Rationale: The Global Burden of Disease program identified smoking and ambient and household air pollution as the main drivers of death and disability from chronic obstructive pulmonary disease (COPD).Objectives: To estimate the attributable risk of chronic airflow obstruction (CAO), a quantifiable characteristic of COPD, due to several risk factors.Methods: The Burden of Obstructive Lung Disease study is a crosssectional study of adults, aged $40, in a globally distributed sample of 41 urban and rural sites. Based on data from 28,459 participants, we estimated the prevalence of CAO, defined as a postbronchodilator FEV 1 -to-FVC ratio less than the lower limit of normal, and the relative risks associated with different risk factors. Local relative risks were estimated using a Bayesian hierarchical model borrowing information from across sites.From these relative risks and the prevalence of risk factors, we estimated local population attributable risks. Measurements and Main Results:The mean prevalence of CAO was 11.2% in men and 8.6% in women. The mean population attributable risk for smoking was 5.1% in men and 2.2% in women. The next most influential risk factors were poor education levels, working in a dusty job for $10 years, low body mass index, and a history of tuberculosis. The risk of CAO attributable to the different risk factors varied across sites.Conclusions: Although smoking remains the most important risk factor for CAO, in some areas, poor education, low body mass index, and passive smoking are of greater importance. Dusty occupations and tuberculosis are important risk factors at some sites.

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In this paper, we propose novel methods of quantifying expert opinion about prior distributions for multinomial models. Two different multivariate priors are elicited using median and quartile assessments of the multinomial probabilities. First, we start by eliciting a univariate beta distribution for the probability of each category. Then we elicit the hyperparameters of the Dirichlet distribution, as a tractable conjugate prior, from those of the univariate betas through various forms of reconciliation using least-squares techniques. However, a multivariate copula function will give a more flexible correlation structure between multinomial parameters if it is used as their multivariate prior distribution. So, second, we use beta marginal distributions to construct a Gaussian copula as a multivariate normal distribution function that binds these marginals and expresses the dependence structure between them. The proposed method elicits a positive-definite correlation matrix of this Gaussian copula. The two proposed methods are designed to be used through interactive graphical software written in Java.