Methodologic Article

THE MINIMIZATION OF TRAVEL EFFORT AS A DELINEATING INFLUENCE FOR URBAN HOSPITAL SERVICE AREAS James Studnicki Using a study population of 16,080 live births occurring to residents of Baltimore City in 16 hospitals in 1969, this research measured the existing flow of these patients against the flow “expected” in an optimal accessibility model (where each birth would occur at the hospital with the shortest travel time to the residence of the mother). The results of the study indicate that there is a general pattern of distance minimizing in travel for hospital admission with 50 per cent of the births occurring to women who travelled to one of the four closest hospitals of the 16 alternatives. However, a surprisingly large proportion (20 per cent) of the study population exhibited extreme spatial inefficiency by traveling to those four hospitals of the 16 alternatives which were farthest from their residence. A stepwise regression analysis identified five variables ‘which best explained variation in the spatial efficiency of these urban obstetrical patients: low hospital occupancy, high total hospital admissions, average extra travel time potential (a measure of the difficulty of “spatial choices” facing patients depending upon the location of their residence with respect to the alternative hospitals), race, and the importance of the hospital‘s obstetrical service (a ratio of births to total admissions).

How simple the planning of hospital services would be if patients would only conform to the behavioral expectations of the planners! For example, what if all hospital admissions were to occur at the facilities which were nearest to the residence of each patient? Each hospital could then simply extend “boundaries” to those discrete pieces of ground which are closer to it than any other facility, and the people living within this bounded territory would be the population served by that particular hospital. This geographical area would provide the planning denominator for each facility, and the population within the area could be monitored as to size, demographic characteristics, incidence of diseases, and hospital utilization. Hospitals would be able to expand or contract certain services on the basis of population changes and could, on the basis of the perfect information provided by the consistent behavior of all patients, coordinate activities with one another SO that duplication of services and physical facilities would be avoided. Hospital services which could operate economically on the demand generated by single geographical areas would be permitted at each hospital, and services which require a larger population base for economical operation could be restricted to selected facilities serving several geographical areas. The basic advantage for planners in this system of optimal spatial behavior is that it allows for perfect predictability concerning the actual distribution of patients to hospitals. Theoretically at least, wide variation in occupancy rates would be eliminated and occupancy would remain uniformly high. In short, future demand at each hospital would be easily predictable, depending only upon the efficiency of the system for monitoring the populations within each geographically delineated service area. International Journal of Health Services, Volume 5, Number 4, 1975 0 1976, Baywood Publishing CO.

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doi: 10.2190/405U-V9TP-VJUA-QLTT http://baywood.com

680 I Studnicki In addition to the obvious advantages it would have for hospital planners, this arrangement should hold some attraction to the people served. It is reasonable to assume that, whenever a person must travel from his home or office to obtain a good or a service, the decision of where the good or service is to be obtained is based at least in part upon the amount of effort required to go from his origin to the alternative destination choices (1). The behavioral assumption which underlies this expectation is that human beings tend to minimize the effort required to interact with the people and places around them (2). This minimization of effort may be viewed conceptually in a variety of ways: as a tendency to reduce the actual or opportunity costs of travel (3); as a tendency to reduce the psychological discomfort associated with leaving familiar surroundings (2); or as a tendency to reduce the ‘‘friction” to personal mobility incurred by increasing distances between points of origin and destinations (4). The actual effort is often referred to as physical accessibility and is most. often measured in simple linear distance or travel time equivalents (1). Whatever the measure used, the expectation is the same: that patients should tend not to travel farther or longer than necessary for admission to a hospital. Consequently, numerous planning projects have been concerned with dividing a region into “study districts,” “areas of major influence,” “catchment areas,” or “service areas.” These geographical subdivisions are constructed on the often unstated assumption that each hospital provides services to the residents of its service area, and that the residents of the service area obtain their hospital services at that hospital (5). That is, these studies have obliquely assumed that the optimal spatial model described above does accurately describe the pattern of patient travel to hospitals. Unfortunately, this spatial model does not in fact exist. It is clear that these service area delineations are not consistent with the actual flows of patients to hospitals. Patients, especially in metropolitan areas, are not distributed to hospitals in an optimal spatial pattern. All patients are not admitted at the hospital which is the most physically accessible to them. Much of the deviation from this optimal pattern probably exists because of the strong role of the physician in hospital admissions, and the bewildering patterns of admitting privileges and hospital selection among urban physicians. In addition, there is evidence that a given distance or travel time from a person’s residence to alternative destination points may not affect every person’s travel pattern in the same way. There may be characteristics of the individual which affect his willingness or ability to opt for less accessible destinations. On the other hand, one hospital, because of certain characteristics, may be more attractive than another which is physically closer. These elements may interact to establish preferred relationships which can be viewed as the component parts of the total system. Without an understanding of the ways in which differences in patients and hospitals affect their interaction, explanations of spatial behavior remain superficial. This article is a review of research in a metropolitan area aimed at comparing existing hospital inpatient travel patterns with an optimal spatial model. In addition, the variation in these travel patterns was related to the characteristics of both the patients and the hospitals in an attempt to describe the relationship of each variable with a constructed indicator of spatial efficiency. The following section is a brief review of relevant literature. LITERATURE REVIEW OF RELEVANT SPATIAL STUDIES Studies which attempt to characterize human mobility on the basis of distances travelled or travel times incurred, whether the measure of effort is viewed as the outcome

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variable or as an explanatory variable for other outcomes, are often called spatial studies. It is difficult to pinpoint a single origin for spatial studies but, since about 1960, a large proportion are found in the literature of geography (6), social studies (7), and general urban studies (8). In addition, considerable literature is developing on the methodological techniques utilized in carrying out spatial studies (9). Spatial studies pertaining to health care utilization are seen as far back as the 1940s, but only since the 1960s have the more sophisticated methodologies been applied to health care problems. Also, with few exceptions, the most productive spatial studies in health care come from multidisciplinary teams working in metropolitan areas (10, 11). For purposes of this research, it is valuable to highhght three recurring “themes” in the spatial literature pertaining to hospital care: (a) the existence of independent geographical service areas, (b) the overlapping and interpenetrating nature of metropolitan hospital catchment areas, and (c) the differential spatial effects of personal and resource characteristics. If physical accessibility is an influence on the patterns of distribution of patients to hospitals, it should be possible to establish the existence of independent geographical service areas. An independent geographical service area can be defined as a territory, with identifiable boundaries, within which a group of potential patients live and also receive hospital care. There is virtually no interarea travel by patients seeking hospital care. Even though the following illustrative studies point out very clearly that independent geographical service areas do exist, it is important to note that they are found only in two specific situations. First, we find the independent geographical service area in rural locations where there may be only one or two hospitals serving a rather extensive area. Here, utilization of a particular facility is almost always a function of locational availability (12). That is, rural patients will go to the hospital nearest their home, either because it is the only facility within the area or because the distance or travel time to the next nearest hospital is prohibitive. We can expect to find independent geographical service areas, then, when alternative hospital choices are few and effort required to travel to the alternative facilities is great (13). The second situation in which we are likely to find independent geographical service areas is where the territory under consideration is so large and contains so many hospitals and such a large population that the service area concept retains usefulness for only the grossest types of comparisons. For example, the Baltimore Standard Metropolitan Statistical Area (SMSA) can be considered as an independent geographical service area. It appears that only a minute proportion of people living within Baltimore City and the five surrounding counties are hospitalized outside this area (14). However, this information is of little value if one is interested in how the hospitals within the Baltimore SMSA divide the patient population. In the United States, national surveys done by Mountin, Pennell, and Hoge (15) and Dickinson (16) and area studies by Ciocco and Altman(17) in western Pennsylvania and by Poland and Lembcke (18) in Kansas and Missouri have all established the existence of independent geographical service areas and contrasted them in terms of size, population, and relative wealth of medical resources. In large cities and their surrounding urbanized areas, the effect of physical accessibility on the distribution of patients to hospitals becomes confused by the large number of alternative hospitals, the relatively small distances between choices, and the large numbers of patients serviced. However, a few studies have attempted to analyze metropolitan geographical areas relating to the hospital-patient spatial relationship. These studies of patient origin have all demonstrated that hospital “trade areas” or “catchment areas” or “geographical draw areas” may be identified by collective patient travel patterns. While the methods and techniques used in arriving at and analyzing these service areas differ

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widely, they all report one common conclusion. That is, while the physical relationship between a hospital and a patient’s residence is undoubtedly an influence on the distribution of patients to hospitals, metropolitan patients are not distributed in a way that minimizes aggregate distance travelled or travel time incurred. Spatial research in a number of American metropolitan areas has clearly demonstrated that travel expectations based on physical accessibility alone are not often validated. Drosness, Reed, and Lubin (19) in California, the Citizens Hospital Study Committee (20) in northeastern Ohio, Cherniak and Schneider (1, pp. 29-33) in Cincinnati, Devise (21) and Morrill and Earickson (22) in Chicago, and Weiss, Greenlick, and Jones (23) in Portland all identified “boundary jumping” behavior by 30 to 70 per cent of the inpatient populations studied. A further element of complexity in the distribution of patients to hospitals in metropolitan areas is the heterogeneity of the interacting elements. Both hospitals and patients differ so much that it is extremely difficult to speak in absolute terms about the spatial behavior bringing them together. In fact, there has been some research aimed at establishing the characteristics of patients and hospital destinations that make them more or less attractive to one another. Among the variables studied for their association with changes in the travel patterns of patients have been hospital bed size, volume of service, location, and length of stay.(22); the type of care received (eg. obstetrics, pediatrics, medicine-surgery)(24); the religious affiliation of the patient and the hospital (3, p. 30); and occupational status, sex, and age (25). To summarize very briefly, then, it seems that the metropolitan areas present a special challenge in seeking to explain the effect of physical accessibility on the distribution of patients to hospitals. Evidence appears at least to be contradictory. The tendency to minimize effort in travel exists in the city as well as rural areas. However, a number of studies have shown that the flows of metropolitan patients to hospitals do not perfectly correspond to effort-minimizing spatial expectations. In addition, existing evidence indicates that differences in both the patient populations and the hospitals may affect the nature of their spatial interaction. The remaining sections of this article deal specifically with the objectives, methods, and findings of the research project.

STUDY OBJECTIVES The overall goal of this research was to gain a fuller understanding of the process of hospital “selection” by a group of urban hospital inpatients. The focus of the study was the patients’ spatial behavior, i.e. the way in which the travel time from residence to the various hospitals was associated with admission patterns. While no formal hypothesis was offered prior to the study, owing partly to the relatively large number of variables included in the initial phases of data analysis, it was hoped that a “profde” of characteristics would emerge that would help to explain or predict the spatial efficiency of patient travel behavior. Further, on the basis of current literature, it was anticipated that the variables selected for the analysis would adequately differentiate urban hospital inpatients in terms of their “attractiveness” to the various facilities. More specifically, the research had two primary objectives:

To determine the degree of correspondence between the existing flow of a group of urban patients to hospitals and the flow that would be “expected” on the basis of an optimal accessibility model. (An optimal accessibility model is one in which all

Travel Effort and Urban Hospital Service Areas / 683 patients are admitted to the hospital with the shortest travel time to the center of the census tract in which they reside, i.e. the model with the greatest spatial efficiency.) To describe the characteristics of patients and hospitals that are associated with variation in this degree of correspondence and the nature of their associations. Basically, for each hospital and patient variable we wanted to know the direction and strength of association with, and its ability to explain variation in, the spatial efficiency of this group of patients. METHODS Study Population and Variables The study population for t h i s research was composed of all obstetrical patients at 16 selected study hospitals in Baltimore City who had live births during calendar year 1969. The total number of births accounted for by this study population was 16,080, and 200 of Baltimore City’s 201 census tracts recorded at least a single birth. Obstetrical patients were selected as the study population because obstetrical service tends to be available at most metropolitan general hospitals, thus maximizing the number of alternative “choices.” In addition, there is some evidence to suggest that obstetrical patients provide an accurate indicator of the travel patterns for all inpatient hospital admissions (14, pp. 155-161; 24, p. 33). Finally, data for obstetrical patients were easily available from a central birth registry, eliminating the need to approach individual hospitals for patient information. For each census tract, the following variables were computed and included in the preliminary analysis stage : Average extra travel time incurred (see Appendix) Average extra travel time potential (see Appendix) Source of prenatal care (public versus private) Lateness or absence of prenatal care Education of mother Age of mother 7. Medical assistance participation 8. Race 9. Total population 10. Total number of births 11. Travel time to nearest hospital 12. Total annual hospital admissions 13. Total annual hospital births 14. Number of hospital beds 15. Hospital population pressures (see Appendix) 16. Hospital occupancy rates 17. Hospital per diem costs 18. Hospital complexity (scope or range of services) 19. Hospital competitive spatial position (see Appendix) 20. Importance of hospital obstetrical services (ratio of births to total admissions) 21. Importance of city patients to hospital obstetrical services 1. 2. 3. 4. 5. 6.

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Data Analysis and Presentation The data analysis reflected the major study objectives. The first study objective was to determine the degree of correspondence between the real flow of these urban patients to hospitals and the flow expected on the basis of the optimal accessibility model. Basically, there were four measures of model correspondence. Briefly, they were:

1. Number and percentage of births occum'ng at the most accessible hospital. 2. Number and percentage of births occum*ngwithin accessibility quartiles. For each census tract, the 16 study hospitals were grouped in quartiles on the basis of their travel times from the center of each census tract. That is, those four hospitals with the shortest travel times composed the fust quartile, those four hospitals with the next shortest travel times composed the second quartile, and so on. Hospitals falling within each quartile may be the same or may differ from census tract to census tract, depending upon patterns of hospital location with respect to the 200 census tracts under consideration. 3. Average extra travel time incurred. The meaning and method of computation for this variable pertaining to its use in describing the behavior of patients living within census tracts are discussed in the Appendix.

4. Hospital population average extra travel time incurred. This measure is computed for each hospital in the following manner. The number of births occurring in each hospital in each census tract is multiplied by the average extra travel time already computed for each census tract. For each hospital, these 200 products (number of births multiplied by the average extra travel time) are added. This sum is the total amount of extra travel time incurred by the 1969 Baltimore City obstetrical load at each hospital. This total amount of extra travel time is divided by the number of city births occurring at each hospital. The resulting measure is the average extra travel time for each hospital's city obstetrical patient populations at each of the 16 alternative hospitals. The second major objective of the study was to describe the nature of the associations of the selected hospital and patient characteristics with the variation in the degree of correspondence between the existing travel pattern of this patient population and the pattern which would pertain in the optimal accessibility model. Briefly, this study objective was accomplished through the use of three analytical stages using the census tract as the unit of analysis. 1. Describing individual variables. Each variable category (a total of 44) was described through the use of means, standard deviations, and frequency distributions. 2. Describing relationships among variable pctirs. This description was accomplished through the use of cross tabulations, plots, and correlation analysis.

3. Stepwise regression analysis. Because much intercorrelation exists among these variables, it is somewhat difficult to determine (on the basis of correlation alone) which variables are the most important ones in explaining the variation among census tracts in model correspondence. With average extra travel time incurfed as the dependent variable,

Travel Effort and Urban Hospital Service Areas / 685

this analysis attempted to identify those variables, hopefully few in number, which are the most powerful “explainers” of the identified variation. FINDINGS Of the total study population, 3961 births, or 25 per cent of the total, occurred at the most accessible hospital. In other words, one fourth of these obstetrical patients conformed to the proposed optimal spatial model. Extending our analysis of model correspondence to the accessibility quartiles, we find that 7988 births, or 50 per cent 0f the total, occurred at hospitals within the first quartile, 271 1 or 17 per cent occurred at hospitals within the second quartile, 2071 or 13 per cent at hospitals within the third quartile, and 3310 or 20 per cent at hospitals within the fourth quartile. Of course, the surprising finding here is that there are more births occurring at hospitals within the fourth quartile than in either the second or third quartiles. Expressed in another way, one birth in every five occurred in the hospitals that, of 16 alternative choices, were the four most distant from the patients in terms of travel time. In the optimal accessibility model, all births would have occurred at the single most accessible hospital. In a distribution of patients which was not optimal spatially but still consistent with anticipated spatial behavior, it would be expected that each quartile would contain a progressively lower percentage of births (i.e. a “distance decay” function). The findings of this analysis, however, indicate that a substantial proportion of the study population is behaving in a way that is considered spatially extreme. That is, not only do they not make the “best” spatial choices, they make the “worst The average extra travel times computed for each hospital obstetrical patient population were compared with the relative percentages of those populations that were non-white and medical assistance recipients. The relationships indicated by these three measures were consistent. Especially at the extremes, for those hospitals with the largest and smallest amounts of average extra travel time the association between spatial behavior, race, and medical assistance status is striking. Non-white patients and those that are medical assistance recipients-and these two variables are highly intercorrelated-are incurring the greatest amounts of average extra travel time (see Table 1). The average extra travel times incurred for each census tract ranged from a low of 2.23 minutes to a high of 13.35 minutes. The mean average extra travel time incurred was 6.25 minutes and the standard deviation for the 200 census tract values was 2.16 minutes. The frequency distribution shows 135 of the 200 census tracts with average extra travel times incurred between 4 and 8 minutes. Also,27 census tracts have values under 4 minutes and 38 census tracts have values over 8 minutes. In addition, the use of plots, cross-tabulations, and correlation techniques allowed for a basic description of the nature of the associations between “spatial efficiency” (average extra travel time) and the various patient and hospital characteristics. Briefly, average extra travel time incurred for the census tracts was positively associated with the percentage of mothers on medical assistance, the percentage of mothers under 20 years of age, the percentage of mothers with less than 12 years of education, the percentage of mothers presenting for prenatal care in the third trimester of pregnancy or not at all, the percentage of births occurring at hospitals with less than 75 per cent occupancy, the percentage of births at hospitals with less than 250 beds and more than 500 beds, the percentage of births at hospitals with fewer than 15,000 total annual admissions, the

.”

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Studnicki Table 1

Average extra travel time, percentage of non-white and percentage of medical assistance recipients for live births to residents of Baltimore City at 16 hospitals in 1969 Hospital No. 1 2 3 4 5

6 7 8 9 10 11 12 13 14 15 16

Average Extra Travel Time (min) 8.13 7.96 7.35 7.33 7.24 6.80 6.40 6.34 6.33 6.23 6.19 5.78 5.76 5.53 5.42 5.35

(Rank)

Live Births to Non-Whites

Live Births to (Rank) Aid Recipients (Rank)

(%I

(%I

(1) (2) (3) (4)

(51

(6) (7) (8 )

(9 1 (10) (1 1) (12) (13) (14) (15) (16)

1.oo 0.88 0.55 0.8 1 0.83 0.29 0.48 0.71 0.2 1 0.21 0.18 0.40 0.71 0.18 0.06 0.12

(1) (2) (7) (4) (3) (10)

(8 1 (5)

(1 1) (1 2) (13) (9) (6) (14) (16) (15)

0.40 0.4 1 0.38 0.39 0.09 0.18 0.25 0.12 0.16 0.16 0.10 0.12 0.39 0.09 0.00

0.02

(2) (1) (5)

(3) (13) (7) (6) (10) (8 1

(9) (12) (1 1) (4) (14) (16) (15)

percentage of births at hospitals with fewer than 2,500 total annual births, the percentage of births at hospitals with a ratio of city births to all births of 0.67 or greater, the percentage of births at hospitals with a ratio of births to admissions of 0.18 or greater, the percentage of births at hospitals most complex in scope (26 or more reported facilities and services), the percentage of births at hospitals with fewer than 200,000 people residing within 10 minutes of travel time, and the percentage of births at spatially isolated hospitals (caposite travel time to the three nearest hospitals greater than 25 minutes). Average extra travel time incurred for the census tracts was negatively associated with the percentage of mothers receiving prenatal care from a private physician, the percentage of total white births, the percentage of mothers aged 20-39 years, the travel time to the nearest hospital, the percentage of births at hospitals with more than 15,000total annual admissions, the percentage of births at hospitals with more than 2,500 total annual births, the percentage of births at hospitals with a ratio of city births to all births of less than 0.33, the percentage of births at hospitals with 400 t o 500 beds, and the percentage of births at hospitals with 86 per cent or greater occupancy. Tables 2 and 3 summarize the correlation analysis. The final stepwise regression analysis produced a multiple correlation coefficient (R) of 0.7816 and a coefficient of multiple determination ( R z )of 0.6110. However, after the entrance of five variables into the regression equation, R was 0.7031 and RZ was 0.4944, and the addition of subsequent variables resulted in an increase in RZ of 0.01 or less. The five most important variables in explaining the variation in average extra travel time and their relative contribution of increase in the coefficient of multiple determination were as follows: percentage of births occurring at hospitals with 75 per cent or less occupancy (0.2710); percentage of births occurring at hospitals with 15,000 or more total

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Table 2 Correlation coefficients for selected patient variables and average extra travel time incurred (significance at the 0.01 level) Correlations

r ~~

Positive Mothers on medical assistance (%) Mothers under 20 years of age (%) Mothers with less than 12 years of education (%) Mothers with no prenatal care or care in third trimester only (76) Negative Mothers receiving prenatal care from private M.D.(76) Total white births (%) Mothers aged 20-39 years (%) Time to nearest hospital

0.42 0.38 0.34 0.30 -0.44 -0.40 -0.37 -0.35

Table 3 Correlation coefficients for selected hospital variables and average extra travel time incurred (significance at the 0.01 level) Correlations Positive Births at hospitals with less than 75% occupancy (76) Births at hospitals with more than 500 beds (%) Births at hospitals with 1,500-2,500 total births (76) Births at hospitals with fewer than 250 beds (76) Births at hospitals with 10,000-15,000 total admissions (%) Births at hospitals with ratio of city births to all births of 0.67 or more (%) Births at hospitals with ratio of births to admissions of 0.18 or more (%) Births at hospitals most complex in scope (%) Births at hospitals with less than 200,000 population within 10 minutes’ travel time (%) Births at hospitals with fewer than 10,000 total admissions (%) Births at spatially isolated hospitals (%) Negative Births at hospitals with more than 15,000 total admissions (5%) Births at hospitals with more than 2,500 total births (%) Births at hospitals with ratio of city births to all births of less than 0.33 (%) Births at hospitals with 400-500 beds (%) Births at hospitals with 86% or more occupancy (%)

r 0.52 0.35 0.35 0.33 0.33 0.3 1 0.30 0.28 0.26 0.26 0.25 -0.47 -0.41 -0.39 -0.36 -0.36

admissions (0.0627); average extra travel time potential (0.0643); percentage of total white births (0.06202); and percentage of births occurring at hospitals with a ratio of births to admissions of 0.13 or less (0.0363). Table 4 summarizes the results of the stepwise regression. The strong association between average extra travel time incurred and hospitals with a low occupancy rate presents a problem in interpretation, since occupancy appears to be more a result of patient travel patterns than an influence on them. In other words, the behavioral relationship between occupancy and travel time is unclear. However, using the

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/ Studnicki Table 4 Summary of stepwise regression analysis for average extra travel time incurred and five important independent variables Summary

Multiple Correlation coefficient (R) Coefficient Of multiple determination ( R 2 ) Standad error O f estimate

0.7816 (all variables) 0.6110 (all variables) 1.4602 (all variables)

Analysis of Variance (All Variables)

Degrees of Freedom Regression Residual

30 169

-

Variable Births at hospitals with less than 75% occupancy (%I Births at hospitals with 15,000or more total admissions (%) Average extra travel time potential Total white births (%) Births at hospitals with a births/ admissions ratio of less than 0.13 (%)

Sum of Squares

Mean Squares

565.909 360.334

18.864 2.132

Final Regression Coefficient

R

R2

F Ratio

8.847 InFinal creased Standard R2 Error

5.74137

0.5206 0.2710 0.2710 5.35029

-6.92653 0.27667 -1.28996

0.5777 0.3337 0.0627 3.99955 0.6308 0.3980 0.0643 0.06849 0.6768 0.4581 0.0602 0.93845

-6.39451

0.703 1 0.4944 0.0363 4.45088

results of the hospital level analysis as a clue, looking at those hospitals which show low occ~pancyrates, it is noted that they account for 5934 of the live births to city residents in 1969 and that 4709 or 79 per cent of that number are non-white. In effect, what we are seeing in the occupancy variable is the strong relationship between average extra travel time incurred and the racial composition of the obstetrical patient populations. Because of the way in which the variable was stated, the effect of race was “masked” by the oc~~pancy rates. only four of the 16 study hospitals had total annual admissions of 15,000 or more, and interestingly two of these hospitals had largely white obstetrical populations and two were largely non-white. In addition, these patient populations were very efficient spatidy, incurring relatively small amounts of average extra travel time. This finding indicates that race and total hospital admissions are unique influences upon aggregate travel patterns. The behavioral phenomenon expressed by this variable is consistent; that is, those hospitals which are located close to the patients they serve can be expected to generate a high total of admissions. f i e relationship expressed between average extra travel time incurred and the percentage of white births within each census tract is somewhat confused by the fact that Baltimore City’s census tracts tend to be nearly a l l white or nearly all non-white. In the middle ranges of average extra travel time, census tracts are just as likely to be nearly all white as nearly all non-white. However, those census tracts incurring extreme amounts of average extra travel time (high or low) are clearly differentiated by race. That is, those

Travel Effort and Urban Hospital Service Areas / 689 census tracts which incur the greatest amount of average extra travel time are exclusively non-white, and those census tracts which incur the smallest amounts of average extra travel time are exclusively white. As previously mentioned, average extra travel time potential is really a measure of the “difficulty” of alternative hospital choices faced by the patients within census tracts. As expected, those patients who face more difficult alternatives generally incur greater amounts of average extra travel time. It is also interesting to note that, because of patterns of residence, white patients on the average face slightly more difficult choices than do non-whites. In other words, if the difficulty of alternative choices was the only determinant of average extra travel time incurred, and the effects of other variables were neutralized, whites would incur slightly greater extra travel times than non-whites. The ratio of births to admissions is a variable which indicates how important the obstetrical service is to the hospital’s overall patient population. This ratio ranges from a low of 0.08 to a high of 0.23. The analysis indicates that as the obstetrical service increases in importance so does the average extra travel time incurred by that category of patients. It is interesting to note that as the total absolute number of births at each hospital increases, average extra travel time for those patient populations decreases. That is, even though average extra travel time is positively correlated with the importance of the hospital obstetrical service, it is negatively correlated with the size of the hospital obstetrical service. INTERPRETATIONS AND IMPLICATIONS FOR PLANNING In the United States, planning agencies concerned with the distribution of patients to hospitals in metropolitan areas have emphasized the use of geographically delineated catchment areas from which a hospital’s patients are expected to come. It is hoped that the catchment areas will provide planning “denominators” for establishing utilization rates and projecting future needs. The areas are largely predicated on the assumption that patients tend to be admitted to the hospital or hospitals that require the least amount of travel effort. The planning value of the catchment area concept, then, is in direct proportion t o the extent that the actual behavior of the patients in travel corresponds to this expectation. The results of this study have shown very clearly that, for 20 per cent of the obstetrical patients in Baltimore City, the geographically delineated catchment area concept based on physical accessibility is meaningless. The hospital “system” works in such a way that these patients must bypass at least 12 hospital alternatives which are spatially more accessible in order to gain hospital admission. The inescapable conclusion is that the non-white, and especially the non-white poor, do not really face a range of 16 alternative choices at all. For these patients, organizational factors (patterns of physician location and hospital privileges, differential hospital admission “paths” on the basis of payment status, etc.) alone are responsible for the location of their eventual admission. Spatial factors simply do not influence this decision. On the other hand, the use of the catchment area concept for patients residing in Baltimore City is probably reasonably accurate if we are speaking about white patients who can afford to pay by insurance or out of pocket. Although there are exceptions, white obstetrical patients generally tend to minimize travel time in their patterns of travel for hospital admission. In terms of future schemes of health services delivery in the United States, certain basic problems have been highlighted by this research. The relationship between the

690 / Studnicki admitting physician and the hospitals at which he holdsstaff privileges must be assessed in detail relating to its impact on the rationalization of services in urban areas. Although this relationship was not the specific focus of this research, it is the physician that largely determines the distribution of patients to resources by means of his admitting privileges and Practices. Regional planning schemes that act at the level of the hospital alone are doomed to failure because it is the medical staff that controls the regional distribution of Patients. In a real sense, then, both the hospital administration and the patient are passive observers in the hospital admission process. Even though we have used the word “choice” in speaking about Baltimore patients facing 16 alternative hospitals, it is clear that the patient generally selects only the physician. The hospital admission process, including the selection of a particular hospital, is in the hands of the physician. A logical extension of this thinking is that problems of hospital occupancy, coordination of services, and fiscal viability might be solved by a regional coordination of the admitting privileges and practices of the various medical staffs. The suggestion here is that regional schemes of hospital regulation (certification of need, for example) would do well to recognize the individual practitioners, as well as the bricks and mortar, as regional resources. The findings of this research also suggest that the payment status of the individual (in Baltimore City highly correlated with race) is an important determinant in the distribution of urban patients to hospitals. It seems clear that a national health insurance scheme making Americans indistinguishable from one another in terms of their ability to pay for services would alter the current spatial patterns of urban hospital populations. Once again, the mechanism for the alteration would rest with admitting physicians who, presumably, would find it easier to accept all patients with the expectation of uniform reimbursement. Under such a system, the data suggest that the “minimization of effort” model would be more accurate since it would eliminate the long standing spatially inefficient relationships between certain hospitals and p u b k patients. It is unlikely, however, that equalization of purchasing power will completely eliminate all barriers to equal access. Race, for example, appears to have its own special influence in the distribution of patients to health resources, quite distinct from the influence of payment status. Finally, the results of this study encourage a new evaluation of a number of well accepted, and practiced, planning “assumptions.” This research calls into question the assumption that, by placing proposed new facilities in certain geographical areas and restricting the growth of hospitals in other geographical areas, control may be exerted over the flow of patients to balance or optimize the distribution of patients to hospitals. In fact, it appears that changes in the composition of individual hospital patient populations (i.e. changes in racial and payment status percentages) are likely to produce more dramatic changes in aggregate travel patterns than any new master plan for future hospital location. Also called into question is the assumption that hospitals drawing patients from the same geographical area are “competing” with one another. Hospitals, via their medical staffs, probably do compete for patients with “favorable” characteristics-that is, whites who are insured or can pay out of pocket. However, there is very little evidence that there is much competition among hospitals for non-white medical assistance recipients. Finally there is the assumption that by drawing concentric circles of increasing size, hospitals can accurately describe the geographical areas from which their patients are drawn and thus estimate the potential population they are serving. Clearly, this planning device can now be viewed, at least for Baltimore City, as fairly accurate for certain

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hospitals and completely erroneous for others. Populations to be served result from many diverse influences of which simple physical accessibility is but a single one. Projections of future demand for individual hospital facilities would do well to recognize the multiple causation of that demand. In conclusion, this research has demonstrated that the often used, but little understood, geographical catchment area concept is subject to severe limitations in metropolitan hospital planning. On the other hand, the knowledge as to why such limitations exist adds a new dimension of sensitivity and insight to its use. APPENDIX Average Extm i’?avel Rme Incurred

This variable describes the degree of correspondence between the existing distribution of patients to hospitals and the distribution expected in an optimal accessibility model. In a simplified example, it is computed in the following manner. 1. Determine the travel times from the census tract to each of the 16 alternative study hospitals. 2. Identify the single hospital which has the shortest travel time to the census tract under consideration. 3. Subtract the travel time to the nearest hospital from the travel time to each of the alternative hospital choices. The travel times that remain are called “extra” since they represent time incurred by patients not behaving in an optimal spatial manner. 4. Multiply the number of patients from the census tract admitted at each hospital by the “extra” travel time value for each alternative hospital arrived at in step 3. 5. Add the total amounts of “extra” travel time incurred. This final total is the time incurred by all the patients in the census tract. 6. Finally, divide t h i s total “extra” travel time incurred by the number of births occurring in the census tract. The resulting variable is Average Extra Travel Time Incurred. Average Extra i’?avel Rme Potential This variable is a measure of the range of alternative choices, measured in travel time, facing the patients residing within each census tract. It is an “average” in that it is the sum of the extra travel time values of all alternative hospitals divided by their number. Expressed in another way, Average Extra Travel Time Potential would be equal to Average Extra Travel Time Incurred if (a) no patients were admitted to the hospital with the shortest travel time to the center of the census tract, and (b) the total number of census tract patients was divided evenly among the alternative hospital choices. The meaning of this variable becomes clear if we visualize two census tracts fcr which the Ayerage Extra Travel Times Incurred are exactly the same but the Average Extra Travel Time Potentials are quite different. It can be seen that the patients living in the census tract with the larger travel time potential are demonstrating greater “spatial efficiency” than those in the census tract with the smaller travel time potential. Although the patients in the two census tracts are incurring the same absolute amounts of average extra travel time, the range of alternative choices in one tract is more “difficult” than in the other.

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Hospital Population Pressures The study hospitals were divided into three categories on the basis of the total population residing within 10 minutes’ travel time of each facility. For each census tract, the total number of live births occurring in the hospitals of each category was computed as a percentage of all live births. The three categories were (1) the percentage of births occurring at hospitals with fewer than 200,000 people residing within 10 minutes’ travel time; (2) the percentage of births occurring at hospitals with 200,000 to 300,000 people residing within 10 minutes’ travel time; and (3) the percentage of births occurring at hospitals with more than 300,000 people residing within 10 minutes’ travel time.

Hospital Competitive Spatial Position The 16 study hospitals were divided into three categories on the basis of their competitive spatial positions; that is, their relative nearness to or isolation from other hospitals. The individual hospital variables were computed by adding, for each hospital, the total travel times to the three nearest hospitals. For each census tract, the total number of live births occurring in the hospitals of each category was computed as a percentage of all live births. The three categories were (1) the percentage of births occurring at spatially clustered hospitals (composite travel time to three nearest hospitals less than 15 minutes); (2) the percentage of births occurring at spatially intermediate hospitals (composite travel time to three nearest hospitals between 15 and 25 minutes); and (3). the percentage of births occurring at spatially isolated hospitals (composite travel time to three nearest hospitals greater than 25 minutes). REFERENCES 1. Cherniak, H. D., and Schneider, J. B. A New Approach to the Delineation of Hospital Service Areas. Regional Science Research Institute Discussion Paper Series Number 16, pp. 7-8, August 1967. 2. Doxiadis, C. Ekistics, the science of human settlements. Science 170(3956): 392-394,1970. 3. Earickson, R. The Spatial Behavior of Hospital Patients: A Behavioral Approach to Spatial Interaction in Metropolitan Chicago, p. 55. University of Chicago Department of Geography, Chicago, 1970.

4. Huff, D. L. The use ofgravity models in social research. In Mathematical Explorations in Behavioml Science, edited by F. Masserick and P. Ratoosh. Dorsey Press, Chicago, 1965. 5. Bachi, R. Standard Distance Measures and Related Methods for Spatial Analysis. Regional Science Association Papers, Vol. X , pp. 83-112,1963. 6. Buttimer, A. Social Geography. International Encyclopedia of the Social Sciences, Vol. VI,pp. 134-145,1968. 7. Webber, M. M. Culture, Territoriality, and the Elastic Mile. Papers and Proceedings o f the Regional Science Association, Vol. XIII, pp. 5969,1964. 8. Lansing, J. B., and Hendricks, G. Automobile Ownership andResidentinl Density. University of Michigan Survey Research Center, Institute for Social Research, Ann Arbor, 1967. 9. Stevens, B. H. A review of the literature on linear methods and models for spatial analysis. Journal of the American Institute ofplanners XXVI: 253-259,1966. 10. Schneider, J. B. Measuring, evaluating and redesigning hospital-physician-patient spatial relationships in metropolitan areas. Inquiry V(2): 2443, 1968. 11. Devise, P. Hospital Study Districts for Metropolitan Chicago: A Geographic Analysis and Methodology. Hospital Planning Council for Metropolitan Chicago, Chicago, 1966. 12. Jehlik, P. J., and McNamara, R. L. The relation of distance to the differential use of certain health personnel and facilities and to the extent of bed illness.Rura1 Sociology 17: 261-265,1952. 13. Shannon, G. W., Bashshur, R. L., and Metzner, C. A. The concept of distance as a factor in accessibility and utilization of health care.Medica1 Care Review 26(2): 143-161,1969.

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14. Rosenfeld, E. D. Survey and Report o f Hospital Facility and Service Needs of the State of Maryland, pp. 174-184. E. D. Rosenfeld Associates, Inc., New York, July 1966. 15. Mountin, J. W., Pennell, E. H., and Hoge, V. M. Health Service Areas: Requirements for General Hospitals and Health Centers. Public Health Bulletin 292. U.S. Government Printing Office, Washington, D.C., 1945. 16. Dickinson, F. A medical service area map of the United States: A progress report. JAMA 139: 1021-1028,1949. 17. Ciocco, A., and Altman, I. Medical Service and Distances Traveled for Physician a r e in Western 18. 19. 20. 21. 22.

Pennsylvania: Part I-Medical Service Areas as Indicated by Intercounty Movement of Patients. United States Public Health Service Monograph 19,1954. Poland, E., and Lembcke, P. A. Delineation of Hospital Service Districts; A Fundamental Requirement in Hospital Planning. Missouri Community Studies, Inc., Kansas City, 1962. Drosness, D. L., Reed, I. M., and Lubin, J. W. The application of computer graphics to patient origin study techniques. Public Health Reports 80(1): 3340,1965. Citizens Hospital Study Committee. Hospitals and Their Use in Northeastern Ohio. Citizens Hospital Study Committee, Cleveland, 1961. Devise, P. Methods and concepts of an interdisciplinary regional hospital study. Health Sew. Res 3(3): 166-173,1968. Morrill, R., and Earickson, R. Hospital variation and patient travel distances. Inquiry 5(4): 26-34,

1968. 23. Weiss, J. E., Greenlick, M. R., and Jones, J. F. Determinants of Medical Care Utilization: The Impact of Ecological Factors. Paper Presented to the American Public Health Association at the 98th Annual Meeting, pp. 4-7, Houston, October 1970. 24. Drosness, D., and Lubin, J. Planning can be based on patient travel.Mod. Hosp., April 1966.. 25. Weiss, J. E., and Greenlick, M. R. Determinants of medical care utilization: The effect of social class and distance on contacts within the medical system.Med. a r e 8(6): 456462,1970.

Manuscript submitted for publication, March 26,1973 Direct reprint requests to: Dr. James Studnicki Department of Public Health Administration School of Hygiene and Public Health Johns Ypkins University 615 North Wolfe Street Baltimore, Maryland 21205