Effects of rainfall on human leptospirosis in Thailand: evidence of multi-province study using distributed lag non-linear model

Abstract

Leptospirosis is a zoonotic bacterial disease that remains an important public health problem, especially in tropical developing countries. Many previous studies in Thailand have revealed the outbreak of human leptospirosis after heavy rainfall, but research determining its quantitative risks associated with rainfall, especially at the national level, remains limited. This study aims to examine the association between rainfall and human leptospirosis across 60 provinces of Thailand. A quasi-Poisson regression framework combined with the distributed lag non-linear model was used to estimate province-specific association between rainfall and human leptospirosis, adjusting for potential confounders. Province-specific estimates were then pooled to derive regional and national estimates using random-effect meta-analysis. The highest risk of leptospirosis associated with rainfall at national level was observed at the same month (lag 0). Using 0 cm/month of rainfall as a reference, the relative risks of leptospirosis associated with heavy (90th percentile), very heavy (95th percentile), and extremely heavy (99th percentile) rainfall at the national level were 1.0994 (95% CI 0.9747, 1.2401), 1.1428 (95% CI 1.0154, 1.2862), and 1.1848 (95% CI 1.0494, 1.3378), respectively. The highest risk of human leptospirosis associated with rainfall was observed in the northern and north-eastern regions. Specifically, the relative risks of leptospirosis associated with extremely heavy rainfall in northern and north-eastern regions were 1.2362 (95% CI 0.9110, 1.6775) and 1.2046 (95% CI 0.9728, 1.4918), respectively. Increasing rainfall was associated with increased risks of leptospirosis, especially in the northern and northeastern regions of Thailand. This finding could be used for precautionary warnings against heavy rainfall.

Supplementary information: The online version contains supplementary material available at 10.1007/s00477-022-02250-x.

Keywords: Distributed lag non-linear model; Human leptospirosis; Rainfall; Thailand.

Fig. 1

Geographical regions of Thailand (a), as well as the average of monthly cumulative rainfall (b) and the total number of human leptospirosis (c) for each province during the period of 2007–2017, excepting for those in a province of Nong Bua Lam Phu where the data was used during the period of 2014–2017, all maps were produced by QGIS version 3.8

Fig. 2

Province-specific effects of rainfall on leptospirosis, estimating through BLUP approach, at lag 0 month. The RR and its 95% CI of leptospirosis associated with exposure to rainfall at 90th, 95th, and 99th percentiles of province-specific monthly cumulative rainfall relative to 0 cm is shown. The word “All” indicate the pooled RR obtained from multivariate meta-analysis

Fig. 3

The national and region-specific rainfall-leptospirosis associations at different lag structures, estimated through multivariate meta-regression model. Grey solid line indicates rainfall level at 0 cm, whereas vertical black, blue, and red dotted lines indicate monthly cumulative rainfall level at 90th, 95th, and 99th percentile, respectively, for each region. I² and p-value of Q test is the measure of the residual heterogeneity of the association between rainfall and leptospirosis at lag 0 month for each region obtained through multivariate meta-regression model

Fig. 4

The pooled national and region-specific rainfall-leptospirosis associations at lag 0 month, estimated through multivariate meta-regression model. Black solid line indicates pooled RR with its 95% confidence interval shown as black dashed lines. Grey dashed lines define province-specific RR incorporated in meta-analysis through BLUP method. Grey solid line indicates rainfall level at 0 cm, whereas vertical black, blue, and red dotted lines indicate monthly cumulative rainfall level at 90th, 95th, and 99th percentile, respectively. I² and p-value of Q test indicate the measure of the residual heterogeneity obtained from multivariate meta-regression model

Fig. 5

The national rainfall-leptospirosis association (lag 0 month) at specific level of geographical and population characteristics, predicted through multivariate meta-regression model. The figure shows predicted curve of the association between rainfall and leptospirosis with no predictor (a) and at 25th (black solid line) and 75th (blue dashed line) percentiles of DTR (b), latitude (c), number of population (d), proportion of population with higher education (e), and proportion of population with registered agricultural workers (f). The shaded areas define the corresponding 95% confidence intervals. Grey solid line indicates rainfall level at 0 cm, whereas vertical black, blue, and red dotted lines indicate monthly cumulative rainfall level at 90th, 95th, and 99th percentile, respectively