Malawi Demographic Health Survey Data Research Articles

Shared geographic spatial risk of childhood undernutrition in Malawi: An application of joint spatial component model

ObjectivesThis study aimed at assessing shared spatial risk of childhood undernutrition indicators in Malawi. Study designCross-sectional design. MethodsThe shared spatial component model was fitted to childhood undernutrition indicators, namely: stunting, wasting and underweight, using 5066 child records of the 2015/16 Malawi demographic health survey data. The spatial components were districts, and were modeled by the convolution prior, with the structured components being assigned the conditional autoregressive distribution. ResultsThere is significant clustering of shared spatial risk of stunting and wasting (Moran I = 0.464, p-value = 0.009), and wasting and underweight (Moran I = 0.392, p-value = 0.026), and the risk maps show southern districts, followed by central districts being at greater risk of jointly having stunting and wasting, wasting and underweight, compared to the northern region districts. The shared spatial risk of stunting and underweight is randomly dispersed across the country (Moran I = - 0.044, p-value = 0.539). ConclusionInterventions to reduce the shared risk of child undernutrition should focus on the southern region districts and those in the central region, and a suggestion is made to address the issue of overpopulation and effects of climate change.

Climate and location as determinants of childhood stunting, wasting, and overweight: An application of semiparametric multivariate probit model

ObjectiveThe aim of this study was to investigate the effects of climate and location while using the multivariate model of malnutrition. MethodsThe joint semiparametric model of stunting, wasting, and overweight was fitted to 2015 child Malawi demographic health survey data with 5149 records. The MDHS was a cross-sectional study. The smooth functions for the non-parametric terms were the regression splines and the effect of location was smoothed by the Markov random field (MRF). ResultsRainfall had a positive effect on stunting (β = 0.076, P = 0.044) and overweight (β = 0.854, P = 0.039). Mean temperature (β = 1.220, P = 0.031) and distance to water body (β = 0.009, P = 0.049) also had a positive effect on wasting. Increased length of rainy season was associated with reduced overweight (β = –0.163, P = 0.042). Location was not a significant predictor of all malnutrition indicators, although there was observable spatial variation regarding overweight and wasting. There was significant positive correlation between stunting and overweight (ρ = 0.234; 95% confidence interval, 0.135–0.324). The findings on socioeconomic determinants are consistent with the literature. ConclusionNutrition interventions may target hot spot areas that have shown increased risk for overweight and wasting. The strategies to minimize malnutrition should focus on consequences of climate change like high rainfall, length of season, and temperature.

Spatial quantile regression with application to high and low child birth weight in Malawi

BackgroundChild low and high birth weight are important public health problems. Many studies have looked at factors of low and high birth weight using mean regression. This study aimed at using quantile regression to find out determinants of low and high birth weight.MethodsSpatial quantile regression models at 0.05 and 0.95 percentiles of birth weight were fitted to 13,087 children birth weight in kilograms using Malawi demographic health survey data of 2010 study. Full Bayesian method by integrated nested Laplace approximations (INLA) was used to estimate the model. Second order random walk priors were assigned for mother age and antenatal visits for pregnancy while Gaussian markov random field prior was used for district of the child.ResultsResidual spatial patterns reveal areas in the southern region promoting high birth weight while areas in the central and northern region promote low birth weight. Most fixed effects findings are consistent with the literature. Richest family, normal mother body mass index (BMI), mother over weight (BMI > 25 kg/m2), birth order 2–3, mother secondary education and height (≥150 cm) negate low birth weight while weight 45–70 kg promote low birth weight. Birth order category 6+, mother height (≥150 cm) and poor wealth quintile, promote high birth weight, while richer and richest wealth quintiles and education categories: primary, secondary, and higher, and mother overweight (BMI > 25 kg/m2) reduce high birth weight. Antenatal visits for pregnancy reduce both low and high birth weight.ConclusionStrategies to reduce low and high birth weight should simultaneously address mother education, weight gain during pregnancy and poverty while targeting areas increasing low and high birth weight.

Analysis on the Association Among Stunting, Wasting and Underweight in Malawi: An Application of a Log-Linear Model for the Three-Way Table.

Childhood undernutrition is an important public health problem. Many studies have investigated the factors of childhood undernutrition, but not the association between the undernutrition indicators. This study aimed at investigating the association between the childhood undernutrition indicators. A loglinear model of cell counts of a three way table of stunting, wasting, and underweight was fitted based on the 2010 Malawi demographic health survey data. Interaction terms in the model depicted deviations from independence. A multiple correspondence analysis of undernutrition indicators was also plotted to have a visual impression of association of the undernutrition variables. A loglinear model showed that underweight was associated with both stunting (P<0.001), and wasting (P<0.001). There was no association between stunting and wasting (P=1). Furthermore there was no three way association of stunting, wasting and underweight (P=1). Lack of three way interaction of stunting, wasting and underweight means that childhood undernutrition multidimensional nature is still valid, and no each indicator can represent the other.

Attitudes of Violence and Risk for HIV: Impact on Women’s Health in Malawi

This study was designed to investigate the pathways that lead to HIV exposure, based on Malawi Demographic Health Survey data (2010). It examines the factors that correlate to gender violence in Malawi, including cultural attitudes towards violence, spousal violence factors, and HIV risk factors. Structural equation modeling (SEM) identifies associations among these constructs. A perfect model fit was achieved to build a simultaneous model that includes attitudes towards violence, violence factors, and HIV risk (GFI = .998). Education attainment (.039) and number of partners (.120) had a weaker association with HIV risk than condom use (.418) and HIV testing (.412). We hypothesized that gender attitudes and incidents of violence would be related to greater risks for HIV infection among women. SEM affirmed a robust association between attitudes towards violence and how women in Malawi perceive gender violence. We conclude that policy and practice design should acknowledge the impact of cultural, educational, and familial characteristics on the populations in order to achieve robust change to reduce HIV transmissions especially among women in Malawi.