Phthalate and phthalate replacement concentrations in relationship to adiposity in a multi-racial cohort of children.

BACKGROUND/OBJECTIVE: Phthalates and phthalate replacements are used in multiple everyday products, making many of them bioavailable to children. Experimental studies suggest that phthalates and their replacements may be obesogenic, however, epidemiologic studies remain inconsistent. Therefore, our objective was to examine the association between phthalates, phthalate replacements and childhood adiposity/obesity markers in children. SUBJECTS/METHODS: A cross-sectional study was conducted in 630 racial/ethnically diverse children ages 4-8 years. Urinary oxidative metabolites of DINCH and DEHTP, three low molecular weight (LMW) phthalates, and eleven high molecular weight (HMW) phthalates were measured. Weight, height, waist circumference and % body fat were measured. Composite molar sum groups (nmol/ml) were natural log-transformed. Linear regression models adjusted for urine specific gravity, sex, age, race-ethnicity, birthweight, breastfeeding, reported activity level, mother's education and pre-pregnancy BMI. RESULTS: All children had LMW and HMW phthalate metabolites and 88% had DINCH levels above the limit of detection. One unit higher in the log of DINCH was associated with 0.106 units lower BMI z-score [ß = -0.106 (95% CI: -0.181, -0.031)], 0.119 units lower waist circumference z-score [ß = -0.119 (95% CI: -0.189, -0.050)], and 0.012 units lower percent body fat [ß = -0.012 (95% CI: -0.019, -0.005)]. LMW and HMW group values were not associated with adiposity/obesity. CONCLUSIONS: We report an inverse association between child urinary DINCH levels, a non-phthalate plasticizer that has replaced DEHP in several applications, and BMI z-score, waist circumference z-score and % body fat in children. Few prior studies of phthalates and their replacements in children have been conducted in diverse populations. Moreover, DINCH has not received a great deal of attention or regulation, but it is a common exposure. In summary, understanding the ubiquitous nature of these chemical exposures and ultimately their sources will contribute to our understanding of their relationship with obesity.

Bayesian hierarchical profile regression for binary covariates.

Dysphagia, a common result of other medical conditions, is caused by malfunctions in swallowing physiology resulting in difficulty eating and drinking. The Modified Barium Swallow Study (MBSS), the most commonly used diagnostic tool for evaluating dysphagia, can be assessed using the Modified Barium Swallow Impairment Profile (MBSImP™). The MBSImP assessment tool consists of a hierarchical grouped data structure with multiple domains, a set of components within each domain which characterize specific swallowing physiologies, and a set of tasks scored on a discrete scale within each component. We lack sophisticated approaches to extract patterns of physiologic swallowing impairment from the MBSImP task scores within a component while still recognizing the nested structure of components within a domain. We propose a Bayesian hierarchical profile regression model, which uses a Bayesian profile regression model in conjunction with a hierarchical Dirichlet process mixture model to (1) cluster subjects into impairment profile patterns while respecting the hierarchical grouped data structure of the MBSImP, and (2) simultaneously determine associations between latent profile cluster membership for all components and the outcome of dysphagia severity. We apply our approach to a cohort of patients referred for an MBSS and assessed using the MBSImP. Our research results can be used to inform appropriate intervention strategies, and provide tools for clinicians to make better multidimensional management and treatment decisions for patients with dysphagia.

Children's physical activity in family child care homes: Influence of quality status, environment and policy features, and child characteristics.

BACKGROUND: About half of preschool-age children are not meeting recommendations of 15 min/h of physical activity (PA), and nearly one out of seven children between the ages of 2-5 years are living with obesity. Furthermore, children attending family child care homes (FCCHs), compared with larger child care centers, engage in lower levels of PA and appear to be at a higher risk of obesity. Therefore, examining PA and multi-level factors that influence PA in children who attend FCCHs is essential. METHODS: The Childcare Home Eating and Exercise Study (CHEER) examined PA behaviors of 184 children enrolled in 56 FCCHs and FCCH quality status, environment and policy features, and child characteristics. PA was assessed by accelerometer, and FCCH environment and policy was assessed via structured observation. Multiple linear regression was used to model associations between school day total PA and FCCH quality status, environment and policy features, and child characteristics. RESULTS: Child participants were on average 3.1 years old; participants were non-Hispanic Black (47.3%), Non-Hispanic White (42.9%), other race/ethnicity (7.1%), and Hispanic/Latin (2.7%). Children in FCCH settings participated in 11.2 min/h of total PA, which is below the recommended 15 min per hour. The PA environment and policy observation yielded a score of 11.8 out of a possible 30, which is not supportive of child PA. There were no associations between total child PA and FCCH quality status, environment and policy features, and child characteristics in these FCCH settings. CONCLUSIONS: This study was unique in its examination of PA and a comprehensive set of factors that may influence PA at the individual, organizational, environmental, and policy levels in a diverse sample of children attending FCCHs in South Carolina. Additional research is needed to better understand how to increase children's physical activity while they are in the FCCH setting. This research should use multi-level frameworks and apply longitudinal study designs.

Increasing Preterm Delivery and Small for Gestational Age Trends in South Carolina during the COVID-19 Pandemic.

Preterm delivery (PTD) complications are a major cause of childhood morbidity and mortality. We aimed to assess trends in PTD and small for gestational age (SGA) and whether trends varied between race-ethnic groups in South Carolina (SC). We utilized 2015-2021 SC vital records linked to hospitalization and emergency department records. PTD was defined as clinically estimated gestation less than (<) 37 weeks (wks.) with subgroup analyses of PTD < 34 wks. and < 28 wks. SGA was defined as infants weighing below the 10th percentile for gestational age. This retrospective study included 338,532 (243,010 before the COVID-19 pandemic and 95,522 during the pandemic) live singleton births of gestational age ≥ 20 wks. born to 260,276 mothers in SC. Generalized estimating equations and a change-point during the first quarter of 2020 helped to assess trends. In unadjusted analyses, pre-pandemic PTD showed an increasing trend that continued during the pandemic (relative risk (RR) = 1.04, 95% CI: 1.02-1.06). PTD < 34 wks. rose during the pandemic (RR = 1.07, 95% CI: 1.02-1.12) with a significant change in the slope. Trends in SGA varied by race and ethnicity, increasing only in Hispanics (RR = 1.02, 95% CI: 1.00-1.04) before the pandemic. Our study reveals an increasing prevalence of PTD and a rise in PTD < 34 wks. during the pandemic, as well as an increasing prevalence of SGA in Hispanics during the study period.

Geographic, Patient, and VA Medical Center Variation in the Receipt and Mode of Primary Care in a National Sample of Veterans with Diabetes during 2020.

While telemedicine infrastructure was in place within the Veterans Health Administration (VHA) healthcare system before the onset of the COVID-19 pandemic, geographically varying ordinances/closures disrupted vital care for chronic disease patients such as those with type 2 diabetes. We created a national cohort of 1,647,158 non-Hispanic White, non-Hispanic Black, and Hispanic veterans with diabetes including patients with at least one primary care visit and HbA1c lab result between 3.5% and 20% in the fiscal year (FY) 2018 or 2019. For each VAMC, the proportion of telehealth visits in FY 2019 was calculated. Two logistic Bayesian spatial models were employed for in-person primary care or telehealth primary care in the fourth quarter of the FY 2020, with spatial random effects incorporated at the VA medical center (MC) catchment area level. Finally, we computed and mapped the posterior probability of receipt of primary care for an "average" patient within each catchment area. Non-Hispanic Black veterans and Hispanic veterans were less likely to receive in-person primary care but more likely to receive tele-primary care than non-Hispanic white veterans during the study period. Veterans living in the most socially vulnerable areas were more likely to receive telehealth primary care in the fourth quarter of FY 2020 compared to the least socially vulnerable group but were less likely to receive in-person care. In summary, racial minorities and those in the most socially vulnerable areas were less likely to receive in-person primary care but more likely to receive telehealth primary care, potentially indicating a disparity in the impact of the pandemic across these groups.

SpaceANOVA: Spatial Co-occurrence Analysis of Cell Types in Multiplex Imaging Data Using Point Process and Functional ANOVA.

Multiplex imaging platforms have enabled the identification of the spatial organization of different types of cells in complex tissue or the tumor microenvironment. Exploring the potential variations in the spatial co-occurrence or colocalization of different cell types across distinct tissue or disease classes can provide significant pathological insights, paving the way for intervention strategies. However, the existing methods in this context either rely on stringent statistical assumptions or suffer from a lack of generalizability. We present a highly powerful method to study differential spatial co-occurrence of cell types across multiple tissue or disease groups, based on the theories of the Poisson point process and functional analysis of variance. Notably, the method accommodates multiple images per subject and addresses the problem of missing tissue regions, commonly encountered due to data-collection complexities. We demonstrate the superior statistical power and robustness of the method in comparison with existing approaches through realistic simulation studies. Furthermore, we apply the method to three real data sets on different diseases collected using different imaging platforms. In particular, one of these data sets reveals novel insights into the spatial characteristics of various types of colorectal adenoma.

Policies, Practices, and Environmental Characteristics Among Family Child Care Homes in South Carolina.

Background: Child care program requirements have adopted nutrition and physical activity standards to address childhood obesity, but few studies have examined the effects of these standards in family child care homes (FCCHs). Methods: In a cross-sectional study (2017-2019), the Childcare Home Eating and Exercise study examined self-reported provider characteristics and observed policies and practices related to physical activity and nutrition in FCCHs in South Carolina. Two-sample t-tests were used to compare observed nutrition and physical activity policy, practice, and environment scores in child care homes that participated in versus did not participate in the state's ABC Quality program, which is designed to improve child care and includes policies and practices intended to increase physical activity levels and improve diet quality. Results: Environment and Policy Assessment and Observation results for nutrition and physical activity were 7.5 out of 21 and 11.8 out of 30, respectively, indicating much room for improvement in nutrition and physical activity policies, practices, and environment in South Carolina FCCHs. The study found one difference between FCCHs that did and did not participate in the ABC Quality program; non-ABC homes provided more time for physical activity. Conclusions: Future research should develop ways to strengthen the guidelines and improve the implementation of obesity prevention standards in FCCHs.

Bayesian kernel machine regression for count data: modelling the association between social vulnerability and COVID-19 deaths in South Carolina.

The COVID-19 pandemic created an unprecedented global health crisis. Recent studies suggest that socially vulnerable communities were disproportionately impacted, although findings are mixed. To quantify social vulnerability in the US, many studies rely on the Social Vulnerability Index (SVI), a county-level measure comprising 15 census variables. Typically, the SVI is modelled in an additive manner, which may obscure non-linear or interactive associations, further contributing to inconsistent findings. As a more robust alternative, we propose a negative binomial Bayesian kernel machine regression (BKMR) model to investigate dynamic associations between social vulnerability and COVID-19 death rates, thus extending BKMR to the count data setting. The model produces a 'vulnerability effect' that quantifies the impact of vulnerability on COVID-19 death rates in each county. The method can also identify the relative importance of various SVI variables and make future predictions as county vulnerability profiles evolve. To capture spatio-temporal heterogeneity, the model incorporates spatial effects, county-level covariates, and smooth temporal functions. For Bayesian computation, we propose a tractable data-augmented Gibbs sampler. We conduct a simulation study to highlight the approach and apply the method to a study of COVID-19 deaths in the US state of South Carolina during the 2021 calendar year.

A Bayesian zero-inflated beta-binomial model for longitudinal data with group-specific changepoints.

Timeline followback (TLFB) is often used in addiction research to monitor recent substance use, such as the number of abstinent days in the past week. TLFB data usually take the form of binomial counts that exhibit overdispersion and zero inflation. Motivated by a 12-week randomized trial evaluating the efficacy of varenicline tartrate for smoking cessation among adolescents, we propose a Bayesian zero-inflated beta-binomial model for the analysis of longitudinal, bounded TLFB data. The model comprises a mixture of a point mass that accounts for zero inflation and a beta-binomial distribution for the number of days abstinent in the past week. Because treatment effects appear to level off during the study, we introduce random changepoints for each study group to reflect group-specific changes in treatment efficacy over time. The model also includes fixed and random effects that capture group- and subject-level slopes before and after the changepoints. Using the model, we can accurately estimate the mean trend for each study group, test whether the groups experience changepoints simultaneously, and identify critical windows of treatment efficacy. For posterior computation, we propose an efficient Markov chain Monte Carlo algorithm that relies on easily sampled Gibbs and Metropolis-Hastings steps. Our application shows that the varenicline group has a short-term positive effect on abstinence that tapers off after week 9.

Implementation Mapping for Managing Patients at High Risk for Hereditary Cancer.

INTRODUCTION: Currently, no standard workflow exists for managing patients with pathogenic variants that put them at higher risk for hereditary cancers. Therefore, follow-up care for individuals with pathogenic variants is logistically challenging and results in poor guideline adherence. To address this challenge, authors created clinical management strategies for individuals identified at high risk for hereditary cancers. METHODS: An implementation mapping approach was used to develop and evaluate the establishment of a Hereditary Cancer Clinic at the Medical University of South Carolina throughout in 2022. This approach consisted of 5 steps: conduct a needs assessment, identify objectives, select implementation strategies, produce implementation protocols, and develop an evaluation plan. The needs assessment consisted of qualitative interviews with patients (n=11), specialists (n=9), and members of the implementation team (n=4). Interviews were coded using the Consolidated Framework for Implementation Research to identify barriers and facilitators to establishment of the Hereditary Cancer Clinic. Objectives were identified, and then the team selected implementation strategies and produced implementation protocols to address concerns identified during the needs assessment. Authors conducted a second round of patient interviews to assess patient education materials. RESULTS: The research team developed a long-term evaluation plan to guide future assessment of implementation, service, and clinical/patient outcomes. CONCLUSIONS: This approach provides the opportunity for real-time enhancements and impact, with strategies for care specialists, patients, and implementation teams. Findings support ongoing efforts to improve patient management and outcomes while providing an opportunity for long-term evaluation of implementation strategies and guidelines for patients at high risk for hereditary cancers.

A Bayesian framework for pathway-guided identification of cancer subgroups by integrating multiple types of genomic data.

In recent years, comprehensive cancer genomics platforms, such as The Cancer Genome Atlas (TCGA), provide access to an enormous amount of high throughput genomic datasets for each patient, including gene expression, DNA copy number alterations, DNA methylation, and somatic mutation. While the integration of these multi-omics datasets has the potential to provide novel insights that can lead to personalized medicine, most existing approaches only focus on gene-level analysis and lack the ability to facilitate biological findings at the pathway-level. In this article, we propose Bayes-InGRiD (Bayesian Integrative Genomics Robust iDentification of cancer subgroups), a novel pathway-guided Bayesian sparse latent factor model for the simultaneous identification of cancer patient subgroups (clustering) and key molecular features (variable selection) within a unified framework, based on the joint analysis of continuous, binary, and count data. By utilizing pathway (gene set) information, Bayes-InGRiD does not only enhance the accuracy and robustness of cancer patient subgroup and key molecular feature identification, but also promotes biological understanding and interpretation. Finally, to facilitate an efficient posterior sampling, an alternative Gibbs sampler for logistic and negative binomial models is proposed using Pólya-Gamma mixtures of normal to represent latent variables for binary and count data, which yields a conditionally Gaussian representation of the posterior. The R package "INGRID" implementing the proposed approach is currently available in our research group GitHub webpage (https://dongjunchung.github.io/INGRID/).

Evaluation of Bayesian spatiotemporal infectious disease models for prospective surveillance analysis.

BACKGROUND: COVID-19 brought enormous challenges to public health surveillance and underscored the importance of developing and maintaining robust systems for accurate surveillance. As public health data collection efforts expand, there is a critical need for infectious disease modeling researchers to continue to develop prospective surveillance metrics and statistical models to accommodate the modeling of large disease counts and variability. This paper evaluated different likelihoods for the disease count model and various spatiotemporal mean models for prospective surveillance. METHODS: We evaluated Bayesian spatiotemporal models, which are the foundation for model-based infectious disease surveillance metrics. Bayesian spatiotemporal mean models based on the Poisson and the negative binomial likelihoods were evaluated with the different lengths of past data usage. We compared their goodness of fit and short-term prediction performance with both simulated epidemic data and real data from the COVID-19 pandemic. RESULTS: The simulation results show that the negative binomial likelihood-based models show better goodness of fit results than Poisson likelihood-based models as deemed by smaller deviance information criteria (DIC) values. However, Poisson models yield smaller mean square error (MSE) and mean absolute one-step prediction error (MAOSPE) results when we use a shorter length of the past data such as 7 and 3 time periods. Real COVID-19 data analysis of New Jersey and South Carolina shows similar results for the goodness of fit and short-term prediction results. Negative binomial-based mean models showed better performance when we used the past data of 52 time periods. Poisson-based mean models showed comparable goodness of fit performance and smaller MSE and MAOSPE results when we used the past data of 7 and 3 time periods. CONCLUSION: We evaluate these models and provide future infectious disease outbreak modeling guidelines for Bayesian spatiotemporal analysis. Our choice of the likelihood and spatiotemporal mean models was influenced by both historical data length and variability. With a longer length of past data usage and more over-dispersed data, the negative binomial likelihood shows a better model fit than the Poisson likelihood. However, as we use a shorter length of the past data for our surveillance analysis, the difference between the Poisson and the negative binomial models becomes smaller. In this case, the Poisson likelihood shows robust posterior mean estimate and short-term prediction results.

Young Children's Dietary Quality in Family Child Care and in Their Own Home.

BACKGROUND: Some evidence suggests that children may have higher quality dietary intake in early care and education settings, compared with their respective homes, but no studies have explored these differences among children in less formal family child care. OBJECTIVE: The purpose of this study was to compare dietary quality via the Healthy Eating Index 2015 among children in family child care and in their own home. DESIGN: This was a cross-sectional analysis of baseline dietary intake data from the Childcare Home Eating and Exercise Research study, a natural experiment, using directly observed dietary data in child care and 24-hour recall data in homes among children in South Carolina. PARTICIPANTS/SETTING: Participants were 123 children in 52 family child-care homes between 2018 and 2019. MAIN OUTCOME MEASURE: The main outcome was total and component Healthy Eating Index 2015 scores. STATISTICAL ANALYSIS: The analysis was a hierarchical linear regression of children nested within family child care homes adjusting for child, provider, facility, and parent characteristics, including sex, age, race, ethnicity, and income, with parameters and SEs estimated via bootstrap sampling. RESULTS: Children had a mean ± SD Healthy Eating Index 2015 score of 60.3 ± 12.1 in family child-care homes and 54.3 ± 12.9 in their own home (P < 0.001). In adjusted analysis and after accounting for clustering of children in family child care homes, total HEI-2015 scores were lower at home than in care (ß = -5.18 ± 1.47; 95% CI -8.05 to -2.30; P = 0.003). CONCLUSIONS: Children had healthier dietary intake in family child-care homes vs their respective homes.

Geographic variation in diabetic retinopathy screening within the Veterans Health Administration.

AIMS: Diabetic retinopathy (DR) remains the leading cause of vision impairment among working-age adults in the United States. The Veterans Health Administration (VA) supplemented its DR screening efforts with teleretinal imaging in 2006. Despite its scale and longevity, no national data on the VA's screening program exists since 1998. Our objective was to determine the influence of geography on diabetic retinopathy screening adherence. METHODS: Setting: VA national electronic medical records. STUDY POPULATION: A national cohort of 940,654 veterans with diabetes (defined as two or more diabetes ICD-9 codes (250.xx)) without a history of DR. EXPOSURES: 125 VA Medical Center catchment areas, demographics, comorbidity burden, mean HbA1c levels, medication use and adherence, as well as utilization and access metrics. MAIN OUTCOME MEASURE: Screening for diabetic retinopathy within the VA medical system within a 2-year period. RESULTS: Within a 2-year time frame 74 % of veterans without a history of DR received retinal screenings within the VA system. After adjustment for age, gender, race-ethnic group, service-connected disability, marital status, and the van Walraven Elixhauser comorbidity score, the prevalence of DR screening varied by VA catchment area with values ranging from 27 % to 86 %. These differences persisted after further adjusting for mean HbA1c level, medication use and adherence as well as utilization and access metrics. CONCLUSIONS: The wide variability in DR screening across 125 VA catchment areas indicates the presence of unmeasured determinants of DR screening. These results are relevant to clinical decision making in DR screening resource allocation.

SpaceANOVA: Spatial co-occurrence analysis of cell types in multiplex imaging data using point process and functional ANOVA.

Motivation: Multiplex imaging platforms have enabled the identification of the spatial organization of different types of cells in complex tissue or tumor microenvironment (TME). Exploring the potential variations in the spatial co-occurrence or co-localization of different cell types across distinct tissue or disease classes can provide significant pathological insights, paving the way for intervention strategies. However, the existing methods in this context either rely on stringent statistical assumptions or suffer from a lack of generalizability. Results: We present a highly powerful method to study differential spatial co-occurrence of cell types across multiple tissue or disease groups, based on the theories of the Poisson point process (PPP) and functional analysis of variance (FANOVA). Notably, the method accommodates multiple images per subject and addresses the problem of missing tissue regions, commonly encountered in such a context due to the complex nature of the data-collection procedure. We demonstrate the superior statistical power and robustness of the method in comparison to existing approaches through realistic simulation studies. Furthermore, we apply the method to three real datasets on different diseases collected using different imaging platforms. In particular, one of these datasets reveals novel insights into the spatial characteristics of various types of precursor lesions associated with colorectal cancer. Availability: The associated R package can be found here, https://github.com/sealx017/SpaceANOVA.

A multivariate spatiotemporal model for tracking COVID-19 incidence and death rates in socially vulnerable populations.

Recent studies have produced inconsistent findings regarding the association between community social vulnerability and COVID-19 incidence and death rates. This inconsistency may be due, in part, to the fact that these studies modeled cases and deaths separately, ignoring their inherent association and thus yielding imprecise estimates. To improve inferences, we develop a Bayesian multivariate negative binomial model for exploring joint spatial and temporal trends in COVID-19 infections and deaths. The model introduces smooth functions that capture long-term temporal trends, while maintaining enough flexibility to detect local outbreaks in areas with vulnerable populations. Using multivariate autoregressive priors, we jointly model COVID-19 cases and deaths over time, taking advantage of convenient conditional representations to improve posterior computation. As such, the proposed model provides a general framework for multivariate spatiotemporal modeling of counts and rates. We adopt a fully Bayesian approach and develop an efficient posterior Markov chain Monte Carlo algorithm that relies on easily sampled Gibbs steps. We use the model to examine incidence and death rates among counties with high and low social vulnerability in the state of Georgia, USA, from 15 March to 15 December 2020.

Differential prevalence of Adverse Childhood Experiences (ACEs) by gender and substance used in individuals with cannabis, cocaine, opioid, and tobacco use disorders.

Background: Adverse childhood experiences (ACEs) show a graded association with the development of substance use disorders (SUDs) and engagement in risky substance use behaviors. Women are overrepresented among individuals with more severe childhood adversity (≥4 types of ACEs) and may be at particular risk for aberrant substance use.Objectives: To assess the prevalence of ACEs among men and women with cannabis, opioid, cocaine, and tobacco use disorders.Methods: Non-treatment-seeking individuals participating in clinical addiction research at a single site completed the ACE questionnaire and provided a detailed substance use history. Data were analyzed using proportional odds models and logistic regression.Results: Most participants (424/565; 75%) reported at least one ACE, and more than one-quarter (156/565; 27%) reported severe childhood adversity. Relative to men (n = 283), women (n = 282) reported more ACEs (OR = 1.49; p = .01) and more experiences of emotional/physical abuse (OR = 1.52; p = .02), sexual abuse (OR = 4.08; p = .04), and neglect (OR = 2.30; p < .01). Participants in the cocaine (OR = 1.87; n = .01) and opioid (OR = 2.21; p = .01) use disorder, but not cannabis use disorder (OR = 1.46; p = .08), studies reported more severe adversity relative to the tobacco group. Relative to tobacco users, emotional/physical abuse (OR = 1.92; p = .02) and neglect (OR = 2.46; p = .01) scores were higher in cocaine users and household dysfunction scores were higher in opioid users (OR = 2.67; p = .01).Conclusion: The prevalence of ACEs differs with respect to both participant gender and primary substance used. Novel SUD treatment strategies that incorporate ACEs may be uniquely beneficial in specific subpopulations of people with SUDs.

Predictors of Treatment Completion among Women Receiving Integrated Treatment for Comorbid Posttraumatic Stress and Substance Use Disorders.

Background: Retention in treatment for individuals with comorbid posttraumatic stress disorder (PTSD) and substance use disorders (SUD) is an area of concern in treatment outcome studies. The current study explores key variables related to retention in a group of women with comorbid PTSD and SUD enrolled in community SUD treatment randomized to eight weekly sessions of a trauma adapted mindfulness-based relapse prevention (TA-MBRP) or an integrated coping skills (ICS) group intervention. Methods: Two unadjusted and adjusted logistic discrete failure time (DFT) models were fit to examine associations between participants and the time (in weeks) to treatment completion status. Key covariates of interest, including time-varying PTSD Symptom Scale-Self Report (PSS) total score, time-varying Five Factors Mindfulness Questionnaire (FFMQ) total score, group assignment, baseline endorsements of substance use and demographics such as age, race and employment status were fit into the model. Results: In the adjusted PSS model, increased levels of PTSD symptom severity (PSS) scores at week 5 and 7 (PSS OR: 1:06: OR 1.13, respectively) were associated with higher odds of non-completion. In the FFMQ model, increased levels of FFMQ scores at week 6 (OR: 0:92) were associated with lower odds of non-completion. In both models, assignment to the ICS control group and unemployment were associated with lower odds of completion and baseline use of cocaine and sedatives were associated with higher odds of completion. Conclusion: Monitoring PTSD symptom severity and measures of mindfulness can inform providers on strategies to enhance retention early in treatment for individuals with comorbid PTSD/SUD.ClinicalTrials.gov # NCT02755103.

A Bayesian multivariate mixture model for high throughput spatial transcriptomics.

High throughput spatial transcriptomics (HST) is a rapidly emerging class of experimental technologies that allow for profiling gene expression in tissue samples at or near single-cell resolution while retaining the spatial location of each sequencing unit within the tissue sample. Through analyzing HST data, we seek to identify sub-populations of cells within a tissue sample that may inform biological phenomena. Existing computational methods either ignore the spatial heterogeneity in gene expression profiles, fail to account for important statistical features such as skewness, or are heuristic-based network clustering methods that lack the inferential benefits of statistical modeling. To address this gap, we develop SPRUCE: a Bayesian spatial multivariate finite mixture model based on multivariate skew-normal distributions, which is capable of identifying distinct cellular sub-populations in HST data. We further implement a novel combination of Pólya-Gamma data augmentation and spatial random effects to infer spatially correlated mixture component membership probabilities without relying on approximate inference techniques. Via a simulation study, we demonstrate the detrimental inferential effects of ignoring skewness or spatial correlation in HST data. Using publicly available human brain HST data, SPRUCE outperforms existing methods in recovering expertly annotated brain layers. Finally, our application of SPRUCE to human breast cancer HST data indicates that SPRUCE can distinguish distinct cell populations within the tumor microenvironment. An R package spruce for fitting the proposed models is available through The Comprehensive R Archive Network.

Bayesian negative binomial regression with spatially varying dispersion: Modeling COVID-19 incidence in Georgia.

Overdispersed count data arise commonly in disease mapping and infectious disease studies. Typically, the level of overdispersion is assumed to be constant over time and space. In some applications, however, this assumption is violated, and in such cases, it is necessary to model the dispersion as a function of time and space in order to obtain valid inferences. Motivated by a study examining spatiotemporal patterns in COVID-19 incidence, we develop a Bayesian negative binomial model that accounts for heterogeneity in both the incidence rate and degree of overdispersion. To fully capture the heterogeneity in the data, we introduce region-level covariates, smooth temporal effects, and spatially correlated random effects in both the mean and dispersion components of the model. The random effects are assigned bivariate intrinsic conditionally autoregressive priors that promote spatial smoothing and permit the model components to borrow information, which is appealing when the mean and dispersion are spatially correlated. Through simulation studies, we show that ignoring heterogeneity in the dispersion can lead to biased and imprecise estimates. For estimation, we adopt a Bayesian approach that combines full-conditional Gibbs sampling and Metropolis-Hastings steps. We apply the model to a study of COVID-19 incidence in the state of Georgia, USA from March 15 to December 31, 2020.