Prioritising communicable disease research in Afghanistan: an application of the Child Health and Nutrition Research Initiative (CHNRI) methodology
Bibliographic record
Abstract
INTRODUCTION: Communicable disease control in Afghanistan has deteriorated amid growing fragility, health system disruption and declining international aid since the 2021 regime change. Outbreaks of measles, pertussis, pneumonia, cholera, malaria, dengue, Crimean-Congo haemorrhagic fever, tuberculosis and polio continue to plague the population in Afghanistan. This study addresses a critical evidence gap by systematically ranking research priorities for communicable diseases in Afghanistan. METHODS: This study applied the Child Health and Nutrition Research Initiative (CHNRI) methodology, which is a widely used approach for systematic, transparent and collaborative research priority setting. It leverages expert consultation to generate, score and rank research questions. This study identified and invited 303 Afghanistan-health researchers, based globally, to complete the survey which consisted of 33 research questions related to communicable diseases that were submitted by 15 researchers. RESULTS: This CHNRI exercise included 44 respondents, 63.6% of whom were of Afghan origin. The top 10 highest-ranked questions focused on identifying barriers to low measles and polio vaccination coverage, assessing disease burden by region and strategies to reduce the incidence of tuberculosis. Respondents of Afghan origin ranked antibiotic resistance and gender-related disparities in tuberculosis as the highest-priority questions. The majority of priority questions were description questions. CONCLUSIONS: Researchers, governments, donors, policy makers and programme implementers can use these findings as a starting point to strategically align research agendas, guide resource allocation, and prioritise evidence-based interventions for life-saving communicable disease prevention and control in Afghanistan.
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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 | 0.385 | 0.287 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".