Pathways to reduced overnight hospitalizations in older adults: Evaluating 62 physical, behavioral, and psychosocial factors
Bibliographic record
Abstract
As our society ages and healthcare costs escalate, researchers and policymakers urgently seek potentially modifiable predictors of reduced healthcare utilization. We aimed to determine whether changes in 62 candidate predictors were associated with reduced frequency, and duration, of overnight hospitalizations. We used data from 11,374 participants in the Health and Retirement Study-a national sample of adults aged >50 in the United States. 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 robust covariate-adjustment, we observed that changes in some health behaviors (e.g., those engaging in frequent physical activity had 0.80 the rate of overnight hospital stays (95% CI [0.74, 0.87])), physical health conditions (e.g., those with cancer had 1.57 the rate of overnight hospital stays (95% CI [1.35, 1.82])), and psychosocial factors (e.g., those who helped friends/neighbors/relatives 100-199 hours/year had 0.73 the rate of overnight hospital stays (95% CI [0.63, 0.85])) were associated with subsequent hospitalizations. Findings for both the frequency, and duration, of hospitalizations were mostly similar. Changes in a number of diverse factors were associated with decreased frequency, and duration, of overnight hospitalizations. Notably, some psychosocial factors (e.g., informal helping) had effect sizes equivalent to or larger than some physical health conditions (e.g., diabetes) and health behaviors (e.g., smoking). These psychosocial factors are mostly modifiable and with further research could be novel intervention targets for reducing hospitalizations.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".