11 NA MAAL2**041977 2,015 8,850 2015-12-23 Nancy Ferrer Tratamiento Seguimiento 3 meses 26/04/1977 42 Mujer 24/12/2015 M-PAI CT Hogar Crea femenino privado Alcohol Cocaína Marihuana 4 8 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 8 0 0 10 S S 12 METROPOLITANA NA 2015-12-24 24/12/2015 2015-12-23 1977-04-26 38.66119 38.65845 2
27422ef13e861f75de8482a2d3cbbe31 NA YALO2**101972 2,018 62,688 2018-06-28 Marcelo Vallecillo Tratamiento Seguimiento 9 meses 25/10/1972 47 Mujer 29/06/2017 PG-PAB CESFAM Irene Frei publico Cocaína Alcohol SIN CONSUMO 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 17 0 0 12 S S 18 METROPOLITANA NA 2017-06-29 29/06/2017 2018-06-28 1972-10-25 44.67625 45.67283 2
27422ef13e861f75de8482a2d3cbbe31 NA YALO2**101972 2,018 62,689 2018-06-28 Marcelo Vallecillo Tratamiento Seguimiento 12 meses 25/10/1972 47 Mujer 29/06/2017 PG-PAB CESFAM Irene Frei publico Cocaína Alcohol SIN CONSUMO 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 17 0 0 12 S S 18 METROPOLITANA NA 2017-06-29 29/06/2017 2018-06-28 1972-10-25 44.67625 45.67283 2
2744173abe6d9b760a34c7fd53e1d3f1 NA EVRO2**071991 2,016 17,663 2016-11-25 V Berrios Tratamiento Seguimiento 3 meses 24/07/1991 28 Mujer 28/12/2015 PG-PAI COSAM El Bosque publico Pasta Base Cocaína Marihuana 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 METROPOLITANA No deseo completar el form 2015-12-28 28/12/2015 2016-11-25 1991-07-24 24.42984 25.34155 3
2744173abe6d9b760a34c7fd53e1d3f1 NA EVRO2**071991 2,016 17,664 2016-11-25 V berrios Tratamiento Seguimiento 6 meses 24/07/1991 28 Mujer 28/12/2015 PG-PAI COSAM El Bosque publico Pasta Base Cocaína Marihuana 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 METROPOLITANA No deseo completar el form 2015-12-28 28/12/2015 2016-11-25 1991-07-24 24.42984 25.34155 3
2744173abe6d9b760a34c7fd53e1d3f1 NA EVRO2**071991 2,016 17,665 2016-11-25 Viviana Berrios Tratamiento Seguimiento 9 meses 24/07/1991 28 Mujer 28/12/2015 PG-PAI COSAM El Bosque publico Pasta Base Cocaína Marihuana 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 METROPOLITANA no deseo completar el formulario 2015-12-28 28/12/2015 2016-11-25 1991-07-24 24.42984 25.34155 3
2746589e9f6246240b560441b1bd8381 NA ANAG2**011980 2,017 34,090 2017-01-11 Rosa Concha Michea Tratamiento Seguimiento 3 meses 29/01/1980 39 Mujer 04/04/2016 M-PAI Comunidad Terapeutica Tabor privado Pasta Base Marihuana NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 19 8 12 19 S S 19 DE ANTOFAGASTA NA 2016-04-04 04/04/2016 2017-01-11 1980-01-29 36.18070 36.95277 2
2746589e9f6246240b560441b1bd8381 NA ANAG2**011980 2,017 34,091 2017-01-11 Rosa Concha Michea Tratamiento Seguimiento 6 meses 29/01/1980 39 Mujer 04/04/2016 M-PAI Comunidad Terapeutica Tabor privado Pasta Base Marihuana NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 19 8 12 19 S S 19 DE ANTOFAGASTA NA 2016-04-04 04/04/2016 2017-01-11 1980-01-29 36.18070 36.95277 2
274be75cb504db06cdea692b974c62ff NA JUUR1**111967 2,018 64,010 2018-02-20 Andrea Molina Tratamiento Seguimiento 6 meses 14/11/1967 51 Hombre 02/08/2017 PG-PAB CESFAM Rio Bueno publico Alcohol NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 15 24 0 13 S S 15 DE LOS RIOS NA 2017-08-02 02/08/2017 2018-02-20 1967-11-14 49.71663 50.26968 2
274be75cb504db06cdea692b974c62ff NA JUUR1**111967 2,018 64,011 2018-02-20 Andrea Molina Egreso Egreso 14/11/1967 51 Hombre 02/08/2017 PG-PAB CESFAM Rio Bueno publico Alcohol NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 15 24 0 13 S S 15 DE LOS RIOS NA 2017-08-02 02/08/2017 2018-02-20 1967-11-14 49.71663 50.26968 2
274f4519fdb55ff2c49021040d3e578b NA CLAL1**051991 2,016 18,186 2016-02-17 Mauricio Pavez Ingreso Inicio Tratamiento 11/05/1991 28 Hombre 17/02/2016 PG-PAB COSAM La Pintana publico Cocaína Alcohol Marihuana 4 4 28 2 0 2 2 0 0 0 0 0 N N N S 0 N 1 6 0 0 10 S S 5 METROPOLITANA NA 2016-02-17 17/02/2016 2016-02-17 1991-05-11 24.77207 24.77207 2
274f4519fdb55ff2c49021040d3e578b NA CLAL1**051991 2,016 18,187 2016-02-17 MAURICIO PAVEZ Tratamiento Seguimiento 3 meses 11/05/1991 28 Hombre 17/02/2016 PG-PAB COSAM La Pintana publico Cocaína Alcohol Marihuana 4 4 28 2 0 0 NA NA 0 0 0 0 N N N S 0 N 1 6 0 0 10 S S 5 METROPOLITANA NA 2016-02-17 17/02/2016 2016-02-17 1991-05-11 24.77207 24.77207 2
275cc08346d34756e5104f7a07610c93 NA MALU1**081967 2,015 1,506 2015-07-28 amapola quinteros Ingreso Inicio Tratamiento 30/08/1967 52 Hombre 16/06/2015 PG-PAB Centro de Tratamiento adicciones Esperanza, Hospital Santa Cruz publico Alcohol NA NA 11 6 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 13 20 0 11 S S 15 DEL LIBERTADOR GENERAL BERNARDO OHIGGINS NA 2015-06-16 16/06/2015 2015-07-28 1967-08-30 47.79466 47.90965 2
275cc08346d34756e5104f7a07610c93 NA MALU1**081967 2,015 1,507 2015-07-28 amapola quinteros Tratamiento Seguimiento 3 meses 30/08/1967 52 Hombre 16/06/2015 PG-PAB Centro de Tratamiento adicciones Esperanza, Hospital Santa Cruz publico Alcohol NA NA 11 6 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 13 20 0 11 S S 15 DEL LIBERTADOR GENERAL BERNARDO OHIGGINS NA 2015-06-16 16/06/2015 2015-07-28 1967-08-30 47.79466 47.90965 2
275fd81565de9d1b7fb66ade67f4e82b NA REME1**071976 2,016 10,667 2016-07-26 Ps. Carlos Flores Tratamiento Seguimiento 9 meses 28/07/1976 43 Hombre 28/07/2015 PG-PAI COSAM Recoleta publico Cocaína Alcohol Marihuana 4 1 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 13 20 0 13 S S 14 METROPOLITANA NA 2015-07-28 28/07/2015 2016-07-26 1976-07-28 38.99795 39.99452 2
275fd81565de9d1b7fb66ade67f4e82b NA REME1**071976 2,016 10,668 2016-07-26 Ps. Carlos Flores Tratamiento Seguimiento 12 meses 28/07/1976 43 Hombre 28/07/2015 PG-PAI COSAM Recoleta publico Cocaína Alcohol Marihuana 4 1 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 13 20 0 14 S S 14 METROPOLITANA NA 2015-07-28 28/07/2015 2016-07-26 1976-07-28 38.99795 39.99452 2
2769fac28ad897d20448c8c7e1073d82 NA ELRI1**031970 2,015 2,074 2015-07-10 LUCILA GONZÁLEZ Ingreso Inicio Tratamiento 29/03/1970 49 Hombre 19/06/2015 PG-PAI Centro de Tratamiento Parentesis Ambulatorio (Temuco) privado Alcohol Marihuana NA 5 7 28 2 0 0 0 0 0 0 0 0 N N N N 0 S 1 8 26 0 10 S S 10 DE LA ARAUCANIA NA 2015-06-19 19/06/2015 2015-07-10 1970-03-29 45.22382 45.28131 2
2769fac28ad897d20448c8c7e1073d82 NA ELRI1**031970 2,015 2,075 2015-07-10 lucila Gonzalez Tobar Tratamiento Seguimiento 3 meses 29/03/1970 49 Hombre 19/06/2015 PG-PAI Centro de Tratamiento Parentesis Ambulatorio (Temuco) privado Alcohol Marihuana NA 5 7 28 2 0 0 0 0 0 0 0 0 N N N N 0 S 1 8 26 0 10 S S 10 DE LA ARAUCANIA NA 2015-06-19 19/06/2015 2015-07-10 1970-03-29 45.22382 45.28131 2
2769fac28ad897d20448c8c7e1073d82 NA ELRI1**031970 2,016 9,994 2016-02-29 MADELEN ESCOBAR Tratamiento Seguimiento 6 meses 29/03/1970 49 Hombre 19/06/2015 PG-PAI Centro de Tratamiento Parentesis Ambulatorio (Temuco) privado Alcohol Marihuana NA 2 18 28 3 0 0 0 0 0 0 0 0 N N N N 1 N 0 14 18 0 18 S N 13 DE LA ARAUCANIA NA 2015-06-19 19/06/2015 2016-02-29 1970-03-29 45.22382 45.92197 2
2769fac28ad897d20448c8c7e1073d82 NA ELRI1**031970 2,016 9,995 2016-02-29 MADELEN ESCOBAR Tratamiento Seguimiento 9 meses 29/03/1970 49 Hombre 19/06/2015 PG-PAI Centro de Tratamiento Parentesis Ambulatorio (Temuco) privado Alcohol Marihuana NA 2 18 28 3 0 0 0 0 0 0 0 0 N N N N 1 N 0 14 18 0 18 S N 13 DE LA ARAUCANIA NA 2015-06-19 19/06/2015 2016-02-29 1970-03-29 45.22382 45.92197 2
2769fac28ad897d20448c8c7e1073d82 NA ELRI1**031970 2,016 9,997 2016-10-12 LAURA CARTES Tratamiento Seguimiento 15 meses 29/03/1970 49 Hombre 19/06/2015 PG-PAI Centro de Tratamiento Parentesis Ambulatorio (Temuco) privado Alcohol Marihuana NA 0 0 28 3 0 0 0 0 0 0 0 0 N N N N 0 N 0 15 28 0 15 S S 13 DE LA ARAUCANIA NA 2015-06-19 19/06/2015 2016-10-12 1970-03-29 45.22382 46.54073 2
2769fac28ad897d20448c8c7e1073d82 NA ELRI1**031970 2,016 9,998 2016-10-12 Laura Cartes Tratamiento NA 29/03/1970 49 Hombre 19/06/2015 PG-PAI Centro de Tratamiento Parentesis Ambulatorio (Temuco) privado Alcohol Marihuana NA 0 0 28 3 0 0 0 0 0 0 0 0 N N N N 0 N 0 15 28 0 15 S S 13 DE LA ARAUCANIA NA 2015-06-19 19/06/2015 2016-10-12 1970-03-29 45.22382 46.54073 2
278a4b4d96b161932c139926b77deb80 NA PADI2**081971 2,018 67,962 2018-07-26 Maria Victoria Valdivia Tratamiento Seguimiento 6 meses 30/08/1971 48 Mujer 15/11/2017 PG-PAI Centro Tratamiento Adicciones Unidos, Hospital Santa Cruz publico Alcohol NA NA 21 16 0 0 0 0 0 0 0 40 28 40 S N N N 9 N 1 10 0 0 10 S S 0 DEL LIBERTADOR GENERAL BERNARDO OHIGGINS NA 2017-11-15 15/11/2017 2018-07-26 1971-08-30 46.21218 46.90486 2
278a4b4d96b161932c139926b77deb80 NA PADI2**081971 2,018 67,963 2018-07-26 Maria Victoria Valdivia Tratamiento Seguimiento 9 meses 30/08/1971 48 Mujer 15/11/2017 PG-PAI Centro Tratamiento Adicciones Unidos, Hospital Santa Cruz publico Alcohol NA NA 21 16 0 0 0 0 0 0 0 40 28 40 S N N N 9 N 1 10 0 0 10 S S 0 DEL LIBERTADOR GENERAL BERNARDO OHIGGINS NA 2017-11-15 15/11/2017 2018-07-26 1971-08-30 46.21218 46.90486 2
278a4b4d96b161932c139926b77deb80 NA PADI2**081971 2,019 105,337 2019-09-03 Fernando Arenas Ingreso Inicio Tratamiento 30/08/1971 48 Mujer 05/07/2019 PG-PAI Centro Tratamiento Adicciones Unidos, Hospital Santa Cruz publico Alcohol NA NA 28 24 0 0 0 0 0 0 0 50 28 50 N N N N 0 N 0 10 12 0 17 S S 10 DEL LIBERTADOR GENERAL BERNARDO OHIGGINS NA 2019-07-05 05/07/2019 2019-09-03 1971-08-30 47.84668 48.01095 2
278a4b4d96b161932c139926b77deb80 NA PADI2**081971 2,019 105,338 2019-09-03 Fernando Arenas Arias Tratamiento Seguimiento 3 meses 30/08/1971 48 Mujer 05/07/2019 PG-PAI Centro Tratamiento Adicciones Unidos, Hospital Santa Cruz publico Alcohol NA NA 28 24 0 0 0 0 0 0 0 50 28 50 N N N N 0 N 0 10 12 0 17 S S 10 DEL LIBERTADOR GENERAL BERNARDO OHIGGINS NA 2019-07-05 05/07/2019 2019-09-03 1971-08-30 47.84668 48.01095 2
279648fc80ce09d39fe76fcb4428c77c NA JUMU1**071979 2,019 86,907 2019-05-07 nicolas chanquey Ingreso Inicio Tratamiento 04/07/1979 40 Hombre 18/12/2017 PG-PAB CESFAM Renaico publico Pasta Base Alcohol NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 15 20 0 17 S S 17 DE LA ARAUCANIA NA 2017-12-18 18/12/2017 2019-05-07 1979-07-04 38.45859 39.84120 4
279648fc80ce09d39fe76fcb4428c77c NA JUMU1**071979 2,019 86,910 2019-05-07 nicolas chanquey Tratamiento Seguimiento 9 meses 04/07/1979 40 Hombre 18/12/2017 PG-PAB CESFAM Renaico publico Pasta Base Alcohol NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 15 20 0 14 S S 15 DE LA ARAUCANIA NA 2017-12-18 18/12/2017 2019-05-07 1979-07-04 38.45859 39.84120 4
279648fc80ce09d39fe76fcb4428c77c NA JUMU1**071979 2,019 86,911 2019-05-07 nicolas chanquey Tratamiento Seguimiento 12 meses 04/07/1979 40 Hombre 18/12/2017 PG-PAB CESFAM Renaico publico Pasta Base Alcohol NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 15 20 0 16 S S 14 DE LA ARAUCANIA NA 2017-12-18 18/12/2017 2019-05-07 1979-07-04 38.45859 39.84120 4
279648fc80ce09d39fe76fcb4428c77c NA JUMU1**071979 2,019 86,912 2019-05-07 nicolas chanquey Tratamiento Seguimiento 15 meses 04/07/1979 40 Hombre 18/12/2017 PG-PAB CESFAM Renaico publico Pasta Base Alcohol NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 15 20 0 13 S S 13 DE LA ARAUCANIA NA 2017-12-18 18/12/2017 2019-05-07 1979-07-04 38.45859 39.84120 4
279648fc80ce09d39fe76fcb4428c77c NA JUMU1**071979 2,019 86,908 2019-05-08 nicolas chanquey Tratamiento Seguimiento 3 meses 04/07/1979 40 Hombre 18/12/2017 PG-PAB CESFAM Renaico publico Pasta Base Alcohol NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 15 20 0 16 S S 16 DE LA ARAUCANIA NA 2017-12-18 18/12/2017 2019-05-08 1979-07-04 38.45859 39.84394 2
279648fc80ce09d39fe76fcb4428c77c NA JUMU1**071979 2,019 86,909 2019-05-08 nicolas chanquey Tratamiento Seguimiento 6 meses 04/07/1979 40 Hombre 18/12/2017 PG-PAB CESFAM Renaico publico Pasta Base Alcohol NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 15 20 0 16 S S 16 DE LA ARAUCANIA NA 2017-12-18 18/12/2017 2019-05-08 1979-07-04 38.45859 39.84394 2
2799b56c23bf57ef71c90eabea64e592 NA NETA1**041976 2,016 30,246 2016-11-03 jonathan marin Ingreso Inicio Tratamiento 12/04/1976 43 Hombre 07/10/2016 PG-PAI PAI Fundacion Casa de La Esperanza Ovalle (Casa de La Esperanza Ovalle) privado Pasta Base Cocaína Alcohol 1 1 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 13 0 0 13 S S 13 DE COQUIMBO NA 2016-10-07 07/10/2016 2016-11-03 1976-04-12 40.48734 40.56126 2
2799b56c23bf57ef71c90eabea64e592 NA NETA1**041976 2,016 30,247 2016-11-03 jonathan marin Tratamiento Seguimiento 3 meses 12/04/1976 43 Hombre 07/10/2016 PG-PAI PAI Fundacion Casa de La Esperanza Ovalle (Casa de La Esperanza Ovalle) privado Pasta Base Cocaína Alcohol 1 1 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 13 0 0 13 S S 13 DE COQUIMBO NA 2016-10-07 07/10/2016 2016-11-03 1976-04-12 40.48734 40.56126 2
279abcc65e1ba66971306d88acacb03b NA JOPA1**101958 2,016 12,911 2016-04-04 Isabela Gutierrez Tratamiento Seguimiento 6 meses 03/10/1958 61 Hombre 01/10/2015 PG-PAI COSAM Puerto Montt publico Alcohol Cocaína NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 8 24 0 12 S S 12 DE LOS LAGOS NA 2015-10-01 01/10/2015 2016-04-04 1958-10-03 56.99384 57.50308 2
279abcc65e1ba66971306d88acacb03b NA JOPA1**101958 2,016 12,912 2016-04-04 ISABELA GUTIERREZ LAMAS Tratamiento Seguimiento 9 meses 03/10/1958 61 Hombre 01/10/2015 PG-PAI COSAM Puerto Montt publico Alcohol Cocaína NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 8 24 0 12 S S 12 DE LOS LAGOS NA 2015-10-01 01/10/2015 2016-04-04 1958-10-03 56.99384 57.50308 2
27d7247c1ea9a6b249b0e60053e75bbb NA JUVA2**061973 2,017 53,232 2017-08-16 GUISELLA STEVENS BRIONES Ingreso Inicio Tratamiento 07/06/1973 46 Mujer 05/07/2017 PG-PAB Consultorio Pica publico Otros Alcohol NA 9 10 0 0 0 0 0 0 0 40 28 40 N N N N 0 N 0 10 0 0 7 S S 12 DE TARAPACA NA 2017-07-05 05/07/2017 2017-08-16 1973-06-07 44.07666 44.19165 2
27d7247c1ea9a6b249b0e60053e75bbb NA JUVA2**061973 2,017 53,233 2017-08-16 GUISELLA STEVENS BRIONES Tratamiento Seguimiento 3 meses 07/06/1973 46 Mujer 05/07/2017 PG-PAB Consultorio Pica publico Otros Alcohol NA 0 0 0 0 0 0 0 0 0 15 28 15 N N N N 0 N 0 13 0 0 10 S S 15 DE TARAPACA Fibromialgia Fibrosis pulmonar Asma 2017-07-05 05/07/2017 2017-08-16 1973-06-07 44.07666 44.19165 2
27d90df4606d393b4dde9d9a9288c5aa NA JEAL1**041987 2,016 21,303 2016-03-03 Mauricio Pavez Ingreso Inicio Tratamiento 20/04/1987 32 Hombre 03/03/2016 PG-PAI COSAM La Pintana publico Pasta Base Alcohol Marihuana 0 0 0 0 28 0 0 0 0 0 0 0 N N N N 0 N 0 7 8 0 12 S S 0 METROPOLITANA NA 2016-03-03 03/03/2016 2016-03-03 1987-04-20 28.87064 28.87064 2
27d90df4606d393b4dde9d9a9288c5aa NA JEAL1**041987 2,016 21,304 2016-03-03 Mauricio Pavez Tratamiento Seguimiento 3 meses 20/04/1987 32 Hombre 03/03/2016 PG-PAI COSAM La Pintana publico Pasta Base Alcohol Marihuana 0 0 0 0 28 0 0 0 0 0 0 0 N N N N 0 N 0 7 8 0 12 S S 1 METROPOLITANA NA 2016-03-03 03/03/2016 2016-03-03 1987-04-20 28.87064 28.87064 2
27ea2c805e2f4330a5029b6cb9d3576e NA PAPI1**031985 2,019 91,261 2019-02-19 mario Tratamiento Seguimiento 3 meses 02/03/1985 34 Hombre 05/09/2018 PG-PAI COSAM El Bosque publico Pasta Base Cocaína Marihuana 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA NA NA 0 19 22 0 18 S S 16 METROPOLITANA NA 2018-09-05 05/09/2018 2019-02-19 1985-03-02 33.51129 33.96851 2
27ea2c805e2f4330a5029b6cb9d3576e NA PAPI1**031985 2,019 91,262 2019-02-19 mario Tratamiento Seguimiento 6 meses 02/03/1985 34 Hombre 05/09/2018 PG-PAI COSAM El Bosque publico Pasta Base Cocaína Marihuana 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA NA NA 0 19 22 0 18 S S 16 METROPOLITANA NA 2018-09-05 05/09/2018 2019-02-19 1985-03-02 33.51129 33.96851 2
27f891ed0241841c5340536a7aa08753 NA WALO1**011981 2,017 34,476 2017-01-23 ASISTENTE SOCIAL Tratamiento Seguimiento 6 meses 28/01/1981 38 Hombre 27/04/2016 PG-PAI COSAM Central publico Pasta Base NA NA 14 3 0 0 0 0 0 0 0 7 14 7 N N N N 0 N 0 17 0 0 20 S S 15 DE ANTOFAGASTA NA 2016-04-27 27/04/2016 2017-01-23 1981-01-28 35.24435 35.98631 2
27f891ed0241841c5340536a7aa08753 NA WALO1**011981 2,017 34,477 2017-01-23 ASISTENTE SOCIAL Tratamiento Seguimiento 9 meses 28/01/1981 38 Hombre 27/04/2016 PG-PAI COSAM Central publico Pasta Base NA NA 14 3 0 0 0 0 0 0 0 7 14 7 N N N N 0 N 0 17 0 0 20 S S 15 DE ANTOFAGASTA NA 2016-04-27 27/04/2016 2017-01-23 1981-01-28 35.24435 35.98631 2
27f891ed0241841c5340536a7aa08753 NA WALO1**011981 2,017 34,478 2017-04-25 ASISTENTE SOCIAL Tratamiento Seguimiento 12 meses 28/01/1981 38 Hombre 27/04/2016 PG-PAI COSAM Central publico Pasta Base NA NA 3 4 0 0 0 0 0 0 0 7 28 7 N N N N 0 N 0 15 0 0 10 S S 20 DE ANTOFAGASTA NA 2016-04-27 27/04/2016 2017-04-25 1981-01-28 35.24435 36.23819 2
27f891ed0241841c5340536a7aa08753 NA WALO1**011981 2,017 34,479 2017-04-25 ASISTENTE SOCIAL Egreso Egreso 28/01/1981 38 Hombre 27/04/2016 PG-PAI COSAM Central publico Pasta Base NA NA 3 4 0 0 0 0 0 0 0 7 28 7 N N N N 0 N 0 15 0 0 10 S S 20 DE ANTOFAGASTA NA 2016-04-27 27/04/2016 2017-04-25 1981-01-28 35.24435 36.23819 2
27fc4f766852f125b1206a32768ec0c9 NA MOVA1**041983 2,015 8,892 2015-12-17 PAULA MEDINA Ingreso Inicio Tratamiento 05/04/1983 36 Hombre 01/12/2015 PG-PAB CESFAM Colina publico Cocaína Alcohol NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 15 23 0 13 S S 20 METROPOLITANA NA 2015-12-01 01/12/2015 2015-12-17 1983-04-05 32.65708 32.70089 2
27fc4f766852f125b1206a32768ec0c9 NA MOVA1**041983 2,015 8,893 2015-12-17 PAULA MEDINA Tratamiento Seguimiento 3 meses 05/04/1983 36 Hombre 01/12/2015 PG-PAB CESFAM Colina publico Cocaína Alcohol NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 15 23 0 13 S S 20 METROPOLITANA NA 2015-12-01 01/12/2015 2015-12-17 1983-04-05 32.65708 32.70089 2
27fd883b9bbad7d00bb37a84839f9d54 NA GLRO2**031953 2,016 28,883 2016-12-06 JORGE PÉREZ ARRIAGADA Tratamiento Seguimiento 3 meses 01/03/1953 66 Mujer 19/08/2016 M-PAI COSAM Melipilla publico Otros NA NA 2 3 0 0 0 0 0 0 0 10 28 10 N N N N 0 N 0 10 2 0 7 S S 15 METROPOLITANA NA 2016-08-19 19/08/2016 2016-12-06 1953-03-01 63.46886 63.76728 2
27fd883b9bbad7d00bb37a84839f9d54 NA GLRO2**031953 2,016 28,884 2016-12-06 jorge perez Tratamiento Seguimiento 6 meses 01/03/1953 66 Mujer 19/08/2016 M-PAI COSAM Melipilla publico Otros NA NA 2 3 0 0 0 0 0 0 0 10 28 10 N N N N 0 N 0 10 2 0 7 S S 15 METROPOLITANA NA 2016-08-19 19/08/2016 2016-12-06 1953-03-01 63.46886 63.76728 2
28071b0fab627c6eb3873446653429b5 NA JERI2**051993 2,016 11,943 2016-08-04 Loreto Hernandez Tratamiento Seguimiento 6 meses 12/05/1993 26 Mujer 24/08/2015 PG-PAI Hospital Salvador, Unidad de Farmacodependencia publico Alcohol Inhalables: neopren, GHB, óxido nitroso (gas hilarante), “poppers”, solventes, gasolina, diluyente Marihuana 2 2 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 15 0 0 12 S S 12 METROPOLITANA NA 2015-08-24 24/08/2015 2016-08-04 1993-05-12 22.28337 23.23066 3
28071b0fab627c6eb3873446653429b5 NA JERI2**051993 2,016 11,944 2016-08-04 Loreto Hernández Tratamiento Seguimiento 9 meses 12/05/1993 26 Mujer 24/08/2015 PG-PAI Hospital Salvador, Unidad de Farmacodependencia publico Alcohol Inhalables: neopren, GHB, óxido nitroso (gas hilarante), “poppers”, solventes, gasolina, diluyente Marihuana 2 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 15 0 0 12 S S 12 METROPOLITANA NA 2015-08-24 24/08/2015 2016-08-04 1993-05-12 22.28337 23.23066 3
28071b0fab627c6eb3873446653429b5 NA JERI2**051993 2,016 11,945 2016-08-04 Loreto Hernandez Tratamiento Seguimiento 12 meses 12/05/1993 26 Mujer 24/08/2015 PG-PAI Hospital Salvador, Unidad de Farmacodependencia publico Alcohol Inhalables: neopren, GHB, óxido nitroso (gas hilarante), “poppers”, solventes, gasolina, diluyente Marihuana 2 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 15 0 0 10 S S 12 METROPOLITANA NA 2015-08-24 24/08/2015 2016-08-04 1993-05-12 22.28337 23.23066 3
2813ed2cbda9f2985c9edbd01279ccbf NA FELO1**031978 2,018 79,102 2018-11-14 Loreto Escobar Tratamiento Seguimiento 3 meses 31/03/1978 41 Hombre 11/06/2018 PG-PAI Hospital Hanga Roa publico Pasta Base Marihuana Cocaína 2 4 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 15 20 0 15 S S 20 METROPOLITANA NA 2018-06-11 11/06/2018 2018-11-14 1978-03-31 40.19713 40.62423 2
2813ed2cbda9f2985c9edbd01279ccbf NA FELO1**031978 2,018 79,103 2018-11-14 Loreto Escobar Tratamiento Seguimiento 6 meses 31/03/1978 41 Hombre 11/06/2018 PG-PAI Hospital Hanga Roa publico Pasta Base Marihuana Cocaína 2 4 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 15 20 0 15 S S 20 METROPOLITANA NA 2018-06-11 11/06/2018 2018-11-14 1978-03-31 40.19713 40.62423 2
2815dbd0e3c879156a7e47d19e650623 NA PELI1**091963 2,017 33,018 2017-06-23 paula marcos Tratamiento Seguimiento 6 meses 05/09/1963 56 Hombre 18/01/2016 PG-PAB COSAM Macul publico Alcohol NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 9 24 0 10 S S 12 METROPOLITANA NA 2016-01-18 18/01/2016 2017-06-23 1963-09-05 52.36961 53.79877 4
2815dbd0e3c879156a7e47d19e650623 NA PELI1**091963 2,017 33,019 2017-06-23 paula marcos Tratamiento Seguimiento 9 meses 05/09/1963 56 Hombre 18/01/2016 PG-PAB COSAM Macul publico Alcohol NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 8 20 0 11 S S 11 METROPOLITANA NA 2016-01-18 18/01/2016 2017-06-23 1963-09-05 52.36961 53.79877 4
2815dbd0e3c879156a7e47d19e650623 NA PELI1**091963 2,017 33,020 2017-06-23 zorai8da harris Tratamiento Seguimiento 12 meses 05/09/1963 56 Hombre 18/01/2016 PG-PAB COSAM Macul publico Alcohol NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 9 24 0 10 S S 15 METROPOLITANA NA 2016-01-18 18/01/2016 2017-06-23 1963-09-05 52.36961 53.79877 4
2815dbd0e3c879156a7e47d19e650623 NA PELI1**091963 2,017 33,021 2017-06-23 zoraida harris Tratamiento Seguimiento 15 meses 05/09/1963 56 Hombre 18/01/2016 PG-PAB COSAM Macul publico Alcohol NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 6 24 0 11 S S 14 METROPOLITANA NA 2016-01-18 18/01/2016 2017-06-23 1963-09-05 52.36961 53.79877 4
281805435a5b1e05791f492e53b387dd NA OSHE1**121982 2,015 1,687 2015-06-15 Yillian Pakomio Bahamondes Ingreso Inicio Tratamiento 16/12/1982 36 Hombre 18/05/2015 PG-PAB Hospital Hanga Roa publico Marihuana Alcohol NA 2 16 28 3 0 0 0 0 0 0 0 0 N N N N 0 N 0 20 20 0 20 S S 20 METROPOLITANA NA 2015-05-18 18/05/2015 2015-06-15 1982-12-16 32.41889 32.49555 2
281805435a5b1e05791f492e53b387dd NA OSHE1**121982 2,015 1,688 2015-06-15 yillian pakomio Tratamiento Seguimiento 3 meses 16/12/1982 36 Hombre 18/05/2015 PG-PAB Hospital Hanga Roa publico Marihuana Alcohol NA 2 16 28 3 0 0 0 0 0 0 0 0 N N N N 0 N 0 20 20 0 20 S S 20 METROPOLITANA NA 2015-05-18 18/05/2015 2015-06-15 1982-12-16 32.41889 32.49555 2
282227259ba5f322def6eee9046b02b3 NA RUES1**121993 2,019 103,682 2019-08-05 Paola Jouannet Styl Ingreso Inicio Tratamiento 31/12/1993 25 Hombre 04/06/2019 PG-PAI Hospital El Pino publico Cocaína Alcohol NA 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 METROPOLITANA Paciente no adhiere a instancias terapéuticas. Por lo tanto no se le ha podido aplicar FICHA TOP 2019-06-04 04/06/2019 2019-08-05 1993-12-31 25.42368 25.59343 2
282227259ba5f322def6eee9046b02b3 NA RUES1**121993 2,019 103,683 2019-08-05 Paola Jouannet Styl Ingreso NA 31/12/1993 25 Hombre 04/06/2019 PG-PAI Hospital El Pino publico Cocaína Alcohol NA 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 METROPOLITANA Paciente no adhiere a instancias terapéuticas. Por lo tanto no se le ha podido aplicar FICHA TOP 2019-06-04 04/06/2019 2019-08-05 1993-12-31 25.42368 25.59343 2
282c449e5b4bf7b69c7ae421ce0c424b NA JOVA1**111974 2,018 60,436 2018-10-18 Jezabel González Tratamiento Seguimiento 12 meses 03/11/1974 45 Hombre 02/02/2017 PG-PAI Hospital de Melipilla publico Alcohol Cocaína Pasta Base 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 11 26 0 10 S S 12 METROPOLITANA TOP de egreso de tratamiento. Usuario con patología dual 2017-02-02 02/02/2017 2018-10-18 1974-11-03 42.25051 43.95619 2
282c449e5b4bf7b69c7ae421ce0c424b NA JOVA1**111974 2,018 60,437 2018-10-18 JEZABEL GONZALEZ Egreso Egreso 03/11/1974 45 Hombre 02/02/2017 PG-PAI Hospital de Melipilla publico Alcohol Cocaína Pasta Base 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 11 26 0 10 S S 12 METROPOLITANA Usuario Patología Dual 2017-02-02 02/02/2017 2018-10-18 1974-11-03 42.25051 43.95619 2
2830748c1df910e56b9fef378dae17dd NA EUSA1**111967 2,019 89,070 2019-03-27 Lorena Lucero Tratamiento Seguimiento 6 meses 29/11/1967 51 Hombre 01/06/2018 PG-PAI Hospital Claudio Vicuna de San Antonio publico Pasta Base Alcohol Marihuana 11 5 28 4 22 0 0 0 0 0 0 0 N N N N 0 N 0 7 24 0 9 N N 5 DE VALPARAISO NA 2018-06-01 01/06/2018 2019-03-27 1967-11-29 50.50513 51.32375 2
2830748c1df910e56b9fef378dae17dd NA EUSA1**111967 2,019 89,071 2019-03-27 LORENA LUCERO Tratamiento Seguimiento 9 meses 29/11/1967 51 Hombre 01/06/2018 PG-PAI Hospital Claudio Vicuna de San Antonio publico Pasta Base Alcohol Marihuana 11 5 28 4 22 0 0 0 0 0 0 0 N N N N 1 N 0 7 24 0 9 N N 5 DE VALPARAISO NA 2018-06-01 01/06/2018 2019-03-27 1967-11-29 50.50513 51.32375 2
2834bfed18b3c258b139520a89cca033 NA ALAR1**121995 2,016 15,332 2016-01-21 LEONARDO CORNEJO Ingreso Inicio Tratamiento 16/12/1995 23 Hombre 02/12/2015 PG-PR CTR Nehuen (Residencial Melipilla) publico Otros Opioides Analgésicos: morfina, codeína, meperidina, demerol, tramadol, tramal. Sedantes: diazepam, Valium, clonazepam, Ravotril, alprazolam, adax, barbitúricos, fenobarbital. Marihuana 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 12 0 0 17 S S 14 METROPOLITANA NA 2015-12-02 02/12/2015 2016-01-21 1995-12-16 19.96167 20.09856 2
2834bfed18b3c258b139520a89cca033 NA ALAR1**121995 2,016 15,333 2016-01-21 LEONARDO CORNEJO Tratamiento Seguimiento 3 meses 16/12/1995 23 Hombre 02/12/2015 PG-PR CTR Nehuen (Residencial Melipilla) publico Otros Opioides Analgésicos: morfina, codeína, meperidina, demerol, tramadol, tramal. Sedantes: diazepam, Valium, clonazepam, Ravotril, alprazolam, adax, barbitúricos, fenobarbital. Marihuana 0 0 0 0 0 0 0 0 0 NA NA NA N N N N 0 NA 0 12 0 0 17 S S 14 METROPOLITANA NA 2015-12-02 02/12/2015 2016-01-21 1995-12-16 19.96167 20.09856 2
2838a147ffce57326611de9c08c3e184 NA JOBE1**111973 2,018 60,259 2018-02-08 geraldine Tratamiento Seguimiento 6 meses 06/11/1973 46 Hombre 31/01/2017 PG-PR CTR Nehuen (Residencial Melipilla) publico Pasta Base Marihuana Cocaína 0 0 0 0 0 0 0 0 0 30 28 30 N N N N 0 N 0 20 11 0 10 S S 15 METROPOLITANA NA 2017-01-31 31/01/2017 2018-02-08 1973-11-06 43.23614 44.25736 2
2838a147ffce57326611de9c08c3e184 NA JOBE1**111973 2,018 60,260 2018-02-08 geraldine Tratamiento Seguimiento 9 meses 06/11/1973 46 Hombre 31/01/2017 PG-PR CTR Nehuen (Residencial Melipilla) publico Pasta Base Marihuana Cocaína 0 0 0 0 0 0 0 0 0 30 28 30 N N N N 0 N 0 20 11 0 10 S S 15 METROPOLITANA NA 2017-01-31 31/01/2017 2018-02-08 1973-11-06 43.23614 44.25736 2
2839b336e51c79ed6cfbe5f10e5f1c08 NA PAZA2**111974 2,018 69,221 2018-01-26 Rodrigo Manriquez Ingreso Inicio Tratamiento 09/11/1974 44 Mujer 18/10/2017 M-PAI Comunidad Terapeutica Talita Kum privado Pasta Base Cocaína NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 20 0 0 10 S S 17 METROPOLITANA NA 2017-10-18 18/10/2017 2018-01-26 1974-11-09 42.94045 43.21424 2
2839b336e51c79ed6cfbe5f10e5f1c08 NA PAZA2**111974 2,018 69,222 2018-01-26 Rodrigo Manriquez Tratamiento Seguimiento 3 meses 09/11/1974 44 Mujer 18/10/2017 M-PAI Comunidad Terapeutica Talita Kum privado Pasta Base Cocaína NA 0 0 0 0 0 0 0 1 28 0 0 0 N N N N 0 N 0 20 0 0 10 S S 17 METROPOLITANA NA 2017-10-18 18/10/2017 2018-01-26 1974-11-09 42.94045 43.21424 2
28430e9c4c1fbbbd9bd6f9c518d52bea NA CRMO1**101983 2,017 38,819 2017-03-07 carina otey Tratamiento Seguimiento 3 meses 12/10/1983 36 Hombre 03/10/2016 PG-PAI COSAM Schneider (CSMC Schneider-CESAMCO) publico Pasta Base Alcohol NA 8 2 13 3 1 0 0 0 0 0 0 0 N N N N 0 N 0 12 24 0 15 S S 10 DE LOS RIOS NA 2016-10-03 03/10/2016 2017-03-07 1983-10-12 32.97741 33.40178 3
28430e9c4c1fbbbd9bd6f9c518d52bea NA CRMO1**101983 2,017 38,820 2017-03-07 carina Tratamiento Seguimiento 6 meses 12/10/1983 36 Hombre 03/10/2016 PG-PAI COSAM Schneider (CSMC Schneider-CESAMCO) publico Pasta Base Alcohol NA 8 2 13 2 1 0 0 0 0 0 0 0 N N N N 0 N 0 12 24 0 15 S S 10 DE LOS RIOS NA 2016-10-03 03/10/2016 2017-03-07 1983-10-12 32.97741 33.40178 3
28430e9c4c1fbbbd9bd6f9c518d52bea NA CRMO1**101983 2,017 38,821 2017-03-07 Carina Otey Egreso Egreso 12/10/1983 36 Hombre 03/10/2016 PG-PAI COSAM Schneider (CSMC Schneider-CESAMCO) publico Pasta Base Alcohol NA 8 2 13 2 0 0 0 0 0 0 0 0 N N N N 0 N 0 12 24 0 15 S S 12 DE LOS RIOS NA 2016-10-03 03/10/2016 2017-03-07 1983-10-12 32.97741 33.40178 3
284796c497c897f751c53554c01b1b0f NA BEES1**061968 2,018 60,912 2018-08-29 Nicolas Chanquey Santos Tratamiento Seguimiento 12 meses 25/06/1968 51 Hombre 29/03/2017 PG-PAB CESFAM Renaico publico Alcohol SIN CONSUMO NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 17 24 0 15 S S 16 DE LA ARAUCANIA NA 2017-03-29 29/03/2017 2018-08-29 1968-06-25 48.75838 50.17659 2
284796c497c897f751c53554c01b1b0f NA BEES1**061968 2,018 60,913 2018-08-29 Nicolas Chanquey Santos Tratamiento Seguimiento 15 meses 25/06/1968 51 Hombre 29/03/2017 PG-PAB CESFAM Renaico publico Alcohol SIN CONSUMO NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 17 24 0 12 S S 16 DE LA ARAUCANIA NA 2017-03-29 29/03/2017 2018-08-29 1968-06-25 48.75838 50.17659 2
284796c497c897f751c53554c01b1b0f NA BEES1**061968 2,018 60,914 2018-10-02 Nicolas Chanquey Santos Tratamiento NA 25/06/1968 51 Hombre 29/03/2017 PG-PAB CESFAM Renaico publico Alcohol SIN CONSUMO NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 17 20 0 15 S S 17 DE LA ARAUCANIA NA 2017-03-29 29/03/2017 2018-10-02 1968-06-25 48.75838 50.26968 2
284796c497c897f751c53554c01b1b0f NA BEES1**061968 2,018 60,915 2018-10-02 Nicolas Chanquey Santos Egreso Egreso 25/06/1968 51 Hombre 29/03/2017 PG-PAB CESFAM Renaico publico Alcohol SIN CONSUMO NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 18 20 0 18 S S 17 DE LA ARAUCANIA NA 2017-03-29 29/03/2017 2018-10-02 1968-06-25 48.75838 50.26968 2
285ac1361424d16ae87e9f8f122cb999 NA LORI2**101969 2,017 32,791 2017-01-23 Marillac Figueroa Tratamiento Seguimiento 9 meses 29/10/1969 50 Mujer 02/12/2015 M-PAI CTA Dianova Vina del Mar privado Cocaína NA NA 8 3 8 1 0 5 4 0 0 0 0 0 N N N N 0 N 0 10 0 0 10 S S 5 DE VALPARAISO NA 2015-12-02 02/12/2015 2017-01-23 1969-10-29 46.09172 47.23614 2
285ac1361424d16ae87e9f8f122cb999 NA LORI2**101969 2,017 32,792 2017-01-23 Marillac Figueroa Tratamiento Seguimiento 12 meses 29/10/1969 50 Mujer 02/12/2015 M-PAI CTA Dianova Vina del Mar privado Cocaína NA NA 8 3 8 1 0 5 4 0 0 0 0 0 N N N N 0 N 0 10 0 0 10 S S 5 DE VALPARAISO NA 2015-12-02 02/12/2015 2017-01-23 1969-10-29 46.09172 47.23614 2
285ac1361424d16ae87e9f8f122cb999 NA LORI2**101969 2,017 32,793 2017-05-03 Marillac Figueroa Tratamiento Seguimiento 15 meses 29/10/1969 50 Mujer 02/12/2015 M-PAI CTA Dianova Vina del Mar privado Cocaína NA NA 10 15 4 1 0 0 0 0 0 0 0 0 N N N N 0 N 0 16 0 0 15 S S 15 DE VALPARAISO NA 2015-12-02 02/12/2015 2017-05-03 1969-10-29 46.09172 47.50992 2
285ac1361424d16ae87e9f8f122cb999 NA LORI2**101969 2,017 32,794 2017-05-03 Marillac Figueroa Tratamiento NA 29/10/1969 50 Mujer 02/12/2015 M-PAI CTA Dianova Vina del Mar privado Cocaína NA NA 10 15 4 1 0 0 0 0 0 0 0 0 N N N N 0 N 0 16 0 0 15 S S 15 DE VALPARAISO NA 2015-12-02 02/12/2015 2017-05-03 1969-10-29 46.09172 47.50992 2
28828be5b927bca86c8d0d008fa3130f NA CESA2**041968 2,017 35,667 2017-05-04 Yamilet Fuentes Maureira Tratamiento Seguimiento 6 meses 29/04/1968 51 Mujer 30/06/2016 PG-PAB CESFAM Irene Frei publico Alcohol SIN CONSUMO NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 16 23 0 15 S S 15 METROPOLITANA NA 2016-06-30 30/06/2016 2017-05-04 1968-04-29 48.16975 49.01300 2
28828be5b927bca86c8d0d008fa3130f NA CESA2**041968 2,017 35,668 2017-05-04 Yamilet Fuentes Maureira Tratamiento Seguimiento 9 meses 29/04/1968 51 Mujer 30/06/2016 PG-PAB CESFAM Irene Frei publico Alcohol SIN CONSUMO NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 16 23 0 15 S S 15 METROPOLITANA NA 2016-06-30 30/06/2016 2017-05-04 1968-04-29 48.16975 49.01300 2
28869f995c45cd410d86d308163a5d31 NA CRVE2**011986 2,016 16,235 2016-11-09 RENATA DIAZ ZAMBRANO Tratamiento Seguimiento 6 meses 06/01/1986 33 Mujer 11/01/2016 M-PAI CT y Rehabilitacion PAI Mujeres, Arica (CORFAL) privado Alcohol NA NA 1 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 20 0 5 S S 1 DE ARICA Y PARINACOTA NA 2016-01-11 11/01/2016 2016-11-09 1986-01-06 30.01232 30.84189 2
28869f995c45cd410d86d308163a5d31 NA CRVE2**011986 2,016 16,236 2016-11-09 RENATA DIAZ ZAMBRANO Tratamiento Seguimiento 9 meses 06/01/1986 33 Mujer 11/01/2016 M-PAI CT y Rehabilitacion PAI Mujeres, Arica (CORFAL) privado Alcohol NA NA 1 6 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 20 0 5 S S 1 DE ARICA Y PARINACOTA NA 2016-01-11 11/01/2016 2016-11-09 1986-01-06 30.01232 30.84189 2
288a4cef3eaa4ba3ece2c0b6fb3369e2 NA MACL1**071983 2,016 31,639 2016-12-12 juan pablo pérez Ingreso Inicio Tratamiento 09/07/1983 36 Hombre 19/10/2016 PG-PAI Alsino (La Florida) publico Marihuana Pasta Base Cocaína 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 0 0 15 S S 10 METROPOLITANA NA 2016-10-19 19/10/2016 2016-12-12 1983-07-09 33.28131 33.42916 2
288a4cef3eaa4ba3ece2c0b6fb3369e2 NA MACL1**071983 2,016 31,640 2016-12-12 Juan Pablo perez Tratamiento Seguimiento 3 meses 09/07/1983 36 Hombre 19/10/2016 PG-PAI Alsino (La Florida) publico Marihuana Pasta Base Cocaína 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 0 0 15 S S 10 METROPOLITANA NA 2016-10-19 19/10/2016 2016-12-12 1983-07-09 33.28131 33.42916 2
288a4cef3eaa4ba3ece2c0b6fb3369e2 NA MACL1**071983 2,017 41,007 2017-09-27 JUAN PABLO PEREZ Tratamiento Seguimiento 9 meses 09/07/1983 36 Hombre 19/10/2016 PG-PAI Alsino (La Florida) publico Marihuana Pasta Base Cocaína 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 16 16 0 15 S S 18 METROPOLITANA NA 2016-10-19 19/10/2016 2017-09-27 1983-07-09 33.28131 34.22040 2
288a4cef3eaa4ba3ece2c0b6fb3369e2 NA MACL1**071983 2,017 41,008 2017-09-27 Juan Pablo Donoso Tratamiento Seguimiento 12 meses 09/07/1983 36 Hombre 19/10/2016 PG-PAI Alsino (La Florida) publico Marihuana Pasta Base Cocaína 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 16 0 0 15 S S 18 METROPOLITANA NA 2016-10-19 19/10/2016 2017-09-27 1983-07-09 33.28131 34.22040 2
28a75475d0e926e083395dc4a58fb295 NA ALNU1**081968 2,016 26,954 2016-11-30 Cristián Suazo Jara Ingreso Inicio Tratamiento 02/08/1968 51 Hombre 08/03/2016 PG-PAB CESFAM Quinta Bella publico Sedantes: diazepam, Valium, clonazepam, Ravotril, alprazolam, adax, barbitúricos, fenobarbital. Alcohol Pasta Base 1 1 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 13 20 0 14 S S 18 METROPOLITANA NA 2016-03-08 08/03/2016 2016-11-30 1968-08-02 47.59754 48.32854 2
28a75475d0e926e083395dc4a58fb295 NA ALNU1**081968 2,016 26,955 2016-11-30 Cristián Suazo Jara Tratamiento Seguimiento 3 meses 02/08/1968 51 Hombre 08/03/2016 PG-PAB CESFAM Quinta Bella publico Sedantes: diazepam, Valium, clonazepam, Ravotril, alprazolam, adax, barbitúricos, fenobarbital. Alcohol Pasta Base 1 1 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 19 20 0 12 S S 20 METROPOLITANA NA 2016-03-08 08/03/2016 2016-11-30 1968-08-02 47.59754 48.32854 2
28bf3975a86a3f41716399c01d6a8a8d NA WLAR1**061985 2,016 10,267 2016-01-11 carolina Araya Tratamiento Seguimiento 6 meses 21/06/1985 34 Hombre 25/06/2015 PG-PAI CT Aiwin privado Alcohol Marihuana NA 9 8 12 3 0 0 0 0 0 0 0 0 N N N N 0 N 0 7 0 0 9 N S 6 DE LOS RIOS NA 2015-06-25 25/06/2015 2016-01-11 1985-06-21 30.00958 30.55715 2
28bf3975a86a3f41716399c01d6a8a8d NA WLAR1**061985 2,016 10,268 2016-01-11 Carolina Araya Tratamiento Seguimiento 9 meses 21/06/1985 34 Hombre 25/06/2015 PG-PAI CT Aiwin privado Alcohol Marihuana NA 11 5 12 3 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 5 0 12 N N 7 DE LOS RIOS NA 2015-06-25 25/06/2015 2016-01-11 1985-06-21 30.00958 30.55715 2
28cb08050f682e7e4f391793bdf2c5d0 NA MICO1**021958 2,015 3,047 2015-07-30 juan gallardo Ingreso Inicio Tratamiento 24/02/1958 61 Hombre 20/07/2015 PG-PR Comunidad Terapeutica San Francisco de Asis privado Pasta Base Alcohol Sedantes: diazepam, Valium, clonazepam, Ravotril, alprazolam, adax, barbitúricos, fenobarbital. 0 0 0 0 0 0 0 0 0 0 0 0 S N N S NA N 2 9 0 0 7 N N 7 METROPOLITANA NA 2015-07-20 20/07/2015 2015-07-30 1958-02-24 57.39904 57.42642 2
28cb08050f682e7e4f391793bdf2c5d0 NA MICO1**021958 2,015 3,048 2015-07-30 juan gallardo Tratamiento Seguimiento 3 meses 24/02/1958 61 Hombre 20/07/2015 PG-PR Comunidad Terapeutica San Francisco de Asis privado Pasta Base Alcohol Sedantes: diazepam, Valium, clonazepam, Ravotril, alprazolam, adax, barbitúricos, fenobarbital. 0 0 0 0 0 0 0 0 0 0 0 0 S N N S 0 N 2 9 0 0 7 N N 7 METROPOLITANA NA 2015-07-20 20/07/2015 2015-07-30 1958-02-24 57.39904 57.42642 2
28e793655b86acf4a9280e14bdcf0ab6 NA DAGA1**111986 2,019 93,779 2019-06-07 Monica Bonnefoy Ingreso Inicio Tratamiento 26/11/1986 32 Hombre 03/12/2018 PG-PAI Comunidad Terapeutica para Adultos Vinculos (C.T. Vinculos - Chiloe) privado Cocaína NA NA 8 6 1 1 0 1 3 0 0 0 0 0 S N N N 0 N 1 12 24 0 15 S S 14 DE LOS LAGOS NA 2018-12-03 03/12/2018 2019-06-07 1986-11-26 32.01917 32.52841 3
28e793655b86acf4a9280e14bdcf0ab6 NA DAGA1**111986 2,019 93,780 2019-06-07 Monica Bonnefoy Tratamiento Seguimiento 3 meses 26/11/1986 32 Hombre 03/12/2018 PG-PAI Comunidad Terapeutica para Adultos Vinculos (C.T. Vinculos - Chiloe) privado Cocaína NA NA 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 DE LOS LAGOS se ingresa top con fecha 07/06/2019. 2018-12-03 03/12/2018 2019-06-07 1986-11-26 32.01917 32.52841 3
28e793655b86acf4a9280e14bdcf0ab6 NA DAGA1**111986 2,019 93,781 2019-06-07 Monica Bonnefoy Tratamiento Seguimiento 6 meses 26/11/1986 32 Hombre 03/12/2018 PG-PAI Comunidad Terapeutica para Adultos Vinculos (C.T. Vinculos - Chiloe) privado Cocaína NA NA 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 DE LOS LAGOS se ingresa top con fechas 07/06/2019 2018-12-03 03/12/2018 2019-06-07 1986-11-26 32.01917 32.52841 3
28ef12f8fec4611a850f60550eed647a NA PABR1**051966 2,017 35,447 2017-10-20 viviana berrios Tratamiento Seguimiento 3 meses 10/05/1966 53 Hombre 16/05/2016 PG-PAB COSAM El Bosque publico Alcohol Marihuana NA 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 METROPOLITANA no deseo llenar formulario 2016-05-16 16/05/2016 2017-10-20 1966-05-10 50.01780 51.44695 5
28ef12f8fec4611a850f60550eed647a NA PABR1**051966 2,017 35,448 2017-10-20 viviana berrios Tratamiento Seguimiento 6 meses 10/05/1966 53 Hombre 16/05/2016 PG-PAB COSAM El Bosque publico Alcohol Marihuana NA 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 METROPOLITANA no deseo llenar formulario 2016-05-16 16/05/2016 2017-10-20 1966-05-10 50.01780 51.44695 5
28ef12f8fec4611a850f60550eed647a NA PABR1**051966 2,017 35,449 2017-10-20 viviana berrios Tratamiento Seguimiento 9 meses 10/05/1966 53 Hombre 16/05/2016 PG-PAB COSAM El Bosque publico Alcohol Marihuana NA 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 METROPOLITANA no deseo llenar formulario 2016-05-16 16/05/2016 2017-10-20 1966-05-10 50.01780 51.44695 5
28ef12f8fec4611a850f60550eed647a NA PABR1**051966 2,017 35,450 2017-10-20 VIVIANA BERRIOS Tratamiento Seguimiento 12 meses 10/05/1966 53 Hombre 16/05/2016 PG-PAB COSAM El Bosque publico Alcohol Marihuana NA 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 METROPOLITANA NO DESEO LLENAR FORMULARIO 2016-05-16 16/05/2016 2017-10-20 1966-05-10 50.01780 51.44695 5
28ef12f8fec4611a850f60550eed647a NA PABR1**051966 2,017 35,451 2017-10-20 VIVIANA BERRIOS Tratamiento Seguimiento 15 meses 10/05/1966 53 Hombre 16/05/2016 PG-PAB COSAM El Bosque publico Alcohol Marihuana NA 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 METROPOLITANA NO DESEO LLENAR FORMULARIO 2016-05-16 16/05/2016 2017-10-20 1966-05-10 50.01780 51.44695 5
28ef12f8fec4611a850f60550eed647a NA PABR1**051966 2,017 35,452 2017-12-11 viviana Tratamiento NA 10/05/1966 53 Hombre 16/05/2016 PG-PAB COSAM El Bosque publico Alcohol Marihuana NA 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 METROPOLITANA no desea llenado documento 2016-05-16 16/05/2016 2017-12-11 1966-05-10 50.01780 51.58932 2
28ef12f8fec4611a850f60550eed647a NA PABR1**051966 2,017 35,453 2017-12-11 viviana berrios Egreso Egreso 10/05/1966 53 Hombre 16/05/2016 PG-PAB COSAM El Bosque publico Alcohol Marihuana NA 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 METROPOLITANA no desea realizar documento 2016-05-16 16/05/2016 2017-12-11 1966-05-10 50.01780 51.58932 2
28f27678bb7db64466cf14e7e7d03cd0 NA DIVA1**011994 2,016 14,108 2016-02-04 Mariana Herrera Tratamiento Seguimiento 3 meses 11/01/1994 25 Hombre 17/11/2015 PG-PAI COSAM La Pintana publico Cocaína Alcohol Marihuana 12 4 20 3 0 1 1 0 0 0 0 0 N N N N 0 N 0 12 20 0 17 S S 15 METROPOLITANA NA 2015-11-17 17/11/2015 2016-02-04 1994-01-11 21.84805 22.06434 2
28f27678bb7db64466cf14e7e7d03cd0 NA DIVA1**011994 2,016 14,109 2016-02-04 MARIANA HERRERA Tratamiento Seguimiento 6 meses 11/01/1994 25 Hombre 17/11/2015 PG-PAI COSAM La Pintana publico Cocaína Alcohol Marihuana 12 4 20 3 0 1 1 0 0 0 0 0 N N N N 0 N 0 12 20 0 17 S S 15 METROPOLITANA NA 2015-11-17 17/11/2015 2016-02-04 1994-01-11 21.84805 22.06434 2
28fbdd4baa57c897c5cb902e11fc8b20 NA SIJA2**041975 2,018 65,272 2018-09-13 Patricia Muñoz Araya Tratamiento Seguimiento 9 meses 09/04/1975 44 Mujer 26/09/2017 M-PR Complejo Hospitalario San José de Maipo/ Hospital San Jose de Maipo publico Alcohol NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 S 1 18 0 4 20 S S 15 METROPOLITANA NA 2017-09-26 26/09/2017 2018-09-13 1975-04-09 42.46680 43.43053 2
28fbdd4baa57c897c5cb902e11fc8b20 NA SIJA2**041975 2,018 65,273 2018-09-13 Patricia Muñoz Araya Tratamiento Seguimiento 12 meses 09/04/1975 44 Mujer 26/09/2017 M-PR Complejo Hospitalario San José de Maipo/ Hospital San Jose de Maipo publico Alcohol NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 S 1 18 0 4 20 S S 15 METROPOLITANA NA 2017-09-26 26/09/2017 2018-09-13 1975-04-09 42.46680 43.43053 2
29064b22ec3b860cf40e873cf1ab8606 NA ROMO2**071973 2,018 70,787 2018-02-22 FERNANDO PEÑA Ingreso Inicio Tratamiento 19/07/1973 46 Mujer 20/12/2017 PG-PAB CESFAM Symon Ojeda publico Alcohol NA NA 22 16 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 0 0 10 S S 10 METROPOLITANA NA 2017-12-20 20/12/2017 2018-02-22 1973-07-19 44.42163 44.59685 2
29064b22ec3b860cf40e873cf1ab8606 NA ROMO2**071973 2,018 70,788 2018-02-22 FERNANDOPEÑA Tratamiento Seguimiento 3 meses 19/07/1973 46 Mujer 20/12/2017 PG-PAB CESFAM Symon Ojeda publico Alcohol NA NA 22 5 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 0 0 10 S S 10 METROPOLITANA NA 2017-12-20 20/12/2017 2018-02-22 1973-07-19 44.42163 44.59685 2
29075f62c2c918d6977dc0931ac55bae NA YERA2**031976 2,018 69,357 2018-01-23 r vega Ingreso Inicio Tratamiento 17/03/1976 43 Mujer 20/10/2017 PG-PAB COSAM Cerrillos publico Cocaína Alcohol Otros 0 0 0 0 0 0 0 0 0 20 28 20 N N N N 0 N 0 10 20 0 8 S S 10 METROPOLITANA NA 2017-10-20 20/10/2017 2018-01-23 1976-03-17 41.59343 41.85352 2
29075f62c2c918d6977dc0931ac55bae NA YERA2**031976 2,018 69,358 2018-01-23 r vega Tratamiento Seguimiento 3 meses 17/03/1976 43 Mujer 20/10/2017 PG-PAB COSAM Cerrillos publico Cocaína Alcohol Otros 0 0 0 0 0 0 0 0 0 20 28 20 N N N N 0 N 0 10 20 0 8 S S 10 METROPOLITANA NA 2017-10-20 20/10/2017 2018-01-23 1976-03-17 41.59343 41.85352 2
290f3370e10675cb56455d0f8a719c6d NA JOMA1**011994 2,017 39,595 2017-04-20 Francisca Martinez Tratamiento Seguimiento 3 meses 01/01/1994 25 Hombre 04/11/2016 PG-PAI Centro Tratamiento Adicciones Unidos, Hospital Santa Cruz publico Alcohol Sedantes: diazepam, Valium, clonazepam, Ravotril, alprazolam, adax, barbitúricos, fenobarbital. NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 18 20 20 20 S S 18 DEL LIBERTADOR GENERAL BERNARDO OHIGGINS NA 2016-11-04 04/11/2016 2017-04-20 1994-01-01 22.84189 23.29911 2
290f3370e10675cb56455d0f8a719c6d NA JOMA1**011994 2,017 39,596 2017-04-20 Francisca Martinez Egreso Egreso 01/01/1994 25 Hombre 04/11/2016 PG-PAI Centro Tratamiento Adicciones Unidos, Hospital Santa Cruz publico Alcohol Sedantes: diazepam, Valium, clonazepam, Ravotril, alprazolam, adax, barbitúricos, fenobarbital. NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 18 20 20 20 S S 18 DEL LIBERTADOR GENERAL BERNARDO OHIGGINS NA 2016-11-04 04/11/2016 2017-04-20 1994-01-01 22.84189 23.29911 2
29153a9ca4b74ae7478ee1e01fa80933 NA ELRO2**011960 2,018 62,904 2018-01-27 ALEJANDRA VALDIVIA Tratamiento Seguimiento 3 meses 23/01/1960 59 Mujer 08/07/2017 PG-PAB CESFAM Karol Wojtyla publico Pasta Base NA NA 0 0 0 0 2 0 0 0 0 0 0 0 N N N N 28 N 0 0 0 0 0 S S 0 METROPOLITANA NA 2017-07-08 08/07/2017 2018-01-27 1960-01-23 57.45654 58.01232 2
29153a9ca4b74ae7478ee1e01fa80933 NA ELRO2**011960 2,018 62,905 2018-01-27 ALEJANDRA VALDIVIA Tratamiento Seguimiento 6 meses 23/01/1960 59 Mujer 08/07/2017 PG-PAB CESFAM Karol Wojtyla publico Pasta Base NA NA 0 0 0 0 2 0 0 0 0 0 0 0 N N N N 28 N 0 0 0 0 0 S S 0 METROPOLITANA NA 2017-07-08 08/07/2017 2018-01-27 1960-01-23 57.45654 58.01232 2
293981cc899a41a4ba13acfefc0db307 NA ARPR1**091978 2,015 1,369 2015-05-29 Maria Teresa Valenzuela Ingreso Inicio Tratamiento 27/09/1978 41 Hombre 01/06/2015 PG-PAI CT Cantares privado Pasta Base NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 28 0 10 S S 10 DEL LIBERTADOR GENERAL BERNARDO OHIGGINS NA 2015-06-01 01/06/2015 2015-05-29 1978-09-27 36.67625 36.66804 2
293981cc899a41a4ba13acfefc0db307 NA ARPR1**091978 2,015 1,370 2015-05-29 Maria Teresa Valenzuela Tratamiento Seguimiento 3 meses 27/09/1978 41 Hombre 01/06/2015 PG-PAI CT Cantares privado Pasta Base NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 22 0 10 S S 10 DEL LIBERTADOR GENERAL BERNARDO OHIGGINS NA 2015-06-01 01/06/2015 2015-05-29 1978-09-27 36.67625 36.66804 2
293981cc899a41a4ba13acfefc0db307 NA ARPR1**091978 2,016 9,599 2016-02-08 maria teresa valenzuela Tratamiento Seguimiento 6 meses 27/09/1978 41 Hombre 01/06/2015 PG-PAI CT Cantares privado Pasta Base NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 20 24 0 18 S S 20 DEL LIBERTADOR GENERAL BERNARDO OHIGGINS NA 2015-06-01 01/06/2015 2016-02-08 1978-09-27 36.67625 37.36619 2
293981cc899a41a4ba13acfefc0db307 NA ARPR1**091978 2,016 9,600 2016-02-08 maria teresa valenzuela Tratamiento Seguimiento 9 meses 27/09/1978 41 Hombre 01/06/2015 PG-PAI CT Cantares privado Pasta Base NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 20 24 0 18 S S 20 DEL LIBERTADOR GENERAL BERNARDO OHIGGINS NA 2015-06-01 01/06/2015 2016-02-08 1978-09-27 36.67625 37.36619 2
29457ccc924d811f66aff428c205de80 NA ROSA1**091964 2,017 53,885 2017-12-26 ELENA QUIJADA Tratamiento Seguimiento 3 meses 06/09/1964 55 Hombre 28/06/2017 PG-PAI Centro Anun Coronel (población general) publico Pasta Base Alcohol NA 2 6 0 0 6 0 0 0 0 0 0 0 N N N N 0 N 0 15 16 0 16 S S 15 DEL BIO-BIO NA 2017-06-28 28/06/2017 2017-12-26 1964-09-06 52.80767 53.30322 2
29457ccc924d811f66aff428c205de80 NA ROSA1**091964 2,017 53,886 2017-12-26 ELENA QUIJADA Tratamiento Seguimiento 6 meses 06/09/1964 55 Hombre 28/06/2017 PG-PAI Centro Anun Coronel (población general) publico Pasta Base Alcohol NA 2 6 0 0 6 0 0 0 0 0 0 0 N N N N 0 N 0 15 16 0 16 S S 15 DEL BIO-BIO NA 2017-06-28 28/06/2017 2017-12-26 1964-09-06 52.80767 53.30322 2
2958ea90cc0119f73b37ac0b58a42b6e NA CRMO1**121991 2,018 64,953 2018-05-26 ROXANA PRIETO Tratamiento Seguimiento 3 meses 05/12/1991 27 Hombre 26/09/2017 PG-PAB Consultorio Raul Branes publico Pasta Base Cocaína Sedantes: diazepam, Valium, clonazepam, Ravotril, alprazolam, adax, barbitúricos, fenobarbital. 9 4 2 1 16 0 0 5 1 0 0 0 N N N N 12 N 0 7 21 0 9 S S 9 METROPOLITANA NA 2017-09-26 26/09/2017 2018-05-26 1991-12-05 25.80972 26.47228 2
2958ea90cc0119f73b37ac0b58a42b6e NA CRMO1**121991 2,018 64,954 2018-05-26 ROXANA PRIETO Tratamiento Seguimiento 6 meses 05/12/1991 27 Hombre 26/09/2017 PG-PAB Consultorio Raul Branes publico Pasta Base Cocaína Sedantes: diazepam, Valium, clonazepam, Ravotril, alprazolam, adax, barbitúricos, fenobarbital. 9 4 2 1 16 0 0 5 1 0 0 0 N N N N 12 N 0 7 21 0 9 S S 9 METROPOLITANA NA 2017-09-26 26/09/2017 2018-05-26 1991-12-05 25.80972 26.47228 2
295e483113b67f5c6005418e3fa3e0f8 NA MACO1**101972 2,018 63,646 2018-07-19 Luis Maulen Tratamiento Seguimiento 12 meses 26/10/1972 47 Hombre 26/07/2017 PG-PR Comunidad Terapeutica El Sendero de Paternitas privado Alcohol Cocaína Pasta Base 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 15 12 0 15 S S 16 METROPOLITANA NA 2017-07-26 26/07/2017 2018-07-19 1972-10-26 44.74743 45.72758 2
295e483113b67f5c6005418e3fa3e0f8 NA MACO1**101972 2,018 63,647 2018-07-19 luis maulen Egreso Egreso 26/10/1972 47 Hombre 26/07/2017 PG-PR Comunidad Terapeutica El Sendero de Paternitas privado Alcohol Cocaína Pasta Base 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 15 8 0 18 S S 15 METROPOLITANA NA 2017-07-26 26/07/2017 2018-07-19 1972-10-26 44.74743 45.72758 2
2965552c61096092d9e4397d32df31f7 NA FEHE1**051986 2,018 79,607 2018-12-20 CAMILA OLMEDO Ingreso Inicio Tratamiento 01/05/1986 33 Hombre 27/06/2018 PG-PAB CESFAM El Quisco publico Pasta Base Marihuana NA 0 0 20 1 0 0 0 0 0 0 0 0 N N N N 0 N 0 17 24 0 19 S S 20 DE VALPARAISO NA 2018-06-27 27/06/2018 2018-12-20 1986-05-01 32.15606 32.63792 2
2965552c61096092d9e4397d32df31f7 NA FEHE1**051986 2,018 79,608 2018-12-20 CAMILA OLMEDO Tratamiento Seguimiento 3 meses 01/05/1986 33 Hombre 27/06/2018 PG-PAB CESFAM El Quisco publico Pasta Base Marihuana NA 0 0 20 1 0 0 0 0 0 0 0 0 N N N N 0 N 0 17 24 0 19 S S 20 DE VALPARAISO NA 2018-06-27 27/06/2018 2018-12-20 1986-05-01 32.15606 32.63792 2
296c640c619e692875d334a1a1e9411a NA JUSA2**021992 2,016 15,938 2016-01-07 miguel santelices Ingreso Inicio Tratamiento 06/02/1992 27 Mujer 07/01/2016 M-PAI Programa Mujeres La Pintana publico Cocaína Pasta Base Marihuana 7 20 7 4 7 4 7 0 0 0 0 0 N N N S 1 S 2 10 7 0 19 S S 2 METROPOLITANA NA 2016-01-07 07/01/2016 2016-01-07 1992-02-06 23.91786 23.91786 2
296c640c619e692875d334a1a1e9411a NA JUSA1**021992 2,016 16,093 2016-01-07 miguel santelices Ingreso Inicio Tratamiento 06/02/1992 27 Hombre 09/01/2016 M-PAI Programa Mujeres La Pintana publico Cocaína Pasta Base Marihuana 7 20 7 4 7 4 7 0 0 0 0 0 N N N S 1 S 2 10 7 0 19 S S 2 METROPOLITANA NA 2016-01-09 09/01/2016 2016-01-07 1992-02-06 23.92334 23.91786 2
297050fbd8d591849f6d505f1c5b6ab8 NA JUSA1**101982 2,019 89,112 2019-02-27 Richard Vergara Tratamiento Seguimiento 6 meses 24/10/1982 37 Hombre 04/06/2018 PG-PAB CESFAM Romeral publico Alcohol NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 17 0 0 18 S N 17 DEL MAULE NA 2018-06-04 04/06/2018 2019-02-27 1982-10-24 35.61123 36.34497 2
297050fbd8d591849f6d505f1c5b6ab8 NA JUSA1**101982 2,019 89,113 2019-02-27 Teresa Muñoz Reveco Tratamiento Seguimiento 9 meses 24/10/1982 37 Hombre 04/06/2018 PG-PAB CESFAM Romeral publico Alcohol NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 17 20 0 18 S N 17 DEL MAULE NA 2018-06-04 04/06/2018 2019-02-27 1982-10-24 35.61123 36.34497 2
2983b6964e4d695e2b3387d17aa6d479 NA BRBA1**071992 2,016 22,968 2016-09-28 SERGIO EDUARDO MORALES ARAYA Tratamiento Seguimiento 3 meses 18/07/1992 27 Hombre 05/05/2016 PG-PAI Comunidad Terapeutica Ambulatoria Joven Atrevete privado Pasta Base Alcohol NA 0 0 0 0 1 0 0 0 0 0 0 0 N N N N 0 N 0 15 24 0 20 S S 10 DE COQUIMBO NA 2016-05-05 05/05/2016 2016-09-28 1992-07-18 23.79740 24.19713 2
2983b6964e4d695e2b3387d17aa6d479 NA BRBA1**071992 2,016 22,969 2016-09-28 SERGIO EDUARDO MORALES ARAYA Tratamiento Seguimiento 6 meses 18/07/1992 27 Hombre 05/05/2016 PG-PAI Comunidad Terapeutica Ambulatoria Joven Atrevete privado Pasta Base Alcohol NA 0 0 0 0 1 0 0 0 0 0 0 0 N N N N 0 N 0 15 24 0 20 S S 10 DE COQUIMBO NA 2016-05-05 05/05/2016 2016-09-28 1992-07-18 23.79740 24.19713 2
29a8e54ad04a03d97bbb71e2d015e244 NA MAQU1**081979 2,016 12,711 2016-08-29 Jose Gomez Ingreso Inicio Tratamiento 20/08/1979 40 Hombre 28/09/2015 PG-PAB Comunidad Terapeutica La Roca, Viña del Mar (Ambulatorio) privado Cocaína Alcohol Marihuana 1 6 3 1 0 2 4 0 0 0 0 0 N N N N 0 N 0 5 28 0 20 S S 7 DE VALPARAISO NA 2015-09-28 28/09/2015 2016-08-29 1979-08-20 36.10678 37.02669 2
29a8e54ad04a03d97bbb71e2d015e244 NA MAQU1**081979 2,016 12,713 2016-08-29 Jose Gomez Tratamiento Seguimiento 6 meses 20/08/1979 40 Hombre 28/09/2015 PG-PAB Comunidad Terapeutica La Roca, Viña del Mar (Ambulatorio) privado Cocaína Alcohol Marihuana 1 6 3 1 0 2 4 0 0 0 0 0 N N N N 0 N 0 5 28 0 20 S S 7 DE VALPARAISO NA 2015-09-28 28/09/2015 2016-08-29 1979-08-20 36.10678 37.02669 2
29a91ec0aeb08a4634c6b606c38f7553 NA GUVE2**011954 2,017 53,896 2017-08-25 FRANCISCA AHUMADA GONZALEZ Ingreso Inicio Tratamiento 24/01/1954 65 Mujer 25/07/2017 M-PR Comunidad Terapeutica La Ruka privado Pasta Base Cocaína Sedantes: diazepam, Valium, clonazepam, Ravotril, alprazolam, adax, barbitúricos, fenobarbital. 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 20 0 0 5 S S 10 DEL LIBERTADOR GENERAL BERNARDO OHIGGINS NA 2017-07-25 25/07/2017 2017-08-25 1954-01-24 63.49897 63.58385 2
29a91ec0aeb08a4634c6b606c38f7553 NA GUVE2**011954 2,017 53,897 2017-08-25 FRANCISCA AHUMADA GONZALEZ Tratamiento Seguimiento 3 meses 24/01/1954 65 Mujer 25/07/2017 M-PR Comunidad Terapeutica La Ruka privado Pasta Base Cocaína Sedantes: diazepam, Valium, clonazepam, Ravotril, alprazolam, adax, barbitúricos, fenobarbital. 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 20 0 0 5 S S 10 DEL LIBERTADOR GENERAL BERNARDO OHIGGINS NA 2017-07-25 25/07/2017 2017-08-25 1954-01-24 63.49897 63.58385 2
29b93a33e7344cc48d3cbf4a5facad38 NA JAFU1**061991 2,017 41,545 2017-01-16 JOSE VALENZZUELA Ingreso Inicio Tratamiento 12/06/1991 28 Hombre 03/01/2017 PG-PAI Casa Chica Hospital Higueras publico Pasta Base Alcohol Marihuana 4 10 4 2 28 0 0 0 0 0 0 0 N S N N 0 N 1 1 0 0 1 S S 3 DEL BIO-BIO NA 2017-01-03 03/01/2017 2017-01-16 1991-06-12 25.56331 25.59890 2
29b93a33e7344cc48d3cbf4a5facad38 NA JAFU1**061991 2,017 41,546 2017-01-16 JOSE VALENZUELA Tratamiento Seguimiento 3 meses 12/06/1991 28 Hombre 03/01/2017 PG-PAI Casa Chica Hospital Higueras publico Pasta Base Alcohol Marihuana 4 10 4 2 28 0 0 0 0 0 0 0 N S N N 0 N 1 1 0 0 1 S S 3 DEL BIO-BIO NA 2017-01-03 03/01/2017 2017-01-16 1991-06-12 25.56331 25.59890 2
29caa66d0c8beb9d1e11d61a8bd946aa NA ALPE1**111991 2,017 45,046 2017-07-19 Claudia Ruz A. Tratamiento Seguimiento 6 meses 13/11/1991 27 Hombre 02/01/2017 PG-PAI CTA Villa Alemana (CTA Penablanca) publico Cocaína Alcohol NA 1 1 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 0 0 15 S S 18 DE VALPARAISO NA 2017-01-02 02/01/2017 2017-07-19 1991-11-13 25.13895 25.68104 2
29caa66d0c8beb9d1e11d61a8bd946aa NA ALPE1**111991 2,017 45,047 2017-07-19 CLAUDIA RUZ Egreso Egreso 13/11/1991 27 Hombre 02/01/2017 PG-PAI CTA Villa Alemana (CTA Penablanca) publico Cocaína Alcohol NA 1 1 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 12 0 15 S S 18 DE VALPARAISO NA 2017-01-02 02/01/2017 2017-07-19 1991-11-13 25.13895 25.68104 2
29d578d1c1448c0ca962f92193597319 NA MANI2**051992 2,019 89,526 2019-10-08 Alejandra Ley Tratamiento Seguimiento 12 meses 05/05/1992 27 Mujer 12/07/2018 PG-PAB CESFAM Paine publico Cocaína Alcohol NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 15 0 0 15 S S 18 METROPOLITANA NA 2018-07-12 12/07/2018 2019-10-08 1992-05-05 26.18480 27.42505 3
29d578d1c1448c0ca962f92193597319 NA MANI2**051992 2,019 89,527 2019-10-08 Alejandra Ley Tratamiento Seguimiento 15 meses 05/05/1992 27 Mujer 12/07/2018 PG-PAB CESFAM Paine publico Cocaína Alcohol NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 15 0 0 15 S S 18 METROPOLITANA NA 2018-07-12 12/07/2018 2019-10-08 1992-05-05 26.18480 27.42505 3
29d578d1c1448c0ca962f92193597319 NA MANI2**051992 2,019 89,528 2019-10-08 Alejandra ley Egreso Egreso 05/05/1992 27 Mujer 12/07/2018 PG-PAB CESFAM Paine publico Cocaína Alcohol NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 15 0 0 15 S S 18 METROPOLITANA NA 2018-07-12 12/07/2018 2019-10-08 1992-05-05 26.18480 27.42505 3
29dfc3d0cddcc80a89bf86074c2bd97b NA JOZU1**021996 2,018 80,504 2018-10-03 madelene neira Ingreso Inicio Tratamiento 20/02/1996 23 Hombre 04/06/2018 PG-PAB CESFAM Carol Urzúa publico Cocaína Marihuana SIN CONSUMO 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 METROPOLITANA paciente no se logra ubicar. 2018-06-04 04/06/2018 2018-10-03 1996-02-20 22.28611 22.61739 2
29dfc3d0cddcc80a89bf86074c2bd97b NA JOZU1**021996 2,018 80,505 2018-10-03 madelene neira Tratamiento Seguimiento 3 meses 20/02/1996 23 Hombre 04/06/2018 PG-PAB CESFAM Carol Urzúa publico Cocaína Marihuana SIN CONSUMO 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 METROPOLITANA paciente no se logra ubicar. 2018-06-04 04/06/2018 2018-10-03 1996-02-20 22.28611 22.61739 2
29e3f04b8822a31eefb5d2b38a2d32c4 NA PESE1**091965 2,017 47,021 2017-10-10 Verenice Gomez Tratamiento Seguimiento 6 meses 28/09/1965 54 Hombre 06/03/2017 PG-PAB CESFAM Penco (Centro Nehuen) publico Alcohol NA NA 0 0 4 1 0 0 0 0 0 0 0 0 N N N N 0 N 0 18 0 0 14 S S 20 DEL BIO-BIO NA 2017-03-06 06/03/2017 2017-10-10 1965-09-28 51.43600 52.03285 3
29e3f04b8822a31eefb5d2b38a2d32c4 NA PESE1**091965 2,017 47,022 2017-10-10 verenice gómez Tratamiento Seguimiento 9 meses 28/09/1965 54 Hombre 06/03/2017 PG-PAB CESFAM Penco (Centro Nehuen) publico Alcohol NA NA 0 0 4 1 0 0 0 0 0 0 0 0 N N N N 0 N 0 18 0 0 14 S S 20 DEL BIO-BIO NA 2017-03-06 06/03/2017 2017-10-10 1965-09-28 51.43600 52.03285 3
29e3f04b8822a31eefb5d2b38a2d32c4 NA PESE1**091965 2,017 47,023 2017-10-10 VERENICE GOMEZ HIDALGO Egreso Egreso 28/09/1965 54 Hombre 06/03/2017 PG-PAB CESFAM Penco (Centro Nehuen) publico Alcohol NA NA 0 0 4 1 0 0 0 0 0 0 0 0 N N N N 0 N 0 18 0 0 14 S S 20 DEL BIO-BIO NA 2017-03-06 06/03/2017 2017-10-10 1965-09-28 51.43600 52.03285 3
29ee8838e51c4d4c32f9f41b3b279ee6 NA LIQU1**061973 2,016 22,755 2016-12-07 m palacios Tratamiento Seguimiento 3 meses 15/06/1973 46 Hombre 17/03/2016 PG-PAI COSAM Estacion Central publico Cocaína Pasta Base Marihuana 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 18 28 0 19 S S 20 METROPOLITANA NA 2016-03-17 17/03/2016 2016-12-07 1973-06-15 42.75428 43.47981 2
29ee8838e51c4d4c32f9f41b3b279ee6 NA LIQU1**061973 2,016 22,756 2016-12-07 m palacios Tratamiento Seguimiento 6 meses 15/06/1973 46 Hombre 17/03/2016 PG-PAI COSAM Estacion Central publico Cocaína Pasta Base Marihuana 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 18 28 0 18 S S 20 METROPOLITANA NA 2016-03-17 17/03/2016 2016-12-07 1973-06-15 42.75428 43.47981 2
29f052da87e352531d2118b72b64ce72 NA CAJI1**051998 2,018 64,341 2018-02-08 Cinthya Alvarado Tratamiento Seguimiento 6 meses 21/05/1998 21 Hombre 22/08/2017 PG-PAI Complejo Miraflores publico Cocaína Alcohol NA 1 20 0 0 0 0 0 0 0 0 0 0 N N N S 3 N 1 15 14 0 15 S S 20 DE MAGALLANES Y LA ANTARTICA CHILENA NA 2017-08-22 22/08/2017 2018-02-08 1998-05-21 19.25530 19.72074 2
29f052da87e352531d2118b72b64ce72 NA CAJI1**051998 2,018 64,342 2018-02-08 Cinthya Alvarado Egreso Egreso 21/05/1998 21 Hombre 22/08/2017 PG-PAI Complejo Miraflores publico Cocaína Alcohol NA 1 20 0 0 0 0 0 0 0 0 0 0 N N N S 3 N 1 15 14 0 15 S S 20 DE MAGALLANES Y LA ANTARTICA CHILENA NA 2017-08-22 22/08/2017 2018-02-08 1998-05-21 19.25530 19.72074 2
29f382d0469295e2c4433a97e2485536 NA JOGO1**061964 2,016 22,084 2016-05-10 Maria Teresa Valenzuela Ingreso Inicio Tratamiento 09/06/1964 55 Hombre 21/04/2016 PG-PAI CT Cantares privado Alcohol Marihuana NA 28 4 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 23 0 17 S S 7 DEL LIBERTADOR GENERAL BERNARDO OHIGGINS NA 2016-04-21 21/04/2016 2016-05-10 1964-06-09 51.86585 51.91786 2
29f382d0469295e2c4433a97e2485536 NA JOGO1**061964 2,016 22,085 2016-05-10 Maria Teresa Valenzuela Tratamiento Seguimiento 3 meses 09/06/1964 55 Hombre 21/04/2016 PG-PAI CT Cantares privado Alcohol Marihuana NA 28 8 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 23 0 17 S S 7 DEL LIBERTADOR GENERAL BERNARDO OHIGGINS NA 2016-04-21 21/04/2016 2016-05-10 1964-06-09 51.86585 51.91786 2
2a0482fbaac44063ffb8edba1a8dc021 NA ESMO2**121987 2,019 92,385 2019-05-13 Paola Jouannet Styl Tratamiento Seguimiento 6 meses 01/12/1987 31 Mujer 19/10/2018 M-PAI Hospital El Pino publico Cocaína Pasta Base Marihuana 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 METROPOLITANA Usuaria no asiste a instancias terapeuticas,ni se le encuentra en el domicilio, por ello no se puede aplicar ficha TOP 2018-10-19 19/10/2018 2019-05-13 1987-12-01 30.88296 31.44695 2
2a0482fbaac44063ffb8edba1a8dc021 NA ESMO2**121987 2,019 92,386 2019-05-13 Paola Jouannet Styl Tratamiento Seguimiento 9 meses 01/12/1987 31 Mujer 19/10/2018 M-PAI Hospital El Pino publico Cocaína Pasta Base Marihuana 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 METROPOLITANA Usuaria no asiste a instancias terapeuticas,ni se le encuentra en el domicilio, por ello no se puede aplicar ficha TOP 2018-10-19 19/10/2018 2019-05-13 1987-12-01 30.88296 31.44695 2
2a096607d50161adbd52cc4c1b3ae17a NA MAHE2**061961 2,015 2,012 2015-07-06 LORETO CANCINO ACEVEDO Ingreso Inicio Tratamiento 05/06/1961 58 Mujer 16/06/2015 PG-PAB CESFAM La Florida, Talca publico Alcohol NA NA 5 6 0 0 0 0 0 0 0 0 0 0 S N N S 0 N 2 10 28 0 10 S S 7 DEL MAULE NA 2015-06-16 16/06/2015 2015-07-06 1961-06-05 54.02875 54.08350 2
2a096607d50161adbd52cc4c1b3ae17a NA MAHE2**061961 2,015 2,013 2015-07-06 LORETO CANCINO ACEVEDO Tratamiento Seguimiento 3 meses 05/06/1961 58 Mujer 16/06/2015 PG-PAB CESFAM La Florida, Talca publico Alcohol NA NA 5 6 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 28 0 10 S S 7 DEL MAULE NA 2015-06-16 16/06/2015 2015-07-06 1961-06-05 54.02875 54.08350 2
2a096607d50161adbd52cc4c1b3ae17a NA MAHE2**061961 2,016 14,156 2016-03-10 PAOLA ALBORNOZ FIGUEROA Ingreso Inicio Tratamiento 06/06/1961 58 Mujer 01/09/2015 PG-PAI COSAM Talca publico Alcohol SIN CONSUMO NA 9 3 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 5 20 0 5 S S 5 DEL MAULE NA 2015-09-01 01/09/2015 2016-03-10 1961-06-06 54.23682 54.75975 3
2a096607d50161adbd52cc4c1b3ae17a NA MAHE2**061961 2,016 14,157 2016-03-10 PAOLA ALBORNOZ FIGUEROA Tratamiento Seguimiento 3 meses 06/06/1961 58 Mujer 01/09/2015 PG-PAI COSAM Talca publico Alcohol SIN CONSUMO NA 9 3 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 5 20 0 5 S S 5 DEL MAULE NA 2015-09-01 01/09/2015 2016-03-10 1961-06-06 54.23682 54.75975 3
2a096607d50161adbd52cc4c1b3ae17a NA MAHE2**061961 2,016 14,158 2016-03-10 PAOLA ALBORNOZ FIGUEROA Tratamiento Seguimiento 6 meses 06/06/1961 58 Mujer 01/09/2015 PG-PAI COSAM Talca publico Alcohol SIN CONSUMO NA 9 3 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 5 20 0 5 S S 5 DEL MAULE NA 2015-09-01 01/09/2015 2016-03-10 1961-06-06 54.23682 54.75975 3
2a0b64b6befd1e05b29a192553100511 NA JOCO1**071980 2,015 1,086 2015-06-01 CAROLINA CONTRERAS Ingreso Inicio Tratamiento 01/07/1980 39 Hombre 27/05/2015 PG-PAB CESFAM Colón publico Alcohol Cocaína NA 12 8 0 0 0 0 0 0 0 0 0 0 N N N S 0 N 1 5 0 0 10 S S 8 DEL MAULE NA 2015-05-27 27/05/2015 2015-06-01 1980-07-01 34.90212 34.91581 2
2a0b64b6befd1e05b29a192553100511 NA JOCO1**071980 2,015 1,087 2015-06-01 CAROLINA CONTRERAS RETAMAL Tratamiento Seguimiento 3 meses 01/07/1980 39 Hombre 27/05/2015 PG-PAB CESFAM Colón publico Alcohol Cocaína NA 12 8 0 0 0 0 0 0 0 0 0 0 N N N S 0 N 1 5 0 0 10 S S 8 DEL MAULE NA 2015-05-27 27/05/2015 2015-06-01 1980-07-01 34.90212 34.91581 2
2a0d1b7b0780a8019041d258f6727a77 NA CAOR1**041951 2,018 76,583 2018-11-16 carolina labra Tratamiento Seguimiento 3 meses 15/04/1951 68 Hombre 04/05/2018 PG-PAB CESFAM Estacion publico Alcohol NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 6 0 0 14 S S 11 DE ATACAMA NA 2018-05-04 04/05/2018 2018-11-16 1951-04-15 67.05270 67.58932 2
2a0d1b7b0780a8019041d258f6727a77 NA CAOR1**041951 2,018 76,584 2018-11-16 carolina labra Tratamiento Seguimiento 6 meses 15/04/1951 68 Hombre 04/05/2018 PG-PAB CESFAM Estacion publico Alcohol NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 6 0 0 12 S S 10 DE ATACAMA NA 2018-05-04 04/05/2018 2018-11-16 1951-04-15 67.05270 67.58932 2
2a143662d1a390c6b41d4268fbfe6113 NA CABA2**041985 2,017 37,490 2017-09-01 Yohana Cabrera A Tratamiento Seguimiento 12 meses 22/04/1985 34 Mujer 31/08/2016 M-PR CT Puerta Abierta (Estacion Central) privado Pasta Base Alcohol Marihuana 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 19 20 0 19 S S 20 METROPOLITANA NA 2016-08-31 31/08/2016 2017-09-01 1985-04-22 31.35934 32.36140 2
2a143662d1a390c6b41d4268fbfe6113 NA CABA2**041985 2,017 37,491 2017-09-01 Johana Cabrera Egreso Egreso 22/04/1985 34 Mujer 31/08/2016 M-PR CT Puerta Abierta (Estacion Central) privado Pasta Base Alcohol Marihuana 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 17 20 0 18 S S 14 METROPOLITANA NA 2016-08-31 31/08/2016 2017-09-01 1985-04-22 31.35934 32.36140 2
2a2acb3c130f745da92d5f617907ce18 NA RODI2**031971 2,017 49,842 2017-10-19 viviana berrios Ingreso Inicio Tratamiento 11/03/1971 48 Mujer 13/02/2017 PG-PAI COSAM El Bosque publico Pasta Base Marihuana Cocaína 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 METROPOLITANA no deseo llenar formulario 2017-02-13 13/02/2017 2017-10-19 1971-03-11 45.93018 46.60917 3
2a2acb3c130f745da92d5f617907ce18 NA RODI2**031971 2,017 49,843 2017-10-19 viviana berrios Tratamiento Seguimiento 3 meses 11/03/1971 48 Mujer 13/02/2017 PG-PAI COSAM El Bosque publico Pasta Base Marihuana Cocaína 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 METROPOLITANA no deseo llenar formulario 2017-02-13 13/02/2017 2017-10-19 1971-03-11 45.93018 46.60917 3
2a2acb3c130f745da92d5f617907ce18 NA RODI2**031971 2,017 49,844 2017-10-19 VIVIANA BERRIOS Tratamiento Seguimiento 6 meses 11/03/1971 48 Mujer 13/02/2017 PG-PAI COSAM El Bosque publico Pasta Base Marihuana Cocaína 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 METROPOLITANA NO DESEO LLENAR FORMULARIO 2017-02-13 13/02/2017 2017-10-19 1971-03-11 45.93018 46.60917 3
2a2f7db604c7ebf5eee4aeecc247a24c NA MATA1**041958 2,018 75,791 2018-07-26 A Ingreso Inicio Tratamiento 26/04/1958 61 Hombre 16/04/2018 PG-PAI COSAM La Pintana publico Cocaína Alcohol NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 7 24 0 10 S S 10 METROPOLITANA NA 2018-04-16 16/04/2018 2018-07-26 1958-04-26 59.97262 60.24914 2
2a2f7db604c7ebf5eee4aeecc247a24c NA MATA1**041958 2,018 75,792 2018-07-26 A Tratamiento Seguimiento 3 meses 26/04/1958 61 Hombre 16/04/2018 PG-PAI COSAM La Pintana publico Cocaína Alcohol NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 7 24 0 10 S S 10 METROPOLITANA NA 2018-04-16 16/04/2018 2018-07-26 1958-04-26 59.97262 60.24914 2
2a2f7db604c7ebf5eee4aeecc247a24c NA MATA1**041958 2,019 88,129 2019-05-02 ANGELICA Tratamiento Seguimiento 9 meses 26/04/1958 61 Hombre 16/04/2018 PG-PAI COSAM La Pintana publico Cocaína Alcohol NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 12 0 0 10 S S 20 METROPOLITANA NA 2018-04-16 16/04/2018 2019-05-02 1958-04-26 59.97262 61.01574 2
2a2f7db604c7ebf5eee4aeecc247a24c NA MATA1**041958 2,019 88,130 2019-05-02 ANGELICA Tratamiento Seguimiento 12 meses 26/04/1958 61 Hombre 16/04/2018 PG-PAI COSAM La Pintana publico Cocaína Alcohol NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 12 0 0 10 S S 20 METROPOLITANA NA 2018-04-16 16/04/2018 2019-05-02 1958-04-26 59.97262 61.01574 2
2a4d3e18e48a3d8706a14e295fe73e99 NA PASA1**061975 2,017 48,620 2017-06-28 BYRON URRUTIA Ingreso Inicio Tratamiento 21/06/1975 44 Hombre 10/04/2017 PG-PAB COSAM Talagante publico Alcohol NA NA 2 4 0 0 0 0 0 0 0 5 28 5 N N N N 0 N 0 15 20 20 16 S S 15 METROPOLITANA NA 2017-04-10 10/04/2017 2017-06-28 1975-06-21 41.80424 42.02053 2
2a4d3e18e48a3d8706a14e295fe73e99 NA PASA1**061975 2,017 48,621 2017-06-28 BYRON URRUTIA Tratamiento Seguimiento 3 meses 21/06/1975 44 Hombre 10/04/2017 PG-PAB COSAM Talagante publico Alcohol NA NA 2 4 0 0 0 0 0 0 0 5 28 5 N N N N 0 N 0 15 20 20 16 S S 15 METROPOLITANA NA 2017-04-10 10/04/2017 2017-06-28 1975-06-21 41.80424 42.02053 2
2a4ddd9ca75c30595f431f642cb6d33b NA ENSI1**021957 2,016 29,965 2016-12-09 Ana Maria Correa Tratamiento Seguimiento 3 meses 10/02/1957 62 Hombre 01/08/2016 PG-PAB COSAM Curanilahue (población general) publico Alcohol NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 17 20 0 17 S S 15 DEL BIO-BIO NA 2016-08-01 01/08/2016 2016-12-09 1957-02-10 59.47159 59.82752 2
2a4ddd9ca75c30595f431f642cb6d33b NA ENSI1**021957 2,016 29,966 2016-12-09 ANA MARIA CORREA Tratamiento Seguimiento 6 meses 10/02/1957 62 Hombre 01/08/2016 PG-PAB COSAM Curanilahue (población general) publico Alcohol NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 17 20 0 17 S S 15 DEL BIO-BIO NA 2016-08-01 01/08/2016 2016-12-09 1957-02-10 59.47159 59.82752 2
2a55fcd169a8f8a254c268faf5853434 NA ALIG1**091988 2,017 58,706 2017-12-13 DANIEL ORIAS Ingreso Inicio Tratamiento 30/09/1988 31 Hombre 03/11/2017 PG-PAI Centro Anun Coronel (población general) publico Pasta Base Alcohol NA 0 0 0 0 8 0 0 0 0 0 0 0 N N N N 0 N 0 14 15 0 15 S S 16 DEL BIO-BIO NA 2017-11-03 03/11/2017 2017-12-13 1988-09-30 29.09240 29.20192 2
2a55fcd169a8f8a254c268faf5853434 NA ALIG1**091988 2,017 58,707 2017-12-13 DANIEL ORIAS Tratamiento Seguimiento 3 meses 30/09/1988 31 Hombre 03/11/2017 PG-PAI Centro Anun Coronel (población general) publico Pasta Base Alcohol NA 0 0 0 0 8 0 0 0 0 0 0 0 N N N N 0 N 0 14 15 0 15 S S 16 DEL BIO-BIO NA 2017-11-03 03/11/2017 2017-12-13 1988-09-30 29.09240 29.20192 2
2a6211dd222e4d64aaacf1153754b15b NA ANAR2**011991 2,018 75,387 2018-04-23 As Marjorie Martinez Ingreso Inicio Tratamiento 13/01/1991 28 Mujer 20/04/2018 M-PAI Centro de Responsabilidad de Salud Mental del Complejo Asistencial Dr.Victor Rios Ruiz publico Pasta Base NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 12 0 0 11 S S 12 DEL BIO-BIO NA 2018-04-20 20/04/2018 2018-04-23 1991-01-13 27.26626 27.27447 2
2a6211dd222e4d64aaacf1153754b15b NA ANAR2**011991 2,018 75,389 2018-04-23 As Marjorie Martine Tratamiento Seguimiento 6 meses 13/01/1991 28 Mujer 20/04/2018 M-PAI Centro de Responsabilidad de Salud Mental del Complejo Asistencial Dr.Victor Rios Ruiz publico Pasta Base NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 12 0 0 11 S S 12 DEL BIO-BIO NA 2018-04-20 20/04/2018 2018-04-23 1991-01-13 27.26626 27.27447 2
2a6df2e7c24399df11c15ff46079b63e NA FRGA1**121960 2,016 15,809 2016-09-23 viviana Ingreso Inicio Tratamiento 08/12/1960 58 Hombre 12/05/2015 PG-PAB COSAM El Bosque publico Alcohol Marihuana NA 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 METROPOLITANA no desea llenar formulario 2015-05-12 12/05/2015 2016-09-23 1960-12-08 54.42300 55.79192 3
2a6df2e7c24399df11c15ff46079b63e NA FRGA1**121960 2,016 15,810 2016-09-23 viviana Tratamiento Seguimiento 3 meses 08/12/1960 58 Hombre 12/05/2015 PG-PAB COSAM El Bosque publico Alcohol Marihuana NA 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 METROPOLITANA no desea llenar formulario 2015-05-12 12/05/2015 2016-09-23 1960-12-08 54.42300 55.79192 3
2a6df2e7c24399df11c15ff46079b63e NA FRGA1**121960 2,016 15,811 2016-09-23 viviana Tratamiento Seguimiento 6 meses 08/12/1960 58 Hombre 12/05/2015 PG-PAB COSAM El Bosque publico Alcohol Marihuana NA 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 METROPOLITANA no desea 2015-05-12 12/05/2015 2016-09-23 1960-12-08 54.42300 55.79192 3
2a74383c4eae0037bb0a0fc098332260 NA MABU1**111983 2,017 52,194 2017-09-12 ps judith lecaros Ingreso Inicio Tratamiento 11/11/1983 35 Hombre 13/06/2017 PG-PAB CESFAM Sarmiento publico Alcohol NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 17 24 0 16 S S 17 DEL MAULE NA 2017-06-13 13/06/2017 2017-09-12 1983-11-11 33.58795 33.83710 2
2a74383c4eae0037bb0a0fc098332260 NA MABU1**111983 2,017 52,195 2017-09-12 ps judith lecaros Tratamiento Seguimiento 3 meses 11/11/1983 35 Hombre 13/06/2017 PG-PAB CESFAM Sarmiento publico Alcohol NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 17 20 0 14 S S 16 DEL MAULE NA 2017-06-13 13/06/2017 2017-09-12 1983-11-11 33.58795 33.83710 2
2a74383c4eae0037bb0a0fc098332260 NA MABU1**111983 2,018 62,767 2018-06-12 Judith lecaros Tratamiento Seguimiento 6 meses 11/11/1983 35 Hombre 13/06/2017 PG-PAB CESFAM Sarmiento publico Alcohol NA NA 0 0 5 1 0 0 0 0 0 0 0 0 N N N N 0 N 0 13 20 0 17 S S 15 DEL MAULE NA 2017-06-13 13/06/2017 2018-06-12 1983-11-11 33.58795 34.58453 3
2a74383c4eae0037bb0a0fc098332260 NA MABU1**111983 2,018 62,768 2018-06-12 Judith Lecaros Tratamiento Seguimiento 9 meses 11/11/1983 35 Hombre 13/06/2017 PG-PAB CESFAM Sarmiento publico Alcohol NA NA 0 0 5 1 0 0 0 0 0 0 0 0 N N N N 0 N 0 12 20 0 14 S S 14 DEL MAULE NA 2017-06-13 13/06/2017 2018-06-12 1983-11-11 33.58795 34.58453 3
2a74383c4eae0037bb0a0fc098332260 NA MABU1**111983 2,018 62,769 2018-06-12 Judith Lecaros Tratamiento Seguimiento 12 meses 11/11/1983 35 Hombre 13/06/2017 PG-PAB CESFAM Sarmiento publico Alcohol NA NA 0 0 5 1 0 0 0 0 0 0 0 0 N N N N 0 N 0 12 20 0 15 S S 13 DEL MAULE NA 2017-06-13 13/06/2017 2018-06-12 1983-11-11 33.58795 34.58453 3
2a8b982197c72d531196c9a5dc8c4522 NA ALSE1**091997 2,018 74,154 2018-05-22 CHRISTIAN ROZAS JORQUERA Ingreso Inicio Tratamiento 16/09/1997 22 Hombre 05/12/2017 PG-PAB Hospital de la Familia y la Comunidad de Mulchen publico Pasta Base Marihuana NA 1 6 9 2 2 0 0 0 0 0 0 0 N N N N 0 N 0 10 6 0 20 S S 10 DEL BIO-BIO NA 2017-12-05 05/12/2017 2018-05-22 1997-09-16 20.21903 20.67899 2
2a8b982197c72d531196c9a5dc8c4522 NA ALSE1**091997 2,018 74,155 2018-05-22 CHRISTIAN ROZAS JORQUERA Tratamiento Seguimiento 3 meses 16/09/1997 22 Hombre 05/12/2017 PG-PAB Hospital de la Familia y la Comunidad de Mulchen publico Pasta Base Marihuana NA 1 6 9 2 2 0 0 0 0 0 0 0 N N N N 0 N 0 10 6 0 20 S S 10 DEL BIO-BIO NA 2017-12-05 05/12/2017 2018-05-22 1997-09-16 20.21903 20.67899 2


As can be seen in Table 10, in some cases the only thing that varies is the stage of treatment ot the TOP variable; in others also varies the qualitative observations or “comments” section; and in very few, there are changes in variables relative to the content of the evaluation. Can a single combination of the date of application and HASH have different dates of admission?

5. Deletion of nearly exact duplicated cases


We needed to reduce the number of cases, but in the meantime, reduce the loss of information due to specific changes in each entry. That is why we selected almost every variable to detect duplicated cases, excepting the following:

  • SENDA ID (ID): The HASH Key identifier was the one recommended by SENDA Professionals.
  • SENDA ID (masked characters 5 & 6) (id_mod): The HASH Key identifier was the one recommended by SENDA Professionals.
  • Year of the Dataset (Source) (ano_bd) (TABLE): Let us detect cases that repeat among different yearly datasets.
  • Events in the Dataset/ Row Number (row): We excluded this identifier because it is a unique number of each event that we arbitrarily generated.
  • Name of the TOP Interviewer (Nombre.Apliacador.del.TOP): Identify each interviewer is not relevant for characterizing each user and their transitions at this stage.
  • Observations (OBS): This variable was created in the context of the study, to include part of the process of standardization of the dataset. Not relevant to characterize each user and their transitions.
  • Age at the Time of Admission (Edad_al_ing): Not included because it depends mainly on already present variables (Date of Birth and Date of Admission)
  • Age at the Time of Application (Edad_at_ap): Not included because it depends mainly on already present variables (Date of Birth and Date of Application of TOP)


#create vector with variable names
#names_top <- names(CONS_TOP_df_dup_ENE_2020_prev2[,-c("id_mod","ano_bd","row","TABLE","Nombre.Apliacador.del.TOP")])
names_top <-names(subset(CONS_TOP_df_dup_ENE_2020_prev2, select = -c(ID,id_mod, ano_bd, row, Nombre.Apliacador.del.TOP, OBS, Edad_al_ing, Edad_at_ap)))
#Group by duplicated rows 
as.data.table(CONS_TOP_df_dup_ENE_2020_prev2)[, dup_todo_TOP := .N, by = names_top] %>%
dplyr::mutate(OBS=case_when(dup_todo_TOP>1~glue::glue("{OBS};1.4.Had a duplicated events with almost every variable in common"),
                            TRUE~OBS))%>% 
  data.table::as.data.table() %>%
  assign("CONS_TOP_df_dup_ENE_2020_prev3",.,envir = .GlobalEnv)
#summarise duplicates and times
as.data.table(CONS_TOP_df_dup_ENE_2020_prev3)[, dup_todo_TOP := .N, by = names_top] %>%
  dplyr::group_by(dup_todo_TOP) %>%
  dplyr::summarise(n=n()) %>%
  mutate(perc = round(n / sum(n),3)*100) %>%
  mutate(perc = paste0(perc,"%")) %>%
  dplyr::mutate(unique_cases= formatC(n/dup_todo_TOP, format="f", big.mark=",", digits=0)) %>%
  data.frame() %>% 
  dplyr::rename("Times present in Dataset"=dup_todo_TOP, "Number of Rows"=`n`, "%"=perc, "Unique Cases"=unique_cases) %>%
  knitr::kable(.,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
               caption="Table 11. Duplicated cases in almost every variable",
                 align ="cccc")  %>%
   kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 10)
`summarise()` ungrouping output (override with `.groups` argument)
Table 11. Duplicated cases in almost every variable
Times present in Dataset Number of Rows % Unique Cases
1 106,975 99.7% 106,975
2 168 0.2% 84
3 63 0.1% 21
4 20 0% 5
5 10 0% 2
6 18 0% 3
7 14 0% 2
8 8 0% 1
9 9 0% 1
10 10 0% 1
12 12 0% 1 
data.table::data.table(CONS_TOP_df_dup_ENE_2020_prev3) %>%
  dplyr::arrange(desc(ano_bd)) %>%
  dplyr::distinct_at(.vars=names_top, .keep_all = TRUE) %>%
  dplyr::arrange(HASH_KEY, fech_ing, fech_ap_top, desc(ano_bd)) %>%
  data.table::as.data.table() %>%
  assign("CONS_TOP_df_dup_ENE_2020_prev4",.,envir = .GlobalEnv)
#Unlike base sorting with sort(), NA are: always sorted to the end for local data, even when wrapped with desc().

 

As seen in Table 11, not many cases were dropped, which can be interpreted as different information regarding users and specific treatments in each of the remaining 107,096 rows, in at least one of the 42 selected variables.


We must consider that this dataset is focused on the contents of the treatment outcomes profile. Additionally, in Table 10, we observed that many entries with the same dates of application and HASH had nearly the same values in variables of each application, we excluded variables related to the source of information provided (e.g., it comes from one yearly or another, and the name of the interviewer), among others not relevant for the study. The variables related to the stage of treatment (TOP or Etapa.del.Tratamiento) were not considered, because in many cases, registries with the same date of application were categorized with different stages. Even some variables relative to the characteristics of the center were omitted (such as the region of the center or “Región.Centro” and their name “Nombre.Centro”).


The following variables were used as a criterion to estimate duplicated rows in the dataset, meaning that any lost information by the deduplication would not correspond to a loss of information related to the questionnaire itself.

  • Masked Identifier (RUN) (HASH_KEY)
  • Type of Center (Tipo.Centro)
  • Primary Substance of Consumption (1) (Sustancia.Principal.1)
  • Primary Substance of Consumption (2) (Sustancia.Principal.2)
  • Primary Substance of Consumption (3) Sustancia.Principal.3)
  • Total Alcohol (Total.OH)
  • Dose of Alcohol (Dósis.OH)
  • Total Marijuana (Total.THC)
  • Dose of Marijuana (Dósis.THC)
  • Total Cocaine Paste Base (Total.PBC)
  • Amount of Cocaine Paste Base (Dósis.PBC)
  • Total Snort Cocaine (Total.COC)
  • Dose of Snort Cocaine (Dósis.COC)
  • Total Sedatives and Tranquillizers (Total.BZD)
  • Dose of Sedatives and Tranquillizers (Dósis.BZD)
  • Total Other Substances (Total.Otra)
  • Dose of Other Substances (Dósis.Otra)
  • Theft (Hurto)
  • Robbery (Robo)
  • Drug selling (Venta.Drogas)
  • Fights (Riña)
  • Total Domestic Violence (Total.VIF)
  • Another Action (Otro)
  • Total Behavior that transgresses social norms (Total.Transgresión)
  • Psychological Health (Salud.Psicológica)
  • Total of Paid Work (Total.Trabajo)
  • Total College or school (Total.Educación)
  • Total Physical Health (Salud.Física)
  • Stable Place to Live (Lugar.Vivir)
  • Housing conditions (Vivienda)
  • Total Quality of Life (QoL) (Calidad.Vida)
  • Date of Admission to Treatment (fech_ing)
  • Date of Application of TOP (fech_ap_top)
#create vector with variable names
names_top2 <- c('HASH_KEY', 'Tipo.Centro', 'Sustancia.Principal.1', 'Sustancia.Principal.2', 'Sustancia.Principal.3', 'Total.OH', 'Dósis.OH', 'Total.THC', 'Dósis.THC', 'Total.PBC', 'Dósis.PBC', 'Total.COC', 'Dósis.COC', 'Total.BZD', 'Dósis.BZD', 'Total.Otra', 'Dósis.Otra', 'Hurto', 'Robo', 'Venta.Drogas', 'Riña', 'Total.VIF', 'Otro', 'Total.Transgresión', 'Salud.Psicológica', 'Total.Trabajo', 'Total.Educación', 'Salud.Física', 'Lugar.Vivir', 'Vivienda', 'Calidad.Vida', 'fech_ing', 'fech_ap_top')
#Group by duplicated rows 
as.data.table(CONS_TOP_df_dup_ENE_2020_prev4)[, dup_contents_TOP := .N, by = names_top2] %>%
  dplyr::mutate(OBS=case_when(dup_contents_TOP>1~glue::glue("{OBS};1.5.Had a duplicated event with relevant variables in common"),
                              TRUE~OBS))%>% 
  data.table::as.data.table() %>%
  assign("CONS_TOP_df_dup_ENE_2020_prev5",.,envir = .GlobalEnv)
#summarise duplicates and times
as.data.table(CONS_TOP_df_dup_ENE_2020_prev5)[, dup_contents_TOP := .N, by = names_top2] %>%
  dplyr::group_by(dup_contents_TOP) %>%
  dplyr::summarise(n=n()) %>%
  mutate(perc = round(n / sum(n),3)*100) %>%
  mutate(perc = paste0(perc,"%")) %>%
  dplyr::mutate(unique_cases= formatC(n/dup_contents_TOP, format="f", big.mark=",", digits=0)) %>%
  data.frame() %>%
  dplyr::rename("Times present in Dataset"=dup_contents_TOP, "Number of Rows"=`n`, "%"=perc, "Unique Cases"=unique_cases) %>%
  knitr::kable(.,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
               caption="Table 12. Duplicated cases in variables related to the content of TOPs",
                 align ="cccc")  %>%
   kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 10)
`summarise()` ungrouping output (override with `.groups` argument)
Table 12. Duplicated cases in variables related to the content of TOPs
Times present in Dataset Number of Rows % Unique Cases
1 99,890 93.3% 99,890
2 6,160 5.8% 3,080
3 777 0.7% 259
4 184 0.2% 46
5 55 0.1% 11
6 30 0% 5 
data.table::data.table(CONS_TOP_df_dup_ENE_2020_prev5) %>%
  dplyr::arrange(desc(ano_bd)) %>%
  dplyr::distinct_at(.vars=names_top2, .keep_all = TRUE) %>%
  dplyr::arrange(HASH_KEY, fech_ing, fech_ap_top, desc(ano_bd)) %>%
  data.table::as.data.table() %>%
  assign("CONS_TOP_df_dup_ENE_2020_prev6",.,envir = .GlobalEnv)
#Unlike base sorting with sort(), NA are: always sorted to the end for local data, even when wrapped with desc().


What happens if a case have the same date of application, but a different date of admission?. Is it possible that a user could be interviewed in the same date for different treatments? We should consider these inconsistencies in order to provide valid cases. There are around 67 users with the same dates of application but different date of admission. We sent these cases to SENDA professionals in order to discard any error.

CONS_TOP_df_dup_ENE_2020_prev6 %>% 
  dplyr::mutate(concatenation_hash_date_ing_date_adm=paste0(HASH_KEY, fech_ing, fech_ap_top)) %>% 
  dplyr::distinct(concatenation_hash_date_ing_date_adm, .keep_all = TRUE) %>% #descartar todas las combinaciones de filas con la misma fecha de todo y el mismo HASH.
  dplyr::mutate(concatenation_hash_date_ap_top=paste0(HASH_KEY, fech_ap_top)) %>% 
  dplyr::filter(duplicated(concatenation_hash_date_ap_top)) %>% #dejo a los que tienen duplicados la fecha de aplicación y el hash, ya que la tercera variable, en este caso, la fecha de ingreso es distinta.
      dplyr::arrange(concatenation_hash_date_ap_top) %>%  
      dplyr::select(HASH_KEY,fech_ing, fech_ap_top,concatenation_hash_date_ap_top) %>% 
  dplyr::inner_join(CONS_TOP_df_dup_ENE_2020_prev6, by=c("HASH_KEY", "fech_ap_top")) %>% #
  dplyr::select(-concatenation_hash_date_ap_top, -ID, -fech_ing.x) %>%
  dplyr::select(HASH_KEY, fech_ap_top, fech_ing.y,id_mod, ano_bd, TOP, Etapa.del.Tratamiento,everything()) %>%
  dplyr::rename("HASH Key"=HASH_KEY, "Date of\nAdmission"=fech_ing.y, "Date of\nApplication"=fech_ap_top, "ID"=id_mod, "Stage of\n Treatment"=Etapa.del.Tratamiento) %>%
  knitr::kable(.,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
               caption="Table 13. Same date of the application of TOP, but different dates of Admission",
                 align =rep("c",58))  %>%
   kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 8) %>%
  scroll_box(width = "100%", height = "350px")
Table 13. Same date of the application of TOP, but different dates of Admission
HASH Key Date of Application Date of Admission ID ano_bd TOP Stage of Treatment hash_rut_completo row Fecha.Aplicación.TOP Nombre.Apliacador.del.TOP Fecha.Nacimiento Edad Sexo Fecha.de.Ingreso.a.Tratamiento Plan.de.Tratamiento Nombre.del.Centro Tipo.Centro Sustancia.Principal.1 Sustancia.Principal.2 Sustancia.Principal.3 Total.OH Dósis.OH Total.THC Dósis.THC Total.PBC Dósis.PBC Total.COC Dósis.COC Total.BZD Dósis.BZD Total.Otra Dósis.Otra Hurto Robo Venta.Drogas Riña Total.VIF Otro Total.Transgresión Salud.Psicológica Total.Trabajo Total.Educación Salud.Física Lugar.Vivir Vivienda Calidad.Vida Región.Centro Comentario fech_ing_sin_fmt fech_nac OBS Edad_al_ing Edad_at_ap dup_todo_TOP dup_contents_TOP
0669c73ad96e50fb9605c167cc40693e 2019-05-02 2019-05-02 HEDI1**041973 2,019 Ingreso Inicio Tratamiento NA 102,653 2019-05-02 Ma. Inés Asenjo 05/04/1973 46 Hombre 02/05/2019 PG-PAB CT Peulla publico Alcohol NA NA 10 8 0 0 0 0 0 0 0 0 0 0 N N N S 0 N 1 18 20 0 16 S S 17 DE LOS LAGOS NA 02/05/2019 1973-04-05 46.0725530 46.07255 1 1
0669c73ad96e50fb9605c167cc40693e 2019-05-02 2019-08-01 HEDI1**041973 2,019 Ingreso Inicio Tratamiento NA 106,016 2019-05-02 Ma. Inés Asenjo 05/04/1973 46 Hombre 01/08/2019 PG-PAI CT Peulla publico Alcohol NA NA 10 8 0 0 0 0 0 0 0 0 0 0 N N N S 0 N 1 18 20 0 16 S S 17 DE LOS LAGOS NA 01/08/2019 1973-04-05 46.3216975 46.07255 1 1
06fc770daebcb112ef7fa528740522c3 2016-07-19 2016-07-12 FEVI1**061969 2,016 Ingreso Inicio Tratamiento NA 26,295 2016-07-19 joel montenegro 10/06/1969 50 Hombre 12/07/2016 PG-PR Comunidad Terapeutica Manresa privado Pasta Base Marihuana Alcohol 12 15 8 1 28 0 0 0 0 0 0 0 N N N S 0 N 1 7 28 0 17 N N 11 METROPOLITANA NA 12/07/2016 1969-06-10 47.0882957 47.10746 1 1
06fc770daebcb112ef7fa528740522c3 2016-07-19 2016-09-28 FEVI1**061969 2,016 Ingreso Inicio Tratamiento NA 29,762 2016-07-19 Joel Montenegro 10/06/1969 50 Hombre 28/09/2016 PG-PR Comunidad Terapeutica Manresa privado Pasta Base NA NA 12 15 8 1 28 0 0 0 0 0 0 0 N N N S 0 N 1 7 28 0 17 N N 11 METROPOLITANA NA 28/09/2016 1969-06-10 47.3018480 47.10746 1 1
1130bdb42a6d45af0c22af05f65528a6 2015-09-01 2015-09-01 JOHE1**031981 2,015 Ingreso Inicio Tratamiento NA 5,929 2015-09-01 daniel araya 05/03/1981 38 Hombre 01/09/2015 PG-PAB COSAM Melipilla publico Marihuana NA NA 1 12 28 2 0 1 0 0 0 0 0 0 N N N N 0 N 0 10 24 0 20 S S 20 METROPOLITANA NA 01/09/2015 1981-03-05 34.4914442 34.49144 1 1
1130bdb42a6d45af0c22af05f65528a6 2015-09-01 2016-05-31 JOHE1**031981 2,015 Ingreso Inicio Tratamiento NA 8,965 2015-09-01 Barbara Marquez 05/03/1981 38 Hombre 31/05/2016 PG-PAB COSAM Melipilla publico Alcohol Marihuana NA 16 12 28 2 0 1 4 0 0 0 0 0 N N N N 0 N 0 10 24 0 19 S S 20 METROPOLITANA NA 31/05/2016 1981-03-05 35.2388775 34.49144 1 1
170bf1afd78b1746f98572d2ae3b4486 2017-05-09 2017-07-21 SEAE1**031980 2,017 Ingreso Inicio Tratamiento NA 55,228 2017-05-09 Victor Hugo Sepulveda 27/03/1980 39 Hombre 21/07/2017 PG-PAB CT Pucon publico Alcohol Marihuana Cocaína 13 10 13 3 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 20 NA 13 S S 13 DE LA ARAUCANIA NA 21/07/2017 1980-03-27 37.3169062 37.11704 1 1
170bf1afd78b1746f98572d2ae3b4486 2017-05-09 2017-10-02 SEAE1**031980 2,017 Ingreso Inicio Tratamiento NA 57,456 2017-05-09 Víctor Hugo 27/03/1980 39 Hombre 02/10/2017 PG-PAI CT Pucon publico Alcohol Marihuana NA 13 10 13 3 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 20 NA 13 S S 13 DE LA ARAUCANIA NA 02/10/2017 1980-03-27 37.5167693 37.11704 1 1
1cc000a06c8b10808b63f2899629f291 2015-06-26 2015-07-01 JOCH1**061962 2,015 Ingreso Inicio Tratamiento NA 2,683 2015-06-26 Patricio Plata 30/06/1962 57 Hombre 01/07/2015 PG-PAI Consultorio Alejandro Gutierrez publico Alcohol NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 15 24 0 15 S S 15 DE AYSEN DEL GENERAL CARLOS IBAÑES DEL CAMPO NA 01/07/2015 1962-06-30 53.0020534 52.98836 1 1
1cc000a06c8b10808b63f2899629f291 2015-06-26 2016-03-04 JOCH1**071962 2,015 Ingreso Inicio Tratamiento NA 8,953 2015-06-26 PATRICIO PLATA 30/07/1962 57 Hombre 04/03/2016 PG-PAB Consultorio Alejandro Gutierrez publico Alcohol NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 15 24 0 15 S S 15 DE AYSEN DEL GENERAL CARLOS IBAÑES DEL CAMPO NA 04/03/2016 1962-07-30 53.5961670 52.90623 1 1
1d218db35e6345729f939c01fffc235e 2017-11-27 2017-11-21 JOSI1**071986 2,017 Ingreso Inicio Tratamiento NA 58,051 2017-11-27 Ts(p)Exequeil Pino 15/07/1986 33 Hombre 21/11/2017 PG-PAI Comunidad Terapeutica Ambulatoria Joven Atrevete privado Alcohol Marihuana Pasta Base 8 10 0 0 16 0 0 0 0 0 0 0 S S N N 5 N 2 15 6 0 15 S S 5 DE COQUIMBO NA 21/11/2017 1986-07-15 31.3538672 31.37029 1 1
1d218db35e6345729f939c01fffc235e 2017-11-27 2018-01-08 JHSI1**071986 2,017 Ingreso Inicio Tratamiento NA 58,793 2017-11-27 Exequiel Pino 15/07/1986 33 Hombre 08/01/2018 PG-PAI Comunidad Terapeutica Ambulatoria Joven Atrevete privado Pasta Base Marihuana Alcohol 8 10 0 0 16 0 0 0 0 0 0 0 S S N N 5 N 2 15 6 0 15 S S 5 DE COQUIMBO NA 08/01/2018 1986-07-15 31.4852841 31.37029 1 1
1e59a2135274d745735dd08bfaaeeabb 2016-04-08 2016-04-08 JAEN1**021966 2,016 Ingreso Inicio Tratamiento NA 21,836 2016-04-08 As. Alejandro Pustela 24/02/1966 53 Hombre 08/04/2016 PG-PAI COSAM Penaflor publico Pasta Base Alcohol NA 1 2 0 0 3 0 0 0 0 0 0 0 N N N N 0 N 0 11 10 0 15 S S 10 METROPOLITANA NA 08/04/2016 1966-02-24 50.1190965 50.11910 1 1
1e59a2135274d745735dd08bfaaeeabb 2016-04-08 2016-04-08 JAEN1**021966 2,016 Ingreso Inicio Tratamiento NA 22,789 2016-04-08 AS.Pustela 24/02/1966 53 Hombre 08/04/2016 PG-PAI COSAM Penaflor publico Pasta Base Cocaína Alcohol 1 2 0 0 3 0 0 0 0 0 0 0 N N N N 0 N 0 11 10 0 15 S S 10 METROPOLITANA NA 08/04/2016 1966-02-24 50.1190965 50.11910 1 1
1e59a2135274d745735dd08bfaaeeabb 2016-04-08 2016-06-08 JAEN1**021966 2,016 Ingreso Inicio Tratamiento NA 24,954 2016-04-08 AS Alejandro Pustela 24/02/1966 53 Hombre 08/06/2016 PG-PAI COSAM Penaflor publico Pasta Base NA NA 1 2 0 0 3 0 0 0 0 0 0 0 N N N N 0 N 0 11 6 0 15 S S 10 METROPOLITANA NA 08/06/2016 1966-02-24 50.2861054 50.11910 1 1
1ec3f2c2efb06ba95486d0324984a867 2016-09-23 2015-08-24 MERO2**021960 2,016 Tratamiento Seguimiento 9 meses NA 13,479 2016-09-23 viviana 13/02/2015 59 Mujer 24/08/2015 PG-PAI COSAM El Bosque publico Alcohol NA NA 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 METROPOLITANA no desea realizar llenado 24/08/2015 1960-02-13 ;1.3.Replaced invalid age at TOP application;1.3.Invalid Age at the Time of Application of TOP;1.5.Had a duplicated event with relevant variables in common 0.5256674 56.61054 1 2
1ec3f2c2efb06ba95486d0324984a867 2016-09-23 2016-08-24 MERO2**021960 2,016 Ingreso Inicio Tratamiento NA 29,255 2016-09-23 viviana 13/02/1960 59 Mujer 24/08/2016 PG-PAB COSAM El Bosque publico Alcohol NA NA 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 METROPOLITANA no desea el llenado 24/08/2016 1960-02-13 56.5284052 56.61054 1 1
2006e00472c8f736506b013889525f0a 2017-12-07 2017-08-10 LUCA1**071967 2,017 Tratamiento Seguimiento 3 meses NA 54,166 2017-12-07 PRISCILA VARGAS 30/07/1967 52 Hombre 10/08/2017 PG-PAB Hospital Quellon publico Alcohol NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 12 12 0 15 S S 14 DE LOS LAGOS NA 10/08/2017 1967-07-30 50.0314853 50.35729 1 1
2006e00472c8f736506b013889525f0a 2017-12-07 2018-01-25 LUCA1**071967 2,017 Ingreso Inicio Tratamiento NA 58,860 2017-12-07 PRISCILA VARGAS 30/07/1967 52 Hombre 25/01/2018 Otro Hospital Quellon publico Alcohol NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 12 12 0 15 S S 14 DE LOS LAGOS NA 25/01/2018 1967-07-30 50.4914442 50.35729 1 1
2056dffeac7afd78936d6b93d0db682f 2018-07-18 2018-07-19 MIBE1**121965 2,018 Ingreso Inicio Tratamiento NA 81,147 2018-07-18 NICOLE GUZMAN 01/12/1965 53 Hombre 19/07/2018 PG-PAI Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado Pasta Base Alcohol Marihuana 19 20 19 1 19 0 0 0 0 0 0 0 N N N N 0 N 0 7 0 0 10 N N 10 DE ARICA Y PARINACOTA NA 19/07/2018 1965-12-01 52.6297057 52.62697 1 1
2056dffeac7afd78936d6b93d0db682f 2018-07-18 NA MIBE1**121965 2,018 Ingreso Inicio Tratamiento NA 81,986 2018-07-18 nicole guzman 01/12/1965 53 Hombre NA NA Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado NA NA NA 19 20 19 1 19 0 0 0 0 0 0 0 N N N N 0 N 0 7 0 0 10 N N 10 DE ARICA Y PARINACOTA NA NA 1965-12-01 NA 52.62697 1 1
21164542ab872932cfb7ce440ba06628 2015-08-11 2015-08-07 JEKR2**041982 2,015 Ingreso Inicio Tratamiento NA 4,848 2015-08-11 Paula Guerra 01/04/1982 37 Mujer 07/08/2015 M-PAI COSAM Renca publico Pasta Base Cocaína Marihuana 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 0 0 10 S S 12 METROPOLITANA NA 07/08/2015 1982-04-01 33.3497604 33.36071 1 1
21164542ab872932cfb7ce440ba06628 2015-08-11 2015-08-11 JEKR2**041982 2,015 Ingreso Inicio Tratamiento NA 4,368 2015-08-11 Paula Guerra 01/04/1982 37 Mujer 11/08/2015 PG-PAI COSAM Renca publico Pasta Base Cocaína Marihuana 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 0 0 10 S S 12 METROPOLITANA NA 11/08/2015 1982-04-01 33.3607118 33.36071 1 1
240a18b230d6ed56b6bd89856379d5a7 2017-11-27 2017-11-27 JOAL1**121989 2,017 Ingreso Inicio Tratamiento NA 57,880 2017-11-27 PRISCILA VARGAS 13/12/1989 29 Hombre 27/11/2017 PG-PAB Hospital Quellon publico Cocaína Alcohol Marihuana 0 0 2 3 0 0 0 0 0 0 0 0 N N N N 2 N 0 10 28 0 15 S S 13 DE LOS LAGOS NA 27/11/2017 1989-12-13 27.9561944 27.95619 1 1
240a18b230d6ed56b6bd89856379d5a7 2017-11-27 2018-01-25 JOAL1**121989 2,017 Ingreso Inicio Tratamiento NA 58,862 2017-11-27 PRISCILA VARGAS 13/12/1989 29 Hombre 25/01/2018 Otro Hospital Quellon publico Cocaína Alcohol Marihuana 0 0 2 3 0 0 0 0 0 0 0 0 N N N N 2 N 0 10 28 0 15 S S 13 DE LOS LAGOS NA 25/01/2018 1989-12-13 28.1177276 27.95619 1 1
24e23951c537c78033a00e1b548eef6c 2015-09-24 2015-09-08 DAPO1**091981 2,015 Ingreso Inicio Tratamiento NA 6,313 2015-09-24 marcelo palacios 08/09/1981 38 Hombre 08/09/2015 PG-PAB COSAM Estacion Central publico Cocaína Alcohol NA 1 1 0 0 0 3 10 0 0 0 0 0 N S N N 0 N 1 10 0 0 15 N N 5 METROPOLITANA NA 08/09/2015 1981-09-08 33.9986311 34.04244 1 1
24e23951c537c78033a00e1b548eef6c 2015-09-24 2015-09-10 DAPO1**091981 2,015 Ingreso Inicio Tratamiento NA 6,372 2015-09-24 marcelo palacios 08/09/1981 38 Hombre 10/09/2015 PG-PAB COSAM Estacion Central publico Cocaína Alcohol NA 0 1 0 0 0 3 10 0 0 0 0 0 N S N N 0 N 1 10 0 0 15 N N 5 METROPOLITANA NA 10/09/2015 1981-09-08 34.0041068 34.04244 1 1
296c640c619e692875d334a1a1e9411a 2016-01-07 2016-01-07 JUSA2**021992 2,016 Ingreso Inicio Tratamiento NA 15,938 2016-01-07 miguel santelices 06/02/1992 27 Mujer 07/01/2016 M-PAI Programa Mujeres La Pintana publico Cocaína Pasta Base Marihuana 7 20 7 4 7 4 7 0 0 0 0 0 N N N S 1 S 2 10 7 0 19 S S 2 METROPOLITANA NA 07/01/2016 1992-02-06 23.9178645 23.91786 1 1
296c640c619e692875d334a1a1e9411a 2016-01-07 2016-01-09 JUSA1**021992 2,016 Ingreso Inicio Tratamiento NA 16,093 2016-01-07 miguel santelices 06/02/1992 27 Hombre 09/01/2016 M-PAI Programa Mujeres La Pintana publico Cocaína Pasta Base Marihuana 7 20 7 4 7 4 7 0 0 0 0 0 N N N S 1 S 2 10 7 0 19 S S 2 METROPOLITANA NA 09/01/2016 1992-02-06 23.9233402 23.91786 1 1
2af49dc9cc22575093820d6ba8b5c2ff 2019-03-29 2019-03-21 PESE1**041975 2,019 Ingreso Inicio Tratamiento NA 99,635 2019-03-29 JAIME CARPIO 09/04/1975 44 Hombre 21/03/2019 PG-PAI Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado Alcohol NA NA 14 20 0 0 0 0 0 0 0 0 0 0 N N N S 0 N 1 6 0 0 9 S S 5 DE ARICA Y PARINACOTA NA 21/03/2019 1975-04-09 43.9479808 43.96988 1 1
2af49dc9cc22575093820d6ba8b5c2ff 2019-03-29 NA PESE1**041975 2,019 Ingreso Inicio Tratamiento NA 100,959 2019-03-29 JAIME CARPIO 09/04/1975 44 Hombre NA NA Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado NA NA NA 14 10 0 0 0 0 0 0 0 0 0 0 N N N S 0 N 1 6 0 0 9 S S 5 DE ARICA Y PARINACOTA NA NA 1975-04-09 NA 43.96988 1 1
2e365b7e16eaa98d21cc94a759735395 2016-02-03 2016-01-25 ANNU2**031991 2,016 Ingreso Inicio Tratamiento NA 16,610 2016-02-03 lady chavez 21/03/1991 28 Mujer 25/01/2016 M-PR Comunidad Terapeutica CEPAS (Mujeres) privado Pasta Base Alcohol NA 26 3 0 0 28 0 0 0 0 0 0 0 S N S N 0 N 2 0 0 0 10 S S 2 DE ANTOFAGASTA NA 25/01/2016 1991-03-21 24.8487337 24.87337 1 1
2e365b7e16eaa98d21cc94a759735395 2016-02-03 2016-02-27 ANNU2**031991 2,016 Ingreso Inicio Tratamiento NA 19,084 2016-02-03 lady chavez 21/03/1991 28 Mujer 27/02/2016 M-PR Comunidad Terapeutica CEPAS (Mujeres) privado Pasta Base Alcohol NA 26 3 0 0 28 0 0 0 0 0 0 0 S N S N 0 N 2 0 0 0 10 S S 2 DE ANTOFAGASTA NA 27/02/2016 1991-03-21 24.9390828 24.87337 1 1
35191527fe9ad9a9342bcec1214ab5cb 2015-10-15 2015-10-15 PALE1**101993 2,015 Ingreso Inicio Tratamiento NA 6,810 2015-10-15 mauricio pavez 12/10/1993 26 Hombre 15/10/2015 PG-PAB COSAM La Pintana publico Sedantes: diazepam, Valium, clonazepam, Ravotril, alprazolam, adax, barbitúricos, fenobarbital. Marihuana Alcohol 2 2 16 2 0 0 0 0 0 0 0 0 N N N S 0 N 1 10 20 0 14 S S 15 METROPOLITANA NA 15/10/2015 1993-10-12 22.0068446 22.00684 1 1
35191527fe9ad9a9342bcec1214ab5cb 2015-10-15 2016-04-12 PALE1**101993 2,015 Ingreso Inicio Tratamiento NA 8,960 2015-10-15 Mauricio Pavez 12/10/1993 26 Hombre 12/04/2016 PG-PAB COSAM La Pintana publico Sedantes: diazepam, Valium, clonazepam, Ravotril, alprazolam, adax, barbitúricos, fenobarbital. Marihuana Alcohol 2 2 16 2 0 0 0 0 0 0 0 0 N N N S 0 N 1 10 20 0 14 S S 15 METROPOLITANA NA 12/04/2016 1993-10-12 22.4996578 22.00684 1 1
35bbdacdfb89a9b151e436ef684f0df2 2016-07-27 2016-07-25 PAAR1**041979 2,016 Ingreso Inicio Tratamiento NA 27,481 2016-07-27 Barbara Marquez 14/04/1979 40 Hombre 25/07/2016 PG-PAB COSAM Melipilla publico Pasta Base NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 16 12 0 20 S S 20 METROPOLITANA NA 25/07/2016 1979-04-14 37.2813142 37.28679 1 1
35bbdacdfb89a9b151e436ef684f0df2 2016-07-27 2016-10-26 PAAR1**041979 2,016 Ingreso Inicio Tratamiento NA 30,601 2016-07-27 Barbara Marquez 04/04/1979 40 Hombre 26/10/2016 PG-PAB COSAM Melipilla publico Pasta Base Alcohol NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 16 24 0 20 S S 20 METROPOLITANA NA 26/10/2016 1979-04-04 37.5633128 37.31417 1 1
35bbdacdfb89a9b151e436ef684f0df2 2016-09-13 2016-07-25 PAAR1**041979 2,016 Tratamiento Seguimiento 3 meses NA 27,482 2016-09-13 BÁRBARA MÁRQUEZ 14/04/1979 40 Hombre 25/07/2016 PG-PAB COSAM Melipilla publico Pasta Base NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 18 24 0 15 S S 20 METROPOLITANA NA 25/07/2016 1979-04-14 37.2813142 37.41821 1 1
35bbdacdfb89a9b151e436ef684f0df2 2016-09-13 2016-10-26 PAAR1**041979 2,016 Tratamiento Seguimiento 3 meses NA 30,602 2016-09-13 Barbara Marquez 04/04/1979 40 Hombre 26/10/2016 PG-PAB COSAM Melipilla publico Pasta Base Alcohol NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 18 24 0 15 S S 20 METROPOLITANA NA 26/10/2016 1979-04-04 37.5633128 37.44559 1 1
3ec371bde328f876a0f41f4840940cc0 2016-01-18 2016-01-18 ALFU1**071990 2,016 Ingreso Inicio Tratamiento NA 16,129 2016-01-18 marcela perez 25/07/1990 29 Hombre 18/01/2016 PG-PAI Comunidad Terapeutica Horizonte (Santiago) privado Cocaína Alcohol Pasta Base 8 10 28 1 0 7 8 0 0 0 0 0 N N S S 0 N 2 1 18 0 20 N N 5 METROPOLITANA NA 18/01/2016 1990-07-25 25.4839151 25.48392 1 1
3ec371bde328f876a0f41f4840940cc0 2016-01-18 2016-01-20 ALFU1**071990 2,016 Ingreso Inicio Tratamiento NA 17,233 2016-01-18 marcela perez 25/07/1990 29 Hombre 20/01/2016 PG-PAI Comunidad Terapeutica Horizonte (Santiago) privado Cocaína Alcohol Pasta Base 8 10 28 1 0 7 8 0 0 0 0 0 N N S S 0 N 2 1 18 0 20 N N 5 METROPOLITANA NA 20/01/2016 1990-07-25 25.4893908 25.48392 1 1
41ac26fe2096716ad90fb01a7d3383c5 2015-06-02 2015-05-25 CEMO2**031958 2,015 Ingreso Inicio Tratamiento NA 390 2015-06-02 Claudia Echeverría 30/03/1958 61 Mujer 25/05/2015 PG-PAB CESFAM Villa O’Higgins publico Alcohol NA NA 2 1 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 0 0 20 S S 18 METROPOLITANA Paciente que estuvo en tratamiento previo (por 5 meses) en CESFAM José Alvo por depresión. Adicción se develó posteriormente. 25/05/2015 1958-03-30 ;1.5.Had a duplicated event with relevant variables in common 57.1526352 57.17454 1 2
41ac26fe2096716ad90fb01a7d3383c5 2015-06-02 2015-05-25 CEMO2**031958 2,015 Tratamiento Seguimiento 3 meses NA 391 2015-06-02 Claudia Echeverria Letelier 30/03/1958 61 Mujer 25/05/2015 PG-PAB CESFAM Villa O’Higgins publico Alcohol NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 0 0 20 S S 18 METROPOLITANA NA 25/05/2015 1958-03-30 57.1526352 57.17454 1 1
41ac26fe2096716ad90fb01a7d3383c5 2015-06-02 2016-02-10 CEMO2**031958 2,015 Ingreso Inicio Tratamiento NA 8,929 2015-06-02 claudia echeverria letelier 30/03/1958 61 Mujer 10/02/2016 PG-PAB CESFAM Villa O’Higgins publico Alcohol NA NA 2 1 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 0 0 20 S S 18 METROPOLITANA NA 10/02/2016 1958-03-30 57.8672142 57.17454 1 1
461102578616ab581c2d956606425149 2015-08-14 2015-08-03 JUVA1**071980 2,015 Ingreso Inicio Tratamiento NA 5,471 2015-08-14 cristina silva 12/07/1980 39 Hombre 03/08/2015 PG-PAI Establecimiento Comunidad Terapeutica Valparaiso (C.T. Mosaico) privado Cocaína Alcohol NA 0 0 0 0 0 1 2 0 0 0 0 0 N N N N 0 N 0 15 0 20 20 S S 17 DE VALPARAISO NA 03/08/2015 1980-07-12 35.0581793 35.08830 1 1
461102578616ab581c2d956606425149 2015-08-14 2016-01-11 JUVA1**071980 2,015 Ingreso Inicio Tratamiento NA 8,906 2015-08-14 cristina silva 11/07/1980 39 Hombre 11/01/2016 PG-PAI Establecimiento Comunidad Terapeutica Valparaiso (C.T. Mosaico) privado Cocaína Alcohol NA 0 0 0 0 0 1 2 0 0 0 0 0 N N N N 0 N 0 15 0 20 20 S S 17 DE VALPARAISO NA 11/01/2016 1980-07-11 35.5017112 35.09103 1 1
461102578616ab581c2d956606425149 2015-10-13 2015-08-03 JUVA1**071980 2,015 Egreso Egreso NA 5,472 2015-10-13 edith vergara 12/07/1980 39 Hombre 03/08/2015 PG-PAI Establecimiento Comunidad Terapeutica Valparaiso (C.T. Mosaico) privado Cocaína Alcohol NA 0 0 0 0 0 1 1 0 0 0 0 0 N N N N 0 N 0 17 0 20 20 S S 20 DE VALPARAISO NA 03/08/2015 1980-07-12 35.0581793 35.25257 1 1
461102578616ab581c2d956606425149 2015-10-13 2016-01-11 JUVA1**071980 2,015 Tratamiento Seguimiento 3 meses NA 8,907 2015-10-13 EDITH VERGARA 11/07/1980 39 Hombre 11/01/2016 PG-PAI Establecimiento Comunidad Terapeutica Valparaiso (C.T. Mosaico) privado Cocaína Alcohol NA 0 0 0 0 1 0 0 0 0 0 0 0 N N N N 0 N 0 17 0 20 20 S S 20 DE VALPARAISO NA 11/01/2016 1980-07-11 35.5017112 35.25530 1 1
46d595681ed89f9701d213c85941a14c 2016-10-03 2016-10-03 SEAL1**071971 2,016 Ingreso Inicio Tratamiento NA 29,906 2016-10-03 CARMEN GONZALEZ 31/07/1971 48 Hombre 03/10/2016 PG-PAI Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado Alcohol NA NA 21 15 0 0 0 0 0 0 0 0 0 0 N N N N 3 N 0 0 0 0 10 S S 20 DE ARICA Y PARINACOTA NA 03/10/2016 1971-07-31 45.1772758 45.17728 1 1
46d595681ed89f9701d213c85941a14c 2016-10-03 2017-01-04 SEAL1**071971 2,016 Ingreso Inicio Tratamiento NA 31,876 2016-10-03 CARMEN GONZÁLEZ 31/07/1971 48 Hombre 04/01/2017 PG-PAI Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado Alcohol NA NA 21 15 0 0 0 0 0 0 0 0 0 0 N N N N 3 N 0 0 0 0 10 S S 20 DE ARICA Y PARINACOTA NA 04/01/2017 1971-07-31 45.4318960 45.17728 1 1
5179f59954372145835a03803ff0f12b 2017-12-15 2017-11-20 DASO1**091966 2,017 Ingreso Inicio Tratamiento NA 58,485 2017-12-15 M FERNANDA SANTANDER 18/09/1966 53 Hombre 20/11/2017 PG-PAB CESFAM Leonera publico Alcohol NA NA 14 4 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 24 0 15 S S 15 DEL BIO-BIO NA 20/11/2017 1966-09-18 51.1731691 51.24162 1 1
5179f59954372145835a03803ff0f12b 2017-12-15 2017-12-04 DASO1**091966 2,017 Ingreso Inicio Tratamiento NA 58,711 2017-12-15 M Fernanda Santander 18/09/1966 53 Hombre 04/12/2017 PG-PAB CESFAM Leonera publico Alcohol NA NA 14 4 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 24 0 15 S S 15 DEL BIO-BIO NA 04/12/2017 1966-09-18 51.2114990 51.24162 1 1
52fd1c48dc83f1f8a2a9ccf65dcd3c1a 2016-10-17 2016-10-12 JAHI1**071991 2,016 Ingreso Inicio Tratamiento NA 30,987 2016-10-17 Francisca González 20/07/1991 28 Hombre 12/10/2016 PG-PR Comunidad Terapeutica Casa Acogida La Esperanza San Joaquin privado Cocaína Marihuana Alcohol 8 24 24 5 3 8 6 0 0 0 0 0 N N N S 2 N 1 15 15 0 17 S S 18 METROPOLITANA NA 12/10/2016 1991-07-20 25.2320329 25.24572 1 1
52fd1c48dc83f1f8a2a9ccf65dcd3c1a 2016-10-17 2017-03-11 JAHI1**071991 2,016 Ingreso Inicio Tratamiento NA 32,004 2016-10-17 Francisca González 20/07/1991 28 Hombre 11/03/2017 PG-PR Comunidad Terapeutica Casa Acogida La Esperanza San Joaquin privado Cocaína Alcohol Marihuana 8 24 24 5 3 8 6 0 0 0 0 0 N N N S 2 N 1 15 15 0 17 S S 18 METROPOLITANA NA 11/03/2017 1991-07-20 25.6427105 25.24572 1 1
52fd1c48dc83f1f8a2a9ccf65dcd3c1a 2017-01-23 2016-10-12 JAHI1**071991 2,017 Tratamiento Seguimiento 3 meses NA 39,863 2017-01-23 Natalia Pereira 20/07/1991 28 Hombre 12/10/2016 PG-PR Comunidad Terapeutica Casa Acogida La Esperanza San Joaquin privado Cocaína Marihuana Alcohol 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 16 0 0 15 S S 18 METROPOLITANA NA 12/10/2016 1991-07-20 25.2320329 25.51403 1 1
52fd1c48dc83f1f8a2a9ccf65dcd3c1a 2017-01-23 2017-03-11 JAHI1**071991 2,017 Tratamiento Seguimiento 3 meses NA 50,384 2017-01-23 Natalia Pereira 20/07/1991 28 Hombre 11/03/2017 PG-PR Comunidad Terapeutica Casa Acogida La Esperanza San Joaquin privado Cocaína Alcohol Marihuana 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 16 0 0 15 S S 18 METROPOLITANA NA 11/03/2017 1991-07-20 25.6427105 25.51403 1 1
5dec4a32784e54b9f52f771fd1ed052e 2016-11-23 2016-02-16 ROSO1**091984 2,016 Tratamiento Seguimiento 6 meses NA 20,192 2016-11-23 RICARDO KAID 08/09/1984 35 Hombre 16/02/2016 PG-PAB CESFAM San Juan de Dios publico Pasta Base Alcohol Marihuana 0 0 1 1 0 0 0 0 0 0 0 0 N N N N 0 S 1 15 20 0 17 S S 20 DEL MAULE NA 16/02/2016 1984-09-08 ;1.5.Had a duplicated event with relevant variables in common 31.4387406 32.20808 1 3
5dec4a32784e54b9f52f771fd1ed052e 2016-11-23 2017-04-11 ROSO1**091984 2,016 Ingreso Inicio Tratamiento NA 32,000 2016-11-23 RICARDO KAID 08/09/1984 35 Hombre 11/04/2017 PG-PAB CESFAM San Juan de Dios publico Pasta Base Marihuana Alcohol 0 0 1 1 0 0 0 0 0 0 0 0 N N N N 0 S 1 15 20 0 17 S S 20 DEL MAULE NA 11/04/2017 1984-09-08 32.5886379 32.20808 1 1
69a383bcdb5d3357665316611218df75 2017-09-11 2017-09-11 ABLE2**011987 2,017 Ingreso Inicio Tratamiento NA 55,688 2017-09-11 PS GOMEZ 27/01/1987 32 Mujer 11/09/2017 PG-PAB Programa Ambulatorio Básico Vivir (PROSEC) privado Pasta Base Cocaína Alcohol 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 16 0 0 16 S S 16 DE VALPARAISO NA 11/09/2017 1987-01-27 30.6228611 30.62286 1 1
69a383bcdb5d3357665316611218df75 2017-09-11 2018-04-02 ABLE2**011987 2,017 Ingreso Inicio Tratamiento NA 58,896 2017-09-11 PS. PAMELA GOMEZ 27/01/1987 32 Mujer 02/04/2018 PG-PAB Programa Ambulatorio Básico Vivir (PROSEC) privado Pasta Base NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 16 0 0 16 S S 16 DE VALPARAISO NA 02/04/2018 1987-01-27 31.1786448 30.62286 1 1
69a383bcdb5d3357665316611218df75 2017-11-07 2017-09-11 ABLE2**011987 2,017 Tratamiento Seguimiento 3 meses NA 55,689 2017-11-07 TO LOPEZ 27/01/1987 32 Mujer 11/09/2017 PG-PAB Programa Ambulatorio Básico Vivir (PROSEC) privado Pasta Base Cocaína Alcohol 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 16 17 0 10 S S 17 DE VALPARAISO NA 11/09/2017 1987-01-27 30.6228611 30.77892 1 1
69a383bcdb5d3357665316611218df75 2017-11-07 2018-04-02 ABLE2**011987 2,017 Tratamiento Seguimiento 3 meses NA 58,897 2017-11-07 TO. PALOMA LOPEZ 27/01/1987 32 Mujer 02/04/2018 PG-PAB Programa Ambulatorio Básico Vivir (PROSEC) privado Pasta Base NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 16 17 0 10 S S 17 DE VALPARAISO NA 02/04/2018 1987-01-27 31.1786448 30.77892 1 1
6cbd28cc3294a9b048f1ddf20ca316ba 2018-07-20 2018-07-25 JOSE1**021985 2,018 Ingreso Inicio Tratamiento NA 81,149 2018-07-20 NICOLE GUZMAN 12/02/1985 34 Hombre 25/07/2018 PG-PAI Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado Pasta Base Marihuana NA 2 7 6 1 6 0 0 0 0 0 0 0 N N N N 0 N 0 10 0 0 19 N N 0 DE ARICA Y PARINACOTA NA 25/07/2018 1985-02-12 33.4455852 33.43190 1 1
6cbd28cc3294a9b048f1ddf20ca316ba 2018-07-20 NA JOSE1**021985 2,018 Ingreso Inicio Tratamiento NA 81,989 2018-07-20 nicole guzman 12/02/1985 34 Hombre NA NA Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado NA NA NA 2 7 6 1 6 0 0 0 0 0 0 0 N N N N 0 N 0 10 0 0 19 N N 0 DE ARICA Y PARINACOTA NA NA 1985-02-12 NA 33.43190 1 1
6dd1ccd2d63f98770ea8a05d60cfbe54 2015-08-19 2015-08-19 LUSO1**121974 2,015 Ingreso Inicio Tratamiento NA 4,780 2015-08-19 Jorge Perez 24/12/1974 44 Hombre 19/08/2015 PG-PAB COSAM Melipilla publico Alcohol NA NA 3 17 0 0 0 5 3 0 0 20 28 20 N N N N 0 N 0 2 24 0 6 S S 2 METROPOLITANA NA 19/08/2015 1974-12-24 40.6516085 40.65161 1 1
6dd1ccd2d63f98770ea8a05d60cfbe54 2015-08-19 2016-05-20 LUSO1**121974 2,015 Ingreso Inicio Tratamiento NA 8,967 2015-08-19 JORGE PEREZ ARRIAGADA 24/12/1974 44 Hombre 20/05/2016 PG-PAI COSAM Melipilla publico Alcohol NA NA 3 17 0 0 0 5 3 0 0 20 28 20 N N N N 0 N 0 2 24 0 6 S S 2 METROPOLITANA NA 20/05/2016 1974-12-24 41.4045175 40.65161 1 1
6e4b0d4a876cddd054328643d8c9e937 2017-03-29 2017-03-28 JUAS1**061984 2,017 Ingreso Inicio Tratamiento NA 46,396 2017-03-29 JORGE PÉREZ ARRIAGADA 28/06/1984 35 Hombre 28/03/2017 PG-PAI COSAM Melipilla publico Pasta Base Marihuana Alcohol 0 0 1 1 28 0 0 0 0 1 28 1 N N N N 0 N 0 3 16 0 6 S S 10 METROPOLITANA NA 28/03/2017 1984-06-28 32.7474333 32.75017 1 1
6e4b0d4a876cddd054328643d8c9e937 2017-03-29 2017-07-10 JUAS1**061984 2,017 Ingreso Inicio Tratamiento NA 52,840 2017-03-29 jorge pérez 28/06/1984 35 Hombre 10/07/2017 PG-PAB COSAM Melipilla publico Pasta Base Marihuana Cocaína 0 0 1 1 28 0 0 0 0 1 28 1 N N N N 0 N 0 3 16 0 3 S S 6 METROPOLITANA NA 10/07/2017 1984-06-28 33.0321697 32.75017 1 1
6f6fbb99dd2898d477a1cb38958d766f 2017-02-20 2017-02-02 JUBU1**041978 2,017 Ingreso Inicio Tratamiento NA 43,977 2017-02-20 ana cordova figueroa 30/04/1978 41 Hombre 02/02/2017 PG-PAB CESFAM Santiago Nueva Extremadura publico Pasta Base Marihuana Sedantes: diazepam, Valium, clonazepam, Ravotril, alprazolam, adax, barbitúricos, fenobarbital. 1 4 8 1 5 0 0 1 28 10 28 10 S N N N 0 N 1 15 17 0 10 S S 10 METROPOLITANA NA 02/02/2017 1978-04-30 38.7624914 38.81177 1 1
6f6fbb99dd2898d477a1cb38958d766f 2017-02-20 2017-04-28 JUBU1**041978 2,017 Ingreso Inicio Tratamiento NA 48,767 2017-02-20 ana cordova 30/04/1978 41 Hombre 28/04/2017 PG-PAB CESFAM Santiago Nueva Extremadura publico Pasta Base Cocaína NA 1 4 8 1 5 0 0 1 28 10 28 10 S N N N 0 N 1 15 17 0 10 S S 10 METROPOLITANA NA 28/04/2017 1978-04-30 38.9952088 38.81177 1 1
71c48221ba078ae722997b095b9b2f20 2017-02-15 2016-05-24 JODI1**101955 2,017 Tratamiento Seguimiento 3 meses NA 35,378 2017-02-15 yessica aguirre 27/10/1955 64 Hombre 24/05/2016 PG-PAI COSAM Melipilla publico Alcohol NA NA 4 8 0 0 0 0 0 1 28 0 0 0 N N N N 0 N 0 12 0 0 15 S S 10 METROPOLITANA NA 24/05/2016 1955-10-27 ;1.5.Had a duplicated event with relevant variables in common 60.5749487 61.30595 1 2
71c48221ba078ae722997b095b9b2f20 2017-02-15 2017-02-15 JODI1**101955 2,017 Ingreso Inicio Tratamiento NA 44,136 2017-02-15 YESSICA AGUIRRE 27/10/1955 64 Hombre 15/02/2017 PG-PAI COSAM Melipilla publico Alcohol NA NA 4 8 0 0 0 0 0 1 28 0 0 0 N N N N 0 N 0 12 0 0 15 S S 10 METROPOLITANA NA 15/02/2017 1955-10-27 61.3059548 61.30595 1 1
71c48221ba078ae722997b095b9b2f20 2017-02-15 2017-02-15 JODI1**101955 2,017 Tratamiento Seguimiento 3 meses NA 44,137 2017-02-15 yessica Aguirre 27/10/1955 64 Hombre 15/02/2017 PG-PAI COSAM Melipilla publico Alcohol NA NA 4 8 0 0 0 0 0 1 28 0 0 0 NA NA NA NA 0 N 0 12 0 0 15 S S 10 METROPOLITANA NA 15/02/2017 1955-10-27 61.3059548 61.30595 1 1
78059b1f8d40154766855e1fb67f3f3d 2015-08-13 2015-08-10 EMMU2**121967 2,015 Ingreso Inicio Tratamiento NA 4,333 2015-08-13 Mario Vidal 21/12/1967 51 Mujer 10/08/2015 PG-PAI Hospital Felix Bulnes publico Alcohol NA NA 21 8 0 0 0 0 0 0 0 0 0 0 N N N N 22 N 0 8 0 0 8 S S 4 METROPOLITANA NA 10/08/2015 1967-12-21 47.6358658 47.64408 1 1
78059b1f8d40154766855e1fb67f3f3d 2015-08-13 2016-11-24 EMMU2**121967 2,015 Ingreso Inicio Tratamiento NA 8,977 2015-08-13 MARIO VIDAL 21/12/1967 51 Mujer 24/11/2016 PG-PAI Hospital Felix Bulnes publico Alcohol NA NA 21 8 0 0 0 0 0 0 0 0 0 0 N N N N 22 N 0 8 0 0 8 S S 4 METROPOLITANA NA 24/11/2016 1967-12-21 48.9281314 47.64408 1 1
78059b1f8d40154766855e1fb67f3f3d 2016-02-15 2015-08-10 EMMU2**121967 2,016 Tratamiento Seguimiento 3 meses NA 11,166 2016-02-15 MARIO VIDAL 21/12/1967 51 Mujer 10/08/2015 PG-PAI Hospital Felix Bulnes publico Alcohol NA NA 28 6 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 5 22 0 0 S S 4 METROPOLITANA NA 10/08/2015 1967-12-21 47.6358658 48.15332 1 1
78059b1f8d40154766855e1fb67f3f3d 2016-02-15 2016-11-24 EMMU2**121967 2,016 Tratamiento Seguimiento 3 meses NA 31,738 2016-02-15 MARIO VIDAL 21/12/1967 51 Mujer 24/11/2016 PG-PAI Hospital Felix Bulnes publico Alcohol NA NA 28 6 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 5 22 0 0 S S 4 METROPOLITANA NA 24/11/2016 1967-12-21 48.9281314 48.15332 1 1
780c3340df74f1eb49884b6811ecaac2 2019-01-17 2019-01-11 BRIG1**011972 2,019 Ingreso Inicio Tratamiento NA 95,374 2019-01-17 Pamela Rosas 03/01/1972 47 Hombre 11/01/2019 PG-PAB CT Peulla publico Alcohol Marihuana NA 5 2 0 0 0 0 0 0 0 0 0 0 N N N N 1 N 0 12 16 0 15 S S 14 DE LOS LAGOS NA 11/01/2019 1972-01-03 47.0225873 47.03901 1 1
780c3340df74f1eb49884b6811ecaac2 2019-01-17 2019-08-01 BRIG1**011972 2,019 Ingreso Inicio Tratamiento NA 106,079 2019-01-17 PAMELA ROSAS 03/01/1972 47 Hombre 01/08/2019 PG-PAI CT Peulla publico Alcohol Marihuana NA 5 12 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 12 16 0 15 S S 14 DE LOS LAGOS NA 01/08/2019 1972-01-03 47.5756331 47.03901 1 1
85a3eb94a06fb3a230d3074cd7717112 2017-08-04 2017-08-02 MACA1**101991 2,017 Ingreso Inicio Tratamiento NA 54,367 2017-08-04 JAIME CARPIO 26/10/1991 28 Hombre 02/08/2017 PG-PAI Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado Alcohol Marihuana Cocaína 2 5 2 2 0 10 1 0 0 0 0 0 N N N N 0 N 0 12 24 0 9 S S 14 DE ARICA Y PARINACOTA NA 02/08/2017 1991-10-26 25.7686516 25.77413 1 1
85a3eb94a06fb3a230d3074cd7717112 2017-08-04 2017-10-02 MACA1**101991 2,017 Ingreso Inicio Tratamiento NA 56,642 2017-08-04 JAIME CARPIO 26/10/1991 28 Hombre 02/10/2017 PG-PAI Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado Alcohol Marihuana Cocaína 2 5 2 2 0 10 1 0 0 0 0 0 N N N N 0 N 0 12 24 0 9 S S 14 DE ARICA Y PARINACOTA NA 02/10/2017 1991-10-26 25.9356605 25.77413 1 1
86ff8826f6dfbfcd99b454e9f7e39a9d 2015-05-27 2015-05-27 FESI1**071993 2,015 Ingreso Inicio Tratamiento NA 516 2015-05-27 CARMEN GONZALEZ 13/07/1993 26 Hombre 27/05/2015 PG-PAI Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado Pasta Base Alcohol Marihuana 19 34 23 5 19 0 0 0 0 0 0 0 N S N N 0 N 1 10 0 0 7 S S 15 DE ARICA Y PARINACOTA NA 27/05/2015 1993-07-13 21.8699521 21.86995 1 1
86ff8826f6dfbfcd99b454e9f7e39a9d 2015-05-27 NA FESI1**071993 2,015 Tratamiento Seguimiento 3 meses NA 2,432 2015-05-27 CARMEN GONZALEZ 13/07/1993 26 Hombre NA NA Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado NA NA NA 19 34 23 5 19 0 0 0 0 0 0 0 N S N N 0 N 1 10 0 0 7 S S 15 DE ARICA Y PARINACOTA NA NA 1993-07-13 NA 21.86995 1 1
86ff8826f6dfbfcd99b454e9f7e39a9d 2016-11-28 2016-08-12 FESI1**071993 2,016 Ingreso Inicio Tratamiento NA 28,477 2016-11-28 JAIME CARPIO 13/07/1993 26 Hombre 12/08/2016 PG-PAI Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado Pasta Base Marihuana Alcohol 3 8 3 12 3 0 0 0 0 0 0 0 N N S S 0 N 2 14 0 0 10 S S 15 DE ARICA Y PARINACOTA NA 12/08/2016 1993-07-13 23.0828200 23.37851 1 1
86ff8826f6dfbfcd99b454e9f7e39a9d 2016-11-28 NA FESI1**071993 2,016 Ingreso Inicio Tratamiento NA 31,297 2016-11-28 Jaime Carpio 13/07/1993 26 Hombre NA NA Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado NA NA NA 3 8 3 12 3 0 0 0 0 0 0 0 N N S S 0 N 2 14 0 0 10 S S 15 DE ARICA Y PARINACOTA NA NA 1993-07-13 NA 23.37851 1 1
8908871912b72c6e9d1a4ac1378956da 2015-06-23 2015-05-26 CIBI1**111953 2,015 Ingreso Inicio Tratamiento NA 878 2015-06-23 Vicente Baeza 15/11/1953 65 Hombre 26/05/2015 PG-PAB Hospital Lord Cochrane publico Alcohol NA NA 3 15 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 15 20 0 15 S S 14 DE AYSEN DEL GENERAL CARLOS IBAÑES DEL CAMPO NA 26/05/2015 1953-11-15 61.5249829 61.60164 1 1
8908871912b72c6e9d1a4ac1378956da 2015-06-23 2016-01-04 CIBI1**111953 2,015 Ingreso Inicio Tratamiento NA 8,861 2015-06-23 vicente baeza 05/11/1953 66 Hombre 04/01/2016 PG-PAI Hospital Lord Cochrane publico Alcohol SIN CONSUMO NA 3 15 0 0 0 0 0 0 0 0 0 0 N N N N 0 NA 0 15 16 0 15 S S 14 DE AYSEN DEL GENERAL CARLOS IBAÑES DEL CAMPO NA 04/01/2016 1953-11-05 62.1629021 61.62902 1 1
8b95c46913c47663ee014af760d26895 2017-10-10 2017-07-03 HEOV1**071987 2,017 Tratamiento Seguimiento 3 meses NA 53,939 2017-10-10 MILISEN DIAZ VIDAL 27/07/1987 32 Hombre 03/07/2017 PG-PAI CESFAM Purranque publico Alcohol NA NA 1 9 0 0 0 0 0 0 0 10 1 10 N N N N 3 N 0 10 16 0 15 S S 15 DE LOS LAGOS NA 03/07/2017 1987-07-27 29.9356605 30.20671 1 1
8b95c46913c47663ee014af760d26895 2017-10-10 2017-10-03 HEOV1**071987 2,017 Ingreso Inicio Tratamiento NA 57,452 2017-10-10 MILISEN DIAZ 27/07/1987 32 Hombre 03/10/2017 PG-PAB CESFAM Purranque publico Alcohol Marihuana Inhalables: neopren, GHB, óxido nitroso (gas hilarante), “poppers”, solventes, gasolina, diluyente 1 9 0 0 0 0 0 0 0 10 1 10 N N N N 3 N 0 10 16 0 15 S S 15 DE LOS LAGOS NA 03/10/2017 1987-07-27 30.1875428 30.20671 1 1
9183cfa162edf4392c9f7ab6ff1f97aa 2017-10-12 2017-05-04 KACA2**061986 2,017 Ingreso Inicio Tratamiento NA 49,046 2017-10-12 karin echeverria 20/06/1986 33 Mujer 04/05/2017 M-PAI Centro de Responsabilidad de Salud Mental del Complejo Asistencial Dr.Victor Rios Ruiz publico Pasta Base Alcohol NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 0 0 10 S S 15 DEL BIO-BIO NA 04/05/2017 1986-06-20 30.8720055 31.31280 1 1
9183cfa162edf4392c9f7ab6ff1f97aa 2017-10-12 2018-04-02 KACA2**061986 2,017 Tratamiento Seguimiento 6 meses NA 58,898 2017-10-12 As Karin Echeverria 20/06/1986 33 Mujer 02/04/2018 M-PAI Centro de Responsabilidad de Salud Mental del Complejo Asistencial Dr.Victor Rios Ruiz publico Pasta Base Alcohol Cocaína 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 0 0 10 S S 15 DEL BIO-BIO NA 02/04/2018 1986-06-20 31.7837098 31.31280 1 1
925a178b8bc390b30355651b76ea29c0 2017-06-06 2017-06-22 LUBU1**031975 2,017 Ingreso Inicio Tratamiento NA 51,705 2017-06-06 Victor Sepulveda 31/03/1975 44 Hombre 22/06/2017 PG-PAB CT Pucon publico Alcohol NA NA 16 10 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 14 24 NA 14 S S 15 DE LA ARAUCANIA NA 22/06/2017 1975-03-31 42.2286105 42.18480 1 1
925a178b8bc390b30355651b76ea29c0 2017-06-06 2017-10-04 LUBU1**031975 2,017 Ingreso Inicio Tratamiento NA 57,361 2017-06-06 Victor Sepulveda 31/03/1975 44 Hombre 04/10/2017 PG-PAI CT Pucon publico Alcohol NA NA 16 10 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 14 24 NA 14 S S 15 DE LA ARAUCANIA NA 04/10/2017 1975-03-31 42.5133470 42.18480 1 1
9567fa91d7d04026200044c3e5ba9ab6 2018-03-06 2018-03-08 CHVI1**111970 2,018 Ingreso Inicio Tratamiento NA 72,984 2018-03-06 JAIME CARPIO 26/11/1970 48 Hombre NA NA Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado NA NA NA 1 4 0 0 25 0 0 0 0 0 0 0 N N N N 0 N 0 5 0 0 3 N N 1 DE ARICA Y PARINACOTA NA NA 1970-11-26 1.1.Replace miss date admission w TOPs w same stage & user 47.2799452 47.27447 1 1
9567fa91d7d04026200044c3e5ba9ab6 2018-03-06 2018-03-08 CHVI1**111970 2,018 Ingreso Inicio Tratamiento NA 74,509 2018-03-06 JAIME CARPIO 26/11/1970 48 Hombre 08/03/2018 PG-PAI Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado Pasta Base SIN CONSUMO NA 1 4 0 0 25 0 0 0 0 0 0 0 N N N N 0 N 0 5 0 0 3 N N 1 DE ARICA Y PARINACOTA NA 08/03/2018 1970-11-26 47.2799452 47.27447 1 1
9567fa91d7d04026200044c3e5ba9ab6 2018-03-06 NA CHVI1**111970 2,018 Ingreso Inicio Tratamiento NA 74,605 2018-03-06 JAIME CARPIO 26/11/1970 48 Hombre NA NA Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado NA NA NA 1 4 0 0 25 0 0 0 0 0 0 0 N N N N 0 N 0 5 0 0 3 N N 1 DE ARICA Y PARINACOTA NA NA 1970-11-26 NA 47.27447 1 1
95da07e03a0f93fef88062111eed37f4 2017-05-26 2017-05-26 ELTO2**011963 2,017 Ingreso Inicio Tratamiento NA 50,219 2017-05-26 PRISCILA VARGAS 14/01/1963 56 Mujer 26/05/2017 PG-PAB Hospital Quellon publico Alcohol NA NA 1 17 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 6 0 18 S S 15 DE LOS LAGOS NA 26/05/2017 1963-01-14 54.3627652 54.36277 1 1
95da07e03a0f93fef88062111eed37f4 2017-05-26 2017-08-01 ELTO2**011963 2,017 Ingreso Inicio Tratamiento NA 53,892 2017-05-26 PRISCILA VARGAS VERA 14/01/1963 56 Mujer 01/08/2017 PG-PAI Hospital Quellon publico Alcohol NA NA 1 17 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 6 0 18 S S 15 DE LOS LAGOS NA 01/08/2017 1963-01-14 54.5462012 54.36277 1 1
9deef0191942bff7c7993d2140b9f775 2019-09-04 2019-03-01 FERO1**101986 2,019 Tratamiento Seguimiento 6 meses NA 98,559 2019-09-04 Joaquin Medina 29/10/1986 33 Hombre 01/03/2019 PG-PAI Comunidad Terapeutica para Adultos Vinculos (C.T. Vinculos - Chiloe) privado Pasta Base NA NA 16 2 0 0 28 0 0 0 0 0 0 0 S N N N 0 N 1 5 24 0 7 S S 7 DE LOS LAGOS NA 01/03/2019 1986-10-29 32.3367556 32.84873 1 1
9deef0191942bff7c7993d2140b9f775 2019-09-04 2019-09-12 FERO1**101986 2,019 Ingreso Inicio Tratamiento NA 106,430 2019-09-04 Joaquin Medina 29/10/1986 33 Hombre 12/09/2019 PG-PR Comunidad Terapeutica para Adultos Vinculos (C.T. Vinculos - Chiloe) privado Pasta Base NA NA 16 2 0 0 28 0 0 0 0 0 0 0 S N N N 0 N 1 5 24 0 7 S S 7 DE LOS LAGOS NA 12/09/2019 1986-10-29 32.8706366 32.84873 1 1
a11b9369bb6a8d4cfe5cd73a82d28482 2015-06-16 2015-06-17 DEDI1**111993 2,015 Ingreso Inicio Tratamiento NA 1,527 2015-06-16 JAIME CARPIO ARAYA 14/11/1993 25 Hombre 17/06/2015 PG-PAI Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado Pasta Base Marihuana Alcohol 13 13 4 4 26 0 0 0 0 0 0 0 S N N S 0 N 2 15 24 0 10 S S 9 DE ARICA Y PARINACOTA NA 17/06/2015 1993-11-14 21.5879535 21.58522 1 1
a11b9369bb6a8d4cfe5cd73a82d28482 2015-06-16 NA DEDI1**111993 2,015 Ingreso Inicio Tratamiento NA 4,362 2015-06-16 JAIME CARPIO 14/11/1993 25 Hombre NA NA Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado NA NA NA 13 13 4 4 26 0 0 0 0 0 0 0 S N N S 0 N 2 15 24 0 10 S S 9 DE ARICA Y PARINACOTA NA NA 1993-11-14 NA 21.58522 1 1
a257cc0c9564f57dd29eb27b9b033477 2018-10-19 2018-10-02 MAVE1**021976 2,018 Ingreso Inicio Tratamiento NA 83,808 2018-10-19 Patricia Muñoz Araya 28/02/1976 43 Hombre 02/10/2018 M-PR Complejo Hospitalario San José de Maipo/ Hospital San Jose de Maipo publico Cocaína Alcohol NA 4 6 0 0 0 1 4 0 0 0 0 0 N N N N 0 N 0 2 5 0 10 S S 2 METROPOLITANA NA 02/10/2018 1976-02-28 42.5927447 42.63929 1 1
a257cc0c9564f57dd29eb27b9b033477 2018-10-19 2018-10-04 MAVE1**021976 2,018 Ingreso Inicio Tratamiento NA 84,567 2018-10-19 Patricia Muñoz Araya 28/02/1976 43 Hombre 04/10/2018 PG-PR Complejo Hospitalario San José de Maipo/ Hospital San Jose de Maipo publico Cocaína Alcohol NA 4 6 0 0 0 1 4 0 0 0 0 0 N N N N 0 N 0 2 5 0 10 S S 2 METROPOLITANA NA 04/10/2018 1976-02-28 42.5982204 42.63929 1 1
a3b7575d3b6f2300d5fbccb6dca39117 2015-08-07 2015-06-15 LUCU1**051963 2,015 Ingreso Inicio Tratamiento NA 2,285 2015-08-07 FELIPE CABRERA 25/05/1963 56 Hombre 15/06/2015 PG-PAB Hospital de Tome, Centro Superarte publico Alcohol Marihuana NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 18 3 0 10 S S 15 DEL BIO-BIO NA 15/06/2015 1963-05-25 52.0574949 52.20260 1 1
a3b7575d3b6f2300d5fbccb6dca39117 2015-08-07 2016-04-01 LUCU1**051963 2,015 Ingreso Inicio Tratamiento NA 8,959 2015-08-07 FELIPE CABRERA 25/05/1963 56 Hombre 01/04/2016 PG-PAI Hospital de Tome, Centro Superarte publico Alcohol Marihuana NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 18 3 0 10 S S 15 DEL BIO-BIO NA 01/04/2016 1963-05-25 52.8542094 52.20260 1 1
b2b1275db04d0bb0926ff016d1f70c38 2016-06-03 2016-05-02 ALHE2**081982 2,016 Ingreso Inicio Tratamiento NA 23,780 2016-06-03 DAVID GALLEGOS 16/08/1982 37 Mujer 02/05/2016 PG-PAI Centro Comunitario Cerro Navia Joven privado Cocaína Marihuana Pasta Base 7 7 4 2 0 0 0 0 0 0 0 0 N N N S 3 N 1 10 0 0 5 S S 5 METROPOLITANA NA 02/05/2016 1982-08-16 33.7111567 33.79877 1 1
b2b1275db04d0bb0926ff016d1f70c38 2016-06-03 2016-06-21 ALHE2**081982 2,016 Ingreso Inicio Tratamiento NA 25,622 2016-06-03 DAVID GALLEGOS 16/08/1982 37 Mujer 21/06/2016 PG-PAI Centro Comunitario Cerro Navia Joven privado Pasta Base Marihuana Alcohol 7 7 2 2 0 0 0 0 0 0 0 0 N N N S 3 N 1 10 0 0 5 S S 5 METROPOLITANA NA 21/06/2016 1982-08-16 33.8480493 33.79877 1 1
b46a8e93ae3916dfd133691fc8e89e3e 2016-08-31 2016-08-16 HEUR1**011964 2,016 Ingreso Inicio Tratamiento NA 28,010 2016-08-31 Patricia Muñoz Araya 09/01/1964 55 Hombre 16/08/2016 PG-PR Complejo Hospitalario San José de Maipo/ Hospital San Jose de Maipo publico Cocaína Alcohol NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 0 20 0 9 S S 2 METROPOLITANA NA 16/08/2016 1964-01-09 52.6023272 52.64339 1 1
b46a8e93ae3916dfd133691fc8e89e3e 2016-08-31 2016-10-03 HEUR1**101964 2,016 Ingreso Inicio Tratamiento NA 30,703 2016-08-31 Patricia Muñoz Araya 09/10/1964 55 Hombre 03/10/2016 PG-PR Complejo Hospitalario San José de Maipo/ Hospital San Jose de Maipo publico Cocaína Alcohol NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 0 20 0 9 S S 2 METROPOLITANA NA 03/10/2016 1964-10-09 51.9835729 51.89322 1 1
b46a8e93ae3916dfd133691fc8e89e3e 2016-08-31 2016-10-03 HEUR1**101964 2,016 Egreso Egreso NA 30,704 2016-08-31 PATRICIA MUÑOZ ARAYA 09/10/1964 55 Hombre 03/10/2016 PG-PR Complejo Hospitalario San José de Maipo/ Hospital San Jose de Maipo publico Cocaína Alcohol NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 16 0 0 17 S S 17 METROPOLITANA NA 03/10/2016 1964-10-09 51.9835729 51.89322 1 1
bc234258283c9e5d5116a38449e16c01 2019-04-24 2019-04-17 FRCA1**081984 2,019 Ingreso Inicio Tratamiento NA 100,990 2019-04-24 juan parra 29/08/1984 35 Hombre 17/04/2019 PG-PAB COSAM La Granja publico Pasta Base Cocaína Alcohol 7 1 0 0 11 0 0 0 0 0 0 0 S S N N 0 N 2 10 0 0 15 S S 18 METROPOLITANA NA 17/04/2019 1984-08-29 34.6310746 34.65024 1 1
bc234258283c9e5d5116a38449e16c01 2019-04-24 2019-05-02 FRCA1**081984 2,019 Ingreso Inicio Tratamiento NA 102,376 2019-04-24 TECNICO EN REHABILITACION 29/08/1984 35 Hombre 02/05/2019 PG-PAI COSAM La Granja publico Pasta Base Cocaína Alcohol 7 1 0 0 11 0 0 0 0 0 0 0 S S N N 0 N 2 10 0 0 15 S S 18 METROPOLITANA NA 02/05/2019 1984-08-29 34.6721424 34.65024 1 1
c03f161d476b48e451ba2558cd120e5e 2016-09-23 2016-05-11 CAVA2**041995 2,016 Tratamiento Seguimiento 3 meses NA 25,308 2016-09-23 viviana 28/04/1995 24 Mujer 11/05/2016 PG-PAI COSAM El Bosque publico Cocaína Marihuana NA 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 METROPOLITANA no desea realizar formulario 11/05/2016 1995-04-28 21.0376454 21.40726 1 1
c03f161d476b48e451ba2558cd120e5e 2016-09-23 2016-08-31 CAVA2**041995 2,016 Ingreso Inicio Tratamiento NA 29,256 2016-09-23 viviana 28/04/1995 24 Mujer 31/08/2016 PG-PAI COSAM El Bosque publico Cocaína Alcohol Pasta Base 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA 0 NA 0 0 0 0 0 NA NA 0 METROPOLITANA no desea realizar formulario 31/08/2016 1995-04-28 21.3442847 21.40726 1 1
c6f534c74d5c7478aa32901849314c92 2016-03-11 2016-02-25 DEME2**021983 2,016 Ingreso Inicio Tratamiento NA 19,267 2016-03-11 marina stuardo 20/02/1983 36 Mujer 25/02/2016 PG-PAI COSAM San Bernardo publico Pasta Base Alcohol Marihuana 0 0 0 0 1 0 0 0 0 0 0 0 N N N N 0 N 0 10 0 0 10 S S 10 METROPOLITANA NA 25/02/2016 1983-02-20 33.0130048 33.05407 1 1
c6f534c74d5c7478aa32901849314c92 2016-03-11 2016-03-31 DEME2**021983 2,016 Tratamiento Seguimiento 3 meses NA 21,080 2016-03-11 marina stuardo 20/02/1983 36 Mujer 31/03/2016 PG-PAI COSAM San Bernardo publico Pasta Base Alcohol NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 0 0 10 S S 10 METROPOLITANA NA 31/03/2016 1983-02-20 33.1088296 33.05407 1 1
d7fc8c449101a24d6540b26010ce2477 2019-04-09 2018-10-18 MACA1**101982 2,019 Egreso Egreso NA 91,788 2019-04-09 PS DANIELA SANCHEZ 12/10/1982 37 Hombre 18/10/2018 PG-PR CEADT privado Alcohol NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 20 0 0 20 S S 20 DEL MAULE NA 18/10/2018 1982-10-12 36.0164271 36.49008 1 1
d7fc8c449101a24d6540b26010ce2477 2019-04-09 2019-04-09 MACA1**101982 2,019 Ingreso Inicio Tratamiento NA 100,202 2019-04-09 lrojas 12/10/1982 37 Hombre 09/04/2019 PG-PAB CESFAM Armando Williams publico Alcohol NA NA 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 S 1 6 0 0 12 N S 10 DEL MAULE NA 09/04/2019 1982-10-12 36.4900753 36.49008 1 1
d96f99d4f07fc912d2d42e1fd7ad8b50 2015-08-19 2015-08-14 PECA1**071976 2,015 Ingreso Inicio Tratamiento NA 5,356 2015-08-19 Paula Saenz 01/07/1976 43 Hombre 14/08/2015 PG-PAI Unidad de Dependencias CABL 1 publico Pasta Base Cocaína Alcohol 4 4 0 0 28 2 2 0 0 0 0 0 N N N N 0 N 0 14 28 0 15 N N 3 METROPOLITANA NA 14/08/2015 1976-07-01 39.1184120 39.13210 1 1
d96f99d4f07fc912d2d42e1fd7ad8b50 2015-08-19 2015-12-09 PECA1**071976 2,015 Tratamiento Seguimiento 3 meses NA 8,601 2015-08-19 Paula Saenz 01/07/1976 43 Hombre 09/12/2015 PG-PAI Unidad de Dependencias CABL 1 publico Pasta Base Marihuana Cocaína 4 4 0 0 28 2 2 0 0 0 0 0 N N N N 0 N 0 14 28 0 15 N N 3 METROPOLITANA NA 09/12/2015 1976-07-01 39.4387406 39.13210 1 1
dcbebbdebac062c872b382b9236248ab 2018-01-15 2018-01-15 PAJA1**101980 2,018 Ingreso Inicio Tratamiento NA 69,181 2018-01-15 NICOL GUZMAN 11/10/1980 39 Hombre 15/01/2018 PG-PAI Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado Alcohol NA NA 22 6 0 0 0 0 0 1 6 0 0 0 N N N N 0 N 0 5 0 0 8 S S 2 DE ARICA Y PARINACOTA NA 15/01/2018 1980-10-11 37.2621492 37.26215 1 1
dcbebbdebac062c872b382b9236248ab 2018-01-15 NA PAJA1**101990 2,018 Ingreso Inicio Tratamiento NA 69,925 2018-01-15 NICOLE GUZMAN 11/10/1990 29 Hombre NA NA Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado NA NA NA 22 6 0 0 0 0 0 1 6 0 0 0 N N N N 0 N 0 5 0 0 8 S S 2 DE ARICA Y PARINACOTA NA NA 1990-10-11 NA 27.26352 1 1
dd2396af89c91d6069861edc1f952bce 2017-05-25 2017-05-25 JOGU2**121971 2,017 Ingreso Inicio Tratamiento NA 50,139 2017-05-25 PRISCILA VARGAS 16/12/1971 47 Mujer 25/05/2017 PG-PAB Hospital Quellon publico Alcohol NA NA 14 6 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 17 0 0 14 S S 17 DE LOS LAGOS NA 25/05/2017 1971-12-16 45.4401095 45.44011 1 1
dd2396af89c91d6069861edc1f952bce 2017-05-25 2017-08-01 JOGU2**121971 2,017 Ingreso Inicio Tratamiento NA 53,991 2017-05-25 PRISCILA VARGAS 16/12/1971 47 Mujer 01/08/2017 PG-PAI Hospital Quellon publico Alcohol NA NA 14 6 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 17 0 0 14 S S 17 DE LOS LAGOS NA 01/08/2017 1971-12-16 45.6262834 45.44011 1 1
e120f5cb25ff889b2f090872c5543192 2017-07-26 2016-08-23 MAGA1**111968 2,017 Tratamiento Seguimiento 9 meses NA 37,707 2017-07-26 TECNICO EN REHABILITACION 29/11/1968 50 Hombre 23/08/2016 PG-PAI COSAM La Granja publico Pasta Base Alcohol Marihuana 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 20 8 0 19 S S 20 METROPOLITANA NA 23/08/2016 1968-11-29 47.7316906 48.65435 1 1
e120f5cb25ff889b2f090872c5543192 2017-07-26 2017-09-02 MAGA1**111968 2,017 Ingreso Inicio Tratamiento NA 55,506 2017-07-26 TECNICO EN REHABILITACION 29/11/1968 50 Hombre 02/09/2017 PG-PAB COSAM La Granja publico Pasta Base Alcohol Anfetaminas 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 20 8 0 19 S S 20 METROPOLITANA NA 02/09/2017 1968-11-29 48.7583847 48.65435 1 1
e18667e0e92270ff05eaeb21a6fdd509 2017-08-08 2017-07-31 CAME2**071979 2,017 Ingreso Inicio Tratamiento NA 54,153 2017-08-08 SANDRA RAMIRE4Z 23/07/1979 40 Mujer 31/07/2017 PG-PAI COSAM La Granja publico Alcohol Marihuana Cocaína 0 0 0 0 0 0 0 0 0 0 0 0 N N N N 0 N 0 10 28 0 15 S S 15 METROPOLITANA NA 31/07/2017 1979-07-23 38.0232717 38.04517 1 1
e18667e0e92270ff05eaeb21a6fdd509 2017-08-08 2017-11-02 CAME2**071979 2,017 Ingreso Inicio Tratamiento NA 57,506 2017-08-08 SANDRA RAMIREZ 23/07/1979 40 Mujer 02/11/2017 PG-PAB COSAM La Granja publico Alcohol NA NA 0 0 0 0 0 0 0 7 28 0 0 0 N N N N 0 N 0 10 28 0 15 S S 15 METROPOLITANA NA 02/11/2017 1979-07-23 38.2806297 38.04517 1 1
e1cd21bb52065f68035d8d9f9443ae45 2017-08-29 2017-08-28 VIIN1**121970 2,017 Ingreso Inicio Tratamiento NA 55,052 2017-08-29 TECNICO EN REHABILITACION 15/12/1970 48 Hombre 28/08/2017 PG-PAB COSAM La Granja publico Alcohol NA NA 5 2 0 0 15 1 1 0 0 0 0 0 N N N N 1 N 0 10 19 0 10 S S 7 METROPOLITANA NA 28/08/2017 1970-12-15 46.7022587 46.70500 1 1
e1cd21bb52065f68035d8d9f9443ae45 2017-08-29 2017-09-01 VIIN1**121970 2,017 Ingreso Inicio Tratamiento NA 55,768 2017-08-29 JUAN LUIS PARRA 15/12/1970 48 Hombre 01/09/2017 PG-PAI COSAM La Granja publico Cocaína Alcohol Marihuana 5 1 0 0 15 1 1 0 0 0 0 0 N N N N 1 N 0 10 19 0 10 S S 7 METROPOLITANA NA 01/09/2017 1970-12-15 46.7132101 46.70500 1 1
eb006ea910509ffb7b68d26e28b9d216 2017-02-21 2017-01-16 CHNA1**041972 2,017 Ingreso Inicio Tratamiento NA 43,431 2017-02-21 ALEJANDRO RODRIGUEZ NARANJO 04/04/1972 47 Hombre 16/01/2017 PG-PAB CESFAM Pedro Leon Gallo publico Alcohol Marihuana Anfetaminas 21 9 0 0 0 0 0 0 0 0 0 0 N N N N 21 N 0 0 7 0 4 S S 7 DE ATACAMA PACIENTE SEÑALA QUE SU CONSUMO HA SIDO PROBLEMATICO SE ENCUENTRA CON UN JUICIO LABORAL, ADEMAS PRESENTA DEMANDO POR PEN ALIMENTO Y SUSPENCION LICENCIA 16/01/2017 1972-04-04 44.7857632 44.88433 1 1
eb006ea910509ffb7b68d26e28b9d216 2017-02-21 2017-02-27 CRNA1**041972 2,017 Ingreso Inicio Tratamiento NA 45,187 2017-02-21 ALEJANDRO RODRIGUEZ NARANJO 04/04/1972 47 Hombre 27/02/2017 PG-PAB CESFAM Pedro Leon Gallo publico Alcohol Anfetaminas Marihuana 21 5 0 0 0 0 0 0 0 0 0 0 N N N N 21 N 0 0 7 0 4 S S 7 DE ATACAMA NA 27/02/2017 1972-04-04 44.9007529 44.88433 1 1
f081ac52232781af3b5024fb7a88acb8 2017-05-16 2017-04-13 YEPO1**111974 2,017 Ingreso Inicio Tratamiento NA 48,398 2017-05-16 Nicole Guzmán 19/11/1974 44 Hombre 13/04/2017 PG-PAI Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado Pasta Base Alcohol Marihuana 6 6 3 1 26 0 0 0 0 0 0 0 S S N N 0 N 2 5 0 0 10 S S 5 DE ARICA Y PARINACOTA NA 13/04/2017 1974-11-19 42.3983573 42.48871 1 1
f081ac52232781af3b5024fb7a88acb8 2017-05-16 NA YEPO1**111974 2,017 Ingreso Inicio Tratamiento NA 50,749 2017-05-16 Nicole Guzmán 19/11/1974 44 Hombre NA NA Centro de Rehabilitacion Sede San Jose de Arica (Casa Acogida La Esperanza Arica) privado NA NA NA 6 6 3 1 26 0 0 0 0 0 0 0 S S N N 0 N 2 5 0 0 10 S S 5 DE ARICA Y PARINACOTA NA NA 1974-11-19 NA 42.48871 1 1
f82d8b940c8ecc8f983a93f5e2bf2d61 2019-05-10 2019-05-10 CAPE1**101990 2,019 Ingreso Inicio Tratamiento NA 102,849 2019-05-10 carlos gallardo 17/10/1990 29 Hombre 10/05/2019 PG-PAB CT Peulla publico Pasta Base Alcohol Marihuana 3 1 3 1 1 0 0 0 0 0 0 0 N N N N 0 N 0 16 0 0 18 S S 10 DE LOS LAGOS NA 10/05/2019 1990-10-17 28.5612594 28.56126 1 1
f82d8b940c8ecc8f983a93f5e2bf2d61 2019-05-10 2019-08-01 CAPE1**101990 2,019 Ingreso Inicio Tratamiento NA 105,995 2019-05-10 Angelo Palma 17/10/1990 29 Hombre 01/08/2019 PG-PAI CT Peulla publico Pasta Base Alcohol Marihuana 3 1 3 1 1 0 0 0 0 0 0 0 N N N N 0 N 0 16 0 0 18 S S 10 DE LOS LAGOS NA 01/08/2019 1990-10-17 28.7885010 28.56126 1 1
fba81efc185eaeffb213f1608ce38af6 2015-06-24 2015-08-01 CLDI1**061980 2,015 Ingreso Inicio Tratamiento NA 5,409 2015-06-24 Humberto Chamorro 06/06/1980 39 Hombre 01/08/2015 PG-PAI COSAM Lota publico Cocaína Sedantes: diazepam, Valium, clonazepam, Ravotril, alprazolam, adax, barbitúricos, fenobarbital. Pasta Base 0 0 0 0 0 2 1 0 0 0 0 0 N N N N 0 N 0 12 28 0 14 S S 9 DEL BIO-BIO NA 01/08/2015 1980-06-06 35.1512663 35.04723 1 1
fba81efc185eaeffb213f1608ce38af6 2015-06-24 2015-09-01 CLDI1**061980 2,015 Ingreso Inicio Tratamiento NA 5,503 2015-06-24 HUMBERTO CHAMORRO 06/06/1980 39 Hombre 01/09/2015 PG-PAI COSAM Lota publico Cocaína Sedantes: diazepam, Valium, clonazepam, Ravotril, alprazolam, adax, barbitúricos, fenobarbital. Alcohol 0 0 0 0 0 2 1 0 0 0 0 0 N N N N 0 N 0 12 28 0 14 S S 9 DEL BIO-BIO NA 01/09/2015 1980-06-06 35.2361396 35.04723 1 1 
    CONS_TOP_df_dup_ENE_2020_prev6 %>% 
    dplyr::filter(!is.na(fech_ing)) %>%
    dplyr::mutate(concatenation_hash_date_ing_date_adm=paste0(HASH_KEY, fech_ing, fech_ap_top)) %>% 
    dplyr::distinct(concatenation_hash_date_ing_date_adm, .keep_all = TRUE) %>% #descartar todas las combinaciones de filas con la misma fecha de todo y el mismo HASH.
    dplyr::mutate(concatenation_hash_date_ap_top=paste0(HASH_KEY, fech_ap_top)) %>% 
    dplyr::filter(duplicated(concatenation_hash_date_ap_top)) %>% #dejo a los que tienen duplicados la fecha de aplicación y el hash, ya que la tercera variable, en este caso, la fecha de ingreso es distinta.
    dplyr::arrange(concatenation_hash_date_ap_top) %>%  
    dplyr::select(HASH_KEY,fech_ing, fech_ap_top,concatenation_hash_date_ap_top) %>% 
    dplyr::inner_join(CONS_TOP_df_dup_ENE_2020_prev6, by=c("HASH_KEY", "fech_ap_top")) %>% #
    dplyr::select(ID,HASH_KEY, fech_ap_top, fech_ing.y,id_mod, ano_bd, TOP, Etapa.del.Tratamiento,everything()) %>%
    dplyr::group_by(concatenation_hash_date_ap_top) %>%
    dplyr::filter(!duplicated(fech_ing.y)) %>%
      #134 cases may be affected with this problem, 67 concatenations of hash date and ap top.
      write.csv2(file =paste0(path,"/Maureen/_9.mismo_HASH_y_fecha_de_aplicacion_TOP_mas_de_una_fecha_de_ingreso.csv"))


Considering these cases are still being cleared, we would only consider as our key date the date of application only.


CONS_TOP_df_dup_ENE_2020_prev6 %>%
    dplyr::rename("dosisoh"=`Dósis.OH`, "dosisthc"=`Dósis.THC`, "dosispbc"=`Dósis.PBC`, "dosiscoc"=`Dósis.COC`, "dosisbzd"=`Dósis.BZD`, "dosisotra"=`Dósis.Otra`,"rina"=`Riña`, "totaltransgresion"=`Total.Transgresión`, "saludpsicologica"=`Salud.Psicológica`, "totaleducacion"=`Total.Educación`, "saludfisica"=`Salud.Física`) %>%
  as.data.frame() %>%
  write.csv2(., file ="CONS_TOP_df_dup_ENE_2020.csv")


6. TOPs in C1

CONS_C1_df_dup_ENE_2020_unique_HASH_date<- dplyr::select(CONS_C1_df_dup_ENE_2020, hash_key, fech_ing) %>% dplyr::mutate(fech_ing= as.Date(as.character(fech_ing))) %>% group_by(hash_key, fech_ing) %>% dplyr::mutate(row_first=row_number()) %>% ungroup() %>% dplyr::filter(row_first==1) 

n_cases_hash1<-dplyr::left_join(CONS_TOP_df_dup_ENE_2020_prev6,CONS_C1_df_dup_ENE_2020_unique_HASH_date, by= c("HASH_KEY"="hash_key", "fech_ing")) %>% dplyr::filter(is.na(row_first)) %>% nrow()
n_cases_hash2<-dplyr::left_join(CONS_TOP_df_dup_ENE_2020_prev6,CONS_C1_df_dup_ENE_2020_unique_HASH_date, by= c("HASH_KEY"="hash_key", "fech_ing")) %>% dplyr::filter(is.na(row_first)) %>% distinct(HASH_KEY) %>% nrow()


dplyr::left_join(CONS_TOP_df_dup_ENE_2020_prev6,CONS_C1_df_dup_ENE_2020_unique_HASH_date, by= c("HASH_KEY"="hash_key", "fech_ing")) %>% dplyr::filter(is.na(row_first)) %>% group_by(is.na(fech_ing)) %>% summarise(n=n()) %>%
  data.frame() %>% 
  dplyr::rename("Missing Data in\n Date of Admission"=is.na.fech_ing., "Number of Rows"=`n`) %>%
  knitr::kable(.,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
               caption="Table 14. Cases product of the combination of HASHs and dates of admission that are not present in C1 Dataset",
                 align ="cccc")  %>%
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 10)
`summarise()` ungrouping output (override with `.groups` argument)
Table 14. Cases product of the combination of HASHs and dates of admission that are not present in C1 Dataset
Missing Data in Date of Admission Number of Rows
FALSE 160
TRUE 1,073

As seen in Table 14, a small fraction of the combination of dates and HASHs was not present in C1 Dataset. Many of them due to missing variables in the date of admission. There are 1233 cases of 643 distinct HASHs that were not included in the C1 dataset.


7. Probabilistic Deduplication

In order to catch some differences that would probabilistically match in terms of HASH-Key and date of admission, we ran data into a package in the software Stata called dtalink, with the following criteria:

  1. Hash Key, with a weight applied of 125 points, in case this variable match, but minus 125 in case that does not match
  2. Date of TOP Application will add 125 points with the same condition, and with a calliper of 5 days.
  3. Type of center (if it is private or public) it was weighted with 5 points if matchs (1.25%), and minus 5 points if it does not.
  4. For the variables related to the evaluation contents, each one was weighted with 5 points (1.25%) and minus 5 points, with a total weight of 145 (or 36.25%).
  5. We used the age as blocks, to reduce computation time and match each cases within people of the same age
  6. Matches will be considered worthy of analysis with 280 or more points (70%)

The code used in Stata is shown here:

  

 export_top<-
 data.frame(final="clear all") %>% 
  rbind("ssc install dtalink") %>% 
  rbind(paste0('import delimited "', gsub('/', '\\', path, fixed=T),'\\CONS_TOP_df_dup_ENE_2020.csv"'))%>%
  dplyr::rename("*final"="final") %>% 
  rbind('qui generate id_match = _n')%>%
  rbind('cap drop _id _matchID _matchflag _score')%>%
  rbind('gen fech_ap_top_mod = date(fech_ap_top, "YMD")')%>%
  
  rbind('dtalink hash_key 125 -125 fech_ap_top_mod 125 -125 5 tipocentro 5 -5  sustanciaprincipal1 5 -5 sustanciaprincipal2 5 -5 sustanciaprincipal3 5 -5 totaloh 5 -5 dosisoh 5 -5 totalthc 5 -5 dosisthc 5 -5 totalpbc 5 -5 dosispbc 5 -5 totalcoc 5 -5 dosiscoc 5 -5 totalbzd 5 -5 dosisbzd 5 -5 totalotra 5 -5 dosisotra 5 -5 hurto 5 -5 robo 5 -5 ventadrogas 5 -5 rina 5 -5 totalvif 5 -5 otro 5 -5 totaltransgresion 5 -5 saludpsicologica 5 -5 totaltrabajo 5 -5 totaleducacion 5 -5 saludfisica 5 -5 lugarvivir 5 -5 vivienda 5 -5 calidadvida 5 -5, block(edad) cutoff(300)') %>% 
  rbind('drop if missing(_score)') %>% 
  rbind(paste0('qui save "', gsub('/', '\\', path, fixed=T),'\\_CONS_TOP_df_match75_05_02_2020.dta", replace'))

export_top%>% knitr::kable("html") %>% 
kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size =10)
*final
clear all
ssc install dtalink
import delimited “C:Fondecytunidad (github)_TOP_df_dup_ENE_2020.csv”
qui generate id_match = _n
cap drop _id _matchID _matchflag _score
gen fech_ap_top_mod = date(fech_ap_top, “YMD”)
dtalink hash_key 125 -125 fech_ap_top_mod 125 -125 5 tipocentro 5 -5 sustanciaprincipal1 5 -5 sustanciaprincipal2 5 -5 sustanciaprincipal3 5 -5 totaloh 5 -5 dosisoh 5 -5 totalthc 5 -5 dosisthc 5 -5 totalpbc 5 -5 dosispbc 5 -5 totalcoc 5 -5 dosiscoc 5 -5 totalbzd 5 -5 dosisbzd 5 -5 totalotra 5 -5 dosisotra 5 -5 hurto 5 -5 robo 5 -5 ventadrogas 5 -5 rina 5 -5 totalvif 5 -5 otro 5 -5 totaltransgresion 5 -5 saludpsicologica 5 -5 totaltrabajo 5 -5 totaleducacion 5 -5 saludfisica 5 -5 lugarvivir 5 -5 vivienda 5 -5 calidadvida 5 -5, block(edad) cutoff(300)
drop if missing(_score)
qui save “C:Fondecytunidad (github)_CONS_TOP_df_match75_05_02_2020.dta”, replace 
write.table(export_top, file = paste0(path,"/SUD_CL/__stata_dtalink_top.do"), sep = "",row.names = FALSE, quote = FALSE,fileEncoding="UTF-8")


*should be in the same folder of the .Rmd
cap do __stata_dtalink_top.do


There were 4009 cases with probabilistic matches, and 3128 HASHs involved in these matches.

8. Transformations in TOPs Dataset

First, we can see that there are many types of plans that are variations from the original ones, such as PG-PAI 2. Following SENDA professionals’ indications, we recoded these variations and collapsed the categories in M-PAI, P-PR, PG-PAI, and PG-PR. Must note that some cases are identified as men but they are in women-specific treatments.

CONS_TOP_df_dup_ENE_2020_prev6 %>%
    dplyr::count(Plan.de.Tratamiento, Sexo) %>% 
    dplyr::group_by(Sexo) %>% 
    mutate(prop = paste0(round(100 * prop.table(n)),"%")) %>%
    data.table::data.table() %>% 
    reshape::melt(id.vars = c(1:2)) %>% 
    reshape::cast(.,Plan.de.Tratamiento~Sexo+variable) %>% 
    data.table::as.data.table() %>%
    knitr::kable(.,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
                   caption="Table 15. Type of Plan by Sex", col.names= c("Type of\n Treatment", " Man (n)", "Man (%)","Women (n)","Women (%)"), 
                 align =rep('c', 5))  %>%
    kable_styling(bootstrap_options = c("striped", "hover"),font_size = 10) %>%
  add_footnote( c("PR= Residential Program" ,"PAB= Basic Outpatient Program", "PAI= Intensive Outpatient Treatment", "Otro= Other (less frequent and often private or not part of SENDA programs)"), notation = "none")
Table 15. Type of Plan by Sex
Type of Treatment Man (n) Man (%) Women (n) Women (%)
M-PAI 42 0% 7977 27%
M-PAI2 NA NA 17 0%
M-PR 42 0% 5268 18%
M-PR2 NA NA 12 0%
Otro 229 0% 98 0%
PG-PAB 26002 35% 6487 22%
PG-PAI 34328 46% 8830 30%
PG-PR 12277 17% 487 2%
PG PAI 2 55 0% 52 0%
NA 1084 1% 4 0%
PR= Residential Program
PAB= Basic Outpatient Program
PAI= Intensive Outpatient Treatment
Otro= Other (less frequent and often private or not part of SENDA programs) 
#3 PR fijo, PRFlexible y PR fijo 2 se pasan a PG PR.//Pr-flexible no puede estar en LV//no hay casos
#4 tipo_de_plan: si tenemos un plan dentro del programa Alcohol-plan, se cambia a PG-plan // Sin casos
#5 tipo_de_plan: si tenemos un plan OTRO-plan se cambia a PG-plan // sin casos

#replace tipo_de_plan = "M-PAI" if tipo_de_plan=="M-PAI2"
#replace tipo_de_plan = "M-PR" if tipo_de_plan=="M-PR2"
#replace tipo_de_plan = "PG-PAI" if tipo_de_plan=="PG PAI 2"
#replace tipo_de_plan = "PG-PAB" if tipo_de_plan=="Otro"
#replace tipo_de_plan = "PG-PAB" if tipo_de_plan=="CALLE"
#replace tipo_de_plan = "" if tipo_de_plan=="NA"
CONS_TOP_df_dup_ENE_2020_prev6 %>%
  dplyr::mutate(OBS=if_else(Plan.de.Tratamiento %in% unlist(c("M-PAI2","M-PR2","PG PAI 2","Otro","CALLE")),paste0(as.character(OBS),";","1.6. Collapsed Treatment Plans"),as.character(OBS),missing=as.character(OBS)))%>%
  dplyr::mutate(tipo_de_plan=dplyr::recode(Plan.de.Tratamiento,"M-PAI2"= "M-PAI", "M-PR2"="M-PR","PG PAI 2"="PG-PAI", "Otro"="PG-PR")) %>%
#PARA VER LO QUE PASA
  #  dplyr::count(Plan.de.Tratamiento, Sexo) %>% 
#    dplyr::group_by(Sexo) %>% 
#    mutate(prop = paste0(round(100 * prop.table(n)),"%")) %>%
#    data.table::data.table() %>% 
#    reshape::melt(id.vars = c(1:2)) %>% 
#    reshape::cast(.,Plan.de.Tratamiento~Sexo+variable) %>% 
#    data.table::as.data.table() %>%
#  as.factor(ty)
  assign("CONS_TOP_df_dup_ENE_2020_prev7",.,envir = .GlobalEnv)


Considering the abovementioned observation, we changed those mens with Masculine identity (obtained from the C1 dataset) into a general population (PR) plan. 

#CONS_TOP_df_dup_ENE_2020_prev7 %>% dplyr::filter(tipo_de_plan=="M-PAI"|tipo_de_plan=="M-PR", Sexo=="Mujer") %>% #dplyr::inner_join(dplyr::select(CONS_C1_df_dup_ENE_2020,HASH_KEY, identidad.de.genero), by="HASH_KEY") %>% dplyr::select(HASH_KEY, id_mod, #tipo_de_plan, identidad.de.genero) %>% dplyr::filter(!is.na(identidad.de.genero)) %>% data.table::as.data.table() %>% print()

CONS_TOP_df_dup_ENE_2020_prev7 %>% dplyr::filter(tipo_de_plan=="M-PAI"|tipo_de_plan=="M-PR", Sexo=="Hombre") %>% dplyr::inner_join(dplyr::select(CONS_C1_df_dup_ENE_2020,hash_key, identidad_de_genero), by=c("HASH_KEY"="hash_key")) %>% dplyr::select(HASH_KEY, id_mod, tipo_de_plan, identidad_de_genero) %>% dplyr::filter(!is.na(identidad_de_genero)) %>%
  data.table::as.data.table() %>%
    knitr::kable(.,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
                   caption="Table 16. Gender Identity of Men that were in Women-specific Plans", col.names= c("HASH Key", " ID", "Type of\n Plan","Gender Identity"),
                 align =rep('c', 5))  %>%
    kable_styling(bootstrap_options = c("striped", "hover"),font_size = 10) %>%
  add_footnote( c("Femenino= Femenine Gender Identity","Masculino= Masculine Gender Identity","PAI= Intensive Outpatient Treatment","PR= Residential Program"), notation = "none")
Table 16. Gender Identity of Men that were in Women-specific Plans
HASH Key ID Type of Plan Gender Identity
04499997449e5301cc00f1d3695f0a95 RAGO1**101981 M-PAI Femenino
04499997449e5301cc00f1d3695f0a95 RAGO1**101981 M-PAI Femenino
3de5250359b1dc9510992ed92659a530 MALE1**121971 M-PAI Masculino
539f826e6e3bf189d3dcc73db6116711 JAMA1**061998 M-PR Femenino
539f826e6e3bf189d3dcc73db6116711 JAMA1**061998 M-PR Femenino
78ff5c983fac3d4a90d88e8bacc6f835 NAPO1**092001 M-PR Femenino
a92ab8f10fa05d0d9dcb5855c0ec0092 RITR1**111984 M-PAI Masculino
a92ab8f10fa05d0d9dcb5855c0ec0092 RITR1**111984 M-PAI Masculino
Femenino= Femenine Gender Identity
Masculino= Masculine Gender Identity
PAI= Intensive Outpatient Treatment
PR= Residential Program 
CONS_C1_df_dup_ENE_2020_unique_gender<- dplyr::select(CONS_C1_df_dup_ENE_2020,hash_key, identidad_de_genero) %>% dplyr::filter(!is.na(identidad_de_genero)) %>% group_by(hash_key) %>% dplyr::mutate(row_join=row_number()) %>%ungroup() %>% dplyr::filter(row_join==1) %>% dplyr::mutate(identidad_de_genero=as.character(identidad_de_genero)) %>% data.table::as.data.table(.)

CONS_TOP_df_dup_ENE_2020_prev7 %>%
    dplyr::left_join(CONS_C1_df_dup_ENE_2020_unique_gender, by=c("HASH_KEY"="hash_key")) %>%
    dplyr::mutate(tipo_plan_m=ifelse(tipo_de_plan %in% c("M-PAI", "M-PR", "M-PAB"),1,0)) %>%
    dplyr::mutate(OBS= case_when((identidad_de_genero=="Masculino" & Sexo=="Hombre" & tipo_plan_m==1)~ paste0(as.character(OBS),";","1.7. Standardized Plans By Sex & Gender Id"),TRUE~as.character(OBS)))%>%
    dplyr::mutate(tipo_de_plan=ifelse(identidad_de_genero=="Masculino" & Sexo=="Hombre" & tipo_de_plan=="M-PAB","PG-PAB", tipo_de_plan)) %>%
      dplyr::mutate(tipo_de_plan=ifelse(identidad_de_genero=="Masculino" & Sexo=="Hombre" & tipo_de_plan=="M-PAI","PG-PAI", tipo_de_plan)) %>%
    dplyr::mutate(tipo_de_plan=ifelse(identidad_de_genero=="Masculino" & Sexo=="Hombre" & tipo_de_plan=="M-PR","PG-PR", tipo_de_plan)) %>% 
    #PARA VER LO QUE PASA
#    dplyr::count(tipo_de_plan, Sexo) %>% 
#    dplyr::group_by(Sexo) %>% 
#    mutate(prop = paste0(round(100 * prop.table(n)),"%")) %>%
#    data.table::data.table() %>% 
#    reshape::melt(id.vars = c(1:2)) %>% 
#    reshape::cast(.,tipo_de_plan~Sexo+variable) %>% 
#    data.table::as.data.table() %>%
#  print()
  dplyr::select(-dup_todo_TOP,-dup_contents_TOP,-row_join,-tipo_plan_m) %>%
  as.data.frame() %>%
  assign("CONS_TOP_df_dup_ENE_2020_prev8",.,envir = .GlobalEnv)

Also, we generated an ID code of the center of treatment, in order to mask this name and avoid any possible identification of specific cases. However, two centers could not be obtained from C1 dataset because they were not available: “Centro de Tratamiento San Francisco” and “Kausana CIP CRC Antofagasta”. These centers will be consulted to the SENDA professional in order to clarify if they can be replace for another one with a center ID number .

#Centro de Tratamiento San Francisco    220
#Kausana CIP CRC Antofagasta    1
#Ambos no tienen un par en C1 para dejarles código. El problema es que el Centro de Tratamiento San Francisco agrupa a 220 personas.
distinct_HASH_center_not_present_in_C1<- CONS_TOP_df_dup_ENE_2020_prev8 %>% dplyr::filter(Nombre.del.Centro=="Centro de Tratamiento San Francisco") %>% distinct(HASH_KEY) 
CONS_C1_df_dup_ENE_2020 %>%
dplyr::filter(hash_key %in% as.character(as.vector(unlist(as.data.table(unlist(distinct_HASH_center_not_present_in_C1)))))) %>%
dplyr::select(nombre_centro) %>% distinct(nombre_centro) %>%
        knitr::kable(.,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
                   caption="Table 17. Different Centers of Treatment that would not be matched with C1 dataset", 
                   #col.names= c("Duplicated", " Frequencies", "Percentage"),
                   align =rep('c', 102))  %>%
      kable_styling(bootstrap_options = c("striped", "hover"),font_size = 8) %>%
  scroll_box(width = "100%", height = "250px")
Table 17. Different Centers of Treatment that would not be matched with C1 dataset
nombre_centro
Comunidad Terapeutica Villamavida
CADEM de Chillan
COSAM Arauco
Centro Anun Coronel (población general)
CESFAM Chiguayante
CESFAM Boca Sur
COSAM Concepcion
Programa Residencial Adultos Población General Padre Chango
Centro de Responsabilidad de Salud Mental del Complejo Asistencial Dr.Victor Rios Ruiz
CESFAM Penco (Centro Nehuen)
Hospital Santa Bárbara
Hospital de Tome, Centro Superarte
COSAM Hualpen
COSAM Curanilahue (población general)
Casa Chica Hospital Higueras
COSAM LEBU
Comunidad Terapeutica CEPAS (Hombres)
CT Padre Chango (PG)
CT Aiwin
COSAM Canete PG
COSAM Alenmoguen
CESFAM Los Cerros
CT Carpe Diem, El Bosque
COSAM Lota
Comunidad Terapeutica Hogar Crea, Antofagasta
COSAM Newen


Also, we collapsed the primary substance of consumption into fewer categories that would be more representative of the patterns of consumption among the Chilean population in terms of SUDs.


#data.table(table(CONS_TOP_df_dup_ENE_2020_prev8$Sustancia.Principal.1))
CONS_TOP_df_dup_ENE_2020_prev8 %>% 
  dplyr::mutate(sus_prin1=as.character(Sustancia.Principal.1)) %>%
  dplyr::mutate(sus_prin1= dplyr::recode(sus_prin1,
                                              "Hipnóticos "= "Tranquilizantes e Hipnóticos",
                                              "Sedantes:  diazepam, Valium, clonazepam, Ravotril, alprazolam, adax, barbitúricos, fenobarbital." = "Tranquilizantes e Hipnóticos",
                                              "Anfetaminas"="Estimulante tipo anfetaminas",
                                              "Extasis"="Estimulante tipo anfetaminas",
                                              "Fenilciclidina"="Estimulante tipo anfetaminas",
                                              "Metanfetaminas y otros derivados"="Estimulante tipo anfetaminas",
                                              "Otros Estimulantes"="Estimulante tipo anfetaminas",
                                              "LSD"="Alucinógenos",
                                              "Otros Alucinógenos"="Alucinógenos",
                                              "Crack"="Pasta Base",
                                              "Heroína"="Opioides",
                                              "Metadona"="Opioides",
                                              "Otros Opioides Analgésicos: morfina, codeína, meperidina,  demerol, tramadol, tramal."="Opioides",
                                              "Inhalables: neopren, GHB, óxido nitroso (gas hilarante), \"poppers\", solventes, gasolina, diluyente"="Inhalables",
                                              "Esteroides Anabólicos"="Otros",
                                              "Hongos"="Alucinógenos")) %>%
dplyr::mutate(OBS=case_when(sus_prin1=="SIN CONSUMO"~paste0(OBS,";","1.8.Primary Substance1, Invalid due to No Consumption"),
                            TRUE~OBS))%>%    
dplyr::mutate(sus_prin1= dplyr::na_if(sus_prin1, "SIN CONSUMO")) %>%
  dplyr::mutate(sus_prin2=as.character(Sustancia.Principal.2)) %>%
  dplyr::mutate(sus_prin2= dplyr::recode(sus_prin2,
                                              "Hipnóticos "= "Tranquilizantes e Hipnóticos",
                                              "Sedantes:  diazepam, Valium, clonazepam, Ravotril, alprazolam, adax, barbitúricos, fenobarbital." = "Tranquilizantes e Hipnóticos",
                                              "Anfetaminas"="Estimulante tipo anfetaminas",
                                              "Extasis"="Estimulante tipo anfetaminas",
                                              "Fenilciclidina"="Estimulante tipo anfetaminas",
                                              "Metanfetaminas y otros derivados"="Estimulante tipo anfetaminas",
                                              "Otros Estimulantes"="Estimulante tipo anfetaminas",
                                              "LSD"="Alucinógenos",
                                              "Otros Alucinógenos"="Alucinógenos",
                                              "Crack"="Pasta Base",
                                              "Heroína"="Opioides",
                                              "Metadona"="Opioides",
                                              "Otros Opioides Analgésicos: morfina, codeína, meperidina,  demerol, tramadol, tramal."="Opioides",
                                              "Inhalables: neopren, GHB, óxido nitroso (gas hilarante), \"poppers\", solventes, gasolina, diluyente"="Inhalables",
                                              "Esteroides Anabólicos"="Otros",
                                              "Hongos"="Alucinógenos")) %>%
dplyr::mutate(OBS=case_when(sus_prin2=="SIN CONSUMO"~paste0(OBS,";","1.8.Primary Substance2, Invalid due to No Consumption"),
                            TRUE~OBS))%>%
dplyr::mutate(sus_prin2= dplyr::na_if(sus_prin2, "SIN CONSUMO")) %>%
  dplyr::mutate(sus_prin3=as.character(Sustancia.Principal.3)) %>%
  dplyr::mutate(sus_prin3= dplyr::recode(sus_prin3,
                                              "Hipnóticos "= "Tranquilizantes e Hipnóticos",
                                              "Sedantes:  diazepam, Valium, clonazepam, Ravotril, alprazolam, adax, barbitúricos, fenobarbital." = "Tranquilizantes e Hipnóticos",
                                              "Anfetaminas"="Estimulante tipo anfetaminas",
                                              "Extasis"="Estimulante tipo anfetaminas",
                                              "Fenilciclidina"="Estimulante tipo anfetaminas",
                                              "Metanfetaminas y otros derivados"="Estimulante tipo anfetaminas",
                                              "Otros Estimulantes"="Estimulante tipo anfetaminas",
                                              "LSD"="Alucinógenos",
                                              "Otros Alucinógenos"="Alucinógenos",
                                              "Crack"="Pasta Base",
                                              "Heroína"="Opioides",
                                              "Metadona"="Opioides",
                                              "Otros Opioides Analgésicos: morfina, codeína, meperidina,  demerol, tramadol, tramal."="Opioides",
                                              "Inhalables: neopren, GHB, óxido nitroso (gas hilarante), \"poppers\", solventes, gasolina, diluyente"="Inhalables",
                                              "Esteroides Anabólicos"="Otros",
                                              "Hongos"="Alucinógenos")) %>%
dplyr::mutate(OBS=case_when(sus_prin3=="SIN CONSUMO"~paste0(OBS,";","1.8.Primary Substance3, Invalid due to No Consumption"),
                            TRUE~OBS))%>%
dplyr::mutate(sus_prin3= dplyr::na_if(sus_prin3, "SIN CONSUMO")) %>%
  assign("CONS_TOP_df_dup_ENE_2020_prev9",.,envir = .GlobalEnv)
CONS_TOP_df_dup_ENE_2020_prev9 %>% 
  dplyr::left_join(dplyr::select(CONS_C1_df_dup_ENE_2020, nombre_centro, id_centro) %>% 
  dplyr::distinct(nombre_centro, id_centro), by=c("Nombre.del.Centro"="nombre_centro")) %>% 
  #dplyr::select(-row_join) %>% 
  assign("CONS_TOP_df_dup_ENE_2020_prev9",.,envir = .GlobalEnv)
CONS_TOP_df_dup_ENE_2020_prev9 %>%
  dplyr::mutate(TOP=as.factor(TOP)) %>%
  dplyr::mutate(Etapa.del.Tratamiento=as.factor(Etapa.del.Tratamiento)) %>%
  dplyr::mutate(Sexo=as.factor(Sexo)) %>%
  dplyr::mutate(Tipo.Centro=as.factor(Tipo.Centro)) %>%
  dplyr::mutate(Hurto=as.factor(Hurto)) %>%
  dplyr::mutate(Robo=as.factor(Robo)) %>%
  dplyr::mutate(Venta.Drogas=as.factor(Venta.Drogas)) %>%
  dplyr::mutate(Riña=as.factor(Riña)) %>%
  dplyr::mutate(Otro=as.factor(Otro)) %>%
  dplyr::mutate(Lugar.Vivir=as.factor(Lugar.Vivir)) %>%
  dplyr::mutate(Vivienda=as.factor(Vivienda)) %>%
  dplyr::mutate(tipo_de_plan=as.factor(tipo_de_plan)) %>%
  dplyr::mutate(sus_prin1=as.factor(sus_prin1)) %>%
  dplyr::mutate(sus_prin2=as.factor(sus_prin2)) %>%
  dplyr::mutate(sus_prin3=as.factor(sus_prin3)) %>%  
  as.data.frame(.) %>%
  assign("CONS_TOP_df_dup_ENE_2020_prev9",.,envir = .GlobalEnv)

  metadata(CONS_TOP_df_dup_ENE_2020_prev9)$name <- "SENDA Treatment Outcomes Profile"
  metadata(CONS_TOP_df_dup_ENE_2020_prev9)$description <- "Information About Treatment Outcomes Profile of users of SENDA, 2015 to 2019"
  #http://docshare03.docshare.tips/files/29337/293377101.pdf Paper de TOP Validación Chilena ACC
codebook::var_label(CONS_TOP_df_dup_ENE_2020_prev9) <- 
  list(HASH_KEY = 'Codificación del RUN/Masked Identifier (RUN)',
    hash_rut_completo = 'HASH alternativo, en el escenario en que se asuma que el individuo al que se le codificó el RUN presente mayor edad/Alternative HASH-Key',
    id_mod = 'ID de SENDA para Presentación en Página Web (enmascara caracteres 5 y 6)/SENDA ID (masked characters 5 & 6)',
    ID = 'Codigo Identificación de SENDA/SENDA ID',
    ano_bd = 'Año de la Base de Datos/Year of the Dataset (Source)',
    row = 'Numerador de los eventos presentes en la Base de Datos/Events in the Dataset',
    Fecha.Aplicación.TOP = '(original, Recodificado en fech_ap_top)/',
    Nombre.Apliacador.del.TOP = 'Nombre Aplicador del TOP/Name of the TOP Interviewer',
    TOP = 'TOP',
    Etapa.del.Tratamiento = 'Etapa del Tratamiento/Stage of Treatment',
    Fecha.Nacimiento = 'Fecha de Nacimiento/Date of Birth',
    Edad = 'Edad (número entero)/Year (Discrete Number)',
    Sexo = 'Sexo/Sex',
    Fecha.de.Ingreso.a.Tratamiento = '(original, Recodificado en fech_ing)/',
    Plan.de.Tratamiento = '(original, Recodificado en tipo_de_plan)/',
    Nombre.del.Centro = 'Nombre del Centro de Tratamiento/Treatment Center',
    Tipo.Centro = 'Tipo de Centro/Type of Center',
    Sustancia.Principal.1 = '(original, Recodificado en sus_prin1)/',
    Sustancia.Principal.2 = '(original, Recodificado en sus_prin2)/',
    Sustancia.Principal.3 = '(original, Recodificado en sus_prin3)/',
    Total.OH = 'Total Alcohol/Total Alcohol',
    Dósis.OH = 'Dosis Consumo de Alcohol/Amount of Alcohol',
    Total.THC = 'Total Marihuana/Total Marijuana',
    Dósis.THC = 'Dosis Marihuana/Dose of Marijuana',
    Total.PBC = 'Total Pasta Base de Cocaína/Total Cocaine Paste Base',
    Dósis.PBC = 'Dosis Pasta Base de Cocaína/Dose of Cocaine Paste Base',
    Total.COC = 'Total Cocaína/Total Snort Cocaine',
    Dósis.COC = 'Dosis Cocaína/Dose of Snort Cocaine',
    Total.BZD = 'Total Sedantes o Tranquilizantes/Total Sedatives and Tranquillizers',
    Dósis.BZD = 'Dosis Sedantes o Tranquilizantes/Dose of Sedatives and Tranquillizers',
    Total.Otra = 'Total Otra sustancia problema/Total Other Substances',
    Dósis.Otra = 'Dosis Otra sustancia problema/Dose of Other Substances',
    Hurto = 'Hurto/Theft',
    Robo = 'Robo/Robbery',
    Venta.Drogas = 'Venta de Drogas/Drug selling',
    Riña = 'Riña/Fights',
    Total.VIF = ' Total Violencia Intrafamiliar/Total Domestic Violence',
    Otro = 'Otra Acción/Another Action',
    Total.Transgresión = 'Total Transgresión a la Norma Social/Total Behavior that transgresses social norms',
    Salud.Psicológica = 'Salud Psicológica/Psychological Health',
    Total.Trabajo = 'Total Trabajo Pagado Formal o Informal/Total of Paid Work',
    Total.Educación = 'Total Asistencia a Establecimiento Educacional o Capacitación Laboral/Total College or school ',
    Salud.Física = 'Total Salud Física/Total Physical Health',
    Lugar.Vivir = 'Lugar estable para vivir/Stable Place to Live',
    Vivienda = 'Vivienda con Condiciones Básicas/Housing conditions',
    Calidad.Vida = 'Total Calidad de Vida/Total Quality of Life (QoL)',
    Región.Centro = 'Región del Centro/Chilean Region of the Center',
    Comentario = 'Comentarios relacionados con la aplicación del TOP/Comments related to the application of TOP',
    fech_ing = 'Fecha de Ingreso a Tratamiento/Date of Admission to Treatment',
    fech_ing_sin_fmt = 'Fecha de Ingreso de Tratamiento (Sin Formato de Fecha)/Date of Admission (unformatted)',
    fech_ap_top = 'Fecha de Aplicación de TOP/Date of Application of TOP',
    fech_nac = 'Fecha de Nacimiento/Date of Birth',
    OBS = 'Observaciones al Proceso de Limpieza y Estandarización de Casos/Observations to the Process of Data Tidying & Standardization',
    Edad_al_ing = 'Edad a la Fecha de Ingreso a Tratamiento (numérico continuo)/Age at Admission to Treatment',
    Edad_at_ap = 'Edad a la Aplicación del Tratamiento (numérico continuo)/Age at the Application of TOP',
    tipo_de_plan = 'Tipo de Plan/Type of Plan',
    identidad_de_genero = 'Identidad de Género/Gender Identity',
    sus_prin1 = 'Sustancia Principal de Consumo (1)/Primary Substance of Consumption (1)',
    sus_prin2 = 'Sustancia Principal de Consumo (2)/Primary Substance of Consumption (2)',
    sus_prin3 = 'Sustancia Principal de Consumo (3)/Primary Substance of Consumption (3)',
    id_centro = 'ID de Centro/Center ID')

  as.data.frame(CONS_TOP_df_dup_ENE_2020_prev9) %>%
  janitor::clean_names()%>%
  assign("CONS_TOP_df_dup_ENE_2020_prev9",.,envir = .GlobalEnv)
  
  #PARA EXPORTAR LABELS A EXCEL
  #data.table::data.table(table(CONS_TOP_df_dup_ENE_2020_prev9$identidad.de.genero, exclude=NULL)) %>% mutate(export=paste0(row_number(),".",V1)) %>% select(-V1) %>% select(export,N)%>% copiar_nombres()
  #es mejor hacerlo con el paquete RIO, en el caso de STATA
save.image(paste0(gsub("/SUD_CL","",path),"/4.Rdata"))
unlink(paste0(path, '/*_cache'), recursive = TRUE)
#save.image("G:/Mi unidad/Alvacast/SISTRAT 2019 (github)/4.Rdata")
library(reclin)
#load("G:/Mi unidad/Alvacast/SISTRAT 2019 (github)/5.RData")
aplicador_top2<-
CONS_TOP_df_dup_ENE_2020_prev9 %>%
  dplyr::group_by(nombre_apliacador_del_top) %>%
  dplyr::mutate(rn=row_number())%>%
  dplyr::ungroup() %>%
  dplyr::filter(rn==1)%>%
  dplyr::select(nombre_apliacador_del_top, region_centro,id_centro) %>%
  dplyr::mutate(nombre_apliacador_del_top=tolower(nombre_apliacador_del_top)) %>%
  dplyr::mutate(nombre_apliacador_del_top=stringr::str_replace_all(nombre_apliacador_del_top,c("ps." = "", "á" = "a", "é" = "e", "í"="i", "ó"="o","ú"="u","ps "=" ","ts."="","t.s."="","ã\u0081"="a","ts "=" ", "tr."="", "ì"="i"))) %>%
  dplyr::mutate(nombre_apliacador_del_top=str_trim(nombre_apliacador_del_top))
  
  linked_data_set <- pair_blocking(as.data.frame(aplicador_top2), as.data.frame(aplicador_top2))
  #Error: no se puede ubicar un vector de tamaño  36.2 Mb
  compare_pairs(linked_data_set,c("region_centro","id_centro"),
      default_comparator = jaro_winkler(0.9)) %>%
  score_problink(var = "weight") %>%
  select_n_to_m("weight", var = "ntom", threshold = 0) %>%
  link()
  
try(linked_data_set <- pair_blocking(CONS_TOP_df_dup_ENE_2020_prev9, CONS_TOP_df_dup_ENE_2020_prev9, "ID.centro") 
    %>%
  compare_pairs(by = c("Nombre.Apliacador.del.TOP"),
      default_comparator = jaro_winkler(0.8)) %>%
  score_problink(var = "weight") %>%
  select_n_to_m("weight", var = "ntom", threshold = 0) %>%
  link()
    )
#load("G:/Mi unidad/Alvacast/SISTRAT 2019 (github)/4.RData")
library(reclin)
try(linked_data_set <- pair_blocking(CONS_TOP_df_dup_ENE_2020_prev9, CONS_TOP_df_dup_ENE_2020_prev9, "id_centro") 
    %>%
  compare_pairs(by = c("", "firstname", "address", "sex"),
      default_comparator = jaro_winkler(0.9)) %>%
  score_problink(var = "weight") %>%
  select_n_to_m("weight", var = "ntom", threshold = 0) %>%
  link()
    )
try(linked_data_set <- pair_blocking(CONS_TOP_df_dup_ENE_2020_prev9, CONS_TOP_df_dup_ENE_2020_prev9, "ID.centro") 
    %>%
  compare_pairs(by = c("Nombre.Apliacador.del.TOP"),
      default_comparator = jaro_winkler(0.8)) %>%
  score_problink(var = "weight") %>%
  select_n_to_m("weight", var = "ntom", threshold = 0) %>%
  link()
    )


  require(RecordLinkage)
  require(dplyr)

############################# ene2020  
#load("G:/Mi unidad/Alvacast/SISTRAT 2019 (github)/5.RData")
CONS_C1_df_dup_ENE_2020 %>%
  dplyr::mutate(Edad= ifelse(is.na(Edad),0,Edad)) %>% #Para prevenir  Error in if (v1 == d1) return(x) : missing value where TRUE/FALSE needed
  dplyr::filter(!is.na(fech_ing)) %>% #Para prevenir  Error in if (v1 == d1) return(x) : missing value where TRUE/FALSE needed
dplyr::select("concat", "Edad") %>%
assign("CONS_C1_df_dup_ENE_2020_concat",.,envir = .GlobalEnv)

#Pairing
  pairs_edad=RLBigDataDedup(CONS_C1_df_dup_ENE_2020_concat, blockfld="Edad",convert.na = TRUE,strcmp=TRUE ,strcmpfun=levenshteinSim)
  pairs_edad_jaro=RLBigDataDedup(CONS_C1_df_dup_ENE_2020_concat, blockfld="Edad",convert.na = TRUE,strcmp=TRUE ,strcmpfun=levenshteinSim)
  getExpectedSize(pairs_edad) #no funciona
  getParetoThreshold(pairs_edad, quantil = 0.95) # no funciona
  
#Unsupervised classification by clustering  
  bclust_edad<- classifyUnsup(pairs_edad, method="bclust")
  k_means_edad<- classifyUnsup(pairs_edad, method="kmeans")
  
  p_edad=epiWeights(pairs_edad,withProgressBar=(sink.number()==0))
  
  edad_weights_em <- emWeights(p_edad,cutoff = 0.90, verbose = TRUE) #
  classify_edad <- epiClassify(p_edad,0.88,withProgressBar=(sink.number()==0))  
  classify_edad_em <- emClassify(classify_edad, 0.6)
  match_edad <- classify_edad_em$prediction
  results_edad <- cbind(classify_edad_em$pairs,match_edad)
  getPairs(classify_edad_em, min.weight = 0.5)
  getPairs(rpairs,min.weight=0.5, max.weight=0.5, filter.match="match")
  classify_edad
  ##strcomp
############################# APR-2020  -TOP
  CONS_TOP_df_dup_ENE_2020_prev9 %>%
    dplyr::select("ID.centro","Nombre.Apliacador.del.TOP") %>%
  dplyr::mutate(ID.centro= ifelse(is.na(ID.centro),0,ID.centro)) %>% #Para prevenir  Error in if (v1 == d1) return(x) : missing value where TRUE/FALSE needed
  as.data.frame() %>%
  assign("CONS_TOP_df_dup_ENE_2020_prev9_concat",.,envir = .GlobalEnv)
    pairs_id_aplicador=RecordLinkage::RLBigDataDedup(CONS_TOP_df_dup_ENE_2020_prev9_concat, blockfld="ID.centro",strcmp=TRUE ,strcmpfun = "jarowinkler", phonetic = numeric(0),phonfun = "soundex")
  getExpectedSize(pairs_id_aplicador) 
  getParetoThreshold(pairs_id_aplicador, quantil = 0.95)
bclust_ID<- classifyUnsup(pairs_id_aplicador, method="bclust") #no funciona
k_means_ID<- classifyUnsup(pairs_id_aplicador, method="kmeans")#no funciona
p_ID=epiWeights(pairs_id_aplicador,withProgressBar=(sink.number()==0))
classify_ID <- epiClassify(pairs_id_aplicador,0.88,withProgressBar=(sink.number()==0))   #0s and mixed positive/negative subscripts not allowed
classify_ID_em <- emClassify(ID_weights_em, 0.6) #No weights in rpairs!, LO PROBÉ CON PAIRS_ID_APLICADOR TAMBIÉN
ID_weights_em <- emWeights(pairs_id_aplicador,cutoff = 0.90, verbose = TRUE) #
getPairs(pairs_id_aplicador, min.weight = 0.5)
getPairs(p_ID,min.weight=0.5, max.weight=0.5, filter.match="match")

pairs_ID_jaro=RLBigDataDedup(CONS_TOP_df_dup_ENE_2020_prev9_concat, blockfld="ID.centro",strcmp=TRUE)
getExpectedSize(pairs_ID_jaro) #no funciona
getParetoThreshold(pairs_ID_jaro, quantil = 0.95) #Error in plot.window(...) : se necesitan valores finitos de 'ylim'
getExpectedSize(pairs_ID_jaro
classifyUnsup(pairs_ID_jaro, method="bclust") #no funciona Wrong class for rpairs!