The impact of COVID-19 on health service utilization in sub-Saharan Africa—a scoping review
Bibliographic record
Abstract
BACKGROUND: Despite comparatively low rates of COVID-19 admissions and recorded deaths in sub-Saharan Africa (SSA), the pandemic still had significant impact on health service utilization (HSU). The aim of this scoping review is to synthesize the available evidence of HSU in SSA during the pandemic, focusing on types of studies, changes in HSU compared with the pre-pandemic period, and changes among specific patient groups. METHODS: The scoping review was guided by the methodological framework for conducting scoping reviews developed by Arksey 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 SSA countries. Studies were mainly quantitative (192; 73.3%), involving multiple centers (163; 62.2%), conducted in hospitals (205; 78.2%), and in urban settings (121; 46.2%). The median number of participants was 836.5 (IQR: 101.5-5897) involving 62.5% females. Most studies (92; 35.1%) focused on communicable diseases and mainly among outpatients (90; 34.2%). Maternal and child health studies formed the largest patient group (58; 22.1%) followed by people living with HIV (32; 12.2%). Change in HSU was reported in 249 (95.0%) studies with 221 (84.4%) 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). The patient group with the largest percentage decrease was cardiovascular diseases (68.0%; IQR: 16.7-71.1) and the lowest percentage decrease was in patients with infections (27.0%; IQR: 16.6-45.6). CONCLUSIONS: A large body of literature is available on the effects of the pandemic on HSU in SSA. Most studies report decreases in HSU during the pandemic. However, patterns differ widely across disease categories, patient groups, and during different time periods of the pandemic.
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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 | 0.024 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.023 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".