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      Quantitative Analysis of Burden of Infectious Diarrhea Associated with Floods in Northwest of Anhui Province, China: A Mixed Method Evaluation

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          Abstract

          Background

          Persistent and heavy rainfall in the upper and middle Huaihe River of China brought about severe floods during the end of June and July 2007. However, there has been no assessment on the association between the floods and infectious diarrhea. This study aimed to quantify the impact of the floods in 2007 on the burden of disease due to infectious diarrhea in northwest of Anhui Province.

          Methods

          A time-stratified case-crossover analysis was firstly conducted to examine the relationship between daily cases of infectious diarrhea and the 2007 floods in Fuyang and Bozhou of Anhui Province. Odds ratios (ORs) of the flood risk were quantified by conditional logistic regression. The years lived with disability (YLDs) of infectious diarrhea attributable to floods were then estimated based on the WHO framework of the calculating potential impact fraction in the Burden of Disease study.

          Results

          A total of 197 infectious diarrheas were notified during the exposure and control periods in the two study areas. The strongest effect was shown with a 2-day lag in Fuyang and a 5-day lag in Bozhou. Multivariable analysis showed that floods were significantly associated with an increased risk of the number cases of infectious diarrhea (OR = 3.175, 95%CI: 1.126–8.954 in Fuyang; OR = 6.754, 95%CI: 1.954–23.344 in Bozhou). Attributable YLD per 1000 of infectious diarrhea resulting from the floods was 0.0081 in Fuyang and 0.0209 in Bozhou.

          Conclusions

          Our findings confirm that floods have significantly increased the risks of infectious diarrhea in the study areas. In addition, prolonged moderate flood may cause more burdens of infectious diarrheas than severe flood with a shorter duration. More attention should be paid to particular vulnerable groups, including younger children and elderly, in developing public health preparation and intervention programs. Findings have significant implications for developing strategies to prevent and reduce health impact of floods.

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          Most cited references24

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          The Association Between Extreme Precipitation and Waterborne Disease Outbreaks in the United States, 1948–1994

          Rainfall and runoff have been implicated in site-specific waterborne disease outbreaks. Because upward trends in heavy precipitation in the United States are projected to increase with climate change, this study sought to quantify the relationship between precipitation and disease outbreaks. The US Environmental Protection Agency waterborne disease database, totaling 548 reported outbreaks from 1948 through 1994, and precipitation data of the National Climatic Data Center were used to analyze the relationship between precipitation and waterborne diseases. Analyses were at the watershed level, stratified by groundwater and surface water contamination and controlled for effects due to season and hydrologic region. A Monte Carlo version of the Fisher exact test was used to test for statistical significance. Fifty-one percent of waterborne disease outbreaks were preceded by precipitation events above the 90th percentile (P = .002), and 68% by events above the 80th percentile (P = .001). Outbreaks due to surface water contamination showed the strongest association with extreme precipitation during the month of the outbreak; a 2-month lag applied to groundwater contamination events. The statistically significant association found between rainfall and disease in the United States is important for water managers, public health officials, and risk assessors of future climate change.
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            Case-crossover analyses of air pollution exposure data: referent selection strategies and their implications for bias.

            The case-crossover design has been widely used to study the association between short-term air pollution exposure and the risk of an acute adverse health event. The design uses cases only; for each individual case, exposure just before the event is compared with exposure at other control (or "referent") times. Time-invariant confounders are controlled by making within-subject comparisons. Even more important in the air pollution setting is that time-varying confounders can also be controlled by design by matching referents to the index time. The referent selection strategy is important for reasons in addition to control of confounding. The case-crossover design makes the implicit assumption that there is no trend in exposure across the referent times. In addition, the statistical method that is used-conditional logistic regression-is unbiased only with certain referent strategies. We review here the case-crossover literature in the air pollution context, focusing on key issues regarding referent selection. We conclude with a set of recommendations for choosing a referent strategy with air pollution exposure data. Specifically, we advocate the time-stratified approach to referent selection because it ensures unbiased conditional logistic regression estimates, avoids bias resulting from time trend in the exposure series, and can be tailored to match on specific time-varying confounders.
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              • Record: found
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              • Article: not found

              Global health impacts of floods: epidemiologic evidence.

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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2013
                6 June 2013
                : 8
                : 6
                : e65112
                Affiliations
                [1 ]Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan City, Shandong Province, P.R. China
                [2 ]School of Public Health, China Studies Centre, The University of Sydney, New South Wales, Australia
                [3 ]National Institute for Communicable Disease Control and Prevention, China CDC, Beijing City, P.R. China
                Wadsworth Center, United States of America
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: GD YZ BJ. Performed the experiments: GD LG WM. Analyzed the data: GD LG XL JL. Contributed reagents/materials/analysis tools: QL BJ. Wrote the paper: GD YZ.

                Article
                PONE-D-13-05453
                10.1371/journal.pone.0065112
                3675108
                23762291
                fdb85878-8d8d-4840-84f1-2e376b22cfc2
                Copyright @ 2013

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 2 February 2013
                : 22 April 2013
                Page count
                Pages: 9
                Funding
                This study was supported by the National Basic Research Program of China (973 Program) (Grant NO. 2012CB955500-955502). Ying Zhang received the Australian National Health and Medical Research Council Public Health Training Fellowship (627049). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Agriculture
                Agricultural Production
                Environmental Impacts
                Biology
                Population Biology
                Population Dynamics
                Disease Dynamics
                Mathematics
                Statistics
                Biostatistics
                Medicine
                Epidemiology
                Environmental Epidemiology
                Infectious Disease Epidemiology
                Global Health
                Non-Clinical Medicine
                Health Care Policy
                Health Risk Analysis
                Health Statistics
                Public Health

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                Uncategorized

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