Pathways to Reduced Overnight Hospitalizations: Evaluating 62 Physical, Behavioral, and Psychosocial Factors
Bibliographic record
Abstract
Abstract As healthcare costs rise steadily and rapidly, researchers and policymakers are increasingly interested in reducing healthcare utilization costs. Growing evidence documents many factors that may influence healthcare utilization; however, less is known about how changes in candidate predictors influence subsequent healthcare utilization. Using data from 11,374 participants in the Health and Retirement Study (HRS)—a diverse, longitudinal, and nationally representative sample of older adults in the United States, we evaluated a large range of candidate predictors of overnight hospitalizations. Using generalized linear regression models with a lagged exposure-wide approach, we evaluated if changes in 62 predictors over four-years (between t0;2006/2008 and t1;2010/2012) were associated with subsequent hospitalizations during the two years prior to t2 (2012-2014 (Cohort A) or 2014-2016 (Cohort B)). After adjustment for a rich set of baseline covariates, changes in some health behaviors (e.g., frequent physical activity), physical health conditions (e.g., no physical functioning limitations), and psychosocial factors (e.g., higher purpose in life, lower anxiety, more volunteering) were associated with decreased hospitalizations four years later. However, there was little evidence that other factors (e.g. smoking, obesity) were associated with subsequent hospitalizations. Notably, some psychosocial factors had effect sizes as large as some physical health conditions. Several indicators of physical health, health behaviors, and psychosocial well-being may predict subsequent hospitalizations, and these factors may be novel targets for interventions and policies aiming to reduce healthcare costs in older adults.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".