Bureau of Communicable Disease, New York City Department of Health and Mental Hygiene, Queens, New York.
Disaster Med Public Health Prep. 2013 Oct;7(5):513-21. doi: 10.1017/dmp.2013.98.
Hurricane Sandy's October 29, 2012 arrival in New York City caused flooding, power disruption, and population displacement. Infectious disease risk may have been affected by floodwater exposure, residence in emergency shelters, overcrowding, and lack of refrigeration or heating. For 42 reportable diseases that could have been affected by hurricane-related exposures, we developed methods to assess whether hurricane-affected areas had higher disease incidence than other areas of NYC.
We identified post-hurricane cases as confirmed, probable, or suspected cases with onset or diagnosis between October 30 and November 26 that were reported via routine passive surveillance. Pre-hurricane cases for the same 4-week period were identified in 5 prior years, 2007-2011. Cases were geocoded to the census tract of residence. Using data compiled by the NYC Office of Emergency Management, we determined (1) the proportion of the population in each census tract living in a flooded block and (2) the subset of flooded tracts severely "impacted", e.g., by prolonged service outages or physical damage. A separate multivariable regression model was constructed for each disease, modeling the outcome of case counts using a negative binomial distribution. Independent variables were: neighborhood poverty; whether cases were pre- or post-hurricane (time); the proportion of the population flooded in impacted and not impacted tracts; and interaction terms between the flood/impact variables and time. Models used repeated measures to adjust for correlated observations from the same tract and an offset term of the log of the population size. Sensitivity analyses assessed the effects of case count fluctuations and accounted for variations in reporting volume by using an offset term of the log of total cases.
Only legionellosis was statistically significantly associated with increased occurrence in flooded/impacted areas post-hurricane, adjusting for baseline differences (P = .04). However, there was only 1 legionellosis case post-hurricane in a flooded/impacted area.
Hurricane Sandy did not appear to elevate reportable disease incidence in NYC. Defining and acquiring reliable data and meta-data regarding hurricane-affected areas was a challenge in the weeks post-storm. Relevant metrics could be developed during disaster preparedness planning. These methods to detect excess disease can be adapted for future emergencies.
2012 年 10 月 29 日，飓风“桑迪”袭击纽约市，导致洪水泛滥、电力中断和人口流离失所。传染病风险可能受到洪水暴露、居住在紧急避难所、过度拥挤以及缺乏冷藏或供暖等因素的影响。对于可能受到飓风相关暴露影响的 42 种法定报告疾病，我们开发了评估受飓风影响地区疾病发病率是否高于纽约市其他地区的方法。
我们将 10 月 30 日至 11 月 26 日期间报告的发病或确诊的确诊、可能或疑似病例确定为灾后病例，这些病例是通过常规被动监测报告的。在同一 4 周期间，我们在 2007-2011 年的 5 年前病例中确定了前飓风病例。病例被地理编码到居住的普查区。利用纽约市应急管理办公室汇编的数据，我们确定了：(1) 每个普查区居住在被洪水淹没的街区的人口比例；(2) 被洪水淹没的普查区中严重“受灾”的部分，例如，由于服务中断时间较长或物理损坏。为每种疾病构建了一个单独的多变量回归模型，使用负二项式分布对病例计数的结果进行建模。独立变量为：邻里贫困；病例是前飓风还是后飓风（时间）；受灾和未受灾普查区的人口淹没比例；以及洪水/影响变量与时间之间的交互项。模型使用重复测量来调整来自同一普查区的相关观测值，并使用人口对数的偏移项进行调整。敏感性分析通过使用病例总数对数的偏移项来评估病例计数波动的影响，并考虑报告量的变化，以评估病例计数波动的影响。
仅军团病在后飓风期间在洪水泛滥/受灾地区的发生与增加呈统计学显著相关，调整了基线差异（P =.04）。然而，在洪水泛滥/受灾地区只有 1 例军团病在后飓风期间发生。
飓风“桑迪”似乎没有提高纽约市法定报告疾病的发病率。在风暴后几周内，定义和获取有关受飓风影响地区的可靠数据和元数据是一项挑战。在灾害备灾规划期间，可以制定相关指标。这些检测疾病过量的方法可以适用于未来的紧急情况。
