Urban college student self-report of hookah use with health care providers

Objective: This study's purpose was to describe urban college students’ communication about hookah with health care providers. Participants: Participants included a random sample of undergraduate urban college students and health care providers. Methods: Students surveyed determined the epidemiology of hookah use in this population, how many health care providers asked about hookah, and how many students admitted hookah use to a physician. Results: Of 375 students surveyed, 78 (20.8%) had never tried it, 284 (75.7%) had smoked hookah at least once, and 64 students (22.6%) were classified as frequent hookah smokers. Only 15 (4.7%) reported a health care provider asking about hookah during visits, whereas 36 (12.7%) admitted their hookah use to a health care provider. Conclusion: Hookah use was found to be highly prevalent among students in one urban university. This study supports the hypothesis that few health care providers broach the topic with patients. Additional research on health consequences of hookah use, education, and improved screening is warranted.     

Dynamic latent trait models with mixed hidden Markov structure for mixed longitudinal outcomes

We propose a general Bayesian joint modeling approach to model mixed longitudinal outcomes from the exponential family for taking into account any differential misclassification that may exist among categorical outcomes. Under this framework, outcomes observed without measurement error are related to latent trait variables through generalized linear mixed effect models. The misclassified outcomes are related to the latent class variables, which represent unobserved real states, using mixed hidden Markov models (MHMMs). In addition to enabling the estimation of parameters in prevalence, transition and misclassification probabilities, MHMMs capture cluster level heterogeneity. A transition modeling structure allows the latent trait and latent class variables to depend on observed predictors at the same time period and also on latent trait and latent class variables at previous time periods for each individual. Simulation studies are conducted to make comparisons with traditional models in order to illustrate the gains from the proposed approach. The new approach is applied to data from the Southern California Children Health Study to jointly model questionnaire-based asthma state and multiple lung function measurements in order to gain better insight about the underlying biological mechanism that governs the inter-relationship between asthma state and lung function development.     

Generalized additive models for longitudinal data

We introduce a class of models for longitudinal data by extending the generalized estimating equations approach of Liang and Zeger (1986) to incorporate the flexibility of nonparametric smoothing. The algorithm provides a unified estimation procedure for marginal distributions from the exponential family. We propose pointwise standard-error bands and approximate likelihood-ratio and score tests for inference. The algorithm is formally derived by using the penalized quasilikelihood framework. Convergence of the estimating equations and consistency of the resulting solutions are discussed. We illustrate the algorithm with data on the population dynamics of Colorado potato beetles on potato plants.