Data from: Spatial variation in housing construction material in low- and middle-income countries

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 to identify and target resources towards
populations at 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. The strongest determinants of
dwelling component quality related to urbanization and economic
development, suggesting that housing improvement programs should focus on
supply-side interventions that provide the resources for these
improvements directly to the populations that need them. These findings
are made available to the reader as files that can be imported into a GIS
for integration into relevant analysis to derive improved estimates of
preventable health burdens attributed to housing.