Strategies to tackle non-communicable diseases in Afghanistan: A scoping review
Bibliographic record
Abstract
Non-communicable diseases (NCDs) and their risk factors are the leading cause of death worldwide and contribute to 74.3% of deaths globally in 2019. The burden of NCDs is escalating in Afghanistan. Currently, every seconds, people in Afghanistan are dying of NCDs. Addressing this challenge in Afghanistan needs effective and practical interventions. This study aimed to identify the strategies developed and implemented in countries with low non-communicable premature death. To conduct a scoping review, we followed the six-step Arksey and O'Malley protocol and searched for eligible articles on eight international databases and the gray literature. The study followed the Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA) guidelines. The inclusion criteria were English documents and evidence produced up to 30 November 2021 for the control of NCDs. We excluded incomplete texts, duplicates, and dissertations due to lack of access. We used EndNote X9 and MaxQDA software for data management and analysis. We conducted content analysis for this study. A total of 122 documents developed between 1984 and 2021 met the inclusion criteria. We identified 35 strategies from which the most used strategies were related to unhealthy diets and smoking cessation programs. Canada (26.4%), Korea (19.8%), and the United Kingdom (19%) have the most publications on the control and prevention of NCDs among the countries included in the study. Most strategies were implemented over 2 years (41%). This study recommends specific interventions to control and prevent NCDs for the main risk factors of tobacco use, unhealthy diet, physical inactivity, and the main non-communicable diseases such as heart diseases, cancers, diabetes, and chronic obstructive pulmonary diseases. Afghanistan Ministry of Public Health, the WHO country office, and other involved stakeholders can use the findings of this review to design and implement strategies for controlling and preventing NCDs in Afghanistan. International organizations such as the World Health Organization, United Nations Agencies, the World Bank, and other involving communities should invest in strengthening good health governance in Afghanistan. The Afghan Government should focus on promoting and funding health literacy among the public and self-care to control and prevent NCDs.
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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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".