Crowding Effects Dominate Demographic Attributes in COVID-19 Cases.
Crowding Effects Dominate Demographic Attributes in COVID-19 Cases.
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DOI：
10.1016/j.ijid.2020.10.063
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发表时间：
2021-01
期刊：
影响因子：
--
通讯作者：
Naha S
中科院分区：
文献类型：
--
作者：
Federgruen A;Naha S
感染率的横断面研究对于了解COVID流行背后的驱动因素至关重要。这项研究旨在确定推动COVID-19发病率的人口统计和社会经济指标。本文评估这些因素在邮政编码粒度。平均家庭规模是唯一最重要的解释变量; 65岁或65岁以上人口的百分比以及贫困线以下人口的百分比也有很强的正相关性。人口密度本身对发病率没有显著的积极影响。着眼于可能的公共政策影响，我们的目标是确定在相对紧凑的地理区域内观察到的COVID-19发病率大幅变化的社会经济和人口因素，并量化这些因素中每一个的相对影响。我们以国际比较为起点。考虑到不同邮政编码的病例发生率差异很大，由约175个邮政编码组成的纽约市是进行上述研究的理想竞技场。我们使用邮政编码粒度的数据进行了系统的回归研究。我们的模型规格是基于一个完善的流行病学模型，解释了家庭规模对R 0的影响。平均家庭规模成为COVID-19发病率大幅变化背后最重要的单一驱动因素。它独立解释了62%的变异。65岁以上人口的百分比和贫困线以下人口的百分比也与邮政编码的发病率密切相关。至于族裔/种族特征，非裔美国人、西班牙裔和亚裔在人口中所占百分比有很大关联，但影响程度较小。(The亚洲人在邮政编码内的比例呈负相关。与普遍的看法相反，人口密度本身并没有显著的积极影响（除了大家庭规模导致的高人口）。我们的研究结果支持已实施和拟议的隔离患者和将受感染者与家庭或宿舍隔离的政策;他们还支持新修订的养老院入院政策。
Cross-sectional studies of infection rates are critical to understand the drivers behind the COVID epidemic. This study sought to identify demographic and socio-economic indicators that drive the incidence rate of COVID-19. The paper assesses these factors at zip-code granularity. The average household size is the single most important explanatory variable; the percentage of the population 65 or older, and that below the poverty line are also strongly positively associated. Population density, per se, does not have a significantly positive impact on incidence rates. With an eye toward possible public policy implications, our objective is to identify the socio-economic and demographic factors that drive the large variation in COVID-19 incidence rates observed within relatively compact geographic regions, and to quantify the relative impact of each of these factors. We use international comparisons as a starting point. New York City, consisting of some 175 zip codes, is an ideal arena to pursue the above study given the large variation in case incidence rates across zip codes. We conducted systematic regression studies employing data with zip code granularity. Our model specifications are based on a well-established epidemiologic model that explains the effects of household sizes on R0. Average household size emerges as the single most important driver behind the large variation in COVID-19 incidence rates. It independently explains 62% of the variation. The percentage of the population above the age of 65 and the percentage below the poverty line are also strongly positively associated with zip code incidence rates. As to ethnic/racial characteristics, the percentages of African Americans, Hispanics and Asians within the population are significantly associated, but the magnitude of the impact is smaller. (The proportion of Asians within a zip code has a negative association.) Contrary to common belief, population density, by itself, does not have a significantly positive impact (other than when a high population is driven by large household sizes). Our findings support implemented and proposed policies to quarantine patients and separate infected individuals from families or dormitories; they also support newly revised nursing home admission policies.
影响因子：
4.3
作者：
BECKER, NG;DIETZ, K
通讯作者：
DIETZ, K
影响因子：
1.4
作者：
BECKER, NG
通讯作者：
BECKER, NG
影响因子：
5.6
作者：
Millett, Gregorio A.;Jones, Austin T.;Sullivan, Patrick S.
通讯作者：
Sullivan, Patrick S.