A comparative analysis of transportation-based accessibility to mental health services

https://doi.org/10.1016/j.trd.2020.102278Get rights and content

Highlights

  • GIS-based analysis was conducted to assess the accessibility to mental health services in Florida.

  • Accessibility of vulnerable population such as seniors was studied.

  • Counties with the least accessibility are mainly clustered in Northwest Florida.

  • Counties with a poor access are mostly located in rural areas.

Abstract

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.

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., 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.

Section snippets

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

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

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 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

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,

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 (34)

  • Caliper Corporation, 2019. Maptitude Mapping Software. URL...
  • Capital Health, 2019. URL...
  • H.T. Chang et al.

    Home healthcare services in Taiwan: a nationwide study among the older population

    BMC Health Serv. Res.

    (2010)
  • CITILABS, 2017. CITILABS Home. URL...
  • E. Finch et al.

    Measuring access to primary healthcare services after stroke: a spatial analytic approach

    Brain Impair

    (2019)
  • Florida Behavioral Health Association, 2015. Mental Health in...
  • FSUTMS, 2018. Florida Statewide Network Model. URL...
  • Cited by (0)

    View full text