Payment Of Inpatient Services Is Utilized By Which Of The Following Prospective Payment Systems?

Wellness Care Financ Rev. 1990 Winter; 12(2): 37–54.

Prospective payment system and other effects on mail-hospital Services

Abstract

The effects of the prospective payment organisation and other factors on the apply of post-hospital services were investigated for iv groups of diagnostically related Medicare discharges. Furnishings on specific services and total Medicare payments were analyzed at the beneficiary level using a Tobit regression technique. The utilization information base of operations consisted of more than than 30,000 discharge episode records for the years 1981-86. The postal service-hospital flow for each Medicare casher encompassed the 60 days following discharge from the infirmary. Influences on both the level and timing of wellness care services during this period were appraised. The influence of the prospective payment organization was measured through the financial bear upon and risk that information technology imposed on the discharging infirmary.

Introduction

Under Medicare's prospective payment organisation (PPS), hospitals are paid a predetermined amount per Medicare discharge. This departure from cost-based reimbursement may give hospitals an incentive to economize on inpatient services. During the several studies conducted to investigate the early on effects of PPS on infirmary inpatient use, it was found that shorter lengths of stay were induced (Guterman and Dobson, 1986; Feder, Hadley, and Zuckerman, 1987; and Gianfrancesco, 1987). Shortened lengths of stay and other reductions in inpatient care induced past PPS may, in plow, have resulted in a more intensive utilize of postal service-hospital services. After prove, notwithstanding, suggests that the initial furnishings of PPS on inpatient care may accept been transitory (Prospective Payment Assessment Commission, 1988; Gianfrancesco, 1988). Changes in the use of post-hospital services under PPS may take besides been brought about by changes in hospital case mix. For example, prove exists that PPS (or the tighter peer review that accompanied information technology) initially encouraged lower admission rates, implying college proportions of more than serious cases. Such cases are likely to consume larger volumes of post-infirmary care.

Effects of PPS and other factors on mail service-hospital intendance were estimated by applying a Tobit regression technique to discharge episode data for the years 1981-86. This menses consists of approximately three pre- and 3 mail service-PPS years. Efforts were made to control for differences in use because of changes in infirmary case mix. This was in role achieved through separate analyses of specific diagnostic groups. Post-hospital use of services was investigated for four diagnostic categories: pneumonia; stroke; hip replacement, arthritis; and hip replacement, fracture. To farther control for changes in hospital case mix, indicators of case severity were specified in the regressions.

The following types of post-infirmary care were analyzed at the casher level: care in skilled nursing facilities (SNFs), home health services, not-inpatient doc services, use of durable medical equipment (DME), and rehospitalizations. Full post-hospital intendance per discharge, measured in Medicare payments per discharge, was also subjected to assay. Beneficiary utilise of post-hospital services was appraised for the lx-day period following discharge from the hospital; PPS and other effects on both the level and timing of each service were estimated.

Earlier evidence

Several studies accept been done in which the effects of PPS on mail service-infirmary care were investigated. For the nearly role, these provide only indirect evidence in that no attempts were made to link the diverse health care services to hospital stays. Also, the evidence is essentially descriptive, relying on pre- and post-PPS comparisons. The post-obit summarizes the findings from these studies.

Guterman and Dobson (1986) reported statistics on the use of SNF, home wellness, and hospital outpatient services for the period earlier the Tax Disinterestedness and Fiscal Responsibility Deed (TEFRA) through the start twelvemonth of PPS. When measured in existent terms, annual rates of increase for SNF and home health services for the get-go PPS yr (1983-84) exceeded those for the pre-TEFRA period (1973-82). Notwithstanding, they were lower than those for the TEFRA yr (1982-83). Annual rates of increment for outpatient hospital and medico services were markedly lower for the start PPS year than for the pre-TEFRA period.

DesHarnais et al. (1987) looked at the destination of Medicare patients upon belch from the hospital. The proportions of cases discharged to SNFs, dwelling house wellness care, and rehospitalization were investigated. Bodily proportions for the offset PPS year were compared with predicted values extrapolated from 1980-83 trends. Although the actual proportions were all greater than the predicted, the difference was statistically significant only for home health care.

A similar study was undertaken by Long et al. (1987). The principal divergence between this and the preceding study is that some attempt was fabricated to hold constant the effects of case mix on post-hospital use of services. In their findings, Long et al. showed that changes in the proportions of cases discharged to SNFs and habitation wellness care were positive and considerably larger in the first PPS yr than in the preceding years (1980-83). No tests for statistical significance were made.

Lewis et al. (1987) focused on changes in the composition of SNF admissions under the PPS. Three data samples, all from southern California, were compared. These included two pre-PPS samples (1980 and 1982-83) and ane for the offset PPS twelvemonth (1984). Their findings contained prove of a dramatic increase during the first PPS year in the proportion of SNF admissions that were Medicare beneficiaries.

In another report, Lyles (1986) investigated the bear upon of PPS on nursing domicile use in Oregon. Although SNFs were included, the nursing domicile data were mostly for intermediate intendance facilities. Changes in employ that occurred during 1982-83 were compared with changes that occurred during 1983-84. The percentage change in total admissions was considerably greater during the 1983-84 menses, and the percent change in full patient days was considerably lower.

Fisher (1987) compared pre-PPS (1982) and mail-PPS (1985) distributions of doc charges among the various health care settings. The results were that physician inpatient charges declined significantly as a percent of the full, and those in other settings increased.

A study past Weinberger, Ault, and Vinicor (1988) focused on a single diagnosis, diabetes mellitus. Mail service-infirmary use of services was compared for two cohorts of patients, one pre-PPS (1981) and the other, mail service-PPS (1983-84). The results were that there were more clinic visits, emergency room visits, and infirmary readmissions among the mail service-PPS cohort.

Of the before studies of post-hospital care nether PPS, that by the RAND Corporation is the most thorough (Neu and Harrison, 1988). This study was based on 1981 and 1984-85 samples of Medicare beneficiaries. Postal service-hospital employ of SNF and domicile health services was compared for the 2 periods. SNF services were directly linked to the hospital stay (i.e., that qualifying beneficiaries for Medicare coverage), and the dwelling house health services linked to the infirmary stay varied, depending on what post-infirmary period (60 or 190 days) was specified. Also, to limit the furnishings of changes in hospital example mix between 1981 and 1984-85, post-infirmary use of the services mentioned was investigated separately for selected diagnosis-related groups (DRGs). (Changes in the DRG composition of the Medicare hospital population seem to accept accounted for most of the changes in post-hospital intendance between the two periods.) The proportions of cases inside each DRG using SNF or abode wellness services were generally college under PPS. However, SNF days per beneficiary were considerably lower, whereas abode health visits per beneficiary were considerably higher. Information technology was also revealed from these findings that there was little correlation betwixt the utilise of SNF and the employ of abode health services.

A major limitation of the studies just mentioned is that they are essentially descriptive, and no endeavour was made to define and exam for a structural relationship between PPS and the apply of post-infirmary care. The author of this report provides a conceptual framework for understanding the furnishings of PPS on these services and tests this framework using regression techniques.

Conceptual framework

PPS was perceived as having two potentially conflicting effects on the commutation of post-hospital care. The start arises from its financial touch on the discharging hospital. Under PPS, a hospital may feel an increase or decrease in its overall operating ratio, depending on whether it incurs a Medicare gain or loss.^one

The incentive to economize on inpatient care and substitute postal service-infirmary services was reasoned to be negatively related to this fiscal impact. Hospitals experiencing decreases in their overall operating ratios would have more than incentive to substitute post-hospital services, and those experiencing increases would take less incentive. Although this symmetrical behavior is inconsistent with profit maximization, hospitals for the about part are not profit maximizers. They are more accurately described as quantity or quality maximizers whose financial objectives may be simply to suspension fifty-fifty. If this is the instance, and then a financial gain under PPS would relax the incentive to substitute mail-hospital care whereas a financial loss would intensify information technology.

The second effect of PPS derives from the fiscal risk that it imposes on the discharging hospital. Fiscal risk is greater for hospitals that are more dependent on Medicare patient volumes. Any modify in payment policy under PPS would take larger effects on the overall operating ratios of these institutions relative to those of hospitals that are less Medicare dependent. Therefore, the range over which a hospital'due south overall operating ratio can vary under PPS depends on the relative importance of its Medicare business concern. The upshot of PPS on a hospital's overall operating ratio depends on both the size of the hospital's Medicare gain or loss and the relative importance of its Medicare business concern. Therefore, a hospital with a large Medicare gain or loss and low Medicare dependence tin experience the same fiscal bear on as one with a small gain or loss and high dependence. All the same, the range of potential increase or decrease in its overall operating ratio is greater for the infirmary that is more dependent on Medicare. Hospitals more dependent on Medicare and, thereby, at greater risk from PPS, may be more inclined to economize on inpatient services so as to reduce the possibility of large negative financial impacts.² To the extent that post-infirmary services substitute for inpatient care, their use would be positively influenced by the risk that PPS imposes on the discharging hospital. Under the price-based reimbursement that preceded PPS, Medicare dependence did not have take a chance implications of the kind described hither.

Shown in Figure 1 is the causeless relationship between the use of post-hospital care and the influence of PPS on the discharging hospital. Post-hospital use is measured on the vertical axis, and the fiscal impact of PPS is measured on the horizontal axis. Differing levels of risk imposed past PPS are represented by curves Risk 1 and Risk 2, with the latter reflecting the higher risk associated with greater Medicare dependence. The downward slopes of these curves betoken the negative human relationship between the use of mail-infirmary care and the financial touch on of PPS. In the absence of PPS—i.e., nether price-based reimbursement—at that place would be no effects on hospital operating ratios, and Medicare dependence would pose no risks of the kind described hither. Hospital A represents this situation. Unlike situations nether PPS are represented by hospitals B, C, and D.

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Assumed human relationship between use of mail-hospital services and the effects of the prospective payment arrangement on the discharging hospital

Hospitals B and C are characterized by the same Medicare dependence and face the same risk from PPS. However, the actual financial impact of PPS differs for each (because of differences in efficiency, for example). Infirmary B experiences an increase in its overall operating ratio, and hospital C experiences a decrease. The assumed incentives are such that C substitutes more postal service-hospital intendance than B. Alternatively, hospitals B and D experience the same financial bear upon of PPS but confront different risks, hospital D being more dependent on Medicare. Considering of this greater gamble, D also substitutes more than post-hospital care than B. Thus, the affect of PPS on the use of mail-infirmary care is causeless to depend on the internet effect of these two potentially conflicting influences.

Data

The principal data used in this report are from the Tracer Belch Episode Files constructed by Abt Associates, Inc. (as part of the Prospective Payment and Analytic Support Studies performed under contract for the Health Care Financing Administration (HCFA). In that location were four episode files to depict from, each containing health service use and other data for diagnostically related Medicare discharges. The diagnostic groups represented past the files are pneumonia, stroke, hip replacement, and hernia. Episodes for the hernia grouping showed little mail service-infirmary care and, for this reason, were excluded from the analysis. Each of the iii remaining groups contained multiple DRGs. To achieve a higher degree of within-group homogeneity, 1 or ii DRGs were selected from each group. Hip replacement cases were also divided into 2 subgroups: arthritis-related and fracture-related. The resulting 4 groups consist of the following DRGs or DRG parts:

Pneumonia: DRGs 89 and 90 (all primary diagnoses).
Stroke: DRG xiv (all main diagnoses).
Hip replacement (arthritis): DRG 209 (all master procedures from the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-nine-CM), beginning with 715).
Hip replacement (fracture): DRG 209 (all ICD-9-CM principal procedures beginning with fourscore-829).

The effects of PPS and other factors on mail-infirmary use were analyzed separately for each of these groups. The selection of specific DRGs or DRG parts enabled improve command, though even so imperfect, for temporal and cross-exclusive variations in hospital case mix.

Each of the four diagnostic groups consisted of several m episodes. These occurred within the period 1981-86, which consists of approximately iii pre-PPS and 3 postal service-PPS years. Specifically, in that location were viii,815 pneumonia episodes, 6,993 stroke, seven,138 hip replacements (arthritis), and 9,713 hip replacements (fracture). Episodes were constructed by first selecting random samples (pneumonia and stroke) or the universe (hip replacement) of hospital discharges from each year's v-per centum Medicare provider assay and review (MEDPAR) extract file.^three Using each beneficiary'due south hospital stay as a starting point, use of other health care services was determined for the 60 days following discharge and for the threescore days preceding admission. These determinations were made by linking to MEDPAR records beneficiary claims data contained in other files.

Several types of health service use data were contained in each episode record, including inpatient infirmary utilise from the MEDPAR files (for all stays during the episode); SNF use from the SNF stay files; home health employ from the Home Health Agency Pecker Record Files; physician, DME, and all other noninstitutional utilise from the 5-percent Function B Payment Record files; and hospital clinic and emergency room employ from the 5-percent Part B Outpatient Bill Records files. Utilize was measured in physical units when available and otherwise in charges. Provider charges and Medicare payments were indicated for all services. Clinic and emergency room visits were excluded from the analysis considering extremely depression mean values and broad twelvemonth-to-year fluctuations fabricated the information highly doubtable.⁴

As indicated, an episode is divers to begin 60 days before the admission engagement for the sampled infirmary stay and to end 60 days subsequently the belch engagement. Use information were recorded for the focal hospital stay, for the 60- and xxx-day periods earlier the admission date and for the 7-24-hour interval, 14-day and lx-day periods after the belch date. This breakdown enabled measurement and analysis of the timing equally well as the volume of post-hospital care.

In addition to utilise information, the episode records contained descriptive data pertaining to the beneficiary and the discharging hospital. Other information, still, were too necessary to construct measures of PPS influence and, in general, to more fully specify the regression models. These information were taken from Medicare Toll Reports, the PPS Impact File, surveys of the American Infirmary Association, the Area Resource File, the 1981-86 Basic Economical Activity Surface area (BEAA) Quarter Per Capita File, and other sources available at Abt Assembly, Inc.

Measures of postal service-hospital service use

Several measures of post-infirmary service utilize were analyzed. These reflect both the volumes of services consumed during the sixty-mean solar day menstruation post-obit discharge and their timing. PPS may have afflicted non only the level of post-hospital care but also its timing. Hospitals at greater take chances and experiencing fiscal losses under PPS, as argued, would be more likely to substitute post-hospital services for inpatient intendance. This substitution might be reflected in more than immediate too as higher levels of apply. In contrast, hospitals at lower take a chance and experiencing fiscal gains would be less likely to substitute post-hospital care, and this would take the opposite outcome on the level and timing of these services. The following measures were subjected to assay.

Skilled nursing facility days—total SNF days used by each beneficiary during the post-60-twenty-four hour period period.
Skilled nursing facility timing—the timing of SNF use measured past the ratio of SNF days in the post-14-24-hour interval menses to those in the postal service-60-twenty-four hours period.
Home health visits—full home wellness visits used past each beneficiary within the post-60-day period.
Home wellness timing—the timing of home wellness use measured as described previously.
Medico charges—full noninstitutional physician charges for each beneficiary during the post-60-day period. All charges were expressed in 1981 dollars and standardized for cross-sectional differences. A Consumer Toll Index was available for each canton and each year (1981-86) from Abt's Surface area Resource File. This enabled deflation of all charges to their 1981 levels. Deflated charges were then divided past the 1984 Medicare wage index (applicable to each county) to adapt for cross-sectional differences in prices. The county used in making these adjustments was the beneficiary's county of residence.
Physician timing—the timing of physician charges measured as described previously.
Durable medical equipment charges—full DME charges for each beneficiary within the mail-sixty-24-hour interval period. All charges were expressed in 1981 dollars as described previously.
Durable medical equipment timing—the timing of DME charges measured as described previously.
Rehospitalization days—total rehospitalization days per beneficiary within the post-60-mean solar day period.
Rehospitalization timing—the timing of rehospitalization days as measured previously.
Medicare payments—total Medicare payments for each beneficiary for all post-infirmary services used during the post-sixty-solar day period. Total payments were expressed in 1981 dollars and adjusted for cross-sectional differences every bit described previously.

Table 1 contains, for each of the four diagnostic groups and each of the years 1981-86, mean quantities of the only mentioned services used during the sixty days following hospital discharge. Medicare payments for mail-hospital services mostly increased during the menstruum, as did dwelling health visits and physician and DME charges. Patterns for SNF and rehospitalization days are mixed, varying by diagnostic group. It is impossible, however, to determine from the descriptive evidence if and to what extent trends in mail-infirmary utilise were influenced by PPS.

Tabular array 1

Hateful values of individual use of mail-infirmary service during 60-24-hour interval period following hospital belch, diagnostic group: 1986

Diagnostic group and service	1981	1982	1983	1984	1985	1986

	Mean value
Pneumonia
Skilled nursing facility days	0.7	0.eight	0.half dozen	0.6	0.nine	0.8
Home wellness visits	1.2	one.3	1.7	2.i	ii.2	2.2
Physician charges	32	37	43	49	55	56
Durable medical equipment charges	23	28	37	46	57	59
Rehospitalization days	1.two	1.6	1.5	i.3	1.iii	1.4
Medicare payments	$503	$613	$646	$692	$739	$813
Stroke
Skilled nursing facility days	iv.five	four.six	5.3	5.2	4.7	4.0
Domicile health visits	3.5	four.seven	5.4	6.two	6.iv	6.2
Dr. charges	36	42	45	50	55	59
Durable medical equipment charges	25	32	41	l	52	53
Rehospitalization days	ii.9	3.5	iii.vi	3.9	4.0	4.4
Medicare payments	$1,100	$i,357	$i,550	$1,704	$i,745	$1,825
Hip replacement, arthritis
Skilled nursing facility days	1.0	1.1	one.1	1.one	1.iii	1.2
Dwelling health visits	ii.ane	iii.ane	4.i	four.viii	5.1	5.3
Physician charges	29	29	37	40	45	44
Durable medical equipment charges	11	15	18	25	32	28
Rehospitalization days	1.i	1.2	1.1	.9	1.0	1.2
Medicare payments	$465	$580	$634	$672	$746	$811
Hip replacement, fracture
Skilled nursing facility days	7.6	viii.i	8.9	viii.viii	7.9	7.5
Home health visits	3.7	five.0	v.4	6.ii	6.half dozen	half dozen.half dozen
Md charges	41	47	41	55	55	61
Durable medical equipment charges	20	31	27	37	45	41
Rehospitalization days	ane.3	1.7	i.3	1.4	1.4	1.7
Medicare payments	$868	$1,046	$i,040	$1,200	$1,224	$i,351

Regression model

To isolate the effects of PPS and other factors for each diagnostic group, Tobit regression models were estimated for the several measures of postal service-hospital use. Because loftier proportions of beneficiaries did non use specific post-infirmary services, the data contained a large number of observations with goose egg values and were not normally distributed. The application of standard regression techniques (ordinary to the lowest degree squares) to such information would have generated biased results. A Tobit regression model corrects for these data characteristics (Maddala, 1983).

The main explanatory variables reflect the risk and fiscal impact imparted by PPS on the discharging infirmary. These effects, which could have potentially conflicting influences on the utilise of postal service-hospital care, were captured by the post-obit measures.

Financial touch on

The fiscal impact of PPS on the discharging infirmary was measured by the increase or subtract in its overall operating ratio that information technology was projected to experience under the fully implemented PPS. To obtain a more accurate mensurate of financial affect, the projected ratio was calculated bold that the hospital did non alter its costs and patient volumes in response to PPS.^v If the discharge occurred prior to the implementation of PPS or was from a hospital not subject to PPS, this measure out had a value of zero, reflecting cost-based reimbursement. Operating ratio increases under PPS were assumed to dampen incentives to substitute post-hospital care and decreases, to reinforce incentives.

Risk

PPS risk faced by the discharging hospital was captured past its (1984) ratio of Medicare discharges to total discharges, that is, its Medicare dependence. If a hospital was not subject to PPS or a discharge occurred prior to its implementation, this measure was given a value of zero. In the absence of PPS (under cost-based reimbursement), Medicare dependence has no risk implications. Greater Medicare dependence was expected to positively influence the use of post-hospital services.

In addition to the effects of PPS on the discharging hospital, several other factors were expected to influence beneficiaries' utilise of post-infirmary services. The measures specified in the regression models to capture these influences are summarized in the following.

Case severity

Differences in case severity amidst the beneficiaries in each diagnostic grouping were captured by a set of variables reflecting each casher'southward employ of wellness care services in the 60-twenty-four hours flow preceding the focal hospital stay. The more serious cases inside each diagnostic grouping were reasoned to consume relatively high levels of care in the pre-lx-day period, thus, the rationale for using these measures every bit indicators of case severity. To suit for the effects of supply constraints on the levels of these services, each beneficiary'south apply of a service during the pre-60-24-hour interval period was divided by the average pre-60-day use of that service for all beneficiaries within the same diagnostic group and State and with the same year of discharge. Case severity was expected to exert a positive influence on mail-hospital intendance.

Need

The use of post-hospital care may also be explained by other beneficiary characteristics that influence demand. These include historic period, sex, race, and income. A positive relationship was expected between age and post-hospital use, whereas the furnishings of sex and race were uncertain. Race was included to capture cultural, social, and economic influences on the use of mail-hospital services. Although the relationship between income and post-hospital use was expected generally to be positive, it would also depend on the type of care. Some types of intendance might be inferior substitutes for others and, thus, would bear a negative relationship with income.

Geographic

Measures were included to capture geographic furnishings on the utilize of post-hospital services. These were based on a canton classification developed for all U.S. counties past the Appalachian Regional Commission. Counties were ranked (1-10) according to geographic isolation, where one is the to the lowest degree isolated (key metropolitan) and 10 the virtually isolated (rural). Rankings were determined primarily by population size, density and concentration, and worker commuting patterns. A higher ranking for a casher's county of residence was believed to generally imply less access to post-infirmary care. Still, certain types of post-hospital intendance, such as home health services, may be relatively more than available in remote areas because of the scarcity of others. For these, the effects of geographic isolation on mail-hospital apply might exist positive.

The ratio of the beneficiary's to the discharging hospital's county ranking was included to mensurate the caste of rural-urban migration for hospital inpatient services. It was expected that higher levels of post-hospital use would exist associated with beneficiaries who migrated to more urban areas for their hospital intendance. Such patients demonstrate a greater willingness and ability to overcome geographic barriers to obtain health care services. There were also reasons for suspecting a negative relationship. Physicians located in urban areas far removed from their rural patients might be less able to schedule post-infirmary care because of unfamiliarity with the local services available to these patients.

Supply

Supply measures were specified for each blazon of post-hospital care. These were casher average utilise rates at the BEAA or State level. In the absence of straight measures of the availability of each service, such aggregate measures of use were judged to exist reasonable proxies for the supply constraints facing beneficiaries. Beneficiaries located in States or BEAAs with more arable supplies were expected to utilise higher levels of post-infirmary care and to obtain quicker access to these services. Although the geographic and supply variables were expected to be correlated, this correlation is far from perfect. The geographic measures are at the county level, whereas the supply measures are at the broader State and Basic Economic Activity Area levels. Also, the supply of health care services does not depend solely on such geographic factors as population density. Some rural areas have relatively abundant supplies (due east.g., some of the North Primal States), and others take relatively poor supplies (eastward.g., some of the Appalachian States). The supply measures also served equally instrumental variables controlling for interdependence among the diverse types of intendance used by each beneficiary. For example, the book of home wellness services consumed by a Medicare beneficiary may, to the extent that they are complements, depend on the book of SNF services consumed. Both quantities depend on the supply of each type of care available to the beneficiary.

Hospital

Hospital characteristics may also influence the quality and quantity of inpatient services and, in turn, touch mail-hospital use. Several hospital measures were specified to capture these effects. The discharging hospital'due south size (beds), educational activity status, and scope (number) of services were expected to be positively related to the quality of inpatient care. Higher quality inpatient care may suggest a bottom need for postal service-hospital services. Type of hospital control may besides influence post-hospital use of services because of differing incentives to economize on inpatient care. For-profit hospitals were expected to exist more sensitive to PPS and other financial incentives affecting the use of these services.⁶ Lastly, hospitals with high occupancy rates may also be more inclined to substitute intendance in other settings considering tighter capacity constraints might induce such hospitals to economize on inpatient services.

Trends

The concluding variables included in the regression models were a set of binaries reflecting each year during the 1981-86 period. These measures captured trends in mail service-hospital apply. For example, there appears to have been a motion, which pre-dates PPS, away from inpatient infirmary care to care in other settings.

Before the models were estimated, some adjustments to the data were fabricated. Steps were taken to improve ensure that each episode record centered on the focal hospital stay. To reduce the possibility that the focal stay was in fact a rehospitalization, all records that showed a infirmary stay within the 30-day menstruation preceding the focal stay were excluded. Also, records were excluded if subsequent hospitalizations were judged unlikely to have been related to the focal stay. A frequency analysis of chief diagnoses or of principal procedures was performed for rehospitalizations. Diagnoses or procedures with low relative frequencies were causeless to be unrelated to the focal stay. A relative frequency of 0.02 was used every bit a cutoff. This was purely a matter of judgment based on observed distributions. A better approach was not available at the fourth dimension. In any case, this method, though crude, should accept reduced the possibility that a rehospitalization was for an illness unrelated to the focal infirmary stay.

Finally, records were excluded if the patient died in the hospital or within the post-lx-day catamenia. The inclusion of such records would distort the human relationship between case severity and the utilize of post-hospital services. Use would be misleadingly low for the most severe cases (i.e., those who died). Although the exclusion of deaths can also create bias if instance severity is imperfectly measured, it was felt that this was a lesser problem.

Regression results

Tobit regression results for each of the 5 types of mail service-hospital care and for post-infirmary Medicare payments are presented in Tables ii through vii.⁷ Regression models were estimated separately for each of the 4 diagnostic groups.

Table 2

Individual utilize of mail service-hospital skilled nursing facility care: Tobit regression results, 1981-86

Variable	Pneumonia		Stroke		Hip replacement, arthritis		Hip replacement, fracture

	SNF days	SNF timing	SNF days	SNF timing	SNF days	SNF timing	SNF days	SNF timing
Prospective payment system
Risk	—	—	^***25.846	^**.360	—	—	^***13.954	^***.303
Risk	—	—	(ix.034)	(5.633)	—	—	(xv.869)	(14.745)
Financial touch on	—	—	—	—	^*−30.997	^*−1,489	^*−17.029	^*−.38
Financial touch on	—	—	—	—	(3.086)	(3.657)	(3.274)	(3.240)
Case severity
Pre-adm HOSP days	^**2.156	—	—	—	—	—	^**.559	^*.011
Pre-adm HOSP days	(iii.603)	—	—	—	—	—	(4.947)	(3.587)
Pre-adm SNF days	^***.779	^***.024	—	—	^***.746	^***.034	^***.435	^***.008
Pre-adm SNF days	(35.761)	(33.235)	—	—	(13.157)	(14.306)	(31.780)	(xix.824)
Pre-adm HH visits	—	—	—	—	^*.007	—	^***.344	^**.005
Pre-adm HH visits	—	—	—	—	(iii.750)	—	(12.906)	(5.625)
Pre-adm PHYS charges	^***1.731	^***.049	^*.850	—	—	—	^***.518	^***.011
Pre-adm PHYS charges	(eleven.178)	(8.552)	(ii.937)	—	—	—	(vii.106)	(6.482)
Pre-adm DME charges	—	—	—	—	^*.331	^*.012	^***−.613	^***−.012
Pre-adm DME charges	—	—	—	—	(iii.726)	(2.797)	(xix.099)	(13.853)
Need
Historic period	^***1.500	^***.048	^***1.222	^***.020	^***1.649	^***.069	^*** i.025	^***.020
Historic period	(63.853)	(58.333)	(81.856)	(75.918)	(141.290)	(127.154)	(271.895)	(205.912)
Sex activity (one =male)	—	—	^***−7.730	^***−.103	^***−12.866	^***−.590	—	^**−.057
Sex activity (one =male)	—	—	(12.789)	(7.430)	(47.370)	(50.241)	—	(4.010)
RaceA	—	—	—	—	^*−half-dozen.689	—	^**6.344	^**.122
RaceA	—	—	—	—	(2.695)	—	(five.188)	(iii.819)
RaceB	—	—	—	—	^**−14.637	^*−.554	—	^**−.207
RaceB	—	—	—	—	(4.765)	(iii.283)	—	(iv.989)
Income	—	—	—	—	—	—	^***.882	^***.024
Income	—	—	—	—	—	—	(10.768)	(15.738)
Geographic
Isolation	—	—	—	—	—	—	—	—
Rural-Urban Migr	—	—	—	—	^**5.038	^**.188	^**2.944	^*.044
Rural-Urban Migr	—	—	—	—	(5.739)	(4.100)	(5.956)	(2.636)
Supply
HOSP stays per bene	—	—	—	—	^*128.075	—	—	—
HOSP stays per bene	—	—	—	—	(ii.597)	—	—	—
SNF admissions per bene	^***7,649.340	^*279.382	^***8,813.320	^***154.926	^***four,296.330	^***194.112	^***5,715.08	^***133.397
SNF admissions per bene	(35.107)	(3.552)	(89.099)	(89.904)	(38.390)	(40.352)	(203.411)	(217.003)
HH visits per bene	^*−28.225	—	—	—	—	—	—	^***−.399
HH visits per bene	(2.926)	—	—	—	—	—	—	(9.303)
PHYS charges per bene	^**.105	—	—	—	—	—	—	^***.002
PHYS charges per bene	(three.321)	—	—	—	—	—	—	(17.180)
DME charges per bene	—	—	—	—	^*.069	^**.003	^***.092	^*.001
DME charges per bene	—	—	—	—	(3.137)	(3.816)	(16.951)	(three.741)
Hospital
Beds	^*.015	—	—	—	—	—	—	—
Beds	(2.819)	—	—	—	—	—	—	—
Teaching	^*5.152	—	—	—	—	—	—	—
Teaching	(2.893)	—	—	—	—	—	—	—
Number of services	^**−1.858	—	—	^*.018	—	—	—	^*.011
Number of services	(4.111)	—	—	(2.781)	—	—	—	(3.317)
Occupancy	—	—	—	^**−.227	—	—	—	^**−.130
Occupancy	—	—	—	(iv.315)	—	—	—	(3.993)
Profit	—	—	^**8.231	—	—	—	—	—
Profit	—	—	(iii.888)	—	—	—	—	—
Nonprofit	—	—	—	^*.093	—	—	—	—
Nonprofit	—	—	—	(2.899)	—	—	—	—
Tendency	none	none	decreasing	none	none	none	none	decreasing

Tabular array 7

Individual use of post-infirmary Medicare payments: Tobit regression results, 1981-86

Variable	Pneumonia	Stroke	Hip replacement, arthritis	Hip replacement, fracture

	MEDPAY	MEDPAY	MEDPAY	MEDPAY
Prospective payment system
Financial impact	—	^**−1,582.960	^**−932.686	^**−838.192
Financial impact	—	(iv.337)	(4.962)	(5.538)
Risk	—	^**693.479	—	—
Risk	—	(five.415)	—
Case severity
Pre-adm Hosp days	^***lx.033	—	—	—
Pre-adm Hosp days	(ten.511)	—	—	—
Pre-adm SNF days	—	—	—	—
Pre-adm SNF days	—	—	—	—
Pre-adm HH visits	^***31.149	—	^*half dozen.710	^***17.4965
Pre-adm HH visits	(37.835)	—	(3.344)	(20.468)
Pre-adm PHYS charges	^***73.447	^***57.362	^***30.391	^***33.139
Pre-adm PHYS charges	(58.520)	(9.663)	(14.311)	(xviii.785)
Preadm DME charges	^***58.966	^*15.665	^***16.376	^**11.208
Preadm DME charges	(123.655)	(3.642)	(10.937)	(five.978)
Need
Historic period	^**4.185	^***−xi.473	^***22.588	^***fifteen.218
Historic period	(5.339)	(6.982)	(59.861)	(46.470)
Sex (1 = male)	^***117.208	—	—	^**109.934
Sex (1 = male)	(9.418)	—	—	(5.469)
RaceA	^*−176.972	—	—	—
RaceA	(2.776)	—	—	—
RaceB	^*−246.136	—	—	^***386.195
RaceB	(3.695)	—	—	(7.551)
Income	—	—	—	—
Income	—	—	—	—
Geographic
Isolation	—	—	—	—
Isolation	—	—	—	—
Rural-Urban Migr	—	—	^***148.653	^**92.915
Rural-Urban Migr	—	—	(9.185)	(4.322)
Supply
HOSP stays per bene	—	—	—	—
HOSP stays per bene	—	—	—	—
SNF admissions per bene	^**−33,202.600	^***94,281.800	—	^**33,807.700
SNF admissions per bene	(iv.480)	(ix.281)	—	(five.191)
HH visits per bene	—	—	^***588.882	^***1,265.240
HH visits per bene	—	—	(8.263)	(47.941)
PHYS charges per bene	^***two.263	^**3.454	^***3.020	^***2.747
PHYS charges per bene	(nine.228)	(6.365)	(17.010)	(17.022)
DME charges per bene	—	—	^*i.616	^***3.321
DME charges per bene	—	—	(3.052)	(xv.782)
Hospital
Beds	—	—	^**.254	^**.216
Beds	—	—	(half-dozen.324)	(4.213)
Teaching	^***138.642	—	—	—
Teaching	(12.294)	—	—	—
Number of services	—	—	^***−37.075	—
Number of services	—	—	(12.323)	—
Occupancy	^*207.867	—	^***393.367	—
Occupancy	(3.335)	—	(10.864)	—
Profit	—	^**329.327	—	^***255.398
Profit	—	(5.344)	—	(12.977)
Nonprofit	—	—	—	—
Nonprofit	—	—	—	—
Trend	increasing	increasing	increasing	increasing

Effects

PPS affected the level and timing of post-hospital SNF services for stroke, hip replacement (arthritis), and hip replacement (fracture). Mail service-hospital SNF care relating to pneumonia does not seem to have been affected. The coefficients of PPS financial touch on and gamble variables, when meaning, take signs predicted by the conceptual framework. The significance and negative sign of the PPS fiscal impact variable suggests that the incentive to substitute mail service-hospital SNF intendance was dampened or reinforced depending on whether the discharging hospital experienced an increment or decrease in its overall operating ratio. This effect of PPS on both the level and timing of post-infirmary SNF care was observed for hip replacement (arthritis) and hip replacement (fracture). The positive sign of the PPS take chances variable suggests a stronger tendency among hospitals more dependent on Medicare (i.e. at greater take chances) to substitute mail service-hospital SNF intendance for inpatient services. For stroke and hip replacement (fracture), this trend is reflected both in more post-hospital SNF days and in their more immediate use following discharge.

Post-hospital home wellness services were more than weakly affected by PPS. Pneumonia and hip replacement (arthritis) prove results consistent with the conceptual framework. For these diagnoses, only the financial impact variable is significant. Both the level and the timing of mail service-hospital home wellness services were negatively affected when the discharging hospital experienced an increase in its overall operating ratio and positively affected when the hospital experienced a decrease. The level and timing of post-infirmary home health services for stroke patients appear to accept been unaffected by PPS. Although the PPS risk variable is significantly related to the timing of postal service-infirmary home health services for hip replacement (fracture), its coefficient has the wrong sign.

Post-infirmary physician services were most affected past PPS for him replacement (arthritis). For this diagnosis, PPS financial affect and risk variables are pregnant and have the predicted signs for both the level and the timing of these services. The financial bear upon of PPS on the discharging hospital also has the predicted negative upshot on the levels of mail-hospital md care relating to stroke and hip replacement (fracture). For this latter condition, the timing of these services was as well affected in the predicted positive fashion by the take a chance that PPS imposed on the discharging infirmary.

The effects of PPS on postal service-hospital use of DME appear to have been limited to pneumonia. For this condition, all the same, the furnishings of PPS seem strong. PPS financial impact and risk variables are significant and have the predicted signs for both the level and the timing of DME services.

Results for rehospitalizations are the weakest. The only effect of PPS consistent with the conceptual framework was observed for stroke. Here, the PPS run a risk variable is significant and has the predicted positive sign for the timing of rehospitalizations. Although the PPS fiscal impact variable is significantly related to the timing of rehospitalizations for pneumonia, its coefficient has the wrong sign.

Post-hospital Medicare payments for stroke, hip replacement (arthritis), and hip replacement (fracture) were significantly affected by PPS. All observed effects are in the management predicted by the conceptual framework. For stroke, the PPS financial impact variable and the run a risk variable are both significant, whereas only the financial bear on variable is significant for hip replacement (arthritis) and hip replacement (fracture). PPS does not appear to have affected mail-hospital Medicare payments for pneumonia.

The observed furnishings of PPS on the level and timing of postal service-infirmary services are generally consistent with the conceptual framework. That pregnant effects were not observed in all instances does not undermine the validity of these findings. Each diagnosis and post-hospital service represents a unlike population for which PPS effects can be measured. It is not unreasonable to assume that the substitutability of a post-hospital service for inpatient hospital intendance and, therefore, the ability of PPS to influence this substitution varies depending on the diagnosis and type of service.

Conclusion

The author investigated the effects of PPS and other factors on the use of post-hospital care. Individual use of each of several types of post-hospital care was analyzed separately for iv diagnostic groups. Effects on both the level and the timing of use were estimated for a 60-day period following discharge from the hospital.

Of those studied, the types of mail service-infirmary care that announced to have been near affected past PPS are SNF and md services. Considerably weaker effects were observed for dwelling wellness services and DME. Results for rehospitalizations showed very little influence of PPS. The furnishings of PPS also varied depending on diagnosis. For instance, significant furnishings of PPS on post-hospital utilize of DME were observed for pneumonia just, and these announced to have been adequately potent (as gauged by consistency with the conceptual framework). More importantly, the effects of PPS on overall mail-hospital use, as measured past mail-hospital Medicare payments, likewise varied with diagnosis. The strongest overall furnishings were observed for stroke, whereas no effects were observed for pneumonia.

As predicted by the conceptual framework, the use of post-hospital care was well-nigh always negatively related to the fiscal bear upon of PPS on the discharging infirmary and positively related to PPS risk faced by that infirmary (gauged by its Medicare dependence). These effects were observed for both the level and the timing of post-hospital services. The fact that PPS did not take a pregnant effect on some diagnoses and types of mail-infirmary intendance is quite reasonable and does not undermine the validity of the findings. Post-hospital care is unlikely to exist equally substitutable for hospital inpatient care in all instances.

Because of its potentially conflicting influences on the discharging hospital, the direction of the overall impact of PPS on the apply of postal service-hospital care is difficult to predict. During the initial years of PPS, most hospitals experienced Medicare gains, increasing their overall operating ratio. For this reason, the financial bear on of PPS may accept been, on balance, to dampen the exchange of post-hospital services for hospital inpatient intendance. To some extent, these effects were countered by the positive influence of PPS risk. Medicare margins, nevertheless, take been steadily falling nether PPS from their initial high levels. Every bit more hospitals feel smaller Medicare gains or experience losses, the financial impact of PPS will tend to work more in the same management every bit its risk issue. Consequently, the overall impact of PPS would likely be to increase the utilize of post-hospital services.

Other influences on the use of postal service-hospital care have been revealed in this study. Some of these, such as the unremarkably positive furnishings of age and case severity (gauged by pre-admission service employ) are obvious. Amidst the more noteworthy findings is the tendency of beneficiaries who migrate to more urban locations for their hospital care to have greater admission to post-hospital services. Also of significance are the positive furnishings of a discharging infirmary'due south occupancy rate and for-profit status and the substitutability and complementarity observed amongst specific types of post-infirmary intendance.

Finally, it is cautioned that the four diagnostic groups studied may not be representative of the entire population of Medicare hospital discharges. This limits the generalizability of the findings. Nonetheless, it is doubtful that an assay based on other diagnostic groups would yield radically different results, peculiarly with respect to the nature of the influence of PPS on the mail-hospital care.

Definition of terms

SNF days: Post-hospital skilled nursing facility days (as defined in text).
SNF timing: Post-hospital skilled nursing facility timing (equally defined in text.).
HH visits: Post-hospital home health visits.
HH timing: Postal service-hospital home wellness timing.
PHYS chrgs: Postal service-hospital physician charges.
PHYS timing: Post-infirmary physical timing.
DME chrgs: Post-infirmary durable medical equipment charges.
DME timing: Post-hospital durable medical equipment timing.
RHSP days: Mail-hospital rehospitalization days.
RHSP timing: Mail-hospital rehospitalization timing.
MEDPAY: Mail service-hospital Medicare payments.
Financial impact: The financial impact imposed by PPS on the discharging hospital.
Risk: The risk imposed past PPS on the discharging hospital.
Pre-adm HOSP days: Number of hospital days used by each beneficiary during the xxx-day period preceding the focal infirmary stay (adjusted for differences in supply).
Pre-adm SNF days: Number of skilled nursing facility days used by each beneficiary during the xxx-twenty-four hours period preceding the focal hospital stay (adjusted for differences in supply).
Pre-adm HH visits: Number of home wellness visits used past each beneficiary during the 30-twenty-four hours period preceding the focal hospital stay (adjusted for differences in supply).
Pre-adm PHYS charges: Doctor charges incurred past each casher during the 30-24-hour interval catamenia preceding the focal hospital stay (adjusted for differences in supply and cross-sectional and temporal price differences).
Pre-adm DME charges: Durable medical equipment charges incurred by each beneficiary during the 30-mean solar day period preceding the focal hospital stay (adjusted for differences in supply and temporal differences in prices).
Historic period: Beneficiary historic period at time of focal infirmary stay.
Sex: Beneficiary sex.
RaceA: A binary variable taking a value of 1 if casher is white, and 0 if beneficiary is blackness or of other race.
RaceB: A binary variable taking a value of 1 if beneficiary is black, and 0 if beneficiary is white or of other race.
Income: Per capita income of county in which beneficiary resides.
Isolation: Appalachian Regional Commission lawmaking for beneficiary'south county of residence.
Rural-Urban Migr: Ratio of Appalachian Regional Commission codes for casher's county and discharging hospital's canton.
HOSP stays per bene: Annual number of hospital stays per Medicare casher for the BEAA in which each beneficiary resides.
SNF admissions per bene: Annual number of skilled nursing facility admissions per Medicare casher for the Country in which each beneficiary resides.
HH visits per bene: Almanac number of home health visits per Medicare beneficiary for the BEAA in which each casher resides.
PHYS charges per bene: Annual physician charges per Medicare beneficiary for the State in which each beneficiary resides.
DME charges per bene: Annual durable medical equipment charges per Medicare beneficiary for the Land in which each beneficiary resides.
Beds: The bed complement of the discharging infirmary.
Instruction: A binary variable taking a value of ane if the discharging hospital is a teaching institution and 0 otherwise.
Number of services: Number of medical services offered past the discharging hospital.
Occupancy: The occupancy rate of the discharging hospital.
Profit: A binary variable taking a value of 1 if the discharging hospital is for profit and 0 if nonprofit or government.
Nonprofit: A binary variable taking a value of 1 if the discharging hospital is nonprofit and 0 if for turn a profit or government.
Trend: A set of five binary variables to reflect the years 1981-86.

Tabular array 3

Individual use of mail service-hospital home health services: Tobit regression results, 1981-86

Variable	Pneumonia		Stroke		Hip replacement, arthritis		Hip replacement, fracture

	HH visits	HH timing	HH visits	HH timing	HH visits	HH timing	HH visits	HH timing
Prospective payment system
Financial bear on	^**−20.238	^***−.965	—	—	^***−23.261	^***−.762	—	—
Financial bear on	(5.182)	(9.307)	—	—	(11.813)	(12.116)	—	—
Run a risk	—	—	—	—	—	—	—	^*−. 116
Run a risk	—	—	—	—	—	—	—	(three.337)
Instance severity
Pre-adm HOSP days	^***.987	^***.032	—	—	—	—	^***−.655	^***−.015
Pre-adm HOSP days	(7.412)	(6.586)	—	—	—	—	(12.450)	(7.192)
Pre-adm SNF days	—	—	—	—	—	^*−.014	—	^**−.006
Pre-adm SNF days	—	—	—	—	—	(2.881)	—	(iv.472)
Pre-adm HH visits	^***one.551	^***.040	^***.931	^***.017	^***.386	^***.005	^***.544	^***.009
Pre-adm HH visits	(396.528)	(221.815)	(104.007)	(75.593)	(59.152)	(12.291)	(ninety.467)	(28.457)
Pre-adm PHYS charges	—	—	—	—	—	—	—	—
Pre-adm PHYS charges	—	—	—	—	—	—	—	—
Pre-adm DME charges	^***.695	^***.019	^***.438	^***.008	^**.443	^***.011	^***.457	^***.013
Pre-adm DME charges	(56.878)	(35.715)	(22.919)	(fifteen.352)	(35.360)	(20.608)	(42.010)	(43.386)
Demand
Historic period	^***.402	^***.013	^**.003	^***.467	^***.009	—	^***−.185	^***−.009
Historic period	(75.348)	(65.412)	(5.311)	(85.683)	(35.561)	—	(24.080)	(72.071)
Sex (1 =male)	—	—	^**−2.136	—	^***−9.105	^***−.185	—	—
Sex (1 =male)	—	—	(5.218)	—	(166.851)	(67.176)	—	—
RaceA	—	—	—	—	—	—	—	—
RaceA	—	—	—	—	—	—	—	—
RaceB	—	—	^**6.856	^***.257	^***half dozen.590	^*.149	^***8.948	^**.131
RaceB	—	—	(6.141)	(14.216)	(7.213)	(3.499)	(xv.149)	(iv.029)
Income	—	—	—	—	^*.323	^***.018	—	—
Income	—	—	—	—	(two.854)	(8.395)	—	—
Geographic
Isolation	—	—	—	—	—	—	—	^*.008
Isolation	—	—	—	—	—	—	—	(2.672)
Rural-Urban Migr	^***3.288	^***.127	^*1.934	—	^***ii.367	^***.076	^***2.576	^***.057
Rural-Urban Migr	(viii.206)	(9.827)	(2.631)	—	(viii.330)	(eight.243)	(11.346)	(half-dozen.719)
Supply
HOSP stays per bene	^***−169.728	^***−6.945	—	—	^***−134.003	^***−iii.573	^**−62.702	^**−2.122
HOSP stays per bene	(xv.992)	(21.690)	—	—	(sixteen.966)	(11.407)	(4.628)	(6.401)
SNF admissions per bene	—	—	—	—	—	—	—	^*−12.531
SNF admissions per bene	—	—	—	—	—	—	—	(2.836)
HH visits per bene	^***34.336	^***1.013	^***46.647	^***.828	^***fifty.347	^***1.187	^***47.153	^***.642
HH visits per bene	(61.557)	(43.919)	(100.322)	(56.568)	(213.299)	(111.810)	(240.510)	(53.464)
PHYS charges per bene	^***.049	^***.001	^***.071	^***.001	^**.104	^***.003	^***.107	^***.002
PHYS charges per bene	(8.162)	(7.133)	(xvi.622)	(9.184)	(69.034)	(62.470)	(94.380)	(l.731)
DME charges per bene	—	—	—	—	—	—	—	—
DME charges per bene	—	—	—	—	—	—	—	—
Hospital
Beds	—	—	^***.007	—	—	^*−.0001	^***.010	^***.0002
Beds	—	—	(eight.800)	—	—	(six.234)	(34.473)	(thirteen.229)
Teaching	—	—	—	^**.041	—	—	^*−.993	—
Teaching	—	—	—	(4.351)	—	—	(two.997)	—
Number of services	—	—	—	—	^**−.439	—	^***−.679	^**−.012
Number of services	—	—	—	—	(v.996)	—	(14.986)	(v.518)
Occupancy	—	—	^**five.578	—	^***9.350	^***.249	^***vii.993	^***.169
Occupancy	—	—	(4.112)	—	(21.718)	(xiv.566)	(xix.337)	(10.411)
Profit	—	—	^**iii.569	—	—	^*.079	^**2.338	—
Profit	—	—	(3.919)	—	—	(ii.870)	(3.917)	—
Nonprofit	—	—	—	—	^***−iii.714	^**−.077	—	—
Nonprofit	—	—	—	—	(11.124)	(iv.597)	—	—
Trend	Increasing	Increasing	Increasing	Increasing	Increasing	Increasing	Increasing	Increasing

Table 4

Private utilise of post-hospital physician services: Tobit regression results, 1981-86

Variable	Pneumonia		Stroke		Hip replacement, arthritis		Hip replacement, fracture

	PHYS chrgs	PHYS timing	PHYS chrgs	PHYS timing	PHYS chrgs	PHYS timing	PHYS chrgs	PHYS timing
Prospective payment organization
Financial touch on	—	—	^*−59.546	—	^***−246.426	^***−.548	^***−190.839	—
Financial touch on	—	—	(iii.073)	—	(9.931)	(seven.912)	(18.246)	—
Risk	—	—	—	—	^*49.002	^**.173	—	^*.075
Risk	—	—	—	—	(2.792)	(v.602)	—	(2.879)
Example severity
Pre-adm HOSP days	—	—	—	—	—	—	^**2.620	—
Pre-adm HOSP days	—	—	—	—	—	—	(four.687)	—
Pre-adm SNF days	—	—	—	—	—	^**.007	—	—
Pre-adm SNF days	—	—	—	—	—	(4.211)	—	—
Pre-adm HH visits	^**−1.047	^*−.003	^*−.753	—	—	^*−.003	—	—
Pre-adm HH visits	(5.126)	(3.599)	(iii.695)	—	—	(2.584)	—	—
Pre-adm PHYS charges	^***25.490	^***.038	^***16.673	^***.041	^***30.948	^***.047	^***20.941	^***.030
Pre-adm PHYS charges	(i,035.67)	(217.572)	(415.209)	(211.101)	(489.033)	(195.769)	(512.178)	(149.140)
Pre-adm DME charges	—	—	^**.944	—	—	—	—	—
Pre-adm DME charges	—	—	(6.170)	—	—	—	—	—
Demand
Age	—	—	^*−.337	—	^***1.589	^***.005	^*.523	^***.003
Age	—	—	(2.915)	—	(viii.606)	(14.258)	(3.482)	(18.002)
Sex (1 =male)	^**−half dozen.898	^**−.021	—	—	—	—	^**−fourteen.372	^***−.071
Sex (1 =male)	(four.526)	(3.840)	—	—	—	—	(five.893)	(nineteen.286)
RaceA	—	—	^**−21.664	—	—	—	—	—
RaceA	—	—	(five.757)	—	—	—	—	—
RaceB	—	—	^***−35.432	—	—	—	—	—
RaceB	—	—	(12.019)	—	—	—	—	—
Income	—	—	—	—	^**4.442	—	^**2.840	^*.006
Income	—	—	—	—	(iv.156)	—	(5.042)	(2.795)
Geographic
Isolation	—	^**−.006	—	—	—	^***.015	—	—
Isolation	—	(4.722)	—	—	—	(ten.075)	—	—
Rural-Urban Migr	—	—	—	—	—	^***.075	—	—
Rural-Urban Migr	—	—	—	—	—	(10.671)	—	—
Supply
HOSP stays per bene	^***−507.087	^***−i.709	^***−574.391	^***−i.588	—	^***−3.379	^***−ane,054.06	^***−2.559
HOSP stays per bene	(12.053)	(12.666)	(13.373)	(viii.277)	—	(15.081)	(214.229)	(19.411)
SNF admissions per bene	—	—	—	—	—	—	—	—
SNF admissions per bene	—	—	—	—	—	—	—	—
HH visits per bene	^***−43.714	—	^*−28.980	^**−.130	—	—	—	—
HH visits per bene	(7.076)	—	(ii.955)	(4.869)	—	—	—	—
PHYS charges per bene	^***.716	^***.001	^***.674	^***.001	^***.961	^***.001	^***.612	^*.0004
PHYS charges per bene	(128.916)	(45.221)	(116.993)	(15.222)	(49.984)	(4.120)	(54.476)	(3.641)
DME charges per bene	^**.168	^*.0004	^***.329	^***.001	^***.692	—	^***.345	^*.0005
DME charges per bene	(5.176)	(iii.559)	(18.438)	(11.617)	(16.230)	—	(10.946)	(3.776)
Hospital
Beds	—	^***−.0001	—	—	^*.032	—	^*.023	—
Beds	—	(8.985)	—	—	(2.956)	—	(three.200)	—
Educational activity	^**6.970	^**.024	—	^**−.023	^**12.599	—	—	—
Educational activity	(four.322)	(5.123)	—	(four.705)	(three.942)	—	—	—
Number of services	—	—	—	—	—	—	^***−4.002	^**−.008
Number of services	—	—	—	—	—	—	(nine.356)	(5.192)
Occupancy	—	—	—	^*.061	—	—	^**26.173	—
Occupancy	—	—	—	(3.298)	—	—	(3.737)	—
Turn a profit	—	—	—	—	^*29.366	—	—	^***.062
Turn a profit	—	—	—	—	(3.268)	—	—	(6.673)
Nonprofit	^*viii.142	—	^**10.290	—	—	^*−.049	—	^*.033
Nonprofit	(3.407)	—	(4.764)	—	—	(2.709)	—	(3.348)
Trend	Increasing	Increasing	Increasing	Increasing	Increasing	Increasing	Increasing	Increasing

Table 5

Individual use of post-hospital durable medical equipment: Tobit regression results, 1981-86

Variable	Pneumonia		Stroke		Hip replacement, arthritis		Hip replacement, fracture

	DME chrgs	DME timing	DME chrgs	DME timing	DME chrgs	DME timing	DME chrgs	DME timing
Prospective payment system
Fiscal impact	^**−308.213	^**−.454	—	—	—	—	—	—
Fiscal impact	(3.885)	(5.657)	—	—	—	—	—	—
Adventure	^**3.761	^*.004	—	—	—	—	—	—
Adventure	(3.988)	(three.706)	—	—	—	—	—	—
Case severity
Pre-adm HOSP days	^***21.528	^***.027	—	—	—	^**−.010	—	^**−.010
Pre-adm HOSP days	(x.120)	(11.549)	—	—	—	(5.266)	—	(four.599)
Pre-adm SNF days	—	—	—	—	—	—	^**−ane.760	^*−.004
Pre-adm SNF days	—	—	—	—	—	—	(five.104)	(3.323)
Pre-adm HH visits	^***7.310	^***.011	^***4.413	^***.009	—	—	^***1.719	—
Pre-adm HH visits	(20.402)	(33.245)	(13.446)	(20.465)	—	—	(8.334)	—
Pre-adm PHYS charges	^*6.436	—	—	—	^**ii.865	^*.008	—	—
Pre-adm PHYS charges	(2.751)	—	—	—	(four.312)	(3.354)	—	—
Pre-adm DME charges	^***66.803	^***.051	^***25.010	^***.030	^***15.996	^***.047	^***19.279	.034
Pre-adm DME charges	(ane,594.030)	(640.147)	(499.376)	(228.454)	(490.012)	(363.651)	(896.166)	(357.854)
Demand
Age	^***two.590	^***.003	^***ii.723	^***.006	—	—	^***−i.483	^***−.008
Age	(xi.000)	(xiii.754)	(13.222)	(19.607)	—	—	(16.375)	(57.245)
Sex (1 = male)	^***43.547	^**.038	^**−30.382	—	^**−30.018	^***−.099	—	^***−.057
Sex (1 = male)	(7.313)	(4.062)	(v.819)	—	(17.941)	(16.922)	—	(six.735)
RaceA	—	—	—	—	—	—	^*30.186	—
RaceA	—	—	—	—	—	—	(three.160)	—
RaceB	—	—	^***119.625	^***.237	—	—	^***98.003	—
RaceB	—	—	(9.685)	(ten.574)	—	—	(xviii.507)	—
Income	—	—	—	—	—	—	—	^*.009
Income	—	—	—	—	—	—	—	(3.369)
Geographic
Isolation	^***−15.616	^***−.017	^***−13.692	^***−.024	—	—	^***−v.578	^***−.013
Isolation	(13.755)	(11.981)	(15.897)	(14.191)	—	—	(11.779)	(vii.913)
Rural-Urban Migr	—	—	—	—	^***2.367	—	^*12.997	—
Rural-Urban Migr	—	—	—	—	(viii.330)	—	(three.055)	—
Supply
HOSP stays per bene	—	—	^**1,373.100	^**2.337	—	—	^**593.332	—
HOSP stays per bene	—	—	(5.311)	(iv.435)	—	—	(4.426)	—
SNF admissions per bene	—	—	—	—	^*four,779.050	—	—	^***−20.008
SNF admissions per bene	—	—	—	—	(ii.694)	—	—	(8.483)
HH visits per bene	^**−176.444	^**−.197	—	—	^***132.596	^***.463	—	—
HH visits per bene	(four.579)	(4.048)	—	—	(fourteen.026)	(fourteen.605)	—	—
PHYS charges per bene	^***.889	^***.001	^***.824	^***.001	^***.764	^***.003	^***.947	^***.002
PHYS charges per bene	(8.199)	(ix.716)	(12.383)	(nine.292)	(34.408)	(40.562)	(74.351)	(44.368)
DME charges per bene	^***2.264	^***.002	^***ane.648	^***.003	^***1.733	^***.005	^***1.534	^***.003
DME charges per bene	(37.066)	(26.948)	(32.551)	(24.078)	(113.882)	(80.192)	(123.688)	(seventy.966)
Hospital
Beds	—	^*−.0001	—	—	—	^*.0001	—	^*.0001
Beds	—	(2.768)	—	—	—	(three.099)	—	(2.904)
Pedagogy	^***46.432	^***.052	—	—	^*−11.178	—	^**−11.271	^**−.033
Pedagogy	(7.438)	(6.729)	—	—	(3.422)	—	(three.856)	(four.233)
Number of services	—	—	—	—	—	^*−.011	^**−iv.301	—
Number of services	—	—	—	—	—	(2.958)	(six.217)	—
Occupancy	—	—	—	—	—	^***.189	^**34.808	—
Occupancy	—	—	—	—	—	(six.811)	(iii.849)	—
Profit	—	—	—	—	—	—	—	—
Profit	—	—	—	—	—	—	—	—
Nonprofit	—	—	—	—	—	^***−.146	—	—
Nonprofit	—	—	—	—	—	(xiv.659)	—	—
Trend	Increasing	None	Increasing	None	Increasing	Increasing	Increasing	Increasing

Table 6

Private utilize of mail service-hospital rehospitalization days: Tobit regression results, 1981-86

Variable	Pneumonia		Stroke		Hip replacement, arthritis		Hip replacement, fracture

	RHSP days	RHSP timing	RHSP days	RHSP timing	RHSP days	RHSP timing	RHSP days	RHSP timing
Prospective payment organization
Financial impact	—	^*1.001	—	—	—	—	—	—
Financial impact	—	(2.817)	—	—	—	—	—	—
Risk	—	—	—	^**.356	—	—	—	—
Risk	—	—	—	(4.148)	—	—	—	—
Example severity
Pre-adm HOSP days	^***i.136	—	—	—	^**.334	—	^**.558	—
Pre-adm HOSP days	(15.161)	—	—	—	(3.811)	—	(five.764)	—
Pre-adm SNF days	—	—	—	—	—	—	—	—
Pre-adm SNF days	—	—	—	—	—	—	—	—
Pre-adm HH visits	—	—	—	—	—	—	—	—
Pre-adm HH visits	—	—	—	—	—	—	—	—
Pre-adm PHYS charges	^***.433	^**.027	—	—	—	—	—	—
Pre-adm PHYS charges	(7.720)	(three.986)	—	—	—	—	—	—
Pre-adm DME charges	^***.414	^***.020	—	^*−.012	—	—	—	—
Pre-adm DME charges	(27.398)	(8.517)	—	(2.951)	—	—	—	—
Need
Age	—	—	^***−.009	^***.309	—	—	^**.118	—
Age	—	—	(eleven.855)	(13.618)	—	—	(iii.979)	—
Sexual practice (1 =male person)	^***2.242	^**.131	—	—	^***four.085	^***.219	^***four.227	^*.130
Sexual practice (1 =male person)	(x.866)	(4.326)	—	—	(13.454)	(vii.111)	(12.660)	(ii.663)
RaceA	—	—	—	—	—	—	—	—
RaceA	—	—	—	—	—	—	—	—
RaceB	^**−4.680	—	—	—	—	—	—	—
RaceB	(4.078)	—	—	—	—	—	—	—
Income	—	^*−.037	—	—	^**−.734	^**−.052	—	—
Income	—	(3.406)	—	—	(four.841)	(4.596)	—	—
Geographic
Isolation	—	—	—	—	—	—	—	—
Isolation	—	—	—	—	—	—	—	—
Rural-Urban Migr	—	—	—	—	^**2.925	^**.211	—	^**−.164
Rural-Urban Migr	—	—	—	—	(4.314)	(4.050)	—	(four.556)
Supply
HOSP stays per bene	—	—	—	—	—	—	^*86.211	^*4.842
HOSP stays per bene	—	—	—	—	—	—	(three.750)	(two.735)
SNF admissions per bene	^*−504.295	—	—	—	—	^**−67.174	—	—
SNF admissions per bene	(3.220)	—	—	—	—	(3.831)	—	—
HH visits per bene	—	—	—	—	—	—	^***17.947	^**.748
HH visits per bene	—	—	—	—	—	—	(fourteen.240)	(5.567)
PHYS charges per bene	—	—	—	—	—	—	—	—
PHYS charges per bene	—	—	—	—	—	—	—	—
DME charges per bene	—	—	—	—	—	—	—	—
DME charges per bene	—	—	—	—	—	—	—	—
Hospital
Beds	^**−.005	^**−.0004	—	—	—	—	—	—
Beds	(iv.727)	(iv.350)	—	—	—	—	—	—
Teaching	—	—	—	—	—	—	—	—
Teaching	—	—	—	—	—	—	—	—
Number of services	—	—	—	—	^*−.509	—	—	—
Number of services	—	—	—	—	(2.931)	—	—	—
Occupancy	^*3.481	—	—	—	^**viii.151	—	—	—
Occupancy	(ii.924)	—	—	—	(5.751)	—	—	—
Profit	—	—	—	—	—	—	—	—
Profit	—	—	—	—	—	—	—	—
Nonprofit	—	—	^**−2.942	^**−.132	—	—	—	—
Nonprofit	—	—	(3.825)	(iv.846)	—	—	—	—
Trend	None	None	None	None	Increasing	Increasing	None	None

Footnotes

Reprint requests: Frank D. Gianfrancesco, Ph.D., 19008 Whetstone Circumvolve, Gaithersburg, Maryland 20879.

^oneA hospital's overall operating ratio is calculated as (total revenue – total cost)/total revenue, and the increase or decrease in this ratio attributable to a Medicare gain or loss is (Medicare revenue – Medicare cost)/total acquirement.

²This behavior is consistent with risk aversion.

^threeBeneficiaries represented in the MEDPAR extract file correspond to those in other 5-per centum files.

⁴Hateful mail service-hospital clinic and emergency room visits were mostly betwixt 0.05 and 0.10. This is unrealistic and sharply contrasts with the mean values observed for other post-hospital services (Tabular array 1).

⁵The impact of PPS on each hospital's overall operating ratio is (Medicare revenue – Medicare cost)/full revenue. Medicare revenues were based on payments that would be under the fully implemented PPS and on pre-PPS (TEFRA period) patient volumes, eastward.g., lengths of stay. Medicare costs reflect pre-PPS patient volumes and costs per day. The use of pre-PPS values allows for a more authentic assessment of the financial touch of PPS because it adjusts for hospital behavioral responses to PPS that would obscure this effect.

^viThe incentive of for-profit hospitals is obvious. PPS revenues are undiminished if post-infirmary care is substituted for some inpatient services, thus enabling them to earn higher profits.

⁷ R ² for the several regression equations was generally depression, ranging below 0.20. This is not unusual for very big samples of individual-level data. Undoubtedly, almost of the variation in mail service-infirmary use of health care services is explained by individual characteristics for which measures were unavailable.

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