Deaths from acute abdominal conditions and geographical access to surgical care in India: a nationally representative spatial analysis
Bibliographic record
Abstract
BACKGROUND: Few population-based studies quantify mortality from surgical conditions and relate mortality to access to surgical care in low-income and middle-income countries. METHODS: We linked deaths from acute abdominal conditions within a nationally representative, population-based mortality survey of 1·1 million households in India to nationally representative facility data. We calculated total and age-standardised death rates for acute abdominal conditions. Using 4064 postal codes, we undertook a spatial clustering analysis to compare geographical access to well-resourced government district hospitals (24 h surgical and anaesthesia services, blood bank, critical care beds, basic laboratory, and radiology) in high-mortality or low-mortality clusters from acute abdominal conditions. FINDINGS: 923 (1·1%) of 86,806 study deaths at ages 0-69 years were identified as deaths from acute abdominal conditions, corresponding to 72,000 deaths nationally in 2010 in India. Most deaths occurred at home (71%) and in rural areas (87%). Compared with 567 low-mortality geographical clusters, the 393 high-mortality clusters had a nine times higher age-standardised acute abdominal mortality rate and significantly greater distance to a well-resourced hospital. The odds ratio (OR) of being a high-mortality cluster was 4·4 (99% CI 3·2-6·0) for living 50 km or more from well-resourced district hospitals (rising to an OR of 16·1 [95% CI 7·9-32·8] for >100 km). No such relation was seen for deaths from non-acute surgical conditions (ie, oral, breast, and uterine cancer). INTERPRETATION: Improvements in human and physical resources at existing government hospitals are needed to reduce deaths from acute abdominal conditions in India. Full access to well-resourced hospitals within 50 km by all of India's population could have avoided about 50,000 deaths from acute abdominal conditions, and probably more from other emergency surgical conditions. FUNDING: Bill & Melinda Gates Foundation, Dalla Lana School of Public Health, Canadian Institute of Health Research.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".