Literature DB >> 15377931

Examining differences in death rates for medicaid and non-medicaid nursing home residents.

Jennifer L Troyer1.   

Abstract

OBJECTIVES: The purpose of this work was to examine differences in the probability of death for Medicaid and privately funded nursing home residents, controlling for differences in facility, market, and resident characteristics.
METHODS: Given that Medicaid residents are more likely to die in nursing facilities, the probability of dying within 1 or 2 years is estimated using a series of probit models, controlling for whether a resident is Medicaid-funded or privately-funded. As resident characteristics, market attributes, facility characteristics, and facility fixed effects are sequentially added to the specifications, the gap in the probability of death between Medicaid and private-pay residents is considered.
RESULTS: The overall mortality rate for Medicaid residents was 14.8% points higher than the death rate for privately funded residents. When considering death within 1 year and 2 years, Medicaid resident death rates were 4.2% and 7.8% higher, respectively. The apparent difference in mortality declines as one controls for resident, market, and facility characteristics. Both facility characteristics and facility fixed effects are relatively important in explaining differences in death rates between Medicaid and private-pay residents.
CONCLUSIONS: Differences in death rates between Medicaid and private-pay residents are relatively small, suggesting that residents with different payment sources are not treated differently in nursing homes in ways that impact the probability of death. However, policymakers may want to look more closely at whether Medicaid residents are segregated into lower-quality facilities.

Mesh:

Year:  2004        PMID: 15377931     DOI: 10.1097/00005650-200410000-00007

Source DB:  PubMed          Journal:  Med Care        ISSN: 0025-7079            Impact factor:   2.983


  9 in total

1.  Validation of the Minimum Data Set in identifying hospitalization events and payment source.

Authors:  Shubing Cai; Dana B Mukamel; Peter Veazie; Helena Temkin-Greener
Journal:  J Am Med Dir Assoc       Date:  2010-08-07       Impact factor: 4.669

2.  Hospitalization of nursing home residents: the effects of states' Medicaid payment and bed-hold policies.

Authors:  Orna Intrator; David C Grabowski; Jacqueline Zinn; Mark Schleinitz; Zhanlian Feng; Susan Miller; Vince Mor
Journal:  Health Serv Res       Date:  2007-08       Impact factor: 3.402

3.  Organizational characteristics and cancer care for nursing home residents.

Authors:  Jan P Clement; Cathy J Bradley; Chunchieh Lin
Journal:  Health Serv Res       Date:  2009-09-23       Impact factor: 3.402

4.  Medicaid and Nursing Home Choice: Why Do Duals End Up in Low-Quality Facilities?

Authors:  Hari Sharma; Marcelo Coca Perraillon; Rachel M Werner; David C Grabowski; R Tamara Konetzka
Journal:  J Appl Gerontol       Date:  2019-04-08

5.  Nursing Home Quality as a Common Good.

Authors:  David C Grabowski; Jonathan Gruber; Joseph J Angelelli
Journal:  Rev Econ Stat       Date:  2008-11-01

6.  Medicaid bed-hold policy and Medicare skilled nursing facility rehospitalizations.

Authors:  David C Grabowski; Zhanlian Feng; Orna Intrator; Vincent Mor
Journal:  Health Serv Res       Date:  2010-12       Impact factor: 3.402

7.  Hospitalizations in nursing homes: does payer source matter? Evidence from New York State.

Authors:  Dana B Mukamel; Peter Veazie; Paul Katz; Helena Temkin-Greener
Journal:  Med Care Res Rev       Date:  2011-04-07       Impact factor: 3.929

8.  Constructing a Measure of Private-pay Nursing Home Days.

Authors:  Kali S Thomas; Benjamin Silver; Pedro L Gozalo; David Dosa; David C Grabowski; Rajesh Makineni; Vincent Mor
Journal:  Med Care       Date:  2018-05       Impact factor: 2.983

9.  Nursing home 5-star rating system exacerbates disparities in quality, by payer source.

Authors:  R Tamara Konetzka; David C Grabowski; Marcelo Coca Perraillon; Rachel M Werner
Journal:  Health Aff (Millwood)       Date:  2015-05       Impact factor: 9.048

  9 in total

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