Prior incarceration and food insecurity trajectories through older adulthood: findings from the Health and Retirement Study
Bibliographic record
Abstract
INTRODUCTION: Prior cross-sectional research has identified incarceration as a risk factor for food insecurity across the life course. However, there is a lack of longitudinal studies on the relationship between prior incarceration and food insecurity over time. METHODS: This study uses biennial data across 10 time points from the Health and Retirement Study (years 2012-2022) to examine the association between prior incarceration and longitudinal trajectories of food insecurity among adults aged 55 and older in the USA (N=8229). Group-based trajectory modelling was used to assess patterns of food insecurity status over time. Multinomial logistic regression assessed the relationship between prior incarceration and food insecurity trajectory group membership. RESULTS: Three food insecurity trajectory groups were identified: no food insecurity (86.2%), declining food insecurity (11.0%) and chronic food insecurity (2.8%). Results from the multinomial logistic regression demonstrated that a history of incarceration was significantly associated with a higher likelihood of membership in the Declining Food Insecurity (relative risk ratio (RRR)=1.80, 95% CI 1.24 to 2.60) and Chronic Food Insecurity groups (RRR=2.14, 95% CI 1.35 to 3.39), relative to No Food Insecurity group after adjusting for covariates. However, after controlling for household income and wealth, this association was attenuated and remained statistically significant only for the Declining Food Insecurity group (RRR=1.59, 95% CI 1.06 to 2.37). CONCLUSIONS: A history of incarceration is associated with a greater risk of food insecurity across older adulthood, though this relationship appears to be largely due to disparities in socioeconomic status.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".