Bayesian Zero- Inflated Poisson model for prognosis of demographic factors associated with using crystal meth in Tehran population

Background: Use of methamphetamine (MA) and other stimulants has increased steadily over the past 10 years. Risk factor evaluation to reduce the problem in the community is one solution to protect people from addiction. This study aimed at using Bayesian zeroinflated Poisson (ZIP) model to investigate the relationship between the number of using crystal meth and some demographic factors in Tehran population. Methods: A cross-sectional study was conducted to investigate crystal meth abuse in Tehran, the capital of Iran, in 2012. Stratified sampling method was used to select samples from 22 urban areas of Tehran. Trained researchers referred to the public places, such as streets, parks, squares, and libraries, to perform face-to-face interviews with the randomly selected samples. Bayesian ZIP model was used to perform the analysis, and SAS 9.3 program was used for data analysis. Results: A total of 993 individuals were studied. According to Bayesian ZIP model, sex (mean= -0.27, 95%CI (-0.485, -0.061)), age (mean= 0.03, 95%CI (0.018, 0.043)), high school level education (mean= 1.276, 95%CI (0.699, 01.9)), diploma level education (mean = 10.4, 95%CI (0.511, 1.69)), and university level education (mean= 0.69, 95%CI (0.142, 1.33)) were all found to have significant associations with crystal meth usage, being the dependent variable. Conclusion: Males, those with higher education levels, and older people in Tehran population are more likely to use crystal meth. This demographic information may be useful in designing preventive programs. Moreover, it is better to analyze count data with excessive zeroes using Bayesian zero- inflated model instead of the usual count models.