Mapping local variation in household overcrowding across Africa from 2000 to 2018: a modelling study

Abstract

Background: Household overcrowding is a serious public health threat associated with high morbidity and mortality. Rapid population growth and urbanisation contribute to overcrowding and poor sanitation in low-income and middle- income countries, and are risk factors for the spread of infectious diseases, including COVID-19, and antimicrobial resistance. Many countries do not have adequate surveillance capacity to monitor household overcrowding. Geostatistical models are therefore useful tools for estimating household overcrowding. In this study, we aimed to estimate household overcrowding in Africa between 2000 and 2018 by combining available household survey data, population censuses, and other country-specific household surveys within a geostatistical framework.

Methods: We used data from household surveys and population censuses to generate a Bayesian geostatistical model of household overcrowding in Africa for the 19-year period between 2000 and 2018. Additional sociodemographic and health-related covariates informed the model, which covered 54 African countries.

Findings: We analysed 287 surveys and population censuses, covering 78 695 991 households. Spatial and temporal variability arose in household overcrowding estimates over time. 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 the north of Africa region (16% [14-19]). Overall, 474·4 million (95% uncertainty interval [UI] 250·1 million-740·7 million) people were estimated to be living in overcrowded conditions in Africa in 2018, a 62·7% increase from the estimated 291·5 million (180·8 million-417·3 million) people who lived in overcrowded conditions in the year 2000. 48·5% (229·9 million) of people living in overcrowded conditions came from six African countries (Nigeria, Ethiopia, Democratic Republic of the Congo, Sudan, Uganda, and Kenya), with a combined population of 538·3 million people.

Interpretation: This study incorporated survey and population censuses data and used geostatistical modelling to estimate continent-wide overcrowding over a 19-year period. Our analysis identified countries and areas with high numbers of people living in overcrowded conditions, thereby providing a benchmark for policy planning and the implementation of interventions such as in infectious disease control.

Funding: UK Department of Health and Social Care, Wellcome Trust, Bill & Melinda Gates Foundation.

Figure 1

Box and whisker plots of household overcrowding comparisons across Africa by region Median range and IQR of household overcrowding in Africa by modelling regions for the year 2000 (shown in red) and 2018 (shown in blue).

Figure 2

The proportion of overcrowded households in low-income and middle-income countries within Africa, 2018 Modelled estimates are shown by national-level aggregation (A), state (level 1) administrative divisions (B), district (level 2) administrative divisions (C), and 5 × 5 km pixels (D). Pixels (1 × 1 km resolution) with a total population density fewer than ten individuals per 1 × 1 km pixel are shown in grey.

Figure 3

The change in proportion of overcrowded households within Africa from 2000 to 2018 Pixels (1 × 1 km resolution) with a total population density fewer than ten individuals per 1 × 1 km pixel are shown in grey.

Figure 4

Within-country variation in household overcrowding in 2000 and 2018 (A) Bars show the range in household overcrowding within each country. Grey bars represent estimates for the year 2000, and coloured bars represent estimates for 2018. Black dots represent the mean proportions for household overcrowding for each country. (B) Bars show the mean relative deviation of household overcrowding in administrative level 2 (districts) from the national level household overcrowding estimates. Grey bars represent estimates for the year 2000, and coloured bars represent estimates for 2018. The 2018 colours are based on the country's region, and countries are ordered (along the x-axis) on the basis of mean overcrowding proportions in the year 2018 (low to high). Countries are labelled using International Organization for Standardization (ISO) codes. AGO=Angola. BEN=Benin. BDI=Burundi. BFA=Burkina Faso. BWA=Botswana. CAF=Central African Republic. CIV=Cote d'Ivore. CMR=Cameroon. COD=Congo (the Demographic Republic of the). COG=Congo. COM=Comoros. CPV=Cabo Verde. DJI=Djibouti. DZA=Algeria. EGY=Egypt. ERI=Eritrea. ESH=Western Sahara. ETH=Ethiopia. GAB=Gabon. GHA=Ghana. GIN=Guinea. GMB=Gambia. GNB=Guinea Bissau. GNQ=Equatorial Guinea. KEN=Kenya. LBR=Liberia. LBY=Libya. LSO=Lesotho. MAR=Morocco. MDG=Madagascar. MLI=Mali. MOZ=Mozambique. MRT=Mauritania. MWI=Malawi. NAM=Namibia. NER=Niger. NGA=Nigeria. RWA=Rwanda. STP=Sao Tome and Principe. TGO=Togo. SDN=Sudan. SEN=Senegal. SLE=Sierra Leone. SOM=Somalia. SSD=South Sudan. SWZ=Eswatini. TCD=Chad. TUN=Tunisia. TZA=Tanzania, the United Republic of. UGA=Uganda. ZAF=South Africa. ZMB=Zambia. ZWE=Zimbabwe.

Figure 5

Population counts of people living in overcrowded conditions in Africa, 2018 Modelled estimates are shown by national level aggregation (A), state (level 1) administrative divisions (B), district (level 2) administrative divisions (C), and 5 × 5 km pixels (D). Pixels (1 × 1 km resolution) with a total population density fewer than ten individuals per 1 × 1 km pixel are shown in grey.