The spectrum of health conditions in community-based cross-sectional surveys in Southeast Asia 2010-21: a scoping review
Bibliographic record
Abstract
Abstract Background Southeast Asia is undergoing an epidemiological transition with non-communicable illnesses becoming increasingly important, yet infectious diseases (tuberculosis, HIV, hepatitis B, malaria) remain widely prevalent in some populations, while emerging and zoonotic diseases threaten. There are also limited population-level estimates of many important heath conditions. This restricts evidence-based decision-making for disease control and prevention priorities. Cross-sectional surveys can be efficient epidemiological tools to measure the prevalence of a wide range of diseases, but no systematic assessment of their coverage of different health conditions has been produced for the region. Methods We conducted a systematic search in Medline, Embase, Global Health, CINAHL, Scopus, Web of Science Core Collection, and Global Index Medicus, and additionally Google Scholar. Our inclusion criteria were cross-sectional surveys conducted with community-based recruitment, in Bangladesh, Cambodia, Laos, Myanmar, and Thailand, published between January 1, 2010 and January 27, 2021, and reporting the prevalence of any health condition. Results 542 publications from 337 surveys were included. Non-communicable conditions (n = 205) were reported by more surveys than infectious conditions (n = 124). Disability (n = 49), self-report history of any disease or symptoms (n = 35), and self-perceived health status (n = 34), which reflect a holistic picture of health, were studied by many fewer surveys. In addition, 45 surveys studied symptomatic conditions which overlap between non-communicable and infectious conditions. The most surveyed conditions were undernutrition, obesity, hypertension, diabetes, intestinal parasites, malaria, anemia, diarrhea, fever, and acute respiratory infections. These conditions overlap with the most important causes of death and disability in the Global Burden of Disease study. However, other high-burden conditions (e.g. hearing loss, headache disorder, low back pain, chronic liver and kidney diseases, and cancers) were rarely studied. Conclusion There were relatively few recent surveys from which to estimate representative prevalences and trends of health conditions beyond those known to be high burden. Expanding the spectrum of health conditions in cross-sectional surveys could improve understanding of evolving disease patterns in the region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.027 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".