The relationship between meteorological factors and the risk of bacillary dysentery in Hunan Province, China

Abstract

Meteorological factors are known to influence the morbidity of bacillary dysentery in humans in a number of different regions. To investigate the effect of meteorological factors on the incidence of this disease in the Hunan Province of China, Xiangxi, the region with the highest annual average incidence of bacillary dysentery in the Hunan province from 2005 to 2010 (44.07 cases per 100 000 people) was selected as the area of study, based on spatial descriptive analysis. Spearman correlation analysis showed that average temperature (without lag), relative humidity (with a 1‐month lag), rainfall (with a 2‐month lag), hours of sunshine (without lag), and air pressure (with a 2‐month lag) were all significantly correlated with the number of dysentery cases in Xiangxi. Multivariate analysis showed that average temperature, relative humidity, rainfall, and hours of sunshine appeared to increase the incidence of dysentery cases in Xiangxi (0.05–2.72%). Multivariate Poisson regression showed no correlation between air pressure and bacillary dysentery incidence. Average temperature was found to be the most important of the meteorological factors considered (it was found that a 1 degC rise in temperature corresponds to a 2.72% increase in the number of cases of bacillary dysentery).

The long-term effects of meteorological parameters on pertussis infections in Chongqing, China, 2004–2018

“…About the positive correlation between them, this is congruous with the recent findings from the studies in Jinan 14,26 and Auckland 67 , which found that temperature was positively associated with pertussis among different age groups and could be considered as a good predictor. Also, in line with the findings that have been observed in other infectious diseases, such as HFMD (in Guangdong) 68 , bacillary dysentery (in Hunan Province) 31 , and scarlet fever (in Hefei City) 69 . Prior work discovered that extreme heat was positively linked to respiratory diseases 70 , and the patients visiting respiratory emergency department significantly increased during hot weather 71 .…”

“…As for such a discrepancy, this is largely due to the fact that temperature and rainfall were only included in that study 14 . Currently, many water-borne pathogens and vector-borne infectious diseases have been found to be positively linked to precipitation, such as dengue fever 19 , bacillary dysentery 31 , HFRS 72 , etc., which because rainfall can mainly impact such diseases via affecting the development of pathogens and the vectors/hosts 74 . However, there is a current scarcity of relative study and biological evidence to clarify the relationship between rainfall and respiratory diseases, albeit several studies exhibited that precipitation may be associated with scarlet fever 17 , 75 and mumps 57 , their findings are contradictory.…”

“…S9 ) 20 , we thus used a negative binomial multivariable regression to assess the independent effect of variations in monthly meteorological factors on the morbidity of pertussis. In this multivariable regression analysis, given that the climatic drivers for the months prior to the occurrence of infectious diseases are frequently critical 20 , 22 , 31 , 32 , and that the majority of infected people often have a mean incubation period ranging from 2 to 21 days (but some patients’ symptoms can last for 1–2 months 33 ) and there is a delay of up to 2 weeks from the confirmed cases to the Statutory Infectious Disease Reporting System 14 , so we included climatic factors with a lag of 0–2 months in our regression model. Meanwhile, given that the multicollinearity among the covariates can lead to unstable estimated parameters, hence we used the variance inflation factor (VIF) to evaluate the degree of multicollinearity 34 .…”

Association between meteorological indicators and COVID-19 pandemic in Pakistan

“…Li et al ( 2019 ) used the Poisson model to explore the association between total cases and meteorological variables. Poisson regression is a commonly used classical model, which was based upon the classical assumptions.…”

“…In the present study, the GAM was applied to establish a dose-response function, which connected different dose variables (outbreak days, average temperature, rainfall, and humidity), with a probability of COVID-19 infection (response variable). Li et al (2019) used the Poisson model to explore the association between total cases and meteorological variables. Poisson regression is a commonly used classical model, which was based upon the classical assumptions.…”