“Health divide” between indigenous and non-indigenous populations in Kerala, India: Population based study
Bibliographic record
Abstract
BACKGROUND: The objective of this study is to investigate the magnitude and nature of health inequalities between indigenous (Scheduled Tribes) and non-indigenous populations, as well as between different indigenous groups, in a rural district of Kerala State, India. METHODS: A health survey was carried out in a rural community (N = 1660 men and women, 18-96 years). Age- and sex-standardised prevalence of underweight (BMI < 18.5 kg/m2), anaemia, goitre, suspected tuberculosis and hypertension was compared across forward castes, other backward classes and tribal populations. Multi-level weighted logistic regression models were used to estimate the predicted prevalence of morbidity for each age and social group. A Blinder-Oaxaca decomposition was used to further explore the health gap between tribes and non-tribes, and between subgroups of tribes. RESULTS: Social stratification remains a strong determinant of health in the progressive social policy environment of Kerala. The tribal groups are bearing a higher burden of underweight (46.1 vs. 24.3%), anaemia (9.9 vs. 3.5%) and goitre (8.5 vs. 3.6%) compared to non-tribes, but have similar levels of tuberculosis (21.4 vs. 20.4%) and hypertension (23.5 vs. 20.1%). Significant health inequalities also exist within tribal populations; the Paniya have higher levels of underweight (54.8 vs. 40.7%) and anaemia (17.2 vs. 5.7%) than other Scheduled Tribes. The social gradient in health is evident in each age group, with the exception of hypertension. The predicted prevalence of underweight is 31 and 13 percentage points higher for Paniya and other Scheduled Tribe members, respectively, compared to Forward Caste members 18-30 y (27.1%). Higher hypertension is only evident among Paniya adults 18-30 y (10 percentage points higher than Forward Caste adults of the same age group (5.4%)). The decomposition analysis shows that poverty and other determinants of health only explain 51% and 42% of the health gap between tribes and non-tribes for underweight and goitre, respectively. CONCLUSIONS: Policies and programmes designed to benefit the Scheduled Tribes need to promote their well-being in general but also target the specific needs of the most vulnerable indigenous groups. There is a need to enhance the capacity of the disadvantaged to equally take advantage of health opportunities.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".