Floods and cause-specific mortality in the UK: a nested case-control study.

BACKGROUND: Floods are the most frequent weather-related disaster, causing significant health impacts worldwide. Limited studies have examined the long-term consequences of flooding exposure. METHODS: Flood data were retrieved from the Dartmouth Flood Observatory and linked with health data from 499,487 UK Biobank participants. To calculate the annual cumulative flooding exposure, we multiplied the duration and severity of each flood event and then summed these values for each year. We conducted a nested case-control analysis to evaluate the long-term effect of flooding exposure on all-cause and cause-specific mortality. Each case was matched with eight controls. Flooding exposure was modelled using a distributed lag non-linear model to capture its nonlinear and lagged effects. RESULTS: The risk of all-cause mortality increased by 6.7% (odds ratio (OR): 1.067, 95% confidence interval (CI): 1.063-1.071) for every unit increase in flood index after confounders had been controlled for. The mortality risk from neurological and mental diseases was negligible in the current year, but strongest in the lag years 3 and 4. By contrast, the risk of mortality from suicide was the strongest in the current year (OR: 1.018, 95% CI: 1.008-1.028), and attenuated to lag year 5. Participants with higher levels of education and household income had a higher estimated risk of death from most causes whereas the risk of suicide-related mortality was higher among participants who were obese, had lower household income, engaged in less physical activity, were non-moderate alcohol consumers, and those living in more deprived areas. CONCLUSIONS: Long-term exposure to floods is associated with an increased risk of mortality. The health consequences of flooding exposure would vary across different periods after the event, with different profiles of vulnerable populations identified for different causes of death. These findings contribute to a better understanding of the long-term impacts of flooding exposure.

Community-wide Mortality Rates in Beijing, China, During the July 2012 Flood Compared with Unexposed Periods.

BACKGROUND: On 21-22 July 2012, Beijing, China, suffered its heaviest rainfall in 60 years. Two studies have estimated the fatality toll of this disaster using a traditional surveillance approach. However, traditional surveillance can miss disaster-related deaths, including a substantial number of deaths from natural causes triggered by disaster exposure. Here, we investigated community-wide mortality risk during this flood compared with rates in unexposed reference periods. METHODS: We compared community-wide mortality rates on the peak flood day and the four following days to seasonally matched nonflood days in previous years (2008-2011), controlling for potential confounders, to estimate the relative risks (RRs) of daily mortality among Beijing residents associated with this flood. RESULTS: On 21 July 2012, the flood-associated RRs were 1.34 (95% confidence interval = 1.11, 1.61) for all-cause, 1.37 (1.01, 1.85) for circulatory, and 4.40 (2.98, 6.51) for accidental mortality, compared with unexposed periods. We observed no evidence of increased risk of respiratory mortality. For the flood period of 21-22 July 2012, we estimated a total of 79 excess deaths among Beijing residents; by contrast, only 34 deaths were reported among Beijing residents in a study using a traditional surveillance approach. CONCLUSIONS: To our knowledge, this is the first study analyzing community-wide changes in mortality rates during the 2012 flood in Beijing and one of the first to do so for any major flood worldwide. This study offers critical evidence on flood-related health impacts, as urban flooding is expected to become more frequent and severe in China.

Assessing climate change and health vulnerability at the local level: Travis County, Texas.

We created a measure to help comprehend population vulnerability to potential flooding and excessive heat events using health, built environment and social factors. Through principal component analysis (PCA), we created non-weighted sum index scores of literature-reviewed social and built environment characteristics. We created baseline poor health measures using 1999-2005 age-adjusted cardiovascular and combined diabetes and hypertension mortality rates to correspond with social-built environment indices. We mapped US Census block groups by linked age-adjusted mortality and a PCA-created social-built environment index. The goal was to measure flooding and excessive heat event vulnerability as proxies for population vulnerability to climate change for Travis County, Texas. This assessment identified communities where baseline poor health, social marginalisation and built environmental impediments intersected. Such assessments may assist targeted interventions and improve emergency preparedness in identified vulnerable communities, while fostering resilience through the focus of climate change adaptation policies at the local level.

Health effects of flooding in rural Bangladesh.

BACKGROUND: There is little information available on nontraumatic health risks as the result of floods, and on the factors that determine vulnerability to them (especially in low-income settings). We estimated the pattern of mortality, diarrhea, and acute respiratory infection following the 2004 floods in rural Bangladesh. METHODS: We conducted controlled interrupted time-series analysis of adverse health outcomes, from 2001 to 2007, in a cohort of 211,000 residents of the Matlab region classified as flooded or nonflooded in 2004. Ratios of mortality, diarrhea, and acute respiratory infection rates in flooded compared with nonflooded areas were calculated by week for mortality and diarrhea, and by month for acute respiratory infection. We controlled for baseline differences as well as normal seasonal patterns in the flooded and nonflooded areas. Variations in flood-related health risks were examined by age, income level, drinking-water source, latrine type, and service area. RESULTS: After fully controlling for pre-flood rate differences and for seasonality, there was no clear evidence of excesses in mortality or diarrhea risk during or after flooding. For acute respiratory infection, we found no evidence of excess risk during the flood itself but a moderate increase in risk during the 6 months after the flood (relative risk = 1.25 [95% confidence interval = 1.06-1.47]) and the subsequent 18 months. CONCLUSIONS: We found little evidence of increased risk of diarrhea or mortality following the floods, but evidence of a moderate elevation in risk of acute respiratory infection during the 2 years after flooding. The discrepancies between our results and the apparent excesses for mortality and diarrhea reported in other situations, using less- controlled estimates, emphasize the importance of stringent confounder control.