Adult mortality trends in Matlab, Bangladesh: an analysis of cause-specific risks
Bibliographic record
Abstract
OBJECTIVE: With socioeconomic development, improvement in preventing and curing infectious diseases, and increased exposure to non-communicable diseases (NCDs) risk factors (eg, overweight/obesity, sedentary lifestyle), the majority of adult deaths in Bangladesh in recent years are due to NCDs. This study examines trends in cause-specific mortality risks using data from the Matlab Health and Demographic Surveillance System (HDSS). DESIGN, SETTINGS AND PARTICIPANTS: We conducted a follow-up study from 2003 to 2017 using data from Matlab HDSS, which covers a rural population of 0.24 million (in 2018) in Chandpur, Bangladesh. HDSS assessed the causes of all deaths using verbal autopsy and classified the causes using the 10th revision of the International Statistical Classification of Diseases. We examined 19 327 deaths involving 2 279 237 person-years. METHODS: We calculated annual cause-specific mortality rates and estimated adjusted proportional HRs using a Cox proportional hazards model. RESULTS: All-cause mortality risk declined over the study period among people aged 15 and older, but the risk from stroke increased, and from heart disease and cancers remained unchanged. These causes were more common among middle-aged and older people and thus bore the most burden. Mortality from causes other than NCDs-namely, infectious and respiratory diseases, injuries, endocrine disorders and others-declined yet still constituted over 30% of all deaths. Thus, the overall mortality decline was associated with the decline of causes other than NCDs. Mortality risk sharply increased with age. Men had higher mortality than women from heart disease, cancers and other causes, but not from stroke. Lower household wealth quintile people have higher mortality than higher household wealth quintile people, non-Muslims than Muslims. CONCLUSION: Deaths from stroke, heart disease and cancers were either on the rise or remained unchanged, but other causes declined continuously from 2003 to 2017. Immediate strengthening of the preventive and curative healthcare systems for NCDs management is a burning need.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".