Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile.
Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile.
复制标题
DOI：
10.1126/science.abg5298
复制
发表时间：
2021-05-28
期刊：
影响因子：
--
通讯作者：
Santillana M
中科院分区：
文献类型：
--
作者：
Mena GE;Martinez PP;Mahmud AS;Marquet PA;Buckee CO;Santillana M
智利的圣地亚哥是一个高度隔离的城市，有着明显的富裕和贫困区。这种情况提供了一个窗口，说明社会因素如何推动严重急性呼吸道综合征冠状病毒2（SARS-CoV-2）在一个收入高度不平等的经济脆弱社会中的流行。Mena等人分析了SARS-CoV-2的发病率和死亡率，以了解疾病负担的空间变化。低收入城市的感染死亡率较高，原因是合并症和缺乏获得保健的机会。各市镇之间在医疗保健提供系统质量方面的差异在测试延迟和能力方面变得明显。这些指标在很大程度上解释了COVID-19漏报和死亡的差异，并表明这些不平等对年轻人的影响不成比例。在SARS-CoV-2大流行期间，在一个经济脆弱的大城市，卫生保健不平等的后果加剧了。COVID-19危机暴露了社区之间的重大不平等。了解使某些群体特别脆弱的社会风险因素对于确保对这一流行病和未来的流行病采取更有效的干预措施至关重要。在这里，我们把社会经济地位作为一个风险因素。虽然人们普遍认为社会和经济不平等对健康结果有负面影响，但社会经济地位影响疾病结果的机制仍不清楚。这些机制可以通过一系列系统性结构因素来调节，如获得保健和经济安全网。我们通过提供疾病发病率和死亡率及其对圣地亚哥（一个高度隔离的城市和智利首都）人口和社会经济阶层的依赖的深入表征来解决这一差距。结合公开数据来源，我们对第一波大流行期间的病例发病率和死亡率进行了全面分析。我们将COVID-19结果与行为和医疗保健系统因素相关联，同时研究它们与年龄和社会经济地位的相互作用。为了克服不完整病例计数数据的内在偏差，我们使用了详细的死亡率数据。我们开发了一个简约的高斯过程模型来研究超额死亡及其不确定性，并使用一种新的正则化最大似然反卷积方法从死亡时间序列中重建真实发病率。为了按年龄和社会经济地位估计感染死亡率，我们实施了一个分层贝叶斯模型，该模型在考虑病例信息不完整性的同时调整了报告偏倚。根据健康和行为指标，我们发现COVID-19结果与社会经济地位之间存在强有力的关联。具体来说，我们发现，在社会经济地位较低的城市，在大流行早期几乎没有检测，而且封锁对人员流动性的影响并不像在更富裕的地区那样大。这些地点的检测阳性率和检测延迟率高得多，表明卫生保健系统遏制疫情蔓延的能力受损。我们还发现，2020年5月至7月期间的死亡人数比正常年份增加了73%，而社会经济水平较低的城市受到的打击最严重，无论是与COVID-19相关的死亡人数还是超额死亡人数。最后，感染死亡率的社会经济梯度在较年轻的年龄组中似乎特别陡峭，反映出基线健康状况较差，社会经济地位低的城市获得保健的机会有限。总之，这些研究结果突出了在一个高度隔离的城市中社会经济和医疗保健差异的重大后果，并提供了实用的方法论方法，可用于根据公共数据描述其他城市中心的COVID-19负担和死亡率，即使报告不完整和有偏见。左边的地图显示了这项研究中所包括的城市，并根据其社会经济地位得分进行了着色。对于COVID-19死亡和超额死亡之间的比较（右上），COVID-19确认的死亡以浅绿色显示，COVID-19归因的死亡以深绿色显示。以灰色显示的超额死亡数对应于观察到的死亡数和预测的死亡数之间的差异。使用高斯过程模型估计预测死亡。阴影表示超额死亡的95%可信区间。感染死亡率（右下）是通过实施分层贝叶斯模型推断的，垂直线代表年龄和社会经济地位的可信区间。COVID-19疫情对城市的影响尤为严重。在这里，我们提供了一个深入的表征疾病的发病率和死亡率及其依赖于人口和社会经济阶层在圣地亚哥，一个高度隔离的城市和智利首都。我们的分析显示，社会经济地位与COVID-19结果和公共卫生能力之间存在密切联系。生活在社会经济地位低的城市的人在封锁期间的流动性并没有像那些生活在更富裕的城市的人那样减少。在这些地方，大流行早期的检测量可能不足，检测阳性率和检测延迟率都要高得多。我们发现社会经济地位与死亡率之间存在很强的关联，无论是COVID-19导致的死亡还是超额死亡。最后，我们表明，在低收入城市的年轻人感染死亡率较高。总之，这些结果突出了社会经济不平等对健康结果的严重后果。
Santiago, Chile, is a highly segregated city with distinct zones of affluence and deprivation. This setting offers a window on how social factors propel the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in an economically vulnerable society with high levels of income inequality. Mena et al. analyzed incidence and mortality attributed to SARS-CoV-2 to understand spatial variations in disease burden. Infection fatality rates were higher in lower-income municipalities because of comorbidities and lack of access to health care. Disparities between municipalities in the quality of their health care delivery system became apparent in testing delays and capacity. These indicators explain a large part of the variation in COVID-19 underreporting and deaths and show that these inequalities disproportionately affected younger people. Science, abg5298, this issue p. eabg5298 The consequences of health care inequalities have been exacerbated during the SARS-CoV-2 pandemic in a large, economically vulnerable city. The COVID-19 crisis has exposed major inequalities between communities. Understanding the societal risk factors that make some groups particularly vulnerable is essential to ensure more effective interventions for this and future pandemics. Here, we focus on socioeconomic status as a risk factor. Although it is broadly understood that social and economic inequality has a negative impact on health outcomes, the mechanisms by which socioeconomic status affects disease outcomes remain unclear. These mechanisms can be mediated by a range of systemic structural factors, such as access to health care and economic safety nets. We address this gap by providing an in-depth characterization of disease incidence and mortality and their dependence on demographic and socioeconomic strata in Santiago, a highly segregated city and the capital of Chile. Combining publicly available data sources, we conducted a comprehensive analysis of case incidence and mortality during the first wave of the pandemic. We correlated COVID-19 outcomes with behavioral and health care system factors while studying their interaction with age and socioeconomic status. To overcome the intrinsic biases of incomplete case count data, we used detailed mortality data. We developed a parsimonious Gaussian process model to study excess deaths and their uncertainty and reconstructed true incidence from the time series of deaths with a new regularized maximum likelihood deconvolution method. To estimate infection fatality rates by age and socioeconomic status, we implemented a hierarchical Bayesian model that adjusts for reporting biases while accounting for incompleteness in case information. We find robust associations between COVID-19 outcomes and socioeconomic status, based on health and behavioral indicators. Specifically, we show in lower–socioeconomic status municipalities that testing was almost absent early in the pandemic and that human mobility was not reduced by lockdowns as much as it was in more affluent locations. Test positivity and testing delays were much higher in these locations, indicating an impaired capacity of the health care system to contain the spread of the epidemic. We also find that 73% more deaths than in a normal year were observed between May and July 2020, and municipalities at the lower end of the socioeconomic spectrum were hit the hardest, both in relation to COVID-19–attributed deaths and excess deaths. Finally, the socioeconomic gradient of the infection fatality rate appeared particularly steep for younger age groups, reflecting worse baseline health status and limited access to health care in municipalities with low socioeconomic status. Together, these findings highlight the substantial consequences of socioeconomic and health care disparities in a highly segregated city and provide practical methodological approaches useful for characterizing the COVID-19 burden and mortality in other urban centers based on public data, even if reports are incomplete and biased. The map on the left shows the municipalities that were included in this study, colored by their socioeconomic status score. For the comparison between COVID-19 deaths and excess deaths (top right), COVID-19–confirmed deaths are shown in light green and COVID-19–attributed deaths in dark green. Excess deaths, shown in gray, correspond to the difference between observed and predicted deaths. Predicted deaths were estimated using a Gaussian process model. The shading indicates 95% credible intervals for the excess deaths. The infection fatality rates (bottom right) were inferred by implementing a hierarchical Bayesian model, with vertical lines representing credible intervals by age and socioeconomic status. The COVID-19 pandemic has affected cities particularly hard. Here, we provide an in-depth characterization of disease incidence and mortality and their dependence on demographic and socioeconomic strata in Santiago, a highly segregated city and the capital of Chile. Our analyses show a strong association between socioeconomic status and both COVID-19 outcomes and public health capacity. People living in municipalities with low socioeconomic status did not reduce their mobility during lockdowns as much as those in more affluent municipalities. Testing volumes may have been insufficient early in the pandemic in those places, and both test positivity rates and testing delays were much higher. We find a strong association between socioeconomic status and mortality, measured by either COVID-19–attributed deaths or excess deaths. Finally, we show that infection fatality rates in young people are higher in low-income municipalities. Together, these results highlight the critical consequences of socioeconomic inequalities on health outcomes.
登录
查看更多内容
影响因子：
6.3
作者：
Aburto JM;Kashyap R;Schöley J;Angus C;Ermisch J;Mills MC;Dowd JB
通讯作者：
Dowd JB
影响因子：
16.6
作者：
Gozzi N;Tizzoni M;Chinazzi M;Ferres L;Vespignani A;Perra N
通讯作者：
Perra N
DOI：
10.1080/10618600.2016.1172487
发表时间：
2017-01-01
影响因子：
2.4
作者：
de Valpine, Perry;Turek, Daniel;Bodik, Rastislav
通讯作者：
Bodik, Rastislav
影响因子：
5.7
作者：
Hoeting, JA;Madigan, D;Volinsky, CT
通讯作者：
Volinsky, CT
影响因子：
10.6
作者：
GREEN, PJ
通讯作者：
GREEN, PJ