Projected burden of disease for bacillary dysentery due to flood events in Guangxi, China

Sci Total Environ. 2017 Dec 1:601-602:1298-1305. doi: 10.1016/j.scitotenv.2017.05.020. Epub 2017 Jun 9.

Abstract

Many researchers have been studying the influence of floods on intestinal infection in recent years. This study aimed to project the future disease burden of bacillary dysentery associated with floods in Guangxi, China. Relying on the longitudinal data, a generalized additive mixed model was applied to quantify the relationship between the monthly morbidity of bacillary dysentery and floods with two severity levels from 2004 to 2010, controlling for other meteorological variables. Years Lived with Disability (YLDs) was used as the measure of the burden of bacillary dysentery in the future of Guangxi, China. According to the generalized additive mixed model, the relative risks (RR) of moderate and severe floods on the morbidity of bacillary dysentery were 1.17 (95% CI: 1.03-1.33) and 1.39 (95% CI: 1.14-1.70), respectively. The regression analysis also indicated that the flood duration was negatively associated with the morbidity of bacillary dysentery (with RR: 0.63, 95% CI: 0.44-0.90). Considering the effects of floods only, compared with the YLDs in 2010, increasing flood events may lead to a 4.0% increase in the YLDs for bacillary dysentery by 2020, 2100, 0.0% by 2050, and an 8.0% increase by 2030 in Guangxi, if other factors remain constant. Considering all potential changes include floods, temperature and population size, the YLDs for bacillary dysentery may increase by up to 16.0% by 2020, 20.0% by 2030, 2050, and 0.0% by 2100, compared to that in 2010 under the moderate flood scenario; Under the severe flood scenario, the YLDs for bacillary dysentery may increase by up to 16.0% by 2020, 20.0% by 2030, 2050, and 4.0% by 2100.

Keywords: Bacillary dysentery; Floods; Poisson regression; Projection; Years Lived with Disability.

MeSH terms

  • China / epidemiology
  • Dysentery, Bacillary / epidemiology*
  • Floods*
  • Humans
  • Morbidity
  • Regression Analysis
  • Risk Assessment