Medicaid and the Uninsured
Relationship between State Medicaid
Policies, Nursing Home Racial
Composition, and the Risk of
Hospitalization for Black and
Andrea Gruneir, Susan C. Miller, Zhanlian Feng, Orna Intrator,
and Vincent Mor
Objective. To examine racial differences in the risk of hospitalization for nursing
home (NH) residents.
Certification and Reporting data from 2000 were merged with independently collected
Medicaid policy data.
Study Design. One hundred and fifty day follow-up of 516,082 long-stay residents.
Principle Findings. 18.5 percent of white and 24.1 percent of black residents were
odds (95 percent confidence interval [CI]51.15–1.25) of hospitalization than residents
in NHs with no blacks. Ten-dollar increments in Medicaid rates reduced the odds of
hospitalization by 4 percent (95 percent CI50.93–1.00) for white residents and 22
percent (95 percent CI50.69–0.87) for black residents.
Conclusions. Our findings illustrate theeffectofcontextualforces onracial disparities
in NH care.
Key Words. Nursing home, Medicaid, racial disparities
Racial disparities in the quality of nursing home (NH) care have been well
documented. Black residents are less likely than their white counterparts to
receive appropriate pharmacologic management for a myriad of conditions
1999; Won et al. 1999; Spooner et al. 2001) and less likely to receive physical
therapy upon admission (Harada et al. 2000). Black residents are also more
likely to be hospitalized than are white residents (Culler, Parchman, and
rHealth Research and Educational Trust
Przybylski 1998). Even at the end-of-life, the risk of hospitalization for black
white residents (Mor, Papandonatos, and Miller 2005). Thishas beenpartially
attributed to an apparent preference for more aggressive care among blacks
that may be related to concerns about undertreatment (Caralis, Davis, and
Wright 1993; Dula 1994; O’Brien et al. 1995; Shepardson et al. 1999; Hopp
and Duffy 2000; Degenholtz et al. 2002; Mitchell et al. 2007).
Facility characteristics, such as staffing and payer mix, have also been shown
to be associated with hospitalization (Intrator, Castle, and Mor 1999; Intrator,
Zinn, and Mor 2004; Mor, Papandonatos, and Miller 2005). This is partic-
care is dependent on the availability of resources (Mor et al. 2004; Miller et al.
2006) but NHs that are largely Medicaid-reliant are often disadvantaged
because Medicaid rates are generally below private pay rates and sometimes
below actual costs of care (Seidman 2002). Such facilities disproportionately
serve black residents (Mor et al. 2004).
Studies have shown a persistent association between state Medicaid re-
imbursement policies and hospitalization. Ten dollar differences in per diem
Medicaid rates are associated with a 5–9 percent decrease in individual risk of
hospitalization (Intrator and Mor 2004) while the presence of a bed hold
policy is associated with an increased risk of hospitalization by nearly 40
percent (Intrator et al. 2007; Gruneir et al. 2007). Whether the effects of these
policies differ for black and white residents has never been examined. Given
the concentration of black residents in NHs that are characterized by reliance
on Medicaid, this presents an important question (Smith et al. 2007).
The purpose of this study is to quantify the effect of Medicaid reim-
bursement policies, facility racial composition, and specific resident charac-
teristics on the differential risk of hospitalization for black and white NH
residents. We examined the effect of the average Medicaid per diem rate and
the presence of a bed hold policy in each state. We also examined the risk of
Address correspondence to Andrea Gruneir, Ph.D., Kunin-Lunenfeld Applied Research Unit,
BaycrestCentrefor GeriatricCare,Toronto,ONM6A2E1 (Canada). SusanC. Miller, Ph.D.,and
Vincent Mor, Ph.D., are with the Center for Gerontology and Health Care Research and the
Orna Intrator, Ph.D., are with the Center for Gerontology and Health Care Research, Brown
University, Providence, RI.
870 HSR: Health Services Research 43:3 (June 2008)
hospitalization for residents in NHs with varying proportions of black resi-
dents and for residents who were older and more functionally impaired.
This study is a subanalysis of data presented by Intrator et al. (2007). We
provide a brief description of the methods, which are fully described in the
main paper (Intrator et al. 2007).
Data from five sources were merged to create a hierarchical database. Res-
ident data were obtained from the Minimum Data Set (MDS), a federally
mandated assessment instrument (Mor 2004), and Medicare inpatient files.
NH data were obtained from the Online Survey Certification and Reporting
(OSCAR) database, which consists of the annually collected surveys required
for certified NHs. The area resource file (ARF) was used to characterize the
market, which was defined as the county (Banaszak-Holl, Zinn, and Mor
1996). Finally, we obtained data on Medicaid reimbursement policies from a
survey of state Medicaid offices conducted by the Center forGerontology and
Health Care Research at Brown University (Grabowski et al. 2004).
The sample consisted of all urban markets in the continental United States.
Inclusion of NHs was restricted to those that were free-standing and had at
least 20 beds. This resulted in a total of 8,997 NHs in 813 counties.
Resident assessments were selected from the second quarter of 2000.
Inclusion was restricted to residents who were long-stay (over 90 days) and
older than age 65. Residents were excluded if they were in a coma, in a swing-
bed facility, enrolled in an HMO during 2000 (for completeness of claims), or
could not be matched to Medicare claims. Of all 1,740,074 residents in 2000,
572,557 met these inclusion criteria.
Medicare data were used to identify residents who were hospitalized or who
died (whichever came first) within 150 days of the baseline assessment. When
there was no evidence of NH discharge, we assumed that the resident re-
not hospitalized) and residents who remained in the NH because they likely
differed on unobserved characteristics associated with hospitalization. There
were 54,870 (9.6 percent) residents who died but were not hospitalized during
State Policies. We examined average per diem Medicaid reimbursement and
the presence of a bed hold policy. In multivariable analyses, payment rates
were standardized to a $100 mean and $10 increments. A bed hold policy
guarantees that Medicaid will pay some portion of the per diem rate to the
NH if the resident is hospitalized. We grouped all states that had any form of
bed hold policy into a single category.
Resident Race. Individual race was taken from the MDS and residents were
classified as either non-Hispanic white, non-Hispanic black, or other. The
‘‘other’’ group consisted of residents identified as Hispanic, Asian/Pacific
Islander, and American Indian/Alaskan native. We combined these groups
into one category because they account for a small proportion of residents
NH Racial Composition. We categorized NHs by the percent of all residents
that were identified as non-Hispanic black. Using the distribution quartiles,
we created an ordinal variable with the following cut-offs: 0–0.83, 0.84–3.6,
3.7–12.9, 13 percent and greater.
Modifiers of the Effect of Race. Based on prior research (Mor, Papandonatos,
and Miller 2005), we chose to test age and functional ability as effect measure
modifiers. In the multivariable model, age was centered at the mean
(84 years) and standardized to 10-year increments. Functional status was
measured on a six-point scale of the resident’s ability to perform activities of
daily living (ADLs) (Morris, Fries, and Morris 1999). We categorized
residents as having: no or mild impairment (0–1), moderate impairment
(2–3), and severe impairment (4–5).
Final estimates were adjusted for several potential confounders.
Resident-level confounders included gender, education, do not resuscitate
(DNR) and do not hospitalize (DNH) orders, weight categories, cognitive
impairment (measured on the Cognitive Performance Scale [Morris et al.
1994]), diagnoses (diabetes, cancer, emphysema/COPD, and heart failure),
872 HSR: Health Services Research 43:3 (June 2008)
unstable medical condition, fever, more than nine prescribed medications,
use of antipsychotic or hypnotic medications, and hospice enrollment.
Facility-level characteristics that were controlled for included: staffing
(nurse practitioner or physician assistant, physician presence, nursing hours
per resident day, and registered nurse presence), number of beds, profit
status, chain membership, percent of residents on each Medicaid (485
percent), Medicare (415 percent), and private pay (435 percent), and
occupancy (490 percent). Aggregated MDS data were used to calculate
mean case-mix index (Fries et al. 1994).
Market variables included: per capita income, percent of population
older than 75 years, number of hospital beds per 1,000 persons older than
75 years, and adjusted hospital wage index. Market competitiveness was
quantified by the Herfindahl index and dichotomized at 0.1 to identify highly
competitive markets (Zinn 1994).
We used descriptive statistics to characterize the sample and a multilevel
model to estimate effect sizes and standard errors. We modeled the odds of
being hospitalized against the odds of remaining in the NH and censored
residents who died during follow-up; the same strategy was used in the main
study (Intrator et al. 2007).
Differential effects of race were estimatedwith multiplicative interaction
terms. We included interactions between resident race and each age, ADL
status, and state Medicaid rate. We had tested interactions between race and
bed hold policy but decided not to include them because there was no ev-
idence of a differential effect. Because of the uneven distribution of NHs that
served predominantly black residents across states (10 states had no NHs in
the top quartile), we chose not to include interactions between NH racial
composition and state policies. Descriptive statistics were carried out in SAS
(Research Triangle Institute Inc., Research Triangle Park, NC) and the mul-
tilevel model was estimated with MLWiN (Multilevel Models Project, Institute
of Education, University of London, London, U.K.).
Of the 517,687 residents who met the inclusion criteria and did not die during
follow-up, 1,605 had missing data (most commonly: DNR/DNH [1,009],
diagnoses , and race ). Of the remaining 516,082 residents, a
total of 19.3 percent were hospitalized, 18.5 percent of white and 24.1 percent
of black residents. The sample was largely female and older than 75.
Either Hospitalized or Remained in the Nursing Home by the End of the 150
Day Follow-up Period (N5516,082)
Characteristics of Long-Stay Nursing Home Residents Who Were
Less than high school
More than high school
Physical impairment (ADL)
Do not hospitalize
Do not resuscitate
874HSR: Health Services Research 43:3 (June 2008)
Eleven percent of residents were black and 4 percent were identified as
‘‘other.’’ A minority of residents had minimal ADL impairment (14.3 percent)
and the remainder was split between moderate and severe impairment
Intrator et al. (2007). Across NHs, the mean percent of residents identified as
black was 11.4 (SD518.4) but the distribution was highly skewed; nearly 25
percent of NHs had fewer than 1 percent of residents identified as black while
shown). When stratified by quartiles of the percent of residents identified as
black, clear trends in NH ownership, staffing, and resource availability
emerged (Table 2).
Identified as Black (Divided into Distribution Quartiles)
Characteristics of Nursing Homes by the Percent of Residents
Part of a chain, %
Special care unit, %
44.55 nursing hours/resident-day, %
Nurse practitioner or physician’s
Percent of nurses that are RNs,
Resident payer mix
Percent Medicare, mean (SD)
Percent Medicaid, mean (SD)
Percent private pay, mean (SD)
Resource poor facilityn, %
Number of states with X number
of NHs within each quartilew
X ? 50 NHs
NP5nurse practitioner; PA5physician’s assistant.
nResource poor designation based on overall payer mix.
wEach state is represented within each column (quartile) so that the sum of each is 48.
39.8 (20.1)36.6 (19.4) 31.8 (18.8)27.3 (18.6)
Three-quarters of states reported a bed hold policy and the average
Medicaid per diem payment rate was $104 (SD520).
Results of the multilevel model are presented in Table 3. It revealed
incremental increases in the risk of hospitalization for all residents in a NH as
the percent of black residents in the NH increased. Residents in states with a
bed hold policy were more likely to be hospitalized than were residents in
Table3: Multivariable Multi-Level Model Results
Percent of residents in NH reported black
1st quartile (lowest)
4th quartile (highest)
Bed hold policy
Medicaid rate, $10 increments
Older age, 10 year increments
self pay, occupancy, profit status, chain membership, education, DNH, DNR, diagnoses, stability
ofcondition,fever,weightandweightchange,cognitive impairment, medicationuse,andhospice.
wRace main effect. Applies to younger residents, with no/mild ADL impairment, in states with
lower Medicaid reimbursement rates.
NH5nursing home; DNR5do not resuscitate; DNH5do not hospitalize; ADL5activities of
876HSR: Health Services Research 43:3 (June 2008)
interval [CI]: 1.12–1.68). The effect of the Medicaid rate differed by resident
white residents in states with higher rates ($10 increase) showed a 4 percent
decreased probability of hospitalization (AOR: 0.96, 95 percent CI: 0.93–
1.00) while black residents showed a 22 percent decreased probability of
hospitalization (AOR: 0.78, 95 percent CI: 0.69–0.87).
risk of hospitalization. For younger, minimally impaired residents, blacks had
0.77–0.88). With increasing age, the odds of hospitalization were still lower for
black (AOR: 0.85, 95 percent CI: 0.76–0.94) compared with white residents
(AOR: 0.97, 95 percent CI: 0.95–0.98). Although impairment increased the
risk of hospitalization for all residents, the odds were substantially higher for
black residents with severe impairment than for similar whites (black AOR:
1.66, 95 percent CI: 1.40–1.96; White AOR: 1.25, 95 percent CI: 1.22–1.29).
We found that the context of NH care contributed to the differential risk of
hospitalization for Black and White residents but that state Medicaid reim-
bursement policies may have helped to ameliorate that disparity. Across NH
racial composition quartiles, trends in staffing and facility organization were
highly suggestive of lower quality in NHs with higher concentrations of black
residents. The same pattern emerged in the outcome such that regardless of a
identified as black in the NH increased.
Facility resource availability, as measured by the percent of residents on
Medicaid, private pay, and a composite descriptor of payer mix, also tracked
closely with facility racial profile. Under these conditions, it is not surprising
that black residents should benefit more than white residents from higher
Medicaid rates. Facilities with resources from private pay residents or other
sources are going to be better able to offset the lower revenue generated by
Medicaid recipients. When this affords more staff and services, it likely ben-
efits all residents in the NH. However, NHs with a greater reliance on Med-
icaid will have fewer overall resources to draw from.
These findings illustrate the role of structural inequities in creating and
maintaining racial disparities in NH care. The correlation between facility
racial composition and resource accessibility is, at least partially, perpetuated
segregation (Reed and Andes 2001; Angelelli, Grabowski, and Mor 2006;
Smith and Mor 2006; Smith et al. 2007). While the root of racial disparities in
reliant NHs could be one mechanism by which to diminish these systemic
disparities. This will be increasingly important as the site of long-term care
services increasingly shifts to community-based programs and Medicaid NHs
are pressed to provide care to more impaired residents.
residents after controlling for contextual factors suggests that individual-level
functional impairment is not surprising but the differences between black and
white residents were striking, particularly among the most impaired. The
particularly around end-of-life decisions, may partially account for these find-
ings. Mortality follow-back studies have shown that family members of black
decedents were less likely to report that death was expected, even if the res-
ident was highly impaired before death (Williams 2006), and more likely to
report problems in physician communication than were family members of
white decedents (Welch, Teno, and Mor 2005).
There are limitations to this study. We did not differentiate between
unavoidable and potentially preventable hospitalizations. Regardless, evi-
rates (Carter and Porell 2005). Second, our estimates are based on cross-
sectional data, which could be endogenous in that states with more generous
paymentratesmight have lowerratesofhospitalizationduetosomeunknown
common factor like state wealth. Third, we did not have individual data on
NH payer source. Valid measures of individual payment would have been
useful in teasing out the effects of resident versus NH resource availability and
the implications for disparities in care.
This study revealed the influence of individual and organizational factors on
the disparate risk of hospitalization for black and white NH residents. While
characteristics that affect individualized care are important, the concentration
of black residents in NHs with the fewest resources is an undeniable contrib-
878 HSR: Health Services Research 43:3 (June 2008)
utor to racial disparities in NH quality. That higher Medicaid payment rates
reduced the risk of hospitalization substantially more for black than for white
residents suggests that efforts to reduce racial disparities in NH care must
include strategies to better support Medicaid-reliant NHs.
We would like to acknowledge Jacqueline Zinn, David Grabowski, and Mark
Schleinitz for their role in developing the study. We would also like to thank
Nancy C. Grossman for her assistance in collecting the state policy data and
Jeffrey Hiris, Chris Brostrup-Jensen, and Yuwei Cang for help with the data
management and analysis. This research was supported by National Institute
on Aging grants RO1 AG20557 and RO1 AG23622 (PI on both: Vincent
Gruneir was also supported by an AARP Scholar’s Award. An earlier version
of this paper was presented as a poster at the AcademyHealth Annual Re-
search Meeting in Orlando, Florida in June 2007.
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