THE GEOSPATIAL ASSESSMENT OF THE RELATIONSHIP BETWEEN ALTITUDE AND MORTALITY IN INDIA
Bibliographic record
Abstract
High altitude environmental stressors have numerous and varied physiological effects on human health. Currently, several studies have illustrated high altitude affects human health, yet there are limited analyses on suitable data that investigate the influence of high altitude in resident populations over a large extent. In India, there is still inadequate data regarding altitude and mortality. The aim of this study was to conduct a preliminary analysis of the association between altitude and mortality in India. The assessment of mortality rates in India at different altitudes integrated geospatial concepts through a Geographic Information System (GIS) and Digital Elevation Model (DEM). Furthermore, surveyed health data was obtained from the Million Death Study (MDS) as well as the Special Fertility and Mortality Survey (SFMS) datasets. Death occurrence was geo-located via post offices in India and the corresponding altitude was extracted in the DEM. The statistical analysis included Pearson correlation and linear regression to examine the leading death causes in a wide range of age groups, from neonates to 69 year-olds, as well as risk factors in India. Although allcause mortality rate has no significant association with altitudes, there was a positive correlation between altitude and prematurity and low birth weight mortality rate in neonates (boys r=0.72, p<0.03; girls r=0.89, p<0.00). Additionally, higher drinking rates for males were found at greater altitude (r=0.92, p<0.00). The preliminary results from the study confirmed that there is a relationship between particular mortality rate, behavioural factors, and altitude in India, but further investigations need to be undertaken to assess the total effect of altitude on mortality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".