Georg Holländer, FMedSci
- （共催 ノーベルファーマ）
John E. Ware Jr., PhD
現在わが国の医療費の半分以上を消費している高齢者においては、複数の慢性疾患を併存している状態 (multi-morbidity) は当たり前のことになっている。高齢者を対象とする臨床研究においても、このmulti-morbidityを無視して科学的な分析は実施できない。これらを科学的に測定し、適切に調整しなければならない。multi-morbidityを測定する数少ない指標の一つであるCharlson Indexは、死亡をアウトカムとして重みづけをvalidateされたものであり、レガシー的な指標になりつつある。John Wareと福原は、このmulti-morbidityを測る画期的な指標QDISに関して長年にわたり共同で作業を行ってきた。今回のシンポジウムでは、その開発検証の過程や結果とともに、様々な慢性疾患に活用して得た成果も報告する。
Measuring multimorbidity: New approach for the next generation
Over a quarter of US adults (27.2%) have multiple chronic conditions (MCC) that adversely impact health status, functioning, or health-related quality of life (QOL). MCC are most prevalent among women, those over age 65, and those living in rural areas. One-third have three or more MCC. Because MCC are predictive of mortality, disability, response to treatment, healthcare utilization, expenditures, and declines in QOL (particularly physical QOL), accounting for differences in MCC is essential for evaluating health outcomes and clinical effectiveness. MCC data can also enhance the staging of individual patient complexity, aid in treatment planning, and improve the application of quality-of-care guidelines. Therefore, if we improve our estimates of the impact of MCC we can improve comparative effectiveness research. With better measurement of the impact of MCC, we can improve patient care by identifying individuals more or less likely to benefit from specific treatments and services.
Among the first MCC measures to incorporate both the number of conditions and the differences in their impact, the Charlson Comorbidity Index (CCI) has been the most frequently and extensively studied. While the CCI is useful for some case-mix adjustment purposes, it has been criticized because it relies on mortality weighting, it omits some common medical conditions, and it assigns the same weight to everyone with a given condition.
In this symposium, we introduce a recently developed disease-specific QOL measure, the 7-item Disease-specific Impact Scale (QDIS-7). The QDIS-7 reliably measures the QOL impact attributed to a specific disease or its treatments. Unlike conventional disease-specific QOL scales, the QDIS-7 is standardized across different conditions, which enables estimation of MCC’s aggregate QOL impact. We also systematically compare predictive validity for older and newer methods for aggregating the impact of MCC. We give examples of practical approaches using QDIS to achieve greater measurement efficiency, compare disease-specific QOL burden across MCC’s aggregate their QOL impact and determine when computerized adaptive assessment is likely to identify clinically important differences in disease-specific QOL
- John E. Ware, Jr., PhD
Dr. Ware, is Research Professor in the Department of Medicine, Tufts University School of Medicine, Visiting Professor in the College of Health Solutions, Arizona State University and has lectured on patient reported outcome measurement (PROM) at Harvard T.H. Chan School of Public Health for more than 25 years.
He is an internationally recognized PROM expert and an elected member of the National Academy of Medicine. He led development of outcome measures in the RAND Health Insurance Experiment and was Principal Investigator for the Medical Outcomes Study, where he developed the SF-36® Health Survey, and for the International Quality of Life Assessment Project translations of the SF-36 for use in multinational clinical trials and population health surveys.
In the 1990’s, Dr. Ware was among the first to apply “modern” psychometric methods and computerized adaptive testing (CAT) to generic and disease-specific measures to standardize outcomes and measure population health more efficiently. He founded QualityMetric to develop web-based PROs and served as its CEO and CSO for 10 years. Dr. Ware has published more than 450 peer-reviewed articles. His current work focuses on the integration of disease-specific and generic PROMs using CAT survey logic to make patient screening and outcomes monitoring more practical and useful in clinical research and practice.
- Shunichi Fukuhara, MD, DMSc, MACP
Dr. Shunichi Fukuhara is a Professor Emeritus and a former Dean of School of Public Health in the Kyoto University Graduate School of Medicine (2013-16). In 2012, He was appointed to be a Vice-President of Fukushima Medical University. He is also an Adjunct Professor of Johns Hopkins University Blooming School of Public Health.
During 1980-83, he received clinical training at University of California at San Francisco, certified in American Board of Internal Medicine and is currently a MACP. After his clinical training and practice as a cardiologist, he received training in clinical epidemiology at Harvard University and received MSc.
Since 1990, he has participated in the International Quality of Life Assessment (IQOLA) project led by Professor John Ware on behalf of Japan.
In 2000, he founded Kyoto University’s Department of Healthcare Epidemiology. He played a central role in Japanese steering committee of a large-scale multinational outcomes study on end-stage renal disease (DOPPS). He and his group has also conducted various kinds of cohort in communities and registry studies on chronic conditions. Since then he and his department have published more than 500 original articles in international peer-review journals. During his 20 years’ chairpersonship, his department has graduated 111 students (85 received Master and Doctoral degree) and about 70% of them became faculty members at leading medical universities, including 10 Professors and more than 20 Associate Professors.
In 2015, He presided the 7th meeting of the World Health Summit (WHS) in Berlin. In 2016, he was elected as the first president of Society for Clinical Epidemiology.
- （共催 IQVIA）
- レセプト等に基づくデータベース研究を実施する際に重要な要素の1つとして、アウトカム定義とその妥当性評価が挙げられる。そのために、バリデーション研究が実施され、専門医の判断とデータベースに基づくアウトカム定義が比較される。バリデーション研究における妥当性の指標としては、感度・特異度や陽性的中率などを用いることが推奨されているが、最終的にどの定義を採用すれば良いかの定量的基準はコンセンサスがない状況である。そこで本講演では、ROC曲線を用いた新しい考え方と基準を提案し、Youden Index等の既存の基準との比較や使い分けについて実践的な議論を行う。
- Speaker 1
Masakata Taguri, PhD
- Department of Health Data Science, Tokyo Medical University
- Speaker 2
Emily Bratton MSPH PhD
- Director, Global Epidemiology
Real World Solutions, IQVIA
Keiko Asao, MD, PhD
- Associate Principal, HEOR
Real World Evidence Solutions, IQVIA Solutions Japan K.K.
- Speaker 1