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Background Housing is essential to human well-being but neglected in global health. Today, housing in Africa is rapidly improving alongside economic development, creating an urgent need to understand how these changes can benefit health. We hypothesised that improved housing is associated with better health in children living in sub-Saharan Africa (SSA). We conducted a cross-sectional analysis of housing conditions relative to a range of child health outcomes in SSA. Methods and findings Cross-sectional data were analysed for 824,694 children surveyed in 54 Demographic and Health Surveys, 21 Malaria Indicator Surveys, and two AIDS Indicator Surveys conducted in 33 countries between 2001 and 2017 that measured malaria infection by microscopy or rapid diagnostic test (RDT), diarrhoea, acute respiratory infections (ARIs), stunting, wasting, underweight, or anaemia in children aged 0–5 years. The mean age of children was 2.5 years, and 49.7% were female. Housing was categorised into a binary variable based on a United Nations definition comparing improved housing (with improved drinking water, improved sanitation, sufficient living area, and finished building materials) versus unimproved housing (all other houses). Associations between house type and child health outcomes were determined using conditional logistic regression within surveys, adjusting for prespecified covariables including age, sex, household wealth, insecticide-treated bed net use, and vaccination status. Individual survey odds ratios (ORs) were pooled using random-effects meta-analysis. Across surveys, improved housing was associated with 8%–18% lower odds of all outcomes except ARI (malaria infection by microscopy: adjusted OR [aOR] 0.88, 95% confidence intervals [CIs] 0.80–0.97, p = 0.01; malaria infection by RDT: aOR 0.82, 95% CI 0.77–0.88, p < 0.001; diarrhoea: aOR 0.92, 95% CI 0.88–0.97, p = 0.001; ARI: aOR 0.96, 95% CI 0.87–1.07, p = 0.49; stunting: aOR 0.83, 95% CI 0.77–0.88, p < 0.001; wasting: aOR 0.90, 95% CI 0.83–0.99, p = 0.03; underweight: aOR 0.85, 95% CI 0.80–0.90, p < 0.001; any anaemia: aOR 0.87, 95% CI 0.82–0.92, p < 0.001; severe anaemia: aOR 0.89, 95% CI 0.84–0.95, p < 0.001). In comparison, insecticide-treated net use was associated with 16%–17% lower odds of malaria infection (microscopy: aOR 0.83, 95% CI 0.78–0.88, p < 0.001; RDT: aOR 0.84, 95% CI 0.79–0.88, p < 0.001). Drinking water source and sanitation facility alone were not associated with diarrhoea. The main study limitations are the use of self-reported diarrhoea and ARI, as well as potential residual confounding by socioeconomic position, despite adjustments for household wealth and education. Conclusions In this study, we observed that poor housing, which includes inadequate drinking water and sanitation facility, is associated with health outcomes known to increase child mortality in SSA. Improvements to housing may be protective against a number of important childhood infectious diseases as well as poor growth outcomes, with major potential to improve children’s health and survival across SSA., In a cross-sectional analysis, Lucy S. Tusting and colleagues investigate housing conditions and child health outcomes in sub-Saharan Africa., Author summary Why was this study done? Children’s health has improved substantially in sub-Saharan Africa since 2000, but mortality among children under 5 years old is still 14 times higher than among children living in developed regions. Adequate housing is essential to human well-being but is neglected in public health. Today African housing is rapidly changing alongside economic development, presenting a major opportunity for health. However, to realise this potential, we urgently need to understand better how housing conditions affect children’s health. What did the researchers do and find? We assessed whether improved housing is associated with a lower risk of five major causes of death in African children: malaria, diarrhoea, acute respiratory infection, growth failure, and anaemia. We performed a cross-sectional analysis using data from 33 African countries collected in 77 national surveys conducted between 2001 and 2017. We analysed data from 824,694 children aged 0–5 years living in 468,471 households. Children living in housing with improved drinking water and sanitation, sufficient living area, and durable construction had a reduced likelihood of four major causes of death: malaria (12%–18% reduction), diarrhoea (8% reduction), growth failure (stunting: 17% reduction, wasting: 10% reduction, underweight: 15% reduction), and anaemia (11%–13% reduction) after accounting for differences in levels of urbanisation, poverty, vaccinations, and other public health interventions. However, there was no detectable relationship between housing conditions and acute respiratory infection. As a comparison with mainstream health interventions, children sleeping under insecticide-treated bed nets were 16%–17% less likely to be infected with malaria compared with children not sleeping under bed nets, but the quality of drinking water and sanitation alone were not associated with any reduction in diarrhoea. What do these findings mean? Improvements to housing may help to reduce the burden of disease among children in sub-Saharan Africa alongside wider poverty reduction. Housing improvements may have a similar effect on malaria to insecticide-treated bed nets. Community-level experimental studies are needed to replicate our findings in different African settings. The main limitation of this study was the use of observational data, which means that it is not possible to fully account for factors that may confound the relationship between housing and health, such as household wealth.

Lorenz von Seidlein and colleagues discuss improving house designs in rural Africa to benefit health.

Background Long-lasting insecticidal nets (LLINs) and indoor residual spraying of insecticide (IRS) are the primary vector control interventions used to prevent malaria in Africa. Although both interventions are effective in some settings, high-quality evidence is rarely available to evaluate their effectiveness following deployment by a national malaria control program. In Uganda, we measured changes in key malaria indicators following universal LLIN distribution in three sites, with the addition of IRS at one of these sites. Methods and Findings Comprehensive malaria surveillance was conducted from October 1, 2011, to March 31, 2016, in three sub-counties with relatively low (Walukuba), moderate (Kihihi), and high transmission (Nagongera). Between 2013 and 2014, universal LLIN distribution campaigns were conducted in all sites, and in December 2014, IRS with the carbamate bendiocarb was initiated in Nagongera. High-quality surveillance evaluated malaria metrics and mosquito exposure before and after interventions through (a) enhanced health-facility-based surveillance to estimate malaria test positivity rate (TPR), expressed as the number testing positive for malaria/number tested for malaria (number of children tested for malaria: Walukuba = 42,833, Kihihi = 28,790, and Nagongera = 38,690); (b) cohort studies to estimate the incidence of malaria, expressed as the number of episodes per person-year [PPY] at risk (number of children observed: Walukuba = 340, Kihihi = 380, and Nagongera = 361); and (c) entomology surveys to estimate household-level human biting rate (HBR), expressed as the number of female Anopheles mosquitoes collected per house-night of collection (number of households observed: Walukuba = 117, Kihihi = 107, and Nagongera = 107). The LLIN distribution campaign substantially increased LLIN coverage levels at the three sites to between 65.0% and 95.5% of households with at least one LLIN. In Walukuba, over the 28-mo post-intervention period, universal LLIN distribution was associated with no change in the incidence of malaria (0.39 episodes PPY pre-intervention versus 0.20 post-intervention; adjusted rate ratio [aRR] = 1.02, 95% CI 0.36–2.91, p = 0.97) and non-significant reductions in the TPR (26.5% pre-intervention versus 26.2% post-intervention; aRR = 0.70, 95% CI 0.46–1.06, p = 0.09) and HBR (1.07 mosquitoes per house-night pre-intervention versus 0.71 post-intervention; aRR = 0.41, 95% CI 0.14–1.18, p = 0.10). In Kihihi, over the 21-mo post-intervention period, universal LLIN distribution was associated with a reduction in the incidence of malaria (1.77 pre-intervention versus 1.89 post-intervention; aRR = 0.65, 95% CI 0.43–0.98, p = 0.04) but no significant change in the TPR (49.3% pre-intervention versus 45.9% post-intervention; aRR = 0.83, 95% 0.58–1.18, p = 0.30) or HBR (4.06 pre-intervention versus 2.44 post-intervention; aRR = 0.71, 95% CI 0.30–1.64, p = 0.40). In Nagongera, over the 12-mo post-intervention period, universal LLIN distribution was associated with a reduction in the TPR (45.3% pre-intervention versus 36.5% post-intervention; aRR = 0.82, 95% CI 0.76–0.88, p < 0.001) but no significant change in the incidence of malaria (2.82 pre-intervention versus 3.28 post-intervention; aRR = 1.10, 95% 0.76–1.59, p = 0.60) or HBR (41.04 pre-intervention versus 20.15 post-intervention; aRR = 0.87, 95% CI 0.31–2.47, p = 0.80). The addition of three rounds of IRS at ~6-mo intervals in Nagongera was followed by clear decreases in all outcomes: incidence of malaria (3.25 pre-intervention versus 0.63 post-intervention; aRR = 0.13, 95% CI 0.07–0.27, p < 0.001), TPR (37.8% pre-intervention versus 15.0% post-intervention; aRR = 0.54, 95% CI 0.49–0.60, p < 0.001), and HBR (18.71 pre-intervention versus 3.23 post-intervention; aRR = 0.29, 95% CI 0.17–0.50, p < 0.001). High levels of pyrethroid resistance were documented at all three study sites. Limitations of the study included the observational study design, the lack of contemporaneous control groups, and that the interventions were implemented under programmatic conditions. Conclusions Universal distribution of LLINs at three sites with varying transmission intensity was associated with modest declines in the burden of malaria for some indicators, but the addition of IRS at the highest transmission site was associated with a marked decline in the burden of malaria for all indicators. In highly endemic areas of Africa with widespread pyrethroid resistance, IRS using alternative insecticide formulations may be needed to achieve substantial gains in malaria control., In this prospective observational study, Grant Dorsey and colleagues measure changes in malaria burden after long-lasting insecticidal net distribution and indoor residual spraying at three sites of in Uganda., Author Summary Why Was This Study Done? Long-lasting insecticidal nets (LLINs), which prevent mosquitoes from biting people while they sleep, and indoor residual spraying of insecticides (IRS) in houses, which prevents mosquitoes from resting in houses, are the main tools used to prevent malaria in Africa. Although LLINs and IRS have been shown to be effective, changes in the behavior of mosquitoes and people as well as the emergence of mosquitoes resistant to insecticides could compromise the benefits of these interventions. Recently the government of Uganda distributed free LLINs throughout the country and began IRS in selected areas. In this “real world” setting, it is important to monitor for changes in the burden of malaria following the scale-up of LLIN distribution and IRS. What Did the Researchers Do and Find? The researchers conducted comprehensive malaria surveillance between October 1, 2011, and March 31, 2016, at three sites in Uganda that differed in the intensity of malaria transmission. Between 2013 and 2014, LLINs were distributed to the entire population at all three sites, and in December 2014, IRS with an insecticide different from that used in the LLINs was started in the site with the highest level of malaria transmission. The researchers found that following LLIN distribution, there were only modest declines in some measures of malaria burden at the three sites, and no changes in other measures. In contrast, following the addition of IRS at the highest transmission site, all measures of malaria burden declined dramatically. The research also documented a high level of resistance to the type of insecticide used in LLINS at all three sites. What Do These Findings Mean? These findings suggest that in countries like Uganda, which has a heavy burden of malaria, LLINs alone may not be adequate to substantially drive down the burden of malaria, and the addition of IRS using a different insecticide may be needed to have a major impact. However, it should be noted that IRS is more expensive and harder to implement than distributing LLINs, and generally needs to be repeated every 6–12 months to have a sustained effect.

The Malaria Eradication Research Agenda (malERA) Consultative Group on Vector Control outline the research needed to ensure vector control at every stage of malaria eradication., Different challenges are presented by the variety of malaria transmission environments present in the world today. In each setting, improved control for reduction of morbidity is a necessary first step towards the long-range goal of malaria eradication and a priority for regions where the disease burden is high. For many geographic areas where transmission rates are low to moderate, sustained and well-managed application of currently available tools may be sufficient to achieve local elimination. The research needs for these areas will be to sustain and perhaps improve the effectiveness of currently available tools. For other low-to-moderate transmission regions, notably areas where the vectors exhibit behaviours such as outdoor feeding and resting that are not well targeted by current strategies, new interventions that target predictable features of the biology/ecologies of the local vectors will be required. To achieve elimination in areas where high levels of transmission are sustained by very efficient vector species, radically new interventions that significantly reduce the vectorial capacity of wild populations will be needed. Ideally, such interventions should be implemented with a one-time application with a long-lasting impact, such as genetic modification of the vectorial capacity of the wild vector population.