Assessing research activity on priority interventions for non-communicable disease prevention in low- and middle-income countries: a bibliometric analysis
Bibliographic record
Abstract
INTRODUCTION: Action is urgently needed to curb the rising rates of non-communicable diseases (NCDs) in low- and middle-income countries (LMICs) and reduce the resulting social and economic burdens. There is global evidence about the most cost-effective interventions for addressing the main NCD risk factors such as tobacco use, unhealthy diets, physical inactivity, and alcohol misuse. However, it is unknown how much research is focused on informing the local adoption and implementation of these interventions. OBJECTIVE: To assess the degree of research activity on NCD priority interventions in LMICs by using bibliometric analysis to quantify the number of relevant peer-reviewed scientific publications. METHODS: A multidisciplinary, multi-lingual journal database was searched for articles on NCD priority interventions. The interventions examined emphasise population-wide, policy, regulation, and legislation approaches. The publication timeframe searched was the year 2000-2011. Of the 11,211 articles yielded, 525 met the inclusion criteria. RESULTS: Over the 12-year period, the number of articles published increased overall but differed substantially between regions: Latin America & Caribbean had the highest (127) and Middle East & North Africa had the lowest (11). Of the risk factor groups, 'tobacco control' led in publications, with 'healthy diets and physical activity' and 'reducing harmful alcohol use' in second and third place. Though half the publications had a first author from a high-income country institutional affiliation, developing country authorship had increased in recent years. CONCLUSIONS: While rising global attention to NCDs has likely produced an increase in peer-reviewed publications on NCDs in LMICs, publication rates directly related to cost-effective interventions are still very low, suggesting either limited local research activity or limited opportunities for LMIC researchers to publish on these issues. More research is needed on high-priority interventions and research funders should re-examine if intervention research is enough of a funding priority.
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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 | 0.073 | 0.301 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.013 |
| Bibliometrics | 0.302 | 0.363 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| 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".