Ramesh Balaji, Callender Rashida, Zaitchik Benjamin F, Jagger Meredith, Swarup Samarth, Gohlke Julia M
College of Public Health The Ohio State University Columbus OH USA.
Department of Statistics Rice University Houston TX USA.
Geohealth. 2023 Apr 21;7(4):e2022GH000710. doi: 10.1029/2022GH000710. eCollection 2023 Apr.
Remotely sensed inundation may help to rapidly identify areas in need of aid during and following floods. Here we evaluate the utility of daily remotely sensed flood inundation measures and estimate their congruence with self-reported home flooding and health outcomes collected via the Texas Flood Registry (TFR) following Hurricane Harvey. Daily flood inundation for 14 days following the landfall of Hurricane Harvey was acquired from FloodScan. Flood exposure, including number of days flooded and flood depth was assigned to geocoded home addresses of TFR respondents ( = 18,920 from 47 counties). Discordance between remotely-sensed flooding and self-reported home flooding was measured. Modified Poisson regression models were implemented to estimate risk ratios (RRs) for adverse health outcomes following flood exposure, controlling for potential individual level confounders. Respondents whose home was in a flooded area based on remotely-sensed data were more likely to report injury (RR = 1.5, 95% CI: 1.27-1.77), concentration problems (1.36, 95% CI: 1.25-1.49), skin rash (1.31, 95% CI: 1.15-1.48), illness (1.29, 95% CI: 1.17-1.43), headaches (1.09, 95% CI: 1.03-1.16), and runny nose (1.07, 95% CI: 1.03-1.11) compared to respondents whose home was not flooded. Effect sizes were larger when exposure was estimated using respondent-reported home flooding. Near-real time remote sensing-based flood products may help to prioritize areas in need of assistance when on the ground measures are not accessible.
遥感洪水淹没信息有助于在洪水期间及过后迅速确定需要援助的地区。在此,我们评估了每日遥感洪水淹没测量数据的效用,并估计了这些数据与通过德克萨斯洪水登记处(TFR)收集的、在哈维飓风过后自报的房屋洪水情况及健康结果之间的一致性。哈维飓风登陆后14天的每日洪水淹没数据来自FloodScan。洪水暴露情况,包括被洪水淹没的天数和洪水深度,被分配到TFR受访者(来自47个县,共18,920人)经地理编码的家庭住址。我们测量了遥感洪水与自报房屋洪水之间的不一致性。实施修正泊松回归模型来估计洪水暴露后不良健康结果的风险比(RRs),同时控制潜在的个体层面混杂因素。根据遥感数据,房屋位于洪水区域的受访者比房屋未被洪水淹没的受访者更有可能报告受伤(RR = 1.5,95%置信区间:1.27 - 1.77)、注意力不集中问题(1.36,95%置信区间:1.25 - 1.49)、皮疹(1.31,95%置信区间:1.15 - 1.48)、疾病(1.29,95%置信区间:1.17 - 1.43)、头痛(1.09,95%置信区间:1.03 - 1.16)和流鼻涕(1.07,95%置信区间:1.03 - 1.11)。当使用受访者报告的房屋洪水情况来估计暴露程度时,效应量更大。在无法获取实地测量数据时,基于近实时遥感的洪水产品可能有助于确定需要援助的优先区域。