A comparative systematic review of risk factors, prevalence, and challenges contributing to non-communicable diseases in South Asia, Africa, and Caribbeans
Bibliographic record
Abstract
BACKGROUND: Non-communicable diseases (NCDs) are a global epidemic challenging global public health authorities while imposing a heavy burden on healthcare systems and economies. AIM: To explore and compare the prevalence of NCDs in South Asia, the Caribbean, and non-sub-Saharan Africa, aiming to identify both commonalities and differences contributing to the NCD epidemic in these areas while investigating potential recommendations addressing the NCD epidemic. METHOD: A comprehensive search of relevant literature was carried out to identify and appraise published articles systematically using the Cochrane Library, Ovid, Google Scholar, PubMed, Science Direct, and Web of Science search engines between 2010 and 2023. A total of 50 articles fell within the inclusion criteria. RESULTS: Numerous geographical variables, such as lifestyle factors, socio-economic issues, social awareness, and the calibre of the local healthcare system, influence both the prevalence and treatment of NCDs. The NCDs contributors in the Caribbean include physical inactivity, poor fruit and vegetable intake, a sedentary lifestyle, and smoking, among others. While for South Asia, these were: insufficient societal awareness of NCDs, poverty, urbanization, industrialization, and inadequate regulation implementation in South Asia. Malnutrition, inactivity, alcohol misuse, lack of medical care, and low budgets are responsible for increasing NCD cases in Africa. CONCLUSION: Premature mortality from NCDs can be avoided using efficient treatments that reduce risk factor exposure for individuals and populations. Proper planning, implementation, monitoring, training, and research on risk factors and challenges of NCDs would significantly combat the situation in these regions.
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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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.028 | 0.030 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".