Enhancing the Trajectories of Cancer Health Disparities Research: Improving Clinical Applications of Diversity, Equity, Inclusion, and Accessibility.

In order to accurately detect and prevent racial disparities, self-reported race (SRR) and ethnicity remain valuable tools; however, inaccurate capture of patient identity and broad aggregation of minoritized race groups present challenges for data interpretation. Also, although SRR is a proxy for shared social/cultural experience, it is not an accurate representation of shared endogenous factors. Biological investigations into cancer disparities, particularly those involving genetic features, should be framed in the context of genetic background or ancestry, as these are heritable aspects of population health. In reality, both genetics and environment work in concert to influence cancer risk and clinical outcomes. The best opportunity to define actionable means for reducing health disparities is in rigorous and comprehensive generation of rich data sets that characterize environmental, biological, and genetic components of disparate disease burden. To translate this pivotal disparities research into clinical tools and improved policies, we describe a diversity, equity, inclusion, and accessibility (DEIA) framework, which will increase participation from diverse backgrounds, reexamine previous research with a rigorous evaluation of appropriate SRR groupings, and engage community leaders to ensure that future research addresses the needs of communities at increased risk. On this path forward, we may finally end cancer disparities.

Social and Clinical Determinants of COVID-19 Outcomes: Modeling Real-World Data from a Pandemic Epicenter.

IMPORTANCE: As the United States continues to accumulate COVID-19 cases and deaths, and disparities persist, defining the impact of risk factors for poor outcomes across patient groups is imperative. OBJECTIVE: Our objective is to use real-world healthcare data to quantify the impact of demographic, clinical, and social determinants associated with adverse COVID-19 outcomes, to identify high-risk scenarios and dynamics of risk among racial and ethnic groups. DESIGN: A retrospective cohort of COVID-19 patients diagnosed between March 1 and August 20, 2020. Fully adjusted logistical regression models for hospitalization, severe disease and mortality outcomes across 1-the entire cohort and 2- within self-reported race/ethnicity groups. SETTING: Three sites of the NewYork-Presbyterian health care system serving all boroughs of New York City. Data was obtained through automated data abstraction from electronic medical records. PARTICIPANTS: During the study timeframe, 110,498 individuals were tested for SARS-CoV-2 in the NewYork-Presbyterian health care system; 11,930 patients were confirmed for COVID-19 by RT-PCR or covid-19 clinical diagnosis. MAIN OUTCOMES AND MEASURES: The predictors of interest were patient race/ethnicity, and covariates included demographics, comorbidities, and census tract neighborhood socio-economic status. The outcomes of interest were COVID-19 hospitalization, severe disease, and death. RESULTS: Of confirmed COVID-19 patients, 4,895 were hospitalized, 1,070 developed severe disease and 1,654 suffered COVID-19 related death. Clinical factors had stronger impacts than social determinants and several showed race-group specificities, which varied among outcomes. The most significant factors in our all-patients models included: age over 80 (OR=5.78, p= 2.29x10-24) and hypertension (OR=1.89, p=1.26x10-10) having the highest impact on hospitalization, while Type 2 Diabetes was associated with all three outcomes (hospitalization: OR=1.48, p=1.39x10-04; severe disease: OR=1.46, p=4.47x10-09; mortality: OR=1.27, p=0.001). In race-specific models, COPD increased risk of hospitalization only in Non-Hispanics (NH)-Whites (OR=2.70, p=0.009). Obesity (BMI 30+) showed race-specific risk with severe disease NH-Whites (OR=1.48, p=0.038) and NH-Blacks (OR=1.77, p=0.025). For mortality, Cancer was the only risk factor in Hispanics (OR=1.97, p=0.043), and heart failure was only a risk in NH-Asians (OR=2.62, p=0.001). CONCLUSIONS AND RELEVANCE: Comorbidities were more influential on COVID-19 outcomes than social determinants, suggesting clinical factors are more predictive of adverse trajectory than social factors.