An observational, multigenerational platform for health systems and population health interventions to minimise intergenerational transmission of non-communicable diseases in India: the YUVAAN cohort study protocol
Bibliographic record
Abstract
Introduction: Non-communicable diseases (NCDs) pose a significant health burden in India, with preventable risk factors contributing to their prevalence. In rural populations, inequalities may be reinforced by health risks passed down through generations. Taking a life course perspective, this multigenerational cohort study aims to investigate behavioural, socioecological, and socioeconomic determinants of growth and NCD risk, as well as healthcare access and utilisation among preadolescents and their parents. Methods and analysis: The study is being implemented by the Hirabai Cowasji Jehangir Medical Research Institute (HCJMRI) using a prospective, multigenerational cohort design to investigate NCD risk over 15 years. Data are being collected from 14 villages around Pune, Maharashtra, India. The target population is asymptomatic (ie, healthy) children aged 8-10 years and their parents. The study commenced on 13 September 2022. Participants (children and their parents) are being enrolled through household visits, and by arranging subsequent visits to the primary health facility of HCJMRI. After obtaining informed consent from participants (parents and their children), comprehensive data are being collected from both children and parents, including clinical, behavioural, healthcare access and utilisation, as well as socioeconomic determinants of health. Clinical assessments include anthropometric measurements, blood samples for a wide range of NCD indicators, bone health and muscle function. The long-term data analysis plan includes longitudinal modelling, time-series analyses, structural equation modelling, multilevel modelling and sex-based analyses to investigate growth trajectories and intergenerational patterns of health risks. As of November 2024, 1070 families from 14 villages have been enrolled (1264 preadolescents and 2140 parents). Given the double burden of malnutrition, with undernutrition and overweight/obesity coexisting among children and parents in India, the study findings will contribute to the development of focused interventions aimed at lowering NCDs, addressing the generational transmission of health risks, and improving health outcomes for rural communities. Ethics and dissemination: Ethics approval was obtained from the institutional ethics committee, Ethics Committee Jehangir Clinical Development Centre, No: JCDC/BHR/24/047. Trial registration number: NCT05603793.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".