Factors influencing uptake of COVID-19 diagnostics in Sub-Saharan Africa: a rapid scoping review
Bibliographic record
Abstract
BACKGROUND: Diagnostics are critical for preventing COVID-19 transmission, enabling disease management and engagement with care. However, COVID-19 testing uptake remained low in low- and middle- income countries in Sub-Saharan Africa (SSA) during the recent pandemic, due to issues of supply, access and acceptability. Early studies conducted outside of the region provide insight into uptake of COVID-19 testing, however there has been no systematic research within the region. The aim of this scoping review is to investigate factors influencing uptake of COVID-19 testing in different settings across SSA. MATERIALS AND METHODS: Inclusion criteria was any study employing qualitative or mixed methodologies, addressing uptake of COVID-19 testing conducted in SSA. MEDLINE, PubMed, Google Scholar, Web of Science, and Africa-Wide Information were searched. Thematic content analysis was conducted across all included articles until saturation was attained. RESULTS: In total 2994 articles were identified and fourteen reviewed. Structural, social, epidemiological, informational, and political elements affected how the public interacted with COVID-19 testing. Coverage was limited by insufficient diagnostic capabilities caused by a shortage of laboratory resources and trained personnel. False information spread through social media led to testing misperceptions and apprehension. Testing hesitancy was ascribed to fear of restrictive measures and the possibility of social harms if positive. Facility-based testing was physically inaccessible and perceived as lacking privacy, whereas self-testing distributed by the community removed lengthy distances and prevented stigma. Perceptions that COVID-19 was not severe and low numbers of confirmed cases in comparison to other settings undermined public urgency for testing. Low testing frequency led to low-rate assumptions, which in turn generated denial and othering narratives. Politicians' acceptance or denial of COVID-19 affected the mobilization of the health system, and their model actions-such as testing openly-promoted public confidence and involvement in interventions. CONCLUSIONS: This review emphasizes the necessity of strong political commitments to enhancing health systems for future pandemic preparedness. Response plans should consider contextual elements that affect how people react to interventions and perceive health emergencies. Community-driven self-testing distribution could enhance the uptake of diagnostics through addressing socio-economic constraints impacting facility-delivered testing.
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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 | 0.018 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.023 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".