Dataset Information

Prevalence and risk factors of stroke in the elderly in Northern China: data from the National Stroke Screening Survey.

ABSTRACT:

Background

The overall global burden of stroke is considerable and increasing. In China, stroke is the leading cause of death and disability.

Methods

For this study, we used data from the National Stroke Screening Survey in 2012 and the 2010 Chinese population from sixth National Census of Populations to calculate a standardized (by age, gender, and education) stroke prevalence. Prevalence, risk factors, and management of stroke were compared by gender, age, and site.

Findings

The standardized prevalence rate of survival stroke patients in study population aged 60 and older was 4.94% in total. Hypertension was the most prevalent risk factor for stroke. Compared to men, women were more likely to have diabetes, obesity, elevated low-density lipoprotein cholesterol (LDL-C), and atrial fibrillation (P?InterpretationThe stroke prevalence in China is in line with median worldwide stroke prevalence. Traditional risk factors remain highly prevalent in stroke survivors, among which hypertension was the most common. Stroke prevalence rates and risk factors varied by age, sex, and sociogeological factors.

SUBMITTER: Xia X

PROVIDER: S-EPMC6517347 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prevalence and risk factors of stroke in the elderly in Northern China: data from the National Stroke Screening Survey.

Xia Xiaoshuang X Yue Wei W Chao Baohua B Li Mei M Cao Lei L Wang Lin L Shen Ying Y Li Xin X

Journal of neurology 20190415 6

<h4>Background</h4>The overall global burden of stroke is considerable and increasing. In China, stroke is the leading cause of death and disability.<h4>Methods</h4>For this study, we used data from the National Stroke Screening Survey in 2012 and the 2010 Chinese population from sixth National Census of Populations to calculate a standardized (by age, gender, and education) stroke prevalence. Prevalence, risk factors, and management of stroke were compared by gender, age, and site.<h4>Findings< ...[more]

PMID: 30989368

Similar Datasets

Project description:ObjectivesThis study reports the life satisfaction of middle-aged and elderly patients who had a stroke in China, and explores its association with patients' sociodemographic characteristics, health status, lifestyles and family relationship.DesignCross-sectional survey.Setting and participantsThe samples of this study were selected from the data of China Health and Retirement Longitudinal Study (CHARLS) in 2018, which covered 28 provinces, 150 countries/districts and 450 village/urban communities. 1154 patients who had a stroke aged 45 and over were included in this study as qualified samples.Outcome measuresDescriptive analysis was used to report sociodemographic characteristics, health status, lifestyles, family relationship and life satisfaction of middle-aged and elderly patients who had a stroke. χ2 analysis and binary logistic regression were used to analyse the factors influencing the life satisfaction of the patients who had a stroke.ResultsOverall, 83.1% of patients who had a stroke were satisfied with their lives, although only 8.7% rated their own health as being good. Patients who had a stroke who were male, elderly, married, living with their spouses and having a pension were more likely to report satisfaction with life (p<0.05). Self-rated health, health satisfaction, chronic lung disease, fall, pain, ability to work and family relationships were also significantly associated with life satisfaction (p<0.05). Patients who drank alcohol (86.8% vs 81.7%, p=0.041), had physical activity (84.4% vs 75.6%, p=0.004) and had social activity (85.3% vs 80.8%, p=0.041) were more satisfied with their lives than those who did not. Multivariable analysis confirmed that age, health satisfaction, physical pain, working ability, relationships with spouse and with children had significant effects on life satisfaction of patients who had a stroke (p<0.05).ConclusionsOur study indicates the importance of improving the overall health of patients who had a stroke and mediating factors, such as pain management, and work ability, spouse and children relationship in improving the life satisfaction of patients in the poststroke rehabilitation.

Project description:BACKGROUND:With the character of high incidence, high prevalence and high mortality, stroke has brought a heavy burden to families and society in China. In 2009, the Ministry of Health of China launched the China national stroke screening and intervention program, which screens stroke and its risk factors and conducts high-risk population interventions for people aged above 40?years old all over China. In this program, stroke risk factors include hypertension, diabetes, dyslipidemia, smoking, lack of exercise, apparently overweight and family history of stroke. People with more than two risk factors or history of stroke or transient ischemic attack (TIA) are considered as high-risk. However, it is impossible for this criterion to classify stroke risk levels for people with unknown values in fields of risk factors. The missing of stroke risk levels results in reduced efficiency of stroke interventions and inaccuracies in statistical results at the national level. In this paper, we use 2017 national stroke screening data to develop stroke risk classification models based on machine learning algorithms to improve the classification efficiency. METHOD:Firstly, we construct training set and test sets and process the imbalance training set based on oversampling and undersampling method. Then, we develop logistic regression model, Naïve Bayesian model, Bayesian network model, decision tree model, neural network model, random forest model, bagged decision tree model, voting model and boosting model with decision trees to classify stroke risk levels. RESULT:The recall of the boosting model with decision trees is the highest (99.94%), and the precision of the model based on the random forest is highest (97.33%). Using the random forest model (recall: 98.44%), the recall will be increased by about 2.8% compared with the method currently used, and several thousands more people with high risk of stroke can be identified each year. CONCLUSION:Models developed in this paper can improve the current screening method in the way that it can avoid the impact of unknown values, and avoid unnecessary rescreening and intervention expenditures. The national stroke screening program can choose classification models according to the practice need.

Dataset Information

Prevalence and risk factors of stroke in the elderly in Northern China: data from the National Stroke Screening Survey.

Background

Methods

Findings

Publications

Prevalence and risk factors of stroke in the elderly in Northern China: data from the National Stroke Screening Survey.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets