Simulating the Risk of Stroke in China Using Markov Model Based on National Screening Data

In recent years, the increasing incidence and prevalence of stroke has brought a heavy economic burden on families and society in China. The Ministry of Health of the Peoples' Republic of China initiated a national stroke screening and intervention program in 2011 for stroke prevention and control. In this paper, we build Markov models for Chinese aged 40-59-years-old based on the stroke screening cohort data in 2013-2017 in order to study the 10-year risk of stroke for patients with different chronic diseases and targeted prevention and control measures for them. The results show that the stroke risk of people with hypertension is higher than that of people with diabetes or dyslipidemia. Considering the high prevalence of hypertension in China, it is more important to control hypertension for stroke prevention and control. For patients with diabetes and dyslipidemia, interventions should be taken according to gender and age. The simulation results can provide decision-making basis at the national level for guiding key population to control chronic diseases for stroke prevention.

[1]  Liping Liu,et al.  Stroke and stroke care in China: huge burden, significant workload, and a national priority. , 2011, Stroke.

[2]  F. Song,et al.  Prevalence of metabolic syndrome among middle-aged and elderly adults in China: current status and temporal trends , 2018, Annals of medicine.

[3]  H. Zhang,et al.  Carotid Atherosclerosis Detected by Ultrasonography: A National Cross‐Sectional Study , 2018, Journal of the American Heart Association.

[4]  P. Schnatz,et al.  Dyslipidemia in Menopause: Mechanisms and Management , 2006, Obstetrical & gynecological survey.

[5]  Li-sheng Liu,et al.  Efficacy of folic acid therapy in primary prevention of stroke among adults with hypertension in China: the CSPPT randomized clinical trial. , 2015, JAMA.

[6]  Lei Pan,et al.  Red Meat Consumption and the Risk of Stroke: A Dose-Response Meta-analysis of Prospective Cohort Studies. , 2016, Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association.

[7]  Wenzhi,et al.  The Stroke Riskometer™ App: Validation of a data collection tool and stroke risk predictor , 2014, International journal of stroke : official journal of the International Stroke Society.

[8]  Chien-Jen Chen,et al.  Description and Prediction of the Development of Metabolic Syndrome: A Longitudinal Analysis Using a Markov Model Approach , 2013, PloS one.

[9]  R B D'Agostino,et al.  Stroke risk profile: adjustment for antihypertensive medication. The Framingham Study. , 1994, Stroke.

[10]  Stefan Walzer,et al.  A Simulation Model for Peripheral Arterial Disease: Methods and Application of A Markov Model , 2016 .

[11]  T. Greenhalgh,et al.  Economic evaluation of type 2 diabetes prevention programmes: Markov model of low- and high-intensity lifestyle programmes and metformin in participants with different categories of intermediate hyperglycaemia , 2018, BMC Medicine.

[12]  H. Zhang,et al.  Prevalence of atrial fibrillation in different socioeconomic regions of China and its association with stroke: Results from a national stroke screening survey. , 2018, International journal of cardiology.

[13]  J. Geleijnse,et al.  Dairy Consumption and Risk of Stroke: A Systematic Review and Updated Dose–Response Meta‐Analysis of Prospective Cohort Studies , 2016, Journal of the American Heart Association.

[14]  Ping Zhang,et al.  Overweight and Obesity in Young Adulthood and the Risk of Stroke: a Meta-analysis. , 2016, Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association.

[15]  S. Larsson,et al.  Differing association of alcohol consumption with different stroke types: a systematic review and meta-analysis , 2016, BMC Medicine.

[16]  Xiaoyun Tang,et al.  Prediction of the development of metabolic syndrome by the Markov model based on a longitudinal study in Dalian City , 2018, BMC Public Health.

[18]  Mei Li,et al.  Discover high-risk factor combinations using Bayesian network from national screening data in China , 2017, 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[19]  Dan Ye,et al.  CSDC — A nationwide screening platform for stroke control and prevention in China , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[20]  Dong Zhao,et al.  Future Cardiovascular Disease in China: Markov Model and Risk Factor Scenario Projections From the Coronary Heart Disease Policy Model–China , 2010, Circulation. Cardiovascular quality and outcomes.

[21]  Juan Xiao,et al.  Description and prediction of the development of metabolic syndrome in Dongying City: a longitudinal analysis using the Markov model , 2014, BMC Public Health.

[22]  A. Zinsmeister,et al.  Clinical course and costs of care for Crohn's disease: Markov model analysis of a population-based cohort. , 1999, Gastroenterology.

[23]  N. Sims,et al.  Mitochondria, oxidative metabolism and cell death in stroke. , 2010, Biochimica et biophysica acta.

[24]  B. Gage,et al.  Selecting Patients With Atrial Fibrillation for Anticoagulation: Stroke Risk Stratification in Patients Taking Aspirin , 2004, Circulation.

[25]  Ashkan Afshin,et al.  Projection of Diabetes Population Size and Associated Economic Burden through 2030 in Iran: Evidence from Micro-Simulation Markov Model and Bayesian Meta-Analysis , 2015, PloS one.

[26]  X. Ji,et al.  Gender Differences in Risks of Coronary Heart Disease and Stroke in Patients with Type 2 Diabetes Mellitus and Their Association with Metabolic Syndrome in China , 2016, International journal of endocrinology.

[27]  Yong Jiang,et al.  Prevalence, Incidence, and Mortality of Stroke in China: Results from a Nationwide Population-Based Survey of 480 687 Adults , 2017, Circulation.

[28]  Ji Lin,et al.  Projection of the future diabetes burden in the United States through 2060 , 2018, Population Health Metrics.