R: Determinants of Childhood Malnutrition in Zambia
ZambiaNutrition
R Documentation
Determinants of Childhood Malnutrition in Zambia
Description
The Demographic Health Surveys (DHS) of Zambia was conducted 1992. The survey is produced jointly
by Macro International, a USAIDfunded firm specializing in demographic research, and the national
statistical agency of the country.
Malnutrition among children is usually determined by assessing an anthropometric status of the
children relative to a reference standard. In our example, malnutrition is measured by stunting
or insufficient height for age, indicating chronic malnutrition. Stunting for a child i
is determined using a Z-score defined as
stunting_i = (AI_i - MAI)/σ
where AI refers to the child's anthropometric indicator (height at a certain age in our
example), while MAI and σ correspond to the median and the standard deviation in
the reference population, respectively.
The main interest is on modeling the dependence of malnutrition on covariates including the
age of the child, the body mass index of the child's mother, the district the child lives in and
some further categorial covariates.
Usage
data("ZambiaNutrition")
Format
A data frame containing 4847 observations on 8 variables.
stunting:
standardised Z-score for stunting.
mbmi:
body mass index of the mother.
agechild:
age of the child in months.
district:
district where the mother lives.
memployment:
mother's employment status with categories ‘working’ and ‘not working’.
meducation:
mother's educational status with categories for complete primary but
incomplete secondary ‘no/incomplete’, complete secondary or higher ‘minimum primary’ and no
education or incomplete primary ‘minimum secondary’.
urban:
locality of the domicile with categories ‘yes’ and ‘no’.
gender:
gender of the child with categories ‘male’ and ‘female’.
Kandala, N. B., Lang, S., Klasen, S., Fahrmeir, L. (2001): Semiparametric Analysis of the
Socio-Demographic and Spatial Determinants of Undernutrition in Two African Countries.
Research in Official Statistics, 1, 81–100.
See Also
bayesx
Examples
## Not run:
## load zambia data and map
data("ZambiaNutrition")
data("ZambiaBnd")
## estimate model
zm <- bayesx(stunting ~ memployment + meducation + urban + gender +
sx(mbmi) + sx(agechild) + sx(district, bs = "mrf", map = ZambiaBnd) +
sx(district, bs = "re"), iter = 12000, burnin = 2000, step = 10,
data = ZambiaNutrition)
summary(zm)
## plot smooth effects
plot(zm, term = c("sx(bmi)", "sx(agechild)", "sx(district)"), map = ZambiaBnd)
## for more examples
demo("zambia")
## End(Not run)