Causal decomposition to evaluate movement behaviour changes on cardiovascular health inequalities
Bibliographic record
Abstract
Abstract Background Individuals of lower socioeconomic position (SEP) are at increased risk of developing cardiovascular disease (CVD). Variability in movement behaviours, including physical activity, sedentary behaviour, and sleep, contributes to CVD risk and socioeconomic gradients in cardiovascular health. Movement behaviour habits often begin early in life, with adolescence representing a sensitive period for establishing behaviour patterns that persist into adulthood. This study aims to evaluate the extent to which socioeconomic inequalities in adult CVD risk can be mitigated by using causal decomposition to model interventional scenarios on unhealthy movement behaviours during adolescence. Methods We will conduct a population-based cohort study of adolescents from the Add Health study, recruited in 1994-1995 from the United States and followed into adulthood. Unhealthy movement behaviours - low moderate-to-vigorous physical activity, high recreational screen time, and short sleep duration - will be operationalized based on the 24-hour Movement Guidelines and measured twice during adolescence (12-24 years). Parental education and family financial hardship will be used to measure SEP. The outcome will be the 10-year risk of CVD, as measured in adulthood (33-41 years) using a validated risk score. G-formula-based causal decomposition with time-varying covariates will be used to quantify the change of socioeconomic inequalities in adult CVD risk under two interventional scenarios: (1) elimination (unhealthy movement behaviours are eliminated in the population during adolescence) and, (2) equalization (the distributions of unhealthy movement behaviours in the low SEP group during adolescence are equalized to those in the high SEP group). Conclusions This study will use causal decomposition methods to estimate policy-relevant effects, providing evidence to guide interventions targeting adolescent movement behaviours and reduce socioeconomic inequalities in CVD risk.
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 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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".