Copyright 2001 by The Gerontological Society of America The Gerontologist
Vol. 41, No. 5, 597–604
State Medicaid Nursing Home Reimbursement
Rates: Adjusting for Ancillaries
James Swan, PhD,
1Valli Bhagavatula, MBBS,
1Amit Algotar, MBBS,
1Mouhammad Seirawan, MBBS,
1Wendy Clemeña, BS,
2and Charlene Harrington, PhD
3 Purpose: State variation in inclusion of ancillary servicesin daily Medicaid nursing home reimbursement rates, ver-sus covering ancillary costs outside of such rates, makes rate comparisons difficult. The purpose of this study is to adjust for inclusion of ancillaries when comparing Medic-aid rates across states. Design and Methods: Data for 1987–1998 were drawn from a national survey of Medic-aid reimbursement. Employing a random-effects model, the PANEL option in the LIMDEP software was used to esti-mate effects on state average Medicaid nursing facility constant-dollar rates of the inclusion in those rates of a set of ancillaries: physical therapy, occupational therapy, prescription drugs, nonprescription drugs, durable medi-cal equipment (DME), medimedi-cal supplies, and physician services. Results: Rates averaged higher when they in-cluded occupational therapy, physician services, nonpre-scription drugs, and both DME and medical supplies. Adjusting for the inclusion of ancillaries leads to a much different ranking of states than for unadjusted rates.
Implications: Public and industry policy makers should consider the inclusion of ancillaries in rates when consid-ering the relative adequacy of rates across states.
Key Words: Medicaid, Nursing facility, Ancillary services
Medicaid nursing home expenditures have re-cently grown at a slower rate than in the past (Levit et al., 2000), and well below the average growth for national health expenditures; but expenditures are large enough to give policy makers ample reason for concern (Coleman 1996). Nursing home expendi-tures were $87.8 billion in 1998, and these were
ex-pected to grow to $148.3 billion in 2007 (Levit et al., 2000). Medicaid covered 46% of nursing facility payment in 1998, for 60 to 65% of residents (HCFA, 2000a; Levit et al., 2000; Short, Feinleib, & Cun-ningham, 1994).
Medicaid nursing home reimbursement methods and per diem reimbursement rates are of great im-portance in part because they influence the costs of providing care (Harrington & Swan, 1987). How-ever, these policies serve goals beyond cost con-straint: equitable payment to providers, access for Medicaid eligibles, and quality of care received. Con-cern is heightened because of the myriad of ways in which these goals may conflict (Gertler, 1991; Hola-han & Sulvetta, 1989; Swan, Harrington, & Grant, 1988; Swan, Harrington, Grant, Leuhrs, & Preston, 1993). Of particular concern is the issue of adequate payment, that which is adequate to promote access and cover the costs of higher quality care. Medic-aid reimbursement rates were on average substan-tially below Medicare nursing home reimbursement, which was $301 per day in 1997 (American Health Care Association [AHCA], 1999) compared with only $95.09 for Medicaid (Swan et al., 2000), al-though it should be noted that the Medicare data include much payment to hospital-based facilities, which are more expensive.
A new Institute of Medicine report (Wunderlich & Kohler, 2000) raised concerns about whether state Medicaid reimbursement rates are adequate to ensure high quality of nursing home care. The report pointed to the quality of care problems in many nursing homes and the inadequate staffing levels and urged new research on the relationship between reim-bursement rates, access, and quality. The Health Care Financing Administration (HCFA, 2000b) also reported serious understaffing in nursing homes throughout the United States, which was found to be related to poor quality of care. State legislators have also been interested in comparing their Medicaid re-imbursement rates with other states and some have made new efforts to increase rates in order to im-prove staffing levels. For example, a 1999 survey of states found that seven states implemented wage pass-throughs for nursing home workers in their 1999 Medicaid reimbursement rates (HCFA, 2000b).
Funding for this study was provided by the Health Care Financing Administration and the U.S. Department of Housing and Urban Devel-opment under Cooperative Agreement 18-C-90034. The conclusions are those of the authors, and should not be attributed to the funding agencies.
Address correspondence to James H. Swan, PhD, Department of Pub-lic Health Sciences, Box 152, Wichita State University, 1845 N. Fairmount, Wichita, KS 67260-0152. E-mail: swan@chp.twsu.edu
1Department of Public Health Sciences, Wichita State University,
Kansas.
2University of Kansas Medical Center, Kansas City, Kansas.
Nursing facilities depend highly on Medicaid as a payer (Levit et al., 2000). They additionally rely on state Medicaid programs to pay rates adequate to cover costs of care (Batavia, Ozminkowski, Gaumer & Gabay, 1993; Reid & Coburn, 1996; Stone & Reublinger, 1995). Because of impacts on access and quality, Medicaid nursing facility reimbursement policies are of intense interest to the consumer and consumer advocacy groups (Sparer, 1993; U.S. Gen-eral Accounting Office, 1995). Whatever their rela-tive interest in these varied goals, policy makers are understandably concerned with the wide variation across states in per diem rates, particularly because the ratio of the highest average state rate has been four and six times higher than the lowest average state rate every year for over two decades (Swan et al., 2000).
Comparison of rates is made complex, however, by such factors as interstate differences in what is in-cluded in daily rates. States vary widely in whether they include various ancillary services and commodi-ties in daily rates versus covering the costs of such ancillaries outside of the rates (Swan et al., 2000). This study focuses on the question of whether ancil-laries are included in the Medicaid nursing facility per diem rate and how ancillaries influence the inter-state comparison of such rates. It does not attempt to establish overall costs for a day of care, which would necessitate different estimation techniques and con-sideration of additional elements beyond the list of ancillaries considered here.
Background
Cost accounting for ancillary services and prod-ucts is difficult enough in itself, for example where payers reimburse separately for ancillaries (Health-care Financial Management, 1998; Sutter & Keough, 1999). Average costs of providing a given type of an-cillary (e.g., physical therapy) may vary considerably across facilities and providers, depending particu-larly on both volume and intensity of use of the an-cillary (Sutter & Keough, 1999). Thus, although some types of providers (e.g., hospitals) may fare quite well in being separately reimbursed for the pro-vision of ancillaries (Larkin, 1999; Pretzer, 1999), nursing facilities may not do so well. For example, low reimbursement is a reason physicians are reluc-tant to provide care in nursing facilities to their pa-tients who are residents, even though regular moni-toring by physicians has the potential for improving the quality of required resident assessments (McCart-ney & McCart(McCart-ney, 1997).
Ancillary services are important to nursing facility care. “Ancillary” is defined as “being auxiliary or supplementary” (Merriam Webster Medical Dictio-nary, 1997). An ancillary service for nursing facility care is any service considered auxiliary or supple-mentary to nursing care. Such ancillaries include var-ious therapies, drugs, supplies, and equipment (The Balanced Budget Act of 1997, 1999), and an array of other items. Each state can have its own list of
ancil-laries, and searches have not dislodged any HCFA designation. The list used here resulted from re-searchers’ (Swan et al., 1988) presenting a list of seven ancillaries found to be commonly referenced in state Medicaid reimbursement policies.
When ancillaries are included in rates, however, there can be considerably greater variation, com-bined with no assurance that average costs are even considered. This is particularly true in recent years in the Medicaid program, where concerns with con-straining costs may outweigh concerns with ade-quately paying for each type of care (Swan & Pick-ard, in press).
Policies to include ancillaries in rates have effects other than cost. In particular, including ancillaries in rates may be a disincentive to provision of the ancil-laries, because a facility receives no additional reve-nue from providing a given ancillary already in-cluded in its daily rate. This is of particular concern because of the relatively low rate of provision of such ancillaries as rehabilitative therapies in the United States compared to other countries (Berg et al., 1997). Likewise, policies related to reimbursement of pre-scription drugs, and their influence on actual use of drugs in nursing facilities, are important. This is of concern especially in light of the estimates that the costs in morbidity and mortality associated with drug treatment in nursing facilities may outweigh the actual costs of drug treatment (Bootman, Harrison, & Cox, 1997). Few studies have examined ancillary rate policies and their effects on nursing homes.
Although there are wider implications of reim-bursement policies for ancillaries, the focus here re-mains on the implications for reimbursement sys-tems. The question is how inclusion of ancillaries in the per diem nursing facility rate influences the inter-state comparison of such rates. This approach can-not hope to account for the costs of the ancillaries, instead focusing on evidence of presumed state ad-justment of rates to cover such costs.
Methods
A variety of approaches might be used to value the costs of ancillaries. The experience collecting survey data from states suggests that most are unable to provide average costs for major cost centers, much less for specific ancillaries. Although costs of ancil-laries might be sought in facility cost reports (Swan & Pickard, 2001), the contents of cost reports vary widely and seldom contain the wealth of detail that would allow the costing of specific ancillaries. Con-sequently, the approach used here is to attempt to model the costs of ancillaries through the use of panel regression analysis over a multiyear period, with appropriate adjustment for correlated error over time.
Data for a 12-year period, 1987–1998, were drawn from a series of national surveys of Medicaid long-term care reimbursement. The reimbursement rate data were collected from a series of telephone surveys conducted between 1983 and 1999 to collect
data from 1987–1998 on Medicaid long-term care payment methods. A combination of telephone inter-viewing and mail surveys elicited information to clar-ify responses. The data in this study are collected at the end of the state fiscal year to represent actual re-imbursement rates for the year. In contrast, HCFA studies (HCFA 1992; HCIA, 1992) report informa-tion derived from state Medicaid plans and amend-ments, estimating ahead of time what rates will be each year; they are not adjusted when states make changes in their rates during a year. Thus, the rates presented here are outputs—average rates determined to have been used during a given year. These may differ somewhat from but be more accurate than esti-mates based on state plans.
Employing a random-effects model, estimated us-ing the PANEL option in LIMDEP (Greene, 1995), we separately estimated effects on state average daily Medicaid nursing facility rates in constant dollars and in actual dollars of the inclusion of a set of ancil-lary services in those rates. Ancillaries considered were: physical therapy, occupational therapy, pre-scription drugs, nonlegend drugs, durable medical equipment (DME), medical supplies, and physician services. Table 1 reports what states included each of these ancillaries in their rates in 1998.
A linear regression model seems reasonable for the issue because inclusion of ancillaries should, in the-ory, involve the increase of per diem rates by some dollar amount for every ancillary included in a state’s rates. Although the intent was to adjust only for the ancillaries, not for other factors that may influence costs, these other factors may influence both rates and policies regarding ancillaries, producing spurious relationships between ancillaries and rates. Therefore, state characteristics and policies that correlate with both the inclusion of some ancillaries and with rates were used as controls in two equations estimated as well as an equation using only the ancillaries as pre-dictors. State characteristics considered were: total population, percentage of population aged 65 or older, percentage of population in metropolitan ar-eas, per capita personal income in constant dollars, Medicaid home- and community-based waiver dol-lars per capita, and nursing facility beds per aged population. Reimbursement system measures were a set of dummy variables representing whether the state had a case-mix payment system and whether the re-imbursement system was one of the following types: class, facility or patient-specific prospective unad-justed, facility or patient-specific prospective adunad-justed, or a combination prospective/retrospective system (the contrast was a retrospective system).
Because state rate-setting policies (basic method and use of case-mix) may be endogenous with inclu-sion of ancillaries, equations were estimated both in-cluding and exin-cluding such policy measures. No matter which measures were included in an equation, the adjustment of rates for the inclusion of ancillaries uses only the coefficient on the ancillaries.
By the logic of the model, all coefficients on ancil-laries should be positive, representing additional per
diem payment to cover the costs of providing these ancillaries. Some coefficients may be found to be negative, however, perhaps even significant. It does not seem logical that rates would have been reduced when an ancillary was included in a rate. It does seem likely, however, that states may sometimes be less generous, providing both lower rates and includ-ing more services to be covered by those rates; thus, a negative coefficient on an ancillary may be seen as spurious, resulting from an unmeasured tendency to be less generous. Thus, such a finding must be treated with extreme caution.
Comparison of ancillary-adjusted rates was done for only the latest year, 1998. Adjustment involved the subtracting of the coefficient for each ancillary from a state’s 1998 rate if the state included that an-cillary in the 1998 rate. This was done to attempt to compare rates after presumed state coverage of ancil-lary costs was subtracted out. The intent was neither to place a total value on a day of care (which in any case could not be done without considering other in-clusions besides these seven ancillary services) nor to estimate actual costs of ancillary services. Therefore, the coefficients for excluded ancillaries were not added to the rate to produce a “total value” rate.
As noted, only the coefficients on ancillaries were used for this adjustment, even though additional variables were employed in two of the three estima-tion equaestima-tions. Where the coefficient was negative, this resulted in an addition to the rate.
Results
States varied widely in their inclusion of ancillaries in daily rates in 1998 (Table 1). Every state included at least one of the ancillaries in the daily rate. Only three included prescription drugs in rates (those states included all seven types of ancillaries consid-ered). By contrast, almost all of the states included nonprescription drugs (47 states) and medical sup-plies (48 states). All but 5 states treated physical and occupational therapy in the same way, 31 including both, and 15 excluding both.
Table 2 reports the three equations estimated to predict daily rates. Although negative coefficients on ancillaries were estimated in all equations, none was significant in the equation that included only the an-cillary-inclusion measures. Two coefficients were sig-nificant, that on the inclusion of nonlegend drugs and that on the inclusion of DME, the inclusion of either estimated to increase 1998 rates by about $6.50. There were negative but insignificant coeffi-cients on physical therapy and on medical supplies.
The findings are somewhat different when other factors are controlled for. The coefficient on DME remains positive and significant (about $6.00), but the coefficient on nonprescription drugs no longer is, whereas that on prescription drugs is. It should be noted, however, that only three states included pre-scription drugs, so the prepre-scription-drug estimate would adjust rates for only these three states. Fur-ther, and more disturbingly, the coefficient on
medi-cal supplies is negative and significant in both equa-tions with controls, suggesting a tendency for states that include medical supplies in their rates to also have somewhat lower rates. What is more disturbing is that in 1998 all but three states included medical supplies in daily rates, so that almost all states would have their rates adjusted upward for the inclusion of medical supplies in rates. Thus, results of adjusting
for ancillaries while controlling for other factors should be considered very carefully.
Table 3 provides rates adjusted by subtracting the coefficients on the ancillaries from the two models from the state average nursing facility per diem rates for 1998. The ancillary-only model yields adjusted rates that average about $7.00 less than actual rates, so about 7% of average rates across states (all figures
Table 1. Medicaid Nursing Home Inclusion of Ancillaries in Daily Rate: Fiscal Year 1998
State Physical Therapy Occupational Therapy Nonprescription Drug Prescription Drug Medical Supplies Durable Medical Equipment Physician Services
Alabama No No Yes No Yes No No
Alaska Yes Yes Yes No Yes No No
Arizona No No Yes No Yes Yes No
Arkansas Yes Yes Yes No Yes Yes No
California No No Yes No Yes No No
Colorado Yes Yes Yes No Yes Yes Yes
Connecticut Yes No Yes No Yes Yes No
Delaware Yes Yes Yes Yes Yes Yes Yes
District Of Columbia Yes Yes Yes No Yes Yes Yes
Florida Yes Yes Yes No Yes Yes No
Georgia Yes Yes Yes No Yes Yes Yes
Hawaii No No No No Yes No No
Idaho Yes Yes Yes No Yes Yes No
Illinois Yes Yes Yes No Yes Yes No
Indiana Yes Yes Yes No Yes No No
Iowa No No Yes No Yes Yes No
Kansas Yes No Yes No No Yes No
Kentucky Yes Yes Yes No Yes No No
Louisiana No No Yes No Yes Yes No
Maine No No Yes No Yes Yes No
Maryland Yes Yes Yes No Yes Yes No
Massachusetts No No Yes No Yes No Yes
Michigan No No Yes No Yes Yes No
Minnesota Yes Yes Yes No Yes Yes No
Mississippi No No Yes No Yes Yes No
Missouri Yes Yes Yes No Yes Yes No
Montana No No Yes No Yes No No
Nebraska No No No No Yes No No
Nevada No No Yes No Yes No No
New Hampshire Yes No No No Yes Yes No
New Jersey No No Yes No Yes Yes No
New Mexico Yes Yes Yes No Yes No No
New York Yes Yes Yes Yes Yes Yes Yes
North Carolina Yes Yes Yes No Yes Yes No
North Dakota Yes Yes Yes No Yes Yes No
Ohio No No Yes No Yes Yes No
Oklahoma Yes Yes Yes No Yes Yes Yes
Oregon Yes Yes Yes No Yes No No
Pennsylvania Yes Yes Yes No Yes Yes Yes
Rhode Island Yes Yes No No Yes No No
South Carolina Yes Yes Yes No Yes No No
South Dakota Yes Yes Yes Yes Yes Yes Yes
Tennessee Yes No Yes No Yes No No
Texas Yes Yes Yes No Yes Yes No
Utah No Yes Yes No Yes Yes No
Vermont Yes Yes Yes No Yes No No
Virginia Yes Yes Yes No No No No
Washington Yes Yes Yes No No No No
West Virginia Yes Yes Yes No Yes No Yes
Wisconsin No No Yes No Yes Yes No
Wyoming Yes Yes Yes No Yes Yes No
No. of States With 35 32 47 3 48 32 10
Source: Medicaid Reimbursement Survey, Institute for Health & Aging, University Of California San Francisco, 1989; Medicaid
unweighted for state size). By contrast, the model controlling for state characteristics and policies yields adjusted rates averaging almost exactly the same as actual rates; this suggests that the costs of ancillaries were not subtracted out of the rates on average, al-though differing state rates were adjusted for inclu-sion or excluinclu-sion of ancillaries. The states were ranked by their actual 1998 rates and by each of their ancillary-adjusted rates and are reported in Ta-ble 3 according to their rankings.
Table 4 reports the rankings from Table 3 in a manner more convenient for comparison. Adjusting for the inclusion of ancillaries in rates leads in some cases to a very different ranking of states than ob-tained from ranking unadjusted rates. Delaware, Idaho, Michigan, Missouri, North Dakota, and Wis-consin all drop at least five positions when the ancil-lary-only model is used; but only Missouri does when other factors are controlled for, with Michigan and Delaware coming close by, dropping four posi-tions. Likewise, Kentucky, Montana, Nebraska, Oregon, and Rhode Island each climb at least five positions using the ancillary-only model; but only Kentucky and Oregon do so when other controls are added to the model. Nebraska’s rank climbs 16 places in the ancillary-only model, attributable to its inclusion in its rates of the single ancillary medical supplies, which had a nonsignificant but negative co-efficient in the ancillary-only model. It climbs only one place in the full-control model, even though the medical-supplies coefficient is significantly negative because the value of the coefficient is smaller and be-cause the adjusted rates for the other states tend to be higher than in the ancillary-only model.
Overall, most states’ rankings do not change much. With the ancillary-only model, 14 states show the same rank, and 14 others change rank by only one position. Where all controls are entered, 13 states remain unchanged in rank, while 12 change by only one rank. Thus, adjusting for ancillaries only, over half of the states (28) do not change by more than one place from their ranking of their unadjusted rates, and under half of the states plus DC (25) do using the model with all of the controls. Still, the many states that do change their ranks appreciably, and the few who change considerably, show the value of adjusting for ancillaries.
Discussion
Inclusion of ancillaries in calculating the Medicaid per diem nursing facility rate, as opposed to reim-bursing such ancillaries separately, is an important discretionary policy that may impact not just rates paid, but also the costs borne, by Medicaid programs for nursing facility care. It may also affect access to nursing facilities, resident access to care within facili-ties, quality of care delivered, and equity across pro-viders. The comparative rates paid by different states are therefore of extreme importance. This article re-ports adjustments of average state Medicaid daily nursing facility rates for the inclusion of a set of an-cillary services, with a concern for greater validity of interstate comparisons of such rates.
We conclude that public and industry policy mak-ers, as well as researchmak-ers, should consider the inclu-sion of ancillaries in rates when considering the
rela-Table 2. Panel Regression Analysis: Medicaid Per Diem Nursing Facility Reimbursement Rates by Inclusion of Ancillaries in Rate-Setting: 1987–1998
Medicaid Nursing Facility Per Diem Rates
Control for Ancillaries Only Control for State Characteristics Control Also for System Type
Independent Variables Coeff. t score Coeff. t score Coeff. t score
Intercept 81.61 17.05** 82.25 6.20** 74.07 13.62**
Ancillaries Included in Rate:
Physical therapy 1.23 0.43 2.36 1.26 2.25 1.19
Occupational therapy 2.11 0.82 1.58 0.96 1.47 0.88
Physician services 2.07 1.11 0.81 0.69 0.57 0.49
Medical supplies 4.39 1.59 3.72 2.17* 3.73 2.13*
Durable medical equipment 6.54 3.71** 5.98 5.39** 5.93 5.31**
Non-legend drugs 6.66 3.55** 1.27 1.06 0.29 0.24
Prescription drugs 0.97 0.31 5.09 2.53* 4.62 2.30*
n of Cases 6022
Dependent Variable Mean 87.94
df 7 14 19
R2 Total Fixed Model .912** .967** .968**
R2 Predictors Over F.M. .007** .059** .060**
R2 Random Model .138 .586 .593
Notes: All rates adjusted to 1998 dollars using CPI. There are 51 ‘states’ for 12 years, with data on per diem rates missing for some
states in some years (10 total).
Source: Medicaid Reimbursement Survey, Institute for Health & Aging, University Of California San Francisco, 1989; Medicaid
Re-imbursement Surveys, Department Of Public Health Sciences, Wichita State University, Yearly 1992–1999. *p .05, 2-tailed test; **p .01, 2-tailed test.
tive adequacy of per diem rates across states. Ancillaries as a group, and some specific ancillaries, show signif-icant influence on average state Medicaid nursing fa-cility rates. More importantly, some states show con-siderable changes in their ranking vis à vis other states when their rates are adjusted for inclusion of ancillaries. Some or all ancillaries should be adjusted for when comparing rates across states.
Lower ranking should not in itself be taken as payment of an inferior rate. Some rankings are deter-mined by only a few cents’ difference in rates. How-ever, after adjustment, 1998 rates still show from 5–1 to 4–1 ratios of highest to lowest adjusted average rates across states; and breaks between some ranks involve many dollars’ difference. This extreme varia-tion exists even after the list of ancillaries, and in
Table 3. State Ranks: 1998 Medicaid Nursing Facility Rates, Actual Versus Adjusted
Actual Rates Ancillary-Adjusted Rates Full-Model-Adjusted Rates
Rank State Rate $s Rank State Rate $s Rank State Rate $s
51 Arkansas 61.98 51 Arkansas 52.29 51 Arkansas 60.27
50 Oklahoma 64.20 50 Oklahoma 52.44 50 Louisiana 63.05
49 Louisiana 65.54 49 Louisiana 56.73 49 Oklahoma 63.06
48 Texas 71.12 48 Kansas 60.01 48 Kansas 68.01
47 Iowa 71.70 47 Texas 61.43 47 Iowa 69.21
46 Kansas 71.98 46 Iowa 62.89 46 Texas 69.41
45 Illinois 74.23 45 South Dakota 64.23 45 South Dakota 71.20
44 South Dakota 76.96 44 Illinois 64.54 44 Illinois 72.52
43 Georgia 78.43 43 Georgia 66.67 43 Georgia 77.29
42 Virginia 70.47 42 Mississippi 71.79 42 Mississippi 78.11
41 Indiana 80.32 41 Virginia 71.93 41 Utah 79.15
40 Mississippi 80.60 40 Utah 72.19 40 Virginia 79.96
39 Nebraska 81.96 39 Indiana 77.17 39 Indiana 84.54
38 South Carolina 82.75 38 Missouri 78.65 38 Nebraska 85.69
37 Utah 83.11 37 South Carolina 79.60 37 California 86.56
36 California 83.12 36 California 80.85 36 Missouri 86.63
35 Tennessee 83.16 35 Tennessee 82.12 35 South Carolina 86.97
34 Nevada 86.17 34 Michigan 82.68 34 Tennessee 88.85
33 Montana 87.54 33 Wisconsin 82.89 33 Michigan 89.00
32 Missouri 88.34 32 Nevada 83.90 32 Wisconsin 89.21
31 Kentucky 88.81 31 Wyoming 84.09 31 Nevada 89.61
30 Oregon 89.18 30 Idaho 84.57 30 Montana 90.98
29 Michigan 91.49 29 North Dakota 84.62 29 Arizona 91.33
28 Wisconsin 91.70 28 Arizona 85.01 28 Wyoming 92.07
27 Wyoming 93.78 27 Montana 85.27 27 Idaho 92.55
26 Arizona 93.82 26 North Carolina 85.43 26 North Dakota 92.60
25 Idaho 94.26 25 Kentucky 85.66 25 Kentucky 93.03
24 North Dakota 94.31 24 Oregon 86.03 24 Oregon 93.40
23 North Carolina 95.12 23 Nebraska 86.35 23 North Carolina 93.41
22 Florida 97.99 22 Florida 88.30 22 Florida 96.28
21 Alabama 98.69 21 Maryland 89.19 21 Maryland 97.17
20 Maryland 98.88 20 Colorado 89.79 20 Colorado 100.41
19 Colorado 101.55 19 Delaware 95.83 19 Alabama 102.13
18 Rhode Island 103.97 18 Alabama 96.42 18 Delaware 102.80
17 Vermont 104.10 17 Minnesota 96.78 17 Minnesota 104.76
16 West Virginia 106.27 16 Vermont 100.95 16 Ohio 107.47
15 Minnesota 106.47 15 West Virginia 101.05 15 Vermont 108.32
14 Delaware 108.56 14 Ohio 101.15 14 Rhode Island 108.48
13 Ohio 109.96 13 Pennsylvania 102.47 13 West Virginia 111.06
12 Pennsylvania 114.23 12 New Jersey 106.95 12 Pennsylvania 113.09
11 New Hampshire 115.07 11 Maine 106.96 11 New Jersey 113.27
10 New Jersey 115.76 10 Rhode Island 107.48 10 Maine 113.28
9 Maine 115.77 9 Washington 108.46 9 New Hampshire 115.12
8 Washington 116.00 8 Massachusetts 112.29 8 Washington 116.49
7 Massachusetts 116.63 7 New Hampshire 114.15 7 Massachusetts 120.64
6 New Mexico 129.04 6 New Mexico 125.89 6 New Mexico 133.26
5 Hawaii 130.42 5 Connecticut 126.25 5 Connecticut 133.59
4 Connecticut 133.83 4 Hawaii 134.81 4 Hawaii 134.15
3 New York 158.93 3 New York 146.20 3 New York 153.17
2 District of Columbia 179.94 2 District of Columbia 168.18 2 District of Columbia 178.80
1 Alaska 253.48 1 Alaska 250.33 1 Alaska 257.70
Mean 100.01 92.98 99.98
Source: Medicaid Reimbursement Survey, Institute for Health & Aging, University Of California San Francisco, 1989; Medicaid
some equations other factors, have been taken into account. This surely demonstrates great differences across states in the adequacy of their rates to cover good quality care, to provide adequate access to care, and to fairly reimburse providers for the care they give.
This study is only of rates and their predictors, not of such other issues as quality, access, or equity. However, average state rates, as well as rates applied to specific facilities, can be and are used in studies fo-cusing on these topics (e.g., studies of the relative
quality provided or of access to facilities). We sug-gest that rates used in studies of such topics be ad-justed for such factors as the inclusion of ancillaries in rates, in order to better understand the effects of rates on these factors net the effects of differential in-clusion in them of ancillaries.
There are limitations to this analysis. First, no ad-justment was made for state fiscal year, so rates for some states are not adjusted for being up to 6 months older than rates for other states. Further, input fac-tors that may be driving rates are not fully controlled.
Table 4. Comparison of State Rankings for 1998
State Actual Rate Rank Ancillary Only Rank State Measures Rank Full Model Rank
Alabama 21 18 19 19 Alaska 1 1 1 1 Arizona 26 28 29 29 Arkansas 51 51 51 51 California 36 36 38 37 Colorado 19 20 20 20 Connecticut 4 5 5 5 Delaware 14 19 18 18 District of Columbia 2 2 2 2 Florida 22 22 22 22 Georgia 43 43 43 43 Hawaii 5 4 4 4 Idaho 25 30 27 27 Illinois 45 44 44 44 Indiana 41 39 39 39 Iowa 47 46 47 47 Kansas 46 48 48 48 Kentucky 31 25 25 25 Louisiana 49 49 50 50 Maine 9 11 11 10 Maryland 20 21 21 21 Massachusetts 7 8 7 7 Michigan 29 34 33 33 Minnesota 15 17 17 17 Mississippi 40 42 42 42 Missouri 32 38 37 36 Montana 33 27 30 30 Nebraska 39 23 36 38 Nevada 34 32 31 31 New Hampshire 11 7 9 9 New Jersey 10 12 12 11 New Mexico 6 6 6 6 New York 3 3 3 3 North Carolina 23 26 24 23 North Dakota 24 29 26 26 Ohio 13 14 16 16 Oklahoma 50 50 49 49 Oregon 30 24 23 24 Pennsylvania 12 13 10 12 Rhode Island 18 10 14 14 South Carolina 38 37 35 35 South Dakota 44 45 45 45 Tennessee 35 35 34 34 Texas 48 47 46 46 Utah 37 40 41 41 Vermont 17 16 15 15 Virginia 42 41 40 40 Washington 8 9 8 8 West Virginia 16 15 13 13 Wisconsin 28 33 32 32 Wyoming 27 31 28 28
Source: Medicaid Reimbursement Survey, Institute for Health & Aging, University Of California San Francisco, 1989; Medicaid
Thus, for example, New York will have higher rates than Kansas because land prices, labor, and so on are more expensive in New York. Average income per capita, percentage metropolitan, and nursing facility beds per aged should partially, but not fully, adjust for such factors. Better adjustment for costs of ancil-laries would involve pricing of their costs and some determination of how those costs were, or were not, actually reflected in state rate setting.
References
American Health Care Association (AHCA). (1999). Facts and trends: The nursing facility sourcebook. Washington, DC: Author.
Batavia, A., Ozminkowski, R., Gaumer, G., & Gabay, M. (1993). Lessons for states in inpatient ratesetting under the Boren amendment. Health Care Financing Review, 15(2), 137–154.
Berg, K., Sherwood, S., Murphy, K., Carpenter, I., Gilgen, R., & Phillips, C. D. (1997). Rehabilitation in nursing homes: A cross-national com-parison of recipients. Age and Ageing, 26(S2), 37–42.
Bootman, J. L., Harrison, D. L., Cox, E. (1997). The health care cost of drug-related morbidity and mortality in nursing facilities. Archives of Internal Medicine, 157, 2089–2096.
Coleman, B. (1996). New directions for state long-term care systems. Vol-ume I: overview (Report No. 9602). Washington, DC: AARP Public Policy Institute.
Gertler, P. (1991). Subsidies, quality, and regulation in the U.S. nursing home industry. In G. Duru & J. H. P. Paelinck (Eds.), Econometrics of health care (pp. 117–130). Netherlands: Kluwer Academic.
Greene, W. H. (1995). User’s manual: LIMDEP version 7.0 [Software manual]. New York: Econometric Software.
Harrington, C., & Swan, J. H. (1987). The impact of state Medicaid nurs-ing home policies on utilization and expenditures. Inquiry, 24(3), 157– 172.
Health Care Financing Administration (HCFA). (1992). Medicaid sp-DATA system: Characteristics of Medicaid state programs (HCFA Publication No. 02178). Washington, DC: Author.
Health Care Financing Administration (HCFA). (2000a). National health expenditures 1960–1998. Washington, DC: Author. On-line: www. hcfa.gov
Health Care Financing Administration (HCFA). (2000b). Appropriateness of minimum nurse staffing ratios in nursing homes. Volume I-III. Washington, DC: Author. On-line: www.hcfa.gov
HCIA & Arthur Andersen & Company. (1992). The guide to the nursing home industry. Baltimore: Author.
Healthcare Financial Management (HFM). (1998). Consolidated billing deadline looms for SNFs: holds implications for ancillary providers. Healthcare Financial Management, 52(5), 9.
Holahan, J., & Sulvetta, M. (1989). Assessing Medicare reimbursement options for skilled nursing facility care. Health Care Financing Re-view, 10(3), 13–27.
Larkin, H. (1999). Get the most from staff and ancillaries: Top perform-ing groups reveal their secrets. Medical Economics, 76(1), 106–111. Levit, K., Cowan, C., Lazenby, H., Sensenig, A., McDonnell, P., Stiller, J.,
Martin, A., & the Health Accounts Team. (2000). Health pending in 1998: Signals of change. Health Affairs, 19(1), 124–132.
McCartney, R. D., and McCartney, B. J. (1997). Nursing home visits: An efficient system for the busy physician. Geriatrics, 52(5), 57–69. Medicare balanced budget refinements: Hearing before the Subcommittee
on Health of the Committee on Ways and Means, House of Repre-sentatives, 106th Cong., 1st Sess. 87 (1999) (testimony of Carol Keehan).
Merriam Webster Medical Dictionary, network edition. (1997). http:// www.personalmd.com/mwmedical/medical.htm
Pretzer, M. (1999). Thar’s gold. Medical economics, 76(23), 114–117. Reid, W., & Coburn, A. (1996). Managerial responses to Medicaid
pro-spective payment in the nursing home sector. Hospital & Health Services Administration, 41, 283–296.
Short, P., Feinleib, S. & Cunningham, P. (1994). Expenditures and sources of payment for persons in nursing and personal care homes, (AHCPR Publication No. 94-0032, National Medical Expenditure Survey Research Findings 19). Rockville, MD: Public Health Service, Agency for Health Care Policy & Research.
Sparer, M. (1993). States in a reformed health system: Lessons from nurs-ing home policy. Health Affairs, 12 (1), 7–20.
Stone, D., & Reublinger, V. (1995). Long-term care reimbursement issues. Clinics in Geriatric Medicine, 11, 517–529.
Sutter, R. N., & Keough, C. L. (1999). Providers prevail in prudent-buyer therapy litigation. Healthcare Financial Management, 53(10), 64–65. Swan, J. H., Harrington, C., Clemeña, W., de Wit, S., Pickard, R., &
Studer, L. (2000). State Medicaid nursing facility reimbursement meth-ods through 1997. Medical Care Research and Review, 57(3), 361–378. Swan, J. H., Harrington, C., & Grant, L. (1988). State Medicaid reim-bursement for nursing homes, 1978–86. Health Care Financing Review, 9(3), 33–50.
Swan, J. H., Harrington, C., Grant, L., Leuhrs, J., & Preston, S. (1993). Trends in Medicaid nursing home reimbursement: 1978–89. Health Care Financing Review, 14(4), 111–131.
Swan, J. H., & Pickard, R. B. (2001). Explaining state nursing home reim-bursement rates. Journal of Health & Social Policy, 12(4), 53–73. Swan, J. H., & Pickard, R. B. (in press). Nursing facility effects of
Medic-aid case-mix payment in three states. Journal of Health and Social Policy.
U.S. General Accounting Office (USGAO). (1995). Medicaid: Spending pressures drive states toward program reinvention (Publication No. GAO/HEHS-95-122). Washington, DC: U.S. Government Printing Office.
Wunderlich, G. S., & Kohler, P. (Eds). (2000). Improving the quality of long-term care. Washington, DC: National Academy Press.
Received January 26, 2001 Accepted June 5, 2001
