This page will be used to comment and understand the steps, advances and doubts about the process of data preparation and standardization of the SISTRAT C1 (Convenio 1) which can be translated to Agreement on Collaboration, Technical Assistance and Transfer of Resources to General Population (“Convenio de Colaboración Técnica y de Transferencia de Recursos para Programa de Población General”). Must note that it does not include Adolescent criminal offenders (C2).

To see the TOP or Profile of Treatment Results (“Perfil de Resultados de Tratamiento”) data preparation, go to this webpage.


Building dataset

Define working directories that contain different text files that will be merged, correspondent to the years of the C1 Sistrat.

dir_c1 <-paste0(gsub("/SUD_CL","",path),"/Encriptado c1/Personas tratadas c1/")
dir_top <-paste0(gsub("/SUD_CL","",path),"/encriptados TOP/")
#In my case,
#dir_c1 <-toString(paste0("G:/Mi unidad/Alvacast/SISTRAT 2019 (github)","/Encriptado c1/Personas tratadas c1/"))
#dir_top <-toString(paste0("G:/Mi unidad/Alvacast/SISTRAT 2019 (github)","/encriptados TOP/"))

Define files in the Directory folder, as long as they have the excel extension and they do not start with ~ (represent a working temporary file in excel)

SISTRAT_c1<-list.files(path=toString(dir_c1), all.files=T, pattern="^[2].*\\s*txt$")

SISTRAT_c1 is composed by the following files: * 2010tab-Resultado-20191113 (1).txt * 2011tab -Resultado-20191113.txt * 2012tab-Resultado-20191113.txt * 2013tab-Resultado-20191113.txt * 2014tab-Resultado-20191113.txt * 2015tab-Resultado-20191113.txt * 2016tab-Resultado-20191113.txt * 2017tab-Resultado-20191113.txt * 2018tab-Resultado-20191113.txt * 2019_EneOcttab-Resultado-20191113.txt

Function to read tab delimited text files, with UTF-8 encoding and, based on the first 7 letters of its name, assign them an object name.

read_excel_mult <- function(dir, filename) {
  assign(paste0(substr(filename, 1, 7)),read.delim(paste0(dir, filename),
        na.strings="null", header = T, fileEncoding="UTF-8"),envir = .GlobalEnv)
  }


Import datasets

To import the datasets, we apply the previous function to every dataset

for (x in SISTRAT_c1) {
  read_excel_mult(as.character(dir_c1), x)
}


Normalize datasets

Normalize datasets by defining a common name for hash key’s (“HASH_KEY”) and assign them a standardized name.

tab_10_18_mod <- function(x,y) {  get(x) %>% dplyr::rename("HASH_KEY" = !!names(.[91])) %>%
    as.data.frame() %>% 
    assign(paste0(y, as.character(x)),.,envir = .GlobalEnv)
}
for (i in paste0(c(2010:2018),"tab")) {tab_10_18_mod(i,y="c1_")}

`2019_En` %>% dplyr::rename("HASH_KEY" = !!names(.[92])) %>%
  as.data.frame() %>% 
  assign(paste0("c1_", "2019tab"),.,envir = .GlobalEnv)


Append the datasets

Once column names are normalized, bind every year’s datasets by their rows.

CONS_C1=rbindlist(mget(paste0("c1_",c(2010:2019),"tab")), idcol="TABLE", fill=T)
CONS_C1 <- CONS_C1 %>% dplyr::mutate(row=1:nrow(CONS_C1)) %>% dplyr::select(row,everything())


Define and format variable names

Redefine variable names, create a column named ano_bd with the year. Also, reorder the variables to get the identifiers in the first columns.

Rename Variables

CONS_C1 %>% 
  dplyr::rename(id=`Codigo.Identificación`) %>%
  dplyr::rename(fech_ing=`Fecha.Ingreso.a.Tratamiento`) %>%
  dplyr::rename(fech_egres=`Fecha.Egreso.de.Tratamiento`) %>%
  dplyr::rename(dias_trat=`Dias.en.Tratamiento`) %>%
  dplyr::rename(eva_consumo=`Evaluación.al.Egreso.Respecto.al.Patrón.de.consumo`) %>%
  dplyr::rename(eva_fam=`Evaluación.al.Egreso.Respecto.a.Situación.Familiar`) %>%
  dplyr::rename(eva_sm=`Evaluación.al.Egreso.Respecto.Salud.Mental`) %>%
  dplyr::rename(eva_fisica=`Evaluación.al.Egreso.Respecto.Salud.Física`) %>%
  dplyr::rename(eva_transgnorma=`Evaluación.al.Egreso.Respecto.Trasgresión.a.la.Norma.Social`) %>% 
  dplyr::rename(eva_relinterp=`Evaluación.al.Egreso.Respecto.Relaciones.Interpersonales`) %>%
  dplyr::rename(eva_ocupacion=`Evaluación.al.Egreso.Respecto.a.Situación.Ocupacional`) %>%
  dplyr::rename(evaluacindelprocesoteraputico=`Evaluación.del.Proceso.Terapéutico`) %>%
  dplyr::rename(nmesesentratamiento=`N.Meses.en.Tratamiento`) %>%
  dplyr::rename(motivodeegreso=`Motivo.de.Egreso`) %>%
  dplyr::rename(tipo_centro=`Tipo.Centro`) %>%
  dplyr::mutate(ano_bd=as.numeric(substr(TABLE,4,7))) %>%
  select(row, TABLE, HASH_KEY,ano_bd, everything()) %>%
  dplyr::arrange(id) %>%
  assign("CONS_C1_df",.,envir = .GlobalEnv)

Relevant variables are converted into factors.

CONS_C1_df %>%
  dplyr::mutate(motivodeegreso=as.factor(motivodeegreso)) %>%
  dplyr::mutate(evaluacindelprocesoteraputico=as.factor(evaluacindelprocesoteraputico)) %>%
  dplyr::mutate(eva_consumo=as.factor(eva_consumo)) %>%
  dplyr::mutate(eva_fam=as.factor(eva_fam)) %>%  
  dplyr::mutate(eva_relinterp=as.factor(eva_relinterp)) %>%  
  dplyr::mutate(eva_ocupacion=as.factor(eva_ocupacion)) %>%
  dplyr::mutate(eva_sm=as.factor(eva_sm)) %>%  
  dplyr::mutate(eva_fisica=as.factor(eva_fisica)) %>%
  dplyr::mutate(eva_transgnorma=as.factor(eva_transgnorma)) %>%  
  dplyr::mutate(sexo=as.factor(Sexo)) %>%
  dplyr::mutate(embarazo=as.factor(`X.Se.trata.de.una.mujer.embarazada.`)) %>%
  dplyr::mutate(tipo_de_plan=as.factor(`Tipo.de.Plan`)) %>%  
  dplyr::mutate(tipo_de_programa=as.factor(`Tipo.de.Programa`)) %>%  
#ACTUALIZATION: JANUARY 2020
  dplyr::mutate(`tipo_centro`=as.factor(`tipo_centro`)) %>%
  dplyr::mutate(`Servicio.de.Salud`=as.factor(`Servicio.de.Salud`)) %>%
  dplyr::mutate(SENDA=as.factor(SENDA)) %>%
  dplyr::mutate(Origen.de.Ingreso=as.factor(Origen.de.Ingreso)) %>%
  dplyr::mutate(País.Nacimiento=as.factor(País.Nacimiento)) %>%
  dplyr::mutate(Nacionalidad=as.factor(Nacionalidad)) %>%
  dplyr::mutate(Etnia=as.factor(Etnia)) %>%
  dplyr::mutate(Estado.Conyugal=as.factor(Estado.Conyugal)) %>%
  dplyr::mutate(Sustancia.de.Inicio=as.factor(Sustancia.de.Inicio)) %>%
  dplyr::mutate(X.Se.trata.de.una.mujer.embarazada.=as.factor(X.Se.trata.de.una.mujer.embarazada.)) %>%
  dplyr::mutate(Escolaridad..último.año.cursado.=as.factor(Escolaridad..último.año.cursado.)) %>%
  dplyr::mutate(Condicion.Ocupacional=as.factor(Condicion.Ocupacional)) %>%
  dplyr::mutate(Categoría.Ocupacional=as.factor(Categoría.Ocupacional)) %>%
  dplyr::mutate(Rubro.Trabaja=as.factor(Rubro.Trabaja)) %>%
  dplyr::mutate(Con.Quién.Vive=as.factor(Con.Quién.Vive)) %>%
  dplyr::mutate(Tipo.de.vivienda=as.factor(Tipo.de.vivienda)) %>%
  dplyr::mutate(Tenencia.de.la.vivienda=as.factor(Tenencia.de.la.vivienda)) %>%
  dplyr::mutate(Sustancia.Principal=as.factor(Sustancia.Principal)) %>%
  dplyr::mutate(Otras.Sustancias.nº1=as.factor(Otras.Sustancias.nº1)) %>%
  dplyr::mutate(Otras.Sustancias.nº2=as.factor(Otras.Sustancias.nº2)) %>%
  dplyr::mutate(Otras.Sustancias.nº3=as.factor(Otras.Sustancias.nº3)) %>%
  dplyr::mutate(Frecuencia.de.Consumo..Sustancia.Principal.=as.factor(Frecuencia.de.Consumo..Sustancia.Principal.)) %>%
  dplyr::mutate(Vía.Administración..Sustancia.Principal.=as.factor(Vía.Administración..Sustancia.Principal.)) %>%
  dplyr::mutate(Diagnóstico.Trs..Consumo.Sustancia=as.factor(Diagnóstico.Trs..Consumo.Sustancia)) %>%
  dplyr::mutate(Diagnóstico.Trs..Psiquiátrico.DSM.IV=as.factor(Diagnóstico.Trs..Psiquiátrico.DSM.IV)) %>%
  dplyr::mutate(Diagnóstico.Trs..Psiquiátrico.SUB.DSM.IV=as.factor(Diagnóstico.Trs..Psiquiátrico.SUB.DSM.IV)) %>%
  dplyr::mutate(X2.Diagnóstico.Trs..Psiquiátrico.DSM.IV=as.factor(X2.Diagnóstico.Trs..Psiquiátrico.DSM.IV)) %>%
  dplyr::mutate(X2.Diagnóstico.Trs..Psiquiátrico.SUB.DSM.IV=as.factor(X2.Diagnóstico.Trs..Psiquiátrico.SUB.DSM.IV)) %>%
  dplyr::mutate(X3.Diagnóstico.Trs..Psiquiátrico.DSM.IV=as.factor(X3.Diagnóstico.Trs..Psiquiátrico.DSM.IV)) %>%
  dplyr::mutate(X3.Diagnóstico.Trs..Psiquiátrico.SUB.DSM.IV=as.factor(X3.Diagnóstico.Trs..Psiquiátrico.SUB.DSM.IV)) %>%
  dplyr::mutate(Diagnóstico.Trs..Psiquiátrico.CIE.10=as.factor(Diagnóstico.Trs..Psiquiátrico.CIE.10)) %>%
  dplyr::mutate(Diagnóstico.Trs..Psiquiátrico.SUB.CIE.10=as.factor(Diagnóstico.Trs..Psiquiátrico.SUB.CIE.10)) %>%
  dplyr::mutate(X2.Diagnóstico.Trs..Psiquiátrico.CIE.10=as.factor(X2.Diagnóstico.Trs..Psiquiátrico.CIE.10)) %>%
  dplyr::mutate(X2.Diagnóstico.Trs..Psiquiátrico.SUB.CIE.10=as.factor(X2.Diagnóstico.Trs..Psiquiátrico.SUB.CIE.10)) %>%
  dplyr::mutate(X3.Diagnóstico.Trs..Psiquiátrico.CIE.10=as.factor(X3.Diagnóstico.Trs..Psiquiátrico.CIE.10)) %>%
  dplyr::mutate(X3.Diagnóstico.Trs..Psiquiátrico.SUB.CIE.10=as.factor(X3.Diagnóstico.Trs..Psiquiátrico.SUB.CIE.10)) %>%
  dplyr::mutate(Diagnóstico.Trs..Físico=as.factor(Diagnóstico.Trs..Físico)) %>%
  dplyr::mutate(Otros.Problemas.de.Atención.de.Salud.Mental=as.factor(Otros.Problemas.de.Atención.de.Salud.Mental)) %>%
  dplyr::mutate(Compromiso.Biopsicosocial=as.factor(Compromiso.Biopsicosocial)) %>%
  dplyr::mutate(DIAGNOSTICO.GLOBAL.DE.NECESIDADES.DE.INTEGRACION.SOCIAL=as.factor(DIAGNOSTICO.GLOBAL.DE.NECESIDADES.DE.INTEGRACION.SOCIAL)) %>%
  dplyr::mutate(DIAGNOSTICO.DE.NECESIDADES.DE.INTEGRACIóN.SOCIAL.EN.CAPITAL.HUMANO=as.factor(DIAGNOSTICO.DE.NECESIDADES.DE.INTEGRACIóN.SOCIAL.EN.CAPITAL.HUMANO)) %>%
  dplyr::mutate(DIAGNOSTICO.DE.NECESIDADES.DE.INTEGRACIóN.SOCIAL.EN.CAPITAL.FISICO=as.factor(DIAGNOSTICO.DE.NECESIDADES.DE.INTEGRACIóN.SOCIAL.EN.CAPITAL.FISICO)) %>%
  dplyr::mutate(DIAGNOSTICO.DE.NECESIDADES.DE.INTEGRACIóN.SOCIAL.EN.CAPITAL.SOCIAL=as.factor(DIAGNOSTICO.DE.NECESIDADES.DE.INTEGRACIóN.SOCIAL.EN.CAPITAL.SOCIAL)) %>%
  dplyr::mutate(Usuario.de.Tribunales..Tratamiento.Drogas=as.factor(Usuario.de.Tribunales..Tratamiento.Drogas)) %>%
  dplyr::mutate(Consentimiento.Informado=as.factor(Consentimiento.Informado)) %>%
  dplyr::mutate(motivodeegreso=as.factor(motivodeegreso)) %>%
  dplyr::mutate(Tipo.Centro.Derivación=as.factor(Tipo.Centro.Derivación)) %>%
  dplyr::mutate(evaluacindelprocesoteraputico=as.factor(evaluacindelprocesoteraputico)) %>%
  dplyr::mutate(eva_consumo=as.factor(eva_consumo)) %>%
  dplyr::mutate(eva_fam=as.factor(eva_fam)) %>%
  dplyr::mutate(eva_relinterp=as.factor(eva_relinterp)) %>%
  dplyr::mutate(eva_ocupacion=as.factor(eva_ocupacion)) %>%
  dplyr::mutate(eva_sm=as.factor(eva_sm)) %>%
  dplyr::mutate(eva_fisica=as.factor(eva_fisica)) %>%
  dplyr::mutate(eva_transgnorma=as.factor(eva_transgnorma)) %>%
  dplyr::mutate(Diagnóstico.Trastorno.Psiquiátrico.CIE.10.al.Egreso=as.factor(Diagnóstico.Trastorno.Psiquiátrico.CIE.10.al.Egreso)) %>%
  dplyr::mutate(DIAGNOSTICO.GLOBAL.DE.NECESIDADES.DE.INTEGRACION.SOCIAL.1=as.factor(DIAGNOSTICO.GLOBAL.DE.NECESIDADES.DE.INTEGRACION.SOCIAL.1)) %>%
  dplyr::mutate(DIAGNOSTICO.DE.NECESIDADES.DE.INTEGRACIóN.SOCIAL.EN.CAPITAL.HUMANO.1=as.factor(DIAGNOSTICO.DE.NECESIDADES.DE.INTEGRACIóN.SOCIAL.EN.CAPITAL.HUMANO.1)) %>%
  dplyr::mutate(DIAGNOSTICO.DE.NECESIDADES.DE.INTEGRACIóN.SOCIAL.EN.CAPITAL.FISICO.1=as.factor(DIAGNOSTICO.DE.NECESIDADES.DE.INTEGRACIóN.SOCIAL.EN.CAPITAL.FISICO.1)) %>%
  dplyr::mutate(DIAGNOSTICO.DE.NECESIDADES.DE.INTEGRACIóN.SOCIAL.EN.CAPITAL.SOCIAL.1=as.factor(DIAGNOSTICO.DE.NECESIDADES.DE.INTEGRACIóN.SOCIAL.EN.CAPITAL.SOCIAL.1)) %>%
  dplyr::mutate(Motivo.de.egreso.Alta.Administrativa=as.factor(Motivo.de.egreso.Alta.Administrativa)) %>%
  dplyr::mutate(Ha.estado.embarazada.egreso.=as.factor(Ha.estado.embarazada.egreso.)) %>%
  dplyr::mutate(identidad.de.genero=as.factor(identidad.de.genero)) %>%
  dplyr::mutate(discapacidad=as.factor(discapacidad)) %>%
  dplyr::mutate(Opción.discapacidad=as.factor(Opción.discapacidad)) %>%
  dplyr::mutate(sexo=as.factor(sexo)) %>%
  dplyr::mutate(embarazo=as.factor(embarazo)) %>%
  dplyr::mutate(tipo_de_plan=as.factor(tipo_de_plan)) %>%
  dplyr::mutate(tipo_de_programa=as.factor(tipo_de_programa)) %>%
  assign("CONS_C1_df",.,envir = .GlobalEnv)


Standardize Dates


Dates of admission and discharge


Most of the dates were not formatted equally. When standardized, some cases failed to transform dates, specifically in dates of discharge, which contained 87 cases, while only one case was problematic in admission dates.

CONS_C1_df %>%
  dplyr::mutate(fech_ing= ifelse(row=="14504", "10/01/2011",
                          ifelse(row=="88379", "09/07/2015", #ACTUALIZACION 2020-01-17: change of date of admission
                          ifelse(row=="79428", "09/07/2015", #ACTUALIZACION 2020-01-17: change of date of admission             
                                        fech_ing))))  %>%
  dplyr::mutate(fech_ing= lubridate::parse_date_time(fech_ing, c("%d/%m/%Y"),exact=T)) %>% #un caso falla en ser transformado
  dplyr::mutate(fech_egres_sin_fmt= fech_egres) %>% #me quedo con esta variable por si acaso
  dplyr::mutate(fech_egres= ifelse(row=="36308", "02/04/2013",
                            ifelse(row=="14083", "03/05/2011",
                            ifelse(row=="6349", "01/07/2010",
                            ifelse(row=="42741", "02/08/2013",
                            ifelse(row=="8608","01/02/2011",
                            ifelse(row=="11709","01/02/2011",
                            ifelse(row=="40486","03/07/2013",
                            ifelse(row=="42521","09/07/2013",
                            ifelse(row=="5757", "04/10/2010", #cambiado
                            ifelse(row=="39507", "02/07/2013",
                            ifelse(row=="3195", "04/10/2010",
                            ifelse(row=="37845", "01/07/2013",
                            ifelse(row=="40180", "07/08/2013",
                            ifelse(row=="28008", "27/11/2012",
                            ifelse(row=="35971", "01/03/2013",
                            ifelse(row=="5172", "03/05/2011",
                            ifelse(row=="10415", "03/05/2011",
                            ifelse(row=="16385", "30/08/2011",
                            ifelse(row=="39932", "02/08/2013",
                            ifelse(row=="16983", "06/09/2011",
                            ifelse(row=="88379", "11/04/2016", #ACTUALIZATION 2020-01-17: change of date of discharge
                            ifelse(row=="79428", "11/04/2016", #ACTUALIZATION 2020-01-17: change of date of discharge
                            ifelse(row=="37004", "02/08/2013",fech_egres)))))))))))))))))))))))) %>%
  dplyr::mutate(fech_egres= lubridate::parse_date_time(stringr::str_trim(fech_egres), 
                                                       orders = c("%d/%m/%Y", "%d/%m/%y","%d%m%Y"),exact=T)) %>%
  dplyr::arrange(desc(ano_bd)) %>% 
  assign("CONS_C1_df",.,envir = .GlobalEnv) #87 failed to parse, bajé a 37, y 30 después de quitar espacios

For discharge dates, many cases had different formats. Those had to be transformed once the first formats were traduced (e.g. 30-12-2019 is interpreted first in “%d/%m/%Y” format. If it is not traduced, it interprets in “DD/MM/YY” format (or “%d/%m/%y”). We can see in Table 1 in non-formatted date of discharge (fech_egres_sin_fmt) how it was traduced into an plausible discharge date (fech_egres). One criterion is that in many cases, the second and or third 0 would possibly correspond to a month character. On the contrary, “7082013” or “9072013” would be “2013-8-70” and “2013-7-90”, respectively.

Table 1. Problematic cases of dates which required a manual change
Case HASH ID Date of Admission Date of Discharge Date of Discharge (No Format) Days in Treatment
36,308 b6373dccc2b0b5ffba42db746b17c692 CAPE1**051992 2013-01-29 2013-04-02 2013/04/02 NA
42,741 090f64b945afce17900e82eccb8f8da6 COMO2**091988 2013-07-02 2013-08-02 2082013 NA
40,486 4fc0a1cff37ad152767eb323ed3b43c0 ELPO2**111980 2013-05-02 2013-07-03 307203 NA
42,521 c668aa3153ea4a1a4435c45557feb80b HUDO1**091969 2013-06-28 2013-07-09 9072013 NA
39,507 2f477adf8b5c282660fa16180eb5096d LIDO2**031992 2013-04-29 2013-07-02 2072013 NA
37,845 fd919f36ada0b9423cf3acdead577150 MAZA1**111968 2013-03-18 2013-07-01 1072013 NA
40,180 c8a0c6ccce12e9d3fbc74c06d6a06664 MIMA2**111973 2013-05-29 2013-08-07 7082013 NA
35,971 132aa32661fbedca4ecdd1e2018c58e3 PARA1**021977 2013-01-02 2013-03-01 01-03-2013 NA
39,932 c87466931290f44c2c6a89bd127d31e5 RELA2**011969 2013-05-22 2013-08-02 2082013 NA
37,004 ad7eb82186ec39a61ad29aed61522c2a SO-A2**061980 2013-02-08 2013-08-02 2082013 NA
28,008 3d50c479484a18e510d6be8f8a35a1ed OMHU1**041990 2012-06-25 2012-11-27 27-11-2012 NA
14,083 68930653a30b13a6f6f7853d6683dff3 CLFI1**031973 2011-02-10 2011-05-03 30520011 NA
14,504 a7a1fcb4a638a7b194063d95e34320a9 CLVE2**111966 2011-01-10 2011-08-30 30/08/2011 232
11,709 52eeb63fbce34e95c7687dd6007efce8 DASA2**031986 2008-08-08 2011-02-01 01-02-2011 NA
10,415 4aa3462c09ad1a7cc1e6d249f620d715 PATR1**061986 2010-04-26 2011-05-03 3052011 NA
16,385 1454ce9c37a56516ce054a3513361a66 RAPA1**051983 2011-04-18 2011-08-30 30/08/20011 NA
16,983 5ebc5439f9db863ff8744778280360db SERI1**011960 2011-05-13 2011-09-06 06-09-2011 NA
6,349 9a8fef6d37e8010c477c25db3e5e8455 CLGO1**101984 2010-06-17 2010-07-01 01707/2010 NA
8,608 52eeb63fbce34e95c7687dd6007efce8 DASA2**031986 2008-08-08 2011-02-01 01-02-2011 NA
5,757 4485da22c54dbef2c45c61a92b5f7cac JAGO1**121972 2010-05-03 2010-10-04 4102010 NA
3,195 07a0a0c984ae3d9dfcd30b55fcb6beb2 LUME1**091957 2009-10-07 2010-10-04 4102010 NA
5,172 4aa3462c09ad1a7cc1e6d249f620d715 PATR1**061986 2010-04-26 2011-05-03 3052011 NA


As we can see in Table 2, some non-formatted dates of discharge (fech_egres_sin_fmt), had insufficient information to traduce into valid dates.

Table 2. Non-interpretable discharge dates
Year case DischargeDate DischargeDate-NoFmt
2,011 12,006 NA 5
2,011 12,005 NA 5
2,011 12,227 NA 25
2,010 8,239 NA 15
2,010 9,043 NA 5
2,010 2,603 NA 20/0/2010
2,010 9,042 NA 5
2,010 194 NA 15
2,010 9,353 NA 25


UPDATE: 2019-12-11, had to format this date because it was badly labelled. CONS_C1_df[CONS_C1_df$row=="5757","fech_egres"] <- parse_date_time(str_trim("04/10/2010"), orders = c("%d/%m/%Y"),exact=T). UPDATE: 2020-01-17. Thanks to SENDA’s experts, they provided us with information about a case with missing values in dates of treatment. This date had to be formatted because it was badly labeled. CONS_C1_df[CONS_C1_df$row=="88379","fech_ing"] <- parse_date_time(str_trim("09/07/2015"), orders = c("%d/%m/%Y"),exact=T). UPDATE: 2020-01-17, this date had to be formatted because it was badly labeled. CONS_C1_df[CONS_C1_df$row=="79428","fech_ing"] <- parse_date_time(str_trim("09/07/2015"), orders = c("%d/%m/%Y"),exact=T). UPDATE: 2020-01-17, this date had to be formatted because it was badly labeled. CONS_C1_df[CONS_C1_df$row=="88379","fech_egres"] <- parse_date_time(str_trim("11/04/2016"), orders = c("%d/%m/%Y"),exact=T). UPDATE: 2020-01-17, this date had to be formatted because it was badly labeled. CONS_C1_df[CONS_C1_df$row=="79428","fech_egres"] <- parse_date_time(str_trim("11/04/2016"), orders = c("%d/%m/%Y"),exact=T). Still, there are 9752 cases with NULL dates of discharge.

Data Cleaning Steps provided by SENDA’s professional

These rules were provided by SENDA’s professionals, in order to select adequate data for the analysis.


1. Exclude non-SENDA treatments

In the first place, we should discard treatments not provided by SENDA. NULL values correspond to a single HASH key (“72c54d822de128e52f10511f3eb2d19f”, in datasets from 2010 and 2011) that did not have a plan and program.

Table 3. Treatment Admissions
SENDA Frequency %
No 7,762 4.8%
Si 155,382 95.2%
NA 2 0%
No= Admission under another agreement
Si= Admission under SENDA agreement


As we can see in Table 3, around 4.8% of cases had a treatment that could not be part of SENDA’s agreements. The case that did not take part under SENDA categories was admitted to a SENDA agreement in 2009/12/10 and in 2009/12/14. Attending to what was informed by SENDAs professional in January 2020, many programs that was pointed as not part of SENDA may actually be part of SENDA. That is why we keep these programs anyways, despite what was originally stated.


2. Explore the type of plan


It is necessary to explore if there is any case that may have a treatment under a probation/parole (“libertad vigilada”) plan. It is necessary to consider the 3 null values in type of plans, equivalent to 2 HASHs, dropping the case with a plan in Probation/Parole.

## `summarise()` regrouping output by 'SENDA' (override with `.groups` argument)
Table 4. Treatment Plans
Type of Plan No SENDA(n) No SENDA(%) Yes SENDA(n) Yes SENDA(%) NA (n) NA (%)
CALLE 5 0% 10 0% NA NA
M-PAB 6 0% 109 0% NA NA
M-PAI 137 2% 10533 7% NA NA
M-PAI2 NA NA 16 0% NA NA
M-PR 251 3% 7588 5% NA NA
M-PR2 NA NA 10 0% NA NA
Otro 1778 23% 90 0% NA NA
PAI LV NA NA 1 0% NA NA
PG-PAB 2236 29% 54075 35% NA NA
PG-PAI 2233 29% 66300 43% NA NA
PG-PR 1116 14% 16599 11% NA NA
PG PAI 2 NA NA 50 0% NA NA
NA NA NA 1 0% 2 100%
PR= Residential Program
PAB= Basic Outpatient Program
PAI= Intensive Outpatient Treatment
PAI LV= Probation/Parole
PAI= Intensive Outpatient Treatment

Table 4 shows that there is only 1 case in the probation/parole plan.


3. Changes in programs

Change fixed residential, flexible residential and residential programs into a general population residential program (PG PR). Additionally, flexible residential program cannot be under a probation/parole program.

Must note that there are some plans that have a number 2 but they do not contain more than 0% of the total of the datasets. Also there are some plans related to the Treatment of People Living on the Streets Program (Programa de Tratamiento de Personas en Situación de Calle). We should change plans ‘Otro’ and ‘Calle’ into ‘General Population’. Plans with number 2 should be grouped with their respective plan, leaving only M-PAI, M-PR, PG-PAB, PG-PAI, and PG-PR. For the moment, we are not sure what is the fixed and flexible residential programs.

Table 5. Contingency Tables of Programs by Plans
Type of Program CALLE M-PAB M-PAI M-PAI2 M-PR M-PR2 Otro PAI LV PG-PAB PG-PAI PG-PR PG PAI 2 NA
Libertad Vigilada 0 0 0 0 0 0 0 0 0 2 0 0 0
Otro 0 0 3 0 3 0 890 0 32 35 12 2 0
Programa Alcohol 0 0 0 0 0 0 0 0 0 80 0 0 0
Programa Calles 15 0 0 0 0 0 0 0 0 0 0 0 0
Programa Específico Mujeres 0 115 9,741 16 7,590 10 7 0 19 48 3 16 0
Programa Población General 0 0 926 0 246 0 971 1 56,260 68,368 17,700 32 0
NA 0 0 0 0 0 0 0 0 0 0 0 0 3


4. Plans within Alcohol Program

If there is a plan within the alcohol program, the plan must be modified into the general population plan.


Every of the 10, 80 cases in the alcohol program should be in the general population plan.


5. Another Plan

If there is any plan correspondent to Another (“Otro”), it changes into a general population program.

There are 1868 cases with treatments categorized as Another (“Otro”) plan.


6. Plan & Programs by Sex

Women should be in the M-PAI in which they actually are categorized in the general population program.


As can be seen in Table 6, around 58% of women are in the general population programs.Should they be removed or changed?

Table 6. Plan by Sex
Plan Men (n) Men (%) Women (n) Women (%)
CALLE 12 0% 3 0%
M-PAB NA NA 115 0%
M-PAI 86 0% 10,584 23%
M-PAI2 NA NA 16 0%
M-PR 68 0% 7,771 17%
M-PR2 NA NA 10 0%
Otro 1,305 1% 563 1%
PAI LV 1 0% NA NA
PG-PAB 44,527 38% 11,784 26%
PG-PAI 54,142 46% 14,391 31%
PG-PR 17,057 15% 658 1%
PG PAI 2 27 0% 23 0%
NA 3 0% NA NA
PR= Residential Program
PAB= Basic Outpatient Program
PAI= Intensive Outpatient Treatment
PAI LV= Probation/Parole
PAI= Intensive Outpatient Treatment

There are still a few doubts about these criteria:

  • Why are there men and women with female gender identity that are in the general population?, (Basic outpatient, intensive outpatient, Residential and Outpatient 2)

  • Should the 2 men and 12 women in M-PAI but with masculine gender identity be in the general population (PG PAI)?

  • If there is a woman with masculine gender identity in M-PR, should they not be in the general population (PG_PR)?


7. Plan & Programs by Sex

Only plans “M-PAI”, “M-PR”, “PG-PAB”, “PG-PAI” and “PG-PR” must be preserved. Considering Table 4, what happens with M-PAB (basic outpatient for women), “Calle”, “PG PAI 2”, “M-PAI2”, “M-PR2”, “PAI LV” or another (“Otro”)? (See Table 4)


8. Age of consumption onset

Treat the age of consumption onset as missing data if it is less than 5 years.

Figure 1. Histogram of Age of Consumption Begin
  par(mfrow = c(1,2)) 
  hist(CONS_C1_df$Edad.Inicio.Consumo, main="Distribution of Age\nof Consumption Begin", 
       xlab="Age", ylab="Freq", breaks=30, xlim=c(0,80))
  hist(CONS_C1_df$Edad.Inicio.Consumo, main="Distribution of Age\nof Consumption Begin (zoom)", 
       xlab="Age", ylab="Freq", breaks=30, xlim=c(0,10), ylim=c(0,20000))


There are around 1117 cases that showed a consumption onset of less than 5 years old. These should be declared as missing data.


9. Delete duplicated cases (same date of admission and ID)

In this case, we included the HASH-KEY as another and more robust criteria to filter duplicate cases.

Table 7. Repeated cases /Multiple Entrances
Frequencies Percentage
Repeated 45,526 27.9%
Unique 117,620 72.1%

 

As seen in Table 7, there are more than a quarter of cases that may be replicating across the different datasets. Considering the difficulties involved in determining repeated cases without dropping relevant information due to specific changes, this will be done posteriorly, once dataset is a bit more normalized.


 

10. Current Age

Consider age as missing age if the case was less than 18 years and greater than 90. UPDATE: This had been reconsidered as a result of a join with SENDA’s professionals, that stated that in specific cases people would have entered treatment when they were not yet adults (<18 years old).


Figure 2. Histogram of Actual Age
    par(mfrow = c(1,3)) 
    hist(CONS_C1_df$Edad, main="Distrbution of Age", 
         xlab="Age", ylab="Freq", breaks=60)
    hist(CONS_C1_df$Edad, main="Distrbution of Age (zoom -)", 
         xlab="Age", ylab="Freq", breaks=60, xlim=c(0,20), ylim=c(0,2000))
    hist(CONS_C1_df$Edad, main="Distrbution of Age (zoom +)", 
         xlab="Age", ylab="Freq", breaks=60, xlim=c(100,120), ylim=c(0,100)) 


    CONS_C1_df %>%
      dplyr::filter(Edad<18) %>%
      dplyr::summarise(n())

    CONS_C1_df %>%
      dplyr::filter(Edad>90) %>%
      dplyr::summarise(n())

There were 673 cases with less than 18 years, 24 cases with more than 90 years. Considering the complexities involved in the age and what should be done to retrieve it adequately, this will be done posteriorly, once other variables are normalized.


11. Days in Treatment

Consider days in treatment as missing if the case had more than 1095 days. Do the same for months in treatment.


Figure 3. Histogram of Days In Treatment
    par(mfrow = c(1,2)) 
    hist(CONS_C1_df$dias_trat, main="Distribution of\nDays in Treatment", 
         xlab="Days", ylab="Freq",xlim=c(-100,4100), breaks=60)
    hist(CONS_C1_df$dias_trat, main="Distribution of\nDays in Treatment (zoom +)", 
         xlab="Days", ylab="Freq", breaks=60, xlim=c(1000,4100), ylim=c(0,2000)) 

        #values out of boundaries

As seen in Figure 3, there are 2320 cases with more than 1095 days in treatment. On the other hand, there are 280 cases with negative or no days of treatment. Due to the complexities involved in the number of treatment days, deletion will be done posteriorly.


12. Pregnancy and Sex

Pregnant men should be considered as missing values. As seen in Table 8, 4 cases of men also reported pregnancy. These cases should be declared as missing.

Table 8. Pregnancy by Sex
Pregnancy Man (n) Man (%) Women (n) Women (%)
No 9515 8% 43466 95%
Si 4 0% 2430 5%
NA 107709 92% 22 0%
Si= Pregnant
No= Not Pregnant


13. Type of plan and Sex

See plans M-PR and M-PAI. Check if there are men. Keep the entries only if they identify as women.

As seen in Table 9, it must be noted that many cases do not answer this question, or perhaps it’s more plausible, this question was not present in previous datasets. This is why we may exclude NA’s (missing values) for the following analysis.

Table 9. Sex by Gender Identity
Gender Identity Man (n) Man (%) Women (n) Women (%)
Femenino 65 0% 3249 7%
Masculino 8012 7% 60 0%
NA 109151 93% 42609 93%
Femenino= Feminine
Masculino= Masculine


 

In Table 10, it can be seen that there are 2 men with a masculine gender identity that are subscribed to the M-PAI plan. However, what happens with the women with masculine gender identity in the M-PAI plan?

Table 10. Type of Plan by Gender Identity and Sex
Plan Fem.-Men(n) Fem.-Men(%) Masc.-Men(n) Masc.-Men(%) Fem.-Women(n) Fem.-Women(%) Masc.-Women(n) Masc.-Women(%)
M-PAI 2 3% 2 0% 877 27% 12 20%
M-PAI2 NA NA NA NA 16 0% NA NA
M-PR 2 3% NA NA 505 16% 1 2%
M-PR2 NA NA NA NA 10 0% NA NA
Otro 1 2% 25 0% 15 0% NA NA
PG-PAB 22 34% 2688 34% 690 21% 19 32%
PG-PAI 33 51% 4060 51% 1098 34% 28 47%
PG-PR 5 8% 1212 15% 26 1% NA NA
PG PAI 2 NA NA 25 0% 12 0% NA NA
PR= Residential Program
PAB= Basic Outpatient Program
PAI= Intensive Outpatient Treatment
PAI LV= Probation/Parole
PAI= Intensive Outpatient Treatment


This can be done by replacing plans of men with masculine identities that are in M- plans, into the General Population. This should be resolved in the following secitons.


14. Days of treatment

Consider days of treatment as missing if there are values of 0 and negative values.


As shown in Figure 3, there are 280 values of 0 or negative values. This must be resolved once treatment days are standardized and overlappings in treatments are corrected.

    CONS_C1_df %>%
    dplyr::filter(dias_trat<1) %>%
      dplyr::select(-id,-TABLE,-14,-16,-17,-26,-27,-28,-29,-35,-36,-37,-88,-93,-94,-96,-101) %>%
      dplyr::select(row, ano_bd, HASH_KEY, hash_rut_completo, id_mod, dias_trat,fech_ing, fech_egres,tipo_de_plan, tipo_de_programa, 
                    ID.centro,motivodeegreso, everything()) %>%
      head() %>%
 knitr::kable(.,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
               caption="Table 11. Cases with lower dates of treatment",
                 align =rep('c', 101))  %>%
  kable_styling(bootstrap_options = c("striped", "hover"),font_size = 8) %>%
  scroll_box(width = "100%", height = "350px")
Table 11. Cases with lower dates of treatment
row ano_bd HASH_KEY hash_rut_completo id_mod dias_trat fech_ing fech_egres tipo_de_plan tipo_de_programa ID.centro motivodeegreso Nombre.Centro tipo_centro Región.del.Centro Servicio.de.Salud Tipo.de.Programa Tipo.de.Plan SENDA Dias.en.SENDA Edad Nombre.Usuario Comuna.Residencia Origen.de.Ingreso País.Nacimiento Nacionalidad Etnia Estado.Conyugal Fecha.Ultimo.Tratamiento Sustancia.de.Inicio Edad.Inicio.Consumo X.Se.trata.de.una.mujer.embarazada. Escolaridad..último.año.cursado. Con.Quién.Vive Tipo.de.vivienda Tenencia.de.la.vivienda Sustancia.Principal Otras.Sustancias.nº1 Otras.Sustancias.nº2 Otras.Sustancias.nº3 Frecuencia.de.Consumo..Sustancia.Principal. Edad.Inicio..Sustancia.Principal. Vía.Administración..Sustancia.Principal. Diagnóstico.Trs..Consumo.Sustancia Diagnóstico.Trs..Psiquiátrico.DSM.IV Diagnóstico.Trs..Psiquiátrico.SUB.DSM.IV X2.Diagnóstico.Trs..Psiquiátrico.DSM.IV X2.Diagnóstico.Trs..Psiquiátrico.SUB.DSM.IV X3.Diagnóstico.Trs..Psiquiátrico.DSM.IV X3.Diagnóstico.Trs..Psiquiátrico.SUB.DSM.IV Diagnóstico.Trs..Psiquiátrico.CIE.10 Diagnóstico.Trs..Psiquiátrico.SUB.CIE.10 X2.Diagnóstico.Trs..Psiquiátrico.CIE.10 X2.Diagnóstico.Trs..Psiquiátrico.SUB.CIE.10 X3.Diagnóstico.Trs..Psiquiátrico.CIE.10 X3.Diagnóstico.Trs..Psiquiátrico.SUB.CIE.10 Diagnóstico.Trs..Físico Otros.Problemas.de.Atención.de.Salud.Mental Compromiso.Biopsicosocial DIAGNOSTICO.GLOBAL.DE.NECESIDADES.DE.INTEGRACION.SOCIAL DIAGNOSTICO.DE.NECESIDADES.DE.INTEGRACIóN.SOCIAL.EN.CAPITAL.HUMANO DIAGNOSTICO.DE.NECESIDADES.DE.INTEGRACIóN.SOCIAL.EN.CAPITAL.FISICO DIAGNOSTICO.DE.NECESIDADES.DE.INTEGRACIóN.SOCIAL.EN.CAPITAL.SOCIAL Fecha.Ingreso.a.Convenio.SENDA Usuario.de.Tribunales..Tratamiento.Drogas Consentimiento.Informado Tipo.Centro.Derivación evaluacindelprocesoteraputico eva_consumo eva_fam eva_relinterp eva_ocupacion eva_sm eva_fisica eva_transgnorma Diagnóstico.Trastorno.Psiquiátrico.CIE.10.al.Egreso DIAGNOSTICO.GLOBAL.DE.NECESIDADES.DE.INTEGRACION.SOCIAL.1 DIAGNOSTICO.DE.NECESIDADES.DE.INTEGRACIóN.SOCIAL.EN.CAPITAL.HUMANO.1 DIAGNOSTICO.DE.NECESIDADES.DE.INTEGRACIóN.SOCIAL.EN.CAPITAL.FISICO.1 DIAGNOSTICO.DE.NECESIDADES.DE.INTEGRACIóN.SOCIAL.EN.CAPITAL.SOCIAL.1 Motivo.de.egreso.Alta.Administrativa Consorcio Ha.estado.embarazada.egreso. sexo embarazo ano_nac
53,692 2,014 44e3de7dae9efc5f37695d7bef2a19ee CAGA2**121982 0 2014-01-28 2014-01-28 M-PR Programa Específico Mujeres 159 Abandono Comunidad Terapeutica Crem (Hogares Crem) privado METROPOLITANA Metropolitano Oriente Programa Específico Mujeres M-PR Si 0 36 OCULTO MACUL Otro Centro Tratamiento Drogas Chile Chile No pertenece Soltero último 6 meses Alcohol 13 No UNIVERSITARIA INCOMPLETA Únicamente con hijos y padres o familia de origen Casa Allegado Alcohol Cocaína Marihuana Hongos 4-6 días - semana 13 Oral (bebida o comida) Dependencia En estudio NA NA NA NA NA En estudio NA NA NA NA NA Sin trastorno Abuso Sexual Severo NA NA NA NA 28/01/2014 No Si NA Logro M?mo Logro Mínimo Logro Mínimo Logro Mínimo Logro Mínimo Logro Mínimo Logro Mínimo Logro Mínimo NA NA NA NA NA NA Opazo y Compañia Ltda. NA Mujer No 1,982
53,553 2,014 86abe004a5c39f635f562c49d2329f7d JODI2**021993 0 2014-01-06 2014-01-06 Otro Programa Específico Mujeres 219 Alta Admnistrativa Comunidad Terapeutica Romeral (Dianova Mujeres Maule) privado DEL MAULE Maule Programa Específico Mujeres Otro Si 0 26 OCULTO TALCA Otros de la Red de Salud General Público Chile Chile No pertenece Soltero último 6 meses Marihuana 13 No BASICA INCOMPLETA Únicamente con padres o familia de origen Casa Allegado Pasta Base Marihuana NA NA Todos los días 15 Fumada o Pulmonar (aspiración de gases o vapores) Dependencia NA NA NA NA NA NA En estudio NA NA NA NA NA ETS Explotaci?omercial Sexual Moderado NA NA NA NA 06/01/2014 No Si NA Logro M?mo Logro Mínimo Logro Mínimo Logro Mínimo Logro Mínimo Logro Mínimo Logro Mínimo Logro Mínimo NA NA NA NA NA NA ONG. Dianova Chile NA Mujer No 1,993
53,402 2,014 ac3c9557b7a403430b8058e3099fa221 PAAL1**071976 0 2014-01-27 2014-01-27 Otro Programa Población General 303 Alta Admnistrativa CTR Nehuen (Residencial Melipilla) publico METROPOLITANA Metropolitano Occidente Programa Población General Otro Si 0 43 OCULTO MELIPILLA Consulta Espontánea Chile Chile No pertenece Casado último 12 meses Alcohol 19 NA BASICA COMPLETA Únicamente con la pareja e hijos Mediagua Propia Pasta Base NA NA NA 2-3 días - semana 24 Fumada o Pulmonar (aspiración de gases o vapores) Dependencia NA NA NA NA NA NA En estudio NA NA NA NA NA En estudio Sin otros problemas de salud mental Severo NA NA NA NA 27/01/2014 No Si NA Logro M?mo Logro Mínimo Logro Mínimo Logro Mínimo Logro Mínimo Logro Mínimo Logro Mínimo Logro Mínimo NA NA NA NA NA NA SERVICIO DE SALUD METROPOLITANO OCCIDENTE NA Hombre NA 1,976
36,706 2,013 b24c50c72c85669ea416ec86b3b11681 ALAC1**031981 0 2013-02-04 2013-02-04 PG-PAI Programa Población General 148 Abandono COSAM Quilicura publico METROPOLITANA Metropolitano Norte Programa Población General PG-PAI No NA 38 OCULTO QUILICURA Consulta Espontánea Chile Chile No pertenece Soltero NA Marihuana 17 NA MEDIA COMPLETA Únicamente con pareja NA NA Pasta Base NA NA NA 4-6 días - semana 28 Fumada o Pulmonar (aspiración de gases o vapores) Dependencia NA NA NA NA NA NA En estudio NA NA NA NA NA Sin trastorno Otros Severo NA NA NA NA NA No Si NA Logro M?mo Logro Mínimo Logro Mínimo Logro Mínimo Logro Mínimo Logro Mínimo Logro Mínimo Logro Mínimo NA NA NA NA NA NA SERVICIO DE SALUD METROPOLITANO NORTE NA Hombre NA 1,981
36,713 2,013 c4f4e254f4e5224db6b81d5f0fdddb6e ALAG1**021994 0 2013-02-06 2013-02-06 PG-PAI Programa Población General NA Derivación NA NA NA NA Programa Población General PG-PAI Si 0 25 OCULTO SANTIAGO CENTRO Otros de la Red de Salud General Privado Chile Chile Mapuche Casado NA Alcohol 0 NA BASICA COMPLETA Solo Casa Arrienda Alcohol Marihuana Cocaína Pasta Base Todos los días 12 Fumada o Pulmonar (aspiración de gases o vapores) Dependencia NA NA NA NA NA NA En estudio NA NA NA NA NA Otras Enfermedades o Condiciones de Riesgo Vital NA Severo NA NA NA NA 06/02/2013 No Si Otro Centro Logro M?mo Logro Alto Logro Alto Logro Alto Logro Alto Logro Alto Logro Intermedio Logro Alto NA NA NA NA NA NA NA NA Hombre NA 1,994
45,004 2,013 ace460ae4527071c9039dc91dfd58092 ALCA1**121956 0 2013-10-14 2013-10-14 PG-PAB Programa Población General 241 Alta Admnistrativa CRS La Florida publico METROPOLITANA Metropolitano Sur Oriente Programa Población General PG-PAB No NA 62 OCULTO LA FLORIDA Estab. de APS Chile Chile No pertenece Casado NA Alcohol 16 NA MEDIA INCOMPLETA Únicamente con la pareja e hijos Departamento Propia Alcohol Cocaína NA NA 2-3 días - semana 16 Oral (bebida o comida) Consumo Perjudicial En estudio NA NA NA NA NA En estudio NA NA NA NA NA En estudio Sin otros problemas de salud mental Moderado NA NA NA NA NA No No NA Logro M?mo Logro Mínimo Logro Mínimo Logro Mínimo Logro Mínimo Logro Mínimo Logro Mínimo Logro Mínimo NA NA NA NA NA NA SERVICIO DE SALUD METROPOLITANO SUR ORIENTE NA Hombre NA 1,956


15. Age of Consumption Onset and Current Age

Consider the age of consumption onset as missing data if it is greater than the current age.

In 780 cases, the age of consumption onset is greater than the current age. This will be corrected once ages are normalized.


CONS_C1_df %>% dplyr::mutate(edad_inicio_cons= ifelse(Edad.Inicio.Consumo>Edad, 1, 0)) %>% dplyr::filter(edad_inicio_cons==1) %>%
  dplyr::select(row, HASH_KEY, ano_bd, id_mod, Edad.Inicio.Consumo,Edad) %>% 
  head(50) %>%
 knitr::kable(.,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
               caption="Table 12. Cases with age of consumption begin greater than the actual age",
                 align =rep('c', 101))  %>%
  kable_styling(bootstrap_options = c("striped", "hover"),font_size = 8) %>%
  scroll_box(width = "100%", height = "350px")
Table 12. Cases with age of consumption begin greater than the actual age
row HASH_KEY ano_bd id_mod Edad.Inicio.Consumo Edad
155,268 fb74493f56becc488867fe53c5573a08 2,019 ALAL2**012001 25 18
161,209 2a33829bf3464ee50d4c8c5087a81093 2,019 CORU2**021992 32 27
150,494 69c9e351e5132a9b487eaeaea44c0c77 2,019 GOMI1**062000 25 19
158,818 d6d3a38f49580254e4a9a876b2209b29 2,019 ISAM1**062001 20 18
151,947 910ad076dd1e17a321519f65c5feaa99 2,019 JALI1**061979 52 40
160,567 4935ae99848d64148eb2f1a0230c4ebb 2,019 KATE2**072001 24 18
159,445 a0eee7839dc689824f91b86db156f82f 2,019 MAES2**072001 22 18
153,734 7d92e6e8d55a12c1b308c78dbad0f5b9 2,019 MAPE1**031995 29 24
148,287 e586f94b6247f0e208a906f97f2420f2 2,019 NECU1**052000 21 19
146,598 1d91d6b2b75fe7a4d8e2a8dfebcfa16c 2,019 NELA1**121999 54 19
159,705 a6929b6c7987cce64b0ca1b3f3299bea 2,019 ROCO2**072001 20 18
160,788 f90dd528fbe2005a3a3cf88e4f873c83 2,019 RUTR2**051997 24 22
131,749 9018b21f72370bdeca005ab540d7c7be 2,018 ALNE1**101999 25 20
127,314 fc87d7f86b4e9f568fb50eccb29f3891 2,018 ANAR1**041999 24 20
136,733 07402f400b254ee4662b8a831365612b 2,018 DIJI1**051992 34 27
138,430 445bfb8e194056cb920c163d851c3f92 2,018 ELCA1**052000 30 19
125,808 578ae693be289de023125ddd2777dd1c 2,018 GARO1**012015 15 4
144,205 69c9e351e5132a9b487eaeaea44c0c77 2,018 GOMI1**062000 25 19
125,800 8d2dfe24c7a24d541a13b4e82b82eede 2,018 HEAV1**112014 13 4
131,633 e0a8c5bbde98b9ed30a52230a7a8b0cb 2,018 JACE2**061999 50 20
142,720 0c9be3e89c1a7576227d2204aada9c24 2,018 JUVA1**111993 26 25
126,276 9b2836d1a04bffaf371d93392573f692 2,018 MAGU2**051996 43 23
129,532 b24908c527faa1b7bd5a267d5dcabd45 2,018 MAHE2**091999 62 20
131,012 eaf995cb33d947ef6b637e0f89907f96 2,018 MIPE1**091999 25 20
127,827 6fc3007c689e78c3a6af220449514b4e 2,018 MYLO2**051986 46 33
140,084 e586f94b6247f0e208a906f97f2420f2 2,018 NECU1**052000 21 19
133,368 1d91d6b2b75fe7a4d8e2a8dfebcfa16c 2,018 NELA1**121999 54 19
130,850 865d8750fff6ca0711f52527bcd70b96 2,018 PAAV2**101997 25 22
129,149 ac8234f0a33399c702103d076a9a8de5 2,018 REVA2**081999 30 20
132,807 2174b5058b56e4471af11248a73f215b 2,018 ROVE1**121999 25 19
125,944 711029aaa5f478f2c55223d285eb84fe 2,018 SHMA1**082013 15 6
127,280 45fce40da4dfc892ba5d4154f8d6b5b6 2,018 SOVA2**121998 46 20
125,778 ad16844e62fd0a0db5ddd59ccffccbf6 2,018 SUUB2**102013 26 6
127,320 5ca64697dd399116abdd351906e52ab1 2,018 YESA1**031999 21 20
136,457 744991c3ca5e12e87f2a7925d254e5f6 2,018 YOBO2**022000 23 19
125,184 9018b21f72370bdeca005ab540d7c7be 2,017 ALNE1**101999 25 20
116,020 fc87d7f86b4e9f568fb50eccb29f3891 2,017 ANAR1**041999 24 20
106,706 c51ce03a395b620b21a70b0d028a77e6 2,017 ANMA1**031996 27 23
120,750 bde31338de6a454e11a31da13e9aa8ee 2,017 BAGA2**041996 25 23
106,134 68a1abebf175673e62b97b65fde63b50 2,017 BRHE1**092015 15 4
119,326 4908c125c91bed71becbd7cf1077e5b2 2,017 CALU2**071999 24 20
105,458 2cdd842e8b1102992ff2c060e7b46178 2,017 CASA1**122014 18 4
105,847 87afef689812b1ad6f576f0e83e5c963 2,017 DABU1**032015 14 4
107,001 31d3a088c8730a02c4ef7239a02e511f 2,017 DIBU2**041996 30 23
105,661 0d5782868d35d58892a0f938813f7608 2,017 EDHO1**052015 17 4
106,644 2817347e37b21d9ff166af0685b417ff 2,017 ENSA1**031996 25 23
108,082 d9e020a08875ef763ce0c3e06cbf1b9d 2,017 ERVA1**081995 25 24
124,962 99246ebba3341f26ce047e32d9c3742b 2,017 FEJA1**121999 36 19
105,599 e89eb1528cbb255f7a94ade170a93768 2,017 FRJA1**042015 16 4
124,952 4e11314413f0172ea97c6926639bff6f 2,017 FRMU1**011996 25 23


Around 666 cases could change once dates are resolved, due to less than 18 years of age.

See here for duplicates treatment.