Consolidate the database
Se genera la base de datos con las nuevas variables solicitadas. Conste que esta base de datos ya filtraba a los menores de edad pero no a aquellos que no reportaron edad válida.
invisible("84,936 x 254 # 2020-10-25")
invisible("84,944 x 277 # 2020-12-20")
prueba<-
CONS_C1_df_dup_SEP_2020 %>%
dplyr::group_by(hash_key) %>%
dplyr::mutate(menor_edad=dplyr::case_when(edad_al_ing<18~1,TRUE~0),menor_edad=sum(menor_edad,na.rm=T)) %>%
dplyr::ungroup() %>%
dplyr::filter(edad_al_ing>=18| is.na(edad_al_ing)) %>% #Sólo asà llegamos a 109,642 casos, igual que en STATA
dplyr::group_by(hash_key) %>%
dplyr::mutate(dup2=row_number()) %>%
dplyr::mutate(duplicates_filtered2=n()) %>%
dplyr::mutate(max_cum_dias_trat_sin_na=max(cum_dias_trat_sin_na, na.rm=T)) %>%
dplyr::mutate(max_cum_diff_bet_treat=max(cum_diff_bet_treat, na.rm=T)) %>%
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
#Formatear dÃas de tratamiento y diferencia en dÃas excliyendo a los casos a los que le borramos un tratamiento menor a 18.
#local vars `" "diff_bet_treat_" "dias_treat_imp_sin_na_" "cum_diff_bet_treat_" "cum_dias_trat_sin_na_" "mean_cum_dias_trat_sin_na_" "mean_cum_diff_bet_treat_" "'
dplyr::ungroup() %>%
dplyr::filter(dup2==1) %>% #84,936 x 254 # 2020-10-25,
dplyr::mutate(cum_dias_trat_sin_na_1= dplyr::case_when(menor_edad>0~cum_dias_trat_sin_na_2,TRUE~cum_dias_trat_sin_na_1)) %>%
dplyr::mutate(cum_dias_trat_sin_na_2= dplyr::case_when(menor_edad>0~cum_dias_trat_sin_na_3,TRUE~cum_dias_trat_sin_na_2)) %>%
dplyr::mutate(cum_dias_trat_sin_na_3= dplyr::case_when(menor_edad>0~cum_dias_trat_sin_na_4,TRUE~cum_dias_trat_sin_na_3)) %>%
dplyr::mutate(cum_dias_trat_sin_na_4= dplyr::case_when(menor_edad>0~cum_dias_trat_sin_na_5,TRUE~cum_dias_trat_sin_na_4)) %>%
dplyr::mutate(cum_dias_trat_sin_na_5= dplyr::case_when(menor_edad>0~cum_dias_trat_sin_na_6,TRUE~cum_dias_trat_sin_na_5)) %>%
dplyr::mutate(cum_dias_trat_sin_na_6= dplyr::case_when(menor_edad>0~cum_dias_trat_sin_na_7,TRUE~cum_dias_trat_sin_na_6)) %>%
dplyr::mutate(cum_dias_trat_sin_na_7= dplyr::case_when(menor_edad>0~cum_dias_trat_sin_na_8,TRUE~cum_dias_trat_sin_na_7)) %>%
dplyr::mutate(cum_dias_trat_sin_na_8= dplyr::case_when(menor_edad>0~cum_dias_trat_sin_na_9,TRUE~cum_dias_trat_sin_na_8)) %>%
dplyr::mutate(cum_dias_trat_sin_na_9= dplyr::case_when(menor_edad>0~cum_dias_trat_sin_na_10,TRUE~cum_dias_trat_sin_na_9)) %>%
dplyr::mutate(cum_dias_trat_sin_na_10= dplyr::case_when(menor_edad>0~NA_real_,TRUE~cum_dias_trat_sin_na_10)) %>%
dplyr::mutate(cum_diff_bet_treat_1= dplyr::case_when(menor_edad>0~cum_diff_bet_treat_2,TRUE~cum_diff_bet_treat_1)) %>%
dplyr::mutate(cum_diff_bet_treat_2= dplyr::case_when(menor_edad>0~cum_diff_bet_treat_3,TRUE~cum_diff_bet_treat_2)) %>%
dplyr::mutate(cum_diff_bet_treat_3= dplyr::case_when(menor_edad>0~cum_diff_bet_treat_4,TRUE~cum_diff_bet_treat_3)) %>%
dplyr::mutate(cum_diff_bet_treat_4= dplyr::case_when(menor_edad>0~cum_diff_bet_treat_5,TRUE~cum_diff_bet_treat_4)) %>%
dplyr::mutate(cum_diff_bet_treat_5= dplyr::case_when(menor_edad>0~cum_diff_bet_treat_6,TRUE~cum_diff_bet_treat_5)) %>%
dplyr::mutate(cum_diff_bet_treat_6= dplyr::case_when(menor_edad>0~cum_diff_bet_treat_7,TRUE~cum_diff_bet_treat_6)) %>%
dplyr::mutate(cum_diff_bet_treat_7= dplyr::case_when(menor_edad>0~cum_diff_bet_treat_8,TRUE~cum_diff_bet_treat_7)) %>%
dplyr::mutate(cum_diff_bet_treat_8= dplyr::case_when(menor_edad>0~cum_diff_bet_treat_9,TRUE~cum_diff_bet_treat_8)) %>%
dplyr::mutate(cum_diff_bet_treat_9= dplyr::case_when(menor_edad>0~cum_diff_bet_treat_10,TRUE~cum_diff_bet_treat_9)) %>%
dplyr::mutate(cum_diff_bet_treat_10= dplyr::case_when(menor_edad>0~NA_real_,TRUE~cum_diff_bet_treat_10)) %>%
dplyr::mutate(tipo_de_plan_2_1= dplyr::case_when(menor_edad>0~as.character(tipo_de_plan_2_2),TRUE~as.character(tipo_de_plan_2_1))) %>%
dplyr::mutate(tipo_de_plan_2_2= dplyr::case_when(menor_edad>0~as.character(tipo_de_plan_2_3),TRUE~as.character(tipo_de_plan_2_2))) %>%
dplyr::mutate(tipo_de_plan_2_3= dplyr::case_when(menor_edad>0~as.character(tipo_de_plan_2_4),TRUE~as.character(tipo_de_plan_2_3))) %>%
dplyr::mutate(tipo_de_plan_2_4= dplyr::case_when(menor_edad>0~as.character(tipo_de_plan_2_5),TRUE~as.character(tipo_de_plan_2_4))) %>%
dplyr::mutate(tipo_de_plan_2_5= dplyr::case_when(menor_edad>0~as.character(tipo_de_plan_2_6),TRUE~as.character(tipo_de_plan_2_5))) %>%
dplyr::mutate(tipo_de_plan_2_6= dplyr::case_when(menor_edad>0~as.character(tipo_de_plan_2_7),TRUE~as.character(tipo_de_plan_2_6))) %>%
dplyr::mutate(tipo_de_plan_2_7= dplyr::case_when(menor_edad>0~as.character(tipo_de_plan_2_8),TRUE~as.character(tipo_de_plan_2_7))) %>%
dplyr::mutate(tipo_de_plan_2_8= dplyr::case_when(menor_edad>0~as.character(tipo_de_plan_2_9),TRUE~as.character(tipo_de_plan_2_8))) %>%
dplyr::mutate(tipo_de_plan_2_9= dplyr::case_when(menor_edad>0~as.character(tipo_de_plan_2_10),TRUE~as.character(tipo_de_plan_2_9))) %>%
dplyr::mutate(tipo_de_plan_2_10= dplyr::case_when(menor_edad>0~NA_character_,TRUE~as.character(tipo_de_plan_2_10))) %>%
dplyr::mutate(motivodeegreso_mod_imp_1= dplyr::case_when(menor_edad>0~as.character(motivodeegreso_mod_imp_2),TRUE~as.character(motivodeegreso_mod_imp_1))) %>%
dplyr::mutate(motivodeegreso_mod_imp_2= dplyr::case_when(menor_edad>0~as.character(motivodeegreso_mod_imp_3),TRUE~as.character(motivodeegreso_mod_imp_2))) %>%
dplyr::mutate(motivodeegreso_mod_imp_3= dplyr::case_when(menor_edad>0~as.character(motivodeegreso_mod_imp_4),TRUE~as.character(motivodeegreso_mod_imp_3))) %>%
dplyr::mutate(motivodeegreso_mod_imp_4= dplyr::case_when(menor_edad>0~as.character(motivodeegreso_mod_imp_5),TRUE~as.character(motivodeegreso_mod_imp_4))) %>%
dplyr::mutate(motivodeegreso_mod_imp_5= dplyr::case_when(menor_edad>0~as.character(motivodeegreso_mod_imp_6),TRUE~as.character(motivodeegreso_mod_imp_5))) %>%
dplyr::mutate(motivodeegreso_mod_imp_6= dplyr::case_when(menor_edad>0~as.character(motivodeegreso_mod_imp_7),TRUE~as.character(motivodeegreso_mod_imp_6))) %>%
dplyr::mutate(motivodeegreso_mod_imp_7= dplyr::case_when(menor_edad>0~as.character(motivodeegreso_mod_imp_8),TRUE~as.character(motivodeegreso_mod_imp_7))) %>%
dplyr::mutate(motivodeegreso_mod_imp_8= dplyr::case_when(menor_edad>0~as.character(motivodeegreso_mod_imp_9),TRUE~as.character(motivodeegreso_mod_imp_8))) %>%
dplyr::mutate(motivodeegreso_mod_imp_9= dplyr::case_when(menor_edad>0~as.character(motivodeegreso_mod_imp_10),TRUE~as.character(motivodeegreso_mod_imp_9))) %>%
dplyr::mutate(motivodeegreso_mod_imp_10= dplyr::case_when(menor_edad>0~NA_character_,TRUE~as.character(motivodeegreso_mod_imp_10))) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_1= dplyr::case_when(menor_edad>0~mean_cum_dias_trat_sin_na_2,TRUE~mean_cum_dias_trat_sin_na_1)) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_2= dplyr::case_when(menor_edad>0~mean_cum_dias_trat_sin_na_3,TRUE~mean_cum_dias_trat_sin_na_2)) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_3= dplyr::case_when(menor_edad>0~mean_cum_dias_trat_sin_na_4,TRUE~mean_cum_dias_trat_sin_na_3)) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_4= dplyr::case_when(menor_edad>0~mean_cum_dias_trat_sin_na_5,TRUE~mean_cum_dias_trat_sin_na_4)) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_5= dplyr::case_when(menor_edad>0~mean_cum_dias_trat_sin_na_6,TRUE~mean_cum_dias_trat_sin_na_5)) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_6= dplyr::case_when(menor_edad>0~mean_cum_dias_trat_sin_na_7,TRUE~mean_cum_dias_trat_sin_na_6)) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_7= dplyr::case_when(menor_edad>0~mean_cum_dias_trat_sin_na_8,TRUE~mean_cum_dias_trat_sin_na_7)) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_8= dplyr::case_when(menor_edad>0~mean_cum_dias_trat_sin_na_9,TRUE~mean_cum_dias_trat_sin_na_8)) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_9= dplyr::case_when(menor_edad>0~mean_cum_dias_trat_sin_na_10,TRUE~mean_cum_dias_trat_sin_na_9)) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_10= dplyr::case_when(menor_edad>0~NA_real_,TRUE~mean_cum_dias_trat_sin_na_10)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_1= dplyr::case_when(menor_edad>0~mean_cum_diff_bet_treat_2,TRUE~mean_cum_diff_bet_treat_1)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_2= dplyr::case_when(menor_edad>0~mean_cum_diff_bet_treat_3,TRUE~mean_cum_diff_bet_treat_2)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_3= dplyr::case_when(menor_edad>0~mean_cum_diff_bet_treat_4,TRUE~mean_cum_diff_bet_treat_3)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_4= dplyr::case_when(menor_edad>0~mean_cum_diff_bet_treat_5,TRUE~mean_cum_diff_bet_treat_4)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_5= dplyr::case_when(menor_edad>0~mean_cum_diff_bet_treat_6,TRUE~mean_cum_diff_bet_treat_5)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_6= dplyr::case_when(menor_edad>0~mean_cum_diff_bet_treat_7,TRUE~mean_cum_diff_bet_treat_6)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_7= dplyr::case_when(menor_edad>0~mean_cum_diff_bet_treat_8,TRUE~mean_cum_diff_bet_treat_7)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_8= dplyr::case_when(menor_edad>0~mean_cum_diff_bet_treat_9,TRUE~mean_cum_diff_bet_treat_8)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_9= dplyr::case_when(menor_edad>0~mean_cum_diff_bet_treat_10,TRUE~mean_cum_diff_bet_treat_9)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_10= dplyr::case_when(menor_edad>0~NA_real_,TRUE~mean_cum_diff_bet_treat_10)) %>%
dplyr::mutate(diff_bet_treat_1= dplyr::case_when(menor_edad>0~diff_bet_treat_2,TRUE~diff_bet_treat_1)) %>%
dplyr::mutate(diff_bet_treat_2= dplyr::case_when(menor_edad>0~diff_bet_treat_3,TRUE~diff_bet_treat_2)) %>%
dplyr::mutate(diff_bet_treat_3= dplyr::case_when(menor_edad>0~diff_bet_treat_4,TRUE~diff_bet_treat_3)) %>%
dplyr::mutate(diff_bet_treat_4= dplyr::case_when(menor_edad>0~diff_bet_treat_5,TRUE~diff_bet_treat_4)) %>%
dplyr::mutate(diff_bet_treat_5= dplyr::case_when(menor_edad>0~diff_bet_treat_6,TRUE~diff_bet_treat_5)) %>%
dplyr::mutate(diff_bet_treat_6= dplyr::case_when(menor_edad>0~diff_bet_treat_7,TRUE~diff_bet_treat_6)) %>%
dplyr::mutate(diff_bet_treat_7= dplyr::case_when(menor_edad>0~diff_bet_treat_8,TRUE~diff_bet_treat_7)) %>%
dplyr::mutate(diff_bet_treat_8= dplyr::case_when(menor_edad>0~diff_bet_treat_9,TRUE~diff_bet_treat_8)) %>%
dplyr::mutate(diff_bet_treat_9= dplyr::case_when(menor_edad>0~diff_bet_treat_10,TRUE~diff_bet_treat_9)) %>%
dplyr::mutate(diff_bet_treat_10= dplyr::case_when(menor_edad>0~NA_real_,TRUE~diff_bet_treat_10)) %>%
dplyr::mutate(dias_treat_imp_sin_na_1= dplyr::case_when(menor_edad>0~dias_treat_imp_sin_na_2,TRUE~dias_treat_imp_sin_na_1)) %>%
dplyr::mutate(dias_treat_imp_sin_na_2= dplyr::case_when(menor_edad>0~dias_treat_imp_sin_na_3,TRUE~dias_treat_imp_sin_na_2)) %>%
dplyr::mutate(dias_treat_imp_sin_na_3= dplyr::case_when(menor_edad>0~dias_treat_imp_sin_na_4,TRUE~dias_treat_imp_sin_na_3)) %>%
dplyr::mutate(dias_treat_imp_sin_na_4= dplyr::case_when(menor_edad>0~dias_treat_imp_sin_na_5,TRUE~dias_treat_imp_sin_na_4)) %>%
dplyr::mutate(dias_treat_imp_sin_na_5= dplyr::case_when(menor_edad>0~dias_treat_imp_sin_na_6,TRUE~dias_treat_imp_sin_na_5)) %>%
dplyr::mutate(dias_treat_imp_sin_na_6= dplyr::case_when(menor_edad>0~dias_treat_imp_sin_na_7,TRUE~dias_treat_imp_sin_na_6)) %>%
dplyr::mutate(dias_treat_imp_sin_na_7= dplyr::case_when(menor_edad>0~dias_treat_imp_sin_na_8,TRUE~dias_treat_imp_sin_na_7)) %>%
dplyr::mutate(dias_treat_imp_sin_na_8= dplyr::case_when(menor_edad>0~dias_treat_imp_sin_na_9,TRUE~dias_treat_imp_sin_na_8)) %>%
dplyr::mutate(dias_treat_imp_sin_na_9= dplyr::case_when(menor_edad>0~dias_treat_imp_sin_na_10,TRUE~dias_treat_imp_sin_na_9)) %>%
dplyr::mutate(dias_treat_imp_sin_na_10= dplyr::case_when(menor_edad>0~NA_real_,TRUE~dias_treat_imp_sin_na_10)) %>%
# diff y dias treat
dplyr::mutate(dias_treat_imp_sin_na_four = rowMeans(dplyr::select(., dias_treat_imp_sin_na_4, dias_treat_imp_sin_na_5, dias_treat_imp_sin_na_6, dias_treat_imp_sin_na_7, dias_treat_imp_sin_na_8, dias_treat_imp_sin_na_9, dias_treat_imp_sin_na_10), na.rm=T)) %>% #,na.rm=F
dplyr::mutate(diff_bet_treat_four = rowMeans(dplyr::select(., diff_bet_treat_4, diff_bet_treat_5, diff_bet_treat_6, diff_bet_treat_7, diff_bet_treat_8, diff_bet_treat_9, diff_bet_treat_10), na.rm=T)) %>% #,na.rm=F
# cum
dplyr::mutate(cum_dias_trat_sin_na_four = rowMeans(dplyr::select(., cum_dias_trat_sin_na_4, cum_dias_trat_sin_na_5, cum_dias_trat_sin_na_6, cum_dias_trat_sin_na_7, cum_dias_trat_sin_na_8, cum_dias_trat_sin_na_9, cum_dias_trat_sin_na_10), na.rm=T)) %>% #,na.rm=F
dplyr::mutate(cum_diff_bet_treat_four = rowMeans(dplyr::select(., cum_diff_bet_treat_4, cum_diff_bet_treat_5, cum_diff_bet_treat_6, cum_diff_bet_treat_7, cum_diff_bet_treat_8, cum_diff_bet_treat_9, cum_diff_bet_treat_10), na.rm=T)) %>% #,na.rm=F
# mean cum
dplyr::mutate(mean_cum_dias_trat_sin_na_four = rowMeans(dplyr::select(., mean_cum_dias_trat_sin_na_4, mean_cum_dias_trat_sin_na_5, mean_cum_dias_trat_sin_na_6, mean_cum_dias_trat_sin_na_7, mean_cum_dias_trat_sin_na_8, mean_cum_dias_trat_sin_na_9, mean_cum_dias_trat_sin_na_10), na.rm=T)) %>% #,na.rm=F
dplyr::mutate(mean_cum_diff_bet_treat_four = rowMeans(dplyr::select(., mean_cum_diff_bet_treat_4, mean_cum_diff_bet_treat_5, mean_cum_diff_bet_treat_6, mean_cum_diff_bet_treat_7, mean_cum_diff_bet_treat_8, mean_cum_diff_bet_treat_9, mean_cum_diff_bet_treat_10), na.rm=T)) %>% #,na.rm=F
# tipo de plana
dplyr::mutate(tipo_de_plan_2_mod=dplyr::case_when(grepl("PAB",tipo_de_plan_2)~"PAB",
grepl("PAI",tipo_de_plan_2)~"PAI",
grepl("PR",tipo_de_plan_2)~"PR",
TRUE~NA_character_)) %>%
dplyr::mutate(tipo_de_plan_2_mod=factor(tipo_de_plan_2_mod)) %>%
dplyr::mutate(estatus_ocupacional= dplyr::case_when(!is.na(cat_ocupacional)&!is.na(estatus_ocupacional)~"Empleado",
TRUE~as.character(estatus_ocupacional)))%>%
dplyr::mutate(estatus_ocupacional= as.factor(estatus_ocupacional))%>%
dplyr::mutate(cnt_mod_cie_10_dg_cons_sus_or= dplyr::case_when(as.character(dg_trs_cons_sus_or)=="Drug dependence"~dg_total_cie_10+1,
TRUE~dg_total_cie_10))%>%
dplyr::mutate(freq_cons_sus_prin= dplyr::case_when(as.character(freq_cons_sus_prin)=="Did not use"~"Less than 1 day a week",
TRUE~as.character(freq_cons_sus_prin)))%>%
dplyr::mutate(freq_cons_sus_prin= as.factor(freq_cons_sus_prin)) %>%
#edad más 65
dplyr::mutate(mas_65= dplyr::case_when(edad_al_ing>65~1,
TRUE~0))%>%
dplyr::mutate(mas_65= as.factor(mas_65)) %>%
dplyr::mutate(comorbidity_icd_10=dplyr::case_when(dg_total_cie_10>=2~ "Two or more",
dg_total_cie_10==1~ "One",
as.character(dg_cie_10_rec)=="Diagnosis unknown (under study)"~"Diagnosis unknown (under study)",
as.character(dg_cie_10_rec)=="Without psychiatric comorbidity"~"Without psychiatric comorbidity")) %>%
dplyr::mutate(comorbidity_icd_10=as.factor(comorbidity_icd_10)) %>%
# dplyr::select(-menor_edad) %>%
dplyr::mutate(no_group=1) %>%
dplyr::mutate(had_readm=dplyr::case_when(duplicates_filtered2>1~1,
TRUE~0)) %>%
dplyr::mutate(n_treats=factor(dplyr::case_when(duplicates_filtered2>3~"04 or more",
duplicates_filtered2==3~"03",
duplicates_filtered2==2~"02",
duplicates_filtered2==1~"01")))
Updated in 2021 June & August
We only left users in treatments in General Population (PG) and missing values. Additionally, we recategorized the variable ‘Living with’ (con_quien_vive).
#janitor::tabyl(CONS_C1_df_dup_SEP_2020$otros_probl_at_sm_or)
#round(prop.table(table(CONS_C1_df_dup_SEP_2020$otros_probl_at_sm_or)),3)
#With With Other Alone With With
#relatives couple and children children only couple only
#38105 21806 7275 8026 3191 6540
library(readr)
## Warning: package 'readr' was built under R version 4.0.5
prueba2<-
prueba %>%
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#AUG 2021:
dplyr::mutate(con_quien_vive_joel=dplyr::case_when(
grepl("Solo$",con_quien_vive, ignore.case=T)~"Alone",
grepl("Con abuelos",con_quien_vive, ignore.case=T)~"Family of origin",
grepl("Con hermanos",con_quien_vive, ignore.case=T)~"Family of origin",
grepl("Con la madre \\(sola\\)",con_quien_vive, ignore.case=T)~"Family of origin",
grepl("Con otro pariente",con_quien_vive, ignore.case=T)~"Others",
grepl("con hijos y padres o familia",con_quien_vive, ignore.case=T)~"Family of origin",
grepl("con la pareja y padres o familia de origen",con_quien_vive, ignore.case=T)~"With couple/children",
grepl("con padres o familia de origen",con_quien_vive, ignore.case=T)~"Family of origin",
#2021-10-01
grepl("Únicamente con hijos",con_quien_vive, ignore.case=T)~"With couple/children",
grepl("Únicamente con pareja",con_quien_vive, ignore.case=T)~"With couple/children",
#2021-10-01
grepl("Con la Pareja, Hijos y Padres o Familia de Origen",con_quien_vive, ignore.case=T)~"With couple/children",
grepl("Hijos y Padres o Familia de Origen",con_quien_vive, ignore.case=T)~"Family of origin",
#2021-10-01
grepl("Únicamente con la pareja e hijos",con_quien_vive, ignore.case=T)~"With couple/children",
grepl("Con amigos",con_quien_vive, ignore.case=T)~"Others",
grepl("Con otro NO pariente",con_quien_vive, ignore.case=T)~"Others",
grepl("*Otros$",con_quien_vive, ignore.case=T)~"Others")) %>%
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#janitor::tabyl(con_quien_vive, con_quien_vive_rec)
dplyr::filter(!grepl("M-",tipo_de_plan_2)) %>%
#janitor::tabyl(embarazo)
#No Si <NA>
#76081 496 89
#prueba2 %>% janitor::tabyl(embarazo,tiene_menores_de_edad_a_cargo)
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
dplyr::mutate(numero_de_hijos_mod_joel=dplyr::case_when(
grepl("Si$",embarazo, ignore.case=T)~as.integer(numero_de_hijos_mod+1),
T~as.integer(numero_de_hijos_mod)))%>%
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
dplyr::filter(edad_al_ing_grupos=="18-29") %>%
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
dplyr::mutate(dom_violence=factor(dplyr::case_when(
grepl("Violencia Intrafamiliar$",otros_probl_at_sm_or, ignore.case=T)~1,
is.na(otros_probl_at_sm_or)~NA_real_,
T~0),levels=c(0,1),labels=c("No domestic violence","Domestic violence"))) %>%
dplyr::mutate(sex_abuse=factor(dplyr::case_when(
grepl("Abuso Sexual",otros_probl_at_sm_or, ignore.case=T)~1,
is.na(otros_probl_at_sm_or)~NA_real_,
T~0),levels=c(0,1),labels=c("No sexual abuse","Sexual abuse"))) %>%
dplyr::filter(!grepl("Others",con_quien_vive_joel)) %>%
#5 de agosto, paso de 26,236 a 23,979, descartando 2,257 casos que tienen "otros".
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#TENENCIA VIVIENDA
#Allegado, Arrienda, Cedida, Ocupación Irregular, Otros, Paga dividendo, Propia
#vive transitoriamente en casa ajena
dplyr::mutate(tenencia_de_la_vivienda_mod=
factor(dplyr::case_when(tenencia_de_la_vivienda_mod=="Allegado"~"Stays temporarily with a relative",
tenencia_de_la_vivienda_mod=="Arrienda"~"Renting",
tenencia_de_la_vivienda_mod=="Cedida"~"Owner/Transferred dwellings/Pays Dividends",
tenencia_de_la_vivienda_mod=="Ocupación Irregular"~"Illegal Settlement",
tenencia_de_la_vivienda_mod=="Otros"~"Others",
tenencia_de_la_vivienda_mod=="Paga dividendo"~"Owner/Transferred dwellings/Pays Dividends",
tenencia_de_la_vivienda_mod=="Propia"~"Owner/Transferred dwellings/Pays Dividends",
T~NA_character_))) %>%
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#DAR ESTRUCTURA ORDINAL#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
dplyr::mutate(escolaridad_rec=parse_factor(as.character(escolaridad_rec),levels=c('3-Completed primary school or less', '2-Completed high school or less', '1-More than high school'), ordered =T,trim_ws=T,include_na =F, locale=locale(encoding = "Latin1"))) %>%
dplyr::mutate(freq_cons_sus_prin=parse_factor(as.character(freq_cons_sus_prin),levels=c('Less than 1 day a week','2 to 3 days a week','4 to 6 days a week','1 day a week or more','Daily'), ordered =T,trim_ws=F,include_na =F)) %>% #, locale=locale(encoding = "Latin1")
dplyr::mutate(compromiso_biopsicosocial=parse_factor(as.character(compromiso_biopsicosocial),levels=c('1-Mild', '2-Moderate','3-Severe'), ordered =T,trim_ws=F,include_na =F)) %>% #, locale=locale(encoding = "Latin1")
dplyr::mutate(comorbidity_icd_10=parse_factor(as.character(comorbidity_icd_10),levels=c('Without psychiatric comorbidity', 'Diagnosis unknown (under study)','One','Two or more'), ordered =T,trim_ws=F,include_na =F)) %>% #, locale=locale(encoding = "Latin1")
dplyr::mutate(cnt_mod_cie_10_or=parse_factor(as.character(cnt_mod_cie_10_or),levels=c('0', '1','2','3'), ordered =T,trim_ws=F,include_na =F)) %>% #, locale=locale(encoding = "Latin1")
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#dplyr::mutate(edad_al_ing_grupos=if_else(edad_al_ing_grupos=='18-29','<18-29',as.character(edad_al_ing_grupos),NA_character_)) %>%
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#FRECUENCIA CONSUMO: Regroup 1 dÃa a la semana y No usó en el último mes
dplyr::mutate(freq_cons_sus_prin=if_else(freq_cons_sus_prin=='Did not use','Less than 1 day a week',as.character(freq_cons_sus_prin),NA_character_))
#"escolaridad_rec", "freq_cons_sus_prin", "num_otras_sus_mod", "numero_de_hijos_mod_joel", "cnt_mod_cie_10_or", "comorbidity_icd_10", "compromiso_biopsicosocial"
#numero_de_hijos_mod #Número de Hijos (Valor Max.)/Number of Children (Max. Value)
#hijos_trat_res #Tiene Hijos en Ingreso a Tratamiento Residencial del Último Registro/Have Children in Residential Treatment of the Last Entry
#prueba2 %>% janitor::tabyl(numero_de_hijos_mod,embarazo)
#prueba2 %>% janitor::tabyl(numero_de_hijos_mod,hijos_trat_res) #me parece que está bien, no hay contradicciones
#prueba2 %>% janitor::tabyl(con_quien_vive,con_quien_vive_joel)
#prueba2 %>% janitor::tabyl(con_quien_vive,con_quien_vive_joel) %>% copiar_nombres()
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
prueba2%>%
dplyr::arrange(hash_key, fech_ing)%>%
rio::export(file = paste0(gsub("analisis_joel.Rmd","",path),"CONS_C1_df_dup_SEP_2020_joel_2021.dta"))
In the data base for 2021, only includes general population or mixed-gender programs at baseline. The variable whether the respondent had children at admission to a residential treatment had many missing values (16%). In the cases that is feasible to have children, it shows 3% of missing values.
Must note that there are patients that may respond that they have children in treatment and respond that they have more than a child. But there can be people that did not report children and having children in residential treatments.
- ‘Starting substance’ (
sus_ini_mod_mvv)= Most vulnerable value.
- ‘Number of children’(
numero_de_hijos_mod_joel)= If a user was pregnant, we added one children into the count of the number of children, because of the sparsity of pregnancy status (5.22%).
- ‘Primary or main substance’(
sus_principal_mod)= We used the primary substance at admission of the largest treatment for continuous treatments. Every substance was chosen from the largest treatment, excepting cases where the value was not available in the largest treatment.
- ‘Tenure status of households’ (
tenencia_de_la_vivienda_mod)= Three categories were collapsed into one single condition: Owner (28.66%), with Transferred dwellings (3.76%) and Pays Dividend (1.79%).
- ‘Educational Attainment’ (
escolaridad_rec)= We selected the most vulnerable category among continuous treatments for the same user.
- ‘Consumption frequency of primary or main substance’(
freq_cons_sus_prin)= Among the categories of the Frequency of Drug Consumption, we grouped Less than 1 day a week (3.16%) with Did not use in the last 30 days (1.80%)
Total
#browse hash_key duplicates_filtered2 cum_diff_bet_treat dup2_real diff_bet_treat if hash_key=="0737aacbb7efdd418f7a37ce3386ce5e"|hash_key=="07668f2d3e4f6beb7975e43ee96eac80"
#diff_bet_treat_five+ cum_dias_trat_sin_na_five+ cum_diff_bet_treat_five+
# mean_cum_dias_trat_sin_na_five+ mean_cum_diff_bet_treat_five,
attr(prueba$n_treats,"label") <- "No. of treatments with 18+ at admission between 2010 and 2019"
attr(prueba$comorbidity_icd_10,"label") <- "Comorbidity ICD-10 (with amount of different diagnosis)"
attr(prueba$dias_treat_imp_sin_na_four,"label") <- "Days of Treatment (Fourth or those that follow)"
attr(prueba$diff_bet_treat_four,"label") <- "Days of Difference Between Treatments (Fifth treatment or those that folow)"
attr(prueba$cum_dias_trat_sin_na_four,"label") <- "Cumulative Days of Treatment (Fourth or those that follow)"
attr(prueba$cum_diff_bet_treat_four,"label") <- "Cumulative Difference Between Treatments (Fifth or those that follow)" #hash_key
attr(prueba$mean_cum_dias_trat_sin_na_four,"label") <- "Average Cumulative Days of Treatment (Fourth or those that follow)"
attr(prueba$mean_cum_diff_bet_treat_four,"label") <- "Average Cumulative Difference Between Treatments (Fifth or those that follow)"
attr(prueba$cnt_mod_cie_10_dg_cons_sus_or,"label") <- "Total count of Psychiatric & Drug dependence Diagnostics"
attr(prueba$max_cum_diff_bet_treat,"label") <- "Max. Cumulative Difference Between Treatments"
attr(prueba$max_cum_dias_trat_sin_na,"label") <- "Max. Cumulative Days of Treatment"
attr(prueba$tipo_de_plan_2_mod,"label") <- "Type of Plan (Independently of the Program)"
attr(prueba$condicion_ocupacional_corr,"label") <- 'Occupational Status Corrected(f)'
attr(prueba$cat_ocupacional_corr,"label") <- 'Occupational Category Corrected(f)'
attr(prueba$compromiso_biopsicosocial,"label") <- 'Biopsychosocial Compromise'
attr(prueba$tenencia_de_la_vivienda_mod,"label") <- 'Tenure status of households'
#attr(prueba$,"label")<-"Starting Substance"
attr(prueba$sus_principal_mod,"label")<-"Starting Substance"
attr(prueba$cum_dias_trat_sin_na_1,"label") <- "Cum. Days of Treatment (1st Treatment)"
attr(prueba$cum_dias_trat_sin_na_2,"label") <- "Cum. Days of Treatment (2nd Treatment)"
attr(prueba$cum_dias_trat_sin_na_3,"label") <- "Cum. Days of Treatment (3rd Treatment)"
attr(prueba$cum_dias_trat_sin_na_4,"label") <- "Cum. Days of Treatment (4th Treatment)"
attr(prueba$cum_dias_trat_sin_na_5,"label") <- "Cum. Days of Treatment (5th Treatment)"
attr(prueba$cum_dias_trat_sin_na_6,"label") <- "Cum. Days of Treatment (6th Treatment)"
attr(prueba$cum_dias_trat_sin_na_7,"label") <- "Cum. Days of Treatment (7th Treatment)"
attr(prueba$cum_dias_trat_sin_na_8,"label") <- "Cum. Days of Treatment (8th Treatment)"
attr(prueba$cum_dias_trat_sin_na_9,"label") <- "Cum. Days of Treatment (9th Treatment)"
attr(prueba$cum_dias_trat_sin_na_10,"label") <-"Cum. Days of Treatment (10th Treatment)"
attr(prueba$cum_diff_bet_treat_1,"label") <- "Cum. Diff Between Treatments (1st Treatment)"
attr(prueba$cum_diff_bet_treat_2,"label") <- "Cum. Diff Between Treatments (2nd Treatment)"
attr(prueba$cum_diff_bet_treat_3,"label") <- "Cum. Diff Between Treatments (3rd Treatment)"
attr(prueba$cum_diff_bet_treat_4,"label") <- "Cum. Diff Between Treatments (4th Treatment)"
attr(prueba$cum_diff_bet_treat_5,"label") <- "Cum. Diff Between Treatments (5th Treatment)"
attr(prueba$cum_diff_bet_treat_6,"label") <- "Cum. Diff Between Treatments (6th Treatment)"
attr(prueba$cum_diff_bet_treat_7,"label") <- "Cum. Diff Between Treatments (7th Treatment)"
attr(prueba$cum_diff_bet_treat_8,"label") <- "Cum. Diff Between Treatments (8th Treatment)"
attr(prueba$cum_diff_bet_treat_9,"label") <- "Cum. Diff Between Treatments (9th Treatment)"
attr(prueba$cum_diff_bet_treat_10,"label") <-"Cum. Diff Between Treatments (10th Treatment)"
attr(prueba2$n_treats,"label") <- "No. of treatments with 18+ at admission between 2010 and 2019"
attr(prueba2$comorbidity_icd_10,"label") <- "Comorbidity ICD-10 (with amount of different diagnosis)"
attr(prueba2$dias_treat_imp_sin_na_four,"label") <- "Days of Treatment (Fourth or those that follow)"
attr(prueba2$diff_bet_treat_four,"label") <- "Days of Difference Between Treatments (Fifth treatment or those that folow)"
attr(prueba2$cum_dias_trat_sin_na_four,"label") <- "Cumulative Days of Treatment (Fourth or those that follow)"
attr(prueba2$cum_diff_bet_treat_four,"label") <- "Cumulative Difference Between Treatments (Fifth or those that follow)" #hash_key
attr(prueba2$mean_cum_dias_trat_sin_na_four,"label") <- "Average Cumulative Days of Treatment (Fourth or those that follow)"
attr(prueba2$mean_cum_diff_bet_treat_four,"label") <- "Average Cumulative Difference Between Treatments (Fifth or those that follow)"
attr(prueba2$cnt_mod_cie_10_dg_cons_sus_or,"label") <- "Total count of Psychiatric & Drug dependence Diagnostics"
attr(prueba2$max_cum_diff_bet_treat,"label") <- "Max. Cumulative Difference Between Treatments"
attr(prueba2$max_cum_dias_trat_sin_na,"label") <- "Max. Cumulative Days of Treatment"
attr(prueba2$tipo_de_plan_2_mod,"label") <- "Type of Plan (Independently of the Program)"
attr(prueba2$condicion_ocupacional_corr,"label") <- 'Occupational Status Corrected(f)'
attr(prueba2$cat_ocupacional_corr,"label") <- 'Occupational Category Corrected(f)'
attr(prueba2$con_quien_vive_joel,"label") <- 'Whom you live with(cohabitation status) (Recoded) (f)'
attr(prueba2$compromiso_biopsicosocial,"label") <- 'Biopsychosocial Compromise'
attr(prueba2$tenencia_de_la_vivienda_mod,"label") <- 'Tenure status of households'
attr(prueba2$sus_ini_mod_mvv,"label")<-"Starting Substance"
attr(prueba2$cum_dias_trat_sin_na_1,"label") <- "Cum. Days of Treatment (1st Treatment)"
attr(prueba2$cum_dias_trat_sin_na_2,"label") <- "Cum. Days of Treatment (2nd Treatment)"
attr(prueba2$cum_dias_trat_sin_na_3,"label") <- "Cum. Days of Treatment (3rd Treatment)"
attr(prueba2$cum_dias_trat_sin_na_4,"label") <- "Cum. Days of Treatment (4th Treatment)"
attr(prueba2$cum_dias_trat_sin_na_5,"label") <- "Cum. Days of Treatment (5th Treatment)"
attr(prueba2$cum_dias_trat_sin_na_6,"label") <- "Cum. Days of Treatment (6th Treatment)"
attr(prueba2$cum_dias_trat_sin_na_7,"label") <- "Cum. Days of Treatment (7th Treatment)"
attr(prueba2$cum_dias_trat_sin_na_8,"label") <- "Cum. Days of Treatment (8th Treatment)"
attr(prueba2$cum_dias_trat_sin_na_9,"label") <- "Cum. Days of Treatment (9th Treatment)"
attr(prueba2$cum_dias_trat_sin_na_10,"label") <-"Cum. Days of Treatment (10th Treatment)"
attr(prueba2$dias_treat_imp_sin_na_1,"label") <- "Days of Treatment (1st Treatment)"
attr(prueba2$dias_treat_imp_sin_na_2,"label") <- "Days of Treatment (2nd Treatment)"
attr(prueba2$dias_treat_imp_sin_na_3,"label") <- "Days of Treatment (3rd Treatment)"
attr(prueba2$dias_treat_imp_sin_na_4,"label") <- "Days of Treatment (4th Treatment)"
attr(prueba2$dias_treat_imp_sin_na_5,"label") <- "Days of Treatment (5th Treatment)"
attr(prueba2$dias_treat_imp_sin_na_6,"label") <- "Days of Treatment (6th Treatment)"
attr(prueba2$dias_treat_imp_sin_na_7,"label") <- "Days of Treatment (7th Treatment)"
attr(prueba2$dias_treat_imp_sin_na_8,"label") <- "Days of Treatment (8th Treatment)"
attr(prueba2$dias_treat_imp_sin_na_9,"label") <- "Days of Treatment (9th Treatment)"
attr(prueba2$dias_treat_imp_sin_na_10,"label") <-"Days of Treatment (10th Treatment)"
attr(prueba2$cum_diff_bet_treat_1,"label") <- "Cum. Diff Between Treatments (1st Treatment)"
attr(prueba2$cum_diff_bet_treat_2,"label") <- "Cum. Diff Between Treatments (2nd Treatment)"
attr(prueba2$cum_diff_bet_treat_3,"label") <- "Cum. Diff Between Treatments (3rd Treatment)"
attr(prueba2$cum_diff_bet_treat_4,"label") <- "Cum. Diff Between Treatments (4th Treatment)"
attr(prueba2$cum_diff_bet_treat_5,"label") <- "Cum. Diff Between Treatments (5th Treatment)"
attr(prueba2$cum_diff_bet_treat_6,"label") <- "Cum. Diff Between Treatments (6th Treatment)"
attr(prueba2$cum_diff_bet_treat_7,"label") <- "Cum. Diff Between Treatments (7th Treatment)"
attr(prueba2$cum_diff_bet_treat_8,"label") <- "Cum. Diff Between Treatments (8th Treatment)"
attr(prueba2$cum_diff_bet_treat_9,"label") <- "Cum. Diff Between Treatments (9th Treatment)"
attr(prueba2$cum_diff_bet_treat_10,"label") <-"Cum. Diff Between Treatments (10th Treatment)"
attr(prueba2$numero_de_hijos_mod_joel,"label") <-"Number of Children (Max. Value), adding 1 if pregnant at admission"
attr(prueba2$sex_abuse,"label") <-"Sexual abuse"
attr(prueba2$dom_violence,"label") <-"Domestic violence"
attr(prueba2$freq_cons_sus_prin,"label") <-"Frequency of drug use in the primary substance"
#n_treats mean_cum_diff_bet_treat_four mean_cum_diff_bet_treat_four cum_diff_bet_treat_four cum_dias_trat_sin_na_four diff_bet_treat_four dias_treat_imp_sin_na_four cnt_mod_cie_10_dg_cons_sus_or max_cum_diff_bet_treat max_cum_dias_trat_sin_na cum_diff_bet_treat_10 cum_dias_trat_sin_na_10 tipo_de_plan_2_mod
library(compareGroups)
table3 <- compareGroups::compareGroups(no_group ~ sexo_2+ escolaridad_rec+ estado_conyugal_2+ compromiso_biopsicosocial+ edad_ini_cons+ edad_al_ing+ sus_ini_mod+ sus_ini_mod_mvv+ freq_cons_sus_prin+ via_adm_sus_prin_act+ con_quien_vive_joel+ numero_de_hijos_mod_joel+ condicion_ocupacional_corr+ cat_ocupacional_corr+ abandono_temprano+ dg_cie_10_rec+ dias_treat_imp_sin_na+ cnt_diagnostico_trs_fisico+ cnt_otros_probl_at_sm_or+ tipo_de_plan_2_mod+ tenencia_de_la_vivienda_mod+ cum_dias_trat_sin_na_1+ cum_dias_trat_sin_na_2+ cum_dias_trat_sin_na_3+ cum_dias_trat_sin_na_4+ cum_dias_trat_sin_na_5+ cum_dias_trat_sin_na_6+ cum_dias_trat_sin_na_7+ cum_dias_trat_sin_na_8+ cum_dias_trat_sin_na_9+ cum_dias_trat_sin_na_10+ dias_treat_imp_sin_na_1+ dias_treat_imp_sin_na_2+ dias_treat_imp_sin_na_3+ dias_treat_imp_sin_na_4+ dias_treat_imp_sin_na_5+ dias_treat_imp_sin_na_6+ dias_treat_imp_sin_na_7+ dias_treat_imp_sin_na_8+ dias_treat_imp_sin_na_9+ dias_treat_imp_sin_na_10+ cum_diff_bet_treat_1+ cum_diff_bet_treat_2+ cum_diff_bet_treat_3+ cum_diff_bet_treat_4+ cum_diff_bet_treat_5+ cum_diff_bet_treat_6+ cum_diff_bet_treat_7+ cum_diff_bet_treat_8+ cum_diff_bet_treat_9+ cum_diff_bet_treat_10+ duplicates_filtered+ max_cum_dias_trat_sin_na+ max_cum_diff_bet_treat+ cnt_mod_cie_10_dg_cons_sus_or+ cnt_mod_cie_10_or+ dg_total_cie_10+ dias_treat_imp_sin_na_four+ diff_bet_treat_four+ cum_dias_trat_sin_na_four+ cum_diff_bet_treat_four+ mean_cum_dias_trat_sin_na_four+ mean_cum_diff_bet_treat_four+ comorbidity_icd_10+ n_treats+ sex_abuse+ dom_violence,
method= c(sexo_2=3,
escolaridad_rec=3,
estado_conyugal_2=3,
compromiso_biopsicosocial=2,
edad_ini_cons=2,
edad_al_ing=2,
sus_ini_mod=3,
sus_ini_mod_mvv=3,
freq_cons_sus_prin=3,
via_adm_sus_prin_act=3,
con_quien_vive_joel=3,
numero_de_hijos_mod_joel=2,
condicion_ocupacional_corr=3,
cat_ocupacional_corr=3,
abandono_temprano=3,
dg_cie_10_rec=3,
dias_treat_imp_sin_na=2,
cnt_mod_cie_10_or=3,
cnt_diagnostico_trs_fisico=2,
cnt_otros_probl_at_sm_or=2,
tipo_de_plan_2_mod=3,
tenencia_de_la_vivienda_mod=2,
cum_dias_trat_sin_na_1= 2,
cum_dias_trat_sin_na_2= 2,
cum_dias_trat_sin_na_3= 2,
cum_dias_trat_sin_na_4= 2,
cum_dias_trat_sin_na_5= 2,
cum_dias_trat_sin_na_6= 2,
cum_dias_trat_sin_na_7= 2,
cum_dias_trat_sin_na_8= 2,
cum_dias_trat_sin_na_9= 2,
cum_dias_trat_sin_na_10=2,
dias_treat_imp_sin_na_1= 2,
dias_treat_imp_sin_na_2= 2,
dias_treat_imp_sin_na_3= 2,
dias_treat_imp_sin_na_4= 2,
dias_treat_imp_sin_na_5= 2,
dias_treat_imp_sin_na_6= 2,
dias_treat_imp_sin_na_7= 2,
dias_treat_imp_sin_na_8= 2,
dias_treat_imp_sin_na_9= 2,
dias_treat_imp_sin_na_10=2,
cum_diff_bet_treat_1= 2,
cum_diff_bet_treat_2= 2,
cum_diff_bet_treat_3= 2,
cum_diff_bet_treat_4= 2,
cum_diff_bet_treat_5= 2,
cum_diff_bet_treat_6= 2,
cum_diff_bet_treat_7= 2,
cum_diff_bet_treat_8= 2,
cum_diff_bet_treat_9= 2,
cum_diff_bet_treat_10= 2,
duplicates_filtered= 3,
max_cum_dias_trat_sin_na= 2,
max_cum_diff_bet_treat= 2,
cnt_mod_cie_10_dg_cons_sus_or= 2,
dg_total_cie_10 = 3,
comorbidity_icd_10 = 3,
dias_treat_imp_sin_na_four = 2,
diff_bet_treat_four = 2,
cum_dias_trat_sin_na_four = 2,
cum_diff_bet_treat_four = 2,
mean_cum_dias_trat_sin_na_four = 2,
mean_cum_diff_bet_treat_four = 2,
n_treats = 3,
sex_abuse = 3,
dom_violence = 3
),
data = prueba2,
include.miss = T,
var.equal=T
)#cie_10 cat_ocupacional estatus_ocupacional
pvals <- getResults(table3)
#p.adjust(pvals, method = "BH")
restab3 <- createTable(table3,show.p.overall = F)
compareGroups::export2md(restab3, size=9, first.strip=T, hide.no="no", position="center",col.names=c("Variables","Total"),
format="html",caption= "Table 1. Summary descriptives table")%>%
kableExtra::add_footnote(c("Note. Variables of C1 dataset had to be standardized before comparison;", "Continuous variables are presented as Medians and Percentiles 25 and 75 were shown;", "Categorical variables are presented as number (%)"), notation = "none")%>%
kableExtra::scroll_box(width = "100%", height = "600px")
Table 1. Summary descriptives table
|
Variables
|
Total
|
|
|
N=23979
|
|
Sexo Usuario/Sex of User:
|
|
|
Men
|
19850 (82.8%)
|
|
Women
|
4129 (17.2%)
|
|
escolaridad_rec:
|
|
|
3-Completed primary school or less
|
5634 (23.5%)
|
|
2-Completed high school or less
|
14518 (60.5%)
|
|
1-More than high school
|
3751 (15.6%)
|
|
‘Missing’
|
76 (0.32%)
|
|
Estado Conyugal/Marital Status:
|
|
|
Married/Shared living arrangements
|
5127 (21.4%)
|
|
Separated/Divorced
|
501 (2.09%)
|
|
Single
|
18294 (76.3%)
|
|
Widower
|
30 (0.13%)
|
|
‘Missing’
|
27 (0.11%)
|
|
Biopsychosocial Compromise:
|
|
|
1-Mild
|
2240 (9.34%)
|
|
2-Moderate
|
13949 (58.2%)
|
|
3-Severe
|
7352 (30.7%)
|
|
‘Missing’
|
438 (1.83%)
|
|
Edad de Inicio de Consumo/Age of Onset of Drug Use
|
15.0 [13.0;16.0]
|
|
Edad a la Fecha de Ingreso a Tratamiento (numérico continuo) (Primera Entrada)/Age at Admission to Treatment (First Entry)
|
25.4 [22.6;27.7]
|
|
Sustancia de Inicio (Sólo más frecuentes)/Starting Substance (Only more frequent):
|
|
|
Alcohol
|
10341 (43.1%)
|
|
CocaÃna
|
934 (3.90%)
|
|
Marihuana
|
9165 (38.2%)
|
|
Otros
|
411 (1.71%)
|
|
Pasta Base
|
1054 (4.40%)
|
|
‘Missing’
|
2074 (8.65%)
|
|
Starting Substance:
|
|
|
Alcohol
|
10182 (42.5%)
|
|
Cocaine hydrochloride
|
970 (4.05%)
|
|
Marijuana
|
9255 (38.6%)
|
|
Other
|
398 (1.66%)
|
|
Cocaine paste
|
1100 (4.59%)
|
|
‘Missing’
|
2074 (8.65%)
|
|
Frequency of drug use in the primary substance:
|
|
|
1 day a week or more
|
1616 (6.74%)
|
|
2 to 3 days a week
|
7059 (29.4%)
|
|
4 to 6 days a week
|
4105 (17.1%)
|
|
Daily
|
9911 (41.3%)
|
|
Less than 1 day a week
|
1166 (4.86%)
|
|
‘Missing’
|
122 (0.51%)
|
|
VÃa de Administración de la Sustancia Principal (Se aplicaron criterios de limpieza)(f)/Route of Administration of the Primary or Main Substance (Tidy)(f):
|
|
|
Smoked or Pulmonary Aspiration
|
13902 (58.0%)
|
|
Intranasal (powder aspiration)
|
5575 (23.2%)
|
|
Injected Intravenously or Intramuscularly
|
16 (0.07%)
|
|
Oral (drunk or eaten)
|
4470 (18.6%)
|
|
Other
|
12 (0.05%)
|
|
‘Missing’
|
4 (0.02%)
|
|
Whom you live with(cohabitation status) (Recoded) (f):
|
|
|
Alone
|
1335 (5.57%)
|
|
Family of origin
|
15455 (64.5%)
|
|
With couple/children
|
7189 (30.0%)
|
|
Number of Children (Max. Value), adding 1 if pregnant at admission
|
1.00 [0.00;1.00]
|
|
Occupational Status Corrected(f):
|
|
|
Employed
|
10423 (43.5%)
|
|
Inactive
|
2019 (8.42%)
|
|
Looking for a job for the first time
|
97 (0.40%)
|
|
No activity
|
1333 (5.56%)
|
|
Not seeking for work
|
216 (0.90%)
|
|
Unemployed
|
9891 (41.2%)
|
|
Occupational Category Corrected(f):
|
|
|
Employer
|
322 (1.34%)
|
|
Other
|
222 (0.93%)
|
|
Salaried
|
7039 (29.4%)
|
|
Self-employed
|
1934 (8.07%)
|
|
Unpaid family labour
|
65 (0.27%)
|
|
Volunteer worker
|
39 (0.16%)
|
|
‘Missing’
|
14358 (59.9%)
|
|
Abandono temprano(<3 meses)/ Early Drop-out(<3 months):
|
|
|
Mayor o igual a 90 dÃas
|
17383 (72.5%)
|
|
Menos de 90 dÃas
|
6596 (27.5%)
|
|
Diagnóstico CIE-10 (1 o más)(Recodificado)/Psychiatric Diagnoses (ICD-10)(one or more)(Recoded):
|
|
|
Without psychiatric comorbidity
|
9259 (38.6%)
|
|
Diagnosis unknown (under study)
|
5198 (21.7%)
|
|
With psychiatric comorbidity
|
9522 (39.7%)
|
|
DÃas de Tratamiento (valores perdidos en la fecha de egreso se reemplazaron por la diferencia con 2019-11-13)/Days of Treatment (missing dates of discharge were replaced with difference from 2019-11-13)
|
147 [84.0;254]
|
|
Recuento de Diagnóstico de Trastorno FÃsico/Count of Physical Disorder
|
0.00 [0.00;0.00]
|
|
Recuento de Otros Problemas de Atención Vinculados a Salud Mental/Count of Other problems linked to Mental Health
|
0.00 [0.00;1.00]
|
|
Type of Plan (Independently of the Program):
|
|
|
PAB
|
9277 (38.7%)
|
|
PAI
|
11390 (47.5%)
|
|
PR
|
3286 (13.7%)
|
|
‘Missing’
|
26 (0.11%)
|
|
Tenure status of households:
|
|
|
Illegal Settlement
|
191 (0.80%)
|
|
Others
|
562 (2.34%)
|
|
Owner/Transferred dwellings/Pays Dividends
|
7502 (31.3%)
|
|
Renting
|
3872 (16.1%)
|
|
Stays temporarily with a relative
|
10646 (44.4%)
|
|
‘Missing’
|
1206 (5.03%)
|
|
Cum. Days of Treatment (1st Treatment)
|
147 [84.0;254]
|
|
Cum. Days of Treatment (2nd Treatment)
|
318 [207;489]
|
|
Cum. Days of Treatment (3rd Treatment)
|
485 [335;704]
|
|
Cum. Days of Treatment (4th Treatment)
|
638 [433;897]
|
|
Cum. Days of Treatment (5th Treatment)
|
805 [550;1041]
|
|
Cum. Days of Treatment (6th Treatment)
|
944 [716;1152]
|
|
Cum. Days of Treatment (7th Treatment)
|
1076 [888;1279]
|
|
Cum. Days of Treatment (8th Treatment)
|
1192 [1152;1232]
|
|
Cum. Days of Treatment (9th Treatment)
|
1403 [1403;1403]
|
|
Cum. Days of Treatment (10th Treatment)
|
1622 [1622;1622]
|
|
Days of Treatment (1st Treatment)
|
147 [84.0;254]
|
|
Days of Treatment (2nd Treatment)
|
137 [77.0;239]
|
|
Days of Treatment (3rd Treatment)
|
134 [73.0;237]
|
|
Days of Treatment (4th Treatment)
|
126 [70.0;224]
|
|
Days of Treatment (5th Treatment)
|
143 [67.0;260]
|
|
Days of Treatment (6th Treatment)
|
145 [76.8;199]
|
|
Days of Treatment (7th Treatment)
|
120 [23.0;175]
|
|
Days of Treatment (8th Treatment)
|
40.0 [29.5;84.5]
|
|
Days of Treatment (9th Treatment)
|
211 [211;211]
|
|
Days of Treatment (10th Treatment)
|
219 [219;219]
|
|
Cum. Diff Between Treatments (1st Treatment)
|
0.00 [0.00;0.00]
|
|
Cum. Diff Between Treatments (2nd Treatment)
|
801 [405;1390]
|
|
Cum. Diff Between Treatments (3rd Treatment)
|
1155 [686;1680]
|
|
Cum. Diff Between Treatments (4th Treatment)
|
1341 [886;1972]
|
|
Cum. Diff Between Treatments (5th Treatment)
|
1458 [1047;1970]
|
|
Cum. Diff Between Treatments (6th Treatment)
|
1509 [1160;2294]
|
|
Cum. Diff Between Treatments (7th Treatment)
|
1188 [1184;1422]
|
|
Cum. Diff Between Treatments (8th Treatment)
|
1706 [1706;1706]
|
|
Cum. Diff Between Treatments (9th Treatment)
|
1944 [1944;1944]
|
|
Cum. Diff Between Treatments (10th Treatment)
|
.
|
|
Número de Tratamientos por HASH (Total)/Number of Treatments by User (Total):
|
|
|
1
|
18472 (77.0%)
|
|
2
|
3908 (16.3%)
|
|
3
|
1087 (4.53%)
|
|
4
|
347 (1.45%)
|
|
5
|
111 (0.46%)
|
|
6
|
37 (0.15%)
|
|
7
|
14 (0.06%)
|
|
8
|
2 (0.01%)
|
|
10
|
1 (0.00%)
|
|
Max. Cumulative Days of Treatment
|
182 [98.0;332]
|
|
Max. Cumulative Difference Between Treatments
|
0.00 [0.00;0.00]
|
|
Total count of Psychiatric & Drug dependence Diagnostics
|
1.00 [1.00;2.00]
|
|
cnt_mod_cie_10_or:
|
|
|
0
|
9259 (38.6%)
|
|
1
|
14314 (59.7%)
|
|
2
|
365 (1.52%)
|
|
3
|
41 (0.17%)
|
|
Conteo de Diagnósticos CIE-10(sólo diagnósticos)/Count of ICD-10 Diagnostics(only diagnoses):
|
|
|
0
|
14457 (60.3%)
|
|
1
|
9116 (38.0%)
|
|
2
|
365 (1.52%)
|
|
3
|
41 (0.17%)
|
|
Days of Treatment (Fourth or those that follow)
|
138 [78.8;227]
|
|
Days of Difference Between Treatments (Fifth treatment or those that folow)
|
277 [140;485]
|
|
Cumulative Days of Treatment (Fourth or those that follow)
|
692 [460;936]
|
|
Cumulative Difference Between Treatments (Fifth or those that follow)
|
1412 [1031;1995]
|
|
Average Cumulative Days of Treatment (Fourth or those that follow)
|
163 [110;221]
|
|
Average Cumulative Difference Between Treatments (Fifth or those that follow)
|
340 [236;488]
|
|
Comorbidity ICD-10 (with amount of different diagnosis):
|
|
|
Without psychiatric comorbidity
|
9259 (38.6%)
|
|
Diagnosis unknown (under study)
|
5198 (21.7%)
|
|
One
|
9116 (38.0%)
|
|
Two or more
|
406 (1.69%)
|
|
No. of treatments with 18+ at admission between 2010 and 2019:
|
|
|
01
|
18475 (77.0%)
|
|
02
|
3907 (16.3%)
|
|
03
|
1085 (4.52%)
|
|
04 or more
|
512 (2.14%)
|
|
Sexual abuse:
|
|
|
No sexual abuse
|
18880 (78.7%)
|
|
Sexual abuse
|
289 (1.21%)
|
|
‘Missing’
|
4810 (20.1%)
|
|
Domestic violence:
|
|
|
No domestic violence
|
14304 (59.7%)
|
|
Domestic violence
|
4865 (20.3%)
|
|
‘Missing’
|
4810 (20.1%)
|
|
Note. Variables of C1 dataset had to be standardized before comparison;
|
|
Continuous variables are presented as Medians and Percentiles 25 and 75 were shown;
|
|
Categorical variables are presented as number (%)
|
Primary substance
table4 <- compareGroups::compareGroups(sus_principal_mod ~ sexo_2+ escolaridad_rec+ compromiso_biopsicosocial+ estado_conyugal_2+ edad_ini_cons+ edad_al_ing+ sus_ini_mod+ sus_ini_mod_mvv+ freq_cons_sus_prin+ via_adm_sus_prin_act+ con_quien_vive_joel+ numero_de_hijos_mod_joel+ condicion_ocupacional_corr+ cat_ocupacional_corr+ abandono_temprano+ dg_cie_10_rec+ dias_treat_imp_sin_na+ cnt_diagnostico_trs_fisico+ cnt_otros_probl_at_sm_or+ tipo_de_plan_2_mod+ tenencia_de_la_vivienda_mod+ cum_dias_trat_sin_na_1+ cum_dias_trat_sin_na_2+ cum_dias_trat_sin_na_3+ cum_dias_trat_sin_na_4+ cum_dias_trat_sin_na_5+ cum_dias_trat_sin_na_6+ cum_dias_trat_sin_na_7+ cum_dias_trat_sin_na_8+ cum_dias_trat_sin_na_9+ cum_dias_trat_sin_na_10+ dias_treat_imp_sin_na_1+ dias_treat_imp_sin_na_2+ dias_treat_imp_sin_na_3+ dias_treat_imp_sin_na_4+ dias_treat_imp_sin_na_5+ dias_treat_imp_sin_na_6+ dias_treat_imp_sin_na_7+ dias_treat_imp_sin_na_8+ dias_treat_imp_sin_na_9+ dias_treat_imp_sin_na_10+ cum_diff_bet_treat_1+ cum_diff_bet_treat_2+ cum_diff_bet_treat_3+ cum_diff_bet_treat_4+ cum_diff_bet_treat_5+ cum_diff_bet_treat_6+ cum_diff_bet_treat_7+ cum_diff_bet_treat_8+ cum_diff_bet_treat_9+ cum_diff_bet_treat_10+ duplicates_filtered+ max_cum_dias_trat_sin_na+ max_cum_diff_bet_treat+ cnt_mod_cie_10_dg_cons_sus_or+ cnt_mod_cie_10_or+ dg_total_cie_10+ dias_treat_imp_sin_na_four+ diff_bet_treat_four+ cum_dias_trat_sin_na_four+ cum_diff_bet_treat_four+ mean_cum_dias_trat_sin_na_four+ mean_cum_diff_bet_treat_four+ comorbidity_icd_10+ n_treats+ sex_abuse+ dom_violence,
method= c(
sexo_2=3,
escolaridad_rec=3,
compromiso_biopsicosocial=2,
estado_conyugal_2=3,
edad_ini_cons=2,
edad_al_ing=2,
sus_ini_mod=3,
sus_ini_mod_mvv=3,
freq_cons_sus_prin=3,
via_adm_sus_prin_act=3,
con_quien_vive_joel=3,
numero_de_hijos_mod_joel=2,
condicion_ocupacional_corr=3,
cat_ocupacional_corr=3,
abandono_temprano=3,
dg_cie_10_rec=3,
dias_treat_imp_sin_na=2,
cnt_mod_cie_10_or=3,
cnt_diagnostico_trs_fisico=2,
cnt_otros_probl_at_sm_or=2,
tipo_de_plan_2_mod=3,
tenencia_de_la_vivienda_mod=2,
cum_dias_trat_sin_na_1= 2,
cum_dias_trat_sin_na_2= 2,
cum_dias_trat_sin_na_3= 2,
cum_dias_trat_sin_na_4= 2,
cum_dias_trat_sin_na_5= 2,
cum_dias_trat_sin_na_6= 2,
cum_dias_trat_sin_na_7= 2,
cum_dias_trat_sin_na_8= 2,
cum_dias_trat_sin_na_9= 2,
cum_dias_trat_sin_na_10=2,
dias_treat_imp_sin_na_1= 2,
dias_treat_imp_sin_na_2= 2,
dias_treat_imp_sin_na_3= 2,
dias_treat_imp_sin_na_4= 2,
dias_treat_imp_sin_na_5= 2,
dias_treat_imp_sin_na_6= 2,
dias_treat_imp_sin_na_7= 2,
dias_treat_imp_sin_na_8= 2,
dias_treat_imp_sin_na_9= 2,
dias_treat_imp_sin_na_10=2,
cum_diff_bet_treat_1= 2,
cum_diff_bet_treat_2= 2,
cum_diff_bet_treat_3= 2,
cum_diff_bet_treat_4= 2,
cum_diff_bet_treat_5= 2,
cum_diff_bet_treat_6= 2,
cum_diff_bet_treat_7= 2,
cum_diff_bet_treat_8= 2,
cum_diff_bet_treat_9= 2,
cum_diff_bet_treat_10= 2,
duplicates_filtered=3,
max_cum_dias_trat_sin_na= 2,
max_cum_diff_bet_treat= 2,
cnt_mod_cie_10_dg_cons_sus_or= 3,
dg_total_cie_10 = 3,
comorbidity_icd_10 = 3,
dias_treat_imp_sin_na_four = 2,
diff_bet_treat_four = 2,
cum_dias_trat_sin_na_four = 2,
cum_diff_bet_treat_four = 2,
mean_cum_dias_trat_sin_na_four = 2,
mean_cum_diff_bet_treat_four = 2,
n_treats = 3,
sex_abuse = 3,
dom_violence= 3
),
data = prueba2,
include.miss = T,
var.equal=T
)#cie_10 cat_ocupacional estatus_ocupacional
pvals <- getResults(table4)
#p.adjust(pvals, method = "BH")
restab4 <- createTable(table4, show.p.overall = T)
compareGroups::export2md(restab4, size=9, first.strip=T, hide.no="no", position="center",
format="html",caption= "Table 2. Summary descriptives table by Primary Substance at Admission")%>%
kableExtra::add_footnote(c("Note. Variables of C1 dataset had to be standardized before comparison;", "Continuous variables are presented as Medians and Percentiles 25 and 75 were shown;", "Categorical variables are presented as number (%)"), notation = "none")%>%
kableExtra::scroll_box(width = "100%", height = "600px")
Table 2. Summary descriptives table by Primary Substance at Admission
|
|
Alcohol
|
Cocaine hydrochloride
|
Marijuana
|
Other
|
Cocaine paste
|
p.overall
|
|
|
N=4142
|
N=5394
|
N=2872
|
N=295
|
N=11276
|
|
|
Sexo Usuario/Sex of User:
|
|
|
|
|
|
<0.001
|
|
Men
|
3321 (80.2%)
|
4474 (82.9%)
|
2356 (82.0%)
|
223 (75.6%)
|
9476 (84.0%)
|
|
|
Women
|
821 (19.8%)
|
920 (17.1%)
|
516 (18.0%)
|
72 (24.4%)
|
1800 (16.0%)
|
|
|
escolaridad_rec:
|
|
|
|
|
|
.
|
|
3-Completed primary school or less
|
900 (21.7%)
|
873 (16.2%)
|
536 (18.7%)
|
62 (21.0%)
|
3263 (28.9%)
|
|
|
2-Completed high school or less
|
2376 (57.4%)
|
3441 (63.8%)
|
1714 (59.7%)
|
152 (51.5%)
|
6835 (60.6%)
|
|
|
1-More than high school
|
852 (20.6%)
|
1063 (19.7%)
|
614 (21.4%)
|
79 (26.8%)
|
1143 (10.1%)
|
|
|
‘Missing’
|
14 (0.34%)
|
17 (0.32%)
|
8 (0.28%)
|
2 (0.68%)
|
35 (0.31%)
|
|
|
Biopsychosocial Compromise:
|
|
|
|
|
|
<0.001
|
|
1-Mild
|
647 (15.6%)
|
538 (9.97%)
|
442 (15.4%)
|
19 (6.44%)
|
594 (5.27%)
|
|
|
2-Moderate
|
2601 (62.8%)
|
3339 (61.9%)
|
1795 (62.5%)
|
156 (52.9%)
|
6058 (53.7%)
|
|
|
3-Severe
|
814 (19.7%)
|
1406 (26.1%)
|
560 (19.5%)
|
115 (39.0%)
|
4457 (39.5%)
|
|
|
‘Missing’
|
80 (1.93%)
|
111 (2.06%)
|
75 (2.61%)
|
5 (1.69%)
|
167 (1.48%)
|
|
|
Estado Conyugal/Marital Status:
|
|
|
|
|
|
.
|
|
Married/Shared living arrangements
|
1019 (24.6%)
|
1350 (25.0%)
|
465 (16.2%)
|
36 (12.2%)
|
2257 (20.0%)
|
|
|
Separated/Divorced
|
108 (2.61%)
|
128 (2.37%)
|
48 (1.67%)
|
8 (2.71%)
|
209 (1.85%)
|
|
|
Single
|
3005 (72.5%)
|
3908 (72.5%)
|
2352 (81.9%)
|
250 (84.7%)
|
8779 (77.9%)
|
|
|
Widower
|
5 (0.12%)
|
3 (0.06%)
|
4 (0.14%)
|
1 (0.34%)
|
17 (0.15%)
|
|
|
‘Missing’
|
5 (0.12%)
|
5 (0.09%)
|
3 (0.10%)
|
0 (0.00%)
|
14 (0.12%)
|
|
|
Edad de Inicio de Consumo/Age of Onset of Drug Use
|
15.0 [13.0;16.0]
|
15.0 [13.0;16.0]
|
15.0 [13.0;16.0]
|
14.0 [13.0;16.0]
|
14.0 [13.0;16.0]
|
<0.001
|
|
Edad a la Fecha de Ingreso a Tratamiento (numérico continuo) (Primera Entrada)/Age at Admission to Treatment (First Entry)
|
25.7 [23.1;27.9]
|
25.6 [23.0;27.8]
|
23.6 [21.2;26.5]
|
24.3 [21.1;27.8]
|
25.5 [22.8;27.8]
|
<0.001
|
|
Sustancia de Inicio (Sólo más frecuentes)/Starting Substance (Only more frequent):
|
|
|
|
|
|
0.000
|
|
Alcohol
|
3256 (78.6%)
|
2366 (43.9%)
|
982 (34.2%)
|
79 (26.8%)
|
3658 (32.4%)
|
|
|
CocaÃna
|
44 (1.06%)
|
604 (11.2%)
|
22 (0.77%)
|
4 (1.36%)
|
260 (2.31%)
|
|
|
Marihuana
|
513 (12.4%)
|
1913 (35.5%)
|
1490 (51.9%)
|
121 (41.0%)
|
5128 (45.5%)
|
|
|
Otros
|
43 (1.04%)
|
65 (1.21%)
|
41 (1.43%)
|
65 (22.0%)
|
197 (1.75%)
|
|
|
Pasta Base
|
28 (0.68%)
|
57 (1.06%)
|
32 (1.11%)
|
2 (0.68%)
|
935 (8.29%)
|
|
|
‘Missing’
|
258 (6.23%)
|
389 (7.21%)
|
305 (10.6%)
|
24 (8.14%)
|
1098 (9.74%)
|
|
|
Starting Substance:
|
|
|
|
|
|
.
|
|
Alcohol
|
3249 (78.4%)
|
2335 (43.3%)
|
973 (33.9%)
|
79 (26.8%)
|
3546 (31.4%)
|
|
|
Cocaine hydrochloride
|
45 (1.09%)
|
627 (11.6%)
|
24 (0.84%)
|
4 (1.36%)
|
270 (2.39%)
|
|
|
Marijuana
|
520 (12.6%)
|
1922 (35.6%)
|
1498 (52.2%)
|
121 (41.0%)
|
5194 (46.1%)
|
|
|
Other
|
41 (0.99%)
|
63 (1.17%)
|
39 (1.36%)
|
65 (22.0%)
|
190 (1.68%)
|
|
|
Cocaine paste
|
29 (0.70%)
|
58 (1.08%)
|
33 (1.15%)
|
2 (0.68%)
|
978 (8.67%)
|
|
|
‘Missing’
|
258 (6.23%)
|
389 (7.21%)
|
305 (10.6%)
|
24 (8.14%)
|
1098 (9.74%)
|
|
|
Frequency of drug use in the primary substance:
|
|
|
|
|
|
.
|
|
1 day a week or more
|
384 (9.27%)
|
502 (9.31%)
|
125 (4.35%)
|
14 (4.75%)
|
591 (5.24%)
|
|
|
2 to 3 days a week
|
1817 (43.9%)
|
1934 (35.9%)
|
639 (22.2%)
|
52 (17.6%)
|
2617 (23.2%)
|
|
|
4 to 6 days a week
|
758 (18.3%)
|
941 (17.4%)
|
416 (14.5%)
|
42 (14.2%)
|
1948 (17.3%)
|
|
|
Daily
|
964 (23.3%)
|
1630 (30.2%)
|
1579 (55.0%)
|
166 (56.3%)
|
5572 (49.4%)
|
|
|
Less than 1 day a week
|
196 (4.73%)
|
354 (6.56%)
|
99 (3.45%)
|
19 (6.44%)
|
498 (4.42%)
|
|
|
‘Missing’
|
23 (0.56%)
|
33 (0.61%)
|
14 (0.49%)
|
2 (0.68%)
|
50 (0.44%)
|
|
|
VÃa de Administración de la Sustancia Principal (Se aplicaron criterios de limpieza)(f)/Route of Administration of the Primary or Main Substance (Tidy)(f):
|
|
|
|
|
|
.
|
|
Smoked or Pulmonary Aspiration
|
0 (0.00%)
|
0 (0.00%)
|
2872 (100%)
|
34 (11.5%)
|
10996 (97.5%)
|
|
|
Intranasal (powder aspiration)
|
0 (0.00%)
|
5394 (100%)
|
0 (0.00%)
|
14 (4.75%)
|
167 (1.48%)
|
|
|
Injected Intravenously or Intramuscularly
|
0 (0.00%)
|
0 (0.00%)
|
0 (0.00%)
|
16 (5.42%)
|
0 (0.00%)
|
|
|
Oral (drunk or eaten)
|
4142 (100%)
|
0 (0.00%)
|
0 (0.00%)
|
225 (76.3%)
|
103 (0.91%)
|
|
|
Other
|
0 (0.00%)
|
0 (0.00%)
|
0 (0.00%)
|
5 (1.69%)
|
7 (0.06%)
|
|
|
‘Missing’
|
0 (0.00%)
|
0 (0.00%)
|
0 (0.00%)
|
1 (0.34%)
|
3 (0.03%)
|
|
|
Whom you live with(cohabitation status) (Recoded) (f):
|
|
|
|
|
|
<0.001
|
|
Alone
|
311 (7.51%)
|
199 (3.69%)
|
148 (5.15%)
|
16 (5.42%)
|
661 (5.86%)
|
|
|
Family of origin
|
2434 (58.8%)
|
3384 (62.7%)
|
2031 (70.7%)
|
222 (75.3%)
|
7384 (65.5%)
|
|
|
With couple/children
|
1397 (33.7%)
|
1811 (33.6%)
|
693 (24.1%)
|
57 (19.3%)
|
3231 (28.7%)
|
|
|
Number of Children (Max. Value), adding 1 if pregnant at admission
|
1.00 [0.00;1.00]
|
1.00 [0.00;1.00]
|
0.00 [0.00;1.00]
|
0.00 [0.00;1.00]
|
1.00 [0.00;2.00]
|
<0.001
|
|
Occupational Status Corrected(f):
|
|
|
|
|
|
.
|
|
Employed
|
2371 (57.2%)
|
2833 (52.5%)
|
1284 (44.7%)
|
96 (32.5%)
|
3839 (34.0%)
|
|
|
Inactive
|
410 (9.90%)
|
368 (6.82%)
|
418 (14.6%)
|
43 (14.6%)
|
780 (6.92%)
|
|
|
Looking for a job for the first time
|
16 (0.39%)
|
14 (0.26%)
|
22 (0.77%)
|
2 (0.68%)
|
43 (0.38%)
|
|
|
No activity
|
163 (3.94%)
|
226 (4.19%)
|
200 (6.96%)
|
27 (9.15%)
|
717 (6.36%)
|
|
|
Not seeking for work
|
20 (0.48%)
|
48 (0.89%)
|
24 (0.84%)
|
5 (1.69%)
|
119 (1.06%)
|
|
|
Unemployed
|
1162 (28.1%)
|
1905 (35.3%)
|
924 (32.2%)
|
122 (41.4%)
|
5778 (51.2%)
|
|
|
Occupational Category Corrected(f):
|
|
|
|
|
|
.
|
|
Employer
|
88 (2.12%)
|
83 (1.54%)
|
34 (1.18%)
|
2 (0.68%)
|
115 (1.02%)
|
|
|
Other
|
48 (1.16%)
|
46 (0.85%)
|
30 (1.04%)
|
4 (1.36%)
|
94 (0.83%)
|
|
|
Salaried
|
1618 (39.1%)
|
1929 (35.8%)
|
878 (30.6%)
|
50 (16.9%)
|
2564 (22.7%)
|
|
|
Self-employed
|
399 (9.63%)
|
559 (10.4%)
|
217 (7.56%)
|
23 (7.80%)
|
736 (6.53%)
|
|
|
Unpaid family labour
|
5 (0.12%)
|
16 (0.30%)
|
11 (0.38%)
|
0 (0.00%)
|
33 (0.29%)
|
|
|
Volunteer worker
|
7 (0.17%)
|
8 (0.15%)
|
9 (0.31%)
|
1 (0.34%)
|
14 (0.12%)
|
|
|
‘Missing’
|
1977 (47.7%)
|
2753 (51.0%)
|
1693 (58.9%)
|
215 (72.9%)
|
7720 (68.5%)
|
|
|
Abandono temprano(<3 meses)/ Early Drop-out(<3 months):
|
|
|
|
|
|
<0.001
|
|
Mayor o igual a 90 dÃas
|
3214 (77.6%)
|
3998 (74.1%)
|
2184 (76.0%)
|
221 (74.9%)
|
7766 (68.9%)
|
|
|
Menos de 90 dÃas
|
928 (22.4%)
|
1396 (25.9%)
|
688 (24.0%)
|
74 (25.1%)
|
3510 (31.1%)
|
|
|
Diagnóstico CIE-10 (1 o más)(Recodificado)/Psychiatric Diagnoses (ICD-10)(one or more)(Recoded):
|
|
|
|
|
|
<0.001
|
|
Without psychiatric comorbidity
|
1652 (39.9%)
|
2156 (40.0%)
|
1100 (38.3%)
|
75 (25.4%)
|
4276 (37.9%)
|
|
|
Diagnosis unknown (under study)
|
720 (17.4%)
|
1141 (21.2%)
|
568 (19.8%)
|
59 (20.0%)
|
2710 (24.0%)
|
|
|
With psychiatric comorbidity
|
1770 (42.7%)
|
2097 (38.9%)
|
1204 (41.9%)
|
161 (54.6%)
|
4290 (38.0%)
|
|
|
DÃas de Tratamiento (valores perdidos en la fecha de egreso se reemplazaron por la diferencia con 2019-11-13)/Days of Treatment (missing dates of discharge were replaced with difference from 2019-11-13)
|
165 [96.0;281]
|
150 [87.0;251]
|
157 [91.0;274]
|
161 [89.0;298]
|
135 [75.8;240]
|
<0.001
|
|
Recuento de Diagnóstico de Trastorno FÃsico/Count of Physical Disorder
|
0.00 [0.00;0.00]
|
0.00 [0.00;0.00]
|
0.00 [0.00;0.00]
|
0.00 [0.00;0.00]
|
0.00 [0.00;0.00]
|
0.005
|
|
Recuento de Otros Problemas de Atención Vinculados a Salud Mental/Count of Other problems linked to Mental Health
|
0.00 [0.00;1.00]
|
0.00 [0.00;1.00]
|
0.00 [0.00;1.00]
|
0.00 [0.00;1.00]
|
0.00 [0.00;1.00]
|
<0.001
|
|
Type of Plan (Independently of the Program):
|
|
|
|
|
|
.
|
|
PAB
|
2120 (51.2%)
|
2321 (43.0%)
|
1380 (48.1%)
|
88 (29.8%)
|
3368 (29.9%)
|
|
|
PAI
|
1811 (43.7%)
|
2605 (48.3%)
|
1331 (46.3%)
|
175 (59.3%)
|
5468 (48.5%)
|
|
|
PR
|
208 (5.02%)
|
465 (8.62%)
|
157 (5.47%)
|
31 (10.5%)
|
2425 (21.5%)
|
|
|
‘Missing’
|
3 (0.07%)
|
3 (0.06%)
|
4 (0.14%)
|
1 (0.34%)
|
15 (0.13%)
|
|
|
Tenure status of households:
|
|
|
|
|
|
.
|
|
Illegal Settlement
|
18 (0.43%)
|
22 (0.41%)
|
22 (0.77%)
|
3 (1.02%)
|
126 (1.12%)
|
|
|
Others
|
88 (2.12%)
|
109 (2.02%)
|
90 (3.13%)
|
4 (1.36%)
|
271 (2.40%)
|
|
|
Owner/Transferred dwellings/Pays Dividends
|
1403 (33.9%)
|
1454 (27.0%)
|
945 (32.9%)
|
90 (30.5%)
|
3610 (32.0%)
|
|
|
Renting
|
821 (19.8%)
|
941 (17.4%)
|
445 (15.5%)
|
53 (18.0%)
|
1612 (14.3%)
|
|
|
Stays temporarily with a relative
|
1648 (39.8%)
|
2647 (49.1%)
|
1216 (42.3%)
|
135 (45.8%)
|
5000 (44.3%)
|
|
|
‘Missing’
|
164 (3.96%)
|
221 (4.10%)
|
154 (5.36%)
|
10 (3.39%)
|
657 (5.83%)
|
|
|
Cum. Days of Treatment (1st Treatment)
|
165 [96.0;281]
|
150 [87.2;251]
|
157 [91.0;274]
|
161 [89.0;300]
|
135 [76.0;240]
|
<0.001
|
|
Cum. Days of Treatment (2nd Treatment)
|
367 [235;577]
|
320 [212;491]
|
351 [239;520]
|
402 [288;592]
|
305 [196;468]
|
<0.001
|
|
Cum. Days of Treatment (3rd Treatment)
|
550 [383;784]
|
483 [344;704]
|
500 [384;676]
|
600 [411;820]
|
473 [320;690]
|
0.013
|
|
Cum. Days of Treatment (4th Treatment)
|
810 [573;994]
|
659 [378;840]
|
595 [437;780]
|
913 [766;1162]
|
608 [432;874]
|
0.013
|
|
Cum. Days of Treatment (5th Treatment)
|
597 [430;972]
|
854 [506;1000]
|
866 [776;925]
|
2000 [2000;2000]
|
786 [554;1067]
|
0.364
|
|
Cum. Days of Treatment (6th Treatment)
|
1022 [669;1087]
|
803 [727;1080]
|
984 [940;995]
|
2230 [2230;2230]
|
949 [714;1175]
|
0.471
|
|
Cum. Days of Treatment (7th Treatment)
|
876 [778;975]
|
887 [581;957]
|
1348 [1348;1348]
|
. [.;.]
|
1178 [1029;1300]
|
0.101
|
|
Cum. Days of Treatment (8th Treatment)
|
. [.;.]
|
. [.;.]
|
. [.;.]
|
. [.;.]
|
1192 [1152;1232]
|
.
|
|
Cum. Days of Treatment (9th Treatment)
|
. [.;.]
|
. [.;.]
|
. [.;.]
|
. [.;.]
|
1403 [1403;1403]
|
.
|
|
Cum. Days of Treatment (10th Treatment)
|
. [.;.]
|
. [.;.]
|
. [.;.]
|
. [.;.]
|
1622 [1622;1622]
|
.
|
|
Days of Treatment (1st Treatment)
|
165 [96.0;281]
|
150 [87.0;251]
|
157 [91.0;274]
|
161 [89.0;298]
|
135 [75.8;240]
|
<0.001
|
|
Days of Treatment (2nd Treatment)
|
160 [92.0;278]
|
139 [79.0;241]
|
147 [80.2;262]
|
215 [106;307]
|
129 [72.0;224]
|
<0.001
|
|
Days of Treatment (3rd Treatment)
|
135 [81.0;232]
|
140 [75.0;223]
|
138 [73.2;251]
|
108 [33.0;189]
|
133 [72.0;239]
|
0.881
|
|
Days of Treatment (4th Treatment)
|
152 [70.2;336]
|
124 [61.5;217]
|
128 [80.5;206]
|
75.0 [46.5;195]
|
124 [70.2;215]
|
0.505
|
|
Days of Treatment (5th Treatment)
|
182 [76.8;232]
|
151 [74.5;226]
|
330 [213;388]
|
588 [588;588]
|
141 [63.0;254]
|
0.268
|
|
Days of Treatment (6th Treatment)
|
67.0 [64.0;175]
|
85.0 [36.5;180]
|
144 [130;195]
|
230 [230;230]
|
154 [106;198]
|
0.373
|
|
Days of Treatment (7th Treatment)
|
31.0 [20.5;41.5]
|
175 [99.0;215]
|
364 [364;364]
|
. [.;.]
|
120 [28.0;146]
|
0.223
|
|
Days of Treatment (8th Treatment)
|
. [.;.]
|
. [.;.]
|
. [.;.]
|
. [.;.]
|
40.0 [29.5;84.5]
|
.
|
|
Days of Treatment (9th Treatment)
|
. [.;.]
|
. [.;.]
|
. [.;.]
|
. [.;.]
|
211 [211;211]
|
.
|
|
Days of Treatment (10th Treatment)
|
. [.;.]
|
. [.;.]
|
. [.;.]
|
. [.;.]
|
219 [219;219]
|
.
|
|
Cum. Diff Between Treatments (1st Treatment)
|
0.00 [0.00;0.00]
|
0.00 [0.00;0.00]
|
0.00 [0.00;0.00]
|
0.00 [0.00;0.00]
|
0.00 [0.00;74.0]
|
<0.001
|
|
Cum. Diff Between Treatments (2nd Treatment)
|
812 [413;1310]
|
799 [405;1420]
|
950 [533;1661]
|
947 [733;1511]
|
774 [392;1366]
|
0.120
|
|
Cum. Diff Between Treatments (3rd Treatment)
|
1075 [645;1639]
|
1221 [794;1732]
|
1209 [696;1821]
|
1274 [858;1290]
|
1146 [680;1667]
|
0.867
|
|
Cum. Diff Between Treatments (4th Treatment)
|
1424 [1098;2117]
|
1141 [888;2078]
|
1428 [1187;1640]
|
1398 [1398;1398]
|
1384 [880;1978]
|
0.971
|
|
Cum. Diff Between Treatments (5th Treatment)
|
1695 [960;1700]
|
1497 [1072;2131]
|
1446 [1432;1668]
|
1415 [1415;1415]
|
1470 [1062;1992]
|
0.992
|
|
Cum. Diff Between Treatments (6th Treatment)
|
1628 [1295;1961]
|
2411 [1953;2594]
|
1827 [1827;1827]
|
. [.;.]
|
1287 [1088;1906]
|
0.452
|
|
Cum. Diff Between Treatments (7th Treatment)
|
. [.;.]
|
. [.;.]
|
. [.;.]
|
. [.;.]
|
1188 [1184;1422]
|
.
|
|
Cum. Diff Between Treatments (8th Treatment)
|
. [.;.]
|
. [.;.]
|
. [.;.]
|
. [.;.]
|
1706 [1706;1706]
|
.
|
|
Cum. Diff Between Treatments (9th Treatment)
|
. [.;.]
|
. [.;.]
|
. [.;.]
|
. [.;.]
|
1944 [1944;1944]
|
.
|
|
Cum. Diff Between Treatments (10th Treatment)
|
.
|
.
|
.
|
.
|
.
|
.
|
|
Número de Tratamientos por HASH (Total)/Number of Treatments by User (Total):
|
|
|
|
|
|
.
|
|
1
|
3435 (82.9%)
|
4367 (81.0%)
|
2422 (84.3%)
|
246 (83.4%)
|
8002 (71.0%)
|
|
|
2
|
537 (13.0%)
|
781 (14.5%)
|
328 (11.4%)
|
35 (11.9%)
|
2227 (19.7%)
|
|
|
3
|
116 (2.80%)
|
176 (3.26%)
|
91 (3.17%)
|
11 (3.73%)
|
693 (6.15%)
|
|
|
4
|
46 (1.11%)
|
43 (0.80%)
|
25 (0.87%)
|
2 (0.68%)
|
231 (2.05%)
|
|
|
5
|
3 (0.07%)
|
17 (0.32%)
|
3 (0.10%)
|
0 (0.00%)
|
88 (0.78%)
|
|
|
6
|
3 (0.07%)
|
7 (0.13%)
|
2 (0.07%)
|
1 (0.34%)
|
24 (0.21%)
|
|
|
7
|
2 (0.05%)
|
3 (0.06%)
|
1 (0.03%)
|
0 (0.00%)
|
8 (0.07%)
|
|
|
8
|
0 (0.00%)
|
0 (0.00%)
|
0 (0.00%)
|
0 (0.00%)
|
2 (0.02%)
|
|
|
10
|
0 (0.00%)
|
0 (0.00%)
|
0 (0.00%)
|
0 (0.00%)
|
1 (0.01%)
|
|
|
Max. Cumulative Days of Treatment
|
194 [106;350]
|
181 [99.0;313]
|
182 [98.0;331]
|
200 [95.0;337]
|
179 [93.0;336]
|
<0.001
|
|
Max. Cumulative Difference Between Treatments
|
0.00 [0.00;0.00]
|
0.00 [0.00;0.00]
|
0.00 [0.00;0.00]
|
0.00 [0.00;0.00]
|
0.00 [0.00;123]
|
<0.001
|
|
Total count of Psychiatric & Drug dependence Diagnostics:
|
|
|
|
|
|
.
|
|
0
|
1108 (26.8%)
|
954 (17.7%)
|
719 (25.0%)
|
33 (11.2%)
|
1450 (12.9%)
|
|
|
1
|
1965 (47.4%)
|
2914 (54.0%)
|
1377 (47.9%)
|
138 (46.8%)
|
6303 (55.9%)
|
|
|
2
|
1003 (24.2%)
|
1438 (26.7%)
|
745 (25.9%)
|
115 (39.0%)
|
3376 (29.9%)
|
|
|
3
|
60 (1.45%)
|
75 (1.39%)
|
28 (0.97%)
|
9 (3.05%)
|
137 (1.21%)
|
|
|
4
|
6 (0.14%)
|
13 (0.24%)
|
3 (0.10%)
|
0 (0.00%)
|
10 (0.09%)
|
|
|
cnt_mod_cie_10_or:
|
|
|
|
|
|
.
|
|
0
|
1652 (39.9%)
|
2156 (40.0%)
|
1100 (38.3%)
|
75 (25.4%)
|
4276 (37.9%)
|
|
|
1
|
2405 (58.1%)
|
3139 (58.2%)
|
1729 (60.2%)
|
207 (70.2%)
|
6834 (60.6%)
|
|
|
2
|
74 (1.79%)
|
84 (1.56%)
|
38 (1.32%)
|
13 (4.41%)
|
156 (1.38%)
|
|
|
3
|
11 (0.27%)
|
15 (0.28%)
|
5 (0.17%)
|
0 (0.00%)
|
10 (0.09%)
|
|
|
Conteo de Diagnósticos CIE-10(sólo diagnósticos)/Count of ICD-10 Diagnostics(only diagnoses):
|
|
|
|
|
|
.
|
|
0
|
2372 (57.3%)
|
3297 (61.1%)
|
1668 (58.1%)
|
134 (45.4%)
|
6986 (62.0%)
|
|
|
1
|
1685 (40.7%)
|
1998 (37.0%)
|
1161 (40.4%)
|
148 (50.2%)
|
4124 (36.6%)
|
|
|
2
|
74 (1.79%)
|
84 (1.56%)
|
38 (1.32%)
|
13 (4.41%)
|
156 (1.38%)
|
|
|
3
|
11 (0.27%)
|
15 (0.28%)
|
5 (0.17%)
|
0 (0.00%)
|
10 (0.09%)
|
|
|
Days of Treatment (Fourth or those that follow)
|
149 [74.8;322]
|
138 [74.0;218]
|
144 [99.0;218]
|
75.0 [46.5;226]
|
136 [79.1;223]
|
0.719
|
|
Days of Difference Between Treatments (Fifth treatment or those that folow)
|
308 [170;552]
|
340 [171;558]
|
330 [138;365]
|
70.5 [70.5;70.5]
|
256 [132;483]
|
0.695
|
|
Cumulative Days of Treatment (Fourth or those that follow)
|
831 [585;1012]
|
676 [412;902]
|
683 [499;809]
|
913 [766;1397]
|
684 [449;929]
|
0.047
|
|
Cumulative Difference Between Treatments (Fifth or those that follow)
|
1694 [1127;2134]
|
1370 [1032;2078]
|
1520 [1385;1640]
|
1406 [1406;1406]
|
1411 [1018;2019]
|
0.940
|
|
Average Cumulative Days of Treatment (Fourth or those that follow)
|
202 [144;248]
|
161 [98.3;201]
|
149 [125;192]
|
228 [191;302]
|
159 [108;217]
|
0.014
|
|
Average Cumulative Difference Between Treatments (Fifth or those that follow)
|
356 [272;518]
|
295 [241;493]
|
347 [296;407]
|
316 [316;316]
|
341 [228;483]
|
0.990
|
|
Comorbidity ICD-10 (with amount of different diagnosis):
|
|
|
|
|
|
.
|
|
Without psychiatric comorbidity
|
1652 (39.9%)
|
2156 (40.0%)
|
1100 (38.3%)
|
75 (25.4%)
|
4276 (37.9%)
|
|
|
Diagnosis unknown (under study)
|
720 (17.4%)
|
1141 (21.2%)
|
568 (19.8%)
|
59 (20.0%)
|
2710 (24.0%)
|
|
|
One
|
1685 (40.7%)
|
1998 (37.0%)
|
1161 (40.4%)
|
148 (50.2%)
|
4124 (36.6%)
|
|
|
Two or more
|
85 (2.05%)
|
99 (1.84%)
|
43 (1.50%)
|
13 (4.41%)
|
166 (1.47%)
|
|
|
No. of treatments with 18+ at admission between 2010 and 2019:
|
|
|
|
|
|
<0.001
|
|
01
|
3435 (82.9%)
|
4368 (81.0%)
|
2422 (84.3%)
|
246 (83.4%)
|
8004 (71.0%)
|
|
|
02
|
537 (13.0%)
|
781 (14.5%)
|
328 (11.4%)
|
36 (12.2%)
|
2225 (19.7%)
|
|
|
03
|
116 (2.80%)
|
175 (3.24%)
|
91 (3.17%)
|
10 (3.39%)
|
693 (6.15%)
|
|
|
04 or more
|
54 (1.30%)
|
70 (1.30%)
|
31 (1.08%)
|
3 (1.02%)
|
354 (3.14%)
|
|
|
Sexual abuse:
|
|
|
|
|
|
.
|
|
No sexual abuse
|
3147 (76.0%)
|
4124 (76.5%)
|
2263 (78.8%)
|
227 (76.9%)
|
9119 (80.9%)
|
|
|
Sexual abuse
|
60 (1.45%)
|
65 (1.21%)
|
23 (0.80%)
|
6 (2.03%)
|
135 (1.20%)
|
|
|
‘Missing’
|
935 (22.6%)
|
1205 (22.3%)
|
586 (20.4%)
|
62 (21.0%)
|
2022 (17.9%)
|
|
|
Domestic violence:
|
|
|
|
|
|
<0.001
|
|
No domestic violence
|
2278 (55.0%)
|
3228 (59.8%)
|
1764 (61.4%)
|
161 (54.6%)
|
6873 (61.0%)
|
|
|
Domestic violence
|
929 (22.4%)
|
961 (17.8%)
|
522 (18.2%)
|
72 (24.4%)
|
2381 (21.1%)
|
|
|
‘Missing’
|
935 (22.6%)
|
1205 (22.3%)
|
586 (20.4%)
|
62 (21.0%)
|
2022 (17.9%)
|
|
|
Note. Variables of C1 dataset had to be standardized before comparison;
|
|
Continuous variables are presented as Medians and Percentiles 25 and 75 were shown;
|
|
Categorical variables are presented as number (%)
|
Living with
table5 <- compareGroups::compareGroups(con_quien_vive_joel ~ sus_principal_mod+ sexo_2+ escolaridad_rec+ compromiso_biopsicosocial+ estado_conyugal_2+ edad_ini_cons+ edad_al_ing+ sus_ini_mod+ sus_ini_mod_mvv+ freq_cons_sus_prin+ via_adm_sus_prin_act+ numero_de_hijos_mod_joel+ condicion_ocupacional_corr+ cat_ocupacional_corr+ abandono_temprano+ dg_cie_10_rec+ dias_treat_imp_sin_na+ cnt_diagnostico_trs_fisico+ cnt_otros_probl_at_sm_or+ tipo_de_plan_2_mod+ tenencia_de_la_vivienda_mod+ cum_dias_trat_sin_na_1+ cum_dias_trat_sin_na_2+ cum_dias_trat_sin_na_3+ cum_dias_trat_sin_na_4+ cum_dias_trat_sin_na_5+ cum_dias_trat_sin_na_6+ cum_dias_trat_sin_na_7+ cum_dias_trat_sin_na_8+ cum_dias_trat_sin_na_9+ cum_dias_trat_sin_na_10+ dias_treat_imp_sin_na_1+ dias_treat_imp_sin_na_2+ dias_treat_imp_sin_na_3+ dias_treat_imp_sin_na_4+ dias_treat_imp_sin_na_5+ dias_treat_imp_sin_na_6+ dias_treat_imp_sin_na_7+ dias_treat_imp_sin_na_8+ dias_treat_imp_sin_na_9+ dias_treat_imp_sin_na_10+ cum_diff_bet_treat_1+cum_diff_bet_treat_2+ cum_diff_bet_treat_3+ cum_diff_bet_treat_4+ cum_diff_bet_treat_5+ cum_diff_bet_treat_6+ cum_diff_bet_treat_7+ cum_diff_bet_treat_8+ cum_diff_bet_treat_9+ cum_diff_bet_treat_10+ duplicates_filtered+max_cum_dias_trat_sin_na+ max_cum_diff_bet_treat+ cnt_mod_cie_10_dg_cons_sus_or+ cnt_mod_cie_10_or+ dg_total_cie_10+dias_treat_imp_sin_na_four+ diff_bet_treat_four+ cum_dias_trat_sin_na_four+ cum_diff_bet_treat_four+ mean_cum_dias_trat_sin_na_four+ mean_cum_diff_bet_treat_four+ comorbidity_icd_10+ n_treats+ sex_abuse+ dom_violence,
method= c(sus_principal_mod=3,
sexo_2=3,
escolaridad_rec=3,
compromiso_biopsicosocial=2,
estado_conyugal_2=3,
edad_ini_cons=2,
edad_al_ing=2,
sus_ini_mod=3,
sus_ini_mod_mvv=3,
freq_cons_sus_prin=3,
via_adm_sus_prin_act=3,
numero_de_hijos_mod_joel=2,
condicion_ocupacional_corr=3,
cat_ocupacional_corr=3,
abandono_temprano=3,
dg_cie_10_rec=3,
dias_treat_imp_sin_na=2,
cnt_mod_cie_10_or=3,
cnt_diagnostico_trs_fisico=2,
cnt_otros_probl_at_sm_or=2,
tipo_de_plan_2_mod=3,
tenencia_de_la_vivienda_mod=2,
cum_dias_trat_sin_na_1= 2,
cum_dias_trat_sin_na_2= 2,
cum_dias_trat_sin_na_3= 2,
cum_dias_trat_sin_na_4= 2,
cum_dias_trat_sin_na_5= 2,
cum_dias_trat_sin_na_6= 2,
cum_dias_trat_sin_na_7= 2,
cum_dias_trat_sin_na_8= 2,
cum_dias_trat_sin_na_9= 2,
cum_dias_trat_sin_na_10=2,
dias_treat_imp_sin_na_1= 2,
dias_treat_imp_sin_na_2= 2,
dias_treat_imp_sin_na_3= 2,
dias_treat_imp_sin_na_4= 2,
dias_treat_imp_sin_na_5= 2,
dias_treat_imp_sin_na_6= 2,
dias_treat_imp_sin_na_7= 2,
dias_treat_imp_sin_na_8= 2,
dias_treat_imp_sin_na_9= 2,
dias_treat_imp_sin_na_10=2,
cum_diff_bet_treat_1= 2,
cum_diff_bet_treat_2= 2,
cum_diff_bet_treat_3= 2,
cum_diff_bet_treat_4= 2,
cum_diff_bet_treat_5= 2,
cum_diff_bet_treat_6= 2,
cum_diff_bet_treat_7= 2,
cum_diff_bet_treat_8= 2,
cum_diff_bet_treat_9= 2,
cum_diff_bet_treat_10= 2,
duplicates_filtered=3,
max_cum_dias_trat_sin_na= 2,
max_cum_diff_bet_treat= 2,
cnt_mod_cie_10_dg_cons_sus_or= 3,
dg_total_cie_10 = 3,
comorbidity_icd_10 = 3,
dias_treat_imp_sin_na_four = 2,
diff_bet_treat_four = 2,
cum_dias_trat_sin_na_four = 2,
cum_diff_bet_treat_four = 2,
mean_cum_dias_trat_sin_na_four = 2,
mean_cum_diff_bet_treat_four = 2,
n_treats = 3,
sex_abuse = 3,
dom_violence = 3
),
data = prueba2,
include.miss = T,
var.equal=T,
max.xlev = 10,
max.ylev = 10
)#cie_10 cat_ocupacional estatus_ocupacional
pvals <- getResults(table5)
#p.adjust(pvals, method = "BH")
restab5 <- createTable(table5, show.p.overall = T)
compareGroups::export2md(restab5, size=9, first.strip=T, hide.no="no", position="center",col.names=c("Variables","Alone","Family of origin", "With couple", "P-value"),
format="html",caption= "Table 4. Summary descriptives table by With whom they live")%>%
kableExtra::add_footnote(c("Note. Variables of C1 dataset had to be standardized before comparison;", "Continuous variables are presented as Medians and Percentiles 25 and 75 were shown;", "Categorical variables are presented as number (%)"), notation = "none")%>%
kableExtra::scroll_box(width = "100%", height = "600px")
Table 4. Summary descriptives table by With whom they live
|
Variables
|
Alone
|
Family of origin
|
With couple
|
P-value
|
|
|
N=1335
|
N=15455
|
N=7189
|
|
|
Sustancia Principal de Consumo (Sólo más frecuentes)(f)/Primary or Main Substance of Consumption at Admission (Only more frequent)(f):
|
|
|
|
<0.001
|
|
Alcohol
|
311 (23.3%)
|
2434 (15.7%)
|
1397 (19.4%)
|
|
|
Cocaine hydrochloride
|
199 (14.9%)
|
3384 (21.9%)
|
1811 (25.2%)
|
|
|
Marijuana
|
148 (11.1%)
|
2031 (13.1%)
|
693 (9.64%)
|
|
|
Other
|
16 (1.20%)
|
222 (1.44%)
|
57 (0.79%)
|
|
|
Cocaine paste
|
661 (49.5%)
|
7384 (47.8%)
|
3231 (44.9%)
|
|
|
Sexo Usuario/Sex of User:
|
|
|
|
<0.001
|
|
Men
|
1171 (87.7%)
|
13154 (85.1%)
|
5525 (76.9%)
|
|
|
Women
|
164 (12.3%)
|
2301 (14.9%)
|
1664 (23.1%)
|
|
|
escolaridad_rec:
|
|
|
|
.
|
|
3-Completed primary school or less
|
359 (26.9%)
|
3333 (21.6%)
|
1942 (27.0%)
|
|
|
2-Completed high school or less
|
767 (57.5%)
|
9420 (61.0%)
|
4331 (60.2%)
|
|
|
1-More than high school
|
201 (15.1%)
|
2660 (17.2%)
|
890 (12.4%)
|
|
|
‘Missing’
|
8 (0.60%)
|
42 (0.27%)
|
26 (0.36%)
|
|
|
Biopsychosocial Compromise:
|
|
|
|
<0.001
|
|
1-Mild
|
92 (6.89%)
|
1328 (8.59%)
|
820 (11.4%)
|
|
|
2-Moderate
|
642 (48.1%)
|
8856 (57.3%)
|
4451 (61.9%)
|
|
|
3-Severe
|
588 (44.0%)
|
4988 (32.3%)
|
1776 (24.7%)
|
|
|
‘Missing’
|
13 (0.97%)
|
283 (1.83%)
|
142 (1.98%)
|
|
|
Estado Conyugal/Marital Status:
|
|
|
|
.
|
|
Married/Shared living arrangements
|
101 (7.57%)
|
858 (5.55%)
|
4168 (58.0%)
|
|
|
Separated/Divorced
|
55 (4.12%)
|
361 (2.34%)
|
85 (1.18%)
|
|
|
Single
|
1171 (87.7%)
|
14203 (91.9%)
|
2920 (40.6%)
|
|
|
Widower
|
1 (0.07%)
|
24 (0.16%)
|
5 (0.07%)
|
|
|
‘Missing’
|
7 (0.52%)
|
9 (0.06%)
|
11 (0.15%)
|
|
|
Edad de Inicio de Consumo/Age of Onset of Drug Use
|
14.0 [13.0;16.0]
|
15.0 [13.0;16.0]
|
15.0 [13.0;16.0]
|
<0.001
|
|
Edad a la Fecha de Ingreso a Tratamiento (numérico continuo) (Primera Entrada)/Age at Admission to Treatment (First Entry)
|
26.3 [23.5;28.3]
|
24.7 [22.0;27.3]
|
26.4 [24.1;28.3]
|
<0.001
|
|
Sustancia de Inicio (Sólo más frecuentes)/Starting Substance (Only more frequent):
|
|
|
|
<0.001
|
|
Alcohol
|
587 (44.0%)
|
6505 (42.1%)
|
3249 (45.2%)
|
|
|
CocaÃna
|
37 (2.77%)
|
548 (3.55%)
|
349 (4.85%)
|
|
|
Marihuana
|
511 (38.3%)
|
6157 (39.8%)
|
2497 (34.7%)
|
|
|
Otros
|
26 (1.95%)
|
250 (1.62%)
|
135 (1.88%)
|
|
|
Pasta Base
|
68 (5.09%)
|
636 (4.12%)
|
350 (4.87%)
|
|
|
‘Missing’
|
106 (7.94%)
|
1359 (8.79%)
|
609 (8.47%)
|
|
|
Starting Substance:
|
|
|
|
<0.001
|
|
Alcohol
|
581 (43.5%)
|
6385 (41.3%)
|
3216 (44.7%)
|
|
|
Cocaine hydrochloride
|
39 (2.92%)
|
571 (3.69%)
|
360 (5.01%)
|
|
|
Marijuana
|
514 (38.5%)
|
6228 (40.3%)
|
2513 (35.0%)
|
|
|
Other
|
24 (1.80%)
|
245 (1.59%)
|
129 (1.79%)
|
|
|
Cocaine paste
|
71 (5.32%)
|
667 (4.32%)
|
362 (5.04%)
|
|
|
‘Missing’
|
106 (7.94%)
|
1359 (8.79%)
|
609 (8.47%)
|
|
|
Frequency of drug use in the primary substance:
|
|
|
|
<0.001
|
|
1 day a week or more
|
55 (4.12%)
|
904 (5.85%)
|
657 (9.14%)
|
|
|
2 to 3 days a week
|
328 (24.6%)
|
4334 (28.0%)
|
2397 (33.3%)
|
|
|
4 to 6 days a week
|
204 (15.3%)
|
2737 (17.7%)
|
1164 (16.2%)
|
|
|
Daily
|
699 (52.4%)
|
6768 (43.8%)
|
2444 (34.0%)
|
|
|
Less than 1 day a week
|
45 (3.37%)
|
633 (4.10%)
|
488 (6.79%)
|
|
|
‘Missing’
|
4 (0.30%)
|
79 (0.51%)
|
39 (0.54%)
|
|
|
VÃa de Administración de la Sustancia Principal (Se aplicaron criterios de limpieza)(f)/Route of Administration of the Primary or Main Substance (Tidy)(f):
|
|
|
|
.
|
|
Smoked or Pulmonary Aspiration
|
798 (59.8%)
|
9266 (60.0%)
|
3838 (53.4%)
|
|
|
Intranasal (powder aspiration)
|
205 (15.4%)
|
3505 (22.7%)
|
1865 (25.9%)
|
|
|
Injected Intravenously or Intramuscularly
|
2 (0.15%)
|
12 (0.08%)
|
2 (0.03%)
|
|
|
Oral (drunk or eaten)
|
328 (24.6%)
|
2662 (17.2%)
|
1480 (20.6%)
|
|
|
Other
|
1 (0.07%)
|
7 (0.05%)
|
4 (0.06%)
|
|
|
‘Missing’
|
1 (0.07%)
|
3 (0.02%)
|
0 (0.00%)
|
|
|
Number of Children (Max. Value), adding 1 if pregnant at admission
|
1.00 [0.00;1.00]
|
0.00 [0.00;1.00]
|
1.00 [1.00;2.00]
|
0.000
|
|
Occupational Status Corrected(f):
|
|
|
|
<0.001
|
|
Employed
|
648 (48.5%)
|
5601 (36.2%)
|
4174 (58.1%)
|
|
|
Inactive
|
57 (4.27%)
|
1231 (7.97%)
|
731 (10.2%)
|
|
|
Looking for a job for the first time
|
3 (0.22%)
|
78 (0.50%)
|
16 (0.22%)
|
|
|
No activity
|
86 (6.44%)
|
1015 (6.57%)
|
232 (3.23%)
|
|
|
Not seeking for work
|
34 (2.55%)
|
154 (1.00%)
|
28 (0.39%)
|
|
|
Unemployed
|
507 (38.0%)
|
7376 (47.7%)
|
2008 (27.9%)
|
|
|
Occupational Category Corrected(f):
|
|
|
|
.
|
|
Employer
|
14 (1.05%)
|
163 (1.05%)
|
145 (2.02%)
|
|
|
Other
|
17 (1.27%)
|
128 (0.83%)
|
77 (1.07%)
|
|
|
Salaried
|
432 (32.4%)
|
3833 (24.8%)
|
2774 (38.6%)
|
|
|
Self-employed
|
119 (8.91%)
|
953 (6.17%)
|
862 (12.0%)
|
|
|
Unpaid family labour
|
1 (0.07%)
|
52 (0.34%)
|
12 (0.17%)
|
|
|
Volunteer worker
|
2 (0.15%)
|
25 (0.16%)
|
12 (0.17%)
|
|
|
‘Missing’
|
750 (56.2%)
|
10301 (66.7%)
|
3307 (46.0%)
|
|
|
Abandono temprano(<3 meses)/ Early Drop-out(<3 months):
|
|
|
|
<0.001
|
|
Mayor o igual a 90 dÃas
|
865 (64.8%)
|
11229 (72.7%)
|
5289 (73.6%)
|
|
|
Menos de 90 dÃas
|
470 (35.2%)
|
4226 (27.3%)
|
1900 (26.4%)
|
|
|
Diagnóstico CIE-10 (1 o más)(Recodificado)/Psychiatric Diagnoses (ICD-10)(one or more)(Recoded):
|
|
|
|
<0.001
|
|
Without psychiatric comorbidity
|
453 (33.9%)
|
5718 (37.0%)
|
3088 (43.0%)
|
|
|
Diagnosis unknown (under study)
|
346 (25.9%)
|
3322 (21.5%)
|
1530 (21.3%)
|
|
|
With psychiatric comorbidity
|
536 (40.1%)
|
6415 (41.5%)
|
2571 (35.8%)
|
|
|
DÃas de Tratamiento (valores perdidos en la fecha de egreso se reemplazaron por la diferencia con 2019-11-13)/Days of Treatment (missing dates of discharge were replaced with difference from 2019-11-13)
|
125 [68.0;223]
|
148 [84.0;257]
|
147 [86.0;253]
|
<0.001
|
|
Recuento de Diagnóstico de Trastorno FÃsico/Count of Physical Disorder
|
0.00 [0.00;0.00]
|
0.00 [0.00;0.00]
|
0.00 [0.00;0.00]
|
0.015
|
|
Recuento de Otros Problemas de Atención Vinculados a Salud Mental/Count of Other problems linked to Mental Health
|
0.00 [0.00;1.00]
|
0.00 [0.00;1.00]
|
0.00 [0.00;1.00]
|
<0.001
|
|
Type of Plan (Independently of the Program):
|
|
|
|
.
|
|
PAB
|
391 (29.3%)
|
5506 (35.6%)
|
3380 (47.0%)
|
|
|
PAI
|
586 (43.9%)
|
7508 (48.6%)
|
3296 (45.8%)
|
|
|
PR
|
356 (26.7%)
|
2423 (15.7%)
|
507 (7.05%)
|
|
|
‘Missing’
|
2 (0.15%)
|
18 (0.12%)
|
6 (0.08%)
|
|
|
Tenure status of households:
|
|
|
|
0.000
|
|
Illegal Settlement
|
53 (3.97%)
|
48 (0.31%)
|
90 (1.25%)
|
|
|
Others
|
74 (5.54%)
|
374 (2.42%)
|
114 (1.59%)
|
|
|
Owner/Transferred dwellings/Pays Dividends
|
258 (19.3%)
|
5345 (34.6%)
|
1899 (26.4%)
|
|
|
Renting
|
555 (41.6%)
|
1154 (7.47%)
|
2163 (30.1%)
|
|
|
Stays temporarily with a relative
|
145 (10.9%)
|
7879 (51.0%)
|
2622 (36.5%)
|
|
|
‘Missing’
|
250 (18.7%)
|
655 (4.24%)
|
301 (4.19%)
|
|
|
Cum. Days of Treatment (1st Treatment)
|
125 [68.0;224]
|
148 [84.0;257]
|
147 [86.0;253]
|
<0.001
|
|
Cum. Days of Treatment (2nd Treatment)
|
285 [174;423]
|
320 [206;493]
|
326 [218;489]
|
<0.001
|
|
Cum. Days of Treatment (3rd Treatment)
|
463 [260;644]
|
474 [326;700]
|
522 [361;730]
|
0.008
|
|
Cum. Days of Treatment (4th Treatment)
|
526 [339;755]
|
633 [426;878]
|
671 [486;941]
|
0.059
|
|
Cum. Days of Treatment (5th Treatment)
|
690 [532;1120]
|
798 [541;1047]
|
876 [576;1021]
|
0.604
|
|
Cum. Days of Treatment (6th Treatment)
|
460 [356;565]
|
897 [712;1153]
|
1006 [868;1144]
|
0.106
|
|
Cum. Days of Treatment (7th Treatment)
|
477 [376;578]
|
1165 [993;1331]
|
1076 [1074;1254]
|
0.100
|
|
Cum. Days of Treatment (8th Treatment)
|
. [.;.]
|
1152 [1131;1172]
|
1273 [1273;1273]
|
0.221
|
|
Cum. Days of Treatment (9th Treatment)
|
. [.;.]
|
1403 [1403;1403]
|
. [.;.]
|
.
|
|
Cum. Days of Treatment (10th Treatment)
|
. [.;.]
|
1622 [1622;1622]
|
. [.;.]
|
.
|
|
Days of Treatment (1st Treatment)
|
125 [68.0;223]
|
148 [84.0;257]
|
147 [86.0;253]
|
<0.001
|
|
Days of Treatment (2nd Treatment)
|
119 [57.0;204]
|
137 [76.0;239]
|
139 [83.0;245]
|
<0.001
|
|
Days of Treatment (3rd Treatment)
|
127 [71.0;220]
|
127 [70.0;229]
|
148 [84.8;246]
|
0.020
|
|
Days of Treatment (4th Treatment)
|
143 [57.2;248]
|
125 [70.0;215]
|
131 [71.0;231]
|
0.997
|
|
Days of Treatment (5th Treatment)
|
199 [139;335]
|
138 [67.8;254]
|
145 [59.2;260]
|
0.567
|
|
Days of Treatment (6th Treatment)
|
28.0 [17.5;38.5]
|
149 [121;195]
|
116 [68.5;205]
|
0.121
|
|
Days of Treatment (7th Treatment)
|
16.5 [13.2;19.8]
|
169 [58.5;244]
|
53.0 [52.0;120]
|
0.075
|
|
Days of Treatment (8th Treatment)
|
. [.;.]
|
84.5 [62.2;107]
|
19.0 [19.0;19.0]
|
0.221
|
|
Days of Treatment (9th Treatment)
|
. [.;.]
|
211 [211;211]
|
. [.;.]
|
.
|
|
Days of Treatment (10th Treatment)
|
. [.;.]
|
219 [219;219]
|
. [.;.]
|
.
|
|
Cum. Diff Between Treatments (1st Treatment)
|
0.00 [0.00;0.00]
|
0.00 [0.00;0.00]
|
0.00 [0.00;0.00]
|
0.812
|
|
Cum. Diff Between Treatments (2nd Treatment)
|
715 [380;1315]
|
779 [396;1355]
|
872 [441;1492]
|
0.075
|
|
Cum. Diff Between Treatments (3rd Treatment)
|
920 [405;1812]
|
1128 [674;1648]
|
1235 [770;1697]
|
0.436
|
|
Cum. Diff Between Treatments (4th Treatment)
|
1290 [1044;2121]
|
1196 [843;1722]
|
1448 [1149;2190]
|
0.034
|
|
Cum. Diff Between Treatments (5th Treatment)
|
2020 [1858;2182]
|
1470 [1091;1768]
|
1418 [944;2384]
|
0.513
|
|
Cum. Diff Between Treatments (6th Treatment)
|
2536 [2415;2657]
|
1576 [1244;2085]
|
1221 [1015;1287]
|
0.174
|
|
Cum. Diff Between Treatments (7th Treatment)
|
. [.;.]
|
1418 [1300;1537]
|
1188 [1188;1188]
|
1.000
|
|
Cum. Diff Between Treatments (8th Treatment)
|
. [.;.]
|
1706 [1706;1706]
|
. [.;.]
|
.
|
|
Cum. Diff Between Treatments (9th Treatment)
|
. [.;.]
|
1944 [1944;1944]
|
. [.;.]
|
.
|
|
Cum. Diff Between Treatments (10th Treatment)
|
.
|
.
|
.
|
.
|
|
Número de Tratamientos por HASH (Total)/Number of Treatments by User (Total):
|
|
|
|
.
|
|
1
|
1033 (77.4%)
|
11903 (77.0%)
|
5536 (77.0%)
|
|
|
2
|
207 (15.5%)
|
2540 (16.4%)
|
1161 (16.1%)
|
|
|
3
|
71 (5.32%)
|
673 (4.35%)
|
343 (4.77%)
|
|
|
4
|
15 (1.12%)
|
233 (1.51%)
|
99 (1.38%)
|
|
|
5
|
7 (0.52%)
|
69 (0.45%)
|
35 (0.49%)
|
|
|
6
|
0 (0.00%)
|
27 (0.17%)
|
10 (0.14%)
|
|
|
7
|
2 (0.15%)
|
8 (0.05%)
|
4 (0.06%)
|
|
|
8
|
0 (0.00%)
|
1 (0.01%)
|
1 (0.01%)
|
|
|
10
|
0 (0.00%)
|
1 (0.01%)
|
0 (0.00%)
|
|
|
Max. Cumulative Days of Treatment
|
154 [80.0;304]
|
183 [98.0;332]
|
186 [100;338]
|
<0.001
|
|
Max. Cumulative Difference Between Treatments
|
0.00 [0.00;0.00]
|
0.00 [0.00;0.00]
|
0.00 [0.00;0.00]
|
0.883
|
|
Total count of Psychiatric & Drug dependence Diagnostics:
|
|
|
|
.
|
|
0
|
224 (16.8%)
|
2493 (16.1%)
|
1547 (21.5%)
|
|
|
1
|
695 (52.1%)
|
8175 (52.9%)
|
3827 (53.2%)
|
|
|
2
|
399 (29.9%)
|
4536 (29.3%)
|
1742 (24.2%)
|
|
|
3
|
16 (1.20%)
|
226 (1.46%)
|
67 (0.93%)
|
|
|
4
|
1 (0.07%)
|
25 (0.16%)
|
6 (0.08%)
|
|
|
cnt_mod_cie_10_or:
|
|
|
|
.
|
|
0
|
453 (33.9%)
|
5718 (37.0%)
|
3088 (43.0%)
|
|
|
1
|
860 (64.4%)
|
9441 (61.1%)
|
4013 (55.8%)
|
|
|
2
|
19 (1.42%)
|
264 (1.71%)
|
82 (1.14%)
|
|
|
3
|
3 (0.22%)
|
32 (0.21%)
|
6 (0.08%)
|
|
|
Conteo de Diagnósticos CIE-10(sólo diagnósticos)/Count of ICD-10 Diagnostics(only diagnoses):
|
|
|
|
.
|
|
0
|
799 (59.9%)
|
9040 (58.5%)
|
4618 (64.2%)
|
|
|
1
|
514 (38.5%)
|
6119 (39.6%)
|
2483 (34.5%)
|
|
|
2
|
19 (1.42%)
|
264 (1.71%)
|
82 (1.14%)
|
|
|
3
|
3 (0.22%)
|
32 (0.21%)
|
6 (0.08%)
|
|
|
Days of Treatment (Fourth or those that follow)
|
149 [51.8;234]
|
138 [77.8;225]
|
137 [85.4;236]
|
0.966
|
|
Days of Difference Between Treatments (Fifth treatment or those that folow)
|
464 [238;588]
|
258 [112;416]
|
288 [164;682]
|
0.064
|
|
Cumulative Days of Treatment (Fourth or those that follow)
|
590 [364;936]
|
683 [454;932]
|
727 [518;999]
|
0.083
|
|
Cumulative Difference Between Treatments (Fifth or those that follow)
|
1760 [1044;2121]
|
1345 [943;1759]
|
1518 [1174;2252]
|
0.081
|
|
Average Cumulative Days of Treatment (Fourth or those that follow)
|
133 [91.0;209]
|
162 [107;218]
|
171 [116;229]
|
0.080
|
|
Average Cumulative Difference Between Treatments (Fifth or those that follow)
|
348 [261;497]
|
312 [222;424]
|
373 [286;544]
|
0.064
|
|
Comorbidity ICD-10 (with amount of different diagnosis):
|
|
|
|
<0.001
|
|
Without psychiatric comorbidity
|
453 (33.9%)
|
5718 (37.0%)
|
3088 (43.0%)
|
|
|
Diagnosis unknown (under study)
|
346 (25.9%)
|
3322 (21.5%)
|
1530 (21.3%)
|
|
|
One
|
514 (38.5%)
|
6119 (39.6%)
|
2483 (34.5%)
|
|
|
Two or more
|
22 (1.65%)
|
296 (1.92%)
|
88 (1.22%)
|
|
|
No. of treatments with 18+ at admission between 2010 and 2019:
|
|
|
|
0.426
|
|
01
|
1034 (77.5%)
|
11905 (77.0%)
|
5536 (77.0%)
|
|
|
02
|
206 (15.4%)
|
2540 (16.4%)
|
1161 (16.1%)
|
|
|
03
|
71 (5.32%)
|
671 (4.34%)
|
343 (4.77%)
|
|
|
04 or more
|
24 (1.80%)
|
339 (2.19%)
|
149 (2.07%)
|
|
|
Sexual abuse:
|
|
|
|
0.034
|
|
No sexual abuse
|
1053 (78.9%)
|
12207 (79.0%)
|
5620 (78.2%)
|
|
|
Sexual abuse
|
27 (2.02%)
|
175 (1.13%)
|
87 (1.21%)
|
|
|
‘Missing’
|
255 (19.1%)
|
3073 (19.9%)
|
1482 (20.6%)
|
|
|
Domestic violence:
|
|
|
|
<0.001
|
|
No domestic violence
|
745 (55.8%)
|
9509 (61.5%)
|
4050 (56.3%)
|
|
|
Domestic violence
|
335 (25.1%)
|
2873 (18.6%)
|
1657 (23.0%)
|
|
|
‘Missing’
|
255 (19.1%)
|
3073 (19.9%)
|
1482 (20.6%)
|
|
|
Note. Variables of C1 dataset had to be standardized before comparison;
|
|
Continuous variables are presented as Medians and Percentiles 25 and 75 were shown;
|
|
Categorical variables are presented as number (%)
|
Readmissions
table6 <- compareGroups::compareGroups(had_readm ~ sexo_2+ escolaridad_rec+ compromiso_biopsicosocial+ estado_conyugal_2+ edad_ini_cons+ edad_al_ing+ sus_ini_mod+ sus_ini_mod_mvv+ freq_cons_sus_prin+ via_adm_sus_prin_act+ con_quien_vive_joel+ numero_de_hijos_mod_joel+ condicion_ocupacional_corr+ cat_ocupacional_corr+ abandono_temprano+ dg_cie_10_rec+ dias_treat_imp_sin_na+ cnt_diagnostico_trs_fisico+ cnt_otros_probl_at_sm_or+ tipo_de_plan_2_mod+ tenencia_de_la_vivienda_mod+ cum_dias_trat_sin_na_1+ cum_dias_trat_sin_na_2+ cum_dias_trat_sin_na_3+ cum_dias_trat_sin_na_4+ cum_dias_trat_sin_na_5+ cum_dias_trat_sin_na_6+ cum_dias_trat_sin_na_7+ cum_dias_trat_sin_na_8+ cum_dias_trat_sin_na_9+ cum_dias_trat_sin_na_10+ dias_treat_imp_sin_na_1+ dias_treat_imp_sin_na_2+ dias_treat_imp_sin_na_3+ dias_treat_imp_sin_na_4+ dias_treat_imp_sin_na_5+ dias_treat_imp_sin_na_6+ dias_treat_imp_sin_na_7+ dias_treat_imp_sin_na_8+ dias_treat_imp_sin_na_9+ dias_treat_imp_sin_na_10+ cum_diff_bet_treat_1+cum_diff_bet_treat_2+ cum_diff_bet_treat_3+ cum_diff_bet_treat_4+ cum_diff_bet_treat_5+ cum_diff_bet_treat_6+ cum_diff_bet_treat_7+ cum_diff_bet_treat_8+ cum_diff_bet_treat_9+ cum_diff_bet_treat_10+ duplicates_filtered+ max_cum_dias_trat_sin_na+ max_cum_diff_bet_treat+ cnt_mod_cie_10_dg_cons_sus_or+ cnt_mod_cie_10_or+ dg_total_cie_10+dias_treat_imp_sin_na_four+ diff_bet_treat_four+ cum_dias_trat_sin_na_four+ cum_diff_bet_treat_four+ mean_cum_dias_trat_sin_na_four+ mean_cum_diff_bet_treat_four+ comorbidity_icd_10+ n_treats+ sex_abuse+ dom_violence,
method= c(
sexo_2=3,
escolaridad_rec=3,
compromiso_biopsicosocial=2,
estado_conyugal_2=3,
edad_ini_cons=2,
edad_al_ing=2,
sus_ini_mod=3,
sus_ini_mod_mvv=3,
freq_cons_sus_prin=3,
via_adm_sus_prin_act=3,
con_quien_vive_joel=3,
numero_de_hijos_mod_joel=2,
condicion_ocupacional_corr=3,
cat_ocupacional_corr=3,
abandono_temprano=3,
dg_cie_10_rec=3,
dias_treat_imp_sin_na=2,
cnt_mod_cie_10_or=3,
cnt_diagnostico_trs_fisico=2,
cnt_otros_probl_at_sm_or=2,
tipo_de_plan_2_mod=3,
tenencia_de_la_vivienda_mod=2,
cum_dias_trat_sin_na_1= 2,
cum_dias_trat_sin_na_2= 2,
cum_dias_trat_sin_na_3= 2,
cum_dias_trat_sin_na_4= 2,
cum_dias_trat_sin_na_5= 2,
cum_dias_trat_sin_na_6= 2,
cum_dias_trat_sin_na_7= 2,
cum_dias_trat_sin_na_8= 2,
cum_dias_trat_sin_na_9= 2,
cum_dias_trat_sin_na_10=2,
dias_treat_imp_sin_na_1= 2,
dias_treat_imp_sin_na_2= 2,
dias_treat_imp_sin_na_3= 2,
dias_treat_imp_sin_na_4= 2,
dias_treat_imp_sin_na_5= 2,
dias_treat_imp_sin_na_6= 2,
dias_treat_imp_sin_na_7= 2,
dias_treat_imp_sin_na_8= 2,
dias_treat_imp_sin_na_9= 2,
dias_treat_imp_sin_na_10=2,
cum_diff_bet_treat_1= 2,
cum_diff_bet_treat_2= 2,
cum_diff_bet_treat_3= 2,
cum_diff_bet_treat_4= 2,
cum_diff_bet_treat_5= 2,
cum_diff_bet_treat_6= 2,
cum_diff_bet_treat_7= 2,
cum_diff_bet_treat_8= 2,
cum_diff_bet_treat_9= 2,
cum_diff_bet_treat_10= 2,
duplicates_filtered=3,
max_cum_dias_trat_sin_na= 2,
max_cum_diff_bet_treat= 2,
cnt_mod_cie_10_dg_cons_sus_or= 3,
dg_total_cie_10 = 3,
comorbidity_icd_10 = 3,
dias_treat_imp_sin_na_four = 2,
diff_bet_treat_four = 2,
cum_dias_trat_sin_na_four = 2,
cum_diff_bet_treat_four = 2,
mean_cum_dias_trat_sin_na_four = 2,
mean_cum_diff_bet_treat_four = 2,
n_treats = 3,
sex_abuse = 3,
dom_violence = 3
),
data = prueba2,
include.miss = T,
var.equal=T
)#cie_10 cat_ocupacional estatus_ocupacional
pvals <- getResults(table6)
#p.adjust(pvals, method = "BH")
restab6 <- createTable(table6, show.p.overall = T)
compareGroups::export2md(restab6, size=9, first.strip=T, hide.no="no", position="center",col.names=c("Variables","Had no Readmissions","Had Readmissions", "P-value"),
format="html",caption= "Table 5. Summary descriptives table by Readmissions")%>%
kableExtra::add_footnote(c("Note. Variables of C1 dataset had to be standardized before comparison;", "Continuous variables are presented as Medians and Percentiles 25 and 75 were shown;", "Categorical variables are presented as number (%)"), notation = "none")%>%
kableExtra::scroll_box(width = "100%", height = "600px")
Table 5. Summary descriptives table by Readmissions
|
Variables
|
Had no Readmissions
|
Had Readmissions
|
P-value
|
|
|
N=18475
|
N=5504
|
|
|
Sexo Usuario/Sex of User:
|
|
|
0.001
|
|
Men
|
15373 (83.2%)
|
4477 (81.3%)
|
|
|
Women
|
3102 (16.8%)
|
1027 (18.7%)
|
|
|
escolaridad_rec:
|
|
|
<0.001
|
|
3-Completed primary school or less
|
4166 (22.5%)
|
1468 (26.7%)
|
|
|
2-Completed high school or less
|
11046 (59.8%)
|
3472 (63.1%)
|
|
|
1-More than high school
|
3202 (17.3%)
|
549 (9.97%)
|
|
|
‘Missing’
|
61 (0.33%)
|
15 (0.27%)
|
|
|
Biopsychosocial Compromise:
|
|
|
<0.001
|
|
1-Mild
|
1840 (9.96%)
|
400 (7.27%)
|
|
|
2-Moderate
|
11058 (59.9%)
|
2891 (52.5%)
|
|
|
3-Severe
|
5244 (28.4%)
|
2108 (38.3%)
|
|
|
‘Missing’
|
333 (1.80%)
|
105 (1.91%)
|
|
|
Estado Conyugal/Marital Status:
|
|
|
0.105
|
|
Married/Shared living arrangements
|
3918 (21.2%)
|
1209 (22.0%)
|
|
|
Separated/Divorced
|
397 (2.15%)
|
104 (1.89%)
|
|
|
Single
|
14121 (76.4%)
|
4173 (75.8%)
|
|
|
Widower
|
18 (0.10%)
|
12 (0.22%)
|
|
|
‘Missing’
|
21 (0.11%)
|
6 (0.11%)
|
|
|
Edad de Inicio de Consumo/Age of Onset of Drug Use
|
15.0 [13.0;16.0]
|
14.0 [13.0;16.0]
|
0.088
|
|
Edad a la Fecha de Ingreso a Tratamiento (numérico continuo) (Primera Entrada)/Age at Admission to Treatment (First Entry)
|
25.3 [22.5;27.7]
|
25.5 [22.8;27.8]
|
<0.001
|
|
Sustancia de Inicio (Sólo más frecuentes)/Starting Substance (Only more frequent):
|
|
|
<0.001
|
|
Alcohol
|
7890 (42.7%)
|
2451 (44.5%)
|
|
|
CocaÃna
|
688 (3.72%)
|
246 (4.47%)
|
|
|
Marihuana
|
6926 (37.5%)
|
2239 (40.7%)
|
|
|
Otros
|
298 (1.61%)
|
113 (2.05%)
|
|
|
Pasta Base
|
667 (3.61%)
|
387 (7.03%)
|
|
|
‘Missing’
|
2006 (10.9%)
|
68 (1.24%)
|
|
|
Starting Substance:
|
|
|
<0.001
|
|
Alcohol
|
7839 (42.4%)
|
2343 (42.6%)
|
|
|
Cocaine hydrochloride
|
703 (3.81%)
|
267 (4.85%)
|
|
|
Marijuana
|
6951 (37.6%)
|
2304 (41.9%)
|
|
|
Other
|
295 (1.60%)
|
103 (1.87%)
|
|
|
Cocaine paste
|
681 (3.69%)
|
419 (7.61%)
|
|
|
‘Missing’
|
2006 (10.9%)
|
68 (1.24%)
|
|
|
Frequency of drug use in the primary substance:
|
|
|
0.003
|
|
1 day a week or more
|
1272 (6.88%)
|
344 (6.25%)
|
|
|
2 to 3 days a week
|
5493 (29.7%)
|
1566 (28.5%)
|
|
|
4 to 6 days a week
|
3140 (17.0%)
|
965 (17.5%)
|
|
|
Daily
|
7539 (40.8%)
|
2372 (43.1%)
|
|
|
Less than 1 day a week
|
935 (5.06%)
|
231 (4.20%)
|
|
|
‘Missing’
|
96 (0.52%)
|
26 (0.47%)
|
|
|
VÃa de Administración de la Sustancia Principal (Se aplicaron criterios de limpieza)(f)/Route of Administration of the Primary or Main Substance (Tidy)(f):
|
|
|
.
|
|
Smoked or Pulmonary Aspiration
|
10250 (55.5%)
|
3652 (66.4%)
|
|
|
Intranasal (powder aspiration)
|
4504 (24.4%)
|
1071 (19.5%)
|
|
|
Injected Intravenously or Intramuscularly
|
15 (0.08%)
|
1 (0.02%)
|
|
|
Oral (drunk or eaten)
|
3695 (20.0%)
|
775 (14.1%)
|
|
|
Other
|
7 (0.04%)
|
5 (0.09%)
|
|
|
‘Missing’
|
4 (0.02%)
|
0 (0.00%)
|
|
|
Whom you live with(cohabitation status) (Recoded) (f):
|
|
|
0.935
|
|
Alone
|
1034 (5.60%)
|
301 (5.47%)
|
|
|
Family of origin
|
11905 (64.4%)
|
3550 (64.5%)
|
|
|
With couple/children
|
5536 (30.0%)
|
1653 (30.0%)
|
|
|
Number of Children (Max. Value), adding 1 if pregnant at admission
|
1.00 [0.00;1.00]
|
1.00 [0.00;2.00]
|
<0.001
|
|
Occupational Status Corrected(f):
|
|
|
<0.001
|
|
Employed
|
8278 (44.8%)
|
2145 (39.0%)
|
|
|
Inactive
|
1562 (8.45%)
|
457 (8.30%)
|
|
|
Looking for a job for the first time
|
72 (0.39%)
|
25 (0.45%)
|
|
|
No activity
|
1021 (5.53%)
|
312 (5.67%)
|
|
|
Not seeking for work
|
157 (0.85%)
|
59 (1.07%)
|
|
|
Unemployed
|
7385 (40.0%)
|
2506 (45.5%)
|
|
|
Occupational Category Corrected(f):
|
|
|
<0.001
|
|
Employer
|
256 (1.39%)
|
66 (1.20%)
|
|
|
Other
|
176 (0.95%)
|
46 (0.84%)
|
|
|
Salaried
|
5527 (29.9%)
|
1512 (27.5%)
|
|
|
Self-employed
|
1584 (8.57%)
|
350 (6.36%)
|
|
|
Unpaid family labour
|
50 (0.27%)
|
15 (0.27%)
|
|
|
Volunteer worker
|
30 (0.16%)
|
9 (0.16%)
|
|
|
‘Missing’
|
10852 (58.7%)
|
3506 (63.7%)
|
|
|
Abandono temprano(<3 meses)/ Early Drop-out(<3 months):
|
|
|
0.547
|
|
Mayor o igual a 90 dÃas
|
13375 (72.4%)
|
4008 (72.8%)
|
|
|
Menos de 90 dÃas
|
5100 (27.6%)
|
1496 (27.2%)
|
|
|
Diagnóstico CIE-10 (1 o más)(Recodificado)/Psychiatric Diagnoses (ICD-10)(one or more)(Recoded):
|
|
|
0.134
|
|
Without psychiatric comorbidity
|
7191 (38.9%)
|
2068 (37.6%)
|
|
|
Diagnosis unknown (under study)
|
4006 (21.7%)
|
1192 (21.7%)
|
|
|
With psychiatric comorbidity
|
7278 (39.4%)
|
2244 (40.8%)
|
|
|
DÃas de Tratamiento (valores perdidos en la fecha de egreso se reemplazaron por la diferencia con 2019-11-13)/Days of Treatment (missing dates of discharge were replaced with difference from 2019-11-13)
|
146 [84.0;254]
|
148 [85.0;254]
|
0.399
|
|
Recuento de Diagnóstico de Trastorno FÃsico/Count of Physical Disorder
|
0.00 [0.00;0.00]
|
0.00 [0.00;0.00]
|
0.949
|
|
Recuento de Otros Problemas de Atención Vinculados a Salud Mental/Count of Other problems linked to Mental Health
|
0.00 [0.00;1.00]
|
0.00 [0.00;1.00]
|
<0.001
|
|
Type of Plan (Independently of the Program):
|
|
|
<0.001
|
|
PAB
|
7376 (39.9%)
|
1901 (34.5%)
|
|
|
PAI
|
8901 (48.2%)
|
2489 (45.2%)
|
|
|
PR
|
2184 (11.8%)
|
1102 (20.0%)
|
|
|
‘Missing’
|
14 (0.08%)
|
12 (0.22%)
|
|
|
Tenure status of households:
|
|
|
0.056
|
|
Illegal Settlement
|
145 (0.78%)
|
46 (0.84%)
|
|
|
Others
|
424 (2.29%)
|
138 (2.51%)
|
|
|
Owner/Transferred dwellings/Pays Dividends
|
5782 (31.3%)
|
1720 (31.2%)
|
|
|
Renting
|
2999 (16.2%)
|
873 (15.9%)
|
|
|
Stays temporarily with a relative
|
8239 (44.6%)
|
2407 (43.7%)
|
|
|
‘Missing’
|
886 (4.80%)
|
320 (5.81%)
|
|
|
Cum. Days of Treatment (1st Treatment)
|
146 [84.0;254]
|
148 [85.0;255]
|
0.380
|
|
Cum. Days of Treatment (2nd Treatment)
|
. [.;.]
|
318 [207;489]
|
.
|
|
Cum. Days of Treatment (3rd Treatment)
|
. [.;.]
|
485 [335;704]
|
.
|
|
Cum. Days of Treatment (4th Treatment)
|
. [.;.]
|
638 [433;897]
|
.
|
|
Cum. Days of Treatment (5th Treatment)
|
. [.;.]
|
805 [550;1041]
|
.
|
|
Cum. Days of Treatment (6th Treatment)
|
. [.;.]
|
944 [716;1152]
|
.
|
|
Cum. Days of Treatment (7th Treatment)
|
. [.;.]
|
1076 [888;1279]
|
.
|
|
Cum. Days of Treatment (8th Treatment)
|
. [.;.]
|
1192 [1152;1232]
|
.
|
|
Cum. Days of Treatment (9th Treatment)
|
. [.;.]
|
1403 [1403;1403]
|
.
|
|
Cum. Days of Treatment (10th Treatment)
|
. [.;.]
|
1622 [1622;1622]
|
.
|
|
Days of Treatment (1st Treatment)
|
146 [84.0;254]
|
148 [85.0;254]
|
0.399
|
|
Days of Treatment (2nd Treatment)
|
. [.;.]
|
137 [77.0;239]
|
.
|
|
Days of Treatment (3rd Treatment)
|
. [.;.]
|
134 [73.0;237]
|
.
|
|
Days of Treatment (4th Treatment)
|
. [.;.]
|
126 [70.0;224]
|
.
|
|
Days of Treatment (5th Treatment)
|
. [.;.]
|
143 [67.0;260]
|
.
|
|
Days of Treatment (6th Treatment)
|
. [.;.]
|
145 [76.8;199]
|
.
|
|
Days of Treatment (7th Treatment)
|
. [.;.]
|
120 [23.0;175]
|
.
|
|
Days of Treatment (8th Treatment)
|
. [.;.]
|
40.0 [29.5;84.5]
|
.
|
|
Days of Treatment (9th Treatment)
|
. [.;.]
|
211 [211;211]
|
.
|
|
Days of Treatment (10th Treatment)
|
. [.;.]
|
219 [219;219]
|
.
|
|
Cum. Diff Between Treatments (1st Treatment)
|
0.00 [0.00;0.00]
|
407 [149;926]
|
0.000
|
|
Cum. Diff Between Treatments (2nd Treatment)
|
. [.;.]
|
801 [405;1390]
|
.
|
|
Cum. Diff Between Treatments (3rd Treatment)
|
. [.;.]
|
1155 [686;1680]
|
.
|
|
Cum. Diff Between Treatments (4th Treatment)
|
. [.;.]
|
1341 [886;1972]
|
.
|
|
Cum. Diff Between Treatments (5th Treatment)
|
. [.;.]
|
1458 [1047;1970]
|
.
|
|
Cum. Diff Between Treatments (6th Treatment)
|
. [.;.]
|
1509 [1160;2294]
|
.
|
|
Cum. Diff Between Treatments (7th Treatment)
|
. [.;.]
|
1188 [1184;1422]
|
.
|
|
Cum. Diff Between Treatments (8th Treatment)
|
. [.;.]
|
1706 [1706;1706]
|
.
|
|
Cum. Diff Between Treatments (9th Treatment)
|
. [.;.]
|
1944 [1944;1944]
|
.
|
|
Cum. Diff Between Treatments (10th Treatment)
|
.
|
.
|
.
|
|
Número de Tratamientos por HASH (Total)/Number of Treatments by User (Total):
|
|
|
.
|
|
1
|
18472 (100.0%)
|
0 (0.00%)
|
|
|
2
|
3 (0.02%)
|
3905 (70.9%)
|
|
|
3
|
0 (0.00%)
|
1087 (19.7%)
|
|
|
4
|
0 (0.00%)
|
347 (6.30%)
|
|
|
5
|
0 (0.00%)
|
111 (2.02%)
|
|
|
6
|
0 (0.00%)
|
37 (0.67%)
|
|
|
7
|
0 (0.00%)
|
14 (0.25%)
|
|
|
8
|
0 (0.00%)
|
2 (0.04%)
|
|
|
10
|
0 (0.00%)
|
1 (0.02%)
|
|
|
Max. Cumulative Days of Treatment
|
146 [84.0;254]
|
380 [239;583]
|
0.000
|
|
Max. Cumulative Difference Between Treatments
|
0.00 [0.00;0.00]
|
630 [232;1268]
|
0.000
|
|
Total count of Psychiatric & Drug dependence Diagnostics:
|
|
|
<0.001
|
|
0
|
3463 (18.7%)
|
801 (14.6%)
|
|
|
1
|
9719 (52.6%)
|
2978 (54.1%)
|
|
|
2
|
5036 (27.3%)
|
1641 (29.8%)
|
|
|
3
|
232 (1.26%)
|
77 (1.40%)
|
|
|
4
|
25 (0.14%)
|
7 (0.13%)
|
|
|
cnt_mod_cie_10_or:
|
|
|
0.238
|
|
0
|
7191 (38.9%)
|
2068 (37.6%)
|
|
|
1
|
10972 (59.4%)
|
3342 (60.7%)
|
|
|
2
|
278 (1.50%)
|
87 (1.58%)
|
|
|
3
|
34 (0.18%)
|
7 (0.13%)
|
|
|
Conteo de Diagnósticos CIE-10(sólo diagnósticos)/Count of ICD-10 Diagnostics(only diagnoses):
|
|
|
0.226
|
|
0
|
11197 (60.6%)
|
3260 (59.2%)
|
|
|
1
|
6966 (37.7%)
|
2150 (39.1%)
|
|
|
2
|
278 (1.50%)
|
87 (1.58%)
|
|
|
3
|
34 (0.18%)
|
7 (0.13%)
|
|
|
Days of Treatment (Fourth or those that follow)
|
. [.;.]
|
138 [78.8;227]
|
.
|
|
Days of Difference Between Treatments (Fifth treatment or those that folow)
|
. [.;.]
|
277 [140;485]
|
.
|
|
Cumulative Days of Treatment (Fourth or those that follow)
|
. [.;.]
|
692 [460;936]
|
.
|
|
Cumulative Difference Between Treatments (Fifth or those that follow)
|
. [.;.]
|
1412 [1031;1995]
|
.
|
|
Average Cumulative Days of Treatment (Fourth or those that follow)
|
. [.;.]
|
163 [110;221]
|
.
|
|
Average Cumulative Difference Between Treatments (Fifth or those that follow)
|
. [.;.]
|
340 [236;488]
|
.
|
|
Comorbidity ICD-10 (with amount of different diagnosis):
|
|
|
0.254
|
|
Without psychiatric comorbidity
|
7191 (38.9%)
|
2068 (37.6%)
|
|
|
Diagnosis unknown (under study)
|
4006 (21.7%)
|
1192 (21.7%)
|
|
|
One
|
6966 (37.7%)
|
2150 (39.1%)
|
|
|
Two or more
|
312 (1.69%)
|
94 (1.71%)
|
|
|
No. of treatments with 18+ at admission between 2010 and 2019:
|
|
|
0.000
|
|
01
|
18475 (100%)
|
0 (0.00%)
|
|
|
02
|
0 (0.00%)
|
3907 (71.0%)
|
|
|
03
|
0 (0.00%)
|
1085 (19.7%)
|
|
|
04 or more
|
0 (0.00%)
|
512 (9.30%)
|
|
|
Sexual abuse:
|
|
|
<0.001
|
|
No sexual abuse
|
14413 (78.0%)
|
4467 (81.2%)
|
|
|
Sexual abuse
|
203 (1.10%)
|
86 (1.56%)
|
|
|
‘Missing’
|
3859 (20.9%)
|
951 (17.3%)
|
|
|
Domestic violence:
|
|
|
<0.001
|
|
No domestic violence
|
10974 (59.4%)
|
3330 (60.5%)
|
|
|
Domestic violence
|
3642 (19.7%)
|
1223 (22.2%)
|
|
|
‘Missing’
|
3859 (20.9%)
|
951 (17.3%)
|
|
|
Note. Variables of C1 dataset had to be standardized before comparison;
|
|
Continuous variables are presented as Medians and Percentiles 25 and 75 were shown;
|
|
Categorical variables are presented as number (%)
|
library(ggplot2)
jpg_path<-rstudioapi::getSourceEditorContext()$path
if (grepl("CISS Fondecyt",jpg_path)==T){
jpg_path<-paste0("C:/Users/CISS Fondecyt/Mi unidad/Alvacast/SISTRAT 2019 (github)/")
} else if (grepl("andre",jpg_path)==T){
jpg_path<-paste0('C:/Users/andre/Desktop/SUD_CL/')
} else if (grepl("E:",jpg_path)==T){
jpg_path<-paste0("E:/Mi unidad/Alvacast/SISTRAT 2019 (github)/")
} else {
jpg_path<-paste0("G:/Mi unidad/Alvacast/SISTRAT 2019 (github)/")
}
prueba2 %>%
# dplyr::group_by(hash_key)%>%
# dplyr::mutate(n_hash=n())%>%
#dplyr::filter(n_hash>1)%>%
# slice(1) %>%
# ungroup() %>%
dplyr::select(dias_treat_imp_sin_na_1,dias_treat_imp_sin_na_2,dias_treat_imp_sin_na_3,dias_treat_imp_sin_na_four) %>% #mean_cum_dias_trat_sin_na_1
tidyr::gather(option,value) %>%
dplyr::mutate(option=dplyr::case_when(option=="dias_treat_imp_sin_na_1"~"01",
option=="dias_treat_imp_sin_na_2"~"02",
option=="dias_treat_imp_sin_na_3"~"03",
option=="dias_treat_imp_sin_na_four"~"04 or more")) %>%
dplyr::mutate(option=factor(option)) %>%
dplyr::group_by(option) %>%
dplyr::mutate(count = length(na.omit(value))) %>%
dplyr::ungroup() %>%
ggplot(aes(x = option, y=value,group= option)) +
stat_summary(fun = mean, geom="bar",alpha=.8, na.rm = T)+
stat_summary(fun = median, geom="point", na.rm = T)+
stat_summary(fun = median,
fun.min = function(x) quantile(x,.25, na.rm = T),
fun.max = function(x) quantile(x,.75, na.rm = T),
geom = "errorbar", width = 0.5)+
geom_label(inherit.aes = FALSE, data = . %>% group_by(option) %>% slice(1),
aes(label = paste0(count, " Obs."), x = option), y = -0.5)+
geom_label(inherit.aes = FALSE, data = . %>% group_by(option) %>% slice(1),
aes(label = paste0(count, " Obs."), x = option), y = -0.5)+
theme_bw()+
theme(plot.caption = element_text(hjust = 0, face= "italic",size=9))+
#geom_bar(stat = "identity")+
#geom_errorbar() +
labs(x="Number of Treatment of Each User", y="Days in Treatment",caption=paste0("Note. Bars=Means, Dots= Medians, Error bars= Percentiles 25 and 75"))
## Warning: attributes are not identical across measure variables;
## they will be dropped
## Warning: Removed 64324 rows containing non-finite values (stat_summary).
library(ggplot2)
prueba2 %>%
# dplyr::group_by(hash_key)%>%
# dplyr::mutate(n_hash=n())%>%
#dplyr::filter(n_hash>1)%>%
# slice(1) %>%
# ungroup() %>%
dplyr::select(diff_bet_treat_1,diff_bet_treat_2,diff_bet_treat_3,diff_bet_treat_four) %>% #mean_cum_dias_trat_sin_na_1
tidyr::gather(option,value) %>%
dplyr::mutate(option=dplyr::case_when(option=="diff_bet_treat_1"~"01",
option=="diff_bet_treat_2"~"02",
option=="diff_bet_treat_3"~"03",
option=="diff_bet_treat_four"~"04 or more")) %>%
dplyr::mutate(option=factor(option)) %>%
dplyr::group_by(option) %>%
dplyr::mutate(count = length(na.omit(value))) %>%
dplyr::ungroup() %>%
ggplot(aes(x = option, y=value,group= option)) +
stat_summary(fun = mean, geom="bar",alpha=.8, na.rm = T)+
stat_summary(fun = median, geom="point", na.rm = T)+
stat_summary(fun = median,
fun.min = function(x) quantile(x,.25, na.rm = T),
fun.max = function(x) quantile(x,.75, na.rm = T),
geom = "errorbar", width = 0.5)+
#geom_label(inherit.aes = F,aes(x = option, y=value, label = max(count, na.rm=T)), vjust = -0.5)+
geom_label(inherit.aes = FALSE, data = . %>% group_by(option) %>% slice(1),
aes(label = paste0(count, " Obs."), x = option), y = -0.5)+
theme_bw()+
theme(plot.caption = element_text(hjust = 0, face= "italic",size=9))+
#geom_bar(stat = "identity")+
#geom_errorbar() +
labs(x="Number of Treatment of Each User", y="Difference (in days) with the posterior treatment",caption=paste0("Note. Bars=Means, Dots= Medians, Error bars= Percentiles 25 and 75"))
## Warning: attributes are not identical across measure variables;
## they will be dropped
## Warning: Removed 88138 rows containing non-finite values (stat_summary).
library(ggplot2)
f3<-
prueba2 %>%
# dplyr::group_by(hash_key)%>%
# dplyr::mutate(n_hash=n())%>%
#dplyr::filter(n_hash>1)%>%
# slice(1) %>%
# ungroup() %>%
dplyr::select(con_quien_vive_joel,dias_treat_imp_sin_na_1,dias_treat_imp_sin_na_2,dias_treat_imp_sin_na_3,dias_treat_imp_sin_na_four) %>% #mean_cum_dias_trat_sin_na_1con_quien_vive_joel
tidyr::gather(option,value,-con_quien_vive_joel) %>%
dplyr::mutate(option=dplyr::case_when(option=="dias_treat_imp_sin_na_1"~"01",
option=="dias_treat_imp_sin_na_2"~"02",
option=="dias_treat_imp_sin_na_3"~"03",
option=="dias_treat_imp_sin_na_four"~"04 or more")) %>%
dplyr::mutate(option=factor(option)) %>%
dplyr::group_by(con_quien_vive_joel,option) %>%
dplyr::mutate(count = length(na.omit(value))) %>%
dplyr::ungroup() %>%
ggplot(aes(x = option, y=value,group= option)) +
stat_summary(fun = mean, geom="bar",alpha=.8, na.rm = T)+
stat_summary(fun = median, geom="point", na.rm = T)+
stat_summary(fun = median,
fun.min = function(x) quantile(x,.25, na.rm = T),
fun.max = function(x) quantile(x,.75, na.rm = T),
geom = "errorbar", width = 0.5)+
geom_label(inherit.aes = FALSE, data = . %>% group_by(option,con_quien_vive_joel) %>% slice(1),
aes(label = paste0("n=",count), x = option), y = -0.5)+
geom_label(inherit.aes = FALSE, data = . %>% group_by(option,con_quien_vive_joel) %>% slice(1),
aes(label = paste0("n=",count), x = option), y = -0.5)+
facet_wrap(~con_quien_vive_joel)+
theme_bw()+
theme(plot.caption = element_text(hjust = 0, face= "italic",size=9))+
#geom_bar(stat = "identity")+
#geom_errorbar() +
labs(x="Number of Treatment of Each User", y="Days in Treatment",caption=paste0("Note. Bars=Means, Dots= Medians, Error bars= Percentiles 25 and 75"))
## Warning: attributes are not identical across measure variables;
## they will be dropped
f3
## Warning: Removed 64324 rows containing non-finite values (stat_summary).
no_mostrar=0
if(no_mostrar==1){
jpeg(paste0(jpg_path,"fig3_joel.jpg"), height=10, width= 10, res= 320, units = "in")
f3
dev.off()
}
Imputation (October 2021)
We generated a plot to see all the missing values in the sample.
#<div style="border: 1px solid #ddd; padding: 5px; overflow-y: scroll; height:400px; overflow-x: scroll; width:100%">
library(dplyr)
library(ggplot2)
#
# sexo_2=3,
# escolaridad_rec=3, 76 (0.32%)
# estado_conyugal_2=3, 27 (0.11%)
# edad_ini_sus_prin=2, 2755 (11,5%)
# edad_al_ing=2,
# sus_ini_mod=3, 2074 (8.65%)
# sus_ini_mod_mvv=3, 2074 (8.65%)
# freq_cons_sus_prin=3, 122 (0.51%)
# via_adm_sus_prin_act=3, 4 (0.02%)
# con_quien_vive_joel=3,
# numero_de_hijos_mod_joel=2, 293 (1,2%)
# condicion_ocupacional_corr=3, 14358 (59.9%)
# cat_ocupacional_corr=3, 14358 (59.9%)
# abandono_temprano=3,
# dg_cie_10_rec=3,
# dias_treat_imp_sin_na=2,
# cnt_mod_cie_10_or=3,
# cnt_diagnostico_trs_fisico=2,
# cnt_otros_probl_at_sm_or=2,
# tipo_de_plan_2_mod=3, 26 (0.11%)
# cnt_mod_cie_10_dg_cons_sus_or= 3,
# dg_total_cie_10 = 3,
# comorbidity_icd_10 = 3,
# compromiso_biopsicosocial 438 (1.83%)
# tenencia_de_la_vivienda_mod 1206 (5.03%)
vector_variables<-
c("sexo_2", "escolaridad_rec", "estado_conyugal_2", "edad_ini_cons", "sus_ini_mod_mvv", "freq_cons_sus_prin", "condicion_ocupacional_corr", "via_adm_sus_prin_act", "con_quien_vive_joel", "numero_de_hijos_mod_joel", "condicion_ocupacional_corr", "dias_treat_imp_sin_na", "cnt_mod_cie_10_or", "tipo_de_plan_2_mod", "comorbidity_icd_10", "compromiso_biopsicosocial", "tenencia_de_la_vivienda_mod","tipo_centro","macrozona","compromiso_biopsicosocial")
missing.values<-
prueba2 %>%
rowwise %>%
dplyr::mutate_at(.vars = vars(vector_variables),
.funs = ~ifelse(is.na(.), 1, 0)) %>%
dplyr::ungroup() %>%
dplyr::summarise_at(vars(vector_variables),~sum(.))
#t(missing.values)
miss_val_bar<-
data.table::melt(missing.values) %>%
mutate(perc=scales::percent(value/nrow(prueba2))) %>%
arrange(desc(perc))
plot_miss<-
missing.values %>%
data.table::melt() %>% #condicion_ocupacional_corr
dplyr::filter(!variable %in% c("row", "hash_key", "dias_treat_imp_sin_na", "dup")) %>%
dplyr::mutate(perc= value/sum(nrow(prueba2))) %>%
dplyr::mutate(label_text= paste0("Variable= ",variable,"<br>n= ",value,"<br>",scales::percent(round(perc,3)))) %>%
dplyr::mutate(perc=perc*100) %>%
ggplot() +
geom_bar(aes(x=factor(variable), y=perc,label= label_text), stat = 'identity') +
sjPlot::theme_sjplot()+
# scale_y_continuous(limits=c(0,1), labels=percent)+
theme(axis.text.x = element_text(angle = 90, hjust = 1, size=9))+
labs(x=NULL, y="% of Missing Values", caption=paste0("Nota. Percentage of missing values (n= ",format(nrow(prueba2),big.mark=","),")"))
ggplotly(plot_miss, tooltip = c("label_text"))%>% layout(xaxis= list(showticklabels = T), height = 600, width=800) %>% layout(yaxis = list(tickformat='%', range = c(0, 15)))
#</div>
From the figure above, we could see that the Age of Onset of Drug Use (edad_ini_cons), Starting substance (sus_ini_mod_mvv), the Substance Tenure status of households (tenencia_de_la_vivienda_mod) and the Biopsychosocial Involvement (compromiso_biopsicosocial) had a proportion of missing values, but no greater than 5%. This is why we imputed these values under MAR assumption.
vector_variables_only_for_imputation<-
c("row","hash_key", "edad_al_ing", "sexo_2", "escolaridad_rec", "estado_conyugal_2", "edad_ini_cons", "sus_ini_mod_mvv", "freq_cons_sus_prin", "condicion_ocupacional_corr", "via_adm_sus_prin_act", "con_quien_vive_joel", "numero_de_hijos_mod_joel", "condicion_ocupacional_corr", "dias_treat_imp_sin_na", "tipo_de_plan_2_mod", "comorbidity_icd_10", "compromiso_biopsicosocial", "tenencia_de_la_vivienda_mod", "tipo_centro", "macrozona", "compromiso_biopsicosocial")#cnt_mod_cie_10_or",
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
prueba2_miss<-
prueba2 %>%
#dplyr::group_by(hash_key) %>%
#dplyr::mutate(rn=row_number()) %>%
#dplyr::ungroup() %>%
#:#:#:#:#:#:#:#:#:#:#:
# ORDINALIZAR LAS VARIABLES ORDINALES:
dplyr::select_(.dots = vector_variables_only_for_imputation) %>%
dplyr::mutate(numero_de_hijos_mod_joel=dplyr::case_when(numero_de_hijos_mod_joel>3~"4 or more",
T~as.character(numero_de_hijos_mod_joel))) %>%
#dplyr::select(-hash_key) %>%
as.data.frame()
#CONS_C1_df_dup_SEP_2020 %>% janitor::tabyl(evaluacindelprocesoteraputico)
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
time_before_missforest<-Sys.time()
# for (var in colnames(prueba2_miss)) {
# attr(prueba2_miss[[deparse(as.name(var))]], "label") <- NULL
# }
library(missRanger)
set.seed(2125)
prueba2_imp <- missRanger(
prueba2_miss,
formula = . ~ . - row - hash_key,
num.trees = 200,
returnOOB=T,
maxiter=50,
verbose = 2,
seed = 2125)
time_after_missforest<-Sys.time()
paste0("Time in imputation process: ");time_after_missforest-time_before_missforest
ggplotly(DataExplorer::plot_missing(prueba2,missing_only = T))
