Social influence in childhood obesity interventions: a systematic review

The objective of this study is to understand the pathways through which social influence at the family level moderates the impact of childhood obesity interventions. We conducted a systematic review of obesity interventions in which parents' behaviours are targeted to change children's obesity outcomes, because of the potential social and environmental influence of parents on the nutrition and physical activity behaviours of children. PubMed (1966–2013) and the Web of Science (1900–2013) were searched, and 32 studies satisfied our inclusion criteria. Results for existing mechanisms that moderate parents' influence on children's behaviour are discussed, and a causal pathway diagram is developed to map out social influence mechanisms that affect childhood obesity. We provide health professionals and researchers with recommendations for leveraging family-based social influence mechanisms to increase the efficacy of obesity intervention programmes.National Institutes of Health (U.S.). Office of Behavioral and Social Sciences ResearchNational Heart, Lung, and Blood Institute (Grant 1R21HL113680-01

Dynamics of Implementation and Maintenance of Organizational Health Interventions

In this study, we present case studies to explore the dynamics of implementation and maintenance of health interventions. We analyze how specific interventions are built and eroded, how the building and erosion mechanisms are interconnected, and why we can see significantly different erosion rates across otherwise similar organizations. We use multiple comparative obesity prevention case studies to provide empirical information on the mechanisms of interest, and use qualitative systems modeling to integrate our evolving understanding into an internally consistent and transparent theory of the phenomenon. Our preliminary results identify reinforcing feedback mechanisms, including design of organizational processes, motivation of stakeholders, and communication among stakeholders, which influence implementation and maintenance of intervention components. Over time, these feedback mechanisms may drive a wedge between otherwise similar organizations, leading to distinct configurations of implementation and maintenance processes

Effect of age on semen parameters in infertile men after varicocelectomy

Kamaleddin Hassanzadeh-Nokashty1, Parisa Yavarikia2, Alireza Ghaffari3, Samad Hazhir1, Mohammadali Hassanzadeh11Department of Urology, 2Faculty of Nursery and Midwifery, 3Department of Internal Medicine, Tabriz University of Medical Sciences, Tabriz, IranBackground: The effectiveness of varicocelectomy in restoration of fertility and improvement of semen parameters is still controversial. The purpose of this study was to determine the effect of age on semen parameters following varicocelectomy in a group of infertile men.Methods: Improvements in sperm count, morphology, and motility were studied in 67 infertile patients 4–10 months after varicocelectomy.Results: The mean age of the patients was 30.48 ± 7.49 years. Significant improvements in total sperm count, percentage normal morphology, and motility were noted in all age groups (P < 0.05). Patients aged <25 years demonstrated the greatest increase in sperm counts, normal morphology, and motility following varicocelectomy. There was a significant negative correlation between age and sperm count, sperm morphology, and sperm motility (P < 0.05).Conclusion: The effect of age on improvement in sperm parameters after varicocelectomy is inconsistent with some reports in the literature, and could be attributable to the duration of infertility prior to surgery; in the long term, varicoceles are known to have deleterious effects on testis biology.Keywords: sperm count, sperm morphology, sperm motility, varicocel

Comparison of two linear models and the nonlinear meta-model with the underlying true model.

<p>The predicted outcome is fluid leakage rate and its expected value under the true data generating process (left), and each model is shown using color maps. Black dots in the two middle charts identify the original data points used in estimation of the two linear models. However, these “raw” data points are not used in GMA estimation, only the coefficients of the two linear models (3+2 coefficients) and two R² terms (total of 7 signatures) are used for estimation of the non-linear meta-model (graphed on the right).</p

Overview of GMA.

<p>Prior studies provide the vector of empirical signatures, . The hypothesized meta-model is estimated by simulating those signatures and matching them against empirical ones.</p

Evolution and Reproducibility of Simulation Modeling in Epidemiology and Health Policy Over Half a Century

Abstract Simulation models are increasingly being used to inform epidemiologic studies and health policy, yet there is great variation in their transparency and reproducibility. In this review, we provide an overview of applications of simulation models in health policy and epidemiology, analyze the use of best reporting practices, and assess the reproducibility of the models using predefined, categorical criteria. We identified and analyzed 1,613 applicable articles and found exponential growth in the number of studies over the past half century, with the highest growth in dynamic modeling approaches. The largest subset of studies focused on disease policy models (70%), within which pathological conditions, viral diseases, neoplasms, and cardiovascular diseases account for one-third of the articles. Model details were not reported in almost half of the studies. We also provide in-depth analysis of modeling best practices, reporting quality and reproducibility of models for a subset of 100 articles (50 highly cited and 50 randomly selected from the remaining articles). Only 7 of 26 in-depth evaluation criteria were satisfied by more than 80% of samples. We identify areas for increased application of simulation modeling and opportunities to enhance the rigor and documentation in the conduct and reporting of simulation modeling in epidemiology and health policy.</jats:p