BACKGROUND: Hospitalizations of long-stay nursing home (NH) residents are common. The high estimates of potentially avoidable hospitalizations in NHs suggest that efforts to reduce avoidable hospitalizations may be effective in lowering health care expenditures as well as improving the quality of care for NH residents. OBJECTIVE: To determine the relationship between clinical risk factors, facility characteristics and State policy variables, and both avoidable and unavoidable hospitalizations. METHOD: Hospitalization risk is estimated using competing risks proportional hazards regressions. Three hospitalization measures were constructed: (1) ambulatory care-sensitive conditions (ACSCs); (2) additional NH-sensitive avoidable conditions (ANHACs); and (3) nursing home "unavoidable" conditions (NHUCs). In all models, we include clinical risk factors, facility characteristics, and State policy variables that may influence the decision to hospitalize. SUBJECTS: The population of interest is a cohort of long-stay NH residents. Data are from the Nursing Home Stay file, a sample of residents in 10% of certified NHs in the United States (2006-2008). RESULTS: Three fifths of hospitalizations were potentially avoidable and the majority was for infections, injuries, and congestive heart failure. Clinical risk factors include renal disease, diabetes, and a high number of medications among others. Staffing, quality, and reimbursement affect avoidable, but not unavoidable hospitalizations. CONCLUSIONS: A NH-sensitive measure of avoidable hospitalizations identifies both clinical facility and policy risk factors, emphasizing the potential for both reimbursement and clinical strategies to reduce hospitalizations from NHs.
BACKGROUND: Hospitalizations of long-stay nursing home (NH) residents are common. The high estimates of potentially avoidable hospitalizations in NHs suggest that efforts to reduce avoidable hospitalizations may be effective in lowering health care expenditures as well as improving the quality of care for NH residents. OBJECTIVE: To determine the relationship between clinical risk factors, facility characteristics and State policy variables, and both avoidable and unavoidable hospitalizations. METHOD: Hospitalization risk is estimated using competing risks proportional hazards regressions. Three hospitalization measures were constructed: (1) ambulatory care-sensitive conditions (ACSCs); (2) additional NH-sensitive avoidable conditions (ANHACs); and (3) nursing home "unavoidable" conditions (NHUCs). In all models, we include clinical risk factors, facility characteristics, and State policy variables that may influence the decision to hospitalize. SUBJECTS: The population of interest is a cohort of long-stay NH residents. Data are from the Nursing Home Stay file, a sample of residents in 10% of certified NHs in the United States (2006-2008). RESULTS: Three fifths of hospitalizations were potentially avoidable and the majority was for infections, injuries, and congestive heart failure. Clinical risk factors include renal disease, diabetes, and a high number of medications among others. Staffing, quality, and reimbursement affect avoidable, but not unavoidable hospitalizations. CONCLUSIONS: A NH-sensitive measure of avoidable hospitalizations identifies both clinical facility and policy risk factors, emphasizing the potential for both reimbursement and clinical strategies to reduce hospitalizations from NHs.
Authors: Eduard E Vasilevskis; Joseph G Ouslander; Amanda S Mixon; Susan P Bell; J Mary Lou Jacobsen; Avantika A Saraf; Daniel Markley; Kelly C Sponsler; Jill Shutes; Emily A Long; Sunil Kripalani; Sandra F Simmons; John F Schnelle Journal: J Am Geriatr Soc Date: 2016-12-16 Impact factor: 5.562
Authors: Daniel S Stadler; Brant J Oliver; Jennifer G Raymond; George F Routzhan; Ellen A Flaherty; James E Stahl; John A Batsis; Stephen J Bartels Journal: J Am Med Dir Assoc Date: 2019-05-06 Impact factor: 4.669
Authors: Joseph G Ouslander; Ilkin Naharci; Gabriella Engstrom; Jill Shutes; David G Wolf; Maria Rojido; Ruth Tappen; David Newman Journal: J Am Med Dir Assoc Date: 2016-06-24 Impact factor: 4.669
Authors: Hanna T Webb; Kristi M Lieb; Timothy E Stump; Kathleen T Unroe; Jennifer L Carnahan Journal: J Am Med Dir Assoc Date: 2021-06-25 Impact factor: 4.669
Authors: Justin Blackburn; Casey P Balio; Jennifer L Carnahan; Nicole R Fowler; Susan E Hickman; Greg A Sachs; Wanzhu Tu; Kathleen T Unroe Journal: BMC Health Serv Res Date: 2021-05-24 Impact factor: 2.655