Document Type : Research articles


1 Department of Health policy and Management, Iranian Center of Excellence in Health Management, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran.

2 Tabriz Health Service Management Research Center, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran.

3 Health Services Management Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.

4 Road Traffic Injury Research Center, Tabriz University of Medical Sciences, Tabriz, Iran. 5 Department of Statistics and Epidemiology, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran.

5 Non-Communicable Diseases Research Center Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.

6 Department of Health Administration and Public Health, John G. Rangos School of Health Sciences, Duquesne University, Pittsburgh, PA, USA.


Background and Objectives: Globally, cardiovascular disease (CVD) is the number one cause of mortality. In this regard, this study aimed to provide policies for the management of CVD by focusing on the reduction of myocardial infarction (MI) mortality rate in Iran.
Materials and Methods: The sequential mixed methods design will be employed to foresight the prevalence of MI in Iran in the next 10 years. This study consists of five phases and in the first phase, the risk factors of cardiovascular disease will be investigated using a systematic review. In the second phase, the uncertainty and impact of those factors will be demonstrated by the experts. Moreover, the impact/uncertainty grid will be used to identify the drivers that are less important and critical uncertainties. In the third phase, the cross-impact matrix will be developed by Scenario wizard, and the scenario logic and the scenarios will be developed. Once the scenario logic is established, details can be added to the scenarios. The next phase consists of statistical estimations of the rate of mortality due to heart attack using artificial neural networks. Finally, the policies will be developed based on the opinions of the panel of experts. The initial results will be published in mid-2020.
 Results: This future study will develop policies to prevent from MI with scenario-based and modeling approaches. The findings can be useful for healthcare professionals and it can improve our understanding of the future of MI to enhance the management of MI patients.
 Conclusion: The obtained policies will help policymakers to make evidence-based decisions, re-design structures, and processes of healthcare interventions, and also plan to decrease MI mortality rate.


  1. Joseph P, Leong D, Mckee M, Anand SS, Schwalm JD, Teo K, et al. Reducing ‎the global burden of cardiovascular disease, part 1: the epidemiology and risk factors. Circ Res. 2017;121(6):677-94.‎ doi: 10.1161/CIRCRESAHA.117.308903. [PubMed: 28860318].
  2. Roth GA, Johnson C, Abajobir A, Abd-Allah F, Abera SF, Abyu G, et al. Global, ‎regional, and national burden of cardiovascular diseases for 10 causes, 1990 to 2015. J Am Coll Cardiol. 2017;70(1):1-25.‎ doi: 10.1016/j.jacc.2017.04.052. [PubMed: 28527533].
  3. Mota JDO, Boué G, Guillou S, Pierre F, Membré JM. Estimation of the burden of ‎disease attributable to red meat consumption in France: Influence on colorectal cancer and ‎cardiovascular diseases. Food Chem Toxicol. 2019;130:174-86.‎ doi: 10.1016/j.fct.2019.05.023. [PubMed: 31103738].
  4. Fahimfar N, Khalili D, Sepanlou SG, Malekzadeh R, Azizi F, Mansournia MA, et ‎al. Cardiovascular mortality in a Western Asian country: results from the Iran cohort ‎consortium. BMJ Open. 2018;8(7):e020303.‎ doi: 101136/bmjopen-2017-020303. [PubMed: 29980541].
  5. Tran DT, Ohinmaa A, Thanh NX, Welsh RC, Kaul P. The healthcare cost burden ‎of acute myocardial infarction in Alberta, Canada. Pharmacoecon Open. 2018;2(4):433-42.‎ doi: 10.1007/s41669-017-0061-0. [PubMed: 29623635].
  6. Alwan A. Global status report on noncommunicable diseases 2010. Geneva: World Health ‎Organization; 2011.‎
  7. Lagerweij GR, de Wit GA, Moons KG, van der Schouw YT, Verschuren WM, ‎Dorresteijn JA, et al. A new selection method to increase the health benefits of CVD prevention ‎strategies. Eur J Prev Cardiol. 2018;25(6):642-50.‎ doi: 10.1177/2047487317752948. [PubMed: 29411690].
  8. Nation M, Crusto C, Wandersman A, Kumpfer KL, Seybolt D, Morrissey-Kane ‎E, et al. What works in prevention: principles of effective prevention programs. Am Psychol. 2003;58(6-7):449-56.‎ doi: 10.1037/0003-066x.58.6-7.449. [PubMed: 12971191].
  9. World Health Organization. Prevention of cardiovascular disease. Geneva: World Health ‎Organization; 2007.‎
  10. Ghazizadeh-Hashemi SH, Larijani B. National action plan for prevention and control of non communicable diseases and the related risk factors in the Islamic Republic of Iran, 2015–2025. Tehran, Iran: Aftabe Andisheh Publications; 2015. P. 47-65.
  11. Zannad F, Kessler M, Lehert P, Grünfeld J, Thuilliez C, Leizorovicz A, et al. ‎Prevention of cardiovascular events in end-stage renal disease: results of a randomized trial of ‎fosinopril and implications for future studies. Kidney Int. 2006;70(7):1318-24.‎ doi: 10.1038/ [PubMed: 16871247].
  12. Heidenreich PA, Trogdon JG, Khavjou OA, Butler J, Dracup K, Ezekowitz MD, ‎et al. Forecasting the future of cardiovascular disease in the United States: a policy statement ‎from the American Heart Association. Circulation. 2011;123(8):933-44.‎ doi: 10.1161/CIR.0b013e31820a55f5. [PubMed: 21262990].
  13. Dombayci C, Espuña A. Building decision making models through conceptual ‎constraints: multi-scale process model implementations. Operat Res Proc ‎‎2016. 2018;12:77-83.‎ doi: 10.1007/978-3-319-55702-1_12.
  14. Haluza D, Jungwirth D. ICT and the future of healthcare: aspects of pervasive ‎health monitoring. Inform Health Soc Care. 2018;43(1):1-11.‎ doi: 10.1080/17538157.2016.1255 215. [PubMed: 28005444].
  15. Van Notten P. Scenario development: a typology of approaches. Think Scenarios Rethink Educ. 2006;6:66-92.‎ doi: 10.1787/9789264023642-en.
  16. Montazer GA, Falahati N. Iranian higher education future scenarios derived by ‎information technology. J Sci Technol Policy. 2015;7(1):47-82.‎
  17. MacGregor H, Lally S, Bloom G, Davies M, Henson S, Mejía Acosta A, et al. ‎Non-communicable disease and development: future pathways. IDS Evidence Report 100. Brighton: IDS; ‎2014.
  18. Wepner B, Giesecke S. Drivers, trends and scenarios for the future of health in ‎Europe. Impressions from the FRESHER project. Eur J Futures Res. ‎‎2018;6(1):2.‎ doi: 10.1007/s40309-017-0118-4.
  19. Johnson RB, Onwuegbuzie AJ, Turner LA. Toward a definition of mixed ‎methods research. J Mixed Methods Res. 2007;1(2):112-33.‎ doi: 10.1177/1558689806298224.
  20. Carayon P, Kianfar S, Li Y, Xie A, Alyousef B, Wooldridge A. A systematic ‎review of mixed methods research on human factors and ergonomics in health care. Appl Ergon. 2015;51:291-321.‎ doi: 10.1016/j.apergo.2015.06.001 . [PubMed: 26154228].
  21. Joanna Briggs Institute. Joanna Briggs Institute reviewers’ manual. Australia: ‎The Joanna Briggs Institute; 2014.‎
  22. Goharinezhad S, Maleki M, Baradaran HR, Ravaghi H. Futures of elderly care in ‎Iran: a protocol with scenario approach. Med J Islam Repub Iran. ‎‎2016;30:416.‎ [PubMed: 28210581].
  23. Amer M, Daim TU, Jetter A. A review of scenario planning. Futures. 2013;46:23-‎‎40.‎ doi: 10.1016/j.futures.2012.10.003.
  24. Goff DC, Lloyd-Jones DM, Bennett G, Coady S, D’agostino RB, Gibbons R, et ‎al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American ‎College of Cardiology/American heart association task force on practice guidelines. J Am Coll Cardiol. 2014;63(25 Part B):2935-59.‎ doi: 10.1016/j.jacc.2013.11.005. [PubMed: 24239921].
  25. Song X, Mitnitski A, MacKnight C, Rockwood K. Assessment of individual risk ‎of death using self‐report data: an artificial neural network compared with a frailty index. J Am Geriatr Soc. 2004;52(7):1180-4.‎ doi: 10111/j.1532-5415.2004.52319.x. [PubMed: 15209659].
  26. Taghipour H, Nowrouz P, Jafarabadi MA, Nazari J, Hashemi AA, Mosaferi M, et ‎al. E-waste management challenges in Iran: presenting some strategies for improvement of ‎current conditions. Waste Manag Res. 2012;30(11):1138-44.‎ doi: 10.1177/0734242X11420328. [PubMed: 21945991].
  27. Shakerkhatibi M, Dianat I, Jafarabadi MA, Azak R, Kousha A. Air pollution and ‎hospital admissions for cardiorespiratory diseases in Iran: artificial neural network versus ‎conditional logistic regression. Int J Environ Sci Technol. ‎‎2015;12(11):3433-42.‎ doi: 10.1007/s13762-015-0884-0.
  28. Gholipour K, Asghari-Jafarabadi M, Iezadi S, Jannati A, Keshavarz S. Modelling the prevalence of diabetes mellitus risk factors based on artificial neural network and multiple regression. East Mediterr Health J. 2018;24(8):770-7. doi: 10.26719/emhj.18.012. [PubMed: 30328607].
  29. Djalalinia S, Modirian M, Sheidaei A, Yoosefi M, Zokaiee H, Damirchilu B, et al. ‎Protocol design for large–scale cross–sectional studies of surveillance of risk factors of ‎non–communicable diseases in Iran: STEPs 2016. Arch Iran Med. 2017;20(9):608-16.‎ [PubMed: 29048923].
  30. Niakan Kalhori SR, Tayefi B, Noori A, Mearaji M, Rahimzade S, Zandian E, et al. Inpatient data, inevitable need for policy making at national and sub-national levels: a lesson learned from NASBOD. Arch Iran Med. 2014;17(1):16-21. [PubMed: 24444090].
  31. Saaty TL. Decision making with the analytic hierarchy process. Int J Serv Sci. 2008;1(1):83-98.‎ doi: 10.1504 /IJSSCI.2008.017590.
  32. Bradfield R, El-Sayed H. Four scenarios for the future of the pharmaceutical ‎industry. Technol Anal Strateg Manag. 2009;21(2):195-212.‎ doi: 10.1080/09537320802625280.