Type 2 diabetes and the double burden of malnutrition in rural south India: A mixed-methods examination of a public health crisis
Bibliographic record
Abstract
India is experiencing a nutrition transition characterized by shifting diets and physical activity patterns that are driving increased prevalence of obesity, type 2 diabetes, and the double burden of malnutrition (defined as co-occurring under- and over-nutrition). The objective of this research was to describe and determine factors associated with these phenomena in rural Tamil Nadu, India using a mixed methods (qualitative and quantitative) study design. In-depth interviews (n=61+54) and focus groups (n=8) assessed perceptions of diabetes, nutrition, and the local food environment. Randomly selected adults (n=753) participated in a socio-demographic survey, food frequency questionnaire, and bio-metric measurements (waist/hip circumference ratio [WHR], body mass index [BMI], blood hemoglobin, and oral glucose tolerance test). Age- and sex-standardized prevalences of health outcomes were: overweight, 34%; pre-diabetes, 9.5%; type 2 diabetes, 10.8%; underweight, 23%; and anemia, 47%. Prevalence of co-morbid anemia plus overweight was 22.6% in women and 12.0% in men, while prevalence of co-morbid anemia plus diabetes was 5.6% of men and women. Multivariable linear and logistic regressions were used to identify factors associated with health outcomes at p<0.05. Factors [odds ratios] associated with obesity included physical activity [0.8], wealth index [1.1], high caste [4.6], rurality index [0.4], and tobacco use [0.2]. Factors [ORs] associated with diabetes included physical activity [0.8], BMI [1.9], WHR [1.6], high caste [2.4], rurality index [0.8], and tobacco use [2.8]. Factors [ORs] associated with co-morbid anemia and overweight included female sex [2.3], rurality index [0.7], high caste [0.7], wealth index [1.1], livestock ownership [0.5], and meat intake [0.8]. Factors [ORs] associated with co-morbid anemia and diabetes included age [1.1], rurality index [0.8], family history of diabetes [4.9], and BMI [2.1]. Local explanatory models of diabetes cited “poor diet”, “tension”, and “tradition” (family history). Illness narratives described “fear” and “loss of control” upon diagnosis with diabetes. Food environment characteristics affecting food choices and consumption included individual factors (age, gender), socio-economic factors (wealth, caste, religion), access to government entitlements, occupation, and media exposure. Results shed light on public health issues in rural India and carry implications for policy, practice, and future 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".