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IntroductionAmerican Indian (AI) people experience a disproportionate tobacco and marijuana burden which may have been exacerbated by the COVID-19 pandemic. Little is known about the tobacco and marijuana habits of American Indian individuals during the COVID-19 pandemic. The objective of this study is to examine tobacco and marijuana use as well as change in use during the COVID-19 pandemic among the American Indian community.MethodsThis cross-sectional study analyzes survey data from a convenience sample of American Indian individuals residing in California and Oklahoma and included adults with and without cancer that resided in both rural and urban areas (n=1068).ResultsDuring October 2020 - January 2021, 36.0% of participants reported current use of tobacco products, 9.9% reported current use of marijuana products, and 23.7% reported increased use of tobacco and/or marijuana in the past 30 days, with no difference between those with cancer and those without cancer. Tobacco use was associated with marital status, age, employment status, COVID-19 exposure, COVID-19 beliefs, and alcohol consumption. Marijuana use was associated with COVID-19 beliefs, alcohol consumption, and income level. Increased tobacco and/or marijuana use was associated with baseline use of those products. Nearly a quarter of participants reported increased use of tobacco and/or marijuana products during the COVID-19 pandemic.ConclusionsWe observed high rates of tobacco use during the COVID-19 pandemic, consistent with other studies. Research is needed to examine whether tobacco and marijuana use will decrease to pre-pandemic levels post-pandemic or if these behaviors will persist post-pandemic. Given these findings, there is a pressing need to increase access to evidence-based tobacco and marijuana treatment services in the AI population post COVID-19 pandemic.

ObjectivesAssess the percentage of cancer-related appointment delays, cancelations, and the unavailability of medications experienced by American Indian participants during the COVID-19 pandemic.MethodsThis cross-sectional survey study was completed between October 2020 and July 2021 by 360 individuals with cancer who lived in California and Oklahoma. Binary and multivariate logistic regression analysis was completed in SAS 9.4.ResultsDuring the initial Covid-19 pandemic, almost one-third (30%) of respondents delayed cancer-related appointments, 42% canceled cancer-related appointments, and one-quarter (24%) were unable to access prescription medications or over-the-counter medications (27%) due to COVID-19. People who underwent testing for COVID-19 were five times more likely to delay a medical appointment [adjusted odds ratio (aOR) = 5.3, 95% CI:2.4, 11.7] and people who followed three or more social distancing measures were more than six times more likely to cancel medical appointments (aOR:6.3, 95% CI:2.9, 13.9).ConclusionThis study identifies delays, cancelations, and medication inaccessibility people identifying as American Indian faced during the coronavirus pandemic. Disparities in healthcare delivery could contribute to increased morbidity and mortality rates of cancer.

PurposeLittle is known about the association of psychosocial factors with health-related quality of life (HRQoL) among American Indians with type 2 diabetes (T2D). This study described functional social support, emotional support, coping, resilience, post-traumatic stress disorder, and HRQoL, among American Indians by diabetes status and, among those with diabetes, examined the association of these factors with HRQoL.MethodsUsing data from the Cherokee Nation Health Survey collected between 2017 and 2019, we evaluated differences in each measure of interest according to diabetes status, using t-test and Chi-squared tests of association. We used weighted multiple logistic regression to examine associations between multiple psychosocial factors and HRQoL among those with diabetes.ResultsCompared to individuals without diabetes, participants with diabetes rated their functional social support (4.62 vs. 4.56, respectively) and coping (2.65 vs. 2.61, respectively) slightly lower and were more likely to report ≥ 15 days of poor physical (14% vs. 26%, respectively) and mental health (14% vs. 17%, respectively) in the past month. Odds of reporting poor overall health increased more than sixfold for those who were dissatisfied/very dissatisfied with life (AOR = 6.70). Resilience scores reduced odds of reporting ≥ 15 days with poor physical health, while experiences of post-traumatic stress doubled these odds.ConclusionOur study yielded insights into the risk as well as protective factors associated with diabetes outcomes in a large sample of American Indians with T2D. Researchers should design pragmatic trials that deepen understanding of preventive as well as treatment leverage through greater attention to experiences that compromise HRQoL.

Background: Oklahoma’s cumulative COVID-19 incidence is higher in rural than urban counties and higher than the overall US incidence. Furthermore, fewer Oklahomans have received at least one COVID-19 vaccine compared to the US average. Our goal is to conduct a randomized controlled trial using the multiphase optimization strategy (MOST) to test multiple educational interventions to improve uptake of COVID-19 vaccination among underserved populations in Oklahoma. Methods: Our study uses the preparation and optimization phases of the MOST framework. We conduct focus groups among community partners and community members previously involved in hosting COVID-19 testing events to inform intervention design (preparation). In a randomized clinical trial, we test three interventions to improve vaccination uptake: (1) process improvement (text messages); (2) barrier elicitation and reduction (electronic survey with tailored questions/prompts); and (2) teachable moment messaging (motivational interviewing) in a three-factor fully crossed factorial design (optimization). Discussion: Because of Oklahoma’s higher COVID-19 impact and lower vaccine uptake, identifying community-driven interventions is critical to address vaccine hesitancy. The MOST framework provides an innovative and timely opportunity to efficiently evaluate multiple educational interventions in a single study. Trial Registration: ClinicalTrials.gov: NCT05236270, First Posted: February 11, 2022, Last Update Posted: August 31, 2022. [ABSTRACT FROM AUTHOR]

High-dimensional data have been regarded as one of the most important types of big data in practice. It happens frequently in practice including genetic study, financial study, and geographical study. Missing data in high dimensional data analysis should be handled properly to reduce nonresponse bias. We discuss some modern machine learning techniques including penalized regression approaches, tree-based approaches, and deep learning (DL) for handling missing data with high dimensionality. Specifically, our proposed methods can be used for estimating general parameters of interest including population means and percentiles with imputation-based estimators, propensity score estimators, and doubly robust estimators. We compare those methods through some limited simulation studies and a real application. Both simulation studies and real application show the benefits of DL and XGboost approaches compared with other methods in terms of balancing bias and variance. [ABSTRACT FROM AUTHOR]