Exposure density and neighborhood disparities in COVID-19 infection risk.
Exposure density and neighborhood disparities in COVID-19 infection risk.
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DOI：
10.1073/pnas.2021258118
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发表时间：
2021-03-30
影响因子：
11.1
通讯作者：
Kontokosta CE
中科院分区：
文献类型：
--
作者：
Hong B;Bonczak BJ;Gupta A;Thorpe LE;Kontokosta CE
我们提出了一种计算方法，以高空间和时间分辨率测量暴露密度，以了解COVID-19传播风险的邻里差异。通过整合地理位置数据和粒度土地利用信息，我们能够确定特定社区的活动范围以及住宅，非住宅和户外活动的活动性质。然后，我们分析了基于当地风险因素，建筑环境特征和社会经济不平等的社会距离政策的差异行为反应。我们的研究结果突出了种族和少数民族以及低收入家庭在健康结果方面的显著差异。暴露密度提供了一个额外的指标，以进一步解释和理解COVID-19对脆弱社区的不同影响。尽管人们越来越意识到脆弱社区之间COVID-19感染风险的差异，但尚未充分研究单个社区规模的行为干预的效果。我们开发了一种方法来量化邻里活动行为在高空间和时间分辨率和测试是否，以及在何种程度上，行为反应的社会经济和人口特征不同的社交距离政策。我们将暴露密度（）定义为一个特定区域内局部活动量和不同土地利用类型中活动比例的度量。使用纽约市的详细社区数据，我们使用匿名智能手机地理位置数据在3个月的时间内，覆盖超过1200万个独特的设备和rasshop-granular土地使用信息，以情境化观察到的活动，量化社区暴露密度。接下来，我们通过估计强制居家令之前和之后按土地使用类型划分的社区活动变化，分析社区社交距离的差异。最后，我们评估了本地人口，社会经济和建筑环境密度特征对感染率和死亡率的影响，以确定与暴露风险相关的健康结果的差异。我们的研究结果表明，在居家令之后，社区之间存在不同的行为模式，这些暴露密度的变化对感染风险有直接和可测量的影响。值得注意的是，我们发现，在研究期间，全市暴露密度再降低10%，可以挽救1，849至4，068人的生命，主要是在低收入和少数民族社区。
We present a computational approach to measure exposure density at high spatial and temporal resolution to understand neighborhood disparities in transmission risk of COVID-19. By integrating geolocation data and granular land-use information, we are able to establish both the extent of activity in a particular neighborhood and the nature of that activity across residential, nonresidential, and outdoor activities. We then analyze the differential behavioral response to social-distancing policies based on local risk factors, built-environment characteristics, and socioeconomic inequality. Our results highlight the significant disparities in health outcomes for racial and ethnic minorities and lower-income households. Exposure density provides an additional metric to further explain and understand the disparate impact of COVID-19 on vulnerable communities. Although there is increasing awareness of disparities in COVID-19 infection risk among vulnerable communities, the effect of behavioral interventions at the scale of individual neighborhoods has not been fully studied. We develop a method to quantify neighborhood activity behaviors at high spatial and temporal resolutions and test whether, and to what extent, behavioral responses to social-distancing policies vary with socioeconomic and demographic characteristics. We define exposure density () as a measure of both the localized volume of activity in a defined area and the proportion of activity occurring in distinct land-use types. Using detailed neighborhood data for New York City, we quantify neighborhood exposure density using anonymized smartphone geolocation data over a 3-mo period covering more than 12 million unique devices and rasterize granular land-use information to contextualize observed activity. Next, we analyze disparities in community social distancing by estimating variations in neighborhood activity by land-use type before and after a mandated stay-at-home order. Finally, we evaluate the effects of localized demographic, socioeconomic, and built-environment density characteristics on infection rates and deaths in order to identify disparities in health outcomes related to exposure risk. Our findings demonstrate distinct behavioral patterns across neighborhoods after the stay-at-home order and that these variations in exposure density had a direct and measurable impact on the risk of infection. Notably, we find that an additional 10% reduction in exposure density city-wide could have saved between 1,849 and 4,068 lives during the study period, predominantly in lower-income and minority communities.
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DOI：
10.1177/0033354920976575
发表时间：
2021-03
期刊：
Public health reports (Washington, D.C. : 1974)
影响因子：
--
作者：
Abouk R;Heydari B
通讯作者：
Heydari B
影响因子：
29.9
作者：
Jay J;Bor J;Nsoesie EO;Lipson SK;Jones DK;Galea S;Raifman J
通讯作者：
Raifman J
DOI：
10.1098/rsif.2007.1197
发表时间：
2008-06-06
期刊：
Journal of the Royal Society, Interface
影响因子：
--
作者：
Caley P;Philp DJ;McCracken K
通讯作者：
McCracken K
影响因子：
56.9
作者：
Chinazzi, Matteo;Davis, Jessica T.;Vespignani, Alessandro
通讯作者：
Vespignani, Alessandro
影响因子：
5.6
作者：
Scannell Bryan M;Sun J;Jagai J;Horton DE;Montgomery A;Sargis R;Argos M
通讯作者：
Argos M