Pesticide-related mental health issues in Thailand, an upper-middle-income country, are not well known. This study aimed to investigate the association between the history of occupational exposure to pesticides and the mental health of Thai farmers. A cross-sectional study was carried out in the areas around Chiang Mai, a large city in Northern Thailand, between June 2020 and January 2021. A total of 6974 farmers from six districts were interviewed to determine whether they regularly experienced symptoms related to mental health by the Self-Reporting Questionnaire (SRQ-20) as well as their lifetime history of agricultural pesticide exposure from 31 active ingredients and five functional categories: insecticides, herbicides, fungicides, rodenticides, and molluscicides. The cut-off of 6 was used to evaluate probable mental disorder. Most of the farmers under investigation were men (53.8%), with a mean age of 55.2 (11.7) years, and were involved mainly in the planting of rice, fruit, and vegetables. About 86.7% reported having used pesticides on their crops at some point in their lives—mostly glyphosate, paraquat, 2,4-D, methomyl, and carbofuran. All functional groups, as well as pesticide classes like organochlorines, organophosphates, and carbamates, were significantly associated with a higher risk of probable mental disorder based on exposure duration, frequency, personal protective equipment usage, and hygienic behavior. In a model with multiple pesticides, there was an association between mental disorder and exposure to endosulfan (AOR = 2.27, 95%CI = 1.26–4.08) and methyl parathion (AOR = 2.26, 95%CI = 1.26–4.06). Having previously reported pesticide poisoning symptoms was related to mental disorder (AOR = 7.97, 95%CI = 5.16–12.31), the findings provided evidence of pesticide exposure posing a risk to farmers’ mental health, particularly long-term and high-intensity exposure.

The quality of life (QoL) of elderly diabetic patients may be affected by caregiver factors, but this has received little empirical support. The objective of this cross-sectional study is to determine the influence of family caregivers’ diabetes knowledge and behavior on the QoL among elderly patients with diabetes mellitus (DM). The participants included 354 elderly patients with Type 2 DM and their family caregivers, who were recruited through multistage sampling from five districts in Chiang Mai, Thailand. Face-to-face interviews with DM patients were conducted using the Thai Simplified Diabetes Knowledge Scale (T-SDKS), the Thai version of the Diabetes Self-Management Questionnaire (DSMQ) for self-care behaviors, and the Thai version of the World Health Organization Quality of Life for Older People (WHOQOL-OLD) scale. For caregivers, their diabetes knowledge was measured by T-SDKS and patient-care or supportive behaviors were developed based on DSMQ. The results showed a moderate level of QoL among elderly diabetic patients. According to simple linear regression analysis, the QoL score among elderly DM patients was positively associated with their diabetes knowledge (B = 1.25), self-care behaviors (B = 3.00), caregivers’ knowledge (B = 0.97), and supportive behavior from caregivers (B = 2.92) at a significance level of p < 0.01. In the multivariable model, patients’ self-care behaviors (B = 1.58, p = 0.001), caregivers’ knowledge (B = 0.58, p = 0.001), and patient-care behaviors (B = 1.38, p = 0.004) were significantly associated with QoL among DM patients when controlling for patient factors, including age, body mass index (BMI), education, and living arrangements, which accounted for 27.0% of the variance. This indicates that caregivers’ adequate diabetes knowledge and appropriate supportive behaviors may impact the QoL of elderly diabetic patients. Health care providers should prioritize motivating and empowering family caregivers to pay more attention to the patient for the success goal.

This study aims to identify and evaluate a robust and replicable public health predictive model that can be applied to the COVID-19 time-series dataset, and to compare the model performance after performing the 7-day, 14-day, and 28-day forecast interval. The seasonal autoregressive integrated moving average (SARIMA) model was developed and validated using a Thailand COVID-19 open dataset from 1 December 2021 to 30 April 2022, during the Omicron variant outbreak. The SARIMA model with a non-statistically significant p-value of the Ljung–Box test, the lowest AIC, and the lowest RMSE was selected from the top five candidates for model validation. The selected models were validated using the 7-day, 14-day, and 28-day forward-chaining cross validation method. The model performance matrix for each forecast interval was evaluated and compared. The case fatality rate and mortality rate of the COVID-19 Omicron variant were estimated from the best performance model. The study points out the importance of different time interval forecasting that affects the model performance.

Objectives: Fine particulate matter pollution has emerged as a significant life-threatening issue in Thailand. Recognizing the importance of environmental health literacy (EHL) in disease prevention is crucial for protecting public health. This study investigated EHL levels and aimed to identify associated factors among village health volunteers (VHVs) in the upper northern region of Thailand.Methods: A cross-sectional study was conducted to collect data from 710 VHVs using the EHL assessment tool developed by the Department of Health, Thailand.Results: The overall EHL score was moderate (mean, 3.28 out of a possible 5.0), with the highest and lowest domain-specific mean score for the ability to make decisions (3.52) and the ability to access (3.03). Multiple linear regression revealed that the factors associated with EHL score were area of residence (urban areas in Chiang Mai: B=0.254; urban areas in Lampang: B=0.274; and rural areas in Lampang: B=0.250 compared to rural areas in Chiang Mai), higher education levels (senior high school: B=0.212; diploma/high vocational certificate: B=0.350; bachelor’s degree or above: B=0.528 compared to elementary school or lower), having annual health checkups compared to not having annual health check-ups (B=0.142), monthly family income (B=0.004), and individuals frequently facing air pollution issues around their residence (B=0.199) compared to those who reported no such issues.Conclusions: The VHVs exhibited moderate EHL associated with residence area, education, health check-ups, family income, and residential air pollution. Considering these factors is vital for enhancing VHVs’ EHL through strategic interventions.

In Thailand, emergency illnesses are life-threatening conditions that constitute serious health problems and quick access to definitive care can improve the survival rate of the elderly dramatically. Currently, the pre-hospital emergency medical services have limitations which prevent the treatment from getting to the accident site on time. In this research, we proposed the A-SA SOS application, a mobile-and IoT-based pre-hospital emergency service for the elderly. The system helps the elderly to send the request to the nearest village health volunteers via a mobile application and smart device. After reaching the elderly, the village health volunteers help carry out basic life support to increase the survival rate before sending the patients directly to the Emergency Management System (EMS) agency. To evaluate the system, we tested it for three months in the Sub-district of Suthep in Chiang Mai city. Finally, the incident report showed that the average time to reach the scene (4.91±0.56) to help elderly patients was less than the standard criteria of an average 3 minutes.

This report aims to provide practical advice about the implementation of a public health monitoring system using both geographic information system technology and mobile health, a term used for healthcare delivery via mobile devices. application amongst household residents and community stakeholders in the limited resource community. A public health monitoring system was implemented in a semi-rural district in Thailand. The challenges encountered during implementation were documented qualitatively in a series of monthly focus group discussions, several community hearings, and many targeted interviews. In addition, lessons learned from the expansion of the program to 75 other districts throughout Thailand were also considered. All challenges proved solvable yielding several key pieces of advice for future project implementation teams. Specifically, communication between team members, anticipating technological challenges, and involvement of community members are critical. The problems encountered in our project were mainly related to the capabilities of the data collectors and technical issues of mobile devices, internet coverage, and the GIS application itself. During the implementation phase, progressive changes needed to be made to the system promptly, in parallel with community team building in order to get the highest public health impact.