Evaluation of the PM2.5 Health Risk Surveillance System in Nong Khai Province, Thailand: A Pilot in Health Region 8

Abstract

Fine particulate matter (PM2.5) pollution poses a global public health concern. In Thailand, Nong Khai Province, located in the 8th Regional Health, has frequently recorded 24-hour average PM2.5 levels exceeding national standards, often reaching levels that adversely affect health. Major sources of PM2.5 include open burning and transboundary haze, impacting a large segment of the population. This study aimed to evaluate the disease surveillance system for PM2.5 exposure-related diseases in Nong Khai province as a pilot area. A mixed-method research, using quantitative and qualitative approaches, was conducted in 2024, assessing both quantity and quality attributes of the disease surveillance system. The qualitative component included interviews with both executive and operational officers from relevant government agencies in the area between 21 February and 28 June 2024, capturing insights from both leadership and frontline personnel. The quantitative component utilized stratified sampling to collect data from patient medical records, which were then analyzed using descriptive statistics, including frequencies and percentages. Findings from the qualitative study revealed that all 13 interviewed officials (100%) acknowledged the importance of the surveillance system. They found it easy to report and noted its usefulness in disease prevention and control. However, issues such as reporting delays and ambiguities in disease coding guidelines were identified. The system was adaptable, with minimal disruption to routine operations. In the quantitative assessment, 345 medical records were reviewed, with 181 cases matched case definitions. The surveillance system demonstrated 100% sensitivity and a positive predictive value (PPV) of 53.1%. Completeness of data for age, gender, and nationality was 100%, and for ICD-10 codes was 98.8%. Accuracy levels for age, gender, nationality, and ICD-10 coding were 98.0%, 92.5%, 98.3%, and 94.8%, respectively. Data on age, gender, and date of service were considered to be representative. In conclusion, the study highlighted that the disease surveillance system demonstrated strong qualitative adaptability and high sensitivity in quantitative measures, but identified gaps in data accuracy and coding clarity. It is recommended that the system be enhanced with automated, real-time reporting capabilities, directly integrating with hospital HIS systems to reduce errors and improve efficiency. Additionally, the adoption of comprehensive disease coding, including codes like Z58.1 for PM2.5 exposure, is encouraged to strengthen surveillance, prevention, and control efforts.