Background: Identification of risk factors is critical to preventing the childhood obesity epidemic. Risk factors that contribute to obesity are multifactorial. However, limited research has focused on identifying obesity risk factors using an ecological approach.Methods: Baseline self-report survey data from the STRONG Kids program were used. The sample consisted of 329 parent-child dyads recruited from childcare programs in east-central Illinois. Child height and weight were measured and converted to age-and sex-specific z-scores using standard growth charts. An ecological model provided the theoretical framework for the selection of 22 previously reported childhood obesity risk factors. Multiple logistic regression analyses were used to identify risk factors.Results: Of 22 potential risk factors, three were found to be significantly associated with child overweight/obesity. These included child nighttime sleep duration (v 2 = 8.56; p = 0.003), parent BMI (v 2 = 5.62; p = 0.01), and parental restrictive feeding for weight control (v 2 = 4.77; p = 0.02). Children who slept for 8 hours and less were 2.2 times more likely to be overweight/obese [95% confidence interval (CI): 1.3-3.7), whereas children with an overweight/obese parent were 1.9 times more likely to be overweight/ obese (95% CI: 1.12-3.2). Finally, children whose parents used restrictive feeding practices were 1.75 times more likely to be overweight/obese (95% CI: 1.06-2.9).Conclusions: Using an ecological approach, we conclude that childhood obesity prevention efforts may benefit from targeting the key risk factors of child sleep duration, parent BMI, and parental restrictive feeding practices as focus areas for obesity prevention.
High-dimensional longitudinal data arises frequently in biomedical and genomic research. It is important to select relevant covariates when the dimension of the parameters diverges as the sample size increases. We propose the penalized quadratic inference function to perform model selection and estimation simultaneously in the framework of a diverging number of regression parameters.The penalized quadratic inference function can easily take correlation information from clustered data into account, yet it does not require specifying the likelihood function. This is advantageous compared to existing model selection methods for discrete data with large cluster size. In addition, the proposed approach enjoys the oracle property, which is able to identify non-zero components consistently with probability tending to 1, and any finite linear combination of the estimated nonzero components also follows an asymptotic normal distribution. We propose an efficient algorithm by selecting an effective tuning parameter to solve the penalized quadratic inference function. Monte Carlo simulation studies illustrate that the proposed method selects the correct model with a high frequency and estimates covariate effects accurately even when the dimension of parameters is high.We illustrate the proposed approach through analyzing periodontal disease data.
Home specimen self-collection kits with central laboratory testing may improve persistence with PrEP and enhance telehealth programs. We offered Iowa TelePrEP clients the choice of using a home kit or visiting a laboratory site for routine monitoring. Mixed-methods evaluation determined the proportion of clients who chose a kit, factors influencing choice, associations between kit use and completion of indicated laboratory monitoring, and user experience. About 46% (35/77) chose to use a kit. Compared to laboratory site use, kit use was associated with higher completion of extra-genital swabs (OR 6.33, 95% CI 1.20–33.51, for anorectal swabs), but lower completion of blood tests (OR 0.21, 95% CI 0.06–0.73 for creatinine). Factors influencing choice included self-efficacy to use kits, time/convenience, and privacy/confidentiality. Clients reported kit use was straight-forward but described challenges with finger prick blood collection. Telehealth PrEP programs should offer clients home kits and support clients with blood collection and kit completion.
Background A growing amount of evidence indicates in utero and early life growth has profound, long-term consequences for an individual’s health throughout the life course; however, there is limited data in preterm infants, a vulnerable population at risk for growth abnormalities. Objective To address the gap in knowledge concerning early growth and its determinants in preterm infants. Methods A retrospective cohort study was performed using a population of preterm (< 37 weeks gestation) infants obtained from an electronic medical record database. Weight z-scores were acquired from discharge until roughly two years corrected age. Linear mixed effects modeling, with random slopes and intercepts, was employed to estimate growth trajectories. Results Thirteen variables, including maternal race, hypertension during pregnancy, preeclampsia, first trimester body mass index, multiple status, gestational age, birth weight, birth length, head circumference, year of birth, length of birth hospitalization stay, total parenteral nutrition, and dextrose treatment, were significantly associated with growth rates of preterm infants in univariate analyses. A small percentage (1.32% - 2.07%) of the variation in the growth of preterm infants can be explained in a joint model of these perinatal factors. In extremely preterm infants, additional variation in growth trajectories can be explained by conditions whose risk differs by degree of prematurity. Specifically, infants with periventricular leukomalacia or retinopathy of prematurity experienced decelerated rates of growth compared to infants without such conditions. Conclusions Factors found to influence growth over time in children born at term also affect growth of preterm infants. The strength of association and the magnitude of the effect varied by gestational age, revealing that significant heterogeneity in growth and its determinants exists within the preterm population.
We develop new modeling for personalized treatment for longitudinal studies involving high heterogeneity of treatment effects. Incorporating subjectspecific information into the treatment assignment is crucial since different individuals can react to the same treatment very differently. We estimate unobserved subject-specific treatment effects through conditional random-effects modeling, and apply the random forest algorithm to allocate effective treatments for individuals. The advantage of our approach is that random-effects estimation does not rely on the normality assumption. In theory, we show that the proposed random-effect estimator is consistent and more efficient than the random-effect estimator that ignores correlation information from longitudinal data. Simulation studies and a data example from an HIV clinical trial also confirm that the proposed method can efficiently identify the best treatments for individual patients.Key words and phrases: Generalized linear mixed model, penalized quasi-likelihood, personalized treatment, quadratic inference functions, random forest.
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