Arch Iran Med
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Iran 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. ⋯ Results 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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Statistical modeling and developing new methods for estimating burden of diseases, injuries and risk factors is a fundamental concern in studying the country health situation for better health management and policy making. Bayesian autoregressive multilevel model is a strong method for this kind of study though in complex situations it has its own challenges. Our study aims to describe the way of modeling space and time data through an autoregressive multilevel model and address challenges in complex situation. ⋯ Our analyses will include different existing sources of data in Iran for 24 years through a rational and reasonable model to estimate burden of diseases, injuries and risk factors for Iran at national, regional and provincial levels while considering several kinds of uncertainties. Comprehensive and realistic estimates are always an issue of request that will be obtained through a suitable statistical modeling considering all dimensions and then can be used for making better decision in real situations.
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It is expected that gastrointestinal (GI) and liver diseases inflict considerable burden on health systems in Iran; therefore, highlighting the significance of GI disorders across the other most burdensome diseases requires comprehensive assessment and regular updates of the statistics of such diseases in Iran. ⋯ Results of the present study will have implications for policy making; as they allow for understanding geographic distributions of the selected GI diseases, and identifying health disparities across provinces.
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Estimating burden of disease, injuries and risk factors is crucial for health policy decision making. The Burden of Diseases (BoD) studies provide data about the magnitude and distribution of health problems among the population at national and sub-national levels. The BoD studies are designed to use secondary data for estimating prevalence and incidence of diseases, injuries and risk factors. However, due to the scarcity of data sometimes it becomes unavoidable to collect data from medical records. Among all needed source of data, including surveys, registries, censuses, inpatient and outpatient data, hospital data are an essential source for BoD studies. Hospital Data Survey (HDS) aims to estimate the prevalence and incidence of diseases and injuries that led to admission to hospitals. This paper aims to describe the required steps for data gathering, sampling, analytical methods, and other needed procedures for HDS. ⋯ The designed questionnaire includes demographic data, current health status, diseases, injuries and co-morbidities with their ICD10 codes, curative procedures, and treatment. A pilot study was conducted on 302 medical records from 6 hospitals to evaluate the validity and reliability of the questionnaire. Sampling frame was designed and probability proportional was used after being tested in the pilot study. In the next step, we will collect 367500 medical files from 863 hospitals (0.5% of all inpatient records in hospitals from1996 - 2013). The HDS is the first national study in Iran that is gathering data through an online-offline web-based system based on electronic version of the questionnaire which makes the process of data cleaning and analyses more comfortable.
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Identifying the burden of disease and its inequality between geographical regions is an important issue to study health priorities. Estimating burden of diseases using statistical models is inevitable especially in the context of rare data availability. To this purpose, the spatio-temporal model can provide a statistically sound approach for explaining the response variable observed over a region and various times. However, there are some methodological challenges in analysis of these complex data. Our primary objective is to provide some remedies to overcome these challenges. ⋯ This study aims to combine different available data sources and produce precise and reliable evidences for Iranian burden of diseases and risk factors and their disparities among geographical regions over time. Providing appropriate statistical methods and models for analyzing the data is undoubtedly crucial to circumvent the problems and obtain satisfactory estimates of model parameters and reach accurate assessment.