Original Article Submitted: 7.1.2015 Accepted: 7.1.2015 Conflict of interest None.
DOI: 10.1111/ddg.12626
Methods of analyzing regional dermatological care as exemplified by the city of Hamburg
Jobst Augustin1, Stefan Erasmi2, Michael Reusch3, Matthias Augustin1 (1) Institute for Healthcare Research in Dermatology and the Care Professions (IVDP), University Hospital Hamburg-Eppendorf, Hamburg (2) Geographical Institute of the University of Göttingen, Dept. of Cartography, GIS and Remote Sensing, Göttingen (3) Dermatology Group Practice am Tibarg and Institute for Strategic Analysis in Dermatology (IStAD), Hamburg
Summary Background: The rural-urban divide is often linked to regional inequalities in healthcare. However, studies have also shown regional healthcare disparities within urban areas. To evaluate these studies, further parameters such as accessibility must be added to the standard criteria. The objective of this study was to present methodic tools for evaluating dermatological healthcare provision in Hamburg, primarily focusing on accessibility. Methods: Analyzing data from 97 districts, the geographical distribution of 101 dermatologists and the physician-patient ratio were determined. In a second step, network analysis regarding accessibility was performed. Results: There are regional inequalities in Hamburg with respect to dermatological care. Depending on the district, the physician-patient ratio ranges from 44.9 % (undersupply) to > 500 % (oversupply). Similar differences exist regarding accessibility. Although 94.5 % of the population of Hamburg is able to reach the nearest dermatologist within ten minutes (by car), it may take more than 30 minutes depending on district and mode of transportation. Conclusions: Analysis of the physician-patient ratio reveals differences regarding dermatological care in Hamburg. However, results of the network analysis show that these differences do not significantly affect access to dermatological care. Therefore, network analysis should be used as an additional tool to evaluate regional healthcare provision.
Introduction One of the major challenges in the German healthcare system is to ensure comprehensive, uniform, and quick access to medical care for all population groups. To implement this goal, a system (“requirements planning”) has been introduced, which is aimed at controlling the number of statutory health insurance physicians, taking – among other factors – regional aspects into account. Regional disparities in medical care, however, show that the desired goal of homogenous healthcare provision has not been fully achieved yet [1].
The current health policy debate on regional specialist care provision primarily focuses on rural areas. Gress and Stegmüller [2], however, have pointed out that healthcare disparities are not limited to rural areas. In cities, too, healthcare provision is not at all homogenous due to differences in socioeconomic and sociodemographic factors. Thus, regarding current healthcare provision as “optimal” is inappropriate in this context. In large cities such as Hamburg or Berlin, partly because of their heterogeneous social structure, it is particularly important to consider the healthcare situation on a smaller scale. The study by Pieper and Schweikart [3]
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using Berlin as an example illustrates this. When regarding Berlin as just one region, as is the case in the “requirements planning” system, the physician-patient ratio for pediatricians, for example, is 134.7 %. However, when considered on a smaller scale, for instance, at the level of statistical areas, a ratio of well below 100 % can be discerned, thus highlighting regional differences in specialist care (with the example of pediatricians in this case). It is therefore obvious that large metropolitan areas cannot just be considered as a whole, but also have to be viewed on a smaller scale. Moreover, the evaluation of regional healthcare provision in the past often showed methodological flaws, as only ratios were used (for example, population-physician ratio) to describe the state of healthcare provision. Neither the individual care provided by the physician [4] nor physician accessibility for patients is considered in this context. The latter is particularly important, for, here too, small-scale analysis at times reveals pronounced differences [5]. Thus, not only does the sheer presence of a physician play a significant role with respect to healthcare provision, but also his accessibility. For a sustained and objective consideration of equivalent living conditions in different city districts or regions, accessibility and network analysis provide a factual, “realistic”, and more nuanced picture of the situation than would be possible at the level of planning areas or administrative units. Indicators can be varied and range from simple indices to equipment features to complex activity-based accessibility indicators that take space and time into account [6, 7]. Regarding dermatological healthcare provision in Germany, there have so far only been few small-scale geographical analyses examining physician accessibility. Representing the first hypothesis-generating study on this topic, our study analyzed patient transit time to the nearest dermatologist. In general, such analyses can be applied to any city or region, provided there is no limitation due to missing data. In this study, Hamburg was chosen as an example because of the quality of available data. Moreover, based on its distinct topography (inner-city bodies of water, industrial areas), Hamburg is also particularly suitable to demonstrate the potential of network analysis. The objective of this study was to present methods for evaluating regional dermatological healthcare provision using the city of Hamburg as an example, and thus to identify possible healthcare disparities at the district level.
Material and Methods The first step in describing the healthcare situation was to determine the physician-patient ratio. Thus, under or oversupplied regions can easily be identified. The physician-patient ratio at the urban district level was calculated using the number of dermatologists per district. The population figures
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used came from the regional Office of Statistics for Hamburg and Schleswig-Holstein [8]. Based on 2011 figures, physician numbers were provided by the Hamburg Medical Association. Overall, data included 128 dermatologists (outpatient/inpatient), 27 of whom, however, only treated privately insured patients (non-statutory health insurance patients). Although this proportion is somewhat higher in Hamburg compared with other cities, this study only considered dermatologists who participated in “broad” healthcare provision or to whom virtually every citizen of Hamburg had access. Thus, only the remaining 101 dermatologists were used in calculating the physician-patient ratio. With respect to network analysis, the 101 dermatologists were again selected according to location. For instance, a group practice with three dermatologists was counted only once. Following this selection, the network analysis included 75 locations. The addresses of the locations first had to be geocoded, that is, converted to coordinates and then assigned to Hamburg’s various districts. When calculating the physician-patient ratio (PPR), only districts with at least one practicing dermatologist were included. The calculation was performed according to the following equation: PPR = optimal care ratio/care ratio x 100. Here, the optimal care ratio is the physician-patient ratio for certain areal subdivisions as defined by Article 6 Section 3 of the requirements planning guideline issued by KBV (Federal Association of Statutory Health Insurance Physicians) [9, 10]. Depending on the degree of population density, there are ten predetermined areal categories for which the numbers of various specialists have been calculated. The city of Hamburg is classified as category 1 (metropolitan areas/core cities) in the requirements planning guideline. According to this categorization, the optimal number of dermatologists (physician-patient ratio) for the city of Hamburg is one physician for every 21,703 people [10, 11]. This figure corresponds to specialist coverage of 100 %. Undersupply exists when the number of specialists is > 50 % below optimal coverage. On the other hand, oversupply exists, when the optimal figure is exceeded by > 10 % (ratio is 10 % > the physician-patient ratio). To better account for the age structure of the population when calculating the physician-patient ratio, a demographic factor was introduced on January 1, 2013, as part of the reform of the requirements planning guidelines. The demographic factor includes an age factor (percentage of the population under and over 65 years) and a “service requirement” factor specific to various specialty groups. The latter is calculated on the basis of billing statistics provided by KBV (National Association of Statutory Health Insurance Physicians). For dermatologists, it is specified as 1.862 [10]. In this study, taking infrastructure into account, the “regional physician-patient ratio” is defined on the basis of accessibility. Based on the actual road network and using transit times within the road network in a geographical
© 2015 Deutsche Dermatologische Gesellschaft (DDG). Published by John Wiley & Sons Ltd. | JDDG | 1610-0379/2015/1307
Original Article Analysis of dermatological care in Hamburg
Figure 1 Overview of the procedure used for calculating the physician-patient ratio and the geographical accessibility of dermatologists in Hamburg.
information system (GIS), geographical accessibility was calculated by means of network analysis. Here, calculation starts at the target points, in this case the location of the doctors’ offices, and, using fixed parameters (distance/time), calculates catchment areas within which the parameters for accessibility of a respective physician are the same. Any number of homogenous categories (care categories) can be defined, for example, by specifying time or distance intervals. Within these categories, the parameters are not differentiated further but regarded as constant. A multimodal road network is used as the basis for determining accessibility, which comprises roads (streets, footpaths) on the one hand, and the entire rail network of the city of Hamburg on the other hand. This allows for the weighting of different parameters within the network dataset with respect to assessing both the distance and in particular also the time to the nearest doctor, which is not possible in a Euclidean distance model (beeline). Factors such as the traffic and parking situation cannot be considered because of missing (hard) data. The network analysis dataset is based on the freely available data of Openstreetmap [12]. For network analysis, this dataset (streets, paths, rail network) had to be transferred to a dataset that allowed for area-wide routing. Here, it is particularly important to ensure error-free topology of nodes and edges as well as attribution of costs (for example, average speed on certain transit segments) and restrictions (for example, stairs, one-way streets) in the road network. In terms of processing the network dataset and performing network analysis, two different scenarios were postulated. In the first scenario, the patient drove to the doctor by car. The second scenario assumed a pedestrian who would be able to use the subway/urban train or regional train as needed. Here, all train stops were presumed to be barrier-free. Departure
times and train frequencies were considered in the form of a “delay factor” or an average waiting time for the next train. This part of the study was performed using the ArcGIS 10.1 geographical information system (GIS) marketed by ESRI Inc. Redlands, CA, USA. The healthcare categories for distance and time to the nearest dermatologist were then blended with spatial distribution data of settlement areas as well as population data within the urban districts. Consequently, this blending process enables the graphic illustration of catchment areas and provides an estimate of the percentage of developed areas and of inhabitants who live in a certain healthcare category. Figure 1 summarizes the underlying data and procedural steps. A lthough individual districts are used as level of analysis, u rban boroughs (higher level) are also included in the results. Maps show a number assigned to each district. The coding of boroughs and districts can be found in the attached table.
Results In a first step, to obtain an overview of the situation regarding dermatological care in Hamburg, the distribution of dermatologists as well as the physician-patient ratio at the district level were ascertained. Thirty-nine urban districts have at least one out of the 101 dermatologists included in this study. The districts of Neustadt (62) and Hamburg-Altstadt (31) show the highest number, with four dermatologists each. Notably, there are hardly any dermatologists in the entire boroughs of Bergedorf (Bd), Hamburg-Mitte (Mt), and parts of the district of Harburg (Hb) (Figure 2). In addition, the illustration shows the physician-patient ratio at the district level. Ten districts are below 90 % and
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Figure 2 Physician-patient ratio (for dermatologists) in Hamburg (provided by BVDD). *For details see Table 3.
therefore below optimal care. However, only two districts, Wilhelmsburg (94) and Rahlstedt (72), can be categorized as “undersupplied” (less than 50 %) according to the guidelines issued by the Joint Federal Committee [10]. Based on these guidelines, 28 districts are classified as “oversupplied” (over 110%). These include, in particular, districts close to the city center such as Hamburg-Altstadt (31), Neustadt, and St. Georg (82), all showing a physician-patient ratio of more than 400 %. Overall, the average physician-patient ratio in Hamburg is 127 %. Against the backdrop of the heterogeneous dermatological care situation and the fact that not every district has a dermatologist, the question arises as to how to evaluate the accessibility of dermatologists overall. This question is also important, as Hamburg’s natural topographical obstacles such as the Rivers Elbe and Alster are reflected in the accessibility of doctors. First, the distance to the nearest dermatologist was calculated using network analysis (Figure 3). According to the distribution of dermatologists, the nearest (less than 1 km) dermatologists are most frequently found in the city center and the centers of the respective urban districts. The greatest distances to the nearest dermatologist tend to be in peripheral districts and in the districts south of the Elbe, with the exception of Harburg (36). Based on Figure 3, Figure 4 also shows the distance to the nearest dermatologist, but focuses on the transit time in minutes. It becomes clear that, in large parts of Hamburg (94.5 % of the population), the nearest dermatologist can be reached by car within ten minutes. This applies to nearly the
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entire metropolitan area north of the Elbe. In some peripheral districts and in parts of the city south of the Elbe, longer transit times have to be accepted. Thus, 5.5 % of the population of Hamburg requires more than ten minutes by car to get to the nearest dermatologist. As in many other large cities, public transportation is very frequently used in Hamburg. Thus, not only do patients use their own cars when seeing a doctor, but also the subway as well as urban and regional trains. In Figure 5, this fact was taken into account in the calculation of accessibility, showing the distance to the nearest dermatologist in minutes. In this case, however, only train rides (all stops) and walking distances were considered, not car rides. Here, too, the distribution of dermatologists becomes apparent in the accessibility pattern, but the result differs from accessibility by car. Compared to using a car, reaching the nearest dermatologist tends to take longer, which, above all, is due to the time it takes to walk to the nearest rail station. Transit times of less than 20 minutes (48.4 % of the population of Hamburg) are possible in the city center as well as in some districts. However, in many parts of the city, it takes longer (than by car), often more than 30 minutes (26.7 % of the population of Hamburg). This applies to the borough of Bergedorf (Bd) and also to the western districts of the borough of Hamburg-Mitte (Mt). Table 1 shows the percentages of the population divided into various care categories (time (in minutes) to the nearest dermatologist using the subway/urban train) at the district level. Only districts with more than 5,000 inhabitants are shown. It can be seen from the table
© 2015 Deutsche Dermatologische Gesellschaft (DDG). Published by John Wiley & Sons Ltd. | JDDG | 1610-0379/2015/1307
Original Article Analysis of dermatological care in Hamburg
Figure 3 Distance to the nearest dermatologist (km). *For details see Tables 2 and 3.
Figure 4 Distance to the nearest dermatologist (by car in minutes; min). *For details see Tables 2 and 3.
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Table 1 Percentage of the population (of Hamburg) per care category at the district level (time required to reach the nearest dermatologist using subway/urban train; only districts with more than 5 000 inhabitants are shown). District
0–10
10–20
20–30
30–40
40–50
50–60
> 60
1 Allermöhe
–
–
20.8
22.5
19.2
28.4
9.1
2 Alsterdorf
–
29.8
60.5
9.7
–
–
–
5 Altona-Altstadt
38.8
53.8
6.3
0.8
0.3
0.0
–
6 Altona-Nord
13.7
30.1
31.6
20.9
3.7
–
–
7 Barmbek-Nord
23.3
55.6
21.1
–
–
–
–
8 Barmbek-Süd
2.5
80.0
17.5
–
–
–
–
9 Bergedorf
8.9
21.0
41.3
19.1
5.7
3.0
0.9
10 Bergstedt
–
–
1.6
24.3
46.9
27.2
0.1
12 Billstedt
5.4
20.5
31.3
39.3
3.6
–
–
14 Blankenese
16.6
43.6
20.7
13.1
5.4
0.5
–
15 Borgfelde
–
75.5
24.5
–
–
–
–
16 Bramfeld
16.4
42.8
38.9
2.0
–
–
–
19 Dulsberg
–
93.9
6.1
–
–
–
–
20 Duvenstedt
–
–
–
–
0.2
13.0
86.8
21 Eißendorf
–
5.9
15.3
18.2
21.5
22.8
16.3
22 Eidelstedt
11.6
32.0
38.4
18.1
0.0
–
–
23 Eilbek
7.9
89.8
2.4
–
–
–
–
24 Eimsbüttel
61.3
38.0
0.7
–
–
–
–
25 Eppendorf
53.3
38.0
8.7
–
–
–
–
26 Farmsen-Berne
10.2
39.5
44.2
6.1
–
–
–
28 Fuhlsbüttel
25.9
61.6
11.2
0.4
0.9
0.0
–
29 Groß Borstel
–
7.9
31.8
43.5
16.8
–
–
30 Groß Flottbek
21.6
60.2
18.2
–
–
–
–
33 Hamm-Mitte
–
23.6
76.2
0.2
–
–
–
34 Hamm-Nord
–
59.7
40.3
–
–
–
–
36 Harburg
28.3
49.0
17.8
2.8
2.0
0.1
0.0
37 Harvestehude
67.2
32.8
–
–
–
–
–
–
–
29.2
34.6
15.9
10.2
10.0
39 Heimfeld
0.3
21.7
25.9
19.1
12.7
11.7
8.5
40 Hoheluft-Ost
75,9
24.1
–
–
–
–
–
41 Hoheluft-West
42.4
57.3
0.3
–
–
–
–
42 Hohenfelde
17.3
82.7
–
–
–
–
–
43 Horn
–
6.4
56.7
36.3
0.7
–
–
44 Hummelsbüttel
–
1.8
29.0
54.0
11.8
3.4
–
45 Iserbrook
–
33.7
57.8
8.5
–
–
–
16.2
41.5
23.1
19.1
–
–
–
38 Hausbruch
46 Jenfeld
Continued
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Original Article Analysis of dermatological care in Hamburg
Table 1 Continued. District
0–10
10–20
20–30
30–40
40–50
50–60
> 60
47 Kirchwerder
–
–
–
–
–
–
100.0
50 Langenhorn
7.6
21.4
37.0
26.1
7.2
0.7
–
–
0.5
3.4
11.5
14.7
27.7
42.2
52 Lohbrügge
3.7
9.5
16.4
30.2
28.3
11.7
0.2
53 Lokstedt
12.9
66.7
20.4
–
–
–
–
–
–
4.6
35.8
47.1
12.6
–
55 Marienthal
8.1
24.3
34.4
33.1
–
–
–
56 Marmstorf
–
–
–
1.6
19.5
53.7
25.1
60 Neugraben-Fischbek
9.2
30.2
34.5
16.2
8.2
1.5
0.1
62 Neustadt
63.8
36.2
–
–
–
–
–
63 Niendorf
5.7
43.9
36.1
8.9
5.1
0.3
–
–
124
62.1
25.4
0.2
–
–
51 Lemsahl – Mellingstedt
54 Lurup
64 Nienstedten 66 Ohlsdorf
1.4
254
66.6
6.6
–
–
–
67 Osdorf
12.8
41.3
34.5
11.0
0.4
–
–
68 Othmarschen
4.3
25.1
39.1
28.7
2.8
0.0
–
69 Ottensen
23.0
45.8
27.7
3.5
0.1
–
–
70 Poppenbüttel
18.7
28.2
32.9
18.5
1.6
0.1
–
72 Rahlstedt
6.0
18.2
33.2
29.9
10.5
0.7
1.6
74 Rissen
14.1
41.9
33.9
4.5
5.6
0.1
–
–
–
28.1
62.6
6.7
0.5
2.1
56.0
44.0
–
–
–
–
–
–
46.5
47.7
5.8
–
–
–
78 Sasel
0.2
14.6
30.0
30.8
24.2
0.2
–
79 Schnelsen
17.1
37.2
39.0
6.4
0.2
0.1
0.0
82 St. Georg
58.4
41.6
–
–
–
–
–
83 St. Pauli
16.0
82.6
1.2
0.0
0.0
0.0
0.1
84 Steilshoop
0.8
16.5
43.3
39.4
–
–
–
85 Stellingen
0.8
12.8
67.4
17.9
1.2
–
–
87 Tonndorf
15.4
38.2
25.0
21.4
–
–
–
88 Uhlenhorst
3.9
64.0
32.1
–
–
–
–
90 Volksdorf
10.9
28.0
51.0
9.8
0.2
–
–
Finkenwerder
–
–
–
–
–
–
100.0
92 Wandsbek
24.1
36.3
24.9
14.7
–
–
–
93 Wellingsbüttel
–
18.3
70.9
10.8
–
–
–
94 Wilhelmsburg
3.0
9.1
10.4
16.3
11.2
9.0
40.9
–
0.7
13.8
34.8
36.1
14.6
–
34.0
49.1
16.9
–
–
–
–
75 Rothenburgsort 76 Rotherbaum 77 Sülldorf
91 Waltershof and
95 Wilstorf 96 Winterhude
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Figure 5 Distance to the nearest dermatologist (by subway/urban train including all stops and walking distance; min). *For details see Tables 2 and 3.
Figure 6 Percentage of the population per care category for three different scenarios with respect to mode of transportation.
that in districts such as Duvenstedt (20) or Kirchwerder (47) nearly all inhabitants require more than 60 minutes to get to a dermatologist. However, even in districts such as Allermöhe (1), the percentage of the population requiring 40 minutes or more to reach the nearest dermatologist is still over 50 %. A different picture is apparent in central districts such
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as Eppendorf (25) or Harvestehude (37). About 65 % of the population of these districts is able to reach a dermatologist within ten minutes. Figure 6 once again contrasts the percentages for the three scenarios (train (not barrier-free), train (barrier-free) and car). Summarized for the entire city, this shows that the percentage
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Original Article Analysis of dermatological care in Hamburg
of the population able to reach a dermatologist within ten minutes is nearly the same for all modes of transportation considered. With regard to accessibility between ten and 30 minutes, train use (not barrier-free) proves advantageous. By contrast, in peripheral districts of the city, using a car is more rational.
study. It provides information about access (in terms of accessibility) to the nearest doctor. Using this kind of analysis, much more nuanced, and in particular population-based, conclusions about regional healthcare provision can be drawn. The result that, for instance, 94.5 % of the population of Hamburg is able to reach the nearest dermatologist within ten minutes by car can be given as an example, and again illustrates the added value of this method (here, as exemplified by the state of dermatological care provision in Hamburg). Furthermore, regardless of district boundaries, regions can be identified in which deficits in accessibility or care provision are apparent. Pieper and Schweikart have reported similar results for Berlin [3]. However, other truly comparable studies are lacking. Network analysis has shown that dermatological healthcare provision may be classified as being better than it would appear, if one merely used the physician-patient ratio and only considered the sheer density of doctors’ offices. A disadvantage of using network analyses, however, is the fact that they (1) require expert knowledge, (2) demand a complex database (road networks), and (3) cannot directly include the current traffic situation (construction sites, parking spaces). Moreover, individual patient behavior in choosing a doctor could not be considered, nor could the care function of other groups of doctors (especially general practitioners). Optimization of the methodology employed can also be achieved by including surrounding regions and other modes of transportation (for example, bus). However, this will likely only affect accessibility in peripheral regions where access to a dermatologist will be somewhat shortened when bus connections are taken into account. With a few exceptions [3, 5] there are no comparable
Discussion The aim of this study was to present methods for evaluating the state of dermatological care using Hamburg as an example. To obtain an initial overview of regional healthcare provision, the physician-patient ratio (according to requirements planning guidelines) [11] was used. Using this ratio, this study highlighted obvious regional district-dependent differences in dermatological care in Hamburg, although Hamburg can overall be classified as slightly “oversupplied” with dermatologists. This aspect is important, as it illustrates the necessity for a more small-scale inner-city analysis. When interpreting the physician-patient ratio, one has to bear in mind that, despite its metropolitan character, Hamburg has many industrial and also agricultural areas with a low population density. Accordingly, only few or no dermatologists have their offices in these regions. This is important not only for Hamburg, but also in terms of healthcare provision for surrounding regions. Despite the easy applicability of the physician-patient ratio and its “ability” to demonstrate regional differences in care, it is merely based on sheer numbers and its validity is therefore limited. To better assess spatial disparities in regional healthcare provision, the network analysis method was used in this
Table 2 Name and numeric coding of Hamburg’s districts. ID
District
ID
District
1
Allermöhe
50
Langenhorn
2
Alsterdorf
51
Lemsahl-Mellingstedt
3
Altengamme
52
Lohbrügge
4
Altenwerder and Moorburg
53
Lokstedt
5
Altona-Altstadt
54
Lurup
6
Altona-Nord
55
Marienthal
7
Barmbek-Nord
56
Marmstorf
8
Barmbek-Süd
57
Moorfleet
9
Bergedorf
58
Neuenfelde
10
Bergstedt
59
Neuengamme
11
Billbrook
60
Neugraben-Fischbek
Continued
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Table 2 Continued.
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ID
District
ID
District
12
Billstedt
61
Neuland and Gut Moor
13
Billwerder
62
Neustadt
14
Blankenese
63
Niendorf
15
Borgfelde
64
Nienstedten
16
Bramfeld
65
Ochsenwerder
17
Cranz
66
Ohlsdorf
18
Curslack
67
Osdorf
19
Dulsberg
68
Othmarschen
20
Duvenstedt
69
Ottensen
21
Eißendorf
70
Poppenbüttel
22
Eidelstedt
71
Rönneburg
23
Eilbek
72
Rahlstedt
24
Eimsbüttel
73
Reitbrook
25
Eppendorf
74
Rissen
26
Farmsen-Berne
75
Rothenburgsort
27
Francop
76
Rotherbaum
28
Fuhlsbüttel
77
Sülldorf
29
Groß Borstel
78
Sasel
30
Groß Flottbek
79
Schnelsen
31
Hamburg-Altstadt
80
Sinstorf
32
Hammerbrook
81
Spadenland
33
Hamm-Mitte
82
St. Georg
34
Hamm-Nord
83
St. Pauli
35
Hamm-Süd
84
Steilshoop
36
Harburg
85
Stellingen
37
Harvestehude
86
Tatenberg
38
Hausbruch
87
Tonndorf
39
Heimfeld
88
Uhlenhorst
40
Hoheluft-Ost
89
Veddel
41
Hoheluft-West
90
Volksdorf
42
Hohenfelde
91
Waltershof and F inkenwerder
43
Horn
92
Wandsbek
44
Hummelsbüttel
93
Wellingsbüttel
45
Iserbrook
94
Wilhelmsburg
46
Jenfeld
95
Wilstorf
47
Kirchwerder
96
Winterhude
48
Kleiner Grasbrook and Steinwerder
97
Wohldorf-Ohlstedt
49
Langenbek
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Table 3 Name and coding of Hamburg’s boroughs. ID
Borough
At
Altona
Bd
Bergedorf
Hb
Harburg
Eb
Eimsbüttel
Mt
Hamburg-Mitte
Nd
Hamburg-Nord
Wb
Wandsbek
studies on accessibility at the regional level. Presumably, this is attributable to the complex methodology of network analysis, hitherto relatively unknown in health sciences. To test the hypothesis as to how far sociodemographic characteristics are able to explain accessibility or access to a dermatologist, available data should be further investigated using indicators – such as unemployment, density of cars, and others – at the district level. Similar to this study, future research is going to employ geographical information systems (GIS) that allow for small-scale healthcare provision analysis [1, 13], taking into account accessibility and other parameters. Geographical information systems may thus provide an important methodological tool for analyzing regional healthcare provision.
Appendix The names, coding, and numerical coding of Hamburg’s boroughs and districts are listed in Tables 2 and 3. Correspondence to Dr. Jobst Augustin Institut für Versorgungsforschung in der Dermatologie und bei Pflegeberufen (IVDP) Universitätsklinikum Hamburg-Eppendorf Martinistraße 52 20246 Hamburg Germany E-mail: [email protected]
References 1
Kistemann T, Schröer MA. Kleinräumige kassenärztliche Versorgung und subjektives Standortverhalten von Vertragsärzten in einem überversorgten Planungsbiet. Gesundheitswesen 2007; 69: 593–600.
2
Greß S, Stegmüller K. Gesundheitliche Versorgung in Stadt und Land – ein Zukunftskonzept. Friedrich-Ebert-Stiftung [Internet]. 2011 [cited 2014 Apr 20]; Available from: http:// library.fes.de/pdf-files/bueros/hessen/07866.pdf. 3 Pieper J, Schweikart J. Methoden zur kleinräumigen Modellierung von Versorgungsdisparitäen in der vertragsärztlichen Versorgung. GIS Zeitschrift für Geoinformatik 2008; 4: 20–6. 4 Fülöp G, Kopetsch T, Schöpe P. Bedarfsgerechte Versorgungsplanung – Entwicklung eines Modells zur Bestimmung zwischenstandötlicher Versorgungsbeziehungen zur Sicherstellung einer flähendeckenden und bedarfsgerechten ambulanten vertragsärztlichen Versorgung. Gesundheits- und Sozialpolitik (GUS): Zeitschrift für das gesamte Gesundheitswesen 2007; 61: 57–63. 5 Pieper J, Schweikart J Kleinräumige Modellierung der vertragsärztlichen Versorgungssituation in Berlin. Zeitschrift für amtliche Statistik Berlin Brandenburg 2009; 2: 22–9. 6 Neumeier S. Modellierung der Erreichbarkeit öfentlicher Apotheken: Untersuchung zum regionalen Versorgungsgrad mit Dienstleistungen der Grundversorgung. Johann Heinrich von Thüen-Institut [Internet] 2013 [cited 2014 Apr 21]; Available from: http://www.econstor.eu/bitstream/10419/88592/1/773916628.pdf. 7 Bleisch A, Koellreuter C. Die Erreichbarkeit von Regionen. IBC Modul Erreichbarkeit, Schlussbericht Phase 1. IBC BAK International Benchmark Club [Internet] 2003 [cited 2014 Apr 19]; DOI: 10.3929/ethz-a-004667517. 8 Statistisches Amt für Hamburg und Schleswig-Holstein. Hamburger Stadtteil-Profile. NORD.regional [Internet]. 2011 [cited 2014 Apr [11]]; 11; Available from: http://www.statistik-nord.de/ uploads/tx_standocuments/NR11_Stadtteil-Profile_2011.pdf. 9 Kassenärztliche Bundesvereinigung (KBV). Grunddaten zur Vertragsärztlichen Versorgung in Deutschland. KBV [Internet] 2011 [cited 2014 Feb 14]; Available from: http://www.kbv. de/125.html. 10 Gemeinsamer Bundesausschuss. Richtlinie des Gemeinsamen Bundesausschusses über die Bedarfsplanung sowie die Maßstäbe zur Feststellung von Üerversorgung und Unterversorgung in der vertragsärztlichen Versorgung (Bedarfsplanungs-Richtlinie). Bundesanzeiger BAnz AT [Internet] 2013 [cited 2014 Feb 15]; Available from: https://www.g-ba.de/ downloads/62–492–850/BPL-RL_2013–12–19_iK-2014–02–26. pdf. 11 Kassenärztliche Vereinigung Hamburg. Bedarfsplan für den Planungsbereich Hamburg der Kassenärztlichen Vereinigung Hamburg, der Landesverbände der Krankenkassen und der Ersatzkassen gemäß § 99 Abs. 1 SGB V i. V. m. § 12 der Zulassungsverordnung für Ärzte. KVH [Internet] 2013 [cited 2014 Feb 20]; Available from: http://www.kvhh.net/media/ public/db/media/1/2013/07/598/bedarfsplanendfassung2013. pdf. 12 OpenStreetMap [cited 2014 Apr 12]; Available from: http:// www.openstreetmap.org/#map = 6/51.330/10.453. 13 Metzmacher A. Das kleinräumige Wohnviertel als küftiges Ma?aller Dinge? KV-Blatt 10. Kassenärztliche Vereinigung Berlin, 2010.
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DOI: 10.1111/ddg.12626
Methods of analyzing regional dermatological care as exemplified by the city of Hamburg
Jobst Augustin1, Stefan Erasmi2, Michael Reusch3, Matthias Augustin1 (1) Institute for Healthcare Research in Dermatology and the Care Professions (IVDP), University Hospital Hamburg-Eppendorf, Hamburg (2) Geographical Institute of the University of Göttingen, Dept. of Cartography, GIS and Remote Sensing, Göttingen (3) Dermatology Group Practice am Tibarg and Institute for Strategic Analysis in Dermatology (IStAD), Hamburg
Summary Background: The rural-urban divide is often linked to regional inequalities in healthcare. However, studies have also shown regional healthcare disparities within urban areas. To evaluate these studies, further parameters such as accessibility must be added to the standard criteria. The objective of this study was to present methodic tools for evaluating dermatological healthcare provision in Hamburg, primarily focusing on accessibility. Methods: Analyzing data from 97 districts, the geographical distribution of 101 dermatologists and the physician-patient ratio were determined. In a second step, network analysis regarding accessibility was performed. Results: There are regional inequalities in Hamburg with respect to dermatological care. Depending on the district, the physician-patient ratio ranges from 44.9 % (undersupply) to > 500 % (oversupply). Similar differences exist regarding accessibility. Although 94.5 % of the population of Hamburg is able to reach the nearest dermatologist within ten minutes (by car), it may take more than 30 minutes depending on district and mode of transportation. Conclusions: Analysis of the physician-patient ratio reveals differences regarding dermatological care in Hamburg. However, results of the network analysis show that these differences do not significantly affect access to dermatological care. Therefore, network analysis should be used as an additional tool to evaluate regional healthcare provision.
Introduction One of the major challenges in the German healthcare system is to ensure comprehensive, uniform, and quick access to medical care for all population groups. To implement this goal, a system (“requirements planning”) has been introduced, which is aimed at controlling the number of statutory health insurance physicians, taking – among other factors – regional aspects into account. Regional disparities in medical care, however, show that the desired goal of homogenous healthcare provision has not been fully achieved yet [1].
The current health policy debate on regional specialist care provision primarily focuses on rural areas. Gress and Stegmüller [2], however, have pointed out that healthcare disparities are not limited to rural areas. In cities, too, healthcare provision is not at all homogenous due to differences in socioeconomic and sociodemographic factors. Thus, regarding current healthcare provision as “optimal” is inappropriate in this context. In large cities such as Hamburg or Berlin, partly because of their heterogeneous social structure, it is particularly important to consider the healthcare situation on a smaller scale. The study by Pieper and Schweikart [3]
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using Berlin as an example illustrates this. When regarding Berlin as just one region, as is the case in the “requirements planning” system, the physician-patient ratio for pediatricians, for example, is 134.7 %. However, when considered on a smaller scale, for instance, at the level of statistical areas, a ratio of well below 100 % can be discerned, thus highlighting regional differences in specialist care (with the example of pediatricians in this case). It is therefore obvious that large metropolitan areas cannot just be considered as a whole, but also have to be viewed on a smaller scale. Moreover, the evaluation of regional healthcare provision in the past often showed methodological flaws, as only ratios were used (for example, population-physician ratio) to describe the state of healthcare provision. Neither the individual care provided by the physician [4] nor physician accessibility for patients is considered in this context. The latter is particularly important, for, here too, small-scale analysis at times reveals pronounced differences [5]. Thus, not only does the sheer presence of a physician play a significant role with respect to healthcare provision, but also his accessibility. For a sustained and objective consideration of equivalent living conditions in different city districts or regions, accessibility and network analysis provide a factual, “realistic”, and more nuanced picture of the situation than would be possible at the level of planning areas or administrative units. Indicators can be varied and range from simple indices to equipment features to complex activity-based accessibility indicators that take space and time into account [6, 7]. Regarding dermatological healthcare provision in Germany, there have so far only been few small-scale geographical analyses examining physician accessibility. Representing the first hypothesis-generating study on this topic, our study analyzed patient transit time to the nearest dermatologist. In general, such analyses can be applied to any city or region, provided there is no limitation due to missing data. In this study, Hamburg was chosen as an example because of the quality of available data. Moreover, based on its distinct topography (inner-city bodies of water, industrial areas), Hamburg is also particularly suitable to demonstrate the potential of network analysis. The objective of this study was to present methods for evaluating regional dermatological healthcare provision using the city of Hamburg as an example, and thus to identify possible healthcare disparities at the district level.
Material and Methods The first step in describing the healthcare situation was to determine the physician-patient ratio. Thus, under or oversupplied regions can easily be identified. The physician-patient ratio at the urban district level was calculated using the number of dermatologists per district. The population figures
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used came from the regional Office of Statistics for Hamburg and Schleswig-Holstein [8]. Based on 2011 figures, physician numbers were provided by the Hamburg Medical Association. Overall, data included 128 dermatologists (outpatient/inpatient), 27 of whom, however, only treated privately insured patients (non-statutory health insurance patients). Although this proportion is somewhat higher in Hamburg compared with other cities, this study only considered dermatologists who participated in “broad” healthcare provision or to whom virtually every citizen of Hamburg had access. Thus, only the remaining 101 dermatologists were used in calculating the physician-patient ratio. With respect to network analysis, the 101 dermatologists were again selected according to location. For instance, a group practice with three dermatologists was counted only once. Following this selection, the network analysis included 75 locations. The addresses of the locations first had to be geocoded, that is, converted to coordinates and then assigned to Hamburg’s various districts. When calculating the physician-patient ratio (PPR), only districts with at least one practicing dermatologist were included. The calculation was performed according to the following equation: PPR = optimal care ratio/care ratio x 100. Here, the optimal care ratio is the physician-patient ratio for certain areal subdivisions as defined by Article 6 Section 3 of the requirements planning guideline issued by KBV (Federal Association of Statutory Health Insurance Physicians) [9, 10]. Depending on the degree of population density, there are ten predetermined areal categories for which the numbers of various specialists have been calculated. The city of Hamburg is classified as category 1 (metropolitan areas/core cities) in the requirements planning guideline. According to this categorization, the optimal number of dermatologists (physician-patient ratio) for the city of Hamburg is one physician for every 21,703 people [10, 11]. This figure corresponds to specialist coverage of 100 %. Undersupply exists when the number of specialists is > 50 % below optimal coverage. On the other hand, oversupply exists, when the optimal figure is exceeded by > 10 % (ratio is 10 % > the physician-patient ratio). To better account for the age structure of the population when calculating the physician-patient ratio, a demographic factor was introduced on January 1, 2013, as part of the reform of the requirements planning guidelines. The demographic factor includes an age factor (percentage of the population under and over 65 years) and a “service requirement” factor specific to various specialty groups. The latter is calculated on the basis of billing statistics provided by KBV (National Association of Statutory Health Insurance Physicians). For dermatologists, it is specified as 1.862 [10]. In this study, taking infrastructure into account, the “regional physician-patient ratio” is defined on the basis of accessibility. Based on the actual road network and using transit times within the road network in a geographical
© 2015 Deutsche Dermatologische Gesellschaft (DDG). Published by John Wiley & Sons Ltd. | JDDG | 1610-0379/2015/1307
Original Article Analysis of dermatological care in Hamburg
Figure 1 Overview of the procedure used for calculating the physician-patient ratio and the geographical accessibility of dermatologists in Hamburg.
information system (GIS), geographical accessibility was calculated by means of network analysis. Here, calculation starts at the target points, in this case the location of the doctors’ offices, and, using fixed parameters (distance/time), calculates catchment areas within which the parameters for accessibility of a respective physician are the same. Any number of homogenous categories (care categories) can be defined, for example, by specifying time or distance intervals. Within these categories, the parameters are not differentiated further but regarded as constant. A multimodal road network is used as the basis for determining accessibility, which comprises roads (streets, footpaths) on the one hand, and the entire rail network of the city of Hamburg on the other hand. This allows for the weighting of different parameters within the network dataset with respect to assessing both the distance and in particular also the time to the nearest doctor, which is not possible in a Euclidean distance model (beeline). Factors such as the traffic and parking situation cannot be considered because of missing (hard) data. The network analysis dataset is based on the freely available data of Openstreetmap [12]. For network analysis, this dataset (streets, paths, rail network) had to be transferred to a dataset that allowed for area-wide routing. Here, it is particularly important to ensure error-free topology of nodes and edges as well as attribution of costs (for example, average speed on certain transit segments) and restrictions (for example, stairs, one-way streets) in the road network. In terms of processing the network dataset and performing network analysis, two different scenarios were postulated. In the first scenario, the patient drove to the doctor by car. The second scenario assumed a pedestrian who would be able to use the subway/urban train or regional train as needed. Here, all train stops were presumed to be barrier-free. Departure
times and train frequencies were considered in the form of a “delay factor” or an average waiting time for the next train. This part of the study was performed using the ArcGIS 10.1 geographical information system (GIS) marketed by ESRI Inc. Redlands, CA, USA. The healthcare categories for distance and time to the nearest dermatologist were then blended with spatial distribution data of settlement areas as well as population data within the urban districts. Consequently, this blending process enables the graphic illustration of catchment areas and provides an estimate of the percentage of developed areas and of inhabitants who live in a certain healthcare category. Figure 1 summarizes the underlying data and procedural steps. A lthough individual districts are used as level of analysis, u rban boroughs (higher level) are also included in the results. Maps show a number assigned to each district. The coding of boroughs and districts can be found in the attached table.
Results In a first step, to obtain an overview of the situation regarding dermatological care in Hamburg, the distribution of dermatologists as well as the physician-patient ratio at the district level were ascertained. Thirty-nine urban districts have at least one out of the 101 dermatologists included in this study. The districts of Neustadt (62) and Hamburg-Altstadt (31) show the highest number, with four dermatologists each. Notably, there are hardly any dermatologists in the entire boroughs of Bergedorf (Bd), Hamburg-Mitte (Mt), and parts of the district of Harburg (Hb) (Figure 2). In addition, the illustration shows the physician-patient ratio at the district level. Ten districts are below 90 % and
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Original Article Analysis of dermatological care in Hamburg
Figure 2 Physician-patient ratio (for dermatologists) in Hamburg (provided by BVDD). *For details see Table 3.
therefore below optimal care. However, only two districts, Wilhelmsburg (94) and Rahlstedt (72), can be categorized as “undersupplied” (less than 50 %) according to the guidelines issued by the Joint Federal Committee [10]. Based on these guidelines, 28 districts are classified as “oversupplied” (over 110%). These include, in particular, districts close to the city center such as Hamburg-Altstadt (31), Neustadt, and St. Georg (82), all showing a physician-patient ratio of more than 400 %. Overall, the average physician-patient ratio in Hamburg is 127 %. Against the backdrop of the heterogeneous dermatological care situation and the fact that not every district has a dermatologist, the question arises as to how to evaluate the accessibility of dermatologists overall. This question is also important, as Hamburg’s natural topographical obstacles such as the Rivers Elbe and Alster are reflected in the accessibility of doctors. First, the distance to the nearest dermatologist was calculated using network analysis (Figure 3). According to the distribution of dermatologists, the nearest (less than 1 km) dermatologists are most frequently found in the city center and the centers of the respective urban districts. The greatest distances to the nearest dermatologist tend to be in peripheral districts and in the districts south of the Elbe, with the exception of Harburg (36). Based on Figure 3, Figure 4 also shows the distance to the nearest dermatologist, but focuses on the transit time in minutes. It becomes clear that, in large parts of Hamburg (94.5 % of the population), the nearest dermatologist can be reached by car within ten minutes. This applies to nearly the
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entire metropolitan area north of the Elbe. In some peripheral districts and in parts of the city south of the Elbe, longer transit times have to be accepted. Thus, 5.5 % of the population of Hamburg requires more than ten minutes by car to get to the nearest dermatologist. As in many other large cities, public transportation is very frequently used in Hamburg. Thus, not only do patients use their own cars when seeing a doctor, but also the subway as well as urban and regional trains. In Figure 5, this fact was taken into account in the calculation of accessibility, showing the distance to the nearest dermatologist in minutes. In this case, however, only train rides (all stops) and walking distances were considered, not car rides. Here, too, the distribution of dermatologists becomes apparent in the accessibility pattern, but the result differs from accessibility by car. Compared to using a car, reaching the nearest dermatologist tends to take longer, which, above all, is due to the time it takes to walk to the nearest rail station. Transit times of less than 20 minutes (48.4 % of the population of Hamburg) are possible in the city center as well as in some districts. However, in many parts of the city, it takes longer (than by car), often more than 30 minutes (26.7 % of the population of Hamburg). This applies to the borough of Bergedorf (Bd) and also to the western districts of the borough of Hamburg-Mitte (Mt). Table 1 shows the percentages of the population divided into various care categories (time (in minutes) to the nearest dermatologist using the subway/urban train) at the district level. Only districts with more than 5,000 inhabitants are shown. It can be seen from the table
© 2015 Deutsche Dermatologische Gesellschaft (DDG). Published by John Wiley & Sons Ltd. | JDDG | 1610-0379/2015/1307
Original Article Analysis of dermatological care in Hamburg
Figure 3 Distance to the nearest dermatologist (km). *For details see Tables 2 and 3.
Figure 4 Distance to the nearest dermatologist (by car in minutes; min). *For details see Tables 2 and 3.
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Table 1 Percentage of the population (of Hamburg) per care category at the district level (time required to reach the nearest dermatologist using subway/urban train; only districts with more than 5 000 inhabitants are shown). District
0–10
10–20
20–30
30–40
40–50
50–60
> 60
1 Allermöhe
–
–
20.8
22.5
19.2
28.4
9.1
2 Alsterdorf
–
29.8
60.5
9.7
–
–
–
5 Altona-Altstadt
38.8
53.8
6.3
0.8
0.3
0.0
–
6 Altona-Nord
13.7
30.1
31.6
20.9
3.7
–
–
7 Barmbek-Nord
23.3
55.6
21.1
–
–
–
–
8 Barmbek-Süd
2.5
80.0
17.5
–
–
–
–
9 Bergedorf
8.9
21.0
41.3
19.1
5.7
3.0
0.9
10 Bergstedt
–
–
1.6
24.3
46.9
27.2
0.1
12 Billstedt
5.4
20.5
31.3
39.3
3.6
–
–
14 Blankenese
16.6
43.6
20.7
13.1
5.4
0.5
–
15 Borgfelde
–
75.5
24.5
–
–
–
–
16 Bramfeld
16.4
42.8
38.9
2.0
–
–
–
19 Dulsberg
–
93.9
6.1
–
–
–
–
20 Duvenstedt
–
–
–
–
0.2
13.0
86.8
21 Eißendorf
–
5.9
15.3
18.2
21.5
22.8
16.3
22 Eidelstedt
11.6
32.0
38.4
18.1
0.0
–
–
23 Eilbek
7.9
89.8
2.4
–
–
–
–
24 Eimsbüttel
61.3
38.0
0.7
–
–
–
–
25 Eppendorf
53.3
38.0
8.7
–
–
–
–
26 Farmsen-Berne
10.2
39.5
44.2
6.1
–
–
–
28 Fuhlsbüttel
25.9
61.6
11.2
0.4
0.9
0.0
–
29 Groß Borstel
–
7.9
31.8
43.5
16.8
–
–
30 Groß Flottbek
21.6
60.2
18.2
–
–
–
–
33 Hamm-Mitte
–
23.6
76.2
0.2
–
–
–
34 Hamm-Nord
–
59.7
40.3
–
–
–
–
36 Harburg
28.3
49.0
17.8
2.8
2.0
0.1
0.0
37 Harvestehude
67.2
32.8
–
–
–
–
–
–
–
29.2
34.6
15.9
10.2
10.0
39 Heimfeld
0.3
21.7
25.9
19.1
12.7
11.7
8.5
40 Hoheluft-Ost
75,9
24.1
–
–
–
–
–
41 Hoheluft-West
42.4
57.3
0.3
–
–
–
–
42 Hohenfelde
17.3
82.7
–
–
–
–
–
43 Horn
–
6.4
56.7
36.3
0.7
–
–
44 Hummelsbüttel
–
1.8
29.0
54.0
11.8
3.4
–
45 Iserbrook
–
33.7
57.8
8.5
–
–
–
16.2
41.5
23.1
19.1
–
–
–
38 Hausbruch
46 Jenfeld
Continued
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Original Article Analysis of dermatological care in Hamburg
Table 1 Continued. District
0–10
10–20
20–30
30–40
40–50
50–60
> 60
47 Kirchwerder
–
–
–
–
–
–
100.0
50 Langenhorn
7.6
21.4
37.0
26.1
7.2
0.7
–
–
0.5
3.4
11.5
14.7
27.7
42.2
52 Lohbrügge
3.7
9.5
16.4
30.2
28.3
11.7
0.2
53 Lokstedt
12.9
66.7
20.4
–
–
–
–
–
–
4.6
35.8
47.1
12.6
–
55 Marienthal
8.1
24.3
34.4
33.1
–
–
–
56 Marmstorf
–
–
–
1.6
19.5
53.7
25.1
60 Neugraben-Fischbek
9.2
30.2
34.5
16.2
8.2
1.5
0.1
62 Neustadt
63.8
36.2
–
–
–
–
–
63 Niendorf
5.7
43.9
36.1
8.9
5.1
0.3
–
–
124
62.1
25.4
0.2
–
–
51 Lemsahl – Mellingstedt
54 Lurup
64 Nienstedten 66 Ohlsdorf
1.4
254
66.6
6.6
–
–
–
67 Osdorf
12.8
41.3
34.5
11.0
0.4
–
–
68 Othmarschen
4.3
25.1
39.1
28.7
2.8
0.0
–
69 Ottensen
23.0
45.8
27.7
3.5
0.1
–
–
70 Poppenbüttel
18.7
28.2
32.9
18.5
1.6
0.1
–
72 Rahlstedt
6.0
18.2
33.2
29.9
10.5
0.7
1.6
74 Rissen
14.1
41.9
33.9
4.5
5.6
0.1
–
–
–
28.1
62.6
6.7
0.5
2.1
56.0
44.0
–
–
–
–
–
–
46.5
47.7
5.8
–
–
–
78 Sasel
0.2
14.6
30.0
30.8
24.2
0.2
–
79 Schnelsen
17.1
37.2
39.0
6.4
0.2
0.1
0.0
82 St. Georg
58.4
41.6
–
–
–
–
–
83 St. Pauli
16.0
82.6
1.2
0.0
0.0
0.0
0.1
84 Steilshoop
0.8
16.5
43.3
39.4
–
–
–
85 Stellingen
0.8
12.8
67.4
17.9
1.2
–
–
87 Tonndorf
15.4
38.2
25.0
21.4
–
–
–
88 Uhlenhorst
3.9
64.0
32.1
–
–
–
–
90 Volksdorf
10.9
28.0
51.0
9.8
0.2
–
–
Finkenwerder
–
–
–
–
–
–
100.0
92 Wandsbek
24.1
36.3
24.9
14.7
–
–
–
93 Wellingsbüttel
–
18.3
70.9
10.8
–
–
–
94 Wilhelmsburg
3.0
9.1
10.4
16.3
11.2
9.0
40.9
–
0.7
13.8
34.8
36.1
14.6
–
34.0
49.1
16.9
–
–
–
–
75 Rothenburgsort 76 Rotherbaum 77 Sülldorf
91 Waltershof and
95 Wilstorf 96 Winterhude
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Original Article Analysis of dermatological care in Hamburg
Figure 5 Distance to the nearest dermatologist (by subway/urban train including all stops and walking distance; min). *For details see Tables 2 and 3.
Figure 6 Percentage of the population per care category for three different scenarios with respect to mode of transportation.
that in districts such as Duvenstedt (20) or Kirchwerder (47) nearly all inhabitants require more than 60 minutes to get to a dermatologist. However, even in districts such as Allermöhe (1), the percentage of the population requiring 40 minutes or more to reach the nearest dermatologist is still over 50 %. A different picture is apparent in central districts such
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as Eppendorf (25) or Harvestehude (37). About 65 % of the population of these districts is able to reach a dermatologist within ten minutes. Figure 6 once again contrasts the percentages for the three scenarios (train (not barrier-free), train (barrier-free) and car). Summarized for the entire city, this shows that the percentage
© 2015 Deutsche Dermatologische Gesellschaft (DDG). Published by John Wiley & Sons Ltd. | JDDG | 1610-0379/2015/1307
Original Article Analysis of dermatological care in Hamburg
of the population able to reach a dermatologist within ten minutes is nearly the same for all modes of transportation considered. With regard to accessibility between ten and 30 minutes, train use (not barrier-free) proves advantageous. By contrast, in peripheral districts of the city, using a car is more rational.
study. It provides information about access (in terms of accessibility) to the nearest doctor. Using this kind of analysis, much more nuanced, and in particular population-based, conclusions about regional healthcare provision can be drawn. The result that, for instance, 94.5 % of the population of Hamburg is able to reach the nearest dermatologist within ten minutes by car can be given as an example, and again illustrates the added value of this method (here, as exemplified by the state of dermatological care provision in Hamburg). Furthermore, regardless of district boundaries, regions can be identified in which deficits in accessibility or care provision are apparent. Pieper and Schweikart have reported similar results for Berlin [3]. However, other truly comparable studies are lacking. Network analysis has shown that dermatological healthcare provision may be classified as being better than it would appear, if one merely used the physician-patient ratio and only considered the sheer density of doctors’ offices. A disadvantage of using network analyses, however, is the fact that they (1) require expert knowledge, (2) demand a complex database (road networks), and (3) cannot directly include the current traffic situation (construction sites, parking spaces). Moreover, individual patient behavior in choosing a doctor could not be considered, nor could the care function of other groups of doctors (especially general practitioners). Optimization of the methodology employed can also be achieved by including surrounding regions and other modes of transportation (for example, bus). However, this will likely only affect accessibility in peripheral regions where access to a dermatologist will be somewhat shortened when bus connections are taken into account. With a few exceptions [3, 5] there are no comparable
Discussion The aim of this study was to present methods for evaluating the state of dermatological care using Hamburg as an example. To obtain an initial overview of regional healthcare provision, the physician-patient ratio (according to requirements planning guidelines) [11] was used. Using this ratio, this study highlighted obvious regional district-dependent differences in dermatological care in Hamburg, although Hamburg can overall be classified as slightly “oversupplied” with dermatologists. This aspect is important, as it illustrates the necessity for a more small-scale inner-city analysis. When interpreting the physician-patient ratio, one has to bear in mind that, despite its metropolitan character, Hamburg has many industrial and also agricultural areas with a low population density. Accordingly, only few or no dermatologists have their offices in these regions. This is important not only for Hamburg, but also in terms of healthcare provision for surrounding regions. Despite the easy applicability of the physician-patient ratio and its “ability” to demonstrate regional differences in care, it is merely based on sheer numbers and its validity is therefore limited. To better assess spatial disparities in regional healthcare provision, the network analysis method was used in this
Table 2 Name and numeric coding of Hamburg’s districts. ID
District
ID
District
1
Allermöhe
50
Langenhorn
2
Alsterdorf
51
Lemsahl-Mellingstedt
3
Altengamme
52
Lohbrügge
4
Altenwerder and Moorburg
53
Lokstedt
5
Altona-Altstadt
54
Lurup
6
Altona-Nord
55
Marienthal
7
Barmbek-Nord
56
Marmstorf
8
Barmbek-Süd
57
Moorfleet
9
Bergedorf
58
Neuenfelde
10
Bergstedt
59
Neuengamme
11
Billbrook
60
Neugraben-Fischbek
Continued
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Table 2 Continued.
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ID
District
ID
District
12
Billstedt
61
Neuland and Gut Moor
13
Billwerder
62
Neustadt
14
Blankenese
63
Niendorf
15
Borgfelde
64
Nienstedten
16
Bramfeld
65
Ochsenwerder
17
Cranz
66
Ohlsdorf
18
Curslack
67
Osdorf
19
Dulsberg
68
Othmarschen
20
Duvenstedt
69
Ottensen
21
Eißendorf
70
Poppenbüttel
22
Eidelstedt
71
Rönneburg
23
Eilbek
72
Rahlstedt
24
Eimsbüttel
73
Reitbrook
25
Eppendorf
74
Rissen
26
Farmsen-Berne
75
Rothenburgsort
27
Francop
76
Rotherbaum
28
Fuhlsbüttel
77
Sülldorf
29
Groß Borstel
78
Sasel
30
Groß Flottbek
79
Schnelsen
31
Hamburg-Altstadt
80
Sinstorf
32
Hammerbrook
81
Spadenland
33
Hamm-Mitte
82
St. Georg
34
Hamm-Nord
83
St. Pauli
35
Hamm-Süd
84
Steilshoop
36
Harburg
85
Stellingen
37
Harvestehude
86
Tatenberg
38
Hausbruch
87
Tonndorf
39
Heimfeld
88
Uhlenhorst
40
Hoheluft-Ost
89
Veddel
41
Hoheluft-West
90
Volksdorf
42
Hohenfelde
91
Waltershof and F inkenwerder
43
Horn
92
Wandsbek
44
Hummelsbüttel
93
Wellingsbüttel
45
Iserbrook
94
Wilhelmsburg
46
Jenfeld
95
Wilstorf
47
Kirchwerder
96
Winterhude
48
Kleiner Grasbrook and Steinwerder
97
Wohldorf-Ohlstedt
49
Langenbek
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Original Article Analysis of dermatological care in Hamburg
Table 3 Name and coding of Hamburg’s boroughs. ID
Borough
At
Altona
Bd
Bergedorf
Hb
Harburg
Eb
Eimsbüttel
Mt
Hamburg-Mitte
Nd
Hamburg-Nord
Wb
Wandsbek
studies on accessibility at the regional level. Presumably, this is attributable to the complex methodology of network analysis, hitherto relatively unknown in health sciences. To test the hypothesis as to how far sociodemographic characteristics are able to explain accessibility or access to a dermatologist, available data should be further investigated using indicators – such as unemployment, density of cars, and others – at the district level. Similar to this study, future research is going to employ geographical information systems (GIS) that allow for small-scale healthcare provision analysis [1, 13], taking into account accessibility and other parameters. Geographical information systems may thus provide an important methodological tool for analyzing regional healthcare provision.
Appendix The names, coding, and numerical coding of Hamburg’s boroughs and districts are listed in Tables 2 and 3. Correspondence to Dr. Jobst Augustin Institut für Versorgungsforschung in der Dermatologie und bei Pflegeberufen (IVDP) Universitätsklinikum Hamburg-Eppendorf Martinistraße 52 20246 Hamburg Germany E-mail: [email protected]
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