ABSTRACT
Background Access to COVID 19 continues to be a significant challenge in most low- and middle-income countries. There have been longstanding health system barriers towards accessing services in these countries over time, with geographic barriers being significant particularly given majority of people live in rural areas with poor facility network coverage. There has been no systematic assessment of existing networks in addition to how they can be optimised. Methods Using Kenya as an example, we mapped facilities providing COVID immunization facilities in the country. We then used a cost distance algorithm to assess geographic access to these facilities using a model that adjusts for differences in travel speeds across differences in elevation, land use and travel roads. We then extracted populations living within 1 hour of these facilities as a proxy of access for the 47 counties in the country. Finally, we used the same cost distance parameters to select facilities that would be used to optimize selection of additional facilities that would move access to 80%. Results We identified 621 facilities that were administering COVID vaccines as of August 2021. The accessibility model highlighted that only 60% of the country’s population was living within 1 hour of these facilities. The scale up activity using the cost distance algorithm identified an additional 560 facilities that can be prioritised to move theoretical access to 80%. These were not evenly distributed in the country, with counties such as Kakamega, Mandera, Nakuru and Nandi having additional 30 facilities selected. Discussion We highlight that 2 in five adults in Kenya live more than one hour from the nearest COVID immunization facility and this may be a barrier towards achieving universal access. We also highlight where efforts to improve coverage by increasing cold chain services can be concentrated to improve access. Furthermore, results can be used to improve decision making on where other interventions like community campaigns can be actively pursued for universal coverage.
ABSTRACT
Background From May 2018 to the end of June 2022, the Democratic Republic of Congo (DRC) experienced six Ebola virus disease outbreaks within its borders. During the 10th EVD outbreak, the largest experienced in the DRC and the second largest and most prolonged EVD outbreak recorded globally, a WHO risk assessment identified nine countries bordering the DRC as moderate to high risk from cross border importation. Burundi, Rwanda, South Sudan and Uganda were classified as priority one countries while Angola, Central African Republic, Congo, Tanzania and Zambia as priority two. These countries implemented varying levels of Ebola virus disease preparedness interventions. This case study highlights the gains and shortfalls with the Ebola virus disease preparedness interventions against the background of a renewed and growing commitment for global epidemic preparedness highlighted during recent World Health Assembly events.Main text Several positive impacts from preparedness support to countries bordering the affected provinces in the DRC were identified, including development of sustained capacities which were leveraged upon to respond to the subsequent COVID-19 pandemic. Shortfalls such as lost opportunities in vertical approaches to response pillars such as surveillance, over dependence on external support and duplication of efforts especially in the areas of capacity building were also identified. A recurrent theme that emerged from this case study is the propensity towards implementing short-term interventions during Ebola virus disease outbreak preparedness and response rather than sustainable investment into strengthening systems for improved health security in alignment with IHR obligations and the Sustainable Development Goals.Conclusions Despite several international frameworks established at the global level for emergency preparedness, a shortfall exists between global policy and practice in countries at high risk of cross border transmission from persistent Ebola virus disease outbreaks in the Democratic Republic of Congo. With renewed global health commitment for country emergency preparedness resulting from the COVID-19 pandemic and cumulating in a resolution for a pandemic preparedness treaty, the time to review and address these gaps and provide recommendations for more sustainable approaches to emergency preparedness towards achieving global health security is now.
Subject(s)
COVID-19ABSTRACT
Background: WHO African Region countries have experienced very different COVID-19 epidemics. This study aimed to identify predictors for the timing of the first COVID-19 case and the per capita mortality rate during the first and second pandemic wave in the region, and to test for any impact of countermeasures.Methods: We performed a region-wide, country-based observational study. Data on COVID-19 cases and deaths for all 47 countries in the WHO African Region were obtained from the WHO COVID-19 Dashboard. A set of predictors classified to nine categories were collected and used as explanatory variables. We applied Cox proportional hazards regression models, generalized linear mixed models and multinomial logistic regression models as appropriate.Findings: Predictors for an earlier first case were a more urban population, high volume of international air travel and more land borders, and better COVID-19 test capacity. Predictors for a high per capita mortality rate during the first wave were a more urban population, more pre-pandemic international air travel and higher prevalence of HIV. The stringency and timing of government restrictions on behaviour were not associated with a lower per capita mortality rate in the first wave. A more urban population and a higher infectious disease resilience score were associated with more stringent restrictions and/or a higher per capita mortality rate in the first wave. The predictor set for the second wave was similar, and first wave per capita mortality predicted that in the second wave. These results were not altered when measures of national testing effort were included in the models.Interpretation: COVID-19 in Africa arrived earlier and caused greater mortality in countries with more international travel and a more urban population. Mortality was exacerbated by high HIV prevalence; it is not clear whether this is a direct or indirect effect. Countries that were better prepared and judged to have more resilient health systems were worst affected, both by the disease and by the imposition of restrictions. The COVID-19 pandemic highlights unanticipated vulnerabilities to infectious disease in Africa.Funding Statement: National Institute for Health Research, Darwin Trust of EdinburghDeclaration of Interests: The authors disclose no conflicts of interest.