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A comparative analysis of transportation-based accessibility to mental health services
Transportation Research Part D 81 (2020) 102278
Contents lists available at ScienceDirect
Transportation Research Part D journal homepage: www.elsevier.com/locate/trd
A comparative analysis of transportation-based accessibility to mental health services
T
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Mahyar Ghorbanzadeha, , Kyusik Kimb, Eren Erman Ozguvena, Mark W. Hornerb a Department of Civil and Environmental Engineering, FAMU–FSU College of Engineering, 2525 Pottsdamer Street, Tallahassee, FL 32310, United States b Department of Geography, Florida State University, 600 W College Avenue, Tallahassee, FL 32306, United States
A R T IC LE I N F O
ABS TRA CT
Keywords: Spatial accessibility Mental health services Vulnerable populations Geographic information systems
The demand for mental health services has been growing stronger over the last couple of decades. This indicates the need to study and assess the access to these mental health services especially with a focus on the vulnerable populations having the greatest need. As such, this paper presents a Geographical Information Systems (GIS)-based analysis in order to study and evaluate the accessibility of mental health facilities using the information on the spatial distributions of population and facilities, and regional traffic characteristics. For this purpose, different age group segments are utilized including the total population as well as those aged between 18 and 21, 22 and 49, 50 and 64, and those aged over 65 and 85. Focusing on the State of Florida, spatially detailed accessibility metrics are calculated with regard to healthcare facilities using travel times between population block groups and these critical mental health facilities. These estimates are used to calculate the weighted county accessibility scores for each county. Findings clearly delineate those counties that lack access to mental facilities, especially those in Northwest Florida, a demographically diverse and substantially rural region. This type of analysis can help planners and policy makers develop better strategies in order to provide adequate mental health care options needed in targeted locations.
1. Introduction The demand for mental health services has been growing stronger over the last couple of decades. Public healthcare providers play a pivotal and important role in meeting the mental needs of the population and specifically seniors that are widely recognized as part of the vulnerable population (Chang et al., 2010). One critical problem associated with this trend is providing access to mental health facilities. According to the National Alliance on Mental Illness, as of 2018, approximately 20% of adults have experienced a mental illness in the United States, which states that 1 out of every 5 persons had some kind of medical need. This report also states that, among those who had a mental disorder, only 43.3% received mental health treatment or professional healthcare. Additionally, 60% of counties in the U.S. do not have a single practicing psychiatrist (NAMI, 2018). This problem especially becomes challenging when populations-at-risk such as the seniors are considered since the mental health of older individuals is generally related to their other health and cognitive issues (Maltz, 2019). Among the U.S. states, the State of Florida ranks among the lowest performing states to mental health facilities with regard to per capita support for mental health services. As of 2016, the ratio of population to these mental health facilities is 750:1, which is higher than the U.S. average (Florida Behavioral Health Association, 2015; Ranks et al.,
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Corresponding author. E-mail addresses: [email protected] (M. Ghorbanzadeh), [email protected] (K. Kim), [email protected] (M.W. Horner).
https://doi.org/10.1016/j.trd.2020.102278 Received 15 October 2019; Received in revised form 13 February 2020; Accepted 13 February 2020 1361-9209/ © 2020 Elsevier Ltd. All rights reserved.
Transportation Research Part D 81 (2020) 102278
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2018). Therefore, equal access to mental health facilities and providing services to all citizens is a major concern for health planners and decision-makers (Rekha et al., 2017). Transportation-based accessibility has been widely studied in the literature with a focus on different facilities such as supermarkets (Niedzielski and Kucharski, 2019; Widener et al., 2015), urban parks (Omer, 2006), nursing homes (Saliba et al., 2004), healthcare facilities (Islam and Aktar, 2011; Perry and Gesler, 2000), and shelters (Kocatepe et al., 2016) using geographical information systems (GIS)-based techniques. For example, Ozel et al. (2016) conducted a GIS-based methodology to assess the accessibility of aging populations to multimodal facilities such as airports, intercity bus and railway stations, and ferry stations in the State of Florida, U.S. Findings showed that, in terms of accessibility to airports and bus stations, aging populations have relatively a better access to these facilities in Florida in comparison to ferry and railway stations. However, Franklin County in Northwest Florida has a poor access to these facilities with respect to its high travel cost. In another study, Horner et al. (2015) assessed the older population’s accessibility to potential facilities such as libraries, pharmacies, and health facilities in Leon County, Florida. The results revealed that the oldest age group (85+) had the highest level of accessibility with the consideration of different time thresholds in comparison to other aging groups (64–75, 75–84). Recently, Chang et al. (2019) employed a gravity-based model to evaluate the urban park accessibility for all types of large housing estates located in Hong Kong with respect to different transportation modes. The findings indicated that public transportation can provide faster travel time than the transportation mode of walking. Additionally, the travel time for public housing residents was approximately 20% longer in comparison to private housing residents. More broadly, several studies have investigated the accessibility to healthcare facilities using geospatial techniques (Finch et al., 2019; Shah et al., 2016). For example, Brabyn and Skelly (2002) implemented a cost path analysis to obtain the minimum travel time and distance from census block centroids to the closest hospitals. Similarly, Agbenyo et al. (2017) used a GIS-based model to assess the household accessibility to health facilities with a case study in Ghana focusing on rural areas. The study’s findings revealed that roadway conditions can dramatically affect households’ access to these facilities. Rekha et al. (2017) applied a three-step floating catchment area (3SFCA) method to measure healthcare accessibility via a case study in India. Also, they conducted a multi-criteria analysis in their study to find the optimal locations for establishing new facilities in the deprived areas. Similarly, Ngamini Ngui and Vanasse (2012) conducted a two-step floating catchment area (2SFCA) method to assess the spatial accessibility to mental health facilities in an urban area in the southwest of Montreal, CA. For this purpose, they considered three main factors including potential mental health services users (demand), mental health services (supply), and the distances between them. The results showed the inaccessible areas and unequal distribution of the facilities in the southwest of Montreal. To the authors’ knowledge, there is no study that has investigated the county-based accessibility to mental health facilities in the U.S. with respect to different population groups, especially with a focus on the vulnerable populations that may be in need of these services the most. As such, this paper reports on a GIS-based analysis of the accessibility of mental health facilities using the information on the spatial distributions of population and facilities, and regional traffic characteristics. For this purpose, different age group segments are utilized including the total population as well as those people aged between 18 and 21, 22 and 49, 50 and 64, and those aged over 65 and 85. Focusing on the State of Florida, spatially detailed accessibility scores are calculated with regard to healthcare facilities using travel times between population block groups and these critical facilities. These scores are used to calculate the weighted county accessibility scores for each county. The travel time costs for each roadway were obtained from the Florida Standard Urban Transportation Model Structure (FSUTMS) model (FSUTMS, 2018). The Network Analyst module in ArcGIS software (“Closest Facility”) was utilized in order to find the optimal path between the centroids of the census population block groups (origins) and the healthcare facilities (destinations). Based on the obtained travel times between each origin-destination pair, the accessibility of each census block group to facilities was visualized using GIS techniques. As a result of this approach, the population block groups with the highest and lowest level of accessibility to healthcare providers were identified. Based on this estimation, county-based mental health facility accessibility scores were calculated for the total population and other age groups in Florida. This was achieved by calculating the county weighted average total cost in which the average travel time for each age group was calculated in each county. This calculation was used to rank the counties in terms of accessibility to healthcare providers for all age groups. The findings of this study can be used in order to identify the population block groups and counties that have less accessibility to the facilities. State officials in the field of public health and health planners can improve the accessibility of these locations by designing targeted interventions. 2. Study area and data description The State of Florida, U.S. was selected as the study area for our accessibility analysis in this paper. As of 2017, Florida’s population was approximately 20 million, and there were 3,926,889 people aged 65 and up, making up approximately 20% of the total population. The overview of the study area is shown in Fig. 1. In the current study, different data sources are used including population block groups, mental health facilities, and the roadway network. Population block group data were obtained from the U.S. Census Bureau for the 11,405 population block groups in the State of Florida (US Census, 2017). The U.S. Census of 2017 data was the most recent one that was available. It should be noted that this dataset is based on the 2013–2017 American Community Survey (ACS) estimates. The distributions of the total population, as well as other age groups (age 18–21, age 22–49, age 50–64, age 65+, age 85+) in the entire state are shown in Fig. 2. In this study, the geometric centroids of population block groups were considered as travel origins. The population block groups along with their geometric centroids are shown in Fig. 3a. Furthermore, 2019 healthcare data provided by Caliper Corporation was utilized in which four different geographic data files are included: Healthcare Organizations, Healthcare Providers, Hospitals, and Primary Care Service Areas. For the purposes of this study, healthcare providers that offer mental and behavioral support to the public were selected using appropriate healthcare provider 2
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Fig. 1. An overview of the study area.
taxonomy codes (Washington Publishing Company, 2019). Although the data provides information related to providers’ locations, an initial assessment of the data in Maptitude GIS (Caliper Corporation, 2019) revealed that these locations did not match with their actual practice locations. Therefore, a geocoding process was conducted using their location addresses in order to create a new dataset. To implement this geocoding process, we utilized Maptitude’s built-in functions as well as MapQuest Lat/Lng Finder (MapQuest Developer, 2019). By aggregating the data using their locations, the new dataset was obtained which could provide a more accurate census of the mental health facilities locations in Florida. The finalized dataset included the 12,847 actual practice locations where 30,161 providers work (i.e., many locations have more than one provider). Based on the taxonomy codes of these facilities and our research on the mental health care provider data, we believe that these locations do provide some type of healthcare service. Note that this is an assumption made in the paper. Also, Fig. 4 presents the number of mental health facilities and their corresponding providers in the whole State of Florida. As seen, most of the mental health services providers and locations are found in urban areas (12,450) and only a few of them are located in rural areas (397). We have also checked these locations with the databases of selected insurance companies and health management organizations (HMOs) such as Florida Blue Cross Blue Shield and Capital Health Plan (Capital Health, 2019), and we believe that we have captured the vast majority of the facility locations of interest. These facilities constitute the destinations for the trips in the analysis. In addition, roadways that connect the origins and destinations are 3
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Fig. 2. (a) Age 18–21 (b) Age 22–49 (c) Age 50–64 (d) Age 65+ (e) Age 85+ (f) Total population.
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Fig. 3. (a) Census block groups and their corresponding centroids (b) Mental health facilities and roadway network.
35000 30000
30161
29429
25000 20000 Number of Facilities 15000
12847
12450
Number of Providers
10000 5000 397 732 0 Urban
Rural
Total
Fig. 4. The number of facilities and providers in urban and rural areas.
identified based on the FSUTMS model provided for the whole state. The locations of these facilities as well as the roadway network in the study area are shown in Fig. 3b. 3. Methodology 3.1. Accessibility of population block groups to healthcare facilities The proposed approach consists of four distinct steps. In the first step, the travel times were obtained for each roadway link in the transportation network based on the FSUTMS model built in CUBE software (CITILABS, 2017). Basically, there are different costs that can be considered in an accessibility analysis, such as distance and travel time. In this paper, free flow travel time was used as an impedance (cost) to obtain the travel time between origins and destinations. Second, using the travel time values for each link obtained from the FSUTMS model, the Closest Facility tool from the Network Analyst module in ArcGIS software was used to determine the optimal path with the least cost between origins (11,405) and destinations (12,847) as well as the corresponding path travel times. In the next step, based on the obtained travel times between each origin-destination pair, the accessibility scores of each census block group to healthcare facilities were calculated and visualized using GIS-based maps. Ultimately, the population block 5
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Fig. 5. Accessibility of population block groups to facilities.
groups with the highest and lowest level of accessibility were identified in the state through calculating the weighted county averages using the approach presented in the next section. The results of this analysis are shown in Fig. 5. 3.2. County-based accessibility to healthcare facilities for different age groups and total population A metric was defined to measure the accessibility of different age groups including age 18–21, age 22–49, age 50–64, age 65+, age 85+, and total population to healthcare facilities by calculating the weighted average of travel cost for each county. This measure was used to compare the counties with regard to their accessibility to a given facility based on their average travel time. The county weighted average travel cost is defined as follows (Eq. (1)): n
∑ cos ti × popi county weighted average travel cost =
i=1 n
∑ popi
(1)
i=1
where costi is the travel cost (travel time) at population block groupi to the closest facility, and popi is the corresponding population living in the population block groupi. It should be noted that this calculation was conducted for each type of age group and total population as well. Then, based on the obtained average travel cost, the county-based accessibility scores for different population groups were calculated with a focus on mental health facilities. Note that the developed metric provides a spatial accessibility analysis, where issues such as insurance- and other employment-based access are not considered. That is, people can be very close to a mental health care facility; however, that facility may not be a provider for their insurance company. As a separate issue, as spatial units are more aggregate in general (e.g., in rural areas), there can be irregularities or misrepresentations in the true nature of travel costs. This is a well-known issue in network modeling (Gaboardi et al., 2019). More specifically, we should point out that when we work with aggregate data and assume that travel begins and ends at the centroid, we are inherently assuming that all people's travel in a given spatial unit is conducted accordingly. This is an instance of the 'ecological fallacy', and its effects are potentially magnified as a given spatial unit becomes larger relative to the distribution of potential origin and destination points within it. It can serve to introduce some errors into travel cost calculations. We are aware of this issue, as it manifests itself in many transportation modeling scenarios, and we recognize that the only way to truly avoid these effects is to use totally disaggregate point-level origin and destination data. That was not possible in this particular study given the study area size, scope, data availability, and our overall objectives. However, in future work, we could consider survey-level data that incorporated household origin responses. That would be one approach to improving upon an aggregate zone level analysis. Fig. 5 illustrates the accessibility of different population groups with respect to the facilities in the study area. 4. Analysis and results 4.1. Accessibility of population block groups to healthcare facilities In this section, the results of the population block group accessibility analysis are presented for the State of Florida. As seen in Fig. 5, there are several population block groups specifically in South and Northwest Florida that have less accessibility to healthcare 6
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facilities with respect to travel times more than 20 min. According to this figure, there are very few facilities in these areas that provide services to the public, which immediately makes accessibility to these services a challenging problem for the residents of these regions. On the other hand, there are a substantial number of healthcare facilities in Southeast, Central, and Northeast Florida, where large cities such as Miami, Orlando, Tampa, and Jacksonville are located. Therefore, the residents in these regions are closer to these facilities and are more accessible to healthcare providers. Based on the findings, it should be mentioned that most of the areas with poor accessibility to mental health providers include rural communities. More specifically, in some areas, the residents in a population block group have to spend more than 60 min (1 h) to reach the closest mental health facility, for which they may not have resources or they may need assistance. Hence, this can provide vital information for policy makers and health planners in the state to focus specifically on these areas in order to improve their accessibility to mental health facilities. 4.2. County-based accessibility to mental health facilities for different age groups and total population In order to analyze the accessibility of Florida counties demographically, the population component was added to assess the accessibility of total population as well as other age groups in each county. Using Eq. (1), the average travel time of each county was obtained with respect to different population groups. The county-based accessibility maps are presented for each type of age group based on the obtained travel times. Fig. 6 shows the accessibility of population groups to mental health facilities in each county with regard to their corresponding travel time. The ten counties with the highest travel times have been shown in Table 1. Moreover, the location of these counties along with their names is presented in Fig. 7. Results reveal that, in terms of accessibility to mental health facilities, several counties in the state, and more specifically in Northwest Florida, have the least county-based accessibility regardless of which population group we focus on. The counties with the least accessibility are Dixie, Hamilton, Liberty, Lafayette, Glades, Levy, Washington, Taylor, Gulf, and Calhoun counties, which have the highest average travel cost in comparison to the others (i.e., more than 10 min). Table 1 shows the travel time values of these counties for each type of age group separately along with their average travel time. Based on the obtained results, it is imperative to note that most of the counties with poor accessibility appear to be in rural areas. This indicates that their residents have to spend much time to access these facilities. Among these counties, Dixie, Hamilton, and Liberty are the least accessible counties with regard to all age groups in terms of mental health accessibility with the highest average travel time. However, the accessibility of Liberty County, for example, decreases when the county-based accessibility scores are calculated with higher age groups. That is, travel time scores of Liberty County in Table 1 gradually increases from the youth age group (18–21) to the oldest age group (85+). This indicates that the problem is even more complex when seniors are considered. On the other hand, counties such as Broward, Miami-Dade, Pinellas, Palm Beach, Seminole, Orange, Hillsborough, Duval, Sarasota, and Leon are the most accessible counties in terms of accessibility to mental health facilities, which have the lowest average travel cost in comparison to the other counties. Fig. 7 illustrates the location of these counties along with their names. It is worth to mention that large cities of the state such as Miami, Orlando, Tampa, Jacksonville, and Tallahassee are located in these counties. Consequently, the residents in these areas are highly accessible to mental health providers as a result of their low average travel times (i.e., less than 3 min). 5. Conclusions and future work A GIS-based methodology was implemented in this study to assess the spatial accessibility of population block groups to mental health facilities which offer mental and behavioral services to the people in the State of Florida. Furthermore, a metric was developed in order to measure the county-based accessibility with respect to different age groups (i.e., age 18–21, age 22–49, age 50–64, age 65+, age 85+, and total population). The findings showed that several counties such as Dixie, Hamilton, Liberty, Lafayette, Glades, Levy, Washington, Taylor, Gulf, and Calhoun, which are mainly clustered in Northwest Florida, have the least accessibility to the mental health facilities. These counties with a poor access to mental health providers are mostly located in rural areas. On the other hand, counties such as Broward, Miami-Dade, Pinellas, Palm Beach, Seminole, Orange, Hillsborough, Duval, Sarasota, and Leon, in which large cities in Florida are located, are highly accessible to mental health providers with respect to travel time costs. That is, their residents live closer to these critical facilities and the average travel times for these counties are less than 3 min. The knowledge obtained from this study could be useful for decision-makers and health planners in the state to highlight these areas for future improvements. The findings of this study can also be useful to officials by: (a) providing better knowledge of the accessibility of each population block group and county with a focus on vulnerable populations, (b) better planning in order to identify candidate future facility locations, and (c) developing plans to provide better accessibility to mental health facilities, which can include services such as mobile response teams that can provide behavioral health assistance in emergency situations. There are some other areas that can be considered for future research. Besides considering free flow travel time for each roadway link in the roadway network, congested travel time could be another component in order to obtain the travel time cost between origins and destinations. In the current study, the spatial accessibility was assessed with the consideration of travel times in the transportation network between census units and facility locations; however, the number of practitioners varies among the mental health facilities. As a possibility for future work, this study could be extended to account for the number of providers in each location, and perhaps the hours of their availability. These enhancements could potentially be added to the approach developed here, which would be an interesting direction for future work. This may lead in the direction of pursuing other methods of accessibility measurement, such as the two-step floating catchment area (2SFCA) method. As such, mental health facilities with a greater number of physicians will obtain higher weights in terms of accessibility in comparison to the ones with a fewer number of physicians. 7
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(a) Age 18-21
(b) Age 22-49
(d)Age 65+
(c) Age 50-64
(f) Total Population
(e) Age 85+
Fig. 6. County-based accessibility of different age groups and total population to facilities.
This study currently considers only the time spent on the roadways; however, time spent waiting at the facilities can also be critical. Furthermore, future research could also consider the proportion of patients to the total population in each age group instead of considering the number of population in a particular age group. Regarding the issue of aggregation and the broader ecological 8
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Table 1 Travel time of least accessible counties for each type of age groups along with their average travel time.
Fig. 7. Counties with the least and most accessibility to mental health facilities.
fallacy, there is a long line of literature on this topic (e.g., Goodman, 1953; Rosen et al., 2001; Greiner and Quinn, 2009, etc.) and solutions to this problem in spatial analysis and transportation often involve the use of disaggregated data (Gaboardi et al., 2019). Along these lines, point-level measurement frameworks for accessibility have been explored in past work (e.g., Horner and Downs, 2014; Wood and Horner, 2019). The goal of these efforts, and our future work, would be to have better individual household level representations of the population.
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Acknowledgements The authors would like to thank the Florida Department of Transportation and CITILABS for providing data and valuable insight. The contents of this paper and discussion represent the authors’ opinion and do not reflect the official views of the Florida Department of Transportation and CITILABS. References Agbenyo, F., Marshall Nunbogu, A., Dongzagla, A., 2017. Accessibility mapping of health facilities in rural Ghana. J. Transp. Heal. 6, 73–83. https://doi.org/10.1016/ j.jth.2017.04.010. Brabyn, L., Skelly, C., 2002. Modeling population access to New Zealand public hospitals. Int. J. Health Geogr. 1. https://doi.org/10.1186/1476-072X-1-3. Caliper Corporation, 2019. Maptitude Mapping Software. URL https://www.caliper.com/maptitude/gis_software/default.htm. Capital Health, 2019. URL https://capitalhealth.com/directories/provider-directory. Chang, Zheng, Zheng, Jiayu, Li, Weifeng, Li, X., 2019. Public transportation and the spatial inequality of urban park accessibility: new evidence from Hong Kong. Transp. Res. Part D Transp. Environ. 76, 111–122. Chang, H.T., Lai, H.Y., Hwang, I.H., Ho, M.M., Hwang, S.J., 2010. Home healthcare services in Taiwan: a nationwide study among the older population. BMC Health Serv. Res. 10. https://doi.org/10.1186/1472-6963-10-274. CITILABS, 2017. CITILABS Home. URL https://www.citilabs.com/. Finch, E., Liu, Y., Foster, M., Cruwys, T., Fleming, J., Worrall, L., Williams, I., Shah, D., Aitken, P., Corcoran, J., 2019. Measuring access to primary healthcare services after stroke: a spatial analytic approach. Brain Impair. https://doi.org/10.1017/BrImp.2019.11. Florida Behavioral Health Association, 2015. Mental Health in Florida. FSUTMS, 2018. Florida Statewide Network Model. URL http://www.fsutmsonline.net/. Gaboardi, J.D., Folch, D.C., Horner, M.W., 2019. Connecting points to spatial networks: effects on discrete optimization models. Geogr. 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Measuring physical accessibility to health facilities–a case study on Khulna City. World Health Popul. 12, 33–41. https://doi.org/10. 12927/whp.2011.22195. Kocatepe, A., Ozguven, E.E., Ozel, H., Horner, M.W., Moses, R., 2016. Transportation accessibility assessment of critical emergency facilities: aging population-focused case studies in Florida. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) 9755, 407–416. https://doi.org/10.1007/ 978-3-319-39949-2_39. Maltz, M., 2019. Caught in the Eye of the Storm: The Disproportionate Impact of Natural Disasters on the Elderly Population in the United States. MapQuest Developer, 2019. URL https://developer.mapquest.com/documentation/tools/latitude-longitude-finder/. NAMI, 2018. National Alliance on Mental Illness. URL https://nami.org/. Ngamini Ngui, A., Vanasse, A., 2012. Assessing spatial accessibility to mental health facilities in an urban environment. Spat. Spatiotemporal. 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Transportation Research Part D journal homepage: www.elsevier.com/locate/trd
A comparative analysis of transportation-based accessibility to mental health services
T
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Mahyar Ghorbanzadeha, , Kyusik Kimb, Eren Erman Ozguvena, Mark W. Hornerb a Department of Civil and Environmental Engineering, FAMU–FSU College of Engineering, 2525 Pottsdamer Street, Tallahassee, FL 32310, United States b Department of Geography, Florida State University, 600 W College Avenue, Tallahassee, FL 32306, United States
A R T IC LE I N F O
ABS TRA CT
Keywords: Spatial accessibility Mental health services Vulnerable populations Geographic information systems
The demand for mental health services has been growing stronger over the last couple of decades. This indicates the need to study and assess the access to these mental health services especially with a focus on the vulnerable populations having the greatest need. As such, this paper presents a Geographical Information Systems (GIS)-based analysis in order to study and evaluate the accessibility of mental health facilities using the information on the spatial distributions of population and facilities, and regional traffic characteristics. For this purpose, different age group segments are utilized including the total population as well as those aged between 18 and 21, 22 and 49, 50 and 64, and those aged over 65 and 85. Focusing on the State of Florida, spatially detailed accessibility metrics are calculated with regard to healthcare facilities using travel times between population block groups and these critical mental health facilities. These estimates are used to calculate the weighted county accessibility scores for each county. Findings clearly delineate those counties that lack access to mental facilities, especially those in Northwest Florida, a demographically diverse and substantially rural region. This type of analysis can help planners and policy makers develop better strategies in order to provide adequate mental health care options needed in targeted locations.
1. Introduction The demand for mental health services has been growing stronger over the last couple of decades. Public healthcare providers play a pivotal and important role in meeting the mental needs of the population and specifically seniors that are widely recognized as part of the vulnerable population (Chang et al., 2010). One critical problem associated with this trend is providing access to mental health facilities. According to the National Alliance on Mental Illness, as of 2018, approximately 20% of adults have experienced a mental illness in the United States, which states that 1 out of every 5 persons had some kind of medical need. This report also states that, among those who had a mental disorder, only 43.3% received mental health treatment or professional healthcare. Additionally, 60% of counties in the U.S. do not have a single practicing psychiatrist (NAMI, 2018). This problem especially becomes challenging when populations-at-risk such as the seniors are considered since the mental health of older individuals is generally related to their other health and cognitive issues (Maltz, 2019). Among the U.S. states, the State of Florida ranks among the lowest performing states to mental health facilities with regard to per capita support for mental health services. As of 2016, the ratio of population to these mental health facilities is 750:1, which is higher than the U.S. average (Florida Behavioral Health Association, 2015; Ranks et al.,
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Corresponding author. E-mail addresses: [email protected] (M. Ghorbanzadeh), [email protected] (K. Kim), [email protected] (M.W. Horner).
https://doi.org/10.1016/j.trd.2020.102278 Received 15 October 2019; Received in revised form 13 February 2020; Accepted 13 February 2020 1361-9209/ © 2020 Elsevier Ltd. All rights reserved.
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2018). Therefore, equal access to mental health facilities and providing services to all citizens is a major concern for health planners and decision-makers (Rekha et al., 2017). Transportation-based accessibility has been widely studied in the literature with a focus on different facilities such as supermarkets (Niedzielski and Kucharski, 2019; Widener et al., 2015), urban parks (Omer, 2006), nursing homes (Saliba et al., 2004), healthcare facilities (Islam and Aktar, 2011; Perry and Gesler, 2000), and shelters (Kocatepe et al., 2016) using geographical information systems (GIS)-based techniques. For example, Ozel et al. (2016) conducted a GIS-based methodology to assess the accessibility of aging populations to multimodal facilities such as airports, intercity bus and railway stations, and ferry stations in the State of Florida, U.S. Findings showed that, in terms of accessibility to airports and bus stations, aging populations have relatively a better access to these facilities in Florida in comparison to ferry and railway stations. However, Franklin County in Northwest Florida has a poor access to these facilities with respect to its high travel cost. In another study, Horner et al. (2015) assessed the older population’s accessibility to potential facilities such as libraries, pharmacies, and health facilities in Leon County, Florida. The results revealed that the oldest age group (85+) had the highest level of accessibility with the consideration of different time thresholds in comparison to other aging groups (64–75, 75–84). Recently, Chang et al. (2019) employed a gravity-based model to evaluate the urban park accessibility for all types of large housing estates located in Hong Kong with respect to different transportation modes. The findings indicated that public transportation can provide faster travel time than the transportation mode of walking. Additionally, the travel time for public housing residents was approximately 20% longer in comparison to private housing residents. More broadly, several studies have investigated the accessibility to healthcare facilities using geospatial techniques (Finch et al., 2019; Shah et al., 2016). For example, Brabyn and Skelly (2002) implemented a cost path analysis to obtain the minimum travel time and distance from census block centroids to the closest hospitals. Similarly, Agbenyo et al. (2017) used a GIS-based model to assess the household accessibility to health facilities with a case study in Ghana focusing on rural areas. The study’s findings revealed that roadway conditions can dramatically affect households’ access to these facilities. Rekha et al. (2017) applied a three-step floating catchment area (3SFCA) method to measure healthcare accessibility via a case study in India. Also, they conducted a multi-criteria analysis in their study to find the optimal locations for establishing new facilities in the deprived areas. Similarly, Ngamini Ngui and Vanasse (2012) conducted a two-step floating catchment area (2SFCA) method to assess the spatial accessibility to mental health facilities in an urban area in the southwest of Montreal, CA. For this purpose, they considered three main factors including potential mental health services users (demand), mental health services (supply), and the distances between them. The results showed the inaccessible areas and unequal distribution of the facilities in the southwest of Montreal. To the authors’ knowledge, there is no study that has investigated the county-based accessibility to mental health facilities in the U.S. with respect to different population groups, especially with a focus on the vulnerable populations that may be in need of these services the most. As such, this paper reports on a GIS-based analysis of the accessibility of mental health facilities using the information on the spatial distributions of population and facilities, and regional traffic characteristics. For this purpose, different age group segments are utilized including the total population as well as those people aged between 18 and 21, 22 and 49, 50 and 64, and those aged over 65 and 85. Focusing on the State of Florida, spatially detailed accessibility scores are calculated with regard to healthcare facilities using travel times between population block groups and these critical facilities. These scores are used to calculate the weighted county accessibility scores for each county. The travel time costs for each roadway were obtained from the Florida Standard Urban Transportation Model Structure (FSUTMS) model (FSUTMS, 2018). The Network Analyst module in ArcGIS software (“Closest Facility”) was utilized in order to find the optimal path between the centroids of the census population block groups (origins) and the healthcare facilities (destinations). Based on the obtained travel times between each origin-destination pair, the accessibility of each census block group to facilities was visualized using GIS techniques. As a result of this approach, the population block groups with the highest and lowest level of accessibility to healthcare providers were identified. Based on this estimation, county-based mental health facility accessibility scores were calculated for the total population and other age groups in Florida. This was achieved by calculating the county weighted average total cost in which the average travel time for each age group was calculated in each county. This calculation was used to rank the counties in terms of accessibility to healthcare providers for all age groups. The findings of this study can be used in order to identify the population block groups and counties that have less accessibility to the facilities. State officials in the field of public health and health planners can improve the accessibility of these locations by designing targeted interventions. 2. Study area and data description The State of Florida, U.S. was selected as the study area for our accessibility analysis in this paper. As of 2017, Florida’s population was approximately 20 million, and there were 3,926,889 people aged 65 and up, making up approximately 20% of the total population. The overview of the study area is shown in Fig. 1. In the current study, different data sources are used including population block groups, mental health facilities, and the roadway network. Population block group data were obtained from the U.S. Census Bureau for the 11,405 population block groups in the State of Florida (US Census, 2017). The U.S. Census of 2017 data was the most recent one that was available. It should be noted that this dataset is based on the 2013–2017 American Community Survey (ACS) estimates. The distributions of the total population, as well as other age groups (age 18–21, age 22–49, age 50–64, age 65+, age 85+) in the entire state are shown in Fig. 2. In this study, the geometric centroids of population block groups were considered as travel origins. The population block groups along with their geometric centroids are shown in Fig. 3a. Furthermore, 2019 healthcare data provided by Caliper Corporation was utilized in which four different geographic data files are included: Healthcare Organizations, Healthcare Providers, Hospitals, and Primary Care Service Areas. For the purposes of this study, healthcare providers that offer mental and behavioral support to the public were selected using appropriate healthcare provider 2
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Fig. 1. An overview of the study area.
taxonomy codes (Washington Publishing Company, 2019). Although the data provides information related to providers’ locations, an initial assessment of the data in Maptitude GIS (Caliper Corporation, 2019) revealed that these locations did not match with their actual practice locations. Therefore, a geocoding process was conducted using their location addresses in order to create a new dataset. To implement this geocoding process, we utilized Maptitude’s built-in functions as well as MapQuest Lat/Lng Finder (MapQuest Developer, 2019). By aggregating the data using their locations, the new dataset was obtained which could provide a more accurate census of the mental health facilities locations in Florida. The finalized dataset included the 12,847 actual practice locations where 30,161 providers work (i.e., many locations have more than one provider). Based on the taxonomy codes of these facilities and our research on the mental health care provider data, we believe that these locations do provide some type of healthcare service. Note that this is an assumption made in the paper. Also, Fig. 4 presents the number of mental health facilities and their corresponding providers in the whole State of Florida. As seen, most of the mental health services providers and locations are found in urban areas (12,450) and only a few of them are located in rural areas (397). We have also checked these locations with the databases of selected insurance companies and health management organizations (HMOs) such as Florida Blue Cross Blue Shield and Capital Health Plan (Capital Health, 2019), and we believe that we have captured the vast majority of the facility locations of interest. These facilities constitute the destinations for the trips in the analysis. In addition, roadways that connect the origins and destinations are 3
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Fig. 2. (a) Age 18–21 (b) Age 22–49 (c) Age 50–64 (d) Age 65+ (e) Age 85+ (f) Total population.
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Fig. 3. (a) Census block groups and their corresponding centroids (b) Mental health facilities and roadway network.
35000 30000
30161
29429
25000 20000 Number of Facilities 15000
12847
12450
Number of Providers
10000 5000 397 732 0 Urban
Rural
Total
Fig. 4. The number of facilities and providers in urban and rural areas.
identified based on the FSUTMS model provided for the whole state. The locations of these facilities as well as the roadway network in the study area are shown in Fig. 3b. 3. Methodology 3.1. Accessibility of population block groups to healthcare facilities The proposed approach consists of four distinct steps. In the first step, the travel times were obtained for each roadway link in the transportation network based on the FSUTMS model built in CUBE software (CITILABS, 2017). Basically, there are different costs that can be considered in an accessibility analysis, such as distance and travel time. In this paper, free flow travel time was used as an impedance (cost) to obtain the travel time between origins and destinations. Second, using the travel time values for each link obtained from the FSUTMS model, the Closest Facility tool from the Network Analyst module in ArcGIS software was used to determine the optimal path with the least cost between origins (11,405) and destinations (12,847) as well as the corresponding path travel times. In the next step, based on the obtained travel times between each origin-destination pair, the accessibility scores of each census block group to healthcare facilities were calculated and visualized using GIS-based maps. Ultimately, the population block 5
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Fig. 5. Accessibility of population block groups to facilities.
groups with the highest and lowest level of accessibility were identified in the state through calculating the weighted county averages using the approach presented in the next section. The results of this analysis are shown in Fig. 5. 3.2. County-based accessibility to healthcare facilities for different age groups and total population A metric was defined to measure the accessibility of different age groups including age 18–21, age 22–49, age 50–64, age 65+, age 85+, and total population to healthcare facilities by calculating the weighted average of travel cost for each county. This measure was used to compare the counties with regard to their accessibility to a given facility based on their average travel time. The county weighted average travel cost is defined as follows (Eq. (1)): n
∑ cos ti × popi county weighted average travel cost =
i=1 n
∑ popi
(1)
i=1
where costi is the travel cost (travel time) at population block groupi to the closest facility, and popi is the corresponding population living in the population block groupi. It should be noted that this calculation was conducted for each type of age group and total population as well. Then, based on the obtained average travel cost, the county-based accessibility scores for different population groups were calculated with a focus on mental health facilities. Note that the developed metric provides a spatial accessibility analysis, where issues such as insurance- and other employment-based access are not considered. That is, people can be very close to a mental health care facility; however, that facility may not be a provider for their insurance company. As a separate issue, as spatial units are more aggregate in general (e.g., in rural areas), there can be irregularities or misrepresentations in the true nature of travel costs. This is a well-known issue in network modeling (Gaboardi et al., 2019). More specifically, we should point out that when we work with aggregate data and assume that travel begins and ends at the centroid, we are inherently assuming that all people's travel in a given spatial unit is conducted accordingly. This is an instance of the 'ecological fallacy', and its effects are potentially magnified as a given spatial unit becomes larger relative to the distribution of potential origin and destination points within it. It can serve to introduce some errors into travel cost calculations. We are aware of this issue, as it manifests itself in many transportation modeling scenarios, and we recognize that the only way to truly avoid these effects is to use totally disaggregate point-level origin and destination data. That was not possible in this particular study given the study area size, scope, data availability, and our overall objectives. However, in future work, we could consider survey-level data that incorporated household origin responses. That would be one approach to improving upon an aggregate zone level analysis. Fig. 5 illustrates the accessibility of different population groups with respect to the facilities in the study area. 4. Analysis and results 4.1. Accessibility of population block groups to healthcare facilities In this section, the results of the population block group accessibility analysis are presented for the State of Florida. As seen in Fig. 5, there are several population block groups specifically in South and Northwest Florida that have less accessibility to healthcare 6
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facilities with respect to travel times more than 20 min. According to this figure, there are very few facilities in these areas that provide services to the public, which immediately makes accessibility to these services a challenging problem for the residents of these regions. On the other hand, there are a substantial number of healthcare facilities in Southeast, Central, and Northeast Florida, where large cities such as Miami, Orlando, Tampa, and Jacksonville are located. Therefore, the residents in these regions are closer to these facilities and are more accessible to healthcare providers. Based on the findings, it should be mentioned that most of the areas with poor accessibility to mental health providers include rural communities. More specifically, in some areas, the residents in a population block group have to spend more than 60 min (1 h) to reach the closest mental health facility, for which they may not have resources or they may need assistance. Hence, this can provide vital information for policy makers and health planners in the state to focus specifically on these areas in order to improve their accessibility to mental health facilities. 4.2. County-based accessibility to mental health facilities for different age groups and total population In order to analyze the accessibility of Florida counties demographically, the population component was added to assess the accessibility of total population as well as other age groups in each county. Using Eq. (1), the average travel time of each county was obtained with respect to different population groups. The county-based accessibility maps are presented for each type of age group based on the obtained travel times. Fig. 6 shows the accessibility of population groups to mental health facilities in each county with regard to their corresponding travel time. The ten counties with the highest travel times have been shown in Table 1. Moreover, the location of these counties along with their names is presented in Fig. 7. Results reveal that, in terms of accessibility to mental health facilities, several counties in the state, and more specifically in Northwest Florida, have the least county-based accessibility regardless of which population group we focus on. The counties with the least accessibility are Dixie, Hamilton, Liberty, Lafayette, Glades, Levy, Washington, Taylor, Gulf, and Calhoun counties, which have the highest average travel cost in comparison to the others (i.e., more than 10 min). Table 1 shows the travel time values of these counties for each type of age group separately along with their average travel time. Based on the obtained results, it is imperative to note that most of the counties with poor accessibility appear to be in rural areas. This indicates that their residents have to spend much time to access these facilities. Among these counties, Dixie, Hamilton, and Liberty are the least accessible counties with regard to all age groups in terms of mental health accessibility with the highest average travel time. However, the accessibility of Liberty County, for example, decreases when the county-based accessibility scores are calculated with higher age groups. That is, travel time scores of Liberty County in Table 1 gradually increases from the youth age group (18–21) to the oldest age group (85+). This indicates that the problem is even more complex when seniors are considered. On the other hand, counties such as Broward, Miami-Dade, Pinellas, Palm Beach, Seminole, Orange, Hillsborough, Duval, Sarasota, and Leon are the most accessible counties in terms of accessibility to mental health facilities, which have the lowest average travel cost in comparison to the other counties. Fig. 7 illustrates the location of these counties along with their names. It is worth to mention that large cities of the state such as Miami, Orlando, Tampa, Jacksonville, and Tallahassee are located in these counties. Consequently, the residents in these areas are highly accessible to mental health providers as a result of their low average travel times (i.e., less than 3 min). 5. Conclusions and future work A GIS-based methodology was implemented in this study to assess the spatial accessibility of population block groups to mental health facilities which offer mental and behavioral services to the people in the State of Florida. Furthermore, a metric was developed in order to measure the county-based accessibility with respect to different age groups (i.e., age 18–21, age 22–49, age 50–64, age 65+, age 85+, and total population). The findings showed that several counties such as Dixie, Hamilton, Liberty, Lafayette, Glades, Levy, Washington, Taylor, Gulf, and Calhoun, which are mainly clustered in Northwest Florida, have the least accessibility to the mental health facilities. These counties with a poor access to mental health providers are mostly located in rural areas. On the other hand, counties such as Broward, Miami-Dade, Pinellas, Palm Beach, Seminole, Orange, Hillsborough, Duval, Sarasota, and Leon, in which large cities in Florida are located, are highly accessible to mental health providers with respect to travel time costs. That is, their residents live closer to these critical facilities and the average travel times for these counties are less than 3 min. The knowledge obtained from this study could be useful for decision-makers and health planners in the state to highlight these areas for future improvements. The findings of this study can also be useful to officials by: (a) providing better knowledge of the accessibility of each population block group and county with a focus on vulnerable populations, (b) better planning in order to identify candidate future facility locations, and (c) developing plans to provide better accessibility to mental health facilities, which can include services such as mobile response teams that can provide behavioral health assistance in emergency situations. There are some other areas that can be considered for future research. Besides considering free flow travel time for each roadway link in the roadway network, congested travel time could be another component in order to obtain the travel time cost between origins and destinations. In the current study, the spatial accessibility was assessed with the consideration of travel times in the transportation network between census units and facility locations; however, the number of practitioners varies among the mental health facilities. As a possibility for future work, this study could be extended to account for the number of providers in each location, and perhaps the hours of their availability. These enhancements could potentially be added to the approach developed here, which would be an interesting direction for future work. This may lead in the direction of pursuing other methods of accessibility measurement, such as the two-step floating catchment area (2SFCA) method. As such, mental health facilities with a greater number of physicians will obtain higher weights in terms of accessibility in comparison to the ones with a fewer number of physicians. 7
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(a) Age 18-21
(b) Age 22-49
(d)Age 65+
(c) Age 50-64
(f) Total Population
(e) Age 85+
Fig. 6. County-based accessibility of different age groups and total population to facilities.
This study currently considers only the time spent on the roadways; however, time spent waiting at the facilities can also be critical. Furthermore, future research could also consider the proportion of patients to the total population in each age group instead of considering the number of population in a particular age group. Regarding the issue of aggregation and the broader ecological 8
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Table 1 Travel time of least accessible counties for each type of age groups along with their average travel time.
Fig. 7. Counties with the least and most accessibility to mental health facilities.
fallacy, there is a long line of literature on this topic (e.g., Goodman, 1953; Rosen et al., 2001; Greiner and Quinn, 2009, etc.) and solutions to this problem in spatial analysis and transportation often involve the use of disaggregated data (Gaboardi et al., 2019). Along these lines, point-level measurement frameworks for accessibility have been explored in past work (e.g., Horner and Downs, 2014; Wood and Horner, 2019). The goal of these efforts, and our future work, would be to have better individual household level representations of the population.
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Acknowledgements The authors would like to thank the Florida Department of Transportation and CITILABS for providing data and valuable insight. The contents of this paper and discussion represent the authors’ opinion and do not reflect the official views of the Florida Department of Transportation and CITILABS. References Agbenyo, F., Marshall Nunbogu, A., Dongzagla, A., 2017. Accessibility mapping of health facilities in rural Ghana. J. Transp. Heal. 6, 73–83. https://doi.org/10.1016/ j.jth.2017.04.010. Brabyn, L., Skelly, C., 2002. Modeling population access to New Zealand public hospitals. Int. J. Health Geogr. 1. https://doi.org/10.1186/1476-072X-1-3. Caliper Corporation, 2019. Maptitude Mapping Software. URL https://www.caliper.com/maptitude/gis_software/default.htm. Capital Health, 2019. URL https://capitalhealth.com/directories/provider-directory. Chang, Zheng, Zheng, Jiayu, Li, Weifeng, Li, X., 2019. 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