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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.

Item nonresponse is a common issue in surveys. Because unadjusted estimators may be biased in the presence of nonresponse, it is common practice to impute the missing values with the objective of reducing the nonresponse bias as much as possible. However, commonly used imputation procedures may lead to unstable estimators of population totals/means when influential units are present in the set of respondents. In this article, we consider the class of multiply robust imputation procedures that provide some protection against the failure of underlying model assumptions. We develop an efficient version of multiply robust estimators based on the concept of conditional bias, a measure of influence. We present the results of a simulation study to show the benefits of our proposed method in terms of bias and efficiency. [ABSTRACT FROM AUTHOR]

Missing data reduce the representativeness of the sample and can lead to inference problems. In this article, we apply the Bayesian jackknife empirical likelihood (BJEL) method for inference on data that are missing at random, as well as for causal inference. The semiparametric fractional imputation estimator, propensity score‐weighted estimator, and doubly robust estimator are used for constructing the jackknife pseudo values, which are needed for conducting BJEL‐based inference with missing data. Existing methods, such as normal approximation and JEL, are compared with the BJEL approach in a simulation study. The proposed approach shows better performance in many scenarios in terms of credible intervals. Furthermore, we demonstrate the application of the proposed approach for causal inference problems in a study of risk factors for impaired kidney function. [ABSTRACT FROM AUTHOR]

Observational studies are common in epidemiology, medicine, economics, and other research fields. Causal inference has been regarded as one of the most effective tools for conducting statistical inference with observational studies without random treatment assignments. The validity of most existing causal inference procedures depends on the underlying outcome regression and/or propensity score modeling assumptions. In this paper, we propose two new Bayesian multiply robust estimation approaches for causal inference based on the loss-likelihood bootstrap. The proposed methods enjoy the multiple robustness property such that the estimators of treatment effect are consistent if at least one of the outcome regression models or propensity score models is correctly specified. Convenient statistical inference can be conducted using the Bayesian posterior credible intervals. A simulation study shows the benefits of our proposed methods. We further apply our proposed methods by using 2009–2010 National Health and Nutrition Examination Survey (NHANES) data to examine the effect of exposure to perfluoroalkyl acids (PFAs) on kidney function. [ABSTRACT FROM AUTHOR]