CSDC: a nationwide screening platform for stroke control and prevention in China

Stroke in Asia

BACKGROUND

There is a significant burden of stroke in Asia. Asia has the largest population in the world in 2023, estimated at 4.7 billion. Approximately 9.5-10.6 million strokes will be anticipated annually in the backdrop of a diverse group of well-developed and less developed countries with large disparities in stroke care resources. In addition, Asian countries are in varying phases of epidemiological transition.

SUMMARY

In this review, we examined recent epidemiological features of ischaemic stroke and intracerebral haemorrhage in Asia with recent developments in hyperacute stroke reperfusion therapy and technical improvements in intracerebral haemorrhage. The article also discussed the spectrum of cerebrovascular diseases in Asia, which include intracranial atherosclerosis, intracerebral haemorrhage, infective aetiologies of stroke, moyamoya disease, vascular dissection, radiation vasculopathy, and cerebral venous thrombosis.

KEY MESSAGES

The review of selected literature and recent updates calls for attention to the different requirements for resources within Asia and highlights the breadth of cerebrovascular diseases still requiring further research and more effective therapies.

Predicting Recurrence for Patients With Ischemic Cerebrovascular Events Based on Process Discovery and Transfer Learning

The recurrence of Ischemic cerebrovascular events (ICE) often results in a high rate of mortality and disability. However, due to the lack of labeled follow-up data in hospitals, prediction methods using traditional machine learning are usually not available or reliable. Therefore, we propose a new framework for predicting the long-term recurrence risk in patients with ICE after discharge from hospitals based on process mining and transfer learning, to point out high-risk patients for intervention. First, process models are discovered from clinical guidelines for analyzing the similarity of ICE population data collected by different medical institutions, and the control flow found are taken as added characteristics of patients. Then we use the in-hospital data (target domain) and the national stroke screening data (source domain), to develop risk prediction models applying instance filter and weight-based transfer learning method. To verify our method, 205 cases from a tertiary hospital and 2954 cases from the screening cohort (2015-2017) are tested. Experimental results show that our framework can improve the performance of three instance-based transfer algorithms. This study provides a comprehensive and efficient approach for applying transfer learning, to alleviate the limitation of insufficient labeled follow-up data in hospitals.

Additive interaction between potentially modifiable risk factors and ethnicity among individuals in the Han, Tujia and Miao populations with first-ever ischaemic stroke

BACKGROUND

As a country with one-fifth of the global population, China has experienced explosive growth in ischaemic stroke (IS) burden with significant ethnic and geographic disparities. The aim of this study was to examine the differences in potentially modifiable risk factors for ischaemic stroke among the Han population and two ethnic minorities (Tujia and Miao).

METHODS

A case-control study was conducted with 324 cases of first-ever ischaemic stroke from the hospitals of the Xiangxi Tujia and Miao Autonomous Prefecture and 394 controls from communities covering the same area between May 1, 2018, and April 30, 2019. Structured questionnaires were administered, and physical examinations were performed in the same manner for cases and controls. Univariate and multivariate logistic regression analyses with adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were used to examine the association between risk factors and ischaemic stroke. An additive model was used to study the interaction between the modifiable risk factors and ethnicity with R software.

RESULTS

Higher high-sensitivity C-reactive protein levels (OR 50.54, 95%CI 29.76-85.85), higher monthly family income (4.18, 2.40-7.28), increased frequency of hot pot consumption (2.90, 1.21-6.93), diabetes mellitus (2.62, 1.48-4.62), a higher apolipoprotein (Apo)B/ApoA1 ratio (2.60, 1.39-4.85), hypertension (2.52, 1.45-4.40) and moderate-intensity physical activity (0.50, 0.28-0.89) were associated with ischaemic stroke. There was an additive interaction between the ApoB/ApoA1 ratio and ethnicity in the Tujia and Miao populations with first-ever ischaemic stroke (the relative excess risk due to the interaction was 5.75, 95% CI 0.58 ~ 10.92; the attributable proportion due to the interaction was 0.65, 95% CI 0.38 ~ 0.91; the synergy index was 3.66, 95% CI 1.35 ~ 9.93).

CONCLUSIONS

This is the first case-control study examining modifiable risk factors for ischaemic stroke among the Han population and two ethnic minorities (Tujia and Miao) in China. Some differences were observed in the impact of risk factors among these ethnic groups. Our results may help interpret health-related data, including surveillance and research, when developing strategies for stroke prevention.

Health care utilization for patients with stroke: a 3-year cross-sectional study of China's two urban health insurance schemes across four cities

Background

Stroke is a devastating disease and a major cause of death and disability in China. While existing studies focused mainly on differences in stroke patients’ health care utilization by insurance type, this study assesses whether health utilization and medical costs differed by insurance type across four cities in China.

Methods

A 5% random sample from the 2014–2016 China Urban Employees’ Basic Medical Insurance (UEBMI) and Urban Residents’ Basic Medical Insurance (URBMI) claims data were collected across four cities, Beijing, Shanghai, Tianjin, and Chongqing. Descriptive statistics and ordinary least squares regression were employed to analyze the data.

Results

We found that differences in healthcare utilization and inpatient and outpatient medical expenses varied more by city-specific insurance type than they did between the UEBMI and URBMI schemes. For example, the median UEBMI medical outpatient costs in Beijing (RMB500.2) were significantly higher than UEBMI patients in Shanghai (RMB260.8), Tianjin (RMB240.8), and Chongqing (RMB293.0), and Beijing URBMI patients had significantly higher outpatient medical costs (RMB356.9) than URBMI patients in Shanghai (RMB233.4) and Chongqing (RMB211.0), which were significantly higher than Tianjin (RMB156.2). Patients in Chongqing had 66.4% (95% CI: − 0.672, − 0.649) fewer outpatient visits, 13.0% (95% CI: − 0.144, − 0.115) fewer inpatient visits, and 34.2% (95% CI: − 0.366, − 0.318) shorter length of stay than patients in Beijing. The divergence of average length of stay and out-of-pocket (OOP) expenses by insurance type was also greater between cities than the UEMBI-URBMI mean difference.

Conclusions

Significant city-specific differences in stroke patients’ healthcare utilization and medical costs reflected inequalities in health care access. The fragmented social health insurance schemes in China should be consolidated to provide patients in different cities equal financial protection and benefit packages and to improve the equity of stroke patient access to health care.

Risk Factors for Incident Stroke and Its Subtypes in China: A Prospective Study

Background

Managing risk factors is crucial to prevent stroke. However, few cohort studies have evaluated socioeconomic factors together with conventional factors affecting incident stroke and its subtypes in China.

Methods and Results

A 2014 to 2016 prospective study from the China National Stroke Screening and Intervention Program comprised 437 318 adults aged ≥40 years without stroke at baseline. There were 2429 cases of first‐ever stroke during a median follow‐up period of 2.1 years, including 2206 ischemic strokes and 237 hemorrhagic strokes. The multivariable Cox regression analysis indicated that age 50 to 59 years (versus 40–49 years), primary school or no formal education (versus middle school), having >1 child (versus 1 child), living in Northeast, Central, East, or North China (versus Southwest China), physical inactivity, hypertension, diabetes mellitus, and obesity were positively associated with the risk of total and ischemic stroke, whereas age 60 to 69 years and living with spouse or children (versus living alone) were negatively associated with the risk of total and ischemic stroke. Men, vegetable‐based diet, underweight, physical inactivity, hypertension, living in a high‐income region, having Urban Resident Basic Medical Insurance, and New Rural Cooperative Medical System were positively associated with the risk of hemorrhagic stroke, whereas age 60 to 69 years was negatively associated with the risk of hemorrhagic stroke.

Conclusions

We identified socioeconomic factors that complement traditional risk factors for incident stroke and its subtypes, allowing targeting these factors to reduce stroke burden.

What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions

Background

Integrated data repositories (IDRs), also referred to as clinical data warehouses, are platforms used for the integration of several data sources through specialized analytical tools that facilitate data processing and analysis. IDRs offer several opportunities for clinical data reuse, and the number of institutions implementing an IDR has grown steadily in the past decade.

Objective

The architectural choices of major IDRs are highly diverse and determining their differences can be overwhelming. This review aims to explore the underlying models and common features of IDRs, provide a high-level overview for those entering the field, and propose a set of guiding principles for small- to medium-sized health institutions embarking on IDR implementation.

Methods

We reviewed manuscripts published in peer-reviewed scientific literature between 2008 and 2020, and selected those that specifically describe IDR architectures. Of 255 shortlisted articles, we found 34 articles describing 29 different architectures. The different IDRs were analyzed for common features and classified according to their data processing and integration solution choices.

Results

Despite common trends in the selection of standard terminologies and data models, the IDRs examined showed heterogeneity in the underlying architecture design. We identified 4 common architecture models that use different approaches for data processing and integration. These different approaches were driven by a variety of features such as data sources, whether the IDR was for a single institution or a collaborative project, the intended primary data user, and purpose (research-only or including clinical or operational decision making).

Conclusions

IDR implementations are diverse and complex undertakings, which benefit from being preceded by an evaluation of requirements and definition of scope in the early planning stage. Factors such as data source diversity and intended users of the IDR influence data flow and synchronization, both of which are crucial factors in IDR architecture planning.

Using machine learning models to classify stroke risk level based on national screening data

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 risk factors and conducts high-risk population interventions for people aged over 40 years old all over China. In this program, stroke risk factors include hypertension, diabetes, dyslipidemia, atrial fibrillation, smoking, lack of exercise, apparently overweight or obese and family history of stroke. People with more than two risk factors or with a history of stroke or transient ischemic attack (TIA) are considered as high-risk. However, it is impossible for this criterion to classify stroke risk level for people with "unknown" values in the fields of risk factors. The missing of stroke risk levels results in reduced efficiency of stroke interventions and inaccuracies in the statistical results at the national level. In this paper, firstly, we construct the training set and test set and process the imbalanced training set based on oversampling and undersampling method. Then, we develop logistic regression model, decision tree model, neural network model and random forest model for stroke risk classification, and evaluate these models based on the recall and precision. The results show that the model based on random forest achieves best performance considering recall and precision. The models constructed in this paper can improve the screening efficiency and avoid unnecessary rescreening and intervention expenditures.

Using machine learning models to improve stroke risk level classification methods of China national stroke screening

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.

Utilisation of Electronic Health Records for Public Health in Asia: A Review of Success Factors and Potential Challenges

INTRODUCTION

Electronic health records offer a valuable resource to improve health surveillance and evaluation as well as informing clinical decision making. They have been introduced in many different settings, including low- and middle-income countries, yet little is known of the progress and effectiveness of similar information systems within Asia. This study examines the implementation of EHR systems for use at a population health level in Asia and to identify their current role within public health, key success factors, and potential barriers in implementation.

MATERIAL AND METHODS

A systematic search process was implemented. Five databases were searched with MeSH key terms and Boolean phrases. Articles selected for this review were based on hospital provider electronic records with a component of implementation, utilisation, or evaluation for health systems or at least beyond direct patient care. A proposed analytic framework considered three interactive components: the content, the process, and the context.

RESULTS

Thirty-two articles were included in the review. Evidence suggests that benefits are significant but identifying and addressing potential challenges are critical for success. A comprehensive preparation process is necessary to implement an effective and flexible system.

DISCUSSION

Electronic health records implemented for public health can allow the identification of disease patterns, seasonality, and global trends as well as risks to vulnerable populations. Addressing implementation challenges will facilitate the development and efficacy of public health initiatives in Asia to identify current health needs and mitigate future risks.

Discover high-risk factor combinations using Bayesian network from cohort data of National Stoke Screening in China

BACKGROUND

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 the national stroke screening and intervention program in 2011 for stroke prevention and control. In the screening, only those who have been classified to "potential high-risk" group in preliminary screening need further examination and physician confirmation to determine the risk level of stroke in rescreening. However, at the beginning of the program, the "potential high-risk" classification method in the preliminary screening are determined by experts based on their experience. The primary aim of this study is to study the causality of stroke and risk factors in middle-aged population using the cohort data, and to explore whether the stroke screening and intervention program should include more precise "potential high-risk" evaluation criteria for this age group in preliminary screening.

METHOD

We use the cohort data of screening between 2013 and 2017 in this study. After data cleaning, the cohort consists of 48,007 people aged from 40 to 59 who are free of stroke at baseline. We use Bayesian networks to develop models.

RESULT

The results show that the stroke incidence in middle-aged population with certain two risk factors is higher than some of that with three factors, which is in keeping with our previous study results. We can take the ratio of the stroke incidence with combinations of risk factors and incidence without any of the risk factors as a variable threshold. By adjusting the threshold, we can get precise stroke preliminary screening criteria to achieve a balance between economy and efficiency.

CONCLUSION

We find that the criteria used in preliminary screening are not reasonable enough. There is a need for national stroke screening and intervention program to further include some more important risk factors or combinations of two risk factors as classification criteria in the preliminary screening. The results of the study can directly guide stroke screening program in China to make the screening more accurate and efficient.