Community Factors and Hospital Readmission Rates
Bibliographic record
Abstract
Background The environment in which a patient lives influences their health outcomes. However, the degree to which community factors are associated with readmissions is uncertain. Objective To estimate the influence of community factors on the Centers for Medicare & Medicaid Services risk-standardized hospital-wide readmission measure (HWR). Research Design We assessed 71 community factors in 6 domains related to health outcomes: clinical care; health behaviors; social and economic factors; the physical environment; demographics; and social capital. Subjects Medicare fee-for-service patients eligible for the HWR measure between July 2014-June 2015 (n= 6,790,723). Patients were linked to community factors using their 5-digit zip code of residence. Methods We used a random forest algorithm to rank factors for their importance in predicting hospital HWR scores. Factors were entered into 6 domain-specific multivariable regression models in order of decreasing importance. Factors with with P-values <0.10 were retained for a final model, after eliminating any that were collinear. Results Among 71 community factors, 19 were retained in the 6 domain models and the final model. Domains which explained the most to least variance in HWR were: physical environment (R2=15%); clinical care (12%); demographics (11%); social and economic environment (7%); health behaviors (9%); and social capital (8%). In the final model, the 19 factors explained more than a quarter of the variance in readmission rate (R2=27%). Conclusions Readmissions for a wide range of clinical conditions are influenced by factors relating to the communities in which patients reside. These findings can be used to target efforts to keep patients out of the hospital.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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".