딥러닝 기반 생존분석이 적용된 심혈관질환 위험 평가 모델 성능 향상을 위한 콕스 모형과 결합된 하이브리드 접근법: 헬스케어-환경 연계 데이터 활용 연구

학위논문 (박사) -- 서울대학교 대학원 : 의과대학 의과학과, 2020. 8. 박상민 .Background and aims: The contribution of different cardiovascular disease (CVD) risk factors for the risk evaluation and predictive modeling for incident CVD is often debated. Also, to what extent data on CVD risk factors from multiple data categories should be collected for comprehensive risk assessment and predictive modeling for CVD risk using survival analysis is uncertain despite the increasing availability of the relevant data sources. This study aimed to evaluate the contribution of different data categories derived from integrated data on healthcare and environmental exposure to the risk evaluation and prediction models for CVD risk using deep learning based survival analysis in combination with Cox proportional hazards regression and Cox proportional hazards regression. Methods: Information on the comprehensive list of CVD risk factors were collected from systematic reviews of variables included in the conventional CVD risk assessment tools and observational studies from medical literature database (PubMed and Embase). Each risk factor was screened for availability in the National Health Insurance Service-National Sample Cohort (NHIS-NSC) linked to environmental exposure data on cumulative particulate matter and urban green space using residential area code. Individual records of 137,249 patients more than 40 years of age who underwent the biennial national health screening between 2009 and 2010 without previous history of CVD were followed up for incident CVD event from January 1, 2011 to December 31, 2013 in the NHIS-NSC with data linkage to environmental exposure. Statistics-based variable selection methods were implemented as follows: statistical significance, subset with the minimum (best) Akaike Information Criteria (AIC), variables selected from the regularized Cox proportional hazards regression with elastic net penalty, and finally a variable set that commonly meets all the criteria from the abovementioned statistical methods. Prediction models using Cox proportional hazards deep neural network (DeepSurv) and Cox proportional hazards regression were constructed in the training set (80% of the total sample) using input feature sets selected from the abovementioned strategies and progressively adding input features by data categories to examine the relative contribution of each data type to the predictive performance for CVD risk. Performance evaluations of the DeepSurv and Cox proportional hazards regression models for CVD risk were conducted in the test set (20% of the total sample) with Unos concordance statistics (C-index), which is the most up-to-date evaluation metrics for the survival models with right censored data. Results: After the comprehensive review, data synthesis, and availability check, a total of 31 risk factors in the categories of sociodemographic, clinical laboratory test and measurement, lifestyle behavior, family history, underlying medical conditions, dental health, medication, and environmental exposure were identified in the NHIS-NSC linked to environmental exposure data. Among the models constructed with different variable selection methods, using statistically significant variables for DeepSurv (Unos C-index: 0.7069) and all of the variables for Cox proportional hazards regression (Unos C-index: 0.7052) showed improved predictive performance for CVD risk, which was a statistically significant increase (p-value for difference in Unos C-index: <0.0001 for both comparisons) compared to the models with basic clinical factors (age, sex, and body mass index), respectively. When all and statistically significant variables in each data category from sociodemographic to environmental exposure were progressively added as input features into DeepSurv and Cox proportional hazards regression for predictive modeling for CVD risk, the DeepSurv model with statistically significant variables pertaining to the sociodemographic factors, clinical laboratory test and measurement, and lifestyle behavior data showed the notable performance that outperformed Cox proportional hazards regression model with statistically significant variables added up to the medication category. Extensive data linkage to environmental exposure on cumulative particulate matter and urban green space offered only marginal improvement for the predictive performance of DeepSurv and Cox proportional hazards regression models for CVD risk. Conclusion: To obtain the best predictive performance of DeepSurv model for CVD risk with minimum number of input features, information on sociodemographic, clinical laboratory test and measurement, and lifestyle behavior should be primarily collected and used as input features in the NHIS-NSC. Also, the overall performance of DeepSurv for CVD risk assessment was improved with a hybrid approach using statistically significant variables from Cox proportional hazards regression as input features. When all the data categories in the NHIS-NSC linked to environmental exposure data are available, progressively adding variables in each data category could incrementally increase the predictive performance of DeepSurv model for CVD risk with the hybrid approach. Data linkage to the environmental exposure with residential area code in the NHIS-NSC offered marginally improved performance for CVD risk in both DeepSurv model with the hybrid approach and Cox proportional hazards regression model.배경 및 목적: 심혈관질환 위험평가 및 예측모델링에서 다양한 심혈관질환 위험인자들의 모델 성능향상에 대한 기여도는 논란의 요지로 보고되어왔다. 또한, 지속적으로 증가하는 활용 가능한 심혈관질환 관련 데이터의 종류와 양에도 불구하고 포괄적인 심혈관질환 위험평가와 최적의 예측 모형 개발을 위해 데이터를 어느 범위와 수준까지 수집해야 하는지에 대한 근거는 부족한 현황이다. 본 연구에서는 콕스 모형과 결합된 딥러닝 기반 생존분석 접근법 및 콕스 모형을 활용한 심혈관질환 위험평가와 예측모델링에서 헬스케어-환경 연계 데이터 활용방법 및 범주에 따른 모델 성능향상에 대한 기여도를 평가하고자 하였다. 연구 방법: 전통적 심혈관질환 위험 평가 도구 및 관찰 연구들에 포함 된 심혈관질환 위험요인 관련 변수들을 체계적 문헌고찰 방법론을 활용하여 의학연구 문헌데이터베이스 (PubMed and Embase)에서 포괄적으로 정보를 수집하였다. 미세먼지 누적장기노출 및 도시녹지면적에 대한 환경 노출 데이터와 연계 된 국민건강보험공단 표본코호트, (National Health Insurance Service-National Sample Cohort, NHIS-NSC)에서 각 심혈관질환 위험인자들의 데이터 확보 가능성을 검토하였다. NHIS-NSC를 기준으로 2009년에서 2010년 사이에 국가건강검진을 받은 40세 이상 대상자 중 과거 심혈관질환 병력이 없는 대상자 137,249명의 환자에 대한 정보를 수집하여 2011 년 1 월 1 일부터 2013 년 12 월 31 일까지 신규 발생한 심혈관질환에 대해 시간 경과에 따라 추적 조사하였다. 통계 기반 변수선택 방법은 콕스비례위험모형에서 통계적 유의성, 최소 (최상의) Akaike Information Criteria (AIC)의 하위 집합, elastic net penalty로 정규화 된 콕스비례위험모형에서 선택된 변수 및 위에 언급된 모든 기준을 충족하는 변수 세트로 선정하였다. 위에 명시된 통계적 방법 외 모든 데이터 범주에 속한 변수 및 콕스비례위험모형에서 통계적으로 유의미한 변수 (하이브리드 접근법)를 점진적으로 입력 피쳐로 추가하는 전략으로 딥러닝 기반 생존분석 (Cox proportional hazards deep neural network, DeepSurv) 및 콕스비례위험모형에서 예측 모델들을 훈련 세트 (전체 샘플의 80 %)를 기반으로 개발하였다. DeepSurv 및 콕스비례 위험모형을 활용한 심혈관질환 예측 모델의 성능평가는 생존분석을 활용한 예측 모델링에 가장 적합한 평가지표로 알려진 Unos concordance statistics (C-index)를 사용하여 테스트 세트 (총 샘플의 20 %)에서 수행하였다. 결과: 체계적 문헌고찰, 데이터 취합 및 추출 가능성 검토 후, 인구사회학적 요인, 건강검진 및 측정 결과, 생활습관, 가족력, 건강상태, 구강건강, 약물 및 환경 노출 데이터 범주에서 총 31 개의 심혈관질환 위험인자가 지역환경 자료와 연계된 NHIS-NSC에서 확인되었다. 통계 기반 변수선택 방법으로 개발한 심혈관질환 예측 모델 중 콕스비례위험모형에서 통계적으로 유의미한 변수를 DeepSurv에 적용한 하이브리드 접근법이 Uno 's C-index 값 0.7069, 모든 변수를 콕스비례위험모형에 적용한 콕스비례위험모형이 Uno 's C-index 값 0.7052로 나타나 기본 임상 요인 (연령, 성별 및 체질량지수)이 포함된 예측 모델과 비교하여 통계적으로 유의미한 모델 예측력 증가를 보였다 (두 모델 모두 Unos C-index 차이에 대한 p-value : <0.0001). 인구사회학적 특성에서 환경 노출에 이르기까지 각 데이터 범주에서 모두 통계적으로 유의미한 변수들이 심혈관질환 예측 모델링을위한 DeepSurv 및 Cox 비례 위험 회귀에 입력 피쳐로 점진적으로 추가 된 경우, 인구사회학적 요인, 건강검진 및 측정 결과, 생활습관 요인 중 통계적으로 유의미한 변수들로 구성된 DeepSurv 모델이 의약품 사용까지 고려한 Cox 비례 위험 회귀를 기반으로 한 모델 보다 뛰어난 성능을 나타냈다. 미세먼지 및 도시녹지면적에 대한 환경 노출 데이터를 거주지를 기반으로 NHIS-NSC와 연계 후 점진적으로 입력 피쳐로 추가 시 DeepSurv 및 콕스비례위험모형을 활용한 심혈관질환 예측 모델링 성능을 통계적으로 유의미한 수준으로 개선하지 못했다. 결론: 최소 입력 피쳐를 갖춘 생존 분석 기반 심혈관질환 예측 모델에서 최상의 성능을 얻으려면 인구사회학적, 건강검진 및 측정 결과, 및 생활습관에 대한 정보를 NHIS-NSC에서 수집하여 DeepSurv의 입력 피쳐로 활용해야한다. 지역환경 자료와 연계된 NHIS-NSC에서 모든 데이터 범주를 사용할 수 있을 때 점진적으로 각 데이터 범주 중 콕스비례위험모형에서 통계적으로 유의미한 심혈관질환 위험인자를 점진적으로 입력 피쳐로 DeepSurv 모델에 추가하는 하이브리드 접근법에서 심혈관질환 예측 모델링 성능이 점차 향상 될 것으로 기대할 수 있다. 주거 지역 코드를 사용한 NHIS-NSC와 환경 노출 데이터 연계는 DeepSurv 및 콕스비례위험모형 모두에서 심혈관질환 예측 모델링 성능이 향상되었지만 통계적으로 유의미한 증가 수준은 아닌 것으로 나타나 환경 노출 데이터 연계 및 적용 시 검토가 필요할 것으로 추정된다.I. Introduction 1 1. Background 1 2. Research problem 4 3. Hypothesis and objective 6 3.1. Hypothesis 6 3.2. Objective 6 II. Materials and methods 8 1. Comprehensive review and identification of cardiovascular disease (CVD) risk factors 8 1.1. Systematic review on variables included in conventional CVD risk assessment tools 8 1.2. Systematic review on traditional and emerging CVD risk factors from observational studies 9 1.3. Integration of the comprehensive list of CVD risk factors 11 1.4. Screening for data availability 11 2. Cohort analysis for measuring strength of association between risk factors and incident cardiovascular disease 11 2.1 Study population and linkage to environmental exposure data 11 2.2. Variable selection and data processing 15 2.3. Population-based cohort analysis 17 3. Predictive modeling using survival analysis: DeepSurv and Cox proportional hazards regression 17 3.1. Model development 17 3.2. Evaluation of the predictive performance of the models 20 III. Results 21 1. Identification and categorization of cardiovascular disease risk factors 21 2. Magnitude of association between selected risk factors with cardiovascular disease 43 3. Model performance evaluation 56 VI. Discussion 68 1. Key findings and contributions 68 2. Comparison to other studies 69 3. Strengths and limitations 73 4. Implications 74 5. Future perspectives 75 V. Conclusion 77 Reference 78 국문초록 88Docto

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Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154448/1/sim8445_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154448/2/sim8445.pd

다수준 자원 분포와 건강 관련 삶의 질

학위논문(박사) -- 서울대학교대학원 : 보건대학원 보건학과(보건학전공), 2022. 8. 조성일.연구 배경 최근 인구구조의 급격한 고령화와 경제 수준의 발전에 따라 건강패러다임은 생명 연장과 같은 단순한 양적 지표뿐만 아니라 건강관련 삶의 질(health-related quality of life, HRQOL)과 같은 질적 지표에 주목하고 있다. 그러나 경제 자원 및 인적 자원 만으로는 포괄적이고 다층적인 삶의 질을 설명하는 데에 한계가 있다. 단순히 개인이 보유한 자원의 양 그 자체 보다도, 유의미한 자원이 생성되는 기전과, 사회적 관계를 통한 자원의 분포 및 자원의 활용가능성에 대한 폭넓은 고려가 필요하다. 이에 본 연구에서는 사회적 자원과 주관적 사회계층 인식 지표를 활용하여 자원의 개념을 보다 폭 넓게 이해하며, 가구 및 지역사회 수준에서 다층적인 자원의 분포 구조를 포착한다. 또한 다층적 자원이 시간과 공간에 따라 어떻게 분포하는지 확인하며, 이에 대한 건강 영향을 파악하고자 한다. 결과적으로, 삶의 질에 대한 사회적 결정요인의 이해의 틀을 확장시키고, 우리 사회에 내재된 계층 기반의 불평등 현황을 파악한다. 주요 연구목적은 첫째, 가구 환경 및 개인의 자원이 주관적 사회계층 인식의 형성에 미치는 영향을 파악하며, 가구원간 인식 차이를 파악함으로써 가구내 자원의 공유의 기전을 파악한다. 둘째, 건강관련 삶의 질 궤적을 파악하고, 주관적 사회계층 인식과 객관적 사회경제적 수준에 따른 삶의 질 궤적을 파악한 후, 두 궤적의 연관성을 분석함으로써 사회경제적 지위의 종단적인 건강영향을 파악한다. 셋째, 지역사회의 다차원적인 사회 자원의 구성체를 정의하고, 인구집단의 건강에 영향을 미치는 주요한 사회 자원을 파악한다. 넷째, 건강관련 삶의 질의 공간 상관을 파악하고, 사회 자원이 건강에 미치는 효과를 공간적인 비정형성을 기반으로 파악한다. 연구 방법 첫번째 연구에서는 제 8차 한국의료패널 자료(2013년)를 활용해 3,984 가구에서 18세 이상 성인 8330명을 연구대상으로 하였고, 두번째 연구에서는 2009년부터 2018년도까지의 한국의료패널 (총10차 조사) 자료의 균형패널 대상자 자료를 활용하였다. 세번째와 네번째 연구는 지역사회 수준의 연구로서, 통계청(KOSIS)의 공개자료 및 지역사회건강조사 자료를 활용하여 250개 지역사회 수준으로 다양한 사회 자원 변수를 병합하였다. 종속변수인 건강관련 삶의 질 지수(HRQoL)는 EQ-5D 지표를 활용하여 한국인 고유의 가중치를 적용 후 산출하였다. 주관적 사회계층 인식의 지표는 MacArthur scale 을 사용하였다. 사회자본은 사회적 연결망, 신뢰, 사회 참여로 구분하고, 그 외에도 문화, 체육시설, 공원의 수와 같은 문화 자원과, 의사 수, 필수진료과 의원, 병원, 요양병원 수와 같은 의료 자원 및 지역사회의 사회경제적 환경 등을 반영하였다. 분석 방법은 첫번째 연구에서 주관적 사회계층 인식에 대한 가구원간 응답일치도를 평가하기 위하여 집단 내 상관계수를 구하였고, 분산 분해를 통해 변수별 상대적 중요도를 비교하였다. 두번째 연구에서는 그룹 기반의 궤적 모형(Group-based trajectory modeling, GBTM)을 적용하였으며, 특히 객관적, 주관적 지위의 변화 패턴을 한번에 포착하기 위하여 2개 변수의 변화 패턴을 동시에 포집하는 다중 그룹 기반 궤적 모형(multi-GBTM)을 적용하였다. 세번째 연구에서는 주성분 분석 및 주성분 회귀분석을 사용하였다. 네번째 연구에서는 지리적 가중회귀분석(Geographically weighted regression, GWR)을 적용하고 그 회귀계수에 대해 K-means 군집 분석을 사용하였다. 통계 프로그램은 STATA 16, SAS 소프트웨어 9.4 버전, R 4.1.3버전을 이용하였으며, 지리 분석 시 QGIS 3.24 및 GeoDa 1.18.0 프로그램을 보조적으로 이용하였다. 연구 결과 번째 연구에서, 주거안정성과 같은 가구의 부의 수준은 주관적 계층 인식 하락에 대하여 상당한 완충효과를 가지고 있으나, 가구 내에서 서로 자원을 공유하는 기전에 따라 가구원 간 인식에 차이가 있는 것을 확인하였다. 특히 미성년 자녀의 수가 많아질수록 부부간 계층 인식의 격차가 벌어졌고, 자녀 세대, 가구주 세대, 가구주의 부모 세대별로 세대간 인식 차이가 있었다. 즉, 이러한 인식 격차는 가구원으로서 정체성 및 부양의무, 혹은 가구 내에서 젊은 세대에게 자원이 집중되는 양상에 기인한다. 두번째 연구의 궤적 분석 결과, HRQoL은 시간 경과에 따라 지속적으로 최고점인 1점 수준을 유지하거나, 낮은 수준에서 시작하여 하향 곡선을 그리며 건강이 악화되는 형태만 확인되었다. 또한 한국 사회는 부유한 가구가 시간이 지남에 따라 더욱 빠르게 소득증가를 이루며 이러한 경제적 불평등이 사회적 집단화에 기여하고 있었다. 또한 이러한 사회경제적 지표는 중장기적으로 건강 궤적 확률에 영향을 미치며 건강 격차를 악화시키는 것을 확인하였다. 한편, 중장기적인 주관적 계층 인식 수준은 가구 소득의 변화만으로는 설명되지 않았는데, 이는 가구의 범주를 넘어서는 사회적 자원 및 환경의 중요성을 시사한다. 세번째 연구에서, 지역사회는 물리적 시설 환경 및 경제적 수준 이외에도 연결형, 결속형, 인지적 사회 자본과, 의료서비스의 공급 및 수요 환경으로 유형화 되는 특징이 있었다. 이러한 지역사회의 자원 분포의 특성은 근린효과로서 인구집단의 건강관련 삶에 질에 크게 영향을 미쳤다. 특히 단순 시설의 수가 아닌, 미충족 의료필요도와 같은 실질적인 자원의 이용가능성이 인구 건강에 영향을 미쳤다. 네번째 연구에서, 지리적인 거리를 반영한 공간 분석 결과, 건강관련 삶이 질은 높은 수준의 지리적 자기상관을 가졌다. 즉 건강한 지역사회는 건강한 지역끼리 서로 지리적으로 밀접한 공간적 상관성이 있었다. 또한 각 지역사회 자원이 건강에 미치는 효과성은 지역마다 상이하며, 해당 효과성을 군집화 하였을 때 권역 단위에서 집합적으로 작동하는 것을 확인하였다. 서울 및 경기도에서는 사회 신뢰가 유의한 건강 보호 효과가 있었다. 경상도권에서는 건강관련 삶의 질 수준이 낮은 사람이 종교활동을 보다 빈번하게 참여하는 경향이 있었다. 전라도권에서는 벽지 지역의 1인 가구가 건강 위험 요소였으며, 강원 및 충청도권에서는 미충족 의료 필요도가 건강관련 삶의 질과 유의한 부적 연관성을 보였다. 결론 다수준에 걸친 자원의 분포 및 활용가능성의 격차가 건강관련 삶의 질에 미치는 영향을 확인하였다. 한국 사회는 시간 경과에 따라 가구 소득에 기반한 계층화가 견고해지고 있으며 객관적, 주관적 사회 이동의 가능성이 제약되는 경직된 사회이다. 주거안정성은 객관적 소득 대비 주관적 수준이 낮아지는데 대한 보호효과가 있다. 그러나 물리적 주거공간과 경제적 자원을 공유하는 한 가정안에서도 부양의무와 같은 가구원 정체성이 자원 활용에 영향을 미쳐 주관적인 사회계층 인식에 격차를 발생시키고 있다. 한편, 건강관련 삶의 질은 종단적으로 상향곡선을 띄지 않으므로 악화를 방지 및 보호하는 것이 중요하다. 이는 개인과 가구 수준을 넘어 다양한 사회 자원으로의 접근 가능성 및 활용 가능성이 보장되어야 가능한 일이다. 지역 사회의 자원을 유형화해보면, 물리적 시설이외에도 보건의료서비스의 수요와 공급 균형, 그리고 사회자본 환경으로 특성화 되는데, 이러한 지역사회의 자원 유형은 인구집단 건강에 영향을 미친다. 또한 건강한 지역사회는 건강한 지역사회끼리, 건강 박탈지역은 박탈지역끼리 높은 공간적 자기상관을 가지며, 자원의 효과성이 권역별로 군집화 되는 지역성을 띈다는 점에 유의하여 자원 재분배 정책을 수립하여야 한다. 즉, 향후 지역 보건 정책 수립 시에는 단순히 자원의 양을 균등화하는 정책보다, 한정된 자원의 양을 가구 유형별로, 지역별로, 어느 수준으로 집중 분배하는 것이 가장 비용효과적일지에 기반하여 지역 특화 전략을 수립하여야 한다. 이상을 종합하면, 한 개인이 어떠한 가족 역할의 의무를 가지는지, 어느 지역사회에 거주하는지에 따라 자원의 활용가능성과 효과성은 상이함을 보여준다. 이러한 내재적 속성은 중장기적으로, 그리고 공간적으로 더욱 큰 건강 격차를 불러일으킨다는 점에서 건강 형평성 및 자원의 재분배 정책에 시사하는 바가 크다.Background The recent dynamics of population aging and economic development have drawn renewed interest to the health paradigm. Rather than a quantitative indicator, such as a prolonged life, qualitative indicators, such as health-related quality of life (HRQoL), have become of interest. However, concepts of economic or human capital cannot fully explain the quality of life. Moreover, it is not only the amount of resources owned per se but also mechanisms of generation, distribution, and availability of valuable resources that are important for understanding the social determinants of HRQoL. In general, social determinants of health cumulatively operate over long periods of time and are more effectively investigated by longitudinal perspectives. These resources can be multidimensional, ranging from the material environment to social relationships, and can be distributed within a family or among communities. Ecological differentiation stems from community characteristics and is very much a spatial affair. Here, this thesis aims to evaluate the broader concept of resources using a subjective measure of social status and social resource indicators. Then, it aims to capture the structure of multilevel resource distribution as it is dispersed over time and space. Finally, this aims to expand the framework of social determinants of HRQoL and reveal the health inequalities embedded in our society. The study objectives are as follows. First, the determinants of subjective social status (SSS) were investigated among household members, focusing on the household environment. Then, differences in SSS among members and gaps between objective income and SSS levels were assessed. Second, changing patterns of socioeconomic status were investigated over time and longitudinal effects of socioeconomic status on HRQoL trajectories were assessed. Then, combined changes in patterns of objective and subjective status (i.e., multiple socioeconomic status trajectories) and the HRQoL trajectories were derived, with time gaps. The prospective effects of socioeconomic transition on HRQoL trajectories were analyzed. Third, the resource composite was defined at the community level by combining healthcare resources, cultural infrastructure, and social capital, such as social networks, as well as the neighborhood environment. Then, types of outdoor resources that are crucial to population health were investigated. Finally, spatial correlations in HRQoL were determined and effects of social resources on HRQoL were investigated, considering geographical variations. Methods The study population was adults over 18 years old in the eighth wave (2013) of the Korea Health Panel Survey for the first study, composed of 3,984 households and 8,330 individuals. As the second was a longitudinal study, we made the dataset a balanced panel that respondents answered in all ten waves of the Korea Health Panel (2009–2018). As the third and fourth were ecological studies, we collected community variables via two types of data libraries—OSIS and the Community Health Survey website. We then aggregated overall data at the 250 community level. The dependent variable of HRQoL was calculated using the EQ-5D index with the weights for Koreans. We used the MacArthur scale to measure household SSS. The other explanatory variables consisted of social resources (trust, social network, and social participation), cultural resources (cultural and sports infrastructures and parks), healthcare resources (doctors, essential medical clinics, tertiary hospitals, and nursing hospitals), and community’s socioeconomic status. Regarding methodologies, we applied the intra-class correlation coefficient to investigate the response reliabilities on household SSS among household members for the first study. In addition, we assessed the importance of determinants on SSS using variance decomposition. For the second study, we used group-based trajectory modeling to identify health trajectories and group-based multi-trajectory modeling to draw multi-SES trajectories. The third study was analyzed using principal component analysis and principal component regression modeling. For the spatial analysis, the fourth study used the geographically weighted regression (GWR) and k-means clustering of the GWR coefficients. We used the STATA 16, SAS software 9.4 version, R version of 4.1.3., QGIS 3.24 and GeoDa 1.18.0 in the adequate analysis. Results For the first study, Housing safety and household wealth, which contributed to 65.7% of the variance in SSS, act as a buffer to downgrade one's SSS. However, there were significant differences between household members according to the dynamics of relational resource sharing. In particular, the perceptions of married couples were consistent, although this decreased as they nurtured more underage children. There are SSS gaps across generations between the ages of the head of household's parents, head of household, and children. For the second study of trajectory modeling, four types of multi-SES trajectories were derived from 2009 to 2013. In the multi-SES trajectories, the richer in 2009 had steeper income growth during the period, while the shapes of the SSS were kept unchangeable over time. The following HRQoL trajectories from 2013 to 2018 showed three distinctive patterns—the 4.3% of individuals showed a low and declining pattern while the other two trajectories remained high and stable. The objective and subjective socioeconomic status, respectively, at baseline were strongly associated with the following health trajectories. For the third study, the communities can be categorized into several principal components (PC). The seven PCs explicitly represent the community characteristics such as (1) structural environments regarding facilities and physical structure; (2)-(3) the set of demand and supply in healthcare; (4) bridging; (5) cognitive; (6) bonding social capital; and (7) economic affluence of the community. These first to seventh PCs explain 46.4% of the HRQoL variance at the community level and are distinctively associated with the HRQoL level. In particular, the structural environment significantly influences population health, implying the neighborhood effect on health. The fourth spatial analysis study showed that HRQoL at the community level has spatial autocorrelation, which means healthy regions are geographically clustered with healthy ones. Moreover, resources do or do not exert effectiveness depending on the regions. Social trust effectively increases HRQoL only in the Seoul and Gyeonggi-do regions. Meanwhile, the religious activities in the Busan and Gyunsang-do regions unexpectedly showed a negative association with health. Unmet medical needs have become a critical health agenda, specifically in the eastern and interior regions of South Korea. Urbanization of the city was positively associated with health on the west side. The aging index is negatively associated with the north and interior regions. The single-person household has become a risk factor in Jeollanam-do and Gangwon-do regions. This differential effectiveness can be spatially clustered and distinguished into five clusters based on the GWR coefficients. That is, the effectiveness of the resources works collectively with some degree of administrative spatial range. Conclusion This study investigated the distribution of multiple levels of resources across households and communities and their health impacts. Taken together, the results indicate that South Korea is a risk-bearing society. The HRQoL patterns were either stable or decreased, but not increased. In addition, HRQoL was spatially clustered at high and low levels of HRQoL. These health patterns suggest longitudinal deterioration and geographical disparities in health. The availability of resources differed according to household environment and family roles. Furthermore, the effectiveness of social resources in the community, such as social capital, differed according to region. This geographical pattern of resource effects on health indicates a spatially shaped social process that gives rise to social inequality. In sum, these findings suggest that the originating family, and where a person lives, determines their health status, highlighting the importance of resource redistribution in enhancing population health. Considering that the administrative district boundary is an effective policy target, the regional-specific healthcare policy for communities should allocate limited resources to areas and households in need, and not focus on equalizing the resources.Chapter 1. Overall introduction１ 1.1. Study Background２ 1.2. Study design and objectives２４ Chapter 2. Resource sharing model for subjective social status at the household level２７ 2.1. Introduction２８ 2.2. Methods３３ 2.3. Results３８ 2.4. Discussion ５０ 2.5. Supplementary data ５６ Appendix A. Distribution of household income and subjective social status among the study population ５６ Appendix B. Detailed information on the intra-class correlation coefficients of subjective social status ５８ Appendix C. Post hoc analyses ５９ Chapter 3. Trajectories of health-related quality of life by change pattern of objective and subjective social status ６２ 3.1. Introduction ６３ 3.2. Methods ６７ 3.3. Results ７１ 3.4. Discussion ８２ 3.5. Supplementary data ８９ Appendix A. Sensitivity analyses for missing using MCAR and MAR test ８９ Appendix B. Selection criteria for the optimal trajectory model ９５ Chapter 4. Diverse social resources for health-related quality of life in the communities１００ 4.1. Introduction １０１ 4.2. Methods １０５ 4.3. Results １１０ 4.4. Discussion １２１ 4.5. Supplementary data １２５ Appendix A. Correlation of variables and OLS model １２５ Appendix B. Detailed information on PCA and PCR results １２７ Appendix C. Sensitivity analyses: Comparison to other dimension reduction methods１３０ Chapter 5 Spatial dependences of social resources on health-related quality of life１３２ 5.1. Introduction １３３ 5.2. Methods １３６ 5.3. Results １４１ 5.4. Discussion １５４ 5.5. Supplementary data １５９ Appendix A. Spatial distributions of HRQoL in 2011, 2015, and 2019１５９ Appendix B. Detailed information on the 2019 GWR modeling １６０ Appendix C. Sensitivity analyses: Validation of the ‘religious activities’ coefficient １６４ Chapter 6 Overall discussion １６７ 6.1. Summary of the studies (Chapter 2-Chapter5) １６８ 6.2. Framework of Multilevel Resource Distribution １７０ 6.3. Overall discussion １７８ 6.4. Overall conclusions １８６ References １８９ 국문 초록 ２１１박

Economic Crises: Evidence and Insights from East Asia

macroeconomics, Economic Crises, East Asia

Data Science for Finance: Targeted Learning from (Big) Data to Economic Stability and Financial Risk Management

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Statistics and EconometricsThe modelling, measurement, and management of systemic financial stability remains a critical issue in most countries. Policymakers, regulators, and managers depend on complex models for financial stability and risk management. The models are compelled to be robust, realistic, and consistent with all relevant available data. This requires great data disclosure, which is deemed to have the highest quality standards. However, stressed situations, financial crises, and pandemics are the source of many new risks with new requirements such as new data sources and different models. This dissertation aims to show the data quality challenges of high-risk situations such as pandemics or economic crisis and it try to theorize the new machine learning models for predictive and longitudes time series models. In the first study (Chapter Two) we analyzed and compared the quality of official datasets available for COVID-19 as a best practice for a recent high-risk situation with dramatic effects on financial stability. We used comparative statistical analysis to evaluate the accuracy of data collection by a national (Chinese Center for Disease Control and Prevention) and two international (World Health Organization; European Centre for Disease Prevention and Control) organizations based on the value of systematic measurement errors. We combined excel files, text mining techniques, and manual data entries to extract the COVID-19 data from official reports and to generate an accurate profile for comparisons. The findings show noticeable and increasing measurement errors in the three datasets as the pandemic outbreak expanded and more countries contributed data for the official repositories, raising data comparability concerns and pointing to the need for better coordination and harmonized statistical methods. The study offers a COVID-19 combined dataset and dashboard with minimum systematic measurement errors and valuable insights into the potential problems in using databanks without carefully examining the metadata and additional documentation that describe the overall context of data. In the second study (Chapter Three) we discussed credit risk as the most significant source of risk in banking as one of the most important sectors of financial institutions. We proposed a new machine learning approach for online credit scoring which is enough conservative and robust for unstable and high-risk situations. This Chapter is aimed at the case of credit scoring in risk management and presents a novel method to be used for the default prediction of high-risk branches or customers. This study uses the Kruskal-Wallis non-parametric statistic to form a conservative credit-scoring model and to study its impact on modeling performance on the benefit of the credit provider. The findings show that the new credit scoring methodology represents a reasonable coefficient of determination and a very low false-negative rate. It is computationally less expensive with high accuracy with around 18% improvement in Recall/Sensitivity. Because of the recent perspective of continued credit/behavior scoring, our study suggests using this credit score for non-traditional data sources for online loan providers to allow them to study and reveal changes in client behavior over time and choose the reliable unbanked customers, based on their application data. This is the first study that develops an online non-parametric credit scoring system, which can reselect effective features automatically for continued credit evaluation and weigh them out by their level of contribution with a good diagnostic ability. In the third study (Chapter Four) we focus on the financial stability challenges faced by insurance companies and pension schemes when managing systematic (undiversifiable) mortality and longevity risk. For this purpose, we first developed a new ensemble learning strategy for panel time-series forecasting and studied its applications to tracking respiratory disease excess mortality during the COVID-19 pandemic. The layered learning approach is a solution related to ensemble learning to address a given predictive task by different predictive models when direct mapping from inputs to outputs is not accurate. We adopt a layered learning approach to an ensemble learning strategy to solve the predictive tasks with improved predictive performance and take advantage of multiple learning processes into an ensemble model. In this proposed strategy, the appropriate holdout for each model is specified individually. Additionally, the models in the ensemble are selected by a proposed selection approach to be combined dynamically based on their predictive performance. It provides a high-performance ensemble model to automatically cope with the different kinds of time series for each panel member. For the experimental section, we studied more than twelve thousand observations in a portfolio of 61-time series (countries) of reported respiratory disease deaths with monthly sampling frequency to show the amount of improvement in predictive performance. We then compare each country’s forecasts of respiratory disease deaths generated by our model with the corresponding COVID-19 deaths in 2020. The results of this large set of experiments show that the accuracy of the ensemble model is improved noticeably by using different holdouts for different contributed time series methods based on the proposed model selection method. These improved time series models provide us proper forecasting of respiratory disease deaths for each country, exhibiting high correlation (0.94) with Covid-19 deaths in 2020. In the fourth study (Chapter Five) we used the new ensemble learning approach for time series modeling, discussed in the previous Chapter, accompany by K-means clustering for forecasting life tables in COVID-19 times. Stochastic mortality modeling plays a critical role in public pension design, population and public health projections, and in the design, pricing, and risk management of life insurance contracts and longevity-linked securities. There is no general method to forecast the mortality rate applicable to all situations especially for unusual years such as the COVID-19 pandemic. In this Chapter, we investigate the feasibility of using an ensemble of traditional and machine learning time series methods to empower forecasts of age-specific mortality rates for groups of countries that share common longevity trends. We use Generalized Age-Period-Cohort stochastic mortality models to capture age and period effects, apply K-means clustering to time series to group countries following common longevity trends, and use ensemble learning to forecast life expectancy and annuity prices by age and sex. To calibrate models, we use data for 14 European countries from 1960 to 2018. The results show that the ensemble method presents the best robust results overall with minimum RMSE in the presence of structural changes in the shape of time series at the time of COVID-19. In this dissertation’s conclusions (Chapter Six), we provide more detailed insights about the overall contributions of this dissertation on the financial stability and risk management by data science, opportunities, limitations, and avenues for future research about the application of data science in finance and economy