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PurposeTo date, studies pertaining to possible links between body modification and risk‐taking behaviours have been conducted mainly among targeted groups. The objective of this study is to examine the influence of a number of risk‐taking behaviours on the probability of being pierced or tattooed among a general adolescent population.MethodsData come from a cross‐sectional study conducted among a sample of 2180 students aged 12–18. Data were collected directly from students through a self‐report survey.ResultsFindings confirm the “risky” nature of these practices even though the tattooed and pierced subjects of this study were from a general adolescent population. Factors that contribute significantly to the likelihood of teenagers being tattooed or pierced, for both genders, are associated with “externalized risk behaviours” such as multiple drug use, illegal activities, gang affiliation, problem gambling, school truancy and rave attendance.ConclusionNowadays, tattooing and body piercing are perceived by many as body decoration, increasingly belonging to the realm of generational conformity. Contrary to this view, our results suggest that these practices among adolescents are mostly adopted by those who are involved in various deviant or illegal activities, which are often interrelated.

BackgroundComputer simulation models are used increasingly to support public health research and policy, but questions about their quality persist. The purpose of this article is to review the principles and methods for validation of population-based disease simulation models.MethodsWe developed a comprehensive framework for validating population-based chronic disease simulation models and used this framework in a review of published model validation guidelines. Based on the review, we formulated a set of recommendations for gathering evidence of model credibility.ResultsEvidence of model credibility derives from examining: 1) the process of model development, 2) the performance of a model, and 3) the quality of decisions based on the model. Many important issues in model validation are insufficiently addressed by current guidelines. These issues include a detailed evaluation of different data sources, graphical representation of models, computer programming, model calibration, between-model comparisons, sensitivity analysis, and predictive validity. The role of external data in model validation depends on the purpose of the model (e.g., decision analysis versus prediction). More research is needed on the methods of comparing the quality of decisions based on different models.ConclusionAs the role of simulation modeling in population health is increasing and models are becoming more complex, there is a need for further improvements in model validation methodology and common standards for evaluating model credibility.

We describe the development of the first population-based microsimulation model of OA. Strengths of this model include the use of large population databases to derive the key parameters and the application of modern microsimulation technology. Limitations of the model reflect the limitations of administrative and survey data and gaps in the epidemiological and HRQL literature.

Projecting the future trends in the cost of OA enables policy makers to anticipate the significant shifts in its distribution of burden in the future.