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Modern Applied Statistics With S

Purpose—The aim of this guideline is to provide a synopsis of best clinical practices in the rehabilitative care of adults recovering from stroke. Methods—Writing group members were nominated by th...

Guidelines for Adult Stroke Rehabilitation and Recovery: A Guideline for Healthcare Professionals from the American Heart Association/American Stroke Association

Hyperglycemia and Adverse Pregnancy Outcomes

Mercury (Hg) is a global pollutant that affects human and ecosystem health. We synthesize understanding of sources, atmosphere-land-ocean Hg dynamics and health effects, and consider the implications of Hg-control policies. Primary anthropogenic Hg emissions greatly exceed natural geogenic sources, resulting in increases in Hg reservoirs and subsequent secondary Hg emissions that facilitate its global distribution. The ultimate fate of emitted Hg is primarily recalcitrant soil pools and deep ocean waters and sediments. Transfers of Hg emissions to largely unavailable reservoirs occur over the time scale of centuries, and are primarily mediated through atmospheric exchanges of wet/dry deposition and evasion from vegetation, soil organic matter and ocean surfaces. A key link between inorganic Hg inputs and exposure of humans and wildlife is the net production of methylmercury, which occurs mainly in reducing zones in freshwater, terrestrial, and coastal environments, and the subsurface ocean. Elevated human exposure to methylmercury primarily results from consumption of estuarine and marine fish. Developing fetuses are most at risk from this neurotoxin but health effects of highly exposed populations and wildlife are also a concern. Integration of Hg science with national and international policy efforts is needed to target efforts and evaluate efficacy.

http://pubs.acs.org/doi/pdf/10.1021/es305071v

Mercury as a Global Pollutant: Sources, Pathways, and Effects

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A User’s Guide

Model Estimation and Evaluation

<h3>Objective:</h3> To characterize subgroups of persons with Parkinson’s disease (PWP) with similar longitudinal mobility impairment patterns based on an easy-to-use patient-reported outcome (PRO) measure. <h3>Background:</h3> Mobility impairments are common, complex symptoms experienced by PWP. The underlying mechanisms and accrual patterns are not well understood, which impedes therapeutic shared-decision making. <h3>Design/Methods:</h3> Latent class growth analysis (LCGA) uses finite mixture models to estimate discrete groups of trajectory parameters using maximum likelihoods. Using longitudinal data available through Fox Insight Data Exploration Network, LCGA was applied to 16,863 PWP stratified by early (N=8612; &lt;3 years), mid (N=6181; 3–10 years) and later (N=2070; &gt;10 years) disease duration spanning ~4.5 years, to empirically discern clusters of PWP with similar longitudinal mobility impairment patterns. Mobility impairment was measured using a generic health-related quality of life tool that has shown construct validity in PWP (EuroQol 5 dimensions [EQ-5D-5L] mobility component). Associations were evaluated between latent trajectory classes and sociodemographic/clinical factors. <h3>Results:</h3> In the early and mid-disease duration strata, four clusters were identified, while a fifth cluster was identified in the later disease duration strata. The subgroup trajectories ranged from none to moderate mobility problems, with a smaller cluster of subjects with severe mobility problems. The percentage of subjects with moderate (early=17.5%, mid=26.4%, later=32.5%) and severe (early=3.8%, mid=7.4%, later=15.4%) mobility problems at baseline increased across disease duration groups. The trajectories tended to be variable or slightly worsening in the early and later duration group and more stable in the mid-duration group. Across strata, the clusters with moderate to severe mobility problems were associated with more severe impairment, depression, anxiety and arthritis, higher BMI, lower income, and lower education. There were no differences by race. <h3>Conclusions:</h3> The approach described and applied to PWP provides a novel template for using a easy-to-use PRO to improve our understanding of mobility trajectories in Parkinson’s disease. <b>Disclosure:</b> Doug Gunzler has nothing to disclose. Dr. Gunzler has received research support from NIH/NINDS. The institution of Dr. Gunzler has received research support from Biogen. The institution of Dr. Gunzler has received research support from Amneal. Dr. Gunzler has received research support from Michael J Fox Foundation. The institution of Prof. Briggs has received research support from NIH. The institution of Prof. Briggs has received research support from Michael J. Fox Foundation. 

Leveraging patient-reported outcomes to discern mobility trajectories in Parkinson’s disease (P10-11.003)

Measurement Bias, Multiple Indicator Multiple Cause Modeling and Multiple Group Modeling

Growth Mixture Modeling

Introduction and Brief History of Structural Equation Modeling for Health and Medical Research

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Douglas D. Gunzler

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