A semi-Markov multistate cure model for estimating intervention effects in stepped wedge design trials.

Multistate models are useful for studying exposures that affect transitions among a set of health states. However, they can be challenging to apply when exposures are time-varying. We develop a multistate model and a method of likelihood construction that allows application of the model to data in which interventions or other exposures can be time-varying and an individual may to be exposed to multiple intervention conditions while progressing through states. The model includes cure proportions, reflecting the possibility that some individuals will never leave certain states. We apply the approach to analyze patient vaccination data from a stepped wedge design trial evaluating two interventions to increase uptake of human papillomavirus vaccination. The states are defined as the number of vaccine doses the patient has received. We model state transitions as a semi-Markov process and include cure proportions to account for individuals who will never leave a given state (e.g. never receive their next dose). Multistate models typically quantify intervention effects as hazard ratios contrasting the intensities of transitions between states in intervention versus control conditions. For multistate processes, another clinically meaningful outcome is the change in the percentage of the study population that has achieved a specific state (e.g. completion of all required doses) by a specific point in time due to an intervention. We present a method for quantifying intervention effects in this manner. We apply the model to both simulated and real-world data and also explore some conditions under which such models may give biased results.

A stochastic estimation procedure for intermittently-observed semi-Markov multistate models with back transitions.

Multistate models provide an important method for analyzing a wide range of life history processes including disease progression and patient recovery following medical intervention. Panel data consisting of the states occupied by an individual at a series of discrete time points are often used to estimate transition intensities of the underlying continuous-time process. When transition intensities depend on the time elapsed in the current state and back transitions between states are possible, this intermittent observation process presents difficulties in estimation due to intractability of the likelihood function. In this manuscript, we present an iterative stochastic expectation-maximization algorithm that relies on a simulation-based approximation to the likelihood function and implement this algorithm using rejection sampling. In a simulation study, we demonstrate the feasibility and performance of the proposed procedure. We then demonstrate application of the algorithm to a study of dementia, the Nun Study, consisting of intermittently-observed elderly subjects in one of four possible states corresponding to intact cognition, impaired cognition, dementia, and death. We show that the proposed stochastic expectation-maximization algorithm substantially reduces bias in model parameter estimates compared to an alternative approach used in the literature, minimal path estimation. We conclude that in estimating intermittently observed semi-Markov models, the proposed approach is a computationally feasible and accurate estimation procedure that leads to substantial improvements in back transition estimates.

Feasibility of Online Mental Wellness Self-assessment and Feedback for Pediatric and Neonatal Critical Care Nurses.

PURPOSE: The primary goal of this study was to test the feasibility of an educational online self-assessment of burnout, resilience, trauma, depression, anxiety, and common workplace stressors among nurses working in a pediatric intensive care unit or neonatal intensive care unit setting. The secondary, exploratory objectives were to estimate the prevalence of psychiatric symptoms in this sample and to identify those variables that most strongly predict burnout. DESIGN AND METHODS: Data from optional and anonymous online measures were analyzed for 115 nurses (67.9% aged 25-44; 61.7% Caucasian) working in an urban children's hospital pediatric or neonatal ICU. Multiple linear regressions identified demographic variables and workplace stressors that significantly predicted each of three components of burnout. RESULTS: Most respondents found the educational assessment and feedback to be helpful. Choosing nursing as a second career was associated with better resilience. Having worked in ICU settings longer and being older were both linked to lower levels of anxiety. Predictors of burnout varied across the three burnout subscales. CONCLUSIONS: Implementation of an online self-assessment with immediate educational feedback is feasible in critical care settings. The variability of predictors across the three burnout subscales indicates the need for tailored interventions for those at risk. Future research may include follow-up of nurses to examine changes in scores over time and expansion of the tool for other medical personnel. PRACTICE IMPLICATIONS: An educational online self-assessment can be a helpful tool for pediatric critical care nurses experiencing varying degrees of burnout and distress.

Utility of the actiheart accelerometer for estimating exercise energy expenditure in female adolescent runners.

There is a growing need to accurately assess exercise energy expenditure (EEE) in athletic populations that may be at risk for health disorders because of an imbalance between energy intake and energy expenditure. The Actiheart combines heart rate and uniaxial accelerometry to estimate energy expenditure above rest. The authors' purpose was to determine the utility of the Actiheart for predicting EEE in female adolescent runners (N = 39, age 15.7 ± 1.1 yr). EEE was measured by indirect calorimetry and predicted by the Actiheart during three 8-min stages of treadmill running at individualized velocities corresponding to each runner's training, including recovery, tempo, and 5-km-race pace. Repeated-measures ANOVA with Bonferroni post hoc comparisons across the 3 running stages indicated that the Actiheart was sensitive to changes in intensity (p < .01), but accelerometer output tended to plateau at race pace. Pairwise comparisons of the mean difference between Actiheart- and criterion-measured EEE yielded values of 0.0436, 0.0539, and 0.0753 kcal × kg-1 × min-1 during recovery, tempo, and race pace, respectively (p < .0001). Bland-Altman plots indicated that the Actiheart consistently underestimated EEE except in 1 runner's recovery bout. A linear mixed-model regression analysis with height as a covariate provided an improved EEE prediction model, with the overall standard error of the estimate for the 3 speeds reduced to 0.0101 kcal × kg-1 × min-1. Using the manufacturer's equation that combines heart rate and uniaxial motion, the Actiheart may have limited use in accurately assessing EEE, and therefore energy availability, in young, female competitive runners.