Mapping Local Variation in Household Overcrowding Across Africa from 2000 to 2018: A Modelling Study

Background: Household overcrowding is a serious public health threat associated with higher morbidity and mortality. Using the UN/WHO definition of >2 people occupying a sleeping space in a dwelling; rapid growth in population and urbanisation contribute to overcrowding and poor sanitation in low- and middle- income countries (LMICs), and are risk factors in the spread of infectious diseases including SARS-CoV-2 and antimicrobial resistance (AMR). Many countries lack surveillance capacity to monitor household overcrowding, therefore, geostatistical models are useful tools for estimate household overcrowding.Methods: Household survey and population censuses informed a Bayesian geostatistical model of household overcrowding in Africa. Additional sociodemographic and health – related covariates informed the model which covered 54 African countries between 2000-2018.Findings: We analysed 287 surveys and population censuses conducted between 2000-2018, covering 78,695,991 households. Spatial and temporal variability arose in household overcrowding estimates over time. Overall, 474.4 million (95% UI: 250·1-740·7 million) people liveed in overcrowded conditions in 2018, a 62·7% increase from 291·5 million (95% UI: 180·8 - 417·3 million) in 2000. In 2018, the highest overcrowding estimates were observed in the Horn of Africa region (median proportion 62% [IQR: 57 - 63%]); the lowest regional median proportion was estimated for North of Africa (16% [IQR: 14 - 19%]). Household overcrowding inceased 280% in Algeria, (5% in 2000 to 19% in 2018). Large within-country variations were observed in Namibia (35%) and Mozambique (29%).Interpretation: This study incorporates survey and population censuses data utilising geostatistical modelling techniques to estimate continent-wide household overcrowding over nineteen years. Our analysis identifies countries and areas with high numbers of people living in overcrowded conditions, thereby providing a benchmark for policy planning and implementing interventions.Funding: This work was funded by a grant from the UK Department of Health and Social Care, Wellcome Trust (209142/Z/17/Z), and the Bill and Melinda Gates Foundation (OPP1176062).Declaration of Interest: None to declare.