Bayesian Latent Factor Regression for Functional and Longitudinal Data

In studies involving functional data, it is commonly of interest to model the impact of predictors on the distribution of the curves, allowing flexible e ects on not only the mean curve but also the distribution about the mean. Characterizing the curve for each subject as a linear combination of a high-dimensional set of potential basis functions, we place a sparse latent factor regression model on the basis coe cients. We induce basis selection by choosing a shrinkage prior that allows many of the loadings to be close to zero. The number of latent factors is treated as unknown through a highly-e cient, adaptive-blocked Gibbs sampler. Predictors are included on the latent variables level, while allowing different predictors to impact different latent factors. This model induces a framework for functional response regression in which the distribution of the curves is allowed to change flexibly with predictors. The performance is assessed through simulation studies and the methods are applied to data on blood pressure trajectories during pregnancy

Bayesian isotonic regression and trend analysis

1 SUMMARY. In many applications, the mean of a response variable can be assumed to be a non-decreasing function of a continuous predictor, controlling for covariates. In such cases, interest often focuses on estimating the regression function, while also assessing evidence of an association. This article proposes a new framework for Bayesian isotonic regression and order restricted inference based on a constrained piecewise linear model with unknown knot locations, corresponding to thresholds in the regression function. The non-decreasing constraint is incorporated through a prior distribution consisting of a product mixture of point masses (accounting for flat regions) and truncated autoregressive normal densities. An MCMC algorithm is used to obtain a smooth estimate of the regression function and posterior probabilities of an association for different regions of the predictor. Generalizations to categorical outcomes and multiple predictors are described, and the approach is applied to data from a study of pesticide exposure and birth weight

Development of a Health Survey Instrument for 5- to 8-Year-Old Youths

Measuring program outcomes is required for documenting effectiveness of interventions with youths participating in programs funded through the U.S. Department of Agriculture\u27s Children, Youth, and Families at Risk (CYFAR) initiative. The California CYFAR program provided programming for youths aged 5–8, which necessitated the development of an age-appropriate survey measure. Evaluating younger youths to assess healthful living outcomes is challenging, especially with youths in kindergarten through second grade. This article addresses development and testing of the measure and resultant lessons learned. Recommendations for developing an evaluation survey for younger youths are provided

Characteristics associated with US Walk to School programs

Participation in Walk to School (WTS) programs has grown substantially in the US since its inception; however, no attempt has been made to systematically describe program use or factors associated with implementation of environment/policy changes

A Novel Bayesian Spatio-Temporal Surveillance Metric to Predict Emerging Infectious Disease Areas of High Disease Risk

ABSTRACT Identification of areas of high disease risk has been one of the top goals for infectious disease public health surveillance. Accurate prediction of these regions leads to effective resource allocation and faster intervention. This paper proposes a novel prediction surveillance metric based on a Bayesian spatio-temporal model for infectious disease outbreaks. Exceedance probability, which has been commonly used for cluster detection in statistical epidemiology, was extended to predict areas of high risk. The proposed metric consists of three components: the area's risk profile, temporal risk trend, and spatial neighborhood influence. We also introduce a weighting scheme to balance these three components, which accommodates the characteristics of the infectious disease outbreak, spatial properties, and disease trends. Thorough simulation studies were conducted to identify the optimal weighting scheme and evaluate the performance of the proposed prediction surveillance metric. Results indicate that the area's own risk and the neighborhood influence play an important role in making a highly sensitive metric, and the risk trend term is important for the specificity and accuracy of prediction. The proposed prediction metric was applied to the COVID-19 case data of South Carolina from March 12, 2020, and the subsequent 30 weeks of data

Reliability and validity of a nutrition and physical activity environmental self-assessment for child care

<p>Abstract</p> <p>Background</p> <p>Few assessment instruments have examined the nutrition and physical activity environments in child care, and none are self-administered. Given the emerging focus on child care settings as a target for intervention, a valid and reliable measure of the nutrition and physical activity environment is needed.</p> <p>Methods</p> <p>To measure inter-rater reliability, 59 child care center directors and 109 staff completed the self-assessment concurrently, but independently. Three weeks later, a repeat self-assessment was completed by a sub-sample of 38 directors to assess test-retest reliability. To assess criterion validity, a researcher-administered environmental assessment was conducted at 69 centers and was compared to a self-assessment completed by the director. A weighted kappa test statistic and percent agreement were calculated to assess agreement for each question on the self-assessment.</p> <p>Results</p> <p>For inter-rater reliability, kappa statistics ranged from 0.20 to 1.00 across all questions. Test-retest reliability of the self-assessment yielded kappa statistics that ranged from 0.07 to 1.00. The inter-quartile kappa statistic ranges for inter-rater and test-retest reliability were 0.45 to 0.63 and 0.27 to 0.45, respectively. When percent agreement was calculated, questions ranged from 52.6% to 100% for inter-rater reliability and 34.3% to 100% for test-retest reliability. Kappa statistics for validity ranged from -0.01 to 0.79, with an inter-quartile range of 0.08 to 0.34. Percent agreement for validity ranged from 12.9% to 93.7%.</p> <p>Conclusion</p> <p>This study provides estimates of criterion validity, inter-rater reliability and test-retest reliability for an environmental nutrition and physical activity self-assessment instrument for child care. Results indicate that the self-assessment is a stable and reasonably accurate instrument for use with child care interventions. We therefore recommend the Nutrition and Physical Activity Self-Assessment for Child Care (NAP SACC) instrument to researchers and practitioners interested in conducting healthy weight intervention in child care. However, a more robust, less subjective measure would be more appropriate for researchers seeking an outcome measure to assess intervention impact.</p

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/m(2), 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

Validity and Reliability of a School Travel Survey

Despite the growing interest in active (ie, nonmotorized) travel to and from school, few studies have explored the measurement properties to assess active travel. We evaluated the criterion validity and test–retest reliability of a questionnaire with a sample of young schoolchildren to assess travel to and from school, including mode, travel companion, and destination after school

Characteristics Associated with US Walk to School Programs: A cross-sectional study

Abstract Participation in Walk to School (WTS) programs has grown substantially in the US since its inception; however, no attempt has been made to systematically describe program use or factors associated with implementation of environment/policy changes. Objective Describe the characteristics of schools' WTS programs by level of implementation. Methods Representatives from 450 schools from 42 states completed a survey about their WTS program's infrastructure and activities, and perceived impact on walking to school. Level of implementation was determined from a single question to which respondents reported participation in WTS Day only (low), WTS Day and additional programs (medium), or making policy/environmental change (high). Results The final model showed number of community groups involved was positively associated with higher level of implementation (OR = 1.78, 95%CI = 1.44, 2.18), as was funding (OR = 1.56, 95%CI = 1.26, 1.92), years of participation (OR = 1.44, 95% CI = 1.23, 1.70), and use of a walkability assessment (OR = 3.22, 95%CI = 1.84, 5.64). Implementation level was modestly associated with increased walking (r = 0.18). Conclusion Strong community involvement, some funding, repeat participation, and environmental audits are associated with progms that adopt environmental/policy change, and seem to facilitate walking to school