In ropensci/rdhs: API Client and Dataset Management for the Demographic and Health Survey (DHS) Data

Anemia is a common cause of fatigue, and women of childbearing age are at particularly high risk for anemia. The package rdhs can be used to compare estimates of the prevalence of any anemia among women from Demographic and Health Surveys (DHS) conducted in Armenia, Cambodia, and Lesotho.

Setup

Load the rdhs package and other useful packages for analysing data.

## devtools::install_github("ropensci/rdhs")
library(rdhs)
library(data.table)
library(ggplot2)
library(survey)
library(haven)

Using calculated indicators from STATcompiler

Anemia prevalence among women is reported as a core indicator through the DHS STATcompiler (https://www.statcompiler.com/). These indicators can be accessed directly from R via the DHS API with the function dhs_data().

Query the API for a list of all StatCompiler indicators, and then search the indicators for those that have "anemia" in the indicator name. API calls return data.frame objects, so if you prefer to use data.table objects then convert afterwards, or we can set this up within our config using set_rdhs_config.

library(rdhs)
set_rdhs_config(data_frame = "data.table::as.data.table")

indicators <- dhs_indicators()
tail(indicators[grepl("anemia", Label), .(IndicatorId, ShortName, Label)])

The indicator ID "AN_ANEM_W_ANY" reports the percentage of women with any anemia. The function dhs_data() will query the indicator dataset for the value of this indicator for our three countries of interest. First, use dhs_countries() to query the list of DHS countries to identify the DHS country code for each country.

countries <- dhs_countries()
dhscc <- countries[CountryName %in% c("Armenia", "Cambodia", "Lesotho"), DHS_CountryCode]
dhscc

Now query the indicators dataset for the women with any anemia indicator for these three countries.

statcomp <- dhs_data(indicatorIds = "AN_ANEM_W_ANY", countryIds = dhscc)
statcomp[,.(Indicator, CountryName, SurveyYear, Value, DenominatorWeighted)]

ggplot(statcomp, aes(SurveyYear, Value, col=CountryName)) +
  geom_point() + geom_line()

Analyse DHS microdata

Identify surveys that include anemia testing

The DHS API provides the facility to filter surveys according to particular characteristics. We first query the list of survey characteristics and identify the SurveyCharacteristicID that indicates the survey included anemia testing. The first command below queries the API for the full list of survey characteristics, and the second uses grepl() to search SurveyCharacteristicNames that include the word 'anemia'.

surveychar <- dhs_survey_characteristics()
surveychar[grepl("anemia", SurveyCharacteristicName, ignore.case=TRUE)]

The SurveyCharacteristicID = 41 indicates that the survey included anemia testing. Next we query the API to identify the surveys that have this characteristic and were conducted in our countries of interest.

surveys <- dhs_surveys(surveyCharacteristicIds = 41, countryIds = dhscc)
surveys[,.(SurveyId, CountryName, SurveyYear, NumberOfWomen, SurveyNum, FieldworkEnd)]

Finally, query the API identify the individual recode (IR) survey datasets for each of these surveys

datasets <- dhs_datasets(surveyIds = surveys$SurveyId, fileType = "IR", fileFormat="flat")
datasets[, .(SurveyId, SurveyNum, FileDateLastModified, FileName)]

Download datasets

To download datasets we need to first log in to our DHS account, by providing our credentials and setting up our configuration using set_rdhs_config(). This will require providing as arguments your email and project for which you want to download datasets from. You will then be prompted for your password. You can also specify a directory for datasets and API calls to be cached to using cache_path. In order to comply with CRAN, this function will also ask you for your permission to write to files outside your temporary directory, and you must type out the filename for the config_path - "rdhs.json". (See introduction vignette for specific format for config, or ?set_rdhs_config).

## set up your credentials
set_rdhs_config(email = "[e-mail]",
                project = "Joint estimation of HIV epidemic trends and adult mortality")

After this the function get_datasets() returns a list of file paths where the desired datasets are saved in the cache. The first time a dataset is accessed, rdhs will download the dataset from the DHS program website using the supplied credentials. Subsequently, datasets will be simply be located in the cached repository.

datasets$path <- unlist(get_datasets(datasets$FileName))

Identify survey variables

Anemia is defined as having a hemoglobin (Hb) <12.0 g/dL for non-pregnant women or Hb <11.0 g/dL for currently pregnant women^[https://www.measureevaluation.org/prh/rh_indicators/womens-health/womens-nutrition/percent-of-women-of-reproductive-age-with-anemia]. To calculate anemia prevalence from DHS microdata, we must identify the DHS recode survey variables for hemoglobin measurement and pregnancy status. This could be done by consulting the DHS recode manual or the .MAP files accompanying survey datasets. It is convenient though to do this in R by loading the first individual recode dataset and searching the metadata for the variable names corresponding to the hemoglobin measurement and pregnancy status.

head(search_variable_labels(datasets$FileName[10], "hemoglobin")[,1:2])

Variable v042 records the household selection for hemoglobin testing. Variable v455 reports the outcome of hemoglobin measurement and v456 the result of altitude adjusted hemoglobin levels.

ir <- readRDS(datasets$path[10])
table(as_factor(ir$v042))
table(as_factor(ir$v455))
summary(ir$v456)

Variable v454 reports the current pregnancy status used for determining the anemia threshold.

search_variable_labels(datasets$FileName[1], "currently.*pregnant")[,1:2]
table(as_factor(ir$v454))

We also keep a number of other variables related to the survey design and potentially interesting covariates: country code and phase (v000), cluster number (v001), sample weight (v005), age (v012), region (v024), urban/rural residence (v025), and education level (v106).

vars <- c("SurveyId", "CountryName", "SurveyYear", "v000", "v001", "v005",
          "v012", "v024", "v025", "v106", "v042", "v454", "v455", "v456")

Extract survey data

datlst <- list()

for(i in 1:nrow(datasets)){

  if(file.exists(datasets$path[i])){

  print(paste(i, datasets$SurveyId[i]))
  ir <- readRDS(datasets$path[i])

  ir$SurveyId <- datasets$SurveyId[i]
  ir$CountryName <- datasets$CountryName[i]
  ir$SurveyYear <- datasets$SurveyYear[i]

  datlst[[datasets$SurveyId[i]]] <- ir[vars]
  }
}

We use rbind_labelled() to combine datasets with labelled columns. The argument labels describes to combine variable levels for all datasets for v024 (region) while providing a consistent set of value labels to be used for v454 (currently pregnant) for all datasets.

dat <- rbind_labelled(datlst,
                      labels = list(v024 = "concatenate",
                                    v454 = c("no/don't know" = 0L,
                                             "yes" = 1L, "missing" = 9L)))

sapply(dat, is.labelled)
dat$v456 <- zap_labels(dat$v456)
dat <- as_factor(dat)

Data tabulations

It is a good idea to check basic tabulations of the data, especially by survey to identify and nuances Exploratory analysis of variables

with(dat, table(SurveyId, v025, useNA="ifany"))
with(dat, table(SurveyId, v106, useNA="ifany"))
with(dat, table(SurveyId, v454, useNA="ifany"))
with(dat, table(SurveyId, v455, useNA="ifany"))

with(dat, table(v042, v454, useNA="ifany"))

Calculate anemia prevalence

Create indicator variable for 'any anemia'. The threshold depends on pregnancy status.

dat$v456[dat$v456 == 999] <- NA
with(dat, table(v455, is.na(v456)))
dat$anemia <- as.integer(dat$v456  < ifelse(dat$v454 == "yes", 110, 120))
dat$anemia_denom <- as.integer(!is.na(dat$anemia))

Specify survey design using the survey package.

dat$w <- dat$v005/1e6
des <- svydesign(~v001+SurveyId, data=dat, weights=~w)

anemia_prev <- svyby(~anemia, ~SurveyId, des, svyciprop, na.rm=TRUE, vartype="ci")
anemia_denom <- svyby(~anemia_denom, ~SurveyId, des, svytotal, na.rm=TRUE)

anemia_prev <- merge(anemia_prev, anemia_denom[c("SurveyId", "anemia_denom")])
res <- statcomp[,.(SurveyId, CountryName, SurveyYear, Value, DenominatorUnweighted, DenominatorWeighted)][anemia_prev, on="SurveyId"]

res$anemia <- 100*res$anemia
res$ci_l <- 100*res$ci_l
res$ci_u <- 100*res$ci_u
res$anemia_denom0 <- round(res$anemia_denom)

The table below compares the prevalence of any anemia calculated from survey microdata with the estimates from DHS StatCompiler and the weighted denominators for each calculation. The estimates are identical for most cases. There are some small differences to be ironed out, which will require looking at the specific countries to check how their stratification was carried out. (We are hoping to bring this feature in once the DHS program has compiled how sample strata were constructed for all of their studies).

knitr::kable(res[,.(CountryName, SurveyYear, Value, anemia, ci_l, ci_u, DenominatorWeighted, anemia_denom0)], digits=1)


ggplot(res, aes(x=SurveyYear, y=anemia, ymin=ci_l, ymax=ci_u,
                col=CountryName, fill=CountryName)) +
  geom_ribbon(alpha=0.4, linetype="blank") + geom_point() + geom_line()

Regression analysis: relationship between education and anemia

A key use of the survey microdata are to conduct secondary analysis of pooled data from several surveys, such as regression analysis. Here we investigate the relationship between anemia prevalence and education level (v106) for women using logistic regression, adjusting for urban/rural (v025) and fixed effects for each survey.

des <- update(des, v106 = relevel(v106, "primary"))
summary(svyglm(anemia ~ SurveyId + v025 + v106, des, family="binomial"))

The results suggest that anemia prevalence is lower among women with higher education.

ropensci/rdhs documentation built on April 5, 2024, 11:50 a.m.