Application of big data for population-based studies for liver diseases

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1 The Korean Liver Cancer Ass Application of big data for population-based studies for liver diseases Moran Ki, MD., PhD Graduate School of Cancer Science and Policy, National Cancer Center

2 Contents Korean Population-based big data- 특성 Big data 활용연구주제 Research Examples Hepatitis A, B, C Rare liver diseases (BCS, PCS, AIH, Wilson) Viral Hepatitis infection diabetes, Parkinson s dz, HCC Metabolic syndrome HCC Second ca. in HCC patients by radiation exposure Cost-effectiveness of HCV screening

3 Korean Population-based big data- 특성 국민건강영양조사 : 대표적인국가건강조사 병원검진자료 : 일반성인, 직장검진 + 피부양자검진 연령, 성, 지역치우침 표준화필수 검사기관자료 : 검사기관특성중요, 환자 ( 의심 ), 일반인 건강보험자료 : 병원진료자료, 검진자료 건강보험공단 : 2002 년부터 ~ 심사평가원 : 최근 5 년자료만가능 공단표본코호트 (100 만명 ): 2002 년인구 2.2% 의 12 년진료추적 공단검진표본코호트 (50 만명 ):40 세이상 검진자검진 + 진료

4 Korean Population-based big data 국민건강영양조사 (KNHANES) 1998년시작. 2007부터는매년약 6000명서베이. two-stage stratified cluster sampling 대상자별가중치있음예 ) HBsAg seropositivity

6 Fig 1. HBsAg seropositivity by sex and year among 10 years based on the KNHNES I to VI ( ). Age standardization method is applied using the 2005 population estimates.

7 Fig 2. HBsAg seropositivity by age between 1998 and Dotted lines are order three polynomial trend line: R2 =0.784 for 1998, for 2005, and in 2013.

8 Korean Population-based big data 병원검진자료 전국 29개병원의 2009년건강검진자료-약 30만명예 ) HCV : 20+ 성인유병률, HCV RNA 양성자예 ) HBV : 20+ 성인유병률, HBsAg & Ab 양성자

A Nationwide Seroepidemiology of Hepatitis C Virus Infection in South Korea * Do Young Kim 1, * In Hee Kim 2, Sook-Hyang Jeong 3, Yong Kyun Cho 4, JoonHyoek Lee 5, Young-Joo Jin 6, Don Lee 6, Dong

9 A Nationwide Seroepidemiology of Hepatitis C Virus Infection in South Korea * Do Young Kim 1, * In Hee Kim 2, Sook-Hyang Jeong 3, Yong Kyun Cho 4, JoonHyoek Lee 5, Young-Joo Jin 6, Don Lee 6, Dong Jin Suh 6, Kwang-Hyub Han 1, NeungHwa Park 7, Ha Yan Kang 8, Young Kul Jung 9, Young Seok Kim 10, Kyung-Ah Kim 11, Youn Jae Lee 12, ByungSeok Lee 13, HyungJoon Yim 14, HeonJu Lee 15, Soon Koo Baik 16, Won Young Tak 17, Sun Jae Lee 18, Woo Jin Chung 19, Sung-Kyu Choi 20, Eun-Young Cho 21, Jeong Heo 22, Dong Joon Kim 23, Byung-Cheol Song 24, Man Woo Kim 25, Jun Lee 25, Hee Bok Chae 26, DaeHee Choi 27, Hwa Young Choi 28, Moran Ki 28 Departments of Internal Medicine, 1 Yonsei University College of Medicine, 2 Chonbuk National University Medical School and Hospital, 3 Seoul National University Bundang Hospital, 4 Kangbuk Samsung Hospital, 5 Seoul Samsung Hospital, 6 Seoul Asan Hospital, 7 University of Ulsan College of Medicine, 8 Dankook University College of Medicine, 9 Gachon College of Medicine, 10 Soon Chun Hyang University College of Medicine, 11 Inje University Ilsan Paik Hospital, 12 Inje University Pusan Paik Hospital, 13 Chungnam National University College of Medicine, 14 Korea University Ansan Hospital, 15 Yeungnam University, College of Medicine, 16 Yonsei University Wonju College of Medicine, 17 Kyungpook National University College of Medicine, 18 Korea University Guro Hospital, 19 Keimyung University School of Medicine, 20 Chonnam National University Medical School, 21 Wonkwang University College of Medicine, 22 Pusan National University School of Medicine, 23 Hallym University College of Medicine, 24 Jeju National University, 25 Chosun University School of Medicine, 26 Chungbuk National University College of Medicine, 27 Kangwon National University, School of Medicine, 28 Department of Preventive Medicine, Eulji University School of Medicine

10 Subjects and data collection Total of 291,314 adults ( 20 years old) who visited 29 health check-up centers of general hospitals all around South Korea between January and December 2009 Two stages of data collection 1. Results of anti-hcv and other biochemical tests from all the subjects by electronically extracted manner 2. Retrospective review of medical records confined to those of people who were positive for anti-hcv

18 Korean Population-based big data 검사기관자료 SCL , 년전국검사자료 - 약 43 만명 예 ) HAV 항체양성률 : total or IgG 양성자

19 Nationwide Seropositivity of Hepatitis A in Republic of Korea from 2005 to 2014, before and after the Outbreak Peak in 2009 Kyung-Ah Kim, 1 Anna Lee, 2 Moran Ki, 3 Sook-Hyang Jeong 4*

20 Characteristics of the Study population For 25,140 subjects enrolled during , the mean age was 37.4 years and the male-to-female ratio was 1:0.99. For 399,105 subjects enrolled during , the mean age was 37.8 years and the male-to-female ratio was 1:1.42. The male-to-female ratio was reversed in the latter half of the period, probably due to increased prenatal screening of IgG anti- HAV for women of child-bearing age after the outbreak peaked in 2009.

21 Table 2. Area and sex-adjusted seroprevalence of anti-hav from 2005 to 2014* Age (year) ~ ~ ~ ~ ~ ** 98.2** 98.0** 99.0** 98.8** 60~ Overall * Seroprevalence of anti-hav was adjusted by area from 2005 to 2009 and by area and gender from 2010 to 2014 **Persons in their fifties and over sixty were grouped together from 2005 to 2009 because of the small number and similar seroprevalence.

22 Fig. 1A % ~

23 % ~19 20~29 30~39 40~49 50~59 Fig. 1B

24 Korean Population-based big data 건강보험자료 - 대표성 ( 인구 98%) - 정확도 severity 높은질병일수록진단명정확도높음 암, 희귀질환 - 산정특례코드활용 그외질환 : 진료횟수, 검사기록, 처방기록, 처방약물등확인 - 정보 위험요인 : 암발생이전검진기록, 진료기록등 예후요인 : 암발생이후진료내용, 재발확인, 생존확인 ( 통계청자료연계 )

The nationwide Health Insurance Review and Assessment Service claims data and Rare Intractable Disease registration data on PBC, identified with the International Classification of Diseases (ICD) 10

31 The nationwide Health Insurance Review and Assessment Service claims data and Rare Intractable Disease registration data on PBC, identified with the International Classification of Diseases (ICD) 10 code of K74.3, were obtained from 2009 to Age- and gender-specific prevalence and incidence rates of PBC were calculated, and data on complications, comorbidities, prescribed drugs, therapeutic procedures and direct medical costs were analysed.

32 Prevalence, Incidence

Standard population = inhabitants in 2010.

33 Standard population = inhabitants in Adjusted for age. Adjusted for sex. Adjusted for age and sex.

34 Complications

35 Case-fatality rates

36 Comorbidity

37 Direct Medical Cost

Using two large data sources, the Health Insurance Review and Assessment Service Claims database and Rare Intractable Disease registration program database in Korea, we identified all patients with

38 Using two large data sources, the Health Insurance Review and Assessment Service Claims database and Rare Intractable Disease registration program database in Korea, we identified all patients with BCS who were registered under International Classification of Diseases 10 (code I82.0). The age- and sex-adjusted incidence and prevalence of BCS were calculated with analysis of complications and direct medical costs.

Fig. 1. (A) Average annual sex-adjusted prevalence rate per million population and prevalent cases (2009 2013) of Budd Chiari syndrome by age in South Korea.

40 Fig. 1. (A) Average annual sex-adjusted prevalence rate per million population and prevalent cases ( ) of Budd Chiari syndrome by age in South Korea. (B) Average annual sex-adjusted incidence rate per million population and number of incident cases ( ) of Budd Chiari syndrome by age in South Korea.

42 Risk of Hepatocellular Carcinoma in Korean with Metabolic Syndrome Study population National Health Insurance Service-National Health Examination Cohort (NHIS-NHEC) data, The sample cohort of 514,866 participants was randomly selected, comprising 10% of the total eligible population aged 40 to 79 years and have health examination in 2002 or The data combined with National Health Insurance Service data and Statistics Korea for cause of death.

43 Metabolic risk profiles criteria WHO, World Heath Organization; EGIR, Europian Group for the Study of Insulin Resistance; NCEP/ATP, National Cholesterol Education Program Adult Treatment Panel; AACE, American Association of Clinical Endocrinology; IDF, International Diabetes Federation

44 Metabolic risk profiles in this study 1 Body weight BMI 25 kg/m 2 2 Lipid total cholesterol 240 mg/dl 3 Blood pressure SBP 130 mmhg or DBP 85 mmhg or history of hypertension, 2 or more visits in Glucose FBS 100 mg/dl or history of diabetes, 2 or more visits in

45 Other HCC risk variables Alcoholic dis. : history of disease (K70) in Viral hepatitis : history of disease (HBV or HCV) in Statin or Metformin History of treatment between 2002 and follow up end Smoking Non-, former-, or current smokers in questionnaires Item missing non smoker (n=21,568, 4.22%) Exercise No activity, 1-2, and 3 times/wk in questionnaires Item missing no activity (n=12,465, 2.44%)

46 Statistical analysis Cox proportional hazards models were applied to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations between metabolic risk profiles and the risk of HCC. We presented a crude model and an adjusted model including age group, smoking status, alcoholic disease, viral hepatitis, statin or metformin intake, and regular exercise. All statistical analyses were stratified by gender.

47 Study subject Health examination and aged yrs in (N=514,795) Exclude (n=1,667) Item missing in the measurement for metabolic risk profiles n=513,128 Exclude (n=1,893) Diagnosis of HCC before health examination n=511,235 Exclude (n=11) Error of the date for health examination or death Final study subjects n=511,224 47

48 Overall study population characteristics according to metabolic risk profiles and risk factors 48 Sex Age Viral inf. Smoking Exercise Alcohol Statin Metformin Metabolic risk profile Sex 0 (n=109, %) 1 (n=171, %) 2 (n=149, %) 3 (n=69, %) 4 (n=12, %) N % N % N % N % N % Male Female Age group Viral infection HBV HCV HBV&HCV Non-infection Smoking Non-smoker Former smoker Current smoker Exercise No activity times/week times/week Alcohol liver disease Yes No Statin Yes No Metformin Yes No

49 HCC risk by no. of metabolic risk profiles Male Met. risk profile ahr 95% CI P-value One Two Three Four Female One Two Three Four M ( ) ( ) < ( ) < ( ) F ( ) ( ) < ( ) < ( ) Cox proportional hazard regression model including age, BMI, hypertension, FBS, TC, viral hepatitis, alcoholic liver disease, smoking, exercise, statin and metformin variables.

50 Second Cancer Risk after Radiation Exposure in Patients with Hepatocellular Carcinoma Moran Ki 1, Hwa Young Choi 1, Hyunsoon Cho 1,3, Ju Hee Lee 2, Young Hwan Koh 2, Joong-Won Park 1,2 1 Department of Cancer Control and Policy, Graduate School of Cancer Science and Policy, 2 Center for Liver Cancer, 3 Division of Cancer Surveillance and Registration, National Cancer Center, Korea

51 Methods Study subject selection The NHIS claims data 2002~2013, age 20+ years. C22, C22.0, C22.7, C22.9 (ICD-10) as a main disease. Radiation exposure CT, PET-CT from 180 days before HCC to the last day of observation. transarterial chemoembolization (TACE) Secondary cancers C00-C97, D00-D48 (ICD-10) as a main disease 180 days after HCC. Patients were excluded if the disease name includes secondary, benign or malignant neoplasm of liver and intrahepatic bile ducts.

52 Flow Chart

53 Characteristics of the HCC patients Patients (n = 20073) Characteristic no. (%) Sex Male (71.6) Female 5702 (28.4) Age at diagnosis for HCC yr 213 (1.1) yr 1227 (6.1) yr 4250 (21.2) yr 5978 (29.8) yr 5420 (27.0) yr 2540 (12.7) 80 yr 445 (2.2) Patients (n = 20073) Characteristic no. (%) Maximum follow-up 0.5 <3 yr 7482 (37.3) 3 <5 yr 2062 (10.3) 5 <7 yr 1290 (6.4) 7 <10 yr 9239 (46.0) Transarterial chemoembol ization No (55.1) Yes 9004 (44.9)

54 Cumulative incidence (%) Cumulative Incidences of Second Cancers HCC cohort and general population (A), (C) all organ sites. (B), (E) digestive organs (D), (F) respiratory & intrathoracic organs A B Follow-up (yr) C D E F

55 Standardized incidence ratios, absolute excess risks, and 10-year cumulative incidences of second cancers in HCC patients Second cancers Standardized Incidence Ratio Absolute Excess Risk 10-Yr Cumulative Incidence % (95% CI) no./10,000 person-yr (95% CI) (95% CI) Male Female Male Female Male Female Any cancer, excluding HCC* 1.5(1.4, 1.6) 1.7(1.6, 1.9) 65.4(52.2, 78.5) 83.0(63.7, 102.3) 13.2(12.1, 14.3) 14.5(12.9, 16.2) Malignant neoplasms 1.4(1.3, 1.5) 1.4(1.3, 1.6) 43.5(31.5, 55.5) 39.4(22.9, 55.8) 8.9(8.0, 9.9) 8.3(7.3, 9.5) Lip, oral cavity, eye & pharynx 0.8(0.6, 1.1) 0.2(0.0, 0.3) -1.2(-3.7, 1.4) -7.0(-10.4, -3.6) 0.3(0.1, 0.5) 0.1(0.0, 0.3) Digestive organs 1.3(1.2, 1.4) 1.9(1.6, 2.1) 18.8(10.3, 27.2) 25.7(15.6, 35.9) 4.8(4.1, 5.6) 3.2(2.6, 3.9) Respiratory & intrathoracic organs 1.4(1.3, 1.6) 2.4(1.8, 3.0) 10.1(4.7, 15.6) 10.6( ) 1.4(1.0, 1.8) 0.7(0.4, 1.2) Bone 8.4(5.4, 11.3) 2.7(-0.4, 5.8) 3.8(2.2, 5.4) 0.6(-0.6, 1.8) 0.1(0.1, 0.3) 0.0(0.0, 0.0) Skin 2.2(1.4, 3.0) 1.4(0.5, 2.3) 2.3(0.5, 4.1) 0.8(-1.7, 3.2) 0.1(0.0, 0.3) 0.2(0.1, 0.4) Soft tissue 4.0(1.8, 6.1) 2.3(-0.3, 4.8) 1.4(0.3, 2.5) 0.5(-0.8, 1.8) 0.1(0.0, 0.4) 0.0(0.0, 0.0) Breast - 0.9(0.6, 1.1) (-9.0, 4.4) - 1.2(0.9, 1.8) Male genital organs 0.9(0.7, 1.1) (-5.1, 1.9) - 0.7(0.5, 1.1) - Brain & central nervous system 3.0(1.9, 4.0) 2.0(0.8, 3.3) 3.0(1.2, 4.7) 1.6(-0.8, 4.0) 0.2(0.1, 0.3) 0.2(0.1, 0.5) Endocrine glands 1.1(0.7, 1.4) 1.2(0.9, 1.4) 0.3(-1.9, 2.6) 3.1(-3.9, 10.1) 0.4(0.3, 0.7) 1.9(1.4, 2.7) Hematologic malignancy 1.6(1.2, 2.1) 2.5(1.4, 3.6) 0.2(0.0, 0.5) 4.0(0.7, 7.3) 0.5(0.2, 1.3) 0.3(0.1, 0.7) Other malignant cancers 4.1(2.8, 5.3) 2.6(0.9, 4.3) 4.2(2.3, 6.1) 1.7(-0.4, 3.9) 0.2(0.1, 0.4) 0.0(0.0, 0.0) In situ & uncertain behavior neoplasms 2.5(2.2, 2.8) 3.2(2.7, 3.6) 21.9(16.6, 27.1) 43.6(33.6, 53.7) 4.0(3.3, 4.8) 5.7(4.8, 6.9)

56 Multivariable survival analysis of second cancers according to the follow-up period and cumulative radiation dose in HCC patients Variable* 0.5 <3 year (996/7482, 13.3%) 3 <5 year (425/2062, 20.6%) 5 <7 year (366/1290, 28.4%) 7 <10 year (218/9239, 2.4%) n e /n s HR (95% CI) n e /n s HR (95% CI) n e /n s HR (95% CI) n e /n s HR (95% CI) Malignant neoplasms (n=1539) Sex (male vs. female) 0.95 ( ) 0.95 ( ) 0.73 ( ) 1.40 ( ) Age by 10-year increments (ref yr) 1.00 ( ) 0.99 ( ) 0.98 ( ) 1.03 ( ) Cumulative radiation dose (ref msv) 247/ /307 80/210 42/ / ( ) 29/ ( ) 22/ ( ) 18/ ( ) / ( ) 70/ ( ) 51/ ( ) 17/ ( ) / ( ) 131/ ( ) 130/ ( ) 82/ ( ) Digestive organs (n=723) Sex (male vs. female) 1.17 ( ) 1.13 ( ) 0.77 ( ) 1.96 ( ) Age by 10-year increments (ref yr) 1.00 ( ) 1.00 ( ) 0.99 ( ) 1.05 ( ) Cumulative radiation dose (ref msv) 149/ /307 30/210 13/ / ( ) 11/ ( ) 13/ ( ) 5/ ( ) / ( ) 25/ ( ) 21/ ( ) 7/ ( ) / ( ) 60/ ( ) 55/ ( ) 32/ ( ) Respiratory & intrathoracic (n=292) Sex (male vs. female) 1.04 ( ) 1.94 ( ) 1.31 ( ) 3.75 ( ) Age by 10-year increments (ref yr) 1.00 ( ) 1.00 ( ) 0.99 ( ) 1.05 ( ) Cumulative radiation dose (ref msv) 37/ /307 9/210 3/ / ( ) 5/ ( ) 3/ ( ) 4/ ( ) / ( ) 17/ ( ) 10/ ( ) 4/ ( ) / ( ) 26/ ( ) 26/ ( ) 19/ ( ) Hematologic malignancy (n=66) Sex (male vs. female) 0.56 ( ) 0.37 ( ) 1.18 ( ) 4.12 ( ) Age by 10-year increments (ref yr) 1.00 ( ) 1.00 ( ) 1.00 ( ) 1.07 ( ) Cumulative radiation dose (ref msv) 9/1640 3/307 2/210 2/ / ( ) 1/ ( ) 2/ ( ) 1/ ( ) / ( ) 2/ ( ) 6/ ( ) 2/ ( ) 300 0/1032-9/ ( ) 8/ ( ) 4/ ( )

57 Healthcare Costs for Chronic Hepatitis C in South Korea during : An Analysis of National Health Insurance Claims Data Moran Ki 1, Hwa Young Choi 1, Kyung-Ah Kim 2, Eun Sun Jang 3, Sook-Hyang Jeong 3*

58 Methods The Health Insurance Review and Assessment Service database was reviewed, and patients diagnosed with CHC in were extracted. Data regarding detailed healthcare utilization, prescribed drugs, and direct medical costs were obtained. For annual direct healthcare cost calculations, a prevalence-based approach was used.

59 Healthcare costs covered by the National Health Insurance for chronic HCV infection, Variables N % Insurer paym ent + copay ment (million USD) Hospital Copayment (million USD) Proportion of copayment (%) Pharmacy Extra-hospital ph armaceutical cost (million USD) Total healthcare cost (million USD) Healthcare cost per patient (USD) , , , , , Total 340, ,812

60 Healthcare costs covered by the National Health Insurance for chronic HCV infection, Variables N % Hospital (million $, %) Pharmacy Total Extra-hospital Copayment (%) pharmaceutical cost (million $) Insurer payment + copayment healthcare cost (million $) Healthcare cost per patient ($) Tertiary hospital 80, ,791 General hospital 56, ,081 Hospital 17, ,258 Primary clinic 65, Geriatric center 1, ,847 Community health center Total 221, ,812

61 Healthcare costs for chronic HCV infection and related liver diseases, Liver disease status (n) n Inpatients * Outpatients * Total Cost Cost per Cost Cost per cost * n (million $) Patient ($) (million $) patient ($) (million $) Annual cost (million $) Chronic hepatitis (159,959) Liver cirrhosis (14,551) 23, , , , , ,322 13, , HCC (7,140) 6, ,316 6, , Liver transplantati on, 1 st year (118) 2 nd or later year (84) , , , ,

62 Healthcare costs for chronic HCV infection by antiviral treatment and year With antiviral treatment Without antiviral treatment Year n Total healt hcare cost (million $) per patient ($) Pharmaceut ical cost (million $) per patient ($) n Total healt hcare cost (million $) per patient ($) Pharmaceu tical cost (million $) per patient ($) , , ,722 58, , , ,901 58, , , ,859 62, , , ,825 64, , , ,946 61, Total 35, , , , , ,164

63 Pharmaceutical costs for chronic HCV infection by drug types, Drug name No. of patient % Patients receiving monotherapy (n = 2,222) Pharmaceutical cost (million $) Pharmaceutical cost per patient ($) Interferon-α-2a Interferon-α-2b ,355 Peginterferon-α-2a ,177 Peginterferon-α-2b ,041 Ribavirin 1, Total 2, ,266 Patients receiving combination therapy (n = 23,001) Interferon-α-2a + Ribavirin Interferon-α-2b + Ribavirin ,112 Peginterferon-α-2a + Ribavirin 15, ,447 Peginterferon-α-2b + Ribavirin 7, ,020 Total 23, ,188

64 Medical cost per person (USD) Healthcare costs per patient for chronic HCV infection and related liver diseases ,838 6, ,470 1,873 0 Chronic hepatitis LC HCC

65 Healthcare cost per patient (USD) Healthcare costs per patient in patients undergoing liver transplantation following chronic HCV infection , ,511 6,171 6,969 3, Follow-up year

66 Summary Big data 활용연구시대 연구진행순서 Big data가필요한연구인가? 활용가능한 big data 조사, 변수조사 big data의장점과제한점비교 연구대상자선정기준-정의 각종변수기준-정의 자료분석과해석은역학, 통계학자와함께

67 Acknowledgement 정숙향, 분당서울대병원 박중원, 국립암센터 김경아, 일산백병원 장은선, 분당서울대병원 김보현, 국립암센터 오경원, 질병관리본부 손혜숙, 인제대학교 조현순, 국립암센터국제암대학원 최화영, 국립암센터국제암대학원

68 Thank you for your attention!

SERUM CYSTATIN C CONCENTRATION IS A POWERFUL PROGNOSTIC INDICATOR IN PATIENTS WITH CIRRHOTIC ASCITES

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