• Farshad Farzadfar, Alireza Delavari, Reza Malekzadeh, Alireza Mesdaghinia, Hamid Reza Jamshidi, Aliakbar Sayyari, and Bagher Larijani.
• Non-communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran, Endocrinology and Metabolism Research center, Endocrinology and Metabolism Research Institute, Tehran, Iran. f-farzadfar@tums.ac.ir.
• Arch Iran Med. 2014 Jan 1; 17 (1): 7-15.

BackgroundIran has witnessed a substantial demographic and health transition, especially during the past 2 decades, which necessitates updated evidence-based policies at national and indeed at subnational scale. The National and Subnational Burden of Diseases, Injuries, and Risk Factors (NASBOD) Study aims to provide the required evidence based on updated data sources available in Iran and novel methods partly adopted from Global Burden of Disease 2010.ObjectiveThis paper aims at explaining the motives behind the study, the design, the definitions, the metrics, and the challenges due to limitations in data availability.MethodsAll available published and unpublished data sources will be used for estimating the burden of 291 diseases and 67 risk factors from 1990 to 2013 at national and subnational scale. Published data will be extracted through systematic review. Existing population-based data sources include: registries (death and cancer), Demographic and Health Surveys, National Health Surveys, and other population-based surveys such as Non_Communicable Diseases Surveillance Surveys. Covariates will be extracted from censuses and household expenditure surveys. Hospital records and outpatient data will be actively collected as two distinct projects. Due to lack of data points by year and province, statistical methods will be used to impute the lacking data points based on determined covariates. Two main models will be used for data imputation: Bayesian Autoregressive Multi-level models and Spatio-Temporal regression models. The results from all available models will be used in an Ensemble Model to obtain the final estimates. Five metrics will be used for estimating the burden: prevalence, death, Years of Life Lost due to premature death (YLL), Years of Life Lost due to Disability (YLD), and Disability-Adjusted Life Years Lost (DALY). Burden attributable to risk factors will be estimated through comparative risk assessment based on Population Attributable Fraction (PAF). Uncertainty Intervals (UIs) will be calculated and reported for all aforementioned metrics.ResultsWe will estimate trends in terms of prevalence, deaths, YLLs, YLDs, and DALYs for Diseases, Injuries, and Risk Factors province from 1990 to 2013.ConclusionResults of the present study will have implications for policy making as they address health gaps in Iranian population and their inequality between provinces.

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