Geographical Inequalities and Social and Environmental Risk Factors for Under-Five Mortality in Ghana in 2000 and 2010: Bayesian Spatial Analysis of Census Data

Background: Under-five mortality is declining in Ghana and many other countries. Very few studies have measured under-five mortality—and its social and environmental risk factors—at fine spatial resolutions, which is relevant for policy purposes. Our aim was to estimate under-five mortality and its social and environmental risk factors at the district level in Ghana. Methods and Findings: We used 10% random samples of Ghana’s 2000 and 2010 National Population and Housing Censuses. We applied indirect demographic methods and a Bayesian spatial model to the information on total number of children ever born and children surviving to estimate under-five mortality (probability of dying by 5 y of age, 5q0) for each of Ghana’s 110 districts. We also used the census data to estimate the distributions of households or persons in each district in terms of fuel used for cooking, sanitation facility, drinking water source, and parental education. Median district 5q0 declined from 99 deaths per 1,000 live births in 2000 to 70 in 2010. The decline ranged from 40% in southern districts, where it had been lower in 2000, exacerbating existing inequalities. Primary education increased in men and women, and more households had access to improved water and sanitation and cleaner cooking fuels. Higher use of liquefied petroleum gas for cooking was associated with lower 5q0 in multivariate analysis. Conclusions: Under-five mortality has declined in all of Ghana’s districts, but the cross-district inequality in mortality has increased. There is a need for additional data, including on healthcare, and additional environmental and socioeconomic measurements, to understand the reasons for the variations in mortality levels and trends

Summary statistics of district population and under-five mortality and its social and environmental risk factors.

<p>Summary statistics of district population and under-five mortality and its social and environmental risk factors.</p

Under-five mortality (deaths per 1,000 live births) by district in 2000 and 2010.

<p>Under-five mortality (deaths per 1,000 live births) by district in 2000 and 2010.</p

Unexplained district variability in under-five mortality.

<p>(A) Spatially structured random effects [exp(<i>U</i>)] and (B) unstructured random effects [exp(<i>V</i>)] of unexplained district variability in under-five mortality. Random effects < 1 for a district represents a reduction in under-five mortality, and random effects > 1 represents an increase.</p

Change in under-five mortality (deaths per 1,000 live births) by district from 2000 to 2010.

<p>(A) Under-five mortality (deaths per 1,000 live births) in 2000 versus 2010. (B) Tukey mean-difference plot of under-five mortality (deaths per 1,000 live births) in 2000 and 2010. (C) Percent change in under-five mortality by district from 2000 to 2010.</p

Risk ratios and 95% credible intervals (2.5th and 97.5th percentiles of the posterior distributions of effect size parameters from the Bayesian model) from multivariate analysis of the association of under-five mortality with its social and environmental risk factors for 2000, 2010, and the change between 2000 and 2010.

<p>The magnitude of the effect size for each variable represents the proportional change (decrease or increase) in 5q0 for a 10% higher prevalence of that variable (over space in a single census year or change over time between censuses), with the 10% shift coming from the reference variable. The reference variables were wood (for cooking fuel), unimproved sanitation, unimproved drinking water, no education (for maternal and paternal education), and living in rural areas.</p

Distributions of household cooking fuel, sanitation facility, drinking water source, and maternal education by district in 2000 and 2010 (percent of households or persons).

<p>Each bar represents one district, with districts ordered by decreasing prevalence of the worst category for each census.</p