Spatial variation in housing construction material in low- and middle-income countries: A Bayesian spatial prediction model of a key infectious diseases risk factor and social determinant of health

Abstract

Housing infrastructure and quality is a major determinant of infectious disease risk and other health outcomes in regions where vector borne, waterborne and neglected tropical diseases are endemic. It is important to quantify the geographical distribution of improvements to dwelling components to identify and target resources towards populations at risk. This study aimed to model the sub-national spatial variation in housing materials using covariates with quasi-global coverage and use the resulting estimates to map predicted coverage across the world's low- and middle-income countries. Data on materials used in dwelling construction were sourced from nationally representative household surveys conducted since 2005. Materials used for construction of flooring, walls, and roofs were reclassified as improved or unimproved. Households lacking location information were georeferenced using a novel methodology. Environmental and demographic spatial covariates were extracted at those locations for use as model predictors. Integrated nested Laplace approximation models were fitted to obtain, and map predicted probabilities for each dwelling component. The dataset compiled included information from households in 283,000 clusters from 350 surveys. Low coverage of improved housing was predicted across the Sahel and southern Sahara regions of Africa, much of inland Amazonia, and areas of the Tibetan plateau. Coverage of improved roofs and walls was high in the Central Asia, East Asia and Pacific and Latin America and the Caribbean regions. Improvements in all three components, but most notably floors, was low in Sub-Saharan Africa. The strongest determinants of dwelling component quality related to urbanization and economic development, suggesting that programs should focus on supply-side interventions, providing resources for housing improvements directly to the populations that need them. These findings are made available to researchers as files that can be imported into a GIS for integration into relevant analyses to derive improved estimates of preventable health burdens attributed to housing.

Fig 1. Number of nationally representative household surveys included in input dataset by country for included LMICs (small countries represented by circles).

Base map compiled from shapefiles obtained from U.S. Department of State—Humanitarian Information Unit [63] and Natural Earth free vector map data @ naturalearthdata.com that are made available in the public domain with no restrictions.

Fig 2

Coverage of improved material for three dwelling components—a. floors, b. walls, c. roofs–in LMICs predicted by integrated nested Laplace approximation (INLA) models fitted to household survey data with inset maps showing details at a smaller zoom extent. Base maps compiled from shapefiles obtained from U.S. Department of State—Humanitarian Information Unit [63] and Natural Earth free vector map data @ naturalearthdata.com that are made available in the public domain with no restrictions.

Fig 3. Distribution of values predicted for coverage of improved dwelling components by INLA models, stratified by component and world region.

Fig 4. Feature importance for each of the variables and their categories included in the final model for each of the dwelling components (excluding time.

HDI–Human Development Index; EVI–Enhanced Vegetation Index; LST–Land Surface Temperature; ET–Evapotranspiration; GDP–Gross Domestic Product; MENA–Middle East and North Africa; LAC–Latin America and the Caribbean; SA–South Asia; EAP–East Asia and Pacific; SSA–Sub-Saharan Africa. Comparison categories for factor variables are Land cover/use–tree cover; Climate zone–tropical; Region–Europe and Central Asia; Urbanicity—remote).