Impact statement
This scoping review provides a comprehensive synthesis of how geographic information systems (GIS) have been used to study the availability, accessibility and utilization of mental health (MH) services. The findings highlight GIS as a powerful, yet underutilized, tool for identifying gaps in service coverage, visualizing disparities across regions and populations and informing data-driven MH policy and planning. By cataloging a wide range of GIS methods and applications from 58 studies, the review lays critical groundwork for the integration of spatial analysis into global MH research.
The review reveals that GIS has predominantly been applied in high-income settings, with limited application in low- and middle-income countries (LMICs) where treatment gaps are largest. It identifies significant opportunities for expanding GIS use in MH implementation research, trial design and policy advocacy – especially in underserved communities. By uncovering invisible barriers to care through spatial mapping, GIS offers an innovative pathway toward more equitable MH systems.
For policymakers, researchers and practitioners, this review provides both a roadmap and a call to action: to harness the full potential of GIS for strengthening MH services, improving access for marginalized populations and driving evidence-based reforms. The insights from this review can support national and local governments, donors and program implementers in making more informed, targeted and just decisions in mental healthcare delivery.
Introduction
Geographic information systems (GIS) are an innovative computer-based spatial mapping technology that can provide an enhanced understanding of patterns, service needs and environmental interactions related to health problems for improving care (Walsan et al., Reference Walsan, Pai and Dawes2016). These systems are equipped to collect, manage and visualize spatial data, assisting in the analysis and interpretation of geographic information. It can be used to examine, quantify and interpret relationships and features within geographic data (McLafferty, Reference McLafferty2003). It has been widely used in the field of public health, especially for understanding the spatial organization of health care, studying healthcare utilization patterns and mapping the availability of healthcare services (McLafferty, Reference McLafferty2003; Higgs, Reference Higgs2004, Reference Higgs2009; Graves, Reference Graves2008). It also has advanced applications in mapping access disparities, disease surveillance, health inequities and emergency responses (Graves, Reference Graves2008; Higgs, Reference Higgs2009). Through integrated analysis of demographic, environmental and clinical data, GIS has been used to support evidence-based policymaking (Hannum et al., Reference Hannum, Wellstead, Howlett and Gofen2025).
Little is known about GIS approaches that have been used in the analysis of mental healthcare service delivery. This has not only precluded a comprehensive understanding of the full potential of GIS in MH research, implementation science, health planning and service delivery but also limited the possibilities of its usage. Leveraging GIS use in exploring mental healthcare service delivery is especially important considering the global focus shifts toward community MH, implementation research and treatment equity gap, which are profoundly shaped by logistical barriers and the practicality of help-seeking (Thornicroft et al., Reference Thornicroft, Deb and Henderson2016; Kola et al., Reference Kola, Kohrt, Acharya, Mutamba, Kieling, Kumar, Sunkel, Zhang and Hanlon2021; Orozco et al., Reference Orozco, Vigo, Benjet, Borges, Aguilar-Gaxiola, Andrade, Cia, Hwang, Kessler and Piazza2022; Adams, Reference Adams2024; McGinty et al., Reference McGinty, Alegria, Beidas, Braithwaite, Kola, Leslie, Moise, Mueller, Pincus, Shidhaye, Simon, Singer, Stuart and Eisenberg2024). Our scoping review aimed to address this gap by exploring the scope and type of GIS usage in studying three dimensions of MH service delivery (availability, accessibility and utilization), across all geographical locations, settings and populations.
According to the World Health Organization (WHO) Health Systems Framework, parameters for monitoring a healthcare service delivery system include (a) availability of services: physical presence of services, encompassing health infrastructure, core health personnel and aspects of service utilization, for example, the proportion of health facilities offering specific services; (b) accessibility: geographic accessibility or spatial accessibility, in terms of commuting time spent and distance traversed to reach healthcare services, for example, the time taken for a service user to drive to the nearest health facility; and (c) utilization: quantification or description of the use of healthcare services by people to study trends, patterns, variations or for other objectives (World Health Organization, 2010, 2014; Penchansky and Thomas, Reference Penchansky and Thomas1981; Carrasquillo, Reference Carrasquillo2013), for example, number of outpatient department visits per 10,000 population per year.
In the current study, we considered these three dimensions of service delivery – namely service availability, accessibility and utilization – because they can also be spatially analyzed, hence providing an opportunity for GIS applications. Drawing from the key stages in the Tanahashi Framework, these three components have been used to identify bottlenecks in service coverage and identify specific barriers to accessing and receiving effective MH care and measuring progress toward universal health coverage in MH (Tanahashi, Reference Tanahashi1978; De Silva et al., Reference De Silva, Lee, Fuhr, Rathod, Chisholm, Schellenberg and Patel2014).
An integrative review conducted in 2019 reviewed GIS applications that were used to study mental healthcare services but limited its scope only to services provided for serious mental illnesses and to one dimension of service delivery (accessibility; Smith-East and Neff, Reference Smith-East and Neff2020). Our scoping review sought to provide a more comprehensive synthesis by mapping how GIS has been applied across three key dimensions of MH service delivery (availability, accessibility and utilization) across diverse contexts, conditions, settings and populations. This broader focus not only enabled a holistic overview of the literature but also revealed methodological and conceptual gaps that must be addressed to strengthen the use of GIS in advancing equitable, evidence-informed MH care.
Materials and methods
We employed a scoping review methodology, which is designed to map the breadth and nature of the existing literature on a topic (Arksey and O’malley, Reference Arksey and O’malley2005; Peters et al., Reference Peters, Godfrey, Khalil, McInerney, Parker and Soares2015). This approach is particularly well-suited to our study because it allows for an exploratory and flexible examination of diverse evidence, identifies key concepts and knowledge gaps and supports the development of future research priorities. The review was conducted in accordance with the Joanna Briggs Institute Methodology for Scoping Reviews (2020) and incorporated the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) extension for Scoping Reviews Checklist (Tricco et al., Reference Tricco, Lillie, Zarin, O’Brien, Colquhoun, Levac, Moher, Peters, Horsley, Weeks, Hempel, Akl, Chang, McGowan, Stewart, Hartling, Aldcroft, Wilson, Garritty, Lewin, Godfrey, Macdonald, Langlois, Soares-Weiser, Moriarty, Clifford, Tunçalp and Straus2018). The review protocol was published on the Open Science Framework in November 2023 (Registration DOI: 10.17605/OSF.IO/QBPJY).
Eligibility criteria
Peer-reviewed publications in English were included. There were no restrictions on geographical location, year of publication or target population or on design or methodology. Broadly, the scoping review aimed to explore the evidence based on (1) GIS and its various uses in healthcare service delivery (i.e., accessibility, availability and utilization) and (2) MH conditions. Hence, we included any study that 1) used GIS to analyze geographical data and 2) included any MH condition that was diagnosed using one of the following: (a) Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), or the International Classification of Diseases (ICD) diagnostic criteria; (b) positive screen on a validated screening tool (e.g., 9-item questionnaire for depression [PHQ-9] and 7-item questionnaire for anxiety [GAD-7]); or (c) clinician diagnosis. We excluded studies that solely used global positioning systems (GPSs) or Google Maps for data collection and did not analyze geographical data.
Only studies focusing on service delivery (utilization, accessibility and availability) of healthcare services were included. Healthcare services were defined as any primary, secondary and tertiary health care, as well as community MH services, but not interventions which are not traditionally categorized as health care (e.g., social interventions that improve MH). As mentioned in the introduction, we defined service availability as the physical presence of services and encompassed health infrastructure, core health personnel and aspects of service utilization. Related constructs such as service coverage, treatment capacity and equity in service availability were included under the dimension of availability. Accessibility was defined primarily as geographic accessibility or spatial accessibility, in terms of commuting time spent and distance traversed to reach healthcare services (Penchansky and Thomas, Reference Penchansky and Thomas1981). We were also interested in exploring the relationship of accessibility with help-seeking and treatment adherence. Utilization referred to the quantification or description of the use of healthcare services by people to study trends, patterns, variations or for other objectives (Carrasquillo, Reference Carrasquillo2013). This dimension also conceptually encompassed disparities in service use, hotspots and cold spots and underlying factors influencing doctor visits or hospital admissions.
Although we limited the definition of “accessibility” primarily to its geographic aspect, we are aware that it is a broader concept determined by other factors that affect one’s uptake of health care (Andersen and Newman, Reference Andersen and Newman1973). Thus, we used “utilization” as a separate concept to capture studies which might highlight the direct or indirect use of GIS in analyzing any other aspects of MH service delivery, especially non-spatial ones (e.g., acceptability or affordability of services). We also anticipated that exploring the concept “utilization” could help us discover studies that have used GIS to assess inequity or disparities in care and explain variations in healthcare use.
Primary and secondary research papers of any design and methodology (including quantitative and qualitative designs if any) were included if they met the inclusion criteria. Both experimental and quasi-experimental study designs including randomized controlled trials, non-randomized controlled trials and analytical observational studies (prospective and retrospective cohort studies, case–control studies and analytical cross-sectional studies) were considered for inclusion. This review also considered descriptive observational study designs including case reports, case series and descriptive cross-sectional studies for inclusion. We excluded reviews, commentaries and opinion pieces.
Search strategy
Seven electronic databases were searched: Medical Literature Analysis and Retrieval System Online (MEDLINE), PsycINFO, Excerpta Medica Database (Embase), Global Health, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Central Register of Controlled Trials (CENTRAL) and Web of Science. The search was conducted between January 2024 and April 2024, using search terms under the following concepts: MH conditions (e.g., “depression”) and geographical information systems (e.g., “geospatial analysis”). The detailed search strategy for MEDLINE can be found in Supplementary Appendix A, and the search strategies for the other databases were a modification of this strategy based on the requirement of each database. Forward and backward citation chaining of included studies was conducted using Web of Science to find any additional eligible studies not identified through the database search.
Study selection and data extraction
Search results from all electronic databases were merged and imported into EndNote X9 for the removal of duplicates. After automatic and manual de-duplication, the remaining studies were imported into Covidence, an online software for managing systematic reviews. Papers were also manually screened for duplicates on the Covidence platform. A pair of reviewers (BB and RP) independently screened all titles and abstracts and conducted the full-text screening for eligibility. Conflicts were resolved by a third reviewer (LF).
Forward and backward citation chaining of included studies was conducted at this stage using Web of Science to find any additional eligible studies. A data extraction form was developed a priori on Microsoft (MS) Excel to collect data relevant to the objectives of this review and piloted.
Data were extracted by four pairs of researchers (BB and AS, BB and RP, BB and MGP and BB and AF). Inter-rater reliability among the four pairs of raters for data extraction, as measured by Cohen’s Kappa (κ), was deemed excellent (0.81–0.92). Any disagreements between the reviewers during extraction were resolved through discussion till a consensus was reached.
Data analysis and quality assessment
To effectively summarize the findings in accordance with the objectives of the review, we conducted a narrative synthesis (Popay et al., Reference Popay, Roberts, Sowden, Petticrew, Arai, Rodgers, Britten, Roen and Duffy2006). This involved a descriptive analysis of the studies included in the scoping review, using a textual approach to summarize and explain the results of the synthesis (Popay et al., Reference Popay, Roberts, Sowden, Petticrew, Arai, Rodgers, Britten, Roen and Duffy2006). Studies were categorized under service delivery dimensions, and the processes of GIS usage were described. In line with guidelines for scoping reviews (Peters et al., Reference Peters, Godfrey, Khalil, McInerney, Parker and Soares2015), we did not conduct quality assessments of the included studies.
Results
Search results are summarized in Figure 1. Of the 8,142 reports identified, 1945 were duplicates. From the remaining 6,197 papers, we excluded 6,092 that did not meet eligibility criteria at the title and abstract screening stage. In total, 105 full texts were assessed for eligibility. Two studies were excluded at this stage because their objectives did not align with our predefined service delivery dimensions instead focusing on spatial patterns in the prevalence of MH conditions. Based on our eligibility criteria, 47 studies were eligible for inclusion. The forward and backward citation chaining process identified 11 additional eligible studies, leading to a total of N = 58.
PRISMA flow diagram of included and excluded studies.
Study characteristics (Table 1)
Summary characteristics of included studies
The 58 included studies were published between 1998 and 2024, with most publications (n = 45 of 58, 77.9%) clustered between 2014 and 2024. The wide majority of studies were conducted in high-income countries (n = 53, 91.4%), with most (n = 41, 70.7%) originating from the United States. Two studies emerged from upper-middle-income countries (South Africa and China) (Bhana and Pillay, Reference Bhana and Pillay1998; Pang and Lee, Reference Pang and Lee2008) and three from lower-middle-income countries (Nigeria, India and Sri Lanka) (Otun, Reference Otun2016; Rajapakshe et al., Reference Rajapakshe, Edirisuriya, Sivayogan and Kulatunga2019; Roberts et al., Reference Roberts, Shiode, Grundy, Patel, Shidhaye and Rathod2020). The wide majority (n = 56, 96.6%) employed a cross-sectional design, with two exceptions: one utilizing a prospective chart review (Klimas et al., Reference Klimas, O’Reilly, Egan, Tobin and Bury2014) and the other using both longitudinal and cross-sectional methods (Cantor et al., Reference Cantor, DeYoreo, Hanson, Kofner, Kravitz, Salas, Stein and Kapinos2022). None of the studies reported the use of GIS in MH trials. The data used came from a variety of settings including inpatient, outpatient, emergency departments (EDs), community-based settings and primary care settings. Most (n = 32, 55.2%) examined substance-use disorders (SUDs) (mainly opioid use disorders [OUDs]), while others also focused on serious mental illness (e.g., schizophrenia) and common mental disorders (e.g., depression and anxiety). The significant number of papers that focused on SUDs mainly examined OUDs and associated treatment, including medication-assisted treatment (MAT) options (methadone, buprenorphine and naloxone distribution), opioid treatment programs in various settings (clinics and pharmacies) and outpatient treatment for OUD.
Accessibility was the most frequently examined service delivery dimension, with 46 out of 58 studies focusing on this aspect, followed by availability (n = 6) and utilization (n = 6). Types of GIS analysis utilized included variations of spatial analysis (descriptive spatial analysis, spatial regression models and spatiotemporal analysis), hotspot analysis, network analysis, 2-step floating catchment area (2SFCA) method, drive-time comparisons and cluster analysis. Sources of data included provider/specialist directories, Substance Abuse and Mental Health Services Administration (SAMHSA) database, community surveys, inpatient databases, ED databases and census data.
Figure 2 illustrates the conceptual framework used to organise the review findings. The framework builds on the WHO’s Service Coverage Framework and the Tanahashi model of health service delivery, adapted to mental health and GIS contexts.
The conceptual framework used to organize the review findings. The framework builds on the WHO’s Service Coverage Framework and the Tanahashi model of health service delivery, adapted to mental health and GIS contexts.
The following section summaries the results into the three key dimensions of accessibility, availability and utilization. In a fourth theme (“Impact”), we report studies that examined how a service delivery dimension impacted other treatment outcomes.
Availability
Six studies (Pang and Lee, Reference Pang and Lee2008; Perron et al., Reference Perron, Gillespie, Alexander-Eitzman and Delva2010; Goedel et al., Reference Goedel, Shapiro, Cerdá, Tsai, Hadland and Marshall2020; Sutarsa et al., Reference Sutarsa, Banfield, Passioura, Konings and Moore2021; Nolen et al., Reference Nolen, Zang, Chatterjee, Behrends, Green, Kumar, Linas, Morgan, Murphy, Walley, Yan, Schackman and Marshall2022; Oluyomi et al., Reference Oluyomi, Schneider, Christian, Alvarez, Smárason, Goodman and Storch2023) explored availability, generally through measures such as the distribution of facilities across a population and resource availability within 5–10-mile network buffers. Analyses commonly used were hotspot analysis or cluster analysis. Service availability could be further organized as “coverage” and “equity.”
Service coverage
Out of the six, three studies (Pang and Lee, Reference Pang and Lee2008; Sutarsa et al., Reference Sutarsa, Banfield, Passioura, Konings and Moore2021; Nolen et al., Reference Nolen, Zang, Chatterjee, Behrends, Green, Kumar, Linas, Morgan, Murphy, Walley, Yan, Schackman and Marshall2022) explored availability in terms of treatment/service coverage. Nolen et al. (Reference Nolen, Zang, Chatterjee, Behrends, Green, Kumar, Linas, Morgan, Murphy, Walley, Yan, Schackman and Marshall2022) used naloxone coverage ratios (the number of naloxone kits distributed through community-based programs to the number of opioid-related overdose deaths among its residents) to determine if US municipalities with high percentages of racial minorities have equitable access to the overdose antidote naloxone (Nolen et al., Reference Nolen, Zang, Chatterjee, Behrends, Green, Kumar, Linas, Morgan, Murphy, Walley, Yan, Schackman and Marshall2022). Pang and Lee (Reference Pang and Lee2008) used district-based geographic coverage to evaluate the methadone treatment program (MTP) in Hong Kong (Pang and Lee, Reference Pang and Lee2008). Sutarsa et al. (Reference Sutarsa, Banfield, Passioura, Konings and Moore2021) investigated the spatial distribution of MH nurses across Australian local government areas by measuring the number of full-time equivalent MH nurses per 1,00,000 people, revealing significant regional disparities (Sutarsa et al., Reference Sutarsa, Banfield, Passioura, Konings and Moore2021).
Equity in service availability
The remaining three studies (Perron et al., Reference Perron, Gillespie, Alexander-Eitzman and Delva2010; Goedel et al., Reference Goedel, Shapiro, Cerdá, Tsai, Hadland and Marshall2020; Oluyomi et al., Reference Oluyomi, Schneider, Christian, Alvarez, Smárason, Goodman and Storch2023) focused on equity in service availability. Two studies (Perron et al., Reference Perron, Gillespie, Alexander-Eitzman and Delva2010; Goedel et al., Reference Goedel, Shapiro, Cerdá, Tsai, Hadland and Marshall2020) attempted to evaluate treatment capacities of particular regions by identifying the distribution of healthcare facilities, determining population covered by service catchment areas and calculating the total number of resources within 5–10-mile Euclidean buffers from patients’ addresses (i.e., straight-line distances from patients’ addresses). One study examined the geographic distribution of OCD CBT speciality providers across the state of Texas, with particular attention to the relationship with neighborhood socioeconomic disadvantage, insurance status and rural versus urban status (Oluyomi et al., Reference Oluyomi, Schneider, Christian, Alvarez, Smárason, Goodman and Storch2023).
Accessibility
Forty-six studies aimed to explore the spatial accessibility of MH services and substance-use treatment facilities using GIS. Accessibility was most commonly defined as the ease with which individuals can reach and utilize MH services. Temporal accessibility, measured by travel time to the nearest MH facility, and spatial accessibility, measured by distance to nearest facility, were generally used measures to assess accessibility, with a small number of studies also using parameters like population within a convenient distance of services (5–10 miles from a facility or within a 30-minute drive from healthcare services). Only one of the studies used the cost of travel as a metric (Han and Stone, Reference Han and Stone2007). Studies relied on usual data sources (census data, the SAMHSA database, community surveys, etc.) occasionally using them alongside databases linked to law or justice departments like the Drug Enforcement Administration (DEA).
Equity in service accessibility
A substantial number of these papers (n = 16) focused on studying equity of services (Bhana and Pillay, Reference Bhana and Pillay1998; Koizumi et al., Reference Koizumi, Rothbard and Kuno2009; Perron et al., Reference Perron, Gillespie, Alexander-Eitzman and Delva2010; Guerrero et al., Reference Guerrero, Pan, Curtis and Lizano2011; López-Lara et al., Reference López-Lara, Garrido-Cumbrera and Díaz-Cuevas2012; Guerrero et al., Reference Guerrero, Kao and Perron2013; Amiri et al., Reference Amiri, Lutz, Socías, McDonell, Roll and Amram2018; Rajapakshe et al., Reference Rajapakshe, Edirisuriya, Sivayogan and Kulatunga2019; Simmons, Reference Simmons2019; Upadhyay et al., Reference Upadhyay, Aparasu, Rowan, Fleming, Balkrishnan and Chen2019; Wani et al., Reference Wani, Wisdom and Wilson2019; Joudrey et al., Reference Joudrey, Chadi, Roy, Morford, Bach, Kimmel, Wang and Calcaterra2020; Langabeer et al., Reference Langabeer, Stotts, Cortez, Tortolero and Champagne-Langabeer2020; Pustz et al., Reference Pustz, Shrestha, Newsky, Taylor, Fowler, Van Handel, Lingwall and Stopka2022; Katayama et al., Reference Katayama, Woldesenbet, Munir, Bryan, Carpenter and Pawlik2023; Charlesworth et al., Reference Charlesworth, Nagy, Drake, Manibusan and Zhu2024). Twelve focused on the rural–urban divide of mental healthcare services, using spatial analysis to visually map areas with limited access to MH services with help of rural and urban census tracts (Bhana and Pillay, Reference Bhana and Pillay1998; Koizumi et al., Reference Koizumi, Rothbard and Kuno2009; Perron et al., Reference Perron, Gillespie, Alexander-Eitzman and Delva2010; López-Lara et al., Reference López-Lara, Garrido-Cumbrera and Díaz-Cuevas2012; Amiri et al., Reference Amiri, Lutz, Socías, McDonell, Roll and Amram2018; Upadhyay et al., Reference Upadhyay, Aparasu, Rowan, Fleming, Balkrishnan and Chen2019; Wani et al., Reference Wani, Wisdom and Wilson2019; Joudrey et al., Reference Joudrey, Chadi, Roy, Morford, Bach, Kimmel, Wang and Calcaterra2020; Langabeer et al., Reference Langabeer, Stotts, Cortez, Tortolero and Champagne-Langabeer2020; Pustz et al., Reference Pustz, Shrestha, Newsky, Taylor, Fowler, Van Handel, Lingwall and Stopka2022; Katayama et al., Reference Katayama, Woldesenbet, Munir, Bryan, Carpenter and Pawlik2023; Charlesworth et al., Reference Charlesworth, Nagy, Drake, Manibusan and Zhu2024) and generally concluding that rural areas were underserved compared to urban areas. In addition to the usual 30-minute or 60-minute drive times, some studies also used other methods of calculating access like enhanced two-step floating catchment area (E2SFCA) method access score (Charlesworth et al., Reference Charlesworth, Nagy, Drake, Manibusan and Zhu2024), 2SFCA technique with a distance decay function (Amiri et al., Reference Amiri, Lutz, Socías, McDonell, Roll and Amram2018), geospatial distance buffering (Langabeer et al., Reference Langabeer, Stotts, Cortez, Tortolero and Champagne-Langabeer2020) and network analysis (Roberts et al., Reference Roberts, Shiode, Grundy, Patel, Shidhaye and Rathod2020). After visually mapping accessibility, two studies used spatial regression techniques to explore associations with socio-demographic factors that further determined healthcare access (Perron et al., Reference Perron, Gillespie, Alexander-Eitzman and Delva2010; Amiri et al., Reference Amiri, Lutz, Socías, McDonell, Roll and Amram2018). The other four studies studied equity of services by focusing on access for vulnerable populations (elderly, ethnic minorities, socio-economically weak groups) (Guerrero et al., Reference Guerrero, Pan, Curtis and Lizano2011; Guerrero et al., Reference Guerrero, Kao and Perron2013; Rajapakshe et al., Reference Rajapakshe, Edirisuriya, Sivayogan and Kulatunga2019; Simmons, Reference Simmons2019). One study used network analysis methods to map dementia care service points geographically with relation to elderly population density (Rajapakshe et al., Reference Rajapakshe, Edirisuriya, Sivayogan and Kulatunga2019). Simmons (Reference Simmons2019) conducted an optimized hotspot analysis to determine which regions were the most underserved in terms of serious mental illness burden and correlated it to neighborhood poverty (Simmons, Reference Simmons2019). Two studies assessed the distance between Latino-populated census tracts and general MH treatment facilities (Guerrero et al., Reference Guerrero, Pan, Curtis and Lizano2011; Guerrero et al., Reference Guerrero, Kao and Perron2013).
Opioid dependence and accessibility of treatment
A number of papers (n = 12) used different methods to explore the same objective: identifying high-risk zones for opioid dependence in the United States and exploring accessibility of emergency services and inpatient and outpatient treatment for the same. Eight studies mapped overdose incidents and compared them to the location of treatment services (ambulance services and methadone/naloxone facilities), highlighting areas of deprivation and concluding that having a treatment facility within 15- and 30-minute drive time from hotspots of overdose deaths was associated with lower risks of overdoses (Kao et al., Reference Kao, Torres, Guerrero, Mauldin and Bordnick2014; Klimas et al., Reference Klimas, O’Reilly, Egan, Tobin and Bury2014; Burrell et al., Reference Burrell, Ethun, Fawcett, Rickard-Aasen, Williams, Kearney and Pringle2017; Taylor et al., Reference Dworkis, Taylor, Peak and Bearnot2017; Dworkis et al., Reference Dworkis, Weiner, Liao, Rabickow and Goldberg2018; Amram et al., Reference Amram, Socías, Nosova, Kerr, Wood, DeBeck, Hayashi, Fairbairn, Montaner and Milloy2019; Iloglu et al., Reference Iloglu, Joudrey, Wang, Thornhill and Gonsalves2021; Anwar et al., Reference Anwar, Duever and Jayawardhana2022). One study tried to obtain an overall risk score by summing distance scores and overdose scores for each town in a state to create a map which approximated the need for additional emergency resources by town (Schneider et al., Reference Schneider, Carlson and Rosenthal2020). After identifying high-risk areas, they further examined how the inaccessibility of resources affects outcomes in patients with OUDs. One study mapped opioid dependence priority areas and areas with low numbers of DEA-waivered practitioners to identify unmet treatment need priority areas and low MAT capacity priority areas (Topmiller et al., Reference Topmiller, Mallow, Vissman and Grandmont2018). Kleinman (Reference Kleinman2020) used population-weighted mean travel time from census tracts to nearest opioid treatment programs and pharmacies, comparing two models of methadone dispensing and demonstrating that pharmacies were more accessible for this purpose than opioid treatment programs (Kleinman, Reference Kleinman2020). Abell-Hart et al. (Reference Abell-Hart, Rashidian, Teng, Rosenthal and Wang2022) identified several hotspots where patients lived far from naloxone/buprenorphine providers (Abell-Hart et al., Reference Abell-Hart, Rashidian, Teng, Rosenthal and Wang2022).
Accessibility and help-seeking
Two studies examined accessibility and its association with demand for care or help-seeking. Bensley et al. (Reference Bensley, Karriker-Jaffe, Cherpitel, Li, Wallisch and Zemore2021) explored the distance and travel time to nearest treatment services (using network analysis) to show that lower service density was associated with a lower likelihood of considering getting help (Bensley et al., Reference Bensley, Karriker-Jaffe, Cherpitel, Li, Wallisch and Zemore2021). Conversely, Roberts et al. (Reference Roberts, Shiode, Grundy, Patel, Shidhaye and Rathod2020) found no association between travel distance and the probability of seeking treatment for depression (Roberts et al., Reference Roberts, Shiode, Grundy, Patel, Shidhaye and Rathod2020).
Accessibility and treatment adherence
Three studies explored the relationship between treatment accessibility and adherence. Two studies concluded that increased distance (>10 miles) was associated with a higher number of missed doses or lower treatment adherence (Amiri et al., Reference Amiri, Lutz, Socías, McDonell, Roll and Amram2018; Amiri et al., Reference Amiri, Lutz, McDonell, Roll and Amram2020), while another used multivariate logistic regression analysis to demonstrate the relationship between travel distance and treatment completion for minority groups (Upadhyay et al., Reference Upadhyay, Aparasu, Rowan, Fleming, Balkrishnan and Chen2019).
Utilization
Six studies examined service utilization patterns (Perlman et al., Reference Perlman, Law, Luan, Rios, Seitz and Stolee2018; Thurston and Freisthler, Reference Thurston and Freisthler2020; Holmes et al., Reference Holmes, Rishworth and King2022; Schwarz et al., Reference Schwarz, Hemmerling, Kabisch, Galbusera, Heinze, von Peter and Wolff2022; Rhew et al., Reference Rhew, Jacklin, Bright, McCarty, Henning-Smith and Warry2023; Winckler et al., Reference Winckler, Nguyen, Khare, Patel, Crandal, Jenkins, Fisher and Rhee2023) with two studies focusing on equity of services or disparities (Holmes et al., Reference Holmes, Rishworth and King2022; Rhew et al., Reference Rhew, Jacklin, Bright, McCarty, Henning-Smith and Warry2023).
Equity in service utilization
Rhew et al. (Reference Rhew, Jacklin, Bright, McCarty, Henning-Smith and Warry2023) studied rural–urban differences in healthcare utilization for older adults with dementia across the state by exploring hospital admission rates and ED visit rates related to dementia, stratified by rurality and regions (Rhew et al., Reference Rhew, Jacklin, Bright, McCarty, Henning-Smith and Warry2023). Holmes et al. (Reference Holmes, Rishworth and King2022) explored disparities in opioid overdose survival and naloxone administration across different counties in Pennsylvania (Holmes et al., Reference Holmes, Rishworth and King2022).
Patterns of service use
Thurston and Freisthler (Reference Thurston and Freisthler2020) examined the frequency and geographic distribution of EMS calls resulting in naloxone administration (Thurston and Freisthler, Reference Thurston and Freisthler2020). Schwarz et al. (Reference Schwarz, Hemmerling, Kabisch, Galbusera, Heinze, von Peter and Wolff2022) studied the extent to which the location of the service user’s home within the catchment area, as well as the distance between the home and the clinic, influences the utilization of two treatment models (inpatient treatment compared to IEHT) (Schwarz et al., Reference Schwarz, Hemmerling, Kabisch, Galbusera, Heinze, von Peter and Wolff2022). Winckler et al. (Reference Winckler, Nguyen, Khare, Patel, Crandal, Jenkins, Fisher and Rhee2023) measured the rate of MH visits per 1,000 children in specific geographic regions (census tracts) to assess the extent to which MH services were being accessed and used by the target population with the aim of identification of high utilization for the pediatric population (Winckler et al., Reference Winckler, Nguyen, Khare, Patel, Crandal, Jenkins, Fisher and Rhee2023). Perlman et al. (Reference Perlman, Law, Luan, Rios, Seitz and Stolee2018) examined the geographic variation in MH service utilization in Toronto at the neighborhood level identifying hotspots and cold spots, spatial patterns and underlying factors measured by doctor visits and hospital admissions (Perlman et al., Reference Perlman, Law, Luan, Rios, Seitz and Stolee2018).
Impact
Seven studies examined how a service delivery dimension (availability, accessibility or utilization) impacted other outcomes (Kleinman, Reference Kleinman2020; Thurston and Freisthler, Reference Thurston and Freisthler2020; Wei and Chan, Reference Wei and Chan2021; Alibrahim et al., Reference Alibrahim, Marsh, Amaro, Kong, Khachikian and Guerrero2022; Cantor et al., Reference Cantor, DeYoreo, Hanson, Kofner, Kravitz, Salas, Stein and Kapinos2022; Schwarz et al., Reference Schwarz, Hemmerling, Kabisch, Galbusera, Heinze, von Peter and Wolff2022; Charlesworth et al., Reference Charlesworth, Nagy, Drake, Manibusan and Zhu2024).
Program or policy evaluation
Thurston and Freisthler (Reference Thurston and Freisthler2020) examined the frequency and geographic distribution of EMS calls resulting in naloxone administration and identified clusters of naloxone events (Thurston and Freisthler, Reference Thurston and Freisthler2020). They eventually concluded that spatial clusters crossed administrative boundaries (i.e., county lines) suggesting that opioid misuse was less responsive to county-level policies. Cantor et al. (Reference Cantor, DeYoreo, Hanson, Kofner, Kravitz, Salas, Stein and Kapinos2022) assessed the proportion of individuals who had a SUD treatment facility within a 15-minute drive that accepted their specific form of payment – Medicaid, private insurance or cash (Cantor et al., Reference Cantor, DeYoreo, Hanson, Kofner, Kravitz, Salas, Stein and Kapinos2022). The study found that Medicaid beneficiaries faced lower geographic accessibility to SUD treatment services, primarily because fewer facilities accepted Medicaid compared to other payment types.
Impact on treatment choices
Five studies showed how accessibility influenced treatment choices (Kleinman, Reference Kleinman2020; Wei and Chan, Reference Wei and Chan2021; Alibrahim et al., Reference Alibrahim, Marsh, Amaro, Kong, Khachikian and Guerrero2022; Schwarz et al., Reference Schwarz, Hemmerling, Kabisch, Galbusera, Heinze, von Peter and Wolff2022; Charlesworth et al., Reference Charlesworth, Nagy, Drake, Manibusan and Zhu2024). One study compared driving time from zip codes of patients to treatment facilities to show that higher accessibility was observed for counseling services than methadone services (Alibrahim et al., Reference Alibrahim, Marsh, Amaro, Kong, Khachikian and Guerrero2022). Wei and Chan (Reference Wei and Chan2021) compared the distance between the patients’ residence and treatment centers to discover that patients living closer to the treatment center were more likely to choose methadone as treatment, while patients living farther away were more likely to choose sublingual buprenorphine tablets (Wei and Chan, Reference Wei and Chan2021). Another study investigated the extent to which the location of the service user’s home within the catchment area, as well as the distance between the home and the clinic, influences the utilization of inpatient treatment compared to inpatient equivalent home treatment (IEHT) (Schwarz et al., Reference Schwarz, Hemmerling, Kabisch, Galbusera, Heinze, von Peter and Wolff2022). Kleinman (Reference Kleinman2020) used population-weighted mean travel time from census tracts to nearest opioid treatment programs and pharmacies, comparing two models of methadone dispensing and demonstrating that pharmacies were more accessible for this purpose than opioid treatment programs (Kleinman, Reference Kleinman2020). Charlesworth et al. (Reference Charlesworth, Nagy, Drake, Manibusan and Zhu2024) examined access to MH prescribers and non-prescribers in rural areas and found that mental healthcare delivery in rural settings often relied on non-prescribers, owing to limited access to Medicaid-participating prescribers (Charlesworth et al., Reference Charlesworth, Nagy, Drake, Manibusan and Zhu2024).
Discussion
To our knowledge, this is the first scoping review to comprehensively synthesize how GISs have been applied across three core dimensions of MH service delivery spanning diverse populations, settings and geographical regions. Our review builds upon previous literature by moving beyond a narrow focus on serious mental illness and accessibility to encompass a broader spectrum of MH conditions and service delivery dimensions. The findings not only demonstrate a growing literature in GIS applications of MH service delivery but also point to a highly uneven distribution of research (both thematically and geographically) with a concentration of studies in high-income countries and a predominant focus on spatial accessibility. This review has identified several underexplored areas in the application of GIS that have the potential to advance MH service planning and delivery globally, including its use in designing and monitoring clinical trials, supporting implementation research and informing advocacy strategies.
Current scope and patterns of use across studies
About one-third of eligible studies across all three themes had primary objectives related to resource management and planning, focusing on identifying high-risk zones or priority areas for opioid dependence, hotspots of overdose deaths or unmet treatment needs, mapping them against areas where treatment services or providers are located. Treatment or service coverage, another metric of importance to resource planning, was explored by conducting spatial analyses of services delivered in comparison with the target population. In addition to quantifying service gaps, studies focused on this theme also suggested potential interventions, such as expanding treatment infrastructure or modifying service delivery models to enhance access.
Another emerging focus that is consequential for resource allocation was studying equity of services (n = 25), which was explored by looking at disparities in service delivery for marginalized populations and rural/urban areas. Furthermore, these studies explored structural inequities by assessing associations between spatial healthcare access and socioeconomic indicators, race/ethnicity and insurance status, highlighting systemic barriers and advocating for equity-driven policy reforms.
Some studies used GIS for program evaluation or policy impact assessment, like comparing two different models of methadone maintenance programs (Iloglu et al., Reference Iloglu, Joudrey, Wang, Thornhill and Gonsalves2021) and the restrictive payment model of Medicaid (Charlesworth et al., Reference Charlesworth, Nagy, Drake, Manibusan and Zhu2024), often suggesting equity-informed interventions and changes in policy (Kleinman, Reference Kleinman2020; Cantor et al., Reference Cantor, DeYoreo, Hanson, Kofner, Kravitz, Salas, Stein and Kapinos2022).
The strength of existing databases and electronic health records emerged as a major determinant of GIS usage, which could possibly explain why only three studies were conducted in low- and middle-income countries (LMICs) (Otun, Reference Otun2016; Rajapakshe et al., Reference Rajapakshe, Edirisuriya, Sivayogan and Kulatunga2019; Roberts et al., Reference Roberts, Shiode, Grundy, Patel, Shidhaye and Rathod2020). GIS applications in MH research relied heavily on existing databases, including census data, provider directories, community surveys and law enforcement databases. Some of these databases also helped facilitate real-time tracking of healthcare trends, enabling analysis without the need for additional primary data collection. The integration of multiple data sources, such as the SAMHSA database, DEA reports and ED records, allowed for a more comprehensive analysis of MH service distribution (Topmiller et al., Reference Topmiller, Mallow, Vissman and Grandmont2018; Kleinman, Reference Kleinman2020; Iloglu et al., Reference Iloglu, Joudrey, Wang, Thornhill and Gonsalves2021; Abell-Hart et al., Reference Abell-Hart, Rashidian, Teng, Rosenthal and Wang2022; Charlesworth et al., Reference Charlesworth, Nagy, Drake, Manibusan and Zhu2024). Interdisciplinary approaches, such as combining healthcare data with law enforcement statistics, helped studies enhance the scope of their analysis and provide a multidimensional perspective on MH service accessibility and availability (Adelfio et al., Reference Adelfio, Kain, Stenberg and Thuvander2019).
The predominance of opioid-related GIS studies conducted in the United States could be explained by the presence of strong surveillance infrastructure and the policy urgency surrounding the opioid epidemic. Federal databases such as the Centers for Disease Control and Prevention’s (CDC) overdose surveillance and SAMHSA’s treatment facility directories provide high-resolution, publicly available spatial data, enabling fine-grained analyses rarely possible elsewhere. The national prioritization of the opioid crisis has also channeled research funding and policy attention toward this issue, creating a disproportionate body of US-based GIS evidence compared to other MH domains or regions.
Gaps in evidence and future scope of use
While GIS offers powerful tools for studying MH service delivery, existing GIS research in MH is constrained by methodological simplifications that limit cross-context transferability. Many studies assessing temporal accessibility measure travel time in terms of drive times to the nearest facility, implicitly assuming uniform transportation modes and potentially overlooking barriers faced by individuals reliant on public transport. The absence of measures that capture economic or cost-related barriers (such as transportation costs, time lost from work or out-of-pocket expenses) can lead to overestimation of true or effective access, as financial burdens may remain prohibitive despite apparent geographic proximity. Similarly, studies examining service availability often assume that proximity equates to access, ignoring capacity constraints, wait times or service saturation. It is also important to consider that the relevance of geographic location could differ across service types: For emergency services, such as opioid overdose treatment, rapid access is critical, whereas for non-emergency MH services, factors like privacy, stigma or patient comfort may make discrete or neutral service locations preferable to simply prioritizing proximity. Additionally, an exclusive focus on geographic distance may fail to capture other determinants of service use, such as stigma, privacy concerns or service acceptability, as highlighted by Cantor et al. (Reference Cantor, DeYoreo, Hanson, Kofner, Kravitz, Salas, Stein and Kapinos2022), who demonstrated that mapping services without considering payment acceptance could misrepresent true access.
Considering cultural or behavioral determinants of service delivery or integrating multiple dimensions (such as triangulating service utilization with availability) can provide a more accurate picture of true treatment capacity and better reflect the complexity of real-world service provision. Additionally, qualitative research can help elucidate the socio-cultural mechanisms underlying spatial patterns of service delivery, offering nuanced explanations for disparities observed through GIS analyses. Most GIS studies in the review offer static, cross-sectional snapshots of accessibility, overlooking how service reach and population mobility shift over time in response to policy changes, service expansion or closure and seasonal fluctuations in demand. Integrating longitudinal spatial analyses could help capture these temporal dynamics, offering a more realistic representation of equity in access to MH services.
More than 90% of the studies included in this review were conducted in high-income or upper-middle-income countries. The few studies conducted in LMICs leveraged existing administrative datasets or community surveys to generate actionable insights, demonstrating that creative use of available resources can support service planning and policy decisions. Future research in LMICs could build on these approaches by integrating multiple data sources, using open-source geographic data or applying community-driven mapping to expand GIS applications.
A major gap observed in this review was the lack of GIS usage in designing or monitoring trials related to MH service delivery. GIS can optimize recruitment strategies for clinical trials by identifying and targeting specific geographic areas with high prevalence of MH conditions or low service utilization. This can improve inclusivity of trial samples and reflect real-world dynamics (Krzyzanowski et al., Reference Krzyzanowski, Manson, Eder, Kne, Oldenburg, Peterson, Hirsch, Luepker and Duval2019). Furthermore, GIS can help understand and address geographic barriers to participation and retention in trials, such as transportation difficulties or lack of local resources, increasing the external validity of trials (Arnold et al., Reference Arnold, Rerolle, Tedijanto, Njenga, Rahman, Ercumen, Mertens, Pickering, Lin, Arnold, Das, Stewart, Null, Luby, Colford, Hubbard and Benjamin-Chung2024). It could also help in adopting more pragmatic approaches to trials, by informing adaptive trial designs and allowing for dynamic allocation of resources based on geographic disparities in service access (Savoca et al., Reference Savoca, Ludwig, Jones, Jason Clodfelter, Sloop, Bollhalter and Bertoni2017). It can be used to plan and monitor the delivery of community interventions within a trial (Nadkarni et al., Reference Nadkarni, Gandhi, Fernandes, Mirchandani, Kamat, Weiss, Singla, Velleman, Lu, Bhatia, Biswal, Sequeira, D’souza, Raikar and Patel2024). For example, it can help ensure equitable distribution of resources across different geographic areas and track intervention implementation in real time.
There is also scope for expanding GIS usage in MH implementation research, specifically in leveraging it to support integration and coordination of MH services across different sectors (e.g., health care, social services and education). Mapping the distribution of services and identifying gaps in coverage can help improve service linkages and reduce fragmentation of care. It can also identify coordination gaps between primary care, specialized MH facilities and social support systems (Reference Khashoggi and MuradKhashoggi and Murad 2020). GIS can enable monitoring of implementation outcomes of new programs and policies, providing real-time data on service delivery, utilization and outcomes. This information can be used to identify implementation challenges and make necessary adjustments to improve program effectiveness, helping us learn on the go and fundamentally transforming implementation research (Scotch et al., Reference Scotch, Parmanto, Gadd and Sharma2006; McGinty et al., Reference McGinty, Alegria, Beidas, Braithwaite, Kola, Leslie, Moise, Mueller, Pincus, Shidhaye, Simon, Singer, Stuart and Eisenberg2024).
The visual impact of GIS mapping has a strong potential in shaping public health policies and advocacy strategies (Davenhall and Kinabrew, Reference Davenhall, Kinabrew, Kresse and Danko2012; Manjunatha et al., Reference Manjunatha, Madhu, Sahana, Suraj, Mounika Sree, Chaithra and Kameshwari2024). Geospatial representations of treatment gaps, inequities and high-risk zones can provide compelling evidence to justify targeted funding allocations for MH infrastructure in underserved areas. GIS-based spatial equity audits could build a case to demand adjustments of service coverage to ensure marginalized communities are not disproportionately affected by service unavailability (Sharma and Ramesh, Reference Sharma and Ramesh2024). Participatory or community-driven mapping can also be used to advocate for policy reforms addressing systemic disparities and decentralization of MH services, ensuring that rural and remote populations have better access to care (Douglas et al., Reference Douglas, Subica, Franks, Johnson, Leon, Villanueva and Grills2020).
Governments, funders and policymakers can take concrete steps to harness the potential of GIS for equitable MH service delivery. Integrating GIS into national health information systems could enable continuous monitoring of geographic inequities in MH service provision. Training policymakers and planners to interpret and apply GIS data in decision-making can help bridge the gap between technical analysis and governance. In parallel, funding agencies should invest in GIS-based implementation research (particularly in LMICs) promoting the use of open-source tools and participatory, community-engaged approaches.
Another consideration for future research and practice could be the use of interdisciplinary approaches in studying MH service delivery. The intersection of GIS with machine learning and MH sciences offers promising avenues for predictive analytics and precision MH (Kamel Boulos et al., Reference Kamel Boulos, Peng and VoPham2019; Li and Ning, Reference Li and Ning2023; Fadiel et al., Reference Fadiel, Eichenbaum, Abbasi, Lee and Odunsi2024). For example, spatial–temporal artificial intelligence (AI) models could predict future service demand based on socioeconomic shifts, urbanization trends or climate change effects. Integration with mobile health (mHealth) tools could personalize treatment pathways based on an individual’s geographic constraints. Legal and policy studies could utilize GIS to assess the impact of health policy changes on service accessibility over time.
Limitations of the review
There are a number of limitations to our findings and review process. Our findings are presented descriptively as is typical for scoping reviews. We did not include gray literature or publications in languages other than English in our search, which may bias our results. Finally, we restricted the scope of the review to healthcare services, excluding preventive or promotional care delivered in other settings.
Conclusion
GIS has the potential to emerge as a powerful tool in MH research, particularly in mapping disparities, informing service delivery and identifying high-risk zones. However, the existing literature remains concentrated in high-income settings, underscoring the need for context-specific applications in LMICs. Additionally, expanding GIS use in trial design, implementation science and policy advocacy could help bridge critical gaps in MH service delivery, ensuring more equitable and data-driven decision-making. We hope this scoping review provides researchers, policymakers and service providers with an orientation to the current scope of GIS applications in MH service delivery and offers a foundation for advancing this work in diverse and underrepresented contexts.
Open peer review
To view the open peer review materials for this article, please visit http://doi.org/10.1017/gmh.2025.10088.
Supplementary material
The supplementary material for this article can be found at http://doi.org/10.1017/gmh.2025.10088.
Data availability statement
Data availability is not applicable to this article as no new data were created or analyzed in this study.
Author contribution
AN, VP, RV and DRS substantially contributed to the conception or design of the work. BB, RP, AKS, MGP, LF, YG and AF substantially contributed to the acquisition, analysis or interpretation of data for the work. BB, RP, AKS, MGP, LF, YG, DRS, AF, RV, VP, CL, CG, UB and AN drafted the work or revised it critically for important intellectual content. BB, RP, AKS, MGP, LF, YG, DRS, AF, RV, VP, CL, CG, UB and AN finally approved the version to be published. BB, RP, AKS, MGP, LF, YG, DRS, AF, RV, VP, CL, CG, UB and AN agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Financial support
This study was a part of the IMPlementation of evidence-based facility and community interventions to reduce the treatment gap for depRESSion (IMPRESS) program that has been funded through a grant from the National Institute of Mental Health (NIMH), United States (Grant Number R01MH115504).
Competing interests
The authors declare none.
Open access
Comments
To
Professor Judy Bass and Professor Dixon Chibanda,
Editor in Chief
PLOS Global Public Health
Subject: Submission of Manuscript – “The use of Geographic Information Systems (GIS) in studying mental health service delivery: A Scoping Review”
Dear Prof Bass and Prof Chibanda,
I am pleased to submit our manuscript entitled “The use of Geographic Information Systems (GIS) in studying mental health service delivery: A Scoping Review” for consideration of publication in the Global Mental Health journal.
This manuscript presents the first comprehensive scoping review of Geographic Information Systems (GIS) applications in mental health service delivery, systematically mapping the breadth and nature of existing literature across three core service delivery dimensions: availability, accessibility, and utilization. Our review synthesizes findings from 58 peer-reviewed studies, highlighting both the methodological diversity and the significant gaps in current research—particularly the underrepresentation of low- and middle-income countries (LMICs) and the predominance of spatial accessibility as a research focus. By identifying emerging directions and opportunities for GIS in mental health research, implementation science, and policy advocacy, our study offers a timely resource for researchers, policymakers, and practitioners aiming to advance equitable and data-driven mental health service planning globally.
While GIS has been widely applied in general public health for mapping service disparities and informing resource allocation, its use in mental health service delivery has remained limited and fragmented. Previous reviews have focused narrowly on serious mental illness or single service dimensions. Our review builds on and extends this literature by encompassing a broader range of mental health conditions, service settings, and research objectives, and by critically appraising both the strengths and limitations of GIS methodologies in this context.
We have had no prior interactions with the journal regarding this manuscript. The work is original, has not been published elsewhere, and is not under consideration by any other journal.
We believe this manuscript aligns closely with the mission of the Global Mental Health journal to publish rigorous, interdisciplinary research addressing global health challenges and promoting equity in health systems. We hope our findings will stimulate further research and practical applications of GIS in mental health, especially in underrepresented settings.
Thank you for considering our submission. We look forward to your response.
Sincerely,
Abhijit