Nepal Family Cohort study: a study protocol
Bibliographic record
Abstract
Introduction The Nepal Family Cohort study uses a life course epidemiological approach to collect comprehensive data on children’s and their parents’ environmental, behavioural and metabolic risk factors. These factors can affect the overall development of children to adulthood and the onset of specific diseases. Among the many risk factors, exposure to air pollution and lifestyle factors during childhood may impact lung development and function, leading to the early onset of respiratory diseases. The global incidence and prevalence of respiratory diseases are rapidly increasing, with the rate of increase in Nepal being the highest. Although the cohort will primarily focus on respiratory health, other health outcomes such as cardiovascular, metabolic and mental health will be assessed to provide a comprehensive overall health assessment. All other health outcomes are self-reported following doctor diagnosis. Some of these health outcomes will be quality controlled during the follow-up by measuring disease specific markers. Our cohort study will likely provide evidence of risk factors and policy recommendations. Methods and analysis Using a life-course epidemiology approach, we established a longitudinal study to address the determinants of lung health and other health outcomes from childhood to adulthood. The baseline data collection (personal data anonymised) was completed in April 2024, and 16 826 participants (9225 children and 7601 parents) from 5829 families were recruited in different geographical and climate areas (hills and plains) of Nepal. We plan to follow up all the participants every 2–3 years. Descriptive analysis will be used to report demographic characteristics and compare rural and semi-urban regions. A linear regression model will assess the association between air pollution, particularly household air pollution (HAP) exposure, and other lifestyle factors, with lung function adjusted for potential confounders. A two-stage linear regression model will help to evaluate lung development based on exposure to HAP. Ethics Ethical approval was obtained from the Nepal Health Research Council, Kathmandu, Nepal, and McMaster University, Hamilton, Canada. Permissions were obtained from two municipalities where the study sites are located. Parents provided signed informed consent and children their assent. Dissemination Findings will be disseminated through traditional academic pathways, including peer-reviewed publications and conference presentations. We will also engage the study population and local media (ie, research blogs and dissemination events) and prepare research and policy briefings for stakeholders and leaders at the local, provincial and national levels.
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.052 | 0.013 |
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".