Background Tennessee women experience the 12th highest breast cancer mortality in the United States. We examined the geographic differences in breast cancer incidence in Tennessee between Appalachian and non-Appalachian counties from 2005 to 2015. Methods We used ArcGIS 10.7 geospatial analysis and logistic regression on the Tennessee Cancer Registry incidence data for adult women aged ≥ 18 years (N = 59,287) who were diagnosed with breast cancer from 2005 to 2015 to evaluate distribution patterns by Appalachian county designation. The Tennessee Cancer Registry is a population-based, central cancer registry serving the citizens of Tennessee and was established by Tennessee law to collect and monitor cancer incidence. The main outcome was breast cancer stage at diagnosis. Independent variables were age, race, marital status, type of health insurance, and county of residence. Results Majority of the sample were White (85.5%), married (58.6%), aged ≥ 70 (31.3%) and diagnosed with an early stage breast cancer (69.6%). More than half of the women had public health insurance (54.2%), followed by private health insurance coverage (44.4%). Over half of the women resided in non-Appalachian counties, whereas 47.6% were in the Appalachian counties. We observed a significant association among breast cancer patients with respect to marital status and type of health insurance coverage (p =  < 0.0001). While the logistic regression did not show a significant result between county of residence and breast cancer incidence, the spatial analysis revealed geographic differences between Appalachian and non-Appalachian counties. The highest incidence rates of 997.49–1164.59/100,000 were reported in 6 Appalachian counties (Anderson, Blount, Knox, Rhea, Roane, and Van Buren) compared to 3 non-Appalachian counties (Fayette, Marshall, and Williamson). Conclusions There is a need to expand resources in Appalachian Tennessee to enhance breast cancer screening and early detection. Using geospatial techniques can further elucidate disparities that may be overlooked in conventional linear analyses to improve women’s cancer health and associated outcomes.

Objective: To assess differences in the prevalence of anxiety/depression symptoms among cancer patients before (2019) and during the COVID-19 pandemic (2020); and the associations between anxiety/depression and sociodemographic and health behavior factors among cancer patients before and during the pandemic. Methods: We analyzed data from the 2019 (n = 856) and 2020 (n = 626) Health Information National Trends Survey, a nationally representative survey of United States adults aged ≥18 years. Only adults with a cancer diagnosis were used in the analyses. Anxiety/depression was assessed using the Patient Health Questionnaire-4 (low/none [0-2], mild [3-5], moderate [6-8], and severe [9-12]) and dichotomized as low/none and current anxiety/depression (mild/moderate/severe). Multivariate analysis was performed. Results:The prevalence of anxiety/depression symptoms among cancer patients was 32.7% before the COVID-19 pandemic and 31.1% during the pandemic. The odds of anxiety/depression among patients with fair/poor health status was higher during the pandemic relative to before (before: odds ratio [OR] = 1.85 vs. during: OR = 3.89). Participants aged 50-64 years (before: OR = 0.29, 95% confidence interval [95% CI] = 0.11-0.76; during: OR = 0.33, 95% CI = 0.11-0.97) and ≥65 years (before: OR = 0.13, 95% CI = 0.05-0.34; during: OR = 0.18, 95% CI = 0.07-0.47) had lower odds of anxiety/depression before and during the pandemic compared to those aged 35-49 years. Hispanics/Latinos had higher odds of anxiety/depression (OR = 2.70, 95% CI = 1.11-6.57) before the pandemic and lower odds of anxiety/depression during the pandemic (OR = 0.2, 95% CI = 0.05-1.01) compared to non-Hispanic Whites. Those who completed high school (before: OR = 0.08, 95% CI = 0.01-0.42), some college (before: OR = 0.10, 95% CI = 0.02-0.42), ≥college degree had lower odds of anxiety/depression symptoms (before: OR = 0.05, 95% CI = 0.01-0.26; during: OR = 0.06, 95% CI = 0.01-0.61) compared to those with less than a high school education.

Multiple Myeloma (MM) has been and continues to be the subject of many research studies. The main goal is to improve the therapeutic/treatment process of survival of MM patients. Based on the 2012-2016 MM cases and deaths, the number of new cases was 6.9 per 100,000 men and women per year, and the number of deaths was 3.3 per 100,000 men and women per year. It is therefore imperative to research into MM. In the present study, we proposed a data-driven statistical model for the survival time of 48 patients diagnosed with multiple myeloma as a function of 16 attributable risk factors. We identified 9 attributable risk factors out of 16 and one interaction term to be significantly contributing to the survival time. They are Bence Jone protein in urine, blood urea nitrogen (BUN)/serum creatinine, infections, % myeloid cells in peripheral blood, fractures, serum calcium, gender, platelets and age, and white blood cells & total serum protein an interaction term. The proposed model satisfied all the model assumptions, passes the residual analysis test and has very high prediction accuracy. Thus, it passes the goodnessof-fit test and the qualities of a good model. The identified significant attributable risk factors and the interaction has been ranked based on the percent contribution to the survival time. The proposed model was evaluated and compared with other existing models of survival of multiple myeloma. Our model is very accurate and also identifies some new significant risk factors. The study offers an improved strategy for the therapeutic/treatment process of multiple Myeloma Cancer.

Background: Although several studies examined the association between e-cigarettes, substance use, and mental health conditions, there is limited research on whether COVID-19-related stress and health outcomes, mental health symptoms, and substance use differ by the frequency of e-cigarette use during the COVID-19 pandemic. We assessed the association of past 30-day frequent use of e-cigarettes with alcohol, cannabis, anxiety/depression, and COVID-19 impact. Methods: We conducted a national online cross-sectional survey among a random sample of US adults aged 18 years or older (N = 5065) between 13 May 2021, and 9 January 2022. A multinomial logistic regression analysis was performed to assess the study aims. Results: Of the participants, 7.17% reported once to several times per month (OSTPM), 6.95% reported once to several times per week (OSTPW), and 6.57% reported every day to several times per day (ESTPD) use of e-cigarettes in the past month. Alcohol and cannabis use ESTPD and once to several times per week/month (OSTPW/M) were associated with a higher likelihood of e-cigarette use ESTPD and OSTPW/M, respectively. Anxiety/depression was associated with e-cigarette use ESTPD and OSTPW. Individuals who considered social distancing to be stressful were more likely to use e-cigarettes ESTPD and OSTPW/M compared to those that considered social distancing as not stressful. Conclusion: Individuals who engaged in the frequent use of alcohol or cannabis, had depression/anxiety, and considered social distancing to be stressful were more likely to engage in frequent e-cigarette use. Improving efforts geared toward reducing the use of substances may help decrease the health risks associated with e-cigarette use.

Multiple myeloma (MM) is a type of cancer that remains incurable. In the last decade, most research into MM has focused on investigating the improvement in the therapeutic strategy. Our study assesses the survival probability of 48 patients diagnosed with MM based on parametric and non-parametric techniques. We performed parametric survival analysis and found a well-defined probability distribution of the survival time to follow three-parameter lognormal. We then estimated the survival probability and compared it with the commonly used non-parametric Kaplan-Meier survival analysis of the survival times. The comparison of the survival probability estimates of the two methods revealed a better survival probability estimate by the parametric method than the Kaplan-Meier. The parametric survival analysis is more robust and efficient because it is based on a well-defined parametric probabilistic distribution, hence preferred over the non-parametric Kaplan-Meier. This study offers therapeutic significance for further enhancement in the treatment strategy of multiple myeloma cancer.