Diagnostic Infectious Diseases Testing Outside Clinics: A Global Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Background Most people around the world do not have access to facility-based diagnostic testing, and the gap in availability of diagnostic tests is a major public health challenge. Self-testing, self-sampling, and institutional testing outside conventional clinical settings are transforming infectious disease diagnostic testing in a wide range of low- and middle-income countries (LMICs). We examined the delivery models of infectious disease diagnostic testing outside clinics to assess the impact on test uptake and linkage to care. Methods We conducted a systematic review and meta-analysis, searching 6 databases and including original research manuscripts comparing testing outside clinics with conventional testing. The main outcomes were test uptake and linkage to care, delivery models, and adverse outcomes. Data from studies with similar interventions and outcomes within thematic areas of interest were pooled, and the quality of evidence was assessed using GRADE. This study was registered in PROSPERO (CRD42019140828). We identified 10 386 de-duplicated citations, and 76 studies were included. Data from 18 studies were pooled in meta-analyses. Studies focused on HIV (48 studies), chlamydia (8 studies), and multiple diseases (20 studies). HIV self-testing increased test uptake compared with facility-based testing (9 studies: pooled odds ratio [OR], 2.59; 95% CI, 1.06–6.29; moderate quality). Self-sampling for sexually transmitted infections increased test uptake compared with facility-based testing (7 studies: pooled OR, 1.74; 95% CI, 0.97–3.12; moderate quality). Conclusions. Testing outside of clinics increased test uptake without significant adverse outcomes. These testing approaches provide an opportunity to expand access and empower patients. Further implementation research, scale-up of effective service delivery models, and policies in LMIC settings are needed.
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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 | 0.027 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.046 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".