The Relationship Between Meteorological Factors And Dysentery In The High Morbidity Area Of Hunan Province

IntroductionDysentery is a common intestinal infectious disease, which can be divided into bacillary dysentery and amoebic dysentery according to different pathogens. Transmission usually occurs through food, water, person-to-person contact and insect vector. Dysentery cases can be found all over the world, with a higher morbidity in temperate and subtropical regions as well as in developing countries. Male has a higher morbidity than female, and people under 5 years old have the highest morbidity among all the age-group, besides, dysentery is one of the three leading causes of death of children worldwide. The morbidity of dysentery in China was higher than that in developed countries. Dysentery was the fourth highest notified infectious diseases from 2008 to 2012, so it remains an important public health problem in our country. Dysentery outbreak occurred more frequently in Hunan than other province in China, so attention should be paid to formulate prevention and control strategies.The growth and reproduction of pathogen of dysentery as well as transmission are susceptible to meteorological factors. Some studies at home and abroad suggested that the spread of dysentery was related to meteorological factors such as air temperature, air pressure, relative humidity, rainfall and so on. Due to the differences in economic, population and health interventions, the impact modes of meteorological factors on dysentery were not the same, which has led to different conclusions. In addition, most of these studies did not consider the effects of seasonal trends and auto-correlation when they studied the relationship between dysentery and meteorological factors, which can exaggerate or reduce the real influence of meteorological factors on the morbidity of dysentery. Therefore, Hunan province has been chosen in this study to investigate the relationship between meteorological factors and dysentery by considering the seasonal trends and auto-correlation.ObjectivesIn order to clarify the spatial distribution of dysentery in Hunan province and selected the city with highest morbidity to be the focus area, monitoring data of dysentery in Hunan province obtained from national disease report system of CDC has been used and studied. Meteorological data of the focus area were collected to explore the impact of meteorological factors, which can provide scientific evidence to prevent and control dysentery and reference for future research.MethodsSpatial interpolation analysis was used to describe the spatial distribution of the morbidity of dysentery and select the highest morbidity area. Descriptive analysis, Spearman correlation analysis and Time-series Poisson regression models were used to study the influence of meteorological factors on dysentery. Software including ArcGIS9.2, SPSS16.0, R2.15.2 and Origin75 were used in this study.Results1. Morbidity of dysentery from 2005 to 2010 in Hunan province showed significant spatial differences:western, southern and eastern regions were severe than the central and northern regions. Yearly morbidity of dysentery of the cities in Hunan province showed different trends and the morbidity in the same year had a big difference in different city. Xiangxi city had the highest average annual morbidity (44.07/100,000) in Hunan province.2. The peak of dysentery cases usually occurred in July to September, and there was a significant seasonal trend, with the largest number of cases in summer, followed by autumn, and winter had the minimum cases.3. Spearman correlation showed that temperature, wind speed, the minimum relative humidity, precipitation and sunshine hours were positively correlated with the number of dysentery cases (P<0.05), and air pressure was negatively correlated (P <0.05) with it. The influence of meteorological factors on the number of dysentery cases exhibited different lag effects. The lag time of mean air pressure, extreme maximum air pressure, the average maximum temperature, extreme maximum wind speed, minimum relative humidity was one month, extreme minimum air pressure and precipitation lagged of two months, while the lag time of average temperature, average minimum temperature, extreme maximum temperature, extreme minimum temperature, average wind speed and sunshine hours were 0 month which meant no lag time or lag within a month.4. In the time-series Poisson regression model, average temperature, extreme maximum wind speed with one month lag, minimum relative humidity with one month lag, sunshine hours and precipitation with two month lag were positively correlated with the number of dysentery cases, and extreme minimum air pressure with two month lag was negatively correlated with it, which was consistent with the results of Spearman correlation. Of all the mentioned meteorological factors above, average temperature had the largest influence on the number of dysentery cases. If holding other variables constant, each 1℃ rise in average temperature corresponded to an increase of 2.72%(95% CI:1.58%-3.87%) in the monthly number of dysentery cases, and each 1hPa rise in extreme minimum air pressure with two month lag corresponded to an decrease of monthly number of dysentery cases by 1.70%(95% CI: 0.97%-2.42%), and each lm/s rise in extreme maximum wind speed with one month lag corresponded to an increase of 1.70%(95% CI:0.53%-2.87%) in the monthly number of dysentery cases, and each 1% rise of minimum relative humidity with one month lag corresponded to an increase of 0.73%(95% CI:0.31%-1.16%) in the monthly number of dysentery cases, and each 1h rise of sunshine hours corresponded to an increase of 0.13%(95% CI:0.06%-0.21%), and each lmm rise of precipitation with two month lag corresponded to an increase of 0.05%(95% CI:0.02%-0.08%) in the monthly number of dysentery cases. And the time-series Poisson regression model above fitted well (r2=0.86).Conclusions1. Morbidity of dysentery in western, southern and eastern regions were severe than other regions. Yearly morbidity of dysentery of the cities in Hunan province showed different trends, and Xiangxi city had the highest morbidity of all the cities in Hunan province.2. There was a significant seasonal trend in the morbidity of dysentery in Xiangxi city, with the largest number of cases in summer, followed by autumn, and winter had the minimum cases. Meteorological factors including average temperature, extreme minimum air pressure with two month lag, extreme maximum wind speed with one month lag, minimum relative humidity with one month lag, sunshine hours and precipitation with two month lag can influence the morbidity of dysentery. And average temperature had the largest influence on the number of dysentery cases. In addition, meteorological data can be used to predict the number of cases of dysentery take advantage of time-series Poisson regression model with a high precision.Significance and innovation1. The spatial distribution of the morbidity of dysentery in Hunan province was studied and the focus region which should be controlled and prevented was found, which can help to allocate the health resources rationally.2. High precision quantitative analysis was used to study the relationship between meteorological factors and morbidity of dysentery by controlling of seasonal trend and the auto-correlation in the high-risk areas, which can provide a scientific basis to prevent and control dysentery.