Why is the number of COVID-19 cases lower than expected in Sub-Saharan Africa? A cross-sectional analysis of the role of demographic and geographic factors.

Unlike initially predicted by WHO, the severity of the novel coronavirus pandemic has remained relatively low in Sub-Saharan Africa, more than two months after the first confirmed cases were identified. In this paper, we analyze the extent to which demographic and geographic factors associated to the disease explain this phenomenon. We use publicly available data from a cross-section of 182 countries worldwide, and we employ a regression analysis that accounts for possible misreporting of COVID-19 cases, as well as a Ramsey-type specification that preserves degree of freedom. We found that proportion of population aged 65+, population density, and urbanization are significantly positively associated with high numbers of active infected cases, while mean temperature around the first quarter (January-March) is negatively associated to this COVID-19 outcome. These factors are those for which Africa has a comparative advantage. In contrast, factors for which Africa has a relative disadvantage, such as income and quality of health care infrastructure, are found to be insignificant predictors of the spread of the pandemic. These results hold even when accounting for possible underreporting, as well as differences in the duration of the epidemic in each country, as measured by the time elapsed since the first confirmed case occurred. We conclude that differences in demographic and geographic characteristics help understand the relatively low progression of the pandemic in sub-Saharan Africa as well as the gap in the number of active cases between this region and the rest of the World. We also found, however, that this gap is insignificant beyond these factors, and is expected to narrow over time as the pandemic evolves. These results provide insights for relevant urban policies and kinds of development planning to consider in the fight against disease spreads of the coronavirus type.

Early dynamics of transmission and projections of COVID-19 in some West African countries.

The initial cases of novel coronavirus (COVID-19) were identified in most West African countries between late February and early March of 2020. But it is only after March 15, 2020 that the number of cases started rising significantly in these countries. This study analyzes the transmission dynamics of the outbreak in West Africa nearly 5 months after the effective onset. We focus on Cameroon, Ghana, Guinea and Nigeria, which are the four West African countries with the highest numbers of infected cases. We combine models of mathematical epidemiology and publicly available data to estimate the main disease transmission characteristics. In particular, we estimate the initial doubling time, the peak time, the peak rate, the final size and the short-term transmission forecasts of the COVID-19 epidemic for these countries. Policy implications for the effectiveness of control measures and for assessing the potential impact on public health in West Africa are discussed.

The effect of a performance-based financing program on HIV and maternal/child health services in Mozambique-an impact evaluation.

Performance-based financing (PBF) is a mechanism by which health providers are paid on the basis of outputs or results delivered. A PBF program was implemented on the provision of HIV, prevention of mother-to child HIV transmission (PMTCT), and maternal/child health (MCH) services in two provinces of Mozambique. A retrospective case-control study design was used in which PBF provinces were matched with control provinces to evaluate the impact of PBF on 18 indicators. Due to regional heterogeneity, we evaluated the intervention sites (North and South) separately. Beginning January 2011, 11 quarters (33 months or 2.75 years) of data from 134 facilities after matching (84 in the North and 50 in the South) were used. Our econometric framework employed a multi-period, multi-group difference-in-differences model on data that was matched using propensity scoring. The regression design employed a generalized linear mixed model with both fixed and random effects, fitted using the seemingly unrelated regression technique. PBF resulted in positive impacts on MCH, PMTCT and paediatric HIV program outcomes. The majority of the 18 indicators responded to PBF (77% in the North and 66% in the South), with at least half of the indicators demonstrating a statistically significant increase in average output of more than 50% relative to baseline. Excluding pregnant women, the majority of adult HIV treatment indicators did not respond to PBF. On average, it took 18 months (six quarters) of implementation for PBF to take effect, and impact was generally sustained thereafter. Indicators were not sensitive to price, but were inversely correlated to the level of effort associated with marginal output. No negative impacts on incentivized indicators nor spill-over effects on non-incentivized indicators were observed. The PBF program in Mozambique has produced large, sustained increases in the provision of PMTCT, paediatric HIV and MCH services. Our results demonstrate that PBF is an effective strategy for driving down the HIV epidemic and advancing MCH care service delivery as compared with input financing alone.