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Record W4386356495 · doi:10.1101/2023.08.30.23294810

The YUVAAN cohort: an innovative multi-generational platform for health systems and population health interventions to minimize intergenerational transmission of non-communicable diseases in India

2023· preprint· en· W4386356495 on OpenAlexaff
Demi Miriam, Rubina Mandlik, Vivek Patwardhan, Dipali Ladkat, Vaman Khadilkar, Neha Kajale, Chidvilas More, Ketan Gondhalekar, Jasmin Bhawra, Anuradha Khadilkar

Bibliographic record

VenuemedRxiv · 2023
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsWestern UniversityToronto Metropolitan University
Fundersnot available
KeywordsCohortMedicinePsychological interventionEnvironmental healthDisadvantagePopulationCohort studyNon-communicable diseaseProspective cohort studyHealth careGerontologyDemographyPublic healthNursingEconomic growth

Abstract

fetched live from OpenAlex

ABSTRACT Introduction Non-communicable diseases (NCDs) pose a significant health burden in India, with preventable risk factors contributing to their prevalence. Intergenerational inequities can exacerbate the transmission of health risks to further disadvantage vulnerable populations. Taking a life course perspective, this multi-generational cohort study aims to investigate behavioural, socio-ecological, and socio-economic determinants of growth and NCD risk, as well as healthcare access and utilization among rural households that include preadolescent children and their parents. Methods The study is being implemented by Hirabai Cowasji Jehangir Medical Research Institute (HCJMRI) utilizing a prospective multi-generational cohort design to investigate NCD risk across 15 years. Data are being collected from 12 villages around Pune, Maharashtra, India. The primary population enrolled includes apparently asymptomatic (i.e., healthy) children aged 8 to 10 years and their parents. The sample size calculation (N=1300 children) for this longitudinal prospective cohort was driven by the primary objective of assessing trajectories of growth and NCD incidence across generations. A total of 2099 children aged 6 to 10 years have been screened since April 2022, of whom 1471 have been found to be eligible for inclusion in the study. After obtaining informed consent from parents and their children, comprehensive bi-annual data are being collected from both children and parents, including clinical, behavioural, healthcare access and utilization as well as socio-ecological and socio-economic determinants of health. Participants (children and their parents) are being enrolled through household visits, and by arranging subsequent visits to the primary health facility of HCJMRI. 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 modeling, time-series analyses, structural equation modeling, multilevel modeling, and sex and gender-based analyses. Ethics approval has been obtained from the institutional ethics committee, the Ethics Committee Jehangir Clinical Development Centre Pvt Ltd. Written informed consent is obtained from adults and written informed assent from children. Discussion As of May 2023, 378 families from 10 villages have been enrolled, including 432 preadolescents and 756 parents. Preliminary results not only highlight the double burden of malnutrition in the cohort with undernutrition and overweight/obesity coexisting among children and parents, respectively but also identify high rates of diabetes and hypertension among adults in rural areas. Findings can inform the development of targeted interventions to reduce NCDs, address intergenerational health inequities, and improve health outcomes in vulnerable populations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.105
GPT teacher head0.401
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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