Faculty Bibliography

BACKGROUND:Epidemiological evidence on different classes of antihypertensives and risks of Alzheimer's disease and related dementias (ADRD) is inconclusive and limited. This study examined the association between antihypertensive use (including therapy type and antihypertensive class) and ADRD diagnoses among older adults with hypertension. METHODS:A retrospective, cross-sectional study was conducted, involving 539 individuals aged ≥ 65 years who used antihypertensives and had ADRD diagnosis selected from 2013 to 2018 Medical Expenditure Panel Survey (MEPS) data. The predictors were therapy type (monotherapy or polytherapy) and class of antihypertensives defined using Multum Lexicon therapeutic classification (with calcium channel blockers [CCBs] as the reference group). Weighted logistic regression was used to assess the relationships of therapy type and class of antihypertensives use with ADRD diagnosis, adjusting for sociodemographic characteristics and health status. RESULTS:We found no significant difference between monotherapy and polytherapy on the odds of ADRD diagnosis. As to monotherapy, those who used angiotensin-converting enzyme inhibitors (ACEIs) had significantly lower odds of developing AD compared to those who used CCBs (OR 0.36, 95 % CI 0.13-0.99). CONCLUSIONS:Findings of the study suggest the need for evidence-based drug therapy to manage hypertension in later adulthood and warrant further investigation into the mechanism underlying the protective effect of antihypertensives, particularly ACEIs, against the development of AD among older adults with hypertension.

Background Peripheral artery disease (PAD) affects >10 million people in the United States. PAD is associated with poor outcomes, including premature death. Machine learning (ML) has been increasingly used on big data to predict clinical outcomes. This study aims to develop ML models to predict in-hospital mortality in patients hospitalized for PAD based on a national database. Methods and Results Inpatient hospitalization data were obtained from the 2016 to 2019 National Inpatient Sample. A total of 150 921 inpatients were identified with a primary diagnosis of PAD and PAD-related procedures using codes of the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) and International Classification of Diseases, Tenth Revision, Procedure Coding System (ICD-10-PCS). Four ML models, including logistic regression, random forest, light gradient boosting, and extreme gradient boosting models, were trained to predict the risk of in-hospital death based on a selection of variables, including patient characteristics, comorbidities, procedures, and hospital-related factors. In-hospital mortality occurred in 1.8% of patients. The performance of the 4 models was comparable, with the area under the receiver operating characteristic curve ranging from 0.83 to 0.85, sensitivity of 77% to 82%, and specificity of 72% to 75%. These results suggest adequate predictability for clinical decision-making. In all 4 models, the total number of diagnoses and procedures, age, endovascular revascularization procedure, congestive heart failure, diabetes, and diabetes with complications were critical predictors of in-hospital mortality. Conclusions This study demonstrates the feasibility of ML in predicting in-hospital mortality in patients with a primary PAD diagnosis. Findings highlight the potential of ML models in identifying high-risk patients for poor outcomes and guiding personalized intervention.

BACKGROUND AND AIMS/OBJECTIVE:Heart failure (HF) patients are at risk of developing type 2 diabetes. This study examined the association between adherence to the Dietary Approaches to Stop Hypertension (DASH) diet and insulin resistance among U.S. adults with HF. METHODS AND RESULTS/RESULTS:Using data from National Health and Nutrition Examination Survey 1999-2016 cycles, we included 348 individuals aged 20+ years with HF and no history of diabetes. DASH diet adherence index quartile 1 indicated the lowest and quartile 4 indicated the highest adherence. The highest level of insulin resistance was defined by the upper tertile of the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR). Associations between level of insulin resistance and DASH diet adherence and its linear trends were examined using logistic regressions. Trend analyses showed that participants in upper DASH diet adherence index quartiles were more likely older, female, non-Hispanic White, of normal weight, and had lower levels of fasting insulin than those in lower quartiles. Median values of HOMA-IR from lowest to highest DASH diet adherence index quartiles were 3.1 (interquartile range, 1.8-5.5), 2.9 (1.7-5.6), 2.1 (1.1-3.7), and 2.1 (1.3-3.5). Multivariable logistic analyses indicated that participants with the highest compared to the lowest DASH adherence showed 77.1% lower odds of having the highest level of insulin resistance (0.229, 95% confidence interval: 0.073-0.716; p = 0.017 for linear trend). CONCLUSION/CONCLUSIONS:Good adherence to the DASH diet was associated with lower insulin resistance among community-dwelling HF patients. Heart healthy dietary patterns likely protect HF patients from developing type 2 diabetes.

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.