Food insecurity and its association with health and well-being in middle-aged and older adults in India
Bibliographic record
Abstract
AIM: Food insecurity is a global public health concern; however, there is limited knowledge about its health impacts in India. We examined the associations of food insecurity with socioeconomic conditions, chronic disease and various domains of health and well-being in a community sample of middle-aged and older adults (45+ years) in India. METHODS: Cross-sectional nationally representative data were collected in wave 1 (2017-2018) of the Longitudinal Ageing Study in India. Food insecurity was measured by questions of access and availability of food. We used logistic regression analyses to examine associations of food insecurity with poor self-rated health, limitations in activities of daily living (ADLs), instrumental ADLs, low life satisfaction, depression, sleep problems and low body mass. RESULTS: Food insecurity related to all seven indicators of poor health and well-being, even after controlling for material wealth and the presence of multimorbidity (which food insecurity also predicted). Associations with mental health were stronger for those for physical health. For instance, food insecurity related to a threefold increase in probable depression (OR=2.9, 95% CI=2.4 to 3.4) and low life satisfaction (OR=3.4, 95% CI=2.9 to 3.8). CONCLUSIONS: Food insecurity is a powerful social determinant of poor health among older adults in India. Policy measures to improve population health and well-being should closely follow trends in food insecurity, particularly among those living in poverty and with multiple health conditions.
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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 | 0.000 |
| 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".