Assessing the Spatial Accessibility of Healthcare Services in Huangmei County Using GIS

Assessing the Spatial Accessibility of Healthcare Services in Huangmei County using GIS

Haoyun Wang Final Paper for Theory and Practice of Public Informatics Professor Clinton Andrews December 17, 2018

Assessing the Spatial Accessibility of Healthcare Services in Huangmei County using GIS Abstract: Large disparities exist in the development of urban and rural areas in China, especially in the field of healthcare. It is common to see remote rural areas suffer from a shortage of medical resources while urbanized cities and towns enjoy a higher level of accessibility to healthcare service. This research uses Huangmei County, the author’s hometown, as a case study and aims to define and evaluate spatial accessibility and spatial inequity (i.e. unequal spatial accessibility in healthcare services for rural and urban areas) through ArcGIS. Finally, it proposes recommendations for decision-makers in healthcare planning to increase people’s spatial accessibility to healthcare services. Keyword: healthcare services, GIS, spatial accessibility, spatial equity

1. INTRODUCTION

Although China has witnessed great progress in economic development during the past decade, large disparities still exist in the development of urban and rural areas in China, especially in the distribution of public services, such as schools, hospitals, and transit. It is common to see remote rural areas suffer from a shortage of public services while urbanized cities and towns enjoy a higher level of accessibility to public services. The Chinese government has proposed the equalization of public services at the National Congress to spur efforts to increase public services in undeveloped areas and other places that need them most Among the host of public services, healthcare service is considered as one of the fundamental services. An accurate assessment of the current allocation of healthcare services is essential to understand and address existing inequities, thus narrowing gaps in access to healthcare services and promoting overall population health.

The spatial distribution of public services within cities and regions and people’s access to these services have been a central focus in a lot of geographical research. While past studies have varied in contexts, methodological approach and the type of public services, a common task has been to address spatial equity by researching on a question whether the spatial distribution of and access to a particular public service correspond to the geographical variation of ‘need’ for that service. This allows policymakers to assess the effectiveness of existing policies and identify areas of under-provision, thereby beginning the process of improved public service delivery. however, the methods used to calculate accessibility vary among studies and influence the results (Smoyer- Tomic, 2004). Methods measuring spatial accessibility can be roughly classified into four

categories: provider-to-population ratios (PPR), distance to nearest provider, average distance to a set of providers, and gravity models (Guagliardo, 2004). With the development of Geographical Information System (GIS), many new techniques have been introduced based on the gravity model, of which the Two-Step Floating Catchment Area (2SFCA) method has gained the most traction (Luo and Wang, 2003).

This analysis will use Huangmei County, China as its case study. First, the paper discusses the social and demographic background of Huangmei. Then it analyzes supply and demand of healthcare services, spatial accessibility, and spatial equity. Last but not least, it provides recommendations for future healthcare facilities planning in Huangmei County.

2. DATA AND METHODS 2.1 Study area Huangmei County is located in the southeast of Hubei Province, Central China, and is north of the Changjiang River. Huangmei lies at the intersection of Hubei Province, Jiangxi Province, and Anhui Province. Huangmei has a total area of 1,701 km2 and its population comprises nearly one million residents, with one-tenth of its residents concentrating in the central town (county seat and regional economic center) Huangmei (The name of the central town is the same as the name of the county). Huangmei County enjoys a good transportation system, a combination of waterway, highway, expressway and railway. As shown in Figure2, there are high mountains, which are more than 1000m in height, inthe north of the county, restricting the accessibility of residents to public services. Southern areas consists of plains, which make it easy for people to move around.

Figure 1. Overview Mapping of Huangmei Figure2. Elevation Map

2.2 Data The population data was obtained from Huangmei Sixth Population Census (2015) and the statistical yearbook of Huangmei (2015). In this paper, a residential point is used as the unit of analysis. The residential point is the centroid of a community in the urban area or a village in the rural area, representing community/village in this paper. Community/village is also currently the smallest measurement unit of public statistics in China.

The data related to healthcare services was acquired from the Huangmei Health Bureau. It includes the number of healthcare professionals (doctors, nurses, etc.) and beds offered. The longitude and latitude of residential points and healthcare facilities were acquired from Baidu Maps (similar to Google Maps, the online map database typically used in China)then the locations were geocoded in ArcGIS.

The travel impedance data (i.e. the road network), as well as administrative boundary data, were acquired from the 1:25,0000 scale Topographic Database of the National Fundamental Geographic Information System of China.

2.3 Method The following analysis was conducted in the ArcGIS 10.6 version. The distribution of healthcare facilities was analyzed by ArcGIS’sAverage Nearest Neighbor tool. The Closest Facility Analysis in Network Analyst was used to calculate the spatial accessibility metric of each residential point to its nearest healthcare facility based on time cost (i.e. time distance). Then, spatial accessibility score is calculated by integrating the spatial accessibility metric and population together. The steps of this analysis will be discussed in detail in the sections below. Finally, this study proposes locationsto build new hospitals to improve spatial accessibility based on the analysis.

This paper seeks to address these questions: How are the healthcare services distributed in Huangmei? How can we evaluate the spatial accessibility and spatial equity of healthcare services? How is the spatial accessibility and equity in Huangmei in terms of healthcare services? Where should new healthcare facilities be established to increase spatial accessibility and equity? How much will spatial accessibility or equity be improved if the new healthcare facilities are built?

3. HEALTHCARE SERVICES SUPPLY-DEMAND IN HUANGMEI

Healthcare services demand is measured by the total population. A higher number of people

4 should correspond with a higher supply of healthcare facilities. To be specific, it is ideal if healthcare services demand were measured by the number of people who are vulnerable to diseases such as children, elders, and the disabled. Due to the lack of demographic data on these vulnerable groups, the overall population is used as an indicator of the demand. Figure 3 shows the distribution of healthcare services demand (residential points) and supply (healthcare facilities). A large percentage of residents are distributed in the central area and southern area of Huangmei County. With respect to healthcare services supply, there are 169 healthcare facilities in Huangmei County. According to the national criteria of the main functions of healthcare facilities and the number of beds provided, they are divided into five categories and three levels. There are 4 comprehensive hospitals, 3 specialty hospitals, 4 regional hospitals, 20 township hospitals and 138 community clinics. The spatial locations of healthcare facilities are shown in Figure 3.

Demand Supply Figure 3. Distribution of Healthcare Services Demand and Supply (Residential Points and Healthcare Facilities)

Table1. Categories and Main Functions of Healthcare Facilities in Huangmei Category Level Main functions beds number Comprehensive County r\Responsible for providing >20 4 hospital level comprehensive health services, as well as (People’s medical education and conducting Hospital, Second research on a regional basis. It has a large People’ S Hospital number of beds for intensive care and Chinese Medicine additional beds for patients who need Hospital, Tongji long-term care. Hospital, )

Specialty County Deals with specific medical needs such as 50-200 3 hospital level psychiatric treatment (see psychiatric (Children’s hospital) and certain disease categories. Hospital, Women’s Hospital, Orthopaedic Hospital) Regional Municipal Provide basic healthcare services to 50-100 4 hospital level several towns. It is less comprehensive than county-level hospitals and meets the demand of local residents. They are tasked with providing preventive care, minimal health care and rehabilitation services. Township Municipal Provide healthcare services to the local 20-50 20 hospital level town. Usually, one town has its own township hospital. Community Community It is run by a government agency for <10 138 clinic level health services or a private partnership of physicians. It provides assistance to basic healthcare service (such as flu shots). Patients diagnosed with severe diseases will be transferred to the higher level hospitals

In China, beds per 1000 people or healthcare professionals per 1000 people are often chosen as the index to evaluate the supply of healthcare services in a region. Table 2 compares the supply of health care services in Huangmei to that of Hubei Province and the country. Because Hubei province is among the more developed provinces in China while Huangmei County is one of the least developed counties in Hubei Province, healthcare services supply in Huangmei is higher than the national average level but lower than the average level of Hubei province. Beds and healthcare professionals should be increased so that the supply of Huangmei does not lag behind the provincial level. Figure 4 and Table 3 illustrate the disparity in healthcare services supply on the municipal level. Huangmei (the central town) and Xiaochi (the second biggest town) have a much higher level of healthcare services supply than the county average level while the supply in rural areas is below the average level. The distribution of healthcare services is not equal among the municipalities.

Table 2. Comparison of healthcare services supply of Huangmei to Hubei and the nation Index Huangmei Hubei Province National County Beds per 1000 people 3.7 4.6 3.6

Healthcare professionals per 1000 3.9 4.2 3.8 people

Figure 4. Supply of Healthcare services for each municipality

Table 3: Healthcare services supply on municipal level Municipalities Mostly urban Population beds Healthcare Beds per 1000 Healthcare or rural professionals people professionals per 1000 people Huangmei urban 110310 1522 1743 13.8 15.8 Xiaochi urban 109066 1069 1134 9.8 10.4 Caishan rural 103887 301 280 2.9 2.7 Zhuogang rural 98638 296 375 3 3.8 Dahe rural 80191 217 168 2.7 2.1 Konglong rural 72933 248 248 3.4 3.4 Fenlu rural 71501 222 243 3.1 3.4 Xinkai rural 69110 180 242 2.6 3.5 Shanmu rural 63655 159 166 2.5 2.6 Dushan rural 54268 174 163 3.2 3 Kuzhu rural 47265 170 165 3.6 3.5 Xiaxin rural 44827 126 134 2.8 3 Longganhu rural 39057 105 90 2.7 2.3 Wuzu rural 37929 129 121 3.4 3.2 Tingqian rural 35289 67 85 1.9 2.4 Liuzuo rural 27174 33 46 1.2 1.7 Liulin rural 19727 28 36 1.4 1.8 Total 1084827 5044 5440 Average 3.7 3.9

Next, the spatial distribution of healthcare facilities is discussed.

In this case study, the healthcare facilities are presented as points on the maps. The spatial distribution pattern of points can be classified as clustered, dispersed, or random. Spatial distribution patterns can be analyzed by the Average Nearest Neighbor tool in ArcGIS, which measures the distance between each healthcare facility point and its nearest neighbor's location. It then averages all these nearest neighbor distances. If the average distance is less than the average for a hypothetical random distribution, the distribution is considered clustered. If the average distance is greater than a hypothetical random distribution, the healthcare facilities are considered dispersed. The average nearest neighbor ratio (R) is calculated as the observed average distance divided by the expected average distance (with expected average distance being based on a hypothetical random distribution with the same number of points covering the same total area). 01, dispersed pattern, higher r indicates a higher degree of dispersion.

According to Table 4 which summarizes the results of the analysis, comprehensive hospitals and specialty hospitals show clustered patterns while regional hospitals, township hospitals, community clinics show dispersed patterns. In order to achieve the equalization of the healthcare services across the whole county area, the dispersed pattern should be encouraged So that more people can access them.

Table 4. Average Nearest Neighbor Analysis Result Healthcare Comprehensive Specialty Regional Township Community services hospital Hospital hospital hospital clinics Closet index: R 0.86 0.63 3.75 1.17 1.08 Distribution clustered clustered dispersed dispersed dispersed pattern Z score -2.63 -1.78 4.1 2.5 1.83

Figure5. Average Nearest Neighbor Analysis for Community Clinics

4. SPATIAL ACCESSIBILITY AND EQUITY

Spatial accessibility is an important factor for planning public facilities. Accessibility involves not just physical distances and time but social, cultural and gender-based constraints as well (Mitchell 1996; Lindsey et al. 2001). Spatial accessibility to healthcare facilities is adequate when the population is in proportional to the healthcare facilities’ spatial distribution (Langford and Higgs, 2010). Spatial accessibility further differs in the level of services offered to different population groups, which are affected by non-spatial factors (Comber et al., 2008, Tan and Samsudin, 2017). The focus in this paper is to examine the spatial accessibility of each residential point (representing the population of the community/village) to the closest healthcare facilities, regardless of ethnicity, wealth, income, education, age, etc. Due to the lack of detailed sociodemographic data, this analysisonly considers the factors of total population and locations of healthcare facilities in the paper. Measuring spatial accessibility begins the process of assessing spatial equity,identifying under-serviced areas and proposing recommendations for planning policy.

Equity, in a spatial context, can be variously defined and measured (Hay 1995). Spatial equity

involves the consideration of need, justice and fairness in the distribution of spatial inequalities (Talen and Anselin 1998). The determination of what is just, fair or equitable varies and is often based on underlying value systems (Truelove 1993). Facilities are discrete entities, whereas the populations they serve are spatially continuous as they spread throughout the region, inevitably resulting in some inequality in spatial accessibility (Dear 1974). Regardless of where public facilities are located, some persons will always be closer to them than others. As Knox (1978, 414) noted, “the crucial question to ask of planned or established facility location patterns is, therefore, how much inequality is produced, and which groups are most disadvantaged?” Similarly, Massam (1993) asserts that ‘the right location’ for a facility does not exist. The purpose of this paper was not to explore or debate the concept of equity. Rather, it adopted Lucy's (1981) definition of equity that service provision should be equal to need. Resource distribution thus can be considered equitable if it is based on population need. Needs-based criteria have been used in many studies to evaluate the spatial equity of amenity location. Here, a spatial equity score is calculated for each residential point. (Smoyer-Tomic, 2004)

The following paper discusses the calculation of spatial accessibility and spatial equity and what the two indices imply in terms of healthcare facilities.

The first step is to build transportation networks where different categories of roads were assigned corresponding travel speeds. Based on road class, traffic, physical conditions, and the Highway Technical Standards of China, each section of road was given a standard speed for the calculation of travel time: 120 km/h for highway, 100 km/h for state road, 80 km/h for provincial road, 60 km/h for county road, and 40 km/h for village road. The walking speed was set at 5 km/h when residents are required to walk to the closest road. These parameters were set in the Network Analyst Extension in ArcGIS.

Next, Closest Facility Analysis was conducted to calculate the shortest routes from each residential point to the closest healthcare facilities. Here, the distance is based on time cost. Healthcare facilities were loaded as “facilities” and residential points were loaded as “incidents”. Figure 6,7, and 8 show the accessibility analysis for healthcare facilities at the county level (comprehensive hospital, specialty hospital), at the municipal level (regional hospital, township hospital), and at the community level (community clinics). Table 5 shows the spatial accessibility of each category of healthcare facilities based on the percentages of population and area covered within a certain time. The percentage of population served is defined as the population of people

10 who can have access to healthcare facilities with a certain time divided by the total population of Huangmei County. The percentage of area served is defined as the service area with a certain time divided by the total area of Huangmei County.

Figure 6. Accessibility Analysis for Healthcare Facilities at County, Municipal, and Community Levels

Table 5. Comparison of Accessibility to Healthcare Facilities Healthcare services Comprehensive Specialty Regional Township Community hospital hospital hospital hospital clinics average access time (minutes) 36.1 27.4 20.5 14.2 18.6 % of population covered within 0-15 minutes 26.9% 17.5% 36.2% 60.7% 53.1% % of area covered within 0-15 minutes 28.0% 16.9% 38.1% 56.3% 54.2% % of population covered within 15-30 minutes 33.5% 33.2% 49.7% 32.7% 30.6% % of area covered within 30-45 minutes 34.7% 34.9% 47.3% 31.0% 27.3% % of population covered within 30-45 minutes 20.8% 35.9% 10.8% 5.9% 13.5% % of area covered within 30-45 minutes 21.3% 32.8% 11.3% 6.8% 12.9% % of population covered within 45-60 minutes 2.8% 14.2% 3.8% 2.8% 4.8% % of area covered within 45-60 minutes 4.8% 16.2% 3.9% 4.1% 4.0%

Spatial accessibility based on time cost 120.0% 100.0% 80.0% 60.0% 40.0% 20.0% of population served served of population

accumulative percenatge 0.0% 0-15 minutes 15-30minutes 30-45minutes 45-60minutes County level 26.9% 70.4% 91.2% 94.0% Municipal level 53.1% 83.7% 97.2% 100.0% Community level 60.7% 93.4% 99.3% 100.0%

Figure 7. Spatial accessibility analysis based on time cost

Figure 7 summarizes the Table 5, illustrating the cumulative percentage of population served based on time cost. On average, residents can have access to healthcare facilities at all levels within 45 minutes. In terms of the spatial accessibility based on time cost, healthcare facilities at the community level perform the best, followed by those at the municipal level. Residents have the least spatial accessibility to healthcare facilities at the county level. Within 15 minutes of travel, only 26.9 % of the population are served by healthcare facilities at the county level, as there are only 4 comprehensive hospitals and specialty hospitals combined. These hospitals are distributed in the following towns: Huangmei in the north and Xiaochi in the south, the towns with highest and second highest population. Within an hour of travel, 6% of the population still could not have access to healthcare facilities at the county level. These spatially disadvantaged people live in the central area in Huangmei County or in the remote rural areas where road network is not as dense as urban area. In comparison, within 15 minutes, more than half of the population have access to healthcare facilities at the municipal and community levels (53.1% and 60.7 % respectively). Within 60 minutes, all residents have spatial access to healthcare facilities at the municipal and community levels. The results indicate that spatial accessibility to healthcare facilities at county level should be improved.

Finally, the overall spatial accessibility score is calculated for each residential point in the following steps. For each residential point, the spatial accessibility score for each category of healthcare facilities is determined as the multiplicative inverse of the time cost (unit in minutes). A lower time cost indicates a higher spatial accessibility score while a higher time cost indicates a lower spatial accessibility score. Then these scores are multiplied by different assigned weights, the sum of which is divided by the population of the residential point and finally normalized into 0-10

scale. The result is the spatial equity score. If a residential point has a low spatial accessibility score but has a high population, the spatial equity score is very low, indicating high spatial inequity here.

Ei=(a1*1.2+a2*2.1+a3*3.2+a4*4.3+a5*4.7)/Pi

Ei: Spatial equity score for residential point i

a1, a2, a3, a4, a5 : accessibility scores for community clinics, township hospital, regional hospital, specialty hospital, specialty hospital, comprehensive hospital respectively

Pi : population of residential point i The parameters (1.2, 2.1, 3.2, 4.5, 4.7) are the weights assigned to the five categories of healthcare services. They are based on a healthcare survey conducted by the local government in which respondents ranked the categories of the healthcare facilities and attach significance to them in 2016. This survey is used as it is only available official sourece.

Figure 8 below shows the spatial equity score of each residential point, and a Kernel Density map is generated by using spatial interpolation analysis. According to Figure 8, communities/villages in central area and southwest areas (Caishan and Zhuogang) have low spatial equity scores. They are distant to the two main towns (Huangmei and Xiaochi) where the healthcare facilities at county level are located. In addition, these areas experience a lack of convenient transit due to insufficient road supply.

Figure 8. Spatial Equity Scores for Huangmei County

5. IMPROVEMENTS FOR SPATIAL ACCESSIBILITY AND EQUITY

In order to improve the spatial equity based on population needs, two suggestions are proposed. First, transportation infrastructure should be invested in central and southwest areas (Caishan and Zhuogang) to increase spatial accessibility. This solution will be beneficial in the long term, as the construction of roads can benefit the local area in all aspects (e.g. increase the economy and make public services more accessible, including healthcare services). The second solution would be to increase healthcare services supply. New healthcare facilities, especially comprehensive hospitals or specialty hospitals, should be planned to build in areas of spatial inequity, so that the healthcare services supply can meet the population demand. A new comprehensive hospital is proposed for the southwest area (Caishan) for the following reasons. This is one suggestion among all the possible solutions. (1) The southwest area (Caishan) has lower healthcare services supply (2.9 beds per 100 people and 2.7 healthcare professional per 1000 people) compared to the average level of the county (3.7 beds per 100 people and 3.9 healthcare professional per 1000 people). (2) The southwest area (Caishan) has low spatial accessibility and spatial equity indicated by the blue area in Figure 8. (3) According to the Huangmei County 2020 Comprehensive Plan, Caishan is planned to be one of the economic subcenters of Huangmei County and will see increasing population demands. In addition, once the new hospital is built, the adjacent county Wuxue County, which has a close intergovernmental collaboration with Huangmei County will be benefited as well. The funding of the new hospital can be shared with the government of Wuxue County. (4) The southwest area (Caishan) has a sufficient supply of healthcare facilities at the municipal level (regional hospital and township hospital) and community level (community clinic) but it lacks access to healthcare facilities at the county level. Therefore, a new comprehensive hospital is needed here.

Figure 9 shows the location of the new comprehensive hospital and Figure 10 shows the projected change in spatial accessibility after the construction. Within 15 minutes, 51.1% of the population are served by healthcare facilities at county level compared to the previous value of 26.9%. Within 60 minutes, all population in the county are served by healthcare facilities at the county level, municipal level and county level. Therefore, if the new comprehensive hospital is built, spatial accessibility will be greatly improved in Huangmei County.

Figure 9. Location of the new comprehensive hospital

Improved spatial accessibility for healthcare facilities at county level 120.0%

100.0%

80.0%

60.0%

40.0%

of of population served 20.0% accumulative percenatge 0.0% 0-15 minutes 15-30minutes 30-45minutes 45-60minutes present 26.9% 70.4% 91.2% 94.0% future 51.1% 76.5% 96.7% 100.0%

Figure 10. Improved spatial accessibility for healthcare facilities at county level

6. DISCUSSIONS AND CONCLUSION

This paper analyzed the spatial distribution, spatial accessibility and spatial equity of healthcare facilities at three levels (county, municipal, community). Finally, it proposed locations for the new hospitals, and comparisons were performed to demonstrate the improved spatial accessibility

after the construction.

The limitation of this paper is that it only examined the spatial accessibility of each residential point to the closest healthcare facilities, without taking into account ethnicity, wealth, income, education, age, and other factors relevant to the population as well as the quality of the healthcare services. If sociodemographic data in each community/village become more available, more in-depth research can be conducted regarding the correlation analysis of the characteristics of community/villages (e.g. percentage of elder people, percentage of people under poverty line, ethnic group distribution) and spatial equity. There is more than one method to measure spatial accessibility and spatial equity. This paper provides one perspective by conducting a case study of Huangmei in China.

With respect to the supply of new healthcare services, due to the lack of data and limited time, this paper did not analyze the under-serviced population at community/village level in detail nor did it consider the implementation of healthcare facilities planning in terms of funding sources as well as the nature of intergovernmental collaboration.

Access to healthcare services is considered a fundamental right and an important contributor to overall population health. However, there are significant disparities of healthcare services in China from a geographic perspective. When healthcare services are incorporated into the comprehensive plan, spatial accessibility and spatial equity should be emphasized. In the case study of Huangmei, the results showed unmet spatial accessibility and special inequity in certain areas. The construction of a new comprehensive hospital was recommended to improve the spatial equity within Huangmei County. This paper may provide insights into healthcare services spatial planning in Huangmei as well as in similar counties in China.

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