Perceived Discrimination, Experiential Avoidance, and Mental Health among Hispanic Adults in Primary Care

The Hispanic population is the largest minority group in the United States and frequently experiences racial discrimination and mental health difficulties. Prior work suggests that perceived racial discrimination is a significant risk factor for poorer mental health among Hispanic in the United States. However, little work has investigated how perceived racial discrimination relates to anxiety and depression among Hispanic adults. Thus, the current study evaluated the explanatory role of experiential avoidance in the relation between perceived racial discrimination and anxiety/depressive symptoms and disorders among Hispanic adults in primary care. Participants included 202 Spanish-speaking adults ( Mage = 38.99, SD = 12.43, 86.1% female) attending a community-based Federally Qualified Health Center. Results were consistent with the hypothesis that perceived racial discrimination had a significant indirect effect on depression, social anxiety, and anxious arousal symptoms as well as the number of mood and anxiety disorders through experiential avoidance. These findings suggest future work should continue to explore experiential avoidance in the association between perceived racial discrimination and other psychiatric and medical problems among the Hispanic population.

Applying Machine Learning and AI Explanations to Analyze Vaccine Hesitancy

The paper identifies and quantifies the impact of race, poverty, politics, and age on COVID-19 vaccination rates in counties across the continental US. Both traditional Ordinary Least Square (OLS) regression analysis and Random Forest machine learning algorithms are applied to quantify contributing factors for county-level vaccination hesitancy. With the machine learning model, joint effects of multiple variables (race/ethnicity, partisanship, age etc.) are considered simultaneously to capture the unique combination of what factors affect the vaccination rate. By implementing a state-of-the-art Artificial Intelligence Explanations (AIX) algorithm, it is possible to solve the black box problem with machine learning models and provide answers to the "how much" question for each measured impact factor in every county. For most counties a higher percentage vote for Republicans, a greater African American population share, and a higher poverty rate lower the vaccination rate. While a higher Asian population share increases the predicted vaccination rate. The impact on the vaccination rate from the Hispanic population proportion is positive in the OLS model, but only positive for counties with very high Hispanic population (65% and more) in the Random Forest model. Both the proportion of seniors and the one for young people in a county have a significant impact in the OLS model - positive and negative, respectively. In contrast, the impacts are ambiguous in the Random Forest model. Because results vary between geographies and since the AIX algorithm is able to quantify vaccine impacts individually for each county, this research can be tailored to local communities. This way it is a helpful tool for local health officials and other policymakers to improve vaccination rates. An interactive online mapping dashboard that identifies impact factors for individual U.S. counties is available at https://www.cpp.edu/~clange/vacmap.html. It is apparent that the influence of impact factors is not universally the same across different geographies.

APOE Gene Associated with Cholesterol-Related Traits in the Hispanic Population

Genetic variants in the apolipoprotein E (APOE) gene are associated with lipid metabolism and lipid-related traits in the non-Hispanic population. There have been limited studies regarding the association between the APOE gene and hypercholesterolemia in the Hispanic population; therefore, our aim for this study is to examine the APOE gene’s associations with cholesterol level and its related phenotypes. The APOE gene consists of three different alleles, ε2, ε3, and ε4, with ε4 being associated with dementia and cardiovascular diseases. A total of 1,382 subjects were collected from the Texas Alzheimer’s Research and Care Consortium (TARCC, N = 1320) and the Initial Study of Longevity and Dementia from the Rio Grande Valley (ISLD-RGV, N = 62). Questionnaires on demographics, medical history, and blood/saliva samples were collected and APOE genotypes were performed. We observed allele frequencies of the APOE ε3 (96.7%), ε4 (22.6%) and ε2 (6.8%) alleles, respectively. Multivariable logistic regression revealed a significant association between the APOE ε4 allele and hypercholesteremia (p = 1.8 × 10−4) in our studied Hispanic population. We prove for the first time, that the APOE ε4 allele increases the risk for hypercholesterol in Hispanics. Further research is needed to confirm and supports our current findings.

EPID-09. VARIATION IN GLIOMA INCIDENCE AMONG US HISPANICS BY GEOGRAPHIC REGION OF ORIGIN

BACKGROUND Glioma incidence is 25% lower in U.S. Hispanics than in White non-Hispanics. The US Hispanic population is diverse and registry-based analyses may mask incidence differences associated with geographic/ancestral origins. METHODS County-level glioma incidence data in U.S. Hispanics were retrieved from the Central Brain Tumor Registry of the United States (CBTRUS), which includes data from the Centers for Disease Control’s National Program of Cancer Registries and the National Cancer Institute’s Surveillance, Epidemiology, and End Results program and covers ~100% of the U.S. population. American Community Survey (ACS) data were used to determine county-level proportion of the Hispanic population of Mexican/Central American origin, Caribbean origin (Puerto Rican, Cuban, Dominican), or other origin. Incidence rate ratios (IRRs) were generated to assess the association of glioma incidence in Hispanics with predominant origin group. RESULTS Compared to Hispanics living in predominantly Caribbean-origin counties, Hispanics in predominantly Mexican/Central American-origin counties were at lower age-adjusted risk of glioma (IRR=0.83; P< 0.0001), glioblastoma (IRR=0.86; P< 0.0001), diffuse and anaplastic astrocytoma (IRR=0.78; P< 0.0001), oligodendroglioma (IRR=0.82; P< 0.0001), ependymoma (IRR=0.88; P=0.0121), and pilocytic astrocytoma (IRR=0.76; P< 0.0001). Associations were consistent in children and adults, and when using more granular regions of origin. However, Central American origin was associated with modestly increased incidence of several lower-grade glioma histologies. Associations were only partially attenuated after adjusting for state-level estimated of European admixture in Hispanics using 23andMe data. CONCLUSIONS Glioma incidence in U.S. Hispanics differs significantly in association with the geographic origins of the Hispanic community, with those of Mexican/Central American origin at significantly reduced risk relative to those of Caribbean origin. U.S. Hispanics are culturally, socioeconomically, and genetically diverse. Although classified as a single ethnic group in most registry data, more granular analytic approaches could advance cancer epidemiology and disparities research.

329. Severe Obesity: A Critical Risk Factor for In-Hospital Complications and Mortality in the Hispanic Population

Background Obesity, Diabetes mellitus type 2, race and other characteristics has been associated with an increased risk of adverse outcomes in patients with COVID-19 disease. The prevalence of obesity in the United States in 2017-2018 was 42.4%. Webb County, Texas with a 95.6% Hispanic population shows an obesity prevalence of 35.8% in 2014. It is unclear whether obesity increases the risks of complications and mortality in Hispanic population from COVID-19 disease. Methods This is a retrospective cohort study of patients admitted to the hospital with the diagnosis of COVID-19 between March 2020 and August 2020. 950 patients were tested and admitted to the hospital with the diagnosis of COVID-19 pneumonia. Patients were categorized into classes of body habitus by BMI: underweight (< 18.5), normal (18.5-24.9), overweight (25.0-29.9), obesity class 1 (30.0-34.9), obesity class 2 (35.0-39.9), and obesity class 3 ( >40.0). Results 950 Hispanic patients were included (Male-52.8%, Female- 47.2%) in the study. In total, 19.05% of our patients died during the hospitalization with an increased risk of mortality in patients having obesity class 2 (RR 4.14, 95% CI = 2.2–7.7 p=< 0.0001), and obesity class 3 (RR 6.0, 95% CI = 1.3–4.6 p=< 0.0001) compared with those with normal BMI. Mortality was higher in obese patients who required invasive mechanical ventilation at 93.75% compared to obese patients who were non-ventilated at 4.29%. Patients with obesity class 2 and 3 had higher risks of in-hospital complications including AKI requiring renal replacement therapy, ARDS, and arrythmias most commonly atrial fibrillation/flutter at 26.7%, 18.42% and 13.5%. Characteristics of In-hospital complication complications due to COVID-19 disease Conclusion Patients admitted to the hospital with the diagnosis of COVID-19 disease with obesity classes 2 and 3 have a significantly increased risk of mortality as compared to normal and overweight patients. Severe obesity was also associated with increased hospital complications of AKI, ARDS, and Atrial Fibrillation. This further affirms that obesity is a pertinent risk factor to be considered in COVID-19 patients. Disclosures All Authors: No reported disclosures

Why and How Do Cities Plan for Extreme Heat?

Heat is the deadliest weather-related hazard in the United States. This paper studies municipal heat adaptation using survey and planning data from California. We first analyze the characteristics of municipalities that innovate. Cities with heat-related policies have greater degrees of projected extreme heat, leadership support, environmental justice planning, and smaller Hispanic population shares. We then assess specific policy innovations of six large cities by plan type. Some strategies, including expanding tree canopies, have been widely adopted while others, such as cool walls, are rarely included. Findings suggest that planners can—and should—play a central role in heat adaptation planning.