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Health Serv Res. 2008 June; 43(3): 869–881.
PMCID: PMC2442243

Relationship between State Medicaid Policies, Nursing Home Racial Composition, and the Risk of Hospitalization for Black and White Residents



To examine racial differences in the risk of hospitalization for nursing home (NH) residents.

Data Sources

National NH Minimum Data Set, Medicare claims, and Online Survey 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 hospitalized. Residents in NHs with high concentrations of blacks had 20 percent higher odds (95 percent confidence interval [CI]=1.15–1.25) 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 CI=0.93–1.00) for white residents and 22 percent (95 percent CI=0.69–0.87) for black residents.


Our findings illustrate the effect of contextual forces on racial disparities in NH care.

Keywords: 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 (Bernabei et al. 1998; Gambassi et al. 1998; Lapane, Fernandez, and Friedman 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 Przybylski 1998). Even at the end-of-life, the risk of hospitalization for black residents, especially older or functionally impaired residents, is higher than for white residents (Mor, Papandonatos, and Miller 2005). This has been partially 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).

Yet, individual preferences are not the sole risk factor for hospitalization. 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 particularly important in light of NH segregation by race and resources. Quality NH 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 reimbursement 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 reimbursement policies, facility racial composition, and specific resident characteristics 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 hospitalization for residents in NHs with varying proportions of black residents 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 Sources

Data from five sources were merged to create a hierarchical database. Resident 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 for Gerontology and Health Care Research at Brown University (Grabowski et al. 2004).

Study Sample

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.

Outcome: Hospitalization

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 remained in the facility. We distinguished between residents who died (but were 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 follow-up.


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 (4.1 percent).

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), 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 (>85 percent), Medicare (>15 percent), and private pay (>35 percent), and occupancy (>90 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).

Analytic Design

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 estimated with 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 evidence 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 multilevel 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 [469], and race [143]). 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. 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 (Table 1).

Table 1
Characteristics of Long-Stay Nursing Home Residents Who Were Either Hospitalized or Remained in the Nursing Home by the End of the 150 Day Follow-up Period (N=516,082)

A full description of facility and market characteristics are available from Intrator et al. (2007). Across NHs, the mean percent of residents identified as black was 11.4 (SD=18.4) but the distribution was highly skewed; nearly 25 percent of NHs had fewer than 1 percent of residents identified as black while the top percentile reported over 87 percent of residents identified as black (not 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).

Table 2
Characteristics of Nursing Homes by the Percent of Residents Identified as Black (Divided into Distribution Quartiles)

Three-quarters of states reported a bed hold policy and the average Medicaid per diem payment rate was $104 (SD=20).

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 states without a policy (adjusted odds ratio [AOR]: 1.37, 95 percent confidence interval [CI]: 1.12–1.68). The effect of the Medicaid rate differed by resident race. When compared with white residents in states with lower Medicaid rates, 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).

Table 3
Multivariable Multi-Level Model Results

Both age and ADL impairment modified the effect of resident race on the risk of hospitalization. For younger, minimally impaired residents, blacks had lower odds of hospitalization compared with whites (AOR: 0.82, 95 percent CI: 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 reimbursement 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 resident's own race, risk of hospitalization increased as the percent of residents 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 benefits all residents in the NH. However, NHs with a greater reliance on Medicaid 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 by admission patterns that reflect, or perhaps magnify, underlying community 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 NH care is complicated, our research suggests that better support of Medicaid-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.

The persistent difference in hospitalization risk between black and white residents after controlling for contextual factors suggests that individual-level disparities continue to plague NH quality. That hospitalizations increased with functional impairment is not surprising but the differences between black and white residents were striking, particularly among the most impaired. The amount and type of information that black residents and their families receive, particularly around end-of-life decisions, may partially account for these findings. Mortality follow-back studies have shown that family members of black decedents were less likely to report that death was expected, even if the resident 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, evidence shows that NHs with low hospitalization rates have lower percentages of potentially preventable hospitalizations than do NHs with high hospitalization rates (Carter and Porell 2005). Second, our estimates are based on cross-sectional data, which could be endogenous in that states with more generous payment rates might have lower rates of hospitalization due to some unknown 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 contributor 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 Mor). The data were analyzed under Data Use Agreement No. 12432. Andrea 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 Research Meeting in Orlando, Florida in June 2007.

Disclosures: None.

Disclaimers: None.


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