ความเชื่อมโยงปริมาณฝนกับจำนวนผู้ป่วยโรคเมลิออยโดสิสรายจังหวัด ในเขตสุขภาพที่ 10 ประเทศไทย

Darunee  Rodma; Junthana  Tadamnil; Jinutcha  Wongprakob; Werawit  Plaingam; Vanida Durongrittichai

Authors

Darunee Rodma Faculty of Allied Health Sciences, Pathumthani University
Junthana Tadamnil Faculty of Pharmacy, Pathumthani University
Jinutcha Wongprakob Faculty of Pharmacy, Pathumthani University
Werawit Plaingam Faculty of Pharmacy, Pathumthani University
Vanida Durongrittichai Faculty of Nursing, Pathumthani University

Keywords:

Association, rainfall, number of Melioidosis patients, incidence rate ratios

Abstract

This research aimed to analyze the quantitative relationship between monthly rainfall and the monthly number of melioidosis cases in five provinces within Health Region 10. This study was an ecological time-series analysis using secondary monthly data (n=84 months) from January 2018 to December 2024. Data included the number of melioidosis cases and three rainfall patterns (minimum, maximum, and average). Data were analyzed using descriptive statistics, and the association was analyzed using the the Generalized Linear Models (GLM).

The results indicated that the GLM for all five provinces were statistically significant, as evidenced by the Log-likelihood ratio test (p<.05). The model with the highest R² was observed in Amnat Charoen province, which utilized a Quasi-Poisson model with Monthly Average Daily Rainfall as the predictor. In Mukdahan and Yasothon provinces, the Quasi-Poisson model with Monthly Minimum Daily Rainfall provided the best fit. Conversely, for Sisaket and Ubon Ratchathani provinces, the Negative Binomial model with Monthly Minimum Daily Rainfall as the predictor was identified as the most appropriate model. The Incidence Rate Ratios (IRR) for all five provinces ranged between 1.0015 and 1.0030

This study indicates that rainfall is a significant risk factor for Health Region 10, necessitating continuous self-protection campaigns.

References

Arushothy, R., Mohd Ali, MR, Zambri, H.F., Muthu, V., Hashim, R., Chieng, S., & Nathan, S. (2024). Assessing the national antibiotic surveillance data to identify burden for melioidosis in Malaysia, IJID Regions, 10, 94-99. https://doi.org/10.1016/j.ijregi.2023.11.014.

Birnie, E., Virk, H. S., Savelkoel, J., Spijker, R., Limmathurotsakul, D., Devleesschauwer, B., Haagsma, J. A., & Wiersinga, W. J. (2019). Global burden of melioidosis in 2015: a systematic review and data synthesis. The Lancet Infectious Diseases, 19(8), 892-902. https://doi.org/10.1016/S1473-3099(19)30150-2

Currie, B.J., & Meumann, E.M. (2025). Melioidosis in Asia-Pacific Nations: Expanding Boundaries but Unknowns Remain. Respirology, 30(10),917-919. doi: 10.1111/resp.70098

Epidemiology Division, Department of Disease Control. (2025). Digital Disease Surveillance Platform, R506, Disease Surveillance Reporting System 506: Melioidosis. Retrieved November 7, 2025, from http://doe1.moph.go.th/surdata/disease.php?ds=72

Fong, J. H., Pillai, N., Yap, C. G., & Jahan, N. K. (2021). Incidences, Case Fatality Rates and Epidemiology of Melioidosis Worldwide: A Review Paper. Open Access Library Journal, 8(e7537), Article e7537. https://www.scirp.org/journal/paperinformation.aspx?paperid=110072

Gallucci, M. (2019). GAMLj: General analyses for linear models [jamovi module]. [cited 2025 Sep 20]. Available from: https://gamlj.github.io/.

Havlicek, L. L., & Peterson, N. L. (1977). Effect of the violation of assumptions upon significance levels of the Pearson r. Psychological Bulletin, 84(2), 373–377. https://doi.org/10.1037/0033-2909.84.2.373

Jiee, S. F., Lim, K.J., Choon Vui, D.S., Marius, D.P., Illyana, N.S., & Jantim, A. (2023). Extreme Weather and Melioidosis: An Endemic Tropical Disease in the Penampang District of Sabah, Malaysia. Journal of Health Research, 37(5), Article 3.

Limmathurotsakul, D., Golding, N., Dance, D. A., Messina, J. P., Pigott, D. M., Moyes, C. L., Rolim, D. B., Wuthiekanun, V., Bremell, T., Gleeson, F., Watson, S. J., Day, N. P., Hay, S. I., & Peacock, S. J. (2016). Predicted global distribution of Burkholderia pseudomallei and burden of melioidosis. Nature Microbiology, 1(1), 1-5. https://doi.org/10.1038/nmicrobiol.2015.8

Meumann E. M., Limmathurotsakul D., Dunachie S. J., Wiersinga W. J., & Currie B. J. (2024). Burkholderia pseudomallei and melioidosis. Nature Reviews Microbiology, 22(3), 155–169.

MORU Tropical Health Network. (2025). Meliodosis. Microbiology Department at Mahidol Oxford Tropical Medicine Research Unit. Retrieved November 15, 2025, from https://www.tropmedres.ac/units/moru-bangkok/microbiology/significant-achievements/melioidosis

National Hydroinformatics Data Center, Hydro-Informatics Institute (Public Organization). (2025). Monthly Spatial Rainfall by Province. Retrieved November 7, 2025, from https://data.hii.or.th/dataset/38cbfb06-e174-4653-accd-d491121ba752/resource/120aba77-b0a7-44c0-8043-91124dac45ed/download/spatial-rain-hii.csv

Nguyen, M. (2020). A Guide on Data Analysis. Bookdown. [cited 2025 Sep 20]. Retrieved November 15, 2025, from https://bookdown.org/mike/data_analysis/

R Core Team. R: A language and environment for statistical computing. (Version 4.1) [Computer software]. 2021 [cited 2025 Sep 20]. Available from: https://cran.r-project.org. (R packages retrieved from MRAN snapshot 2022-01-01).

The jamovi project. jamovi (Version 2.3) [Computer Software]. 2022 [cited 2025 Sep 20]. Available from: https://www. jamovi.org.

Ver Hoef, J.M., & Boveng, P.L. (2007). Quasi-Poisson vs. negative binomial regression: how should we model overdispersed count data? Ecology, 88(11), 2766-72. doi: 10.1890/07-0043.1.

Prideaux, L., Sandeman, M., Stratton, H., Kelleher, A.D., Smith, S., & Hanson, J. (2025). Melioidosis in People Living With Diabetes; Clinical Presentation, Clinical Course and Implications for Patient Management. Acta Tropica, 263,107559