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RESEARCH ARTICLE |
a Department of Health Care Organization and Policy, School of Public Health, University of Alabama at Birmingham
David C. Grabowski, Department of Health Care Organization & Policy, RPHB 330, 1530 3rd Avenue South, Birmingham, AL 35294-0022 E-mail: grabowsk{at}uab.edu.
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Abstract |
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Methods. Linking national data from the On-Line Survey, Certification, and Reporting system, the Area Resource File, and aggregate reimbursement information, the author examined the effect of Medicaid reimbursement on a range of quality measures in the context of CON and moratoria.
Results. An increase in Medicaid reimbursement improved quality as measured by professional staffing, but there was not a statistically significant effect when quality was measured by nonprofessional staffing, various procedural measures, or regulatory deficiencies. However, this study did not support previous research showing a negative effect of Medicaid reimbursement on nursing home quality in the context of CON laws.
Discussion. This study supports recent trends suggesting that nursing home CON laws may be lessening in importance for the nursing home market. Nevertheless, further work is necessary to determine the quality returns to increased Medicaid reimbursement.
Aseries of studies and reports have documented poor quality for Medicaid residents within nursing homes over the last three decades (e.g.,
Institute of Medicine, Committee on Nursing Home Regulation 1986
;
U.S. General Accounting Office 1998;
U.S. Senate, Subcommittee on Long-Term Care, Senate Special Committee on Aging 1974). A straightforward policy initiative to address this issue of low nursing home quality would be to increase the Medicaid reimbursement rate. Medicaid, the dominant purchaser of nursing home services in the United States, gives financially indigent individuals access to nursing homes by directly reimbursing homes for the care of Medicaid residents. States have broad discretion in determining the method and level of Medicaid reimbursement. Building on the pioneering work of
Scanlon 1980, researchers have questioned the competitiveness of the nursing home market and the effect an increase in Medicaid reimbursement may have on quality. Scanlon posited that bed constraint policies, including certificate-of-need (CON) regulation and bed construction moratoria, place the market in a state of "excess Medicaid demand" where all eligible Medicaid individuals are not able to gain access to a facility. Because of this excess demand, an increase in Medicaid reimbursement may actually serve as a disincentive to the provision of quality (
Gertler 1989;
Nyman 1985). In light of trends showing that excess demand may be lessening in many nursing home markets, in this study I tested whether Medicaid reimbursement increases the quality of care within nursing homes using recent data for all U.S. nursing homes.
Although some states are now repealing them, most states still regulate entry and expansion of firms in their nursing home markets through CON laws. CON laws require that before new firms can enter a market or existing firms can expand, a case must be made to a government health services planning agency that there is a clinically legitimate need for care in the market that cannot now be met with resources available. The stated rationale of these polices is to limit the supply of beds and thereby contain utilization and Medicaid spending. If the bed supply is constrained, it is thought that the resulting excess demand will compel those who would otherwise be nursing home residents to "choose" an alternative form of care such as informal care from family members or formal home- and community-based services. Some states have also used construction moratoria regardless of need. As of 1996, 44 states had CON or construction moratoria policies in place (
Harrington, Swan, Nyman, and Carrillo 1997).
In examining the nursing home market, economic analyses (e.g.,
Cohen and Spector 1996;
Dusansky 1989;
Gertler 1989;
Nyman 1985) have typically employed an excess demand paradigm where CON or construction moratoria policies constrain the supply of nursing home beds. Within this excess demand model, a nursing home first satisfies private-pay demand and then fills the remaining beds with lower paying Medicaid residents. As a result, private-pay demand is still met under a bed constraint, but excess Medicaid demand exists. Despite the differential rates charged by these two payer types, nursing homes are required by law to provide the same level of quality to all residents regardless of payer source (
Health Care Financing Administration 1995). In addition to this legal constraint,
Gertler and Waldman 1992 noted that certain nursing home services such as medical care and dietary services may be produced jointly for both payer types and exhibit economies of joint production. Because of excess Medicaid demand, however, nursing homes do not view a Medicaid payment as a reward for quality because potential Medicaid residents are available regardless of whether minimum quality is exceeded or not. Thus, a higher Medicaid rate is associated with a lower return to raising quality to attract private-pay residents. An increase in the Medicaid rate may paradoxically decrease quality under a bed constraint policy by raising the opportunity cost of providing quality to private-pay residents.
An important series of studies (
Gertler 1989,
Gertler 1992;
Nyman 1985,
Nyman 1988a,
Nyman 1988b,
Nyman 1989b) have tested this relationship between the Medicaid reimbursement rate and the quality of nursing home care under CON policies. Utilizing 1980 New York State data, the Gertler studies used a reduced form model, and quality was represented by the inputs a nursing home uses to produce the goods and services it provides to its patients. In contrast, the Nyman studies used ordinary least squares and two-stage least squares models, and quality was measured by the number of violations for all Wisconsin nursing homes using 1979 or 1983 nursing-home-level data.
Given these varying econometric methodologies and data sources, these studies support the assertion that a change in reimbursement has a paradoxical effect on quality in the context of a bed constraint. First,
Nyman 1985,
Nyman 1988b,
Nyman 1989b found no evidence that quality is higher within homes receiving higher reimbursement rates. In fact, quality actually decreases when Medicaid rates are increased. Second,
Nyman 1988a found that nursing homes located in counties with a tighter bed supply spend significantly less money on resident care. Third,
Gertler 1989 found that an increase in the return on Medicaid residents induces homes to admit more Medicaid residents and to lower quality. Similarly,
Gertler 1992 showed that an increase in the Medicaid reimbursement rate improves access for Medicaid residents, but at the expense of increasing overall Medicaid expenditures and lowering overall quality. Gertler estimated that a 10% increase in total Medicaid expenditures results in a 4.1% increase in Medicaid residents receiving care and a 3.4% decrease in quality.
Although these results from the late 1970s and early 1980s show evidence of this counterintuitive relationship between Medicaid reimbursement and quality, recent studies of the market for nursing home care have found that CON and construction moratorium policies may no longer be important in constraining the growth of the nursing home market. Occupancy rates, an indirect measure of excess demand, have been declining over the last two decades. The national occupancy rate was 92.9% in 1977, 91.8% in 1985, and 87.4% in 1995 (
Strahan 1997). Furthermore, there has been an increase in the number of empty beds per 1,000 elderly (aged 65 and older) individuals. Using 1969 and 1973 data from 43 states,
Scanlon 1980 found 5 empty beds per 1,000 elderly individuals. Using national 1995 and 1996 data, a recent study found 10 empty beds per 1,000 elderly individuals (
Grabowski 1999). More directly, a recent study by
Nyman 1993 supported the hypothesis that excess demand is lessening. Using a three-part test, he found no evidence of excess demand in three states (Wisconsin, Minnesota, and Oregon) in 1988, despite evidence of excess demand 5 years earlier in Wisconsin (
Nyman 1989a).
In terms of recent work directly addressing the effect of the Medicaid reimbursement rate on the provision of nursing home quality, I examined this issue using 1995–96 data for all U.S. nursing homes (
Grabowski 1999). In this study, a multipart model was estimated and nursing home quality was represented by the proportion of residents without facility-acquired pressure ulcers. I found that reimbursement has a positive effect on quality for facilities that care for both Medicaid and private-pay residents, regardless of the tightness of the market. In the case of high Medicaid homes (i.e., homes with predominantly Medicaid residents), higher Medicaid reimbursement increases quality in loose markets, but there is little effect in tighter markets. Nevertheless, this study indicates that there may be a positive relationship between reimbursement and quality for the majority of homes.
Although my 1999 results showed that the importance of CON may be lessening for the nursing home industry, a potential criticism of this study is that it employed only a single measure of quality (i.e., facility-acquired pressure sores). In the current study I extended this analysis by using recent national nursing home data to consider the effect of the Medicaid reimbursement rate on several additional measures of nursing home quality, including professional and nonprofessional staffing levels, medication error rates, physical restraint use, catheter use, feeding tube use, and the number of home-level deficiencies. I found that—regardless of CON regulation—an increase in Medicaid reimbursement improves quality according to a professional staffing measure but has no statistically significant effect according to procedural or composite quality measures. Furthermore, this research does not validate the findings from earlier work that an increase in Medicaid reimbursement decreases quality under excess demand conditions.
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). Collected and maintained by the Health Care Financing Administration (HCFA), the OSCAR data are used to determine whether homes are in compliance with federal regulatory requirements. Every facility is required to have an initial survey to verify compliance. Thereafter, states are required to survey each facility no less often than every 15 months, and the average is about 12 months (
Harrington, Carillo, Thollaug, and Summers 1998). The data for this analysis were collected within the 15-month interval of October 1, 1995, and December 31, 1996. The approximately 1,500 Medicare-only (rehabilitative) homes contained in OSCAR were eliminated from the data set because they were not applicable to this analysis. Furthermore, approximately 200 dually Medicaid-Medicare certified homes were also eliminated from the data set because they served at least 70% Medicare residents and were considered to be primarily rehabilitative care facilities. Nursing homes in the state of Alaska were eliminated because market-level data were not available for this state. Additionally, a small number of homes in Virginia were also eliminated because there was difficulty in linking these homes with their respective county. Thus, the final OSCAR data used within the empirical analysis contained 15,067 homes nationally. As a related point, there were still a small number of Medicare residents (roughly 7%) within dually certified homes. I dealt with these Medicare residents by eliminating them from both the numerator and the denominator of this analysis. Because the OSCAR system is collected at the facility level, I made the assumption that quality is equally distributed across Medicare and non-Medicare residents in eliminating these residents from the analysis.
I used three other data sources to supplement the OSCAR data. First, the OSCAR data were merged with aggregate county-level demographic, socioeconomic, and health status data from the Bureau of Health Professions' Area Resource File. Second, state-level Medicaid reimbursement methods and levels were obtained from several editions of the State Data Book on Long-Term Care Program and Market Characteristics (
Harrington, Swan, Bedney, Carillo, and Studer 1996;
Harrington, Swan, Griffin, et al. 1998). Finally, the 1995 and 1996 HCFA area wage indexes were linked with the data.
Variables
Because nursing home quality is a multidimensional construct encompassing the preferences of multiple actors including policymakers, health care professionals, administrators, owners, investors, third-party insurers, and consumers (
Davis 1991), I used multiple measures of quality in the empirical analyses.
Donabedian 1966 formulated a definition of health care quality based on the triad of structural factors, process-of-care variables, and outcomes or end results of that care. Using this framework, I employed a range of quality measures including professional and nonprofessional staffing levels, medication error rates, physical restraint use, catheter use, feeding tube use, and number of home-level deficiencies (see Table 1 for summary statistics).
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I also used four procedural measures to represent quality. The first measure was the medication error rate for the facility, which is determined by a survey of a select number of medication passes within the home (
Health Care Financing Administration 1995). Given the recent concerns regarding medical errors in health care delivery (
Institute of Medicine 2000), this measure is a timely and important indicator of quality care. The other three procedural measures, the use of urethral catheters, feeding tubes, and physical restraints, may all have a negative effect on resident outcomes. Because labor constitutes 60–70% of nursing home costs, these procedures may be employed as labor-saving practices on the part of nursing homes with potential negative consequences for resident health. Urethral catheterization places the resident at greater risk for urinary tract infection, which may result in hospitalization. Other long-term complications include bladder and renal stones, abscesses, and renal failure. The use of feeding tubes can result in complications including self-extubation, infections, aspiration, unintended misplacement of the tube, and pain. Finally, immobility resulting from the use of physical restraints may increase the risk of pressure ulcers, depression, and mental and physical deterioration and increase the risk of mortality (
Zinn 1993).
The final measure of quality was the number of facility-level deficiencies assigned as part of the Medicaid certification process. Under the direction of HCFA, state surveyors use 175 consolidated measures encompassing structural, procedural, and outcome measures of quality to assign deficiencies. The process measures include whether proper procedures are used in providing each of the major nursing home services. The outcome measures are intended to ensure that problems do not occur within the facility. Examples of negative outcomes that a home may be cited for include a preventable reduction in the range of motion or a failure to maintain acceptable parameters of nutritional status. When a facility fails to meet one of these standards, a deficiency or citation is given to the facility. Several alternative remedies may be imposed on facilities that receive a high number of deficiencies. These punishments include civil penalties of up to $10,000, denial of payment for new admissions, state monitoring, temporary management, immediate termination, and other approaches. The extent and type of enforcement actions depend on the scope of problems (whether deficiencies are isolated, constitute a pattern, or are widespread) and the severity of violations (whether there is risk or harm to the residents;
Institute of Medicine 1996).
In this analysis, I used the county to approximate the market for nursing home care. Most economic studies have used the county as a proxy for the nursing home market (e.g.,
Cohen and Spector 1996;
Nyman 1985;
Zinn 1994). The county may be a reasonable approximation of the market for nursing home care given patterns of funding and resident origin (
Banaszak-Holl, Zinn, and Mor 1996). For example, federal block grant funds for long-term care services are distributed at the county level. Furthermore,
Gertler 1989 found that 75% of residents in New York State facilities had previously lived in the county where the home was located. Similarly,
Nyman 1994 found 80% of residents in Wisconsin facilities chose a nursing home located in the county in which they had resided.
The Medicaid nursing home reimbursement rate is the average rate for the state (M = $86.74). Some states reimburse Medicaid residents in hospital-based nursing homes using a different rate as opposed to the standard rate used in freestanding facilities. In these states, a dummy variable is utilized to indicate this difference (M = 19%). States broadly employ one of four reimbursement methodologies—prospective (77% of all facilities), combination (6%), flat rate (15%), or retrospective (2%). Importantly, whereas almost every state used a retrospective-based system in the 1970s, states have almost universally moved away from this form of reimbursement. In 1995, Pennsylvania and Nebraska were the only states using purely retrospective-based systems, and by 1996, only Nebraska employed this type of system. In practice the various methodologies employed by states can become quite complex, but the systems differ in two other broad ways. First, some states allow an upward adjustment in their prospective rates based on cost information that becomes available during the rate period (M = 41%). Second, a number of states employ case-mix reimbursement methods that pay different rates based on a home's mix of resident needs and the costs of caring for those needs (M = 54%). I used dummy variables to indicate these various differences in payment systems across states.
A range of other facility and market-level characteristics were included in the analysis as independent variables. The first facility characteristic was the total number of residents within a nursing home (M = 96.5 residents). The second facility characteristic was the profit status of the nursing home. Nursing homes operate under one of three categories: for profit (69% of all facilities), not for profit (24%), and government (6%). The third facility characteristic was whether the home is part of a multiple-facility chain. Fifty-three percent of all homes included within this analysis were a member of a group or chain of homes. The fourth facility-level characteristic was whether the nursing home is a hospital-based facility. In 1995–96, 6.5% of all homes were hospital based. Finally, the empirical analysis had to control for the health characteristics of the nursing home resident population, because homes with a sicker resident pool would have more disabled bedridden residents, and thus more bedsores. I included three variables measuring resident acuity within the analysis to control for this issue of case-mix. The first variable was the number of activities of daily living (ADLs) with which the average resident received assistance (M = 3.6 ADLs). The second measure was the proportion of residents requiring skilled nursing care (M = 31%). The final measure was a case-mix index expressed in minutes of staff time typically required for the care of residents on the basis of their dysfunctions and the subsequent procedures they require (M = 134 min). I estimated minutes using weights based on the Management Minutes system designed by Bill Thoms (
Cohen and Spector 1996). Because this measure incorporated the number of residents with pressure sores and the use of tube feedings and catheters, this measure of case-mix was used only in the composite (i.e., deficiencies) and staffing models.
In terms of market-level characteristics, I included a Herfindahl index to help gain insight into the effect of the structure of competition within the marketplace. I constructed this index by summing the squared market shares of all facilities in the county. The index ranged from 0 to 1, with higher values signifying a greater concentration of facilities (M = 0.50). The second market-level measure was the number of empty beds in the market per elderly individuals aged 75 and older. This measure was a proxy for the degree of excess demand present within the county. On average, there were 30 empty beds per 1,000 elderly individuals. The density of individuals aged 75 and older per square mile within the county was also included in the model. On average, there were 9 elderly individuals per square mile within the county where the nursing home was located. This analysis also included the median income within the market. The average median income within this analysis was $16,791. To help account for different input prices faced by firms, I also included the 1995–96 HCFA hospital area wage index in the model (M = $8,263).
Analytic Strategy
This multipart estimation strategy was constructed around the idea that a change in the Medicaid reimbursement rate may affect both a facility's payer mix and the provision of quality (given a particular payer mix). I combined estimates from each of the model's three stages to measure the overall effect of Medicaid reimbursement on nursing home quality. In the first stage, a change in reimbursement affected the home's decision to become an integrated, all-Medicaid, or all-private-pay facility. I estimated the first part of the model with a discrete choice model to determine the change in the probabilities of the three payer mix categories given a shift in reimbursement. I used a polytomous (or multinomial) logit model, a widely used functional form, to estimate these probabilities. Conditional on the home's choosing to be an integrated facility, in the second stage I examined how a change in reimbursement may affect the relative proportions of Medicaid and private-pay residents. I used generalized linear models (GLM) to fit a binomial regression model grouped at the home level (
McCullagh and Nelder 1989). Finally, the third stage showed the effect of reimbursement on quality conditional on a home's choosing a particular regime. This stage was estimated in substantially different ways depending on the structure of the quality measure.
For the quality measures that are represented as a proportion of the total home population, I used GLM to fit a binomial regression model grouped at the home level. These quality measures included the proportion of residents with feeding tubes, catheters, and physical restraints. Because the medication error rate was based on a sample of medication passes, a binomial regression model, benchmarked by the total home population, was also used for this measure. Because the deficiencies measure was a nonnegative count variable, I estimated it using a negative binomial maximum-likelihood regression model. Finally, the staffing measures were represented as the number of staff (i.e., RNs, LPNs, or NAs) per 100 residents and were estimated with a least squares model.
Importantly, this multipart model was neither a structural nor a reduced form of the model. Instead it was a conditional statement about responses given a choice of regime. With this conditional framework, I was able to take account of the inherent endogeneity of payer type and quality. Other analyses have addressed this issue with instrumental variables analysis (
Nyman 1985) or a reduced form model (
Gertler 1989).
Although putting the parts together for the various stages gave us the total prediction, I was ultimately interested in how a change in Medicaid reimbursement affects quality. Rather than deal with analytical partial derivatives of the combined effect, I used numerical increments of overall effects. With a mean Medicaid reimbursement rate of $86.74 (and a standard deviation of 23.19), I selected the rates of $65, $75, $85, $95, and $105 to evaluate the predicted mean quality levels. Because I conditioned on the payer mix regime in the initial stage, I did not include an interaction of payer mix and reimbursement in the latter stages.
To estimate the standard errors, I combined the predicted values from the various stages of the model to get the total effect of reimbursement on quality, using 500 bootstrap samples (or replicates;
Efron and Tibshirani 1993). In constructing these standard errors with a bootstrapping method, there is the potential for bias because the point estimate of the coefficient is taken from the original N observations. To assess whether a bias correction method was necessary, I tested the normality of the standard errors generated via the bootstrap procedure. A chi-square test statistic for normality was constructed for skewness, kurtosis, and an overall joint test of skewness and kurtosis. On the basis of these test statistics, I could not reject the null hypothesis that the standard errors were normally distributed for all the quality measures except deficiencies. On the basis of the results of this test, a bias correction method was implemented for the inference statistics in the deficiency model.
The bias correction method was implemented in the following manner. First, the cell means from the multipart model were subtracted from the individual results from the bootstrap model. This difference was then multiplied by a factor (discussed below) that accounts for clustering. This difference (adjusted for clustering) was then added back to the original cell mean. With this new "bias corrected" estimate, I then calculated the 2.5th and 97.5th percentiles of this distribution to construct a 95% confidence interval around the estimate. Importantly, I did not adjust the cell means for this bias, only the inference statistics.
Because many regulations are particular to a given state or market, observations within states or markets may not be independent of one another. With state or market-level variables being the primary variables of interest, I had to correct inference statistics for intracluster correlation. First, the entire multipart model was estimated and the predicted residual was calculated. I calculated a one-way analysis of variance (ANOVA) to determine the correlation in the residual within states both for the full model and for the Medicaid- and integrated-only models. This ANOVA test provided a "conservative T-deflator" statistic to adjust the standard errors for each quality measure, which are available upon request from the author.
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In examining the results from the multipart model of the effect of Medicaid reimbursement on quality, I obtained different results depending on whether I employed a structural or process measure of quality (see Table 2 ). For the structural or staffing measures of quality, an increase in Medicaid reimbursement had a positive and statistically significant effect on the number of RNs per 100 residents. An increase in the Medicaid reimbursement of $40 increased the number of RNs by 1.42 per 100 residents. However, for the LPN and NA measures, there was a positive but not statistically significant effect.
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As discussed in the previous section, the standard errors for the deficiency measure were found to be skewed and kurtotic. Therefore, I could not test for significance without adjusting for the underlying bias in these standard errors. Importantly, the deficiency results in Table 2 do not show the standard errors in parentheses below the cell mean, but rather a 95% confidence interval that reflects the nonnormal distribution of the errors. For this composite measure of nursing home quality, quality was found to be positively, but not statistically significantly, related to level of Medicaid reimbursement. An increase in the Medicaid rate had a positive effect on quality, with an increase in reimbursement of $40 increasing quality by more than one half (-0.62) of a deficiency citation.
In addition to the multipart model results for all nursing homes, I also isolated those tightest nursing home markets where excess demand was most likely to be present and examined the effect of Medicaid reimbursement on these same quality measures (see Table 3 ). I used the measure of market tightness, empty beds per 1,000 elderly (aged 75 and older) individuals, to examine the tightest quartile of homes. Any homes located in counties with less than 9.8 empty beds per 1,000 elderly individuals were included in this analysis. Similar to the results for all homes discussed previously, I found a positive and statistically significant effect of reimbursement on quality for the RN measure. A $40 increase in Medicaid reimbursement was met with an increase in 1.11 registered nurses per 100 residents. However, the other two staffing measures (i.e., the number of LPNs and NAs) were not statistically significant. Although reimbursement was positively associated with quality in all cases except the feeding tubes measure, the procedural measures were once again not statistically significant from zero. Interestingly, the deficiencies measure, which was not statistically significant in my previous analysis of all homes, was actually statistically significant for those homes in the tightest markets. An increase in Medicaid reimbursement of $40 decreased deficiencies by 2.02 within these tightest markets.
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;
Nyman 1985), using data from the 1970s and early 1980s, found that an increase in Medicaid reimbursement actually decreased nursing home quality because of the presence of excess demand within the market for nursing home care. However, in this current study I was not able to validate these earlier findings using a range of quality measures. These results provide some evidence that excess demand has lessened in importance over time for the market for nursing home care.
A shift in the underlying tightness of the nursing home market may help to explain the contradictory results of this study when put into the context of this existing literature. These earlier studies found an entire state (e.g.,
Gertler 1989;
Nyman 1985)—or even the entire nation (e.g.,
Scanlon 1980)—to be in a state of excess demand. I took the conservative approach of testing for excess demand in those nursing home markets within the top quartile of the tightness measure (as defined by empty beds per elderly individuals aged 75 and older). Clearly, however, the overall market for nursing home beds has been loosening to the point that this top quartile may not be sufficient to detect excess demand. Alternatively, I could continue to raise this excess demand threshold (e.g., the 90th percentile of tightness and above) and repeat these same tests on a smaller sample of homes. However, as this threshold is raised, the problem of excess demand becomes relevant for even fewer nursing home residents. The fact that this analysis was not able to detect excess demand—even at the 75th percentile of the tightness measure—draws into serious question the relevance of this excess demand paradigm for the majority of today's nursing home markets.
Although this study is not a longitudinal analysis, there are a number of explanations for this shift away from an excess demand paradigm within the market for nursing home care. A range of factors may have affected either the demand or supply of nursing home beds over time. These shifts include changes in the effectiveness of CON policies in constraining bed growth, implementation of preadmission Medicaid screening programs, demographic shifts (e.g., decreases in elderly individuals' morbidity over time), and the market-based emergence of potential substitutes to nursing home care (e.g., alternative living arrangements). Further research will be necessary to tie these political, economic, and demographic shifts to the changing nursing home market.
A decrease in excess demand also has important policy implications for the market for nursing home care. Historically, the excess demand for beds created by CON and construction moratoria policies has potentially benefited nursing home operators in at least two ways. First, in markets where regulation creates an excess demand for beds, nursing homes may lower their costs of care by reducing quality of care and choosing not to serve heavy-care Medicaid patients. And second, because CON could limit bed construction by both expansion and entry, these policies may also serve as a mechanism for nursing homes to establish and preserve market power. As such, the excess demand paradigm provided policymakers with a strong rationale for not increasing Medicaid reimbursement rates over time beyond the standard adjustments for inflation. Although this current study has not unequivocally shown that an increase in Medicaid reimbursement improves quality along all dimensions, it has shown that a professional staffing measure is quite responsive to Medicaid reimbursement policy.
This staffing result has strong face validity in that one would expect structural (or resource-based) measures of quality to be quite responsive to changes in reimbursement levels. It is quite interesting, however, that increased Medicaid reimbursement does not necessarily affect quality when represented by procedural or composite measures. Moreover, I found a very low correlation between nursing home staffing and other measures of quality. Increased reimbursement may indeed produce greater numbers of staff, but not necessarily better observed outcomes of care. If this is indeed the case, the results from this study have direct implications for the
Omnibus Budget Reconciliation Act of 1987 (
Omnibus Budget Reconciliation Act of 1987) (OBRA 87), which was passed by Congress with the goal of improving the quality of nursing home care. Although these regulations were quite broad in nature, part of the Act stipulated minimum staffing standards for all licensed nursing homes. The staffing regulations established under OBRA 87 require that Medicaid- and Medicare-certified nursing facilities have LPNs on duty 24 hr/day; an RN on duty at least 8 hr/day, 7 days/week; an RN director of nursing; and certified NAs. A 1996 Institute of Medicine (IOM) report labeled the legislation a landmark statute in part because it recognized that the implementation of its provisions would require increased resources and encouraged state Medicaid programs to adjust their rates accordingly. The IOM report noted that this is arguably the first time that Congress explicitly addressed the connection between quality and increased public spending (
Institute of Medicine 1996). Interestingly, however, the growth in state Medicaid reimbursement rates look fairly comparable in the pre- and post-OBRA 87 periods.
In this article I have highlighted many of the important changes in the nursing home market over the last two decades, but there has been one unfortunate constant in this marketplace—poor quality care. Although many nursing homes provide good care, even in the face of severe financial constraints, poor quality has been found in a number of studies across different populations, time periods, quality measures, and empirical methodologies. In this article I have highlighted two potential explanations for low quality within the market for nursing home care: excess demand and inadequate Medicaid reimbursement. A third explanation not explicitly addressed in this study assumes that nursing home residents (or their families) are incapable of making informed appraisals regarding the relative quality of nursing homes. If policymakers want to address the problem of low-quality nursing home care, they need to identify the underlying source of the problem. If excess demand causes the problem, then there are two policy options: either regulate quality or eliminate excess demand by relaxing or eliminating CON and construction moratoria policies. If inadequate Medicaid reimbursement is the source of the quality problem, then the solution is more straightforward. Finally, if a lack of consumer information leads to low quality, then there is no alternative but direct regulation of quality (through either minimum quality of care standards or other oversight). The empirical evidence from the nursing home literature in support of each of these three explanations as the source of low-quality care is inconclusive, and of course, may be shifting over time. In this current study I provide evidence that excess demand may be lessening in importance and that Medicaid reimbursement may be important in explaining a resource-based measure such as case-mix-adjusted professional staffing.
Several potential limitations apply to the empirical analysis. First, because I used facility-level data, I controlled for resident case-mix with two or three distinct variables (depending on the quality measure), but if these variables did not completely capture the health of residents in the home, omitted variable bias may have occurred. Additionally, because poor quality may increase the observed case-mix in a given nursing home, I also cannot rule out an endogenous relationship between the case-mix and quality measures. Second, the OSCAR system is collected for the purposes of provider certification, which may raise concerns about the accuracy and interstate reliability of the measures. Third, this analysis assumes the county serves as accurate proxy for the market for nursing home care. Fourth, the data used in this article are observational in nature; experimental data do not currently exist (and most likely never will) to address the research questions I analyzed. Fifth, I did not include a measure of the private-pay price in the empirical analysis because it may be endogenous to the provision of quality, but the exclusion of this variable leaves open the issue of omitted variable bias.
Finally, when examining the effect of public policies on behavior, researchers often prefer a time series approach because there might be other factors, unaccounted for by the analysis, that affect the relationship of interest. In this case, a state-specific factor may be driving both the Medicaid reimbursement decisions and the level of nursing home quality within the state. Because the analyses contained within this study are cross-sectional, I cannot unambiguously rule out the presence of a third factor affecting both Medicaid policy and nursing home quality. Ideally, researchers should construct a panel involving some type of natural experiment in which there was a major change in Medicaid reimbursement policy. However, in many states, aggregate Medicaid reimbursement rates have increased fairly steadily over the 1990s, largely to offset inflation. As a result, it may be that relatively little information could be gained from the use of a time series compared with the cross-sectional approach taken here. Nevertheless, the construction of a time series to examine this issue may be an important area for future research.
Given concerns about nursing home costs and the quality returns to increased Medicaid reimbursement, the federal government has been reluctant to encourage states to raise Medicaid rates. Beyond the standard adjustments for inflation, there are actually few historical instances of significant changes in these rates. One notable exception was the decision in 1974 to establish cost-based reimbursement for Medicaid residents as part of
U.S. Public Law 92-603 1974. Following this federal action, almost all states adopted some form of cost-based reimbursement in the 1970s. This shift was met with limited success—almost all states have since shifted back to prospective-based payment systems—but it was attempted in a period when the market for Medicaid residents was commonly thought to be in a state of excess demand. Given observed changes in the degree of excess demand present in the market for nursing home care, this current analysis reopens the question of whether increased Medicaid reimbursement will improve the quality of nursing home care.
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Received for publication April 20, 2000. Accepted for publication October 12, 2000.
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This article has been cited by other articles:
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  E. A. Miller, V. Mor, D. C. Grabowski, and P. L. Gozalo The Devil's in the Details: Trading Policy Goals for Complexity in Medicaid Nursing Home Reimbursement Journal of Health Politics Policy and Law, February 1, 2009; 34(1): 93 - 135. [Abstract] [PDF]
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 C. Harrington, T. Tsoukalas, C. Rudder, R. J. Mollot, and H. Carrillo Variation in the Use of Federal and State Civil Money Penalties for Nursing Homes Gerontologist, October 1, 2008; 48(5): 679 - 691. [Abstract] [Full Text] [PDF]
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J. Goodson, W. Jang, and M. Rantz Nursing Home Care Quality: Insights From a Bayesian Network Approach Gerontologist, June 1, 2008; 48(3): 338 - 348. [Abstract] [Full Text] [PDF]
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 J. H. Swan and C. Harrington California Nursing Facility Quality and Union Environments J Aging Health, April 1, 2007; 19(2): 183 - 199. [Abstract] [PDF]
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N. G. Castle and J. Engberg Organizational Characteristics Associated With Staff Turnover in Nursing Homes Gerontologist, February 1, 2006; 46(1): 62 - 73. [Abstract] [Full Text] [PDF]
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C. Mueller, G. Arling, R. Kane, J. Bershadsky, D. Holland, and A. Joy Nursing Home Staffing Standards: Their Relationship to Nurse Staffing Levels Gerontologist, February 1, 2006; 46(1): 74 - 80. [Abstract] [Full Text] [PDF]
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C. Harrington, J. T. Mullan, and H. Carrillo State Nursing Home Enforcement Systems Journal of Health Politics Policy and Law, February 1, 2004; 29(1): 43 - 74. [Abstract] [PDF]
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C. Harrington, J. O'Meara, M. Kitchener, L. P. Simon, and J. F. Schnelle Designing a Report Card for Nursing Facilities: What Information Is Needed and Why Gerontologist, April 1, 2003; 43(90002): 47 - 57. [Abstract] [Full Text] [PDF]
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