Abstract
Purpose of the Study: To examine how Medicaid capital reimbursement policy is associated with nursing homes (NHs) having high proportions of private rooms and small households. Design and Methods: Through a 2009/2010 NH national survey, we identified NHs having small households and high proportions of private rooms (≥76%). A survey of state Medicaid officials and policy document review provided 2009 policy data. Facility- and county-level covariates were from Online Survey, Certification and Reporting, the Area Resource File, and aggregated resident assessment data (minimum data set). The policy of interest was the presence of traditional versus fair rental capital reimbursement policy. Average Medicaid per diem rates and the presence of NH pay-for-performance (p4p) reimbursement were also examined. A total of 1,665 NHs in 40 states were included. Multivariate logistic regression analyses (with clustering on states) were used. Results: In multivariate models, Medicaid capital reimbursement policy was not significantly associated with either outcome. However, there was a significantly greater likelihood of NHs having many private rooms when states had higher Medicaid rates (per $10 increment; adjusted odds ratio [AOR] 1.13; 95% CI 1.049, 1.228), and in states with versus without p4p (AOR 1.78; 95% CI 1.045, 3.036). Also, in states with p4p NHs had a greater likelihood of having small households (AOR 1.78; 95% CI 1.045, 3.0636). Implications: Higher NH Medicaid rates and reimbursement incentives may contribute to a higher presence of 2 important environmental artifacts of culture change—an abundance of private rooms and small households. However, longitudinal research examining policy change is needed to establish the cause and effect of the associations observed.
Key Words: Culture, Nursing homes, Medicaid/Medicare, Long-term care, Economics
The transformative movement called “nursing home (NH) culture change” encompasses a variety of organizational practices that NHs undertake to change their home and work environments. One important transformation involves the move to an environment designed as a home, rather than an institution (Koren, 2010). Many physical features of traditional NHs make living in them different than living at home. Such features include sharing one’s environment with caregivers, the inability to lock one’s door, having no personal space due to the presence of a roommate, and eating meals with others (Regnier & Denton, 2009). Because NH residents commonly comment that they want to return to their home (Bowman, 2008), it is possible that designing NHs to have some of the same features as their homes in the community may improve residents’ contentment with their current homes and potentially improve their quality of life. This study compares the presence of homelike environmental attributes (i.e., environmental artifacts of culture change) among facilities in states with differing Medicaid regulations and rates. In particular, it examines whether the presence of two more costly attributes of homelike NH environments, an abundance of private rooms and the presence of smaller living units that contain kitchens and dining facilities (i.e., small households), differs when a state’s Medicaid capital reimbursement policies are aligned with promoting these environmental artifacts.
A high prevalence of private rooms is considered an environmental artifact of culture change (Bowman, 2006). Private rooms afford NH residents the solitude lacking in many traditional NH settings, and studies suggest that residing in private versus shared rooms is associated with better resident outcomes (Calkins & Cassella, 2007). However, private rooms cost more than shared rooms to construct (Calkins & Cassella, 2007), and thus facilities may be less likely to choose this option without some type of offset to these higher construction costs. One such offset is that private rooms can attract residents with higher payment sources. In fact, NH administrators report investments in private rooms and in other physical amenities (flat-screen televisions, Wi-Fi, etc.) are made to attract the short-stay Medicare rehabilitation resident (Shield, Looze, Tyler, Lepore, & Miller, 2013). On any given day, 14.4% of U.S. NH residents have Medicare as their payer and another 22.3% have other non-Medicaid payers (largely private pay and Medicare managed care) (American Health Care Association, 2013). Given these averages, we considered a private room prevalence of more than 75% to reflect a NH’s likely adoption of a culture change philosophy. However, we cannot rule out that some facilities may strive for a payer mix almost entirely of Medicare and/or private pay residents; still, that many states’ NH certificate of need regulations require facilities with Medicare-certified beds to also maintain some proportion of Medicaid beds makes this less likely (Troyer, 2002).
Although an abundance of NH private rooms is highly desirable and compatible with having a homelike environment, their presence does not necessarily signal NH adoption of culture change philosophy or of other environmental artifacts of culture change such as the elimination of long corridors or institutional dining areas. However, the introduction of small households within NHs offers this more widespread change.
The introduction of household models in NHs typically accompanies a person-centered/directed care philosophy, inclusive of consistent staff assignment and other hallmarks of NH culture change. Households provide small-scale living communities within NHs (of 10–20 residents) where all activities of daily living occur (Nelson, 2008). Households typically include kitchens, dining rooms, and living rooms; they eliminate long corridors and institutional dining areas common in traditional NHs. The Green House and other small house models extend the household model by providing residence for 7–10 elders in self-contained homes in the community (Nelson, 2008). Although rigorous research is needed to more definitively determine how the household or small house models affect quality outcomes (Shier, Khodyakov, Cohen, Zimmerman, & Saliba, 2014), pre/post comparison has shown benefits to the NH and to residents and family members with the adoption of a household model (AHRQ Innovations Exchange, 2008), and pre/post quasi-experimental study of Green House implementation has shown benefits to residents and families (Kane, Lum, Cutler, Degenholtz, & Yu, 2007; Lum, Kane, Cutler, & Yu, 2008). However, the change to a household model requires substantial financial investment. For example, for one provider organization, the funding of the design and construction of six households with 12–25 beds each cost $30 million (AHRQ Innovations Exchange, 2008). Although the transition to a household model of care is likely to be philosophy driven, this move may occur more frequently in states with Medicaid capital reimbursement policies designed to better offset these costs or to offset these costs more quickly.
Research has shown differing Medicaid policies and rates to be associated with differing NH hospitalization rates (Intrator et al., 2007), and NHs have been found to be responsive to changes in Medicaid regulations and rates (Bowblis, Crystal, Intrator, & Lucas, 2012; Feng, Grabowski, Intrator, Zinn, & Mor, 2008; S. C. Miller, Gozalo, Lima, & Mor, 2011; Mukamel, Spector, & Bajorska, 2005; Werner, Tamara Konetzka, & Liang, 2010). In relation to culture change, previous research found NHs in states with higher Medicaid reimbursement rates had more homelike environments (based on a composite culture change survey score) (S. C. Miller et al., 2013). Also, compared with NHs in states with no Medicaid pay-for-performance (p4p) reimbursement, those in states with p4p systems including culture change performance measures had a two times greater likelihood of having more homelike environments, resident-centered care and staff empowerment; in states with p4p without culture change performance measures (compared with no p4p), NHs had significantly higher environment and staff empowerment scores.
There has been little recent study of the effect of a state’s Medicaid capital reimbursement policy on NH capital spending, but this policy is important to most NHs’ profitability as the capital cost center is most likely to affect whether a facility is profitable or takes a loss (Cohen & Holahan, 1986; Swan, Harrington, de Wit, & Zhong, 1997). Despite varied payment methodologies, most states segment reimbursements into several costs centers such as direct care costs, operating costs, and capital costs, and calculate each one independently. Because the capital cost center reimburses the expense of the NH building, land, and its durable contents, states typically use a facility-specific retrospective rate within the prospective payment system (Harrington, Carrillo, Mullan, & Swan, 1998) as a way for NHs to pass through these costs to the state.
States use a number of formulas to calculate the capital cost rate; however, there are two main approaches: traditional and fair rental. The other much less frequent approach that does not treat capital costs separately is the flat-rate (or modified flat-rate) approach (not studied here). The traditional (historical) approach typically bases reimbursement for capital costs on historical costs and usually includes actual interest expense, lease payments, and sometimes the payment of a return on equity. This method typically has a high level of reimbursement at the front end as depreciation is allotted in equal increments while interest is highest just after the mortgage is taken out. The fair rental approach uses some form of replacement cost basis to determine the NH’s capital value (i.e., the asset or rate base), which is then used as the basis for payment of a fee for capital expenditures, called an imputed fair rental amount. This leads to a steadier level of reimbursement throughout the life span of the facility. Notably, some states view negatively the traditional capital reimbursement approach because its higher near term payments (creating a positive cash flow in earlier loan years) yield to negative cash flows in later years as depreciation reimbursement does not meet the principal payments on the loan. This causes a variety of perverse incentives, including refinancing or sale of the home, as well as “creative” transactions to increase reimbursement (Boerstler, Carlough, & Schlenker, 1992; Cohen & Holahan, 1986).
Although capital reimbursement reflects only 10%–50% of NH costs, reduction in its reimbursement has been a focus of states who have found overall reductions of Medicaid NH rates harder to accomplish following the Omnibus Budget Reconciliation Act of 1987 and repeal of the Boren amendment (E. A. Miller, 2006). Therefore, to contain capital costs, many states moved from a traditional to a fair rental capital reimbursement approach; one state used the fair rental approach in 1984, and by 1998, 18 states had adopted it (E. A. Miller, 2006).
Conceptual Framework
Economic theory supports the notion that NHs with better financial performance will have a greater ability to invest in the costlier environmental artifacts of culture change studied here—a high proportion of private rooms (≥76%) and the presence of small households. In this study, a NH’s payer mix and occupancy rate serves as a proxy for financial performance, and controlling for this and for other facility- and county-level variables, we hypothesize that the traditional (vs fair market value) method of capital reimbursement will be associated with greater investment. The traditional method reimburses costs more generously in the near term and thus investment will be less costly to NHs (in the near term), leading we believe to a greater presence of the study outcomes. Also, considering financial performance and given previous research findings (S. C. Miller et al., 2013), we believe NHs in states with higher Medicaid NH rates and with payment incentives for performance (p4p) will have made greater investments in the culture change artifacts studied. We speculate that these additional Medicaid dollars offset to some extent the dollars needed by NHs to cross-subsidize (with Medicare and private payment sources) the lower margins associated with Medicaid payment (Konetzka, Norton, & Stearns, 2006; Mor, Zinn, Angelelli, Teno, & Miller, 2004), thus allowing for more net revenues for investment.
Although higher net revenues may be attributable to a NH’s Medicaid regulatory environment, NH investments may be targeted to attract higher paying Medicare or paying residents, not Medicaid residents (Mukamel et al., 2005; Shield et al., 2013). Considering this, our private room outcome is set purposively high to reflect an investment with a greater likelihood of being driven by culture change and/or more reflective of common good. Also, even being in a state with traditional capital reimbursement, higher Medicaid rates and/or p4p payment may not be sufficient for a NH to offset the financial consequences of having a high proportion of (lower margin) Medicaid residents (and thus low proportions of [higher margin] Medicare and/or private pay residents); therefore, control for payer mix is essential to isolate the regulatory association.
In our conceptual framework, several other facility- and county-level factors are considered confounders. Nonprofit versus for-profit ownership is viewed as an important confounder because it is likely nonprofit NHs’ investment decisions are more value laden than those of for-profit NHs. Also, NH investments vary based on market competition and the demand for NH beds (Mukamel et al., 2005), and these factors are considered in our analyses.
Given this is a cross-sectional study, we cannot examine how regulatory/rate changes affect investment but rather we observe how the presence of facility investments differs for NHs in states with varying regulations and rates. Also, because investments require providers to prepare cost and investment requests and file for supplemental funding, etc., we would expect a time lag between policy change and capital investment. In relation to capital reimbursement policy, although we do not have change data, the presence of traditional reimbursement is likely to be long standing while more recent changes are likely to reflect a move to a fair market value versus a traditional reimbursement approach (E. A. Miller, 2006). If this is the case, our findings would be biased toward the null. In terms of higher Medicaid rates, our assumption is that rate generosity is consistent in the several years preceding our observation, and in relation to p4p, only one state (Colorado) implemented its program in 2009, whereas the others implemented p4p programs in 2002 (Iowa), 2003 (Utah), 2005 (Kansas), 2006 (Ohio), and 2007 (Georgia). Considering the timing of p4p implementation and the well-known anticipatory responses of NHs to planned regulatory change, it is expected that much of the investment observed in 2009/2010 will reflect the presence of this policy. However, we cannot know from this study whether the observed outcomes were accomplished in the long run or short run. Still, we do know that the establishment of small households is a more recent phenomenon.
This study merges data from a nationally representative survey of NH administrators with Medicaid reimbursement and policy data to understand how capital investments differ in NHs in states with differing Medicaid regulations and rates. Specifically, it expands upon previous research by focusing on how a state’s capital reimbursement policy is associated with the presence of two environmental artifacts of NH culture change requiring substantial capital investment. It also provides insights into how Medicaid p4p regulations and more generous Medicaid payments are associated with costly investments. Findings are intended to inform future regulatory research, and thus policy decisions aimed at improving the quality of care and life in U.S. NHs.
Methods
NH Survey and Study Sample
This research is part of a larger study on the implementation of culture change practices in U.S. NHs (S. C. Miller et al., 2013). The larger study collected survey data from NH directors of nursing and administrators on three culture change domains: (a) a NH’s (physical) environment, (b) staff empowerment, and (c) resident choice and decision making (i.e., resident-centered care). The selection and cognitive-based testing of survey items are described in detail elsewhere (S. C. Miller et al., 2013; Tyler et al., 2011). Surveys were administered to a stratified, proportionate random sample of directors of nursing and administrators at 4,149U.S. NHs; contact was achieved at 3,539 of these facilities. Surveys were completed between August 2009 and April 2011 (only 2.9% completed in 2011). The cooperation rate (i.e., proportion of responses when contact with a NH administrator was achieved) was 62.6% (n = 2,215). Survey weights were developed to adjust for the stratified sample design, allowing for generalizations at the national level. There was no evidence of survey nonresponse bias (Clark, Roman, Rogers, Tyler, & Mor, 2013).
Abbreviated surveys containing a subset of questions were offered, and a small number of these short surveys were completed by NH administrators (55; 2.5% of 2,215). These respondents were removed from our analyses because the short survey did not contain study outcomes (n = 2,160). Analyses found no significant differences between respondents who answered abbreviated versus full surveys, suggesting no survey bias exists. Of the 2,160 NHs remaining, 97 (4.5%) were hospital based and thus were removed. Also, given the study’s focus on the comparison of NH physical environments in states with the traditional versus fair rental approach of capital reimbursement, 335 (16.2%) NHs in 12 states with flat-rate (or modified flat-rate) capital reimbursement were removed (n = 1,728). Last, facilities with missing outcomes data (N = 36; 2.0%) or covariate data (N = 37; 2.1%) were removed, resulting in a final study n of 1,665 nonhospital-based NHs.
Variables of Interest and Other Data Sources
NH Physical Environment.
Study outcomes reflect costly capital investments and represent two (of the eight) physical environment survey items. The first item asked the administrator to indicate the percentage of the NH residents with private rooms; it provided the following response categories: 0%, 1%–4%, 5%–25%, 26%–75%, and 76%–100%. From these categorical responses, we created a dichotomous variable representing the proportion of NHs with 76% or more of its residents having private rooms. Six percent of the study NHs had 76% or more residents living in private rooms. The second question asked about the presence of households within NHs. We prefaced the question by the following statement, In order to make the nursing home less like an institution and more like a home, some nursing homes have redesigned their facilities into small households or neighborhoods that include kitchen and dining facilities. We then asked the administrator, “Do any of your residents live in SMALL HOUSEHOLDS that include kitchen and dining facilities?” Response categories were yes or no, with 7.2% of the study NH administrators responding that any residents lived in small households.
State Policies and Covariates.
A 2011 survey administered to state Medicaid officials by our institution provided data on the capital reimbursement approach used by states in 2009. Officials were asked whether the capital reimbursement approach used in 2009 was a fair rental, traditional historic, or a flat-rate (or modified flat-rate) approach. Also, in 2009, Medicaid State Plans (which contain information on capital reimbursement policies) for the 48 continental U.S. states were requested from the Center for Medicare and Medicaid Services regional offices; plans for 18 states were procured. Then, for 20 states, the plans were obtained from state Medicaid offices or their Web sites. For five states, the plans were unavailable, so information on a state’s capital reimbursement approach was obtained from state legislative or administrative codes. All information retrieved was reviewed and whether the capital reimbursement approach was traditional, fair rental, or flat-rate was determined. We classified Ohio as a traditional system, but note that, in 2009, it was undergoing a transition to a flat-rate system.
Two additional 2009 policy variables were included in the analyses—a state’s average Medicaid NH per diem reimbursement rate and whether a state had Medicaid p4p reimbursement. Unlike previous research, we did not examine p4p by whether the p4p reimbursement system did or did not include culture change performance measures because in this study, only two states had p4p with culture change measures (and study NHs were primarily from one of the two states). We obtained data on these policy variables from our organization’s 2011 survey of Medicaid officials. Data on states having Medicaid NH p4p reimbursement programs in 2009 were also obtained from a study by Werner and colleagues (2010). However, unlike Werner and colleagues, we excluded Vermont as a p4p state as it provided a bonus to qualifying NHs, not a NH per diem add-on as the other p4p states. This decision was consistent with Vermont’s survey response. Table 1 shows the distribution of the 2009 rates and polices.
Table 1.
State Average Medicaid Rates and Capital Reimbursement and Pay-for-Performance Policies (in 2009)
| State | Average medicaid rate ($) | Capital reimbursement approach | Pay-for-performance (any) | |
|---|---|---|---|---|
| Traditional | Fair rental | |||
| Alabama | 166.42 | FR | ||
| Arkansas | 143.59 | FR | ||
| California | 162.45 | FR | ||
| Colorado | 174.61 | FR | p4p (with culture change measures) | |
| Connecticut | 216.69 | FR | ||
| Delaware | 210.65 | T | ||
| Florida | 192.49 | FR | ||
| Georgia | 135.59 | T | p4p | |
| Iowa | 126.10 | T | p4p | |
| Illinois | 117.44 | T | ||
| Indiana | 151.15 | FR | ||
| Kansas | 135.21 | T | p4p | |
| Kentucky | 138.17 | T | ||
| Maryland | 218.25 | FR | ||
| Maine | 176.94 | T | ||
| Michigan | 160.08 | T | ||
| Minnesota | 162.62 | FR | ||
| Montana | 158.84 | FR | ||
| Mississippi | 176.94 | FR | ||
| North Carolina | 156.59 | T | ||
| North Dakota | 180.90 | T | ||
| Nebraska | 120.46 | T | ||
| New Hampshire | 194.97 | T | ||
| New Jersey | 174.08 | T | ||
| Nevada | 175.81 | FR | ||
| New York | 228.52 | T | ||
| Ohioa | 167.25 | T | p4p | |
| Pennsylvania | 188.70 | FR | ||
| Rhode Island | 186.49 | FR | ||
| South Carolina | 147.58 | FR | ||
| South Dakota | 114.03 | T | ||
| Tennessee | 147.38 | T | ||
| Utah | 149.95 | FR | p4p (with culture change measures | |
| Virginia | 150.23 | FR | ||
| Vermont | 180.92 | T | ||
| Washington | 164.93 | T | ||
| Wisconsin | 162.79 | T | ||
| Wyoming | 158.17 | T | ||
aIn 2009, Ohio was undergoing a transition to a flat-rate system.
Other covariates included variables derived from minimum data set, Medicare claims and enrollment data, Online Survey, Certification and Reporting (OSCAR) data, and the Area Resource File. NH OSCAR data closest in time to the date of a NH’s survey response were used. Resident aggregated data were from the 2009 minimum data set and were used to derive a variable representing the percentage of a facility’s residents who were non-Hispanic black. The following variables came from OSCAR data: the percentage of residents with Medicaid and with Medicare as a payer; for-profit status (yes/no); chain status (yes/no); government ownership (yes/no); presence of an Alzheimer’s special care unit (yes/no); facility size (number of beds); occupancy rate (standardized); and a county-level Herfindahl index and average occupancy rate. Whether the NH was in rural county, and demand variables including the proportion of population age 75+ and the percentage of the 75+ population that was non-Hispanic white were from the Area Resource File. We used a variable derived from the 2009 Medicare enrollment file to estimate the poverty level of the 75+ population; it represents the proportion of 75+ enrollees who were Medicare/Medicaid (dually) eligible. A county-level Herfindahl index was included to control for the competitiveness of the NH market; both higher market competition and excess demand have been found to be associated with greater NH investment (Mukamel et al., 2005).
Analytic Strategy
Percentages and means (with standard deviations) were used to describe the study NHs and the prevalence of outcomes. Descriptive analyses were weighted to adjust for sampling design. Multivariate logistic regression models with clustering on state were used to examine the associations between the regulatory/rate variables and study outcomes. To cluster, we used the Huber–White robust variance estimator, which produces robust standard errors of the parameter estimates by accounting for intrafacility autocorrelation. Multivariate analyses were not weighted because we could not simultaneously apply sample weights and use the Huber–White variance estimator. However, weighted nonclustered models resulted in very similar coefficient estimates. Also, multivariate sensitivity analyses with differing cutoffs for the private room outcome were conducted and supported the cutoff used. Statistical analyses were conducted using Stata, version 12.1 (“Stata: Data Analysis and Statistical Software,” 2012).
Results
Descriptive Findings
Table 2 describes the prevalence of Medicaid policies and characteristics of NHs in states with traditional and fair rental capital expenditure reimbursement approaches. Thirty-four percent of the NHs in traditional capital reimbursement states (approximately half from Ohio) also had Medicaid p4p reimbursement, whereas only 4.3% of the NHs in fair rental value states also had p4p. The average Medicaid daily per diem rate was approximately $11 more in fair rental versus tradition capital reimbursement states. In states with a fair rental reimbursement approach (vs in states with a traditional approach), NHs were more often for profit, more frequently had Alzheimer’s special care units, and less frequently resided in rural counties (Table 2).
Table 2.
Medicaid Policies and Nursing Home (NH) and County Characteristics (n = 1,665)a
| Capital reimbursement approach | |||
|---|---|---|---|
| All | Traditional | Fair rental | |
| Sample size | n = 1,665 | n = 876 | n = 789 |
| Estimated population size | N = 10,684.3 | N = 5,621.3 | N = 5,063.0 |
| % or mean (SD) | % or mean (SD) | % or mean (SD) | |
| Medicaid policies and NH characteristics | |||
| Pay-for-performance reimbursement (any) | 19.9% | 34.0% | 4.3% |
| Average Medicaid daily rate | 162.8 (28.7) | 157.3 (30.9) | 168.7 (24.8) |
| NH and county characteristics | |||
| Average percent residents with Medicaid as payer | 60.1 (21.1) | 60.5 (20.2) | 59.7 (22.0) |
| Average percent residents with Medicare as payer | 14.7 (12.3) | 14.1% (12.4) | 15.4% (12.3) |
| Average percent private pay residents | 25.2 (17.6) | 25.4 (17.1) | 24.9 (18.1) |
| Average percent non-Hispanic black | 10.9 (17.5) | 10.5 (17.1) | 11.3 (17.9) |
| Average occupancy rate | 86.4 (11.5) | 86.9 (11.2) | 85.7 (11.9) |
| For-profit ownership | 71.3% | 69.4% | 73.3% |
| Facility is part of chain | 56.6% | 56.5% | 56.8% |
| Facility is government owned | 4.6% | 5.3% | 3.7% |
| Facility has Alzheimer’s unit | 19.1% | 16.2% | 22.3% |
| Total beds | 109.9 (58.0) | 108.0 (60.8) | 111.9 (54.7) |
| Facility in rural county | 30.3% | 35.9% | 24.1% |
| Average county-level Herfindahl index | 0.19 (0.23) | 0.21 (0.23) | 0.17 (0.22) |
| Average percent county aged 75 and older | 6.8 (2.2) | 6.7 (2.0) | 6.8 (2.4) |
| Average percent county aged 75 and older non-Hispanic white | 89.5 (11.9) | 91.1 (10.5) | 87.7 (13.0) |
| Average percent county aged 75 and older dual Medicare/ Medicaid eligible | 16.1 (7.6) | 15.7 (0.07) | 16.7 (0.08) |
aWeighted analyses to adjust for sampling design.
The prevalence of weighted unadjusted study outcomes in states with differing Medicaid policy is shown in Table 3. A higher prevalence of many private beds (≥76%) is observed in states with traditional versus fair rental approaches to capital reimbursement. However, NHs residing in p4p states had a higher prevalence of both study outcomes regardless of the capital reimbursement approaches within these states.
Table 3.
Prevalence of Outcomes by State Policiesa
| Reimbursement category | Sample n | Estimated population size | NH with ≥76% of beds private (6.0% for all) | NH with small households (7.2% for all) |
|---|---|---|---|---|
| Traditional | ||||
| No Medicaid pay-for-performance reimbursement | 583 | 3692.5 | 5.4% | 6.8% |
| Any Medicaid pay-for-performance reimbursement | 293 | 1901.5 | 6.8% | 10.4% |
| Fair rental | ||||
| No Medicaid pay-for-performance reimbursement | 752 | 4870.0 | 6.0% | 6.4% |
| Any Medicaid pay-for-performance reimbursement | 37 | 220.3 | 7.5% | 10.0% |
Note: NH = nursing home.
aWeighted analyses to adjust for sampling design.
Multivariate Results
Multivariate models show no statistically significant association between capital reimbursement policy and the study outcomes (Table 4). However, a higher Medicaid rate (per $10 increment) and being in a state with a p4p payment system were both significantly associated with greater odds of NHs having a high proportion of private rooms (adjusted odds ratio [AOR] 1.13; 95% CI 1.049, 1.229 and AOR 1.78; 95% CI 1.044, 3.036, respectively). In relation to small households, NHs in states with a Medicaid p4p reimbursement system had a greater likelihood of having small household (AOR 1.68; 95% CI 0.954, 2.963; p = .076). Full model results are shown in Supplementary Appendices A and B.
Table 4.
Multivariate Logistic Regression Resultsa,b
| Model 1 | Model 2 | Model 3 | |
|---|---|---|---|
| AOR (95% CI) | AOR (95% CI) | AOR (95% CI) | |
| Outcome: private rooms ≥76% | |||
| Traditional | 0.91 (0.479, 1.742) | 1.10 (0.632, 1.927) | 0.946 (0.533, 1.681) |
| Medicaid rate (per $10 increment) | 1.12 (1.036, 1.216)* | 1.13 (1.049, 1.228)* | |
| Pay-for-performance reimbursement | 1.781 (1.044, 3.036)** | ||
| Outcome: presence of small households | |||
| Traditional | 1.22 (0.764, 1.935) | 1.11 (0.737, 1.669) | 0.93 (0.578, 1.512) |
| Medicaid rate | 0.93 (0.853, 1.022) | 0.93 (0.855, 1.018) | |
| Pay-for-performance reimbursement | 1.68 (0.954, 2.963)*** | ||
Note: AOR = adjusted odds ratio.
aClustered by state of nursing home.
bModels controlled for percentage of residents with Medicaid, Medicare, and who were non-Hispanic black; for nursing home, for-profit status, chain status, government owned status, presence of Alzheimer’s special care unit, size of facility (number of beds), occupancy rate; and for rural county location, county-level Herfindahl index, percentage of the county population aged 75 and older, percentage of county population aged 75 and older that was non-Hispanic white, and percentage of the Medicare population aged 75 and older that were Medicare/Medicaid dual eligible.
*p ≤ .01. **p < .05. ***p < .10.
Discussion
Findings did not support our hypothesis that a more generous capital reimbursement policy (a traditional vs fair market value approach) is associated with NH investment in the two costly environmental artifacts of culture change studied. However, similar to previous research (S. C. Miller et al., 2013) and similar to research by Grabowski, Elliot, Leitzell, Cohen, and Zimmerman (2014), we did find a state’s Medicaid rate and the presence of Medicaid p4p regulation were associated with the presence of these costly investments. In states with higher Medicaid rates and p4p regulation, NHs had a significantly greater likelihood of an abundance of private rooms (≥76%). Also, in states with (vs without) p4p, NHs had a greater likelihood of having small households. Although findings do not support our capital reimbursement hypothesis, they do support the notion that higher NH Medicaid rates and p4p Medicaid regulation may contribute to a higher presence of two important environmental artifacts of culture change. Still, longitudinal research examining policy change is needed to determine the cause and effect of the associations observed.
Our null finding relating to capital reimbursement policy may in part speak to other unobserved differences in states with traditional versus fair rental value reimbursement approaches. It is possible, for example, that states that switched to the fair rental model (in the 1990s) also put other unobserved policy mechanisms into place. Also, as aptly described by E. A. Miller, Mor, Grabowski, and Gozalo (2009) “The Devil’s in the Details,” and we did not study whether differing features of states’ traditional or fair rental capital reimbursement approaches may modify the associations observed. In this regard, we did not consider whether or how capital spending for renovations, new additions, or other physical plant change may increase a facility’s capital expenditure rate (i.e., whether “rebasing” of rates is allowed and the specifics of rebasing policy). For example, we would expect NH investments to be greater when rebasing is allowed and is done more frequently and with current cost report data (reflecting more current expenditures). However, some states rebase reimbursement rates annually, whereas others do so every 2 years (E. A. Miller et al., 2009). This is of interest for future study as are case studies of states with differing capital reimbursement policies.
Similar to previous research (S. C. Miller et al., 2013) and to findings by Grabowski and colleagues (2014), we found higher Medicaid NH reimbursement rates to be significantly associated with NHs having a high proportion of private rooms (≥76%).
Also, Medicaid p4p payment regulation was significantly associated with a high proportion of private rooms. In this research, we considered the high presence of private rooms (≥76%) to represent a culture change artifact because we believed NHs who invest this disproportionately in private rooms would have more of a value-driven motivation. In fact, we found facilities with this high proportion of private rooms also had a significantly higher culture change composite scores (derived from all survey responses). Congruent with our conceptual framework, NHs in states with higher rates and p4p payment may be better equipped financially to make the costly investments studied. Conversely, higher rates and/or p4p may be markers of other unmeasured state attributes.
NHs in states with p4p payment regulation also had a greater likelihood of having small households (p = .076). Our previous culture change research found NHs in states with p4p had a greater likelihood of having a higher NH environment domain score, but the effect size was almost double for NHs in states having p4p programs including culture change performance measures versus no such measures (S. C. Miller et al., 2013). However, in the present study, we dichotomized the p4p policy variable (state did or did not have any Medicaid p4p) because only 37 NHs were in two states with p4p programs that included culture change measures were included in our analyses (and most NHs were in one of the two states). Still, for sensitivity analyses, we ran multivariate models with two p4p variables indicating p4p with or without culture change measures (compared with no p4p). For the private room model, the adjusted odds ratios and confidence intervals for the two p4p variables were almost identical (data not shown). For the household model, the effect sizes and significance levels were similar for the two p4p payment variables (AOR 1.78; 95% CI 0.850, 3.716 for p4p with and AOR 1.66; 95% CI 0.870, 3.172 for p4p without culture change measures). As speculated, therefore, it may be that the additional dollars available through p4p programs enable greater NH capital investment. However, given this study’s small number of NHs in states having p4p with culture change measures, and considering that more states are adopting such measures (see below paragraph), additional research is recommended to more fully understand the impact of incorporating culture change performance measures into NH p4p programs.
Similar to findings by Grabowski and colleagues (2014) and as surmised from our conceptual framework, we found NHs with higher proportions of Medicaid residents had a lower likelihood of having a high proportion of private rooms and small households. Conversely, with higher proportions of Medicare residents, NHs had higher likelihoods of these environmental artifacts of culture change (see Supplementary Appendices). It is likely that for NHs with high proportions of Medicaid residents, higher Medicaid rates or p4p programs may be insufficient to offset the lower revenues resulting from a mostly Medicaid payer mix. Therefore, a restructuring of p4p programs to provide larger or better targeted incentives may be indicated. The Ohio NH Medicaid program has begun to embark on such a restructured p4p program. Ohio made two major changes to its NH Medicaid p4p program—it increased the proportion of the Medicaid per diem rate affected by a NH’s performance (from 1.7% to 10%), and it added quality indicators focusing on culture change aligned practices (i.e., resident choice) and environmental attributes (i.e., private rooms, other). Longitudinal tracking and study of such state Medicaid NH efforts is critical to gain an improved understanding of how increasing and better targeting change incentives may affect desired practice change, and ultimately resident outcomes.
Limitations
This study has limitations that deserve comment. Importantly, this is a cross-sectional study; thus, findings reflect associations observed and not cause and effect relationships. In relation to this, there may be other unobserved state attributes (perhaps reflecting state generously) or reimbursement approaches/rules that affect the willingness of NHs to make the costly investments studied, thus confounding our results. Also, we did not examine the details of the capital reimbursement and p4p approaches/programs studied, and such examination could provide important insights. Furthermore, we do not know from this study the extent to which the capital investments observed reflected short- and/or long-run investments, and longitudinal research is needed to better understand the timing of policy changes and capital investment. Last, the study is generalizable to nonhospital-based NHs in the 38 study states but not to all U.S. NHs.
Conclusions
Although we did not find a state’s regulatory approach to NH capital reimbursement (i.e., traditional vs fair rental) to be significantly associated with NH investment in the costly environmental attributes of culture change studied, the availability of higher Medicaid reimbursement rates and p4p opportunities to increase individual rates was associated with NHs having an abundance of private rooms (≥76%) and small households. This higher availability of Medicaid dollars may allow NHs to offset to some extent the dollars needed to cross-subsidize (with Medicare and private payment sources) the lower margins associated with Medicaid payment; therefore, NHs may be in a better financial position to make the more costly investments studied. If state regulators wish to improve the rate of uptake of culture change initiatives, they must consider whether their state’s Medicaid reimbursement approaches and regulations clearly encourage or discourage culture change investments, and whether they represent sufficient monetary value to incentivize NHs with higher Medicaid case mixes. NH design modifications in accordance with culture change have the potential to affect the common good. Therefore, the importance of culture change investment should be among considerations legislators (and their aides) weigh when they write Medicaid regulations and rules. NH administrators may be more willing to incur capital cost to advance a culture change agenda when reimbursement systems are clear about their goals and provide meaningful monetary rewards.
Supplementary Material
Supplementary material can be found at: http://gerontologist.oxfordjournals.org.
Funding
This research was made possible by a grant from The Retirement Research Foundation (2008-086) and from the Shaping Long Term Care in America Project funded by the National Institute on Aging (1P01AG027296).
Acknowledgment
During this study, Mr. N. Cohen was a student in the Department of Community Health at Brown University.
References
- AHRQ Innovations Exchange (2008). Creation of households program in nursing home improves residents’ health status, reduces staff turnover, and boosts demand for services Rockville, MD: Agency for Healthcare Research and Quality. Retrieved from http://www.innovations.ahrq.gov/content.aspx?id=2051 [Google Scholar]
- American Health Care Association (2013). LTC stats: Nursing facility patient characteristics report -- June 2013 update Retrieved from http://www.ahcancal.org/research_data/oscar_data/NursingFacilityPatientCharacteristics/LTC%20STATS_HSNF_PATIENT_2013Q2_FINAL.pdf
- Boerstler H., Carlough T., Schlenker R. E. (1992). Nursing home capital reimbursement: Issues for education of health care administrators. The Journal of Health Administration Education, 10, 563–572 [PubMed] [Google Scholar]
- Bowblis J. R., Crystal S., Intrator O., Lucas J. A. (2012). Response to regulatory stringency: The case of antipsychotic medication use in nursing homes. Health Economics, 21, 977–993. 10.1002/hec.1775 [DOI] [PubMed] [Google Scholar]
- Bowman C. S. (2006). Development of the artifacts of culture change tool Retrieved from Edu-Catering, LLP website: http://www.artifactsofculturechange.org/Data/Documents/artifacts.pdf
- Bowman C. S. (2008). The environmental side of the culture change movement: Identifying barriers and potential solutions to furthering innovation in nursing homes. Paper presented at the Creating Home in the Nursing Home: A National Symposium on Culture Change and the Environment Requirements, Washington, DC Retrieved from http://www.pioneernetwork.net/Data/Documents/Creating-Home-Bkgrnd-Paper.pdf [Google Scholar]
- Calkins M., Cassella C. (2007). Exploring the cost and value of private versus shared bedrooms in nursing homes. The Gerontologist, 47, 169–183 [DOI] [PubMed] [Google Scholar]
- Clark M. A., Roman A., Rogers M. L., Tyler D. A., Mor V. (2013). Surveying multiple health professional team members within institutional settings: An example from the nursing home industry. Manuscript submitted for publication. [DOI] [PMC free article] [PubMed]
- Cohen J., Holahan J. (1986). An evaluation of current approaches to nursing home capital reimbursement. Inquiry, 23, 23–39 [PubMed] [Google Scholar]
- Feng Z., Grabowski D. C., Intrator O., Zinn J., Mor V. (2008). Medicaid payment rates, case-mix reimbursement, and nursing home staffing–1996-2004. Medical Care, 46, 33–40. 10.1097/MLR.0b013e3181484197 [DOI] [PubMed] [Google Scholar]
- Grabowski D. C., Elliot A., Leitzell B., Cohen L. W., Zimmerman S. (2014). Who are the innovators? Nursing homes implementing culture change. The Gerontologist, 54(Suppl. 1), S65–S75. 10.1093/geront/gnt144 [DOI] [PubMed]
- Harrington C., Carrillo H., Mullan J., Swan J. H. (1998). Nursing facility staffing in the states: The 1991 to 1995 period. Medical Care Research and Review, 55, 334–363 [DOI] [PubMed] [Google Scholar]
- Intrator O., Grabowski D. C., Zinn J., Schleinitz M., Feng Z., Miller S., Mor V. (2007). Hospitalization of nursing home residents: The effects of states’ Medicaid payment and bed-hold policies. Health Services Research, 42, 1651–1671. 10.1111/j.1475-6773.2006.00670.x [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kane R. A., Lum T. Y., Cutler L. J., Degenholtz H. B., Yu T. C. (2007). Resident outcomes in small-house nursing homes: A longitudinal evaluation of the initial green house program. Journal of the American Geriatrics Society, 55, 832–839. 10.1111/ j.1532-5415.2007.01169.x [DOI] [PubMed] [Google Scholar]
- Konetzka R. T., Norton E. C., Stearns S. C. (2006). Medicare payment changes and nursing home quality: Effects on long-stay residents. International Journal of Health Care Finance and Economics, 6, 173–189. 10.1007/s10754-006-9000-9 [DOI] [PubMed] [Google Scholar]
- Koren M. J. (2010). Person-centered care for nursing home residents: The culture-change movement. Health Affairs (Millwood), 29, 312–317. 10.1377/hlthaff.2009.0966 [DOI] [PubMed] [Google Scholar]
- Lum T. Y., Kane R. A., Cutler L. J., Yu T. C. (2008). Effects of Green House nursing homes on residents’ families. Health Care Financing Review, 30, 35–51 [PMC free article] [PubMed] [Google Scholar]
- Miller E. A. (2006). Explaining incremental and non-incremental change: Medicaid nursing facility reimbursement policy, 1980–98. State Politics & Policy Quarterly, 6, 117–150. 10.2307/41289383 [Google Scholar]
- Miller E. A., Mor V., Grabowski D. C., Gozalo P. L. (2009). The devil’s in the details: Trading policy goals for complexity in Medicaid nursing home reimbursement. Journal of Health Politics, Policy and Law, 34, 93–135. 10.1215/03616878-2008-993 [DOI] [PubMed] [Google Scholar]
- Miller S. C., Gozalo P., Lima J. C., Mor V. (2011). The effect of Medicaid nursing home reimbursement policy on Medicare hospice use in nursing homes. Medical Care, 49, 797–802. 10.1097/Mlr.0b013e318223c0ae [DOI] [PMC free article] [PubMed] [Google Scholar]
- Miller S. C., Looze J., Shield R., Clark M. A., Lepore M., Tyler D. … Mor V. (2013). Culture change practice in U.S. nursing homes: Prevalence and variation by state Medicaid reimbursement policies. The Gerontologist. Advance online publication. 10.1093/geront/gnt020 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mor V., Zinn J., Angelelli J., Teno J. M., Miller S. C. (2004). Driven to tiers: Socioeconomic and racial disparities in the quality of nursing home care. The Milbank Quarterly, 82, 227–256. 10.1111/j.0887-378X.2004.00309.x [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mukamel D. B., Spector W. D., Bajorska A. (2005). Nursing home spending patterns in the 1990s: The role of nursing home competition and excess demand. Health Services Research, 40, 1040–1055. 10.1111/j.1475-6773.2005.00394.x [DOI] [PMC free article] [PubMed] [Google Scholar]
- Nelson G. G. (2008). Household models for nursing home environments Retrieved from Pioneer Network in Culture Change website: http://pioneernetwork.net/Data/Documents/NelsonHousehold- ResidentialPaper.pdf
- Regnier V., Denton A. (2009). Ten new and emerging trends in residential group living environments. NeuroRehabilitation, 25, 169–188. 10.3233/NRE-2009-0514 [DOI] [PubMed] [Google Scholar]
- Shield R. R., Looze J., Tyler D. A., Lepore M. J., Miller S. C. (2013). Why and how do nursing homes implement culture change practices? Insights from qualitative interviews in a mixed methods study. Journal of Applied Gerontology. Advance online publication. 10.1177/0733464813491141 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Shier V., Khodyakov D., Cohen L. W., Zimmerman S., Saliba D. (2014). What does the evidence really say about culture change in nursing homes? The Gerontologist, 54(Suppl. 1), S6–S16. 10.1093/geront/gnt147. [DOI] [PubMed]
- Stata: Data Analysis and Statistical Software (Version 12) [Software]. (2012). College Station, TX: StataCorp LP; Retrieved from http://www.stata.com [Google Scholar]
- Swan J. H., Harrington C., de Wit S. K., Zhong M. (1997). State-imposed limits on Medicaid reimbursement for nursing facility care. American Journal of Public Health, 87, 1211–1213 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Troyer J. L. (2002). Cross-subsidization in nursing homes: Explaining rate differentials among payer types. Southern Economic Journal, 68, 750–773 [Google Scholar]
- Tyler D. A., Shield R. R., Rosenthal M., Miller S. C., Wetle T., Clark M. A. (2011). How valid are the responses to nursing home survey questions? Some issues and concerns. The Gerontologist, 51, 201–211. 10.1093/geront/gnq095 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Werner R. M., Tamara Konetzka R., Liang K. (2010). State adoption of nursing home pay-for-performance. Medical Care Research and Review, 67, 364–377. 10.1177/1077558709350885 [DOI] [PubMed] [Google Scholar]