健康医疗大数据驱动的健康管理学理论方法体系

doi:10.6040/j.issn.1671-7554.0.2017.430

摘要/Abstract

摘要： 当今互联网、云计算和物联网技术的成熟和发展,医疗/卫生信息化的广泛普及,使得医疗/卫生相关数据正在以惊人的速度增长;同时,组学技术(基因组、影像组等)的推广应用,以及可穿戴移动医疗与移动健康技术的迅猛发展,促使健康医疗领域快速进入“大数据”时代;“健康医疗大数据驱动的健康管理”的新时代已经来临,即大数据驱动的健康/疾病管理实践已经成为现实。基于此,创建了涵盖健康/疾病检测、风险评估与干预的“大数据驱动的健康/疾病管理学的理论方法体系”。该体系包括健康医疗大数据背景下生命历程流行病学与暴露组学理论指导的健康/疾病管理学理论框架与概念模型、健康/疾病检测指标筛选及证据获取的理论方法、健康/疾病风险评估的理论方法、制定健康/疾病干预策略的理论方法。该体系对于指导大数据驱动的健康/疾病管理的理论和实践、推进健康/疾病管理学转型升级和健康医疗大数据产业化发展具有理论和实际意义。

关键词: 健康医疗大数据, 健康/疾病管理学, 理论方法体系, 健康/疾病检测, 风险评估, 干预策略

Abstract: Todays Internet, cloud computing and networking technology is mature and developed, and the widespread adoption of medical/health information makes medical/health-related data grow at a staggering rate. At the same time, 山东大学学报 (医学版)55卷6期 -薛付忠.健康医疗大数据驱动的健康管理学理论方法体系 \=-the popularization and application of omics technology(genome, radiomics, etc.)and the rapid development of wearable mobile medical care and mobile health technologies have promoted health care filed quickly into the “big data” era; This suggests that a new era for “health care big data-driven health/disease management” has come, that is, big data-driven health/disease management practices have become a reality. Based on this, we have developed a “health care big data-driven theory and methodology of health/disease management” that covers health/disease detection, risk assessment and intervention. The system includes the health/disease management theoretical framework and conceptual model by the guidance of life course epidemiology and exposome theory under the background of healthcare big data, health/disease detection index screening and evidence acquisition methods, health/disease risk assessment methods and the development of a theoretical approach to health/disease intervention strategies. It is of great theoretical and practical significance to guide the theory and practice development of big data-driven health/disease management, promote the transformation and upgrading of health/disease management and the industrialization of healthcare big data.

Key words: health/disease management, intervention strategy, theory and methodology, health/disease detection, healthcare big data, risk assessment

中图分类号:

薛付忠. 健康医疗大数据驱动的健康管理学理论方法体系[J]. 山东大学学报（医学版）, 2017, 55(6): 1-29.

XUE Fuzhong. Healthcare big data-driven theory and methodology for health management[J]. JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES), 2017, 55(6): 1-29.

参考文献

[1] 中华医学会健康管理学分会. 健康管理概念与学科体系的中国专家初步共识[J]. 中华健康管理学杂志, 2009, 3(3): 141-147. Chinese Health Management Association Editorial Board of Chinese Journal of Health Management. Expert consensus on definition and discipline system of health management[J]. China Health Management Journal, 2009, 3(3): 141-147.
[2] Roger SB, JoAnne MF, Nathan DW, 等. 预防心脏病: Braunwald心脏病学姊妹篇[M]. 北京: 北京大学医学出版社, 2013.
[3] Mortimer JT, Shanahan MJ. Handbook of the life course[M]. New York: Springer Science & Business Media, 2003.
[4] Kuh D, Ben-Shlomo Y, Lynch J, et al. Life Course Epidemiology[J]. J Epidemiol Community Health, 2015, 57(10): 67-75.
[5] Wild CP. Complementing the genome with an “exposome”: The outstanding challenge of environmental exposure measurement in molecular epidemiology[J]. Cancer Epidemiol Biomarkers Prev, 2005, 14(8): 1847-1850.
[6] Wild CP. The exposome: from concept to utility[J]. Int J Epidemiol, 2012, 41(1): 24-32.
[7] Wu Y, Liu X, Li X, et al. Estimation of 10-year risk of fatal and nonfatal ischemic cardiovascular diseases in Chinese adults.[J]. Circulation, 2006, 114(21): 2217-2225.
[8] Cai X. Lifetime risks of cardiovascular disease[J]. N Engl J Med, 2012, 366(17): 321-329.
[9] Stamatakis E, Hamer M, Mishra GD. Early adulthood television viewing and cardiometabolic risk profiles in early middle age: results from a population, prospective cohort study[J]. Diabetologia, 2012, 55(2): 311-320.
[10] Weintraub WS, Daniels SR, Burke LE, et al. Value of primordial and primary prevention for cardiovascular disease: a policy statement from the American Heart Association.[J]. Circulation, 2011, 124(8): 967. doi: 10.1161/CIR.0b013e3182285a81.
[11] Dzau VJ, Antman EM, Black HR, et al. The cardiovascular disease continuum validated: clinical evidence of improved patient outcomes: part I: Pathophysiology and clinical trial evidence(risk factors through stable coronary artery disease)[J] Circulation, 2006, 114(25):2850-2870.
[12] O'Rourke MF, Safar ME, Dzau V. The cardiovascular continuum extended: aging effects on the aorta and microvasculature[J]. Vasc Med, 2010, 15(6): 461-468.
[13] Vassiliadis E, Barascuk N, Didangelos A, et al. Novel cardiac-specific biomarkers and the cardiovascular continuum[J]. Biomarker Insights, 2012, 7(7): 45-57.
[14] Duncan SC, Duncan TE, Hops H. Analysis of longitudinal data within accelerated longitudinal designs[J]. Psychological Methods, 1996, 1(3): 236-248.
[15] 唐文清, 张敏强, 黄宪,等. 加速追踪设计的方法和应用[J]. 心理科学进展, 2014(2): 369-380.
[16] 刘娅飞,邢娉,徐秀琴,等.山东多中心健康管理纵向观察队列[J].山东大学学报(医学版),2017,55(6):30-36. LIU Yafei, XING Ping, XU Xiuqin, et al. Shandong multi-center longitudinal cohort for health management: a brief introduction[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 30-36.
[17] Tibshirani RJ. Regression shrinkage and selection via the LASSO[J]. J R Stat Soc B, 1996, 58: 267-288.
[18] Hoerl AE, Kennard RW. Ridge Regression: based estimation for nonorthogonal problems[J]. Technometrics, 2000, 42(1): 55-67.
[19] Fan J, Li R. Variable selection via nonconcave penalized likelihood and its oracle properties[J]. J Am Statist Ass, 2001, 96(456): 1348-1360.
[20] Zou H, Hastie T. Regularization and variable selection via the elastic net[J]. J R Stat Soc B, 2005, 67(2): 301-320.
[21] Wang S, Zhang C, Zhang G, et al. Association between white blood cell count and non-alcoholic fatty liver disease in urban Han Chinese: a prospective cohort study[J]. Bmj Open, 2016, 6(6). doi: 10.1136/bmjopen-2015-010342.
[22] Ding L, Zhang C, Zhang G, et al. A new insight into the role of plasma fibrinogen in the development of metabolic syndrome from a prospective cohort study in urban Han Chinese population[J]. Diabetol Metab Syndr, 2015, 7(1): 110. doi: 10.1186/s13098-015-0103-7.
[23] Zhu Z, Liu Y, Zhang C, et al. Identification of cardiovascular risk components in urban Chinese with metabolic syndrome and application to coronary heart disease prediction: A Longitudinal Study[J]. Plos One, 2013, 8(12): 84204. doi: 10.1371/journal.pone.0084204.
[24] Zhang W, Wang L, Chen Y, et al. Identification of hypertension predictors and application to hypertension prediction in an urban Han Chinese population: A Longitudinal Study, 2005-2010[J]. Prev Chronic Dis, 2015, 12(10): 184. doi: 10.5888/pcd12.150192.
[25] 于涛,刘焕乐,冯新,等.基于健康管理队列的高血压风险预测模型[J].山东大学学报(医学版),2017,55(6):61-65. YU Tao, LIU Huanle, FENG Xin, et al. A hypertension risk prediction model based on health management cohort[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 61-65.
[26] Zhang W, Chen Q, Yuan Z, et al. A routine biomarker-based risk prediction model for metabolic syndrome in urban Han Chinese population.[J]. BMC Public Health, 2015, 15(1): 1-9.
[27] 孙苑潆,杨亚超,曲明苓,等.健康管理人群代谢综合征发病风险预测模型[J].山东大学学报(医学版),2017,55(6): 87-92. SUN Yuanying, YANG Yachao, QU Mingling, et al. A prediction model for metabolic syndrome risk: a study based on the health management cohort[J].Journal of Shandong University(Health Sciences), 2017, 55(6): 87-92.
[28] Ding L, Li J, Wang C, et al. Incidence of atrial fibrillation and its risk prediction model based on a prospective urban Han Chinese cohort.[J]. J Hum Hypertens, 2017. doi: 10.1038/jhh.2017.23.
[29] Cao J, Wang C, Zhang G, et al. Incidence and simple prediction model of hyperuricemia for urban Han Chinese adults: A prospective cohort study:[J]. Int J Environ Res Public Health, 2017, 14(1). doi: 10.3390/ijerph14010067.
[30] 周苗,夏同耀,孙爱玲,等.健康管理人群慢性肾脏病风险预测模型[J].山东大学学报(医学版),2017,55(6):98-103. ZHOU Miao, XIA Tongyao, SUN Ailing,et al. Risk prediction model of chronic kidney disease in health management population[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 98-103.
[31] 苏萍,杨亚超,杨洋,等. 健康管理人群2型糖尿病发病风险预测模型[J]. 山东大学学报(医学版),2017, 55(6): 82-86. SU Ping, YANG Yachao, YANG Yang, et al. Prediction models on the onset risks of type 2 diabetes among the health management population[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 82-86.
[32] 张振堂,杨洋,韩福俊,等.基于社区2型糖尿病患者的心脑血管事件5年风险预测模型[J].山东大学学报(医学版),2017,55(6): 108-113. ZHANG Zhentang, YANG Yang, Han Fujun, et al. A cardiovascular risk score system for type 2 diabetes: a prospective cohort study among Chinese community population[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 108-113.
[33] 李江冰,宋心红,林海燕,等.健康管理人群缺血性异常心电图的影响因素[J].山东大学学报(医学版),2017,55(6): 77-81. LI Jiangbing, SONG Xinhong, LIN Haiyan, et al. The influencing factors of ischemic ECG abnormalities in a large health check-up population[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 77-81.
[34] 许艺博,季晓康,李向一,等. 尿液pH与代谢综合征的相关性。山东大学学报(医学版),2016,54(12): 82-85. XU Yibo, JI Xiaokang, LI Xiangyi, et al. Analysis on the relationship between urine pH and metabolic syndrome. Journal of Shandong University(Health Sciences), 2016, 54(12): 82-85.
[35] 于媛媛,王春霞,苏萍,等. 健康管理队列白内障发病风险预测模型[J]. 山东大学学报(医学版),2017,55(6): 104-107. YU Yuanyuan, WANG Chunxia, SU Ping, et al. A prediction model to estimate risks of cataract based on health management cohort[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 104-107.
[36] 柳晓涓,蒋正,康凤玲,等. 中性粒细胞计数与非酒精性脂肪肝关联性的前瞻性队列研究[J]. 山东大学学报(医学版),2017,55(6): 119-123. LIU Xiaojuan, JIANG Zheng, KANG Fengling, et al. Association between neutrophil count and nonalcoholic fatty liver disease: a prospective cohort study[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 119-123.
[37] 张光,王广银,吴红彦,等. 健康管理人群高脂血症风险预测模型[J]. 山东大学学报(医学版),2017,55(6): 72-76. ZHANG Guang, WANG Guangyin,WU Hongyan, et al. A prediction model of hyperlipidemia risk based on the health management population[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 72-76.
[38] 李向一,孙秀彬,鹿玉莲,等. 子宫肌瘤与乳腺增生关联性的队列研究[J]. 山东大学学报(医学版),2016,54(9): 53-58. LI Xiangyi, SUN Xiubin, LU Yulian, et al. Relationship between uterine fibroid and hyperplasia of mammary glands: a cohort study[J]. Journal of Shandong University(Health Sciences), 2016, 54(9): 53-58.
[39] 曹瑾,季晓康,孙秀彬,等. γ-谷氨酰转移酶与高尿酸血症关系的队列分析[J]. 山东大学学报(医学版),2017,55(6): 124-128. CAO Jin, JI Xiaokang, SUN Xiubin, et al. The relationship between γ-glutamyltransferase and hyperuricemia: a cohort study[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 124-128.
[40] Qian Z, Sun X, Ji X, et al. The association between gallstones and metabolic syndrome in urban Han Chinese: a longitudinal cohort study[J]. Sci Rep, 2016, 6: 29937. doi: 10.1038/srep29937.
[41] Liu J, Lin H, Zhang C, et al. Non-alcoholic fatty liver disease associated with gallstones in females rather than males: a longitudinal cohort study in Chinese urban population[J]. BMC Gastroenterol, 2014, 14(1): 1-7.
[42] Zhang T, Zhang C, Zhang Y, et al. Metabolic syndrome and its components as predictors of nonalcoholic fatty liver disease in a northern urban Han Chinese population: a prospective cohort study.[J]. Atherosclerosis, 2015, 240(1): 144-148.
[43] Zhang T, Zhang Y, Zhang C, et al. Prediction of metabolic syndrome by non-alcoholic fatty liver disease in northern urban Han Chinese population: a prospective cohort study.[J]. PLoS One, 2014, 9(5): 96651. doi: 10.1371/journal.pone.0096651.
[44] Zhang Y, Zhang T, Zhang C, et al. Identification of reciprocal causality between non-alcoholic fatty liver disease and metabolic syndrome by a simplified Bayesian network in a Chinese population[J]. BMJ Open, 2015, 5(9): 008204. doi: 10.1136/bmjopen-2015-008204.
[45] Wu S, Lin H, Zhang C, et al. Association between erythrocyte parameters and metabolic syndrome in urban Han Chinese: A longitudinal cohort study[J]. BMC Public Health, 2013, 13(1): 989. doi: 10.1186/1471-2458-13-989.
[46] Meng W, Zhang C, Zhang Q, et al. Association between leukocyte and metabolic syndrome in urban Han Chinese: A longitudinal cohort study[J]. PLoS One, 2012, 7(11): 49875. doi: 10.1371/journal.pone.0049875.
[47] Zhang Q, Zhang C, Song X, et al. A longitudinal cohort based association study between uric acid level and metabolic syndrome in Chinese Han urban male population[J]. BMC Public Health, 2012, 12(1): 1-8.
[48] Wilson J, Jungner G, Wilson J, et al. Principles and practice of screening for disease[J]. WHO Chron, 1967, 22(11): 318.
[49] Beiser A, D'Agostino RB, Seshadri S, et al. Computing estimates of incidence, including lifetime risk: Alzheimers disease in the Framingham Study. The Practical Incidence Estimators(PIE)macro[J]. Stat Med, 2000, 19(11-12): 1495-1522.
[50] Seshadri S, Wolf PA. Lifetime risk of stroke and dementia: current concepts, and estimates from the Framingham Study[J]. Lancet Neurol, 2008, 6(12): 1106-1114.
[51] Ishikawa S, Matsumoto M, Kayaba K, et al. Risk charts illustrating the 10-year risk of stroke among residents of Japanese rural communities: The JMS cohort study[J]. J Epidemiol, 2009, 19(2): 101-106.
[52] Borglykke A, Andreasen AH, Kuulasmaa K, et al. Stroke risk estimation across nine European countries in the MORGAM project[J]. Heart, 2010, 96(24): 1997-2004.
[53] Chien KL, Su TC, Hsu HC, et al. Constructing the prediction model for the risk of stroke in a Chinese population[J]. Stroke, 2010, 41(9): 1858-1864.
[54] Liu J, Hong Y, D'Agostino Sr RB, et al. Predictive value for the Chinese population of the Framingham CHD risk assessment tool compared with the Chinese Multi-Provincial Cohort Study[J]. Jama, 2004, 291(21): 2591-2599.
[55] Gaziano TA, Young CR, Fitzmaurice G, et al. Laboratory-based versus non-laboratory-based method for assessment of cardiovascular disease risk: the NHANES I Follow-up Study cohort.[J]. Lancet, 2008, 371(9616): 923-931.
[56] Emerging Risk Factors Collaboration(ERFC), Di Angelantonio E, Sarwar N, et al. Major lipids, apolipoproteins, and risk of vascular disease[J]. JAMA, 2009, 302(18): 1993-2000.
[57] Kaptoge S, Di AE, Lowe G, et al. C-reactive protein concentration and risk of coronary heart disease, stroke, and mortality: an individual participant meta-analysis[J]. Lancet, 2010, 375(9709): 132-140.
[58] Meschia JF, Worrall BB, Rich SS. Genetic susceptibility to ischemic stroke[J]. Nat Rev Neurol, 2011, 7(7): 369-378.
[59] Kim J, Chae YK. Genomewide association studies of stroke[J]. N Engl J Med, 2009, 50(361): 722. doi: 10.1056/NEJMc091089.
[60] Gail MH. Personalized estimates of breast cancer risk in clinical practice and public health.[J]. Stat Med, 2011, 30(10): 1090. doi: 10.1002/sim.4187.
[61] Gail MH, Brinton LA, Byar DP, et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually[J]. J Natl Cancer Inst, 1989, 81(24): 1879-1886.
[62] Jason PF, Robert JG. A proportional hazards model for the subdistribution of a competing risk[J[. J Am Stat Assoc, 1999, 94(446):496-509.
[63] Moons KG, Kengne AP, Grobbee DE, et al. Risk prediction models: II. External validation, model updating, and impact assessment.[J]. Heart, 2012, 98(9): 691.doi: 10.1136/heartjnl-2011-30 1247.
[64] Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation.[J]. Eur Heart J, 2014, 35(29): 1925. doi: 10.1093/eurheartj/ehu207.
[65] 王停停,王金涛,袁中尚,等. 原因别竞争风险模型及其在健康风险评估中的应用[J]. 山东大学学报(医学版),2017,55(6):42-46. WANG Tingting, WANG Jintao, YUAN Zhongshang, et al. Cause-specific hazard model and its applications in health risk assessment[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 42-46.
[66] Wolbers M, Koller MT, Witteman JC, et al. Prognostic models with competing risks: methods and application to coronary risk prediction.[J]. Epidemiology, 2009, 20(20): 555-561.
[67] 王金涛,苏萍,袁中尚,等. 部分分布竞争风险模型及其在健康风险评估中的应用[J]. 山东大学学报(医学版),2017,55(6): 37-41. WANG Jintao, SU Ping, YUAN Zhongshang, et al. Sub-distribution hazard model and its applications in health risk assessment[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 37-41.
[68] 李敏,王春霞,夏冰,等. 健康管理人群脑卒中风险预测模型[J]. 山东大学学报(医学版),2017,55(6): 93-97. LI Min, WANG Chunxia, XIA Bing, et al. A stroke prediction model for the health management population[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 93-97.
[69] 李吉庆,赵焕宗,宋炳红,等. 基于健康管理队列的心血管事件风险预测模型[J]. 山东大学学报(医学版),2017,55(6): 56-60. LI Jiqing, ZHAO Huanzong, SONG Binghong, et al. Risk prediction model of cardiovascular disease based on health management cohort[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 56-60.
[70] 王春霞,许艺博,杨宁,等. 健康管理人群冠心病风险预测模型[J]. 山东大学学报(医学版), 2017, 55(6): 66-71. WANG Chunxia, XU Yibo, YANG Ning, et al. A prediction model for coronary heart disease risks in health management population[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 66-71.
[71] Friedman J, Hastie T, Tibshirani R. The elements of statistical learning[M]. Berlin; Springer Series in Statistics, 2001.
[72] Chen S, Grant E, Wu TT, et al. Statistical Learning Methods for Longitudinal High-dimensional Data[J]. Wiley Interdiscip Rev Comput Stat, 2014, 6(1): 10-18.
[73] Jajuga K, Sokolowski A, Bock HH. Classification, clustering and data analysis: recent advances and applications[M]. Berlin; Springer, 2002.
[74] Loh WY, Zheng W. Regression trees for longitudinal and multiresponse data[J]. Ann Appl Stat, 2013, 7(1): 495-522.
[75] Ramsay JO, Hooker G, Graves S. Functional data analysis with R and MATLAB[M]. New York; Springer, 2009.
[76] Ziegler, Alexandre. A game theory analysis of options: corporate finance and financial intermediation in continous time[M]. Berlin; Springer, 2004.
[77] Song G, Xue F, Zhang C. A model using texture features to differentiate the nature of thyroid nodules on sonography[J]. J Ultrasound Med, 2015, 34(10): 1753-1760.
[78] Li J, Liu R, Ji X, et al. Insight into the spectrum of coronary atherosclerosis in asymptomatic urban Han Chinese population by coronary computed tomography angiography[J]. PLoS One, 2015, 10(7): 0132188. doi: 10.1371/journal.pone.0132188.
[79] 蒋正, 申振伟, 张光,等. 非酒精性脂肪肝筛查模型[J].山东大学学报(医学版), 2017, 55(6): 114-118. JIANG Zheng, SHEN Zhenwei, ZHANG Guang, et al. A screening tool of non-alcoholic fatty liver disease based on health management subjects[J]. Journal of Shandong University(health Sciences), 2017, 55(6): 114-118.
[80] Splawa-Neyman J, Dabrowska DM, Speed TP. On the application of probability theory to agricultural experiments. Essay on principles. Section 9[J]. Statist Sci, 1990, 5(4): 465-472.
[81] Rubin DB. Estimating causal effects of treatments in randomized and nonrandomized studies[J]. J Educ Psychol, 1974, 66(5): 688-701.
[82] Robins J. A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect[J]. Math Comput Model, 1986, 7(9-12): 1393-1512.
[83] Sherman RE, Anderson SA, Dal Pan GJ, et al. Real-world evidence-what is it and what can it tell us?[J]. N Engl J Med, 2016, 375(23): 2293-2297.
[84] Hulley S, Grady D, Bush T, et al. Randomized trial of estrogen plus progestin for secondary prevention of coronary heart disease in postmenopausal women[J]. JAMA, 1998, 280(7): 605-613.
[85] Mamtani R, Lewis JD, Scott FI, et al. Disentangling the association between statins, cholesterol, and colorectal cancer: a nested case-control study[J]. PloS Med,2016, 13(4): e1002007. doi: 10.1371/journal.pmed.
[86] Rogeberg O. Correlations between cannabis use and IQ change in the Dunedin cohort are consistent with confounding from socioeconomic status[J]. Proc Natl Acad Sci U S A, 2013, 110(11): 4251. doi: 10.1073/pnas.1215678110.
[87] Miller M, Swanson SA, Azrael D. Are we missing something pertinent? A bias analysis of unmeasured confounding in the firearm-suicide literature[J]. Epidemiol Rev, 2016, 38(1). doi: 10.1093/epirev/mxv011.
[88] Wong CM, Lai HK, Tsang H, et al. Satellite-based estimates of long-term exposure to fine particles and association with mortality in elderly Hong Kong residents[J]. Environ Health Perspect, 2015, 123(11): 1167-1172.
[89] Thurston GD, Jiyoung A, Cromar KR, et al. Ambient particulate matter air pollution exposure and mortality in the NIH-AARP diet and health cohort[J]. Environ Health Perspect, 2016, 124(4): 484-490.
[90] Ostro B, Hu J, Goldberg D, et al. Associations of mortality with long-term exposures to fine and ultrafine particles, species and sources: results from the California teachers study cohort[J]. Environ Health Perspect, 2015, 123. doi: 10.1289/ehp.1408565.
[91] Miller KA, Siscovick DS, Sheppard L, et al. Long-term exposure to air pollution and incidence of cardiovascular events in women[J]. N Engl J Med, 2007, 356(5): 447-458.
[92] Milojevic A, Wilkinson P, Armstrong B. Short-term effects of air pollution on a range of cardiovascular events in England and Wales: case-crossover analysis of the MINAP database, hospital admissions and mortality(vol 100, pg 1093, 2013)[J]. Heart,2015, 101(2): 162. doi: 10.1136/heartjnl-2013-304963.
[93] Tseng E, Ho WC, Lin MH, et al. Chronic exposure to particulate matter and risk of cardiovascular mortality: cohort study from Taiwan[J]. BMC Public Health, 2015, 15(1): 936. doi: 10.1186/s12889-015-2272-6.
[94] Beelen R, Raaschou-Nielsen O, Stafoggia M, et al. Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project[J]. Lancet, 2014, 383(9919): 785-795.
[95] Zhang T, Zhang Y, Zhang C, et al. Prediction of metabolic syndrome by non-alcoholic fatty liver disease in northern urban Han Chinese population: a prospective cohort study.[J]. PloS one, 2014, 9(5): 96651. doi: 10.1371/journal.pone.0096651. eCollection 2014.
[96] Liang W, Zhao Y, Lee AH. An investigation of the significance of residual confounding effect.[J]. Biomed Res Int. 2014, 2014(2014): 658056. doi: 10.1155/2014/658056.
[97] Fewell Z, Davey Smith G, Sterne J AC. The impact of residual and unmeasured confounding in epidemiologic studies: a simulation study[J]. Am J Epidemiol, 2007, 166(6): 646-655.
[98] Sperrin M, Candlish J, Badrick E, et al. Collider bias is only a partial explanation for the obesity paradox[J]. Epidemiology, 2016, 27(4): 525-530.
[99] Greenland S, Brumback B. An overview of relations among causal modelling methods[J]. Int J Epidemiol, 2002, 31(5): 1030-1037.
[100] Pearl J. Causality: models, reasoning, and inference[M]. New York; Cambridge University Press, 2000.
[101] Petersen ML, Mj VDL. Causal models and learning from data: integrating causal modeling and statistical estimation[J]. Epidemiology, 2014, 25(3): 418-426.
[102] SAS/STAT^® 13.1 Users Guide. The GENMOD Procedure[M]. Cary, NC; SAS Institute Inc. 2013.
[103] Uddin MJ, Groenwold RHH, Ali MS, et al. Methods to control for unmeasured confounding in pharmacoepidemiology: an overview[J]. Int J Clin Pharm, 2016, 38(3): 714-723.
[104] Lance P. How Do We Know if a Program Made a Difference? A Guide to Statistical Methods for Program Impact Evaluation[M]. Chapel Hill, North Carolina: Measure Evaluation, 2014.
[105] Rosenbaum PR, Rubin DB. The central role of the propoensity score in observational studies for causal effects[J]. Biometrika, 1983, 70(1): 41-55.
[106] Liu W, Kuramoto SJ, Stuart EA. An introduction to sensitivity analysis for unobserved confounding in nonexperimental prevention research[J]. Prev Sci, 2013, 14(6): 570. doi: 10.1007/s11121-012-0339-5.
[107] Wyss R, Lunt M, Brookhart MA, et al. Reducing bias amplification in the presence of unmeasured confounding through out-of-sample estimation strategies for the disease risk score[J]. J Causal Inference, 2014, 2(2): 131-146.
[108] Robins JM. Marginal structural models versus structural nested models as tools for causal inference[M]. New York; Springer, 2000.
[109] Peng D, Vanderweele TJ. Sensitivity analysis without assumptions[J]. Epidemiology, 2016, 27(3): 368-377.
[110] Greenland S. Basic methods for sensitivity analysis of biases[J]. Int J Epidemiol, 1996, 25(6): 1107-1116.
[111] Baiocchi M, Cheng J, Small DS. Instrumental variable methods for causal inference[J]. Stat Med, 2014, 33(13): 2297-2340.
[112] Didelez V, Meng S, Sheehan NA. Assumptions of iv methods for observational epidemiology[J]. Stat Sci, 2010, 25(1): 22-40.
[113] Lipsitch M, Tchetgen TE, Cohen T. Negative controls: a tool for detecting confounding and bias in observational studies[J]. Epidemiology, 2010, 21(3): 383-388.
[114] Tchetgen TE. The control outcome calibration approach for causal inference with unobserved confounding[J]. Am J Epidemiol, 2014, 179(5): 633-640.
[115] Flanders WD, Klein M, Darrow LA, et al. A method to detect residual confounding in spatial and other observational studies[J]. Epidemiology(Cambridge, Mass.), 2011, 22(6): 823-826.
[116] Flanders WD, Klein M, Darrow LA, et al. A method for detection of residual confounding in time-series and other observational studies[J]. Epidemiology, 2011, 22(1): 59-67.
[117] Sofer T, Richardson DB, Colicino E, et al. On negative outcome control of unobserved confounding as a generalization of difference-in-differences[J]. Stat Sci, 2016, 31(3): 348-361.
[118] Ames BN, Cathcart R, Schwiers E, et al. Uric acid provides an antioxidant defense in humans against oxidant-and radical-caused aging and cancer: a hypothesis[J]. P Natl Acad SCI USA, 1981, 78(11): 6858-6862.
[119] Liese AD, Hense HW, Löwel H, et al. Association of serum uric acid with all-cause and cardiovascular disease mortality and incident myocardial infarction in the MONICA Augsburg cohort. World Health Organization Monitoring Trends and Determinants in Cardiovascular Diseases[J]. Epidemiology, 1999, 10(4): 391-397.
[120] Alderman M, Aiyer KJ. Uric acid: role in cardiovascular disease and effects of losartan[J]. Curr Med Res Opin, 2004, 20(3): 369-379.
[121] Sundström J, Sullivan L, D'Agostino RB, et al. Relations of serum uric acid to longitudinal blood pressure tracking and hypertension incidence[J]. Hypertension, 2005, 45(1): 28-33.
[122] Hwu CM, Lin KH. Uric acid and the development of hypertension[J]. Med Sci Monit, 2010, 16(10): 224-230.
[123] Bos MJ, Koudstaal PJ, Hofman A, et al. Uric acid is a risk factor for myocardial infarction and stroke: the Rotterdam study[J]. 2006, 37(6): 1503-1507.
[124] Sinan DO, Kabakci G, Okutucu S, et al. The association between serum uric acid level and coronary artery disease[J]. Int J Clin Pract, 2010, 64(7): 900-907.
[125] Fang J, Alderman MH. Serum Uric Acid and Cardiovascular Mortality: The NHANES I Epidemiologic Follow-up Study, 1971-1992[J]. JAMA, 2000, 283(18): 2404-2410.
[126] Kim SY, Guevara JP, Kim KM, et al. Hyperuricemia and risk of stroke: A systematic review and meta-analysis[J]. Arthritis Rheum, 2009, 61(7): 885. doi: 10.1002/art.24612.
[127] Meisinger C, Koenig W, Baumert J, et al. Uric acid levels are associated with all-cause and cardiovascular disease mortality independent of systemic inflammation in men from the general population the MONICA/KORA cohort study[J]. Arterioscler Thromb Vasc Biol, 2008, 28(6): 1186-1192.
[128] Kim SY, Guevara JP, Kim KM, et al. Hyperuricemia and coronary heart disease: a systematic review and meta-analysis[J]. Arthrit Care Res, 2010, 62(2): 170-180.
[129] Johnson RJ, Kang DH, Feig D, et al. Is there a pathogenetic role for uric acid in hypertension and cardiovascular and renal disease?[J]. Hypertension, 2003, 41(6): 1183. doi: 10.1161/01.HYP.0000069700.62727.C5.
[130] Chiou WK, Wang MH, Huang DH, et al. The relationship between serum uric acid level and metabolic syndrome: differences by sex and age in taiwanese[J]. J Epidemiol, 2010, 20(3): 219-224.
[131] Nan H, Qiao Q, Söderberg S, et al. Serum uric acid and components of the metabolic syndrome in non-diabetic populations in Mauritian Indians and Creoles and in Chinese in Qingdao, China[J]. Metab Syndr Relat Disord, 2008, 6(1): 47-57.
[132] Ryu S, Song J, Choi BY, et al. Incidence and risk factors for metabolic syndrome in korean male workers, ages 30 to 39[J]. Ann Epidemiol, 2007, 17(4): 245-252.
[133] Sui X, Church TS, Meriwether RA, et al. Uric acid and the development of metabolic syndrome in women and men[J]. Metabolism, 2008, 57(6): 845-852.
[134] Yang T, Chu CH, Bai CH, et al. Uric acid level as a risk marker for metabolic syndrome: a Chinese cohort study[J]. Atherosclerosis, 2012, 220(2): 525-531.
[135] Santos RD. Elevated uric acid, the metabolic syndrome and cardiovascular disease: cause, consequence, or just a not so innocent bystander?[J]. Endocrine, 2012, 41(3): 350-352.
[136] Nakagawa T, Hu H, Zharikov S, et al. A causal role for uric acid in fructose-induced metabolic syndrome[J]. Am J Physiol Renal Physiol, 2006, 290(3): 625-631.
[137] Vitart V, Rudan I, Hayward C, et al. SLC2A9 is a newly identified urate transporter influencing serum urate concentration, urate excretion and gout[J]. Nat Genet, 2008, 40(4): 437-442.
[138] Sheehan NA, Didelez V, Burton PR, et al. Mendelian Randomisation and Causal Inference in Observational Epidemiology[J]. Plos Med, 2008, 5(8): 177. doi: 10.1371/journal.pmed.00501 77.
[139] Angrist JD, Imbens GW. Two-stage least squares estimation of average causal effects in models with variable treatment intensity[J]. J Am Stat Assoc, 1995, 90(430): 431-442.
[140] Palmer TM, Nordestgaard BG, Benn M, et al. Association of plasma uric acid with ischaemic heart disease and blood pressure: mendelian randomisation analysis of two large cohorts[J]. BMJ, 2013, 347: 4262. doi: 10.1136/bmj.f4262.

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