Socioeconomic Disparities in Subway Use and COVID-19 Outcomes in New York City.
Socioeconomic Disparities in Subway Use and COVID-19 Outcomes in New York City.
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DOI：
10.1101/2020.05.28.20115949
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发表时间：
2021-07-01
影响因子：
5
通讯作者：
White, Laura F
中科院分区：
文献类型：
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作者：
Sy, Karla Therese L;Martinez, Micaela E;White, Laura F
背景技术背景：美国疾控中心报告称，COVID-19大流行中的种族和民族差异可能部分是由于社会经济劣势，需要个人继续外出工作，以及缺乏带薪病假。然而，仍需要对COVID-19负担的社会经济决定因素进行数据驱动分析。利用来自纽约市（NYC）的数据，我们旨在确定社会经济因素如何影响人员流动和COVID-19负担。方法/总结：纽约市在社区的社会经济地位（SES）和人口统计学方面存在很大的异质性。我们利用这种异质性对人类流动性（即，地铁乘客量）、社会人口因素和2020年4月26日COVID-19发病率。我们还对纽约市的行政区进行了二次分析（相当于该市的县），以评估地铁使用量下降与每个自治市镇结束COVID-19病例指数增长期所需时间之间的关系。中位收入较低的地区，非白人和/或西班牙裔/拉丁裔的比例较高，基本工人的比例较高，在大流行期间，更大比例的医护人员使用了更多的地铁。地铁使用与收入中位数之间的正相关，以及地铁使用与非白人和/或西班牙裔/拉丁裔百分比之间的正相关，在根据基本工作人员的百分比进行调整后并不存在。这表明，基本工作是推动地铁使用在较低的SES邮政编码和社区的颜色。当调整检测工作量时，增加地铁使用与每10万人口中COVID-19病例的发生率较高相关（aRR=1.11; 95%CI：1.03 - 1.19），但当我们调整收入中位数时，这种关联较弱（aRR=1.06; 95%CI：1.00 - 1.12）。所有社会人口学变量与每10万人口中的阳性病例率显著相关，当调整检测工作（未保险的百分比除外）和调整收入和检测工作时。与COVID-19相关性最强的风险因素是从事基本工作的个人百分比（aRR = 1.59，95%CI：1.36 - 1.86）。我们发现，地铁使用下降之前，任何行政命令，并有一个估计28天的滞后期开始减少地铁使用和结束的指数增长期的SARS-CoV-2在纽约市boroughs.Interpretation：我们的研究结果表明，在大流行期间呆在家里的能力受到社会经济地位和工作环境的限制。贫穷的社区并没有像富裕的社区那样减少流动性。此外，社会经济地位较低的社区的疾病负担更高，这可能是由于就地避难能力的不平等，和/或由于现有的其他健康差异过多，增加了对COVID-19的脆弱性。此外，地铁乘客量大幅下降与COVID-19病例指数增长阶段结束之间的滞后时间延长对未来的政策非常重要，因为它表明，如果疫情死灰复燃，并重新发布居家令，那么城市可以预期等待一个月后报告的病例才会趋于平稳。使用纽约市2020年1月至2020年4月的数据，我们发现，在纽约市行政区内，从地铁使用量开始减少到严重急性呼吸道综合征冠状病毒2型指数增长期结束之间，估计有28天的滞后。我们还对人类流动性（即，2020年4月11日当周的地铁乘客量）、社会人口因素和截至2020年4月26日的2019年冠状病毒病（COVID-19）发病率。收入中位数较低的地区、非白人和/或西班牙裔/拉丁裔人口比例较高的地区、必要工作人员比例较高的地区以及医疗保健必要工作人员比例较高的地区在大流行期间流动性更大。当调整必要工作者的百分比时，这些协会并没有保持，这表明必要工作推动了这些地区的人类流动。在对检测工作进行调整后，流动性增加和所有社会人口变量（75岁以上人口的百分比和卫生保健基本工作者的百分比除外）与每10万人中COVID-19病例的较高发病率相关。我们的研究表明，最弱势的社会群体不仅感染COVID-19的风险增加，而且他们缺乏充分参与社交距离干预的特权。
BACKGROUND: The United States CDC has reported that racial and ethnic disparities in the COVID-19 pandemic may in part be due to socioeconomic disadvantages that require individuals to continue to work outside their home and a lack of paid sick leave. However, data-driven analyses of the socioeconomic determinants of COVID-19 burden are still needed. Using data from New York City (NYC), we aimed to determine how socioeconomic factors impact human mobility and COVID-19 burden. Methods/Summary: New York City has a large amount of heterogeneity in socioeconomic status (SES) and demographics among neighborhoods. We used this heterogeneity to conduct a cross-sectional spatial analysis of the associations between human mobility (i.e., subway ridership), sociodemographic factors, and COVID-19 incidence as of April 26, 2020. We also conducted a secondary analysis of NYC boroughs (which are equivalent to counties in the city) to assess the relationship between the decline in subway use and the time it took for each borough to end the exponential growth period of COVID-19 cases.FINDINGS: Areas with the lower median income, a greater percentage of individuals who identify as non-white and/or Hispanic/Latino, a greater percentage of essential workers, and a greater percentage of healthcare workers had more subway use during the pandemic. The positive associations between subway use and median income, and between subway use and percent non-white and/or Hispanic/Latino do not remain when adjusted for the percent of essential workers. This suggests essential work is what drives subway use in lower SES zip codes and communities of color. Increased subway use was associated with a higher rate of COVID-19 cases per 100,000 population when adjusted for testing effort (aRR=1.11; 95% CI: 1.03 - 1.19), but this association was weaker once we adjusted for median income (aRR=1.06; 95% CI: 1.00 - 1.12). All sociodemographic variables were significantly associated with the rate of positive cases per 100,000 population when adjusting for testing effort (except percent uninsured) and adjusting for both income and testing effort. The risk factor with the strongest association with COVID-19 was the percent of individuals in essential work (aRR = 1.59, 95% CI: 1.36 - 1.86). We found that subway use declined prior to any executive order, and there was an estimated 28-day lag between the onset of reduced subway use and the end of the exponential growth period of SARS-CoV-2 within New York City boroughs.INTERPRETATION: Our results suggest that the ability to stay home during the pandemic has been constrained by SES and work circumstances. Poorer neighborhoods are not afforded the same reductions in mobility as their richer counterparts. Furthermore, lower SES neighborhoods have higher disease burdens, which may be due to inequities in ability to shelter-in-place, and/or due to the plethora of other existing health disparities that increase vulnerability to COVID-19. Furthermore, the extended lag time between the dramatic fall in subway ridership and the end of the exponential growth phase for COVID-19 cases is important for future policy, because it demonstrates that if there is a resurgence, and stay-at-home orders are re-issued, then cities can expect to wait a month before reported cases will plateau.Using data from New York City from January 2020 to April 2020, we found an estimated 28-day lag between the onset of reduced subway use and the end of the exponential growth period of severe acute respiratory syndrome coronavirus 2 within New York City boroughs. We also conducted a cross-sectional analysis of the associations between human mobility (i.e., subway ridership) on the week of April 11, 2020, sociodemographic factors, and coronavirus disease 2019 (COVID-19) incidence as of April 26, 2020. Areas with lower median income, a greater percentage of individuals who identify as non-White and/or Hispanic/Latino, a greater percentage of essential workers, and a greater percentage of health-care essential workers had more mobility during the pandemic. When adjusted for the percentage of essential workers, these associations did not remain, suggesting essential work drives human movement in these areas. Increased mobility and all sociodemographic variables (except percentage of people older than 75 years old and percentage of health-care essential workers) were associated with a higher rate of COVID-19 cases per 100,000 people, when adjusted for testing effort. Our study demonstrates that the most socially disadvantaged not only are at an increased risk for COVID-19 infection, they lack the privilege to fully engage in social distancing interventions.