Smoking invariably has environmental, social, economic and health consequences in Ethiopia. Reducing and quitting cigarette smoking improves individual health and increases available household funds for education, food and better economic productivity. Therefore, this study aimed to apply the Bayesian negative binomial logit hurdle and zero-inflated model to determine associated factors of the number of cigarette smokers per day using the smoking intensity data of 2016 Ethiopia Demographic and Health Survey. The survey was a community-based cross-sectional study conducted from January 18 to June 27, 2016. The survey used two stage stratified sampling design. Bayesian analysis of Negative Binomial Logit Hurdle and Zero-inflated models which incorporate both overdispersion and excess zeros and carry out estimation using Markov Chain Monte Carlo techniques. About 94.2% of them never cigarettes smoked per day and the data were found to have excess zeros and overdispersion. Therefore, after considering both the zero counts and the enduring overdispersion, according to the AIC and Vuong tests, the Zero-inflated Negative Binomial and Negative Binomial Logit Hurdle model best fit to the data. The finding Bayesian estimation technique is more robust and precisely due to that it is more popular data analysis method. Furthermore; using Bayesian Zero-inflation and Zero hurdle model the variable: age, residence, education level, internet use, wealth index, marital status, chewed chat, occupation, the media were the most statistically significant determinate factors on the smoking intensity.

在埃塞俄比亚，吸烟总是对环境，社会，经济和健康造成影响。减少和戒烟可改善个人健康，并增加可用于教育，食品和更好的经济生产力的家庭可用资金。因此，本研究旨在应用2016年埃塞俄比亚人口与健康调查的吸烟强度数据，使用贝叶斯负二项式logit障碍和零膨胀模型来确定每天吸烟者数量的相关因素。该调查是一项基于社区的横断面研究，于2016年1月18日至6月27日进行。该调查采用了两阶段分层抽样设计。负二项式Logit障碍和零膨胀模型的贝叶斯分析，这些模型结合了超分散和多余零，并使用Markov Chain Monte Carlo技术进行了估算。其中约94.2％的人每天从不抽烟，并且发现数据中存在过多的零和过度分散。因此，在考虑了零计数和持久的过度分散之后，根据AIC和Vuong检验，零膨胀负二项式和负二项式Logit障碍模型最适合该数据。贝叶斯估计技术的发现更加可靠，因为它是一种更为流行的数据分析方法。此外; 使用贝叶斯零通胀和零障碍模型变量：年龄，居住，受教育程度，互联网使用，财富指数，婚姻状况，咀嚼聊天，职业，