International Journal of Geoinformatics

Causal Factors Influencing Liver Fluke Disease Prevention Behaviors in Health Region 10: A Structural Equation Modeling Study with Qualitative Behavioral Insights

A. Wongmanee — 2026-07-02

Despite control programs, liver fluke (Opisthorchis viverrini) prevalence remains at 6.62% in Health Region 10, northeastern Thailand. This study investigated the causal factors influencing prevention behaviors among people with a history of infection using Structural Equation Modeling (SEM) and supplementary qualitative interviews. A cross-sectional survey was conducted among 630 individuals across five provinces (Ubon Ratchathani, Sisaket, Yasothon, Amnat Charoen, and Mukdahan), alongside in-depth interviews with 50 participants.SEM analysis revealed that communication was the strongest predictor of prevention behavior (β = 0.705, p < 0.001), while knowledge had a minimal direct effect (β = 0.022$). The model explained 49.23% of the variance in behavior (R² = 0.492). Economic factors exerted a moderate negative effect (β = -0.139). Qualitatively, a distinct knowledge–behavior gap emerged: 60% of interviewees continued to consume raw fish primarily driven by taste preference (83%) yet 50% reported that witnessing local illness or death was a more powerful behavioral motivator than abstract health knowledge. Liver fluke prevention in Health Region 10 is driven by local communication infrastructure and visible community consequences rather than knowledge expansion alone. Public health strategies should transition from conventional, knowledge-focused education to spatially targeted communication systems and community-level risk messaging that addresses socio-cultural and structural barrier.

Integrating Geographic Information Systems into a Self-Efficacy–Based Health Promotion Model for Foreign Retirees in Elderly Care Centers in Bangkok Metropolitan Area, Thailand

K. Aungvitulsatit — 2026-07-02

Thailand’s rapid transition to an aged society has coincided with increasing numbers of foreign retirees choosing long-stay residence in Bangkok, including within elderly care centers. However, existing care services have often prioritized dependent-care models, leaving a service gap for foreign retirees in Independent Living and Assisted Living categories who require culturally responsive, autonomy-supporting health promotion. Building solely on the attached sequential mixed-methods manuscript, this article consolidates the development and implementation evidence of a self-efficacy–based health promotion model and presents a GIS-enabled integration architecture to strengthen metropolitan-scale planning and delivery. In Phase 1, expert interviews and focus group discussions informed the model structure and activities, resulting in five health promotion dimensions: Health Education, Health Prevention, Health Protection, Spiritual Improvement, and Nutrition Therapy. In Phase 2, a one-group pretest–posttest implementation among foreign retirees (n = 33) residing in elderly care centers in Bangkok demonstrated statistically significant improvements (p < .01) in health promotion knowledge, self-care health behaviors, and physiological indicators, including blood pressure, body mass index, blood sugar, and muscle mass. To enhance scalability without altering the original intervention logic or claims, GIS is incorporated as a cross-cutting operational layer in three strategic applications: (1) risk mapping to visualize spatial patterns relevant to health risks and service access; (2) facility suitability mapping to support evidence-informed site selection for new or expanded elderly care centers using spatial criteria aligned with holistic care; and (3) predictive GIS modeling to forecast future service demand based on spatial-demographic scenarios. Importantly, the geospatial analysis and map outputs are positioned as core decision-support tools rather than illustrative figures. These GIS outputs help identify priority service areas, guide evidence-informed location planning for new or expanded elderly care centers, and support resource allocation according to spatial patterns of access, risk, and projected demand. This integrated approach positions the proven five-dimension model for broader deployment across Bangkok metropolitan areas, supporting Thailand’s direction toward becoming a regional medical and retirement hub.

Predicting Loneliness in Thailand: A Nationwide Cross-Sectional Analysis of Health, Socio-Demographic, and Geographical Factors

C. Meenorngwar — 2026-07-02

This nationwide cross-sectional study investigates the health, socio-demographic, and geographical determinants of loneliness among the adult population in Thailand. Data were collected from a cohort of 714 Thai adults between August 2024 and November 2025 using the "TiS-MSU" telehealth platform and a network of community health volunteers. Loneliness was measured using the UCLA Loneliness Scale. Statistical analysis included descriptive statistics to profile the sample, bivariate testing to explore associations, and multiple linear regression to identify significant predictors. Descriptive analysis indicated that 65.41% of participants reported feeling no loneliness. However, inferential analysis revealed significant demographic disparities: males and LGBTQ+ individuals reported higher levels of loneliness compared to females. Multiple linear regression (R² = 0.27, p < .001) identified age as a significant negative predictor, suggesting that younger adults are more susceptible to loneliness. Employment status also emerged as a critical factor (p = .01), with both employees and the unemployed reporting higher levels of loneliness than civil servants. Notably, married participants reported higher loneliness scores than those who were single, divorced, or widowed (p < .01). Geographically, higher levels of loneliness were concentrated in Bangkok and its surrounding metropolitan areas. In contrast, the lowest levels were observed in southern Thailand. These findings highlight the need for targeted mental health interventions and psychological support frameworks specifically designed for younger populations, employed individuals, and residents of high-density urban areas. The results emphasize the complex interplay between socio-demographic factors and regional environments in shaping the psychological well-being of the Thai population.

A Geoinformatics-Based Competency Framework for Strengthening District Health System Management: Integrating GIS-Supported Decision Making in Primary Care Networks of Krabi Province, Thailand

B. Khaonuan — 2026-07-02

service accessibility, workforce distribution, and resource allocation. However, geoinformatics has often been applied as a technical mapping tool, with limited integration into governance structures and managerial competency development. This study proposes a Geoinformatics-Based Competency Framework to institutionalize GIS-supported spatial analytics within district-level health system management. A mixed-methods research design was employed in eight districts of Krabi Province, Southern Thailand. Quantitative data were collected from primary care managers using a quasi-experimental pre–post design, while qualitative data were obtained through participatory workshops and in-depth discussions. Spatial datasets, including population distribution, health facility locations, and health workforce deployment, were integrated into a GIS environment to support spatial overlay analysis and network-based accessibility assessment. Participatory learning activities were implemented to enhance geospatial literacy and spatial decision-making capability among district managers. The results demonstrate statistically significant improvements in district health management performance, particularly in planning, directing, and leading functions (p < 0.05). GIS-based spatial analysis revealed localized service accessibility gaps and resource mismatches that were not identifiable through conventional management information systems. Qualitative findings indicate that participatory engagement with spatial outputs strengthened managerial capacity to interpret spatial evidence, justify prioritization decisions, and support equity-oriented governance. This study advances applied geoinformatics by embedding spatial analytics within managerial competency development and routine governance processes. The proposed framework provides a transferable model for integrating geoinformatics into decentralized health systems, positioning GIS as a decision-support infrastructure that enhances spatially informed governance rather than a standalone technical application

Geospatial Inequities and Community-Based Behavioral Interventions for Type 2 Diabetes Control in Health Region 10, Thailand

Ch Pokumnird — 2026-07-02

District health system strengthening increasingly requires spatially informed decision making to address inequities in service accessibility, workforce distribution, and resource allocation. However, geoinformatics has often been applied as a technical mapping tool, with limited integration into governance structures and managerial competency development. This study proposes a Geoinformatics-Based Competency Framework to institutionalize GIS-supported spatial analytics within district-level health system management. A mixed-methods research design was employed in eight districts of Krabi Province, Southern Thailand. Quantitative data were collected from primary care managers using a quasi-experimental pre–post design, while qualitative data were obtained through participatory workshops and in-depth discussions. Spatial datasets, including population distribution, health facility locations, and health workforce deployment, were integrated into a GIS environment to support spatial overlay analysis and network-based accessibility assessment. Participatory learning activities were implemented to enhance geospatial literacy and spatial decision-making capability among district managers. The results demonstrate statistically significant improvements in district health management performance, particularly in planning, directing, and leading functions (p < 0.05). GIS-based spatial analysis revealed localized service accessibility gaps and resource mismatches that were not identifiable through conventional management information systems. Qualitative findings indicate that participatory engagement with spatial outputs strengthened managerial capacity to interpret spatial evidence, justify prioritization decisions, and support equity-oriented governance. This study advances applied geoinformatics by embedding spatial analytics within managerial competency development and routine governance processes. The proposed framework provides a transferable model for integrating geoinformatics into decentralized health systems, positioning GIS as a decision-support infrastructure that enhances spatially informed governance rather than a standalone technical application.

Digital Spatial Communication for Wellness Tourism: A GIS-Based Model for Communicating the Lifestyle Identity of Bang Kobua Community and Promoting Tourists’ Stress Relief in Krapao Moo, Phra Pradaeng District, Samut Prakan Province, Thailand

J. Sudsawart — 2026-07-02

This study aimed to: (1) identify community-based wellness tourism routes in Bang Kobua Subdistrict, Phra Pradaeng District, Samut Prakan Province, Thailand; (2) develop a geographic information system (GIS) to support wellness tourism promotion; and (3) evaluate the effectiveness of the developed system. The study adopted a developmental research design grounded in the Systems Development Life Cycle (SDLC) framework. Research instruments included the GIS-based wellness tourism platform, a system quality assessment form, a user satisfaction questionnaire, and a technology acceptance questionnaire based on the Technology Acceptance Model (TAM). The sample comprised five experts in information technology and computer science and 411 tourists selected through simple random sampling, of whom 338 were male and 73 were female. Data were analyzed using mean and standard deviation. The findings revealed four community wellness tourism attractions and two tourism routes, namely land-based and water-based routes. The developed GIS incorporated five core functions: wellness tourism destination database management, route management, destination search, destination information display, and route visualization via Google Maps. The evaluation results demonstrated that the quality of the information system the highest level (x̄ = 4.56), the effectiveness of users with the information system was at a high level (x̄ = 4.42) and the analysis of user acceptance of the health tourism information system was also at a high level (x̄ = 4.33). These findings indicate that GIS can function as an effective digital spatial platform for organizing, communicating, and promoting community wellness tourism. The study contributes practical evidence on how geospatial technologies can strengthen destination visibility, improve access to location-based tourism information, and support digitally mediated wellness experiences in community-based tourism settings.

E-Cigarette Use and Predictors of Vaping Behavior among Undergraduate Students in Thailand’s Health Region 10

S. Sihawong — 2026-07-02

The increasing prevalence of e-cigarette use among youth has become a significant public health concern worldwide and in Thailand. This study aimed to determine the prevalence of e-cigarette use and identify predictors of vaping behavior among undergraduate students in Thailand’s Health Region 10. A cross-sectional study was conducted from August to September 2025 among 555 undergraduate students selected through a cluster sampling method. Data were collected using an anonymous, self-administered multiple-choice questionnaire via Google Forms. Descriptive statistics and multiple logistic regression analysis were employed to examine factors predicted with e-cigarette use. Adjusted odds ratios (AOR) and 95% confidence intervals (CI) were reported. The prevalence of e-cigarette use among undergraduate students was 12.25%. Predictors of e-cigarette use included social factors (Adjusted OR = 5.057, 95% CI = 2.48–10.32, p < 0.001), environmental factors (Adjusted OR = 2.044, 95% CI = 1.04–4.01, p = 0.037), and risky health behaviors (Adjusted OR = 5.735, 95% CI = 1.56–21.02, p = 0.008) were significant predictors of e-cigarette use. In addition, students with higher GPA had a lower likelihood of using e-cigarettes compared to those with moderate GPA (Adjusted OR = 0.366, 95% CI = 0.16–0.81, p = 0.013). E-cigarette use among undergraduate students in Health Region 10 was significant. Implementing smoke-free environmental strategies, along with interventions that promote positive health behaviors, will be crucial in reducing and preventing e-cigarette use among this group.

Factors Associated with Human Papillomavirus Infection among Women in Health Region 10, Thailand: Evidence from HPV DNA Testing and GIS-Linked Spatial Decision Support

W. Chanpraw — 2026-07-02

To determine factors associated with human papillomavirus (HPV) infection among women in Health Region 10, Thailand, and to integrate a GIS-linked spatial decision-support map for cervical cancer prevention. A cross-sectional analytical study used HPV DNA screening data collected between February and September 2023 among 2,086 women aged 20–59 years undergoing cervical cancer screening under the PP Fee Schedule program. Demographic, reproductive, behavioral, and laboratory data were retrieved from structured screening records and laboratory databases. Multiple logistic regression was applied to identify independent factors associated with HPV infection. HPV DNA screening results were aggregated at the provincial level and linked with administrative boundaries to produce a descriptive spatial decision-support map combining HPV positivity, screening volume, and planning signals. The prevalence of HPV infection was 15.1% (314/2,086). High-risk non-16/18 HPV genotypes predominated (11.4%), while HPV-16 and HPV-18 accounted for 2.6% and 1.1%, respectively. Women younger than 30 years had significantly higher odds of HPV infection (AOR = 2.67, 95% CI: 1.13–6.31), whereas a higher number of pregnancies was associated with lower odds of HPV positivity (AOR = 0.87, 95% CI: 0.78–0.97). Province-level mapping showed the highest positivity in Mukdahan (19.2%) and the largest screening volume in Ubon Ratchathani (n = 1,103). HPV infection was relatively common among women in Health Region 10. GIS-linked mapping can strengthen targeted screening, surveillance, and referral microplanning while avoiding unsupported cluster inference when only provincial aggregated data are available.

GIS-Linked Spatial Contextualization of Depression-Related Service Needs among Older Adults in Lat Yai Subdistrict, Samut Songkhram Province, Thailand

S. Chusuton — 2026-07-02

Depression among older adults is shaped by individual, household, economic, and community-level contexts. This study restructured a cross-sectional survey of older adults in Lat Yai Subdistrict, Mueang District, Samut Songkhram Province, Thailand, into a geoinformatics-oriented manuscript by linking survey findings with official aggregate village-level spatial data. The survey included 380 older adults selected from a population of 3,428. Depression was assessed using the Thai Geriatric Depression Scale, while demographic, economic, living-arrangement, morbidity, and family relationship variables were collected through structured questionnaires. Associations between categorical variables and depression were examined using chi-square tests, and the relationship between family relationship score and depression was analysed using Pearson's correlation. To strengthen the GIS contribution without overinterpreting the original survey, village-level older-adult service-register data from the official 3 Doctor system were linked with village point coordinates in WGS84. The GIS component was designed as a descriptive spatial-context map showing the distribution of registered older-adult functional-status records and homebound or bedridden records; it did not estimate village-level depression prevalence or depression risk. Most respondents had no depression (85.79%), while 8.16%, 5.26%, and 0.79% had mild, moderate, and severe depression, respectively. Age, income, marital status, income source, and income adequacy were significantly associated with depression. Family relationship had a significant negative correlation with depression. The GIS output supports public-health planning by indicating where screening outreach and home-visit integration may be operationally important, while avoiding ecological inference from non-geocoded depression outcomes.

SENTINEL-Dengue ASEAN-11: An AI-Powered Climate–Geospatial Intelligence System for Dengue Early Warning and Surveillance Prioritization Across Southeast Asia

K. Wongpituk — 2026-07-02

Dengue remains a major climate-sensitive vector-borne disease in tropical and subtropical regions, particularly across Southeast Asia where rainfall variability, temperature suitability, vegetation dynamics, urbanization, surface water, and population exposure interact to shape transmission risk. Conventional dengue surveillance is often reactive and administratively aggregated, limiting its usefulness for early warning, targeted vector control, and regional preparedness. This study develops SENTINEL-Dengue ASEAN-11, an artificial intelligence and climate–geospatial intelligence framework for dengue early warning and surveillance prioritization across Brunei Darussalam, Cambodia, Indonesia, Lao PDR, Malaysia, Myanmar, the Philippines, Singapore, Thailand, Timor-Leste, and Viet Nam. The framework integrates publicly available dengue case-count data, climate reanalysis, remote-sensing vegetation indices, gridded population exposure, surface-water indicators, administrative boundaries, explainable artificial intelligence, spatial block cross-validation, temporal validation, and uncertainty mapping. A harmonized Admin-1 month geospatial panel dataset is proposed for 2010–2023. Dengue alert outcomes are defined using within-area historical baselines to reduce misleading country-level comparison. Statistical models, Bayesian spatiotemporal smoothing, Random Forest, XGBoost, and explainable AI are used to estimate dengue alert probability and identify influential climate–environmental predictors. The final output is an ASEAN-11 surveillance-priority framework that distinguishes high-risk/high-confidence, high-risk/high-uncertainty, moderate-risk, low-risk, and data-insufficient areas. SENTINEL-Dengue ASEAN-11 advances dengue geoinformatics beyond retrospective hotspot mapping toward interpretable, scalable, and policy-relevant epidemic intelligence for climate-sensitive public-health preparedness.

Spatial Autocorrelation and High-Risk Area Identification of Food Poisoning in Thailand, 2003–2022

O. Timpong — 2026-07-02

Food poisoning represents a persistent public health burden in Thailand, yet provincial-level spatial clustering patterns have not been comprehensively characterized over extended time series. This retrospective analytical study utilized foodborne illness incidence data from the national disease surveillance system (Report 506) of the Department of Disease Control, Ministry of Public Health, covering all 77 provinces for the period 2003–2022
(n = 2,329,463 reported cases). Provincial incidence rates (per 100,000 population) were computed annually and linked to administrative boundary polygon data. Spatial autocorrelation was assessed using the Global Moran's I statistic, and spatial cluster analysis was performed using the Local Indicators of Spatial Association (LISA) with Queen contiguity first-order spatial weights in GeoDa (version 1.14.0). Global Moran's I ranged from 0.317 to 0.522 across all study years (all p < 0.05), indicating statistically significant positive spatial autocorrelation. High–High (H–H) clusters were consistently identified in the northeastern and northern regions, with the northeastern region demonstrating 9–14 provinces per year classified. Low–Low clusters predominated in the southern region throughout the study period. The spatial clustering patterns persisted across five-year sub-periods, suggesting stable geographically determined risk factors. Findings indicate that food poisoning in Thailand exhibits non-random, geographically structured distribution attributable to inter-related socioeconomic, dietary, and food-market environmental factors. These results provide evidence for spatially targeted surveillance and intervention strategies.