Journal of Kerman University of Medical Sciences (Nov 2018)
Methodological Challenges in Estimation of the Life Time Prevalence of Alcohol Consumption in Iran: Current Practice and Guidelines for Futures Studies
Abstract
Background: The prevalence of alcohol consumption in Iran cannot be estimated because large variations have been observed in the reported prevalences of alcohol consumption. The main aim of this study was to assess the methodological challenges in estimation of the life time prevalence of alcohol consumed in Iran. By the same token we provided a standard approach for future researches in this regard. Methods: Published articles were reviewed systematically. Using the risk of bias tool, 49 out of 600 articles met the eligibility criteria. Based on the population of alcohol consumers, the included studies were categorized into 5 groups: general populations, patients, school students, university students, and specific population (first relatives of opium addicts and women who were faced with epistemic violence). Results: There was a paucity of reliable information in about 40% of provinces. Almost all studies applied the direct size estimation methods, but 11 studies used face to face interview and 38 studies used self-administered questionnaires. The non-response bias seemed prominent in entire studies except those on school students. It is important to mention that 97% of papers in general population did not represent any information about the non-response rate. The reported prevalence ranged from 1.37% in university students to 88.8% in patient population. Even among university students, the prevalence varied considerably (1.37% to 34.7%). Conclusion: The observed huge variations in the reported prevalence of alcohol consumption, due to the methodological consideration, appear largely in Iran. Therefore, it is necessary to develop a standard protocol for data collection and sampling to harmonize the findings in future studies. In addition, it is recommended to assess the frequency of alcohol use by indirect methods such as the Network Scale Up method.
