Impact of incarceration on cardiovascular disease risk factors: a systematic review and meta-regression on weight and BMI change
Bibliographic record
Abstract
OBJECTIVE: Cardiovascular disease is an underappreciated issue in prison medicine. Recent studies have revealed a higher prevalence of cardiovascular disease risk factors (CVDRFs) among individuals in prison, but the impact of incarceration on CVDRFs over time is not well understood. This review aimed to assess available literature and quantify the relationship between incarceration and trends in major CVDRFs in high-income countries. DESIGN: Systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. Meta-regression on weight change and obesity. DATA SOURCES: Medline, Embase, PubMed, Cochrane Central Wiley and Web of Science. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Longitudinal studies reporting on the incidence of, or trends in any CVDRF among current or former people in prison over time, in high-income countries. DATA EXTRACTION AND SYNTHESIS: Two authors independently screened articles for eligibility, extracted data and assessed quality using an adapted version of the Newcastle-Ottawa Scale. Trends in CVDRFs during and following incarceration were summarised and in those with sufficient data a meta-regression was performed. RESULTS: (95% CI -0.9 to 4.6) at 2 years. Weight gain appeared most pronounced right after entering prison and then plateaued at 2 years. Concerning hypertension, the results were inconclusive, despite a trend towards rising blood pressure or prevalence of hypertension during incarceration, and an increased incidence of hypertension following incarceration. Results are contradictory or inconclusive for the other CVDRFs reviewed. CONCLUSION: Possible explanations for the association between incarceration and weight include a sedentary lifestyle, unhealthy diet, forced smoking cessation, psychotropic medication use and high levels of stress. Incarceration may be an independent risk factor for cardiovascular disease.
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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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.049 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".