A mixed method to evaluate burden of malaria due to flooding and waterlogging in Mengcheng County, China: a case study.
BACKGROUND
Malaria 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.


METHODS
A 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.


RESULTS
A 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.


CONCLUSIONS
Flooding 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.
Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan City, Shandong Province, P.R.China; Department of Occupational and Environmental Health, School of Public Health, Taishan Medical College, Taian City, Shandong Province, P.R.China; Shandong University Climate Change and Health Center, Jinan City, Shandong Province, P.R.China.
Guoyong
G
Ding
Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan City, Shandong Province, P.R.China; Shandong University Climate Change and Health Center, Jinan City, Shandong Province, P.R.China.
Lu
L
Gao
Shandong University Climate Change and Health Center, Jinan City, Shandong Province, P.R.China; Department of Environment and Health, School of Public Health, Shandong University, Jinan City, Shandong Province, P.R.China.
Xuewen
X
Li
National Center for Chronic and Noncommunicable Disease Control and Prevention, China CDC, Beijing City, P.R.China.
Maigeng
M
Zhou
Shandong University Climate Change and Health Center, Jinan City, Shandong Province, P.R.China; State Key Laboratory for Infectious Diseases Prevention and Control, National Institute for Communicable Disease Control and Prevention, China CDC, Beijing City, P.R.China.
Qiyong
Q
Liu
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing City, P.R.China.
Hongyan
H
Ren
Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan City, Shandong Province, P.R.China; Shandong University Climate Change and Health Center, Jinan City, Shandong Province, P.R.China.
Baofa
B
Jiang
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D002681:China / Q000453:epidemiology; D002980:Climate; D057231:Climate Change; D017281:Cost of Illness; D018592:Cross-Over Studies; D005260:Female; D055868:Floods; D005843:Geography; D006801:Humans; D015994:Incidence; D008288:Malaria / Q000453:epidemiology*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D015999:Multivariate Analysis; D016016:Proportional Hazards Models; D012306:Risk; D014887:Weather; D055815:Young Adult