A Mixed Method to Evaluate Burden of Malaria Due to Flooding and Waterlogging in Mengcheng County, China: A Case Study

Abstract

BackgroundMalaria is a highly climate-sensitive vector-borne infectious disease that still represents a significant public health problem in Huaihe River Basin. However, little comprehensive information about the burden of malaria caused by flooding and waterlogging is available from this region. This study aims to quantitatively assess the impact of flooding and waterlogging on the burden of malaria in a county of Anhui Province, China.MethodsA mixed method evaluation was conducted. A case-crossover study was firstly performed to evaluate the relationship between daily number of cases of malaria and flooding and waterlogging from May to October 2007 in Mengcheng County, China. Stratified Cox models were used to examine the lagged time and hazard ratios (HRs) of the risk of flooding and waterlogging on malaria. Years lived with disability (YLDs) of malaria attributable to flooding and waterlogging were then estimated based on the WHO framework of calculating potential impact fraction in the Global Burden of Disease study.ResultsA total of 3683 malaria were notified during the study period. The strongest effect was shown with a 25-day lag for flooding and a 7-day lag for waterlogging. Multivariable analysis showed that an increased risk of malaria was significantly associated with flooding alone [adjusted hazard ratio (AHR) = 1.467, 95% CI = 1.257, 1.713], waterlogging alone (AHR = 1.879, 95% CI = 1.696, 2.121), and flooding and waterlogging together (AHR = 2.926, 95% CI = 2.576, 3.325). YLDs per 1000 of malaria attributable to flooding alone, waterlogging alone and flooding and waterlogging together were 0.009 per day, 0.019 per day and 0.022 per day, respectively.ConclusionFlooding and waterlogging can lead to higher burden of malaria in the study area. Public health action should be taken to avoid and control a potential risk of malaria epidemics after these two weather disasters.

A time-trend ecological study for identifying flood-sensitive infectious diseases in Guangxi, China from 2005 to 2012

“…Floods can potentially increase the spread of vector-borne diseases, such as leptospirosis, Japanese encephalitis, malaria, dengue, rabies, etc. (Afzal and Sultan, 2013;Ding et al, 2014;Matsushita et al, 2018;Mohd Radi et al, 2018;Saeed and Piracha, 2016;Smith et al, 2013;Zhang et al, 2016). Similar findings have been reported in our study.…”

“…Flood disasters may lead to infectious disease outbreaks when they result in substantial population displacement, microbes, vectors and reservoir animal hosts exploit the disrupted social and environmental conditions for disease transmission (Kouadio et al, 2012;McMichael, 2015). Studies at home and abroad show that floods can increase the risk of infectious diseases, e.g., cholera (Rieckmann et al, 2018;Sidley, 2008), bacillary dysentery (Liu et al, 2015(Liu et al, , 2017, leptospirosis (Mohd Radi et al, 2018), malaria (Ding et al, 2014) and dengue fever (Dayrit et al, 2018). However, evidence on the association between floods and infectious diseases is far from clear.…”

Climate Change–Related Water Disasters’ Impact on Population Health

“…Disease risk increases as a result of climate change due to related expansions in vector numbers and ranges, shortening of pathogen incubation periods, and disruption and relocation of large human populations (Portier et al., ). Vector‐borne diseases include dengue, dengue hemorrhagic fever, yellow fever, West Nile virus (Ahmed et al., ; Alderman et al., ; Burton, Rabito, Danielson, & Takaro, ; Phung et al., ), Japanese encephalitis (Zhang, Liu, Zhang, & Jiang, ), Ross River virus (Tall, Gatton, & Tong, ), and malaria (Alderman et al., ; Boyce et al., ; Ding et al., ; Gao, Zhang, Ding, Liu, Wang, & Jiang, ).…”

Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

“…In order to test NFIS-WPM (Ave), numbers from 0 to 4 inclusively are given to represent No rain, Light rain, Moderate rain, Heavy rain and Rainstorm, respectively (Figure 4) [37][38][39]. …”