The System Known As Oscar Provides Which Services For Nursing Home Residents?

• Journal List
• Wellness Intendance Financ Rev
• v.21(three); Spring 2000
• PMC4194680

Wellness Care Financ Rev. 2000 Spring; 21(3): 203–229.

Residential Care Supply, Nursing Dwelling Licensing, and Case Mix in Four States

Abstract

Simulation analyses quantify access and standing physical and cognitive impairment patient case-mix changes under two scenarios: with increases in residential intendance supply and with all nursing homes licensed but every bit skilled care facilities. Findings raise caution well-nigh the assumed interplay betwixt residential intendance supply and nursing home use. The proportion of nursing abode patients with only concrete and cognitive impairment likely to exist affected past electric current and emerging long-term intendance (LTC) policy was well under 25 percent of the nursing home population in each of the 4 study States. States varied in LTC supply and utilization controls.

Introduction

Consumers, individual investment, and many State governments view the residential care industry, especially that sector known as assisted living, as a viable alternative for nursing homes for many persons. Residents in this housing take admission to meal and maid services and aid with such tasks as using medications, dressing, grooming, eating, bathing, and transferring. Increasingly also, States accept begun to let those living in residential care facilities (RCFs) to receive extended periods of skilled nursing care and to remain in these facilities even if they become not-convalescent or if they are receiving hospice care (Mollica, 1998).

Arguments favoring the growth and expanded role of assisted living or other forms of RCFs in serving the needs of the frail elderly population include consumer preference, affordability relative to nursing homes, and potential reductions in Land Medicaid expenditures (Wilson, 1993). Even when accepting these arguments on face value, there is piffling empirical basis to guide Country governments in how to achieve the substitution of supportive housing for nursing home care. Should States farther constrain the growth of nursing homes, stimulate the growth of residential care beds, extend admission to assisted living by reshaping the eligibility criteria near those who can remain in supportive housing, or provide financial reimbursement for the home and community-based care (HCBC) (east.thousand., homemakers, personal intendance aides) that may exist needed in such housing? In the absence of their own feel, States look to other States to resolve such questions. Such mimicking may focus on specific policies (e.g., eligibility criteria), while ignoring essential contextual influences (the prevailing ratio of nursing home beds to population), or multiple interactive policies (due east.g., reimbursement for RCF care, licensing standards for nursing homes) that are essential to the success of the adopted new policy.

Investigators (Spector, Reschovsky, and Cohen, 1996) at the Agency for Healthcare Inquiry and Quality (AHRQ) judge that betwixt 25 and 35 percent of the 1-one thousand thousand-plus nursing abode residents are there mainly because of limitations in ability to perform personal care tasks such as bathing, dressing, and airing. They suggest that a subgroup of these individuals tin can be potentially served with home care services or by residence in supportive housing.

The AHRQ estimate of the potentially "relocatable" nursing home population has some important limitations. One of these is that it is based on a national sample of nursing home residents, but with as well few cases to adjust for local or customs-level conditions—such as the availability of alternative services or State policies affecting allowable levels of care. In this article, with nursing home resident characteristics from the nursing habitation minimum information, we utilise simulations to exam the sensitivity of the AHRQ gauge to customs-level contextual factors in four States. These models evaluate how the introduction of two exemplar policies affect instance mix, holding constant diverse facility, Country policy, and community characteristics.

1 policy is the requirement that all nursing facilities in a State run across the standards appropriate for skilled nursing facility (SNF) licensing. Imposition of this standard implies that facilities will exist staffed appropriately to the skilled levels of care and that the facility will accept fewer incentives to serve a population with care needs less than those required and reimbursed in skilled intendance. The second fake policy is one where the State achieves substantial growth in the number of residential care beds per one,000 population. A growth in such supply is assumed to be a necessary condition if RCF intendance is to substitute for nursing home care.

Methods

The principle data sources used in this analysis are those of the On-Line Survey, Certification, and Reporting System (OSCAR) and the Minimum Data Set (MDS), both maintained by HCFA. With these data, it is possible to summate example-mix classifications of the residents of each nursing home and to compare the relationship betwixt case mix and various other facility attributes. These data are supplemented to include customs characteristics using the Area Resource File (ARF).

OSCAR data are available for certified nursing homes in the The states. These data include facility characteristics and staffing, which are used hither. OSCAR data are collected during almanac certification surveys past the State or their contracted agencies. The MDS is specific to each resident, measuring functional abilities, medical problems, and emotional states (such as depression and beliefs bug). MDS data are pooled in this analysis to classify a facility'south case mix. The MDS is collected on all nursing facility residents at or near the time of admission, upon readmission from a hospital, if there is a meaning alter in status, and quarterly.

Community characteristics were obtained from the 1998 ARF, which is a compilation of demography and other county-level data assembled past the Bureau of Wellness Professions, U.S. Department of Health and Human Services. The information elements, whether from OSCAR, MDS, or the ARF, pertain to 1995 or reflect governmental estimates for 1995. Exceptions are that hospital discharges are from 1993, and the per centum of females in the labor force is from 1990.¹ Residential care beds (defined to include all licensed housing by the Country, regardless of the term used by each detail State to describe its supportive housing) information were obtained directly from State licensing and regulatory agencies in each State. Unlicensed RCFs were not counted.

Report States

Analyses are limited to States for which advisable MDS information were available. As of 1995, 11 States were compiling MDS data into statewide information systems as part of case-mix reimbursement demonstrations. Five States (Kansas, Maine, Mississippi, Ohio, and South Dakota) having complete MDS records for most nursing homes were used in these analyses. In these States, freestanding nursing homes were required to submit MDS assessment forms. An all-encompassing number of missing MDS or facility-identification problems precluded using the other States. A total of 1,555 freestanding nursing homes (the unit of assay) were licensed in the five report States in 1995. OSCAR and MDS records were matched for 95.four pct of these facilities.

Infirmary-based facilities were excluded from the analysis because of a high rate of missing MDS records for such facilities (e.g., fifteen of 17 facilities missing MDS records were hospital based in Mississippi, as were 64 of 106 facilities in Ohio). MDS records are needed to calculate case mix.² OSCAR records were non available in 1995 (including a 6-calendar month window on either side of the calendar year) for a total of 26 facilities in these five States. OSCAR data are needed to connect facility and community characteristics to the example-mix data.

Although the states used in this analysis were chosen because of the pragmatic consideration that they provided advisable information, they also reflect a spectrum of State policies relative to nursing homes and residential intendance, and varying market place conditions. Table 1 summarizes selected State LTC policies in effect in 1995. These policies exemplify approaches that can be used singularly or in combination to affect admission into the alternative levels of care. The prevalence of these policies is changing rapidly. For example, by 1998, 28 States provided some course of Medicaid-reimbursed assistance for persons in supportive housing, 24 permitted at least part-time or intermittent nursing to be provided, and 34 permitted residents to be non-ambulatory (Mollica, 1998). Replications of the electric current analyses amongst more than States or within States over time could further delineate these policy options, such equally by considering conversions or closures of nursing home beds, the extent of HCBC bachelor within a community, and the financial standards for Medicaid eligibility.

Table 1

Summary of Land Policies Affecting Nursing Domicile and Residential Care Eligibility and Financing: Selected States, 1995

Policy	Kansas	Maine	Mississippi	Ohio	Due south Dakota
Pre-access screening	Required of all nursing facility admissions prior to and including 1995	Required of all nursing facility admissions simply after 10/1/1995	Minimal screening of nursing facility admissions prior to and during 1995	Required of all nursing facility admissions only later on i/1995	Required of all nursing facility admissions since 1988
Nursing facility eligibility criteria	3 or more ADLs plus 2 or more IADLs and other skilled care needs	3 or more ADLs plus daily skilled nursing or therapy five times per week	three or more ADLs or other skilled nursing or therapy needs	3 or more than ADLs or other skilled care needs	24-hour supervision or skilled nursing needs
Medicaid funds for residential care	HCBC, 1995	None, 1995	None, 1995	None, 1995	Up to $150 per month available in some RCFs
State supplemental payments or other Land funds for residential care	Up to $155 monthly	$49-$219, 1995	None, 1995	None, 1995	Upwardly to $250 per month bachelor in some RCFs
Residential care eligibility	Screening only if HCBC	No exclusions, abode wellness care required for those needing skilled nursing	Convalescent, continent, not-vehement	Few exclusions; upwards to 100 days or more of intermittent nursing permitted	Excludes residents who demand more than than limited easily-on physical assistance or who require ongoing nursing

Even though the sample States are representative of approaches used by other States, the findings should not be interpreted as representing national outcomes. Analyses are specific to each of the sample States.

Kansas and South Dakota had mandatory all-payer pre-access screening processes that preceded and encompassed the study period. All States had been requiring such screening for Medicaid-eligible applicants. There was variation in the specific minimum criteria for nursing home eligibility, but the basic attributes were common—a need for skilled nursing or other skilled care. Reimbursement for publicly subsidized RCF residence was not high in whatsoever State, but three States did offering some such assistance using Medicaid HCBC waivers and/or Country supplemental payments to Supplemental Security Income. Access to residential care, equally reflected in functional and other levels of care excluding placement, was the least restrictive in Maine and Ohio and near restrictive in Mississippi and Due south Dakota. Based on the combination of restrictions and facilitative features of these various policies, Maine and Kansas appear to be the most facilitative of the substitution of residential treat nursing facility placement across all levels of care. South Dakota is besides facilitative merely more than bounded in the concrete and cognitive areas of disability. Ohio's limited RCF exclusions may encourage substitution across a diversity of levels of care, but the absence of reimbursement suggests that this effect volition non be reflected in the lower income population. Mississippi'southward policies reverberate a traditional nursing facility and RCF boundary.

Case-Mix Classification

Resources Utilization Groups, Version Iii (Rug-III) were used to consolidate resident characteristics into a standardized case-mix nomenclature organization. These classifications are based on assessment data in the nursing domicile MDS. These instruments were compiled for agenda year 1995. At that time, 44 RUG-Iii groups, organized by the vii major hierarchical categories shown in Figure 1, could exist derived from the instruments (Fries et al., 1994). Residents qualifying for more ane of these categories are classified by the most resources-intensive grouping. Each of the seven major groups can be subdivided into a 2d- or third-level subclassification based on the specific value of the activities of daily living scale (due east.grand., toileting, eating, bed and chair transferring), whether they are depressed (Clinically Complex just), and whether they are receiving nursing rehabilitation (Cognitively Impaired, Behavior Issues, and Concrete Function only). Sample sizes precluded using these subdivided classes.

RUG-III Case-Mix Definitions

Two sets of example-mix classifications were compiled for each facility. One used merely the MDS records of the facility admissions during the 1995 calendar year. The 2d represents the continuing or average daily case mix in the facility itself. This continuing intendance case mix was calculated by summing the Carpet classifications on each resident from their first MDS assessment in each bachelor quarter in 1995. This process weights each assessment equally in terms of the exposure time within the facility. Table ii shows the means and standard deviations for seven Rug-Three major domains. Admission and standing care instance mix are presented separately.

Table ii

Percentage of Residents in Rug-III Classifications at Time of Nursing Facility¹ Admission, Averaged over Agenda Year 1995: Selected States

Carpeting-III Classification	Kansas		Maine		Mississippi		Ohio		South Dakota
	Percentage	Standard Deviation	Percent	Standard Deviation	Percent	Standard Departure	Percent	Standard Deviation	Percent	Standard Deviation
	Average Across Facilities
Access Case Mix	(n=366)		(n=121)		(due north=142)		(northward=754)		(due north=94)
Rehabilitation	7.19	11.99	9.08	10.65	3.47	7.13	ten.73	14.47	2.18	4.78
Extensive	0.76	2.26	i.05	1.96	two.01	3.51	2.29	five.04	1.34	iii.07
Special Care	six.04	8.40	7.51	6.43	eleven.70	7.97	nine.twoscore	ix.09	12.52	13.36
Clinically Complex Care	33.44	20.19	45.09	17.58	38.09	xiii.67	48.82	17.79	48.08	xx.46
Cognitive Damage	fourteen.72	14.00	10.22	nine.40	13.06	ten.06	xi.15	11.71	10.eighteen	xiv.20
Behavior Bug	1.67	half-dozen.99	0.45	one.64	0.46	ane.37	ane.53	five.89	0.51	1.88
Concrete Functioning	36.18	23.31	26.60	17.00	31.20	14.58	16.08	14.22	25.eighteen	xviii.32
Continuing Case Mix	(n=366)		(n=122)		(n=142)		(n=759)		(n=94)
Rehabilitation	2.71	v.79	2.19	vii.57	0.85	1.41	one.18	1.94	one.51	two.11
Extensive	0.l	0.78	0.92	2.10	1.45	two.04	one.26	two.22	0.74	0.92
Special Care	four.28	2.96	5.17	3.xiii	11.11	6.00	9.42	six.46	5.eleven	2.68
Clinically Circuitous Intendance	30.64	12.20	34.11	nine.05	32.46	8.21	40.72	10.74	38.03	7.82
Cerebral Impairment	16.52	seven.24	thirteen.33	vii.22	13.66	5.82	12.67	7.00	11.55	four.86
Behavior Problems	two.35	7.41	1.05	1.44	0.97	i.65	two.32	4.07	one.sixteen	1.51
Physical Functioning	43.00	xiii.xv	43.23	10.63	39.49	viii.35	32.44	ten.85	41.89	9.08

Analytical Model

Tabular array three shows the descriptive statistics for the contained variables used in the assay. The measures are arranged into three groupings following the economical framework that conceptually guided their selection (Paringer, 1985; Scanlon, 1980a). The start grouping consists of attributes describing the private facilities. Facility attributes, such as the average age of residents and size of facility, are thought to influence the attractiveness of a facility for prospective clientele and to reflect predisposing attributes (e.g., licensing status) relative to the types of clients being sought. Service demand, the second dimension, reflects canton- or market-level attributes. Amidst these are variables shown in prior work to be associated with nursing home use: population size and age structure (Mendelson and Schwartz, 1993; Scanlon, 1980b; Zedlewski and McBride, 1992), and service need generating resource (Feldstein, 1988; Grumbach and Lee, 1991). Other circumstances potentially affecting demand, such as the percent of females in the labor force, may both increase household resources needed to finance care for the elderly and make women unavailable to exist total-time caregivers (Chiswick, 1976).

Table 3

Facility¹ and Marketplace Area Characteristics: Selected States, Calendar Year 1995

Feature	Kansas (due north=366)		Maine (northward=122)		Mississippi (n=142)		Ohio (north=759)		S Dakota (n=94)
	Statistic	Standard Deviation	Statistic	Standard Divergence	Statistic	Standard Deviation	Statistic	Standard Deviation	Statistic	Standard Difference
Facility Attributes
Number Beds in Facility	75.51	36.42	73.06	37.81	103.20	50.97	106.80	63.46	72.99	32.56
Percent NFs That Are For-Profit Facilities	48.09	—	82.00	—	89.forty	—	79.40	—	41.fifty	—
Percent NFs That Are in Corporate Chains	55.46	—	48.40	—	62.00	—	l.l	—	sixty.60	—
Percentage SNF Licensed	51.64	—	100.00	—	57.00	—	69.00	—	57.40	—
Average Age of NF Residents	82.25	5.64	81.87	6.46	lxxx.78	iv.37	79.59	6.69	83.34	2.56
Percentage of Facility Residents Under Medicaid	46.12	18.xix	69.20	17.20	67.71	18.55	66.58	20.52	fifty.36	12.92
Facility Occupancy Rate	84.92	16.74	93.06	half dozen.81	95.26	8.78	85.57	17.18	95.54	4.61
Service Need
Pct Age 65 or Over in County	16.73	4.64	14.05	i.37	12.98	2.14	thirteen.67	ii.16	17.55	4.96
Percent of County Females in Labor Force	52.47	v.85	53.67	iv.27	45.81	6.xx	50.47	5.46	53.32	seven.41
Per Capita Income in Thousands	$nineteen.78	3.92	$19.89	3.xix	$16.xxx	2.65	$21.73	iii.77	$eighteen.73	3.32
Hospital Discharges per one,000 Population	119.49	61.96	127.10	41.99	158.xc	101.90	123.70	63.11	116.lx	ninety.62
Population per Square Mile in Thousands	0.15	0.26	0.13	0.107	0.x	0.09	0.87	0.97	0.031	0.049
Percent of Canton NF Residents with Medicare Payment	14.19	nine.42	12.50	2.93	7.83	v.08	sixteen.68	6.83	37.31	7.76
Percent of County NF Residents with Medicaid Payment	45.08	8.05	66.76	6.59	65.70	12.51	65.11	6.83	50.67	viii.47
Service Supply
NF Beds per 1,000 Population	17.73	xiv.43	8.10	1.76	7.35	two.56	nine.85	3.39	17.47	8.95
NF Residents per ane,000 Age 65 or Over	87.61	63.49	62.43	13.70	58.30	17.60	61.47	34.83	108.twenty	38.51
Number of RCF Beds per 1,000 County Population	ane.94	1.84	iii.92	i.81	i.11	1.26	1.seventy	half-dozen.00	1.43	1.61

A number of other population characteristics (e.g., per centum of aged persons living in poverty, percent of persons age 85 and over) were tested in preliminary analyses using step-wise backward deletion regression. Collinear items constitute during this process were not used in the reduced form models shown. Individual-level measures known to have an influence on nursing home placement, such as living organisation and the presence of family caregivers (Evashwick et al., 1984; Greene and Ondrich, 1990) were not used at all because this information was non bachelor for the study population in the ARF.

Supply is the third dimension in the model. Competing supply is directly measured for nursing homes and licensed residential care beds. Other customs resources, such as home care supply, are indirectly measured using population per square mile. Habitation care services have been shown to increment in the presence of higher population density, and nursing home use rates have been constitute to turn down as contest or population density increases (Dubay, 1993; Scanlon, 1980a).

The Herfindahl alphabetize of competition, in this case the proportion of beds in the discipline nursing facility as a percent of total beds in that community, was also tested, merely the private components of competition were more predictive of case mix than was this alphabetize. Rural versus urban location is represented in population per square mile. Additional information, such as on the number of persons receiving HCBC covered services or the number of home intendance visits per 1,000 population could perhaps further refine the enumeration of culling service supply and demand, just these data are non bachelor in the ARF and require either canton inventories or access to Country administrative records not available to this projection. Moreover, the number of recipients for programs such equally Medicaid HCBC, except in Ohio, is generally small-scale both in absolute terms and in number of recipients relative to those in nursing homes. Recipient figures for 1995 in the sample States were: Kansas, 6,400; Maine, 911; Mississippi, 1,300; Ohio, 14,963; and South Dakota, 550 (Bectel and Tucker, 1998).

The supply of nursing home beds relative to demand is further controlled for in the analysis using iii measures: the per centum of the county'south full nursing facility population existence paid for by Medicaid, the percentage being paid past Medicare, and each nursing facility's occupancy charge per unit. The showtime 2 measures are indicators of prevailing local market place weather and reflect the context for the patient selection decisions of each facility. Occupancy rate is a straight indicator of supply-demand balance as reflected at a facility level.

An ordinary least squares (OLS) regression is used to model the relationship between nursing dwelling case mix and the facility, demand, and supply attributes. The choice of variables in the final model derived start from the theoretical framework and and then from the selection of a reduced-form model that used astern step-wise regression to eliminate highly correlated covariates. The simulation models estimated RUG case-mix proportions using these same OLS models, changing two parameters to represent the faux effects. The fake parameters are the proportion of nursing homes licensed for skilled nursing care and the proportion of licensed residential care beds per one,000 population.

The SNF licensing measure was chosen as a simulation event based on empirical findings (reported afterward) that prove important influences on case mix inside the sample States. The RCF supply measure out was chosen to test the assumption that expansion of this nursing dwelling house alternative would reduce nursing home use among persons with cognitive or physical issues. Analyses and the simulations were conducted separately for each State, thereby holding constant other policies (such as certificate of need, income eligibility, reimbursement levels for LTC services, and expenditures on HCBC) and the regulation-enforcement practices prevailing within each Country. Differences in these policy attributes may have affected the number of persons receiving some level of Medicaid-reimbursed HCBC. Comparing the number of HCBC recipients with Medicaid-reimbursed nursing home recipients, the percentages were equally follows: Kansas, 48.8 per centum; Maine, xiv.9 percent; Mississippi, 11.6 percent; Ohio, 27.0 per centum; Southward Dakota, 12.6 per centum (Bectel and Tucker, 1998).

Findings

Policy and Contextual Relationships

Tables 4 through ​ 11 show the coefficients for the OLS models using nursing facilities as the unit of analysis. Tables 4-​ 7 guess instance mix amidst access patients, and Tables eight-​ xi show the same for continuing care patients. The results shown are for five case-mix groupings, and separate tables are shown for each Country. Persons with cognitive impairment and physical impairment are represented in the case-mix groupings that are thought to be those most likely to exist housed in RCF or community intendance settings every bit the supply of RCFs grows and as other policies facilitate access to these nursing home alternatives. Rehabilitation, special care, and complex care patients are represented by case-mix groupings associated with skilled care. The prevalence of these patients, measured by the percentage of each specific case mix, holding other things abiding, would exist expected to change as the proportion of custodial patients declines. A directly result from RCF policy to skilled care case mix is not expected.

Table 4

Assay of Nursing Facility Admissions Case Mix: Kansas, 1995

Independent Variable	Rehabilitation		Special Care		Complex Intendance		Cognitive Damage		Physical Impairment
	Statistic	T-score	Statistic	T-score	Statistic	T-score	Statistic	T-score	Statistic	T-score
Intercept	-1.twoscore	—	-24.37	—	-29.19	—	35.49	—	46.07	—
Facility Attributes
Facility Average Resident Age	0.08	0.72	*0.eighteen	two.05	**0.58	two.78	0.02	0.15	0.24	1.00
Facility Percent Medicaid	0.06	1.71	-0.03	-ane.fourteen	-0.03	-0.55	0.02	0.44	0.01	0.19
Facility Occupancy	-0.06	-ane.52	0.01	0.45	-0.01	-0.16	*-0.09	-2.08	0.09	1.23
Facility Number of Beds	0.01	0.34	-0.01	-0.65	0.04	ane.18	0.18	0.75	-0.06	-1.48
Facility For-Profit	-0.61	-0.44	-ane.36	-one.28	0.13	0.05	*3.80	2.nineteen	-3.44	-i.21
Facility Chain	one.46	1.04	i.18	1.x	1.17	0.46	2.38	one.36	-4.07	-1.42
Facility SNF Licensed	**vii.17	5.10	0.74	0.68	-0.20	-0.08	**-vi.l	-iii.71	-0.74	-0.26
Demand Variables
Percent Age 65 or Over in County	0.24	ane.07	0.14	0.82	-0.28	-0.68	-0.43	-1.50	0.47	1.02
Canton Percent of Females in Labor Force	0.06	0.33	0.21	1.43	-0.07	-0.21	-0.06	-0.24	-0.29	-0.74
Per Capita Income in Thousands	-0.03	-0.13	-0.16	-0.84	0.71	i.54	-0.36	-one.thirteen	-0.02	-0.03
Population per Square Mile in Thousands	five.07	ane.45	-ane.62	0.61	-3.46	-0.55	2.07	0.48	0.87	0.12
Discharges per Thousand	*-0.02	-2.28	0.00	0.62	0.04	ane.81	-0.00	-0.33	-0.01	-0.60
Canton Medicare Percentage	0.06	0.90	0.08	1.45	0.06	0.48	-0.03	-0.39	-0.27	-ane.90
County Medicaid Percentage	0.03	0.32	0.08	1.xiv	0.thirteen	0.82	-0.00	-0.03	*-0.36	-2.07
Supply Variables
NF Residents per 1,000 Population Age 65 or Over in County	0.00	0.03	0.01	0.30	0.07	one.46	-0.04	-i.31	-0.05	-0.96
NF Beds per 1,000 Population in County	0.01	0.1	0.01	0.12	-0.40	-i.95	0.24	ane.70	0.22	0.94
Number of RCF Beds per 1,000 Canton Population	-0.00	-0.02	0.02	0.36	-0.04	-0.33	0.06	0.73	0.04	0.33
Mean Case-Mix Percent	7.12	—	5.97	—	33.07	—	14.56	—	35.79	—
Adapted Rtwo	**0.187	—	0.036	—	**0.102	—	*0.083	—	**0.129	—

Table 7

Assay of Nursing Facility Admissions Case Mix: Due south Dakota, 1995

Independent Variable	Rehabilitation		Special Intendance		Complex Intendance		Cognitive Impairment		Concrete Harm
	Statistic	T-score	Statistic	T-score	Statistic	T-score	Statistic	T-score	Statistic	T-score
Intercept	-nineteen.81	—	three.02	—	155.14	—	-59.01	—	-15.75	—
Facility Attributes
Facility Average Resident Age	0.eighteen	0.64	0.18	0.22	-0.97	-0.74	0.68	0.80	0.66	0.59
Facility Percentage Medicaid	-0.02	-0.42	-0.13	-0.77	-0.16	-0.60	0.thirty	1.75	0.09	0.39
Facility Occupancy	0.13	i.07	0.fourteen	0.39	0.25	0.44	0.10	0.29	-0.76	-1.59
Facility Number of Beds	0.00	0.07	0.07	one.xix	0.07	0.76	-0.01	-0.24	-0.13	-1.74
Facility For-Turn a profit	*2.79	2.52	*vii.55	2.31	-2.15	-0.42	-one.52	-0.46	-5.89	-one.35
Facility Chain	0.30	0.25	-3.18	-0.91	7.74	1.43	-4.95	-1.42	0.88	0.19
Facility SNF Licensed	ii.49	i.78	2.65	0.64	iii.53	0.55	-3.02	-0.73	-iv.75	-0.86
Demand Variables
Percent Age 65 or Over in County	0.03	0.08	0.44	0.39	-0.80	-0.46	-1.80	-i.62	2.24	1.51
Canton Pct of Females in Labor Strength	-0.01	-0.07	-0.42	-0.78	-0.01	-0.02	0.17	0.32	0.08	0.12
Per Capita Income in Thousands	0.02	0.06	0.57	0.70	-1.78	-i.41	0.65	0.80	0.42	0.39
Population per Square Mile in Thousands	14.75	0.89	41.30	0.85	26.42	0.35	-43.72	-0.89	-27.24	-0.42
Discharges per Thousand	-0.01	-1.46	-0.03	-one.33	-0.04	-1.20	0.04	one.50	0.05	ane.64
County Medicare Percentage	-0.02	-0.33	-0.27	-1.30	0.17	0.52	-0.24	-ane.16	0.36	1.29
County Medicaid Percentage	-0.eleven	-1.23	0.01	0.05	-0.04	-0.08	-0.07	-0.25	0.04	0.ten
Supply Variables
NF Residents per 1,000 Population Age 65 or Over in County	0.02	0.44	0.09	0.55	-0.12	-0.47	-0.xvi	-0.97	0.26	1.xix
NF Beds per 1,000 Population in Canton	-0.15	-0.46	-0.83	-0.84	0.35	0.23	i.90	1.92	-1.76	-1.34
Number of RCF Beds per 1,000 County Population	0.05	0.14	-0.05	-0.05	1.43	0.76	-i.25	-1.27	-0.17	-0.13
Mean Case-Mix Percentage	2.nineteen	—	12.52	—	48.08	—	10.18	—	25.18	—
Adapted Rtwo	*0.290	—	0.214	—	0.187	—	*0.298	—	0.252	—

Table viii

Analysis of Nursing Facility Continuing Care Case Mix: Kansas, 1995

Independent Variable	Rehabilitation		Special Care		Circuitous Care		Cognitive Harm		Physical Damage
	Statistic	T-score	Statistic	T-score	Statistic	T-score	Statistic	T-score	Statistic	T-score
Intercept	10.09	—	ane.76	—	-0.87	—	-17.45	—	8.49	—
Facility Attributes
Facility Average Resident Age	-0.02	-0.37	*0.07	two.xviii	** 0.42	3.41	**0.29	three.89	**0.37	two.98
Facility Percent Medicaid	0.01	0.35	-0.02	-1.77	-0.03	-0.95	*0.05	2.45	-0.02	-0.48
Facility Occupancy	**-0.05	-2.98	-0.01	-1.thirty	0.02	0.47	-0.00	-0.13	0.05	1.29
Facility Number of Beds	**-0.03	-3.30	-0.00	-0.84	0.02	ane.22	0.01	one.14	-0.00	-0.21
Facility For-Profit	-0.27	-0.39	0.07	0.xx	*3.28	2.19	0.08	0.09	-1.78	-1.17
Facility Chain	-0.45	-0.65	-0.30	-0.81	0.34	0.23	*ane.82	2.04	-one.48	-0.97
Facility SNF Licensed	**2.71	iii.86	0.48	1.30	-0.74	-0.49	-0.09	-0.x	-2.02	-ane.32
Need Variables
Per centum Age 65 or Over in Canton	-0.05	-0.43	0.01	0.14	-0.39	-one.61	-0.04	-0.28	0.48	one.91
County Per centum of Females in Labor Forcefulness	-0.01	-0.12	-0.05	-0.96	-0.19	-0.93	0.19	i.57	0.01	0.04
Per Capita Income in Thousands	0.13	0.99	0.04	0.66	0.37	1.35	*-0.37	-2.25	-0.18	-0.63
Population per Square Mile in Thousands	2.92	1.68	*1.81	1.98	1.03	0.28	2.44	one.09	vi.82	1.79
Discharges per 1000	-0.01	-1.74	0.00	0.91	0.01	0.82	0.00	0.x	-0.01	-0.eighty
Canton Medicare Pct	0.02	0.44	0.02	1.37	0.ten	1.28	0.04	0.86	-0.13	-i.66
County Medicaid Percentage	-0.01	-0.20	-0.01	-0.29	0.00	0.00	0.08	1.49	0.04	0.46
Supply Variables
NF Residents per i,000 Population Historic period 65 or Over in County	-0.00	-0.11	0.01	1.02	-0.01	-0.35	-0.02	-i.23	0.02	0.76
NF Beds per 1,000 Population in County	-0.01	-0.18	-0.04	-1.15	0.04	0.32	0.09	1.20	-0.09	-0.69
Number of RCF Beds per one,000 County Population	-0.04	-0.twenty	0.00	0.00	0.29	0.78	-0.31	-1.xl	0.xiv	0.36
Mean Instance-Mix Per centum	ii.69	—	iv.28	—	xxx.55	—	16.47	—	43.09	—
Adjusted R2	**0.135	—	**0.094	—	**0.107	—	**0.104	—	**0.207	—

Table 11

Analysis of Nursing Facility Standing Care Instance Mix: S Dakota, 1995

Independent Variable	Rehabilitation		Special Intendance		Complex Intendance		Cerebral Impairment		Physical Impairment
	Statistic	T-score	Statistic	T-score	Statistic	T-score	Statistic	T-score	Statistic	T-score
Intercept	14.49	—	**49.67	—	43.81	—	xi.26	—	-fifteen.96	—
Facility Attributes
Facility Average Resident Age	-0.thirteen	-one.26	**-0.46	-2.93	0.26	0.55	0.17	0.53	0.xviii	0.39
Facility Percent Medicaid	-0.00	-0.04	-0.04	-i.31	*0.22	two.35	0.02	0.23	*-0.23	-2.50
Facility Occupancy	-0.04	-0.88	-0.07	-0.98	0.xi	0.54	0.12	0.84	-0.xix	-0.96
Facility Number of Beds	0.00	0.17	0.02	1.44	0.01	0.42	-0.01	-0.38	-0.02	-0.51
Facility For-Profit	*0.89	2.16	-0.88	-1.43	*3.57	one.96	0.08	0.07	*-4.25	-ii.40
Facility Chain	0.48	one.09	-0.40	-0.61	2.03	1.04	0.32	0.24	-3.00	-1.59
Facility SNF Licensed	*ane.19	2.29	0.09	0.12	i.47	0.64	0.04	0.02	-2.24	-i.00
Demand Variables
Percent Age 65 or Over in County	0.09	0.68	**0.57	two.76	-0.07	-0.eleven	-0.28	-0.66	-0.31	-0.51
County Percent of Females in Labor Forcefulness	0.05	0.76	0.08	0.82	*-0.lx	-i.99	-0.21	-1.02	*0.63	ii.16
Per Capita Income in Thousands	0.03	0.25	*-0.32	-2.09	0.xvi	0.36	-0.13	-0.42	0.32	0.74
Population per Foursquare Mile in Thousands	0.13	0.02	2.15	0.24	half dozen.78	0.25	4.13	0.22	-13.66	-0.52
Discharges per Thousand	0.00	0.25	-0.00	-0.38	-0.01	-0.69	0.01	0.87	0.00	0.29
Canton Medicare Percentage	-0.03	-one.07	-0.05	-i.34	0.00	-0.28	-0.08	-1.00	0.20	1.76
County Medicaid Percentage	*-0.07	-2.11	-0.07	-1.48	**-0.forty	-ii.66	-0.03	-0.32	**0.62	4.32
Supply Variables
NF Residents per 1,000 Population Age 65 or Over in County	0.01	0.46	**0.09	2.96	0.12	i.32	-0.02	-0.30	*-0.eighteen	-2.06
NF Beds per 1,000 Population in County	-0.05	-0.41	**-0.lx	-3.twenty	-0.87	-1.58	-0.04	-0.ten	**one.46	2.73
Number of RCF Beds per 1,000 County Population	0.02	0.12	-0.17	-0.92	0.ninety	1.64	-0.42	-i.xiii	-0.52	-0.99
Mean Case-Mix Percent	one.51	—	v.11	—	38.03	—	11.56	—	41.89	—
Adjusted Rtwo	**0.496	—	*0.308	—	*0.287	—	0.138	—	**0.501	—

Unadjusted regression coefficients bear witness the relationship between case mix (the result of interest) and the facility, demand, and supply attributes included in the rows of the tables. Of detail interest in these tables is whether a facility is licensed as a SNF and the number of RCF beds. The RCF supply coefficient can exist interpreted equally the unit change in the case-mix percentage that occurs for each unit change in the number of RCF beds per 1,000 population. Negative coefficients for any attribute suggest a reduction of cases in a case-mix group. Coefficients associated with binary variables, such as being licensed as a SNF, tin can exist interpreted as the absolute change in case-mix percentage when a facility is SNF licensed. This attribute has a trend (statistically pregnant in some States) to be associated with increases in the skilled care RUG classifications and to have a negative association with the custodial care Carpeting classifications. RCF supply has similar tendencies, but with no statistically meaning coefficients. The absenteeism of a more consequent upshot among all States on SNF licensing attribute may be partly the result of the assay existence limited to freestanding nursing homes. The other measures show variation amidst the States in the strength and pattern of association with instance mix. As a grouping, these measures serve the role in these analyses of adjusting for differences among counties within a State. An interpretation of their effects or a simulation of changes in these parameters is exterior the scope of this article.

Simulation Results

The simulations are estimated in two models. The starting time sets the county's proportion of nursing homes licensed as SNFs to 100 percent and applies that Country's SNF coefficient from Tables four through ​ 11 to this faux unit mix as appropriate for admission or continuing case mix. All other conditions and relationships reflected in Tables four-​ 11 are held constant. The 2d set of simulations also uses this model, while additionally setting the number of residential intendance beds per one,000 population in the county to be equal to the number in Maine and applying the State's coefficient for RCF beds per 1,000 population. Simulation results shown in Table 12 reflect the case mix amid nursing home admissions given these changes. Table 13 shows similar case-mix simulation results amidst continuing care nursing dwelling residents.

Table 12

Simulation of Admission Example Mix: Selected States

RUG-III Category	Kansas		Mississippi		Ohio		S Dakota
	Percent SNF =100	RCF = Maine	Per centum SNF =100	RCF = Maine	Per centum SNF = 100	RCF = Maine	Per centum SNF = 100	RCF = Maine
Concrete Impairment
Mean SNF Coefficient Value	36.01	36.00	29.39	29.04	12.77	12.66	23.16	22.74
Upper Limit for SNF Coefficient	41.lx	41.threescore	34.62	34.26	15.ten	fourteen.98	33.96	33.54
Lower Limit for SNF Coefficient	xxx.42	thirty.41	24.17	23.82	10.45	10.33	12.36	xi.94
Observed Hateful Carpeting Percentage	36.18	36.18	31.20	31.20	sixteen.08	16.08	25.18	25.18
Cognitive Impairment
Mean SNF Coefficient Value	xi.63	12.67	11.00	12.33	ix.59	9.45	8.90	5.79
Upper Limit for SNF Coefficient	15.09	16.13	15.02	16.34	eleven.68	xi.54	17.00	13.90
Lower Limit for SNF Coefficient	8.18	9.22	6.98	8.31	7.50	seven.36	0.79	-2.32
Observed Mean RUG Pct	xiv.72	14.72	13.06	xiii.06	eleven.15	xi.xv	10.18	10.18
Rehabilitation
Mean SNF Coefficient Value	ten.71	10.61	5.26	5.47	13.18	13.08	3.25	3.36
Upper Limit for SNF Coefficient	xiii.50	13.forty	vii.63	7.84	fifteen.52	25.42	5.99	half dozen.10
Lower Limit for SNF Coefficient	8.31	viii.22	2.89	iii.10	10.84	10.74	0.51	0.62
Observed Hateful Rug Percentage	seven.twenty	7.20	3.47	3.47	10.73	10.73	two.xix	2.19

Table 13

Simulation of Continuing Care Example Mix: Selected States

RUG-III Category	Kansas		Mississippi		Ohio		South Dakota
	Percent SNF = 100	RCF = Maine	Percent SNF = 100	RCF = Maine	Percent SNF = 100	RCF = Maine	Percent SNF = 100	RCF = Maine
Concrete Impairment
Hateful SNF Coefficient Value	42.11	42.37	39.80	42.01	31.xiii	29.42	40.94	39.64
Upper Limit for SNF Coefficient	45.12	45.39	42.98	45.xix	33.00	33.08	45.31	44.01
Lower Limit for SNF Coefficient	39.09	39.36	36.62	38.83	29.30	31.25	36.57	35.27
Observed Mean RUG Percentage	43.09	43.09	39.49	39.49	32.44	32.44	41.89	41.89
Cognitive Impairment
Mean SNF Coefficient Value	xvi.44	fifteen.83	13.81	eleven.61	12.14	12.16	13.58	ten.53
Upper Limit for SNF Coefficient	xviii.21	17.60	sixteen.11	13.91	13.41	thirteen.44	14.65	13.61
Lower Limit for SNF Coefficient	fourteen.66	fourteen.05	11.51	ix.31	10.86	10.88	eight.l	7.46
Observed Mean RUG Percentage	16.47	xvi.47	xiii.66	thirteen.66	12.67	12.67	eleven.56	11.56
Rehabilitation
Mean SNF Coefficient Value	4.00	3.93	one.15	1.08	1.29	1.27	two.02	2.06
Upper Limit for SNF Coefficient	5.37	5.30	ane.68	1.lx	one.64	1.61	iii.04	iii.07
Lower Limit for SNF Coefficient	2.62	ii.55	0.62	0.54	0.94	0.92	1.00	1.04
Observed Mean RUG Per centum	two.69	ii.69	0.85	0.85	1.18	1.xviii	1.51	1.51

For Tables 12 and ​ thirteen, the results shown are limited to three example-mix groupings: persons in nursing homes with cognitive or physical problems equally their predominant problem and patients receiving rehabilitation care as their principal reason for placement. The first set of columns shows the results when only the SNF measure is changed; the second when both the SNF parameter and RCF supply are changed. The values shown in the first row of each group are the predicted or false percentages of patients with the indicated Rug classification. These values represent the case mix equally estimated in each facility so averaged across nursing homes in the Country. The next two rows in each group re-gauge the simulation using the upper and lower limits on the point estimate's confidence interval. This is done to bear witness the confidence interval for the fake instance-mix charge per unit. The simulated estimates can exist contrasted with the last row of each group, which is the observed example-mix distribution as shown in Tabular array 2. The difference between the observed and the estimated values tin can exist interpreted as the probable change in the proportion of patients in a particular RUG classification when the SNF licensing policy is applied and/or when the SNF policy is applied in the presence of a college average number of RCF beds per ane,000 population. Upper and lower jump simulation intervals crossing the observed distribution are not statistically significant.

The values adopted for the faux parameters were those from Maine. This State has LTC policies and an RCF bed supply that are reflective of what appears to be the emerging national direction for these areas of expansion.^three For example:

• Maine's LTC reimbursement and eligibility policies are consistent with those of States such as Oregon, long considered to be the innovator in ensuring admission to community intendance, including RCF care, (Mollica, 1998). Maine's rank in per capita HCBC spending is tenth nationally, and Oregon is ranked 7th (Bectel and Tucker, 1998).

• The number of RCF beds per ane,000 population is two to three times higher in Maine than in the other sample States. Maine (with 33) is among the meridian 7 States nationally in the number of licensed residential care beds per 1,000 anile population. Merely Oregon (with 42) and California (with 44) have more than xl licensed beds per i,000 aged population (Bedney et al., 1996).

Admissions Example Mix

A positive event of the fake policies would be to decrease the pct of nursing dwelling house residents with primarily concrete and/or cerebral impairments and to increase the proportion who require more skilled or other complex clinical care. Among the four States for which simulations were estimated (a simulation was not conducted in Maine), the addition of SNF licensing for all nursing homes was associated with a marginal reduction in the percentage of persons who, at time of nursing home admission, were there primarily due to concrete and/or cognitive impairments. In three of the States, the reduction had an absolute value of near 2 pct (comparing the simulated result with the observed charge per unit) when the SNF coefficient was used. This occurred regardless of the underlying physical-harm prevalence charge per unit. These estimates were sensitive to the confidence ranges in the coefficient effects estimate. These range upwardly to ±12 pct relative to the observed prevalence among upper and lower limits of the SNF licensing effect. Kansas, with the highest percent of observed physical damage cases and the highest proportion of nursing domicile beds per 1,000 population, was the Land to the lowest degree affected by the imitation SNF policy change. In all States, the addition of the faux RCF bed supply did not materially change the admission instance mix over that achieved with the SNF policy change considered lone.

Similar patterns of accented change were observed amongst the cognitive-impairment case mix. The rates of absolute alter varied from ane to three percent using the SNF effect, again with an upper and lower bound on the estimate that crosses the observed rate. This magnitude of change had the event of leaving all States with roughly comparable proportions of patients within this RUG classification. The introduction of changes in RCF supply had an inconsistent effect beyond the States. In two States, it increased the proportion of nursing dwelling house residents with cognitive problems, and in one Land, it had no effect over that of the SNF policy. The fourth State suggested a substantial incremental reduction with this change, merely the estimate was unstable because the coefficient's confidence interval crossed the value of 0 percent.

Rehabilitation example mix was included in the simulation model primarily as a verification bank check. Movements toward more than skilled intendance chapters should be expected to increment the attractiveness of nursing homes for this use, under the assumption of adequate demand. Such a growth in this level of care could occur either as custodial beds are made available through the processes modeled previously or through the utilise of currently vacant nursing home beds. All States reflected an accented increase in the proportion of nursing home residents who would have a rehabilitation nomenclature. This increase ranged from 1 to 3 pct when SNF policy effect was faux, but the confidence interval on this guess suggests the potential for even greater increases. These furnishings did not change when RCF supply context was added to the estimate.

Continuing Care Case Mix

The typical daily demography of nursing resident example mix, every bit reflected in the classifications shown in Table 13, was less affected past the simulated policies than it was among admissions residents. For both physical and cognitive impairment, in that location was virtually no difference in the simulated or estimated prevalence rate from the observed charge per unit. This suggests that the simulated SNF licensing policy had no effect on continuing custodial care case mix, belongings other Land policy attributes constant. The introduction of increased RCF supply, however, did suggest a marginal outcome, having an absolute difference ranging from i to 3 per centum amidst the States. Relative to rehabilitation example mix, the simulation of the SNF licensing policy showed very little, if whatsoever, deviation in the estimated prevalence. RCF supply changes did not touch on this.

Discussion

Amidst the assumptions underlying the adoption of less restrictive State RCF policy are that consumers volition have more choice in where they live and that the State may experience reduced demand for Medicaid-reimbursed nursing home stays. The States included in this analysis provide a range of environmental and policy variations inside which these relationships can be tested. An earlier analysis of the potential number of nursing home residents suggested that as many as 35 pct (i.e., those with predominantly personal intendance needs) might be appropriately managed in settings other than nursing homes (Spector, Reschovsky, and Cohen, 1996). This judge was based on assay of a 1987 national probability sample of nursing abode patients.

Patient characteristics based on Carpet classifications were used to provide a current (i.e., 1995) estimate of nursing home example mix. Among the more than than 1,500 nursing homes used in this analysis, the most mutual classifications are Clinically Circuitous, bookkeeping for i-tertiary to one-half of all residents; Concrete Issues, bookkeeping for almost 20 to xl percent of residents; and Cerebral Impairment, about 10 to 15 percent of residents. Persons in these latter two groups reflect those whose needs are predominantly for personal care. Given these prevalence rates, a State would have to relocate about half of those in the current nursing homes due solely to physical or cognitive impairments to reach a 25-percent reduction among nursing dwelling house residents.

Maine, with a relatively low rate of nursing home beds per ane,000 population and a relatively high rate of RCF beds per 1,000 population, was used every bit an exemplar. Simulation analyses were conducted in 4 States to estimate the change in the nursing home patient population in other States when two of Maine'south policies were stipulated as nowadays. The issue was reflected by reductions in the proportion of nursing abode patients with either physical or cerebral problem classifications. The magnitude of the effect ranged from 0 to iii per centum among united states for physical impairment and 1 to iv percent among those with cerebral damage. Admission case mix was non associated with changes in RCF supply. Standing care case reductions, ranging between 0 and 3 percent for concrete and 0 and 2 percent for cognitive trouble case mix, were less consistent amongst the States. Increases in RCFs were associated with most of the estimated reductions. These effects, even assuming the outer limit of the confidence interval, were besides small to exist statistically significant given the sample bachelor for this assay.

Holding other considerations constant, one might expect that the proportions of physically and cognitively fragile custodial patients in nursing facilities would exist lower in those communities where custodial patients could (both by policy and financing) receive care in RCFs.

Kansas and South Dakota have relatively loftier and comparable ratios of nursing home beds per population, comparable proportions of residents who are Medicaid recipients, and policies that are relatively facilitative of RCF use (Kansas for reimbursement and eligibility, South Dakota for reimbursement only). In both States, the faux case mix suggests that expansion of RCF supply was non sufficient to substantially alter nursing dwelling demand under the existing nursing home reimbursement and utilization controls currently used in States with loftier nursing dwelling bed supply.

Ohio provides some other permutation on the policy environment, this time reflecting an environment with RCF eligibility criteria favorable to higher levels of frailty in RCFs but with no reimbursement programs to facilitate access to this care amidst those with low incomes. This State, in dissimilarity to the preceding two, has a moderate rate of nursing facility beds to the population and residents per population approximating those of Maine. Moreover, of the four States in the simulation, Ohio had the everyman observed Carpeting proportion for physical impairment at admission and among continuing cases even before the simulation. In this context, the simulation of Maine SNF policy produced a 25-per centum reduction in the proportion of physically dumb (4 percent in absolute terms) and a comparable reduction (2 per centum in accented terms) amid the cognitively impaired at admission. There were no effects associated with continuing instance mix. RCF supply simulation added to these changes among the physically impaired continuing cases, where the absolute modify was about 2 percent.

Mississippi is the most traditional of us in this sample, with no financial assistance for RCF care and restrictive exclusions of the fragile from these facilities. This State has the everyman proportion of nursing home beds per ane,000 population, and the number of residents per population is lower than that of Maine. The observed proportions of physically and cognitively dumb nursing home residents are between those of Kansas and South Dakota, simply the simulated SNF policy issue amongst admissions cases is of a magnitude comparable to most of the other States, being about ii percent in absolute terms, and no effect among standing cases. The addition of more RCF supply did non touch on admissions cases but was associated with a small-scale decrease in the proportion of those with cognitive impairment.

Conclusions

A number of States have begun to modify their RCF regulations and other LTC policies to facilitate admission to residential alternatives to nursing home intendance and to perhaps reduce State Medicaid expenditures on this care. Such substitutability assumes some comparability in the level of demand and the availability of an appropriate service supply. Prior piece of work by others has suggested that possibly as many every bit 35 percent of those in nursing homes are in that location mainly considering of personal intendance needs and that some portion of these residents could perhaps be relocated into other forms of care (Spector, Reschovsky, and Cohen, 1996). We attempted to farther quantify the judge of the potentially "relocatable" nursing dwelling population and to appraise how example mix might be expected to alter in the presence of two tested policy scenarios. This was done within the context of measured nursing home and residential care service supply, community-level demand, nursing home attributes, and State policies affecting allowable levels of care. RUG-Three instance classifications were used to differentiate a nursing facility's case mix among both admissions and continuing residents. The analysis was applied to each of 4 States for whom advisable case mix and service supply were available.

The furnishings of State policy scenarios were simulated past the replication of predictive models where the proportion of SNF-licensed nursing homes was gear up to 100 percent and the supply of RCFs per 1,000 population was set to the levels present in Maine in 1995. Land LTC policies (due east.grand., placement eligibility for RCF care and availability of public funds for RCF care) varied among the 4 States, as did the relative supply of nursing beds. Facility attributes, service demand, and nursing home supply characteristics were held constant within each Country.

The findings suggest that nursing facility example mix, as expressed past the proportion of persons with concrete or cognitive impairment, can be affected at time of admission by adoption of policies that restrict nursing domicile operations to skilled levels of care. The magnitude of this effect, though small-scale (1 to viii percent, combining both cognitive and physical Carpet classified cases) in accented reductions, is observed in varying Land conditions. This reduction is present among the 2 written report States with loftier nursing home occupancy rates (95 percent), regardless of the underlying prevalence of physical damage in the patient population, among ane of two States with lower occupancy rates, and amongst ane of two States with a high ratio of nursing dwelling house beds per population. These furnishings were generally not enhanced under the assumption of an expanded RCF supply. This was true fifty-fifty for States having somewhat facilitative (although severely constrained) RCF reimbursement or RCF eligibility criteria.

Amongst the continuing care population, the faux SNF policy had very little effect on either physical- or cognitive-problem case mix inside any of the States. The simulated condition of expanded RCF supply, withal, does advise a minor reduction (most a 1-percentage absolute change) in most of the States and for both groups of atmospheric condition.

Conclusions drawn from these findings are qualified. The foremost limitation is the cantankerous-sectional nature of the analysis. At a betoken in time, 1995 in this example, it seems likely that market forces had established a rest between the need and supply for nursing and residential care beds. This seems to be true regardless of the sources of reimbursement or admission criteria. Recognizing this, it is likely that cross-sectional analyses assess only the very small maladjustment between supply and demand present at whatever i time, perhaps making the policy effects predicted hither downwards biased and express to virtually-term furnishings. For example, the simulated adjustment in market atmospheric condition (i.e., setting all nursing homes to SNF status and doubling or tripling the RCF supply) very probable would produce reductions in nursing dwelling beds over time due to the conversion of some facilities to RCFs or rehabilitation centers, or even closure of facilities. Reductions in nursing dwelling house supply were not simulated in the analysis. Notwithstanding, reductions in bed supply could substantially alter the demand for beds past each of the Carpet classifications. Further work using rates of alter over varying periods (instead of point estimates) among the demand and supply attributes as predictors of rates of change in case mix (again instead of point-in-time estimates of case mix) may produce dissimilar results.

Important, too, is the existing disparity betwixt the number of nursing abode beds per 1,000 population and that of RCF beds in nigh of the study States. Within Maine, the reference case, the ratio is essentially 2:1 (i.e., 8.ane versus 3.ix per i,000 population). In the other iv States the ratio is much higher: Southward Dakota, 12:1; Kansas, 9:1; Mississippi, 7:1; and Ohio, 6:1. A substantial growth in RCF supply or nursing home bed supply reductions would have to occur before most States began to approach the supply mix of Maine. This can be achieved, just it will take time.

Some other caution is that the attainment of a counterbalanced supply mix past itself is non sufficient. Presently, Maine, Ohio, and Mississippi (each with different distributions of nursing home and RCF supply among their counties) have similar numbers of nursing abode residents per one,000 anile persons and like proportions of cognitively dumb Carpet classified cases in nursing homes. These States vary only in the proportion classified with physical issues. This suggests that the forces operating to impact nursing home utilize may exist more than complex than the relatively simple models used hither. These models allowed the outcome of all pre-existing State utilization controls and other policies affecting nursing home placement and retention to be held constant. Viewed inside the context of an individual Land, this was a convenient style to assess the issue of the two specific changes introduced. However, as the simulated effects on case mix were more pronounced in some States than others, it seems advisable that attention exist given to farther depiction of other LTC policies and their implementation and that the supply of alternative services, such as HCBC and unlicensed housing, be incorporated into the analysis. An empirical starting signal for the identification of constructive Country policy mixes could be those States (such every bit Ohio) that already have depression proportions of nursing home patients classified as physically or cognitively dumb.

Many of the limitations associated with measurement, absence of case-mix or policy variation, or the time effects noted here can be overcome with longitudinal replication and refinement in the supply-and-need attributes. The advent of the MDS system within all States will before long make such analyses viable across the country. However, overcoming limitations arising from constrained policy options among the States may crave analysis of these hypothetical innovations. A detail example is that of identifying the level of RCF reimbursement needed to facilitate access to (and stimulate supply of) residential care for low-income persons. Medicaid and State supplemental payments for RCF-level care observed amidst the sample States are well below market rates for such housing. Reimbursement rates more comparable to market place rates could likely reduce some proportion of nursing home residents for whom Medicaid is the primary payer by allowing access to supportive housing. On boilerplate, the current proportion of Medicaid patients would take to be reduced past 50 percentage to effect a 25-percent reduction in the total number of nursing home patients. The price of the daily room charge per unit needed to affect such a change could be determined by examining the income levels of those with physical or cognitive problems entering nursing homes. Equally currently modeled, this effect seems about probable amidst ongoing cases with cerebral bug considering it is among this population where the RCF supply seems to be most associated with case mix.

Although this study has limitations, the underlying findings should non be ignored. They raise a caution about the optimistic assumptions of the interplay between RCF policy and nursing habitation use. To the extent that effects exist, changes in nursing dwelling demand resulting from changes in State policy will not be instantaneous. Moreover, the upper limit of the proportion of nursing home cases with merely physical and cognitive impairment likely to be affected past current and emerging LTC policy appears to be well under 25 percentage of the electric current nursing habitation population. This is known not by the simulation merely by the observed Carpet rates. Furthermore, the findings propose that particular attending be given to ongoing nursing abode residents and the factors influencing the retentiveness of cases with predominately physical or cognitive impairments. These proportions are more similar amid States than instance mix at admission, and they practice not appear to have much association with RCF supply.

Finally, there is the upshot of supply and need and how they interact. States and counties that accept had historically high rates of nursing abode beds per one,000 population may exist somehow fundamentally unlike than those with lower bed supply in their preferences for nursing homes. As State policy and other circumstances brainstorm to alter the presumed balance between the need for and supply of long-term services, the management of adjustment in terms of bed supply and case mix may prove to exist unpredictable inside communities and across the State, as they take shown to be in these simulations.

​

Table 5

Assay of Nursing Facility Admissions Example Mix: Mississippi, 1995

Independent Variable	Rehabilitation		Special Care		Complex Care		Cognitive Harm		Physical Impairment
	Statistic	T-score	Statistic	T-score	Statistic	T-score	Statistic	T-score	Statistic	T-score
Intercept	-ii.23	—	**59.33	—	45.36	—	-two.28	—	-12.28	—
Facility Attributes
Facility Average Resident Historic period	0.19	one.28	**-0.65	-3.46	-0.xx	-0.62	0.10	0.40	*0.70	2.xx
Facility Percent Medicaid	-0.05	-1.25	0.02	0.46	-0.04	-0.42	-0.04	-0.63	0.12	1.45
Facility Occupancy	**-0.28	-3.53	-0.00	-0.02	0.16	0.ninety	0.20	one.49	-0.07	-0.twoscore
Facility Number of Beds	0.02	one.l	-0.00	-0.27	0.03	i.20	0.01	0.46	*-0.06	-two.16
Facility For-Profit	*iv.88	2.54	1.26	0.50	0.48	0.eleven	-1.27	-0.39	-vii.29	-1.72
Facility Chain	-1.75	-1.39	1.59	0.96	*6.53	two.26	-2.60	-1.21	-2.13	-0.77
Facility SNF Licensed	**iv.23	3.54	one.66	1.07	1.70	0.62	*-4.53	-2.23	-4.28	-1.62
Demand Variables
Pct Historic period 65 or Over in County	-0.02	-0.03	-0.03	-0.05	-1.47	-1.41	-0.24	-0.31	*ii.15	2.xiv
Canton Pct of Females in Labor Forcefulness	**0.41	2.83	-0.08	-0.45	0.20	0.lx	-0.36	-i.48	-0.34	-1.06
Per Capita Income in Thousands	-0.11	-0.28	0.86	1.69	-0.03	-0.03	0.xxx	0.46	-0.93	-1.08
Population per Square Mile in Thousands	-0.43	-0.03	-22.99	-i.42	-22.63	-0.fourscore	20.71	0.98	35.54	1.30
Discharges per Thousand	-0.00	-0.30	0.00	0.50	0.01	0.43	-0.01	-0.73	-0.00	-0.05
County Medicare Pct	0.08	0.71	0.03	0.21	0.01	0.04	0.01	0.06	-0.24	-0.91
County Medicaid Percentage	-0.02	-0.39	-0.ten	-1.33	0.01	0.07	0.11	ane.15	-0.00	-0.01
Supply Variables
NF Residents per 1,000 Population 65 or Over in County	*-0.19	-2.fifteen	-0.03	-0.29	*-0.46	-2.34	0.02	0.17	**0.72	3.79
NF Beds per i,000 Population in County	one.06	1.64	0.nineteen	0.22	*3.40	2.29	0.20	0.18	**-5.37	-iii.76
Number of RCF Beds per 1,000 County Population	-0.00	-0.07	-0.01	-0.32	0.01	0.25	-0.00	-0.1	0.00	0.02
Hateful Case-Mix Percent	3.47	—	eleven.lxx	—	38.09	—	thirteen.07	—	31.20	—
Adjusted Rtwo	**0.402	—	*0.190	—	0.148	—	0.135	—	**0.305	—

Tabular array 6

Analysis of Nursing Facility Admissions Case Mix: Ohio, 1995

Independent Variable	Rehabilitation		Special Intendance		Complex Care		Cognitive Impairment		Concrete Impairment
	Statistic	T-score	Statistic	T-score	Statistic	T-score	Statistic	T-score	Statistic	T-score
Intercept	2.threescore	—	7.71	—	-17.25	—	fourteen.99	—	**63.91	—
Facility Attributes
Facility Average Resident Age	0.04	0.61	-0.01	-0.26	**0.38	3.77	0.02	0.36	*-0.xv	-2.01
Facility Percent Medicaid	**-0.10	-3.72	0.02	1.22	**0.12	3.48	0.03	1.36	**-0.08	-ii.98
Facility Occupancy	*0.06	2.07	0.01	0.48	0.06	1.43	-0.04	-1.44	*-0.07	-2.53
Facility Number of Beds	**0.04	4.19	0.00	0.5	-0.00	-0.03	*-0.02	-2.48	**-0.02	-two.95
Facility For-Profit	**5.25	4.25	-0.18	-0.21	-ane.89	-ane.1	0.38	0.35	**-3.91	-3.18
Facility Chain	**ii.69	ii.77	0.46	0.67	0.26	0.nineteen	-0.84	-0.96	*-two.06	-2.13
Facility SNF Licensed	**7.93	6.65	**4.16	iv.92	*3.46	2.21	**5.08	-iv.77	**-10.72	-9.02
Demand Variables
Percent Age 65 or Over in Canton	0.eighteen	0.44	-0.01	-0.05	0.22	0.forty	-0.19	-0.53	-0.17	-0.43
County Per centum of Females in Labor Force	0.10	0.44	-0.02	-0.xiv	0.20	0.64	0.04	0.nineteen	-0.21	-0.92
Per Capita Income in Thousands	-0.06	-0.18	-0.16	-0.64	-0.27	-0.56	-0.02	-0.05	0.forty	ane.xiii
Population per Square Mile in Thousands	0.98	0.93	0.01	0.01	-2.73	-1.87	*ii.14	ii.27	-0.63	-0.6
Discharges per Grand	0.01	1.24	0.01	i.59	0.02	1.61	-0.01	-0.77	**-0.05	-four.11
County Medicare Per centum	**-0.37	-3.95	-0.01	-0.13	*0.33	2.54	0.05	0.58	0.01	0.12
County Medicaid Percentage	-0.13	-1.thirty	0.05	0.70	0.x	0.77	-0.02	-0.27	-0.04	-0.42
Supply Variables
NF Residents per ane,000 Population Age 65 or Over in County	0.01	0.62	0.01	0.68	-0.01	-0.51	0.02	1.08	-0.03	-1.57
NF Beds per 1,000 Population in County	-0.38	-one.80	*-0.33	-ii.18	0.21	0.72	*-0.02	-2.48	0.40	1.93
Number of RCF Beds per 1,000 Canton Population	-0.04	-0.56	0.00	0.05	0.08	0.74	-0.06	-0.87	-0.05	-0.67
Hateful Case-Mix Pct	10.73	—	9.xl	—	48.82	—	xi.xv	—	16.08	—
Adjusted R2	**0.264	—	**0.063	—	**0.068	—	*0.104	—	**0.244	—

Table 9

Analysis of Nursing Facility Continuing Intendance Instance Mix: Mississippi, 1995

Independent Variable	Rehabilitation		Special Care		Complex Care		Cognitive Impairment		Concrete Impairment
	Statistic	T-score	Statistic	T-score	Statistic	T-score	Statistic	T-score	Statistic	T-score
Intercept	3.21	—	**83.47	—	viii.48	—	0.56	—	-9.ten	—
Facility Attributes
Facility Average Resident Age	-0.06	-1.75	**-0.92	-7.98	*0.46	2.44	0.09	0.66	**0.58	2.97
Facility Percent Medicaid	-0.01	-0.69	-0.03	-0.93	0.04	0.90	0.00	0.06	0.01	0.21
Facility Occupancy	0.00	0.22	-0.02	-0.26	*-0.22	-ii.14	0.10	1.33	0.13	1.28
Facility Number of Beds	-0.00	-0.67	**-0.03	-2.91	** 0.04	two.65	0.01	ane.xi	-0.02	-ane.46
Facility For-Turn a profit	0.38	0.90	1.54	1.01	-0.39	-0.16	-0.73	-0.39	-1.30	-0.50
Facility Chain	0.16	0.58	*0.84	0.83	*3.69	2.22	-i.84	-one.49	-2.12	-1.24
Facility SNF Licensed	**0.72	ii.67	-0.47	-0.49	-ane.04	-0.66	0.35	0.30	0.72	0.44
Demand Variables
Percent Age 65 or Over in County	-0.06	-0.54	0.thirty	0.83	0.28	0.46	0.28	0.62	-0.78	-1.27
County Percent of Females in Labor Force	**0.09	two.68	-0.02	-0.15	0.22	1.fifteen	-0.182	-1.29	-0.22	-1.14
Per Capita Income in Thousands	-0.x	-ane.19	0.32	i.03	-0.76	-1.50	-0.01	-0.02	0.53	1.01
Population per Square Mile in Thousands	-2.91	-i.05	-0.00	-0.54	-8.24	-0.51	14.86	1.23	eight.18	0.49
Discharges per Chiliad	0.00	one.22	0.01	1.58	-0.00	-0.24	-0.00	-0.40	-0.00	-0.46
County Medicare Percentage	*0.05	2.02	*0.24	two.53	0.21	1.39	-0.02	-0.15	0.04	0.27
County Medicaid Pct	-0.01	-0.45	0.01	0.23	-0.01	-0.18	-0.00	-0.01	0.01	0.08
Supply Variables
NF Residents per 1,000 Population Age 65 or Over in County	-0.02	-1.03	0.07	0.96	-0.19	-1.69	0.xv	1.77	0.01	0.ten
NF Beds per 1,000 Population in Canton	0.fifteen	1.01	-0.65	-1.26	1.xv	1.36	-1.02	-1.82	0.30	0.34
Number of RCF Beds per 1,000 Canton Population	-0.03	-0.28	*-0.69	-two.03	0.77	1.38	-0.78	-1.90	0.79	1.38
Hateful Case-Mix Percent	0.95	—	11.11	—	32.46	—	xiii.66	—	39.49	—
Adjusted R2	**0.237	—	**0.468	—	**0.234	—	0.155	—	*0.216	—

Table 10

Analysis of Nursing Facility Continuing Care Case Mix: Ohio, 1995

Contained Variable	Rehabilitation		Special Care		Circuitous Care		Cognitive Harm		Physical Harm
	Statistic	T-score	Statistic	T-score	Statistic	T-score	Statistic	T-score	Statistic	T-score
IIntercept	ane.62	—	9.27	—	**48.24	—	*twenty.58	—	-7.98	—
Facility Attributes
Facility Average Resident Age	0.00	0.38	**-0.18	-5.19	-0.11	-1.78	-0.02	-0.55	**0.51	viii.94
Facility Percent Medicaid	-0.00	-1.12	0.01	0.72	-0.02	-0.91	0.01	0.93	-0.02	-0.96
Facility Occupancy	**0.01	ii.59	0.01	0.53	0.01	0.49	-0.02	-1.20	-0.01	-0.26
Facility Number of Beds	**0.00	three.39	**0.01	3.01	0.01	1.36	-0.01	-1.forty	**-0.02	-3.04
Facility For-Turn a profit	0.15	0.82	**one.86	3.23	-one.00	-0.97	-0.55	-0.82	-one.eleven	-one.15
Facility Concatenation	0.18	i.25	*-0.89	-1.98	-0.23	-0.28	0.50	0.94	0.50	0.66
Facility SNF Licensed	*0.35	two.00	**three.xi	5.61	**3.83	3.86	**-0.73	-ii.66	**-4.26	-4.5
Demand Variables
Percent Age 65 or Over in County	0.04	0.67	-0.xiv	-0.fourscore	0.46	i.42	-0.19	-0.88	0.15	0.48
County Pct of Females in Labor Force	-0.04	-1.26	-0.01	-0.11	-0.00	-0.02	0.10	0.77	0.25	ane.42
Per Capita Income in Thousands	0.01	0.11	0.xiii	0.fourscore	0.27	0.93	0.12	0.61	-0.42	-1.54
Population per Square Mile in Thousands	0.17	1.12	0.50	one.03	**-2.51	-two.89	1.04	one.82	0.34	0.41
Discharges per M	0.00	0.87	0.00	0.94	0.01	1.01	0.00	0.14	*-0.02	-2.28
County Medicare Percentage	-0.02	-1.46	-0.04	-0.87	-0.12	-1.55	0.04	0.88	*0.14	i.99
County Medicaid Percentage	-0.01	-0.68	**0.15	three.29	-0.14	-ane.70	0.00	0.09	-0.01	-0.12
Supply Variables
NF Residents per one,000 Population Age 65 or Over in Canton	0.00	0.35	0.00	0.10	0.02	1.27	-0.01	-0.55	-0.02	-1.57
NF Beds per 1,000 Population in County	-0.03	-0.86	*-0.xix	-2.02	-0.25	-one.46	-0.03	-0.27	*0.34	2.09
Number of RCF Beds per 1,000 Canton Population	-0.01	-0.84	0.02	0.59	-0.07	-one.04	0.01	0.27	0.06	0.90
Mean Case-Mix Percent	1.18	—	nine.42	—	40.72	—	12.67	—	32.44	—
Adapted R2	**0.094	—	**0.197	—	**0.071	—	**0.055	—	**0.195	—

Acknowledgments

We thank David Zimmerman, Ph.D., Sara Karon, Ph.D., and Wayne Bigelow, Ph.D., from the Centre for Health Systems Research and Analysis, University of Wisconsin, Madison, for compiling the MDS records and for their collaboration in the analyses that preceded these simulations. We also give thanks Charlene Harrington, Ph.D., from the Department of Social and Behavioral Sciences, University of California, San Francisco, for her assistance in compiling the OSCAR records and for her assistance in conceptualizing the LTC arrangement modeled here. Finally, we thank Susan Raetzman, our program officer at The Commonwealth Fund for her encouragement to write this article.

Footnotes

James Swan is with Wichita State University. Robert Newcomer is with the University of California, San Francisco. This research was funded by Grant Number 96611 from the Republic Fund. The views expressed in this article are those of the authors and exercise non necessarily reflect the views of Wichita State University, the University of California San Francisco, or the Health Intendance Financing Administration (HCFA).

^oneThe percent of females in the labor strength seems to be relatively stable, approximately 50 per centum amid the sample States. The hospital belch rate is much more than variable, both amongst States and over time. The actual corporeality of change was more often than not similar in three States. For example, betwixt 1993 and 1996, the number of hospital admissions per 1,000 population declined in Kansas by 8.9. Comparable numbers were 7.five for Maine and 10.two for Ohio. In Mississippi (one.iv per i,000) there was no substantive alter, whereas in S Dakota (34.5 per 1,000), the modify was much more dramatic. All of this points to the likely improvement in the model if trend information tin be used instead of point-in-fourth dimension estimates (American Hospital Clan, 1994, 1997).

²Although some of the hospital-based facilities may be operation as de facto nursing homes in smaller communities, our assay comparison freestanding with infirmary-based facilities in our five-State data base of operations generally shows that hospital-based facilities have a case mix that is significantly different from that of freestanding nursing facilities. Facilities serving only the mentally ill were also excluded equally being out of scope.

ⁱⁱⁱThe selection of policy or market place parameters from other States, as had been done in the Spector, Reschovsky, and Cohen (1996) written report, was considered during the procedure of choosing the coefficients to apply in the simulation. The choice of parameters was constrained by the desire to use empirically based coefficients in the simulation models. No national studies have compiled market-area inventories of RCF supply or have evaluated the human relationship between RCF supply and nursing home case mix. Absent this data, coefficients estimated among the sample States were needed to represent the effects of nursing home licensing status and RCF supply.

Reprint Requests: Robert Newcomer, Ph.D., Department of Social and Behavioral Sciences, 3333 California Street, Suite 455, University of California, San Francisco, CA 94118. East-mail: ude.fscu.asti@njr

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Articles from Health Care Financing Review are provided here courtesy of Centers for Medicare and Medicaid Services

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The System Known As Oscar Provides Which Services For Nursing Home Residents?,

Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4194680/

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