Demand for Medical Care in a Rural Setting: Racial Comparisons By Laurence A. Miners, Sandra B. Greene, Eva 1. Salber, and Richard M. Schefler Household data from a southern rural community are used to examine racial differences in the utilization of medical care services, and both monetary and nonmonetary determinants of demand are considered. Regression analysis results indicate that office waiting time (for black households) and travel time to the provider (for both black and white households) have a greater impact on demand than price. Racial differences exist in the effects of health insurance coverage and household income on household medical visit expenditures, and both need and household size are found to be consequential determinants of demand.
Previous investigations of the maldistribution and shortage of physicians as well ,as the growth of the egalitarian concept that all people deserve adequate medical care have spurred economists and other social scientists to study the demand for health care in rural areas [1-5]. Luft, Hershey, and Morrell [6] point out that certain minorities are concentrated in rural areas and that rural residents are more likely to have lower incomes. They also state that rural populations have a higher incidence of accidents and disabilities but fewer available physicians. Davis and Marshall [3] indicate that improving rural medical services may yield certain externalities such that regional development may be accelerated and migration from rural to urban areas slowed, if not reversed, thereby ameliorating urban problems. Little has actually been done, however, to quantify the factors influencing the utilization of medical services in rural areas, particularly in the South. In this article we analyze household data from a southern rural community to identify the factors influencing the demand for medical care. Of primary concern are social differences in the utilization of services. The data are stratified by race not only to account for differences in demand between black and white households but also because programs designed to improve the health status of the community This study was supported by grant no. I R03 HS02417-01 from the National Center for Health Services Research, DHEW, and by a grant from the Robert Wood Johnson Foundation. Address communications and requests for reprints to Laurence A. Miners, Lecturer, Department of Economics, SUNY, Stony Brook, NY 11794. Sandra B. Greene is director, Health Economics Research, Blue Cross-Blue Shield of North Carolina; Eva J. Salber is professor of community health sciences, Duke University Medical Center; and Richard M. Scheffler is director, Division of Manpower and Resources 261 Development, Institute of Medicine, National Academy of Sciences.
0017-9124/78/03026115/$02.O0/O @ 1978 Hospital Research and Educational Trust
FALL 1978
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may be most effective when aimed at subsets of the population. The primary purpose of this study is to investigate the effect of variables such as health status, monetary and nonmonetary health care costs, income, and household size on the household's demand for ambulatory medical care.
Related Studies and Development of the Analysis Recent concern over the rising price of health care as well as the possibility of national health insurance legislation has initiated a great deal of research on the cost of medical care [7]. The rising costs of health care are of special concern in rural areas, where families are often unable to pay these costs and are less likely to be covered by health insurance. A few rural health care centers and clinics provide medical services at greatly reduced or no cost to the patient [8-10], but even in these cases, although the monetary cost of receiving care is greatly reduced, some price is still being paid. At many clinics long lines effectively ration care to only those willing (or able) to wait extended periods of time. Travel distance and appointment waiting time may also act as rationing devices. Acton [11] has shown that as monetary prices approach zero, nonmonetary prices become relatively more important to the patient and that the number of outpatient visits decreases as travel distance to the provider increases. (It should be pointed out that since Acton's analysis was done in an urban setting, his results do not bear directly on the present study.) Although the individual has traditionally been treated as the unit of analysis, Becker [12] and Lancaster [13] have shown that the family is a realistic alternative, and there are in fact several reasons for considering the family or the household rather than the individual as the primary unit of analysis. First, the household (especially the husband and wife) decides as a unit which goods and services to demand in the marketplace. Although the actual procurement of goods and services may be carried out by various family members, it is the howehold that determines how expenditures will be made and allocates available income accordingly. Second, certain externalities associated with the use of medical care make the household a more meaningful unit of analysis (e.g., a medical visit by one household member may be of benefit to other members of the household as well; members of one household may also use the same medical care providers). Finally, since larger families tend to have more medical visits than smaller families (if only due to their larger size), we control for family size in our empirical estimates. Salber et al. [14] conducted an in-depth analysis of the data used in this study, and they found significant racial differences in the use of health care. Although both this study and that one attempted to identify factors influencing the demand for medical care, we analyze HEALTH the determinants of medical care expenditures as well. In addition, RSERVIC we present a behavioral model of the household's demand for medical care and employ regression analysis techniques to distinguish the most 262 important determinants of both family medical visits and expendi-
A behavioral model of the use of medical services. Adapted from
Andersen [15].
RURAL MEDI-
CAL CARE
DEMAND
Place of care
Number of acute and chronic conditions
Monetary, nonmonetary prices
Attitudes toward medical care
Number of beddays +
Health insurance costs
Ethnic, social background
-f
+f
Health status
Household income
Demographic
NE-ED
ENABLING FACTORS
PREDISPOSING FACTORS
varables8
tures. Although Salber et al. considered the effects of household income and thereby obtained an indication of the household's ability to pay for medical care, we indclue explicit price measures and an insurance variable in our analysis and are thereby able to ascertain the direct effect of these variables on demand for care. Another salient
distinction between the two studies is the inclusion of three nonmonetary price variables in our analysis. Our behavioral model of household demand for ambulatory medical care is based on Andersen's model of the family's use of health services [15], a model that has been widely tested [16]. Andersen postulated three major determinants of use of health services: predisposing factors, enabling factors, and need for care (see accompanying figure). Actual and perceived need are the most important of the three sets of factors, since, unless a person recognizes the need for care, the enabling and predisposing factors will not be operative. Not surprisingly, in his analysis of the determinants of family hospital use, Andersen [15, p. 54] found that need was the most important factor. Data and Methods Data used in the present study are from Rougemont/Bahama, a rural community in northern Durham County, North Carolina. The estimated population of this area was 2,275. The data were gathered as part of a community survey conducted by the department of community health sciences at Duke University between September 1973 and June 1975. The survey consisted of five visits to each household: an initi'al health survey visit followed by four panel-study visits to provide a detailed monitoring of the severity and duration of illness and disability and to determnine the utilization of medical goods and services. Each of the 704 households in Rougemont/Bahama was visited at least once, and the completion rate was extraordinarily high-94
FL
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MINERS ET AL
percent of the households in the community responded to the initial survey, and over 77 percent completed the entire study. Due to budget limitations, 53 families with incomes of $16,000 or higher were omitted from the panel-study visits, and data from the second and fourth panel-study visits were not included in this study at all. Salber et al. [14] and Thacker, Greene, and Salber [17] provide more detailed discussions and analyses of the survey. Using Andersen's model [15] as the basis for our analysis, we estimated a household demand curve for medical care visits. The model was specified as follows: first, we assumed that the family initially decides whether or not to use medical services and if the decision is positive then decides how much to spend on medical visits; second, since the variables determining the number of visits may differ from those affecting the decision of whether to demand any visits at all, we considered both the number of visits demanded and whether the household completed any medical visits at all as dependent variables. Salber et al. [14] showed that the proportion of people with no doctor visits in the last five years was greater for blacks than for whites (14.7 percent vs. 6.7 percent). However, they also showed that the average number of doctor visits in the past year-given that at least one visit occurred-was greater for blacks (4.7 visits vs. 4.0 visits). Although we stratified the population by race in this study, household size was the only other predisposing variable we included in our analysis. Earlier specifications that included age and education of the head of household yielded estimates that were highly correlated with household income. Monetary price variables on which information was collected included insurance costs and the household's usual out-of-pocket medical expenses. Nonmonetary price measures included travel time, office waiting time, and appointment waiting time. There is a fourth measure of time involved in receiving medical care, namely, the time spent with the provider once the visit actually begins, but, unfortunately, no information on this variable was available from the survey data. The model that we estimated can be stated as follows: (1) Household visits = f (need, enabling factors, predisposing factors); (2) Ambulatory medical care expenses = g (need, enabling factors, predisposing factors). The model was estimated using ordinary least squares (OLS) regression analysis. (For a discussion of regression analysis and the properties of estimators see Kmenta [18].) Factors used in the regressions are listed and defined in Table 1. We expected, a priori, that both pecuniary and nonpecuniary price measures would have a negative effect on demand. Household size (N), costs for medical insurance (COST,), household income (INCOME), and ill health (ACUTE, CHRON) were expected to have a positive influence on demand.
HEALTH
SERVICES RESEARCH
264
Results .eIIs
Means and standard deviations of the variables are presented in Table 2 (p. 266), which shows that white households tended to have
Table 1. Terms Used in Regression Estimation Acronym
Definition
RURAL MEDICAL CARE DEMAND
INCOME vSia
Total yearly household income, in thousands of dollars Binary variable scored 1 if household had one or more medical visits and 0 if none VIS Total number of household medical care visits COSTD Total household direct (out-of-pocket) expenditures for medical care visits, in thousands of dollars (includes X rays and laboratory fees) COST7 Amount household members usually pay for one medical care visit, in dollars COSTI Total yearly cost of household medical insurance premiums, in thousands of dollars Estimated average number of days spent waiting for medical TIMEA care appointments (nonurgent problems) Traveling time to usual medical care facility, in minutes TIMEr TIME,, Estimated average waiting time at usual medical care facility, in minutes ACUTE Total number of acute conditions in household during survey period CHRON Total number of chronic conditions in household during survey period CLINO1 Binary variable scored I if usual source of care for household is a clinic (HMO, hospital, neighborhood health center, etc.) and 0 otherwise NOSUl Binary variable scored 1 if household has no usual source of medical care and 0 otherwise WHITEo1 Binary variable scored 1 for white household and 0 for integrated or black household N Number of persons in household WHITE X INCOME WHITE, interacted with INCOME WHITE X CULN WHITE.I interacted with CLIN. WHITE X NOS WHIT4L" interacted with NOSE WHITE X VIS WHITE. interacted with VIS WHITE X ACUTE WHITE(a interacted with ACUTE WHITE X CHRON WHITE. interacted with CHRON WHITE X COSTz WHITE. interacted with COSTz WHITE X N WHIT4Ll interacted with N WHITE X TIME, WHITE. interacted with TIMEA WHITE XTIME, WHITE4 interacted with TIME,
more medical care visits (VIS) and pay more for them than black households; whites also had higher incomes (INCOME) and higher medical insurance costs (COST,). Appointment time (TIMEA) was longer for white households, whereas waiting time in the office (TIMEw) was longer for black households. The reason for these differences becomes apparent when one considers the values for the binary variables CLINO and NOSo0 (see Table 2), which are determined by
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Table 2. Means and Standard Deviations of Variables Used in the Analysis (Except for binary variables, standard deviations are shown in parentheses) Visit equation Direct-cost equation Variable White Black White Black households households households households VIS .............
8.493 (14.168)
6.613 (8.958)
COSTD ..........
7.786 (4.072) COST1 .......... 0.231 (0.191) CLINo1 .......... INCOME .......
5387 (3.594) 0.131 (0.151)
NOSo ...........
TIMEA ......... 17.109 (23.576) TIME. ......... 27.318 (9.864) TIMEw ......... 50.776
11.965 (11.124) N............... 3.119
(7.574)
266
0.050
(0.086)
7.444
5.402
(4.223)
(3.665)
0.222
0.128
(0.197)
(0.151)
0.196
0.655
0.061
0.079
7.688
(1.605) ACUTE ......... 2.502
4.946 (3.098) 2.957
3.103 (1.609) 2.484
(2.322)
(4.008)
(2.407)
4.736 (3237) 2.920 (3.939)
5.174
3.882
(5.070)
(383)
5.439 (5.098)
(3.937)
CHRON ........
HEALTH SERVICES RESEARCH
0.088
(0.130)
75.172
(106.180)
..........
7.218
(7.933)
7.677 (13.634) 26.591 (15.168)
(91.532)
COSTv
8.583
(13.593)
4.460
the source of care used by the majority of household members. CLINO0 was scored 1 if the household's usual source of care was a clinic, and NOSO was scored 1 if the majority of household members had no usual source of care. Otherwise these variables were given a score of zero. Therefore the mean of each variable indicates the percent of households obtaining care in that manner. A third response (private physician) was possible; however, it was necessary to omit one of the binary variables from the regression analysis in order to avoid an indeterminant solution. The three binary variables sum to one (or 100 percent) by definition. Including all three variables in the analysis would have been analogous to including a constant, and since the regression procedure already contained a constant, the solution to the system would have been indeterminant if all three binary variables had been included.
Table 2 indicates that 19.6 percent of white households had clin- RURAL MEDICAL CARE ics as their usual source of care, whereas the corresponding figure for DEMAND black households was 65.5 percent; 74.3 percent of white households and 26.6 percent of black households responded that they usually received care at a private physician's office. Since appointments are generally necessary for private physicians but not for many clinics, it is not surprising that appointment time was greater for whites than for blacks. Accordingly, blacks spent more time waiting in the office (TIMEw) than whites. (It should be pointed out that information on the time-price variables (TIMET, TIMEw, and TIMEA) was collected for the household as a unit.) White and black households appear to have had approximately the same number of acute conditions (ACUTE), but white households seem to have had a slightly higher average number of chronic conditions (CHRON). The difference in number of conditions could be due to a bias in reporting or to differences in family size between black and white households. If conditions are calculated on an individual basis, whites reported 1.4 acute conditions and 1.42 chronic conditions per year per person, whereas blacks reported only 0.98 acute conditions and 0.83 chronic conditions per year per person. Earlier estimates of our model included two other estimates of need-self-reported health status (respondents were asked to rate individual family health status as excellent, good, fair, or poor) and number of days spent in bed due to illness or injury. All three measures of need were found to be significant and to have the expected influence on demand, but the number of acute and chronic conditions was the most important of these variables. Since the three variables were very highly correlated, measures of health status and days in bed were omitted from the present analysis. The regression results are presented in Table 3 (p. 268). Considering first the household-visit equation for white households, one finds that travel time (TIMET), office waiting time (TIMEw), and the amount usually paid for a visit (COSTv) all have the expected sign. The effects of TIMET and COSTv on household demand for ambulatory medical care were significant at the 90-percent level using a one-tailed test. Unfortunately, the effects of both TIMEA and TIMEw are insignificant. Income did not have a significant impact on household demand for medical care visits, and, although contrary to our expectations, this finding is consistent with the analysis by Salber et al. [14]. The effect of insurance cost (COST,) on demand for medical care was also not statistically significant; however, the OLS regression results in Table 4 (p. 270) indicate that insurance costs may be a significant determinant of the household's decision to enter the medical care market. It is recognized that other estimation techniques, e.g., probit analysis, yield more efficient estimates than OLS. However, the results given in Table 4 are unbiased and are presented as approximates of the "true" parameter values. (See Kmenta [18, pp. 425-427].) FALL 1978 Family size and the number of acute and chronic conditions were all positive and significant as expected.
267
MINERS ET AL.
Table 3. Racial Diferences in Demand and Expenditures for Ambulatory Medical Care: OLS Regression Results (t values are shown in parenthews) Dependent variable
Indepen-
COSTD
IS
den t__
variable
White
Black
households households
TIMEA ......... TIMET
.........
0.0218 (0.51) -0.1563
(1.54)* TIMEw
.........
0.0099
COSTv
.........
-0.1292 (1.48)*
INCOME .......
0.0012
(0.93)
(0.01) COST1 ......... 0.0916 (0.02) 1.1584 N .............. ACUTE ........
CHRON ........
White
0.0288 (0.62)
-0.0568
(1.32)* -0.0073
(1.25) 0.0024 (0.03)
0.0511 (0.25) -5.1322 (1.13) 0.2923
(1.28)*
0.0003 0.1037
(2.45)* -0.0114 (1.96)*
-0.0064 (2.04)
(0.32)
0.9005
0.0187
-0.0018
(4.67)*
(4.98)*
0.6282
1.1093
0.0018
(3.17)*
(6.04)*
(0.59) -0.0021 (0.80)
(1.03) -0.0216
(0.86) ..........
vis ............
5.1495 (1.10)
0.4209 (0.22)
F ratio .........
201 3.719
93 15.742
R2
0.149
0.631
Constant ........
0.0063
(2.30) 0.0206
(0.14)
(1.70)* 0.9751 (2.12)*
CLIN0, .........
NOSo0
Black
households households
0.1377 (2.86)* 0.0016 (2.64)* 0.0238
-0.0135 (0.60) -0.0337
(0.73)
(0.92)
0.0052 (3.17)* 0.0324 (1.17)
223 7.393 0.217
87 3.147 0.244
No. of ob-
servations.
*
.............
Variable is significant at the 10-percent level or lower using a one-tailed test.
The only significant price variable in the visit equation for black households was TIMEr. The level of significance of TIMEw was greater for black households than for white households, and COSTv HEALTH was insignificant for blacks although significant for whites. Since SERVICES COSTv represents out-of-pocket expenses not covered by health insurRESEARCH ance premiums, it was expected that this variable would be more important than overall price in determining the demand for services.
268
The result obtained here implies that monetary price variables are RURAL MEDICARE perhaps less important to black households than nonmonetary ones CAL DEMAND (TIMET, TIMEw). The lack of significance of monetary price for black households could be due to the low price that many blacks pay for care. In many instances those receiving care at clinics either pay nothing or are charged reduced prices. The effects of income, insurance costs, and household size in the equation for black households are similar to those in the equation for white households, and the coefficients for the number of acute and chronic conditions are positive and highly significant for both equations. Examination of the F ratios indicates that both regression equations fit the data rather well. The R2 values are actually low, which is common with cross-sectional data of this type. Columns 3 and 4 in Table 3 present the results for total direct household expenditures on medical care visits, COSTD. It should be remembered that the household expenditure variable (COSTD) is different from the amount that the family usually pays for a medical care visit (COSTV). Data on household medical care expenditures were collected during the panel visits and are lump-sum amounts. Data on the usual-fee variable-the average amount that the household usually paid at its place of care-were collected only once. The nonpecuniary price measures were excluded from the analysis since these variables were not expected to have a significant impact on monetary expenditures. The variables relating to the household's usual source of care (CLINOG, NOS01) were included in the analysis since it was expected that these variables might be determinants of medical care costs, but values for these variables were generally not significant. (The positive and significant coefficient for NOSO1 in the equation for white households indicates that white households tend to pay more for medical services if they have no usual source of care than if they have a private physician.) There are two possible explanations for the lack of significance of these variables. First, it is possible that monetary costs are small or near zero for a large portion of the population; hence place of care has little effect on the amount paid. Second, it is possible that the two variables CLINo1 and NOSo0 did not accurately represent usual sources of care. Since different household members may attend different places of care, the accuracy of these household indexes is suspect. Although INCOME was insignificant for whites and significant for blacks, the opposite was true for the insurance variable COST,. This finding implies that health insurance coverage may be an important determinant of medical care costs for white households, whereas black households tend to pay for medical care out of their own pockets. Household size, as in the visit equation, was significant for both whites and blacks; however, there was a negative impact on household medical visit expenses. When compared with the results in columns 1 and 2 (Table 3), these latter results imply that as household size increases FALL 1978 there tend to be more medical care visits, but household medical expenses actually decrease. There are two possible explanations for this
269
MINERS ET AL.
Table 4. Variables Affecting the Household's Decision to Receive Medical Care: OLS Regression Results (t values are shown in parentheses)
Dependent variable Independent variable
viSa, White households
TIMEA ......................... -0.0004 (0.43) TIMEr ......................... -0.0056 (2.83)*
TIMEw
.........................
COSTv .....................
COSTI ......................... INCOME ....................... N ..........................
CHRON ........................ ........................
No .of observations ........ ..... F ratio .........................
(1.34)* -0.0012
(0.50) -0.00004
(1A3)*
(0.12)
0.0020
(1.14)
0.1474
0.0018
(0.38)
0.1598
(1.43)*
(0.65)
-0.0010
-0.0086
(0.20)
(0.76) 0.0123
0.0015
0.0184 (2.04)* 0.0015
(0.39) Constant
-0.0034
-0.0003
(0.11) ACUTE .....................
Black
households
(0.98)
0.0045 (0.42) 0.0109
(1.09)
0.9862
0.7955
(10.75)*
(7.50)*
201 2.732
93 0854 0.085
R' ............................ 0.114 * Variable is significant at the 10-percent level or lower a one-tailed test.
using
finding. First, larger households (holding income constant) are more likely to be eligible for Medicaid support and thereby have reduced out-of-pocket medical expenses. Second, larger households may pay less for ambulatory medical care due to the setting in which treatment is received (clinic vs. private physician). The correlation coefficient of household size and the percent of households receiving care at clinics was positive, whereas the correlation coefficient of household size and the percent of households receiving care from private physicians was negative. However both of these coefficients were too small to lend much support to this argument. HEALTH The number of acute and chronic conditions had a significant imSERVICES on medical visit costs for white households but not for black. pact RESEARCH Perhaps this is an indication of the large number of blacks who receive free care at clinics, since these variables are important in ex-
270
plaining the demand for visits by black households. Also, for white RURAL MEDICAL CARE households, acute conditions seem to account for a larger part of med- DEMAND ical care expenses than do chronic conditions. As expected, the number of household visits had a positive and significant effect on medical visit costs for both black and white households. Our empirical results lend some support to our hypotheses of the effect of waiting time on the demand for care and of racial differences in the use of medical care services. Travel time had a significant impact on the use of medical care by both black and white households, whereas the amount the household usually paid for care was important only for whites. Insurance premiums had a significant effect on medical care costs for white households, whereas for black households the only significant expenditure variable was total family income. Nevertheless, we considered it desirable to test whether there might be a statistically significant difference between the equations for white households and the equations for black households. In order to test for any differences, we pooled the observations from the black and white subgroups and tested the regression model again with the new combined data set. Murphy [19] describes this procedure as "a test of equality between coefficients in two identical models based on two different data sets." We performed this test for both the household-visit and the household-expenditure equations, and the results indicated an overall significant difference between the white and black expenditure equations but not the household-visit equations. (The calculated F statistics were 2.48 for the expenditure regressions and 0.80 for the visit equations. The former F statistic is significant at the 99-percent confidence level, and the latter is not significant at any acceptable level of confidence.) The results imply that although there was a disparity between white and black expenditures for medical care, there were no significant racial dissimilarities in the utilization of medical care services. Notice that in the household-visit regressions, except for office waiting time and average cost of a medical visit, the equations for blacks and whites were generally comparable in both sign and significance. However, the results for the household-expenditure equations indicated racial differences in method of payment (income vs. health insurance), the effect of acute and chronic conditions, and, in one instance, usual source of care. The lack of a significant racial difference between the two household-visit models does not imply that there were no dissimilarities between the individual variables in the equations for blacks and for whites, only that there was no significant difference overall. The household-visit results shown in Table 5 (p. 272) indicate that when the data sets are pooled, household size and number of chronic conditions have a significant effect on demand when interacted with race. To form an interaction term with race, the appropriate variable was multiplied by WHITE01-i.e., multiplied by 0 or 1. Kmenta [18] and FALL 1978 others have shown that when a dummy variable is included in a regression equation the intercept of the fitted regression line changes
271
MINERS ET AL.
Table 5. Pooled OLS Regression Results with Racial Interaction Terms (t values are shown in parentheses) Dependent variable Independent variable
vis
COSTt
TIMEA ............. 0.0242 (0.72)
TIMEz ............. -0.0974 (l163)* TIMEw ............. -0.0089 (124) COSTr .............. -0.1096 (1.59)* COST1 .............. INCOME ........... N................... ACUTE ............. CHRON ............ CLING .............. NOS1 ............... vis ................. WHITE,u ........... WHITE X N ......... WHITE X CHRON WHITE X INCOME WHITE X CLIN ..... WHITE X NOS ...... WHITE X VIS ....... WHITE-X ACUTE ... Constant ............ No. of observations F ratio ..............
-0.7565 (0.19) 0.0234 (0.12) 0.2530 (0.60)
0.0889 (2.48)* 0o.0063 (1.84)* -0.0083 (2.48)*
0.9130 (3.34)* 1.0916 (3.26)*
(0.44) 0.0012 (0.83) -0.0078 (0.25) -0.0220 (0.35) 0.0046 (2.31)*
0.5742 (0.17) 1.0224 (1.58)* -0.4454 (1.22)
0.0078 (0.21)
-0.0018
-0.0062 (1.65)* -0.0187 (0.48) 0.1539 (2.00)* -0.0030 (1.45)* 0.0198 (3.87)* 2.3160 (0.73) 294 6.700
0.0176 (0.51) 310 6.271 0.223 0.229 R' * Variable is significant at the 10-percent level udng a onetailed test ..................
HEALTH
SERVICES
RESEARCH
272
accordingly. However, if the dummy variable is interacted with specific variables in the regression equation, then the slope of the fitted line varies. Hence it is possible to obtain estimates of the effect of a binary variable on specific variables in the regression equation. In this way the resulting coefficient for the interaction term will indicate racial differences in the effect of the variable on demand even though the combined data set includes observations from both races. As Table 5 shows, the F ratios for both equations were significant, and most of the coefficients were also significant, implying. that our model does at least an adequate job of accounting for the variation in the data. The elasticity measures presented in Table 6 also tend to substantiate many of our earlier hypotheses and results. Elasticity is a
Table 6. Elasticities of Demand and Expediures for Medical Care with Respect to Changes in the Independent Variable
CAL CARE DEMAND
(Elasticities were calculated at the mean for variables significant at the 10-percent level using a one-tailed test) VIS COSTS Independent White Black White Black variable house- house- house- house- VIS COSTD holds holds holds holds -0.3341 TIMEr ....-0.07 -0.2284 -0.0659 -0.0830 TIMEw .... COSTV .... -0.1820 -0.1473 N ....... 0.4254 0.2186 -0.4020 -0.6062 0.4458 -0.8 03059 0.4209 ACUTE ... 02873 0.4027 0.5278 CHRON ... 0.3827 0.6512 0.4748 0.1644 INCOME .. 0.6807 COSTI 0.2263 0.2616 0.0760 NOS.I. 0.0955 VIS 0.1561 0.7507 0.2602
measure of the percentage rate of change in the dependent variable in response to a change in one of the independent variables. Since it is a rate-of-change variable and not attached to a specific measuring unit, it allows comparisons among variables and across data sets. Travel time emerges as the single most important variable in determining the number of physician visits demanded by white households. The difference between the travel time elasticity and the elasticity of visits (demand) with respect to monetary price (COSTv) is particularly noteworthy. Travel time appears more important than waiting time in determining the number of visits by black families, but, as mentioned before, the level of need in the household (as measured by number of acute and chronic conditions) had the largest influence on demand. The racial differences in the effect of need on the demand for medical care suggest that there may be a disparity in the way blacks and whites report acute and chronic conditions or that blacks wait until they are relatively sicker before demanding care. The results for the expenditure equations (columns 3 and 4 in Table 6) indicate that household income was relatively more important for blacks than insurance costs were for whites in explaining medical visit costs. Phelps [20] calculated the elasticity of physician expenses with respect to income to be 0.11-clearly much smaller than our finding for black households. Our finding could have been due to the lack of health insurance coverage of black families in our sample or our concentration on middle and lower-income households. Household size (N) and need (ACUTE) had a larger impact on expenditures of white households than other variables, induding insurance costs. This finding implies that health insurance coverage, although
FALL
1978
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significant, was not overly influential. It should be pointed out that COST, measures only what households paid for health insurance premiums; a more reliable and interesting variable would have included the degree and scope of coverage as well. The pooled regression elasticities indicate the overall importance of need, household size, and travel time (at least for whites) in accounting for the medical care services received by the household. Monetary price, income, and insurance costs, although at times significant, were dominated by these other variables.
Discussion The results indicate that not only are there racial differences in the demand for care but that traditional economic variables such as monetary price and income may not be the most important determinants of demand. The time household members spend traveling to their source of care has a negative and significant influence on the amount of medical services demanded by both black and white households and is the most important determinant of demand for whites. The majority of black households receive care at clinics or hospitals, and, as expected, waiting time also has a negative effect on the demand for care by these households. The importance of travel time and waiting time in our analysis suggests that the current location and distribution of ambulatory medical services in the community may either not be optimal or not be used to full advantage. Furthermore, the influence of these and other nonmonetary price variables on both expenditures and the demand for care appears to be different for blacks and whites. Perhaps what is needed is a more liberal scheduling of physician and clinic hours and efforts to get primary medical care to various subgroups of the population more efficiently. Furthermore it appears that lowering the monetary price (even as low as zero) may not be the absolute answer to the problem. Above all, our results indicate that ill health or need is the primary factor affecting the utilization of services. Although the data we use are limited to one rural community, our results lend support to earlier studies of the demand for medical care [4,11,20].
REFERENCES 1. Fein, R. The Doctor Shortage: An Economic Diagnosis. Washington, DC:
HEALTH SERVICES RESEARCH
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Brookings Institution, 1967. 2. Scheffler, R.M. The regional distribution of physicians and specialists. Rev Reg Stud 2:63 Winter 1972. 3. Davis, K. and R. Marshall. Rural Health Manpower and Delivery. In R.M. Scheffler (ed.), Research in Health Economics: An Annual Compilation of Research. Greenwich, CT: Jai Press (forthcoming). 4. Kane, R.L. and P.F. Westover. Rural health care research: Past accomplishments and future challenges. Health Care Dimensions 3:123 Dec. 1976. 5. Wright, D.D. Recent rural health research. J Community Health 2:60 Fall 1976. 6. Luft, H.S., J.C. Hershey, and J. Morrell. Factors affecting the use of physician services in a rural community. Am J Public Health 66:865 Sept. 1976. 7. Rosett, R.N. (ed.). The Role of Health Insurance in the Health Services Sector. New York: Neale Watson, 1976. 8. Cowen, D.L., D.L. Hochstrasser, C. Friedericks, and T. Payne. Problems in the development of a rural primary care center. J Community Health 2:52 Fall 1976.
9. Garrett, M.L., D.L. Miles, and A.G. LeBaron. Rural areas pose special problems for providing social services. Hospitals 50:77 Nov. 16, 1976. 10. Reid, R.A., J.B. Eberle, L. Gonzales, N.L. Querk, and R. Oseasohn. Rural medical care: An experimental delivery system. Am J Public Health 65:266 Mar.
RURAL MEDICAL CARE DEMAND
1975. 11. Acton, J.P. Nonmonetary factors in the demand for medical services: Some empirical evidence. J Polit Econ 83:595 June 1975. 12. Becker, G.S. A theory of social interactions. J Polit Econ 82:1063 Nov-Dec. 1974. 13. Lancaster, K. The theory of household behavior: Some foundations. Ann Econ Soc Meas 4:5 Winter 1975. 14. Salber, E.J., S.B. Greene, J.J. Feldman, and G. Hunter. Access to health care in a southern rural community. Med Care 14:971 Dec. 1976. 15. Andersen, R. A Behavioral Model of Families' Use of Health Services. Center for Health Administration Studies Research Series 25, University of Chicago, 1968. 16. Andersen, R., J. Kravits, and O.W. Anderson (eds.). Equity in Health Services: Empirical Analysis in Social Policy. Cambridge, MA: Ballinger, 1975. 17. Thacker, S.B., S.B. Greene, and E.J. Salber. Hospitalizations in a southern rural community: An application of the ecology model. Int J Epidemiol 6:55 Mar.
1977.
18. Kmenta, J. Elements of Econometrics. New York: Macmillan, 1971. 19. Murphy, J.L. Introductory Econometrics, p. 237. Homewood, IL: Irwin, 1973. 20. Phelps, C.E. Effects of Insurance on Demand for Medical Care. In R. Andersen, J. Kravits, and O.W. Anderson (eds.), Equity in Health Services: Empirical Analysis in Social Policy, pp. 105-130. Cambridge, MA: Ballinger, 1975.
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Previous investigations of the maldistribution and shortage of physicians as well ,as the growth of the egalitarian concept that all people deserve adequate medical care have spurred economists and other social scientists to study the demand for health care in rural areas [1-5]. Luft, Hershey, and Morrell [6] point out that certain minorities are concentrated in rural areas and that rural residents are more likely to have lower incomes. They also state that rural populations have a higher incidence of accidents and disabilities but fewer available physicians. Davis and Marshall [3] indicate that improving rural medical services may yield certain externalities such that regional development may be accelerated and migration from rural to urban areas slowed, if not reversed, thereby ameliorating urban problems. Little has actually been done, however, to quantify the factors influencing the utilization of medical services in rural areas, particularly in the South. In this article we analyze household data from a southern rural community to identify the factors influencing the demand for medical care. Of primary concern are social differences in the utilization of services. The data are stratified by race not only to account for differences in demand between black and white households but also because programs designed to improve the health status of the community This study was supported by grant no. I R03 HS02417-01 from the National Center for Health Services Research, DHEW, and by a grant from the Robert Wood Johnson Foundation. Address communications and requests for reprints to Laurence A. Miners, Lecturer, Department of Economics, SUNY, Stony Brook, NY 11794. Sandra B. Greene is director, Health Economics Research, Blue Cross-Blue Shield of North Carolina; Eva J. Salber is professor of community health sciences, Duke University Medical Center; and Richard M. Scheffler is director, Division of Manpower and Resources 261 Development, Institute of Medicine, National Academy of Sciences.
0017-9124/78/03026115/$02.O0/O @ 1978 Hospital Research and Educational Trust
FALL 1978
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MINERS ET AL.
may be most effective when aimed at subsets of the population. The primary purpose of this study is to investigate the effect of variables such as health status, monetary and nonmonetary health care costs, income, and household size on the household's demand for ambulatory medical care.
Related Studies and Development of the Analysis Recent concern over the rising price of health care as well as the possibility of national health insurance legislation has initiated a great deal of research on the cost of medical care [7]. The rising costs of health care are of special concern in rural areas, where families are often unable to pay these costs and are less likely to be covered by health insurance. A few rural health care centers and clinics provide medical services at greatly reduced or no cost to the patient [8-10], but even in these cases, although the monetary cost of receiving care is greatly reduced, some price is still being paid. At many clinics long lines effectively ration care to only those willing (or able) to wait extended periods of time. Travel distance and appointment waiting time may also act as rationing devices. Acton [11] has shown that as monetary prices approach zero, nonmonetary prices become relatively more important to the patient and that the number of outpatient visits decreases as travel distance to the provider increases. (It should be pointed out that since Acton's analysis was done in an urban setting, his results do not bear directly on the present study.) Although the individual has traditionally been treated as the unit of analysis, Becker [12] and Lancaster [13] have shown that the family is a realistic alternative, and there are in fact several reasons for considering the family or the household rather than the individual as the primary unit of analysis. First, the household (especially the husband and wife) decides as a unit which goods and services to demand in the marketplace. Although the actual procurement of goods and services may be carried out by various family members, it is the howehold that determines how expenditures will be made and allocates available income accordingly. Second, certain externalities associated with the use of medical care make the household a more meaningful unit of analysis (e.g., a medical visit by one household member may be of benefit to other members of the household as well; members of one household may also use the same medical care providers). Finally, since larger families tend to have more medical visits than smaller families (if only due to their larger size), we control for family size in our empirical estimates. Salber et al. [14] conducted an in-depth analysis of the data used in this study, and they found significant racial differences in the use of health care. Although both this study and that one attempted to identify factors influencing the demand for medical care, we analyze HEALTH the determinants of medical care expenditures as well. In addition, RSERVIC we present a behavioral model of the household's demand for medical care and employ regression analysis techniques to distinguish the most 262 important determinants of both family medical visits and expendi-
A behavioral model of the use of medical services. Adapted from
Andersen [15].
RURAL MEDI-
CAL CARE
DEMAND
Place of care
Number of acute and chronic conditions
Monetary, nonmonetary prices
Attitudes toward medical care
Number of beddays +
Health insurance costs
Ethnic, social background
-f
+f
Health status
Household income
Demographic
NE-ED
ENABLING FACTORS
PREDISPOSING FACTORS
varables8
tures. Although Salber et al. considered the effects of household income and thereby obtained an indication of the household's ability to pay for medical care, we indclue explicit price measures and an insurance variable in our analysis and are thereby able to ascertain the direct effect of these variables on demand for care. Another salient
distinction between the two studies is the inclusion of three nonmonetary price variables in our analysis. Our behavioral model of household demand for ambulatory medical care is based on Andersen's model of the family's use of health services [15], a model that has been widely tested [16]. Andersen postulated three major determinants of use of health services: predisposing factors, enabling factors, and need for care (see accompanying figure). Actual and perceived need are the most important of the three sets of factors, since, unless a person recognizes the need for care, the enabling and predisposing factors will not be operative. Not surprisingly, in his analysis of the determinants of family hospital use, Andersen [15, p. 54] found that need was the most important factor. Data and Methods Data used in the present study are from Rougemont/Bahama, a rural community in northern Durham County, North Carolina. The estimated population of this area was 2,275. The data were gathered as part of a community survey conducted by the department of community health sciences at Duke University between September 1973 and June 1975. The survey consisted of five visits to each household: an initi'al health survey visit followed by four panel-study visits to provide a detailed monitoring of the severity and duration of illness and disability and to determnine the utilization of medical goods and services. Each of the 704 households in Rougemont/Bahama was visited at least once, and the completion rate was extraordinarily high-94
FL
FALL
263
MINERS ET AL
percent of the households in the community responded to the initial survey, and over 77 percent completed the entire study. Due to budget limitations, 53 families with incomes of $16,000 or higher were omitted from the panel-study visits, and data from the second and fourth panel-study visits were not included in this study at all. Salber et al. [14] and Thacker, Greene, and Salber [17] provide more detailed discussions and analyses of the survey. Using Andersen's model [15] as the basis for our analysis, we estimated a household demand curve for medical care visits. The model was specified as follows: first, we assumed that the family initially decides whether or not to use medical services and if the decision is positive then decides how much to spend on medical visits; second, since the variables determining the number of visits may differ from those affecting the decision of whether to demand any visits at all, we considered both the number of visits demanded and whether the household completed any medical visits at all as dependent variables. Salber et al. [14] showed that the proportion of people with no doctor visits in the last five years was greater for blacks than for whites (14.7 percent vs. 6.7 percent). However, they also showed that the average number of doctor visits in the past year-given that at least one visit occurred-was greater for blacks (4.7 visits vs. 4.0 visits). Although we stratified the population by race in this study, household size was the only other predisposing variable we included in our analysis. Earlier specifications that included age and education of the head of household yielded estimates that were highly correlated with household income. Monetary price variables on which information was collected included insurance costs and the household's usual out-of-pocket medical expenses. Nonmonetary price measures included travel time, office waiting time, and appointment waiting time. There is a fourth measure of time involved in receiving medical care, namely, the time spent with the provider once the visit actually begins, but, unfortunately, no information on this variable was available from the survey data. The model that we estimated can be stated as follows: (1) Household visits = f (need, enabling factors, predisposing factors); (2) Ambulatory medical care expenses = g (need, enabling factors, predisposing factors). The model was estimated using ordinary least squares (OLS) regression analysis. (For a discussion of regression analysis and the properties of estimators see Kmenta [18].) Factors used in the regressions are listed and defined in Table 1. We expected, a priori, that both pecuniary and nonpecuniary price measures would have a negative effect on demand. Household size (N), costs for medical insurance (COST,), household income (INCOME), and ill health (ACUTE, CHRON) were expected to have a positive influence on demand.
HEALTH
SERVICES RESEARCH
264
Results .eIIs
Means and standard deviations of the variables are presented in Table 2 (p. 266), which shows that white households tended to have
Table 1. Terms Used in Regression Estimation Acronym
Definition
RURAL MEDICAL CARE DEMAND
INCOME vSia
Total yearly household income, in thousands of dollars Binary variable scored 1 if household had one or more medical visits and 0 if none VIS Total number of household medical care visits COSTD Total household direct (out-of-pocket) expenditures for medical care visits, in thousands of dollars (includes X rays and laboratory fees) COST7 Amount household members usually pay for one medical care visit, in dollars COSTI Total yearly cost of household medical insurance premiums, in thousands of dollars Estimated average number of days spent waiting for medical TIMEA care appointments (nonurgent problems) Traveling time to usual medical care facility, in minutes TIMEr TIME,, Estimated average waiting time at usual medical care facility, in minutes ACUTE Total number of acute conditions in household during survey period CHRON Total number of chronic conditions in household during survey period CLINO1 Binary variable scored I if usual source of care for household is a clinic (HMO, hospital, neighborhood health center, etc.) and 0 otherwise NOSUl Binary variable scored 1 if household has no usual source of medical care and 0 otherwise WHITEo1 Binary variable scored 1 for white household and 0 for integrated or black household N Number of persons in household WHITE X INCOME WHITE, interacted with INCOME WHITE X CULN WHITE.I interacted with CLIN. WHITE X NOS WHIT4L" interacted with NOSE WHITE X VIS WHITE. interacted with VIS WHITE X ACUTE WHITE(a interacted with ACUTE WHITE X CHRON WHITE. interacted with CHRON WHITE X COSTz WHITE. interacted with COSTz WHITE X N WHIT4Ll interacted with N WHITE X TIME, WHITE. interacted with TIMEA WHITE XTIME, WHITE4 interacted with TIME,
more medical care visits (VIS) and pay more for them than black households; whites also had higher incomes (INCOME) and higher medical insurance costs (COST,). Appointment time (TIMEA) was longer for white households, whereas waiting time in the office (TIMEw) was longer for black households. The reason for these differences becomes apparent when one considers the values for the binary variables CLINO and NOSo0 (see Table 2), which are determined by
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1978
265
MINERS ET AL.
Table 2. Means and Standard Deviations of Variables Used in the Analysis (Except for binary variables, standard deviations are shown in parentheses) Visit equation Direct-cost equation Variable White Black White Black households households households households VIS .............
8.493 (14.168)
6.613 (8.958)
COSTD ..........
7.786 (4.072) COST1 .......... 0.231 (0.191) CLINo1 .......... INCOME .......
5387 (3.594) 0.131 (0.151)
NOSo ...........
TIMEA ......... 17.109 (23.576) TIME. ......... 27.318 (9.864) TIMEw ......... 50.776
11.965 (11.124) N............... 3.119
(7.574)
266
0.050
(0.086)
7.444
5.402
(4.223)
(3.665)
0.222
0.128
(0.197)
(0.151)
0.196
0.655
0.061
0.079
7.688
(1.605) ACUTE ......... 2.502
4.946 (3.098) 2.957
3.103 (1.609) 2.484
(2.322)
(4.008)
(2.407)
4.736 (3237) 2.920 (3.939)
5.174
3.882
(5.070)
(383)
5.439 (5.098)
(3.937)
CHRON ........
HEALTH SERVICES RESEARCH
0.088
(0.130)
75.172
(106.180)
..........
7.218
(7.933)
7.677 (13.634) 26.591 (15.168)
(91.532)
COSTv
8.583
(13.593)
4.460
the source of care used by the majority of household members. CLINO0 was scored 1 if the household's usual source of care was a clinic, and NOSO was scored 1 if the majority of household members had no usual source of care. Otherwise these variables were given a score of zero. Therefore the mean of each variable indicates the percent of households obtaining care in that manner. A third response (private physician) was possible; however, it was necessary to omit one of the binary variables from the regression analysis in order to avoid an indeterminant solution. The three binary variables sum to one (or 100 percent) by definition. Including all three variables in the analysis would have been analogous to including a constant, and since the regression procedure already contained a constant, the solution to the system would have been indeterminant if all three binary variables had been included.
Table 2 indicates that 19.6 percent of white households had clin- RURAL MEDICAL CARE ics as their usual source of care, whereas the corresponding figure for DEMAND black households was 65.5 percent; 74.3 percent of white households and 26.6 percent of black households responded that they usually received care at a private physician's office. Since appointments are generally necessary for private physicians but not for many clinics, it is not surprising that appointment time was greater for whites than for blacks. Accordingly, blacks spent more time waiting in the office (TIMEw) than whites. (It should be pointed out that information on the time-price variables (TIMET, TIMEw, and TIMEA) was collected for the household as a unit.) White and black households appear to have had approximately the same number of acute conditions (ACUTE), but white households seem to have had a slightly higher average number of chronic conditions (CHRON). The difference in number of conditions could be due to a bias in reporting or to differences in family size between black and white households. If conditions are calculated on an individual basis, whites reported 1.4 acute conditions and 1.42 chronic conditions per year per person, whereas blacks reported only 0.98 acute conditions and 0.83 chronic conditions per year per person. Earlier estimates of our model included two other estimates of need-self-reported health status (respondents were asked to rate individual family health status as excellent, good, fair, or poor) and number of days spent in bed due to illness or injury. All three measures of need were found to be significant and to have the expected influence on demand, but the number of acute and chronic conditions was the most important of these variables. Since the three variables were very highly correlated, measures of health status and days in bed were omitted from the present analysis. The regression results are presented in Table 3 (p. 268). Considering first the household-visit equation for white households, one finds that travel time (TIMET), office waiting time (TIMEw), and the amount usually paid for a visit (COSTv) all have the expected sign. The effects of TIMET and COSTv on household demand for ambulatory medical care were significant at the 90-percent level using a one-tailed test. Unfortunately, the effects of both TIMEA and TIMEw are insignificant. Income did not have a significant impact on household demand for medical care visits, and, although contrary to our expectations, this finding is consistent with the analysis by Salber et al. [14]. The effect of insurance cost (COST,) on demand for medical care was also not statistically significant; however, the OLS regression results in Table 4 (p. 270) indicate that insurance costs may be a significant determinant of the household's decision to enter the medical care market. It is recognized that other estimation techniques, e.g., probit analysis, yield more efficient estimates than OLS. However, the results given in Table 4 are unbiased and are presented as approximates of the "true" parameter values. (See Kmenta [18, pp. 425-427].) FALL 1978 Family size and the number of acute and chronic conditions were all positive and significant as expected.
267
MINERS ET AL.
Table 3. Racial Diferences in Demand and Expenditures for Ambulatory Medical Care: OLS Regression Results (t values are shown in parenthews) Dependent variable
Indepen-
COSTD
IS
den t__
variable
White
Black
households households
TIMEA ......... TIMET
.........
0.0218 (0.51) -0.1563
(1.54)* TIMEw
.........
0.0099
COSTv
.........
-0.1292 (1.48)*
INCOME .......
0.0012
(0.93)
(0.01) COST1 ......... 0.0916 (0.02) 1.1584 N .............. ACUTE ........
CHRON ........
White
0.0288 (0.62)
-0.0568
(1.32)* -0.0073
(1.25) 0.0024 (0.03)
0.0511 (0.25) -5.1322 (1.13) 0.2923
(1.28)*
0.0003 0.1037
(2.45)* -0.0114 (1.96)*
-0.0064 (2.04)
(0.32)
0.9005
0.0187
-0.0018
(4.67)*
(4.98)*
0.6282
1.1093
0.0018
(3.17)*
(6.04)*
(0.59) -0.0021 (0.80)
(1.03) -0.0216
(0.86) ..........
vis ............
5.1495 (1.10)
0.4209 (0.22)
F ratio .........
201 3.719
93 15.742
R2
0.149
0.631
Constant ........
0.0063
(2.30) 0.0206
(0.14)
(1.70)* 0.9751 (2.12)*
CLIN0, .........
NOSo0
Black
households households
0.1377 (2.86)* 0.0016 (2.64)* 0.0238
-0.0135 (0.60) -0.0337
(0.73)
(0.92)
0.0052 (3.17)* 0.0324 (1.17)
223 7.393 0.217
87 3.147 0.244
No. of ob-
servations.
*
.............
Variable is significant at the 10-percent level or lower using a one-tailed test.
The only significant price variable in the visit equation for black households was TIMEr. The level of significance of TIMEw was greater for black households than for white households, and COSTv HEALTH was insignificant for blacks although significant for whites. Since SERVICES COSTv represents out-of-pocket expenses not covered by health insurRESEARCH ance premiums, it was expected that this variable would be more important than overall price in determining the demand for services.
268
The result obtained here implies that monetary price variables are RURAL MEDICARE perhaps less important to black households than nonmonetary ones CAL DEMAND (TIMET, TIMEw). The lack of significance of monetary price for black households could be due to the low price that many blacks pay for care. In many instances those receiving care at clinics either pay nothing or are charged reduced prices. The effects of income, insurance costs, and household size in the equation for black households are similar to those in the equation for white households, and the coefficients for the number of acute and chronic conditions are positive and highly significant for both equations. Examination of the F ratios indicates that both regression equations fit the data rather well. The R2 values are actually low, which is common with cross-sectional data of this type. Columns 3 and 4 in Table 3 present the results for total direct household expenditures on medical care visits, COSTD. It should be remembered that the household expenditure variable (COSTD) is different from the amount that the family usually pays for a medical care visit (COSTV). Data on household medical care expenditures were collected during the panel visits and are lump-sum amounts. Data on the usual-fee variable-the average amount that the household usually paid at its place of care-were collected only once. The nonpecuniary price measures were excluded from the analysis since these variables were not expected to have a significant impact on monetary expenditures. The variables relating to the household's usual source of care (CLINOG, NOS01) were included in the analysis since it was expected that these variables might be determinants of medical care costs, but values for these variables were generally not significant. (The positive and significant coefficient for NOSO1 in the equation for white households indicates that white households tend to pay more for medical services if they have no usual source of care than if they have a private physician.) There are two possible explanations for the lack of significance of these variables. First, it is possible that monetary costs are small or near zero for a large portion of the population; hence place of care has little effect on the amount paid. Second, it is possible that the two variables CLINo1 and NOSo0 did not accurately represent usual sources of care. Since different household members may attend different places of care, the accuracy of these household indexes is suspect. Although INCOME was insignificant for whites and significant for blacks, the opposite was true for the insurance variable COST,. This finding implies that health insurance coverage may be an important determinant of medical care costs for white households, whereas black households tend to pay for medical care out of their own pockets. Household size, as in the visit equation, was significant for both whites and blacks; however, there was a negative impact on household medical visit expenses. When compared with the results in columns 1 and 2 (Table 3), these latter results imply that as household size increases FALL 1978 there tend to be more medical care visits, but household medical expenses actually decrease. There are two possible explanations for this
269
MINERS ET AL.
Table 4. Variables Affecting the Household's Decision to Receive Medical Care: OLS Regression Results (t values are shown in parentheses)
Dependent variable Independent variable
viSa, White households
TIMEA ......................... -0.0004 (0.43) TIMEr ......................... -0.0056 (2.83)*
TIMEw
.........................
COSTv .....................
COSTI ......................... INCOME ....................... N ..........................
CHRON ........................ ........................
No .of observations ........ ..... F ratio .........................
(1.34)* -0.0012
(0.50) -0.00004
(1A3)*
(0.12)
0.0020
(1.14)
0.1474
0.0018
(0.38)
0.1598
(1.43)*
(0.65)
-0.0010
-0.0086
(0.20)
(0.76) 0.0123
0.0015
0.0184 (2.04)* 0.0015
(0.39) Constant
-0.0034
-0.0003
(0.11) ACUTE .....................
Black
households
(0.98)
0.0045 (0.42) 0.0109
(1.09)
0.9862
0.7955
(10.75)*
(7.50)*
201 2.732
93 0854 0.085
R' ............................ 0.114 * Variable is significant at the 10-percent level or lower a one-tailed test.
using
finding. First, larger households (holding income constant) are more likely to be eligible for Medicaid support and thereby have reduced out-of-pocket medical expenses. Second, larger households may pay less for ambulatory medical care due to the setting in which treatment is received (clinic vs. private physician). The correlation coefficient of household size and the percent of households receiving care at clinics was positive, whereas the correlation coefficient of household size and the percent of households receiving care from private physicians was negative. However both of these coefficients were too small to lend much support to this argument. HEALTH The number of acute and chronic conditions had a significant imSERVICES on medical visit costs for white households but not for black. pact RESEARCH Perhaps this is an indication of the large number of blacks who receive free care at clinics, since these variables are important in ex-
270
plaining the demand for visits by black households. Also, for white RURAL MEDICAL CARE households, acute conditions seem to account for a larger part of med- DEMAND ical care expenses than do chronic conditions. As expected, the number of household visits had a positive and significant effect on medical visit costs for both black and white households. Our empirical results lend some support to our hypotheses of the effect of waiting time on the demand for care and of racial differences in the use of medical care services. Travel time had a significant impact on the use of medical care by both black and white households, whereas the amount the household usually paid for care was important only for whites. Insurance premiums had a significant effect on medical care costs for white households, whereas for black households the only significant expenditure variable was total family income. Nevertheless, we considered it desirable to test whether there might be a statistically significant difference between the equations for white households and the equations for black households. In order to test for any differences, we pooled the observations from the black and white subgroups and tested the regression model again with the new combined data set. Murphy [19] describes this procedure as "a test of equality between coefficients in two identical models based on two different data sets." We performed this test for both the household-visit and the household-expenditure equations, and the results indicated an overall significant difference between the white and black expenditure equations but not the household-visit equations. (The calculated F statistics were 2.48 for the expenditure regressions and 0.80 for the visit equations. The former F statistic is significant at the 99-percent confidence level, and the latter is not significant at any acceptable level of confidence.) The results imply that although there was a disparity between white and black expenditures for medical care, there were no significant racial dissimilarities in the utilization of medical care services. Notice that in the household-visit regressions, except for office waiting time and average cost of a medical visit, the equations for blacks and whites were generally comparable in both sign and significance. However, the results for the household-expenditure equations indicated racial differences in method of payment (income vs. health insurance), the effect of acute and chronic conditions, and, in one instance, usual source of care. The lack of a significant racial difference between the two household-visit models does not imply that there were no dissimilarities between the individual variables in the equations for blacks and for whites, only that there was no significant difference overall. The household-visit results shown in Table 5 (p. 272) indicate that when the data sets are pooled, household size and number of chronic conditions have a significant effect on demand when interacted with race. To form an interaction term with race, the appropriate variable was multiplied by WHITE01-i.e., multiplied by 0 or 1. Kmenta [18] and FALL 1978 others have shown that when a dummy variable is included in a regression equation the intercept of the fitted regression line changes
271
MINERS ET AL.
Table 5. Pooled OLS Regression Results with Racial Interaction Terms (t values are shown in parentheses) Dependent variable Independent variable
vis
COSTt
TIMEA ............. 0.0242 (0.72)
TIMEz ............. -0.0974 (l163)* TIMEw ............. -0.0089 (124) COSTr .............. -0.1096 (1.59)* COST1 .............. INCOME ........... N................... ACUTE ............. CHRON ............ CLING .............. NOS1 ............... vis ................. WHITE,u ........... WHITE X N ......... WHITE X CHRON WHITE X INCOME WHITE X CLIN ..... WHITE X NOS ...... WHITE X VIS ....... WHITE-X ACUTE ... Constant ............ No. of observations F ratio ..............
-0.7565 (0.19) 0.0234 (0.12) 0.2530 (0.60)
0.0889 (2.48)* 0o.0063 (1.84)* -0.0083 (2.48)*
0.9130 (3.34)* 1.0916 (3.26)*
(0.44) 0.0012 (0.83) -0.0078 (0.25) -0.0220 (0.35) 0.0046 (2.31)*
0.5742 (0.17) 1.0224 (1.58)* -0.4454 (1.22)
0.0078 (0.21)
-0.0018
-0.0062 (1.65)* -0.0187 (0.48) 0.1539 (2.00)* -0.0030 (1.45)* 0.0198 (3.87)* 2.3160 (0.73) 294 6.700
0.0176 (0.51) 310 6.271 0.223 0.229 R' * Variable is significant at the 10-percent level udng a onetailed test ..................
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accordingly. However, if the dummy variable is interacted with specific variables in the regression equation, then the slope of the fitted line varies. Hence it is possible to obtain estimates of the effect of a binary variable on specific variables in the regression equation. In this way the resulting coefficient for the interaction term will indicate racial differences in the effect of the variable on demand even though the combined data set includes observations from both races. As Table 5 shows, the F ratios for both equations were significant, and most of the coefficients were also significant, implying. that our model does at least an adequate job of accounting for the variation in the data. The elasticity measures presented in Table 6 also tend to substantiate many of our earlier hypotheses and results. Elasticity is a
Table 6. Elasticities of Demand and Expediures for Medical Care with Respect to Changes in the Independent Variable
CAL CARE DEMAND
(Elasticities were calculated at the mean for variables significant at the 10-percent level using a one-tailed test) VIS COSTS Independent White Black White Black variable house- house- house- house- VIS COSTD holds holds holds holds -0.3341 TIMEr ....-0.07 -0.2284 -0.0659 -0.0830 TIMEw .... COSTV .... -0.1820 -0.1473 N ....... 0.4254 0.2186 -0.4020 -0.6062 0.4458 -0.8 03059 0.4209 ACUTE ... 02873 0.4027 0.5278 CHRON ... 0.3827 0.6512 0.4748 0.1644 INCOME .. 0.6807 COSTI 0.2263 0.2616 0.0760 NOS.I. 0.0955 VIS 0.1561 0.7507 0.2602
measure of the percentage rate of change in the dependent variable in response to a change in one of the independent variables. Since it is a rate-of-change variable and not attached to a specific measuring unit, it allows comparisons among variables and across data sets. Travel time emerges as the single most important variable in determining the number of physician visits demanded by white households. The difference between the travel time elasticity and the elasticity of visits (demand) with respect to monetary price (COSTv) is particularly noteworthy. Travel time appears more important than waiting time in determining the number of visits by black families, but, as mentioned before, the level of need in the household (as measured by number of acute and chronic conditions) had the largest influence on demand. The racial differences in the effect of need on the demand for medical care suggest that there may be a disparity in the way blacks and whites report acute and chronic conditions or that blacks wait until they are relatively sicker before demanding care. The results for the expenditure equations (columns 3 and 4 in Table 6) indicate that household income was relatively more important for blacks than insurance costs were for whites in explaining medical visit costs. Phelps [20] calculated the elasticity of physician expenses with respect to income to be 0.11-clearly much smaller than our finding for black households. Our finding could have been due to the lack of health insurance coverage of black families in our sample or our concentration on middle and lower-income households. Household size (N) and need (ACUTE) had a larger impact on expenditures of white households than other variables, induding insurance costs. This finding implies that health insurance coverage, although
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significant, was not overly influential. It should be pointed out that COST, measures only what households paid for health insurance premiums; a more reliable and interesting variable would have included the degree and scope of coverage as well. The pooled regression elasticities indicate the overall importance of need, household size, and travel time (at least for whites) in accounting for the medical care services received by the household. Monetary price, income, and insurance costs, although at times significant, were dominated by these other variables.
Discussion The results indicate that not only are there racial differences in the demand for care but that traditional economic variables such as monetary price and income may not be the most important determinants of demand. The time household members spend traveling to their source of care has a negative and significant influence on the amount of medical services demanded by both black and white households and is the most important determinant of demand for whites. The majority of black households receive care at clinics or hospitals, and, as expected, waiting time also has a negative effect on the demand for care by these households. The importance of travel time and waiting time in our analysis suggests that the current location and distribution of ambulatory medical services in the community may either not be optimal or not be used to full advantage. Furthermore, the influence of these and other nonmonetary price variables on both expenditures and the demand for care appears to be different for blacks and whites. Perhaps what is needed is a more liberal scheduling of physician and clinic hours and efforts to get primary medical care to various subgroups of the population more efficiently. Furthermore it appears that lowering the monetary price (even as low as zero) may not be the absolute answer to the problem. Above all, our results indicate that ill health or need is the primary factor affecting the utilization of services. Although the data we use are limited to one rural community, our results lend support to earlier studies of the demand for medical care [4,11,20].
REFERENCES 1. Fein, R. The Doctor Shortage: An Economic Diagnosis. Washington, DC:
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Brookings Institution, 1967. 2. Scheffler, R.M. The regional distribution of physicians and specialists. Rev Reg Stud 2:63 Winter 1972. 3. Davis, K. and R. Marshall. Rural Health Manpower and Delivery. In R.M. Scheffler (ed.), Research in Health Economics: An Annual Compilation of Research. Greenwich, CT: Jai Press (forthcoming). 4. Kane, R.L. and P.F. Westover. Rural health care research: Past accomplishments and future challenges. Health Care Dimensions 3:123 Dec. 1976. 5. Wright, D.D. Recent rural health research. J Community Health 2:60 Fall 1976. 6. Luft, H.S., J.C. Hershey, and J. Morrell. Factors affecting the use of physician services in a rural community. Am J Public Health 66:865 Sept. 1976. 7. Rosett, R.N. (ed.). The Role of Health Insurance in the Health Services Sector. New York: Neale Watson, 1976. 8. Cowen, D.L., D.L. Hochstrasser, C. Friedericks, and T. Payne. Problems in the development of a rural primary care center. J Community Health 2:52 Fall 1976.
9. Garrett, M.L., D.L. Miles, and A.G. LeBaron. Rural areas pose special problems for providing social services. Hospitals 50:77 Nov. 16, 1976. 10. Reid, R.A., J.B. Eberle, L. Gonzales, N.L. Querk, and R. Oseasohn. Rural medical care: An experimental delivery system. Am J Public Health 65:266 Mar.
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1975. 11. Acton, J.P. Nonmonetary factors in the demand for medical services: Some empirical evidence. J Polit Econ 83:595 June 1975. 12. Becker, G.S. A theory of social interactions. J Polit Econ 82:1063 Nov-Dec. 1974. 13. Lancaster, K. The theory of household behavior: Some foundations. Ann Econ Soc Meas 4:5 Winter 1975. 14. Salber, E.J., S.B. Greene, J.J. Feldman, and G. Hunter. Access to health care in a southern rural community. Med Care 14:971 Dec. 1976. 15. Andersen, R. A Behavioral Model of Families' Use of Health Services. Center for Health Administration Studies Research Series 25, University of Chicago, 1968. 16. Andersen, R., J. Kravits, and O.W. Anderson (eds.). Equity in Health Services: Empirical Analysis in Social Policy. Cambridge, MA: Ballinger, 1975. 17. Thacker, S.B., S.B. Greene, and E.J. Salber. Hospitalizations in a southern rural community: An application of the ecology model. Int J Epidemiol 6:55 Mar.
1977.
18. Kmenta, J. Elements of Econometrics. New York: Macmillan, 1971. 19. Murphy, J.L. Introductory Econometrics, p. 237. Homewood, IL: Irwin, 1973. 20. Phelps, C.E. Effects of Insurance on Demand for Medical Care. In R. Andersen, J. Kravits, and O.W. Anderson (eds.), Equity in Health Services: Empirical Analysis in Social Policy, pp. 105-130. Cambridge, MA: Ballinger, 1975.
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