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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/8911| The Predictors of COVID-19 Case Fatalities in Nigerian Health Systems: A Secondary Data Analysis | |
| Adewale Akinjeji Dr. Remi Oladigbolu Adetunji Adedokun Ogonna Obudulu Franklin Emerenini | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://doi.org/10.1101/2024.06.01.24308316 | |
| https://www.medrxiv.org/content/10.1101/2024.06.01.24308316v1 | |
| Background COVID-19, caused by the novel SARS-CoV-2 is the worst catastrophe in this century that affected more than 800 million people and caused more than 7 million deaths. During the pandemic, the burden of COVID-19 increased significantly, posing a threat to public health infrastructure, testing protocols, national healthcare capacity, and disease control measures. To assess the impacts of the Nigerian Health Systems on COVID-19 fatalities, the researchers evaluated the association between healthcare system capability and mortality rate of COVID-19 patients through adjustments for healthcare spending as a proportion of the GDP, population density, and the proportion of the population that are 65 years and above across the 36 States and Abuja, FCT.
Methods The study utilized secondary data abstracted from the World Bank records, Worldometer, and Post-Pandemic Health Financing by the States in Nigeria (2020 to 2022). It used data from the 36 States of the country and the FCT, Abuja. The dependent variable was COVID-19 case fatality (Case Fatality Rate across the study areas), the predictor variable was Healthcare Capacity Index (aggregate of number of doctors/nurses/midwives/hospital bed space per 1,000 population categorized into low, middle, and high Healthcare Capacity index), and the covariates were population density, health expenditure as a percentage of GDP, and the proportion of the population that are 65 years and above. A negative binomial regression model was used to assess the predictors of case fatality after adjusting for other covariates at an alpha of <0.05 and 95% confidence interval.
Results Almost half of the States in Nigeria were in the middle Healthcare Capacity Index 16 (43.2%) with only 7 (18.9%) in the high Healthcare Capacity Index (HCI). The regression analysis shows that HCI was a predictor of COVID-19 case fatality as the States with high HCI compared with low HCI were 9.4 times more likely to have lower COVID-19 case fatalities (AOR=0.106, p=0.063, 95% CI[0.010-1.131]), and those with middle HCI compared with low were 6.4 times more likely to have lower COVID-19 case fatality (aOR=0.156, p=0.006, 95% CI [0.041-0.593]). Although States with a higher proportion of the population that were 65 years and above were about 2 times more likely to have higher COVID-19 case fatality (aOR 1.99, p=0.154, 95% CI [0.771-5.172]), this was not statistically significant due to the small sample size (37 States)
Conclusion The research further buttressed the pivotal role that effective multidimensional healthcare capacity is a pertinent strategy to mitigate future case fatalities from Public Health Events of International Concerns (PHEICs). | |
| bioRxiv | |
| 03-06-2024 | |
| Preimpreso | |
| Inglés | |
| Público en general | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Materiales de Consulta y Comunicados Técnicos |
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