Assessing differential impacts of COVID-19 on black communities

Gender and Racial Disparity among Addiction Psychiatry Fellows in the United States

The United States (US) has a culturally diverse population. However, the percentage of underrepresented minorities (URMs) and women in healthcare does not fully reflect their current and future demographics. Our objective was to explore and forecast the gender and racial trends in the US addiction psychiatry fellowship programs. A retrospective analysis was performed using data from Accreditation Council for Graduate Medical Education (ACGME) Resource Books which encompassed US addiction psychiatry fellows from 2007 to 2020. Simple linear and multiple regression were used to predict the 2030 addiction psychiatry workforce. White (Non-Hispanic) and Asian/Pacific Islander had a relative change of -2.8% and -26.1% from 2011 to 2020, respectively. Black (non-Hispanic) had a relative change of + 5.2%. Hispanic and Native American/Alaskan had no relative change during this time. From 2007 to 2020, women's representation relatively decreased by 10.9%. Statistically, these dynamic trends of the addiction psychiatry workforce will continue to exist in 2030. Women and URM addiction psychiatrists play an effective role in addressing substance use disorders (SUD). Unfortunately, the current gender and racial disparities in addiction psychiatry will exist in the future. Understanding the continued gender and racial bias in addiction psychiatry fellowships and devising appropriate recommendations can help mitigate the existing disparities.

Geospatial evaluation of COVID-19 mortality: Influence of socio-economic status and underlying health conditions in contiguous USA

Since its outbreak, COVID-19 disease has claimed over one hundred thousand lives in the United States, resulting to multiple and complex nation-wide challenges. In this study, we employ global and local regression models to assess the influence of socio-economic and health conditions on COVID-19 mortality in contiguous USA. For a start, stepwise and exploratory regression models were employed to isolate the main explanatory variables for COVID-19 mortality from the ensemble 33 socio-economic and health parameters between January 1st and 16th of September 2020. Preliminary results showed that only five out of the examined variables (case fatality rate, vulnerable population, poverty, percentage of adults that report no leisure-time physical activity, and percentage of the population with access to places for physical activity) can explain the variability of COVID-19 mortality across the Counties of contiguous USA within the study period. Consequently, we employ three global and two local regression algorithms to model the relationship between COVID-19 and the isolated socio-economic and health variables. The outcomes of the regression analyses show that the adopted models can explain 61%-81% of COVID-19 mortality across the contiguous USA within the study period. However, MGWR yielded the highest R2 (0.81) and lowest AICc values (4031), emphasizing that it is the most efficient among the adopted regression models. The computed average adjusted R2 values show that local regression models (mean adj. R2 = 0.80) outperformed the global regression models (mean adj. R2 = 0.64), indicating that the former is ideal for modeling spatial causal relationships. The GIS-based optimized cluster analyses results show that hotspots for COVID-19 mortality as well as socioeconomic variables are mostly delineated in the South, Mid-West and Northeast of contiguous USA. COVID-19 mortality exhibited positive and significant association with black race (0.51), minority (0.48) and poverty (0.34). Whereas, the percentage of persons that attended college was negatively associated with poverty (-0.51), obesity (-0.50) and diabetes (-0.45). Results show that education is crucial to improve socio-economic and health conditions of the Americans. We conclude that investing in people's standard of living would reduce the vulnerability of an entire population.