Disproportionate impacts of COVID-19 in a large US city.
Disproportionate impacts of COVID-19 in a large US city.
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DOI：
10.1371/journal.pcbi.1011149
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发表时间：
2023-06
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
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作者：
新冠肺炎对个人的影响不成比例，取决于他们在哪里生活和工作，以及他们的种族、民族和社会经济地位。研究已经记录了整个大流行期间临界点的灾难性差异，但尚未系统地跟踪其随时间的严重程度。使用2020年3月11日至2021年6月1日的匿名住院数据和细粒感染住院率，我们估计了德克萨斯州奥斯汀按年龄段和邮政编码划分的新冠肺炎负担。在这15个月期间，我们估计总体感染率为23.7%(95%CRI：22.5-24.8%)，病例报告率为29.4%(95%CRI：28.0-31.0%)。65岁以上的人感染的可能性低于年轻年龄组(11.2%[95%CRI：10.3-12.0%]比25.1%[95%CRI：23.7-26.4%])，但更有可能住院(1,965/10万比376/10万)，并报告他们的感染(53%[95%CRI：49-57%]比28%[95%CRI：27-30%])。我们使用混合效应泊松回归模型来估计感染和报告率之间的差异作为社会脆弱性的函数。我们比较了脆弱性排名第75个百分位数和第25个百分位数的邮政编码，发现与脆弱性较低的社区相比，更脆弱的社区的感染率是2.5(95%CRI：2.0-3.0)倍，报告率只有70%(95%CRI：60%-82%)。不平等现象持续存在，但在15个月的研究期间显著下降。我们的结果表明，需要进一步的公共卫生努力来缓解当地的新冠肺炎差距，当监测数据有限时，疾控中心的社会脆弱性指数可以作为当地范围内风险的可靠预测指标。根据社区的社会经济和种族构成，新冠肺炎对社区的影响不成比例。研究已经记录了多个地理尺度上的灾难性差异，但尚未跟踪它们如何随着时间的推移而演变。在这里，我们使用细粒度流行病学数据来估计德克萨斯州奥斯汀按年龄组和邮政编码划分的新冠肺炎的时变不同负担。在这15个月期间，我们估计23.7%(95%CRI：22.5-24.8%)的人口受到感染，其中29.4%(95%CRI：28.0-31.0%)报告了感染。感染在该地区的传播并不均匀。65岁以上的人感染的可能性明显低于年轻年龄组(11.2%[95%CRI：10.3-12.0%]比25.1%[95%CRI：23.7-26.4%])，这表明保护这些人口的努力可能是有效的。我们发现，与最不脆弱的邮政编码相比，该地区最脆弱的邮政编码面临的感染风险是最小的邮政编码的2.5倍，而在脆弱的社区报告感染的可能性只有70%。不平等现象持续存在，但在15个月的研究期间显著下降。需要进一步的公共卫生努力来解决当地的新冠肺炎差距。
COVID-19 has disproportionately impacted individuals depending on where they live and work, and based on their race, ethnicity, and socioeconomic status. Studies have documented catastrophic disparities at critical points throughout the pandemic, but have not yet systematically tracked their severity through time. Using anonymized hospitalization data from March 11, 2020 to June 1, 2021 and fine-grain infection hospitalization rates, we estimate the time-varying burden of COVID-19 by age group and ZIP code in Austin, Texas. During this 15-month period, we estimate an overall 23.7% (95% CrI: 22.5–24.8%) infection rate and 29.4% (95% CrI: 28.0–31.0%) case reporting rate. Individuals over 65 were less likely to be infected than younger age groups (11.2% [95% CrI: 10.3–12.0%] vs 25.1% [95% CrI: 23.7–26.4%]), but more likely to be hospitalized (1,965 per 100,000 vs 376 per 100,000) and have their infections reported (53% [95% CrI: 49–57%] vs 28% [95% CrI: 27–30%]). We used a mixed effect poisson regression model to estimate disparities in infection and reporting rates as a function of social vulnerability. We compared ZIP codes ranking in the 75th percentile of vulnerability to those in the 25th percentile, and found that the more vulnerable communities had 2.5 (95% CrI: 2.0–3.0) times the infection rate and only 70% (95% CrI: 60%-82%) the reporting rate compared to the less vulnerable communities. Inequality persisted but declined significantly over the 15-month study period. Our results suggest that further public health efforts are needed to mitigate local COVID-19 disparities and that the CDC’s social vulnerability index may serve as a reliable predictor of risk on a local scale when surveillance data are limited. COVID-19 disproportionately impacted communities based on their socioeconomic and racial composition. Studies have documented catastrophic disparities at multiple geographic scales, but have not yet tracked how they evolved over time. Here, we use fine-grain epidemiological data to estimate the time-varying disparate burden of COVID-19 by age group and ZIP code in Austin, Texas. During this 15-month period, we estimate that 23.7% (95% CrI: 22.5–24.8%) of the population was infected and 29.4% (95% CrI: 28.0–31.0%) of those infections were reported. Infections were not spread evenly across the region. Individuals over 65 were significantly less likely to be infected than younger age groups (11.2% [95% CrI: 10.3–12.0%] vs 25.1% [95% CrI: 23.7–26.4%]), suggesting that efforts to protect those populations may have been effective. We found that the most vulnerable ZIP codes in the region faced 2.5 times the infection risks compared with the least vulnerable ZIP codes, and that infections were only 70% as likely to be reported in vulnerable communities. Inequality persisted but declined significantly over the 15-month study period. Further public health efforts are needed to address local COVID-19 disparities.
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DOI：
10.15585/mmwr.mm7117e3
发表时间：
2022-04-29
期刊：
MMWR. Morbidity and mortality weekly report
影响因子：
--
作者：
通讯作者：
--
影响因子：
13.8
作者：
Anand S;Montez-Rath M;Han J;Cadden L;Hunsader P;Kerschmann R;Beyer P;Boyd SD;Garcia P;Dittrich M;Block GA;Parsonnet J;Chertow GM
通讯作者：
Chertow GM
DOI：
10.15585/mmwr.mm695152e2
发表时间：
2021-01-01
期刊：
MMWR. Morbidity and mortality weekly report
影响因子：
--
作者：
通讯作者：
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DOI：
10.1016/j.ijdrr.2021.102613
发表时间：
2021-10-13
影响因子：
5
作者：
Bixler, R. Patrick;Yang, Euijin;Coudert, Marc
通讯作者：
Coudert, Marc
影响因子：
120.7
作者：
Auger, Katherine A.;Shah, Samir S.;Thomson, Joanna E.
通讯作者：
Thomson, Joanna E.