The spread of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has been unprecedented in its speed and effects. Interruption of its transmission to prevent widespread community transmission is critical because its effects go beyond the number of COVID-19 cases and deaths and affect the health system capacity to provide other essential services. Highlighting the implications of such a situation, the predictions presented here are derived using a Markov chain model, with the transition states and country specific probabilities derived based on currently available knowledge. A risk of exposure, and vulnerability index are used to make the probabilities country specific. The results predict a high risk of exposure in states of small size, together with Algeria, South Africa and Cameroon. Nigeria will have the largest number of infections, followed by Algeria and South Africa. Mauritania would have the fewest cases, followed by Seychelles and Eritrea. Per capita, Mauritius, Seychelles and Equatorial Guinea would have the highest proportion of their population affected, while Niger, Mauritania and Chad would have the lowest. Of the World Health Organization's 1 billion population in Africa, 22% (16%–26%) will be infected in the first year, with 37 (29 – 44) million symptomatic cases and 150 078 (82 735–189 579) deaths. There will be an estimated 4.6 (3.6–5.5) million COVID-19 hospitalisations, of which 139 521 (81 876–167 044) would be severe cases requiring oxygen, and 89 043 (52 253–106 599) critical cases requiring breathing support. The needed mitigation measures would significantly strain health system capacities, particularly for secondary and tertiary services, while many cases may pass undetected in primary care facilities due to weak diagnostic capacity and non-specific symptoms. The effect of avoiding widespread and sustained community transmission of SARS-CoV-2 is significant, and most likely outweighs any costs of preventing such a scenario. Effective containment measures should be promoted in all countries to best manage the COVID-19 pandemic.

The study suggests that there is a financial barrier created by cost-sharing that decreases access to services, especially among the poor in Uganda. However, further studies are needed to clarify issues of utilization by age and gender.

Background: It has been argued that quality improvements that result from user charges reduce their negative impact on utilization especially of the poor. In Uganda, because there was no concrete evidence for improvements in quality of care following the introduction of user charges, the government abolished user fees in all public health units on 1 st March 2001. This gave us the opportunity to prospectively study how different aspects of quality of care change, as a country changes its health financing options from user charges to free services, in a developing country setting. The outcome of the study may then provide insights into policy actions to maintain quality of care following removal of user fees.

No abstract

Background: Innovative strategies such as digital health are needed to ensure attainment of the ambitious universal health coverage in Africa. However, their successful deployment on a wider scale faces several challenges on the continent. This article reviews the key benefits and challenges associated with the application of digital health for universal health coverage and propose a conceptual framework for its wide scale deployment in Africa.Discussion: Digital health has several benefits. These include; improving access to health care services especially for those in hard-to-reach areas, improvements in safety and quality of healthcare services and products, improved knowledge and access of health workers and communities to health information; cost savings and efficiencies in health services delivery; and improvements in access to the social, economic and environmental determinants of health, all of which could contribute to the attainment of universal health coverage. However, digital health deployment in Africa is constrained by challenges such as poor coordination of mushrooming pilot projects, weak health systems, lack of awareness and knowledge about digital health, poor infrastructure such as unstable power supply, poor internet connectivity and lack of interoperability of the numerous digital health systems. Contribution of digital health to attainment of universal health coverage requires the presence of elements such as resilient health system, communities and access to the social and economic determinants of health.Conclusion: Further evidence and a conceptual framework are needed for successful and sustainable deployment of digital health for universal health coverage in Africa.

The move towards universal health coverage is premised on having well-functioning health systems, which can assure provision of the essential health and related services people need. Efforts to define ways to assess functionality of health systems have however varied, with many not translating into concrete policy action and influence on system development. We present an approach to provide countries with information on the functionality of their systems in a manner that will facilitate movement towards universal health coverage. We conceptualise functionality of a health system as being a construct of four capacities: access to, quality of, demand for essential services and its resilience to external shocks. We test and confirm the validity of these capacities as appropriate measures of system functionality. We thus provide results for functionality of the 47 countries of the WHO African Region based on this. The functionality of health systems ranges from 34.4 to 75.8 on a 0–100 scale. Access to essential services represents the lowest capacity in most countries of the region, specifically due to poor physical access to services. Funding levels from public and out-of-pocket sources represent the strongest predictors of system functionality, compared with other sources. By focusing on the assessment on the capacities that define system functionality, each country has concrete information on where it needs to focus, in order to improve the functionality of its health system to enable it respond to current needs including achieving universal health coverage, while responding to shocks from challenges such as the 2019 coronavirus disease. This systematic and replicable approach for assessing health system functionality can provide the guidance needed for investing in country health systems to attain universal health coverage goals.

Countries of the World Health Organization (WHO) African Region have experienced a wide range of coronavirus disease 2019 (COVID-19) epidemics. This study aimed to identify predictors of the timing of the first COVID-19 case and the per capita mortality in WHO African Region countries during the first and second pandemic waves and to test for associations with the preparedness of health systems and government pandemic responses. Using a region-wide, country-based observational study, we found that the first case was detected earlier in countries with more urban populations, higher international connectivity and greater COVID-19 test capacity but later in island nations. Predictors of a high first wave per capita mortality rate included a more urban population, higher pre-pandemic international connectivity and a higher prevalence of HIV. Countries rated as better prepared and having more resilient health systems were worst affected by the disease, the imposition of restrictions or both, making any benefit of more stringent countermeasures difficult to detect. Predictors for the second wave were similar to the first. Second wave per capita mortality could be predicted from that of the first wave. The COVID-19 pandemic highlights unanticipated vulnerabilities to infectious disease in Africa that should be taken into account in future pandemic preparedness planning.

Purpose Nationwide analyses are required to optimise and tailor activities to control future COVID-19 waves of resurgence continent-wide. We compared epidemiological and clinical outcomes of the four COVID-19 waves in the Democratic Republic of Congo (DRC). Methods This retrospective descriptive epidemiological analysis included data from the national line list of confirmed COVID-19 cases in all provinces for all waves between 9 March 2020 and 2 January 2022. Descriptive statistical measures (frequencies, percentages, case fatality rates [CFR], test positivity rates [TPR], and characteristics) were compared using chi-squared or the Fisher–Irwin test. Results During the study period, 72,108/445,084 (16.2%) tests were positive, with 9,641/56,637 (17.0%), 16,643/66,560 (25.0%), 24,172/157,945 (15.3%), and 21,652/163,942 (13.2%) cases during the first, second, third, and fourth waves, respectively. TPR significantly decreased from 17.0% in the first wave to 13.2% in the fourth wave as did infection of frontline health workers (5.2% vs. 0.9%). CFR decreased from 5.1 to 0.9% from the first to fourth wave. No sex- or age-related differences in distributions across different waves were observed. The majority of cases were asymptomatic in the first (73.1%) and second (86.6%) waves, in contrast to that in the third (11.1%) and fourth (31.3%) waves. Conclusion Despite fewer reported cases, the primary waves (first and second) of the COVID-19 pandemic in the DRC were more severe than the third and fourth waves, with each wave being associated with a new SARS-CoV-2 variant. Tailored public health and social measures, and resurgence monitoring are needed to control future waves of COVID-19.