Greg Arling1, Kathleen A Abrahamson, Valerie Cooke, Robert L Kane, Teresa Lewis. 1. Indiana University Center for Aging Research, Regenstrief Institute, and Indiana University School of Medicine, 410 West 10th Street, Suite 2000, Indianapolis, IN 46202-3012, USA. GArling@IUPUI.edu
Abstract
BACKGROUND: Research into nursing home transitions has given limited attention to the facility or community contexts. OBJECTIVE: To identify facility and market factors affecting transitions of nursing home residents back to the community. RESEARCH DESIGN: Multilevel models were used to estimate effects of facility and market factors on facility-level community discharge rates after controlling for resident demographic, health, and functional conditions. Facility discharge rates were adjusted using Empirical Bayes estimation. SUBJECTS: Annual cohort of first-time admissions (N=24,648) to 378 Minnesota nursing facilities in 75 nursing home markets from July 2005 to June 2006. MEASURES: Community discharge within 90 days of admission; facility occupancy, payer mix, ownership, case-mix acuity, size, admissions from hospitals, nurse staffing level, and proportion of admissions preferring or having support to return to the community; and nursing market population size, average occupancy, market concentration, and availability of home and community-based services. RESULTS: Rates of community discharge (Empirical Bayes residual) were highest in facilities with more residents preferring community discharge, more Medicare days, higher nurse staffing levels, and higher occupancy. In addition, facilities had higher community discharge rates if they were located in markets with a greater ratio of home and community-based services recipients to nursing home residents and with larger populations. CONCLUSIONS: State Medicaid programs should undertake system-level interventions that encourage nursing facilities to reduce unused bed capacity, balance the mix of payers, invest in nurse staffing, and take other steps to promote community discharges. In addition, states should increase home and community-based services, particularly in markets with low community discharge rates.
BACKGROUND: Research into nursing home transitions has given limited attention to the facility or community contexts. OBJECTIVE: To identify facility and market factors affecting transitions of nursing home residents back to the community. RESEARCH DESIGN: Multilevel models were used to estimate effects of facility and market factors on facility-level community discharge rates after controlling for resident demographic, health, and functional conditions. Facility discharge rates were adjusted using Empirical Bayes estimation. SUBJECTS: Annual cohort of first-time admissions (N=24,648) to 378 Minnesota nursing facilities in 75 nursing home markets from July 2005 to June 2006. MEASURES: Community discharge within 90 days of admission; facility occupancy, payer mix, ownership, case-mix acuity, size, admissions from hospitals, nurse staffing level, and proportion of admissions preferring or having support to return to the community; and nursing market population size, average occupancy, market concentration, and availability of home and community-based services. RESULTS: Rates of community discharge (Empirical Bayes residual) were highest in facilities with more residents preferring community discharge, more Medicare days, higher nurse staffing levels, and higher occupancy. In addition, facilities had higher community discharge rates if they were located in markets with a greater ratio of home and community-based services recipients to nursing home residents and with larger populations. CONCLUSIONS: State Medicaid programs should undertake system-level interventions that encourage nursing facilities to reduce unused bed capacity, balance the mix of payers, invest in nurse staffing, and take other steps to promote community discharges. In addition, states should increase home and community-based services, particularly in markets with low community discharge rates.
Authors: Denise A Tyler; Renée R Shield; Jill Harrison; Whitney L Mills; Kristen E Morgan; Maxwell E Cutty; Danielle L Coté; Susan M Allen Journal: J Aging Soc Policy Date: 2019-02-14
Authors: Sijiu Wang; Helena Temkin-Greener; Adam Simning; R Tamara Konetzka; Shubing Cai Journal: Health Serv Res Date: 2021-06-17 Impact factor: 3.734
Authors: Lianne Jeffs; Madelyn P Law; Sharon Straus; Roberta Cardoso; Renee F Lyons; Chaim Bell Journal: BMJ Qual Saf Date: 2013-07-12 Impact factor: 7.035