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155 results on '"Hou, Yabing"'

1. Leveraging Internet Search Data to Improve the Prediction and Prevention of Noncommunicable Diseases: Retrospective Observational Study

Author

Xu, Chenjie, Cao, Zhi, Yang, Hongxi, Gao, Ying, Sun, Li, Hou, Yabing, Cao, Xinxi, Jia, Peng, and Wang, Yaogang

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundAs human society enters an era of vast and easily accessible social media, a growing number of people are exploiting the internet to search and exchange medical information. Because internet search data could reflect population interest in particular health topics, they provide a new way of understanding health concerns regarding noncommunicable diseases (NCDs) and the role they play in their prevention. ObjectiveWe aimed to explore the association of internet search data for NCDs with published disease incidence and mortality rates in the United States and to grasp the health concerns toward NCDs. MethodsWe tracked NCDs by examining the correlations among the incidence rates, mortality rates, and internet searches in the United States from 2004 to 2017, and we established forecast models based on the relationship between the disease rates and internet searches. ResultsIncidence and mortality rates of 29 diseases in the United States were statistically significantly correlated with the relative search volumes (RSVs) of their search terms (P

Published
2020
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2. Detecting Lung Cancer Trends by Leveraging Real-World and Internet-Based Data: Infodemiology Study

Author

Xu, Chenjie, Yang, Hongxi, Sun, Li, Cao, Xinxi, Hou, Yabing, Cai, Qiliang, Jia, Peng, and Wang, Yaogang

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundInternet search data on health-related terms can reflect people’s concerns about their health status in near real time, and hence serve as a supplementary metric of disease characteristics. However, studies using internet search data to monitor and predict chronic diseases at a geographically finer state-level scale are sparse. ObjectiveThe aim of this study was to explore the associations of internet search volumes for lung cancer with published cancer incidence and mortality data in the United States. MethodsWe used Google relative search volumes, which represent the search frequency of specific search terms in Google. We performed cross-sectional analyses of the original and disease metrics at both national and state levels. A smoothed time series of relative search volumes was created to eliminate the effects of irregular changes on the search frequencies and obtain the long-term trends of search volumes for lung cancer at both the national and state levels. We also performed analyses of decomposed Google relative search volume data and disease metrics at the national and state levels. ResultsThe monthly trends of lung cancer-related internet hits were consistent with the trends of reported lung cancer rates at the national level. Ohio had the highest frequency for lung cancer-related search terms. At the state level, the relative search volume was significantly correlated with lung cancer incidence rates in 42 states, with correlation coefficients ranging from 0.58 in Virginia to 0.94 in Oregon. Relative search volume was also significantly correlated with mortality in 47 states, with correlation coefficients ranging from 0.58 in Oklahoma to 0.94 in North Carolina. Both the incidence and mortality rates of lung cancer were correlated with decomposed relative search volumes in all states excluding Vermont. ConclusionsInternet search behaviors could reflect public awareness of lung cancer. Research on internet search behaviors could be a novel and timely approach to monitor and estimate the prevalence, incidence, and mortality rates of a broader range of cancers and even more health issues.

Published
2020
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20. Physical Activity, Mental Activity, and Risk of Incident Stroke:A Prospective Cohort Study

Author

Cao, Zhi, Zhang, Jing, Lu, Zuolin, Chen, Han, Min, Jiahao, Hou, Yabing, Wang, Xiaohe, Xu, Chenjie, Cao, Zhi, Zhang, Jing, Lu, Zuolin, Chen, Han, Min, Jiahao, Hou, Yabing, Wang, Xiaohe, and Xu, Chenjie

Abstract

BACKGROUND: Cumulative evidence suggests a correlation between physical or mental activity and the risk of stroke. However, the combined impact of these activities on stroke onset remains unexplored. This study identified physical and mental activity patterns using principal component analysis and investigated their associations with risk of incident stroke in the general population. METHODS: Our study was sourced from the UK Biobank cohort between 2006 and 2010. Information on physical and mental-related activities were obtained through a touch-screen questionnaire. The incident stroke was diagnosed by physicians and subsequently verified through linkage to Hospital Episode Statistics. Principal component analysis was used to identify potential physical and mental activity patterns. Cox proportional hazard regression models were performed to calculate hazard ratios (HRs) and 95% CIs of incident stroke, adjusting for potential confounders. RESULTS: The initial UK Biobank cohort originally consisted of 502 411 individuals, of whom a total of 386 902 participants (aged 38-79 years) without any history of stroke at baseline were included in our study. During a median follow-up of 7.7 years, 6983 (1.8%) cases of stroke were documented. The mean age of the included participants was 55.9 years, and the proportion of women was 55.1%. We found that multiple individual items related to physical and mental activity showed significant associations with risk of stroke. We identified 4 patterns of physical activity and 3 patterns of mental activity using principal component analysis. The adherence to activity patterns of vigorous exercise, housework, and walking predominant patterns were associated with a lower risk of stroke by 17% (HR, 0.83 [95% CI, 0.78-0.89]; 20% (HR, 0.80 [95% CI, 0.75-0.85]; and 20% (HR, 0.80 [95% CI, 0.75-0.86), respectively. Additionally, the transportation predominant pattern (HR, 1.36 [95% CI

Published
2024

29. Additional file 1 of The role of type 2 diabetes in the association between habitual glucosamine use and dementia: a prospective cohort study

Author

Xu, Chenjie, Hou, Yabing, Fang, Xuexian, Yang, Hongxi, and Cao, Zhi

Abstract

Additional file 1: Supplementary Table 1. List of ICD-10 codes for dementia. Supplementary Table 2. Association of habitual glucosamine use and risk of dementia stratified by APOE ε4 status. Supplementary Table 3. Association of habitual glucosamine use and risk of dementia stratified by potential risk factors. Supplementary Table 4. Associations of habitual glucosamine use and the risk of Alzheimer’s disease and vascular dementia stratified by potential risk factors. Supplementary Table 5. Association of glucosamine supplement use and risk of dementia after excluding participants with limited follow-up years (≤ 2 years). Supplementary Table 6. Association of glucosamine supplement use and risk of dementia in completed-case analysis (n = 448,043) and multiple imputation samples. Supplementary Table 7. Association of glucosamine supplement use and risk of dementia in compete risk models. Supplementary Figure 1. Flowchart of participant enrolment.

Published
2023
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33. Additional file 1 of Association of cardiovascular health with diabetic complications, all-cause mortality, and life expectancy among people with type 2 diabetes

Author

Zhang, Yuan, Yang, Rongrong, Hou, Yabing, Chen, Yanchun, Li, Shu, Wang, Yaogang, and Yang, Hongxi

Abstract

Additional file 1: Table S1. Definition of cardiovascular health (CVH) metrics. Table S2. Detailed information on missing covariates. Table S3. Hazard ratios (HRs) and 95% confidence interval (CI) of diabetic complications by weight cardiovascular health (CVH) among people with type 2 diabetes. Table S4. Hazard ratios (HRs) and 95% confidence interval (CI) of all-cause mortality by weight cardiovascular health (CVH) among people with type 2 diabetes. Table S5. Hazard ratios (HRs) and 95% confidence interval (CI) of diabetic complications by cardiovascular health (CVH) among people with type 2 diabetes after excluding first 3 years incidence of diabetic complications during follow-up. Table S6. Hazard ratios (HRs) and 95% confidence interval (CI) of mortality by cardiovascular health (CVH) among people with type 2 diabetes after excluding those who died within the first 3-year follow-up period. Table S7. Hazard ratios (HRs) and 95% confidence interval (CI) of diabetes complications by cardiovascular health (CVH) among people with type 2 diabetes after further adjustment for diabetes duration. Table S8. Hazard ratios (HRs) and 95% confidence interval (CI) of all-cause mortality by weight cardiovascular health (CVH) among people with type 2 diabetes after further adjustment for diabetes duration. Figure S1. Flowchart for the selection of the analyzed study sample from the UK Biobank study. Figure S2. Hazard ratios (HRs) of diabetic complications and all-cause mortality per 1-number increment in ideal cardiovascular health (CVH) metrics according to stratification categories among participants with type 2 diabetes. Figure S3. Years of life expectancy lost by cardiovascular health (CVH) metrics among people with type 2 diabetes after further adjustment for diabetes duration. Figure S4. Years of life expectancy lost by cardiovascular health (CVH) metrics among people with and without type 2 diabetes.

Published
2022
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39. Long-Term Trends in Stroke Management and Burden Among Low-Income Women in a Rural Area From China (1992–2019): A Prospective Population-Based Study

Author

Tian, Xiaobing, primary, Liu, Jie, additional, Yu, Changshen, additional, Hou, Yabing, additional, Zhan, Changqing, additional, Lin, Qiuxing, additional, Zhang, Xinyu, additional, Zhang, Xin, additional, Guo, Dandan, additional, Yang, Qiaoxia, additional, Tu, Jun, additional, Wang, Yaogang, additional, Ning, Xianjia, additional, and Wang, Jinghua, additional

Published
2021
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