Informing a home time measure reflective of quality of life: A data driven investigation of time frames and settings of health care utilization

OBJECTIVE: To evaluate short‐ and long‐term measures of health care utilization—days in the emergency department (ED), inpatient (IP) care, and rehabilitation in a post‐acute care (PAC) facility—to understand how home time (i.e., days alive and not in an acute or PAC setting) corresponds to quality of life (QoL). DATA SOURCES: Survey data on community‐residing veterans combined with multipayer administrative data on health care utilization. STUDY DESIGN: VA or Medicare health care utilization, quantified as days of care received in the ED, IP, and PAC in the 6 and 18 months preceding survey completion, were used to predict seven QoL‐related measures collected during the survey. Elastic net machine learning was used to construct models, with resulting regression coefficients used to develop a weighted utilization variable. This was then compared with an unweighted count of days with any utilization. PRINCIPAL FINDINGS: In the short term (6 months), PAC utilization emerged as the most salient predictor of decreased QoL, whereas no setting predominated in the long term (18 months). Results varied by outcome and time frame, with some protective effects observed. In the 6‐month time frame, each weighted day of utilization was associated with a greater likelihood of activity of daily living deficits (0.5%, 95% CI: 0.1%–0.9%), as was the case with each unweighted day of utilization (0.6%, 95% CI: 0.3%–1.0%). The same was true in the 18‐month time frame (for both weighted and unweighted, 0.1%, 95% CI: 0.0%–0.3%). Days of utilization were also significantly associated with greater rates of instrumental ADL deficits and fair/poor health, albeit not consistently across all models. Neither measure outperformed the other in direct comparisons. CONCLUSIONS: These results can provide guidance on how to measure home time using multipayer administrative data. While no setting predominated in the long term, all settings were significant predictors of QoL measures.