Mapping Geographic Inequities of Dental Clinics Using a Mobile-Based Geospatial Platform: Evidence from a Mid-Sized Urban Setting

Mapping Geographic Inequities of Dental Clinics Using a Mobile-Based Geospatial Platform: Evidence from a Mid-Sized Urban Setting | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mapping Geographic Inequities of Dental Clinics Using a Mobile-Based Geospatial Platform: Evidence from a Mid-Sized Urban Setting Srinivas Pachava, Madhurya Rupa Pasumarthi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8846986/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Geographic inequities in access to oral healthcare services remain a persistent challenge across health systems worldwide. In many settings where private outpatient care predominates, the absence of comprehensive, spatially explicit data on healthcare facility distribution limits evidence-based planning and equitable resource allocation. Aim: To demonstrate the feasibility of using a mobile-based, open-access platform to develop a geospatial inventory of dental clinics and to examine spatial distribution patterns within a representative mid-sized urban setting. Methods: A cross-sectional geospatial survey was conducted over one month in an urban city. Real-time data on dental clinic location and attributes were collected using a smartphone-based application with automatic GPS tagging. Spatial visualization and descriptive analysis were used to identify clustering patterns and service gaps. Results: A total of 165 dental clinics were mapped. Clinics were disproportionately concentrated in central commercial and mixed-use zones, while peripheral urban areas accounted for a small proportion of facilities. The spatial pattern revealed clear inequities in service availability across the urban continuum. Conclusion: Mobile-based geospatial mapping provides a scalable, low-cost approach to documenting private dental healthcare infrastructure. Such methods can enhance health system visibility, support equity-oriented planning, and inform decentralization strategies in comparable urban settings globally. Dental clinic geospatial mapping Epicollect5 mobile data collection public health dentistry Figures Figure 1 INTRODUCTION Unequal geographic distribution of oral healthcare services is a well-documented public health concern across both high-income and low- and middle-income countries. 1 Although the global dental workforce has expanded substantially over recent decades, access to care remains uneven, particularly within rapidly urbanizing environments where service provision is largely market driven. 2 Private dental clinics tend to cluster in economically vibrant urban cores, while peri-urban and peripheral populations often experience reduced accessibility. 3 A major contributor to this imbalance is the limited availability of reliable, spatially explicit data on private healthcare facilities. 4 In many countries, health facility registries are incomplete, fragmented, or poorly integrated into planning systems. This lack of visibility constrains the ability of policymakers to identify service gaps, assess equity, and design targeted interventions. 5 In India, systematic documentation of private dental clinics is further complicated by the absence of a uniformly enforced and streamlined national protocol for clinic registration. While regulatory mechanisms exist, registration processes are often perceived by practitioners as administratively complex, inspection-intensive, and punitive in nature. This regulatory environment has inadvertently discouraged voluntary registration, resulting in avoidance of formal enrollment and leaving a substantial proportion of dental clinics undocumented. Consequently, official records frequently underestimate service availability, particularly in the private sector. 6 In this context, technology-enabled alternatives to administrative registries are increasingly relevant. 7 Mobile-based geospatial data collection platforms allow real-time documentation of healthcare facilities with minimal cost and infrastructure. These tools have been applied in epidemiological surveillance and community mapping, but their potential for systematically documenting private oral healthcare infrastructure remains underexplored. 8 The present study applies a mobile-based geospatial mapping approach to document the distribution of dental clinics within a representative mid-sized urban setting. Rather than focusing on a single city in isolation, the study aims to illustrate a methodological framework applicable to similar urban contexts in India and other rapidly urbanizing regions. AIM AND OBJECTIVES Aim: To demonstrate the application of a mobile-based geospatial data collection platform for mapping dental clinics within a mid-sized urban environment. Objectives: To develop a real-time geospatial database of dental clinics To analyze spatial distribution patterns across urban functional zones To identify geographic inequities in access to dental services To generate evidence relevant for equity-oriented urban health planning METHODS Study Design A cross-sectional, observational geospatial survey was conducted to map the distribution of dental clinics within a representative mid-sized urban environment. The study was designed to assess spatial clustering of private outpatient dental services and to identify potential service gaps across the urban continuum. Study Area The study was conducted in Guntur, a mid-sized city Located in southern Indian state Andhra Pradesh, characterized by rapid urban growth, mixed land-use development, and a predominantly private outpatient healthcare delivery system. Cities of this scale constitute a substantial proportion of India’s urban landscape and function as transitional hubs between rural and metropolitan health systems. Guntur exhibits urban features commonly observed in comparable cities, including a dense central commercial core, surrounding mixed residential commercial zones, and expanding peripheral areas with relatively limited healthcare infrastructure. In terms of urban form and health system organization, the city is comparable to other mid-sized Indian cities such as Vijayawada, Mysuru, and Salem, as well as internationally to rapidly urbanizing cities in low- and middle-income countries where private healthcare provision dominates. 6 The study area encompassed an approximate 10-km radius from the city center, covering central, intermediate, and peripheral zones. This spatial extent was selected to capture socioeconomic diversity and variation in healthcare accessibility. Study Duration The survey was conducted over one month, from April 17 to May 17. Data Collection Tool Data were collected using an open-access, smartphone-based geospatial data collection platform that enables real-time data entry, automatic GPS tagging, photographic documentation, and cloud-based storage. 7 Variables and Data Elements For each dental clinic, the following publicly observable variables were recorded: Clinic name Address Geographic coordinates (latitude and longitude) Photograph of clinic frontage Timestamp of data entry No personal, clinical, or patient-level information was collected. Data Collection Procedure A structured digital form was created within the platform to standardize data entry. Authors systematically traversed predefined urban zones to ensure coverage of central commercial areas, mixed-use neighborhoods, and peripheral localities. Data were entered in real time using smartphones (Android/iOS) with geographic coordinates captured automatically. Completed entries were reviewed for completeness and internal consistency, and the dataset was exported in CSV format for analysis. Technical Steps for Using Epicollect5: Accessing Epicollect5 Epicollect5 can be accessed through a web browser via http://five.epicollect.net or via the mobile application available on Android and Apple devices. To use the web version, users must sign in using a Google account. Creating a New Project After logging in, a new project is created by selecting the “ CREATE PROJECT ” option. During project creation, users can choose the project visibility as public or private. Once the project is created, the project URL is displayed at the top-left corner of the screen. Form Builder Interface Epicollect5 provides a form builder to design questionnaires or databases. The interface consists of three sections: Left panel: input question types Middle panel: form layout Right panel: settings and properties Adding and Customizing Questions Questions are added by dragging the required input type from the left panel to the center layout area. The question text and properties are customized using the properties tab in the settings panel. The entered question text appears exactly as displayed on users’ mobile or desktop screens. Sharing and Access Control After completing the form, the project can be shared with other users. Two access roles are available: Creator : full rights to edit the project and view all data Collector : permission to view the form and upload their own data only. Descriptive spatial analysis was conducted using the platform’s mapping and visualization functions. Clinic locations were plotted to examine clustering patterns across urban zones. Frequencies and proportions were calculated to summarize distribution and identify areas with comparatively limited-service availability. Ethical Considerations The study involved non-intrusive observation of publicly visible healthcare facilities. As no human participants were involved and no identifiable personal data were collected, formal ethical approval and informed consent were not required. RESULTS A total of 165 dental clinics were documented within the study area. Spatial analysis revealed pronounced clustering of clinics within central commercial and mixed residential–commercial zones. In contrast, peripheral urban areas accounted for a relatively small proportion of facilities. The distribution followed a clear core periphery pattern, with high-density service availability in economically active zones and sparse coverage in outer urban areas. This pattern indicates inequities in geographic access to dental services within the city. Table 1 Distribution of dental clinics by urban functional zone Urban functional zone Number of clinics Percentage Central commercial zone 75 45.4 Mixed residential commercial zone 50 30.3 Predominantly residential zone 30 18.2 Peripheral urban zone 10 6.1 Total 165 100.0 Table 1 summarizes clinic distribution by urban functional zone. Central commercial zone showed notable concentration (45.4%) of clinics in the area followed by mixed residential commercial zone (30.3%). Peripheral areas showed the least number of clinics. Table 2 Distribution of dental clinics by distance from city center Distance from city center Number of clinics Percentage Up to 2 km 86 52.1 2.1 to 5 km 58 35.2 More than 5 km 21 12.7 Total 165 100.0 Table 3 Spatial clustering pattern of dental clinics Spatial characteristic Clinics present Percentage High density clusters 90 54.5 Moderate density areas 50 30.3 Low density or sparse areas 25 15.2 Total 165 100.0 The spatial visualization generated showed high-density belts of clinics within inner commercial wards. Peripheral localities exhibited service deserts, highlighting potential gaps in accessibility. DISCUSSION This study demonstrates pronounced spatial inequities in the distribution of dental clinics within a representative midsized urban setting, with clear clustering in central commercial and mixed use zones and limited availability in peripheral urban areas. Such patterns are consistent with long standing observations in oral public health literature, which indicate that dental services, particularly those delivered through private markets, tend to align more closely with economic activity than with population health need. 1,3 Early global analyses by Petersen (2003) established that oral healthcare systems worldwide are strongly shaped by socioeconomic gradients, resulting in concentration of services within urban and economically advantaged areas. Although this work predates the widespread adoption of digital geospatial tools, it provides a conceptual foundation for understanding structural inequities in oral health service distribution. The spatial clustering observed in the present study reflects this enduring pattern. 1 Subsequent population level analyses have reinforced these concerns. Using data from the Global Burden of Disease study, Peres et al. (2019) demonstrated that access to oral healthcare remains uneven despite substantial global growth in the dental workforce. Their findings emphasized that increases in provider numbers do not necessarily translate into equitable access, particularly in rapidly urbanizing regions where private outpatient care predominates 2 . The core periphery distribution pattern observed in Guntur mirrors this global mismatch between workforce availability and service reach. Evidence from spatially explicit studies in middle income countries further corroborates the present findings. In a Brazilian urban analysis, Antunes et al. (2002) reported disproportionate concentration of dental clinics in central urban districts, with peripheral and socioeconomically disadvantaged neighborhoods experiencing reduced access. 16 Similar patterns of spatial clustering of private dental services have been documented in urban studies from South Africa, China, and Southeast Asia during the 2010s, suggesting that this phenomenon persists across different time periods and geographic contexts. More recent research integrating geographic information systems into oral health has demonstrated that geospatial visualization provides insights that are not apparent from aggregate facility counts alone. Studies conducted over the past decade have shown that mapping clinic locations can reveal service deserts within urban environments, even where the overall number of facilities appears adequate. This aligns closely with the present study, in which peripheral urban zones were visibly underserved despite the presence of a substantial number of dental clinics within the city as a whole. 12, 13 The methodological contribution of this study lies in its application of a mobile-based open access platform for real-time documentation of private dental infrastructure. Earlier epidemiological investigations often relied on census data or official registries, which are known to underrepresent private healthcare facilities. 11 By contrast, mobile geospatial approaches, first described for public health surveillance by Aanensen et al. (2009) 7 and later adapted for clinical research by Gohil et al. (2020), 8 enable rapid low-cost generation of spatial health intelligence. The present study extends this methodological approach specifically to oral healthcare infrastructure in a mid-sized urban context. Importantly, while data collection was limited to a single city, the observed spatial patterns are not unique to this setting. Similar configurations have been reported across different decades and regions, suggesting that the findings reflect a broader structural tendency inherent to private oral healthcare systems rather than a localized anomaly. This strengthens the external relevance of the study and supports its applicability to other mid-sized cities in India and to comparable urban environments in other low- and middle-income countries. 12,14 From a policy and planning perspective, these findings highlight the limitations of relying solely on formal registration systems for healthcare infrastructure assessment, particularly in contexts where regulatory processes discourage comprehensive enrollment. 15 Independent geospatial mapping approaches can serve as a complementary strategy, enhancing visibility of private sector services and providing planners with actionable evidence to address geographic inequities in access to oral healthcare. CONCLUSION This study demonstrates the utility of mobile-based geospatial mapping as a low-cost, scalable method for documenting private dental healthcare infrastructure. The findings reveal clear spatial inequities in service distribution, characterized by central clustering and peripheral under-coverage. By improving visibility of existing services, such approaches can support evidence-based urban health planning, guide decentralization strategies, and contribute to more equitable access to oral healthcare. The methodology is transferable and well-suited for comparative studies across mid-sized cities globally. LIMITATIONS The study may have underrepresented clinics without visible signage or those located in less accessible areas. The analysis was limited to spatial distribution and did not assess clinic capacity, service scope, or operating hours. Additionally, findings are based on a single-city survey and should be interpreted in the context of similar urban settings. RECOMMENDATIONS Integration of geospatial mapping into routine health system planning Incentivization of dental service provision in underserved urban zones Expansion of mapping approaches to other healthcare facilities Linkage of spatial data with population and oral health outcome indicators References Petersen PE, The World Oral Health Report (2003) : Continuous improvement of oral health in the 21st century – the approach of the WHO Global Oral Health Programme. Community Dent Oral Epidemiol. 2003;31(Suppl 1):3–24 Peres MA, Macpherson LMD, Weyant RJ, Daly B, Venturelli R, Mathur MR et al (2019) Oral diseases: a global public health challenge. J Dent Res 98(4):373–380 Antunes JLF, Narvai PC, Nugent ZJ (2002) Measuring inequalities in the distribution of dental caries. Community Dent Oral Epidemiol 30(5):332–341 Mohapatra U, Nagarajappa R, Satyarup D (2023) Pandemic transformation of oral public health – A review. J Global Oral Health 6:123–126 World Health Organization (2018) Master Facility List Resource Package: guidance for countries. WHO, Geneva Ministry of Health and Family Welfare, Government of India. The Clinical Establishments (Registration and Regulation) Act. New Delhi: MoHFW (2010) Available from: https://clinicalestablishments.mohfw.gov.in/index.php/en/act Aanensen DM, Huntley DM, Feil EJ, Al-Own F, Spratt BG (2009) EpiCollect: linking smartphones to web applications for epidemiology, ecology and community data collection. PLoS ONE 4(9):e6968 Gohil R, Sharma S, Sachdeva S, Gupta S, Dhillon M (2020) EpiCollect5: a free, fully customizable mobile-based application for data collection in clinical research. J Postgrad Med Educ Res 54(4):248–251 Government Municipal Corporation, Guntur Profile of Guntur city and urban characteristics . Andhra Pradesh: Government of Andhra Pradesh. Available from: https://gmcguntur.ap.gov.in/gmcprofile Epicollect5 Epicollect5 documentation: mobile and web-based geospatial data collection platform . Available fromhttps://docs.epicollect.net/ Nagarajappa S, Vyas S (2021) Smartphone assisted oral health data recording - an android based software application development. Med Pharm Rep 94(3):333–340 Alhagi AA, Ferine TS, Tanwir S, Srivastava R, Galiampalayam S (2021) Rural-urban disparities in patient satisfaction with oral health care: A provincial survey. BMC Oral Health 21(1):261 Athavale AV, Zodpey SP Public Health Informatics in India: The Potential and the Challenges. Indian J Public Health 2010 July-Sep ; 54(3):p 131–136 Dani N, Sood S, Prakash Nupur & Mbarika, Victor & Agrawal, Rajeev. GIS and Telemedicine: eHealth tools for Public Healthcare. eGov.2006 Nov Mathur MR, Williams DM, Reddy KS, Watt RG (2015) Universal health coverage: a unique policy opportunity for oral health. J Dent Res 94(3 Suppl):3S–5S Celeste RK, Moura FR, Santos CP, Tovo MF (2014) Analysis of outpatient care in Brazilian municipalities with and without specialized dental clinics. Cad Saude Publica 30(3):511–521 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8846986","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589341770,"identity":"13055261-d599-44d5-8f5f-144c021bf6e3","order_by":0,"name":"Srinivas Pachava","email":"","orcid":"","institution":"Sibar Institute of Dental Sciences","correspondingAuthor":false,"prefix":"","firstName":"Srinivas","middleName":"","lastName":"Pachava","suffix":""},{"id":589341771,"identity":"bdd06d70-b1ca-4cef-86f8-74761313d474","order_by":1,"name":"Madhurya Rupa Pasumarthi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYHACMwTzAxCzsZOihXEGSAszKVqYecAkAfW67Ye3Pebdc1jefEbuwc82v7bJ8zEzMH74mIPHijNp5cY8zw4bzrmRlyyd23fbsI2ZgVly5jY8Wg7kmEnzHDjMOEMix0A6t+c2I1ALGzMvPi3n34C12AO1GP+27LltT1jLDYgtiUAtZtIMP24nEqHlWZnknAPpyTN43qVZ9jbcTm5jZmzG75fzydsk3hywtp3Bnnv4xo8/t23ntzcf/PARjxYoaAZiYKQwtoE4jA0E1QNBHUQLwx9iFI+CUTAKRsFIAwDDO1CUjulTpAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0009-0703-8654","institution":"Sibar Institute of Dental Sciences","correspondingAuthor":true,"prefix":"","firstName":"Madhurya","middleName":"Rupa","lastName":"Pasumarthi","suffix":""}],"badges":[],"createdAt":"2026-02-11 04:24:41","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8846986/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8846986/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102495194,"identity":"eaa3de01-4c2e-4b3a-a707-c7de9d787a6c","added_by":"auto","created_at":"2026-02-12 09:27:46","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82412,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeospatial Distribution of Dental Clinics Within the Study Area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe spatial visualization generated showed high-density belts of clinics within inner commercial wards. Peripheral localities exhibited service deserts, highlighting potential gaps in accessibility.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8846986/v1/fca4bfc9396995fb4ae1efd2.jpg"},{"id":102495276,"identity":"7f349848-a9b2-4f25-afea-76fb9692d66e","added_by":"auto","created_at":"2026-02-12 09:28:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":668878,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8846986/v1/66d816ff-3a77-4c78-9960-6472d7599ba3.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eMapping Geographic Inequities of Dental Clinics Using a Mobile-Based Geospatial Platform: Evidence from a Mid-Sized Urban Setting\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eUnequal geographic distribution of oral healthcare services is a well-documented public health concern across both high-income and low- and middle-income countries. \u003csup\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e1\u003c/span\u003e\u003c/sup\u003e Although the global dental workforce has expanded substantially over recent decades, access to care remains uneven, particularly within rapidly urbanizing environments where service provision is largely market driven. \u003csup\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e2\u003c/span\u003e\u003c/sup\u003ePrivate dental clinics tend to cluster in economically vibrant urban cores, while peri-urban and peripheral populations often experience reduced accessibility. \u003csup\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eA major contributor to this imbalance is the limited availability of reliable, spatially explicit data on private healthcare facilities. \u003csup\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e4\u003c/span\u003e\u003c/sup\u003e In many countries, health facility registries are incomplete, fragmented, or poorly integrated into planning systems. This lack of visibility constrains the ability of policymakers to identify service gaps, assess equity, and design targeted interventions. \u003csup\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn India, systematic documentation of private dental clinics is further complicated by the absence of a uniformly enforced and streamlined national protocol for clinic registration. While regulatory mechanisms exist, registration processes are often perceived by practitioners as administratively complex, inspection-intensive, and punitive in nature. This regulatory environment has inadvertently discouraged voluntary registration, resulting in avoidance of formal enrollment and leaving a substantial proportion of dental clinics undocumented. Consequently, official records frequently underestimate service availability, particularly in the private sector. \u003csup\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e6\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn this context, technology-enabled alternatives to administrative registries are increasingly relevant. \u003csup\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e7\u003c/span\u003e\u003c/sup\u003e Mobile-based geospatial data collection platforms allow real-time documentation of healthcare facilities with minimal cost and infrastructure. These tools have been applied in epidemiological surveillance and community mapping, but their potential for systematically documenting private oral healthcare infrastructure remains underexplored. \u003csup\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe present study applies a mobile-based geospatial mapping approach to document the distribution of dental clinics within a representative mid-sized urban setting. Rather than focusing on a single city in isolation, the study aims to illustrate a methodological framework applicable to similar urban contexts in India and other rapidly urbanizing regions.\u003c/p\u003e"},{"header":"AIM AND OBJECTIVES","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAim:\u003c/h2\u003e \u003cp\u003eTo demonstrate the application of a mobile-based geospatial data collection platform for mapping dental clinics within a mid-sized urban environment.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eObjectives:\u003c/h3\u003e\n\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTo develop a real-time geospatial database of dental clinics\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo analyze spatial distribution patterns across urban functional zones\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo identify geographic inequities in access to dental services\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo generate evidence relevant for equity-oriented urban health planning\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cstrong\u003eStudy Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA cross-sectional, observational geospatial survey was conducted to map the distribution of dental clinics within a representative mid-sized urban environment. The study was designed to assess spatial clustering of private outpatient dental services and to identify potential service gaps across the urban continuum.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in Guntur, a mid-sized city Located in southern Indian state Andhra Pradesh, characterized by rapid urban growth, mixed land-use development, and a predominantly private outpatient healthcare delivery system. Cities of this scale constitute a substantial proportion of India’s urban landscape and function as transitional hubs between rural and metropolitan health systems.\u003c/p\u003e\n\u003cp\u003eGuntur exhibits urban features commonly observed in comparable cities, including a dense central commercial core, surrounding mixed residential commercial zones, and expanding peripheral areas with relatively limited healthcare infrastructure. In terms of urban form and health system organization, the city is comparable to other mid-sized Indian cities such as Vijayawada, Mysuru, and Salem, as well as internationally to rapidly urbanizing cities in low- and middle-income countries where private healthcare provision dominates.\u003csup\u003e 6\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe study area encompassed an approximate 10-km radius from the city center, covering central, intermediate, and peripheral zones. This spatial extent was selected to capture socioeconomic diversity and variation in healthcare accessibility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Duration\u003c/strong\u003e\u003cbr\u003eThe survey was conducted over one month, from April 17 to May 17.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection Tool\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were collected using an open-access, smartphone-based geospatial data collection platform that enables real-time data entry, automatic GPS tagging, photographic documentation, and cloud-based storage.\u003csup\u003e 7\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariables and Data Elements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor each dental clinic, the following publicly observable variables were recorded:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eClinic name\u003c/li\u003e\n\u003cli\u003eAddress\u003c/li\u003e\n\u003cli\u003eGeographic coordinates (latitude and longitude)\u003c/li\u003e\n\u003cli\u003ePhotograph of clinic frontage\u003c/li\u003e\n\u003cli\u003eTimestamp of data entry\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNo personal, clinical, or patient-level information was collected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection Procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA structured digital form was created within the platform to standardize data entry. Authors systematically traversed predefined urban zones to ensure coverage of central commercial areas, mixed-use neighborhoods, and peripheral localities. Data were entered in real time using smartphones (Android/iOS) with geographic coordinates captured automatically. Completed entries were reviewed for completeness and internal consistency, and the dataset was exported in CSV format for analysis.\u003c/p\u003e\n\u003cp\u003eTechnical Steps for Using Epicollect5:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eAccessing Epicollect5\u003c/strong\u003e\u003cbr\u003eEpicollect5 can be accessed through a web browser via http://five.epicollect.net or via the mobile application available on Android and Apple devices. To use the web version, users must sign in using a Google account.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCreating a New Project\u003c/strong\u003e\u003cbr\u003eAfter logging in, a new project is created by selecting the \u003cstrong\u003e“\u003c/strong\u003eCREATE PROJECT\u003cstrong\u003e”\u003c/strong\u003e option. During project creation, users can choose the project visibility as public or private. Once the project is created, the project URL is displayed at the top-left corner of the screen.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eForm Builder Interface\u003c/strong\u003e\u003cbr\u003eEpicollect5 provides a form builder to design questionnaires or databases.\u003cbr\u003eThe interface consists of three sections:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLeft panel: input question types\u003c/p\u003e\n\u003cp\u003eMiddle panel: form layout\u003c/p\u003e\n\u003cp\u003eRight panel: settings and properties\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eAdding and Customizing Questions\u003c/strong\u003e\u003cbr\u003eQuestions are added by dragging the required input type from the left panel to the center layout area. The question text and properties are customized using the properties tab in the settings panel. The entered question text appears exactly as displayed on users’ mobile or desktop screens.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eSharing and Access Control\u003c/strong\u003e\u003cbr\u003eAfter completing the form, the project can be shared with other users.\u003cbr\u003eTwo access roles are available:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eCreator\u003c/strong\u003e: full rights to edit the project and view all data\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCollector\u003c/strong\u003e: permission to view the form and upload their own data only.\u003c/p\u003e\n\u003cp\u003eDescriptive spatial analysis was conducted using the platform’s mapping and visualization functions. Clinic locations were plotted to examine clustering patterns across urban zones. Frequencies and proportions were calculated to summarize distribution and identify areas with comparatively limited-service availability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study involved non-intrusive observation of publicly visible healthcare facilities. As no human participants were involved and no identifiable personal data were collected, formal ethical approval and informed consent were not required.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 165 dental clinics were documented within the study area. Spatial analysis revealed pronounced clustering of clinics within central commercial and mixed residential\u0026ndash;commercial zones. In contrast, peripheral urban areas accounted for a relatively small proportion of facilities.\u003c/p\u003e \u003cp\u003eThe distribution followed a clear core periphery pattern, with high-density service availability in economically active zones and sparse coverage in outer urban areas. This pattern indicates inequities in geographic access to dental services within the city.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of dental clinics by urban functional zone\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban functional zone\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of clinics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral commercial zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed residential commercial zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredominantly residential zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral urban zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e165\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e100.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003esummarizes clinic distribution by urban functional zone. Central commercial zone showed notable concentration (45.4%) of clinics in the area followed by mixed residential commercial zone (30.3%). Peripheral areas showed the least number of clinics.\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of dental clinics by distance from city center\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from city center\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of clinics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUp to 2 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.1 to 5 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than 5 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e165\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e100.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpatial clustering pattern of dental clinics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpatial characteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinics present\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh density clusters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate density areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow density or sparse areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e165\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e100.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eThe spatial visualization generated showed high-density belts of clinics within inner commercial wards. Peripheral localities exhibited service deserts, highlighting potential gaps in accessibility.\u003c/span\u003e \u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study demonstrates pronounced spatial inequities in the distribution of dental clinics within a representative midsized urban setting, with clear clustering in central commercial and mixed use zones and limited availability in peripheral urban areas. Such patterns are consistent with long standing observations in oral public health literature, which indicate that dental services, particularly those delivered through private markets, tend to align more closely with economic activity than with population health need. \u003csup\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e1,3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eEarly global analyses by Petersen (2003) established that oral healthcare systems worldwide are strongly shaped by socioeconomic gradients, resulting in concentration of services within urban and economically advantaged areas. Although this work predates the widespread adoption of digital geospatial tools, it provides a conceptual foundation for understanding structural inequities in oral health service distribution. The spatial clustering observed in the present study reflects this enduring pattern. \u003csup\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSubsequent population level analyses have reinforced these concerns. Using data from the Global Burden of Disease study, Peres et al. (2019) demonstrated that access to oral healthcare remains uneven despite substantial global growth in the dental workforce. Their findings emphasized that increases in provider numbers do not necessarily translate into equitable access, particularly in rapidly urbanizing regions where private outpatient care predominates \u003csup\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. The core periphery distribution pattern observed in Guntur mirrors this global mismatch between workforce availability and service reach.\u003c/p\u003e \u003cp\u003eEvidence from spatially explicit studies in middle income countries further corroborates the present findings. In a Brazilian urban analysis, Antunes et al. (2002) reported disproportionate concentration of dental clinics in central urban districts, with peripheral and socioeconomically disadvantaged neighborhoods experiencing reduced access. \u003csup\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e16\u003c/span\u003e\u003c/sup\u003eSimilar patterns of spatial clustering of private dental services have been documented in urban studies from South Africa, China, and Southeast Asia during the 2010s, suggesting that this phenomenon persists across different time periods and geographic contexts.\u003c/p\u003e \u003cp\u003eMore recent research integrating geographic information systems into oral health has demonstrated that geospatial visualization provides insights that are not apparent from aggregate facility counts alone. Studies conducted over the past decade have shown that mapping clinic locations can reveal service deserts within urban environments, even where the overall number of facilities appears adequate. This aligns closely with the present study, in which peripheral urban zones were visibly underserved despite the presence of a substantial number of dental clinics within the city as a whole. \u003csup\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e12, 13\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe methodological contribution of this study lies in its application of a mobile-based open access platform for real-time documentation of private dental infrastructure. Earlier epidemiological investigations often relied on census data or official registries, which are known to underrepresent private healthcare facilities. \u003csup\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eBy contrast, mobile geospatial approaches, first described for public health surveillance by Aanensen et al. (2009) \u003csup\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e and later adapted for clinical research by Gohil et al. (2020), \u003csup\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e8\u003c/span\u003e\u003c/sup\u003e enable rapid low-cost generation of spatial health intelligence. The present study extends this methodological approach specifically to oral healthcare infrastructure in a mid-sized urban context.\u003c/p\u003e \u003cp\u003eImportantly, while data collection was limited to a single city, the observed spatial patterns are not unique to this setting. Similar configurations have been reported across different decades and regions, suggesting that the findings reflect a broader structural tendency inherent to private oral healthcare systems rather than a localized anomaly. This strengthens the external relevance of the study and supports its applicability to other mid-sized cities in India and to comparable urban environments in other low- and middle-income countries. \u003csup\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e12,14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eFrom a policy and planning perspective, these findings highlight the limitations of relying solely on formal registration systems for healthcare infrastructure assessment, particularly in contexts where regulatory processes discourage comprehensive enrollment. \u003csup\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e15\u003c/span\u003e\u003c/sup\u003e Independent geospatial mapping approaches can serve as a complementary strategy, enhancing visibility of private sector services and providing planners with actionable evidence to address geographic inequities in access to oral healthcare.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study demonstrates the utility of mobile-based geospatial mapping as a low-cost, scalable method for documenting private dental healthcare infrastructure. The findings reveal clear spatial inequities in service distribution, characterized by central clustering and peripheral under-coverage.\u003c/p\u003e \u003cp\u003eBy improving visibility of existing services, such approaches can support evidence-based urban health planning, guide decentralization strategies, and contribute to more equitable access to oral healthcare. The methodology is transferable and well-suited for comparative studies across mid-sized cities globally.\u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003eLIMITATIONS\u003c/h2\u003e \u003cp\u003eThe study may have underrepresented clinics without visible signage or those located in less accessible areas. The analysis was limited to spatial distribution and did not assess clinic capacity, service scope, or operating hours. Additionally, findings are based on a single-city survey and should be interpreted in the context of similar urban settings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003eRECOMMENDATIONS\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIntegration of geospatial mapping into routine health system planning\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIncentivization of dental service provision in underserved urban zones\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eExpansion of mapping approaches to other healthcare facilities\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLinkage of spatial data with population and oral health outcome indicators\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePetersen PE, The World Oral Health Report (2003) : Continuous improvement of oral health in the 21st century \u0026ndash; the approach of the WHO Global Oral Health Programme. Community Dent Oral Epidemiol. 2003;31(Suppl 1):3\u0026ndash;24\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeres MA, Macpherson LMD, Weyant RJ, Daly B, Venturelli R, Mathur MR et al (2019) Oral diseases: a global public health challenge. J Dent Res 98(4):373\u0026ndash;380\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAntunes JLF, Narvai PC, Nugent ZJ (2002) Measuring inequalities in the distribution of dental caries. Community Dent Oral Epidemiol 30(5):332\u0026ndash;341\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohapatra U, Nagarajappa R, Satyarup D (2023) Pandemic transformation of oral public health \u0026ndash; A review. J Global Oral Health 6:123\u0026ndash;126\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization (2018) Master Facility List Resource Package: guidance for countries. WHO, Geneva\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinistry of Health and Family Welfare, Government of India. The Clinical Establishments (Registration and Regulation) Act. New Delhi: MoHFW (2010) Available from:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://clinicalestablishments.mohfw.gov.in/index.php/en/act\u003c/span\u003e\u003cspan address=\"https://clinicalestablishments.mohfw.gov.in/index.php/en/act\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAanensen DM, Huntley DM, Feil EJ, Al-Own F, Spratt BG (2009) EpiCollect: linking smartphones to web applications for epidemiology, ecology and community data collection. PLoS ONE 4(9):e6968\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGohil R, Sharma S, Sachdeva S, Gupta S, Dhillon M (2020) EpiCollect5: a free, fully customizable mobile-based application for data collection in clinical research. J Postgrad Med Educ Res 54(4):248\u0026ndash;251\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGovernment Municipal Corporation, Guntur \u003cem\u003eProfile of Guntur city and urban characteristics\u003c/em\u003e. Andhra Pradesh: Government of Andhra Pradesh. Available from:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gmcguntur.ap.gov.in/gmcprofile\u003c/span\u003e\u003cspan address=\"https://gmcguntur.ap.gov.in/gmcprofile\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEpicollect5 \u003cem\u003eEpicollect5 documentation: mobile and web-based geospatial data collection platform\u003c/em\u003e. Available \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003efromhttps://docs.epicollect.net/\u003c/span\u003e\u003cspan address=\"http://fromhttps://docs.epicollect.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagarajappa S, Vyas S (2021) Smartphone assisted oral health data recording - an android based software application development. Med Pharm Rep 94(3):333\u0026ndash;340\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlhagi AA, Ferine TS, Tanwir S, Srivastava R, Galiampalayam S (2021) Rural-urban disparities in patient satisfaction with oral health care: A provincial survey. BMC Oral Health 21(1):261\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAthavale AV, Zodpey SP Public Health Informatics in India: The Potential and the Challenges. Indian J Public Health 2010 July-Sep ; 54(3):p 131\u0026ndash;136\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDani N, Sood S, Prakash Nupur \u0026amp; Mbarika, Victor \u0026amp; Agrawal, Rajeev. GIS and Telemedicine: eHealth tools for Public Healthcare. eGov.2006 Nov\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMathur MR, Williams DM, Reddy KS, Watt RG (2015) Universal health coverage: a unique policy opportunity for oral health. J Dent Res 94(3 Suppl):3S\u0026ndash;5S\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCeleste RK, Moura FR, Santos CP, Tovo MF (2014) Analysis of outpatient care in Brazilian municipalities with and without specialized dental clinics. Cad Saude Publica 30(3):511\u0026ndash;521\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Sibar Institute of Dental Sciences","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dental clinic, geospatial mapping, Epicollect5, mobile data collection, public health dentistry","lastPublishedDoi":"10.21203/rs.3.rs-8846986/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8846986/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eGeographic inequities in access to oral healthcare services remain a persistent challenge across health systems worldwide. In many settings where private outpatient care predominates, the absence of comprehensive, spatially explicit data on healthcare facility distribution limits evidence-based planning and equitable resource allocation.\u003c/p\u003e\u003ch2\u003eAim:\u003c/h2\u003e \u003cp\u003eTo demonstrate the feasibility of using a mobile-based, open-access platform to develop a geospatial inventory of dental clinics and to examine spatial distribution patterns within a representative mid-sized urban setting.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eA cross-sectional geospatial survey was conducted over one month in an urban city. Real-time data on dental clinic location and attributes were collected using a smartphone-based application with automatic GPS tagging. Spatial visualization and descriptive analysis were used to identify clustering patterns and service gaps.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eA total of 165 dental clinics were mapped. Clinics were disproportionately concentrated in central commercial and mixed-use zones, while peripheral urban areas accounted for a small proportion of facilities. The spatial pattern revealed clear inequities in service availability across the urban continuum.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eMobile-based geospatial mapping provides a scalable, low-cost approach to documenting private dental healthcare infrastructure. Such methods can enhance health system visibility, support equity-oriented planning, and inform decentralization strategies in comparable urban settings globally.\u003c/p\u003e","manuscriptTitle":"Mapping Geographic Inequities of Dental Clinics Using a Mobile-Based Geospatial Platform: Evidence from a Mid-Sized Urban Setting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-12 09:24:54","doi":"10.21203/rs.3.rs-8846986/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e6567834-e99c-4d29-8558-5ea70ca2dba4","owner":[],"postedDate":"February 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-12T09:24:54+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-12 09:24:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8846986","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8846986","identity":"rs-8846986","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}