591
Yenny Elizalde
2025-11-19
CARGUE BD ORIGINAL
CREACIÓN DE SUB-BASE PARA ANALISIS
library(dplyr)## Warning: package 'dplyr' was built under R version 4.3.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union# Variables para el subset
variables <- c("COD_EVE", "FEC_NOT", "SEMANA", "ANO", "EDAD", "UNI_MED", "nombre_nacionalidad", "SEXO", "AREA", "TIP_SS", "COD_ASE", "PER_ETN", "nom_grupo", "estrato", "GP_DESPLAZ", "GP_MIGRANT", "GP_POBICFB", "GP_VIC_VIO", "GP_OTROS", "fuente", "FEC_CON", "INI_SIN", "TIP_CAS", "PAC_HOS", "FEC_HOS", "FEC_DEF", "AJUSTE", "FECHA_NTO", "CER_DEF", "CBMTE", "FEC_AJU", "confirmados", "consecutive_origen", "va_sispro", "nom_est_f_caso", "Nom_upgd", "Pais_ocurrencia", "Nombre_evento", "Departamento_ocurrencia", "Municipio_ocurrencia", "Pais_residencia", "Departamento_residencia", "Municipio_residencia", "Departamento_Notificacion", "Municipio_notificacion", "CONSECUTIVE_12")
# Crear subset
variables_existentes <- variables[variables %in% names(bases_591)]
datos_subset <- bases_591 %>% select(all_of(variables_existentes))
# Resultados
cat("Subset creado:", ncol(datos_subset), "variables,", nrow(datos_subset), "registros\n")## Subset creado: 46 variables, 814 registroscat("Variables incluidas:\n")## Variables incluidas:print(names(datos_subset))## [1] "COD_EVE" "FEC_NOT"
## [3] "SEMANA" "ANO"
## [5] "EDAD" "UNI_MED"
## [7] "nombre_nacionalidad" "SEXO"
## [9] "AREA" "TIP_SS"
## [11] "COD_ASE" "PER_ETN"
## [13] "nom_grupo" "estrato"
## [15] "GP_DESPLAZ" "GP_MIGRANT"
## [17] "GP_POBICFB" "GP_VIC_VIO"
## [19] "GP_OTROS" "fuente"
## [21] "FEC_CON" "INI_SIN"
## [23] "TIP_CAS" "PAC_HOS"
## [25] "FEC_HOS" "FEC_DEF"
## [27] "AJUSTE" "FECHA_NTO"
## [29] "CER_DEF" "CBMTE"
## [31] "FEC_AJU" "confirmados"
## [33] "consecutive_origen" "va_sispro"
## [35] "nom_est_f_caso" "Nom_upgd"
## [37] "Pais_ocurrencia" "Nombre_evento"
## [39] "Departamento_ocurrencia" "Municipio_ocurrencia"
## [41] "Pais_residencia" "Departamento_residencia"
## [43] "Municipio_residencia" "Departamento_Notificacion"
## [45] "Municipio_notificacion" "CONSECUTIVE_12"CONVERSION VARIABLES
library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionpredatos <- datos_subset
# Crear nuevo dataframe con fechas convertidas
predatos<- predatos %>%
mutate(
FEC_NOT_DATE = as.Date(FEC_NOT),
FEC_CON_DATE = as.Date(FEC_CON),
INI_SIN_DATE = as.Date(INI_SIN),
FEC_HOS_DATE = as.Date(FEC_HOS),
FEC_DEF_DATE = as.Date(FEC_DEF),
FECHA_NTO_DATE = as.Date(FECHA_NTO),
FEC_AJU_DATE = as.Date(FEC_AJU)
)
# Verificar conversiones
cat("Resumen de fechas convertidas:\n")## Resumen de fechas convertidas:fechas_vars <- c("FEC_NOT_DATE", "FEC_CON_DATE", "INI_SIN_DATE",
"FEC_HOS_DATE", "FEC_DEF_DATE", "FECHA_NTO_DATE", "FEC_AJU_DATE")
for(var in fechas_vars) {
if(var %in% names(predatos)) {
cat(paste(var, ":", sum(!is.na(predatos[[var]])), "registros válidos\n"))
}
}## FEC_NOT_DATE : 814 registros válidos
## FEC_CON_DATE : 813 registros válidos
## INI_SIN_DATE : 814 registros válidos
## FEC_HOS_DATE : 551 registros válidos
## FEC_DEF_DATE : 814 registros válidos
## FECHA_NTO_DATE : 807 registros válidos
## FEC_AJU_DATE : 814 registros válidos# Mostrar estructura
str(predatos %>% select(ends_with("_DATE")))## tibble [814 × 7] (S3: tbl_df/tbl/data.frame)
## $ FEC_NOT_DATE : Date[1:814], format: "2023-04-17" "2023-08-10" ...
## $ FEC_CON_DATE : Date[1:814], format: "2023-04-09" "2023-04-08" ...
## $ INI_SIN_DATE : Date[1:814], format: "2023-04-04" "2023-04-08" ...
## $ FEC_HOS_DATE : Date[1:814], format: "2023-04-09" "2023-04-08" ...
## $ FEC_DEF_DATE : Date[1:814], format: "2023-04-17" "2023-04-19" ...
## $ FECHA_NTO_DATE: Date[1:814], format: "2021-10-16" "2023-03-09" ...
## $ FEC_AJU_DATE : Date[1:814], format: "2023-11-01" "2023-11-14" ...CONVERSION VARIABLE EDAD
library(dplyr)
# Conversión directa
predatos <- predatos %>%
mutate(
UNI_MED = as.numeric(as.character(UNI_MED)),
EDAD = as.numeric(as.character(EDAD))
)
# nueva variable
predatos <- predatos %>%
mutate(
edad_meses = case_when(
UNI_MED == 3 ~ 1, # Menor de 1 mes -> 1 mes
UNI_MED == 2 ~ EDAD, # Ya en meses -> mismo valor
UNI_MED == 1 ~ EDAD * 12, # Años -> multiplicar por 12
TRUE ~ NA_real_ # Otros casos -> NA
)
)
# Verificación rápida
cat("Resumen de la conversión:\n")## Resumen de la conversión:cat("UNI_MED = 1 (años):", nrow(predatos %>% filter(UNI_MED == 1)), "registros\n")## UNI_MED = 1 (años): 308 registroscat("UNI_MED = 2 (meses):", nrow(predatos %>% filter(UNI_MED == 2)), "registros\n")## UNI_MED = 2 (meses): 489 registroscat("UNI_MED = 3 (menor 1 mes):", nrow(predatos %>% filter(UNI_MED == 3)), "registros\n")## UNI_MED = 3 (menor 1 mes): 17 registroscat("Rango de edad_meses:", range(predatos$edad_meses, na.rm = TRUE), "\n")## Rango de edad_meses: 1 48# Conversión directa
predatos <- predatos %>%
mutate(
edad_meses = as.numeric(as.character(edad_meses)),
ANO = as.numeric(as.character(ANO)),
SEMANA = as.numeric(as.character(SEMANA)))SUBBASE CON EDAD MODIFICADA
# Variables para el subset
variables2 <- c("COD_EVE", "SEMANA", "ANO", "edad_meses", "nombre_nacionalidad", "SEXO", "AREA", "TIP_SS", "COD_ASE", "PER_ETN", "nom_grupo", "estrato", "GP_DESPLAZ", "GP_MIGRANT", "GP_POBICFB", "GP_VIC_VIO", "GP_OTROS", "fuente", "TIP_CAS", "AJUSTE", "CBMTE", "confirmados", "consecutive_origen", "va_sispro", "nom_est_f_caso", "Pais_ocurrencia", "Nombre_evento", "Departamento_ocurrencia", "Pais_residencia", "Departamento_residencia", "Departamento_Notificacion")
# Crear subset
variables_existentes2 <- variables2[variables2 %in% names(predatos)]
datos_finales <- predatos %>% select(all_of(variables_existentes2))
# Resultados
cat("Subset creado:", ncol(datos_finales), "variables,", nrow(datos_finales), "registros\n")## Subset creado: 31 variables, 814 registroscat("Variables incluidas:\n")## Variables incluidas:print(names(datos_finales))## [1] "COD_EVE" "SEMANA"
## [3] "ANO" "edad_meses"
## [5] "nombre_nacionalidad" "SEXO"
## [7] "AREA" "TIP_SS"
## [9] "COD_ASE" "PER_ETN"
## [11] "nom_grupo" "estrato"
## [13] "GP_DESPLAZ" "GP_MIGRANT"
## [15] "GP_POBICFB" "GP_VIC_VIO"
## [17] "GP_OTROS" "fuente"
## [19] "TIP_CAS" "AJUSTE"
## [21] "CBMTE" "confirmados"
## [23] "consecutive_origen" "va_sispro"
## [25] "nom_est_f_caso" "Pais_ocurrencia"
## [27] "Nombre_evento" "Departamento_ocurrencia"
## [29] "Pais_residencia" "Departamento_residencia"
## [31] "Departamento_Notificacion"DESCRIPTIVO AUTOMATIZADO
#library(SmartEDA)
# similarly, with dplyr syntax: df %>% ExpReport(...)
#ExpReport(
#datos_finales,
#Target="COD_EVE",
#label=NULL,
#op_file="Report.html",
#op_dir=getwd())#library(openxlsx)
# Exportar a Excel
#write.xlsx(datos_finales, file = "datos.xlsx")ANALISIS UNIVARIADO
library(readxl)
library(dplyr)
library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.3.3library(knitr)## Warning: package 'knitr' was built under R version 4.3.3library(kableExtra)## Warning: package 'kableExtra' was built under R version 4.3.3##
## Attaching package: 'kableExtra'## The following object is masked from 'package:dplyr':
##
## group_rowslibrary(lubridate)
library(tidyr)## Warning: package 'tidyr' was built under R version 4.3.3library(purrr)
vars_omitidas <- c("EDAD", "UNI_MED", "CONSECUTIVE_12", "consecutive_origen", "Nom_upgd", "CER_DEF")
datos <- predatos %>% select(-any_of(vars_omitidas))ANALISIS EDAD EN MESES
resumen_edad <- datos %>%
summarise(
Min = min(edad_meses, na.rm = TRUE),
Q1 = quantile(edad_meses, 0.25, na.rm = TRUE),
Media = mean(edad_meses, na.rm = TRUE),
Mediana = median(edad_meses, na.rm = TRUE),
Q3 = quantile(edad_meses, 0.75, na.rm = TRUE),
Max = max(edad_meses, na.rm = TRUE),
SD = sd(edad_meses, na.rm = TRUE),
NA_Count = sum(is.na(edad_meses))
)
print(resumen_edad)## # A tibble: 1 × 8
## Min Q1 Media Mediana Q3 Max SD NA_Count
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 1 4 10.5 9 12 48 10.3 0ggplot(datos, aes(y = edad_meses)) +
geom_boxplot(fill = "#FFB6C1", color = "#555555", alpha = 0.8) + # rosa pastel
theme_minimal() +
labs(
title = "Boxplot de edad_meses",
y = "Edad en meses"
) +
theme(
plot.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 12)
)
ANALISIS POR SEMANA EPIDEMIOLÓGICA
library(dplyr)
library(ggplot2)
df_lineas <- datos %>%
group_by(COD_EVE, SEMANA) %>%
summarise(Frecuencia = n(), .groups = "drop")
ggplot(df_lineas, aes(x = SEMANA, y = Frecuencia, color = factor(COD_EVE))) +
geom_line(linewidth = 1) +
theme_minimal() +
labs(
title = "Tendencia semanal diferenciada por tipo de mortalidad",
x = "Semana",
y = "Frecuencia",
color = "Codigo del evento"
) +
scale_color_brewer(palette = "Set2") # paleta pastel
ANALISIS VARIABLES CATEGORICAS
vars <- c("nombre_nacionalidad", "SEXO", "AREA", "TIP_SS", "PER_ETN",
"estrato", "GP_DESPLAZ", "GP_MIGRANT", "GP_POBICFB",
"GP_VIC_VIO", "GP_OTROS", "fuente", "nom_est_f_caso")
tabla_resumen <- map_df(vars, function(v) {
datos %>%
count(!!sym(v)) %>%
mutate(
Variable = v,
Porcentaje = round((n / sum(n)) * 100, 2) # ← aquí multiplicamos por 100
) %>%
rename(Categoria = !!sym(v), Frecuencia = n) %>%
select(Variable, Categoria, Frecuencia, Porcentaje)
})
kable(tabla_resumen,
caption = "Tabla resumen de frecuencias y porcentaje",
align = "l") %>%
kable_styling(full_width = FALSE, bootstrap_options = c("striped", "hover"))| Variable | Categoria | Frecuencia | Porcentaje |
|---|---|---|---|
| nombre_nacionalidad | COLOMBIA | 783 | 96.19 |
| nombre_nacionalidad | VENEZUELA | 31 | 3.81 |
| SEXO | F | 334 | 41.03 |
| SEXO | M | 480 | 58.97 |
| AREA | 1 | 370 | 45.45 |
| AREA | 2 | 61 | 7.49 |
| AREA | 3 | 383 | 47.05 |
| TIP_SS | C | 66 | 8.11 |
| TIP_SS | E | 1 | 0.12 |
| TIP_SS | I | 15 | 1.84 |
| TIP_SS | N | 76 | 9.34 |
| TIP_SS | S | 656 | 80.59 |
| PER_ETN | 1 | 422 | 51.84 |
| PER_ETN | 3 | 1 | 0.12 |
| PER_ETN | 5 | 44 | 5.41 |
| PER_ETN | 6 | 347 | 42.63 |
| estrato | 1 | 644 | 79.12 |
| estrato | 2 | 104 | 12.78 |
| estrato | 3 | 21 | 2.58 |
| estrato | NA | 45 | 5.53 |
| GP_DESPLAZ | 1 | 13 | 1.60 |
| GP_DESPLAZ | 2 | 801 | 98.40 |
| GP_MIGRANT | 1 | 30 | 3.69 |
| GP_MIGRANT | 2 | 784 | 96.31 |
| GP_POBICFB | 1 | 15 | 1.84 |
| GP_POBICFB | 2 | 799 | 98.16 |
| GP_VIC_VIO | 1 | 3 | 0.37 |
| GP_VIC_VIO | 2 | 811 | 99.63 |
| GP_OTROS | 1 | 771 | 94.72 |
| GP_OTROS | 2 | 43 | 5.28 |
| fuente | 1 | 622 | 76.41 |
| fuente | 2 | 113 | 13.88 |
| fuente | 3 | 13 | 1.60 |
| fuente | 4 | 42 | 5.16 |
| fuente | 5 | 24 | 2.95 |
| nom_est_f_caso | Confirmado por Clínica | 665 | 81.70 |
| nom_est_f_caso | Confirmado por laboratorio | 149 | 18.30 |
ANALISIS CAUSA MUERTE
df_cbmte <- datos %>%
count(CBMTE) %>%
arrange(desc(n))
top10 <- df_cbmte %>% slice(1:10)
otros <- df_cbmte %>% slice(11:n()) %>%
summarise(CBMTE = "Otros", n = sum(n))
df_final <- bind_rows(top10, otros) %>%
arrange(n)
ggplot(df_final, aes(x = n, y = reorder(CBMTE, n), fill = CBMTE)) +
geom_col() +
# ---------- ETIQUETAS ----------
geom_text(aes(label = n),
hjust = -0.1, # Mueve el texto hacia afuera de la barra
size = 3.8) +
scale_fill_brewer(palette = "Set3") +
theme_minimal() +
labs(
title = "Top 10 causas básicas de muerte",
x = "Frecuencia",
y = ""
) +
coord_cartesian(xlim = c(0, max(df_final$n) * 1.15)) + # deja espacio para texto
theme(
legend.position = "none",
plot.title = element_text(size = 14, face = "bold")
)
ANALISIS GEOREFERENCIACION
DEPARTAMENTO OCURRENCIA
library(kableExtra)
tabla_dep <- datos %>%
count(Departamento_ocurrencia, name = "Frecuencia") %>%
arrange(desc(Frecuencia))
# Top 10
top10 <- tabla_dep %>% slice(1:10)
# Calcular "Otros"
otros <- tabla_dep %>%
slice(11:n()) %>%
summarise(
Departamento_ocurrencia = "Otros",
Frecuencia = sum(Frecuencia)
)
# Unir top 10 + otros
tabla_final <- bind_rows(top10, otros) %>%
mutate(
Porcentaje = round((Frecuencia / sum(Frecuencia)) * 100, 2)
)
# ---- Agregar fila TOTAL ----
fila_total <- tibble(
Departamento_ocurrencia = "TOTAL",
Frecuencia = sum(tabla_final$Frecuencia),
Porcentaje = 100
)
tabla_final2 <- bind_rows(tabla_final, fila_total)
# ---- Tabla con formato profesional ----
kable(
tabla_final2,
caption = "Top 10 de Departamento de ocurrencia",
col.names = c("Departamento", "Frecuencia", "Porcentaje (%)"),
align = "l"
) %>%
kable_styling(
bootstrap_options = c("striped", "hover", "condensed"),
full_width = FALSE
) %>%
row_spec(
nrow(tabla_final2), # última fila (TOTAL)
background = "#003366", # azul oscuro
color = "white", # texto blanco
bold = TRUE
)| Departamento | Frecuencia | Porcentaje (%) |
|---|---|---|
| GUAJIRA | 148 | 18.18 |
| CHOCO | 118 | 14.50 |
| ANTIOQUIA | 43 | 5.28 |
| MAGDALENA | 41 | 5.04 |
| CESAR | 39 | 4.79 |
| BOLIVAR | 38 | 4.67 |
| BOGOTA | 34 | 4.18 |
| VALLE | 31 | 3.81 |
| EXTERIOR | 30 | 3.69 |
| META | 28 | 3.44 |
| Otros | 264 | 32.43 |
| TOTAL | 814 | 100.00 |
MUNICIPIO OCURRENCIA
tabla_mun <- datos %>%
count(Municipio_ocurrencia, name = "Frecuencia") %>%
arrange(desc(Frecuencia))
# Top 10
top10 <- tabla_mun %>% slice(1:10)
# Calcular "Otros"
otros <- tabla_mun %>%
slice(11:n()) %>%
summarise(
Municipio_ocurrencia = "Otros",
Frecuencia = sum(Frecuencia)
)
# Unir top 10 + otros
tabla_final_mun <- bind_rows(top10, otros) %>%
mutate(
Porcentaje = round((Frecuencia / sum(Frecuencia)) * 100, 2)
)
# ---- Agregar fila TOTAL ----
fila_total <- tibble(
Municipio_ocurrencia = "TOTAL",
Frecuencia = sum(tabla_final_mun$Frecuencia),
Porcentaje = 100
)
tabla_final_mun2 <- bind_rows(tabla_final_mun, fila_total)
# ---- Tabla con estilo profesional ----
kable(
tabla_final_mun2,
caption = "Top 10 de Municipio de ocurrencia",
col.names = c("Municipio", "Frecuencia", "Porcentaje (%)"),
align = "l"
) %>%
kable_styling(
bootstrap_options = c("striped", "hover", "condensed"),
full_width = FALSE
) %>%
row_spec(
nrow(tabla_final_mun2), # última fila (TOTAL)
background = "#003366", # azul oscuro
color = "white", # texto blanco
bold = TRUE
)| Municipio | Frecuencia | Porcentaje (%) |
|---|---|---|
| URIBIA | 58 | 7.13 |
| BOGOTA | 34 | 4.18 |
| EXTERIOR_VENEZUELA | 29 | 3.56 |
| MAICAO | 28 | 3.44 |
| MANAURE | 26 | 3.19 |
| BAJO BAUDO (PIZARRO) | 22 | 2.70 |
| RIOHACHA | 22 | 2.70 |
| QUIBDO | 18 | 2.21 |
| PUEBLO RICO | 16 | 1.97 |
| CUMARIBO | 15 | 1.84 |
| Otros | 546 | 67.08 |
| TOTAL | 814 | 100.00 |
DEPARTAMENTO RESIDENCIA
tabla_depr <- datos %>%
count(Departamento_residencia, name = "Frecuencia") %>%
arrange(desc(Frecuencia))
# Top 10
top10 <- tabla_depr %>% slice(1:10)
# Calcular "Otros"
otros <- tabla_depr %>%
slice(11:n()) %>%
summarise(
Departamento_residencia = "Otros",
Frecuencia = sum(Frecuencia)
)
# Unir top 10 + otros
tabla_final <- bind_rows(top10, otros) %>%
mutate(
Porcentaje = round((Frecuencia / sum(Frecuencia)) * 100, 2)
)
# ---- Agregar fila TOTAL ----
fila_total <- tibble(
Departamento_residencia = "TOTAL",
Frecuencia = sum(tabla_final$Frecuencia),
Porcentaje = 100
)
tabla_final2 <- bind_rows(tabla_final, fila_total)
# ---- Tabla con fila TOTAL en azul oscuro ----
kable(
tabla_final2,
caption = "Top 10 de Departamento residencia",
col.names = c("Departamento", "Frecuencia", "Porcentaje (%)"),
align = "l"
) %>%
kable_styling(
bootstrap_options = c("striped", "hover", "condensed"),
full_width = FALSE
) %>%
row_spec(
nrow(tabla_final2), # última fila (TOTAL)
background = "#003366", # azul oscuro
color = "white", # texto blanco
bold = TRUE
)| Departamento | Frecuencia | Porcentaje (%) |
|---|---|---|
| GUAJIRA | 140 | 17.20 |
| CHOCO | 119 | 14.62 |
| ANTIOQUIA | 43 | 5.28 |
| EXTERIOR | 42 | 5.16 |
| MAGDALENA | 42 | 5.16 |
| BOLIVAR | 39 | 4.79 |
| CESAR | 38 | 4.67 |
| BOGOTA | 32 | 3.93 |
| VALLE | 32 | 3.93 |
| CORDOBA | 25 | 3.07 |
| Otros | 262 | 32.19 |
| TOTAL | 814 | 100.00 |
MUNICIPIO RESIDENCIA
tabla_munr <- datos %>%
count(Municipio_residencia, name = "Frecuencia") %>%
arrange(desc(Frecuencia))
# Top 10
top10 <- tabla_munr %>% slice(1:10)
# Calcular "Otros"
otros <- tabla_munr %>%
slice(11:n()) %>%
summarise(
Municipio_residencia = "Otros",
Frecuencia = sum(Frecuencia)
)
# Unir top 10 + otros
tabla_final <- bind_rows(top10, otros) %>%
mutate(
Porcentaje = round((Frecuencia / sum(Frecuencia)) * 100, 2)
)
# ---- Agregar fila TOTAL ----
fila_total <- tibble(
Municipio_residencia = "TOTAL",
Frecuencia = sum(tabla_final$Frecuencia),
Porcentaje = 100
)
tabla_final2 <- bind_rows(tabla_final, fila_total)
# ---- Tabla con fila TOTAL en azul oscuro ----
kable(
tabla_final2,
caption = "Top 10 de Municipio residencia",
col.names = c("Municipio", "Frecuencia", "Porcentaje (%)"),
align = "l"
) %>%
kable_styling(
bootstrap_options = c("striped", "hover", "condensed"),
full_width = FALSE
) %>%
row_spec(
nrow(tabla_final2), # última fila (TOTAL)
background = "#003366", # azul oscuro
color = "white", # texto blanco
bold = TRUE
)| Municipio | Frecuencia | Porcentaje (%) |
|---|---|---|
| URIBIA | 58 | 7.13 |
| EXTERIOR_VENEZUELA | 41 | 5.04 |
| BOGOTA | 32 | 3.93 |
| MANAURE | 25 | 3.07 |
| MAICAO | 24 | 2.95 |
| BAJO BAUDO (PIZARRO) | 22 | 2.70 |
| RIOHACHA | 21 | 2.58 |
| CUMARIBO | 20 | 2.46 |
| QUIBDO | 18 | 2.21 |
| PUEBLO RICO | 16 | 1.97 |
| Otros | 537 | 65.97 |
| TOTAL | 814 | 100.00 |
DEPARTAMENTO NOTIFICACION
tabla_depn <- datos %>%
count(Departamento_Notificacion, name = "Frecuencia") %>%
arrange(desc(Frecuencia))
# Top 10
top10 <- tabla_depn %>% slice(1:10)
# Calcular "Otros"
otros <- tabla_depn %>%
slice(11:n()) %>%
summarise(
Departamento_Notificacion = "Otros",
Frecuencia = sum(Frecuencia)
)
# Unir top 10 + otros
tabla_final <- bind_rows(top10, otros) %>%
mutate(
Porcentaje = round((Frecuencia / sum(Frecuencia)) * 100, 2)
)
# ---- Agregar fila TOTAL ----
fila_total <- tibble(
Departamento_Notificacion = "TOTAL",
Frecuencia = sum(tabla_final$Frecuencia),
Porcentaje = 100
)
tabla_final2 <- bind_rows(tabla_final, fila_total)
# ---- Tabla con estilo y fila TOTAL resaltada ----
kable(
tabla_final2,
caption = "Top 10 de Departamento notificación",
col.names = c("Departamento", "Frecuencia", "Porcentaje (%)"),
align = "l"
) %>%
kable_styling(
bootstrap_options = c("striped", "hover", "condensed"),
full_width = FALSE
) %>%
row_spec(
nrow(tabla_final2), # última fila = TOTAL
background = "#003366", # azul oscuro
color = "white", # texto blanco
bold = TRUE
)| Departamento | Frecuencia | Porcentaje (%) |
|---|---|---|
| GUAJIRA | 139 | 17.08 |
| CHOCO | 98 | 12.04 |
| CESAR | 49 | 6.02 |
| BOGOTA | 48 | 5.90 |
| ANTIOQUIA | 43 | 5.28 |
| CORDOBA | 40 | 4.91 |
| ATLANTICO | 39 | 4.79 |
| MAGDALENA | 39 | 4.79 |
| META | 35 | 4.30 |
| VALLE | 34 | 4.18 |
| Otros | 250 | 30.71 |
| TOTAL | 814 | 100.00 |
MUNICIPIO NOTIFICACIÓN
tabla_munn <- datos %>%
count(Municipio_notificacion, name = "Frecuencia") %>%
arrange(desc(Frecuencia))
# Top 10
top10 <- tabla_munn %>% slice(1:10)
# Calcular "Otros"
otros <- tabla_munn %>%
slice(11:n()) %>%
summarise(
Municipio_notificacion = "Otros",
Frecuencia = sum(Frecuencia)
)
# Unir top 10 + otros
tabla_final <- bind_rows(top10, otros) %>%
mutate(
Porcentaje = round((Frecuencia / sum(Frecuencia)) * 100, 2)
)
# ---- Agregar fila TOTAL ----
fila_total <- tibble(
Municipio_notificacion = "TOTAL",
Frecuencia = sum(tabla_final$Frecuencia),
Porcentaje = 100
)
tabla_final2 <- bind_rows(tabla_final, fila_total)
# ---- Tabla con estilo y fila TOTAL en azul oscuro ----
kable(
tabla_final2,
caption = "Top 10 de Municipio notificación",
col.names = c("Municipio", "Frecuencia", "Porcentaje (%)"),
align = "l"
) %>%
kable_styling(
bootstrap_options = c("striped", "hover", "condensed"),
full_width = FALSE
) %>%
row_spec(
nrow(tabla_final2), # última fila (TOTAL)
background = "#003366", # azul oscuro
color = "white", # texto blanco
bold = TRUE
)| Municipio | Frecuencia | Porcentaje (%) |
|---|---|---|
| QUIBDO | 66 | 8.11 |
| MAICAO | 56 | 6.88 |
| BOGOTA | 48 | 5.90 |
| VALLEDUPAR | 40 | 4.91 |
| MONTERIA | 33 | 4.05 |
| BARRANQUILLA | 32 | 3.93 |
| URIBIA | 31 | 3.81 |
| RIOHACHA | 29 | 3.56 |
| VILLAVICENCIO | 25 | 3.07 |
| SANTA MARTHA | 24 | 2.95 |
| Otros | 430 | 52.83 |
| TOTAL | 814 | 100.00 |
ANALISIS BIVARIADO
datosf <- datos_finales
t1 <- table1::table1(~SEXO + edad_meses + AREA + TIP_SS + PER_ETN + estrato + GP_DESPLAZ + GP_MIGRANT + GP_POBICFB + GP_VIC_VIO + nom_est_f_caso + Departamento_residencia + Departamento_ocurrencia | Nombre_evento, data = datosf )
t1| MORTALIDAD POR DESNUTRICIÓN (N=301) |
MORTALIDAD POR EDA 0-4 AÑOS (N=132) |
MORTALIDAD POR IRA (N=381) |
Overall (N=814) |
|
|---|---|---|---|---|
| SEXO | ||||
| F | 130 (43.2%) | 50 (37.9%) | 154 (40.4%) | 334 (41.0%) |
| M | 171 (56.8%) | 82 (62.1%) | 227 (59.6%) | 480 (59.0%) |
| edad_meses | ||||
| Mean (SD) | 9.91 (8.05) | 12.5 (11.8) | 10.3 (11.1) | 10.5 (10.3) |
| Median [Min, Max] | 9.00 [1.00, 48.0] | 10.0 [1.00, 48.0] | 7.00 [1.00, 48.0] | 9.00 [1.00, 48.0] |
| AREA | ||||
| 1 | 120 (39.9%) | 41 (31.1%) | 209 (54.9%) | 370 (45.5%) |
| 2 | 24 (8.0%) | 12 (9.1%) | 25 (6.6%) | 61 (7.5%) |
| 3 | 157 (52.2%) | 79 (59.8%) | 147 (38.6%) | 383 (47.1%) |
| TIP_SS | ||||
| C | 5 (1.7%) | 7 (5.3%) | 54 (14.2%) | 66 (8.1%) |
| E | 1 (0.3%) | 0 (0%) | 0 (0%) | 1 (0.1%) |
| I | 5 (1.7%) | 4 (3.0%) | 6 (1.6%) | 15 (1.8%) |
| N | 28 (9.3%) | 19 (14.4%) | 29 (7.6%) | 76 (9.3%) |
| S | 262 (87.0%) | 102 (77.3%) | 292 (76.6%) | 656 (80.6%) |
| PER_ETN | ||||
| 1 | 182 (60.5%) | 92 (69.7%) | 148 (38.8%) | 422 (51.8%) |
| 5 | 15 (5.0%) | 5 (3.8%) | 24 (6.3%) | 44 (5.4%) |
| 6 | 104 (34.6%) | 35 (26.5%) | 208 (54.6%) | 347 (42.6%) |
| 3 | 0 (0%) | 0 (0%) | 1 (0.3%) | 1 (0.1%) |
| estrato | ||||
| 1 | 262 (87.0%) | 119 (90.2%) | 263 (69.0%) | 644 (79.1%) |
| 2 | 20 (6.6%) | 6 (4.5%) | 78 (20.5%) | 104 (12.8%) |
| 3 | 0 (0%) | 0 (0%) | 21 (5.5%) | 21 (2.6%) |
| Missing | 19 (6.3%) | 7 (5.3%) | 19 (5.0%) | 45 (5.5%) |
| GP_DESPLAZ | ||||
| 1 | 4 (1.3%) | 1 (0.8%) | 8 (2.1%) | 13 (1.6%) |
| 2 | 297 (98.7%) | 131 (99.2%) | 373 (97.9%) | 801 (98.4%) |
| GP_MIGRANT | ||||
| 1 | 7 (2.3%) | 8 (6.1%) | 15 (3.9%) | 30 (3.7%) |
| 2 | 294 (97.7%) | 124 (93.9%) | 366 (96.1%) | 784 (96.3%) |
| GP_POBICFB | ||||
| 1 | 7 (2.3%) | 2 (1.5%) | 6 (1.6%) | 15 (1.8%) |
| 2 | 294 (97.7%) | 130 (98.5%) | 375 (98.4%) | 799 (98.2%) |
| GP_VIC_VIO | ||||
| 1 | 3 (1.0%) | 0 (0%) | 0 (0%) | 3 (0.4%) |
| 2 | 298 (99.0%) | 132 (100%) | 381 (100%) | 811 (99.6%) |
| nom_est_f_caso | ||||
| Confirmado por Clínica | 301 (100%) | 130 (98.5%) | 234 (61.4%) | 665 (81.7%) |
| Confirmado por laboratorio | 0 (0%) | 2 (1.5%) | 147 (38.6%) | 149 (18.3%) |
| Departamento_residencia | ||||
| AMAZONAS | 1 (0.3%) | 2 (1.5%) | 6 (1.6%) | 9 (1.1%) |
| ANTIOQUIA | 19 (6.3%) | 3 (2.3%) | 21 (5.5%) | 43 (5.3%) |
| ARAUCA | 3 (1.0%) | 2 (1.5%) | 6 (1.6%) | 11 (1.4%) |
| ATLANTICO | 12 (4.0%) | 1 (0.8%) | 11 (2.9%) | 24 (2.9%) |
| BOLIVAR | 16 (5.3%) | 1 (0.8%) | 22 (5.8%) | 39 (4.8%) |
| BOYACA | 1 (0.3%) | 1 (0.8%) | 8 (2.1%) | 10 (1.2%) |
| CAQUETA | 5 (1.7%) | 0 (0%) | 2 (0.5%) | 7 (0.9%) |
| CESAR | 22 (7.3%) | 4 (3.0%) | 12 (3.1%) | 38 (4.7%) |
| CHOCO | 50 (16.6%) | 25 (18.9%) | 44 (11.5%) | 119 (14.6%) |
| CORDOBA | 9 (3.0%) | 1 (0.8%) | 15 (3.9%) | 25 (3.1%) |
| EXTERIOR | 13 (4.3%) | 11 (8.3%) | 18 (4.7%) | 42 (5.2%) |
| GUAINIA | 1 (0.3%) | 5 (3.8%) | 4 (1.0%) | 10 (1.2%) |
| GUAJIRA | 66 (21.9%) | 27 (20.5%) | 47 (12.3%) | 140 (17.2%) |
| GUAVIARE | 1 (0.3%) | 0 (0%) | 1 (0.3%) | 2 (0.2%) |
| HUILA | 5 (1.7%) | 1 (0.8%) | 6 (1.6%) | 12 (1.5%) |
| MAGDALENA | 20 (6.6%) | 6 (4.5%) | 16 (4.2%) | 42 (5.2%) |
| META | 7 (2.3%) | 2 (1.5%) | 14 (3.7%) | 23 (2.8%) |
| NARIÑO | 5 (1.7%) | 2 (1.5%) | 13 (3.4%) | 20 (2.5%) |
| NORTE SANTANDER | 1 (0.3%) | 1 (0.8%) | 6 (1.6%) | 8 (1.0%) |
| RISARALDA | 8 (2.7%) | 8 (6.1%) | 9 (2.4%) | 25 (3.1%) |
| SANTANDER | 4 (1.3%) | 6 (4.5%) | 7 (1.8%) | 17 (2.1%) |
| SUCRE | 2 (0.7%) | 3 (2.3%) | 5 (1.3%) | 10 (1.2%) |
| TOLIMA | 2 (0.7%) | 0 (0%) | 8 (2.1%) | 10 (1.2%) |
| VALLE | 11 (3.7%) | 3 (2.3%) | 18 (4.7%) | 32 (3.9%) |
| VICHADA | 17 (5.6%) | 4 (3.0%) | 2 (0.5%) | 23 (2.8%) |
| CASANARE | 0 (0%) | 4 (3.0%) | 2 (0.5%) | 6 (0.7%) |
| CAUCA | 0 (0%) | 7 (5.3%) | 13 (3.4%) | 20 (2.5%) |
| PUTUMAYO | 0 (0%) | 1 (0.8%) | 1 (0.3%) | 2 (0.2%) |
| VAUPES | 0 (0%) | 1 (0.8%) | 4 (1.0%) | 5 (0.6%) |
| BOGOTA | 0 (0%) | 0 (0%) | 32 (8.4%) | 32 (3.9%) |
| CALDAS | 0 (0%) | 0 (0%) | 1 (0.3%) | 1 (0.1%) |
| CUNDINAMARCA | 0 (0%) | 0 (0%) | 3 (0.8%) | 3 (0.4%) |
| QUINDIO | 0 (0%) | 0 (0%) | 2 (0.5%) | 2 (0.2%) |
| SAN ANDRES | 0 (0%) | 0 (0%) | 2 (0.5%) | 2 (0.2%) |
| Departamento_ocurrencia | ||||
| AMAZONAS | 1 (0.3%) | 2 (1.5%) | 6 (1.6%) | 9 (1.1%) |
| ANTIOQUIA | 19 (6.3%) | 3 (2.3%) | 21 (5.5%) | 43 (5.3%) |
| ARAUCA | 3 (1.0%) | 2 (1.5%) | 5 (1.3%) | 10 (1.2%) |
| ATLANTICO | 12 (4.0%) | 1 (0.8%) | 11 (2.9%) | 24 (2.9%) |
| BOLIVAR | 16 (5.3%) | 1 (0.8%) | 21 (5.5%) | 38 (4.7%) |
| BOYACA | 1 (0.3%) | 1 (0.8%) | 9 (2.4%) | 11 (1.4%) |
| CALDAS | 1 (0.3%) | 0 (0%) | 1 (0.3%) | 2 (0.2%) |
| CAQUETA | 5 (1.7%) | 0 (0%) | 2 (0.5%) | 7 (0.9%) |
| CESAR | 22 (7.3%) | 4 (3.0%) | 13 (3.4%) | 39 (4.8%) |
| CHOCO | 49 (16.3%) | 26 (19.7%) | 43 (11.3%) | 118 (14.5%) |
| CORDOBA | 9 (3.0%) | 1 (0.8%) | 15 (3.9%) | 25 (3.1%) |
| EXTERIOR | 10 (3.3%) | 6 (4.5%) | 14 (3.7%) | 30 (3.7%) |
| GUAINIA | 1 (0.3%) | 5 (3.8%) | 4 (1.0%) | 10 (1.2%) |
| GUAJIRA | 69 (22.9%) | 30 (22.7%) | 49 (12.9%) | 148 (18.2%) |
| GUAVIARE | 1 (0.3%) | 0 (0%) | 1 (0.3%) | 2 (0.2%) |
| HUILA | 5 (1.7%) | 1 (0.8%) | 6 (1.6%) | 12 (1.5%) |
| MAGDALENA | 20 (6.6%) | 6 (4.5%) | 15 (3.9%) | 41 (5.0%) |
| META | 11 (3.7%) | 3 (2.3%) | 14 (3.7%) | 28 (3.4%) |
| NARIÑO | 5 (1.7%) | 2 (1.5%) | 13 (3.4%) | 20 (2.5%) |
| NORTE SANTANDER | 1 (0.3%) | 2 (1.5%) | 6 (1.6%) | 9 (1.1%) |
| QUINDIO | 1 (0.3%) | 0 (0%) | 2 (0.5%) | 3 (0.4%) |
| RISARALDA | 8 (2.7%) | 8 (6.1%) | 9 (2.4%) | 25 (3.1%) |
| SANTANDER | 4 (1.3%) | 6 (4.5%) | 7 (1.8%) | 17 (2.1%) |
| SUCRE | 2 (0.7%) | 3 (2.3%) | 4 (1.0%) | 9 (1.1%) |
| TOLIMA | 2 (0.7%) | 0 (0%) | 8 (2.1%) | 10 (1.2%) |
| VALLE | 10 (3.3%) | 2 (1.5%) | 19 (5.0%) | 31 (3.8%) |
| VICHADA | 13 (4.3%) | 4 (3.0%) | 3 (0.8%) | 20 (2.5%) |
| CASANARE | 0 (0%) | 4 (3.0%) | 2 (0.5%) | 6 (0.7%) |
| CAUCA | 0 (0%) | 7 (5.3%) | 13 (3.4%) | 20 (2.5%) |
| PUTUMAYO | 0 (0%) | 1 (0.8%) | 2 (0.5%) | 3 (0.4%) |
| VAUPES | 0 (0%) | 1 (0.8%) | 3 (0.8%) | 4 (0.5%) |
| BOGOTA | 0 (0%) | 0 (0%) | 34 (8.9%) | 34 (4.2%) |
| CUNDINAMARCA | 0 (0%) | 0 (0%) | 4 (1.0%) | 4 (0.5%) |
| SAN ANDRES | 0 (0%) | 0 (0%) | 2 (0.5%) | 2 (0.2%) |
v1 = table(datosf$COD_EVE, datosf$SEXO)
rownames(v1) <- c( "112", "590", "600")
colnames(v1) <- c("F", "H")
addmargins(v1)##
## F H Sum
## 112 130 171 301
## 590 50 82 132
## 600 154 227 381
## Sum 334 480 814library(CGPfunctions)## Warning: package 'CGPfunctions' was built under R version 4.3.3## Warning in .recacheSubclasses(def@className, def, env): undefined subclass
## "ndiMatrix" of class "replValueSp"; definition not updatedPlotXTabs2(data=datosf,x=SEXO,y=COD_EVE)
v2 = table(datosf$COD_EVE, datosf$AREA)
rownames(v2) <- c( "112", "590", "600")
colnames(v2) <- c("1", "2", "3")
addmargins(v2)##
## 1 2 3 Sum
## 112 120 24 157 301
## 590 41 12 79 132
## 600 209 25 147 381
## Sum 370 61 383 814PlotXTabs2(data=datosf,x=AREA,y=COD_EVE)
v3 = table(datosf$COD_EVE, datosf$TIP_SS)
rownames(v3) <- c( "112", "590", "600")
colnames(v3) <- c("C", "E", "I", "N", "S")
addmargins(v3)##
## C E I N S Sum
## 112 5 1 5 28 262 301
## 590 7 0 4 19 102 132
## 600 54 0 6 29 292 381
## Sum 66 1 15 76 656 814PlotXTabs2(data=datosf,x=TIP_SS,y=COD_EVE)
v4 = table(datosf$COD_EVE, datosf$PER_ETN)
rownames(v4) <- c( "112", "590", "600")
colnames(v4) <- c("1", "3", "5", "6")
addmargins(v4)##
## 1 3 5 6 Sum
## 112 182 0 15 104 301
## 590 92 0 5 35 132
## 600 148 1 24 208 381
## Sum 422 1 44 347 814PlotXTabs2(data=datosf,x=PER_ETN,y=COD_EVE)
v5 = table(datosf$COD_EVE, datosf$estrato)
rownames(v5) <- c( "112", "590", "600")
colnames(v5) <- c("1", "2", "3")
addmargins(v5)##
## 1 2 3 Sum
## 112 262 20 0 282
## 590 119 6 0 125
## 600 263 78 21 362
## Sum 644 104 21 769PlotXTabs2(data=datosf,x=estrato,y=COD_EVE)
v6 = table(datosf$COD_EVE, datosf$GP_DESPLAZ)
rownames(v6) <- c( "112", "590", "600")
colnames(v6) <- c("1", "2")
addmargins(v6)##
## 1 2 Sum
## 112 4 297 301
## 590 1 131 132
## 600 8 373 381
## Sum 13 801 814PlotXTabs2(data=datosf,x=GP_DESPLAZ,y=COD_EVE)
v7 = table(datosf$COD_EVE, datosf$GP_MIGRANT)
rownames(v7) <- c( "112", "590", "600")
colnames(v7) <- c("1", "2")
addmargins(v7)##
## 1 2 Sum
## 112 7 294 301
## 590 8 124 132
## 600 15 366 381
## Sum 30 784 814PlotXTabs2(data=datosf,x=GP_MIGRANT,y=COD_EVE)
v8 = table(datosf$COD_EVE, datosf$GP_POBICFB)
rownames(v8) <- c( "112", "590", "600")
colnames(v8) <- c("1", "2")
addmargins(v8)##
## 1 2 Sum
## 112 7 294 301
## 590 2 130 132
## 600 6 375 381
## Sum 15 799 814PlotXTabs2(data=datosf,x=GP_POBICFB,y=COD_EVE)
v9 = table(datosf$COD_EVE, datosf$GP_VIC_VIO)
rownames(v9) <- c( "112", "590", "600")
colnames(v9) <- c("1", "2")
addmargins(v9)##
## 1 2 Sum
## 112 3 298 301
## 590 0 132 132
## 600 0 381 381
## Sum 3 811 814PlotXTabs2(data=datosf,x=GP_VIC_VIO,y=COD_EVE)
v10 = table(datosf$COD_EVE, datosf$nom_est_f_caso)
rownames(v10) <- c( "112", "590", "600")
colnames(v10) <- c("Confirmado por Clínica", "")
addmargins(v10)##
## Confirmado por Clínica Sum
## 112 301 0 301
## 590 130 2 132
## 600 234 147 381
## Sum 665 149 814PlotXTabs2(data=datosf,x=nom_est_f_caso,y=COD_EVE)
ANALISIS FECHAS
DIFERENCIA EN DIAS ENTRE INICIO DE SINTOMAS Y FECHA DE DEFUNCIÓN (DIAS_ENFER)
datos <- datos %>%
mutate(
FEC_DEF = as.Date(FEC_DEF),
INI_SIN = as.Date(INI_SIN),
DIAS_ENFER = as.numeric(FEC_DEF - INI_SIN)
)DIFERENCIA ENTRE INICIO DE SINTOMAS Y FECHA DE CONSULTA (DIAS_CON)
datos <- datos %>%
mutate(
FEC_CON = as.Date(FEC_CON),
INI_SIN = as.Date(INI_SIN),
DIAS_CON = as.numeric(FEC_CON - INI_SIN)
)DIFERENCIA ENTRE FECHA DE CONSULTA Y FECHA DE NOTIFICACIÓN (DIAS_NOTI)
datos <- datos %>%
mutate(
FEC_NOT = as.Date(FEC_CON),
FEC_CON = as.Date(INI_SIN),
DIAS_NOTI = as.numeric(FEC_NOT - FEC_CON)
)ANALISIS DE DIFERENCIA DE FECHAS GENERAL
library(summarytools)
# Filtrar solo las variables numéricas que nos interesan
variables_numericas <- datos %>%
select(DIAS_ENFER, DIAS_CON, DIAS_NOTI)
estadisticas_detalladas <- descr(variables_numericas)
print(estadisticas_detalladas)## Descriptive Statistics
## variables_numericas
## N: 814
##
## DIAS_CON DIAS_ENFER DIAS_NOTI
## ----------------- ---------- ------------ -----------
## Mean 5.99 10.07 5.99
## Std.Dev 14.15 19.04 14.15
## Min 0.00 0.00 0.00
## Q1 0.00 2.00 0.00
## Median 3.00 5.00 3.00
## Q3 6.00 12.00 6.00
## Max 232.00 265.00 232.00
## MAD 4.45 5.93 4.45
## IQR 6.00 10.00 6.00
## CV 2.36 1.89 2.36
## Skewness 9.07 7.33 9.07
## SE.Skewness 0.09 0.09 0.09
## Kurtosis 113.42 75.63 113.42
## N.Valid 813.00 814.00 813.00
## Pct.Valid 99.88 100.00 99.88ANALISIS DE DIFERENCIA DE DIAS POR COD_EVENTO
estadisticas_especificas <- datos %>%
filter(COD_EVE %in% c(112, 590, 600)) %>%
group_by(COD_EVE) %>%
select(DIAS_ENFER, DIAS_CON, DIAS_NOTI) %>%
descr(stats = c("mean", "med", "sd", "min", "max"),
transpose = TRUE)## Adding missing grouping variables: `COD_EVE`print(estadisticas_especificas)## Descriptive Statistics
## datos
## Group: COD_EVE = 112
## N: 301
##
## Mean Median Std.Dev Min Max
## ---------------- ------- -------- --------- ------ --------
## DIAS_CON 8.14 3.00 19.56 0.00 232.00
## DIAS_ENFER 12.42 7.00 21.58 0.00 237.00
## DIAS_NOTI 8.14 3.00 19.56 0.00 232.00
##
## Group: COD_EVE = 590
## N: 132
##
## Mean Median Std.Dev Min Max
## ---------------- ------ -------- --------- ------ --------
## DIAS_CON 4.91 2.00 12.92 0.00 138.00
## DIAS_ENFER 6.45 3.00 13.36 0.00 138.00
## DIAS_NOTI 4.91 2.00 12.92 0.00 138.00
##
## Group: COD_EVE = 600
## N: 381
##
## Mean Median Std.Dev Min Max
## ---------------- ------ -------- --------- ------ --------
## DIAS_CON 4.67 3.00 7.90 0.00 74.00
## DIAS_ENFER 9.46 5.00 18.36 0.00 265.00
## DIAS_NOTI 4.67 3.00 7.90 0.00 74.00datos_largo <- datos %>%
select(COD_EVE, DIAS_ENFER, DIAS_CON, DIAS_NOTI) %>%
pivot_longer(cols = c(DIAS_ENFER, DIAS_CON, DIAS_NOTI),
names_to = "Variable",
values_to = "Valor")
# Boxplot múltiple con facetas
ggplot(datos_largo, aes(x = as.factor(COD_EVE), y = Valor)) +
geom_boxplot(aes(fill = Variable), alpha = 0.7) +
facet_wrap(~ Variable, scales = "free_y") +
labs(title = "Boxplots de Variables Numéricas por evento",
x = "COD_EVE",
y = "Valor") +
theme_minimal() +
scale_x_discrete(labels = c("112", "590", "600")) +
theme(legend.position = "none") ## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
ANALISIS DE CAUSAS BASICAS DE MUERTE (CIE-10)
library(writexl)## Warning: package 'writexl' was built under R version 4.3.3# Crear subset con solo las variables COD_EVE y CBMTE
subset_causas <- datos %>%
select(COD_EVE, CBMTE)
# Ver las primeras filas del subset
head(subset_causas)## # A tibble: 6 × 2
## COD_EVE CBMTE
## <chr> <chr>
## 1 112 A09X
## 2 112 E46X
## 3 112 E40X
## 4 112 J189
## 5 112 E46X
## 6 112 P369# Exportar a Excel
#write_xlsx(subset_causas, "CBMTE.xlsx")# Frecuencias absolutas y relativas
frecuencias_COD_EVE <- subset_causas %>%
count(COD_EVE) %>%
mutate(porcentaje = n/sum(n)*100)
frecuencias_CBMTE <- subset_causas %>%
count(CBMTE) %>%
arrange(desc(n)) %>%
mutate(porcentaje = n/sum(n)*100)
print(frecuencias_COD_EVE)## # A tibble: 3 × 3
## COD_EVE n porcentaje
## <chr> <int> <dbl>
## 1 112 301 37.0
## 2 590 132 16.2
## 3 600 381 46.8print(head(frecuencias_CBMTE, 20)) # Top 20 causas## # A tibble: 20 × 3
## CBMTE n porcentaje
## <chr> <int> <dbl>
## 1 A09X 102 12.5
## 2 E43X 94 11.5
## 3 J189 67 8.23
## 4 J960 59 7.25
## 5 R572 51 6.27
## 6 E46X 42 5.16
## 7 J158 29 3.56
## 8 J159 25 3.07
## 9 A419 18 2.21
## 10 E440 18 2.21
## 11 J129 17 2.09
## 12 J969 16 1.97
## 13 R571 16 1.97
## 14 I469 15 1.84
## 15 J22X 13 1.60
## 16 J219 12 1.47
## 17 J180 11 1.35
## 18 E640 10 1.23
## 19 J069 10 1.23
## 20 R092 10 1.23# Tabla de contingencia
tabla_contingencia <- table(subset_causas$COD_EVE, subset_causas$CBMTE)
# Prueba chi-cuadrado de independencia
chi_cuadrado <- chisq.test(tabla_contingencia)## Warning in chisq.test(tabla_contingencia): Chi-squared approximation may be
## incorrectprint(chi_cuadrado)##
## Pearson's Chi-squared test
##
## data: tabla_contingencia
## X-squared = 963.35, df = 218, p-value < 2.2e-16# Coeficiente V de Cramer (medida de asociación)
library(vcd)## Warning: package 'vcd' was built under R version 4.3.3## Loading required package: gridcramer_v <- assocstats(tabla_contingencia)$cramer
print(paste("V de Cramer:", cramer_v))## [1] "V de Cramer: 0.769245633323916"# Principales causas por COD_EVE
causas_por_grupo <- subset_causas %>%
group_by(COD_EVE, CBMTE) %>%
summarise(n = n(), .groups = 'drop') %>%
group_by(COD_EVE) %>%
arrange(COD_EVE, desc(n)) %>%
mutate(porcentaje_grupo = n/sum(n)*100,
rank = row_number())
# Top 5 causas por grupo
top_causas <- causas_por_grupo %>%
filter(rank <= 5)
print(top_causas)## # A tibble: 15 × 5
## # Groups: COD_EVE [3]
## COD_EVE CBMTE n porcentaje_grupo rank
## <chr> <chr> <int> <dbl> <int>
## 1 112 E43X 90 29.9 1
## 2 112 E46X 39 13.0 2
## 3 112 R572 24 7.97 3
## 4 112 A09X 18 5.98 4
## 5 112 E440 18 5.98 5
## 6 590 A09X 75 56.8 1
## 7 590 R571 9 6.82 2
## 8 590 A419 5 3.79 3
## 9 590 I469 5 3.79 4
## 10 590 E46X 3 2.27 5
## 11 600 J189 59 15.5 1
## 12 600 J960 51 13.4 2
## 13 600 J158 25 6.56 3
## 14 600 J159 24 6.30 4
## 15 600 R572 24 6.30 5# Mapa de calor de frecuencias
library(ggplot2)
heatmap_data <- causas_por_grupo %>%
filter(rank <= 10) # Top 10 causas por grupo
ggplot(heatmap_data, aes(x = factor(COD_EVE), y = reorder(CBMTE, rank),
fill = n)) +
geom_tile() +
scale_fill_gradient(low = "white", high = "lightblue") +
labs(title = "Frecuencia de Causas CIE-10 por COD_EVE",
x = "COD_EVE", y = "CBMTE (Causa CIE-10)") +
theme_minimal()
library(FactoMineR)## Warning: package 'FactoMineR' was built under R version 4.3.3library(factoextra)## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(ggplot2)
library(dplyr)
library(RColorBrewer)
library(ggrepel)
# Configurar tema profesional
theme_profesional <- theme_minimal() +
theme(
text = element_text(family = "sans", size = 12),
plot.title = element_text(face = "bold", size = 16, hjust = 0.5),
plot.subtitle = element_text(size = 12, hjust = 0.5, color = "gray50"),
axis.title = element_text(face = "bold", size = 12),
legend.title = element_text(face = "bold"),
panel.grid.major = element_line(color = "gray90"),
panel.grid.minor = element_blank(),
plot.background = element_rect(fill = "white", color = NA)
)
# Preparar datos filtrados para mejor visualización
causas_frecuentes <- subset_causas %>%
count(CBMTE) %>%
filter(n >= 5) %>% # Aumentar el umbral para mayor claridad
pull(CBMTE)
datos_filtrados <- subset_causas %>%
filter(CBMTE %in% causas_frecuentes) %>%
mutate(COD_EVE = as.factor(COD_EVE),
CBMTE = as.factor(CBMTE))
# Realizar ACM
acm_mejorado <- MCA(datos_filtrados[, c("COD_EVE", "CBMTE")],
graph = FALSE,
ncp = 5) # Mantener más componentes para mejor calidad
# Extraer coordenadas para personalización
coordenadas_var <- as.data.frame(acm_mejorado$var$coord)
coordenadas_var$Variable <- rownames(coordenadas_var)
coordenadas_var$Tipo <- ifelse(grepl("^112|^590|^600", coordenadas_var$Variable),
"COD_EVE", "CBMTE")
coordenadas_var$Codigo <- coordenadas_var$Variable
# Crear paletas de colores profesionales
paleta_cod_eve <- c(
"112" = "#1f77b4", # Azul profesional
"590" = "#ff7f0e", # Naranja profesional
"600" = "#2ca02c" # Verde profesional
)
# Paleta para CBMTE - variaciones de los mismos colores por categoría
generar_paleta_cbmtc <- function(cod_eve, n_colores) {
color_base <- paleta_cod_eve[cod_eve]
if (cod_eve == "112") {
return(colorRampPalette(c("#1f77b4", "#aec7e8"))(n_colores)) # Azules
} else if (cod_eve == "590") {
return(colorRampPalette(c("#ff7f0e", "#ffbb78"))(n_colores)) # Naranjas
} else if (cod_eve == "600") {
return(colorRampPalette(c("#2ca02c", "#98df8a"))(n_colores)) # Verdes
}
}
# Asignar colores a cada CBMTE basado en su COD_EVE predominante
asignar_colores_cbmtc <- function(subset_causas) {
predominio <- subset_causas %>%
count(COD_EVE, CBMTE) %>%
group_by(CBMTE) %>%
slice_max(n, n = 1) %>%
ungroup()
colores_cbmtc <- c()
for (cod in c("112", "590", "600")) {
cbmtcs_cod <- predominio %>%
filter(COD_EVE == cod) %>%
pull(CBMTE)
n_colores <- length(cbmtcs_cod)
if (n_colores > 0) {
paleta <- generar_paleta_cbmtc(cod, n_colores)
colores_cbmtc <- c(colores_cbmtc, setNames(paleta, cbmtcs_cod))
}
}
return(colores_cbmtc)
}
colores_cbmtc <- asignar_colores_cbmtc(datos_filtrados)
# Gráfico principal del ACM
grafico_acm <- fviz_mca_var(acm_mejorado,
repel = TRUE,
col.var = "contrib",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
alpha.var = "contrib",
shape.var = 15,
pointsize = 2) +
theme_profesional +
labs(title = "Análisis de Correspondencias Múltiples",
subtitle = "Relación entre evento y causa basica de muerte (CIE-10)",
x = paste("Dimensión 1 (", round(acm_mejorado$eig[1,2], 1), "%)", sep = ""),
y = paste("Dimensión 2 (", round(acm_mejorado$eig[2,2], 1), "%)", sep = "")) +
scale_color_gradient2(low = "blue", mid = "green", high = "red",
midpoint = median(acm_mejorado$var$contrib[,1]),
name = "Contribución") +
theme(legend.position = "right")## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.print(grafico_acm)
# Gráfico de eigenvalues (varianza explicada)
fviz_eig(acm_mejorado,
addlabels = TRUE,
ylim = c(0, 50),
barfill = "#1f77b4",
barcolor = "#1f77b4") +
theme_profesional +
labs(title = "Varianza Explicada por Cada Dimensión",
subtitle = "Calidad de la representación en el ACM")
# Contribución de variables a las dimensiones
fviz_contrib(acm_mejorado, choice = "var", axes = 1, top = 15) +
theme_profesional +
labs(title = "Contribución de Variables a la Dimensión 1")
fviz_contrib(acm_mejorado, choice = "var", axes = 2, top = 15) +
theme_profesional +
labs(title = "Contribución de Variables a la Dimensión 2")
# Análisis cualitativo de patrones
patrones_interpretacion <- top_causas %>%
group_by(COD_EVE) %>%
summarise(
causas_principales = paste(CBMTE, collapse = ", "),
perfil_epidemiologico = case_when(
any(grepl("^E40|^E43|^E46", CBMTE)) ~ "Desnutrición",
any(grepl("^J18|^J12|^J15", CBMTE)) ~ "Infecciones respiratorias",
any(grepl("^A09", CBMTE)) ~ "Gastroenteritis",
TRUE ~ "Mixto"
)
)
print(patrones_interpretacion)## # A tibble: 3 × 3
## COD_EVE causas_principales perfil_epidemiologico
## <chr> <chr> <chr>
## 1 112 E43X, E46X, R572, A09X, E440 Desnutrición
## 2 590 A09X, R571, A419, I469, E46X Desnutrición
## 3 600 J189, J960, J158, J159, R572 Infecciones respiratoriasRIESGO RELATIVO
EVENTO 112
subset_112 <- subset(datosf, COD_EVE == 112)library(dplyr)
# Crear variable indicadora
datos_112 <- datosf %>%
mutate(Mortalidad_DNT = ifelse(COD_EVE == 112, "Sí", "No"))
# Tabla comparativa
t2 <- table1::table1(~ SEXO + edad_meses + AREA + TIP_SS + PER_ETN + estrato +
GP_DESPLAZ + GP_MIGRANT + GP_POBICFB + GP_VIC_VIO +
nom_est_f_caso + Departamento_residencia + Departamento_ocurrencia |
Mortalidad_DNT,
data = datos_112)
t2| No (N=513) |
Sí (N=301) |
Overall (N=814) |
|
|---|---|---|---|
| SEXO | |||
| F | 204 (39.8%) | 130 (43.2%) | 334 (41.0%) |
| M | 309 (60.2%) | 171 (56.8%) | 480 (59.0%) |
| edad_meses | |||
| Mean (SD) | 10.8 (11.3) | 9.91 (8.05) | 10.5 (10.3) |
| Median [Min, Max] | 8.00 [1.00, 48.0] | 9.00 [1.00, 48.0] | 9.00 [1.00, 48.0] |
| AREA | |||
| 1 | 250 (48.7%) | 120 (39.9%) | 370 (45.5%) |
| 2 | 37 (7.2%) | 24 (8.0%) | 61 (7.5%) |
| 3 | 226 (44.1%) | 157 (52.2%) | 383 (47.1%) |
| TIP_SS | |||
| C | 61 (11.9%) | 5 (1.7%) | 66 (8.1%) |
| I | 10 (1.9%) | 5 (1.7%) | 15 (1.8%) |
| N | 48 (9.4%) | 28 (9.3%) | 76 (9.3%) |
| S | 394 (76.8%) | 262 (87.0%) | 656 (80.6%) |
| E | 0 (0%) | 1 (0.3%) | 1 (0.1%) |
| PER_ETN | |||
| 1 | 240 (46.8%) | 182 (60.5%) | 422 (51.8%) |
| 3 | 1 (0.2%) | 0 (0%) | 1 (0.1%) |
| 5 | 29 (5.7%) | 15 (5.0%) | 44 (5.4%) |
| 6 | 243 (47.4%) | 104 (34.6%) | 347 (42.6%) |
| estrato | |||
| 1 | 382 (74.5%) | 262 (87.0%) | 644 (79.1%) |
| 2 | 84 (16.4%) | 20 (6.6%) | 104 (12.8%) |
| 3 | 21 (4.1%) | 0 (0%) | 21 (2.6%) |
| Missing | 26 (5.1%) | 19 (6.3%) | 45 (5.5%) |
| GP_DESPLAZ | |||
| 1 | 9 (1.8%) | 4 (1.3%) | 13 (1.6%) |
| 2 | 504 (98.2%) | 297 (98.7%) | 801 (98.4%) |
| GP_MIGRANT | |||
| 1 | 23 (4.5%) | 7 (2.3%) | 30 (3.7%) |
| 2 | 490 (95.5%) | 294 (97.7%) | 784 (96.3%) |
| GP_POBICFB | |||
| 1 | 8 (1.6%) | 7 (2.3%) | 15 (1.8%) |
| 2 | 505 (98.4%) | 294 (97.7%) | 799 (98.2%) |
| GP_VIC_VIO | |||
| 2 | 513 (100%) | 298 (99.0%) | 811 (99.6%) |
| 1 | 0 (0%) | 3 (1.0%) | 3 (0.4%) |
| nom_est_f_caso | |||
| Confirmado por Clínica | 364 (71.0%) | 301 (100%) | 665 (81.7%) |
| Confirmado por laboratorio | 149 (29.0%) | 0 (0%) | 149 (18.3%) |
| Departamento_residencia | |||
| AMAZONAS | 8 (1.6%) | 1 (0.3%) | 9 (1.1%) |
| ANTIOQUIA | 24 (4.7%) | 19 (6.3%) | 43 (5.3%) |
| ARAUCA | 8 (1.6%) | 3 (1.0%) | 11 (1.4%) |
| ATLANTICO | 12 (2.3%) | 12 (4.0%) | 24 (2.9%) |
| BOGOTA | 32 (6.2%) | 0 (0%) | 32 (3.9%) |
| BOLIVAR | 23 (4.5%) | 16 (5.3%) | 39 (4.8%) |
| BOYACA | 9 (1.8%) | 1 (0.3%) | 10 (1.2%) |
| CALDAS | 1 (0.2%) | 0 (0%) | 1 (0.1%) |
| CAQUETA | 2 (0.4%) | 5 (1.7%) | 7 (0.9%) |
| CASANARE | 6 (1.2%) | 0 (0%) | 6 (0.7%) |
| CAUCA | 20 (3.9%) | 0 (0%) | 20 (2.5%) |
| CESAR | 16 (3.1%) | 22 (7.3%) | 38 (4.7%) |
| CHOCO | 69 (13.5%) | 50 (16.6%) | 119 (14.6%) |
| CORDOBA | 16 (3.1%) | 9 (3.0%) | 25 (3.1%) |
| CUNDINAMARCA | 3 (0.6%) | 0 (0%) | 3 (0.4%) |
| EXTERIOR | 29 (5.7%) | 13 (4.3%) | 42 (5.2%) |
| GUAINIA | 9 (1.8%) | 1 (0.3%) | 10 (1.2%) |
| GUAJIRA | 74 (14.4%) | 66 (21.9%) | 140 (17.2%) |
| GUAVIARE | 1 (0.2%) | 1 (0.3%) | 2 (0.2%) |
| HUILA | 7 (1.4%) | 5 (1.7%) | 12 (1.5%) |
| MAGDALENA | 22 (4.3%) | 20 (6.6%) | 42 (5.2%) |
| META | 16 (3.1%) | 7 (2.3%) | 23 (2.8%) |
| NARIÑO | 15 (2.9%) | 5 (1.7%) | 20 (2.5%) |
| NORTE SANTANDER | 7 (1.4%) | 1 (0.3%) | 8 (1.0%) |
| PUTUMAYO | 2 (0.4%) | 0 (0%) | 2 (0.2%) |
| QUINDIO | 2 (0.4%) | 0 (0%) | 2 (0.2%) |
| RISARALDA | 17 (3.3%) | 8 (2.7%) | 25 (3.1%) |
| SAN ANDRES | 2 (0.4%) | 0 (0%) | 2 (0.2%) |
| SANTANDER | 13 (2.5%) | 4 (1.3%) | 17 (2.1%) |
| SUCRE | 8 (1.6%) | 2 (0.7%) | 10 (1.2%) |
| TOLIMA | 8 (1.6%) | 2 (0.7%) | 10 (1.2%) |
| VALLE | 21 (4.1%) | 11 (3.7%) | 32 (3.9%) |
| VAUPES | 5 (1.0%) | 0 (0%) | 5 (0.6%) |
| VICHADA | 6 (1.2%) | 17 (5.6%) | 23 (2.8%) |
| Departamento_ocurrencia | |||
| AMAZONAS | 8 (1.6%) | 1 (0.3%) | 9 (1.1%) |
| ANTIOQUIA | 24 (4.7%) | 19 (6.3%) | 43 (5.3%) |
| ARAUCA | 7 (1.4%) | 3 (1.0%) | 10 (1.2%) |
| ATLANTICO | 12 (2.3%) | 12 (4.0%) | 24 (2.9%) |
| BOGOTA | 34 (6.6%) | 0 (0%) | 34 (4.2%) |
| BOLIVAR | 22 (4.3%) | 16 (5.3%) | 38 (4.7%) |
| BOYACA | 10 (1.9%) | 1 (0.3%) | 11 (1.4%) |
| CALDAS | 1 (0.2%) | 1 (0.3%) | 2 (0.2%) |
| CAQUETA | 2 (0.4%) | 5 (1.7%) | 7 (0.9%) |
| CASANARE | 6 (1.2%) | 0 (0%) | 6 (0.7%) |
| CAUCA | 20 (3.9%) | 0 (0%) | 20 (2.5%) |
| CESAR | 17 (3.3%) | 22 (7.3%) | 39 (4.8%) |
| CHOCO | 69 (13.5%) | 49 (16.3%) | 118 (14.5%) |
| CORDOBA | 16 (3.1%) | 9 (3.0%) | 25 (3.1%) |
| CUNDINAMARCA | 4 (0.8%) | 0 (0%) | 4 (0.5%) |
| EXTERIOR | 20 (3.9%) | 10 (3.3%) | 30 (3.7%) |
| GUAINIA | 9 (1.8%) | 1 (0.3%) | 10 (1.2%) |
| GUAJIRA | 79 (15.4%) | 69 (22.9%) | 148 (18.2%) |
| GUAVIARE | 1 (0.2%) | 1 (0.3%) | 2 (0.2%) |
| HUILA | 7 (1.4%) | 5 (1.7%) | 12 (1.5%) |
| MAGDALENA | 21 (4.1%) | 20 (6.6%) | 41 (5.0%) |
| META | 17 (3.3%) | 11 (3.7%) | 28 (3.4%) |
| NARIÑO | 15 (2.9%) | 5 (1.7%) | 20 (2.5%) |
| NORTE SANTANDER | 8 (1.6%) | 1 (0.3%) | 9 (1.1%) |
| PUTUMAYO | 3 (0.6%) | 0 (0%) | 3 (0.4%) |
| QUINDIO | 2 (0.4%) | 1 (0.3%) | 3 (0.4%) |
| RISARALDA | 17 (3.3%) | 8 (2.7%) | 25 (3.1%) |
| SAN ANDRES | 2 (0.4%) | 0 (0%) | 2 (0.2%) |
| SANTANDER | 13 (2.5%) | 4 (1.3%) | 17 (2.1%) |
| SUCRE | 7 (1.4%) | 2 (0.7%) | 9 (1.1%) |
| TOLIMA | 8 (1.6%) | 2 (0.7%) | 10 (1.2%) |
| VALLE | 21 (4.1%) | 10 (3.3%) | 31 (3.8%) |
| VAUPES | 4 (0.8%) | 0 (0%) | 4 (0.5%) |
| VICHADA | 7 (1.4%) | 13 (4.3%) | 20 (2.5%) |
ANALISIS VARIABLE EDAD EN MESES
# Estadísticos básicos con summary
summary(datos_112$edad_meses)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 4.00 9.00 10.49 12.00 48.00# O de manera más detallada
estadisticos_edad <- datos_112 %>%
summarise(
n = n(),
n_no_na = sum(!is.na(edad_meses)),
media = mean(edad_meses, na.rm = TRUE),
mediana = median(edad_meses, na.rm = TRUE),
desviacion = sd(edad_meses, na.rm = TRUE),
minimo = min(edad_meses, na.rm = TRUE),
maximo = max(edad_meses, na.rm = TRUE),
q1 = quantile(edad_meses, 0.25, na.rm = TRUE),
q3 = quantile(edad_meses, 0.75, na.rm = TRUE),
rango_iqr = IQR(edad_meses, na.rm = TRUE)
)
print(estadisticos_edad)## # A tibble: 1 × 10
## n n_no_na media mediana desviacion minimo maximo q1 q3 rango_iqr
## <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 814 814 10.5 9 10.3 1 48 4 12 8library(ggplot2)
# Histograma con curva de densidad
ggplot(datos_112, aes(x = edad_meses)) +
geom_histogram(aes(y = ..density..),
fill = "lightblue", color = "black", alpha = 0.7, bins = 30) +
geom_density(alpha = 0.2, fill = "red") +
labs(title = "Distribución de Edad en Meses con Curva de Densidad",
x = "Edad (meses)",
y = "Densidad") +
theme_minimal()## Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(density)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
GRUPO DE EDAD CADA 6 MESES
datos_112 <- datos_112 %>%
mutate(
edad_grupo_6meses = cut(edad_meses,
breaks = seq(0, 60, by = 6),
labels = c("0-6", "7-12", "13-18", "19-24", "25-30",
"31-36", "37-42", "43-48", "49-54", "55-69"),
include.lowest = TRUE,
right = FALSE)
)# Tabla de frecuencias simple
tabla_edad_grupo2 <- datos_112 %>%
count(edad_grupo_6meses) %>%
arrange(edad_grupo_6meses) %>%
mutate(
porcentaje = round(n / sum(n) * 100, 2),
porcentaje_acumulado = round(cumsum(porcentaje), 2)
)
print(tabla_edad_grupo2, n = Inf)## # A tibble: 6 × 4
## edad_grupo_6meses n porcentaje porcentaje_acumulado
## <fct> <int> <dbl> <dbl>
## 1 0-6 303 37.2 37.2
## 2 7-12 203 24.9 62.2
## 3 13-18 194 23.8 86.0
## 4 25-30 59 7.25 93.2
## 5 37-42 31 3.81 97.0
## 6 49-54 24 2.95 100GRUPO 6 MESES PARA EVENTO 112
tabla_edad_grupo_2_112 <- datos_112 %>%
filter(COD_EVE == 112) %>%
count(edad_grupo_6meses) %>%
arrange(edad_grupo_6meses) %>%
mutate(
porcentaje = round(n / sum(n) * 100, 2),
porcentaje_acumulado = round(cumsum(porcentaje), 2)
)
print(tabla_edad_grupo_2_112, n = Inf)## # A tibble: 6 × 4
## edad_grupo_6meses n porcentaje porcentaje_acumulado
## <fct> <int> <dbl> <dbl>
## 1 0-6 94 31.2 31.2
## 2 7-12 83 27.6 58.8
## 3 13-18 98 32.6 91.4
## 4 25-30 18 5.98 97.3
## 5 37-42 1 0.33 97.7
## 6 49-54 7 2.33 100CALCULO DE RIESGOS RELATIVOS PARA 112
SEXO
library(epiR)## Loading required package: survival## Warning: package 'survival' was built under R version 4.3.3## Package epiR 2.0.84 is loaded## Type help(epi.about) for summary information## Type browseVignettes(package = 'epiR') to learn how to use epiR for applied epidemiological analyses## sexo_tabla <- matrix(c(130, 204, 171, 309), nrow = 2, byrow = TRUE)
colnames(sexo_tabla) <- c("Mortalidad 112","Otra mortalidad")
rownames(sexo_tabla) <- c("Femenino","Masculino")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_sexo <- epi.2by2(dat = sexo_tabla, method = "cohort.count", conf.level = 0.95)
print(resultado_sexo)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 130 204 334 38.92 (33.66 to 44.38)
## Exposure- 171 309 480 35.62 (31.34 to 40.09)
## Total 301 513 814 36.98 (33.65 to 40.40)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 1.09 (0.91, 1.31)
## Inc odds ratio 1.15 (0.86, 1.54)
## Attrib risk in the exposed * 3.30 (-3.46, 10.06)
## Attrib fraction in the exposed (%) 8.47 (-9.77, 23.47)
## Attrib risk in the population * 1.35 (-4.06, 6.77)
## Attrib fraction in the population (%) 3.66 (0.76, 6.88)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 0.919 Pr>chi2 = 0.338
## Fisher exact test that OR = 1: Pr>chi2 = 0.339
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsAREA
CABECERA MUNICIPAL Y CENTRO POBLADO
area_tabla <- matrix(c(24, 37, 120, 250), nrow = 2, byrow = TRUE)
colnames(area_tabla) <- c("Mortalidad 112","Otra mortalidad")
rownames(area_tabla) <- c("Centro Poblado","Cabecera Municipal")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_area <- epi.2by2(dat = area_tabla, method = "cohort.count", conf.level = 0.95)
print(resultado_area)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 24 37 61 39.34 (27.07 to 52.69)
## Exposure- 120 250 370 32.43 (27.68 to 37.46)
## Total 144 287 431 33.41 (28.97 to 38.08)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 1.21 (0.86, 1.71)
## Inc odds ratio 1.35 (0.77, 2.36)
## Attrib risk in the exposed * 6.91 (-6.24, 20.07)
## Attrib fraction in the exposed (%) 17.57 (-18.84, 40.06)
## Attrib risk in the population * 0.98 (-5.55, 7.50)
## Attrib fraction in the population (%) 2.93 (1.62, 4.43)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 1.124 Pr>chi2 = 0.289
## Fisher exact test that OR = 1: Pr>chi2 = 0.307
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsCABECERA MUNICIPAL Y RURAL DISPERSO
area_tabla2 <- matrix(c(157, 226, 120, 250), nrow = 2, byrow = TRUE)
colnames(area_tabla2) <- c("Mortalidad 112","Otra mortalidad")
rownames(area_tabla2) <- c("Rural Disperso","Cabecera Municipal")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_area2 <- epi.2by2(dat = area_tabla2, method = "cohort.count", conf.level = 0.95)
print(resultado_area2)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 157 226 383 40.99 (36.02 to 46.10)
## Exposure- 120 250 370 32.43 (27.68 to 37.46)
## Total 277 476 753 36.79 (33.33 to 40.34)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 1.26 (1.05, 1.53)
## Inc odds ratio 1.45 (1.07, 1.95)
## Attrib risk in the exposed * 8.56 (1.70, 15.42)
## Attrib fraction in the exposed (%) 20.88 (4.44, 34.63)
## Attrib risk in the population * 4.35 (-1.53, 10.24)
## Attrib fraction in the population (%) 11.84 (7.14, 16.94)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 5.930 Pr>chi2 = 0.015
## Fisher exact test that OR = 1: Pr>chi2 = 0.016
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsASEGURAMIENTO
CONTRIBUTIVO VS INDETERMINADO
aseg_tabla1 <- matrix(c(5, 10, 5, 61), nrow = 2, byrow = TRUE)
colnames(aseg_tabla1) <- c("Mortalidad 112","Otra mortalidad")
rownames(aseg_tabla1) <- c("Indeterminado","Contributivo")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_aseg1 <- epi.2by2(dat = aseg_tabla1, method = "cohort.count", conf.level = 0.95)
print(resultado_aseg1)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 5 10 15 33.33 (11.82 to 61.62)
## Exposure- 5 61 66 7.58 (2.51 to 16.80)
## Total 10 71 81 12.35 (6.08 to 21.53)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 4.40 (1.46, 13.29)
## Inc odds ratio 6.10 (1.49, 24.95)
## Attrib risk in the exposed * 25.76 (1.06, 50.45)
## Attrib fraction in the exposed (%) 77.27 (32.23, 91.94)
## Attrib risk in the population * 4.77 (-4.83, 14.37)
## Attrib fraction in the population (%) 38.64 (21.98, 58.80)
## -------------------------------------------------------------------
## Yates corrected chi2 test that OR = 1: chi2(1) = 5.302 Pr>chi2 = 0.021
## Fisher exact test that OR = 1: Pr>chi2 = 0.016
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsCONTRIBUTIVO VS NO ASEGURADO
aseg_tabla2 <- matrix(c(28, 48, 5, 61), nrow = 2, byrow = TRUE)
colnames(aseg_tabla2) <- c("Mortalidad 112","Otra mortalidad")
rownames(aseg_tabla2) <- c("No asegurado","Contributivo")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_aseg2 <- epi.2by2(dat = aseg_tabla2, method = "cohort.count", conf.level = 0.95)
print(resultado_aseg2)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 28 48 76 36.84 (26.06 to 48.69)
## Exposure- 5 61 66 7.58 (2.51 to 16.80)
## Total 33 109 142 23.24 (16.57 to 31.06)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 4.86 (1.99, 11.87)
## Inc odds ratio 7.12 (2.56, 19.81)
## Attrib risk in the exposed * 29.27 (16.68, 41.85)
## Attrib fraction in the exposed (%) 79.44 (52.41, 91.47)
## Attrib risk in the population * 15.66 (6.23, 25.10)
## Attrib fraction in the population (%) 67.40 (45.91, 84.88)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 16.961 Pr>chi2 = <0.001
## Fisher exact test that OR = 1: Pr>chi2 = <0.001
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsCONTRIBUTIVO VS SUBSIDIADO
aseg_tabla3 <- matrix(c(262, 394, 5, 61), nrow = 2, byrow = TRUE)
colnames(aseg_tabla3) <- c("Mortalidad 112","Otra mortalidad")
rownames(aseg_tabla3) <- c("Subsidiado","Contributivo")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_aseg3 <- epi.2by2(dat = aseg_tabla3, method = "cohort.count", conf.level = 0.95)
print(resultado_aseg3)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 262 394 656 39.94 (36.17 to 43.80)
## Exposure- 5 61 66 7.58 (2.51 to 16.80)
## Total 267 455 722 36.98 (33.45 to 40.62)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 5.27 (2.26, 12.31)
## Inc odds ratio 8.11 (3.22, 20.46)
## Attrib risk in the exposed * 32.36 (24.96, 39.77)
## Attrib fraction in the exposed (%) 81.03 (58.34, 91.83)
## Attrib risk in the population * 29.40 (22.11, 36.70)
## Attrib fraction in the population (%) 79.51 (58.64, 92.51)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 26.951 Pr>chi2 = <0.001
## Fisher exact test that OR = 1: Pr>chi2 = <0.001
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsCONTRIBUTIVO VS ESPECIAL
aseg_tabla4 <- matrix(c(1, 0, 5, 61), nrow = 2, byrow = TRUE)
colnames(aseg_tabla4) <- c("Mortalidad 112","Otra mortalidad")
rownames(aseg_tabla4) <- c("Especial","Contributivo")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_aseg4 <- epi.2by2(dat = aseg_tabla4, method = "cohort.count", conf.level = 0.95)
print(resultado_aseg4)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 1 0 1 100.00 (2.50 to 100.00)
## Exposure- 5 61 66 7.58 (2.51 to 16.80)
## Total 6 61 67 8.96 (3.36 to 18.48)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 13.20 (5.68, 30.66)
## Inc odds ratio Inf (NaN, Inf)
## Attrib risk in the exposed * 92.42 (86.04, 98.81)
## Attrib fraction in the exposed (%) 92.42 (58.02, 96.72)
## Attrib risk in the population * 1.38 (-7.97, 10.73)
## Attrib fraction in the population (%) 15.40 (9.09, 25.38)
## -------------------------------------------------------------------
## Yates corrected chi2 test that OR = 1: chi2(1) = 2.098 Pr>chi2 = 0.148
## Fisher exact test that OR = 1: Pr>chi2 = 0.090
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsPERTENENCIA ETNICA
Otro vs Indígena
PE_tabla <- matrix(c(182, 240, 104, 243), nrow = 2, byrow = TRUE)
colnames(PE_tabla) <- c("Mortalidad 112","Otra mortalidad")
rownames(PE_tabla) <- c("Indígena","Otro")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_PE1 <- epi.2by2(dat = PE_tabla, method = "cohort.count", conf.level = 0.95)
print(resultado_PE1)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 182 240 422 43.13 (38.35 to 48.01)
## Exposure- 104 243 347 29.97 (25.20 to 35.09)
## Total 286 483 769 37.19 (33.76 to 40.72)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 1.44 (1.18, 1.75)
## Inc odds ratio 1.77 (1.31, 2.39)
## Attrib risk in the exposed * 13.16 (6.41, 19.91)
## Attrib fraction in the exposed (%) 30.51 (15.78, 42.92)
## Attrib risk in the population * 7.22 (1.31, 13.13)
## Attrib fraction in the population (%) 19.41 (13.81, 25.38)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 14.111 Pr>chi2 = <0.001
## Fisher exact test that OR = 1: Pr>chi2 = <0.001
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtro vs Raizal
PE_tabla2 <- matrix(c(0, 1, 104, 243), nrow = 2, byrow = TRUE)
colnames(PE_tabla2) <- c("Mortalidad 112","Otra mortalidad")
rownames(PE_tabla2) <- c("Raizal","Otro")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_PE2 <- epi.2by2(dat = PE_tabla2, method = "cohort.count", conf.level = 0.95)## Warning in qf(1 - N., 2 * sa, 2 * sc + 2): NaNs producedprint(resultado_PE2)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 0 1 1 0.00 (0.00 to 97.50)
## Exposure- 104 243 347 29.97 (25.20 to 35.09)
## Total 104 244 348 29.89 (25.12 to 35.00)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 0.00 (0.00, NaN)
## Inc odds ratio 0.00 (0.00, NaN)
## Attrib risk in the exposed * -29.97 (-34.79, -25.15)
## Attrib fraction in the exposed (%) -Inf (-Inf, 62.99)
## Attrib risk in the population * -0.09 (-6.90, 6.72)
## Attrib fraction in the population (%) -0.29 (-0.30, -0.28)
## -------------------------------------------------------------------
## Yates corrected chi2 test that OR = 1: chi2(1) = 0.000 Pr>chi2 = 1.000
## Fisher exact test that OR = 1: Pr>chi2 = 1.000
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtro vs Afro
PE_tabla3 <- matrix(c(15, 29, 104, 243), nrow = 2, byrow = TRUE)
colnames(PE_tabla3) <- c("Mortalidad 112","Otra mortalidad")
rownames(PE_tabla3) <- c("Afro","Otro")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_PE3 <- epi.2by2(dat = PE_tabla3, method = "cohort.count", conf.level = 0.95)
print(resultado_PE3)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 15 29 44 34.09 (20.49 to 49.92)
## Exposure- 104 243 347 29.97 (25.20 to 35.09)
## Total 119 272 391 30.43 (25.91 to 35.26)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 1.14 (0.73, 1.77)
## Inc odds ratio 1.21 (0.62, 2.35)
## Attrib risk in the exposed * 4.12 (-10.69, 18.93)
## Attrib fraction in the exposed (%) 12.08 (-40.59, 41.08)
## Attrib risk in the population * 0.46 (-6.17, 7.10)
## Attrib fraction in the population (%) 1.52 (0.47, 2.75)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 0.313 Pr>chi2 = 0.576
## Fisher exact test that OR = 1: Pr>chi2 = 0.603
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsESTRATO
Estrato 1 vs Sin Dato
E1_tabla <- matrix(c(262, 382, 19, 26), nrow = 2, byrow = TRUE)
colnames(E1_tabla) <- c("Mortalidad 112","Otra mortalidad")
rownames(E1_tabla) <- c("Estrato 1","Sin Dato")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_E1 <- epi.2by2(dat = E1_tabla, method = "cohort.count", conf.level = 0.95)
print(resultado_E1)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 262 382 644 40.68 (36.86 to 44.59)
## Exposure- 19 26 45 42.22 (27.66 to 57.85)
## Total 281 408 689 40.78 (37.09 to 44.56)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 0.96 (0.68, 1.37)
## Inc odds ratio 0.94 (0.51, 1.73)
## Attrib risk in the exposed * -1.54 (-16.46, 13.38)
## Attrib fraction in the exposed (%) -3.78 (-41.79, 29.53)
## Attrib risk in the population * -1.44 (-16.33, 13.45)
## Attrib fraction in the population (%) -3.53 (-29.83, 25.43)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 0.041 Pr>chi2 = 0.839
## Fisher exact test that OR = 1: Pr>chi2 = 0.876
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsEstrato 2 vs Sin Dato
E2_tabla <- matrix(c(20, 84, 19, 26), nrow = 2, byrow = TRUE)
colnames(E2_tabla) <- c("Mortalidad 112","Otra mortalidad")
rownames(E2_tabla) <- c("Estrato 2","SD")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_E2 <- epi.2by2(dat = E2_tabla, method = "cohort.count", conf.level = 0.95)
print(resultado_E2)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 20 84 104 19.23 (12.16 to 28.13)
## Exposure- 19 26 45 42.22 (27.66 to 57.85)
## Total 39 110 149 26.17 (19.32 to 34.00)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 0.46 (0.27, 0.77)
## Inc odds ratio 0.33 (0.15, 0.70)
## Attrib risk in the exposed * -22.99 (-39.29, -6.69)
## Attrib fraction in the exposed (%) -119.56 (-266.20, -29.68)
## Attrib risk in the population * -16.05 (-32.11, 0.02)
## Attrib fraction in the population (%) -61.31 (-70.15, -43.14)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 8.592 Pr>chi2 = 0.003
## Fisher exact test that OR = 1: Pr>chi2 = 0.005
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsEstrato 3 vs Sin Dato
E3_tabla <- matrix(c(0, 21, 19, 26), nrow = 2, byrow = TRUE)
colnames(E3_tabla) <- c("Mortalidad 112","Otra mortalidad")
rownames(E3_tabla) <- c("Estrato 3","SD")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_E3 <- epi.2by2(dat = E3_tabla, method = "cohort.count", conf.level = 0.95)## Warning in qf(1 - N., 2 * sa, 2 * sc + 2): NaNs producedprint(resultado_E3)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 0 21 21 0.00 (0.00 to 16.11)
## Exposure- 19 26 45 42.22 (27.66 to 57.85)
## Total 19 47 66 28.79 (18.30 to 41.25)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 0.00 (0.00, NaN)
## Inc odds ratio 0.00 (0.00, NaN)
## Attrib risk in the exposed * -42.22 (-56.65, -27.79)
## Attrib fraction in the exposed (%) -Inf (-Inf, -168.30)
## Attrib risk in the population * -13.43 (-31.53, 4.66)
## Attrib fraction in the population (%) -46.67 (-51.10, -40.24)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 12.451 Pr>chi2 = <0.001
## Fisher exact test that OR = 1: Pr>chi2 = <0.001
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsGRUPOS POBLACIONALES
Desplazado
GPD_tabla <- matrix(c(4, 9, 297, 504), nrow = 2, byrow = TRUE)
colnames(GPD_tabla) <- c("Mortalidad 112","Otra mortalidad")
rownames(GPD_tabla) <- c("No desplazado","Desplazado")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_GPD <- epi.2by2(dat = GPD_tabla, method = "cohort.count", conf.level = 0.95)
print(resultado_GPD)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 4 9 13 30.77 (9.09 to 61.43)
## Exposure- 297 504 801 37.08 (33.72 to 40.53)
## Total 301 513 814 36.98 (33.65 to 40.40)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 0.83 (0.37, 1.88)
## Inc odds ratio 0.75 (0.23, 2.47)
## Attrib risk in the exposed * -6.31 (-31.62, 19.00)
## Attrib fraction in the exposed (%) -20.51 (-193.55, 36.26)
## Attrib risk in the population * -0.10 (-4.81, 4.61)
## Attrib fraction in the population (%) -0.27 (-0.32, -0.21)
## -------------------------------------------------------------------
## Yates corrected chi2 test that OR = 1: chi2(1) = 0.032 Pr>chi2 = 0.859
## Fisher exact test that OR = 1: Pr>chi2 = 0.777
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsMIGRANTE
GPM_tabla <- matrix(c(7, 23, 294, 490), nrow = 2, byrow = TRUE)
colnames(GPM_tabla) <- c("Mortalidad 112","Otra mortalidad")
rownames(GPM_tabla) <- c("No migrante","Migrante")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_GPM <- epi.2by2(dat = GPM_tabla, method = "cohort.count", conf.level = 0.95)
print(resultado_GPM)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 7 23 30 23.33 (9.93 to 42.28)
## Exposure- 294 490 784 37.50 (34.10 to 40.99)
## Total 301 513 814 36.98 (33.65 to 40.40)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 0.62 (0.32, 1.20)
## Inc odds ratio 0.51 (0.22, 1.20)
## Attrib risk in the exposed * -14.17 (-29.68, 1.34)
## Attrib fraction in the exposed (%) -60.71 (-219.71, 9.18)
## Attrib risk in the population * -0.52 (-5.26, 4.22)
## Attrib fraction in the population (%) -1.41 (-1.48, -1.33)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 2.488 Pr>chi2 = 0.115
## Fisher exact test that OR = 1: Pr>chi2 = 0.127
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsICBF
GPI_tabla <- matrix(c(7, 8, 294, 505), nrow = 2, byrow = TRUE)
colnames(GPI_tabla) <- c("Mortalidad 112","Otra mortalidad")
rownames(GPI_tabla) <- c("No ICBF","ICBF")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_GPI <- epi.2by2(dat = GPI_tabla, method = "cohort.count", conf.level = 0.95)
print(resultado_GPI)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 7 8 15 46.67 (21.27 to 73.41)
## Exposure- 294 505 799 36.80 (33.44 to 40.25)
## Total 301 513 814 36.98 (33.65 to 40.40)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 1.27 (0.73, 2.20)
## Inc odds ratio 1.50 (0.54, 4.19)
## Attrib risk in the exposed * 9.87 (-15.60, 35.34)
## Attrib fraction in the exposed (%) 21.15 (-49.16, 48.10)
## Attrib risk in the population * 0.18 (-4.53, 4.89)
## Attrib fraction in the population (%) 0.49 (0.38, 0.62)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 0.616 Pr>chi2 = 0.433
## Fisher exact test that OR = 1: Pr>chi2 = 0.431
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsVICTIMA VIOLENCIA
GPVV_tabla <- matrix(c(3, 0, 298, 513), nrow = 2, byrow = TRUE)
colnames(GPVV_tabla) <- c("Mortalidad 112","Otra mortalidad")
rownames(GPVV_tabla) <- c("No victima violencia","Victima violencia")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_GPVV <- epi.2by2(dat = GPVV_tabla, method = "cohort.count", conf.level = 0.95)
print(resultado_GPVV)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 3 0 3 100.00 (29.24 to 100.00)
## Exposure- 298 513 811 36.74 (33.42 to 40.17)
## Total 301 513 814 36.98 (33.65 to 40.40)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 2.72 (2.49, 2.98)
## Inc odds ratio Inf (NaN, Inf)
## Attrib risk in the exposed * 63.26 (59.94, 66.57)
## Attrib fraction in the exposed (%) 63.26 (15.90, 66.50)
## Attrib risk in the population * 0.23 (-4.46, 4.92)
## Attrib fraction in the population (%) 0.63 (0.57, 0.70)
## -------------------------------------------------------------------
## Yates corrected chi2 test that OR = 1: chi2(1) = 2.776 Pr>chi2 = 0.096
## Fisher exact test that OR = 1: Pr>chi2 = 0.050
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsDEPARTAMENTO DE RESIDENCIA
Otros vs Vichada
Dpto_tabla1 <- matrix(c(17, 6, 89, 287), nrow = 2, byrow = TRUE)
colnames(Dpto_tabla1) <- c("Mortalidad 112","Otra mortalidad")
rownames(Dpto_tabla1) <- c("Vichada","Otros")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_Dpto1 <- epi.2by2(dat = Dpto_tabla1, method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto1)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 17 6 23 73.91 (51.59 to 89.77)
## Exposure- 89 287 376 23.67 (19.46 to 28.30)
## Total 106 293 399 26.57 (22.30 to 31.19)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 3.12 (2.31, 4.23)
## Inc odds ratio 9.14 (3.50, 23.88)
## Attrib risk in the exposed * 50.24 (31.79, 68.70)
## Attrib fraction in the exposed (%) 67.98 (54.00, 75.43)
## Attrib risk in the population * 2.90 (-3.21, 9.00)
## Attrib fraction in the population (%) 10.90 (9.27, 12.70)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 28.046 Pr>chi2 = <0.001
## Fisher exact test that OR = 1: Pr>chi2 = <0.001
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtros vs Caqueta
Dpto_tabla2 <- matrix(c(5, 2, 89, 287), nrow = 2, byrow = TRUE)
colnames(Dpto_tabla2) <- c("Mortalidad 112","Otra mortalidad")
rownames(Dpto_tabla2) <- c("Caqueta","Otros")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_Dpto2 <- epi.2by2(dat = Dpto_tabla2, method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto2)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 5 2 7 71.43 (29.04 to 96.33)
## Exposure- 89 287 376 23.67 (19.46 to 28.30)
## Total 94 289 383 24.54 (20.31 to 29.17)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 3.02 (1.83, 4.99)
## Inc odds ratio 8.06 (1.54, 42.27)
## Attrib risk in the exposed * 47.76 (14.02, 81.50)
## Attrib fraction in the exposed (%) 66.86 (32.78, 76.35)
## Attrib risk in the population * 0.87 (-5.21, 6.96)
## Attrib fraction in the population (%) 3.56 (2.99, 4.19)
## -------------------------------------------------------------------
## Yates corrected chi2 test that OR = 1: chi2(1) = 6.081 Pr>chi2 = 0.014
## Fisher exact test that OR = 1: Pr>chi2 = 0.011
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtros vs Cesar
Dpto_tabla3 <- matrix(c(22, 16, 89, 287), nrow = 2, byrow = TRUE)
colnames(Dpto_tabla3) <- c("Mortalidad 112","Otra mortalidad")
rownames(Dpto_tabla3) <- c("Cesar","Otros")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_Dpto3 <- epi.2by2(dat = Dpto_tabla3, method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto3)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 22 16 38 57.89 (40.82 to 73.69)
## Exposure- 89 287 376 23.67 (19.46 to 28.30)
## Total 111 303 414 26.81 (22.60 to 31.36)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 2.45 (1.76, 3.39)
## Inc odds ratio 4.43 (2.23, 8.81)
## Attrib risk in the exposed * 34.22 (17.95, 50.50)
## Attrib fraction in the exposed (%) 59.12 (41.44, 69.61)
## Attrib risk in the population * 3.14 (-2.91, 9.20)
## Attrib fraction in the population (%) 11.72 (9.75, 13.89)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 20.601 Pr>chi2 = <0.001
## Fisher exact test that OR = 1: Pr>chi2 = <0.001
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtros vs Atlantico
Dpto_tabla4 <- matrix(c(12, 12, 89, 287), nrow = 2, byrow = TRUE)
colnames(Dpto_tabla4) <- c("Mortalidad 112","Otra mortalidad")
rownames(Dpto_tabla4) <- c("Atlantico","Otros")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_Dpto4 <- epi.2by2(dat = Dpto_tabla4, method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto4)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 12 12 24 50.00 (29.12 to 70.88)
## Exposure- 89 287 376 23.67 (19.46 to 28.30)
## Total 101 299 400 25.25 (21.06 to 29.81)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 2.11 (1.36, 3.28)
## Inc odds ratio 3.22 (1.40, 7.43)
## Attrib risk in the exposed * 26.33 (5.87, 46.79)
## Attrib fraction in the exposed (%) 52.66 (22.36, 67.52)
## Attrib risk in the population * 1.58 (-4.47, 7.63)
## Attrib fraction in the population (%) 6.26 (5.06, 7.60)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 8.286 Pr>chi2 = 0.004
## Fisher exact test that OR = 1: Pr>chi2 = 0.007
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtros vs Guaviare
Dpto_tabla5 <- matrix(c(1, 1, 89, 287), nrow = 2, byrow = TRUE)
colnames(Dpto_tabla5) <- c("Mortalidad 112","Otra mortalidad")
rownames(Dpto_tabla5) <- c("Guaviare","Otros")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_Dpto5 <- epi.2by2(dat = Dpto_tabla5, method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto5)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 1 1 2 50.00 (1.26 to 98.74)
## Exposure- 89 287 376 23.67 (19.46 to 28.30)
## Total 90 288 378 23.81 (19.60 to 28.43)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 2.11 (0.52, 8.55)
## Inc odds ratio 3.22 (0.20, 52.08)
## Attrib risk in the exposed * 26.33 (-43.10, 95.76)
## Attrib fraction in the exposed (%) 52.66 (-151.99, 75.29)
## Attrib risk in the population * 0.14 (-5.93, 6.21)
## Attrib fraction in the population (%) 0.59 (0.47, 0.72)
## -------------------------------------------------------------------
## Yates corrected chi2 test that OR = 1: chi2(1) = 0.002 Pr>chi2 = 0.968
## Fisher exact test that OR = 1: Pr>chi2 = 0.420
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtro vs Magdalena
Dpto_tabla6 <- matrix(c(20, 22, 89, 287), nrow = 2, byrow = TRUE)
colnames(Dpto_tabla6) <- c("Mortalidad 112","Otra mortalidad")
rownames(Dpto_tabla6) <- c("Magdalena","Otros")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_Dpto6 <- epi.2by2(dat = Dpto_tabla6, method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto6)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 20 22 42 47.62 (32.00 to 63.58)
## Exposure- 89 287 376 23.67 (19.46 to 28.30)
## Total 109 309 418 26.08 (21.93 to 30.57)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 2.01 (1.40, 2.90)
## Inc odds ratio 2.93 (1.53, 5.62)
## Attrib risk in the exposed * 23.95 (8.25, 39.65)
## Attrib fraction in the exposed (%) 50.29 (26.19, 64.38)
## Attrib risk in the population * 2.41 (-3.61, 8.42)
## Attrib fraction in the population (%) 9.23 (7.42, 11.25)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 11.241 Pr>chi2 = <0.001
## Fisher exact test that OR = 1: Pr>chi2 = 0.001
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtros vs Guajira
Dpto_tabla7 <- matrix(c(66, 74, 89, 287), nrow = 2, byrow = TRUE)
colnames(Dpto_tabla7) <- c("Mortalidad 112","Otra mortalidad")
rownames(Dpto_tabla7) <- c("Guajira","Otros")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_Dpto7 <- epi.2by2(dat = Dpto_tabla7, method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto7)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 66 74 140 47.14 (38.66 to 55.75)
## Exposure- 89 287 376 23.67 (19.46 to 28.30)
## Total 155 361 516 30.04 (26.11 to 34.20)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 1.99 (1.55, 2.56)
## Inc odds ratio 2.88 (1.91, 4.33)
## Attrib risk in the exposed * 23.47 (14.15, 32.79)
## Attrib fraction in the exposed (%) 49.79 (35.12, 60.84)
## Attrib risk in the population * 6.37 (0.53, 12.21)
## Attrib fraction in the population (%) 21.20 (17.25, 25.46)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 26.745 Pr>chi2 = <0.001
## Fisher exact test that OR = 1: Pr>chi2 = <0.001
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsotros vs Antioquia
Dpto_tabla8 <- matrix(c(19, 24, 89, 287), nrow = 2, byrow = TRUE)
colnames(Dpto_tabla8) <- c("Mortalidad 112","Otra mortalidad")
rownames(Dpto_tabla8) <- c("Antioquia","Otros")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_Dpto8 <- epi.2by2(dat = Dpto_tabla8, method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto8)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 19 24 43 44.19 (29.08 to 60.12)
## Exposure- 89 287 376 23.67 (19.46 to 28.30)
## Total 108 311 419 25.78 (21.65 to 30.25)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 1.87 (1.27, 2.73)
## Inc odds ratio 2.55 (1.34, 4.88)
## Attrib risk in the exposed * 20.52 (5.06, 35.97)
## Attrib fraction in the exposed (%) 46.43 (19.21, 62.19)
## Attrib risk in the population * 2.11 (-3.89, 8.11)
## Attrib fraction in the population (%) 8.17 (6.44, 10.11)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 8.489 Pr>chi2 = 0.004
## Fisher exact test that OR = 1: Pr>chi2 = 0.005
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsotros vs Chocó
Dpto_tabla9 <- matrix(c(50, 69, 89, 287), nrow = 2, byrow = TRUE)
colnames(Dpto_tabla9) <- c("Mortalidad 112","Otra mortalidad")
rownames(Dpto_tabla9) <- c("Chocó","Otros")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_Dpto9 <- epi.2by2(dat = Dpto_tabla9, method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto9)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 50 69 119 42.02 (33.03 to 51.41)
## Exposure- 89 287 376 23.67 (19.46 to 28.30)
## Total 139 356 495 28.08 (24.16 to 32.26)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 1.78 (1.34, 2.34)
## Inc odds ratio 2.34 (1.51, 3.61)
## Attrib risk in the exposed * 18.35 (8.49, 28.20)
## Attrib fraction in the exposed (%) 43.66 (25.08, 57.05)
## Attrib risk in the population * 4.41 (-1.43, 10.25)
## Attrib fraction in the population (%) 15.71 (12.29, 19.45)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 15.066 Pr>chi2 = <0.001
## Fisher exact test that OR = 1: Pr>chi2 = <0.001
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsANALISIS DEPARTAMENTO OCURRENCIA
Otros vs Caqueta
Dpto_o_tabla1 <- matrix(c(5, 2, 139, 349), nrow = 2, byrow = TRUE)
colnames(Dpto_o_tabla1 ) <- c("Mortalidad 112","Otra mortalidad")
rownames(Dpto_o_tabla1 ) <- c("Caquetá","Otros")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_Dpto_o1 <- epi.2by2(dat = Dpto_o_tabla1 , method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto_o1)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 5 2 7 71.43 (29.04 to 96.33)
## Exposure- 139 349 488 28.48 (24.52 to 32.71)
## Total 144 351 495 29.09 (25.12 to 33.31)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 2.51 (1.54, 4.09)
## Inc odds ratio 6.28 (1.20, 32.74)
## Attrib risk in the exposed * 42.94 (9.24, 76.65)
## Attrib fraction in the exposed (%) 60.12 (19.72, 70.68)
## Attrib risk in the population * 0.61 (-5.05, 6.27)
## Attrib fraction in the population (%) 2.09 (1.79, 2.41)
## -------------------------------------------------------------------
## Yates corrected chi2 test that OR = 1: chi2(1) = 4.264 Pr>chi2 = 0.039
## Fisher exact test that OR = 1: Pr>chi2 = 0.024
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtros vs Vichada
Dpto_o_tabla2 <- matrix(c(13, 7, 139, 349), nrow = 2, byrow = TRUE)
colnames(Dpto_o_tabla2 ) <- c("Mortalidad 112","Otra mortalidad")
rownames(Dpto_o_tabla2 ) <- c("Vichada","Otros")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_Dpto_o2 <- epi.2by2(dat = Dpto_o_tabla2 , method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto_o2)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 13 7 20 65.00 (40.78 to 84.61)
## Exposure- 139 349 488 28.48 (24.52 to 32.71)
## Total 152 356 508 29.92 (25.97 to 34.11)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 2.28 (1.61, 3.24)
## Inc odds ratio 4.66 (1.82, 11.93)
## Attrib risk in the exposed * 36.52 (15.23, 57.80)
## Attrib fraction in the exposed (%) 56.18 (32.86, 66.97)
## Attrib risk in the population * 1.44 (-4.21, 7.08)
## Attrib fraction in the population (%) 4.80 (4.10, 5.58)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 12.218 Pr>chi2 = <0.001
## Fisher exact test that OR = 1: Pr>chi2 = 0.002
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtros vs Cesar
Dpto_o_tabla3 <- matrix(c(22, 17, 139, 349), nrow = 2, byrow = TRUE)
colnames(Dpto_o_tabla3 ) <- c("Mortalidad 112","Otra mortalidad")
rownames(Dpto_o_tabla3 ) <- c("Cesar","Otros")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_Dpto_o3 <- epi.2by2(dat = Dpto_o_tabla3 , method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto_o3)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 22 17 39 56.41 (39.62 to 72.19)
## Exposure- 139 349 488 28.48 (24.52 to 32.71)
## Total 161 366 527 30.55 (26.64 to 34.68)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 1.98 (1.45, 2.70)
## Inc odds ratio 3.25 (1.67, 6.30)
## Attrib risk in the exposed * 27.93 (11.86, 44.00)
## Attrib fraction in the exposed (%) 49.51 (28.64, 61.61)
## Attrib risk in the population * 2.07 (-3.55, 7.68)
## Attrib fraction in the population (%) 6.76 (5.67, 7.97)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 13.275 Pr>chi2 = <0.001
## Fisher exact test that OR = 1: Pr>chi2 = <0.001
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtros vs Atlantico
Dpto_o_tabla4 <- matrix(c(12, 12, 139, 349), nrow = 2, byrow = TRUE)
colnames(Dpto_o_tabla4 ) <- c("Mortalidad 112","Otra mortalidad")
rownames(Dpto_o_tabla4 ) <- c("Atlantico","Otros")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_Dpto_o4 <- epi.2by2(dat = Dpto_o_tabla4 , method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto_o4)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 12 12 24 50.00 (29.12 to 70.88)
## Exposure- 139 349 488 28.48 (24.52 to 32.71)
## Total 151 361 512 29.49 (25.57 to 33.65)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 1.76 (1.15, 2.68)
## Inc odds ratio 2.51 (1.10, 5.72)
## Attrib risk in the exposed * 21.52 (1.12, 41.92)
## Attrib fraction in the exposed (%) 43.03 (7.68, 60.03)
## Attrib risk in the population * 1.01 (-4.62, 6.63)
## Attrib fraction in the population (%) 3.42 (2.79, 4.13)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 5.093 Pr>chi2 = 0.024
## Fisher exact test that OR = 1: Pr>chi2 = 0.037
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtros vs Caldas y Guaviare
Dpto_o_tabla5 <- matrix(c(1, 1, 139, 349), nrow = 2, byrow = TRUE)
colnames(Dpto_o_tabla5 ) <- c("Mortalidad 112","Otra mortalidad")
rownames(Dpto_o_tabla5 ) <- c("Caldas y Guaviare","Otros")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_Dpto_o5 <- epi.2by2(dat = Dpto_o_tabla5 , method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto_o5)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 1 1 2 50.00 (1.26 to 98.74)
## Exposure- 139 349 488 28.48 (24.52 to 32.71)
## Total 140 350 490 28.57 (24.61 to 32.79)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 1.76 (0.44, 7.07)
## Inc odds ratio 2.51 (0.16, 40.42)
## Attrib risk in the exposed * 21.52 (-47.89, 90.93)
## Attrib fraction in the exposed (%) 43.03 (-202.46, 69.62)
## Attrib risk in the population * 0.09 (-5.57, 5.75)
## Attrib fraction in the population (%) 0.31 (0.25, 0.37)
## -------------------------------------------------------------------
## Yates corrected chi2 test that OR = 1: chi2(1) = 0.000 Pr>chi2 = 1.000
## Fisher exact test that OR = 1: Pr>chi2 = 0.490
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtros vs Magdalena
Dpto_o_tabla6 <- matrix(c(20, 21, 139, 349), nrow = 2, byrow = TRUE)
colnames(Dpto_o_tabla6 ) <- c("Mortalidad 112","Otra mortalidad")
rownames(Dpto_o_tabla6 ) <- c("Magdalena","Otros")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_Dpto_o6 <- epi.2by2(dat = Dpto_o_tabla6 , method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto_o6)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 20 21 41 48.78 (32.88 to 64.87)
## Exposure- 139 349 488 28.48 (24.52 to 32.71)
## Total 159 370 529 30.06 (26.18 to 34.16)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 1.71 (1.21, 2.42)
## Inc odds ratio 2.39 (1.26, 4.55)
## Attrib risk in the exposed * 20.30 (4.48, 36.11)
## Attrib fraction in the exposed (%) 41.61 (14.80, 57.00)
## Attrib risk in the population * 1.57 (-4.02, 7.17)
## Attrib fraction in the population (%) 5.23 (4.24, 6.34)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 7.412 Pr>chi2 = 0.006
## Fisher exact test that OR = 1: Pr>chi2 = 0.012
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtros vs Guajira
Dpto_o_tabla7 <- matrix(c(69, 79, 139, 349), nrow = 2, byrow = TRUE)
colnames(Dpto_o_tabla7 ) <- c("Mortalidad 112","Otra mortalidad")
rownames(Dpto_o_tabla7 ) <- c("Guajira","Otros")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_Dpto_o7 <- epi.2by2(dat = Dpto_o_tabla7 , method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto_o7)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 69 79 148 46.62 (38.39 to 54.99)
## Exposure- 139 349 488 28.48 (24.52 to 32.71)
## Total 208 428 636 32.70 (29.07 to 36.50)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 1.64 (1.31, 2.04)
## Inc odds ratio 2.19 (1.50, 3.20)
## Attrib risk in the exposed * 18.14 (9.16, 27.12)
## Attrib fraction in the exposed (%) 38.90 (23.21, 50.80)
## Attrib risk in the population * 4.22 (-1.19, 9.64)
## Attrib fraction in the population (%) 12.91 (10.38, 15.65)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 16.975 Pr>chi2 = <0.001
## Fisher exact test that OR = 1: Pr>chi2 = <0.001
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtros vs Antioquia
Dpto_o_tabla8 <- matrix(c(19, 24, 139, 349), nrow = 2, byrow = TRUE)
colnames(Dpto_o_tabla8 ) <- c("Mortalidad 112","Otra mortalidad")
rownames(Dpto_o_tabla8 ) <- c("Antioquia","Otros")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_Dpto_o8 <- epi.2by2(dat = Dpto_o_tabla8 , method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto_o8)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 19 24 43 44.19 (29.08 to 60.12)
## Exposure- 139 349 488 28.48 (24.52 to 32.71)
## Total 158 373 531 29.76 (25.89 to 33.84)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 1.55 (1.08, 2.23)
## Inc odds ratio 1.99 (1.06, 3.74)
## Attrib risk in the exposed * 15.70 (0.33, 31.08)
## Attrib fraction in the exposed (%) 35.54 (4.20, 53.47)
## Attrib risk in the population * 1.27 (-4.31, 6.85)
## Attrib fraction in the population (%) 4.27 (3.34, 5.32)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 4.662 Pr>chi2 = 0.031
## Fisher exact test that OR = 1: Pr>chi2 = 0.037
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsEDAD ANALISIS GRUPO POR 6 MESES
Menor de 6 meses vs mayor de 19 meses
Edad_6m_tabla1 <- matrix(c(94, 209, 26, 88), nrow = 2, byrow = TRUE)
colnames(Edad_6m_tabla1 ) <- c("Mortalidad 112","Otra mortalidad")
rownames(Edad_6m_tabla1 ) <- c("Menor de 6m","Mayor de 19m")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_edad_6m_1 <- epi.2by2(dat = Edad_6m_tabla1 , method = "cohort.count", conf.level = 0.95)
print(resultado_edad_6m_1)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 94 209 303 31.02 (25.86 to 36.56)
## Exposure- 26 88 114 22.81 (15.47 to 31.60)
## Total 120 297 417 28.78 (24.48 to 33.38)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 1.36 (0.93, 1.98)
## Inc odds ratio 1.52 (0.92, 2.51)
## Attrib risk in the exposed * 8.22 (-1.08, 17.51)
## Attrib fraction in the exposed (%) 26.48 (-5.64, 50.02)
## Attrib risk in the population * 5.97 (-2.87, 14.81)
## Attrib fraction in the population (%) 20.75 (5.32, 36.79)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 2.728 Pr>chi2 = 0.099
## Fisher exact test that OR = 1: Pr>chi2 = 0.115
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsMayor a 19 meses vs 7 a 12 meses
Edad_6m_tabla2 <- matrix(c(83, 120, 26, 88), nrow = 2, byrow = TRUE)
colnames(Edad_6m_tabla2 ) <- c("Mortalidad 112","Otra mortalidad")
rownames(Edad_6m_tabla2 ) <- c("7-12 m","Mayor a 19m")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_edad_6m_2 <- epi.2by2(dat = Edad_6m_tabla2 , method = "cohort.count", conf.level = 0.95)
print(resultado_edad_6m_2)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 83 120 203 40.89 (34.06 to 47.99)
## Exposure- 26 88 114 22.81 (15.47 to 31.60)
## Total 109 208 317 34.38 (29.17 to 39.90)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 1.79 (1.23, 2.61)
## Inc odds ratio 2.34 (1.39, 3.94)
## Attrib risk in the exposed * 18.08 (7.83, 28.33)
## Attrib fraction in the exposed (%) 44.22 (19.88, 62.03)
## Attrib risk in the population * 11.58 (2.27, 20.89)
## Attrib fraction in the population (%) 33.67 (20.79, 46.95)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 10.577 Pr>chi2 = 0.001
## Fisher exact test that OR = 1: Pr>chi2 = 0.001
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsmayor a 19 meses vs 13 a 18 meses
Edad_6m_tabla3 <- matrix(c(98, 96, 26, 88), nrow = 2, byrow = TRUE)
colnames(Edad_6m_tabla3 ) <- c("Mortalidad 112","Otra mortalidad")
rownames(Edad_6m_tabla3 ) <- c("13-18 m","Mayor a 19m")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_edad_6m_3 <- epi.2by2(dat = Edad_6m_tabla3 , method = "cohort.count", conf.level = 0.95)
print(resultado_edad_6m_3)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 98 96 194 50.52 (43.26 to 57.75)
## Exposure- 26 88 114 22.81 (15.47 to 31.60)
## Total 124 184 308 40.26 (34.74 to 45.97)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 2.21 (1.54, 3.19)
## Inc odds ratio 3.46 (2.05, 5.81)
## Attrib risk in the exposed * 27.71 (17.28, 38.14)
## Attrib fraction in the exposed (%) 54.85 (35.93, 68.96)
## Attrib risk in the population * 17.45 (8.00, 26.90)
## Attrib fraction in the population (%) 43.35 (31.25, 55.46)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 22.921 Pr>chi2 = <0.001
## Fisher exact test that OR = 1: Pr>chi2 = <0.001
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsRIESGO RELATIVO EVENTO 590
subset_590 <- subset(datosf, COD_EVE == 590)library(dplyr)
# Crear variable indicadora
datos_590 <- datosf%>%
mutate(Mortalidad_EDA = ifelse(COD_EVE == 590, "Sí", "No"))
# Tabla comparativa
t3 <- table1::table1(~ SEXO + edad_meses + AREA + TIP_SS + PER_ETN + estrato +
GP_DESPLAZ + GP_MIGRANT + GP_POBICFB + GP_VIC_VIO +
nom_est_f_caso + Departamento_residencia + Departamento_ocurrencia |
Mortalidad_EDA,
data = datos_590)
t3| No (N=682) |
Sí (N=132) |
Overall (N=814) |
|
|---|---|---|---|
| SEXO | |||
| F | 284 (41.6%) | 50 (37.9%) | 334 (41.0%) |
| M | 398 (58.4%) | 82 (62.1%) | 480 (59.0%) |
| edad_meses | |||
| Mean (SD) | 10.1 (9.90) | 12.5 (11.8) | 10.5 (10.3) |
| Median [Min, Max] | 8.00 [1.00, 48.0] | 10.0 [1.00, 48.0] | 9.00 [1.00, 48.0] |
| AREA | |||
| 1 | 329 (48.2%) | 41 (31.1%) | 370 (45.5%) |
| 2 | 49 (7.2%) | 12 (9.1%) | 61 (7.5%) |
| 3 | 304 (44.6%) | 79 (59.8%) | 383 (47.1%) |
| TIP_SS | |||
| C | 59 (8.7%) | 7 (5.3%) | 66 (8.1%) |
| E | 1 (0.1%) | 0 (0%) | 1 (0.1%) |
| I | 11 (1.6%) | 4 (3.0%) | 15 (1.8%) |
| N | 57 (8.4%) | 19 (14.4%) | 76 (9.3%) |
| S | 554 (81.2%) | 102 (77.3%) | 656 (80.6%) |
| PER_ETN | |||
| 1 | 330 (48.4%) | 92 (69.7%) | 422 (51.8%) |
| 3 | 1 (0.1%) | 0 (0%) | 1 (0.1%) |
| 5 | 39 (5.7%) | 5 (3.8%) | 44 (5.4%) |
| 6 | 312 (45.7%) | 35 (26.5%) | 347 (42.6%) |
| estrato | |||
| 1 | 525 (77.0%) | 119 (90.2%) | 644 (79.1%) |
| 2 | 98 (14.4%) | 6 (4.5%) | 104 (12.8%) |
| 3 | 21 (3.1%) | 0 (0%) | 21 (2.6%) |
| Missing | 38 (5.6%) | 7 (5.3%) | 45 (5.5%) |
| GP_DESPLAZ | |||
| 1 | 12 (1.8%) | 1 (0.8%) | 13 (1.6%) |
| 2 | 670 (98.2%) | 131 (99.2%) | 801 (98.4%) |
| GP_MIGRANT | |||
| 1 | 22 (3.2%) | 8 (6.1%) | 30 (3.7%) |
| 2 | 660 (96.8%) | 124 (93.9%) | 784 (96.3%) |
| GP_POBICFB | |||
| 1 | 13 (1.9%) | 2 (1.5%) | 15 (1.8%) |
| 2 | 669 (98.1%) | 130 (98.5%) | 799 (98.2%) |
| GP_VIC_VIO | |||
| 1 | 3 (0.4%) | 0 (0%) | 3 (0.4%) |
| 2 | 679 (99.6%) | 132 (100%) | 811 (99.6%) |
| nom_est_f_caso | |||
| Confirmado por Clínica | 535 (78.4%) | 130 (98.5%) | 665 (81.7%) |
| Confirmado por laboratorio | 147 (21.6%) | 2 (1.5%) | 149 (18.3%) |
| Departamento_residencia | |||
| AMAZONAS | 7 (1.0%) | 2 (1.5%) | 9 (1.1%) |
| ANTIOQUIA | 40 (5.9%) | 3 (2.3%) | 43 (5.3%) |
| ARAUCA | 9 (1.3%) | 2 (1.5%) | 11 (1.4%) |
| ATLANTICO | 23 (3.4%) | 1 (0.8%) | 24 (2.9%) |
| BOGOTA | 32 (4.7%) | 0 (0%) | 32 (3.9%) |
| BOLIVAR | 38 (5.6%) | 1 (0.8%) | 39 (4.8%) |
| BOYACA | 9 (1.3%) | 1 (0.8%) | 10 (1.2%) |
| CALDAS | 1 (0.1%) | 0 (0%) | 1 (0.1%) |
| CAQUETA | 7 (1.0%) | 0 (0%) | 7 (0.9%) |
| CASANARE | 2 (0.3%) | 4 (3.0%) | 6 (0.7%) |
| CAUCA | 13 (1.9%) | 7 (5.3%) | 20 (2.5%) |
| CESAR | 34 (5.0%) | 4 (3.0%) | 38 (4.7%) |
| CHOCO | 94 (13.8%) | 25 (18.9%) | 119 (14.6%) |
| CORDOBA | 24 (3.5%) | 1 (0.8%) | 25 (3.1%) |
| CUNDINAMARCA | 3 (0.4%) | 0 (0%) | 3 (0.4%) |
| EXTERIOR | 31 (4.5%) | 11 (8.3%) | 42 (5.2%) |
| GUAINIA | 5 (0.7%) | 5 (3.8%) | 10 (1.2%) |
| GUAJIRA | 113 (16.6%) | 27 (20.5%) | 140 (17.2%) |
| GUAVIARE | 2 (0.3%) | 0 (0%) | 2 (0.2%) |
| HUILA | 11 (1.6%) | 1 (0.8%) | 12 (1.5%) |
| MAGDALENA | 36 (5.3%) | 6 (4.5%) | 42 (5.2%) |
| META | 21 (3.1%) | 2 (1.5%) | 23 (2.8%) |
| NARIÑO | 18 (2.6%) | 2 (1.5%) | 20 (2.5%) |
| NORTE SANTANDER | 7 (1.0%) | 1 (0.8%) | 8 (1.0%) |
| PUTUMAYO | 1 (0.1%) | 1 (0.8%) | 2 (0.2%) |
| QUINDIO | 2 (0.3%) | 0 (0%) | 2 (0.2%) |
| RISARALDA | 17 (2.5%) | 8 (6.1%) | 25 (3.1%) |
| SAN ANDRES | 2 (0.3%) | 0 (0%) | 2 (0.2%) |
| SANTANDER | 11 (1.6%) | 6 (4.5%) | 17 (2.1%) |
| SUCRE | 7 (1.0%) | 3 (2.3%) | 10 (1.2%) |
| TOLIMA | 10 (1.5%) | 0 (0%) | 10 (1.2%) |
| VALLE | 29 (4.3%) | 3 (2.3%) | 32 (3.9%) |
| VAUPES | 4 (0.6%) | 1 (0.8%) | 5 (0.6%) |
| VICHADA | 19 (2.8%) | 4 (3.0%) | 23 (2.8%) |
| Departamento_ocurrencia | |||
| AMAZONAS | 7 (1.0%) | 2 (1.5%) | 9 (1.1%) |
| ANTIOQUIA | 40 (5.9%) | 3 (2.3%) | 43 (5.3%) |
| ARAUCA | 8 (1.2%) | 2 (1.5%) | 10 (1.2%) |
| ATLANTICO | 23 (3.4%) | 1 (0.8%) | 24 (2.9%) |
| BOGOTA | 34 (5.0%) | 0 (0%) | 34 (4.2%) |
| BOLIVAR | 37 (5.4%) | 1 (0.8%) | 38 (4.7%) |
| BOYACA | 10 (1.5%) | 1 (0.8%) | 11 (1.4%) |
| CALDAS | 2 (0.3%) | 0 (0%) | 2 (0.2%) |
| CAQUETA | 7 (1.0%) | 0 (0%) | 7 (0.9%) |
| CASANARE | 2 (0.3%) | 4 (3.0%) | 6 (0.7%) |
| CAUCA | 13 (1.9%) | 7 (5.3%) | 20 (2.5%) |
| CESAR | 35 (5.1%) | 4 (3.0%) | 39 (4.8%) |
| CHOCO | 92 (13.5%) | 26 (19.7%) | 118 (14.5%) |
| CORDOBA | 24 (3.5%) | 1 (0.8%) | 25 (3.1%) |
| CUNDINAMARCA | 4 (0.6%) | 0 (0%) | 4 (0.5%) |
| EXTERIOR | 24 (3.5%) | 6 (4.5%) | 30 (3.7%) |
| GUAINIA | 5 (0.7%) | 5 (3.8%) | 10 (1.2%) |
| GUAJIRA | 118 (17.3%) | 30 (22.7%) | 148 (18.2%) |
| GUAVIARE | 2 (0.3%) | 0 (0%) | 2 (0.2%) |
| HUILA | 11 (1.6%) | 1 (0.8%) | 12 (1.5%) |
| MAGDALENA | 35 (5.1%) | 6 (4.5%) | 41 (5.0%) |
| META | 25 (3.7%) | 3 (2.3%) | 28 (3.4%) |
| NARIÑO | 18 (2.6%) | 2 (1.5%) | 20 (2.5%) |
| NORTE SANTANDER | 7 (1.0%) | 2 (1.5%) | 9 (1.1%) |
| PUTUMAYO | 2 (0.3%) | 1 (0.8%) | 3 (0.4%) |
| QUINDIO | 3 (0.4%) | 0 (0%) | 3 (0.4%) |
| RISARALDA | 17 (2.5%) | 8 (6.1%) | 25 (3.1%) |
| SAN ANDRES | 2 (0.3%) | 0 (0%) | 2 (0.2%) |
| SANTANDER | 11 (1.6%) | 6 (4.5%) | 17 (2.1%) |
| SUCRE | 6 (0.9%) | 3 (2.3%) | 9 (1.1%) |
| TOLIMA | 10 (1.5%) | 0 (0%) | 10 (1.2%) |
| VALLE | 29 (4.3%) | 2 (1.5%) | 31 (3.8%) |
| VAUPES | 3 (0.4%) | 1 (0.8%) | 4 (0.5%) |
| VICHADA | 16 (2.3%) | 4 (3.0%) | 20 (2.5%) |
ANALISIS VARIABLE EDAD EN MESES
# Estadísticos básicos con summary
summary(datos_590$edad_meses)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 4.00 9.00 10.49 12.00 48.00# O de manera más detallada
estadisticos_edad_590 <- datos_590 %>%
summarise(
n = n(),
n_no_na = sum(!is.na(edad_meses)),
media = mean(edad_meses, na.rm = TRUE),
mediana = median(edad_meses, na.rm = TRUE),
desviacion = sd(edad_meses, na.rm = TRUE),
minimo = min(edad_meses, na.rm = TRUE),
maximo = max(edad_meses, na.rm = TRUE),
q1 = quantile(edad_meses, 0.25, na.rm = TRUE),
q3 = quantile(edad_meses, 0.75, na.rm = TRUE),
rango_iqr = IQR(edad_meses, na.rm = TRUE)
)
print(estadisticos_edad_590)## # A tibble: 1 × 10
## n n_no_na media mediana desviacion minimo maximo q1 q3 rango_iqr
## <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 814 814 10.5 9 10.3 1 48 4 12 8library(ggplot2)
# Histograma con curva de densidad
ggplot(datos_590, aes(x = edad_meses)) +
geom_histogram(aes(y = ..density..),
fill = "lightblue", color = "black", alpha = 0.7, bins = 30) +
geom_density(alpha = 0.2, fill = "red") +
labs(title = "Distribución de Edad en Meses con Curva de Densidad",
x = "Edad (meses)",
y = "Densidad") +
theme_minimal()
GRUPO DE EDAD CADA 6 MESES
datos_590 <- datos_590 %>%
mutate(
edad_grupo_6meses = cut(edad_meses,
breaks = seq(0, 60, by = 6),
labels = c("0-6", "7-12", "13-18", "19-24", "25-30",
"31-36", "37-42", "43-48", "49-54", "55-60"),
include.lowest = TRUE,
right = FALSE)
)# Tabla de frecuencias simple
tabla_edad_grupo_6m <- datos_590 %>%
count(edad_grupo_6meses) %>%
arrange(edad_grupo_6meses) %>%
mutate(
porcentaje = round(n / sum(n) * 100, 2),
porcentaje_acumulado = round(cumsum(porcentaje), 2)
)
print(tabla_edad_grupo_6m, n = Inf)## # A tibble: 6 × 4
## edad_grupo_6meses n porcentaje porcentaje_acumulado
## <fct> <int> <dbl> <dbl>
## 1 0-6 303 37.2 37.2
## 2 7-12 203 24.9 62.2
## 3 13-18 194 23.8 86.0
## 4 25-30 59 7.25 93.2
## 5 37-42 31 3.81 97.0
## 6 49-54 24 2.95 100GRUPO 6 MESES PARA EVENTO 590
tabla_edad_grupo_6m_590 <- datos_590 %>%
filter(COD_EVE == 590) %>%
count(edad_grupo_6meses) %>%
arrange(edad_grupo_6meses) %>%
mutate(
porcentaje = round(n / sum(n) * 100, 2),
porcentaje_acumulado = round(cumsum(porcentaje), 2)
)
print(tabla_edad_grupo_6m_590, n = Inf)## # A tibble: 6 × 4
## edad_grupo_6meses n porcentaje porcentaje_acumulado
## <fct> <int> <dbl> <dbl>
## 1 0-6 37 28.0 28.0
## 2 7-12 46 34.8 62.9
## 3 13-18 24 18.2 81.1
## 4 25-30 10 7.58 88.6
## 5 37-42 9 6.82 95.5
## 6 49-54 6 4.55 100.CALCULO DE RIESGOS RELATIVOS PARA 590
SEXO
library(epiR)
sexo_tabla_2 <- matrix(c(82, 398, 50, 284), nrow = 2, byrow = TRUE)
colnames(sexo_tabla) <- c("Mortalidad 590","Otra mortalidad")
rownames(sexo_tabla) <- c("Femenino","Masculino")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_sexo <- epi.2by2(dat = sexo_tabla_2, method = "cohort.count", conf.level = 0.95)
print(resultado_sexo)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 82 398 480 17.08 (13.82 to 20.75)
## Exposure- 50 284 334 14.97 (11.32 to 19.26)
## Total 132 682 814 16.22 (13.75 to 18.93)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 1.14 (0.83, 1.58)
## Inc odds ratio 1.17 (0.80, 1.72)
## Attrib risk in the exposed * 2.11 (-2.98, 7.21)
## Attrib fraction in the exposed (%) 12.37 (-20.60, 36.59)
## Attrib risk in the population * 1.25 (-3.34, 5.83)
## Attrib fraction in the population (%) 7.68 (-1.71, 17.67)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 0.647 Pr>chi2 = 0.421
## Fisher exact test that OR = 1: Pr>chi2 = 0.441
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsAREA
CABECERA MUNICIPAL Y CENTRO POBLADO
area_tabla5 <- matrix(c(12, 49, 41, 329), nrow = 2, byrow = TRUE)
colnames(area_tabla5) <- c("Mortalidad 590","Otra mortalidad")
rownames(area_tabla5) <- c("Centro Poblado","Cabecera Municipal")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_area3 <- epi.2by2(dat = area_tabla5, method = "cohort.count", conf.level = 0.95)
print(resultado_area3)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 12 49 61 19.67 (10.60 to 31.84)
## Exposure- 41 329 370 11.08 (8.07 to 14.73)
## Total 53 378 431 12.30 (9.35 to 15.77)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 1.78 (0.99, 3.18)
## Inc odds ratio 1.97 (0.97, 4.00)
## Attrib risk in the exposed * 8.59 (-1.88, 19.07)
## Attrib fraction in the exposed (%) 43.67 (-2.04, 67.61)
## Attrib risk in the population * 1.22 (-3.24, 5.67)
## Attrib fraction in the population (%) 9.89 (6.61, 13.67)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 3.584 Pr>chi2 = 0.058
## Fisher exact test that OR = 1: Pr>chi2 = 0.089
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsCABECERA MUNICIPAL Y RURAL DISPERSO
area_tabla6 <- matrix(c(79, 304, 41, 329), nrow = 2, byrow = TRUE)
colnames(area_tabla6) <- c("Mortalidad 590","Otra mortalidad")
rownames(area_tabla6) <- c("Centro Poblado","Cabecera Municipal")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_area4 <- epi.2by2(dat = area_tabla6, method = "cohort.count", conf.level = 0.95)
print(resultado_area4)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 79 304 383 20.63 (16.68 to 25.03)
## Exposure- 41 329 370 11.08 (8.07 to 14.73)
## Total 120 633 753 15.94 (13.39 to 18.75)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 1.86 (1.31, 2.64)
## Inc odds ratio 2.09 (1.39, 3.14)
## Attrib risk in the exposed * 9.55 (4.38, 14.71)
## Attrib fraction in the exposed (%) 46.28 (24.10, 62.12)
## Attrib risk in the population * 4.86 (0.72, 8.99)
## Attrib fraction in the population (%) 30.47 (21.43, 39.74)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 12.800 Pr>chi2 = <0.001
## Fisher exact test that OR = 1: Pr>chi2 = <0.001
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsASEGURAMIENTO
CONTRIBUTIVO VS INDETERMINADO
aseg_tabla5 <- matrix(c(4, 11, 7, 59), nrow = 2, byrow = TRUE)
colnames(aseg_tabla5) <- c("Mortalidad 590","Otra mortalidad")
rownames(aseg_tabla5) <- c("Indeterminado","Contributivo")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_aseg5 <- epi.2by2(dat = aseg_tabla5, method = "cohort.count", conf.level = 0.95)
print(resultado_aseg5)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 4 11 15 26.67 (7.79 to 55.10)
## Exposure- 7 59 66 10.61 (4.37 to 20.64)
## Total 11 70 81 13.58 (6.98 to 23.00)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 2.51 (0.84, 7.50)
## Inc odds ratio 3.06 (0.77, 12.27)
## Attrib risk in the exposed * 16.06 (-7.52, 39.64)
## Attrib fraction in the exposed (%) 60.23 (-18.73, 85.41)
## Attrib risk in the population * 2.97 (-7.55, 13.50)
## Attrib fraction in the population (%) 21.90 (10.27, 37.36)
## -------------------------------------------------------------------
## Yates corrected chi2 test that OR = 1: chi2(1) = 1.492 Pr>chi2 = 0.222
## Fisher exact test that OR = 1: Pr>chi2 = 0.114
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsCONTRIBUTIVO VS NO ASEGURADO
aseg_tabla6 <- matrix(c(19, 57, 7, 59), nrow = 2, byrow = TRUE)
colnames(aseg_tabla6) <- c("Mortalidad 590","Otra mortalidad")
rownames(aseg_tabla6) <- c("No asegurado","Contributivo")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_aseg6 <- epi.2by2(dat = aseg_tabla6, method = "cohort.count", conf.level = 0.95)
print(resultado_aseg6)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 19 57 76 25.00 (15.77 to 36.26)
## Exposure- 7 59 66 10.61 (4.37 to 20.64)
## Total 26 116 142 18.31 (12.32 to 25.67)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 2.36 (1.06, 5.25)
## Inc odds ratio 2.81 (1.10, 7.19)
## Attrib risk in the exposed * 14.39 (2.15, 26.64)
## Attrib fraction in the exposed (%) 57.58 (8.72, 80.82)
## Attrib risk in the population * 7.70 (-2.08, 17.48)
## Attrib fraction in the population (%) 42.07 (19.58, 64.52)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 4.893 Pr>chi2 = 0.027
## Fisher exact test that OR = 1: Pr>chi2 = 0.031
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsCONTRIBUTIVO VS SUBSIDIADO
aseg_tabla7 <- matrix(c(102, 554, 7, 59), nrow = 2, byrow = TRUE)
colnames(aseg_tabla7) <- c("Mortalidad 590","Otra mortalidad")
rownames(aseg_tabla7) <- c("Subsidiado","Contributivo")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_aseg7 <- epi.2by2(dat = aseg_tabla7, method = "cohort.count", conf.level = 0.95)
print(resultado_aseg7)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 102 554 656 15.55 (12.86 to 18.55)
## Exposure- 7 59 66 10.61 (4.37 to 20.64)
## Total 109 613 722 15.10 (12.56 to 17.92)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 1.47 (0.71, 3.02)
## Inc odds ratio 1.55 (0.69, 3.49)
## Attrib risk in the exposed * 4.94 (-2.99, 12.87)
## Attrib fraction in the exposed (%) 31.79 (-34.01, 67.01)
## Attrib risk in the population * 4.49 (-3.38, 12.37)
## Attrib fraction in the population (%) 29.75 (-15.18, 65.20)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 1.143 Pr>chi2 = 0.285
## Fisher exact test that OR = 1: Pr>chi2 = 0.367
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsPERTENENCIA ETNICA
Otro vs Indígena
PE_tabla4 <- matrix(c(92, 330, 35, 312), nrow = 2, byrow = TRUE)
colnames(PE_tabla4) <- c("Mortalidad 590","Otra mortalidad")
rownames(PE_tabla4) <- c("Indígena","Otro")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_PE4 <- epi.2by2(dat = PE_tabla4, method = "cohort.count", conf.level = 0.95)
print(resultado_PE4)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 92 330 422 21.80 (17.95 to 26.05)
## Exposure- 35 312 347 10.09 (7.13 to 13.75)
## Total 127 642 769 16.51 (13.96 to 19.33)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 2.16 (1.50, 3.11)
## Inc odds ratio 2.49 (1.63, 3.78)
## Attrib risk in the exposed * 11.71 (6.66, 16.77)
## Attrib fraction in the exposed (%) 53.73 (33.86, 67.83)
## Attrib risk in the population * 6.43 (2.31, 10.54)
## Attrib fraction in the population (%) 38.93 (28.88, 48.94)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 18.953 Pr>chi2 = <0.001
## Fisher exact test that OR = 1: Pr>chi2 = <0.001
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtro vs Afro
PE_tabla5 <- matrix(c(92, 330, 35, 312), nrow = 2, byrow = TRUE)
colnames(PE_tabla5) <- c("Mortalidad 590","Otra mortalidad")
rownames(PE_tabla5) <- c("Afro","Otro")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_PE5 <- epi.2by2(dat = PE_tabla5, method = "cohort.count", conf.level = 0.95)
print(resultado_PE5)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 92 330 422 21.80 (17.95 to 26.05)
## Exposure- 35 312 347 10.09 (7.13 to 13.75)
## Total 127 642 769 16.51 (13.96 to 19.33)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 2.16 (1.50, 3.11)
## Inc odds ratio 2.49 (1.63, 3.78)
## Attrib risk in the exposed * 11.71 (6.66, 16.77)
## Attrib fraction in the exposed (%) 53.73 (33.86, 67.83)
## Attrib risk in the population * 6.43 (2.31, 10.54)
## Attrib fraction in the population (%) 38.93 (28.88, 48.94)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 18.953 Pr>chi2 = <0.001
## Fisher exact test that OR = 1: Pr>chi2 = <0.001
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsESTRATO
Estrato 1 vs Sin Dato
E4_tabla <- matrix(c(119, 525, 7, 38), nrow = 2, byrow = TRUE)
colnames(E4_tabla) <- c("Mortalidad 590","Otra mortalidad")
rownames(E4_tabla) <- c("Estrato 1","Sin Dato")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_E4 <- epi.2by2(dat = E4_tabla, method = "cohort.count", conf.level = 0.95)
print(resultado_E4)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 119 525 644 18.48 (15.55 to 21.70)
## Exposure- 7 38 45 15.56 (6.49 to 29.46)
## Total 126 563 689 18.29 (15.47 to 21.38)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 1.19 (0.59, 2.39)
## Inc odds ratio 1.23 (0.54, 2.82)
## Attrib risk in the exposed * 2.92 (-8.08, 13.93)
## Attrib fraction in the exposed (%) 15.82 (-59.48, 58.78)
## Attrib risk in the population * 2.73 (-8.24, 13.71)
## Attrib fraction in the population (%) 14.94 (-37.77, 58.04)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 0.240 Pr>chi2 = 0.624
## Fisher exact test that OR = 1: Pr>chi2 = 0.842
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsEstrato 2 vs Sin Dato
E5_tabla <- matrix(c(6, 98, 7, 38), nrow = 2, byrow = TRUE)
colnames(E5_tabla) <- c("Mortalidad 590","Otra mortalidad")
rownames(E5_tabla) <- c("Estrato 2","Sin Dato")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_E5 <- epi.2by2(dat = E5_tabla, method = "cohort.count", conf.level = 0.95)
print(resultado_E5)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 6 98 104 5.77 (2.15 to 12.13)
## Exposure- 7 38 45 15.56 (6.49 to 29.46)
## Total 13 136 149 8.72 (4.73 to 14.46)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 0.37 (0.13, 1.04)
## Inc odds ratio 0.33 (0.10, 1.05)
## Attrib risk in the exposed * -9.79 (-21.28, 1.71)
## Attrib fraction in the exposed (%) -169.63 (-623.34, 0.78)
## Attrib risk in the population * -6.83 (-18.35, 4.69)
## Attrib fraction in the population (%) -78.29 (-103.73, -37.30)
## -------------------------------------------------------------------
## Yates corrected chi2 test that OR = 1: chi2(1) = 2.648 Pr>chi2 = 0.104
## Fisher exact test that OR = 1: Pr>chi2 = 0.063
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsGRUPOS POBLACIONALES
Desplazado
GPD_tabla2 <- matrix(c(1, 12, 131, 670), nrow = 2, byrow = TRUE)
colnames(GPD_tabla2) <- c("Mortalidad 590","Otra mortalidad")
rownames(GPD_tabla2) <- c("Desplazado","No Desplazado")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_GPD2 <- epi.2by2(dat = GPD_tabla2, method = "cohort.count", conf.level = 0.95)
print(resultado_GPD2)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 1 12 13 7.69 (0.19 to 36.03)
## Exposure- 131 670 801 16.35 (13.86 to 19.10)
## Total 132 682 814 16.22 (13.75 to 18.93)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 0.47 (0.07, 3.11)
## Inc odds ratio 0.43 (0.05, 3.31)
## Attrib risk in the exposed * -8.66 (-23.37, 6.05)
## Attrib fraction in the exposed (%) -112.61 (-1098.23, 51.49)
## Attrib risk in the population * -0.14 (-3.74, 3.46)
## Attrib fraction in the population (%) -0.85 (-0.90, -0.80)
## -------------------------------------------------------------------
## Yates corrected chi2 test that OR = 1: chi2(1) = 0.213 Pr>chi2 = 0.645
## Fisher exact test that OR = 1: Pr>chi2 = 0.705
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsMIGRANTE
GPM_tabla2 <- matrix(c(8, 22, 124, 660), nrow = 2, byrow = TRUE)
colnames(GPM_tabla2) <- c("Mortalidad 590","Otra mortalidad")
rownames(GPM_tabla2) <- c("No migrante","Migrante")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_GPM2 <- epi.2by2(dat = GPM_tabla2, method = "cohort.count", conf.level = 0.95)
print(resultado_GPM2)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 8 22 30 26.67 (12.28 to 45.89)
## Exposure- 124 660 784 15.82 (13.33 to 18.56)
## Total 132 682 814 16.22 (13.75 to 18.93)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 1.69 (0.91, 3.12)
## Inc odds ratio 1.94 (0.84, 4.45)
## Attrib risk in the exposed * 10.85 (-5.18, 26.88)
## Attrib fraction in the exposed (%) 40.69 (-13.59, 65.47)
## Attrib risk in the population * 0.40 (-3.20, 4.00)
## Attrib fraction in the population (%) 2.47 (1.95, 3.04)
## -------------------------------------------------------------------
## Yates corrected chi2 test that OR = 1: chi2(1) = 1.769 Pr>chi2 = 0.184
## Fisher exact test that OR = 1: Pr>chi2 = 0.128
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsICBF
GPI_tabla2 <- matrix(c(2, 13, 130, 669), nrow = 2, byrow = TRUE)
colnames(GPI_tabla2) <- c("Mortalidad 590","Otra mortalidad")
rownames(GPI_tabla2) <- c("No ICBF","ICBF")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_GPI2 <- epi.2by2(dat = GPI_tabla2, method = "cohort.count", conf.level = 0.95)
print(resultado_GPI2)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 2 13 15 13.33 (1.66 to 40.46)
## Exposure- 130 669 799 16.27 (13.78 to 19.02)
## Total 132 682 814 16.22 (13.75 to 18.93)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 0.82 (0.22, 3.01)
## Inc odds ratio 0.79 (0.18, 3.55)
## Attrib risk in the exposed * -2.94 (-20.33, 14.46)
## Attrib fraction in the exposed (%) -22.03 (-338.73, 57.73)
## Attrib risk in the population * -0.05 (-3.65, 3.55)
## Attrib fraction in the population (%) -0.33 (-0.45, -0.20)
## -------------------------------------------------------------------
## Yates corrected chi2 test that OR = 1: chi2(1) = 0.000 Pr>chi2 = 1.000
## Fisher exact test that OR = 1: Pr>chi2 = 1.000
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsTIPO_CASO
TC_tabla <- matrix(c(130, 535, 2, 147), nrow = 2, byrow = TRUE)
colnames(TC_tabla) <- c("Mortalidad 590","Otra mortalidad")
rownames(TC_tabla) <- c("Clinica","laboratorio")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_TC <- epi.2by2(dat = TC_tabla, method = "cohort.count", conf.level = 0.95)
print(resultado_TC)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 130 535 665 19.55 (16.60 to 22.77)
## Exposure- 2 147 149 1.34 (0.16 to 4.76)
## Total 132 682 814 16.22 (13.75 to 18.93)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 14.56 (3.65, 58.19)
## Inc odds ratio 17.86 (4.37, 73.04)
## Attrib risk in the exposed * 18.21 (14.67, 21.74)
## Attrib fraction in the exposed (%) 93.13 (75.45, 98.13)
## Attrib risk in the population * 14.87 (11.74, 18.01)
## Attrib fraction in the population (%) 91.72 (74.83, 98.81)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 29.698 Pr>chi2 = <0.001
## Fisher exact test that OR = 1: Pr>chi2 = <0.001
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsDEPARTAMENTO DE RESIDENCIA
Otros vs Casanare
Dpto_tabla9 <- matrix(c(4, 2, 85, 588), nrow = 2, byrow = TRUE)
colnames(Dpto_tabla9) <- c("Mortalidad 590","Otra mortalidad")
rownames(Dpto_tabla9) <- c("Casanare","Otros")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_Dpto9 <- epi.2by2(dat = Dpto_tabla9, method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto9)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 4 2 6 66.67 (22.28 to 95.67)
## Exposure- 85 588 673 12.63 (10.21 to 15.38)
## Total 89 590 679 13.11 (10.66 to 15.88)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 5.28 (2.90, 9.61)
## Inc odds ratio 13.84 (2.50, 76.69)
## Attrib risk in the exposed * 54.04 (16.23, 91.84)
## Attrib fraction in the exposed (%) 81.05 (57.05, 87.21)
## Attrib risk in the population * 0.48 (-3.09, 4.05)
## Attrib fraction in the population (%) 3.64 (3.16, 4.18)
## -------------------------------------------------------------------
## Yates corrected chi2 test that OR = 1: chi2(1) = 10.871 Pr>chi2 = <0.001
## Fisher exact test that OR = 1: Pr>chi2 = 0.003
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtros vs Guainía
Dpto_tabla10 <- matrix(c(5, 5, 85, 588), nrow = 2, byrow = TRUE)
colnames(Dpto_tabla10) <- c("Mortalidad 590","Otra mortalidad")
rownames(Dpto_tabla10) <- c("Guainía","Otros")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_Dpto10 <- epi.2by2(dat = Dpto_tabla10, method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto10)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 5 5 10 50.00 (18.71 to 81.29)
## Exposure- 85 588 673 12.63 (10.21 to 15.38)
## Total 90 593 683 13.18 (10.73 to 15.95)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 3.96 (2.06, 7.59)
## Inc odds ratio 6.92 (1.96, 24.39)
## Attrib risk in the exposed * 37.37 (6.28, 68.46)
## Attrib fraction in the exposed (%) 74.74 (45.42, 84.48)
## Attrib risk in the population * 0.55 (-3.02, 4.12)
## Attrib fraction in the population (%) 4.15 (3.56, 4.81)
## -------------------------------------------------------------------
## Yates corrected chi2 test that OR = 1: chi2(1) = 8.983 Pr>chi2 = 0.003
## Fisher exact test that OR = 1: Pr>chi2 = 0.005
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtros vs Putumayo
Dpto_tabla11 <- matrix(c(1, 1, 85, 588), nrow = 2, byrow = TRUE)
colnames(Dpto_tabla11) <- c("Mortalidad 590","Otra mortalidad")
rownames(Dpto_tabla11) <- c("Putumayo","Otros")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_Dpto11 <- epi.2by2(dat = Dpto_tabla11, method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto11)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 1 1 2 50.00 (1.26 to 98.74)
## Exposure- 85 588 673 12.63 (10.21 to 15.38)
## Total 86 589 675 12.74 (10.32 to 15.49)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 3.96 (0.98, 16.06)
## Inc odds ratio 6.92 (0.43, 111.63)
## Attrib risk in the exposed * 37.37 (-31.97, 106.71)
## Attrib fraction in the exposed (%) 74.74 (-34.62, 86.94)
## Attrib risk in the population * 0.11 (-3.44, 3.66)
## Attrib fraction in the population (%) 0.87 (0.74, 1.01)
## -------------------------------------------------------------------
## Yates corrected chi2 test that OR = 1: chi2(1) = 0.271 Pr>chi2 = 0.603
## Fisher exact test that OR = 1: Pr>chi2 = 0.239
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtros vs Santander
Dpto_tabla12 <- matrix(c(6, 11, 85, 588), nrow = 2, byrow = TRUE)
colnames(Dpto_tabla12) <- c("Mortalidad 590","Otra mortalidad")
rownames(Dpto_tabla12) <- c("Santander","Otros")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_Dpto12 <- epi.2by2(dat = Dpto_tabla12, method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto12)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 6 11 17 35.29 (14.21 to 61.67)
## Exposure- 85 588 673 12.63 (10.21 to 15.38)
## Total 91 599 690 13.19 (10.75 to 15.94)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 2.79 (1.42, 5.48)
## Inc odds ratio 3.77 (1.36, 10.47)
## Attrib risk in the exposed * 22.66 (-0.19, 45.52)
## Attrib fraction in the exposed (%) 64.21 (25.27, 79.51)
## Attrib risk in the population * 0.56 (-3.00, 4.12)
## Attrib fraction in the population (%) 4.23 (3.54, 5.01)
## -------------------------------------------------------------------
## Yates corrected chi2 test that OR = 1: chi2(1) = 5.591 Pr>chi2 = 0.018
## Fisher exact test that OR = 1: Pr>chi2 = 0.016
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtros vs Cauca
Dpto_tabla13 <- matrix(c(7, 13, 85, 588), nrow = 2, byrow = TRUE)
colnames(Dpto_tabla13) <- c("Mortalidad 590","Otra mortalidad")
rownames(Dpto_tabla13) <- c("Cauca","Otros")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_Dpto13 <- epi.2by2(dat = Dpto_tabla13, method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto13)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 7 13 20 35.00 (15.39 to 59.22)
## Exposure- 85 588 673 12.63 (10.21 to 15.38)
## Total 92 601 693 13.28 (10.84 to 16.03)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 2.77 (1.48, 5.20)
## Inc odds ratio 3.72 (1.45, 9.60)
## Attrib risk in the exposed * 22.37 (1.32, 43.42)
## Attrib fraction in the exposed (%) 63.91 (28.45, 78.82)
## Attrib risk in the population * 0.65 (-2.92, 4.21)
## Attrib fraction in the population (%) 4.86 (4.07, 5.75)
## -------------------------------------------------------------------
## Yates corrected chi2 test that OR = 1: chi2(1) = 6.611 Pr>chi2 = 0.010
## Fisher exact test that OR = 1: Pr>chi2 = 0.010
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtros vs Risaralda
Dpto_tabla14 <- matrix(c(8, 17, 85, 588), nrow = 2, byrow = TRUE)
colnames(Dpto_tabla14) <- c("Mortalidad 590","Otra mortalidad")
rownames(Dpto_tabla14) <- c("Risaralda","Otros")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_Dpto14 <- epi.2by2(dat = Dpto_tabla14, method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto14)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 8 17 25 32.00 (14.95 to 53.50)
## Exposure- 85 588 673 12.63 (10.21 to 15.38)
## Total 93 605 698 13.32 (10.89 to 16.07)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 2.53 (1.38, 4.64)
## Inc odds ratio 3.26 (1.36, 7.77)
## Attrib risk in the exposed * 19.37 (0.91, 37.83)
## Attrib fraction in the exposed (%) 60.53 (24.51, 76.68)
## Attrib risk in the population * 0.69 (-2.86, 4.25)
## Attrib fraction in the population (%) 5.21 (4.31, 6.21)
## -------------------------------------------------------------------
## Yates corrected chi2 test that OR = 1: chi2(1) = 6.244 Pr>chi2 = 0.012
## Fisher exact test that OR = 1: Pr>chi2 = 0.012
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtros vs Sucre
Dpto_tabla15 <- matrix(c(3, 7, 85, 588), nrow = 2, byrow = TRUE)
colnames(Dpto_tabla15) <- c("Mortalidad 590","Otra mortalidad")
rownames(Dpto_tabla15) <- c("Sucre","Otros")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_Dpto15 <- epi.2by2(dat = Dpto_tabla15, method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto15)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 3 7 10 30.00 (6.67 to 65.25)
## Exposure- 85 588 673 12.63 (10.21 to 15.38)
## Total 88 595 683 12.88 (10.46 to 15.63)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 2.38 (0.90, 6.25)
## Inc odds ratio 2.96 (0.75, 11.68)
## Attrib risk in the exposed * 17.37 (-11.14, 45.88)
## Attrib fraction in the exposed (%) 57.90 (-19.03, 79.91)
## Attrib risk in the population * 0.25 (-3.30, 3.81)
## Attrib fraction in the population (%) 1.97 (1.61, 2.38)
## -------------------------------------------------------------------
## Yates corrected chi2 test that OR = 1: chi2(1) = 1.327 Pr>chi2 = 0.249
## Fisher exact test that OR = 1: Pr>chi2 = 0.127
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtros vs Exterior
Dpto_tabla16 <- matrix(c(11, 31, 85, 588), nrow = 2, byrow = TRUE)
colnames(Dpto_tabla16) <- c("Mortalidad 590","Otra mortalidad")
rownames(Dpto_tabla16) <- c("Exterior","Otros")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_Dpto16 <- epi.2by2(dat = Dpto_tabla16, method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto16)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 11 31 42 26.19 (13.86 to 42.04)
## Exposure- 85 588 673 12.63 (10.21 to 15.38)
## Total 96 619 715 13.43 (11.01 to 16.15)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 2.07 (1.20, 3.58)
## Inc odds ratio 2.45 (1.19, 5.07)
## Attrib risk in the exposed * 13.56 (0.03, 27.09)
## Attrib fraction in the exposed (%) 51.78 (14.72, 70.68)
## Attrib risk in the population * 0.80 (-2.75, 4.34)
## Attrib fraction in the population (%) 5.93 (4.76, 7.25)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 6.254 Pr>chi2 = 0.012
## Fisher exact test that OR = 1: Pr>chi2 = 0.019
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtros vs Amazonas
Dpto_tabla17 <- matrix(c(2, 7, 85, 588), nrow = 2, byrow = TRUE)
colnames(Dpto_tabla17) <- c("Mortalidad 590","Otra mortalidad")
rownames(Dpto_tabla17) <- c("Amazonas","Otros")
#El grupo de comparación siempre debe estar en la segunda fila (por eso Femenino están al final).
# Calcular riesgo relativo
resultado_Dpto17 <- epi.2by2(dat = Dpto_tabla17, method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto17)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 2 7 9 22.22 (2.81 to 60.01)
## Exposure- 85 588 673 12.63 (10.21 to 15.38)
## Total 87 595 682 12.76 (10.35 to 15.50)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 1.76 (0.51, 6.07)
## Inc odds ratio 1.98 (0.40, 9.67)
## Attrib risk in the exposed * 9.59 (-17.68, 36.87)
## Attrib fraction in the exposed (%) 43.16 (-102.17, 77.69)
## Attrib risk in the population * 0.13 (-3.42, 3.67)
## Attrib fraction in the population (%) 0.99 (0.75, 1.26)
## -------------------------------------------------------------------
## Yates corrected chi2 test that OR = 1: chi2(1) = 0.125 Pr>chi2 = 0.723
## Fisher exact test that OR = 1: Pr>chi2 = 0.322
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsANALISIS DEPARTAMENTO OCURRENCIA
Otros vs Casanare
Dpto_o_tabla9 <- matrix(c(4, 2, 95, 116), nrow = 2, byrow = TRUE)
colnames(Dpto_o_tabla9 ) <- c("Mortalidad 590","Otra mortalidad")
rownames(Dpto_o_tabla9 ) <- c("Casanare","Otros")
# Calcular riesgo relativo
resultado_Dpto_o9 <- epi.2by2(dat = Dpto_o_tabla9 , method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto_o9)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 4 2 6 66.67 (22.28 to 95.67)
## Exposure- 95 116 211 45.02 (38.19 to 52.00)
## Total 99 118 217 45.62 (38.87 to 52.50)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 1.48 (0.82, 2.66)
## Inc odds ratio 2.44 (0.44, 13.62)
## Attrib risk in the exposed * 21.64 (-16.67, 59.96)
## Attrib fraction in the exposed (%) 32.46 (-51.77, 52.92)
## Attrib risk in the population * 0.60 (-8.83, 10.03)
## Attrib fraction in the population (%) 1.31 (0.95, 1.75)
## -------------------------------------------------------------------
## Yates corrected chi2 test that OR = 1: chi2(1) = 0.402 Pr>chi2 = 0.526
## Fisher exact test that OR = 1: Pr>chi2 = 0.415
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtros vs Guainia
Dpto_o_tabla10 <- matrix(c(5, 5, 95, 116), nrow = 2, byrow = TRUE)
colnames(Dpto_o_tabla10 ) <- c("Mortalidad 590","Otra mortalidad")
rownames(Dpto_o_tabla10 ) <- c("Guainia","Otros")
# Calcular riesgo relativo
resultado_Dpto_o10 <- epi.2by2(dat = Dpto_o_tabla10 , method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto_o10)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 5 5 10 50.00 (18.71 to 81.29)
## Exposure- 95 116 211 45.02 (38.19 to 52.00)
## Total 100 121 221 45.25 (38.56 to 52.06)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 1.11 (0.59, 2.10)
## Inc odds ratio 1.22 (0.34, 4.34)
## Attrib risk in the exposed * 4.98 (-26.73, 36.68)
## Attrib fraction in the exposed (%) 9.95 (-92.68, 43.29)
## Attrib risk in the population * 0.23 (-9.16, 9.61)
## Attrib fraction in the population (%) 0.50 (0.12, 0.98)
## -------------------------------------------------------------------
## Yates corrected chi2 test that OR = 1: chi2(1) = 0.000 Pr>chi2 = 1.000
## Fisher exact test that OR = 1: Pr>chi2 = 0.758
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtros vs Santander
Dpto_o_tabla11 <- matrix(c(6, 11, 95, 116), nrow = 2, byrow = TRUE)
colnames(Dpto_o_tabla11 ) <- c("Mortalidad 590","Otra mortalidad")
rownames(Dpto_o_tabla11 ) <- c("Santander","Otros")
# Calcular riesgo relativo
resultado_Dpto_o11 <- epi.2by2(dat = Dpto_o_tabla11 , method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto_o11)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 6 11 17 35.29 (14.21 to 61.67)
## Exposure- 95 116 211 45.02 (38.19 to 52.00)
## Total 101 127 228 44.30 (37.74 to 51.00)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 0.78 (0.40, 1.52)
## Inc odds ratio 0.67 (0.24, 1.87)
## Attrib risk in the exposed * -9.73 (-33.42, 13.96)
## Attrib fraction in the exposed (%) -27.57 (-163.63, 25.53)
## Attrib risk in the population * -0.73 (-10.03, 8.58)
## Attrib fraction in the population (%) -1.64 (-1.96, -1.17)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 0.604 Pr>chi2 = 0.437
## Fisher exact test that OR = 1: Pr>chi2 = 0.613
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtros vs Cauca
Dpto_o_tabla12 <- matrix(c(7, 13, 95, 116), nrow = 2, byrow = TRUE)
colnames(Dpto_o_tabla12) <- c("Mortalidad 590","Otra mortalidad")
rownames(Dpto_o_tabla12) <- c("Cauca","Otros")
# Calcular riesgo relativo
resultado_Dpto_o12 <- epi.2by2(dat = Dpto_o_tabla12, method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto_o12)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 7 13 20 35.00 (15.39 to 59.22)
## Exposure- 95 116 211 45.02 (38.19 to 52.00)
## Total 102 129 231 44.16 (37.65 to 50.82)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 0.78 (0.42, 1.44)
## Inc odds ratio 0.66 (0.25, 1.71)
## Attrib risk in the exposed * -10.02 (-31.98, 11.93)
## Attrib fraction in the exposed (%) -28.64 (-152.15, 22.97)
## Attrib risk in the population * -0.87 (-10.15, 8.41)
## Attrib fraction in the population (%) -1.97 (-2.34, -1.43)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 0.744 Pr>chi2 = 0.388
## Fisher exact test that OR = 1: Pr>chi2 = 0.483
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtros vs Sucre
Dpto_o_tabla13 <- matrix(c(3, 6, 95, 116), nrow = 2, byrow = TRUE)
colnames(Dpto_o_tabla13) <- c("Mortalidad 590","Otra mortalidad")
rownames(Dpto_o_tabla13) <- c("Sucre","Otros")
# Calcular riesgo relativo
resultado_Dpto_o13 <- epi.2by2(dat = Dpto_o_tabla13, method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto_o13)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 3 6 9 33.33 (7.49 to 70.07)
## Exposure- 95 116 211 45.02 (38.19 to 52.00)
## Total 98 122 220 44.55 (37.86 to 51.38)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 0.74 (0.29, 1.89)
## Inc odds ratio 0.61 (0.15, 2.51)
## Attrib risk in the exposed * -11.69 (-43.21, 19.83)
## Attrib fraction in the exposed (%) -35.07 (-276.71, 32.10)
## Attrib risk in the population * -0.48 (-9.87, 8.91)
## Attrib fraction in the population (%) -1.07 (-1.22, -0.85)
## -------------------------------------------------------------------
## Yates corrected chi2 test that OR = 1: chi2(1) = 0.122 Pr>chi2 = 0.727
## Fisher exact test that OR = 1: Pr>chi2 = 0.734
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtros vs Putumayo
Dpto_o_tabla14 <- matrix(c(1, 2, 95, 116), nrow = 2, byrow = TRUE)
colnames(Dpto_o_tabla14) <- c("Mortalidad 590","Otra mortalidad")
rownames(Dpto_o_tabla14) <- c("Putumayo","Otros")
# Calcular riesgo relativo
resultado_Dpto_o14 <- epi.2by2(dat = Dpto_o_tabla14, method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto_o14)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 1 2 3 33.33 (0.84 to 90.57)
## Exposure- 95 116 211 45.02 (38.19 to 52.00)
## Total 96 118 214 44.86 (38.08 to 51.79)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 0.74 (0.15, 3.69)
## Inc odds ratio 0.61 (0.05, 6.84)
## Attrib risk in the exposed * -11.69 (-65.45, 42.07)
## Attrib fraction in the exposed (%) -35.07 (-635.26, 44.72)
## Attrib risk in the population * -0.16 (-9.62, 9.29)
## Attrib fraction in the population (%) -0.37 (-0.41, -0.29)
## -------------------------------------------------------------------
## Yates corrected chi2 test that OR = 1: chi2(1) = 0.000 Pr>chi2 = 1.000
## Fisher exact test that OR = 1: Pr>chi2 = 1.000
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtros vs Risaralda
Dpto_o_tabla15 <- matrix(c(8, 17, 95, 116), nrow = 2, byrow = TRUE)
colnames(Dpto_o_tabla15) <- c("Mortalidad 590","Otra mortalidad")
rownames(Dpto_o_tabla15) <- c("Risaralda","Otros")
# Calcular riesgo relativo
resultado_Dpto_o15 <- epi.2by2(dat = Dpto_o_tabla15, method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto_o15)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 8 17 25 32.00 (14.95 to 53.50)
## Exposure- 95 116 211 45.02 (38.19 to 52.00)
## Total 103 133 236 43.64 (37.22 to 50.23)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 0.71 (0.39, 1.28)
## Inc odds ratio 0.57 (0.24, 1.39)
## Attrib risk in the exposed * -13.02 (-32.50, 6.46)
## Attrib fraction in the exposed (%) -40.70 (-165.82, 15.24)
## Attrib risk in the population * -1.38 (-10.60, 7.85)
## Attrib fraction in the population (%) -3.16 (-3.53, -2.59)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 1.541 Pr>chi2 = 0.214
## Fisher exact test that OR = 1: Pr>chi2 = 0.287
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtros vs Vaupés
Dpto_o_tabla16 <- matrix(c(1, 3, 95, 116), nrow = 2, byrow = TRUE)
colnames(Dpto_o_tabla16) <- c("Mortalidad 590","Otra mortalidad")
rownames(Dpto_o_tabla16) <- c("Vaupés","Otros")
# Calcular riesgo relativo
resultado_Dpto_o16 <- epi.2by2(dat = Dpto_o_tabla16, method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto_o16)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 1 3 4 25.00 (0.63 to 80.59)
## Exposure- 95 116 211 45.02 (38.19 to 52.00)
## Total 96 119 215 44.65 (37.89 to 51.56)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 0.56 (0.10, 3.05)
## Inc odds ratio 0.41 (0.04, 3.98)
## Attrib risk in the exposed * -20.02 (-62.99, 22.94)
## Attrib fraction in the exposed (%) -80.09 (-891.74, 37.04)
## Attrib risk in the population * -0.37 (-9.82, 9.07)
## Attrib fraction in the population (%) -0.83 (-0.85, -0.79)
## -------------------------------------------------------------------
## Yates corrected chi2 test that OR = 1: chi2(1) = 0.084 Pr>chi2 = 0.772
## Fisher exact test that OR = 1: Pr>chi2 = 0.630
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsOtros vs Amazonas
Dpto_o_tabla17 <- matrix(c(2, 7, 95, 116), nrow = 2, byrow = TRUE)
colnames(Dpto_o_tabla17) <- c("Mortalidad 590","Otra mortalidad")
rownames(Dpto_o_tabla17) <- c("Amazonas","Otros")
# Calcular riesgo relativo
resultado_Dpto_o17 <- epi.2by2(dat = Dpto_o_tabla17, method = "cohort.count", conf.level = 0.95)
print(resultado_Dpto_o17)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 2 7 9 22.22 (2.81 to 60.01)
## Exposure- 95 116 211 45.02 (38.19 to 52.00)
## Total 97 123 220 44.09 (37.42 to 50.92)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 0.49 (0.14, 1.69)
## Inc odds ratio 0.35 (0.07, 1.72)
## Attrib risk in the exposed * -22.80 (-50.78, 5.18)
## Attrib fraction in the exposed (%) -102.61 (-616.93, 19.36)
## Attrib risk in the population * -0.93 (-10.32, 8.45)
## Attrib fraction in the population (%) -2.12 (-2.13, -2.04)
## -------------------------------------------------------------------
## Yates corrected chi2 test that OR = 1: chi2(1) = 1.013 Pr>chi2 = 0.314
## Fisher exact test that OR = 1: Pr>chi2 = 0.305
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population unitsEDAD ANALISIS GRUPO POR 6 MESES
Menor de 6 meses vs mayor de 19 meses
Edad_6m_tabla4 <- matrix(c(37, 266, 25, 89), nrow = 2, byrow = TRUE)
colnames(Edad_6m_tabla4) <- c("Mortalidad 590","Otra mortalidad")
rownames(Edad_6m_tabla4) <- c("Menor de 6m","Mayor a 19m")
# Calcular riesgo relativo
resultado_edad_6m_4 <- epi.2by2(dat = Edad_6m_tabla4, method = "cohort.count", conf.level = 0.95)
print(resultado_edad_6m_4)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 37 266 303 12.21 (8.75 to 16.44)
## Exposure- 25 89 114 21.93 (14.72 to 30.65)
## Total 62 355 417 14.87 (11.59 to 18.65)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 0.56 (0.35, 0.88)
## Inc odds ratio 0.50 (0.28, 0.87)
## Attrib risk in the exposed * -9.72 (-18.16, -1.28)
## Attrib fraction in the exposed (%) -79.59 (-181.50, -13.11)
## Attrib risk in the population * -7.06 (-15.39, 1.27)
## Attrib fraction in the population (%) -47.50 (-64.32, -27.00)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 6.181 Pr>chi2 = 0.013
## Fisher exact test that OR = 1: Pr>chi2 = 0.020
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population units7-12 meses vs mayor de 19 meses
Edad_6m_tabla5 <- matrix(c(46, 157, 25, 89), nrow = 2, byrow = TRUE)
colnames(Edad_6m_tabla5) <- c("Mortalidad 590","Otra mortalidad")
rownames(Edad_6m_tabla5) <- c("7-12 meses","Mayor a 19m")
# Calcular riesgo relativo
resultado_edad_6m_5 <- epi.2by2(dat = Edad_6m_tabla5, method = "cohort.count", conf.level = 0.95)
print(resultado_edad_6m_5)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 46 157 203 22.66 (17.09 to 29.04)
## Exposure- 25 89 114 21.93 (14.72 to 30.65)
## Total 71 246 317 22.40 (17.93 to 27.39)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 1.03 (0.67, 1.59)
## Inc odds ratio 1.04 (0.60, 1.81)
## Attrib risk in the exposed * 0.73 (-8.80, 10.26)
## Attrib fraction in the exposed (%) 3.22 (-47.22, 37.28)
## Attrib risk in the population * 0.47 (-8.41, 9.34)
## Attrib fraction in the population (%) 2.09 (-11.89, 17.87)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 0.022 Pr>chi2 = 0.881
## Fisher exact test that OR = 1: Pr>chi2 = 1.000
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population units13-18 meses vs mayor de 19 meses
Edad_6m_tabla6 <- matrix(c(24, 170, 25, 89), nrow = 2, byrow = TRUE)
colnames(Edad_6m_tabla6) <- c("Mortalidad 590","Otra mortalidad")
rownames(Edad_6m_tabla6) <- c("13-18 meses","Mayor a 19m")
# Calcular riesgo relativo
resultado_edad_6m_6 <- epi.2by2(dat = Edad_6m_tabla6, method = "cohort.count", conf.level = 0.95)
print(resultado_edad_6m_6)## Outcome+ Outcome- Total Inc risk *
## Exposure+ 24 170 194 12.37 (8.09 to 17.85)
## Exposure- 25 89 114 21.93 (14.72 to 30.65)
## Total 49 259 308 15.91 (12.01 to 20.48)
##
## Point estimates and 95% CIs:
## -------------------------------------------------------------------
## Inc risk ratio 0.56 (0.34, 0.94)
## Inc odds ratio 0.50 (0.27, 0.93)
## Attrib risk in the exposed * -9.56 (-18.46, -0.66)
## Attrib fraction in the exposed (%) -77.27 (-193.18, -6.69)
## Attrib risk in the population * -6.02 (-14.64, 2.60)
## Attrib fraction in the population (%) -37.84 (-49.64, -22.61)
## -------------------------------------------------------------------
## Uncorrected chi2 test that OR = 1: chi2(1) = 4.904 Pr>chi2 = 0.027
## Fisher exact test that OR = 1: Pr>chi2 = 0.035
## Wald confidence limits
## CI: confidence interval
## * Outcomes per 100 population units