COVID-19 is an active pandemic that likely poses an existential threat to humanity. Frequent handwashing, social distancing, and partial or total lockdowns are among the suite of measures prescribed by the World Health Organization (WHO) and being implemented across the world to contain the pandemic. However, existing inequalities in access to certain basic necessities of life (water, sanitation facility, and food storage) create layered vulnerabilities to COVID-19 and can render the preventive measures ineffective or simply counterproductive. We hypothesized that individuals in households without any of the named basic necessities of life are more likely to violate the preventive (especially lockdown) measures and thereby increase the risk of infection or aid the spread of COVID-19. Based on nationally-representative data for 25 sub-Saharan African (SSA) countries, multivariate statistical and geospatial analyses were used to investigate whether, and to what extent, household family structure is associated with in-house access to basic needs which, in turn, could reflect on a higher risk of COVID-19 infection. The results indicate that approximately 46% of the sampled households in these countries (except South Africa) did not have in-house access to any of the three basic needs and about 8% had access to all the three basic needs. Five countries had less than 2% of their households with in-house access to all three basic needs. Ten countries had over 50% of their households with no in-house access to all the three basic needs. There is a social gradient in in-house access between the rich and the poor, urban and rural richest, male- and female-headed households, among others. We conclude that SSA governments would need to infuse innovative gender- and age-sensitive support services (such as water supply, portable sanitation) to augment the preventive measures prescribed by the WHO. Short-, medium- and long-term interventions within and across countries should necessarily address the upstream, midstream and downstream determinants of in-house access and the full spectrum of layers of inequalities including individual, interpersonal, institutional, and population levels.

ObjectiveThis study sought to investigate the joint effect of household cooking fuel type and urbanicity (rural–urban residency) on anaemia among children under the age of five in sub-Saharan Africa.DesignWe analysed cross-sectional data of 123, 186 children under the age of five from 29 sub-Saharan African countries gathered between 2010 and 2019 by the Demographic and Health Survey programme. Bivariate (χ2 test of independence) and multilevel logistic regression were used to examine the effect of urbanicity-household cooking fuel type on childhood anaemia. Results were reported as adjusted odds ratios (aORs) with 95% CIs at p<0.05.Outcome measuresAnaemia status of children.ResultsMore than half (64%) of children had anaemia. The percentage of children who suffered from anaemia was high in those born to mothers in Western Africa (75%) and low among those born in Southern Africa (54%). Children from rural households that depend on unclean cooking fuels (aOR=1.120; 95% CI 1.033 to 1.214) and rural households that depend on clean cooking fuels (aOR=1.256; 95% CI 1.080 to 1.460) were more likely to be anaemic as compared with children from urban households using clean cooking fuel. Child’s age, sex of child, birth order, perceived birth size, age of mother, body mass index of mother, education, marital status, employment status, antenatal care, wealth quintile, household size, access to electricity, type of toilet facility, source of drinking water and geographic region had significant associations with childhood anaemia status.ConclusionsOur study has established a joint effect of type of household cooking fuel and urbanicity on anaemia among children under the age of five in sub-Saharan Africa. It is therefore critical to promote the usage of clean cooking fuels among households and women in rural areas. These should be done taking into consideration the significant child, maternal, household, and contextual factors identified in this study.

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Introduction The coronavirus 2019 (COVID-19) has overwhelmed the health systems of several countries, particularly those within the African region. Notwithstanding, the relationship between health systems and the magnitude of COVID-19 in African countries have not received research attention. In this study, we investigated the relationship between the pervasiveness of the pandemic across African countries and their Global Health Security Index (GHSI) scores. Materials and methods The study included 54 countries in five regions viz Western (16); Eastern (18); Middle (8); Northern (7); and Southern (5) Africa. The outcome variables in this study were the total confirmed COVID-19 cases (per million); total recoveries (per million); and the total deaths (per million). The data were subjected to Spearman’s rank-order (Spearman’s rho) correlation to determine the monotonic relationship between each of the predictor variables and the outcome variables. The predictor variables that showed a monotonic relationship with the outcome were used to predict respective outcome variables using multiple regressions. The statistical analysis was conducted at a significance level of 0.05. Results Our results indicate that total number of COVID-19 cases (per million) has strong correlations (rs >0.5) with the median age; aged 65 older; aged 70 older; GDP per capita; number of hospital beds per thousand; Human Development Index (HDI); recoveries (per million); and the overall risk environment of a country. All these factors including the country’s commitments to improving national capacity were related to the total number of deaths (per million). Also, strong correlations existed between the total recoveries (per million) and the total number of positive cases; total deaths (per million); median age; aged 70 older; GDP per capita; the number of hospital beds (per thousand); and HDI. The fitted regression models showed strong predictive powers (R-squared>99%) of the variances in the total number of COVID-19 cases (per million); total number of deaths (per million); and the total recoveries (per million). Conclusions The findings from this study suggest that patient-level characteristics such as ageing population (i.e., 65+), poverty, underlying co-morbidities–cardiovascular disease (e.g., hypertension), and diabetes through unhealthy behaviours like smoking as well as hospital care (i.e., beds per thousand) can help explain COVID-19 confirmed cases and mortality rates in Africa. Aside from these, other determinants (e.g., population density, the ability of detection, prevention and control) also affect COVID-19 prevalence, deaths and recoveries within African countries and sub-regions.

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