A call for epidemic modeling to examine historical and structural drivers of racial disparities in infectious disease.
A call for epidemic modeling to examine historical and structural drivers of racial disparities in infectious disease.
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DOI：
10.1016/j.socscimed.2021.113833
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发表时间：
2021-05
期刊：
影响因子：
--
通讯作者：
Van Den Abbeele S
中科院分区：
文献类型：
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作者：
Cassels S;Van Den Abbeele S
种族主义是造成健康差距的根本原因（Link和Elan，1995年; Pirtle，2020年;威廉姆斯和柯林斯，2001年）。根本原因采取动态途径，导致多种不同的健康结果。具体而言，黑人和白色人健康的差异是由于过去和现在的种族主义和结构性不平等，如过度监禁、隔离以及歧视性住房和就业政策（Pirtle，2020年;威廉姆斯和柯林斯，2001年）。Richardson et al.最近发表的论文。（2021）确定了将种族主义与美国黑人感染SARS-CoV-2的风险较高联系起来的两种机制：一线工作中黑人工人的比例过高，以及居住在密度较高的住房中（Richardson等人，2021年）。然后，他们使用流行病模型来研究对路易斯安那州黑人美国人的赔偿如何影响决定COVID-19生殖率R 0的变量。传染病的流行病模型很少包含健康差异的结构性驱动因素，因此值得称赞的是，本文扩展了他们的建模方法，以包括这些驱动因素。然而，为了参数化建模框架，以适应这些更大的历史和结构的驱动程序，重要的假设因果关系的途径，连接结构驱动程序，明确的中介机制的模型是必要的。受过传统训练的流行病学家和社会科学家被教导要警惕缺乏有力经验证据的假设。尽管如此，流行病建模确实是一个平台，在这个平台上，我们可以，有些人可能会认为应该，探索想象的或“激进的方法来解决公共卫生问题”的空间（施瓦茨等人，2016年）。考虑到消除健康方面种族差异的紧迫性和迫切需要，评估大规模结构性干预措施的流行病模型应该成为科学话语的重要组成部分（Richardson等人，2021年）。因此，我们希望这篇论文和我们的回应能够呼吁其他人开发创新的流行病模型，以进一步研究传染病差异的历史和当前结构性驱动因素。需要有两个重要的发展。首先，进一步的研究必须澄清和说明将种族主义和其他结构性因素与健康差距联系起来的干预机制。第二，正如作者所指出的，当客观的、定义明确的参数不可用时，学术界需要接受创造性和想象力的替代方案。传染病的流行模型长期以来一直被用于评估干预措施对人群水平健康结果的影响（Garnett，2002; Heesterbeek et al.，2015年）的报告。在过去的20年里，在将社会和行为决定因素纳入流行病模型方面取得了重大进展（Cassels等人，2008年），因此建模一直是支持社会层面干预措施以减少健康差距的重要组成部分。例如，流行病建模在检查HIV中的种族差异方面至关重要（Goodreau等人，2017），以及评估社会和行为干预（Jenness et al.，2019年）。这些模型已经得到了大量实证研究的支持，以参数化最接近的生物学和行为决定因素，例如种族或血清状态的选择性混合（Beck et al.，2015; Birkett等人，2019年）。下一波流行病建模需要纳入健康差异的“上游”、远端驱动因素（Shannon et al.，2015年）的报告。流行病模型应用于研究a）过去和现在的结构性驱动因素如何影响健康差距，以及B）评估结构性干预措施的潜在影响。关键的挑战是，我们并不总是...
Racism is a fundamental cause of health disparities (Link and Phelan, 1995; Pirtle, 2020; Williams and Collins, 2001). Fundamental causes take dynamic pathways to cause multiple disparate health outcomes. Disparities in Black and white health, specifically, are due to past and present racism and structural inequities such as hyperincarceration, segregation, and discriminatory housing and employment policies (Pirtle, 2020; Williams and Collins, 2001). The recent paper by Richardson et al.(2021) identified two mechanisms that link racism to higher risk of SARS-CoV-2 infection for Black Americans: Black workers overrepresented in front-line work, and living in higher density housing (Richardson et al., 2021). Then they used epidemic modeling to examine how reparations payments to Black Americans in Louisiana could impact variables that determine the COVID-19 reproductive ratio, R0. Rarely have epidemic models for infectious diseases incorporated structural drivers of health disparities, thus it is commendable that this paper extended their modeling approach to include such drivers. However, in order to parameterize modeling frameworks to accommodate these larger historic and structural drivers, significant assumptions regarding causal pathways that link structural drivers to explicit mediating mechanisms in the models are needed. Classically trained epidemiologists and social scientists are taught to be wary of assumptions that lack robust empirical evidence. Nonetheless, epidemic modeling is indeed the platform in which we can, and some may argue should, explore the space of imagined or “radical approaches to public health problems”(Schwartz et al., 2016). Given the urgency and critical need to combat racial disparities in health, epidemic models that evaluate large-scale, structural interventions should be a significant part of scientific discourse (Richardson et al., 2021). Thus, we hope that this paper and our response will act as a call for others to develop innovative epidemic models to further examine historical and current structural drivers of infectious disease disparities. Two important developments are needed. First, further research must clarify and parameterize the intervening mechanisms that link racism and other structural factors to health disparities. Second, as the authors state, the academic community needs to embrace creative and imaginative alternatives when objective, well-defined parameters are not available.Epidemic models of infectious disease have long been used to evaluate the impact of interventions on population-level health outcomes (Garnett, 2002; Heesterbeek et al., 2015). Over the past 20 years, significant progress has been made in incorporating social and behavioral determinants in epidemic models (Cassels et al., 2008), and thus modeling has been an important component in supporting societal-level interventions to reduce health disparities. For example, epidemic modeling has been critical in examining racial disparities in HIV (Goodreau et al., 2017), as well as assessing social and behavioral interventions (Jenness et al., 2019). These models have been bolstered by substantial empirical research to parameterize the proximate biological and behavioral determinants, such as selective mixing by race or serostatus (Beck et al., 2015; Birkett et al., 2019). The next wave of epidemic modeling needs to incorporate ‘upstream,’distal drivers of health disparities (Shannon et al., 2015). Epidemic models should be used to examine a) how past and present structural drivers affect health disparities, and b) evaluate the potential impact of structural interventions. The key challenge is that we do not always have …
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影响因子：
168.9
作者：
Shannon, Kate;Strathdee, Steffanie A.;Goldenberg, Shira M.;Duff, Putu;Mwangi, Peninah;Rusakova, Maia;Reza-Paul, Sushena;Lau, Joseph;Deering, Kathleen;Pickles, Michael R.;Boily, Marie-Claude
通讯作者：
Boily, Marie-Claude
DOI：
10.1126/science.aaa4339
发表时间：
2015-03-13
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Heesterbeek H;Anderson RM;Andreasen V;Bansal S;De Angelis D;Dye C;Eames KT;Edmunds WJ;Frost SD;Funk S;Hollingsworth TD;House T;Isham V;Klepac P;Lessler J;Lloyd-Smith JO;Metcalf CJ;Mollison D;Pellis L;Pulliam JR;Roberts MG;Viboud C;Isaac Newton Institute IDD Collaboration
通讯作者：
Isaac Newton Institute IDD Collaboration
影响因子：
4.4
作者：
Birkett M;Neray B;Janulis P;Phillips G 2nd;Mustanski B
通讯作者：
Mustanski B
影响因子：
3.6
作者：
Garnett, GP
通讯作者：
Garnett, GP
DOI：
10.15585/mmwr.mm6927a3
发表时间：
2020-07-10
期刊：
MMWR. Morbidity and mortality weekly report
影响因子：
--
作者：
Hsu HE;Ashe EM;Silverstein M;Hofman M;Lange SJ;Razzaghi H;Mishuris RG;Davidoff R;Parker EM;Penman-Aguilar A;Clarke KEN;Goldman A;James TL;Jacobson K;Lasser KE;Xuan Z;Peacock G;Dowling NF;Goodman AB
通讯作者：
Goodman AB