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This study estimates the out-of-pocket (OOP) expenditures for different cancer types among survivors with current versus no current cancer condition and across sex, which is understudied in the literature. This is a cross-sectional study of Medical Expenditure Panel Survey data for 2009–2018 where the primary outcome was the average per year OOP expenditure incurred by cancer survivors. Of 189,285 respondents, 15,010 (7.93%) were cancer survivors; among them, 46.28% had a current cancer condition. Average per year OOP expenditure for female survivors with a current condition of breast cancer ($1,730), lung cancer ($1,679), colon cancer ($1,595), melanoma ($1,783), non–Hodgkin lymphoma ($1,656), nonmelanoma/other skin cancer (NMSC, $2,118) and two or more cancers ($2,310) were significantly higher than that of women with no history of cancer ($853, all P < 0.05). Similarly, average per year OOP expenditure for male survivors with a current condition of prostate cancer ($1,457), lung cancer ($1,131), colon cancer ($1,471), melanoma ($1,474), non–Hodgkin lymphoma ($1,653), NMSC ($1,789), and bladder cancer ($2,157) were significantly higher compared with the men with no history of cancer ($621, all P < 0.05). These differences persisted in survivors with no current cancer condition for breast cancer among women; prostate, lung, colon, and bladder cancer among men; and melanoma, NMSC, and two or more cancers among both sexes. OOP expenditure varied across cancer types and by sex for survivors with and without a current cancer condition. These findings highlight the need for targeted interventions for cancer survivors. Significance: Our study found that OOP expenditures among survivors with a current cancer condition for several cancers were significantly higher than that of individuals without a cancer history. These differences persisted in female with breast cancer; male with prostate, lung, colon, and bladder cancer; and survivors of both sexes with melanoma, and NMSC/other skin cancer, even after there was no current cancer condition.

Background: Smoking experimentation in Mexican American youth is problematic. In light of the research showing that preventing smoking experimentation is a valid strategy for smoking prevention, there is a need to identify Mexican American youth at high risk for experimentation.Methods: A prospective population-based cohort of 1,179 adolescents of Mexican descent was followed for 5 years starting in 2005–06. Participants completed a baseline interview at a home visit followed by three telephone interviews at intervals of approximately 6 months and additional interviews at two home visits in 2008–09 and 2010–11. The primary endpoint of interest in this study was smoking experimentation. Information about social, cultural, and behavioral factors (e.g., acculturation, susceptibility to experimentation, home characteristics, and household influences) was collected at baseline using validated questionnaires.Results: Age, sex, cognitive susceptibility, household smoking behavior, peer influence, neighborhood influence, acculturation, work characteristics, positive outcome expectations, family cohesion, degree of tension, ability to concentrate, and school discipline were found to be associated with smoking experimentation. In a validation dataset, the proposed risk prediction model had an area under the receiver operating characteristic curve (AUC) of 0.719 (95% confidence interval, 0.637–0.801) for predicting absolute risk for smoking experimentation within 1 year.Conclusions: The proposed risk prediction model is able to quantify the risk of smoking experimentation in Mexican American adolescents.Impact: Accurately identifying Mexican American adolescents who are at higher risk for smoking experimentation who can be intervened will substantially reduce the incidence of smoking and thereby subsequent health risks. Cancer Epidemiol Biomarkers Prev; 23(10); 2165–74. ©2014 AACR.

Supplementary Table S1. Univariate analysis of the smoking experimentation risk factors that were significant at the 0.05 level of significance (sorted by decreasing significance). Supplementary Table S2: Descriptions of the predictors presented in Table S1. Supplementary Figure S1: ROC curves for 1 to 5-year risk of smoking experimentation.

PURPOSE: Little is known regarding HIV/STI testing preferences for location, staffing, and hours of operation among young black men who have sex with men (YBMSM) in the Southeastern U.S, a population at high risk for HIV/STIs. We used a discrete choice experiment to explore these preferences. METHODS: YBMSM ages 16-35 in Birmingham, Alabama and Jackson, Mississippi completed online surveys evaluating their preferences (best/worst) for HIV/STI testing locations, staffing, hours, method of results notification, and cost. They also selected preferred combinations of these variables through choice tasks. Results were analyzed using joint best/worst and discrete choice experiment models. RESULTS: Between June 2018-December 2019, participants in Alabama (n = 54) and Mississippi (n = 159) completed online surveys. Both groups preferred stationary testing locations over mobile testing vans, with the most significant difference favoring STI testing-only clinics in Mississippi and local health departments in Alabama (p < 0.001). Technician-performed tests or self-testing were significantly less preferred compared to clinician-performed testing for both groups (p < 0.0001 and p < 0.0001, respectively). Free testing and phone results notification (versus text) were preferred by both groups. The most desirable combination among all participants was weekday clinician-performed testing at the health department for $5. CONCLUSION: YBMSM in the Southeastern U.S. prefer traditional testing locations staffed by experienced personnel. Combination choices are influenced by services that are low- or no-cost. More research is needed to inform the best way(s) to provide affordable, high-quality HIV/STI testing services for YBMSM, particularly in the post-COVID-19 era when sexual healthcare delivery models are evolving toward home-based and remote-health focused strategies.

Transplant recipients have a 2- to 4-fold increased risk of developing malignancies over the general population. Cancer is the second most common cause of death for recipients. The magnitude of the risk depends on the cancer type and increases in viral-related malignancies. Skin cancer is the most common. However, data in most cancer registries is limited to cutaneous melanomas, thereby limiting the epidemiologic examination of cancer risk in non-melanoma skin cancer. Our goal was to evaluate post-kidney transplant cancer cases and sites in our population to guide screening recommendations.Between 2009 and 2015, a retrospective study of adult kidney recipients transplanted at East Carolina University was conducted. The first cancer diagnosis after transplant through February 18, 2020, was captured and analyzed. Patient demographics, cancer sites, and histological diagnoses were analyzed and compared. p16 immunohistochemistry was used as a surrogate marker for high-risk human papillomavirus (HPV) infection.Retrospectively, kidney transplant recipients were analyzed (N = 439), the majority were non-Hispanic Black (NHB) individuals, 312 (71.1%), and 127 (28.9%) were non-Hispanic White (NHW) individuals. Of these, 59 (13.4%) developed a posttransplant malignancy, with the majority on sun-exposed skin found in NHW. NHB had all anogenital/mucosa skin cancers on non-sun-exposed skin. Of these detected in NHB, all were squamous cell carcinomas, with five out of six (83.3%) being positive for p16.Posttransplant malignancy differed significantly by race, site, and potential source of etiology. The majority of malignancies are likely explained by acceleration of precursor lesions from prior exposure to ultraviolet rays or HPV.

A primary goal of longitudinal studies is to examine trends over time. Reported results from these studies often depend on strong, unverifiable assumptions about the missing data. Whereas the risk of substantial bias from missing data is widely known, analyses exploring missing-data influences are commonly done either ad hoc or not at all. This article outlines one of the three primary recognized approaches for examining missing-data effects that could be more widely used, i.e. the shared-parameter model (SPM), and explains its purpose, use, limitations and extensions. We additionally provide synthetic data and reproducible research code for running SPMs in SAS, Stata and R programming languages to facilitate their use in practice and for teaching purposes in epidemiology, biostatistics, data science and related fields. Our goals are to increase understanding and use of these methods by providing introductions to the concepts and access to helpful tools.

In 2021, the U.S. Preventive Services Task Force (USPSTF) updated its recommendation to expand lung cancer screening (LCS) eligibility and mitigate disparities. Although this increased the number of non-White individuals who are eligible for LCS, the update's impact on drivers of disparities is less clear. This analysis focuses on racial disparities among Black individuals because members of this group disproportionately share late-stage lung cancer diagnoses, despite typically having a lower intensity smoking history compared to non-Hispanic White individuals.We used data from the National Health Interview Survey to examine the impact of the 2021 eligibility criteria on racial disparities by factors such as education, poverty, employment history, and insurance status. We also examined preventive care use and reasons for delaying medical care.When comparing Black individuals and non-Hispanic White individuals, our analyses show significant differences in who would be eligible for LCS: Those who do not have a high school diploma (28.7% vs. 17.0%, p = 0.002), are in poverty (26.2% vs. 14.9%, p 0.001), and have not worked in the past 12 months (66.5% vs. 53.9%, p = 0.009). Further, our analyses also show that more Black individuals delayed medical care due to not having transportation (11.1% vs. 3.6%, p 0.001) compared to non-Hispanic White individuals.Our results suggest that despite increasing the number of Black individuals who are eligible for LCS, the 2021 USPSTF recommendation highlights ongoing socioeconomic disparities that need to be addressed to ensure equitable access.

Background: Previous studies suggest poor pulmonary function is associated with increased burden of cerebral white matter hyperintensities and brain atrophy among elderly individuals, but the results are inconsistent. Objective: To study the cross-sectional associations of pulmonary function with structural brain variables. Methods: Data from six large community-based samples (N = 11,091) were analyzed. Spirometric measurements were standardized with respect to age, sex, height, and ethnicity using reference equations of the Global Lung Function Initiative. Associations of forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), and their ratio FEV1/FVC with brain volume, gray matter volume, hippocampal volume, and volume of white matter hyperintensities were investigated using multivariable linear regressions for each study separately and then combined using random-effect meta-analyses. Results: FEV1 and FVC were positively associated with brain volume, gray matter volume, and hippocampal volume, and negatively associated with white matter hyperintensities volume after multiple testing correction, with little heterogeneity present between the studies. For instance, an increase of FVC by one unit was associated with 3.5 ml higher brain volume (95% CI: [2.2, 4.9]). In contrast, results for FEV1/FVC were more heterogeneous across studies, with significant positive associations with brain volume, gray matter volume, and hippocampal volume, but not white matter hyperintensities volume. Associations of brain variables with both FEV1 and FVC were consistently stronger than with FEV1/FVC, specifically with brain volume and white matter hyperintensities volume. Conclusion: In cross-sectional analyses, worse pulmonary function is associated with smaller brain volumes and higher white matter hyperintensities burden.

INTRODUCTION A data-driven index of dementia risk based on magnetic resonance imaging (MRI), the Alzheimer's Disease Pattern Similarity (AD-PS) score, was estimated for participants in the Atherosclerosis Risk in Communities (ARIC) study. METHODS AD-PS scores were generated for 839 cognitively non-impaired individuals with a mean follow-up of 4.86 years. The scores and a hypothesis-driven volumetric measure based on several brain regions susceptible to AD were compared as predictors of incident cognitive impairment in different settings. RESULTS Logistic regression analyses suggest the data-driven AD-PS scores to be more predictive of incident cognitive impairment than its counterpart. Both biomarkers were more predictive of incident cognitive impairment in participants who were White, female, and apolipoprotein E gene (APOE) e4 carriers. Random forest analyses including predictors from different domains ranked the AD-PS scores as the most relevant MRI predictor of cognitive impairment. CONCLUSIONS Overall, the AD-PS scores were the stronger MRI-derived predictors of incident cognitive impairment in cognitively non-impaired individuals.

BACKGROUND AND OBJECTIVES: Routine human papillomavirus (HPV) vaccination is recommended at ages 11 to 12 years and may be initiated as early as 9 years of age. METHODS: Data were derived from the National Immunization Survey-Teen, spanning 2008–2018. Using health care provider–verified vaccination histories, we examined trends in human papillomavirus vaccination up-to-date (HPV-UTD) rates within ages 9 to 12 years. Furthermore, we assessed vaccination status by sociodemographic factors and US state of residence. RESULTS: Overall, amid evidence of recent stagnation, HPV vaccination between ages 9 to 12 increased over the years. Initiation rates rose from 17.3% in 2008 to 62.8% in 2018, and HPV-UTD rates rose from 13.5% in 2011 to 32.8% in 2018. After the inception of gender-neutral HPV vaccination, HPV-UTD rates between 2011 and 2018 rose by 31.9% among boys and only 6.6% among girls. For most of the study period, non-Hispanic Black and Hispanic individuals had higher rates of initiation and HPV-UTD than non-Hispanic white individuals. In 2018, vaccination initiation rates exceeded 70% in several states; however, HPV-UTD rates in most US states were CONCLUSIONS: HPV vaccination within ages 9 to 12 years is suboptimal. To leverage the substantial benefits of HPV vaccination within this age range, it is imperative that conscious efforts are taken at the national and state levels to promote HPV vaccination for this age group.

Background: Gender-generated disparities in HPV vaccination uptake have the potential to further perpetuate existing disparities in HPV-associated cancers. This study examines the influence of parent-child gender on intent to not vaccinate against HPV due to safety/side effect concerns. Methods: Study data was from the National Immunization Survey-Teen (2010 - 2019). Weighted prevalence of intention to refuse HPV vaccination due to safety/side effect concerns were stratified by parent-child gender. Multivariable logistic regressions were used to examine for associations between intentions to refuse HPV vaccination due to safety concern/side effects and parent-child gender. Results: Over the study period, intentions to refuse HPV vaccination due to safety concerns rose among all parent-child gender pairings but was highest among respondent mothers when pertaining to their unvaccinated daughters. Results revealed statistically significant increased likelihood of having intentions to refuse HPV vaccination due to safety concerns among all parent-child combinations, compared to father-son pairs. These odds were consistently highest among mother-daughter pairs; 9.25 times more likely (95% CI: 6.34-13.51) than father-son pairs in 2010. In 2019, compared to father - son pairs, fathers were 1.94 (95% CI: 1.21-3.12) times more likely to report intention to not vaccinate against HPV for their daughters; while mothers were 2.23 (95% CI: 1.57-3.17) times more likely to report intentions to refuse HPV vaccination for their sons; and 2.87 (95% CI: 2.02-4.07) more likely for their daughters. Conclusions: Intentions to refuse HPV vaccination due to safety concern/side effects varied by gender of the responding parent and pertinent child. Study findings are persistent and constantly increased over a 10-year period. Interventions aimed at correcting gender-based misperceptions, as well as to counter misinformation about the safety of the HPV vaccine are warranted. Citation Format: Onyema Chido-Amajuoyi, rajesh talluri, Inimfon Jackson, Sahil Shete, Joel Fokom Domgue, Sanjay Shete. HPV non-vaccination due to safety concerns/side effects: Variations in vaccination intentions by Parent-Child Gender, National Immunization Survey - Teen, 2010 - 2019 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2209.

This serial cross-sectional study of the National Health Interview Survey evaluates temporal trends in and sociodemographic and behavioral factors associated with cigarette smoking cessation among adult cancer survivors in the US., Key Points Question What were trends in cigarette smoking cessation rates among US adult cancer survivors from 2000 to 2017? Findings In this serial cross-sectional study that included 381 989 adults, the age-adjusted prevalence of current cigarette smokers at the time of first cancer diagnosis was 24.25% among cancer survivors diagnosed between 2000-2017. Over the study period, the probability of quitting cigarette smoking after cancer diagnosis statistically significantly increased with the year of cancer diagnosis. Meaning In the US adult population of cancer survivors, upward trends in cigarette smoking cessation were observed from 2000 to 2017; however, the prevalence of smoking remained high among this population., Importance Continued cigarette smoking after cancer diagnosis has been shown to adversely alter the cancer survivor’s health outcomes and quality of life. Although considerable progress has been made in reducing cigarette smoking in the United States over the last decades, trends in cigarette smoking cessation among cancer survivors have not yet been fully investigated. Objective To evaluate temporal trends in cigarette smoking cessation and their sociodemographic and behavioral correlates in the US population of adult cancer survivors. Design, Setting, and Participants This investigation was a serial cross-sectional study of the National Health Interview Survey (NHIS) from 2006 to 2018, a household survey of civilian US residents who were 18 years or older. Data analysis was performed from June to October 2019. The NHIS is population based and representative of the US population. Included in this study were 381 989 respondents to the NHIS in 2006 to 2018. Main Outcomes and Measures The primary outcome was the probability of quitting cigarette smoking after first cancer diagnosis. Secondary outcomes were factors associated with quitting cigarette smoking after cancer diagnosis. Results Data on 381 989 adults (weighted N = 239 114 051; mean [SD] age, 48.96 [18.28] years; 211 508 [55.37%] female; 61.90% non-Hispanic White, 13.97% non-Hispanic Black, and 16.22% Hispanic individuals) 18 years or older were analyzed from the 2006 to 2018 NHIS, of whom 8.80% (n = 35 524; weighted n = 21 016 720) were diagnosed with cancer. Among cancer survivors diagnosed between 2000 and 2017, the age-adjusted prevalence of current cigarette smoking at the time of first cancer diagnosis was 24.45% (n = 4054; weighted n = 2 395 173). The probability of reporting a cigarette smoking cessation event after first cancer diagnosis statistically significantly increased with each year of cancer diagnosis (hazard ratio [HR], 1.05; 95% CI, 1.02-1.08), indicating upward trends in the prevalence of quitting cigarette smoking over time. Older individuals (HR, 1.02; 95% CI, 1.01-1.03), individuals diagnosed as having a smoking-related cancer (HR, 1.28; 95% CI, 1.06-1.54), individuals with an undergraduate degree (HR, 1.39; 95% CI, 1.08-1.79) or a postgraduate degree (HR, 1.61; 95% CI, 1.18-2.20), and individuals with obesity (HR, 1.32; 95% CI, 1.06-1.63) had a higher probability of reporting a cigarette smoking cessation event after cancer diagnosis, whereas individuals living below the poverty level (HR, 0.62; 95% CI, 0.48-0.81) had a lower probability of reporting a cigarette smoking cessation event after cancer diagnosis. Conclusions and Relevance In this nationally representative survey of the US adult population, the likelihood of cigarette smoking cessation among cancer survivors increased with the year of cancer diagnosis from 2000 to 2017; however, the improvement is incremental, and the prevalence of smoking remained high among this population. Considering the projected increase in the population of cancer survivors in the United States, urgent action is needed to increase cigarette smoking cessation rates in this high-risk population.

Key Points Question Among adult cancer survivors, do cigarette smoking behaviors vary according to whether the cancer was smoking related or not smoking related? Findings This cross-sectional study found that, in the 2017 US National Health Interview Survey, which included 26 742 respondents aged 18 years or older, current smoking prevalence was higher among smoking-related cancer survivors compared with non–smoking-related cancer survivors (19.78% vs 10.63%). After cancer diagnosis, the odds of continued cigarette smoking were twice as high among those with smoking-related cancers compared with those with non–smoking-related cancers. Meaning Compared with non–smoking-related cancer survivors, smoking-related cancer survivors have a higher risk of being cigarette smokers and of continuing smoking., This cross-sectional study examines cigarette smoking prevalence and behaviors (continuing or quitting smoking) among cancer survivors and compares them between survivors of smoking-related cancers and non–smoking-related cancers., Importance The population of cancer survivors is rapidly growing in the US. Tobacco smoking is associated with many cancers; however, whether cigarette smoking behaviors among cancer survivors vary according to cancer type—that is, smoking-related cancers (SRCs) vs non–smoking-related cancers (NSRCs)—remains unclear. Objectives To examine cigarette smoking prevalence and behaviors (ie, continuing or quitting smoking) among cancer survivors and to compare them between survivors of SRCs and NSRCs. Design, Setting, and Participants This study was a cross-sectional analysis of the 2017 National Health Interview Survey, a household survey of civilian US residents who were aged 18 years or older. The National Health Interview Survey is population based and is representative of the US population. Data analysis was performed from June to October 2019. Main Outcomes and Measures The primary outcomes were prevalence of current cigarette smoking among cancer survivors and prevalence of continuing smoking and quitting smoking after a cancer diagnosis. Secondary outcomes included factors associated with continued smoking vs quitting smoking after a cancer diagnosis. Results A total of 26 742 respondents (mean [SD] age, 50.97 [18.61] years; 14 646 women [51.76%]) to the 2017 National Health Interview Survey were included in this study. Of the 3068 individuals (9.42%) in the study population who had cancer, 589 (19.96%) were SRC survivors, 2297 (74.50%) were NSRC survivors, 168 (4.96%) were survivors of both SRC and NSRC, and the remaining 14 (0.58%) had missing information about the type of cancer. Four hundred forty-nine SRC survivors (54.08%) were women, compared with 1412 NSRC survivors (54.30%). Ninety-six SRC survivors (15.69%) and 151 NSRC survivors (7.99%) were younger than 45 years. Overall, 372 cancer survivors (13.16%) were current smokers. Current smoking prevalence was higher among survivors of SRCs (145 survivors [19.78%]) compared with NSRC survivors (251 survivors [10.63%]). Among cancer survivors, 309 current smokers at cancer diagnosis (43.96%) reported having successfully quit smoking and 372 (56.04%) reported continuing smoking. Among the continuing smokers, 176 (56.49%) reported an unsuccessful quit attempt in the last 12 months. After cancer diagnosis, SRC survivors had higher odds of continued smoking compared with NSRC survivors (odds ratio [OR], 2.10; 95% CI, 1.12-3.93; P = .02). Men (OR, 1.93; 95% CI, 1.05-3.57; P = .04), those with angina pectoris (OR, 5.40; 95% CI, 1.33-21.91; P = .02), and those with chronic bronchitis (OR, 2.55; 95% CI, 1.05-6.19; P = .04) had higher odds of continued smoking, whereas Hispanic participants (compared with non-Hispanic white participants: OR, 0.18; 95% CI, 0.05-0.68; P = .01) and married participants (compared with never married participants: OR, 0.33; 95% CI, 0.12-0.96; P = .04) had lower odds of continued smoking. Conclusions and Relevance These findings suggest that compared with NSRC survivors, SRC survivors may be at higher risk of being cigarette smokers at cancer diagnosis and of continuing smoking afterward. Although smoking cessation interventions are critically important for all cancer survivors, special efforts should target survivors of SRCs.

Obesity and type 2 diabetes are major public health issues with known interdependence. Genetic variants have been associated with obesity, type 2 diabetes, or both; thus, we hypothesize that some single nucleotide polymorphisms (SNPs) associated with both conditions may be mediated through obesity to affect type 2 diabetes or vice versa. We propose a framework for bidirectional mediation analyses. Simulations show that this approach accurately estimates the parameters, whether the mediation is unidirectional or bidirectional. In many scenarios, when the mediator is regressed on the initial variable and the outcome is regressed on the mediator and the initial variable, the resulting residuals are correlated because of other unmeasured covariates not in the model. We show that the proposed model provides accurate estimates in this scenario, too. We applied the proposed approach to investigate the mediating effects of SNPs associated with type 2 diabetes and obesity using genetic data from the Multi-Ethnic Study of Atherosclerosis cohort. Specifically, we used body mass index as a measure for obesity and fasting glucose as a measure for type 2 diabetes. We evaluated the top 6 SNPs associated with both body mass index and fasting glucose. Two SNPs (rs3752355 and rs6087982) had indirect effects on body mass index mediated through fasting glucose (0.2677; 95% confidence interval (CI) [0.0007, 0.6548] and 0.3301; 95% CI [0.0881, 0.8544], respectively). The remaining four SNPs (rs7969190, rs4869710, rs10201400 and rs12421620) directly affect body mass index and fasting glucose without mediating effects.

Vaccination against the human papillomavirus (HPV) is effective at preventing several squamous cell carcinomas, yet the population-level uptake of the vaccine remains low in the US. Several factors contribute to HPV vaccine hesitancy and refusal; of note, safety concerns rank consistently high as a reason for nonvaccination.1,2 The COVID-19 pandemic has brought to the forefront the fragility of public confidence in the safety of vaccines.3 Therefore, this study examines safety concerns or adverse effects of the HPV vaccine as the main reason for nonvaccination over an 11-year period.

This cross-sectional study examines National Health and Nutrition Examination Survey data to assess the prevalence of secondhand smoke exposure in nonsmokers in the US population 3 years and older.

Background The sequence of events preceding incident bacterial vaginosis (iBV) is unclear. Methods African American women who have sex with women, who had no Amsel criteria and Nugent scores of 0-3, were followed for 90 days to detect iBV (defined as a Nugent score of 7-10 on at least 2-3 consecutive days), using self-collected vaginal swab specimens. For women with iBV (cases) and women maintaining normal vaginal flora (healthy women), 16S ribosomal RNA gene sequencing targeting V4 was performed. Longitudinal vaginal microbiome data were analyzed. Results Of 204 women screened, 42 enrolled; of these, 45% developed iBV. Sequencing was performed on 448 specimens from 14 cases and 8 healthy women. Among healthy women, Lactobacillus crispatus dominated the vaginal microbiota in 75%. In contrast, prior to iBV, the vaginal microbiota in 79% of cases was dominated by Lactobacillus iners and/or Lactobacillus jensenii/Lactobacillus gasseri. The mean relative abundance of Prevotella bivia, Gardnerella vaginalis, Atopobium vaginae, and Megasphaera type I became significantly higher in cases 4 days before (P. bivia), 3 days before (G. vaginalis), and on the day of (A. vaginae and Megasphaera type I) iBV onset. The mean relative abundance of Sneathia sanguinegens, Finegoldia magna, BV-associated bacteria 1-3, and L. iners was not significantly different between groups before onset of iBV. Conclusion G. vaginalis, P. bivia, A. vaginae, and Megasphaera type I may play significant roles in iBV.

Background This study aimed to determine if intranasal dexmedetomidine is a superior pre-medication to oral midazolam in older, difficult children. Methods This was conducted as a prospective, single-blind randomized control trial in a tertiary care center. Seventy-five children, age >5 years and weight >20 kg, who needed general anesthesia for dental procedures were randomly assigned to be pre-medicated with either oral midazolam at a dose of 0.5 mg/kg (max 15 mg) or intranasal dexmedetomidine at a dose of 2 mcg/kg (max 100 mcg). The primary outcome studied was the patients' level of sedation when separated from their parents, which was assessed using a 5-point University of Michigan Sedation Scale. Secondary outcome studied was the level of anxiolysis assessed by the acceptance of mask induction using a 4-point scale. All assessments were made by one research person blinded to the study drug. Results The two groups were similar in age, sex, weight, pre-anesthetic behavior, time from pre-medication to anesthesia induction, and surgical time. A significantly higher proportion of patients who received dexmedetomidine had satisfactory sedation at separation from parents (69.4% vs 40.5%, P = .03) compared to those who received midazolam. There were no significant differences in the rate of acceptance of mask induction (80.6% vs 78.4%, P = 1.00). Intranasal dexmedetomidine was tolerated well when administered using a mucosal atomizer and without any clinically significant effect on heart rate or systolic blood pressure. Conclusions Intranasal dexmedetomidine provides higher success rate in sedation and parental separation compared to oral midazolam, in older, difficult children.

Background: Smoking experimentation in Mexican American youth is problematic. In light of the research showing that preventing smoking experimentation is a valid strategy for smoking prevention, there is a need to identify Mexican American youth at high risk for experimentation. Methods: A prospective population-based cohort of 1,179 adolescents of Mexican descent was followed for 5 years starting in 2005–06. Participants completed a baseline interview at a home visit followed by three telephone interviews at intervals of approximately 6 months and additional interviews at two home visits in 2008–09 and 2010–11. The primary endpoint of interest in this study was smoking experimentation. Information about social, cultural, and behavioral factors (e.g., acculturation, susceptibility to experimentation, home characteristics, and household influences) was collected at baseline using validated questionnaires. Results: Age, sex, cognitive susceptibility, household smoking behavior, peer influence, neighborhood influence, acculturation, work characteristics, positive outcome expectations, family cohesion, degree of tension, ability to concentrate, and school discipline were found to be associated with smoking experimentation. In a validation dataset, the proposed risk prediction model had an area under the receiver operating characteristic curve (AUC) of 0.719 (95% confidence interval, 0.637–0.801) for predicting absolute risk for smoking experimentation within 1 year. Conclusions: The proposed risk prediction model is able to quantify the risk of smoking experimentation in Mexican American adolescents. Impact: Accurately identifying Mexican American adolescents who are at higher risk for smoking experimentation who can be intervened will substantially reduce the incidence of smoking and thereby subsequent health risks. Cancer Epidemiol Biomarkers Prev; 23(10); 2165–74. ©2014 AACR.

We propose Bayesian methods for Gaussian graphical models that lead to sparse and adaptively shrunk estimators of the precision (inverse covariance) matrix. Our methods are based on lasso-type regularization priors leading to parsimonious parameterization of the precision matrix, which is essential in several applications involving learning relationships among the variables. In this context, we introduce a novel type of selection prior that develops a sparse structure on the precision matrix by making most of the elements exactly zero, in addition to ensuring positive definiteness – thus conducting model selection and estimation simultaneously. More importantly, we extend these methods to analyze clustered data using finite mixtures of Gaussian graphical model and infinite mixtures of Gaussian graphical models. We discuss appropriate posterior simulation schemes to implement posterior inference in the proposed models, including the evaluation of normalizing constants that are functions of parameters of interest, which result from the restriction of positive definiteness on the correlation matrix. We evaluate the operating characteristics of our method via several simulations and demonstrate the application to real data examples in genomics.

Array-based comparative genomic hybridization (aCGH) is a high-resolution high-throughput technique for studying the genetic basis of cancer. The resulting data consists of log fluorescence ratios as a function of the genomic DNA location and provides a cytogenetic representation of the relative DNA copy number variation. Analysis of such data typically involves estimation of the underlying copy number state at each location and segmenting regions of DNA with similar copy number states. Most current methods proceed by modeling a single sample/array at a time, and thus fail to borrow strength across multiple samples to infer shared regions of copy number aberrations. We propose a hierarchical Bayesian random segmentation approach for modeling aCGH data that utilizes information across arrays from a common population to yield segments of shared copy number changes. These changes characterize the underlying population and allow us to compare different population aCGH profiles to assess which regions of the genome have differential alterations. Our method, referred to as BDSAcgh (Bayesian Detection of Shared Aberrations in aCGH), is based on a unified Bayesian hierarchical model that allows us to obtain probabilities of alteration states as well as probabilities of differential alteration that correspond to local false discovery rates. We evaluate the operating characteristics of our method via simulations and an application using a lung cancer aCGH data set.

Epistasis helps to explain how multiple single-nucleotide polymorphisms (SNPs) interact to cause disease. A variety of tools have been developed to detect epistasis. In this article, we explore the strengths and weaknesses of an information theory approach for detecting epistasis and compare it to the logistic regression approach through simulations. We consider several scenarios to simulate the involvement of SNPs in an epistasis network with respect to linkage disequilibrium patterns among them and the presence or absence of main and interaction effects. We conclude that the information theory approach more efficiently detects interaction effects when main effects are absent, whereas, in general, the logistic regression approach is appropriate in all scenarios but results in higher false positives. We compute epistasis networks for SNPs in the FSD1L gene using a two-phase head and neck cancer genome-wide association study involving 2,185 cases and 4,507 controls to demonstrate the practical application of the methods.

The advent of large, information-rich data sets partnered with advanced computation has made it increasingly possible to inject life into data through interactive visualization. Brown and colleague...

Graphical models are increasingly used in genetic analyses to take into account the complex relationships between genetic and nongenetic factors influencing the phenotypes. We propose a model for determining the network structure of quantitative traits while accounting for the correlated nature of the family-based samples using the kinship coefficient. The Gaussian graphical model of age, systolic blood pressure, diastolic blood pressure, hypertension, blood pressure medication use, and smoking status was derived for three time points using real data. We also explored binary sparse graphical models of single-nucleotide polymorphisms (SNPs), covariates, and quantitative traits for exploratory analysis of the data. We validated the applicability of this method by producing a network graph using 20 causal variants, 21 noncausal variants, and 6 binary and quantitative phenotypes using the simulated data. To improve the model's ability to identify associations between the causal variants and the phenotypes, we intend to conduct follow-up studies investigating how to use the relationships between SNPs and between SNPs and phenotypes when analyzing genome wide association data with multiple phenotypes.

Background Several methods have been proposed to account for multiple comparisons in genetic association studies. However, investigators typically test each of the SNPs using multiple genetic models. Association testing using the Cochran-Armitage test for trend assuming an additive, dominant, or recessive genetic model, is commonly performed. Thus, each SNP is tested three times. Some investigators report the smallest p-value obtained from the three tests corresponding to the three genetic models, but such an approach inherently leads to inflated type 1 errors. Because of the small number of tests (three) and high correlation (functional dependence) among these tests, the procedures available for accounting for multiple tests are either too conservative or fail to meet the underlying assumptions (e.g., asymptotic multivariate normality or independence among the tests). Results We propose a method to calculate the exact p-value for each SNP using different genetic models. We performed simulations, which demonstrated the control of type 1 error and power gains using the proposed approach. We applied the proposed method to compute p-value for a polymorphism eNOS -786T>C which was shown to be associated with breast cancer risk. Conclusions Our findings indicate that the proposed method should be used to maximize power and control type 1 errors when analyzing genetic data using additive, dominant, and recessive models.

Reverse-phase protein array (RPPA) analysis is a powerful, relatively new platform that allows for high-throughput, quantitative analysis of protein networks. One of the challenges that currently limit the potential of this technology is the lack of methods that allow for accurate data modeling and identification of related networks and samples. Such models may improve the accuracy of biological sample classification based on patterns of protein network activation and provide insight into the distinct biological relationships underlying different types of cancer. Motivated by RPPA data, we propose a Bayesian sparse graphical modeling approach that uses selection priors on the conditional relationships in the presence of class information. The novelty of our Bayesian model lies in the ability to draw information from the network data as well as from the associated categorical outcome in a unified hierarchical model for classification. In addition, our method allows for intuitive integration of a priori network information directly in the model and allows for posterior inference on the network topologies both within and between classes. Applying our methodology to an RPPA data set generated from panels of human breast cancer and ovarian cancer cell lines, we demonstrate that the model is able to distinguish the different cancer cell types more accurately than several existing models and to identify differential regulation of components of a critical signaling network (the PI3K-AKT pathway) between these two types of cancer. This approach represents a powerful new tool that can be used to improve our understanding of protein networks in cancer., Comment: Published in at http://dx.doi.org/10.1214/14-AOAS722 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Rare variants have increasingly been cited as major contributors in the disease etiology of several complex disorders. Recently, several approaches have been proposed for analyzing the association of rare variants with disease. These approaches include collapsing rare variants, summing rare variant test statistics within a particular locus to improve power, and selecting a subset of rare variants for association testing, e.g., the step-up approach. We found that (a) if the variants being pooled are in linkage disequilibrium, the standard step-up method of selecting the best subset of variants results in loss of power compared to a model that pools all rare variants and (b) if the variants are in linkage equilibrium, performing a subset selection using step-based selection methods results in a gain of power of association compared to a model that pools all rare variants. Therefore, we propose an approach to selecting the best subset of variants to include in the model that is based on the linkage disequilibrium pattern among the rare variants. The proposed linkage disequilibrium–based variant selection model is flexible and borrows strength from the model that pools all rare variants when the rare variants are in linkage disequilibrium and from step-based selection methods when the variants are in linkage equilibrium. We performed simulations under three different realistic scenarios based on: (1) the HapMap3 dataset of the DRD2 gene, and CHRNA3/A5/B4 gene cluster (2) the block structure of linkage disequilibrium, and (3) linkage equilibrium. We proposed a permutation-based approach to control the type 1 error rate. The power comparisons after controlling the type 1 error show that the proposed linkage disequilibrium–based subset selection approach is an attractive alternative method for subset selection of rare variants.

This paper considers the task of building efficient regression models for sparse multivariate analysis of high dimensional data sets, in particular it focuses on cases where the numbers q of responses Y = (y k , 1 ≤ k ≤ q) and p of predictors X = (xj , 1 ≤ j ≤ p) to analyse jointly are both large with respect to the sample size n, a challenging bi-directional task. The analysis of such data sets arise commonly in genetical genomics, with X linked to the DNA characteristics and Y corresponding to measurements of fundamental biological processes such as transcription, protein or metabolite production. Building on the Bayesian variable selection set-up for the linear model and associated efficient MCMC algorithms developed for single responses, we discuss the generic framework of hierarchical related sparse regressions, where parallel regressions of y k on the set of covariates X are linked in a hierarchical fashion, in particular through the prior model of the variable selection indicators γkj , which indicate among the covariates xj those which are associated to the response y k in each multivariate regression. Structures for the joint model of the γkj , which correspond to different compromises between the aims of controlling sparsity and that of enhancing the detection of predictors that are associated with many responses (‘hot spots’), will be discussed and a new multiplicative model for the probability structure of the γkj will be presented. To perform inference for these models in high dimensional set-ups, novel adaptive MCMC algorithms are needed. As sparsity is paramount and most of the associations expected to be zero, new algorithms that progressively focus on part of the space where the most interesting associations occur are of great interest. We shall discuss their formulation and theoretical properties, and demonstrate their use on simulated and real data from genomics.