Reducing the disparities in healthcare access is one of the important goals in healthcare services and is significant for national health. However, measuring the complexity of access in truly underserved areas is the critical step in designing and implementing healthcare policy to improve those services and to provide additional support. Even though there are methods and tools for modeling healthcare accessibility, the context of data is challenging to interpret at the local level for targeted program implementation due to its complexity. Therefore, the purpose of this study is to develop a concise and context-specific methodology for assessing disparities for a remote province in Thailand to assist in the development and expansion of the efficient use of additional mobile health clinics. We applied the geographic information system (GIS) methodology with the travel time-based approach to visualize and analyze the concealed information of spatial data in the finer analysis resolution of the study area, which was located in the border region of the country, Ubon Ratchathani, to identify the regional differences in healthcare allocation. Our results highlight the significantly inadequate level of accessibility to healthcare services in the regions. We found that over 253,000 of the population lived more than half an hour away from a hospital. Moreover, the relationships of the vulnerable residents and underserved regions across the province are underlined in the study and substantially discussed in terms of expansion of mobile health delivery to embrace the barrier of travel duration to reach healthcare facilities. Accordingly, this research study addresses regional disparities and provides valuable references for governmental authorities and health planners in healthcare strategy design and intervention to minimize the inequalities in healthcare services.

Evacuation shelters are the most important means for safeguarding people in hazardous areas and situations, and thus minimizing losses, particularly those due to a disaster. Therefore, evacuation shelter assignment and evacuation planning are some of the critical factors for reducing vulnerability and increasing resilience in disaster risk reduction. However, an imbalance of shelter distribution and spatial heterogeneity of a population are the critical issues limiting the accessibility of evacuation shelters in real situations. In this study, we propose a methodology for spatial assessment to reduce vulnerability and evaluate the spatial distribution of both shelter demand and resources, considering spatial accessibility. The method was applied to the case study of Mabi, in the context of a disaster caused by the 2018 flooding. We applied this approach to evaluate the area and identified the vulnerability of the evacuation shelters and the residents. The proposed method revealed that 54.55% of the designated evacuation shelters and 59% of the total population were physically vulnerable to the flood. The results highlight, using GIS maps, that the total shelter capacity was significantly decreased to 43.86%. The outcome assessment addressed specific vulnerable shelters and the imbalance between the demand for and resources of each shelter. Accordingly, this study provides practical information and a valuable reference for supporting local governments and stakeholders to improve future disaster planning, prevention, and preparedness.

Globally, rapid economic growth has contributed to an overall increase in the incidence of childhood obesity. Although the prevalence of obesity has been well recognized, the disparities related to a region’s socioeconomic environment in terms of the incidence of obesity are still less understood. Therefore, the purpose of this study was to examine the spatial pattern of childhood obesity and identify the potential associations between childhood obesity and socioeconomic environment in the northeastern region of Thailand, Isaan. Using nationally collected obesity data from children aged 0–5 years in 2019, we employed a geographic information system (GIS) to perform obesity cluster analysis at the smaller regional level, investigating a total of 322 districts in study area. Global and local statistical approaches were applied to calculate spatial associations between the socioeconomic status of neighborhoods and childhood obesity. The study revealed that 12.42% of the total area showed significant clusters at the district level, with high values observed in the western and northeastern areas. The results of the spatial statistical model revealed that childhood obesity was significantly positively associated with areas exhibiting high levels of socioeconomic environment factors. Identifying the associated factors and highlighting geographic regions with significant spatial clusters is a powerful approach towards understanding the role of location and expanding the knowledge on the factors contributing to childhood obesity. Our findings, as a first step, offer valuable references that could support policy-makers and local authorities in enhancing policy development with the aim of reducing childhood obesity and improving public health.

Property crime is a pressing issue in maintaining social order and urban sustainability, particularly in regions marked by pronounced socioeconomic disparity. While the link between socioeconomic stress and crime is well established, regional variations in Thailand have not been fully examined. Therefore, the purpose of this research was to examine spatial patterns of property crime and identify the potential associations between property crime and socioeconomic environment across Thailand. Using nationally compiled property-crime data from official sources across all provinces of Thailand, we employed geographic information system (GIS) tools to conduct a spatial cluster analysis at the sub-national level across 76 provinces. Both global and local statistical techniques were applied to identify spatial associations between property-crime rates and neighborhood-level socioeconomic conditions. The results revealed that property-crime clusters are primarily concentrated in the south, while low-crime areas dominate parts of the north and northeast regions. To analyze the spatial dynamics of property crime, we used geospatial statistical models to investigate the influence of socioeconomic variables across provinces. We found that property-crime rates were significantly associated with monthly income, areas experiencing high levels of household debt, migrant populations, working-age populations, an uneducated labor force, and population density. Identifying associated factors and mapping geographic regions with significant spatial clusters is an effective approach for determining where issues concentrate and for deepening understanding of the underlying patterns and drivers of property crime. This study offers actionable insights for enhancing safety, resilience, and urban sustainability in Thailand’s diverse regional contexts by highlighting geographies of vulnerability.

The medical laboratory plays a crucial role within a hospital setting and is responsible for the examination and analysis of patient specimens to accurately diagnose various ailments. The burden on medical laboratory personnel has significantly increased, particularly in the context of the ongoing global COVID-19 pandemic. Worldwide, the implementation of comprehensive and extended COVID-19 screening programs has placed a significant strain on healthcare professionals. This burden has led to exhaustion among medical employees, limiting their ability to effectively track laboratory resources, such as medical equipment and consumables. Therefore, this study proposed an artificial intelligence (AI)-based solution that contributes to a more efficient and less labor-intensive workflow for medical workers in laboratory settings. With the ultimate goal to reduce the burden on healthcare providers by streamlining the process of monitoring and managing these resources, the objective of this study is to design and develop an AI-based system for consumables tracking in medical laboratories. In this work, the effectiveness of two object detection models, namely, YOLOv5x6 and YOLOv8l, for the administration of consumables in medical laboratories was evaluated and analyzed. A total of 570 photographs were used to create the dataset, capturing the objects in a variety of settings. The findings indicate that both detection models demonstrate a notable capability to achieve a high mean average precision. This underscores the effectiveness of computer vision in the context of consumable goods detection scenarios and provides a reference for the application of real-time detection models in tracking systems within medical laboratories.