Non-communicable diseases are the leading cause of mortality in rural Birbhum, West Bengal, India: a sex-stratified analysis of verbal autopsies from a prospective cohort, 2012–2017
Bibliographic record
Abstract
OBJECTIVES: There is a dearth of data on causes of death in rural India, which impedes identification of public health priorities to guide health interventions. This study aims to offer insights from verbal autopsies, to understand the pattern and distribution of causes of death in a rural area of Birbhum District, West Bengal, India. DESIGN: Causes of death data were retrieved from a prospective vital event surveillance system. SETTING: The Birbhum Population Project, a Health and Demographic Surveillance System, West Bengal, India. PARTICIPANTS: Between January 2012 and December 2017, all deaths were recorded. MAIN OUTCOME MEASURES: Trained Surveyors tracked all deaths prospectively and used a previously validated verbal autopsy (VA) tool to record causes of death. Experienced physicians reviewed completed VA forms, and assigned cause of death using the 10th version of International Classification of Diseases. In addition to cause-specific mortality fraction, cause-specific crude death rate (CDR) among males and females were estimated. RESULTS: A total of 2320 deaths (1348 males and 972 females) were recorded. An estimated CDR was 708/100 000. Over half of all deaths (1176 deaths, 50.7%) were attributed to non-communicable diseases (NCDs), with nearly 30% of all deaths attributed to circulatory system disorders; whereas 24.2% and 3.9% deaths were due to cerebrovascular diseases and ischaemic heart disease, respectively. Equal percent (13%) of males died from external causes and from infectious and parasitic diseases, and 11% died from respiratory system-related diseases. Among females, 12% died from infectious and parasitic diseases. Among children aged 0-4 years, 50% of all male deaths and 45% of all female deaths were attributed to conditions in the perinatal period. CONCLUSIONS: NCDs are the leading cause of death among adults in a select population of rural Birbhum, India. Health programmes for rural India should prioritise plans to mitigate deaths due to 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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".