Evaluation of the PM2.5 Surveillance System in Nakhon Phanom Province, Thailand: A Pilot Case Study

Abstract

Thailand is facing air pollution problems from particulate matter of diameter less than 2.5 micron or PM_2.5 exceeding standard levels every year. Particularly Nakhon Phanom province of Health Region 8 has been significantly affected. This study aimed to evaluate the PM_2.5 surveillance system in Nakhon Phanom both qualitatively and quantitatively to develop policies and operational guidelines and to serve as a case study for Health Region 8. This research employed a mixed-method approach by conducting in-depth interviews with 11 public health officials and stakeholders and reviewing 508 medical records of patients suspected of PM_2.5-related diseases at Nakhon Phanom Hospital between October 1, 2020 and March 31, 2023. The qualitative analysis revealed a high level of acceptance of the surveillance system, primarily due to growing public concern regarding the health impacts of air pollution. The system was found to be relatively simple and flexible in implementation. The quantitative results showed that out of 485 medical records, 442 matched the reporting definitions according to the ICD-10 (International Classification of Disease 10^th revision) criteria, and 355 matched the disease definitions. The system demonstrated high coverage (90.7%), indicating that a substantial proportion of potential cases were included in the surveillance data. However, the positive predictive value was moderate (72.9%), suggesting that while the system is generally accurate, there is room for improvement in differentiating between diseases directly attributable to PM_2.5 exposure and other factors. Additionally, no clear correlation was found between PM_2.5 levels and the number of reported patients. The absence of clear diagnostic criteria for diseases related to PM_2.5 exposure adversely impacts data accuracy and consistency. In conclusion, the PM_2.5 surveillance system in Nakhon Phanom province was functioning satisfactorily, with high coverage and reasonable data accuracy. However, the study highlights the need to improve the diagnostic criteria for PM_2.5-related diseases and the development of learning machine artificial intelligence for coding Z58.1. Furthermore, refinements to the surveillance system would better support the development of evidence-based policies aimed at mitigating the health impacts of air pollution.