Long-term Care Facility Variation in the Incidence of Pneumonia and Influenza
Bibliographic record
Abstract
Abstract Background Pneumonia and influenza (P&I) increase morbidity and mortality among older adults, especially those residing in long-term care facilities (LTCFs). Facility-level characteristics may affect the risk of P&I beyond resident-level risk factors. However, the relationship between facility characteristics and P&I is poorly understood. To address this, we identified potentially modifiable facility-level characteristics that influence the incidence of P&I across LTCFs. Methods We conducted a retrospective cohort study using 2013–2015 Medicare claims linked to Minimum Data Set and LTCF-level data. Short-stay (<100 days) and long-stay (100+ days) LTCF residents were followed for the first occurrence of hospitalization, LTCF discharge, Medicare disenrollment, or death. We calculated LTCF risk-standardized incidence rates (RSIRs) per 100 person-years for P&I hospitalizations by adjusting for over 30 resident-level demographic and clinical covariates using hierarchical logistic regression. Results We included 1 767 241 short-stay (13 683 LTCFs) and 922 863 long-stay residents (14 495 LTCFs). LTCFs with lower RSIRs had more licensed independent practitioners (nurse practitioners or physician assistants) among short-stay (44.9% vs 41.6%, P < .001) and long-stay residents (47.4% vs 37.9%, P < .001), higher registered nurse hours/resident/day among short-stay and long-stay residents (mean [SD], 0.5 [0.7] vs 0.4 [0.4], P < .001), and fewer residents for whom antipsychotics were prescribed among short-stay (21.4% [11.6%] vs 23.6% [13.2%], P < .001) and long-stay residents (22.2% [14.3%] vs 25.5% [15.0%], P < .001). Conclusions LTCF characteristics may play an important role in preventing P&I hospitalizations. Hiring more registered nurses and licensed independent practitioners, increasing staffing hours, and higher-quality care practices may be modifiable means of reducing P&I in LTCFs.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".