A Scenario-based Modelling Study of the Prevention of Myocardial Infarction in Iran
نویسندگان
1 Health Services Management Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
2 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.
3 Non-Communicable Diseases Research Center Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
4 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.
5 Tabriz Health Service Management Research Center, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran.
6 Department of Health Administration and Public Health, John G. Rangos School of Health Sciences, Duquesne University, Pittsburgh, PA, USA.
7
doi
10.32592/ircmj.2020.22.9.88چکیده
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.
