medicare payment changes and nursing home quality- effects on long-stay residents

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Medicare Payment Changes and Nursing Home Quality: Effects on Long-Stay ResidentsAuthor(s): R. Tamara Konetzka, Edward C. Norton, Sally C. StearnsSource: International Journal of Health Care Finance and Economics, Vol. 6, No. 3 (Sep., 2006),pp. 173-189Published by: SpringerStable URL: http://www.jstor.org/stable/20460602 .

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Int JHealth Care Finance Econ (2006) 6:173-189DOI 10.1007/s10754-006-9000-9

Medicare payment changes and nursing home quality:effects on long-stay residentsR. Tamara Konetzka - Edward C. NortonSally C. Stearns

Received: 6May 2005 /Accepted: 16May 2006!Published online: 3 October 2006(C pringer Science+Business Media, LLC 2006

Abstract The Balanced Budget Act of 1997 dramatically changed the way thatMedicare pays skilled nursing facilities, providing a natural experiment in nursing homebehavior. Medicare payment policy (directed at short-stay residents) may have affected outcomes for long-stay, chronic-care residents if services for these residents

were subsidized through cost-shifting prior to implementation of Medicare prospective payment for nursing homes. We link changes in both the form and level ofMedicare payment at the facility level with changes in resident-level quality, as representedby pressure sores and urinary tract infections inMinimum Data Set (MDS) assessments. Results show that long-stay residents experienced increased adverse outcomeswith the elimination ofMedicare cost reimbursement.Keywords Prospective payment * ursing homes * edicare . Quality of careJEL Classification Ill .118 * 51

IntroductionThe Balanced Budget Act of 1997 (BBA) represented "the most far-reaching changesto theMedicare program since its inception" (Ross, 1999). It dramatically changed theform of payment forMedicare services in skilled nursing facilities (SNFs) by replacing the former cost-based reimbursement system with a prospective payment system(PPS). At the same time, the overall level of funding was reduced by several billiondollars, reducing the average reimbursement for the majority of SNFs. Like the PPSsystem in hospitals, the SNF PPS was introduced to counter unsustainable increases

R. T.Konetzka (lE)Department of Health Studies, University of Chicago, 5841 S.Maryland Ave., MC2007, Chicago,IL60637,USAe-mail:[email protected]. C. Norton *S. C. StearnsUniversity of North Carolina atChapel Hill, Chapel Hill, NC 27599-7590,USA

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174 R. T. Konetzka et al.

inMedicare spending while maintaining access and quality. This major policy changeoffered a natural experiment in the effects of Medicare reimbursement on nursinghome quality of care.

Debate over the new policy has focused on Medicare residents only, who are generally short-stay residents with rehabilitation needs. However, most Medicare SNFresidents receive care in nursing facilities that also have large long-stay, chronic-carepopulations, the majority of whom are funded byMedicaid. Facilities blend multiplerevenue streams to cover fixed and operating costs for all residents, and it has longbeen asserted that high private-pay and Medicare margins are used to subsidize substantially lower Medicaid margins. Financial pressures from Medicare may lead toa reduced ability to subsidize quality for the long-stay population. We hypothesize,therefore, that spillover effects from theMedicare budget cuts may result in decreasedquality of care for long-stay residents.

Analysis of provider behavior in the face of these dramatic changes requires several changes in the traditional approach to studying nursing home quality. Prior studies have largely ignored Medicare and used facility-level administrative data with

weak measures of quality and inadequate controls for resident severity. Our theoretical framework is based on a cost-shifting model that interprets Medicare cuts as adecreased ability to subsidize lowMedicaid rates. Our empirical analysis uses detailedresident-level data to assess the effects of changes in both the method and level of

Medicare payments on two common and validated measures of quality for long-stay,chronic-care nursing home residents (pressure sores and urinary tract infections) whilecontrolling for resident severity. These aremore precise measures of quality and severity than those available in the facility-level data used in previous studies. Furthermore,we employ an econometric model that can separate effects of payment changes fromconcurrent trends in the industry, in contrast to the more common before-and-afterstudies. Our results show that selected adverse outcomes among long-stay nursinghome residents increased in response to implementation of Medicare PPS paymentbut did not change significantly in response to short-term changes in average paymentrate.

BackgroundTraditionally, Medicare has been responsible for only a small portion of nursing facility residents and revenues. Medicare only pays for amaximum of 100 days of nursingfacility care per episode of care and only after at least a three-day hospital stay; after the 20th day a large copayment is required. On average, Medicare is responsiblefor only 9% of nursing facility residents and 12% of total nursing facility revenues(AHCA, 2001). However, operating margins are much higher forMedicare residentsthan forMedicaid residents. During the 1990s, nursing homes boosted capacity to takeonMedicare rehab patients as hospital length of stay was reduced. In Ohio, for example, average daily Medicare census almost doubled over the decade (Mehdizadeh& Applebaum, 2003). In part, this expansion may have been an effort to increaseMedicare revenues to supplement the relatively lower Medicaid rates and marginsassociated with themajority of residents inmost facilities, a role that traditionally wasfilled by private-pay patients. Thus, despite the small proportion of Medicare residentsin the average facility, the financial changes brought about by PPS are by no meanstrivial tomany SNFs.X Springer


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Medicare payment changes and nursing home quality: effects on long-stay residents 175. _ . . _ _ . . _ _ . . . _ _ . .~~~~~~~Between 1986 and 1998,payments to SNFs became the fastest-growing egment of

Medicare expenditures, increasing at an average nominal rate of 30% per year (USGAO, 1999). SNFswere reimbursedunder a retrospectivecost-based system,withlimits on routine costs but no limits on ancillary services such as physical and occupational therapy. Although the growth was due in part to a large increase in the numberofMedicare beneficiaries utilizing SNF services (an 80% increasebetween 1990 and1997 alone according to theProspectivePaymentAssessment Commission,1997), thesystem encouraged excessive use of ancillarytherapiesandprovided few incentivesfor cost containment. In response, the BBA of 1997 mandated the implementation ofa PPS for SNFs.

Prior to PPS, SNFs billed Medicare based on routine, ancillary, and capital costsincurred. Routine costs were subject to a limit, while reasonable costs of ancillaryservices and capital were reimbursed in full. Under PPS, all routine and rehabilitationtherapy reimbursement s included in the case-mix-specificrate.Administrative andcapital costs are included as a flat payment based on the average across all facilitiesand are not specific to case-mix.

The SNF PPS system was phased in over 4 years, with the start of each year corresponding to a facility's own fiscal year. In the first year, facilities were reimbursedbased on 25% federal rate and 75% facility-specific rate. A 50/50 mix was used in thesecond year, 75/25 in the third year, and finally 100% of the federal rate in the fourthyear. However, the prospective nature of the rates was implemented immediately; itwas only the level of the rates that varied according to historical costs. Rates weredesigned based on average costs per case-mix category in 1995; each facility's 1995costs determined the flat facility-specific rate and the average cost over all facilitiesdetermined the federal rate. Rates are adjusted for regional wage differences andruralstatus.

PPS was not intended to be budget-neutral. A major goal of the change to prospective rates was to slow the growth inMedicare SNF costs. Therefore, PPS rates wereset to decrease average reimbursements for most facilities, with total savings in 1999estimated ex-ante by the Congressional Budget Office at $ 1.2 billion. In actuality,expenditures were cut by $ 3.4 billion in 1999 (The Lewin Group, 2000). After thenew system was implemented in 1998, numerous media reports emerged regardingthe financial difficulties of SNFs. Over 10% of facilities nationwide filed Chapter 11bankruptcy (Roadman, 2000), including several of the largest chains.

By late 1999, concerns over the industry's financial viability led Congress to reconsider the adequacy of the rates. Congress adjusted the system in the Balanced Budget

Refinement Act (BBRA) of April 2000. Reimbursement rates in allRUG-III groupsincreased at least slightly, and in the 15 groups thought to be most under-reimbursedthe rates increased by 20%. The adjustments were seen as a temporary solution, scheduled to end when an improved classification system was developed. The BBRA alsoallowed facilities with lower facility-specific rates tomove to the full federal rate withthe start of the next cost reporting period rather than following the 4-year phase-in.

Additional rate increases were enacted in the Benefits Improvement and Protection Act of 2000 (BIPA). BIPA provisions are not considered in this analysis becausethey took effect after the end of the study period.

Under PPS, retrospective, cost-based reimbursement was replaced with prospective per diem rates for each Medicare resident, regardless of the actual costs incurredby the facility. The Centers forMedicare and Medicaid Services (CMS), the regulatoryagency responsible for implementing the new program, changed both the marginal

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and the average reimbursement rates, and both changes could affect quality of care,(Cutler, 1995; Norton, Van Houtven, Lindrooth, Normand, & Dickey, 2002). The marginal reimbursement rate fell to zero and the average reimbursement rate decreasedformost facilities (because the program was designed to reduce total costs). Under thecost-based system, because most costs were reimbursed, facilities faced the incentiveto increase utilization not only to improve quality of care but also to provide unnecessary services. In a prospective reimbursement system, a facility receives the rateassociated with each resident's RUG-Ill classification, regardless of services actuallyprovided. The facility has an incentive to minimize costs, thereby increasing margins.Cost-minimization may take the form of reductions in quality of care, although theextent of the decrease is limited by the need to attract residents. The change in average reimbursement affects resources available to provide care. A decrease in averagereimbursement is expected to reduce quality (Cutler, 1995, Norton et al., 2002).

Evidence from the literatureMany studies have been conducted on the effects of hospital PPS, but their applicability to nursing facilities is limited by a fundamental difference between hospital PPSand SNF PPS. Hospital PPS reimburses per admission, while SNF PPS reimburses perdiem. Per-admission reimbursement creates the incentive tominimize length of stay,since costs incurred for additional days are not reimbursed. Per-diem reimbursementcreates no such incentive. Incentives regarding length of stay confound the relationship between reimbursement and outcomes, with the result that conclusions fromhospital PPS may not apply to nursing facilities.

A more appropriate comparison can be found in the literature on Medicaid reimbursement and qualitv of care. Most of these studies focus on the effect of Medicaidrate increases in the presence of excess demand, where facilities have a great dealof choice inwhich residents to accept or reject, Medicaid residents are accepted last,and Medicaid demand exceeds supply (Scanlon, 1980; Nyman, 1985). Recent studieshave established that current market conditions no longer support the excess demandframework in most areas. Even where Certificate of Need laws still exist, they areoften not binding. Occupancy rates have been in steady decline since the mid-1990s(American Health Care Association, 2001), forcing facilities to compete for all typesof residents. Grabowski (2001 a) used national OSCAR data from 1995 to 1996 to findthat an increase inMedicaid reimbursement rates improved quality asmeasured byprofessional staffimg regardless of CON laws, refuting the applicability of the excessdemand literature today. Other measures of quality (regulatory deficiencies, nonprofessional staffing, medical error rate, use of catheters, feeding tubes, and physicalrestraints) led to inconclusive results but did not support the excess demand paradigm.In a related study (Grabowski, 2001b), the same OSCAR data were supplemented

with New York cost report data, and amore outcomes-oriented measure of quality, thepercent of residents with facility-acquired pressure sores, was used. Rates were foundto have a small but statistically significant and positive effect on quality, again refuting the excess demand results using more current data. Finally, a longitudinal studyincluding private-pay price data showed thatMedicaid rate increases were associated

with improved nursing home quality (Grabowski, Feng, Intrator, & Mor, 2004).These newer studies of Medicaid reimbursement offer substantive and method

ological insights. For our purposes they are limited, however, in that they ignoree Springer


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were able to shift the costs ofmaintaining quality for long-stay residents onto Medicareand private-pay residents; after the BBA Medicare cuts, the ability to cross-subsidize

was diminished. While this argument has been implicitly and explicitly raised in thepolicy debate, the applicability of the cost-shifting model to nursing homes has notbeen carefully analyzed in terms of economic theory. We find that a cost-shifting

model fits the pre-BBA scenario in the nursing home sector.Dranove (1988) demonstrates that hospitals serving patients with public funding

and private-pay patients will raise prices to private-pay patients when reimbursementrates from the public funder are cut, i.e. hospitals will cost-shift. The increased revenues from private-pay patients are used to maintain overall revenues that supportthe costs of care for both types of patients. Dranove demonstrates that two conditions

must be met before hospitals will cost-shift: (1) The hospital cannot be a profitmaximizer; and (2) The hospital must have some market power. If these conditionsare met and prices for both payer groups are exogenous, cost-shifting may take theform of changes in quantity or quality of services instead of price.

While anecdotal evidence supports the idea that Medicare revenues were usedto support low Medicaid reimbursement in nursing homes until the PPS cuts, thenursing home sector has a very different market structure than the hospital sector studied by Dranove. It is not immediately clear that either of his conditionsfor cost-shifting is met. First, unlike hospitals, most nursing homes are for-profit(Norton, 2000). Second, as occupancy rates in nursing homes have declined and excess Medicaid demand is no longer as prevalent, nursing homes may not have the

market power they once had. If nursing homes were truly profit-maximizers in acompetitive market, however, we would not expect to see the extent of mixing thatcurrently exists between short-stay rehab patients and long-stay chronic-care patients. Most facilities have largely long-stay populations with a small proportion ofrehab patients, revealing a preference for mixing that ismore consistent with a costshifting model than with a competitive model. We argue that both conditions for costshifting apply sufficiently to the nursing home sector, despite initial appearances to thecontrary.

We start with the first condition. For a number of reasons, for-profit nursing homesmay not behave like profit-maximizers and may find it in their interest to cost-shift:

1. A pure profit-maximizing nursing home would specialize inMedicare or privatepay patients and not accept Medicaid patients ifMedicaid margins are substantially lower. However, Certificate of Need laws often require a facility tomaintainsome Medicaid beds in order to have certified Medicare beds (Troyer, 2002). Inaddition, assuming a typical U-shaped cost curve, costs are minimized at a particular number of residents. In the short run, declining occupancy rates may meanthat facilities have excess capacity they would like to fill in order tominimize costs.

2. A profit-maximizing level of quality may not be consistent with maintaining certification. The nursing home industry is heavily regulated, and quality of carefor long-stay (largely Medicaid) residents must meet minimum standards if thefacility is to maintain certification forMedicare and Medicaid reimbursement.

While requirements also exist forMedicare residents, it is quality among long-stayMedicaid residents that tends to be problematic and where the regulations are of

ten binding. If the profit-maximizing level of quality in theMedicaid population isbelow that required for certification (given lowMedicaid reimbursement rates),

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Medicare payment changes and nursing home quality: effects on long-stay residents 179. . . . ... . . .~~~~~~~~~~~~~~~~~~facilities may cost-shift to provide a higher level of care and thereby maintaincertification.

Together, these two points can explain why for-profit nursing homes are not reallyprofit-maximizers. They may serve both populations and use Medicare revenues tosubsidize quality for Medicaid. However, the plausibility of doing so depends on thetightness of the market and on the elasticities of demand with respect to quality inthe two populations. IfMedicare residents were highly sensitive to quality, itmay notbe possible to divert funds. Arguably, short-stay Medicare residents are less sensitiveto quality than long-stay residents. Short-stay rehab patients are placed in nursing

homes directly after a hospital stay for an acute event. They therefore do not havetime to shop for quality and must rely on discharge planners to find an available bedimmediately. Since their stays are short, switching facilities after the level of qualityis experienced is also unlikely. Long-stay residents and their families, on the otherhand, often have more time to choose a facility and, by definition, more time to switchfacilities if quality is unsatisfactory.

The second condition, that facilities must have market power, is not difficult to applyto the nursing home sector. While the studies that refute the excess demand paradigmargue that the nursing home market has become more competitive than it once was,few would argue that it is truly a competitive market, and certainly not in terms ofquality. Like other health care sectors, asymmetric information precludes perfect competition on quality and leaves providers with some degree of market power. Nursinghome markets are generally local, and proximity may be more important to consumersthan clinical quality even to the extent that information on quality is available. Furthermore, while occupancy rates have declined inmany markets and nursing homes

may find it difficult to fill beds, this isnot true of all nursing home markets. We explorethis assertion in the empirical work by comparing high- and low-occupancy markets.

The particular form of cost-shifting applicable to the nursing home sector is different from the simple price changes that Dranove described for hospitals and requiressome further explanation. Prior to PPS, there were two components toMedicarereimbursement for SNF services. We can characterize these two components as a andiq. The first component, et, represents the reimbursement or payment rate for rou

tine costs that the facility incurs in caring for aMedicare resident-room, board, androutine staffing. The parameter on the second component, ,B, represents the degreeto which facilities are paid for extra services provided (q), such as therapy minutes.

While Medicare rates per service were exogenous, SNFs could increase profits substantially simply by providing a higher intensity of services to each resident coveredby Medicare. It was the quantity of these extra services that led to serious concernsabout cost-containment and precipitated the PPS.

The changes inmarginal and average reimbursement under PPS can be characterized by changes in a and P: the changes in average reimbursement are representedby changes in Ca, hile the elimination of marginal reimbursement is represented by adecrease in ,B(or ,B ffectively going to zero). We will call changes in ac the rate effectand changes in W8he PPS effect. With the elimination of marginal reimbursement,SNFs lost an important mechanism for generating additional Medicare revenues thatcould be used to cross-subsidize quality for Medicaid residents. White (2003) and

Wodchis (2004) demonstrated that, indeed, the number of therapy minutes per resident dropped off dramatically once PPS was implemented. Clearly, a decrease in theaverage Medicare rate should also decrease the revenues available to cross-subsidize.

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180 R. T.Konetzka et al.

We therefore hypothesize that both the PPS effect and an average rate reduction(negative rate effect) should lead to decreased quality of care for long-stay nursinghome residents.

Secondary hypotheses focus on comparisons by proprietary status and marketoccupancy. Because for-profit nursing homes do not or cannot behave like strictprofit-maximizers, we assume that both for-profit and nonprofit facilities were costshifting before PPS and that their ability to do so was diminished after PPS. Wetherefore hypothesize that we will find little difference in results between for-profitand nonprofit homes. This would also be consistent with the effects on staffing foundin Konetzka et al. (2004), where no significant differences emerged by proprietarystatus. Finally, we hypothesize that the tightest markets at baseline, those with marketoccupancy greater than 90%, wielded the greatest power to cost-shift and thereforeshould experience the largest effects on long-stay quality under PPS.

DataWe analyzed clinical outcomes and case-mix data from the Nursing Facility MinimumData Set (MDS), a government-mandated data set containing assessment data on allresidents in all Medicare- or Medicaid-certified nursing facilities. Our analysis useddata from all freestanding nursing homes in Ohio, Kansas, Maine, Mississippi, andSouth Dakota that existed from 1997 to 2000. We chose these states because theyhad reliable data both before and after implementation of PPS and because theyprovided a large sample of facilities and residents from multiple geographic regionsof the U.S. Since hospital-based facilities are considered fundamentally different andoften do not have the large long-stay populations that are the focus of this analysis,we excluded them from the sample. The sample represents approximately 10% offreestanding facilities and residents nationwide. An important potential limitation ofthis analysis is generalizability from the five-state sample to the U.S. Characteristicsof nursing homes in the five states do not appear to diverge widely from nationalnorms, however, and the results are consistent with national results on staffing ratios(Konetzka et al., 2004).

We used quarterly assessments from 1997 to 2000, resulting in a total of 1,215,934MDS patient assessment observations. No assessments were available for the first halfof 1998, resulting in a uniformly smaller sample for that year. Residents with staysless than 90 days do not have quarterly assessments and were excluded. The sampleduring the entire study period included 262,743 residents and 1,406 facilities, thoughthere were fewer residents at any given point in time.

We combined MDS data with facility-level ownership information from CMS'sOnline Survey, Certification and Reporting (OSCAR) database. We calculated eachfacility's percent Medicare and baseline reimbursement levels from Medicare costreports, also from CMS. OSCAR also provided information on the percentage of

Medicare residents.

MethodsThe goal of the study is to estimate both the PPS effect ,, representing the elimination of marginal reimbursement under the new prospective system, and the rateX1 Springer


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Medicare payment changes and nursing home quality: effects on long-stay residents 181. . . . . . . _ . . . . . . . . .~~~~~~~~~~~~~~~~effect a, representing the average level of payment per resident-day. We capitalize onfacility-level variation in both form and level of payment during 1997-2000 to identifythe PPS and rate effects separately. In addition, we employ a modified differencein-differences model to separate the effects of changes inMedicare reimbursementfrom macro trends. Facilities more dependent onMedicare should experience a stronger effect from changes inMedicare reimbursement. Therefore, we interact changesin the policy variables with the baseline (1997) percent of residents in each facility

whose care is paid by Medicare. This argument is similar to that often used in hospitalstudies (Gruber. 1994; Zwanziger, Melnick, & Bamezai, 2000). Unlike most difference-in-differences models, in our study treatment (percent Medicare) is a continuousvariable ranging from zero to one, and is perhaps better thought of as a dose-responseindicator.

Percent Medicare is defined as the percent of annual total residents in a facilitywhose primary payer isMedicare, based on pre-PPS (1997) data. Use of a baselinemeasure avoids contamination of the measure through any endogenous changes inpayer mix in response to the policy. Medicare cost reports for 1997 were used to definethe percent of resident-days payable byMedicare. For facilities witho-ut Medicare costreports in 1997, OSCAR values representing percent Medicare at a single point intime were used. The two measures are strongly correlated (.68 correlation).

The PPS effect is the product of a dummy variable indicating that the facility wassubject to prospective payment and the baseline percent Medicare in each facility.Facilities began receiving payment on a prospective basis at staggered times, depending on each facility's fiscal year start date. Although the majority of facilities weresubject to PPS rates beginning January 1, 1999, almost a quarter implemented PPSat other points between July 1, 1998 and June 30, 1999. If facilities implementingPPS earlier experience changes in quality earlier, the changes are more likely to bedue to PPS than to general trends. There are three sources of variation in the PPSeffect: cross-sectional variation in the percent Medicare at baseline; cross-sectionalvariation in the start of the fiscal year which led to longitudinal variation in time ofimplementation; and longitudinal (pre-post) variation inwhether or not PPS was ineffect.

The rate effect is constructed as the facility-specific average Medicare reimbursement per Medicare resident-day multiplied by the baseline percent Medicare in eachfacility. Because the post-PPS rate could be endogenous if facilities responded tothe policy by changing case-mix, we simulated an exogenous measure of paymentchanges holding case-mix constant at baseline (1997) levels. After 1997, exogenouspayments changed for all facilities due to the phase-in of the federal rates and theBBRA rate increases, but the trajectory of these increases was different for eachfacility depending on historical costs and baseline case-mix. For example, facilities

with lower historical costs (based on 1995 cost reports) received lower facility-specificrates during the phase-in of PPS regardless of baseline case-mix. For these facilities,

movement toward the full federal rate represented an increase, while for high-costfacilities it represented a decrease. We were able to account for the BBRA provision allowing immediate movement to the full federal rate in 2000 by assuming thatfacilities with lower facility-specific rates would prefer to move to the federal rate.

Case-mix provided another source of variation by determining how much a facility'srates were affected by the BBRA increases, under which payment for some severitycategories was increased more than others. These complexities of the payment processprovided a great deal of variation with which to estimate the rate effect. Most facilities

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182 R. T.Konetzka et al.

experienced an initial drop in rates in 1998 and an increase under BBRA in 2000, butthere was variation among facilities in both direction and magnitude during the studyperiod, all of which is captured in our rate effect variable.

The outcome measures used in this analysis-pressure sores and urinary tract infections-were chosen from the literature to reflect important and staffing-dependentaspects of care affecting long-stay residents and because they occur frequently enoughto be sensitive to financial pressures. The Institute of Medicine (1986) listed pressuresores and urinary tract infections among major indicators of quality problems in nursing homes; an extensive clinical review by Zimmerman et al. (1995) recommendedboth as measurable indicators of quality in nursing facilities. More recently, CMSjudged pressure sores and urinary tract infections to be among the chronic-care quality indicators with the highest validity (Morris et al., 2003). Each dependent variable

was analyzed in a separate regression at the resident level to allow for themost preciseresident-specific risk adjustment. Each is a binary variable equal to 1 if the residentexperienced the event during the specified time period and 0 otherwise.

Resident-level severity controls include age, gender, a group of diagnoses, dependence in Activities of Daily Living (ADLs) and a validated measure of cognitivefunctioning called the Cognitive Performance Score (Morris et al., 1994). A squaredterm is included for age to account for potential nonlinearities of effect. We also control for resident payer source, Medicare or private-pay, with Medicaid as the referentcategory. Payer source data in theMDS appears to contain frequent errors and inconsistencies; these data were cleaned to the extent possible but likely contain residual

measurement error. Dependent and explanatory variables and their definitions arelisted in Table 1.

Outcomes are modeled as a function of the PPS effect, rate effect, and residentlevel severity controls, as well as facility-level fixed effects and time fixed effects. Thebasic model for resident i in facility f at time t thus has the following form:Pr (Outcome)ift = ,Po ~, PPS Effectft + ,62Rate Effectft + 163Severityift + Year, + siftwhere the number of facilities is 1,406 and the number of observations per residentranges from 1 to 16 depending on the number of quarters the resident lived in thefacility during the 4-year period of analysis. Because of the potential for omitted facility-level factors and because correlation exists among observations from the sameresident over time, themodel was estimated using facility fixed effects and clusteringon resident. Facility fixed effects also control for time-invariant differences in qualityat the market, county, and state levels, including baseline percent Medicare.

Time fixed effects (three indicator variables for years 1998-2000) are also includedto account for any underlying time trend. We allow these time dummies to pick up the"main effects" of PPS and theMedicare rate since these concepts are not meaningfulto non-Medicare facilities; the PPS effect and rate effect are therefore estimated net ofany underlying trend in non-Medicare facilities. One natural concern in this approachiswhether facilities with noMedicare residents form an appropriate control group, butthe following factors support this choice: (1)Most facilities with some Medicare residents still have amajority of residents who are non-Medicare, long-stay populations;(2) Many facilities with no Medicare residents are still certified forMedicare; and (3)

We have incorporated a test of dose-response by interacting percent Medicare withthe policy variables, so the design does not rest solely on comparison to non-Medicarefacilities. An additional potential concern might be that state-specific Medicaid policychanges were enacted during the study period, especially in light of BBA provisionse Springer


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Medicare payment changes and nursing home quality: effects on long-stay residents 183. *~~~~~~~~~~~~~~~~~~~~~~~~~~Table 1 Summary statistics for the 5-state sample in the analysis of nursing home quality (N=1,215,934)Variable Mean Standard deviationDependent VariablesPressure sore (yes= 1, stage 2-4 last 14days) .05Urinary tract infection (yes= 1, last 30 days) .07Policy variables (N= 1,436,373)PPS effect (percentMedicare x PPS dummy) .04 .05Rate effect (percentMedicare x $ /resident-day/10) 1.60 1.50Timne ummies1997 (referent category) .261998 .131999 .312000 .30Resident Severity ControlsAge (at time of assessment) 82.0 12.0Gender (female= 1) .74ADL dependence (scale of 4-18; 18 ismost dependent) 9.60 4.76Cognitive Impairment (scale of 0-6; 6 ismost impaired) 2.51 1.74Comatose (yes= 1) .003Alzheimers or other dementia diagnosis .72Stroke diagnosis .24Heart disease (ashdor chf) diagnosis .37Cancer diagnosis .08Diabetes diagnosis .22Depression diagnosis .40Resident payer sourceMedicare .0?Private pay .24Medicaid/other (referent category) .70Facility characteristicsat baseline (N= 1, 406)Percent medicare .06For-profitownership (yes= 1) .73Not-for-profit ownership (yes= 1) .23Government ownership (yes= 1) .04Chain ownership (yes= 1) .54

making it easier for states to reduce rates. State-specific changes over time wouldnot be captured in the facility fixed effects or time dummies. Recent studies show,however, that there were no subsequent significant changes inMedicaid payment tonursing homes in the five sample states (Grabowski et al., 2004).

We estimated additional specifications using interaction terms and stratification todiscern differences in the policy variables by proprietary status and market occupancy.

Market occupancy was defined as the number of nursing home residents in the countydivided by the number of nursing home beds. We interacted not-for-profit status withthe policy variables and also ran separate regressions on for-profit and not-for-profitfacilities; similarly, we ran stratified and interacted models formarket occupancy.

Although the dependent variables are dichotomous, maximum likelihood estimation becomes intractable with 1,406 facility fixed effects. An ordinary least squareslinear probability model (LPM) was therefore used to estimate the equation. Aconditional logit was run on a subset of data to test sensitivity of the results tothe potential misspecification; results were not substantially different. Predicted val

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ues from the LPM regressions rarely fell outside the [0,1] range (approximately 3%for pressure sores; 0 for urinary tract infections) rendering moot the common complaint about LPMs. The LPM model also makes interpretation of interaction effectsstraightforward (Ai & Norton, 2003).

We do not assume lags in response to the policy changes, nor do we assume thatfacilities changed behavior in anticipation of PPS. Since facilities were reimbursed ona cost basis until the day that they were subject to PPS, there would be little reasonto cut costs prior to implementation and give up that reimbursement. After PPS

was implemented, staffing ratios appear to have changed without substantial delayKonetzka et al. (2004). This is consistent with tremendously high rates of turnoveramong direct-care staff in nursing homes (American Health Care Association, 2001),

making changes to staffing (at least reductions) relatively easy to implement quickly.

ResultsThe dependent variables measure adverse outcomes, i.e., negative quality. Therefore,the conceptual model predicts that the coefficient on the PPS effect variable shouldbe positive and significant, while the coefficient on the rate effect variable should benegative and significant.

The change from cost reimbursement to prospective payment resulted in an increasein adverse outcomes as exhibited by positive and significant coefficients on the PPSeffect variable (see Table 2). On average, the change to prospective payment increasedthe probability that a nursing home resident acquired a stage-2-or-above pressure soreby .0021 and a urinary tract infection by .0020 on any given quarterly assessment. Thistranslates to roughly 273 additional pressure sores and 260 additional urinary tractinfections per quarterly assessment in the 5-state sample alone, given that there areapproximately 130,000 residents in these facilities at any point in time. A back-ofthe-envelope calculation assuniing similar effects across all states yields over 5,000additional adverse outcomes per quarter in the US.

The rate effect variable, though exhibiting the expected negative sign for bothoutcomes, shows only amarginally significant effect for urinary tract outcomes and isnonsignificant for pressure sores. Magnitudes are very small: a $10 average increasein payment per resident-day translates to a reduction in the probability of a residentacquiring a pressure sore of only .00004 and of acquiring a urinary tract infection ofonly .0001. Together, these effects add up to a difference of approximately 18 adverseoutcomes per quarterly assessment in the 5-state sample.

The year dummies control for overall patterns in the industry unrelated to theincluded measures for PPS or the rate changes. The results show higher incidenceof both outcomes in 1998-2000 compared to 1997, with a monotonic increase forpressure sores but not for urinary tract infections

Resident-level characteristics play a significant role in the probability of both outcomes. Women have higher predicted rates of urinary tract infection but lower ratesof pressure sores. High ADL dependence, a comatose state, or a diagnosis of dementia, heart disease, cancer, or diabetes increases the probability of both outcomes, butgeneral cognitive impairment is associated with a lower probability. A diagnosis ofstroke or depression increases the probability of urinary tract infection but decreasesthe probability of pressure sores. Age is not associated with urinary tract infectionsbut is associated with pressure sores in a nonlinear way: The relationship is negativeX1 Springer


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Medicare payment changes and nursing home quality: effects on long-stay residents 185. . . .~~~~~~~~~~~~~~~~~~~~~~~~~~~~~Table 2 Effects ofMedicare changes on probability of adverse outcomes for long-staynursing homeresidents

Pressure sore Urinary tract infectionPolicy effectsPPS effect .033*** (.010) .032*** (.012)Rate effect -.00069 (.00089) -.0017* (.0010)Time trends1998 .00362***(.00071) .00275***(.00087)1999 .00380*** (.00083) .00167* (.00098)2000 .00483*** (.00086) .0021-** .0010)Resident severity controlsAge -.0133*** (.0024) .0019 (.0022)Age squared .00086*** (.00016) -.00019 (.00015)Female -.01036*** (.00074) .01789*** (.00075)ADL dependence .006013*** (.000083) .004137*** (.000082)Cognitive impairment -.00108*** (.00023) -.00355*** (.00023)Comatose .0397*** (.0099) .0232*** (.0076)Alzheimers or other dementia .0016 (.0012) .0015 (.0014)Stroke -.00394*** (.00075) .00380*** (.00082)Heart disease .00472*** (.00062) .00258*** (.00068)Cancer .0023** (.0010) .0079*** (.0012)Diabetes .02030*** (.00079) .01662*** (.00084)Depression -.00359*** (.00059) .00872*** (.00067)Resident payer sourceMedicare .0549*** (.0017) .0454*** (.0017)Private-pay -.00169** (.00067) .00186** (.00079)Constant .0385*** (.0095) .0115 (.0086)Number of observations 1,215,934 1,215,934Number of facilities 1,406 1,406Notes: Standard errors in parentheses. Estimation includes facility fixed effects and clustering onindividual** significant at 5%; *** significant at 1%

at younger ages but the effect levels off. Medicare residents who become long-stayresidents appear to have higher baseline probability of adverse outcomes, while theeffect of private funding ismixed- a lower likelihood thanMedicaid residents to havepressure sores but a slightly higher likelihood to have urinary tract infections.

We compared policy effects by proprietary status and market occupancy by stratifying the sample. Key policy effects from these stratified analyses are displayed in

Table 3. Overall, the point estimates for nonprofits and for-profits are similar. Whilethe PPS effect is significant at conventional levels for pressure sores in for-profit facilities but not in nonprofit facilities (P < .12), the point estimates are qualitatively thesame. In the case of urinary tract infections, the PPS effect is significant for both typesof facilities but the point estimate for nonprofits is slightly larger. The rate effectreveals some differences inmagnitudes of effect between for-profit and nonprofitfacilities, but the differences do not follow a consistent pattern. The rate effect is quitesmall even for those values that are significant. A combined model (not shown) withan interaction term between nonprofit status and the policy effects revealed similarresults.

Results stratified bymarket occupancy rates also do not reveal a consistent pattern,but there is some indication that themagnitude of increase in adverse outcomes underPPS was larger for facilities in high-occupancy markets at baseline. The point estimate

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Table 3 Effects of Medicare changes on adverse outcomes among long-stay nursing home residentsby proprietary status andmarket occupancyStratum Pressure sore Urinary tract infectionNoniprofit facilities (N= 303,576)PPS effect .036 (.024) .046* (.028)Rate effect _.0059*** (.0017) -.0013 (.0021)For-profitfacilities (N= 858,024)PPS effect .034*** (.012) .031** (.014)Rate effect .0011 (.0011) -.0021* (.0012)Facilities in high-occupancymarkets (N= 311.017)PPS effect .028 (.018) .073*** (.020)Rate effect -.0028 (.0018) -.0024 (.0020)Facilities inLow-Occupancy Markets (N= 904,917)PPS effect .041*** (.013) .012 (.015)Rate effect .0001 (.0010) -.0035*** (.0012)Notes: Standard errors in parentheses. Estimation includes facility fixed effects and clustering onindividual.Regressions included same control variables as listed inTable 2* significant at 10%; ** significant at 5%; *** significant at 1%

on the PPS effect in the UTI regression is .073 in high-occupancy markets and only.012 in low-occupancy markets. The effect of high occupancy works in the opposite

direction for pressure sores, but the difference in the point estimates is not nearlyas dramatic. The rate effect shows the mirror image. For pressure sores the rateeffect is dramatically larger in high-occupancy markets and for UTIs the rate effect issomewhat larger in low-occupancy markets.

DiscussionThis study contributes to the literature on nursing home behavior a conceptualapproach that explicitly includes Medicare, explains how Medicare payment changes

may have diminished facilities' ability to cost-shift, and allows us to analyze importantpolicy changes in both form and level of payment. Empirically, our study improvesupon existing analyses in several important ways. We use clinically detailed, resident-level data and more sensitive measures of quality; we employ rigorous methodsthat include amodified difference-in-differences model and controls for endogeneity;and we relax the implicit assumption thatMedicare payment changes can only affect

Medicare residents. The result is that we find detrimental effects from the eliminationofMedicare cost reimbursement on pressure sores and urinarv tract infections amonglong-stay residents, whereas previous studies that used less sensitive measures, lessrigorous designs, or amore restricted sample found none.

Our study is also important in informing the policy debate on Medicare SNFpayment. First, nursing facilities appear to react to reductions in funding by reducingstaffing, which in turn affects outcomes. The changes found in this study in adverse clinical outcomes among long-stay residents in nursing homes is consistent with changesin staffing found inKonetzka et al. (2004) using OSCAR data and a facility level ofanalysis. That study showed significant decreases in professional staffing ratios withPPS, proportional to the percent Medicare in each facility, that were somewhat mitigated with the BBRA rate increases. Although the evidence is mixed on the exacte Springer


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Medicare payment changes and nursing home quality: effects on long-stay residents 187

nature of the relationsliip (Davis 1991; Zhang & Grabowski, 2003), staffing has beenshown in some studies to have an effect on resident outcomes, especially professionalstaffing (Castle, 2000; CMS, 2001; Cohen & Spector, 1996; Harrington, Zimmerman,Karon, Robinson, & Beutel, 2000; Johnson-Pawlson & Infeld, 1996; Wunderlich &

Kohler, 2000). Financial pressures fromMedicare payment changes would most likelyaffect quality through staffing because staffing comprises the majority of any nursing facility's budget. Reductions in staff lead to reduced surveillance and preventivehealth care that may increase adverse outcomes. There is no reason to assume thatthese staffing changes would affect only Medicare residents, especially if facilities wererelying onMedicare revenues to cross-subsidize care for long-stay Medicaid residents.

The second policy finding is that the PPS effect was large and significant while therate effect was generally not. This, too, is consistent with changes in staffing associated

with the two types of payment changes. White (2005-2006) found that the eliminationof cost reimbursement (our PPS effect) was associated with large reductions in nursestaffing, while changes in average payment rates had a negligible effect. In our results,the PPS effect was consistent across types of facility and was robust to a variety of sensitivity analyses. The elimination of cost reimbursement represented amore dramaticchange to SNFs and appears to have increased the likelihood of adverse outcomes,confirming our first hypothesis. We believe the weakness of the rate effect can beexplained at least in part by several factors. The rate increases under BBRA of 2000were intended to be temporary, and facilities would not be expected to make thesame changes in staffing or other operational decisions under uncertainty as underthe more certain PPS changes. In addition, although staffing reductions are relativelyeasy to implement in an environment with high turnover, increases in staffing maytake longer. A quick response to reductions in funding but a longer-term responseto increases in funding will tend to bias our estimates on the rate effect toward zero.

For these reasons, our estimate of the rate effect is potentially a lower bound on thetrue effect. In any case, the results do provide evidence for our second hypothesisamong nonprofit facilities. Nonprofit facilities do appear to exhibit fewer pressuresores with higher payment rates and more pressure sores with lower payment rates.The weakness and inconsistency of the overall rate effect compared to the PPS effectsuggests, however, that increases in prospective rates may be a weaker policy toolthan cost reimbursement in inducing improvements in quality.Our third and most important policy finding is that changes inMedicare paymentsto SNFs can affect long-stay, chronic-care residents. Although Medicare comprisesonly 12% of nursing facility revenues on average (American Health Care Association, 2001), generally short-stay rehab patients, the financial impact of PPS reductionsled to decreased quality of care for non-Medicare residents because facilities' abilityto cost-shift to support their care was diminished. The recent studies that examinedoutcomes only forMedicare residents before and after PPS may have missed the fulleffect of PPS-induced changes. Furthermore, the outcomes from administrative dataused in previous studies may not have been as sensitive to financial pressures as theless serious but more prevalent outcomes studied here.We believe these empirical results are consistent with a cost-shifting model ofnursing home behavior. Before PPS was implemented, SNFs dramatically increasedtheir capacity to provide short-term rehabilitative care to tap growing demand andgenerous Medicare reimbursement. High service intensity among Medicare residentsproduced revenues that could cross-subsidize a less lucrative long-stay population, apopulation that nonetheless was important for facilities to serve in order tomaintain

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certification. The PPS eliminated this mechanism by eliminating marginal reimbursement for individual services and by reducing average payment rates. The result islower quality among long-stay nursing home residents. Empirically we find no consistent differences between for-profit and nonprofit facilities, supporting our contentionthat for-profit nursing homes are not able to behave like pure profit-maximizers giventhe current regulatory framework and that both types of facilities were cost-shifting.

Our empirical results with regard to market power are more mixed. We expected thatfacilities in high-occupancy markets at baseline would have more power to cost-shiftand therefore would exhibit greater effects of PPS. While several of the magnitudesof effect in high-occupancy markets were dramatically larger than in low-occupancy

markets, the results were not consistent across types of effects (PPS effect and rateeffect) and outcomes. It may be that facility-wide market occupancy is not a veryprecise measure of nursing home market power. Unfortunately, the data precludeexamining market occupancy by payer type.

PPS was implemented as a cost-containment measure forMedicare, but unanticipated decreases in quality of care resulted. From a societal perspective, such a tradeoffbetween cost and quality cannot be viewed in terms of the effect of policy changes on

Medicare residents only. When one provider organization combines revenue streamsfrom several different payers, effects of one payer's policy changes on different resident payer groups should be expected. These indirect effects may have subsequentconsequences for costs toMedicare as well as other payers. Future analyses of pay

ment policies to nursing homes need to take a wider perspective if cost and qualityare to be balanced efficiently for all residents.

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