Prevalence and Social Determinants of Type 2 Diabetes in a Coastal Area of Kerala,India
Bibliographic record
Abstract
INTRODUCTION: Varying prevalence rates of type 2 diabetes have been observed in different parts of the southern state of Kerala, India which is in an advanced stage of epidemiologic transition. Social patterning is evident in diabetes and therefore it was decided to undertake a study on estimating the prevalence of diabetes and associated social determinants. METHODOLOGY: The adopted local self administration unit of the Medical College which is also the field practice area with a population of 25,096 was taken for the study. All the households in the area were visited and the details regarding self reported diabetes was collected after obtaining informed consent and analysis done by multivariate logistic regression. RESULT: The prevalence of self reported diabetes in this coastal area was found to be low at 7.4%. Type 2 diabetes was also found to occur significantly earlier among the respondents belonging to the below poverty line. Age above 40 years (OR 2 95% CI 1.5-2.7, p=.000), marital status (OR 1.9 95% CI 1.1-2.1, p=.006) presence of comorbidities (OR 635 95% CI 389-969, p=.000), more than 8 years of schooling (OR 0.64 95% CI 0.46-0.86, p=.004), living conditions as represented by presence of household source of drinking water(OR 1.4 95% CI 1.01-1.5) were found to be independent predictors. Though there was increasing trend of diabetes among the forward caste line families after backward logistic regression this disappeared leaving behind the proxy of socioeconomic status, household source of drinking water. CONCLUSION: Though, the state of Kerala is in an advanced stage of epidemiologic transition, coastal areas are still in the earlier phases of transition with low prevalence of type 2 diabetes mellitus. Higher education and better living conditions are important social determinants of diabetes though further studies are necessary to delineate the impact of economic status and education.
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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 | 0.000 |
| 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".