The impact of COVID-19 on health service utilization in sub-Saharan Africa – a scoping review
Bibliographic record
Abstract
Abstract Introduction The Coronavirus Disease 2019(COVID-19) pandemic has undoubtedly exposed weaknesses in health systems, especially in sub-Saharan Africa (SSA). Despite comparatively low rates of COVID-19 admissions and recorded deaths in SSA, the pandemic still had a significant impact on health service utilization (HSU). The aim of this scoping review is to synthesize the available evidence on HSU in SSA during the COVID-19 pandemic focusing on changes in HSU generally and amongst particular patient groups studied. Methods The Scoping review was guided by the methodological framework for conducting scoping reviews developed by Arskey and O’Malley. We identified relevant studies through a search of PubMed(MEDLINE), Embase, Scopus and Web of Science. We then provided a general descriptive overview of the extracted data focusing on the types of studies, patient groups and change in HSU. Results We identified 262 studies reporting on HSU in 39 countries in SSA. The median study duration was 364.5[IQR:89 - 730] days. Studies were mainly quantitative 192(73.3%) and retrospective 117(44.7%). The majority were multiple centre studies 163(62.2%), hospital-based 205 (78.2%) and in urban settings 121(46.2%). Median number of participants was 8329[IQR:103-5848] involving 62.7% females. Communicable diseases were the most studied disease category in 92(35.1%) and mainly among out-patients 92(34.2%). Change in HSU was reported in 249(95.0%) of studies with 221(84.4%) of studies reporting a decrease in HSU. The median decrease in HSU was 35.6% [IQR: 19.0-55.8] and median increase was 16.2% [IQR: 9.1-31.9]. HSU was mostly reported among maternal and child health patients 58(22.1%) and people living with Human immunodeficiency virus 32(12.2%). The patient groups with the highest percentage decrease and increase in HSU were cardiovascular diseases 68.0%[IQR:16.7-71.1] and surgical cases 38.3%[IQR 24.0-52.5] respectively. Conclusion HSU was generally reduced during the COVID-19 pandemic among different patient groups in SSA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.020 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".