| BackgroundFloods are recognized to be the most frequent and serious natural disasters in the world and accounted for over 40% of the disasters in the world. Floods are recognized to increase the global burden of disease and will place continuing stress on public health service system. Effects of floods on human health are complex and far-reaching, including mortality, injuries, epidemic of infectious disease and psychosocial health problem. One of the most serious problems following floods is the epidemic of infectious disease. China has vast territory with complicated climate types and experiences frequent floods events. Floods occurred with variety, wide range of affected, uneven distribution of time and space and damage caused by floods were serious. Floods seriously endangered life safety and health conditions of victims. However, little quantitative study had been conducted to explore effects of floods on human health and research methods were limited in China. Meanwhile, the fifth reports of Intergovernmental Panel on Climate Change indicated that floods events are expected to increase in frequency and intensity due to rising sea levels and more frequent and extreme precipitation events. Some study indicated that floods events in Liaoning Province are expected to increase in frequency and intensity under the background of global climate change. With the development of economy society and the rapidly increase of urban population, more and more people will exposed to floods in China.MethodsBacillary dysentery were selected as target disease and Panel Poisson regression model was applied to quantify the relationship between floods and bacillary dysentery in Liaoning Province from 2004 to 2010. The quantitative relationship between floods and bacillary dysentery was applied to project the excess cases of bacillary dysentery associated with floods in 2020,2030,2050 and 2100. Some assumptions and the demographic change were considered in this study. The study mainly includes the following purposes:1. Monthly bacillary dysentery data, meteorological data and demographic data from January 2004 to December 2010 of Liaoning Province were collected. Panel Poisson regression model was applied to quantify the relationship between floods and bacillary dysentery with adjustment meteorological factors, secular trend, seasonal effect and lag effect.2. GIS software was applied to organize the projected daily precipitation data under RCP 4.5 and RCP8.5 in 2020,2030,2050 and 2100 in Liaoning Province. According to the flood classification defined by the Comprehensive Study Group of Major Natural Disasters of the State Science and Technology Commission in China, floods in Liaoning Province in 2020,2030,2050 and 2100 under RCP 4.5 and RCP8.5 scenarios were developed. The demographic data of Liaoning Province in 2010 was selected baseline data and the projected demographic data of Liaoning Province in 2020,2030,2050 and 2100 were calculated based on the natural growth rate published by WHO.3. Excess cases of bacillary dysentery associated with floods in Liaoning Province in 2020,2030,2050 and 2100 were projected under some assumptions in our study. Our study aims to provide inspiration and evidence on infectious diseases control after floods for government and public health institution in Liaoning province.MethodsFirstly, spearman correlation analysis was applied to analyze the lag effect between bacillary dysentery and study factors. Considering the reproducing of pathogen and the incubation period of bacillary dysentery, a time lag of 0-2 months was considered in this study. The lagged value with the maximum correlation coefficient for each variable was selected for inclusion in the subsequent regression analysis. Secondly, Panel Poisson regression model was applied to quantify the relationship between floods and bacillary dysentery and Incidence rate ratio and 95%CI were calculated with adjustment the meteorological factors, secular trend, seasonal effect and lag effect. Thirdly, GIS software was applied to organize the projected daily precipitation under RCP 4.5 and RCP8.5 scenarios in 2020,2030, 2050 and 2100 in Liaoning Province and floods of Liaoning Province in 2020,2030, 2050 and 2100 were developed. Finally, excess cases of bacillary dysentery associated with floods in Liaoning Province in 2020,2030,2050 and 2100 were projected with some assumptions.Results1. Spearman correlation analysis indicated that floods were positively correlated to the monthly morbidity of bacillary dysentery with no lagged effect. It also indicated that the climatic factors, including MAT and MARH were positively correlated to the monthly morbidity of bacillary dysentery with 1-month,0-month lagged, respectively. The lagged effects of all these climatic variables were included in the subsequent panel data analysis.2. Panel Poisson regression model showed that floods were significantly associated with an increased risk of bacillary dysentery in Liaoning Province with adjustment the meteorological factors, secular trend, seasonal effect and lag effect. The IRR of floods on the risk of bacillary dysentery was 1.383 (95%CI: 1.353-1.414).The results also indicated that monthly average temperature and monthly average relative humidity were significantly associated with an increased risk of bacillary dysentery in Liaoning Province.3. Excess cases of bacillary dysentery associated with floods in Liaoning Province in 2020,2030,2050 and 2100 were projected with some assumptions. The results showed that under the RCP4.5 scenarios, excess cases of bacillary dysentery associated with floods in Liaoning Province in 2020,2030,2050 and 2100 were 1111、 355、453、658 with low fertility;1117、357、455、669 with medium fertility and 1131、 362、466、694 with high fertility, respectively. Under the RCP8.5 scenarios, excess cases of bacillary dysentery associated with floods in Liaoning Province in 2020, 2030、2050 and 2100 were 330、1108、616、292 with low fertility; 332、1114、619、 296 with medium fertility and 336、1127、634、308 with high fertility, respectively.ConclusionPanel Poisson regression model was applied to quantify the relationship between floods and bacillary dysentery in our study. Excess cases of bacillary dysentery associated with floods in Liaoning Province in 2020,2030,2050 and 2100 were projected with some assumptions. Study showed that floods were significantly associated with an increased risk of bacillary dysentery in Liaoning Province. Quantifying the relationship between floods and bacillary dysentery in Liaoning Province could provide epidemiological evidence on infectious disease control for governments and public health institution after floods. Under the RCP4.5 scenarios, excess cases of bacillary dysentery associated with floods in Liaoning Province in 2020,2030,2050 and 2100 were 1111、355、453、658 with low fertility; 1117、357、 455、669 with medium fertility and 1131、362、466、694 with high fertility, respectively. Under the RCP8.5 scenarios, excess cases of bacillary dysentery associated with floods in Liaoning Province in 2020,2030,2050 and 2100 were 330、 1108、616、292 with low fertility; 332、1114、619、296 with medium fertility and 336、 1127、634、308 with high fertility, respectively. Study showed that floods still endanger the safety and health conditions of victims and will place stress on public health service system in the future. Relevant public health strategies should be developed at an early stage to prevent and reduce the health burden of floods.LimitationsDue to limitations of monthly data, the lag effect between bacillary dysentery and floods may not be accurately estimated. Many factors such as socioeconomic levels in different regions could not be included in our study. The projection was conducted in some assumptions and this is also one of the main limitations of this study. |
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