Faculty Bibliography

BACKGROUND:While hate crimes rose during the COVID-19 pandemic, few studies examined whether this pandemic-time racial discrimination has led to negative health consequences at the population level. OBJECTIVE:We examined whether experienced and perceived racial discrimination were associated with mental or behavioral health outcomes during the pandemic. DESIGN/METHODS:In October 2020, we conducted a national survey with minorities oversampled that covered respondents' sociodemographic background and health-related information. PARTICIPANTS/METHODS:A total of 2709 participants responded to the survey (response rate: 4.2%). MAIN MEASURES/METHODS:The exposure variables included (1) experienced and encountered racial discrimination, (2) experienced racial and ethnic cyberbullying, and (3) perceived racial bias. Mental health outcomes were measured by psychological distress and self-rated happiness. Measures for behavioral health included sleep quality, change in cigarette smoking, and change in alcohol consumption. Weighted logistic regressions were performed to estimate the associations between the exposure variables and the outcomes, controlling for age, gender, race and ethnicity, educational attainment, household income, eligibility to vote, political party, COVID-19 infection, and geographic region. Separate regressions were performed in the six racial and ethnic subgroups: non-Hispanic White, non-Hispanic Black, Hispanic, East Asian, South Asian, and Southeast Asian respondents. KEY RESULTS/RESULTS:Experienced racial discrimination was associated with higher likelihood of psychological distress (adjusted odds ratio [AOR] = 2.18, 95% confidence interval [95% CI]: 1.34-3.55). Experienced racial discrimination (AOR = 2.31, 95% CI: 1.34-3.99) and perceived racial bias (AOR = 1.05, 95% CI: 1.00-1.09) were both associated with increased cigarette smoking. The associations between racial discrimination and mental distress and substance use were most salient among Black, East Asian, South Asian, and Hispanic respondents. CONCLUSIONS:Racial discrimination may be associated with higher likelihood of distress, and cigarette smoking among racial and ethnic minorities. Addressing racial discrimination is important for mitigating negative mental and behavioral health ramifications of the pandemic.

Racial discrimination has intensified in the U.S. during the COVID-19 pandemic, but how it disrupted healthcare is largely unknown. This study investigates the association of racial discrimination with delaying or forgoing care during the pandemic based on data from a nationally representative survey, the Health, Ethnicity and Pandemic (HEAP) study (n = 2552) conducted in October 2020 with Asians, Hispanics and non-Hispanic Blacks oversampled. Racial discrimination during the pandemic was assessed in three domains: experienced racial discrimination, race-related cyberbullying, and Coronavirus racial bias beliefs. Respondents answered whether they had delayed or forgone any type of healthcare due to the pandemic. Overall, 63.7% of respondents reported delaying or forgoing any healthcare during the pandemic. About 20.3% East/Southeast Asians, 18.6% non-Hispanic Blacks and 15.9% Hispanics reported experiences of racial discrimination, compared with 2.8% of non-Hispanic Whites. Experienced racial discrimination was associated with delaying/forgoing care among non-Hispanic Blacks (Adjusted odds ratios[AOR] = 4.58, 95% confidence interval[CI]: 2.22-9.45), Hispanics (AOR = 3.88, 95%CI: 1.51-9.98), and East/Southeast Asians (AOR = 2.14, 95%CI: 1.22-3.77). Experiencing race-related cyberbullying was significantly associated with delaying/forgoing care among non-Hispanic Blacks (AOR = 1.34, 95%CI: 1.02-1.77) and East/Southeast Asians (AOR = 1.51, 95%CI: 1.19-1.90). Coronavirus racial bias was significantly associated with delaying/forgoing care among East/Southeast Asians (AOR = 1.55, 95%CI: 1.16-2.07). The three domains of racial discrimination were consistently associated with delayed or forgone health care among East/Southeast Asians during the COVID-19 pandemic; some of the associations were also seen among non-Hispanic Blacks and Hispanics. These results demonstrate that addressing racism is important for reducing disparities in healthcare delivery during the pandemic and beyond.

The COVID-19 pandemic has resulted in more than 600 million confirmed cases worldwide since December 2021. Cardiovascular disease (CVD) is both a risk factor for COVID-19 mortality and a complication that many COVID-19 patients develop. This study uses Twitter data to identify the spatiotemporal patterns and correlation of related tweets with daily COVID-19 cases and deaths at the national, regional, and state levels. We collected tweets mentioning both COVID-19 and CVD-related words from February to July 2020 (Eastern Time) and geocoded the tweets to the state level using GIScience techniques. We further proposed and validated that the Twitter user registration state can be a feasible proxy of geotags. We applied geographical and temporal analysis to investigate where and when people talked about COVID-19 and CVD. Our results indicated that the trend of COVID-19 and CVD-related tweets is correlated to the trend of COVID-19, especially the daily deaths. These social media messages revealed widespread recognition of CVD's important role in the COVID-19 pandemic, even before the medical community started to develop consensus and theory supports about CVD aspects of COVID-19. The second wave of the pandemic caused another rise in the related tweets but not as much as the first one, as tweet frequency increased from February to April, decreased till June, and bounced back in July. At the regional level, four regions (Northeast, Midwest, North, and West) had the same trend of related tweets compared to the country as a whole. However, only the Northeast region had a high correlation (0.8-0.9) between the tweet count, new cases, and new deaths. For the second wave of confirmed new cases, the major contributing regions, South and West, did not ripple as many related tweets as the first wave. Our understanding is that the early news attracted more attention and discussion all over the U.S. in the first wave, even though some regions were not impacted as much as the Northeast at that time. The study can be expanded to more geographic and temporal scales, and with more physical and socioeconomic variables, with better data acquisition in the future.

Purpose/UNASSIGNED:Despite escalating racism in the United States during COVID-19, few studies have identified correlates of racism experience among Americans using nationally representative data. This study seeks to quantitatively identify correlates of racism experience and qualitatively categorize racism experience and its coping using nationally representative survey data. Methods/UNASSIGNED:=2,506), a nationally representative survey conducted in October 2020, multivariable logistic regression was estimated to examine the association between self-reported racism experience and selected correlates. Thematic analysis was conducted to qualitatively classify types of racism experience and related coping strategies. Results/UNASSIGNED:When asked whether they had been discriminated or unfairly treated during COVID-19 because of their racial/ethnic background, 19% non-Hispanic Asian and Black respondents said yes, followed by 15% among Hispanics and 3% among non-Hispanic Whites. Besides significant correlates of racism experience identified at the individual and household level, three contextual factors at the neighborhood or state level were associated with lower odds of racism experience, including living in a blue state (adjusted odds ratio [AOR]=0.69, 95% confidence interval [CI]: 0.50-0.95; reference category: red state), living in the top third of the neighborhoods in the sample in terms of racial diversity (AOR=0.65%, 95% CI: 0.42-0.99; reference: bottom third), and coming from neighborhoods with a median population age of 35-39 (AOR=0.67, 95% CI: 0.46-0.98; reference: younger than 35). Prevailing coping strategies against experienced racism included social avoidance, direct confrontation, seeking social and religious support, resorting to hobbies for relief, and taking legal actions. Conclusion/UNASSIGNED:Racism experience is not only correlated with factors at individual level, it is also associated with contextual factors such as political climate, neighborhood diversity, and population age structure. Future efforts in supporting victims of racism might be more cost-effective by focusing on the identified vulnerable groups and related contextual factors.

Telehealth is an important source of health care during the COVID-19 pandemic. Evidence is scarce regarding disparities in telehealth utilization in the United States. We aimed to investigate the prevalence and factors associated with telehealth utilization among US adults. Our data came from the Health, Ethnicity, and Pandemic Study, a nationally representative survey conducted in October 2020, with 2554 adults ≥ 18 and an oversample of racial/ethnic minorities. Telehealth utilization was measured as self-reported teleconsultation with providers via email, text message, phone, video, and remote patient monitoring during the pandemic. Logistic regressions were performed to examine the association between telehealth use and factors at the individual, household, and community levels. Overall, 43% of the sample reported having used telehealth, representing 114.5 million adults in the nation. East and Southeast Asians used telehealth less than non-Hispanic Whites (OR = 0.5, 95% CI: 0.3-0.8). Being uninsured (compared with private insurance: OR = 0.4, 95% CI: 0.2-0.8), and those with limited broadband coverage in the community (OR = 0.5, 95% CI: 0.3-0.8) were less likely to use telehealth. There is a need to develop and implement more equitable policies and interventions at both the individual and community levels to improve access to telehealth services and reduce related disparities.

PURPOSE/OBJECTIVE:Socioeconomic and geographic determinants of medical school application and matriculation may help explain the unequal distribution of physicians in the United States. This study describes trends in MD-granting medical school application and matriculation rates and explores the relationship between county median family income, proximity to a medical school, and medical school application and matriculation rates. METHOD/METHODS:Data were obtained from the Association of American Medical Colleges, including the age, gender, and Federal Information Processing Standards code for county of legal residence for each applicant and matriculant to MD-granting medical schools in the United States from 2001 through 2015. The application and matriculation rates in each county were calculated using the number of applicants and matriculants per 100,000 residents. Counties were classified into 4 groups according to the county median family income (high-income, middle-income, middle-low-income, low-income). The authors performed Chi-square tests to assess trends across the study period and the association of county median family income with application and matriculation rates. RESULTS:There were 581,833 applicants and 262,730 (45.2%) matriculants to MD-granting medical schools between 2001 and 2015. The application rate per 100,000 residents during 2001-2005, 2006-2010, and 2011-2015 was 57.2, 62.7, and 69.0, respectively, and the corresponding matriculation rate was 27.5, 28.1, and 29.8, respectively. The ratio of the application rate in high-income counties to that in low-income counties during the 3 time periods was 1.9, 2.4, and 2.8, respectively. CONCLUSIONS:The application and matriculation rates to MD-granting medical schools increased steadily from 2001 to 2015. Yet, applicants and matriculants disproportionately came from high-income counties. The differences in the application and matriculation rates between low-income and high-income counties grew during this period. Exploring these differences can lead to better understanding of the factors that drive geographic differences in physician access and the associated health disparities across the United States.

Sensory impairments, such as visual and hearing impairments, and cognitive decline are prevalent among mid-age and older adults in China. With 4-year longitudinal data from the China Health and Retirement Longitudinal Study, we assessed the association between self-reported sensory impairments and episodic memory. Multivariate linear mixed-effects models were used to estimate the association of baseline sensory impairment in 2011-2012 with cognitive decline at 2- and 4-year follow-up visits. Among the 13,097 participants, longitudinal associations were identified between having hearing loss (β = -0.14, 95% CI: -0.22, -0.05), having both poor hearing and vision (β = -0.14, 95% CI: -0.23, -0.04) and decline in immediate word recall over 4 years, compared to those without self-reported sensory impairment. In addition, these associations were more significant among those aged 60 and older and among women. Further research is needed to investigate these associations in the longer term, providing evidence to support interventions that can prevent or delay sensory impairments and preserve cognitive functions in older adults.

Importance/UNASSIGNED:Access to primary care clinicians, including primary care physicians and nonphysician clinicians (nurse practitioners and physician assistants) is necessary to improving population health. However, rural-urban trends in primary care access in the US are not well studied. Objective/UNASSIGNED:To assess the rural-urban trends in the primary care workforce from 2009 to 2017 across all counties in the US. Design, Setting, and Participants/UNASSIGNED:In this cross-sectional study of US counties, county rural-urban status was defined according to the national rural-urban classification scheme for counties used by the National Center for Health Statistics at the Centers for Disease Control and Prevention. Trends in the county-level distribution of primary care clinicians from 2009 to 2017 were examined. Data were analyzed from November 12, 2019, to February 10, 2020. Main Outcomes and Measures/UNASSIGNED:Density of primary care clinicians measured as the number of primary care physicians, nurse practitioners, and physician assistants per 3500 population in each county. The average annual percentage change (APC) of the means of the density of primary care clinicians over time was calculated, and generalized estimating equations were used to adjust for county-level sociodemographic variables obtained from the American Community Survey. Results/UNASSIGNED:The study included data from 3143 US counties (1167 [37%] urban and 1976 [63%] rural). The number of primary care clinicians per 3500 people increased significantly in rural counties (2009 median density: 2.04; interquartile range [IQR], 1.43-2.76; and 2017 median density: 2.29; IQR, 1.57-3.23; P < .001) and urban counties (2009 median density: 2.26; IQR. 1.52-3.23; and 2017 median density: 2.66; IQR, 1.72-4.02; P < .001). The APC of the mean density of primary care physicians in rural counties was 1.70% (95% CI, 0.84%-2.57%), nurse practitioners was 8.37% (95% CI, 7.11%-9.63%), and physician assistants was 5.14% (95% CI, 3.91%-6.37%); the APC of the mean density of primary care physicians in urban counties was 2.40% (95% CI, 1.19%-3.61%), nurse practitioners was 8.64% (95% CI, 7.72%-9.55%), and physician assistants was 6.42% (95% CI, 5.34%-7.50%). Results from the generalized estimating equations model showed that the density of primary care clinicians in urban counties increased faster than in rural counties (β = 0.04; 95% CI, 0.03 to 0.05; P < .001). Conclusions and Relevance/UNASSIGNED:Although the density of primary care clinicians increased in both rural and urban counties during the 2009-2017 period, the increase was more pronounced in urban than in rural counties. Closing rural-urban gaps in access to primary care clinicians may require increasingly intensive efforts targeting rural areas.