Public health surveillance through community health workers: a scoping review of evidence from 25 low-income and middle-income countries
Bibliographic record
Abstract
BACKGROUND: The last 3 years have witnessed global health challenges, ranging from the pandemics of COVID-19 and mpox (monkeypox) to the Ebola epidemic in Uganda. Public health surveillance is critical for preventing these outbreaks, yet surveillance systems in resource-constrained contexts struggle to provide timely disease reporting. Although community health workers (CHWs) support health systems in low-income and middle-income countries (LMICs), very little has been written about their role in supporting public health surveillance. This review identified the roles, impacts and challenges CHWs face in public health surveillance in 25 LMICs. METHODS: We conducted a scoping review guided by Arksey and O'Malley's framework. We exported 1,156 peer-reviewed records from Embase, Global Health and PubMed databases. After multiple screenings, 29 articles were included in the final review. RESULTS: CHWs significantly contribute to public health surveillance in LMICs including through contact tracing and patient visitation to control major infectious diseases such as HIV/AIDS, malaria, tuberculosis, Ebola, neglected tropical diseases and COVID-19. Their public health surveillance roles typically fall into four main categories including community engagement; data gathering; screening, testing and treating; and health education and promotion. The use of CHWs in public health surveillance in LMICs has been impactful and often involves incorporation of various technologies leading to improved epidemic control and disease reporting. Nonetheless, use of CHWs can come with four main challenges including lack of education and training, lack of financial and other resources, logistical and infrastructural challenges as well as community engagement challenges. CONCLUSION: CHWs are important stakeholders in surveillance because they are closer to communities than other healthcare workers. Further integration and training of CHWs in public health surveillance would improve public health surveillance because CHWs can provide health data on 'hard-to-reach' populations. CHWs' work in public health surveillance would also be greatly enhanced by infrastructural investments.
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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 | 0.029 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.029 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".