Sot. Sci. Med. Vol. 20, No. 8. pp. 861467. 1989 Printed in Great Britain. All rights reserved

0277-9536/89 $3.00 + 0.00 Copyright Q 1989 Pergamon Press pk

THE IMPACT OF PROSPECTIVELY SET HOSPITAL BUDGETS ON PSYCHIATRIC ADMISSIONS

RICHARD G. FRANK and CATHERINE A. JACKSON

Health Services Research & Development Center, The Johns Hopkins University, School of Hygiene, 624 N Broadway, Baltimore, MD 21205, U.S.A.

Abstract-This article examines the impact of prospectively set hospital budgets on rates of admission of psychiatric patients in New York state, U.S.A. The analysis takes advantage of a natural experiment which took place in the early 1980s. whereby a geographic region adopted a prospective hospital budget reimbursement scheme that differed from the prospective per diem reimbursement scheme used in the rest of the state. The results indicate a strong decrease in psychiatric admissions attributable to the experimental payment method.

Key t+ords-prospective payment, psychiatric care, hospitals, economics

INTRODUCTION

The decade of the 1980s is one in which third party payers for health care have ambitiously pursued new approaches to financing hospital services. Per diem prospective rate setting, per case prospective payment, capitation funding and prospectively set hospital budgets are among those reimbursement systems currently being used in the health care sector. The focus of this research is on the relative impact of two of these reimbursement methods on psychiatric admissions to general hospitals. Specifically, we compare the impact of admissions of prospectively set hospital budgets to that of prospectively set per diem rates.

This comparison is particularly useful since a number of payment systems have been compared to prospective per diem rates [I]. For example, per case prospective payment, an increasingly widely adopted reimbursement method, has been compared to the per diem rate approach. Under per case prospective payment, the incentive to increase admissions has raised concern among regulators. Prospectively set hospital budgets have been proposed as an alter- native system which reduces incentives to admit patients as well as reducing episode costs [2]. For these reasons, we focus on the effect of prospectively set hospital budgets on psychiatric admissions as compared to prospectively set per diem rates.

Research to date on the application of prospective payment to psychiatric inpatient care has found that constraints on reimbursement have led to strong responses on the part of hospitals [3,4]. Evidence suggests that some hospital responses may be un- desirable; specifically, readmission rates appear to be higher under per case prospective payment than under a per service payment system, and limits on length of stay appear to lead to increased numbers of transfers to public mental hospitals.

The research reported here takes advantage of a natural experiment to investigate the magnitude of the response of admissions to prospectively set

budgets. The experimental payment system studied is in effect in the Finger Lakes Region of New York state. Other rural counties in the state serve as control counties. The data used cover one year prior to the start of the experiment (1980) and extend 4 years under the experimental payment system (1981-1984). The study can be viewed as a pretest-post-test control group design because we have data for the study counties for periods before and after the payment system change, and data for the control countries during the same time period.

The rest of the paper is organized into four sec- tions. The second section provides background into the Finger Lakes Hospital Experimental Payment Program (FLHEP) and discusses conceptual issues underlying the empirical model used in the analysis. The third section reviews the empirical approach taken including a description of the data set used and issues related to the specification of the regression models. The fourth section presents the results of the statistical analysis. The last section is a discussion of the findings.

BACKGROUND

The Finger Lakes experiment

The Finger Lakes Hospital Experimental Payment (FLHEP) Program was implemented in January 1981. FLHEP applies to 8 hospitals located in 4 counties in the Finger Lakes region of New York [S]. The area is rural and characterized by a relatively low density of population. Seven of the 8 hospitals paid under the FLHEP program are private nonprofit and one is publicly owned. These hospitals are largely involved with the provision of primary and secondary care. Tertiary care is available in nearby Rochester and Syracuse. The FLHEP program is administered by a free standing corporation organized for the purpose of cooperative health planning in the region. This corporation is known as the Finger Lakes

861

862 RICHARD G. FRANK and CATHERIM A. JACKSON

Area Hospital Corporation (FLAHC). The FLAHC board of directors includes officers from each of the participating hospitals as well as nonvoting members of the health insurance and health planning (HSA) community.

The details of the FLHEP reimbursement formula have been described in considerable detail elsewhere [6]. The FLHEP system affects all payers including Medicare and Medicaid. The system involves a single overall total payment limit for the entire geographic area which is annually adjusted for inflation plus an additional l-2% for new projects, increased in- tensity and population growth or aging. Individual hospitals have their overall revenues determined in advance by FLHEP, but increases in utilization above projected levels can yield increased revenues through a volume adjusted formula. The extent of cost increases are limited however, and increases or decreases in volume for the entire FLAHC do not affect the total payments to all hospitals. As a result, there may be a modest incentive for an individual hospital to maintain or increase volumes, although this is coupled with strong pressure to decrease volumes as a group. Thus, given the rate of inflation and no significant increases in inpatient volumes budgets were predetermined [6, p. 2031.

The comparison intervention is the New York State hospital rate setting program which has been in operation since 1970. Since 1977 it has affected all payers for hospital care. Up until 1982 a single overall per diem rate was set for all patients. This rate was revised annually on the basis of past cost experience and inflation projections. After 1982 overall per diem rates were set but variations from those rates for major payers could occur based on their enrollees’ actual use of services. Payments to hospitals could be reduced by occupancy and length of stay penalties. Thus, the New York System was essentially a per diem system with some utilization controls. One important point to emphasize is that during the early 1980s the State of New York was seriously concerned with excess hospital capacity and was in the process of formulating policy that would penalize hospitals for low occupancy rates (such a formula went into effect in 1985). This means that there were some incentives during the study period for all hospitals to maintain sufficient levels of admissions so as to avoid future penalties. Thus, this should not differentially impact the experimental and control hospitals, since both FLHEP and comparison hospitals were under pressure to maintain occupancy.

Conceptual issues

Under prospectively set budgets, there is no revenue loss due to decreased admissions to an individual hospital. Since experimental hospitals receive no marginal revenues from increased volumes of service, experimental hospitals are expected to discourage admissions and, where possible, shift patients to less expensive treatment settings. In contrast, hospitals under per diem rate setting lose revenues from decreased admissions (although less than under a per case system), and gain marginal revenues from additional admissions. For this reason we expect that there will be a significant decrease in psychiatric admissions attributable to FLHEP when

compared to the volume of psychiatric admissions in non-FLHEP counties. It should be pointed out that hospitals also gain revenues under a per diem rate system by lengthening hospital stays, so long as marginal revenues exceed marginal costs. Given the imposition of length of stay penalties in New York during the 1980s this is a less attractive strategy for hospitals to adopt than under a ‘pure’ per diem rate setting system. This furnishes an additional reason for having an interest in admission rates.

The analysis presented here focuses on psychiatric care provided by acute care general hospitals. Roughly 50% of all psychiatric discharges in the United States are from nonfederal general hospitals (71. While there is considerable controversy regarding the optimal use of inpatient psychiatric care, the predominant view is that acute care hospitalizations are an important element of the mental health service system. Rapid return to the community is also gener- ally considered an important goal of treatment [8]. A reduction in psychiatric admissions to general hospi- tals that is attributable to a payment system can come about for a number of reasons. First, patients who might have been hospitalized under the per diem rate method may be managed on an outpatient basis under fixed budget funding. A second possibility is that patients may be more likely to be referred to a public mental hospital under the fixed budget pay- ment approach leading to reduced general hospital admissions. Third, patients may be encouraged by a hospital to seek care outside the Finger Lakes region in a hospital that offers tertiary care and is payed under the per diem system. Finally, individuals in need of psychiatric care may not receive appropriate care. The second and fourth explanations may imply undesirable outcomes.

Psychiatric admissions may be particularly re- sponsive to payment incentives because of the wide variety of clinical approaches to inpatient psychiatric treatment. There are a large number of competing technologies and ideologies in the area of psychiatric care. These competing approaches imply vastly different admission criteria and treatment regimens [8]. This clinical heterogeneity may allow financial concerns to take on greater importance than they would where clinical approaches are most generally agreed upon.

The view of psychiatric admissions adopted here is that they are influenced by the characteristics of the population (risk factors), the nature of the market for mental health services and the method of hospital payment. Since our principal concern is with hospitals’ willingness to provide care under differing payment arrangements, we focus our attention on the payment variable and include other factors largely to control for possible confounding influences. The resulting model of admissions can be expressed as:

A = F(P, X, M) (1)

where P is an indicator of provider payment method; X is a vector of consumer characteristics (including insurance information); and M is a vector of characteristics of the health care market place. The empirical work presented below uses equation (1) to assess the impact of the prospectively set hospital budgets on psychiatric admissions.

The impact of prospectively set hospital budgets 863

EMPIRICAL IMPLE.MENTATION

Data

The analysis focuses on rates of psychiatric admis- sions (MDCl9) using the county as the unit of observation. We choose the county because we are interested in the impact of payment systems on the overall use of hospital services, not the use of any particular hospital. The data used in this analysis are based on the four FLHEP counties and a sample of 30 nonmetropolitan counties in New York State. The method of choosing comparison counties involved the construction of a 95% confidence interval around the mean percentage of the population living in an urban area in the FLAHC counties. The mean per- centage of urbanization in the 4 FLAHC counties was 29%. The inclusion criteria was the range of 17-50% urbanized. Counties falling above or below this range were excluded from the comparison group. Use of this criterion included 30 countries and elim- inated all the major cities in New York (New York City, Albany, Syracuse, Rochester, Buffalo, etc.) [9].

Our data base combined information from six main sources: the SPARCS discharge abstract data set from the State of New York, the American Hospital Association’s (AHA) Annual Survey, the Area Resource File (ARF), the American Medical Association (AMA), the American Psychological As- sociation’s Directory of Professional Psychologists, and Sales and Marketing Management, Inc. Defi- nitions of variables used in the analysis, the source of the information for each variable, and the mean and standard deviation of the variable are displayed in Table 1. This information was available for the 34 counties over 5 years with the resulting sample size of 170 (34 x 5).

Exploratory analysis of the variable which mea- sures admissions per 1000 population showed the data were not normally distributed. There is a density mass between the values of 2 and 4; that is, 100 of the 170 observations fail within this range. Moreover, no county has more than 8 admissions per 1000

population. Hausman, Hall and Grihches [IO] have shown that the choice of stochastic assumptions may substantially affect the precision of the parameter estimates obtained. We therefore investigated the distribution of admissions per 1000 population. Three distributions were investigated: the lognormal distribution, the Poisson distribution and the uniform distribution. In order to test the discrete Poisson distribution, the number of admissions was rounded to the nearest integer. This meant that if the Poisson distribution was adopted a considerable amount of information would not be used for estimation. A Kolmogorov-Smimoff goodness of fit test was used to test for the distribution of the admissions data against the three theoretical distributions. The tests were performed for both the pooled and year specific data. For the pooled data the test indicated that the lognormal distribution could not be rejected at the 50% confidence level. The uniform could be rejected at the 5% confidence level and the Poisson could be rejected at the 7% level. For the year specific tests (based on 34 observations) only the uniform distribution could be clearly rejected. The lack of significance in these tests is probably due to lack of power. Additionally, the data failed to be consistent with the Poisson property that the mean and variance of the distribution be equal. Table 1 shows the admissions variable has a mean of 3.04 and a vari- ance of 1.64. Finally, the distribution of the admis- sions rates displayed ‘underdispersion’. For these reasons we rejected use of both the Poisson and uniform distributions and based our estimation on the lognormal distribution.

Specification

The empirical model of psychiatric admissions is estimated using three functional forms. These are the double log form for admissions per 1000 population, the double log form for total admissions where population is included as an independent variable [l], and the logistic transformation of the number of admissions per 100 population. The independent variables included in each of the models are the same

Table 1. Variable definitions and descriptive statistics

Variable SOUKC Mean

(SD)

MDCl9 admissions per 1000 county population % Population > 50 years of age

Psychiatrists per 1000 population

Total MDs per 1000 population

Psychologists per 1000 population

Total hospital beds per 1000 population

Psychiatric beds per 1000 population

Medicare recipients per 1000 population

SSDl/per ICOO population

Median family income

FLHEP is in effect

SPARCS

ARF

ARF-AMA

ARF-AMA

APA Dictionary

AHA Tapes

AHA Tapes

ARF

ARF

Sales and Marketing Management

3.04 (1.28) 21.49 (3.76) 0.04

(0.05) 0.94

(0.46) 0.16

(0.22) 3.32

(1.18) 1.06

(4.22) 119.87 (22.34)

0.61 m 071 \--- ,

21,048 (4,133)

0.09 ,n 291

S.S.M. 2*,-

864 RICHARD G. FUNK and CATHERM A. JACKSON

(except, of course, for the population variable in the total admission specification).

The detailed specification reflects the ideas underlying equation (1). Age, income and insurance characteristics of the population are factors that are well known to affect the demand for mental health services [12, 13). We measure the age distribution of the population by including the log of the percentage of the population age 50 years or more (other age breakdowns are not available for all years). The income of the population is measured as the log of the median effective buying income in nominal dollars. We summarize insurance characteristics by including the portion of the population covered by Medicare. We include a measure of the health status of the population by measuring the portion of the Medicare population that is disabled. In addition to these population characteristics, we measure several char- acteristics of the health and mental health service system. These are included in order to control for the availability of various sources of mental health care. For example, in counties with relatively large num- bers of psychiatric beds it may be less costly and more convenient to obtain psychiatric care than in areas with fewer such services. This would imply greater utilization of psychiatric care in the counties with relatively high concentrations of psychiatric services.

Inclusion of variables such as the number of psy- chologists and psychiatrists allows for substitution of outpatient mental health care for inpatient care or complementary relationships such as inpatient re- ferrals from office based providers of mental health. Recent results from the Rand Health Insurance Study [ 141 suggest a complementary relationship between inpatient and outpatient care. The availability of both specialty psychiatric beds and acute care beds are specified in terms of the log of beds per 1000 population. Similarly, the supply of physician, psychiatrist and psychologist services available is specified as the log of the number of practitioners per 1000 population.

Finally, the payment systems variable is defined as a dichotomous dummy variable that takes on a value of 1 for the 4 FLHEP counties for the years 1981-1984 and zero otherwise.

The estimation of the single equation model de- scribed above involves analysis of a pane1 of counties over a S-year period. Combining time-series and cross-section data for use with ordinary least squares generates a regression disturbance term that may contain cross-sectional, time-series and random elements. The possibility of autocorrelation of the disturbance term with the independent variables suggests the need to modify the ordinary least squares estimators. Fixed and random effects estimators have been used to obtain consistent parameter estimates [IS]. The use of random effects (variance component) models requires that one assumes no correlation between the elements of the disturbance term and

independent variables. We assessed this assumption by using a specification test proposed by Hausman [16]. This test involves estimating both fixed-effect and variance components models. Since it is known that the fixed-effects mode1 is consistent but not efficient, comparing the coefficients of the fixed- effects and variance components models will identify any bias due to the variance components approach. We compared fixed-effects coefficient estimates to the Fuller and Battese [ 171 variance components models. A number of coefficients had different signs which we interpret as evidence of correlation between the disturbance terms and the independent variables. We therefore adopted the fixed-effects model where un- observed factors that are correlated with explanatory variables can be accommodated. The cost of the fixed-effects approach is a loss of statistical power due to decreased numbers of degrees of freedom. Never- theless, this more conservative approach seemed to be warranted when a complex phenomenon such as psychiatric admissions was being analyzed.

RESULTS

Table 2 presents trends in psychiatric admissions (MDC19) for the years 1980-1984 where admissions in each year are divided by 1980 admissions. We assess changes in admissions in this manner so as to better capture time trends relative to the pre- experimental year. The table shows a S-year decline in psychiatric admissions in the FLHEP counties of roughly 29% and an increase of roughly 6% in the control counties. The population growth in the two groups was equivalent during the S-year period.

It should be noted that we observed only a single year prior to the implementation of FLHEP (1980) and therefore, we may be overlooking a difference in secular trends between control and experimental counties. In order to obtain a sense of some of the longer run trends in hospital utilization in the FLHEP and control counties, we examined the rates of growth in average monthly Medicare hospital costs for the years 1977-1982 (hospital specific data are not available prior to 1980). Over the 6-year period, the FLHEP counties experienced an average growth rate in Medicare hospital expenditures of 11%, while the control counties had an average growth rate of 13.2%. This difference is rather small compared to the dramatic changes reflected by Table 2.

The fixed-effects regression models reported in Table 3 are designed to control for time trends and other potentially confounding factors so as to obtain consistent estimates of the impact of FLHEP. The inclusion of dummy variables for each year (using 1980 as the reference category) estimates the changes in psychiatric admission rates in each year. Thus the estimate of the FLHEP effect is made holding

Table 2. Psychiatric admissions (MDC 19)

1980 1981 1982 1983 1984

FLHEP counties 100.00 94.41 84.33 74.11 70.57

Control counties lOO.cnl 102.31 101.54 105.93 105.81

The impact of prospectively set hospital budgets

Table 3. Regression results fixed effects models: specification form

Variable LN(ADM) LN(ADM/POP) LN (ADM/lOO)

(I-ADM/lOO)

LN(%pop > JO)

LN(psychiatrist/pop)

LN(MD/pop)

LN(psychologist/pop)

LN+ds/pop)

LN(psych beds/pop)

LN(Medicare POP/pop)

LN(SSIDIS/pop)

LN(Med-family income)

LN(population)

FLHEP

Intercept

R’

N

0.602 (0.491). 0.19e-’

(0.003) - 0.707 (2.55) 0.009

(0.226) 1.395

(2.438) - 0.025

(0.351) 0.428

(0.433) - 0.229 (0.319) 0.459

(1.637) 2.273

(1.113) -0.178 (1.791)

- 13.832 (1.177) 0.96

F(48, 121) 65.35 170

0.330 (1.151) 0.007

(0.014) - 0.673 (2.489) 0.008

(0.177) I.302

(2.363) - 0.026

(“0:;;;)

(0.206) - 0.232

(0.325) 0.51 I

(1.910) -

0.455 (0.277) 0.035

(0.492) - 0.814 (2.111) 0.014

(0.225) 2.138

(2.721) - 0.030

(“o:Z) (0.316)

-0.110 (0.107) 0.625

(1.639) -

- - -0.174 - 0.249

(1.763) (1.767) - 7.707 - 12.859 (1.212) (1.406) 0.91 0.90

F(47.122) 23.62 F(47, 122) 24.96 170 170

865

*I statistics in parentheses. Time and county dummy variables were used in all specifications to produce fixed etkts estimates. Complete results including dummy variable estimates are available from the authors.

constant overall time trends in admissions in the study counties.

Table 3 presents the regression results using three functional forms and fixed-effects estimators [ 181. The left hand column reports results for the dependent variable specified as the log of total admissions, the middle column for the log of admissions per 1000 population, and the right hand column for the logistic model results [19]. The overall explanatory power of all the models is quite high (R* of at least 0.90) as is expected from a fixed-effects model. By and large, we observed a similar pattern of coefficient estimates across models. It is interesting to note that the coefficient for the population variable in the log of total admissions equation is estimated to be 2.27, but the imprecision of the estimate makes it impossible to reject the hypothesis that the coefficient is significantly different from one (or zero). Thus, the model reported in column 2, log of admission per 1000 population, is appropriate.

The variable of central interest to this research is the FLHEP dummy. The estimates for the FLHEP variable are very stable across the three specifications. Table 4 summarizes the estimated impacts of the FLHEP program from the regressions reported in

Table 3. The estimates range from a decline in admissions of 1622% [20]. These estimates are all significant at the 95% confidence level using a one tailed t-test since we had a prior hypothesis regarding the direction of the impact of the program.

Several other findings are notable. The estimated income elasticities range from 0.46 to 0.63, suggesting that a 10% increase in median family income is associated with a 466.3% increase in psychiatric admissions. The estimates are all significant at the 90% level, with the estimate for the log of admissions per 1000 population significant at the 95% level. These income elasticities are substantially larger than in findings in the general medical care sector.

The estimated coefficients for both the psychiatrist and psychologist to population ratios are essentially zero (although positive). In contrast, the physician to population ratio is estimated to have a large negative and significant impact in psychiatric admissions. This may reflect the role of the general medical sector in providing ambulatory psychiatric care in rural areas. For example, hospital outpatient departments and emergency rooms may have greater capacity and willingness to treat psychiatric cases on an outpatient basis in areas where the supply of physicians is

Table 4. Impacts of fixed budget payment method

Model

LN(ADM)* LN(ADM/POP)’ Lo&tic*

FLHEP -0.16 -0.16 - 0.22

Income 0.46 0.51 0.63

l eb - I = impact.

866 RICHARD G. FRANK and

relatively large. This might occur because in highly competitive areas office based (and attending) phys- icians would be willing to work in the outpatient and emergency departments of general hospitals to obtain access to patients (and therefore income).

Finally, the impact of the supply of specialty psychiatric beds was estimated to be negative, although not significantly different from zero at conventional confidence levels. In contrast the number of general medical-surgical beds per 1000 population was estimated to have a strong positive impact on psychiatric admissions. This may reflect the policy pressures in New York State to take action on excess hospital capacity. There probably was a tendency for hospitals in counties with relatively high bed capacity to encourage use of medical-surgical beds as ‘psychiatric scatterbeds’ in order to keep occupancy rates at levels that would not attract regulatory attention.

We probed the robustness of these results by testing several underlying assumptions of the model as well as alternate specifications. First, we were concerned that the two payment schemes might have different effects on the distribution of the error terms. We examined the homogeneity of the variances in the error terms between the FLHEP and control counties. We could not reject the hypothesis that there were no significant differences in the variances of the errors (using an F-test). A second type of problem that could affect the estimates is heteroskedasticity where larger counties might have different variances than smaller ones. Such heterogeneity in variances would result in inefficient estimates. We tested this possibility using a Park-Glejser test of heteroskedasticity [21]. Focusing on county population size, we could not reject the null hypothesis that total county population is not related to the variance of the error term. Finally, in order to assess the sensitivity of the estimates to possible simultaneous equations bias, we reestimated the model excluding the measures of psychologists, psychiatrists and psychiatric beds from the model. The estimates of the FLHEP effect were not sub- stantially altered. (These results are available from the authors upon request.)

DISCUSSION AND IMPLICATIONS

The estimated response of psychiatric admissions to the incentives contained in prospectively set hospital budgets ranges from decreases of l&-22%. This is a substantial impact. These results suggest that hospitals do respond to incentives to decrease admissions and that prospectively set budgets provide such inducements.

The results also provide some clues as to impacts of applying capitated financing to the mentally ill. While fixed budget funding for hospitals is not the same as capitated funding, it does provide similar incentives for reduced hospitalization. As mentioned above there are a number of mechanisms which might explain how admission rates are reduced. One im- portant possibility that we were able to investigate is whether patients left the FLHEP geographic area at rates that were different from individuals in other parts of the state. We found no significant differences

CATHERM A. JACKSON

between FLHEP and control counties in the rates of psychiatric patients being treated outside their home counties. We also sought evidence as to whether there was differential use of public mental hospitals be- tween FLHEP and control county populations. No significant differences were found (using information from New York State’s Office of Mental Health). Thus while this study did not address capitation directly, the results suggest that capitation would decrease admissions substantially.

Two other distinct but not mutually exclusive explanations were suggested earlier. They are: (I) hospitals paid under fixed budgets make more extensive use of their outpatient departments for treating psychiatric patients, thus taking advantage of substitution possibilities between outpatient and inpatient psychiatric care; and (2) FLHEP patients are undertreated or comparison county patients receive ‘unnecessary’ inpatient psychiatric care [22]. The data necessary to test these explanations were not available to us and thus these questions must remain subjects for further research. Our lack of knowledge concerning the two explanations coupled with the large estimated response to FLHEP leads us to conclude that access to care, in particular, needs to be carefully monitored when payment systems containing incentives, similar to those of the FLHEP program, are applied to the mentally ill. This is especially true for the mentally ill who as a group are likely not to function well in the role of consumer.

Acknowledgements-This research was supported by grants from the National Institute of Mental Health and the Robert Wood Johnson Foundation. We are grateful to Laura Morlock, Morris Barer and two anonymous referees for helpful comments on this paper.

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Frank R. G. and Lave J. R. A comparison of hospital responses to payment policies for Medicaid psychiatric patients. Working paper, Johns Hopkins University, 1988. Dowling W. L. Prospective reimbursement of hospitals. Inquiry 11, 163-180, 1974. Frank R. G. and Lave J. R. Per case prospective payment for psychiatric inpatients: an assessment and alternatives. J. Hlrh Polir. Policy Law 11, 83-96, 1986. Rupp A., Steinwachs D. M. and Salkever D. S. The effect of hospital payment method on the pattern and cost of mental health. Hosp. Commun. Psychiur. 35, 460-469, March, 1984. The FLHEP counties are Ontario, Seneca, Wayne and Yates. Famand L. J., Jacobs P. and Dickson W. M. An evaluation of a program to regulate rural hospital costs: The Finger Lakes Hospital Experimental Payment Pro- gram. Inauirv 23. 200-208. 1986. sational- Institute of Mental Health. Mental Healrh U.S. 198% USGPO. Washington. D.C., 1987. Goldman H. H., T&e C. k. and Jencks S. T. The organization of the psychiatric inpatient services system. Med. Care 25, 2-2 I, 1987. The % of the population living in urban areas in FLHEP counties in 1980 was as follows: Ontario 28.7%. Seneca 37.9%, Wayne 21.1%. Yates 24.4%. Hausman J. A., Hall B. and Griliches 2. Economic models for count data with an application to the

The impact of prospectively set hospital budgets 867

patients-R-D relationship. NBER Technical Paper No. 17. 1981.

I I. Using log of total admissions instead of log of admis- sions per 1000 population relaxes the assumption that the population variable has a coefficient of i.

12. Taube C. A.. Kessler L. G. and Bums B. Estimatine the probability and level of ambulatory-mental-health services use. Hlrh Serv. Res. 21, 321-341, 1986.

13. Variables such as sex and race which are also known to affect individual use of mental health service were excluded because they do not vary sufficiently over time, thus making the OLS X’X matrix singular.

14. Manning W. G. et nl. Health insurance and the demand for medical care. Am. Econ. Rev. 251-277, 10 June, 1987.

15. Mundlak Y. On pooling of time series and cross section data. Economerrica 46, 69-85, January. 1978.

16. Hausman J. A. Specification tests in econometrics. Economerrica 46, 1251-1271, 1978.

17. Fuller W. and Battese G. E. Estimation of linear models with crossed error structures. J. Economer. 62, 67-78, 1974.

18. It is important to note that all the variables included in the model vary over time.

19. It should be noted that in order to obtain the odds ratio we use 100 population rather than 1000 population in the logistic specification.

20. Strictly swakina the logistic impact represents a 22% difference in th; relative odds of being-admitted.

21. Judge G. G. er al. The Theory and Pracrice of Econo- merrics. Wiley, New York, 1980.

22. Since New York State has a law which requires that insurers offer minimum levels of mental health benefits, the population in New York State may be relatively well insured. This would be consistent with high levels of utilization among the populations in the control counties.