racial/ethnic disparities in access to medicare home health care: the disparate impact of policy

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Racial/Ethnic Disparities in Accessto Medicare Home Health Care: TheDisparate Impact of PolicyJoan K. Davitt a & Lenard W. Kaye ba School of Social Policy and Practice; and New Courtland Center forTransitions and Health, University of Pennsylvania , Philadelphia,Pennsylvania, USAb Center on Aging; and School of Social Work, University of Maine ,Bangor, Maine, USAPublished online: 21 Sep 2010.

To cite this article: Joan K. Davitt & Lenard W. Kaye (2010) Racial/Ethnic Disparities in Access toMedicare Home Health Care: The Disparate Impact of Policy, Journal of Gerontological Social Work,53:7, 591-612, DOI: 10.1080/01634372.2010.503984

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Journal of Gerontological Social Work, 53:591–612, 2010Copyright © Taylor & Francis Group, LLCISSN: 0163-4372 print/1540-4048 onlineDOI: 10.1080/01634372.2010.503984

Racial/Ethnic Disparities in Access to MedicareHome Health Care: The Disparate Impact

of Policy

JOAN K. DAVITTSchool of Social Policy and Practice; and New Courtland Center for Transitions and Health,

University of Pennsylvania, Philadelphia, Pennsylvania, USA

LENARD W. KAYECenter on Aging; and School of Social Work, University of Maine, Bangor, Maine, USA

The Balanced Budget Act of 1997 dramatically decreased reim-bursements for traditional Medicare home health patients. A mul-tivariate analysis of Medicare Current Beneficiary Survey datashowed that African American and “other” users experiencedgreater decreases in home care between 1996 and 1998 than didWhite users. These results suggest (a) race/ethnicity is an indepen-dent factor in determining service use post-BBA and (b) healthpolicy has a disparate impact on minority older adults. Capitatedpayment systems must be pursued cautiously to avoid negativeeffects on vulnerable populations. The potential for current andfuture Medicare policy changes to negatively affect vulnerablepopulations is also discussed.

KEYWORDS Medicare, home health care, policy, racialdisparities

The Balanced Budget Act of 1997 (BBA) created an Interim Payment System(IPS) that dramatically decreased payments for Medicare fee-for-service

Received 27 April 2010; accepted 22 June 2010.We acknowledge the following organizations that provided support for this research:

The John A. Hartford Foundation, The Andrus Foundation and the Centers for Medicare andMedicaid Services.

Address correspondence to Joan K. Davitt, School of Social Policy and Practice,University of Pennsylvania, 3701 Locust Walk, Philadelphia, PA 19104, USA. E-mail: [email protected]

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592 J. K. Davitt and L. W. Kaye

home health users. The IPS immediately constrained expenditures, whileproviding time for the Centers for Medicare and Medicaid Services (CMS)to develop a new Prospective Payment System (PPS). Congressional BudgetOffice (CBO) projections indicated that home health spending in FY2000would drop from $25.3 to 21.2 billion due to BBA changes. However, actualexpenditures dropped to $9 billion (Leon, Davitt, & Marainen, 2002). Underthe IPS, agencies were paid based on the lesser of reduced per-visit limits oran aggregate per-beneficiary cap, calculated based on average agency (75%)and regional (25%) home health care costs in 1993 (U.S. General AccountingOffice, 1998). The majority of agencies fell under the per-beneficiary limits,as this tended to be the most restrictive of the payment alternatives. Thenew IPS per-beneficiary limit provided a flat rate for each patient served byan agency, without adjusting for patient acuity, thus discouraging agenciesfrom serving potentially high-cost patients (Berke, 1998; Davitt & Marcus,2008; Komisar & Feder, 1998; The Lewin Group, 1998).

Research has demonstrated that African American, Hispanic, and NativeAmerican older adults have higher rates of comorbidity and chronic, dis-abling conditions than their White counterparts (Byrne, Nedleman, & Luke,1994; John, 2004; Johnson & Taylor, 1991; National Center for HealthStatistics, 1998; Rooks & Whitfield, 2004). Minority Medicare beneficiariesare also more likely to rate their health status as fair or poor, comparedto White beneficiaries (John, Hennessey, & Denny, 1999; Williams, 2004).Black, Latino, and Native American beneficiaries are more likely to sufferfrom functional impairments (Hayward & Heron, 1999; Williams, 2004) andfrom chronic conditions, such as diabetes and hypertension, than Whitebeneficiaries (Johnson & Taylor, 1991; Williams, 2004). Although AsianAmericans, generally, seem to have better morbidity and mortality rates thanWhites, native Hawaiians and Pacific Islanders have poorer health outcomesthan both White and other Asian American groups (Braun, Yee, Browne, &Mokuau, 2004).

Having a chronic condition is associated with greater risk of limita-tions in physical functioning and the potential for increased use of healthcare (Mor, Wilcox, Rakowski, & Hiris, 1994). In addition, minority olderadults are less likely to have the resources to pay out of pocket for in-homeservices (Angel & Hogan, 2004). Studies found that more disabled elderlyAfrican Americans, who were eligible for public financing, had much higherrates of formal care use than those ineligible for public financing (Leon,Parente, & Neuman 1997; Liu, Wissoker, & Rimes, 1998; Wallace, Levy-Storms, Andersen, & Kingston, 1997). Wallace and colleagues (1997, p. 10)concluded that “African American elderly depend disproportionately on pub-lic programs for the formal long term care they receive.” Thus, changes inpublic financing that reduce access to in-home care services may dispro-portionately affect minority beneficiaries because both their care needs anddependence on public services are greater.

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Racial Disparities in Home Health Care 593

This article provides an analysis of policy changes related to use ofhome health care and describes the disparate impact of these changes byrace/ethnicity. The incentives in these policy changes generated pressureon agencies to reduce service to potentially high-cost patients, in particu-lar patients with chronic medical conditions and those that would quicklyexhaust the per-beneficiary limit due to multiple visits (Davitt & Choi, 2008b;Leon et al., 2002; Markham-Smith, Maloy, & Hawkins, 1999). Given thegreater likelihood for African American and other minority older adultsto suffer from severe and chronic medical conditions, it is reasonable tohypothesize that they would be at greater risk of reduced access from thesepolicy changes. It is essential to understand whether race-neutral policychanges result in differential effects by race/ethnicity to prevent such gapsin access to care via future health or social welfare policy changes. Forexample, the national standardized base rate, for the PPS (implemented inOctober 2000), was established from reimbursement figures under the IPS.Therefore, access problems under the IPS may continue under the PPS. Also,the potential to adopt similar cost cutting measures in other health care sys-tems both in the United States and abroad, warrants a critical analysis of theimpact of such approaches, especially on vulnerable subgroups. This articleprovides an historical analysis of these reimbursement changes as an exam-ple of a race-neutral policy change that resulted in unintended and disparateconsequences for older minority Medicare beneficiaries.

LITERATURE ON RACIAL DISPARITIES IN HEALTH CARE

Research has documented continued discrepancies by race in access to oruse of specific medical procedures (Eggers & Greenberg, 2000; Kjellstrand,1988). Race has been associated with lower levels of preventive care, whichlead to more serious health outcomes for African Americans (McBean andGornick, 1994; Ries et al., 1994). Likewise, studies have found differences inthe quality, quantity, and intensity of care received by race for inpatient care(Kahn et al., 1994; Lee, Gehlbach, Hosmer, Reti, & Baker, 1997). Differentialaccess and utilization by race has been documented for specific medicalservices (Moy & Hogan, 1993; Mui & Burnette, 1994; Wallace, Levy-Storms,Kingston, & Andersen, 1998) and in Medicare (Lee, et.al. 1997).

Studies have demonstrated dramatic decreases in use of home healthpost-BBA (Davitt & Marcus, 2008; Komisar, 2002; McCall, Komisar, Petersons,& Moore, 2001; McCall, Petersons, Moore, & Korb, 2003; McKnight, 2006).Factors associated with decreased use of Medicare home care after theBBA included: income (Davitt, 2003; Liu, Long, & Dowling, 2003; McKnight,2006), functional impairment and poor health status (Davitt & Marcus, 2008;Liu et al., 2003; McKnight, 2006), having a chronic condition (McCall et al.,2003), age (Fitzgerald et al., 2006; McCall et al., 2003), gender (Fitzgeraldet al., 2006), and not having a prior hospital stay (Liu et al., 2003).

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Some of these studies also demonstrated changes by race/ethnicity.McCall and colleagues (2001) compared Medicare data for 1997 and 1999and found significantly greater decreases in the number of users per 1,000enrollees and in payments per user for non-White beneficiaries. However,they did not control for key predisposing and enabling characteristics. Ina later study, while controlling for certain demographic and system factors,they also found that non-White users experienced differential reductionsin the amount of home health care used (McCall et al., 2003). However,these studies did not control for health or functional status. Finally, Liu andcolleagues (2003) and Fitzgerald et al. (2006) demonstrated no significantdifferences in likelihood of home health use or visits by race during the IPS.

A limited and inconclusive body of literature has examined the asso-ciation between race/ethnicity and home health care outcomes. McCall,Korb, Petersons, and Moore (2002) examined differences in the probabil-ity of experiencing certain adverse outcomes (e.g. hospitalization, skillednursing facility [SNF] admission, ER use or death) between 1997 (pre-IPS)and 1999 (IPS). They found no difference in outcomes by race. In con-trast, other research, using data from 1998 and 1999, showed that AfricanAmericans were less likely to have repeat hospitalizations (Rosati, Liping,Navaie-Waliser, & Feldman, 2003). However, these studies analyzed onlymajor adverse outcomes.

Finally, two studies examined differences in health status outcomesby race/ethnicity. Peng, Navaie-Waliser, and Feldman (2003) found thatHispanic and Asian home health users were more likely to have greaterinstrumental activities of daily living (IADL) impairments at discharge thanWhite users. Also Black, Hispanic, and Asian clients were less likely to reportdepressive symptoms at discharge than White users. However, this samplewas derived from one agency making generalizability difficult. The find-ings also suggested a weak fit for the functional status multivariate models,which might be influenced by the lack of controls for agency, system andneighborhood factors.

A second study sampled OASIS episodes from agencies participating inthe University of Colorado Outcome Reporting and Enhancement research.They used the first full year (2001) of the PPS to examine racial/ethnicdifferences in functional outcomes of home health users, controlling forbaseline status at admission. White users had much better functional out-comes than non-White users at discharge with the greatest differencebeing between African American and White users. However, this study isalso limited because the sampling frame was comprised of a self-selectedgroup of agencies participating in a quality improvement study, and theirmultivariate models controlled only for patient-level risk factors (Brega,Goodrich, Powell, & Grigsby, 2005). Most important, neither Peng et al.(2003) nor Brega et al. (2005) evaluated the influence of policy on outcomedisparities.

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Our study contributes to this existing literature in two crucial ways.First, given the differential health and functional status of older adults ofcolor, the risk for greater need for home health care is likely to differ forminority and White beneficiaries. In examining this issue, it is essential tocontrol for comorbidity and for functional and health status so that changesin use cannot be attributed to a higher/lower patient risk profile. Access tocare is also associated with adequate insurance coverage (Collins, Hall, &Neuhaus 1999; Hargraves & Hadley 2003). Our study controlled for thesefactors in multivariate regression models, thereby expanding the conceptualframework for understanding the contributing factors to disparities. By con-trolling for risk and protective factors, this study furthers the understandingof whether any difference in use can be attributed to race/ethnicity directly,thus suggesting a racial disparity, or to other factors that are associated withhealth care use. Second, the contradictory findings in the existing literaturehighlight a need for further analysis of this issue. Furthermore, in an era offiscal restraint, it is critical to understand the unintended consequences ofshifts to capitated methods of payment and other policy changes, especiallyfor the most vulnerable patients.

METHODS

Conceptualization

This study sought to understand the impact of policy changes on realizedaccess to home health care for beneficiaries in the traditional Medicareprogram and whether such policy changes altered the equitable nature ofthat access (Andersen, 1968). According to Andersen (1995), realized accessrefers to the actual patterns of use (dependent variables in this study) andsatisfaction for a particular population. On the other hand, equitable accessplaces greatest emphasis on illness in determining resource allocation.

To address this question, we conducted secondary analyses deriveddirectly from Andersen’s (1968) behavioral model, which specifies threeindividual-level determinants of health care use: predisposing, enabling andneed characteristics. Individuals are predisposed to use health care basedon certain socio-demographic attributes; in this study, they include age, sex,marital status, education, race, and cognitive impairment. Enabling charac-teristics are those that allow the individual to act on a need or preferencefor care and include: supplemental insurance, Medicaid eligibility, censusregion, rural residence, and availability of caregivers. Finally, need vari-ables refer to the individual’s actual and perceived health status, includingfunctional status, number of diagnoses (measure of comorbidity), and self-reported measures of health status. Realized access was measured via varioususe variables such as number of users, the likelihood of any use, number

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of visits, and home health reimbursements as well as use of and number ofvisits for specific services (the dependent variables).

Data Source

We analyzed data from the Medicare Current Beneficiary Survey (MCBS)Access to Care File with matching claims. The MCBS is a panel study ofa nationally representative sample of aged, disabled, and institutionalizedMedicare beneficiaries, using all Medicare enrollment files (Eppig & Chulis,1997). Interviews are conducted in-person with the beneficiary or a desig-nated proxy. Response rates for the initial interview range between 85% and90%, with subsequent interviews achieving 95% or better (CMS, 2003). Thisstudy employed a comparative analysis of CMS Medicare data for the years1996 and 1998 to determine what changes occurred in service access. Weused 1998, as this was the first year with full participation in the IPS.

Sample

The MCBS sample is derived using a stratified multistage area probabilitydesign with three stages of selection. The primary sampling units are 107metropolitan statistical areas and clusters of nonmetropolitan counties. ThePSUs are grouped into homogeneous strata based on socioeconomic status(SES) and size. The secondary sampling unit consists of over 1,000 zip codeclusters, selected via systematic random sampling. Finally, beneficiaries areselected with stratification by age (Apodaca, Judkins & Lo, 1992).

We analyzed the impact of these policy changes on Medicare beneficia-ries 65 years and older. Beneficiaries with end-stage renal disease (ESRD),disability, and residing in a nursing home were excluded. Research hasshown that disability and ESRD beneficiaries have different use patternsthan older adults; including these groups might confound the results mask-ing important access changes for older adults (Davitt & Marcus, 2008). Thesubsample included 11,467 respondents in 1996 (25,616,283 weighted) and10,540 respondents in 1998 (24,364,446 weighted). Analyses of home healthcare use examined a subset of home health users only, for a total of 2,437users, 1,240 in 1996 (2,475,150 weighted) and 1,197 in 1998 (2,186,681weighted).

Operational Definitions

This analysis focused on whether race/ethnicity might better predict realizedaccess post-BBA than need variables, thus, making access less equitable. Theoriginal data combine race and ethnicity into one variable, which hereto-fore we refer to as race. Unfortunately, the MCBS does not oversample

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by race/ethnicity, leaving several race categories with very small numbers.Therefore, race was collapsed from a six-attribute variable into a three-attribute variable. Those categories with too few respondents were collapsedinto the category other, which included Asian, Hispanic of any race, NativeAmerican, and other. The final multivariate race variable consisted of AfricanAmerican, Other, and White, which represented the excluded group inregression analyses. The other main independent variable was the year ofservice receipt. A dummy variable with categories 0 = 1996 (Pre-IPS) and1 = 1998 (IPS) was created to evaluate differences in utilization patternsover time and was the main independent variable in all regressions.

Health and functional status have been shown to be directly associatedwith healthcare use, thus we controlled for these variables. The followinghealth/functional variables were included as controls in the multivariatemodels: general health status, number of diagnoses (as an indicator ofcomorbidity), and functional impairment in activities of daily living (ADLs).Respondents reported their general health status, utilizing the followingresponse categories: excellent, very good, good, fair, or poor. This vari-able was collapsed into a 2-item variable coded poor to fair and good toexcellent (excluded group in regressions). There were no differences in thestatistical results using a 5-value or 2-value health status variable. Numberof diagnoses provides a simple measure of comorbidity for each patient.The MCBS does not provide a measure of comorbidity nor access to clinicalrecords. We derived this measure by summing the number of diagnoses oneach claim and then dividing by the number of claims for each patient. Thissimple index offers good explanatory power with regard to service use. Italso has demonstrated greater power than the Charlson Comorbidity Indexas that was developed for use with an inpatient sample and to predict mor-tality (Chapelski, Lichtenberg, Dwyer, Youngblade, & Tsai, 1997; Romano,Roos, & Jollis, 1993).

Impairment in ADLs was measured as a dummy variable; either benefi-ciaries had some or no limits. Models were run on continuous and dichoto-mous forms of ADL impairment with very similar results; for ease of inter-pretation we are reporting the dichotomous version here. ADLs include thefollowing items: bathing, dressing, eating, transferring, walking, and toileting.

Other predisposing and enabling control variables in these modelsincluded: patient age, number of caregivers, years of education, gender,Medicaid eligibility, census region (West North Central acted as the excludedgroup in regression models),1 rural residence, marital status (not marriedacted as the excluded group), and supplemental insurance coverage. A newvariable was created for supplemental insurance coverage. This was createdby collapsing the existing variable into two categories, Medicare plus a pub-lic and/or private supplement, and Medicare only (excluded group). Finally,the dummy variable, memory loss, which interferes with activity, was usedas a proxy for cognitive impairment.

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Data Analysis

A two-part model of health care use is recommended for this type of analysis;policy changes may differentially affect entry into the system compared withaccess once in the system (Diehr, Yanez, Ash, Hornbrook, & Lin, 1999).Thus, we examined the likelihood of any home health care use for all visittypes and for each of the six specific service types (skilled nursing, medicalsocial work, home health aide, physical, occupational and speech therapy)and used the chi-square statistic to determine whether the rates of any usediffered between 1996 and 1998 by race.

To determine if the policy change moderated the relationship betweenrace and likelihood of any use (for all or specific services), we ran aseries of logistic regression models, interacting year of service receiptwith race. These models used race (White as the excluded group), yearof service receipt (1996 as the excluded group) and their interaction asthe primary independent variables, while controlling for all other predis-posing, enabling, and need variables. We used the regression coefficientsfor year, race, and their interaction from these models to calculate theadjusted odds ratio of the change in likelihood of any home health useor any use of specific services between 1996 and 1998 by race (Allison,1999).

The second part of the analysis models the amount of use for thosewho received any home health care or any specific services. Three cate-gories of visits were created, including skilled nursing, therapy (physical,occupational and speech), and nonskilled services (medical social work andhome health aide). Collapsing procedures were required due to the low usein some of the specific service types, e.g., occupational therapy and medicalsocial work. For users of home health care services, we examined whetherthe mean number of visits per user (for all visit types and for skilled, non-skilled, and therapy services) and mean reimbursements per user (for allvisit types) differed by race between 1996 and 1998, using a series of loglinear regression models. The log of the key utilization variables, visits andreimbursements (for all visit types) and visits by type were the dependentvariables with year, race, and their interaction as the primary independentvariables, while controlling for all other predisposing, enabling, and needfactors. The regression coefficients for year, race, and their interaction werethen used to calculate the adjusted percent change (log-linear regression)between years by race (Allison, 1999).

All analyses were conducted in WesVar PC software using balancedrepeated replicate (BRR) procedures to produce appropriate standard errorsreflecting both the complex sampling design (i.e. clustering, unequal prob-ability of selection), and the sample weights. BRR also accommodates thenonindependence caused by the presence of some individuals in both the1996 and 1998 samples (CMS, 2003; O’Connell, Chu, & Bailey, 1997).

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RESULTS

Between 1996 and 1998, the total number of home health users in the fee forservice system decreased significantly (–12 %, p = .01). Total visits decreasedby over 38% (p < .001) and total reimbursements by over 33% (p < .001).Likewise, average visits declined by over 30% (p < .001) from 75 visitsper user in 1996 to 52 in 1998, a difference of over 20 visits on average.Payments per user declined over 24% (p < .05), going from $4,769 to $3,585.Home health care experienced the greatest decreases during this time periodcompared to SNF, inpatient, outpatient and physician services. The onlyother significant decrease during this time period was for the number ofSNF care days per user (–19.49%, p < .05) while SNF stays increased (26%,p < .01).

Changes in Users by Race

Table 1 provides the demographic characteristics of the sample for bothusers and nonusers in both years. The number of White users decreased(18%, Chi-Square = 4.3, p = .038), whereas those of other race increased(69%, Chi-Square = 6.6, p = .01) between 1996 and 1998. African Americanusers increased by less than 2%, but this was not significant.

When controlling for other factors in logistic regression models, thelikelihood of any home health use did not change significantly for any racialgroup. Only the likelihood of receiving any medical social work (MSW)services was significant for race. Users of other race experienced a 69%(p = .036) decrease in the likelihood of any MSW services between 1996and 1998; White users saw a 4% increase. African American users did notexperience a significant change in the receipt of MSW services.

Changes in Visits and Reimbursements

Table 2 summarizes the results related to volume of home care use mea-sured by visits and payments per user for all visit types and visits peruser for skilled nursing, and unskilled services. The unadjusted differencein mean visits/payments column shows that users of other race experiencedthe greatest significant decrease in both visits and payments per user. AfricanAmerican users experienced the second highest significant decrease in visitsand reimbursements, and White users experienced the smallest significantdecrease in both visits and reimbursements per user. It is important to notethat African American users received more visits than White users in bothyears. However, they went from receiving 2 times more visits than Whiteusers in 1996 to about 1.5 times more in 1998. However, users of otherrace who had received more visits than White users in 1996 (for all types

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TABLE 1 Demographic Characteristics of Home Health Users and Nonusers in 1996 and 1998

Users Nonusers

Variable 1996 1998 1996 1998

Total 2,475,150 2,186,681 23,141,133 22,177,765(9.7%) (9.0%) (90.3%) (91.0%)

Age 79.49 79.71 74.39 74.78# of caregivers 1.69 1.63 1.21 1.20Years of education 10.43 10.53 11.51 11.66African American 13.36% 14.93% 86.64% 85.07%Other race 5.54% 8.43% 94.46% 91.57%White 9.54% 8.50% 90.46% 91.50%Poor—fair health 21.67% 20.33% 78.36% 79.67%Good—excellent health 6.10% 5.46% 93.90% 94.54%Some ADL limits 24.96% 21.76% 75.04% 78.24%No ADL limits 4.04% 4.05% 95.96% 95.95%Some IADL limits 21.27% 20.05% 78.73% 79.95%No IADL limits 3.39% 3.07% 96.61% 96.93%Some physical limits 11.99% 11.07% 88.01% 88.93%No physical limits 2.38% 2.08% 9.62% 97.92%Cognitive impairment 24.32% 24.41% 75.68% 75.59%No cognitive impairment 8.10% 7.28% 91.90% 92.72%Medicaid eligible 18.24% 17.75% 81.74% 82.25%Not Medicaid eligible 8.78% 8.02% 91.22% 91.98%Male 7.65% 7.16% 92.35% 92.84%Female 11.12% 10.29% 88.88% 89.71%Medicare only 8.97% 7.82% 91.03% 92.18%Medicare plus

supplemental insurance9.75% 9.12% 90.25% 90.88%

Married 6.51% 6.31% 93.49% 93.69%Not married 13.61% 12.44% 86.39% 87.56%Urban 9.68% 8.92% 90.32% 91.08%Non-urban 9.61% 9.11% 90.39% 90.89%

Census regionNew England 16.33% 13.64% 83.67% 86.36%Middle Atlantic 8.89% 9.28% 91.11% 90.72%East North Central 8.49% 8.63% 91.51% 91.37%Puerto Rico 5.72% 6.35% 94.28% 93.65%South Atlantic 10.57% 9.37% 89.43% 90.63%East South Central 11.74% 10.31% 88.26% 89.69%West South Central 10.57% 9.92% 89.43% 90.08%Mountain 8.18% 6.61% 91.82% 93.39%Pacific 10.35% 8.47% 89.65% 91.53%West North Central 6.83% 6.14% 93.17% 93.86%

Note. ADL = activities of daily living. IADL = instrumental activities of daily living.

and skilled nursing), were, in 1998, receiving fewer visits than White users.Similar results can be seen for skilled nursing visits, where users of otherrace experienced greater and significant decreases than African Americanand White users. However, African American users experienced the greatestdecrease in nonskilled services. There were no significant results for therapyservices.

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To test the hypothesis that the policy might moderate the relationshipbetween race and home health use, race was interacted with year of servicewhile controlling for all other variables. Only African Americans experiencedsignificant decreases in visits and reimbursements (for all types) between1996 and 1998. (See Table 2.) Both African American and users of other raceexperienced greater and significant decreases in skilled nursing visits, butAfrican American users experienced a greater decrease in nonskilled visitsbetween 1996 and 1998 than White users.

DISCUSSION

This study indicates that admission to home health was less problematic thanaccess to actual home care services for racial minorities. Clearly, minorityusers experienced greater decreases in visits and reimbursements in totaland for skilled and nonskilled services than White patients between 1996and 1998. Although African American users still received more visits thanWhite users in 1998, users of other race actually received fewer visits thanWhite users in 1998. If the cuts in service were applied equitably acrossgroups, one would expect to see minority users still receiving services at thesame rate as prior to the BBA relative to White users (e.g. 2 times more forAfrican American users in 1996 compared to 1.5 times more in 1998). Thesepatterns occurred in a very brief time period, suggesting that improvement inhealth status for the subgroup would not provide an adequate explanationfor the decreases. Another possible, although unlikely, explanation is thatminority patients on average were receiving an inappropriately high levelof services prior to the BBA. More likely, this suggests that race may bean independent and separate factor in the determination of service deliverypost-BBA.

Users in the other race category saw a significant decrease in accessto MSW services, and were much less likely to receive any MSW servicesin 1998 than White patients. Other research shows that overall use of socialwork services decreased during this time period (Davitt & Choi, 2008a). “Themost dramatic cuts in staffing and visits were for medical social work andhome health aide which are more specifically intended to support chroni-cally ill patients and their families.” (Davitt, 2009, p.305) Cuts in social workraise particular concerns regarding how minority patients are meeting theirneeds for care post-home health services, since social work is generally usedto help with discharge transitions.

Because the decreases were related to use once in the program, ratherthan admission to home health care, it appears that most users would havehad a skilled care need at admission. If minority beneficiaries had chronic yetskilled care needs for home health care, our finding may indicate inappro-priate reductions in service. However, without a review of clinical records, it

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is difficult to determine whether patients continued to have a skilled need.It is possible that the decreases in service were applied to those with onlycustodial needs. It is also possible that these service cuts affected beneficia-ries with legitimate, skilled care needs, either by reducing the frequency ofvisits or the length of stay.

Policy, Practice and Research Implications

The IPS was created to immediately control costs in the Medicare homehealth benefit, while CMS designed a prospective payment system. However,incentives in the IPS did little to help agencies directly discriminate betweenpatients with chronic, but medically complex, needs and those with custodialneeds. The IPS lacked case-mix adjustment that would tie reimbursementsto patient acuity, and enable agencies to serve high-cost patients withoutexhausting the capitated payment. CMS used an aggregate per-beneficiarylimit to simulate case mix adjustment, that is, they expected agencies to bal-ance low and high-cost patients served. Agency reaction to these changeswas exacerbated by the fact that CMS did not provide specific agency capsuntil 6 months after initiation of the IPS and that agencies had never beforeoperated under a per-beneficiary limit. Agency directors, when required toguess their per-beneficiary limit, reacted, in many cases, by cutting ser-vices more drastically than was necessary (Davitt & Choi, 2008a, 2008b)and by keeping all beneficiaries under the anticipated payment cap. Theseincentives encouraged agencies to cut services to all patients but especiallyto clients who were expected to exhaust the per-beneficiary cap. Thus,minority patients may have experienced greater cuts compared to pre-IPSpatterns, because agencies were keeping all patients under the cap, ratherthan balancing the needs of sicker patients with those of less ill patients.

If providers believed that specific patients would be more costly to servebecause of the presence of chronic conditions, they may have made strategicdecisions to curtail service delivery to such patients. Given the evidence ongreater acuity and prevalence of chronic illness among minority elders, agen-cies may have used race as a proxy for potential high cost. Another explana-tion of more dramatic cuts in service to minority users involves advocacy forminority patients. Research has shown that White members of health carestaff are less able at times to identify with minority patients, resulting in lessaggressive advocacy efforts on behalf of minority patients (Smedley, Smith, &Nelson, 2003). Curtailed advocacy during this period, would only exacerbateany general service cuts for minority patients who might have greater needs.

Also, provider stereotypes about other patient characteristics may haveinfluenced practice patterns (Smedley et al., 2003). For example, if providersexpected that minority patients would be more likely to have informal sup-port, which has been suggested in many studies, they may have assumedthat they could safely cut services to these patients. Qualitative research with

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agency directors indicated much greater effort to educate caregivers and shiftcare to the informal sector post-BBA (Davitt, 2009). However, we controlledfor number of caregivers, suggesting that the actual number of availablecaregivers may have been less important than the assumptions staff mighthave made about availability.

Both reductions in access for minority patients and the need forenhanced advocacy suggests a need for greater access to and use of socialwork services within home health care, rather than a reduction in thoseservices. Additional service would be required to help patients and theirfamilies directly by identifying and connecting families to additional in-homeresources to provide care and support the biopsychosocial needs of thepatient and caregiver (Davitt, 2009). Equally important would be the socialworker’s role in providing cultural sensitivity training to staff within homehealth agencies. Social workers may also oversee agency-based evaluationsrelated to quality and access for all patient subgroups. Finally, social workstaff may need to take on the advocacy role on both a patient and systemlevel. Documentation of gaps in access and/or outcomes may be used tosupport advocacy efforts. However, the challenge in expanding the socialwork role in a cost-cutting era is in convincing agency administrators of theneed and potential benefit to such efforts.

The IPS also forced many agencies to close branch offices and/orreduce their service areas to remain solvent (Davitt and Choi, 2008a; Linet al., 2005; U.S. Government Accounting Office, 1998). If minority Medicarebeneficiaries were more likely to reside in racially segregated and/or eco-nomically impoverished neighborhoods with already compromised healthservice access, their access to home health care providers may have beenfurther curtailed by these changes. In this analysis, we were able to con-trol for urbanization and Medicare region. However, future research on suchpolicy changes should investigate measures of service availability in specificcensus areas. If specific geographic areas are found to have little/no access,then incentives could be used to encourage providers to reenter those areas.

Ample research confirms that lower SES leads to poorer health in laterlife and may explain some component of racial disparities in health care(Crimmins, Hayward, & Seeman, 2004; Eggers & Greenberg, 2000). Althoughour multivariate models controlled for SES through use of Medicaid eligibil-ity as a proxy for poverty and the education variable, these are not foolproofmeasures of SES. Thus, it is possible that the results revealed in this analysismay still be related to SES. The explained variance in the regression mod-els ranged from 17% to 27% indicating that additional, relevant variablesare not included in these models. These variables could include individualSES, neighborhood effects variables (e.g. poverty level, racial segregation),and service availability variables (e.g. number of home health agencies in ageographic region). Future research on the disparate impact of health policychanges should include measures of these constructs.

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Another critical area for future research is to document the impact ofhome health reimbursement changes on health outcomes. It is difficult todetermine from our data whether the reduced utilization levels by race alsorepresent a reduction in quality of care that is linked to deterioration inpatient outcomes. Initial research suggests that there may be outcome dis-parities by race in home care (Brega et al., 2005; Peng et al., 2003). However,the existing literature is inconclusive and has methodological limits.

The Current Policy Environment: The Potential to Maintain or ReduceDisparate Impact

The inclusion of case mix adjustment in the PPS, which began in October,2000, has helped agencies to provide services to more costly patients,such as those with medically complex, chronic conditions. The PPS tiesreimbursement to patient need via a comprehensive assessment (OASIS)conducted by the home health agency which appraises patients’ clinicalseverity, functional status and service needs. Since 2000, agencies have beenreceiving a fixed payment for a 60-day episode of care for each patient,which is based on their acuity, originally captured via 80 home healthresource groups (HHRGs). “Thus agencies are paid based on the expectedservice needs for different categories of patients, rather than on actual costto deliver the service (pre-BBA) or on an arbitrarily derived per-beneficiarylimit (IPS)” (Davitt & Choi, 2008b, p. 265).

Changes made to the OASIS assessment beginning in 2008 resulted ina new, four-equation case-mix algorithm with 153 HHRGs. The shift from80 to 153 HHRGs arose from CMS’ concerns regarding the original case mixmodel’s ability to accurately predict service needs (CMS, 2007). The newformula increases the number of therapy thresholds, the diagnosis groupsand adds scoring for certain conditions and secondary diagnoses (Davitt &Choi, 2008b). The formula also recognizes whether a patient is in an early orlater episode of care (CMS, 2007). According to CMS (2007, p. 25358) thesechanges will “ensure that the payment system continues to produce appro-priate compensation for providers while retaining opportunities to managehome health care efficiently.” These changes ideally should help in servinghigher-acuity patients, especially those that need extended episodes of careas well as those with additional diagnoses that were not considered in theoriginal PPS formula.

However, there are two key concerns with the PPS. First, the originalbase rate for the PPS was generated from the drastically reduced IPS expen-ditures. Thus, this program may be continuing disparate impact effects evenwith case mix adjustment. Early studies showed a decline in the likelihoodof use after the PPS, but this decline was much smaller than under theIPS (Fitzgerald, et. al. 2006; Murtagh, McCall, Moore, & Meadow, 2003).

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“While growth rates in terms of total spending moved onto the positive side,they remain well below the average growth rate for the overall Medicareprogram” (Davitt & Choi, 2008b, p. 265). Likewise, more recent data fromMedPAC (2010) indicate that average visits per user have held steady at37 between 2000 and 2008, down from 73 in 1997, just prior to the BBA.Likewise, agencies have continued to curtail access to nonskilled services,especially medical social work and home health aide visits (Davitt, 2009;Davitt & Choi, 2008a; MedPAC, 2010). In fact, CMS data show that the per-cent of home health aide and medical social work visits dropped by 41%and 30%, respectively, between 2000 and 2008 (MedPAC, 2010). This sug-gests that growth increases are not the result of a return to former accesslevels, but rather to growth in the number of users and in costs to pro-vide services. Thus, concerns regarding continuation of disparate impact onminority users need continued evaluation.

The second concern is related to the fact that, under the PPS, the agencycontinues to shoulder the financial risk to serving the patient (Davitt & Choi,2008b). Shifting risk to agencies has historically led to adaptive, gamingpractices to reduce the agency’s financial risk (as seen under the IPS) orenhance profit (Davitt & Choi, 2008b). For example, after the PPS, use oftherapy services increased from 19% in 2000 to 26% in 2008 (MedPAC, 2010)and at least one study showed increased access for ortho and neuro-patients(Murtaugh, et.al., 2003). Increased reimbursement for therapy incentivizedcertain patient groups for whom agencies thought they could receive higherreimbursements (Davitt & Choi, 2008b).

CMS and MedPAC continue to monitor agency practice, in particular,concerns related to payment/cost margins, upcoding, and outlier payments.MedPAC has repeatedly reported that agency payments are higher thantheir actual costs. In 2008, freestanding agencies experienced margins of17.4%; MedPAC (2010) attributed this to lower cost growth and fewer ser-vices being delivered than anticipated. Upcoding, or “overstating the severityof a beneficiary’s condition” accounted for 11.78% of the change in case-mix between 2000 and 2008 according to CMS (2007; MedPAC, 2009; U.S.General Accounting Office, 2009, p. 5). Thus, CMS adjusted the market bas-ket update between 2008 and 2011, reducing it by 2.75% each year (CMS,2007; MedPAC, 2009). Also in 2010, CMS imposed a new agency level capon outlier payments (an added payment meant to encourage agencies toserve high-cost beneficiaries). Agencies will be held to 10% of total pay-ments for outlier claims. Any outlier claims over the 10% cap will notbe paid.

Historical (Davitt & Choi, 2008b) and recent U.S. General AccountingOffice (2009) studies show that much of this fraudulent practice happens ina limited number of agencies. But the remedy for the behavior is applied toall agencies by cutting the base rate via a reduction in the market basket ormaking all agencies subject to an outlier payment limit. There is no question

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that agencies game the system to attempt to provide as much care to patientsas they need (Davitt & Choi, 2008b; Fitzgerald et al, 2006). Likewise, someproviders come into the system intent on reaping as much profit as possi-ble (U.S. General Accounting Office, 1996, 2009). However, such sweepingpolicies do not necessarily eliminate fraudulent providers from the system.It also raises the real prospect that legitimate, high-need, and thus high-cost,beneficiaries will receive fewer visits if all agencies have to cut their servicesto counteract reductions in the base rate or to stay below the 10% outlierlimit. Unfortunately, policy makers tend to focus on overall costs within theprogram and remedies to discourage fraudulent practices, rather than elim-inating fraudulent providers from the system or incentivizing quality care.Likewise, very little research is conducted to simulate or evaluate the impactof such changes on the most vulnerable patients.

Finally, built into the original BBA was a budget neutrality measurethat basically requires CMS to balance any proposed policy changes to keepcosts at a certain level, i.e., what they would have been even without thepolicy change (CMS, 2007; Dombi, 2007; MedPAC; 2010). Thus, if a pro-posed change would increase the cost of the home health program, thenadjustments must be achieved elsewhere to honor the neutrality clause. Thebiggest challenge to this policy is the ability to accurately predict agencyresponse to any changes to estimate the impact on budget neutrality of aparticular recommendation. This was seen when the IPS was implemented.The CBO anticipated that agencies would increase their revenues by serv-ing more patients (Dombi, 2007; Leon et al., 2002). However, a predicted$17 billion reduction in costs in home health care for the first 5 years afterthe BBA, instead, reached $70 billion (Dombi, 2007). Agency reaction wasboth more extreme and different than anticipated (Davitt & Marcus, 2008;Komisar, 2002; McCall et al, 2003; McKnight, 2006), partly because agenciesdid not receive clear guidance on the level of funding they would receive,and cut all patients more severely than expected.

When you combine the neutrality requirement with the gaming behav-iors of agencies, a real issue is created. Gaming has been demonstratedthroughout the Medicare program’s history; agencies tend to modify theirpractice in reaction to policy changes to either capitalize on generous aspectsof changes or to counteract financial harm due to restrictive changes. Theproblem is that it is very challenging to predict how agencies will reactto proposed changes. The impact of such policy estimations on vulnerablepatient groups, those with greater and legitimate need for care, unfortunatelyis overlooked.

On the positive side, CMS is testing models that focus reimbursementincentives on outcomes of care. However, these models currently measurequality (through a series of outcome indicators) on aggregate agency data.Very little attention is being paid to ensuring quality for subgroups of patientswho are most needy and vulnerable. CMS needs to ask the question, quality

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for whom, and ensure that impact assessment does not overlook need inevaluating the effectiveness of home health care.

If frail and disabled older adults of color rely more heavily oncommunity-based care options including home health care, then the criticalquestion, in the wake of BBA cutbacks including IPS, PPS, outlier pay-ment and budget neutrality measures, becomes: How are these vulnerablegroups meeting their needs? The access to care file does not provide data onother service systems (i.e., informal care, private pay, Medicaid). However,research has shown that reimbursement for home health care under theMedicaid system increased during the IPS (Spector, Cohen, & Pesis-Katz,2004). Likewise, McKnight (2006) found an increase in out-of-pocket expen-ditures for home health care. Also, research shows that informal caregivinghas increased for this population (Davitt, 2009; Golberstein, Grabowski,Langa, & Chernew, 2009). This suggests that Medicare beneficiaries abovethe poverty line, but with limited financial and social resources, may con-tinue to have difficulty obtaining needed home health care if they cannotafford to purchase it privately.

NOTE

1. Census regions: 1. New England - Connecticut, Maine, Massachusetts, New Hampshire, RhodeIsland, Vermont; 2. Middle Atlantic - New Jersey, New York, Pennsylvania; 3. East North Central -Illinois, Indiana, Michigan, Ohio, Wisconsin; 4. Puerto Rico; 5. South Atlantic - Delaware, District ofColumbia, Florida, Georgia, Maryland, North Carolina, South Carolina, Virginia, West Virginia; 6. EastSouth Central - Alabama, Kentucky, Mississippi, Tennessee; Puerto Rico; 7. West South Central - Arkansas,Louisiana, Oklahoma, Texas; 8. Mountain - Arizona, Colorado, Idaho, Montana, Nevada, New Mexico,Utah, Wyoming; 9. Pacific - Alaska, California, Hawaii, Oregon, Washington; 10. West North Central -Iowa, Kansas, Minnesota, Missouri, Nebraska, N. Dakota, S. Dakota.

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racial/ethnic disparities in access to medicare home health care: the disparate impact of policy

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