Housing infrastructure and quality is a major determinant of infectious disease risk and other health outcomes in regions of the world where vector borne, waterborne and neglected tropical diseases are endemic. It is important to quantify the geographical distribution of improvements to the major dwelling components in order to identify and target resources towards populations at elevated risk. The aim of this study was to model the sub-national spatial variation in housing materials using covariates with quasi-global coverage and use the resulting estimates to map the predicted coverage across the world’s low- and middle-income countries (LMICs). Data relating to the materials used in dwelling construction were sourced from nationally representative household surveys conducted since 2005. Materials used for construction of flooring, walls, and roof were reclassified as improved or unimproved. Households lacking location information were georeferenced using a novel methodology, and a suite of environmental and demographic spatial covariates were extracted at those locations for use as model predictors. Integrated nested Laplace approximation (INLA) 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, while improvements in all three components, but most notably floors, was low in Sub-Saharan Africa. Human development was by far the strongest determinant of dwelling component quality, though vegetation greenness and land use were also relevant markers These findings allow us to identify and target resources towards populations at elevated risk, and the resulting predictions are made available as supplementary files.

The authors have declared no competing interest.

This research was supported financially by the National Institutes of Health’s National Institute of Allergy and Infectious Diseases (grants 1K01AI168493-01A1 to JMC and 1R03AI151564-01 to MNK) the European Union under the HORIZON EUROPE Programme (Grant Agreement Number: 101137255 with sub-award to JMC) the Engineering in Medicine (EIM) funding program (to VL), the Department of Internal Medicine and the Division of Infectious Diseases and International Health at the University of Virginia. The funders played no role in the design and implementation of the study or the analysis and interpretation of the results

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All human subject information used in this study was anonymized, publicly available secondary data therefore IRB approval was not sought.

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The data used in this analysis are publicly available from the sources listed in table 2 and supplementary file S2. The statistical source code used to generate estimates can be accessed is available from the corresponding author upon reasonable request.