Determinants of Hospital Utilization In the Netherlands
by Jacques van der Gaag, Frans F. H. Rutten, and Bernard M. S. van Praag Hospital use in the Netherlands is examined in a cross-section analysis of 1969 and 1971 data for 120 service regions. Elasticities of admissions with respect to bed supply and supply of general practitioners are calculated, and the substitutability of first level care (by general practitioners) for hospital care is considered. Substitution effects found indicate that the Dutch government's plan to reduce the ratio of hospital beds to population is feasible. In the Netherlands, as in most European countries, health care is taking a growing share of the gross national product (GNP). Over the period from 1953 to 1972, expenditures in the health care sector increased from 3.3 percent to 7.2 percent of the GNP. Recent extrapolations indicate that health care may account for over 12 percent of the GNP by 1980. One of the main causes of this rapid growth in cost is the disproportionate expansion of the hospital sector. In 1971 the number of general short-term hospital beds had increased to 4.45 beds per 1,000 population, 111 percent of the 1960 level; the number of specialists per 1,000 rose over the same period to 134 percent of the 1960 value, while the number of physicians in general practice per 1,000 population decreased to 89 percent of what it had been in 1960. Hospital care accounted for 32.8 percent of total health care costs in 1953, and 43.8 percent in 1970; the estimate for 1980 is 56 percent. The Dutch government has expressed the opinion that the trend to increasing hospitalization should be checked and has indicated as a desirable goal a ratio of 3.27 general hospital beds (or 4.0 total beds, including university and specialty hospitals) per 1,000 population [1]. The purpose of this article is to analyze the use of hospital beds in 120 health service regions in the Netherlands and to consider the possibility of substituting other types of care for specialist inpatient care.
This study was supported by a grant from the Ministry of Public Health and Environmental Hygiene, the Netherlands. The views and conclusions expressed in this report are the authors' and not necessarily those of the ministry. Address communications and requests for reprints to Professor Bemard van Praag, Economics Institute, Leiden University, Groenhovenstraat 5, Leiden, the Netherlands.
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The Dutch Health Care System
In the Netherlands it is usual for a patient to enter the health care system through a visit to a physician in general practice. Although it is possible to enter a higher level of medical care directly (for instance, in an emergency admission to a hospital), the general practitioner normally provides primary (first level) care and decides whether the patient needs specialist care. The specialist decides whether the patient is to be treated as an outpatient (second level of care) or admitted to the hospital (third level). The patient may be sent to a nursing home (fourth level) by either the GP or the specialist. All Dutch families with an annual income below a certain level (f 17,050 in 1971, or about $6,470 in 1976 dollars) are covered by a compulsory insurance system for medical care, the Sickness Fund Organization (SFO). In 1971 about 70 percent of the Dutch population were insured in this manner. Those in higher income groups are covered by a voluntary insurance system (VIS). These two systems differ not only in type of coverage provided but also in the way doctors and other medical personnel are paid for their services. Another characteristic of the Dutch situation is the way hospital prices are set: during the period under consideration the price per bed day of hospitalization was legally fixed at total institutional costs per day divided by 90 percent of the number of beds in that hospital. This rule is obviously meant to protect hospitals against losses, while at the same time securing a safety margin of 10 percent.
The Data The data for this study came from three sources. Most data on general hospitals were collected by the Ministry of Public Health. Financial and demographic data came from the SFO and the Central Bureau of Statistics. The consumer units on which the analysis was based consist of the areas that are served by general hospitals. These areas were constructed from patient origin data for each hospital that specified the municipalities from which its patients came. (In 1971 the Netherlands had 198 general hospitals and 863 municipalities.) Where a municipality was served by more than one hospital, the hospitals were taken together; where part of a municipal population went to hospital A and part to hospital B, the municipality was divided between the areas of the two hospitals. In this way 120 areas were constructed around centers of hospital inpatient care, which made it possible to analyze hospital data and municipal data together for 1969 and 1971.
The Variables Classical economic theory based on a regulating price mechanism does not apply to the health care sector [2,3]. The fact that most people are insured
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against medical costs and therefore do not adjust their demand for medical care price changes, and the circumstance that in most cases the physician decides how much care is "demanded" by his patients, suggest that demand more or less automatically follows the supply of medical care. This means that the dependent variables-number of hospital admissions per 1,000 population (ADM), mean stay (MS), and, as a result, the number of patient days in general hospitals per 1,000 population (PD)-will be largely influenced by the supply of hospital beds. The supply of other medical goods and services also influences the use of hospital facilities. In this work some proxy variables were used for relevant influences for which there were no reliable production functions. The first level of care was represented by the number of general practitioners per 1,000 population (GP). Although Feldstein [2, p. 279] has found the reverse, ADM is in general influenced negatively by GP, indicating that first level care is a substitute for hospital care. Unfortunately, lack of appropriate data prevented inclusion of separate measures of second and third level facilities. The variables representing third level care were number of general hospital beds per 1,000 population (BEDS), the number of specialists per 100 beds (SPEC), and the number of nursing personnel (nurses and assistant nurses) per 100 beds (NURS); these variables were expected to influence hospital use positively [4]. At the same time the number of specialists may be considered as a measure of the supply of second level care, because almost all specialists work both inside and outside the hospital. The supply at the fourth level was measured as the number of nursing home beds per 1,000 population (NH). Because it was desired that only the use of general hospitals be reflected in the final result, the number of admissions to university hospitals (AUH) was included among the regression variables to take account of substitution effects. The percentage of the population insured by SFO was taken as an explanatory variable because differences between SFO and VIS in coverage may cause differences in consumption of medical care [5,6]; because the way in which the physician is paid differs substantially between VIS and SFO and may have considerable impact on the demand for medical care [7]; and because the percentage of the population insured by SFO also indicates the distribution of the population in two income groups in each region, which may also influence hospital use [8]. Because urban areas tend to have higher rates of hospital use, an index of population density (DENS) was included to account for differences in medical consumption between rural and urban districts. Finally, two additional variables were introduced to take the age-sex structure of the population into account. EADM is the national average admission rate per 1,000 weighted by the age-sex distribution in each region and is defined as follows:
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Nij ADMi EADMj =-=1
(1)
XNij where
N,j = number of persons in age-sex group i in region j ADM, = national average admission rate for age-sex group i
n = number of age-sex groups. The other variable, EMS, is the national average mean stay weighted by the age-sex distribution. It is defined as
EMSj
=
Y= Nij ADMi MSi 5 4=1
(2)
Nij ADMi
where Nij ADMi = expected national average admission for age-sex group i in region j MS, = national average mean stay for age-sex group i Because the data on number of admissions per age-sex group were not available for all regions, it was necessary to use the expected national average instead in Eq. 2. The difference between this estimate and the actual value is corrected in Eq. 4 (below) by including the ratio ADM/EADM. The expected relationships between the three dependent variables ADM, MS, and PD and the eight exogenous variables are summarized in the following three structural equations: ADM = EADM x F1 (BEDS, AUH, GP, SPEC, NURS, NH, SFO, DENS) (3) MS = EMS x F2 (BEDS, AUH, GP, SPEC, NURS, NH, SFO, DENS, ADM/EADM) (4) PD=ADMx MS (5) where ADM = admission rate in general hospitals EADM = admission rate expected on the basis of the national average weighted with respect to the age-sex structure of the region MS = mean stay in general hospitals EMS = mean stay as expected on the basis of the national average, weighted with respect to the age-sex distribution of the admitted patients PD = number of patient days in general hospitals per 1,000 population BEDS = number of general hospitals beds per 1,000 population
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AUH = number of admissions in university hospitals per 1,000 population GP = number of general practitioners per 1,000 population SPEC = number of specialists per 100 general hospital beds NURS = number of nursing personnel per 100 general hospital beds NH = number of nursing home beds per 1,000 population SFO = percentage insured by SFO DENS = an index of the population density Equation 3 says that the admission rate equals the expected admission rate (based on the age-sex structure of the area) except for differences due to the influence of the exogenous variables. Equation 4 can be interpreted the same way. However, it includes the ratio ADM/EADM, for two reasons. One reason has been mentioned in connection with Eq. 2. The other reason is more fundamental: a consequence of the 90-percent rule for price setting is that hospitals are forced to reach an occupancy rate of 90 percent. If the occupancy rate exceeds 90 percent the hospital earns large marginal revenues since marginal costs are relatively low. Therefore one may expect that a fall in admission rate as a result of exogenous factors will be compensated by a rise in mean stay. Regression Results Since the system is recursive (with a triangular coefficient matrix and diagonal covariance matrix), Eqs. 3 and 4 may be estimated individually. In order to avoid problems of heteroscedasticity due to large differences in the size of the service areas, each datum was weighted by the square root of the area's population. The multiplicative specification of the functions F1 and F2 (in Eqs. 3 and 4) proved to be most successful, so the data were regressed as logarithms. The estimated coefficients thus represent elasticities; they are shown in Table 1, along with the regression constant C, the standard errors of the estimates (in parentheses), and the multiple correlation coefficient R12, adjusted for degrees of freedom. The number of service areas observed was 120. Table 1. Regression Results: Standardized Elasticity Coefficients of Admissions/ 1000 and Mean Stay in Response to Supply and Population Variables (Standard errors in parentheses) Dependent variable
b
s BEDS
Hb0pbpE bPEC
ADM, 1969 .. 0.54 -0.07 -0.34 -0.07
(0.05) ADM, 1971 .. 0.57 (0.05) MS, 1969 .... 0.72
(0.01) -0.06 (0.01) -0.01 (0.06) (0.01) MS, 1971 .... 0.66 -0.02 (0.04) (0.01)
268
(0.09) -0.27 (0.09) -0.09 (0.04) -0.15 (0.05)
(0.04) -0.04 (0.05) -0.01 (0.02) 0.01
ontn
bNuRs
bNH
bsFo
bDENS
0.08 -0.008 -0.32 -0.05
bADM/IADM
Constant
R2
...
0.12 (0.50) -0.80 (0.65) -2.14 (0.42) -2.26 (0.33)
0.63
(0.06) (0.004) (0.09) (0.01)
0.01 -0.003 -0.04 (0.01) (0.004) (0.12) -0.01 0.001 0.07 (0.03) (0.002) (0.04) 0.05 0.003 0.18 (0.02) (0.03) (0.002) (0.06)
-0.05 (0.01) -0.01 (0.01) -0.01 (0.01)
...
-0.67
(0.05) -0.68 (0.05)
0.62 0.66 0.64
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The estimates for 1969 and 1971 indicate that elasticities were generally stable except for percentage insured by SFO. Other data indicate that the SFO coefficient for 1969 is unreliable; therefore the percentage insured by SFO in a given area probably has no influence on the admission rate in that area. With respect to admission rate, the large positive coefficient of the bed supply (BEDS) and the large negative coefficient of the supply of general practitioners (GP) make these major significant influences. The expected negative coefficient of university hospital admissions (AUH) appears both in 1969 and 1971. Population density (DENS) shows a slight negative influence on admission rate. The influence of BEDS and GP on mean stay is again significant. The positive effect of SFO insurance on mean stay is confirmned by other evidence: the mean stay for SFO-insured patients is longer than for VIS-insured patients (18.2 as compared with 17.0 days in 1971). Especially notable is the large elasticity (-0.15 in 1971) of the supply of general practitioners with respect to mean stay. This means that a 10-percent increase in the number of general practitioners per 1,000 population reduces the mean stay by 1.5 percent, which could imply that the general practitioner acts as a "threshold" to the hospital for the patients with a relatively long mean stay. It will be seen that this assumption is confirmed by the analysis of consumption by age-sex groups. The net effect of the variables on mean stay is a function of several coefficients shown in Table 1. For example, when the number of beds increases by 10 percent, there are two direct effects, namely, a 5.7-percent increase in admission rate and a 6.6-percent increase in mean stay (1971 figures). However, an increase in admission rate of 5.7 percent indirectly causes a decrease in mean stay of 0.68 x 5.7 percent = 3.9 percent. The net effect on mean stay of a 10-percent increase in BEDS is therefore 6.6 percent - 3.9 percent = 2.7 percent. The overall picture may be seen somewhat more easily in Table 2, which Table 2. Regression Results, Reduced Equations: Standardized Elasticity Coefficients of Admissions/ 1000, Mean Stay, and Patient Days in Response to Supply and Population Variables (Standard errors in parentheses) Dependent variable
b
BEDS
ADM, 1969 ... 0.54
(0.05) ADM, 1971 ... 0.57 (0.05) MS, 1969.0.36 (0.04)
MS, 1971 ..... 0.28 (0.04) PD, 1969 ..... 0.90 PD, 1971 .....
Fall 1975
(0.03) 0.85 (0.03)
b
b b bbU0 bH P bSPC bNURS
-0.34 -0.07 (0.01) (0.09) (0.04) -0.06 -0.27 -0.04 (0.01) (0.09) (0.05)
-0.07
0.03 (0.01)
0.14
0.04
(0.08)
(0.03)
0.02
0.03
0.03
bNH
bSFO
0.08 -0.008 (0.06) (0.004) 0.01 -0.003 (0.01) (0.004) 0.05 (0.05) 0.04
-0.32 (0.09) -0.04 (0.12) 0.006 0.28 (0.004) (0.08)
0.001
0.21
DbN bDENs
Constant
j2
-0.05 (0.01) -0.05 (0.01) 0.02 (0.01)
0.12 (0.50)
0.63
0.80 (0.64) -1.98 (0.42)
0.62
0.03
-1.70
(0.01) (0.08) (0.04)
(0.04) (0.003) (0.10)
(0.01)
(0.55)
-0.03 -0.20 -0.03 (0.01) (0.06) (0.02) -0.04 -0.24 -0.01 (0.01) (0.06) (0.03)
0.14 -0.001 0.04 (0.03) (0.003) (0.05)
0.03
(0.01) 0.02
-1.86 (0.30) -2.51
(0.01)
(0.39)
0.05
0.004
0.17
(0.03) (0.002) (0.07)
R2
0.39
0.40 0.83 0.87
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shows estimates of the coefficients for the reduced form equations, including an equation explaining the number of patient days per 1,000 population in terns of the same explanatory variables. The elasticities of this equation could have been derived by adding the respective elasticities of the reduced form equations explaining ADM and MS. However, the standard errors and the value of R2 cannot be found in that way. A difficulty still obscures the analysis. Because many of the 120 service areas were centered around more than one hospital, the means of the variables SPEC and NURS were usually weighted, which could obscure their possible effects. In order to examine these variables more closely, the following straightforward production function for 198 general hospitals was estimated with 1971 data: (6) PDh = y BEDS,,al SPEC,,a2 NURSh,a3 where PDh = total number of patient days in 1971 BEDS,% = total number of beds available SPEC,, = total number of specialists NURSh = total number of nursing personnel The index h was added to distinguish this production function, estimated for 198 general hospitals, from the demand functions described previously for the 120 areas. The results, in Table 3, indicate that again the number of beds appears to be the most important variable. The number of nursing personnel, however, is also significantly and positively related to hospital productivity in terms of patient days. The number of specialists again shows no significant influence.
Analysis by Age-Sex Group Consumption of medical care differs markedly among various age-sex groups. Tables 4 and 5 show that both admission rate and mean stay increase with age from 15-19 years on. Between 20 and 40 years, admission of women greatly exceeds admission of men, reflecting the large number of deliveries in hospital. In the very low and very high age groups men are admitted significantly more than women. The impact of the exogenous variables on admissions and mean stay for age-sex groups was examined estimating Eqs. 3 and 4 for 22 separate age-sex Table 3. Production Function Parameters: Exponents for Contribution of Three Supply Variables to Patient Days (Standard errors in parentheses) BEDS SPEC NURS Constant Dependent variable PD, 1971 ......
270
(al) 0.85 (0.05)
(a2 ) -0.01 (0.01)
(as)
( Y)
R2
0.17 (0.07)
5.88 (0.17)
0.96
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Table 4. 1971 Admission Rates by Age Group and Sex Admissions/1000
Age group
Males
Females
Under 1 .................... 59.150 1- 4 ....................... 15.168 5- 9 ....................... 7.753 10-14 . 4.532 .............. 15-19 . 5.193 .............. 20-24 . 5.546 .............. 25-29 . 5.412 .............. 30-34 . .............. 5.881 35-39 . 6.178 .............. 40-44 . 6.863 .............. 45-49 . 7.902 .............. 50-54 . 9.389 .............. 55-59 . 11.372 ...................... 60-64 . ...................... 13.240 65-69 . ...................... 14.287 70-74 ....................... 16.898 75-79 ............... 18.076 80-84 ....................... 18.379 85 and over ................. 18.073
52.365 11.475 6.202 3.783
7.398 12.613
15.319 12.780 11.188 10.594 10.581 10.388 9.596 10.108 11.497 13.823 15.170 15.995 14.289
groups (without the variables EADM and EMS). Lack of data from many regions caused a reduction in the number of observations from 120 to 44. The results for mean stay were unsatisfactory, in part because of the reduction in the number of observations and probably in part because mean stay fluctuates with the conditions within the hospital [9], which were beyond the scope of this study. For admission rates also the coefficients of most variables were nonsignificant or uninterpretable. However, the variables BEDS and GP show a fairly clear picture; these results are presented in Table 6. With regard to the impact of GP on ADM, Table 6 shows that the influence is clearly negative and significant for age groups from 20-29 years onward. Table 5. 1971 Mean Stay by Age Group and Sex Age group All ages ...............
Males
Females
17.4
16.8 14.9 10.0 11.6 12.7 21.2 31.3
Under 1 .............. 14.9 1- 4 ................. 10.3 5-14 ................ 13.7 15-44 . 16.1 ............ 45-64 ................. 21.1 65 and over .......... 25.1
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Table 6. Elasticities of Admissions/1000 in Age-Sex Groups in Response to Two Supply Variables Age-sex group
0.76(0.21) 0.79(0.23) years, M ........... 0.17(0.28) F ........... 0.28(0.30) .......... 06(0.25) years, M . F ........... 0.20(0.27) years, M ........... 0.31(0.14) F ........... 0.37(0.11) .......... 0.61(0.11) years, M . F .......... 0.61(0.17) years, M .......... 0.81(0.11) F .......... 0.44(0.17) years, M .......... 0.52(0.11)
Under 1*, M F
1- 4
5- 9 10-14
15-19 20-29
30-39
(Standard errors in parentheses) BEDS GP
............. .............
F .......... 0.32(0.16) 40-49 years, M ........... 0.57(0.15) F ........... 0.46(0.09) 50-59 years, M ........... 0.52(0.09) F ........... 0.55(0.10) 60-64 years, M .......... 0.63(0.13) F .......... 0.47(0.10) 65 years and over, M .......... 0.71(0.12) F .......... 0.78(0.10) * Hospital deliveries included.
K2
-0.52(0.38) -1.09(0.41) 0.07(0.51) -0.21(0.54) 0.19(0.45) 0.26(0.50) 0.18(0.24) 0.51(0.30) -0.14(0.26) 0.16(0.20) -0.30(0.20) -0.40(0.29)
0.41 0.43 0.19 0.27 0.07 0.18 0.32 0.27 0.66 0.51 0.84 0.41
-0.67(0.19) -0.30(0.27)
-0.33(0.19) -0.30(0.16) -0.33(0.17) -0.36(0.17) -0.19(0.23) -0.34(0.19)
0.75 0.40 0.78 0.47 0.73 0.52 0.57 0.43
-0.32(0.21) -0.27(0.18)
0.64 0.69
This supports the indications in Table 1 that the patients whom the general practitioner can keep out of hospital are those with a relatively long mean stay. From morbidity data collected by general practitioners it appears that from age 20-29 onward, diagnoses tend to be less "hard," and problems often tend to be more psychological than somatic. It is frequently argued that if the general practitioner can spend more time with patients he can often reveal somatic complaints to be caused by psychological problems and prevent unnecessary entries to higher levels of care. The present results point in the same direction. Admission for the lower age groups are not adequately explained by the eight exogenous variables used. Most likely the other sources of care (e.g., infant welfare centers, school physicians) would need to be included in the analysis. For the older groups the elasticity with respect to BEDS shows a general tendency to rise with age. This becomes much clearer for the groups above 65 years of age, as is shown in Table 7. 272
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Table 7. Elasticities of Admissions/1000 for the Aged in Response to Bed Supply (Standard errors in parentheses) Sex
Male
65-69
70-74
.52 (0.12) .0.65 (0.11)
(0.14) 0.72 (0.14)
...............
Female
0.66
Age 75-79
80-84
0.73 (0.14) 0.76 (0.14)
0.89 (0.14) 0.84 (0.16)
85 and over 1.11
(0.25) 1.30
(0.20)
This finding takes on special significance in connection with the lack of substitution between hospital care and nursing home care revealed in Tables 1 and 2. This negative result does not change when the analysis is focused on the higher age groups. At the same time the admission rate for those ages is very high where there is a large bed supply in general hospitals. The high elasticity suggests that the aged may often be hospitalized on the basis of "social indications" rather than for essential medical care when beds are plentiful and demand is low. Government policy in the Netherlands has aimed at reducing expensive hospital care for the aged, especially for "social indications." It looks as if this goal has not been achieved; nursing homes are apparently not serving as substitutes for hospitals. The major results of the analysis can be summarized as follows: * The number of beds per 1,000 population is the most important determinant of admission rate and mean stay and is therefore an important factor for controlling consumption in the hospital sector. * First level care, measured as the number of general practitioners per 1,000 population, can be an important substitute for hospital care. With respect to mean stay this effect is neutralized by the strong negative influence of admission rate on mean stay, a consequence of the "90-percent occupancy rule" previously mentioned; however, the overall effect of GP on the number of patient days remains negative.
The effects of specialists and nursing personnel per 100 beds on hospital remain unclear, though the latter effect tends to be positive everywhere. As stated before, the number of specialists acts as a measure of both second level and third level care, blurring the distinction between these two supply effects on the demand for hospital care. * The percentage of the population insured by SFO has a considerable positive effect on mean stay. The fact that SFO-insured patients stay longer in general hospitals than VIS-insured patients was already known and does not disappear when a correction for age and sex differences between the two groups is applied. Socioeconomic differences (reflected in the income criterion for SFO membership) might possibly explain the observed differences in hospital use. Separate estimates for each insurance mode show that the differences in payment systems also induce differences in the consumption of medical care. *
use
FaIl 11975 975 Fall
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More research, including separate variables for income, social class, and insurance system, is needed to untangle these complicated effects. * There is no substitution between fourth level care (nursing homes) and hospital use, notwithstanding both general opinion and government policy. Policy Implications
Tlhe analysis has shown that three major factors determine consumption in the hospital sector: age-sex structure of the population, supply of hospital beds, and supply of general practitioners. Given the demographic development of the population, and assuming that medical technology remains constant and the relationships observed for 1971 hold in the coming decade, what will be the consequences of government policies with respect to bed supply and education of general practitioners? Changes in Bed Supply The consequences of two different government policies with regard to bed supply were computed. Alternative I assumes that growth in bed supply follows the trend of the last decade, so that general hospital beds increase from 58,360 (4.45 per 1,000 persons) in 1971 to 75,421 (5.25 per 1,000 persons) in 1984. This is approximately an 18-percent increase in bed/population ratio. Alternative II assumes a smooth reduction in number of beds toward a 1984 bed supply of 3.27 general hospital beds per 1,000 population, equivalent to the 4/1,000 norm. (The latter figure includes university and specialty hospitals Table 8. 1984 Population Distribution, Admissions/1000, and Total Admissions in Six Age Groups Under Two Bed-Supply Alternatives Age
Admissions
All ages
Population (thousands) 14368.9
Under 1 1-14 216.1
15-29
30-49
50-64
65 and over
3 045.2 3 509.1 3 887.4 2 086.6 1 623.7
ALTERNATIVE I
96.7
Rate per 1000 persons . Percent change +10 from 1971 ........ Total admissions ..... 1 388 983 Percent change from 1971 ........ +20
530.2
70.5
80.9
84.0
100.7
147.3
+10 +4 +12 +9 +10 +14 114 576 214 687 283 886 326 542 210121 239 171 0
-5
+21
+36
+23
+38
60.0
67.3
78.0
103.0
ALTERNATIVE II
75.4 Rate per 1000 persons Percent change from 1971 ........ -15 Total admissions ..... 1 083395 Percent change from 1971 -7 ........
274
410.8
63.2
-15 -7 -17 -13 -15 -21 88 774 192 457 210546 261622 162755 167241 -22
-14
-10
+9
-4
-4
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and is the norm the present Dutch government wishes to apply in the future. In 1971 the bed density in 9 percent of the hospital areas was below this level.) Under Alternative II general hospital beds would total 46,985 in 1984, with a 27-percent decrease in bed/population ratio. The number of 1984 admissions for six all-inclusive age groups was calculated from the bed supply changes under each alternative, and age-group elasticities that were averaged from the data in Table 6. The resulting figures are presented in Table 8. The difference in the number of admissions between Alternatives I and II is over 305,000. The relative reduction in admissions under Alternative II is largest for the 65-and-over age group, in keeping with the high elasticity of admissions for this group. The 27-percent reduction in bed/population ratio under Alternative II corresponds to a 23-percent drop in patient days (from Table 2, 0.85 x 27% = 23%). This means that the occupancy rate, 91.5 percent in 1971, will increase to the unacceptably high figure of 96.5 percent. It is evident that a marked reduction in number of beds must be compensated for by other measures. The previous analysis suggests that one such measure is the substitution of first level care for hospital care. First Level Care as a Substitute for Hospital Care The number of general practitioners in 1984 can be predicted fairly accurately from university estimates of the number of physicians graduating in the Table 9. 1984 Population Distribution, Admissions/1000, and Total Admissions in Six Age Groups Under Two Bed-Supply Alternatives with 38-Percent Increase in General Practitioners per 1000 Persons Age
Admissions
All ages
Population (thousands) 14368.4
Under 1 1-14 216.1
15-29
30-49
50 64
65 and
over
3 045.2 3 509.1 3 887.4 2 086.6 1 623.7
ALTERNATIVE I
Rate per 1000 persons Percent change
.
from 1971 ........ Total admissions ..... Percent change
from 1971
87.3 -1 1 254 913
........
+8
Rate per 1000 persons . Percent change from 1971 ........
66.1
485.8
68.2
74.5
71.8
89.1
132.9
+1 +1 +3 -7 -3 +3 104 981 207 683 261 428 279 115 185 916 215 790 -8
-8
+11
+16
+9
+24
60.9
53.6
55.1
66.4
88.6
ALTERNATIVE I
Total admissions
Percent change from 1971
Fall 1975
-25 949 326
-18
366.4
-24 -10 -26 -29 -28 -32 79 179 185 453 188088 214 196 138 550 143 860
-31
-18
-20
-11
-19
-17
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et
al.
coming 5-8 years. A conservative estimate is an increase of 52 percent over 1971 levels, which equals an increase of 38 percent in the ratio of general practitioners to population. This estimate, and age-sex elasticities for BEDS and GP regrouped from Table 6, gives the 1984 admission rate for six age groups as influenced jointly by changes in bed supply and number of general practitioners. The results are shown in Table 9 (p. 275). Because the negative effect of an increase in GP compensates for the positive effect of an increase in bed supply, admission rates do not change significantly under Alternative I. The number of admissions in the different age groups varies considerably, however, as a consequence of the changing age structure of the population. Under Alternative II, both admissions per 1,000 persons and total admissions are considerably reduced. The 38-percent increase in GP will decrease patient days by about 0.24 x 38% = 9.1% (Table 2). Together with the change in bed supply, this will reduce occupancy rates to 81.3 percent and 87.6 percent for Alternatives I and II respectively, in contrast to 91.5 percent in 1971. In neither case would an unacceptable tension exist between demand and supply.
Discussion One must be careful with these extrapolations: a sharp rise in the number of general practitioners and a drop in the number of hospital beds (with a concomitant increase in the number of specialists per bed) would change the structure of the health care sector, and quite possibly the elasticities found for 1969 and 1971. On the other hand, the substitution elasticity of hospital care with respect to number of general practitioners may be halved (assuming a constant 90-percent occupancy and given the predicted increase in general practitioners) without changing the conclusion that realization of 4 beds per 1,000 population in 1984 will lead to a considerable reduction of consumption in the hospital sector, without increasing the tension between demand and supply. Nevertheless, some qualifications are necessary. Only part of the medical sector has been investigated, without consideration of such important issues as prevention, changing medical technology, and changing morbidity patterns. Furthermore, the included variables are only rough characteristics of the health care supply; in regard to hospital beds and specialists, for example, the different specialties are not distinguished, and first level care is represented only by the ratio of general practitioners to population. Moreover, the extrapolations have been based on cross-section results, which raises problems that are extensively discussed by Kuh [11]. On the other hand, the calculations are based on rather conservative extrapolations, and the same conclusion is indicated even if the substitution effect were only half that found. It appears, then, that bed supply is the main variable influencing hospital use, but its effect differs considerably for different age-sex groups. This is also true for the substitution of first level care for hospital care. The total elasticity
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of substitution is considerable (-0.24 in 1971), and given the expected increase in the supply of general practitioners it should be feasible to reduce bed supply to 4 per 1,000 population. For certain age groups (especially the old), however, first level care is not a sufficient substitute for hospital care, and contrary to general opinion, nursing homes do not serve as a substitute either, so other kinds of care (e.g., home care) must be made available. Acknowledgment. The authors thank Ms. G. van Beukering for her creative assistance in programming. REFERENCES
1. State Secretary of Public Health and Environment, The Netherlands. Memorandum on the Structure of Health Care. The Netherlands Ministry of Public Health and Environmental Hygiene, Aug. 9, 1974. 2. Feldstein, M. S. Economic Analysis for Health Service Efficiency. Amsterdam: NorthHolland, 1967. 3. Fuchs, V. R. (ed.). Essays in the Economics of Health and Medical Care. New York: Columbia University Press, 1972. 4. Fuchs, V. R. and M. J. Kramer. Determinants of Expenditures for Physician's Services in the United States 1948-68. National Center for Health Services Research and Development. DHEW Publication No. (HSM )73-3013. Washington: Government Printing Office, 1973. 5. Phelps, C. E. and J. P. Newhouse. Coinsurance, the price of time, and the demand for medical services. Rev Econ Stat 56:334 Aug. 1974. 6. Rosett, R. N. and L. Huang. The effect of health insurance on the demand for medical care. J Polit Econ 81:281 Apr. 1973. 7. Roemer, M. On paying the doctor and the implications of different methods. J Health Hum Behav 3:10 Spring 1962. 8. Klarman, H. E. (ed.). Empirical Studies in Health Economics. Baltimore, MD: Johns Hopkins Press, 1970. 9. Logan, R. F. L., J. S. A. Ashley, R. E. Klein, and D. M. Robson. Dynamics of Medical Care: The Liverpool Study into Use of Hospital Resources. Memoir No. 14, London School of Hygiene and Tropical Medicine, 1972. 10. van der Gaag, J., F. F. H. Rutten, and B. M. S. van Praag. Do Different Financing Systems Cause Different Demands in the Health Care Sector? Economic Institute Report 74.01, Leiden University, 1974. 11. Kuh, E. The validity of cross-sectionally estimated behaviour equations in time-series applications. Econometrica 27:197 Apr. 1959.
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by Jacques van der Gaag, Frans F. H. Rutten, and Bernard M. S. van Praag Hospital use in the Netherlands is examined in a cross-section analysis of 1969 and 1971 data for 120 service regions. Elasticities of admissions with respect to bed supply and supply of general practitioners are calculated, and the substitutability of first level care (by general practitioners) for hospital care is considered. Substitution effects found indicate that the Dutch government's plan to reduce the ratio of hospital beds to population is feasible. In the Netherlands, as in most European countries, health care is taking a growing share of the gross national product (GNP). Over the period from 1953 to 1972, expenditures in the health care sector increased from 3.3 percent to 7.2 percent of the GNP. Recent extrapolations indicate that health care may account for over 12 percent of the GNP by 1980. One of the main causes of this rapid growth in cost is the disproportionate expansion of the hospital sector. In 1971 the number of general short-term hospital beds had increased to 4.45 beds per 1,000 population, 111 percent of the 1960 level; the number of specialists per 1,000 rose over the same period to 134 percent of the 1960 value, while the number of physicians in general practice per 1,000 population decreased to 89 percent of what it had been in 1960. Hospital care accounted for 32.8 percent of total health care costs in 1953, and 43.8 percent in 1970; the estimate for 1980 is 56 percent. The Dutch government has expressed the opinion that the trend to increasing hospitalization should be checked and has indicated as a desirable goal a ratio of 3.27 general hospital beds (or 4.0 total beds, including university and specialty hospitals) per 1,000 population [1]. The purpose of this article is to analyze the use of hospital beds in 120 health service regions in the Netherlands and to consider the possibility of substituting other types of care for specialist inpatient care.
This study was supported by a grant from the Ministry of Public Health and Environmental Hygiene, the Netherlands. The views and conclusions expressed in this report are the authors' and not necessarily those of the ministry. Address communications and requests for reprints to Professor Bemard van Praag, Economics Institute, Leiden University, Groenhovenstraat 5, Leiden, the Netherlands.
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The Dutch Health Care System
In the Netherlands it is usual for a patient to enter the health care system through a visit to a physician in general practice. Although it is possible to enter a higher level of medical care directly (for instance, in an emergency admission to a hospital), the general practitioner normally provides primary (first level) care and decides whether the patient needs specialist care. The specialist decides whether the patient is to be treated as an outpatient (second level of care) or admitted to the hospital (third level). The patient may be sent to a nursing home (fourth level) by either the GP or the specialist. All Dutch families with an annual income below a certain level (f 17,050 in 1971, or about $6,470 in 1976 dollars) are covered by a compulsory insurance system for medical care, the Sickness Fund Organization (SFO). In 1971 about 70 percent of the Dutch population were insured in this manner. Those in higher income groups are covered by a voluntary insurance system (VIS). These two systems differ not only in type of coverage provided but also in the way doctors and other medical personnel are paid for their services. Another characteristic of the Dutch situation is the way hospital prices are set: during the period under consideration the price per bed day of hospitalization was legally fixed at total institutional costs per day divided by 90 percent of the number of beds in that hospital. This rule is obviously meant to protect hospitals against losses, while at the same time securing a safety margin of 10 percent.
The Data The data for this study came from three sources. Most data on general hospitals were collected by the Ministry of Public Health. Financial and demographic data came from the SFO and the Central Bureau of Statistics. The consumer units on which the analysis was based consist of the areas that are served by general hospitals. These areas were constructed from patient origin data for each hospital that specified the municipalities from which its patients came. (In 1971 the Netherlands had 198 general hospitals and 863 municipalities.) Where a municipality was served by more than one hospital, the hospitals were taken together; where part of a municipal population went to hospital A and part to hospital B, the municipality was divided between the areas of the two hospitals. In this way 120 areas were constructed around centers of hospital inpatient care, which made it possible to analyze hospital data and municipal data together for 1969 and 1971.
The Variables Classical economic theory based on a regulating price mechanism does not apply to the health care sector [2,3]. The fact that most people are insured
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against medical costs and therefore do not adjust their demand for medical care price changes, and the circumstance that in most cases the physician decides how much care is "demanded" by his patients, suggest that demand more or less automatically follows the supply of medical care. This means that the dependent variables-number of hospital admissions per 1,000 population (ADM), mean stay (MS), and, as a result, the number of patient days in general hospitals per 1,000 population (PD)-will be largely influenced by the supply of hospital beds. The supply of other medical goods and services also influences the use of hospital facilities. In this work some proxy variables were used for relevant influences for which there were no reliable production functions. The first level of care was represented by the number of general practitioners per 1,000 population (GP). Although Feldstein [2, p. 279] has found the reverse, ADM is in general influenced negatively by GP, indicating that first level care is a substitute for hospital care. Unfortunately, lack of appropriate data prevented inclusion of separate measures of second and third level facilities. The variables representing third level care were number of general hospital beds per 1,000 population (BEDS), the number of specialists per 100 beds (SPEC), and the number of nursing personnel (nurses and assistant nurses) per 100 beds (NURS); these variables were expected to influence hospital use positively [4]. At the same time the number of specialists may be considered as a measure of the supply of second level care, because almost all specialists work both inside and outside the hospital. The supply at the fourth level was measured as the number of nursing home beds per 1,000 population (NH). Because it was desired that only the use of general hospitals be reflected in the final result, the number of admissions to university hospitals (AUH) was included among the regression variables to take account of substitution effects. The percentage of the population insured by SFO was taken as an explanatory variable because differences between SFO and VIS in coverage may cause differences in consumption of medical care [5,6]; because the way in which the physician is paid differs substantially between VIS and SFO and may have considerable impact on the demand for medical care [7]; and because the percentage of the population insured by SFO also indicates the distribution of the population in two income groups in each region, which may also influence hospital use [8]. Because urban areas tend to have higher rates of hospital use, an index of population density (DENS) was included to account for differences in medical consumption between rural and urban districts. Finally, two additional variables were introduced to take the age-sex structure of the population into account. EADM is the national average admission rate per 1,000 weighted by the age-sex distribution in each region and is defined as follows:
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Nij ADMi EADMj =-=1
(1)
XNij where
N,j = number of persons in age-sex group i in region j ADM, = national average admission rate for age-sex group i
n = number of age-sex groups. The other variable, EMS, is the national average mean stay weighted by the age-sex distribution. It is defined as
EMSj
=
Y= Nij ADMi MSi 5 4=1
(2)
Nij ADMi
where Nij ADMi = expected national average admission for age-sex group i in region j MS, = national average mean stay for age-sex group i Because the data on number of admissions per age-sex group were not available for all regions, it was necessary to use the expected national average instead in Eq. 2. The difference between this estimate and the actual value is corrected in Eq. 4 (below) by including the ratio ADM/EADM. The expected relationships between the three dependent variables ADM, MS, and PD and the eight exogenous variables are summarized in the following three structural equations: ADM = EADM x F1 (BEDS, AUH, GP, SPEC, NURS, NH, SFO, DENS) (3) MS = EMS x F2 (BEDS, AUH, GP, SPEC, NURS, NH, SFO, DENS, ADM/EADM) (4) PD=ADMx MS (5) where ADM = admission rate in general hospitals EADM = admission rate expected on the basis of the national average weighted with respect to the age-sex structure of the region MS = mean stay in general hospitals EMS = mean stay as expected on the basis of the national average, weighted with respect to the age-sex distribution of the admitted patients PD = number of patient days in general hospitals per 1,000 population BEDS = number of general hospitals beds per 1,000 population
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der Gaag et al.
AUH = number of admissions in university hospitals per 1,000 population GP = number of general practitioners per 1,000 population SPEC = number of specialists per 100 general hospital beds NURS = number of nursing personnel per 100 general hospital beds NH = number of nursing home beds per 1,000 population SFO = percentage insured by SFO DENS = an index of the population density Equation 3 says that the admission rate equals the expected admission rate (based on the age-sex structure of the area) except for differences due to the influence of the exogenous variables. Equation 4 can be interpreted the same way. However, it includes the ratio ADM/EADM, for two reasons. One reason has been mentioned in connection with Eq. 2. The other reason is more fundamental: a consequence of the 90-percent rule for price setting is that hospitals are forced to reach an occupancy rate of 90 percent. If the occupancy rate exceeds 90 percent the hospital earns large marginal revenues since marginal costs are relatively low. Therefore one may expect that a fall in admission rate as a result of exogenous factors will be compensated by a rise in mean stay. Regression Results Since the system is recursive (with a triangular coefficient matrix and diagonal covariance matrix), Eqs. 3 and 4 may be estimated individually. In order to avoid problems of heteroscedasticity due to large differences in the size of the service areas, each datum was weighted by the square root of the area's population. The multiplicative specification of the functions F1 and F2 (in Eqs. 3 and 4) proved to be most successful, so the data were regressed as logarithms. The estimated coefficients thus represent elasticities; they are shown in Table 1, along with the regression constant C, the standard errors of the estimates (in parentheses), and the multiple correlation coefficient R12, adjusted for degrees of freedom. The number of service areas observed was 120. Table 1. Regression Results: Standardized Elasticity Coefficients of Admissions/ 1000 and Mean Stay in Response to Supply and Population Variables (Standard errors in parentheses) Dependent variable
b
s BEDS
Hb0pbpE bPEC
ADM, 1969 .. 0.54 -0.07 -0.34 -0.07
(0.05) ADM, 1971 .. 0.57 (0.05) MS, 1969 .... 0.72
(0.01) -0.06 (0.01) -0.01 (0.06) (0.01) MS, 1971 .... 0.66 -0.02 (0.04) (0.01)
268
(0.09) -0.27 (0.09) -0.09 (0.04) -0.15 (0.05)
(0.04) -0.04 (0.05) -0.01 (0.02) 0.01
ontn
bNuRs
bNH
bsFo
bDENS
0.08 -0.008 -0.32 -0.05
bADM/IADM
Constant
R2
...
0.12 (0.50) -0.80 (0.65) -2.14 (0.42) -2.26 (0.33)
0.63
(0.06) (0.004) (0.09) (0.01)
0.01 -0.003 -0.04 (0.01) (0.004) (0.12) -0.01 0.001 0.07 (0.03) (0.002) (0.04) 0.05 0.003 0.18 (0.02) (0.03) (0.002) (0.06)
-0.05 (0.01) -0.01 (0.01) -0.01 (0.01)
...
-0.67
(0.05) -0.68 (0.05)
0.62 0.66 0.64
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The estimates for 1969 and 1971 indicate that elasticities were generally stable except for percentage insured by SFO. Other data indicate that the SFO coefficient for 1969 is unreliable; therefore the percentage insured by SFO in a given area probably has no influence on the admission rate in that area. With respect to admission rate, the large positive coefficient of the bed supply (BEDS) and the large negative coefficient of the supply of general practitioners (GP) make these major significant influences. The expected negative coefficient of university hospital admissions (AUH) appears both in 1969 and 1971. Population density (DENS) shows a slight negative influence on admission rate. The influence of BEDS and GP on mean stay is again significant. The positive effect of SFO insurance on mean stay is confirmned by other evidence: the mean stay for SFO-insured patients is longer than for VIS-insured patients (18.2 as compared with 17.0 days in 1971). Especially notable is the large elasticity (-0.15 in 1971) of the supply of general practitioners with respect to mean stay. This means that a 10-percent increase in the number of general practitioners per 1,000 population reduces the mean stay by 1.5 percent, which could imply that the general practitioner acts as a "threshold" to the hospital for the patients with a relatively long mean stay. It will be seen that this assumption is confirmed by the analysis of consumption by age-sex groups. The net effect of the variables on mean stay is a function of several coefficients shown in Table 1. For example, when the number of beds increases by 10 percent, there are two direct effects, namely, a 5.7-percent increase in admission rate and a 6.6-percent increase in mean stay (1971 figures). However, an increase in admission rate of 5.7 percent indirectly causes a decrease in mean stay of 0.68 x 5.7 percent = 3.9 percent. The net effect on mean stay of a 10-percent increase in BEDS is therefore 6.6 percent - 3.9 percent = 2.7 percent. The overall picture may be seen somewhat more easily in Table 2, which Table 2. Regression Results, Reduced Equations: Standardized Elasticity Coefficients of Admissions/ 1000, Mean Stay, and Patient Days in Response to Supply and Population Variables (Standard errors in parentheses) Dependent variable
b
BEDS
ADM, 1969 ... 0.54
(0.05) ADM, 1971 ... 0.57 (0.05) MS, 1969.0.36 (0.04)
MS, 1971 ..... 0.28 (0.04) PD, 1969 ..... 0.90 PD, 1971 .....
Fall 1975
(0.03) 0.85 (0.03)
b
b b bbU0 bH P bSPC bNURS
-0.34 -0.07 (0.01) (0.09) (0.04) -0.06 -0.27 -0.04 (0.01) (0.09) (0.05)
-0.07
0.03 (0.01)
0.14
0.04
(0.08)
(0.03)
0.02
0.03
0.03
bNH
bSFO
0.08 -0.008 (0.06) (0.004) 0.01 -0.003 (0.01) (0.004) 0.05 (0.05) 0.04
-0.32 (0.09) -0.04 (0.12) 0.006 0.28 (0.004) (0.08)
0.001
0.21
DbN bDENs
Constant
j2
-0.05 (0.01) -0.05 (0.01) 0.02 (0.01)
0.12 (0.50)
0.63
0.80 (0.64) -1.98 (0.42)
0.62
0.03
-1.70
(0.01) (0.08) (0.04)
(0.04) (0.003) (0.10)
(0.01)
(0.55)
-0.03 -0.20 -0.03 (0.01) (0.06) (0.02) -0.04 -0.24 -0.01 (0.01) (0.06) (0.03)
0.14 -0.001 0.04 (0.03) (0.003) (0.05)
0.03
(0.01) 0.02
-1.86 (0.30) -2.51
(0.01)
(0.39)
0.05
0.004
0.17
(0.03) (0.002) (0.07)
R2
0.39
0.40 0.83 0.87
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shows estimates of the coefficients for the reduced form equations, including an equation explaining the number of patient days per 1,000 population in terns of the same explanatory variables. The elasticities of this equation could have been derived by adding the respective elasticities of the reduced form equations explaining ADM and MS. However, the standard errors and the value of R2 cannot be found in that way. A difficulty still obscures the analysis. Because many of the 120 service areas were centered around more than one hospital, the means of the variables SPEC and NURS were usually weighted, which could obscure their possible effects. In order to examine these variables more closely, the following straightforward production function for 198 general hospitals was estimated with 1971 data: (6) PDh = y BEDS,,al SPEC,,a2 NURSh,a3 where PDh = total number of patient days in 1971 BEDS,% = total number of beds available SPEC,, = total number of specialists NURSh = total number of nursing personnel The index h was added to distinguish this production function, estimated for 198 general hospitals, from the demand functions described previously for the 120 areas. The results, in Table 3, indicate that again the number of beds appears to be the most important variable. The number of nursing personnel, however, is also significantly and positively related to hospital productivity in terms of patient days. The number of specialists again shows no significant influence.
Analysis by Age-Sex Group Consumption of medical care differs markedly among various age-sex groups. Tables 4 and 5 show that both admission rate and mean stay increase with age from 15-19 years on. Between 20 and 40 years, admission of women greatly exceeds admission of men, reflecting the large number of deliveries in hospital. In the very low and very high age groups men are admitted significantly more than women. The impact of the exogenous variables on admissions and mean stay for age-sex groups was examined estimating Eqs. 3 and 4 for 22 separate age-sex Table 3. Production Function Parameters: Exponents for Contribution of Three Supply Variables to Patient Days (Standard errors in parentheses) BEDS SPEC NURS Constant Dependent variable PD, 1971 ......
270
(al) 0.85 (0.05)
(a2 ) -0.01 (0.01)
(as)
( Y)
R2
0.17 (0.07)
5.88 (0.17)
0.96
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Table 4. 1971 Admission Rates by Age Group and Sex Admissions/1000
Age group
Males
Females
Under 1 .................... 59.150 1- 4 ....................... 15.168 5- 9 ....................... 7.753 10-14 . 4.532 .............. 15-19 . 5.193 .............. 20-24 . 5.546 .............. 25-29 . 5.412 .............. 30-34 . .............. 5.881 35-39 . 6.178 .............. 40-44 . 6.863 .............. 45-49 . 7.902 .............. 50-54 . 9.389 .............. 55-59 . 11.372 ...................... 60-64 . ...................... 13.240 65-69 . ...................... 14.287 70-74 ....................... 16.898 75-79 ............... 18.076 80-84 ....................... 18.379 85 and over ................. 18.073
52.365 11.475 6.202 3.783
7.398 12.613
15.319 12.780 11.188 10.594 10.581 10.388 9.596 10.108 11.497 13.823 15.170 15.995 14.289
groups (without the variables EADM and EMS). Lack of data from many regions caused a reduction in the number of observations from 120 to 44. The results for mean stay were unsatisfactory, in part because of the reduction in the number of observations and probably in part because mean stay fluctuates with the conditions within the hospital [9], which were beyond the scope of this study. For admission rates also the coefficients of most variables were nonsignificant or uninterpretable. However, the variables BEDS and GP show a fairly clear picture; these results are presented in Table 6. With regard to the impact of GP on ADM, Table 6 shows that the influence is clearly negative and significant for age groups from 20-29 years onward. Table 5. 1971 Mean Stay by Age Group and Sex Age group All ages ...............
Males
Females
17.4
16.8 14.9 10.0 11.6 12.7 21.2 31.3
Under 1 .............. 14.9 1- 4 ................. 10.3 5-14 ................ 13.7 15-44 . 16.1 ............ 45-64 ................. 21.1 65 and over .......... 25.1
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Table 6. Elasticities of Admissions/1000 in Age-Sex Groups in Response to Two Supply Variables Age-sex group
0.76(0.21) 0.79(0.23) years, M ........... 0.17(0.28) F ........... 0.28(0.30) .......... 06(0.25) years, M . F ........... 0.20(0.27) years, M ........... 0.31(0.14) F ........... 0.37(0.11) .......... 0.61(0.11) years, M . F .......... 0.61(0.17) years, M .......... 0.81(0.11) F .......... 0.44(0.17) years, M .......... 0.52(0.11)
Under 1*, M F
1- 4
5- 9 10-14
15-19 20-29
30-39
(Standard errors in parentheses) BEDS GP
............. .............
F .......... 0.32(0.16) 40-49 years, M ........... 0.57(0.15) F ........... 0.46(0.09) 50-59 years, M ........... 0.52(0.09) F ........... 0.55(0.10) 60-64 years, M .......... 0.63(0.13) F .......... 0.47(0.10) 65 years and over, M .......... 0.71(0.12) F .......... 0.78(0.10) * Hospital deliveries included.
K2
-0.52(0.38) -1.09(0.41) 0.07(0.51) -0.21(0.54) 0.19(0.45) 0.26(0.50) 0.18(0.24) 0.51(0.30) -0.14(0.26) 0.16(0.20) -0.30(0.20) -0.40(0.29)
0.41 0.43 0.19 0.27 0.07 0.18 0.32 0.27 0.66 0.51 0.84 0.41
-0.67(0.19) -0.30(0.27)
-0.33(0.19) -0.30(0.16) -0.33(0.17) -0.36(0.17) -0.19(0.23) -0.34(0.19)
0.75 0.40 0.78 0.47 0.73 0.52 0.57 0.43
-0.32(0.21) -0.27(0.18)
0.64 0.69
This supports the indications in Table 1 that the patients whom the general practitioner can keep out of hospital are those with a relatively long mean stay. From morbidity data collected by general practitioners it appears that from age 20-29 onward, diagnoses tend to be less "hard," and problems often tend to be more psychological than somatic. It is frequently argued that if the general practitioner can spend more time with patients he can often reveal somatic complaints to be caused by psychological problems and prevent unnecessary entries to higher levels of care. The present results point in the same direction. Admission for the lower age groups are not adequately explained by the eight exogenous variables used. Most likely the other sources of care (e.g., infant welfare centers, school physicians) would need to be included in the analysis. For the older groups the elasticity with respect to BEDS shows a general tendency to rise with age. This becomes much clearer for the groups above 65 years of age, as is shown in Table 7. 272
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Table 7. Elasticities of Admissions/1000 for the Aged in Response to Bed Supply (Standard errors in parentheses) Sex
Male
65-69
70-74
.52 (0.12) .0.65 (0.11)
(0.14) 0.72 (0.14)
...............
Female
0.66
Age 75-79
80-84
0.73 (0.14) 0.76 (0.14)
0.89 (0.14) 0.84 (0.16)
85 and over 1.11
(0.25) 1.30
(0.20)
This finding takes on special significance in connection with the lack of substitution between hospital care and nursing home care revealed in Tables 1 and 2. This negative result does not change when the analysis is focused on the higher age groups. At the same time the admission rate for those ages is very high where there is a large bed supply in general hospitals. The high elasticity suggests that the aged may often be hospitalized on the basis of "social indications" rather than for essential medical care when beds are plentiful and demand is low. Government policy in the Netherlands has aimed at reducing expensive hospital care for the aged, especially for "social indications." It looks as if this goal has not been achieved; nursing homes are apparently not serving as substitutes for hospitals. The major results of the analysis can be summarized as follows: * The number of beds per 1,000 population is the most important determinant of admission rate and mean stay and is therefore an important factor for controlling consumption in the hospital sector. * First level care, measured as the number of general practitioners per 1,000 population, can be an important substitute for hospital care. With respect to mean stay this effect is neutralized by the strong negative influence of admission rate on mean stay, a consequence of the "90-percent occupancy rule" previously mentioned; however, the overall effect of GP on the number of patient days remains negative.
The effects of specialists and nursing personnel per 100 beds on hospital remain unclear, though the latter effect tends to be positive everywhere. As stated before, the number of specialists acts as a measure of both second level and third level care, blurring the distinction between these two supply effects on the demand for hospital care. * The percentage of the population insured by SFO has a considerable positive effect on mean stay. The fact that SFO-insured patients stay longer in general hospitals than VIS-insured patients was already known and does not disappear when a correction for age and sex differences between the two groups is applied. Socioeconomic differences (reflected in the income criterion for SFO membership) might possibly explain the observed differences in hospital use. Separate estimates for each insurance mode show that the differences in payment systems also induce differences in the consumption of medical care. *
use
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More research, including separate variables for income, social class, and insurance system, is needed to untangle these complicated effects. * There is no substitution between fourth level care (nursing homes) and hospital use, notwithstanding both general opinion and government policy. Policy Implications
Tlhe analysis has shown that three major factors determine consumption in the hospital sector: age-sex structure of the population, supply of hospital beds, and supply of general practitioners. Given the demographic development of the population, and assuming that medical technology remains constant and the relationships observed for 1971 hold in the coming decade, what will be the consequences of government policies with respect to bed supply and education of general practitioners? Changes in Bed Supply The consequences of two different government policies with regard to bed supply were computed. Alternative I assumes that growth in bed supply follows the trend of the last decade, so that general hospital beds increase from 58,360 (4.45 per 1,000 persons) in 1971 to 75,421 (5.25 per 1,000 persons) in 1984. This is approximately an 18-percent increase in bed/population ratio. Alternative II assumes a smooth reduction in number of beds toward a 1984 bed supply of 3.27 general hospital beds per 1,000 population, equivalent to the 4/1,000 norm. (The latter figure includes university and specialty hospitals Table 8. 1984 Population Distribution, Admissions/1000, and Total Admissions in Six Age Groups Under Two Bed-Supply Alternatives Age
Admissions
All ages
Population (thousands) 14368.9
Under 1 1-14 216.1
15-29
30-49
50-64
65 and over
3 045.2 3 509.1 3 887.4 2 086.6 1 623.7
ALTERNATIVE I
96.7
Rate per 1000 persons . Percent change +10 from 1971 ........ Total admissions ..... 1 388 983 Percent change from 1971 ........ +20
530.2
70.5
80.9
84.0
100.7
147.3
+10 +4 +12 +9 +10 +14 114 576 214 687 283 886 326 542 210121 239 171 0
-5
+21
+36
+23
+38
60.0
67.3
78.0
103.0
ALTERNATIVE II
75.4 Rate per 1000 persons Percent change from 1971 ........ -15 Total admissions ..... 1 083395 Percent change from 1971 -7 ........
274
410.8
63.2
-15 -7 -17 -13 -15 -21 88 774 192 457 210546 261622 162755 167241 -22
-14
-10
+9
-4
-4
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and is the norm the present Dutch government wishes to apply in the future. In 1971 the bed density in 9 percent of the hospital areas was below this level.) Under Alternative II general hospital beds would total 46,985 in 1984, with a 27-percent decrease in bed/population ratio. The number of 1984 admissions for six all-inclusive age groups was calculated from the bed supply changes under each alternative, and age-group elasticities that were averaged from the data in Table 6. The resulting figures are presented in Table 8. The difference in the number of admissions between Alternatives I and II is over 305,000. The relative reduction in admissions under Alternative II is largest for the 65-and-over age group, in keeping with the high elasticity of admissions for this group. The 27-percent reduction in bed/population ratio under Alternative II corresponds to a 23-percent drop in patient days (from Table 2, 0.85 x 27% = 23%). This means that the occupancy rate, 91.5 percent in 1971, will increase to the unacceptably high figure of 96.5 percent. It is evident that a marked reduction in number of beds must be compensated for by other measures. The previous analysis suggests that one such measure is the substitution of first level care for hospital care. First Level Care as a Substitute for Hospital Care The number of general practitioners in 1984 can be predicted fairly accurately from university estimates of the number of physicians graduating in the Table 9. 1984 Population Distribution, Admissions/1000, and Total Admissions in Six Age Groups Under Two Bed-Supply Alternatives with 38-Percent Increase in General Practitioners per 1000 Persons Age
Admissions
All ages
Population (thousands) 14368.4
Under 1 1-14 216.1
15-29
30-49
50 64
65 and
over
3 045.2 3 509.1 3 887.4 2 086.6 1 623.7
ALTERNATIVE I
Rate per 1000 persons Percent change
.
from 1971 ........ Total admissions ..... Percent change
from 1971
87.3 -1 1 254 913
........
+8
Rate per 1000 persons . Percent change from 1971 ........
66.1
485.8
68.2
74.5
71.8
89.1
132.9
+1 +1 +3 -7 -3 +3 104 981 207 683 261 428 279 115 185 916 215 790 -8
-8
+11
+16
+9
+24
60.9
53.6
55.1
66.4
88.6
ALTERNATIVE I
Total admissions
Percent change from 1971
Fall 1975
-25 949 326
-18
366.4
-24 -10 -26 -29 -28 -32 79 179 185 453 188088 214 196 138 550 143 860
-31
-18
-20
-11
-19
-17
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der Gaag
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coming 5-8 years. A conservative estimate is an increase of 52 percent over 1971 levels, which equals an increase of 38 percent in the ratio of general practitioners to population. This estimate, and age-sex elasticities for BEDS and GP regrouped from Table 6, gives the 1984 admission rate for six age groups as influenced jointly by changes in bed supply and number of general practitioners. The results are shown in Table 9 (p. 275). Because the negative effect of an increase in GP compensates for the positive effect of an increase in bed supply, admission rates do not change significantly under Alternative I. The number of admissions in the different age groups varies considerably, however, as a consequence of the changing age structure of the population. Under Alternative II, both admissions per 1,000 persons and total admissions are considerably reduced. The 38-percent increase in GP will decrease patient days by about 0.24 x 38% = 9.1% (Table 2). Together with the change in bed supply, this will reduce occupancy rates to 81.3 percent and 87.6 percent for Alternatives I and II respectively, in contrast to 91.5 percent in 1971. In neither case would an unacceptable tension exist between demand and supply.
Discussion One must be careful with these extrapolations: a sharp rise in the number of general practitioners and a drop in the number of hospital beds (with a concomitant increase in the number of specialists per bed) would change the structure of the health care sector, and quite possibly the elasticities found for 1969 and 1971. On the other hand, the substitution elasticity of hospital care with respect to number of general practitioners may be halved (assuming a constant 90-percent occupancy and given the predicted increase in general practitioners) without changing the conclusion that realization of 4 beds per 1,000 population in 1984 will lead to a considerable reduction of consumption in the hospital sector, without increasing the tension between demand and supply. Nevertheless, some qualifications are necessary. Only part of the medical sector has been investigated, without consideration of such important issues as prevention, changing medical technology, and changing morbidity patterns. Furthermore, the included variables are only rough characteristics of the health care supply; in regard to hospital beds and specialists, for example, the different specialties are not distinguished, and first level care is represented only by the ratio of general practitioners to population. Moreover, the extrapolations have been based on cross-section results, which raises problems that are extensively discussed by Kuh [11]. On the other hand, the calculations are based on rather conservative extrapolations, and the same conclusion is indicated even if the substitution effect were only half that found. It appears, then, that bed supply is the main variable influencing hospital use, but its effect differs considerably for different age-sex groups. This is also true for the substitution of first level care for hospital care. The total elasticity
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of substitution is considerable (-0.24 in 1971), and given the expected increase in the supply of general practitioners it should be feasible to reduce bed supply to 4 per 1,000 population. For certain age groups (especially the old), however, first level care is not a sufficient substitute for hospital care, and contrary to general opinion, nursing homes do not serve as a substitute either, so other kinds of care (e.g., home care) must be made available. Acknowledgment. The authors thank Ms. G. van Beukering for her creative assistance in programming. REFERENCES
1. State Secretary of Public Health and Environment, The Netherlands. Memorandum on the Structure of Health Care. The Netherlands Ministry of Public Health and Environmental Hygiene, Aug. 9, 1974. 2. Feldstein, M. S. Economic Analysis for Health Service Efficiency. Amsterdam: NorthHolland, 1967. 3. Fuchs, V. R. (ed.). Essays in the Economics of Health and Medical Care. New York: Columbia University Press, 1972. 4. Fuchs, V. R. and M. J. Kramer. Determinants of Expenditures for Physician's Services in the United States 1948-68. National Center for Health Services Research and Development. DHEW Publication No. (HSM )73-3013. Washington: Government Printing Office, 1973. 5. Phelps, C. E. and J. P. Newhouse. Coinsurance, the price of time, and the demand for medical services. Rev Econ Stat 56:334 Aug. 1974. 6. Rosett, R. N. and L. Huang. The effect of health insurance on the demand for medical care. J Polit Econ 81:281 Apr. 1973. 7. Roemer, M. On paying the doctor and the implications of different methods. J Health Hum Behav 3:10 Spring 1962. 8. Klarman, H. E. (ed.). Empirical Studies in Health Economics. Baltimore, MD: Johns Hopkins Press, 1970. 9. Logan, R. F. L., J. S. A. Ashley, R. E. Klein, and D. M. Robson. Dynamics of Medical Care: The Liverpool Study into Use of Hospital Resources. Memoir No. 14, London School of Hygiene and Tropical Medicine, 1972. 10. van der Gaag, J., F. F. H. Rutten, and B. M. S. van Praag. Do Different Financing Systems Cause Different Demands in the Health Care Sector? Economic Institute Report 74.01, Leiden University, 1974. 11. Kuh, E. The validity of cross-sectionally estimated behaviour equations in time-series applications. Econometrica 27:197 Apr. 1959.
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