The community case management of pneumonia in Africa: a review of the evidence
Bibliographic record
Abstract
Pneumonia causes around 750 000 child deaths per year in sub-Saharan African (SSA) countries. The lack of accessibility to prompt and effective treatment is an important contributor to this burden. Community case management of pneumonia (CCMp) uses trained community health workers (CHWs) to administer antibiotics to suspected child pneumonia cases in villages. This strategy has been gaining momentum in low- and middle-income countries, and the World Health Organization and United Nations children's fund have recently encouraged countries to broaden community case management to other diseases. Recommendations in favour CCMp are based on three meta-analyses showing its efficacy to reduce childhood mortality and morbidity attributable to pneumonia although most of the studies in the meta-analyses were conducted in Asian countries. This is problematic as community case management strategies have been implemented in very different ways in Asian and SSA countries, partly due to differences in malaria prevalence. Therefore, we conducted a narrative synthesis to systematically review the evidence on CCMp in SSA. Results show that there is a lack of evidence concerning its efficacy and effectiveness in SSA, irrespective of whether case management is integrated with other diseases or not. CHWs encounter difficulties in counting the respiratory rate. Their adherence to the guidelines is poorer when they are required to manage several illnesses or children with severe signs. CCMp thus encompasses issues of over-treatment and missed treatment, with potentially negative consequences such as increased lethality in severe cases and antibiotics resistance. The current lack of evidence concerning its efficacy, effectiveness and the factors leading to successful implementation, coupled with CHWs' poor adherence, demand a thorough examination of the legitimacy of implementing CCMp in SSA countries.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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".