Around the world, societies are aging at a rapid pace. The demographic transition and the challenges surrounding elderly care are defining issues of our time. Aging populations strain public finances and existing models of social support, affect economic growth, and change disease patterns and prevalence. Many countries, therefore, contemplate policy changes to their retirement, pensions, and health care systems. China, which faces a fast-growing trend of aging cohorts, is no exception.
To alleviate the pressure of elderly care on public finances, the Chinese government has been considering raising retirement ages and corresponding changes in social health insurance and pension policy. A new study now helps evaluate such retirement reforms and provides evidence to inform policy in China and elsewhere by probing the effects of retirement on health care utilization.
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The study’s co-authors, including Karen Eggleston, director of the Asia Health Policy Program at APARC, leverage administrative data from medical claims for over 80,000 insured adults in a megacity in eastern China to explore the effect of retirement on outpatient and inpatient care utilization. In this case, urban employee insurance beneficiaries receive a reduced patient cost-sharing rate upon retirement. By focusing on a relatively well-insured population with comprehensive administrative data on insurance plan design and overall resource use at retirement, the study provides new evidence about mechanisms such as the reduced out-of-pocket price of health care, the opportunity cost of time, and the interaction of these demand-side factors with supply-side incentives. Eggleston and her colleagues report on their findings in the journal Health Economics.
In this relatively well-insured population, annual health care utilization significantly increases primarily because of more intensive use of outpatient care at retirement. This increase in outpatient care stems from a decline in the patient cost-sharing rate, the reduced time constraints upon retirement, and the interaction of these factors with supply-side incentives such as prescribing antibiotics. There is no evidence of change in inpatient care at retirement.
The economics of medical expenditure growth and its interaction with population aging is of considerable policy importance for countries in all income groups. “Our findings may provide useful evidence as one consideration for policymakers in other cities in China and elsewhere looking to increase insurance benefits and control medical spending for burgeoning elderly populations.
The study’s co-authors, including Karen Eggleston, find that health care expenditures among Chinese covered by relatively generous health insurance significantly increase at retirement, primarily due to an increase in the number of outpatient visits.
Diabetes is one of the fastest-growing health challenges of the 21st century. On the frontlines of the epidemic rise in the number of people with diabetes is the Asia-Pacific region. China, in particular, has by far the largest absolute burden of diabetes, with an estimated 116 million adults living with the disease accounting for one-quarter of patients with diabetes globally. By 2045, the number of adults living with diabetes in the country is expected to increase to 147 million, not including the large diaspora community China provides worldwide.
Evaluating the health and economic outcomes of diabetes and its complications is vital for formulating health policy. The existing predictive outcomes models for type 2 diabetes, however, were developed and validated in historical European populations and may not be applicable for East Asian populations with their distinct epidemiology and complications. Additionally, the existing models are typically limited to diabetes alone and ignore the progression from prediabetes to diabetes. The lack of an appropriate simulation model for East Asian individuals and prediabetes is a major gap for the economic evaluation of health interventions.
New collaborative research now addresses these limitations. The research team includes APARC’s Asia Health Policy Program Director Karen Eggleston. The researchers developed and validated a patient-level simulation model for predicting lifetime health outcomes of prediabetes and type 2 diabetes in East Asian populations. They report on their findings in the journal PLOS Medicine.
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The chronic progression to diabetes-related complications is apt for computer simulation modeling due to the long-term nature of health outcomes and the time lag for interventions to impact patient outcomes. It is problematic, however, to estimate the impacts of health interventions on East Asian populations with diabetes using existing models, which were developed and validated in European and North American populations with different epidemiology and outcomes.
To fill in this gap, Eggleston and her colleagues set out to develop and validate an outcomes model for the progression of diabetes and related complications in Chinese populations. They compared this new model, called the Chinese Hong Kong Integrated Modeling and Evaluation (CHIME), to two widely used existing models developed and validated in the United Kingdom (known as the United Kingdom Prospective Diabetes Study Outcomes Model 2, or UKPDS-OM2) and in the United States/Canada (called Risk Equations for Complications of type 2 Diabetes, or RECODe). Despite the continuum of risk across the spectrum of risk factor values, these two existing models ignore the progression from prediabetes to diabetes.
The CHIME integrates prediabetes and diabetes into a comprehensive model comprising 13 outcomes. These include mortality, micro- and macrovascular complications, and the development of diabetes. The researchers developed the CHIME simulation model using data from a population-based cohort of 97,628 participants in Hong Kong with type 2 diabetes (43.5%) or prediabetes (56.5%) from 2006 to 2017. Known as the Hong Kong Clinical Management System (CMS), this cohort makes one of the largest Chinese electronic health informatics systems with detailed clinical records.
The next step was to externally validate the CHIME model against individual-level data from the China Health and Retirement Longitudinal Study (CHARLS) cohort (2011-2018), a nationally representative longitudinal cohort of middle-aged and elderly Chinese residents age 45 and older. The researchers validated the CHIME model against six outcomes measures recorded in the CHARLS data and an additional 80 endpoints from nine published trials of diabetes patients using simulated cohorts of 100,000 individuals.
The researchers found that the CHIME model outperformed the widely used UKPDS-OM2 and RECODe models on the data used, meaning that the validation of the CHIME model was more accurate for trials with mainly Asian participants than trials with mostly non-Asian participants. The results indicate that the CHIME model is a validated tool for predicting the progression of diabetes and its outcomes, particularly among Chinese and East Asian populations, for which the existing models have been unsuitable.
With the new model, clinicians and health economists can evaluate population health status for prediabetes and diabetes using routinely recorded data and therapies related to the long-term management of diabetes. In particular, the CHIME outcomes model enables them to assess patients' quality of life and measure cost per quality-adjusted life-years over the long-time horizon of chronic disease conditions. The new model thus supports the economic evaluation of policy guidelines and clinical treatment pathways to tackle diabetes and prediabetes, address micro- and macrovascular complications associated with these conditions, and improve life expectancy.
A research team including APARC's Karen Eggleston developed a new simulation model that supports the economic evaluation of policy guidelines and clinical treatment pathways to tackle diabetes and prediabetes among Chinese and East Asian populations, for whom existing models may not be applicable.
Does the new wave of digital technologies portend a future in which robots and automation increasingly replace workers and destroy livelihoods? In one of the first studies of service sector robots, APARC experts find evidence to offset dystopian predictions of robot job replacement.
The researchers — Asia Health Policy Program Director Karen Eggleston, SK Center Fellow Yong Suk Lee, and University of Tokyo health economist Toshiaki Iizuka, our former visiting scholar — set out to examine how robots affect labor, productivity, and quality of care in Japan’s nursing homes. Their findings indicate that robot adoption may not be detrimental to labor and may help address the challenges of rapidly aging societies.
Eggleston recently joined the Future Health podcast, an initiative of the New South Wales Ministry of Health, to discuss the study and its implications. The program is available both as a video and audio podcast. Watch and listen below:
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Published by the National Bureau of Economic Research, the study suggests that robot adoption has increased employment opportunities for non-regular care workers, helped mitigate the turnover problem that plagues nursing homes, and provided greater flexibility for workers. It is also published in AHPP's working paper series and is part of a broader research project by Eggleston, Lee, and Iizuka, that explores the impact of robots on nursing home care in Japan and the implications of robotic technologies adoption in aging societies.
The study has attracted media attention. The Financial Times Magazine, in a feature story and podcast, called it “groundbreaking in several ways but perhaps most clearly for setting its sights not on manufacturing but on the services sector, where robots are only just beginning to make their mark.” The Freakonomics Radio podcast also hosted Eggleston and Lee for a conversation about their research as part of an episode on collaborative robots and the future of work.
On the Future Health podcast, Karen Eggleston discusses the findings and implications of her collaborative research into the effects of robot adoption on staffing in Japanese nursing homes.
Peter ("Pete") W. Groeneveld, MD, MS is Professor of Medicine at the University of Pennsylvania’s Perelman School of Medicine and a primary care physician at Philadelphia’s Corporal Michael J. Crescenz VA Medical Center. He is the Founding Director of Penn’s Cardiovascular Outcomes, Quality, and Evaluative Research (CAVOQER) Center, Director of Research at Penn’s Leonard Davis Institute of Health Economics (LDI), Chair of the VA’s Research and Development Committee, Co-Director of Penn’s Master of Science in Health Policy (MSHP) program, and Associate Director of the VA’s Center for Health Equity Research and Promotion. Dr. Groeneveld’s research is focused on the quality, outcomes, costs, and equity of high-technology cardiovascular care, and his methodological expertise is in the analysis of a wide variety of health care data, including administrative claims, clinical registries, electronic medical records, and surveys. His research has been funded by the VA, NIH, AHRQ, and the Commonwealth of Pennsylvania, and he has co-authored over 100 peer-reviewed publications. Dr. Groeneveld is a Fellow of the American Heart Association and of the American College of Physicians, and he is an elected member of the American Society for Clinical Investigation (ASCI).
Title: Cardiology Physician Group Practice Vertical Integration and the Use of Cardiovascular Imaging
Abstract: A substantial proportion of previously independent U.S. cardiology physician practices have become vertically integrated into larger health systems. It is unclear if vertical integration affected the clinical practice patterns of these cardiologists. Longitudinal data from cardiology practice surveys from 2008-2013 were combined with Medicare fee-for-service claims for two common cardiology imaging tests: echocardiograms and cardiac nuclear studies. Cardiologists who transitioned from independent to hospital- or health system-owned practices ordered 17% more echocardiograms and 10% more cardiac nuclear imaging studies after their practices had transitioned. Our findings surprisingly suggest that vertical integration of cardiologists' practices was associated with higher rates of cardiovascular imaging. Potential explanations include preferential integration of group practices with lower pre-integration imaging rates, increased post-integration clinician incentives for ordering tests, and/or reduced administrative barriers to obtaining testing after integration.
Zoom Meeting
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Pascal Geldsetzer, PhD
Assistant Professor of Medicine in the Division of Primary Care and Population Health
Title: Regression Discontinuity in Electronic Health Record Data
Abstract: Regression discontinuity in electronic health record (EHR) data combines the main advantage of randomized controlled trials (causal inference without needing to adjust for confounders) with the large size, low cost, and representativeness of observational studies in routinely collected medical data. Regression discontinuity could be an important tool to help clinical medicine move away from a “one size fits all” approach because, along with the increasing size and availability of EHR data, it would allow for a rigorous examination of how treatment effects vary across highly granular patient subgroups. In addition, given the broad range of health outcomes recorded in EHR data, this design could be used to systematically test for a wide range of unexpected beneficial and adverse health effects of different treatments. I will talk about the broad motivation for this research and discuss examples from some of our ongoing work in this area. If there is time, I will also discuss some of my ongoing research on improving healthcare services for chronic conditions in low- and middle-income country settings.
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Alyce S. Adams, PhD
Professor of Medicine, Stanford's Center for Health Policy & Center for Primary Care and Outcomes Research
Professor of Epidemiology and Population Health in the Stanford School of Medicine
Associate Director for Health Equity and Community Engagement in the Stanford Cancer Institute
Title: Health Policy and the Fight for Equitable Healthcare Outcomes: Why Access Isn’t Enough
Abstract: Using evidence from evaluations of natural experiments, Alyce Adams will discuss the intended and unintended consequences of changes in prescription drug policy at the state and federal level of low income and minority individuals with multiple chronic conditions. We will explore the potential for policy effects to have an immediate and dramatic increase in access to clinically essential treatments. However, she will also discuss where such policies can widen, rather than reduce disparities in treatment. We concluded that increasing access (while critical) is not sufficient to address inequities in treatment use and outcomes among high risk populations. Importantly, new strategies are needed to inform the design of policy interventions that promote access, while simultaneously advancing health equity.
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Alyce Adams is the inaugural Stanford Medicine Innovation Professor and Professor of Health Policy, Epidemiology and Population Health and of Pediatrics (by Courtesy). She also serves as Associate Chair for Community Outreach and Engagement for Stanford Health Policy, and as Associate Director for Partnership and Community Engagement in the Stanford Cancer Institute. Focusing on pharmaceutical treatments for cancer and diabetes, Dr. Adams' research evaluates health system and policy level interventions to improve access, balance the benefits and harms of treatment, improve patient-valued outcomes, and reduce disparities.
Alyce Adams is the inaugural Stanford Medicine Innovation Professor and Professor of Health Policy, Epidemiology and Population Health and of Pediatrics (by Courtesy). She also serves as Associate Chair for Community Outreach and Engagement for Stanford Health Policy, and as Associate Director for Partnership and Community Engagement in the Stanford Cancer Institute. Focusing on pharmaceutical treatments for cancer and diabetes, Dr. Adams' research evaluates health system and policy level interventions to improve access, balance the benefits and harms of treatment, improve patient-valued outcomes, and reduce disparities.
