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.

Novel synthetic cannabinoids are appearing in recreational drug markets worldwide. Pharmacological characterization of these new drugs is needed to inform clinicians, toxicologists, and policy makers who monitor public health. [1-(5-Fluoropentyl)-1H-indol-3-yl](1-naphthyl)methanone (AM-2201) is an abused synthetic cannabinoid that was initially created as a research tool for investigating the endocannabinoid system. Here we measured the pharmacodynamic effects of AM-2201 in rats, and simultaneously determined plasma pharmacokinetics for the parent drug and its metabolites. Male Sprague-Dawley rats were fitted with surgically implanted temperature transponders and indwelling jugular catheters under pentobarbital anesthesia. One week later, rats received subcutaneous injection of AM-2201 (0.1, 0.3, and 1.0 mg/kg) or its vehicle, and serial blood specimens were withdrawn via catheters. Core temperatures and catalepsy were measured just prior to each blood withdrawal, and plasma was assayed for drug and metabolites using liquid chromatography-tandem mass spectrometry. We found that AM-2201 produced dose-related hypothermia and catalepsy that peaked at 2 hours and lasted up to 8 hours. AM-2201 plasma concentrations rose linearly with increasing dose and ranged from 0.14 to 67.9 mg/l. Concentrations of three metabolites, AM-2201 N-(4-hydroxypentyl) (#0.17 mg/l), naphthalen-1-yl-(1-pentylindol-3-yl)methanone (JWH-018) N-(5-hydroxypentyl) (#1.14 mg/l), and JWH-018 N-pentanoic acid (#0.88 mg/l) were detectable but much lower. Peak AM-2201, JWH-018 N-(5-hydroxypentyl), and JWH-018 N-pentanoic acid concentrations occurred at 1.3, 2.4, and 6.5 hours, respectively. Concentrations of AM-2201, JWH-018 N-(5-hydroxypentyl), and JWH-018 N-pentanoic acid were negatively correlated with body temperature, but, given the low concentrations of metabolites detected, AM-2201 is likely the major contributor to pharmacodynamic effects under our experimental conditions.

AM-2201 is a popular synthetic cannabinoid first synthesized in 2000. AM-2201 pharmacokinetic and pharmacodynamic data are scarce, requiring further investigation. We developed a sensitive method for quantifying AM-2201 and 13 metabolites in plasma to provide a tool to further metabolic, pharmacokinetic and pharmacodynamic studies. Analysis was performed by liquid chromatography-tandem mass spectrometry. Chromatographic separation was performed by gradient elution on a biphenyl column with 0.1% formic acid in water/0.1% formic acid in acetonitrile:methanol 50:50 (v/v) mobile phase. Sample preparation (75 μL) consisted of an enzymatic hydrolysis and a supported liquid extraction. The method was validated with human plasma with a 0.025 or 0.050 – 50 μg/L working range, and cross-validated for rat plasma. Analytical recovery was 88.8 – 110.1% of target concentration, and intra- (n = 30) and inter-day (n = 30) imprecision <11.9% coefficient of variation. Method recoveries and matrix effects ranged from 58.4 – 84.4% and −62.1 to −15.6%, respectively. AM-2201 and metabolites were stable (±20%) at room temperature for 24 h, at 4°C for 72 h, and after three freeze-thaw cycles, and for 72 h in the autosampler after extraction. The method was developed for pharmacodynamic and pharmacokinetic studies with controlled administration in rats but is applicable for pre-clinical and clinical research and forensic investigations. Rat plasma specimen analysis following subcutaneous AM-2201 administration demonstrated the suitability of the method. AM-2201, JWH-018 N-(5-hydroxypentyl), and JWH-018 N-pentanoic acid concentrations were 4.8±1.0, 0.15±0.03, and 0.34±0.07 μg/L, respectively, 8h after AM-2201 administration at 0.3 mg/kg (n = 5).

Background Few studies have comprehensively and contextually examined the relationship of variables associated with opioid use. Our purpose was to fill a critical gap in comprehensive risk models of opioid misuse and use disorder in the United States by identifying the most salient predictors. Methods A multivariate logistic regression was used on the 2017 and 2018 National Survey on Drug Use and Health, which included all 50 states and the District of Columbia of the United States. The sample included all noninstitutionalized civilian adults aged 18 and older (N = 85,580; weighted N = 248,008,986). The outcome of opioid misuse and/or use disorder was based on reported prescription pain reliever and/or heroin use dependence, abuse, or misuse. Biopsychosocial predictors of opioid misuse and use disorder in addition to sociodemographic characteristics and other substance dependence or abuse were examined in our comprehensive model. Biopsychosocial characteristics included socioecological and health indicators. Criminality was the socioecological indicator. Health indicators included self-reported health, private health insurance, psychological distress, and suicidality. Sociodemographic variables included age, sex/gender, race/ethnicity, sexual identity, education, residence, income, and employment status. Substance dependence or abuse included both licit and illicit substances (i.e., nicotine, alcohol, marijuana, cocaine, inhalants, methamphetamine, tranquilizers, stimulants, sedatives). Results The comprehensive model found that criminality (adjusted odds ratio [AOR] = 2.58, 95% confidence interval [CI] = 1.98–3.37, p < 0.001), self-reported health (i.e., excellent compared to fair/poor [AOR = 3.71, 95% CI = 2.19–6.29, p < 0.001], good [AOR = 3.43, 95% CI = 2.20–5.34, p < 0.001], and very good [AOR = 2.75, 95% CI = 1.90–3.98, p < 0.001]), no private health insurance (AOR = 2.12, 95% CI = 1.55–2.89, p < 0.001), serious psychological distress (AOR = 2.12, 95% CI = 1.55–2.89, p < 0.001), suicidality (AOR = 1.58, 95% CI = 1.17–2.14, p = 0.004), and other substance dependence or abuse were significant predictors of opioid misuse and/or use disorder. Substances associated were nicotine (AOR = 3.01, 95% CI = 2.30–3.93, p < 0.001), alcohol (AOR = 1.40, 95% CI = 1.02–1.92, p = 0.038), marijuana (AOR = 2.24, 95% CI = 1.40–3.58, p = 0.001), cocaine (AOR = 3.92, 95% CI = 2.14–7.17, p < 0.001), methamphetamine (AOR = 3.32, 95% CI = 1.96–5.64, p < 0.001), tranquilizers (AOR = 16.72, 95% CI = 9.75–28.65, p < 0.001), and stimulants (AOR = 2.45, 95% CI = 1.03–5.87, p = 0.044). Conclusions Biopsychosocial characteristics such as socioecological and health indicators, as well as other substance dependence or abuse were stronger predictors of opioid misuse and use disorder than sociodemographic characteristics.

Background Lung cancer (LC) continues to be the leading cause of cancer deaths in the United States. Surgical treatment has proven to offer a favorable prognosis and a better 5‐year relative survival for patients with early or localized tumors. This novel study investigates the factors associated with the odds of receiving surgical treatment for localized malignant LC in Tennessee. Methods Population‐based data of 9679 localized malignant LC patients from the Tennessee Cancer Registry (2005–2015) were utilized to examine the factors associated with receiving surgical treatment for localized malignant LC. Bivariate and multivariate logistic regression analyses, cross‐tabulation, and Chi‐Square ( 2 ) tests were conducted to assess these factors. Results Patients with localized malignant LC who initiated treatment after 2.7 weeks were 46% less likely to receive surgery (adjusted odds ratio [AOR] = 0.54; 95% confidence interval [CI] = 0.50–0.59; p < 0.0001). Females had a greater likelihood (AOR = 1.14; CI = 1.03–1.24) of receiving surgical treatment compared to men. Blacks had lower odds (AOR = 0.76; CI = 0.65–0.98) of receiving surgical treatment compared to Whites. All marital groups had higher odds of receiving surgical treatment compared to those who were single/never married. Patients living in Appalachian county had lower odds of receiving surgical treatment (AOR = 0.65; CI = 0.59–0.71) compared with those in the non‐Appalachian county. Patients with private (AOR = 2.09; CI = 1.55–2.820) or public (AOR = 1.42; CI = 1.06–1.91) insurance coverage were more likely to receive surgical treatment compared to self‐pay/uninsured patients. Overall, the likelihood of patients receiving surgical treatment for localized malignant LC decreases with age. Conclusion Disparities exist in the receipt of surgical treatment among patients with localized malignant LC in Tennessee. Health policies should target reducing these disparities to improve the survival of these patients.

Introduction: Long–standing disparities in colorectal cancer (CRC) outcomes and survival between Whites and Blacks have been observed. A person–centered approach using latent class analysis (LCA) is a novel methodology to assess and address CRC health disparities. LCA can overcome statistical challenges from subgroup analyses that would normally impede variable–centered analyses like regression. Aim was to identify risk profiles and differences in malignant CRC survivorship outcomes.Methods: We conducted an LCA on the Surveillance, Epidemiology, and End Results data from 1975 to 2016 for adults ≥18 (N = 525,245). Sociodemographics used were age, sex/gender, marital status, race, and ethnicity (Hispanic/Latinos) and stage at diagnosis. To select the best fitting model, we employed a comparative approach comparing sample-size adjusted BIC and entropy; which indicates a good separation of classes.Results: A four–class solution with an entropy of 0.72 was identified as: lowest survivorship, medium-low, medium-high, and highest survivorship. The lowest survivorship class (26% of sample) with a mean survival rate of 53 months had the highest conditional probabilities of being 76–85 years–old at diagnosis, female, widowed, and non-Hispanic White, with a high likelihood with localized staging. The highest survivorship class (53% of sample) with a mean survival rate of 92 months had the highest likelihood of being married, male with localized staging, and a high likelihood of being non-Hispanic White.Conclusion: The use of a person–centered measure with population-based cancer registries data can help better detect cancer risk subgroups that may otherwise be overlooked.

Our purpose was to examine the constellation of health determinants in prostate cancer (PCa) surgical treatment delay using a person-centered approach. PCa is the most common and the second leading cause of death among men in the US. Black men are more likely to have advanced disease, and more than twice as likely to die of PCa than white men. Causes for these disparities are complex and involve multiple determinants of health. Our hypothesis was surgical delay may increase the likelihood of recurrence of disease, influence quality of life and survival in blacks. Methods: We used latent class analysis (LCA), a person-centered methodology to assess health disparities in localized, malignant (ICD-O-3 histology and behavior code) PCa surgical treatment delays while assessing co-occurring risk profiles (i.e., outliers). We used the Tennessee Department of Health cancer registry incidence data from 2005 to 2015 for adults over the age of 18 (N=18,088). We examined the differences in PCa surgical delay, measured in days since diagnosis by multiple determinants of health. Determinants of health used were age groups based on US Preventive Services Task Force recommendations, marital status, race, ethnicity (Hispanic/Latino), county of residence (non-Appalachian or Appalachian) and health insurance (none/self-pay, public, or private). To select the best fitting model, we employed a comparative approach comparing sample size adjusted BIC and entropy; which indicates a reliable separation of classes. Results: The best model was a three-class solution with an entropy of 0.69. The highest surgical delay group (12% of sample) with a 31% likelihood of delaying surgery more than 90 days had the highest conditional probabilities of being black, under 55 years old, living in a non-Appalachian county, and single, with a high probability of having private health insurance. The medium surgical delay group (46% of sample) with a 21% likelihood of delay had the highest conditional probabilities of being 70 and older and having public health insurance, with a high probability of being married, non-Hispanic, white. The lowest surgical delay class (42% of sample) with a 14% likelihood of delay had the high conditional probabilities of being 55-69 years old, non-Hispanic, white, and married, with public health insurance. Conclusion: We identified that even with heath insurance blacks had the highest surgical delay living in non-Appalachian counties, which was almost double that of the whites with lowest delay class. These disparities in PCa surgical delay may explain differences in health outcomes in blacks who are most at-risk; however structural factors, clinical care, and treatment outcomes may play a role in these delay, but were not available for analysis. The use of person-centered approaches can help public health researchers better detect cancer risk subgroups and the underlying determinants that may be overlooked, but needed for tailored interventional programs to address prostate cancer disparities. Citation Format: Francisco Alejandro Montiel Ishino, Xiaohui Liu, Bonita Salmeron, Rina Das, Faustine Williams. Identifying risk subgroups of invasive prostate cancer surgical delay using a person-centered approach: The constellation of health determinants using latent class analysis on cancer registry data [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4337.

Background: Few studies have comprehensively and contextually examined the relationship of variables associated with opioid use. Our purpose was to fill a critical gap in comprehensive risk models of opioid misuse and use disorder in the United States by identifying the most salient predictors.Methods: A multivariate logistic regression was used on the 2017 and 2018 National Survey on Drug Use and Health, which included all 50 states and the District of Columbia of the United States. The sample included all noninstitutionalized civilian adults aged 18 and older (N=85,580; weighted N=248,008,986). The outcome of opioid misuse and/or use disorder was based on reported prescription pain reliever and/or heroin use dependence, abuse, or misuse. Biopsychosocial predictors of opioid misuse and use disorder in addition to sociodemographic characteristics and other substance dependence or abuse were examined in our comprehensive model. Biopsychosocial characteristics included socioecological and health indicators. Criminality was the socioecological indicator. Health indicators included self-reported health, private health insurance, psychological distress, and suicidality. Sociodemographic variables included age, sex/gender, race/ethnicity, sexual identity, education, residence, income, and employment status. Substance dependence or abuse included both licit and illicit substances (i.e., nicotine, alcohol, marijuana, cocaine, inhalants, methamphetamine, tranquilizers, stimulants, sedatives). Results. The comprehensive model found that criminality (adjusted odds ratio [AOR]=2.58, 95% confidence interval [CI]=1.98-3.37, p<0.001), self-reported health (i.e., excellent compared to fair/poor [AOR=3.71, 95%CI=2.19-6.29, p<0.001], good [AOR=3.43, 95%CI=2.20-5.34, p<0.001], and very good [AOR=2.75, 95%CI=1.90-3.98, p<0.001]), no private health insurance (AOR=2.12, 95%CI=1.55-2.89, p<0.001), serious psychological distress (AOR=2.12, 95%CI=1.55-2.89, p<0.001), suicidality (AOR=1.58, 95%CI=1.17-2.14, p=0.004), and other substance dependence or abuse were significant predictors of opioid misuse and/or use disorder. Substances associated were nicotine (AOR=3.01, 95%CI=2.30-3.93, p<0.001), alcohol (AOR=1.40, 95%CI=1.02-1.92, p=0.038), marijuana (AOR=2.24, 95%CI=1.40-3.58, p=0.001), cocaine (AOR=3.92, 95%CI=2.14-7.17, p<0.001), methamphetamine (AOR=3.32, 95%CI=1.96-5.64, p<0.001), tranquilizers (AOR=16.72, 95%CI=9.75-28.65, p<0.001), and stimulants (AOR=2.45, 95%CI=1.03-5.87, p=0.044). Conclusions. Biopsychosocial characteristics such as socioecological and health indicators, as well as other substance dependence or abuse were stronger predictors of opioid misuse and use disorder than sociodemographic characteristics.