Socio-demographic patterning of the individual-level double burden of malnutrition in a rural population in South India: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The double burden of malnutrition is the co-occurrence of undernutrition (e.g. underweight, stunting, and micronutrient deficiencies) and over-nutrition (e.g. obesity, type 2 diabetes, and cardiovascular disease) at the population, household, or individual level. The objectives of this study were to determine the extent and determinants of individual-level co-morbid anemia and overweight and co-morbid anemia and diabetes in a population in rural Tamil Nadu, South India. METHODS: We undertook a cross-sectional study of adults (n = 753) in a rural region of Tamil Nadu, South India. A survey assessed socio-demographic factors, physical activity levels, and dietary intake. Clinical measurements included body-mass index, an oral glucose tolerance test, and blood hemoglobin assessments. Multivariable logistic regression analyses were used to determine associations between risk factors and two co-morbid double burden pairings: (1) anemia and overweight, and (2) anemia and diabetes. RESULTS: Prevalence of co-morbid anemia and overweight was 23.1% among women and 13.1% among men. Prevalence of co-morbid anemia and diabetes was 6.2% among women and 6.3% among men. The following variables were associated with co-morbid anemia and overweight in multivariable models [odds ratio (95% confidence interval)]: female sex [2.3 (1.4, 3.85)], high caste [3.2 (1.34, 7.49)], wealth index [1.1 (1.00, 1.12)], rurality (0.7 [0.56, 0.85]), tobacco consumption [0.6 (0.32, 0.96)], livestock ownership [0.5 (0.29, 0.89)], and energy-adjusted meat intake [1.8 (0.61, 0.94)]. The following variables were associated with co-morbid anemia and diabetes in multivariable models: age [1.1 (1.05, 1.11)], rurality [0.8 (0.57, 0.98)], and family history of diabetes [4.9 (1.86, 12.70). CONCLUSION: This study determined the prevalence and factors associated with individual-level double burden of malnutrition. Women in rural regions of India may be particularly vulnerable to individual-level double burden of malnutrition and should be a target population for any nutrition interventions to address simultaneous over- and undernutrition.
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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 | 0.001 |
| 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".