Nothing
R/RH_DELA_C_SKF.R
In surveyPrev: Mapping the Prevalence of Binary Indicators using Survey Data in Small Areas
Defines functions
RH_DELA_C_SKF
Documented in
RH_DELA_C_SKF
#'RH_DELA_C_SKF
#'BRdata
#'Assistance during delivery from a skilled provider
#'
#'
#' @param Rdata data.frame from surveyPrev::getDHSdata
#'
#' @return A partially processed data.frame that will be used in surveyPrev::getDHSindicator. The whole function can be used as a parameter in surveyPrev::getDHSindicator
#'
#' @author Qianyu Dong
#' @examples
#' \dontrun{
#' dhsData <- getDHSdata(country = "Zambia",
#' indicator = "RH_DELA_C_SKF",
#' year = 2018)
#' data <- getDHSindicator(dhsData, indicator = NULL,
#' FUN = surveyPrev::RH_DELA_C_SKF)
#' }
#' @export
#'
RH_DELA_C_SKF <- function(Rdata){
BRdata <- Rdata %>%
mutate(wt = v005/1000000)
# period and age of child
# choose reference period, last 2 years (24 months) or last 5 years (60 months)
# this is fiver year
BRdata <- BRdata %>%
mutate(period = 60)
# age of child. If b19 is not available in the data use v008 - b3
if ("TRUE" %in% (!("b19" %in% names(BRdata))))
BRdata [[paste("b19")]] <- NA
if ("TRUE" %in% all(is.na(BRdata$b19)))
{ b19_included <- 0} else { b19_included <- 1}
if (b19_included==1) {
BRdata <- BRdata %>%
mutate(age = b19)
} else {
BRdata <- BRdata %>%
mutate(age = v008 - b3)
}
# ** NEW STEP: Explicitly filter the data to keep only recent births **
# This directly changes the number of rows (the sample size).
BRdata <- BRdata %>%
filter(age < period)
BRdata <- BRdata %>%
mutate(rh_del_pv =
case_when(
m3a == 1 ~ 1 ,
m3b == 1 ~ 2,
m3c == 1 | m3d == 1 | m3e == 1 | m3f == 1~ 3 ,
m3g == 1 ~ 4 ,
m3h == 1 | m3i == 1 | m3j == 1 | m3k == 1 | m3l == 1 | m3m == 1 ~ 5 ,
m3n ==1 ~ 6,
m3a ==8 | m3a==9 ~ 9 ,
age>=period ~ 99)) %>%
replace_with_na(replace = list(rh_del_pv = c(99))) %>%
set_value_labels(rh_del_pv = c("Doctor" = 1, "Nurse/midwife"=2, "Country specific health professional"=3, "Traditional birth attendant"=4, "Relative/other"=5, "No one"=6, "Don't know/missing"=9 )) %>%
set_variable_labels(rh_del_pv = "Person providing assistance during delivery")
# //Skilled provider during delivery
# ** Note: Please check the final report for this indicator to determine what provider is considered skilled.
BRdata <- BRdata %>%
mutate(rh_del_pvskill =
case_when(
rh_del_pv %in% c(1,2) ~ 1 ,
rh_del_pv %in% c(3,4,5) ~ 2,
rh_del_pv ==6 ~ 3 ,
rh_del_pv==9 ~ 9 ,
age>=period ~ 99)) %>%
replace_with_na(replace = list(rh_del_pvskill = c(99))) %>%
set_value_labels(rh_del_pvskill = c("Skilled provider" = 1, "Unskilled provider"=2, "No one"=3, "Don't know/missing"=9 )) %>%
set_variable_labels(rh_del_pvskill = "Skilled assistance during delivery")
if (BRdata$v000[1] %in% c("NG7","NG6")) {
# //Skilled provider during delivery -- ****SPECIFIC FOR NIGERIA*******
# ** Note: Please check the final report for this indicator to determine what provider is considered skilled.
BRdata <- BRdata %>%
mutate(rh_del_pvskill =
case_when(
#m3c==1 ~ 1,
rh_del_pv %in% c(1,2) | m3c==1 ~ 1 , # ADD AUXILIARY MIDWIFE HERE FOR NIGERIA (M3C==1)
rh_del_pv %in% c(3, 4, 5) ~ 2,
rh_del_pv ==6 ~ 3 ,
rh_del_pv==9 ~ 9 ,
age>=period ~ 99)) %>%
replace_with_na(replace = list(rh_del_pvskill = c(99))) %>%
set_value_labels(rh_del_pvskill = c("Skilled provider" = 1, "Unskilled provider"=2, "No one"=3, "Don't know/missing"=9 )) %>%
set_variable_labels(rh_del_pvskill = "Skilled assistance during delivery in Nigeria")
}
if (BRdata$v000[1] %in% c("BF8","BF6","TZ8","ET7","ET8","CD6","SN7","SN8","MZ8","RW6","RW7","SL6","SL7","ZM7","ZM8","MZ6")) {
## "Country specific health professional"=3 AS Skilled provider FOR "BF8","BF6","TZ8" ,"ET7","ET8","CD6","SN8","MZ8"
# BF2021 BF8
# Prenatal care provided by a skilled provider
# Pregnancy-related care received from skilled providers, such as
# physicians, health counselors, midwives, registered midwives, nurses,
# health officers, certified birth attendants and auxiliary birth attendants,
# mobile health workers, and community health workers
# Sample: Women aged 15–49 who had a live birth or a stillbirth
# in the 2 years preceding the survey.
# Soins prénatals dispensés par un prestataire qualifié
# Soins relatifs à la grossesse reçus de prestataires qualifiés, comme les
# médecins, conseillers de santé, sages-femmes, maïeuticiens d’état, infirmiers,
# attachés de santé, accoucheuses brevetées et les accoucheuses auxiliaires,
# agent itinérant de santé et agent de santé à base communautaire
# Échantillon : Femmes de 15–49 ans ayant eu une naissance vivante ou un
# mort-né au cours des 2 années précédant l’enquête.
# vars <- grep("^m3[a-n]$", names(BRdata), value = TRUE)
#
# # pull labels
# labels <- sapply(vars, function(v) attr(BRdata[[v]], "label"))
#
# # combine into a data.frame
# labelf=data.frame(variable = vars, label = labels, stringsAsFactors = FALSE)
BRdata <- BRdata %>%
mutate(rh_del_pvskill =
case_when(
#m3c==1 ~ 1,
rh_del_pv %in% c(1,2,3) ~ 1 , # "Country specific health professional"=3 AS Skilled provider
rh_del_pv %in% c( 4, 5) ~ 2,
rh_del_pv ==6 ~ 3 ,
rh_del_pv==9 ~ 9 ,
age>=period ~ 99)) %>%
replace_with_na(replace = list(rh_del_pvskill = c(99))) %>%
set_value_labels(rh_del_pvskill = c("Skilled provider" = 1, "Unskilled provider"=2, "No one"=3, "Don't know/missing"=9 )) %>%
set_variable_labels(rh_del_pvskill = "Skilled assistance during delivery in Nigeria")
}
if (BRdata$v000[1] %in% c("TZ7")) {
# TZ7 has different column definitions
# Skilled assistance during delivery
# Births delivered with the assistance of doctors, assistant medical officers,
# clinical officers/assistant clinical officers, nurse/midwives, and MCH aides
# Sample: All live births in the 5 years before the survey
# variable label
# m3a m3a assistance: doctor/amo
# m3b m3b assistance: clinical officer
# m3c m3c assistance: assistant clinical officer
# m3d m3d na - assistance: cs health professional
# m3e m3e na - assistance: cs health professional
# m3f m3f na - assistance: cs health professional
# m3g m3g assistance: nurse/ midwife
# m3h m3h assistance: assist. nurse
# m3i m3i assistance: maternal and child health (mch) aides
# m3j m3j assistance: community health worker (chw)
# m3k m3k assistance: other
# m3l m3l assistance: traditional birth attendants (tba)
# m3m m3m assistance: relative/friend
# m3n m3n assistance: no one
# TZ8
# > labelf
# variable label
# m3a m3a assistance: doctor/amo
# m3b m3b assistance: clinical officer
# m3c m3c assistance: assistant clinical officer
# m3d m3d assistance: nurse/midwife
# m3e m3e assistance: assistant nurse
# m3f m3f assistance: mch aide
# m3g m3g assistance: traditional birth attendant
# m3h m3h assistance: community health worker
# m3i m3i assistance: relative/neighbor
# m3j m3j na - assistance: cs other person
# m3k m3k assistance: other
# m3l m3l na - assistance: cs other
# m3m m3m na - assistance: cs other
# m3n m3n assistance: no one
# vars <- grep("^m3[a-n]$", names(BRdata), value = TRUE)
#
# # pull labels
# labels <- sapply(vars, function(v) attr(BRdata[[v]], "label"))
#
# # combine into a data.frame
# labelf=data.frame(variable = vars, label = labels, stringsAsFactors = FALSE)
BRdata <- BRdata %>%
mutate(rh_del_pv =
case_when(
m3a == 1 ~ 1 ,
m3b == 1 ~ 2,
m3c == 1 | m3g == 1 | m3h == 1 | m3i == 1~ 3 ,
m3l == 1 ~ 4 ,
m3j == 1 | m3f == 1 | m3j == 1 | m3k == 1 | m3l == 1 | m3m == 1 ~ 5 ,
m3n ==1 ~ 6,
m3a ==8 | m3a==9 ~ 9 ,
age>=period ~ 99)) %>%
replace_with_na(replace = list(rh_del_pv = c(99))) %>%
set_value_labels(rh_del_pv = c("Doctor" = 1, "Nurse/midwife"=2, "Country specific health professional"=3, "Traditional birth attendant"=4, "Relative/other"=5, "No one"=6, "Don't know/missing"=9 )) %>%
set_variable_labels(rh_del_pv = "Person providing assistance during delivery")
BRdata <- BRdata %>%
mutate(rh_del_pvskill =
case_when(
#m3c==1 ~ 1,
rh_del_pv %in% c(1,2,3) ~ 1 , # "Country specific health professional"=3 AS Skilled provider FOR BF
rh_del_pv %in% c( 4, 5) ~ 2,
rh_del_pv ==6 ~ 3 ,
rh_del_pv==9 ~ 9 ,
age>=period ~ 99)) %>%
replace_with_na(replace = list(rh_del_pvskill = c(99))) %>%
set_value_labels(rh_del_pvskill = c("Skilled provider" = 1, "Unskilled provider"=2, "No one"=3, "Don't know/missing"=9 )) %>%
set_variable_labels(rh_del_pvskill = "Skilled assistance during delivery in Nigeria")
}
if (BRdata$v000[1] %in% c("MZ6")) {
#
# labelf
# variable label
# m3a m3a assistance: doctor
# m3b m3b assistance: nurse/medical assistant
# m3c m3c assistance: midwife
# m3d m3d na - assistance: cs health professional
# m3e m3e na - assistance: cs health professional
# m3f m3f na - assistance: cs health professional
# m3g m3g assistance: traditional healer
# m3h m3h assistance: community health worker
# m3i m3i assistance: community health mother and child
# m3j m3j assistance: relatives
# m3k m3k assistance: friends and neighbors
# m3l m3l na - assistance: cs other
# m3m m3m assistance: other
# m3n m3n assistance: no one
# >
#
BRdata <- BRdata %>%
mutate(rh_del_pv =
case_when(
m3a == 1 ~ 1 ,
m3b == 1 ~ 2,
m3c == 1 ~ 3 ,
m3g == 1 ~ 4 ,
m3d == 1 | m3h == 1 | m3e == 1 | m3f == 1 | m3i == 1 | m3j == 1 | m3k == 1 | m3l == 1 | m3m == 1 ~ 5 ,
m3n ==1 ~ 6,
m3a ==8 | m3a==9 ~ 9 ,
age>=period ~ 99)) %>%
replace_with_na(replace = list(rh_del_pv = c(99))) %>%
set_value_labels(rh_del_pv = c("Doctor" = 1, "Nurse/midwife"=2, "Country specific health professional"=3, "Traditional birth attendant"=4, "Relative/other"=5, "No one"=6, "Don't know/missing"=9 )) %>%
set_variable_labels(rh_del_pv = "Person providing assistance during delivery")
BRdata <- BRdata %>%
mutate(rh_del_pvskill =
case_when(
rh_del_pv %in% c(1,2,3) ~ 1 , # "Country specific health professional"=3 AS Skilled provider
rh_del_pv %in% c( 4, 5) ~ 2,
rh_del_pv ==6 ~ 3 ,
rh_del_pv==9 ~ 9 ,
age>=period ~ 99)) %>%
replace_with_na(replace = list(rh_del_pvskill = c(99))) %>%
set_value_labels(rh_del_pvskill = c("Skilled provider" = 1, "Unskilled provider"=2, "No one"=3, "Don't know/missing"=9 )) %>%
set_variable_labels(rh_del_pvskill = "Skilled assistance during delivery")
}
if (BRdata$v000[1] %in% c("ML7")) {
#ML7
# labelf
# variable label
# m3a m3a assistance: doctor
# m3b m3b assistance: nurse/midwife
# m3c m3c assistance: matron
# m3d m3d assistance: trained traditional birth attendant
# m3e m3e na - assistance: cs health professional
# m3f m3f na - assistance: cs health professional
# m3g m3g assistance: traditional birth attendant
# m3h m3h assistance: community health worker
# m3i m3i assistance: relative/ friend
# m3j m3j na - assistance: cs other person
# m3k m3k assistance: other
# m3l m3l na - assistance: cs other
# m3m m3m na - assistance: cs other
# m3n m3n assistance: no one
BRdata <- BRdata %>%
mutate(rh_del_pv =
case_when(
m3a == 1 ~ 1 ,
m3b == 1 ~ 2,
m3c == 1 ~ 3 ,
m3g == 1 ~ 4 ,
m3d == 1 | m3h == 1 | m3e == 1 | m3f == 1 | m3i == 1 | m3j == 1 | m3k == 1 | m3l == 1 | m3m == 1 ~ 5 ,
m3n ==1 ~ 6,
m3a ==8 | m3a==9 ~ 9 ,
age>=period ~ 99)) %>%
replace_with_na(replace = list(rh_del_pv = c(99))) %>%
set_value_labels(rh_del_pv = c("Doctor" = 1, "Nurse/midwife"=2, "Country specific health professional"=3, "Traditional birth attendant"=4, "Relative/other"=5, "No one"=6, "Don't know/missing"=9 )) %>%
set_variable_labels(rh_del_pv = "Person providing assistance during delivery")
BRdata <- BRdata %>%
mutate(rh_del_pvskill =
case_when(
rh_del_pv %in% c(1,2,3) ~ 1 , # "Country specific health professional"=3 AS Skilled provider
rh_del_pv %in% c( 4, 5) ~ 2,
rh_del_pv ==6 ~ 3 ,
rh_del_pv==9 ~ 9 ,
age>=period ~ 99)) %>%
replace_with_na(replace = list(rh_del_pvskill = c(99))) %>%
set_value_labels(rh_del_pvskill = c("Skilled provider" = 1, "Unskilled provider"=2, "No one"=3, "Don't know/missing"=9 )) %>%
set_variable_labels(rh_del_pvskill = "Skilled assistance during delivery")
}
BRdata$rh_del_pvskill= ifelse( BRdata$rh_del_pvskill == 1, 1, 0)
colnames(BRdata)[colnames(BRdata) == 'rh_del_pvskill'] <- "value"
return(BRdata)
}
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Defines functions RH_DELA_C_SKF
Documented in RH_DELA_C_SKF
#'RH_DELA_C_SKF
#'BRdata
#'Assistance during delivery from a skilled provider
#'
#'
#' @param Rdata data.frame from surveyPrev::getDHSdata
#'
#' @return A partially processed data.frame that will be used in surveyPrev::getDHSindicator. The whole function can be used as a parameter in surveyPrev::getDHSindicator
#'
#' @author Qianyu Dong
#' @examples
#' \dontrun{
#' dhsData <- getDHSdata(country = "Zambia",
#' indicator = "RH_DELA_C_SKF",
#' year = 2018)
#' data <- getDHSindicator(dhsData, indicator = NULL,
#' FUN = surveyPrev::RH_DELA_C_SKF)
#' }
#' @export
#'
RH_DELA_C_SKF <- function(Rdata){
BRdata <- Rdata %>%
mutate(wt = v005/1000000)
# period and age of child
# choose reference period, last 2 years (24 months) or last 5 years (60 months)
# this is fiver year
BRdata <- BRdata %>%
mutate(period = 60)
# age of child. If b19 is not available in the data use v008 - b3
if ("TRUE" %in% (!("b19" %in% names(BRdata))))
BRdata [[paste("b19")]] <- NA
if ("TRUE" %in% all(is.na(BRdata$b19)))
{ b19_included <- 0} else { b19_included <- 1}
if (b19_included==1) {
BRdata <- BRdata %>%
mutate(age = b19)
} else {
BRdata <- BRdata %>%
mutate(age = v008 - b3)
}
# ** NEW STEP: Explicitly filter the data to keep only recent births **
# This directly changes the number of rows (the sample size).
BRdata <- BRdata %>%
filter(age < period)
BRdata <- BRdata %>%
mutate(rh_del_pv =
case_when(
m3a == 1 ~ 1 ,
m3b == 1 ~ 2,
m3c == 1 | m3d == 1 | m3e == 1 | m3f == 1~ 3 ,
m3g == 1 ~ 4 ,
m3h == 1 | m3i == 1 | m3j == 1 | m3k == 1 | m3l == 1 | m3m == 1 ~ 5 ,
m3n ==1 ~ 6,
m3a ==8 | m3a==9 ~ 9 ,
age>=period ~ 99)) %>%
replace_with_na(replace = list(rh_del_pv = c(99))) %>%
set_value_labels(rh_del_pv = c("Doctor" = 1, "Nurse/midwife"=2, "Country specific health professional"=3, "Traditional birth attendant"=4, "Relative/other"=5, "No one"=6, "Don't know/missing"=9 )) %>%
set_variable_labels(rh_del_pv = "Person providing assistance during delivery")
# //Skilled provider during delivery
# ** Note: Please check the final report for this indicator to determine what provider is considered skilled.
BRdata <- BRdata %>%
mutate(rh_del_pvskill =
case_when(
rh_del_pv %in% c(1,2) ~ 1 ,
rh_del_pv %in% c(3,4,5) ~ 2,
rh_del_pv ==6 ~ 3 ,
rh_del_pv==9 ~ 9 ,
age>=period ~ 99)) %>%
replace_with_na(replace = list(rh_del_pvskill = c(99))) %>%
set_value_labels(rh_del_pvskill = c("Skilled provider" = 1, "Unskilled provider"=2, "No one"=3, "Don't know/missing"=9 )) %>%
set_variable_labels(rh_del_pvskill = "Skilled assistance during delivery")
if (BRdata$v000[1] %in% c("NG7","NG6")) {
# //Skilled provider during delivery -- ****SPECIFIC FOR NIGERIA*******
# ** Note: Please check the final report for this indicator to determine what provider is considered skilled.
BRdata <- BRdata %>%
mutate(rh_del_pvskill =
case_when(
#m3c==1 ~ 1,
rh_del_pv %in% c(1,2) | m3c==1 ~ 1 , # ADD AUXILIARY MIDWIFE HERE FOR NIGERIA (M3C==1)
rh_del_pv %in% c(3, 4, 5) ~ 2,
rh_del_pv ==6 ~ 3 ,
rh_del_pv==9 ~ 9 ,
age>=period ~ 99)) %>%
replace_with_na(replace = list(rh_del_pvskill = c(99))) %>%
set_value_labels(rh_del_pvskill = c("Skilled provider" = 1, "Unskilled provider"=2, "No one"=3, "Don't know/missing"=9 )) %>%
set_variable_labels(rh_del_pvskill = "Skilled assistance during delivery in Nigeria")
}
if (BRdata$v000[1] %in% c("BF8","BF6","TZ8","ET7","ET8","CD6","SN7","SN8","MZ8","RW6","RW7","SL6","SL7","ZM7","ZM8","MZ6")) {
## "Country specific health professional"=3 AS Skilled provider FOR "BF8","BF6","TZ8" ,"ET7","ET8","CD6","SN8","MZ8"
# BF2021 BF8
# Prenatal care provided by a skilled provider
# Pregnancy-related care received from skilled providers, such as
# physicians, health counselors, midwives, registered midwives, nurses,
# health officers, certified birth attendants and auxiliary birth attendants,
# mobile health workers, and community health workers
# Sample: Women aged 15–49 who had a live birth or a stillbirth
# in the 2 years preceding the survey.
# Soins prénatals dispensés par un prestataire qualifié
# Soins relatifs à la grossesse reçus de prestataires qualifiés, comme les
# médecins, conseillers de santé, sages-femmes, maïeuticiens d’état, infirmiers,
# attachés de santé, accoucheuses brevetées et les accoucheuses auxiliaires,
# agent itinérant de santé et agent de santé à base communautaire
# Échantillon : Femmes de 15–49 ans ayant eu une naissance vivante ou un
# mort-né au cours des 2 années précédant l’enquête.
# vars <- grep("^m3[a-n]$", names(BRdata), value = TRUE)
#
# # pull labels
# labels <- sapply(vars, function(v) attr(BRdata[[v]], "label"))
#
# # combine into a data.frame
# labelf=data.frame(variable = vars, label = labels, stringsAsFactors = FALSE)
BRdata <- BRdata %>%
mutate(rh_del_pvskill =
case_when(
#m3c==1 ~ 1,
rh_del_pv %in% c(1,2,3) ~ 1 , # "Country specific health professional"=3 AS Skilled provider
rh_del_pv %in% c( 4, 5) ~ 2,
rh_del_pv ==6 ~ 3 ,
rh_del_pv==9 ~ 9 ,
age>=period ~ 99)) %>%
replace_with_na(replace = list(rh_del_pvskill = c(99))) %>%
set_value_labels(rh_del_pvskill = c("Skilled provider" = 1, "Unskilled provider"=2, "No one"=3, "Don't know/missing"=9 )) %>%
set_variable_labels(rh_del_pvskill = "Skilled assistance during delivery in Nigeria")
}
if (BRdata$v000[1] %in% c("TZ7")) {
# TZ7 has different column definitions
# Skilled assistance during delivery
# Births delivered with the assistance of doctors, assistant medical officers,
# clinical officers/assistant clinical officers, nurse/midwives, and MCH aides
# Sample: All live births in the 5 years before the survey
# variable label
# m3a m3a assistance: doctor/amo
# m3b m3b assistance: clinical officer
# m3c m3c assistance: assistant clinical officer
# m3d m3d na - assistance: cs health professional
# m3e m3e na - assistance: cs health professional
# m3f m3f na - assistance: cs health professional
# m3g m3g assistance: nurse/ midwife
# m3h m3h assistance: assist. nurse
# m3i m3i assistance: maternal and child health (mch) aides
# m3j m3j assistance: community health worker (chw)
# m3k m3k assistance: other
# m3l m3l assistance: traditional birth attendants (tba)
# m3m m3m assistance: relative/friend
# m3n m3n assistance: no one
# TZ8
# > labelf
# variable label
# m3a m3a assistance: doctor/amo
# m3b m3b assistance: clinical officer
# m3c m3c assistance: assistant clinical officer
# m3d m3d assistance: nurse/midwife
# m3e m3e assistance: assistant nurse
# m3f m3f assistance: mch aide
# m3g m3g assistance: traditional birth attendant
# m3h m3h assistance: community health worker
# m3i m3i assistance: relative/neighbor
# m3j m3j na - assistance: cs other person
# m3k m3k assistance: other
# m3l m3l na - assistance: cs other
# m3m m3m na - assistance: cs other
# m3n m3n assistance: no one
# vars <- grep("^m3[a-n]$", names(BRdata), value = TRUE)
#
# # pull labels
# labels <- sapply(vars, function(v) attr(BRdata[[v]], "label"))
#
# # combine into a data.frame
# labelf=data.frame(variable = vars, label = labels, stringsAsFactors = FALSE)
BRdata <- BRdata %>%
mutate(rh_del_pv =
case_when(
m3a == 1 ~ 1 ,
m3b == 1 ~ 2,
m3c == 1 | m3g == 1 | m3h == 1 | m3i == 1~ 3 ,
m3l == 1 ~ 4 ,
m3j == 1 | m3f == 1 | m3j == 1 | m3k == 1 | m3l == 1 | m3m == 1 ~ 5 ,
m3n ==1 ~ 6,
m3a ==8 | m3a==9 ~ 9 ,
age>=period ~ 99)) %>%
replace_with_na(replace = list(rh_del_pv = c(99))) %>%
set_value_labels(rh_del_pv = c("Doctor" = 1, "Nurse/midwife"=2, "Country specific health professional"=3, "Traditional birth attendant"=4, "Relative/other"=5, "No one"=6, "Don't know/missing"=9 )) %>%
set_variable_labels(rh_del_pv = "Person providing assistance during delivery")
BRdata <- BRdata %>%
mutate(rh_del_pvskill =
case_when(
#m3c==1 ~ 1,
rh_del_pv %in% c(1,2,3) ~ 1 , # "Country specific health professional"=3 AS Skilled provider FOR BF
rh_del_pv %in% c( 4, 5) ~ 2,
rh_del_pv ==6 ~ 3 ,
rh_del_pv==9 ~ 9 ,
age>=period ~ 99)) %>%
replace_with_na(replace = list(rh_del_pvskill = c(99))) %>%
set_value_labels(rh_del_pvskill = c("Skilled provider" = 1, "Unskilled provider"=2, "No one"=3, "Don't know/missing"=9 )) %>%
set_variable_labels(rh_del_pvskill = "Skilled assistance during delivery in Nigeria")
}
if (BRdata$v000[1] %in% c("MZ6")) {
#
# labelf
# variable label
# m3a m3a assistance: doctor
# m3b m3b assistance: nurse/medical assistant
# m3c m3c assistance: midwife
# m3d m3d na - assistance: cs health professional
# m3e m3e na - assistance: cs health professional
# m3f m3f na - assistance: cs health professional
# m3g m3g assistance: traditional healer
# m3h m3h assistance: community health worker
# m3i m3i assistance: community health mother and child
# m3j m3j assistance: relatives
# m3k m3k assistance: friends and neighbors
# m3l m3l na - assistance: cs other
# m3m m3m assistance: other
# m3n m3n assistance: no one
# >
#
BRdata <- BRdata %>%
mutate(rh_del_pv =
case_when(
m3a == 1 ~ 1 ,
m3b == 1 ~ 2,
m3c == 1 ~ 3 ,
m3g == 1 ~ 4 ,
m3d == 1 | m3h == 1 | m3e == 1 | m3f == 1 | m3i == 1 | m3j == 1 | m3k == 1 | m3l == 1 | m3m == 1 ~ 5 ,
m3n ==1 ~ 6,
m3a ==8 | m3a==9 ~ 9 ,
age>=period ~ 99)) %>%
replace_with_na(replace = list(rh_del_pv = c(99))) %>%
set_value_labels(rh_del_pv = c("Doctor" = 1, "Nurse/midwife"=2, "Country specific health professional"=3, "Traditional birth attendant"=4, "Relative/other"=5, "No one"=6, "Don't know/missing"=9 )) %>%
set_variable_labels(rh_del_pv = "Person providing assistance during delivery")
BRdata <- BRdata %>%
mutate(rh_del_pvskill =
case_when(
rh_del_pv %in% c(1,2,3) ~ 1 , # "Country specific health professional"=3 AS Skilled provider
rh_del_pv %in% c( 4, 5) ~ 2,
rh_del_pv ==6 ~ 3 ,
rh_del_pv==9 ~ 9 ,
age>=period ~ 99)) %>%
replace_with_na(replace = list(rh_del_pvskill = c(99))) %>%
set_value_labels(rh_del_pvskill = c("Skilled provider" = 1, "Unskilled provider"=2, "No one"=3, "Don't know/missing"=9 )) %>%
set_variable_labels(rh_del_pvskill = "Skilled assistance during delivery")
}
if (BRdata$v000[1] %in% c("ML7")) {
#ML7
# labelf
# variable label
# m3a m3a assistance: doctor
# m3b m3b assistance: nurse/midwife
# m3c m3c assistance: matron
# m3d m3d assistance: trained traditional birth attendant
# m3e m3e na - assistance: cs health professional
# m3f m3f na - assistance: cs health professional
# m3g m3g assistance: traditional birth attendant
# m3h m3h assistance: community health worker
# m3i m3i assistance: relative/ friend
# m3j m3j na - assistance: cs other person
# m3k m3k assistance: other
# m3l m3l na - assistance: cs other
# m3m m3m na - assistance: cs other
# m3n m3n assistance: no one
BRdata <- BRdata %>%
mutate(rh_del_pv =
case_when(
m3a == 1 ~ 1 ,
m3b == 1 ~ 2,
m3c == 1 ~ 3 ,
m3g == 1 ~ 4 ,
m3d == 1 | m3h == 1 | m3e == 1 | m3f == 1 | m3i == 1 | m3j == 1 | m3k == 1 | m3l == 1 | m3m == 1 ~ 5 ,
m3n ==1 ~ 6,
m3a ==8 | m3a==9 ~ 9 ,
age>=period ~ 99)) %>%
replace_with_na(replace = list(rh_del_pv = c(99))) %>%
set_value_labels(rh_del_pv = c("Doctor" = 1, "Nurse/midwife"=2, "Country specific health professional"=3, "Traditional birth attendant"=4, "Relative/other"=5, "No one"=6, "Don't know/missing"=9 )) %>%
set_variable_labels(rh_del_pv = "Person providing assistance during delivery")
BRdata <- BRdata %>%
mutate(rh_del_pvskill =
case_when(
rh_del_pv %in% c(1,2,3) ~ 1 , # "Country specific health professional"=3 AS Skilled provider
rh_del_pv %in% c( 4, 5) ~ 2,
rh_del_pv ==6 ~ 3 ,
rh_del_pv==9 ~ 9 ,
age>=period ~ 99)) %>%
replace_with_na(replace = list(rh_del_pvskill = c(99))) %>%
set_value_labels(rh_del_pvskill = c("Skilled provider" = 1, "Unskilled provider"=2, "No one"=3, "Don't know/missing"=9 )) %>%
set_variable_labels(rh_del_pvskill = "Skilled assistance during delivery")
}
BRdata$rh_del_pvskill= ifelse( BRdata$rh_del_pvskill == 1, 1, 0)
colnames(BRdata)[colnames(BRdata) == 'rh_del_pvskill'] <- "value"
return(BRdata)
}
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