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.

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/).

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.

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.

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.

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.

Predicting HPV vaccination among Tdap vaccinated adolescents in Georgia at the county level☆.

PURPOSE: Vaccinations are reported at the state level, but services are delivered at the county level through health departments (HD). This research contributes statistical models to predict county level HPV vaccination. METHODS: Using a cross sectional study design, secondary data were analyzed for the years 2016-2018 for all counties of GA. Study population was male and female adolescents aged 13-17 who received the tetanus, diphtheria and pertussis (Tdap) vaccine. The number of administered HPV vaccine doses and HPV vaccination coverage rate were modeled using indicators of HD clinic access, age, sex, race/ethnicity, socioeconomic status, education, median household income, health insurance, and urban/rural residence. RESULTS: By county the number of administered HPV vaccine doses showed a statistically significant positive association with indicators of HD clinic access: public transit and the number of HD private clinics. HPV vaccination coverage showed a statistically significant negative association with White race and rural residency. CONCLUSION: Examining Tdap vaccinated adolescents conservatively predicted HPV vaccination and controlled for multiple confounders such as vaccination ineligibility, vaccine exemption, and vaccine opposition. Within this population, public health professionals and clinicians could use these statistical models to target HPV vaccination efforts among non-Hispanic whites and rural communities at the county level.

Spatial patterns of HPV and Tdap vaccine dose administration and the association of health department clinic access in Georgia counties.

OBJECTIVE: To characterize counties in GA by quantifying administered doses of the HPV and Tdap vaccines collected by the state health department immunization registry and indicators of Health Department (HD) clinic access. METHODS: Using a cross sectional study design, secondary data were collected from public health data sources for the years 2016 to 2018 for 159 counties of Georgia. The study population was male and female adolescents aged 13-17. The number of administered HPV and Tdap vaccine doses were modeled in relation to number of private and public HD clinics, number of HD clinics registered in the VFC program and the availability of public transportation using Poisson regression, negative binomial regression, and Bayesian spatial analysis. RESULTS: Choropleth maps showed similar clustering patterns between administered doses of the HPV vaccine and Tdap vaccine and increased counts of administered vaccine doses in counties with both public and private clinics. Administered doses of HPV and Tdap vaccines were found to exhibit spatial dependence across counties. Accounting for spatial dependence, the availability of public transit had a significant positive effect on administered HPV vaccine doses, while the number of private HD clinics had a significant positive effect on administered Tdap vaccine doses. CONCLUSIONS: Maps at the county level show vaccination variability, clustering patterns and provide additional insights on the access to health care. Bayesian spatial models are needed to accurately identify and estimate factors associated with administering doses of the HPV and Tdap vaccines. Future work is needed to further examine the utilization of HPV vaccination services among urban groupings.

Association between gestational PFAS exposure and Children's adiposity in a diverse population.

Perfluoroalkyl substances (PFAS) are widely distributed suspected obesogens that cross the placenta. However, few data are available to assess potential fetal effects of PFAS exposure on children's adiposity in diverse populations. To address the data gap, we estimated associations between gestational PFAS concentrations and childhood adiposity in a diverse mother-child cohort. We considered 6 PFAS in first trimester blood plasma, measured using ultra-high-performance liquid chromatography with tandem mass spectrometry, collected from non-smoking women with low-risk singleton pregnancies (n = 803). Body mass index (BMI), waist circumference (WC), fat mass, fat-free mass, and % body fat were ascertained in 4-8 year old children as measures of adiposity. We estimated associations of individual gestational PFAS with children's adiposity and overweight/obesity, adjusted for confounders. There were more non-Hispanic Black (31.7 %) and Hispanic (42.6 %) children with overweight/obesity, than non-Hispanic white (18.2 %) and Asian/Pacific Islander (16.4 %) children (p < 0.0001). Perfluorooctane sulfonate (PFOS; 5.3 ng/mL) and perfluorooctanoic acid (2.0 ng/mL) had the highest median concentrations in maternal blood. Among women without obesity (n = 667), greater perfluoroundecanoic acid (PFUnDA) was associated with their children having higher WC z-score (ß = 0.08, 95%CI: 0.01, 0.14; p = 0.02), fat mass (ß = 0.55 kg, 95%CI: 0.21, 0.90; p = 0.002), and % body fat (ß = 0.01 %; 95%CI: 0.003, 0.01; p = 0.004), although the association of PFUnDA with fat mass attenuated at the highest concentrations. Among women without obesity, the associations of PFAS and their children's adiposity varied significantly by self-reported race-ethnicity, although the direction of the associations was inconsistent. In contrast, among the children of women with obesity, greater, PFOS, perfluorononanoic acid, and perfluorodecanoic acid concentrations were associated with less adiposity (n = 136). Our results suggest that specific PFAS may be developmental obesogens, and that maternal race-ethnicity may be an important modifier of the associations among women without obesity.

Associations Between Governor Political Affiliation and COVID-19 Cases, Deaths, and Testing in the U.S.

INTRODUCTION: The response to the COVID-19 pandemic became increasingly politicized in the U.S., and the political affiliation of state leaders may contribute to policies affecting the spread of the disease. This study examines the differences in COVID-19 infection, death, and testing by governor party affiliation across the 50 U.S. states and the District of Columbia. METHODS: A longitudinal analysis was conducted in December 2020 examining COVID-19 incidence, death, testing, and test positivity rates from March 15, 2020 through December 15, 2020. A Bayesian negative binomial model was fit to estimate the daily risk ratios and posterior intervals comparing rates by gubernatorial party affiliation. The analyses adjusted for state population density, rurality, Census region, age, race, ethnicity, poverty, number of physicians, obesity, cardiovascular disease, asthma, smoking, and presidential voting in 2020. RESULTS: From March 2020 to early June 2020, Republican-led states had lower COVID-19 incidence rates than Democratic-led states. On June 3, 2020, the association reversed, and Republican-led states had a higher incidence (risk ratio=1.10, 95% posterior interval=1.01, 1.18). This trend persisted through early December 2020. For death rates, Republican-led states had lower rates early in the pandemic but higher rates from July 4, 2020 (risk ratio=1.18, 95% posterior interval=1.02, 1.31) through mid-December 2020. Republican-led states had higher test positivity rates starting on May 30, 2020 (risk ratio=1.70, 95% posterior interval=1.66, 1.73) and lower testing rates by September 30, 2020 (risk ratio=0.95, 95% posterior interval=0.90, 0.98). CONCLUSIONS: Gubernatorial party affiliation may drive policy decisions that impact COVID-19 infections and deaths across the U.S. Future policy decisions should be guided by public health considerations rather than by political ideology.

Propensity score matching for multilevel spatial data: accounting for geographic confounding in health disparity studies.

BACKGROUND: Diabetes is a public health burden that disproportionately affects military veterans and racial minorities. Studies of racial disparities are inherently observational, and thus may require the use of methods such as Propensity Score Analysis (PSA). While traditional PSA accounts for patient-level factors, this may not be sufficient when patients are clustered at the geographic level and thus important confounders, whether observed or unobserved, vary by geographic location. METHODS: We employ a spatial propensity score matching method to account for "geographic confounding", which occurs when the confounding factors, whether observed or unobserved, vary by geographic region. We augment the propensity score and outcome models with spatial random effects, which are assigned scaled Besag-York-Mollié priors to address spatial clustering and improve inferences by borrowing information across neighboring geographic regions. We apply this approach to a study exploring racial disparities in diabetes specialty care between non-Hispanic black and non-Hispanic white veterans. We construct multiple global estimates of the risk difference in diabetes care: a crude unadjusted estimate, an estimate based solely on patient-level matching, and an estimate that incorporates both patient and spatial information. RESULTS: In simulation we show that in the presence of an unmeasured geographic confounder, ignoring spatial heterogeneity results in increased relative bias and mean squared error, whereas incorporating spatial random effects improves inferences. In our study of racial disparities in diabetes specialty care, the crude unadjusted estimate suggests that specialty care is more prevalent among non-Hispanic blacks, while patient-level matching indicates that it is less prevalent. Hierarchical spatial matching supports the latter conclusion, with a further increase in the magnitude of the disparity. CONCLUSIONS: These results highlight the importance of accounting for spatial heterogeneity in propensity score analysis, and suggest the need for clinical care and management strategies that are culturally sensitive and racially inclusive.

Associations between governor political affiliation and COVID-19 cases, deaths, and testing in the United States.

INTRODUCTION: The response to the COVID-19 pandemic became increasingly politicized in the United States (US) and political affiliation of state leaders may contribute to policies affecting the spread of the disease. This study examined differences in COVID-19 infection, death, and testing by governor party affiliation across 50 US states and the District of Columbia. METHODS: A longitudinal analysis was conducted in December 2020 examining COVID-19 incidence, death, testing, and test positivity rates from March 15 through December 15, 2020. A Bayesian negative binomial model was fit to estimate daily risk ratios (RRs) and posterior intervals (PIs) comparing rates by gubernatorial party affiliation. The analyses adjusted for state population density, rurality, census region, age, race, ethnicity, poverty, number of physicians, obesity, cardiovascular disease, asthma, smoking, and presidential voting in 2020. RESULTS: From March to early June, Republican-led states had lower COVID-19 incidence rates compared to Democratic-led states. On June 3, the association reversed, and Republican-led states had higher incidence (RR=1.10, 95% PI=1.01, 1.18). This trend persisted through early December. For death rates, Republican-led states had lower rates early in the pandemic, but higher rates from July 4 (RR=1.18, 95% PI=1.02, 1.31) through mid-December. Republican-led states had higher test positivity rates starting on May 30 (RR=1.70, 95% PI=1.66, 1.73) and lower testing rates by September 30 (RR=0.95, 95% PI=0.90, 0.98). CONCLUSION: Gubernatorial party affiliation may drive policy decisions that impact COVID-19 infections and deaths across the US. Future policy decisions should be guided by public health considerations rather than political ideology.

Healthy Eating Policy Improves Children's Diet Quality in Early Care and Education in South Carolina.

Policies to promote healthy foods in early care and education (ECE) in the United States exist, but few have been prospectively evaluated. In South Carolina, a statewide program serving low-income children in ECE enacted new policies promoting healthy foods. We conducted an evaluation to measure changes in dietary intake among children in ECE exposed and not exposed to the new policy. Using direct observation, we assessed dietary intake in 112 children from 34 ECE centers in South Carolina and 90 children from 30 ECE centers in North Carolina (a state with no policy). We calculated Healthy Eating Index-2015 (HEI) scores to measure diet quality consumed before and after the policy was enacted. We fit mixed-effects linear models to estimate differences in HEI scores by state from baseline to post-policy, adjusting for child race, number of children enrolled, director education, center years in operation, participation in the Child and Adult Care Food Program (CACFP), and center profit status. The policy increased HEI scores for whole fruits, total fruits, and lean proteins, but decreased scores for dairy. Thus, the policy was associated with some enhancements in dietary intake, but additional support may help improve other components of diet.

Participation in the Child and Adult Care Food Program is associated with fewer barriers to serving healthier foods in early care and education.

BACKGROUND: Early care and education (ECE) is an important setting for influencing young children's dietary intake. There are several factors associated with barriers to healthy eating in ECE, and recent evidence suggests that participation in the Child and Adult Care Food Program (CACFP), the primary national food assistance program in ECE, may be associated with fewer barriers to serving healthier foods. However, no prior studies have examined differences between CACFP participants and non-participants across a large, multi-state sample. This is the first study to examine the association between CACFP participation and barriers to serving healthier foods in ECE using a random sample of directors from two regions across the country. METHODS: We conducted a cross-sectional survey among a random sample of child care center directors from four states (Massachusetts, North Carolina, Rhode Island, and South Carolina). We conducted logistic and Poisson regression to calculate the odds and incidence rate ratios of reporting 1) no barriers, 2) specific barriers (e.g., cost), and 3) the total number of barriers, by CACFP status, adjusting for covariates of interest. RESULTS: We received 713 surveys (36% response rate). About half (55%) of centers participated in CACFP. The most prevalent reported barriers to serving healthier foods were cost (42%) and children's food preferences (19%). Directors from CACFP centers were twice as likely to report no barriers, compared to directors from non-CACFP centers (OR 2.03; 95% CI [1.36, 3.04]; p < 0.01). Directors from CACFP centers were less likely to report cost as a barrier (OR = 0.46; 95% [CI 0.31, 0.67]; p < 0.001), and reported fewer barriers overall (IRR = 0.77; 95% CI [0.64, 0.92]; p < 0.01), compared to directors from non-CACFP centers. CONCLUSIONS: CACFP directors reported fewer barriers to serving healthier foods in child care centers. Still, cost and children's food preferences are persistent barriers to serving healthier foods in ECE. Future research should evaluate characteristics of CACFP participation that may alleviate these barriers, and whether barriers emerge or persist following 2017 rule changes to CACFP nutrition standards.

Barriers and Facilitators to Compliance with a State Healthy Eating Policy in Early Care and Education Centers.

BACKGROUND: Early care and education (ECE) policies can improve childhood obesity risk factors. We evaluated barriers and facilitators to implementing mandatory nutrition standards for foods provided in South Carolina ECE centers serving low-income children, comparing centers participating in the Child and Adult Care Food Program (CACFP) with non-CACFP centers. METHODS: We mailed 261 surveys (demographics, policies and practices, barriers and facilitators) to center directors after new state nutrition standards were implemented in South Carolina. We conducted univariate and bivariate analyses to explore relationships between barriers, facilitators, and center-level characteristics, by CACFP status. RESULTS: We received 163 surveys (62% response rate). Centers had a median [interquartile range (IQR)] of 5 (4-7) classrooms and 59 (37.5-89) total children enrolled. More than half (60.1%) of directors reported they were moderately or fully informed about the standards. The most common barriers were food costs (17.8%) and children's food preferences (17.8%). More non-CACFP directors reported food costs as a barrier (28.6% vs. 6.5%, p < 0.001), having to spend additional money on healthier foods (48.8% vs. 28.6%, p = 0.01), and having to provide additional nutrition education to parents (28.6% vs. 11.7%, p = 0.01), compared with CACFP directors. CONCLUSIONS: Center directors were generally well informed about the nutrition standards. The most common barriers to implementing the standards were food costs and children's food preferences. Centers participating in CACFP may be in a better position to adhere to new state nutrition standards, as they receive some federal reimbursement for serving healthy foods and may be more accustomed to regulation.

State Regulations Promoting Infant Physical Activity in Early Care and Education.

BACKGROUND: State policies have the potential to improve early care and education (ECE) settings, but little is known about the extent to which states are updating their licensing and administrative regulations, especially in response to national calls to action. In 2013, we assessed state regulations promoting infant physical activity in ECE and compared them with national recommendations. To assess change over time, we conducted this review again in 2018. METHODS: We reviewed regulations for all US states for child care centers (centers) and family child care homes (homes) and compared them with three national recommendations: (1) provide daily tummy time; (2) use cribs, car seats, and high chairs for their primary purpose; and (3) limit the use of restrictive equipment (e.g., strollers). We performed exact McNemar's tests to compare the number of states meeting recommendations from 2013 to 2018 to evaluate whether states had made changes over this period. RESULTS: From 2013 to 2018, we observed significant improvement in one recommendation for homes-to use cribs, car seats, and high chairs for their primary purpose (odds ratio 11.0; 95% CI 1.6-47.3; p = 0.006). We did not observe any other significant difference between 2013 and 2018 regulations. CONCLUSIONS: Despite increased awareness of the importance of early-life physical activity, we observed only modest improvement in the number of states meeting infant physical activity recommendations over the past 5 years. In practice, ECE programs may be promoting infant physical activity, but may not be required to do so through state regulations.

Control beliefs and risk for 4-year mortality in older adults: a prospective cohort study.

BACKGROUND: Control beliefs are important psychological factors that likely contribute to heterogeneity in health outcomes for older adults. We evaluated whether control beliefs are associated with risk for 4-year mortality, after accounting for established "classic" biomedical risk factors. We also determined if an enhanced risk model with control beliefs improved identification of individuals with low vs. high mortality risk. METHODS: We used nationally representative data from the Health and Retirement Study (2006-2012) for adults 50 years or older in 2006 (n = 7313) or 2008 (n = 6301). We assessed baseline perceived global control (measured as 2 dimensions-"constraints" and "mastery"), and health-specific control. We also obtained baseline data for 12 established biomedical risk factors of 4-year mortality: age, sex, 4 medical conditions (diabetes mellitus, cancer, lung disease and heart failure), body mass index less than 25 kg/m2, smoking, and 4 functional difficulties (with bathing, managing finances, walking several blocks and pushing or pulling heavy objects). Deaths within 4 years of follow-up were determined through interviews with respondents' family and the National Death Index. RESULTS: After accounting for classic biomedical risk factors, perceived constraints were significantly associated with higher mortality risk (third quartile scores odds ratio [OR] 1.37, 95% CI 1.03-1.81; fourth quartile scores OR 1.45, 95% CI, 1.09-1.92), while health-specific control was significantly associated with lower risk (OR 0.69-0.78 for scores above first quartile). Higher perceived mastery scores were not consistently associated with decreased risk. The enhanced model with control beliefs found an additional 3.5% of participants (n = 222) with low predicted risk of 4-year mortality (i.e., 4% or less); observed mortality for these individuals was 1.8% during follow-up. Compared with participants predicted to have low mortality risk only by the classic biomedical model, individuals identified by only the enhanced model were older, had higher educational status, higher income, and higher prevalence of diabetes mellitus and cancer. CONCLUSION: Control beliefs were significantly associated with risk for 4-year mortality; accounting for these factors improved identification of low-risk individuals. More work is needed to determine how assessment of control beliefs could enable targeting of clinical interventions to support at-risk older adults.

The LZIP: A Bayesian latent factor model for correlated zero-inflated counts.

Motivated by a study of molecular differences among breast cancer patients, we develop a Bayesian latent factor zero-inflated Poisson (LZIP) model for the analysis of correlated zero-inflated counts. The responses are modeled as independent zero-inflated Poisson distributions conditional on a set of subject-specific latent factors. For each outcome, we express the LZIP model as a function of two discrete random variables: the first captures the propensity to be in an underlying "at-risk" state, while the second represents the count response conditional on being at risk. The latent factors and loadings are assigned conditionally conjugate gamma priors that accommodate overdispersion and dependence among the outcomes. For posterior computation, we propose an efficient data-augmentation algorithm that relies primarily on easily sampled Gibbs steps. We conduct simulation studies to investigate both the inferential properties of the model and the computational capabilities of the proposed sampling algorithm. We apply the method to an analysis of breast cancer genomics data from The Cancer Genome Atlas.