Archives of Iranian Medicine
Volume:17 Issue: 1, Jan 2014

Gastrointestinal cancers, including esophageal, gastric, liver and pancreatic are relatively common in Iran. Furthermore, consumption of opium and its derivatives (O&D) are considerable. This study, aimed to examine the association between consumption of O&D and the incidence of upper gastrointestinal (UGI) cancers. In a matched case-control study in Kerman (located in southeast of Iran), 142 patients with UGI cancers and 284 healthy people (matched in terms of age, sex and residence (urban/rural)) were recruited. Variables (using O&D, smoking, alcohol use and diet) were collected using a structured questionnaire. Conditional logistic regression models were used to assess the above mentioned association. Opium use was associated with an increased risk of UGI cancers with an adjusted OR 4.0 (95 % CI: 2.2 – 7.0). A very strong dose-response relation was observed between consumption of O&D and the incidence of UGI cancers. (Three consumption levels-none, low and high; OR = 18.7; 95 % CI: 5.5 – 63.3). This dose-response relationship was also strong even in patients with gastric cancers (OR: 9.2; 95 % CI: 2.5 – 33.7). The results of this study showed that opium consumption can be a strong risk factor for UGI cancers in Iran.

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. This paper aims at explaining the motives behind the study, the design, the definitions, the metrics, and the challenges due to limitations in data availability. All available published and unpublished data sources will be used for estimating the burden of 291 diseases and 67 risk factors from 1990 to 2013 at national and subnational scale.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. We will estimate trends in terms of prevalence, deaths, YLLs, YLDs, and DALYs for Diseases, Injuries, and Risk Factors province from 1990 to 2013. Results of the present study will have implications for policy making as they address health gaps in Iranian population and their inequality between provinces.

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. STUDY DESIGN: 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.

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. We will obtain data from different published and unpublished secondary data sources including population-based health surveys (HHE, NHS, DHS, STEP) at national and provincial levels and we also assess epidemiological studies via systematic review for each disease, injuries and risk factor over the period of 1990-2013. These data generally have a multilevel hierarchy and also time correlation. However, statistical analysis of diseases, injuries and risk factors data is primarily facing the problem of information scarcity. Data are generally too scarce to ensure reliable estimates in many practical problems. Also, there may be nonlinear changes over time, different kind of uncertainties in data and incompatible geographical data. We describe Bayesian autoregressive multilevel modeling approach that provides a natural solution to these problems through its ability to sensibly combine information from several sources of data and available prior information. In this hierarchy model levels of each hierarchy borrow information from each other and also lower levels borrow information from higher levels. We will fit the model using Markov Chain Monte Carlo (MCMC) methods because of its capabilities and benefits in complex cases. 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.

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. Data from nationally representative surveys and systematic reviews have been gathered across contiguous areal units over a period of more than 20 years (1990 – 2013). Generally, observations of areal units are spatially and temporally correlated in such a way that observations closer in space and time tend to be more correlated than observations farther away. It is critical to determine the correlation structure in space-time process which has been observed over a set of irregular regions. Moreover, these data sets are subject to high percentage of missing, including misaligned areal units, areas with small sample size, and may have nonlinear trends over space and time. Furthermore, the Gaussian assumption might be overly restrictive to represent the data. In this setting, the traditional statistical techniques are not appropriate and more flexible and comprehensive methodology is required. Particularly, we focus on approaches that allow extending spatio-temporal models proposed previously in the literature. Since statistical models include both continuous and categorical outcomes, we assume a latent variable framework for describing the underlying structure in mixed outcomes and use a conditionally autoregressive (CAR) prior for the random effects. In addition, we will employ misalignment modeling to combine incompatible areal units between data sources and/or over the years to obtain a unified clear picture of population health status over this period. In order to take parameter uncertainties into account, we pursue a Bayesian sampling-based inference. Hence, a hierarchical Bayes approach is constructed to model the data. The hierarchical structure enables us to “borrow information” from neighboring areal units to improve estimates for areas with missing values and small number of observations. For their general applicability and ease of implementation, the MCMC methods are the most adapted tool to perform Bayesian inference. 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.

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. To assess in-depth sub-national estimates and trends for the incidence and prevalence of selected GI and liver diseases by age, gender and province over the period 1990 – 2013 in Iran. This is a national and sub-national burden of disease study on 21 GI diseases using all available data sources, including cancer registry, death registration system data, hospital data, and all available published data. Analyses will be performed separately by gender, age groups, year, and province. We will conduct 21 separated systematic reviews of the literature for 21 diseases categories through searching online international electronic databases (i.e. the Medline database of the National Library of Medicine, Web of Science, and Scopus), Iranian search engines (i.e., IranMedex, Scientific Information Database (SID), and IRANDOC), and gray literature. We will search the medical literature published between January 1985 and December 2013. We generated two models, Spatio-temporal and Multilevel Autoregressive models, to estimate mean and uncertainty interval for the parameters of interest by gender, age, year, and province. The models will be informed by data of gender, age, year, and province. Markov Chain Monte Carlo (MCMC) methods will be used to perform Bayesian inference in both modeling framework. All programs will be written in R statistical packages (version 3.0.1). We will calculate and present 1990 to 2013 trends in terms of prevalence, years of life lost due to premature mortality (YLLs), years lived with disability (YLDs), and disability-adjusted life years DALYs for the 21 selected GI diseases by gender, and province. We will also quantify the uncertainty interval for the estimates of interest. 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.

Non-communicable diseases (NCDs) and their risk factors are the major public health problems. There are some documented trend and point estimations of metabolic risk factors for Iranian population but there are little information about their exposure distribution at sub-national level and no information about their trends and their effects on the population health. The present study protocol is aimed to provide the standard structure definitions, organization, data sources, methods of data gathering or generating, and data on trend analysis of the metabolic risk factors in NASBOD study. We will estimate 1990 to 2013 trends of prevalence, years of life lost due to premature mortality (YLLs), and years lived with disability (YLDs) and disability-adjusted life years DALYs for MRFs by gender, age group, and province. We will also quantify the uncertainty interval for the estimates of interest. The findings of study could provide practical information regarding metabolic risk factors and their burden for better health policy to reduce the burden of diseases, and to plan cost-effective preventive strategies. The results also could be used for future complementary global, regional, national, and sub national studies.

Development of national evidence-based public health strategies requires a deep understanding of the role of major risk factors (RFs) and the burden of disease (BOD). In this article, we explain the framework for studying the national and sub-national Environmental Burden of Disease (EBD) in Iran as a part of the National and Sub-national Burden of Disease (NASBOD) study.

The distribution of exposures to environmental RFs and their attributable effect size over 1990-2013 will be estimated through comprehensive reviews of either published or unpublished sources. Statistical modeling will be used to impute missing data in the distribution of RFs exposures for each district-year. National and sub-national BOD attributable to these RFs will be estimated in the following metrics: Prevalance, death, years of life lost due to premature death(YLL), years of life lost due to disability (YLD), and disability -adjusted life years last(DALYS). The BOD attributable to the current distribution of exposures will be compared with a counterfactual exposure distribution scenario–here, the theoretical-minimum-risk exposure distribution. Inequalities in the distribution of exposure to RFs will be analyzed and manifested nationwide using geographic information systems.

The EBD study aims to provide an official report to Iranian Ministry of Health and Medical Education, to publish a series of articles on the exposure trends of the selected environmental RFs, to estimate the BOD attributable to these RFs, and to assess inequalities and its determinants in the distribution of exposure to RFs. Iran’s territory is large with diverse population, socioeconomic, and geographic areas. Results of this comparative risk assessment study may pave the way for health policy makers to plan more comprehensive and cost-effective evidence-based strategies.

Non-communicable diseases (NCDs) and their risk factors are a major health threat at the global level, notably for developing countries. The tracking of cardiometabolic risk factors from childhood to adulthood is well documented. Therefore, more attention needs to be directed at primordial and primary prevention of NCDs. Given the high prevalence of NCDs and their risk factors in Iranian population, a study was designed to determine the attributable burden of cardiometabolic risk factors in Iranian pediatric population during past decades.

This paper explains the definitions, organization, data sources, methods of data gathering or generating, data analyses, and the trend analysis of the study. A national expert working group addressed unmet needs and offered consultations on the selection of risk factors and the practical definition of disease. In the later stages, during the course of the study, they will supervise the statistical modeling methods, the interpretation of results, and the publication strategy. Also an international expert advisory group will collaborate with the project team.

The findings of this study could provide basic information regarding NCD related risk factors, and their burden and trends in children, which is necessary for health policy decisions to reduce the burden of disease and to plan cost-effective preventive strategies.