Projection of Diabetes Population Size and Associated Economic Burden through 2030 in Iran: Evidence from Micro-Simulation Markov Model and Bayesian Meta-Analysis

RESEARCH ARTICLE Projection of Diabetes Population Size and Associated Economic Burden through 2030 in Iran: Evidence from Micro-Simulation Markov Model and Bayesian Meta-Analysis Mehdi Javanbakht1*, Atefeh Mashayekhi2, Hamid R. Baradaran3, AliAkbar Haghdoost4, Ashkan Afshin5 1 Health Economics Research Unit, Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, United Kingdom, 2 Endocrine Research Center, Institute of Endocrinology & Metabolism, Iran University of Medical Sciences, Tehran, Iran, 3 Endocrine Research Center, Institute of Endocrinology & Metabolism, Iran University of Medical Sciences, Tehran, Iran, 4 Kerman University of Medical Sciences, Kerman, Iran, 5 Friedman School of Nutrition Science and Policy, Tufts University, Boston, United States of America * OPEN ACCESS Citation: Javanbakht M, Mashayekhi A, Baradaran HR, Haghdoost A, Afshin A (2015) Projection of Diabetes Population Size and Associated Economic Burden through 2030 in Iran: Evidence from MicroSimulation Markov Model and Bayesian MetaAnalysis. PLoS ONE 10(7): e0132505. doi:10.1371/ journal.pone.0132505 Editor: Chen-Wei Pan, Medical College of Soochow University, CHINA Received: March 3, 2015 Accepted: June 15, 2015 Published: July 22, 2015 Copyright: © 2015 Javanbakht et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Abstract Background The aim of this study was to estimate the economic burden of diabetes mellitus (DM) in Iran from 2009 to 2030. Methods A Markov micro-simulation (MM) model was developed to predict the DM population size and associated economic burden. Age- and sex-specific prevalence and incidence of diagnosed and undiagnosed DM were derived from national health surveys. A systematic review was performed to identify the cost of diabetes in Iran and the mean annual direct and indirect costs of patients with DM were estimated using a random-effect Bayesian metaanalysis. Face, internal, cross and predictive validity of the MM model were assessed by consulting an expert group, performing sensitivity analysis (SA) and comparing model results with published literature and national survey reports. Sensitivity analysis was also performed to explore the effect of uncertainty in the model. Data Availability Statement: Data are available from the literature and sources as cited in the paper. Funding: This project was part of MJ Ph.D program thesis and was funded by Iran University of Medical sciences, Grant number: 891113. URL: http://en.iums. ac.ir/. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. Results We estimated 3.78 million cases of DM (2.74 million diagnosed and 1.04 million undiagnosed) in Iran in 2009. This number is expected to rise to 9.24 million cases (6.73 million diagnosed and 2.50 million undiagnosed) by 2030. The mean annual direct and indirect costs of patients with DM in 2009 were US$ 556 (posterior standard deviation, 221) and US $ 689 (619), respectively. Total estimated annual cost of DM was $3.64 (2009 US$) billion (including US$1.71 billion direct and US$1.93 billion indirect costs) in 2009 and is predicted PLOS ONE | DOI:10.1371/journal.pone.0132505 July 22, 2015 1 / 17 Economic Burden of Diabetes through 2030 in Iran to increase to $9.0 (in 2009 US$) billion (including US$4.2 billion direct and US$4.8 billion indirect costs) by 2030. Conclusions The economic burden of DM in Iran is predicted to increase markedly in the coming decades. Identification and implementation of effective strategies to prevent and manage DM should be considered as a public health priority. Introduction Diabetes mellitus (DM) is among the leading causes of mortality worldwide, with an estimated at least 1.3 million deaths attributed to the illness in 2013 alone [1]. In 2013, an estimated 382 million people lived with DM worldwide and current projections suggest this number will rise to 592 million by 2035 [2,3]. Continuously increasing length of the life span of individuals in combination with the growing number of the world population are two underlying factors to the expected explosion in the numbers of diabetic patients and related burden. Diabetes mellitus is a risk factor for other chronic health conditions, such as cardiovascular disease and complications resulting from it include nephropathy, amputations and blindness, all of which impose a burden to society by reducing quality of life, increasing the risk of premature death and raising the economic burden due to absenteeism in the labor market and increased health care costs [4–10]. The estimated worldwide cost of DM and its associated complications was estimated to be at least US$548 billion in 2013[3]. The Middle East and North Africa (MENA) region has the highest diabetes prevalence in the world at 10.9%. It is estimated that about 35 million people are living with diabetes in this region [3]. Iran is amongst the countries with the highest prevalence of DM in the region at 9.94% in the adult population [3]. It has been suggested that socioeconomic development and urbanization have led to changes in lifestyle, such as increased sedentary activity and caloric intake coupled with a loss of traditional healthy dietary habits, which are responsible for the observed rise [11,12]. Furthermore, unlike most developed countries, where approximately half of reported cases are individuals older than 60 years, DM is most prevalent amongst the working population (20–59 years old), making it a major obstacle toward economic growth in Iran and other countries in the MENA region [13–15]. With rising health care costs and limited resources, it is necessary to understand the impact of DM in Iran to inform health policy and health care resource allocation. However, few studies have investigated the economic burden of DM in Iran and even less studies have estimated projections of the economic burden [16–18]. Given that a well-designed and validated model can effectively synthesize and combine data from various sources to generate new insights into the impact of chronic disease on society and reveal important gaps in the knowledge [19,20], therefore we developed this study which is the first study to use a Markov-microsimulation model to estimate the economic burden of DM in Iran from 2009 to 2030 using local database. Methods Model framework A Markov microsimulation (MM) model was constructed to predict the growth of DM within the Iranian population over 22 years and its associated economic burden. The MM model is a computer modelling technique that simulates individual lives. Within the model each person is represented by a record containing a unique identifier an (...truncated)