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Background National rates of COVID-19 infection and fatality have varied dramatically since the onset of the pandemic. Understanding the conditions associated with this cross-country variation is essential to guiding investment in more effective preparedness and response for future pandemics. MethodsDaily SARS-CoV-2 infections and COVID-19 deaths for 177 countries and territories and 181 subnational locations were extracted from the Institute for Health Metrics and Evaluation's modelling database. Cumulative infection rate and infection-fatality ratio (IFR) were estimated and standardised for environmental, demographic, biological, and economic factors. For infections, we included factors associated with environmental seasonality (measured as the relative risk of pneumonia), population density, gross domestic product (GDP) per capita, proportion of the population living below 100 m, and a proxy for previous exposure to other betacoronaviruses. For IFR, factors were age distribution of the population, mean body-mass index (BMI), exposure to air pollution, smoking rates, the proxy for previous exposure to other betacoronaviruses, population density, age-standardised prevalence of chronic obstructive pulmonary disease and cancer, and GDP per capita. These were standardised using indirect age standardisation and multivariate linear models. Standardised national cumulative infection rates and IFRs were tested for associations with 12 pandemic preparedness indices, seven health-care capacity indicators, and ten other demographic, social, and political conditions using linear regression. To investigate pathways by which important factors might affect infections with SARS-CoV-2, we also assessed the relationship between interpersonal and governmental trust and corruption and changes in mobility patterns and COVID-19 vaccination rates. Findings The factors that explained the most variation in cumulative rates of SARS-CoV-2 infection between Jan 1, 2020, and Sept 30, 2021, included the proportion of the population living below 100 m (5•4% [4•0-7•9] of variation), GDP per capita (4•2% [1•8-6•6] of variation), and the proportion of infections attributable to seasonality (2•1% [95% uncertainty interval 1•7-2•7] of variation). Most cross-country variation in cumulative infection rates could not be explained. The factors that explained the most variation in COVID-19 IFR over the same period were the age profile of the country (46•7% [18•4-67•6] of variation), GDP per capita (3•1% [0•3-8•6] of variation), and national mean BMI (1•1% [0•2-2•6] of variation). 44•4% (29•2-61•7) of cross-national variation in IFR could not be explained. Pandemic-preparedness indices, which aim to measure health security capacity, were not meaningfully associated with standardised infection rates or IFRs. Measures of trust in the government and interpersonal trust, as well as less government corruption, had larger, statistically significant associations with lower standardised infection rates. High levels of government and interpersonal trust, as wel...

BackgroundThe COVID-19 pandemic has overwhelmed health systems in both developed and developing nations alike. Africa has one of the weakest health systems globally, but there is limited evidence on how the region is prepared for, impacted by and responded to the pandemic.MethodsWe conducted a scoping review of PubMed, Scopus, CINAHL to search peer-reviewed articles and Google, Google Scholar and preprint sites for grey literature. The scoping review captured studies on either preparedness or impacts or responses associated with COVID-19 or covering one or more of the three topics and guided by Arksey and O’Malley’s methodological framework. The extracted information was documented following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension checklist for scoping reviews. Finally, the resulting data were thematically analysed.ResultsTwenty-two eligible studies, of which 6 reported on health system preparedness, 19 described the impacts of COVID-19 on access to general and essential health services and 7 focused on responses taken by the healthcare systems were included. The main setbacks in health system preparation included lack of available health services needed for the pandemic, inadequate resources and equipment, and limited testing ability and surge capacity for COVID-19. Reduced flow of patients and missing scheduled appointments were among the most common impacts of the COVID-19 pandemic. Health system responses identified in this review included the availability of telephone consultations, re-purposing of available services and establishment of isolation centres, and provisions of COVID-19 guidelines in some settings.ConclusionsThe health systems in Africa were inadequately prepared for the pandemic, and its impact was substantial. Responses were slow and did not match the magnitude of the problem. Interventions that will improve and strengthen health system resilience and financing through local, national and global engagement should be prioritised.