Greta Goto

Bio: Greta Goto is an academic researcher. The author has contributed to research in topics: Racism & Health equity. The author has an hindex of 1, co-authored 2 publications receiving 22 citations.

Structural Racism in the COVID-19 Pandemic: Moving Forward.

Abstract: The COVID-19 pandemic has taken a substantial human, social and economic toll globally, but its impact on Black/African Americans, Latinx, and American Indian/Alaska Native communities in the U.S. is unconscionable. As the U.S. continues to combat the current COVID-19 cycle and prepares for future pandemics, it will be critical to learn from and rectify past and contemporary wrongs. Drawing on experiences in genomic research and intersecting areas in medical ethics, health disparities, and human rights, this article considers three key COVID-19-related issues: research to identify remedies; testing, contact tracing and surveillance; and lingering health needs and disability. It provides a pathway for the future: community engagement to develop culturally-sensitive responses to the myriad genomic/bioethical dilemmas that arise, and the establishment of a Truth and Reconciliation Commission to transition the country from its contemporary state of segregation in healthcare and health outcomes into an equitable and prosperous society for all.

An Open Dialogue on Health Disparities and Structural Racism: Response to Open Peer Commentaries

Abstract: In our target article (Sabatello et al. 2021), we proposed the use of community engagement and the establishment of a Truth and Reconciliation Commission (TRC) as pathways for promoting social just...

科研数据共享的挑战 (The Conundrum of Sharing Research Data)

Abstract: We must all accept that science is data and that data are science, and thus provide for, and justify the need for the support of, much-improved data curation. (Hanson, Sugden, & Alberts) Researchers are producing an unprecedented deluge of data by using new methods and instrumentation. Others may wish to mine these data for new discoveries and innovations. However, research data are not readily available as sharing is common in only a few fields such as astronomy and genomics. Data sharing practices in other fields vary widely. Moreover, research data take many forms, are handled in many ways, using many approaches, and often are difficult to interpret once removed from their initial context. Data sharing is thus a conundrum. Four rationales for sharing data are examined, drawing examples from the sciences, social sciences, and humanities: (1) to reproduce or to verify research, (2) to make results of publicly funded research available to the public, (3) to enable others to ask new questions of extant data, and (4) to advance the state of research and innovation. These rationales differ by the arguments for sharing, by beneficiaries, and by the motivations and incentives of the many stakeholders involved. The challenges are to understand which data might be shared, by whom, with whom, under what conditions, why, and to what effects. Answers will inform data policy and practice. © 2012 Wiley Periodicals, Inc.

The Effects of COVID-19 Spread on the Egyptian Exchange Sectors: Winners and Losers across Time (preprint)

Abstract: This paper attempts to investigate the effects of Coronavirus spread on stock markets using panel data analysis, on daily basis over the period from March 1, 2020 until September 30, 2020. Coronavirus spread has been measured by daily cases and daily deaths per million of population, while stock return is measured by Δ in sectoral indices. This has been conducted after dividing the research period into 6 months from March to September and has been applied on 17 sectors in the Egyptian Exchange. Using panel data analysis, results indicate significant negative industry effects for each of banking sector (BANK), Food, Beverages and Tobacco sector (FOBT) and Health Care & Pharmaceuticals sector (HLTH). Besides, findings show significant positive industry effects for each of Contracting & Construction Engineering sector (COCE), Energy & Support Services sector (ENGY), IT, Media & Communication Services sector (IMCS), Shipping & Transportation Services sector (SHTS) and Trade & Distributors sector (TRDB). The robustness check supports the significant negative industry effects for each of Food, Beverages and Tobacco (FOBT) and Health Care & Pharmaceuticals (HLTH) (as losers) and the significant positive industry effects for each of Energy & Support Services (ENGY), Shipping & Transportation Services (SHTS) and Trade & Distributors (TRDB) (as winners).

Neighborhood Social Cohesion and Inequalities in COVID-19 Diagnosis Rates by Area-Level Black/African American Racial Composition.

Abstract: Geographic inequalities in COVID-19 diagnosis are now well documented. However, we do not sufficiently know whether inequalities are related to social characteristics of communities, such as collective engagement. We tested whether neighborhood social cohesion is associated with inequalities in COVID-19 diagnosis rate and the extent the association varies across neighborhood racial composition. We calculated COVID-19 diagnosis rates in Philadelphia, PA, per 10,000 general population across 46 ZIP codes, as of April 2020. Social cohesion measures were from the Southeastern Pennsylvania Household Health Survey, 2018. We estimated Poisson regressions to quantify associations between social cohesion and COVID-19 diagnosis rate, testing a multiplicative interaction with Black racial composition in the neighborhood, which we operationalize via a binary indicator of ZIP codes above vs. below the city-wide average (41%) Black population. Two social cohesion indicators were significantly associated with COVID-19 diagnosis. Associations varied across Black neighborhood racial composition (p <0.05 for the interaction test). In ZIP codes with ≥41% of Black people, higher collective engagement was associated with an 18% higher COVID-19 diagnosis rate (IRR=1.18, 95%CI=1.11, 1.26). In contrast, areas with <41% of Black people, higher engagement was associated with a 26% lower diagnosis rate (IRR=0.74, 95%CI=0.67, 0.82). Neighborhood social cohesion is associated with both higher and lower COVID-19 diagnosis rates, and the extent of associations varies across Black neighborhood racial composition. We recommend some strategies for reducing inequalities based on the segmentation model within the social cohesion and public health intervention framework.

The Invisibility of Asian Americans in COVID-19 Data, Reporting, and Relief.

Abstract: Without proper recognition of the dual pandemics of COVID-19 and racism that Asian Americans and other racial minorities in the United States are facing, we cannot successfully address structural b...

Structural Racism in the COVID-19 Pandemic: Don't Forget about the Children!

Abstract: The COVID-19 pandemic has been unprecedented, in every sense of the word. At the time of writing, there have been nearly 80 million confirmed cases of coronavirus and nearly 2 million deaths worldw...