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Cover of the journal Social Indicators Research
This study investigates the strength and significance of the associations of health workforce with multiple health outcomes and COVID-19 excess deaths across countries, using the latest WHO dataset.

Multiple log-linear regression analyses, counterfactual scenarios analyses, and Pearson correlation analyses were performed. The average density of health workforce and the average levels of health outcomes were strongly associated with country income level. A higher density of the health workforce, especially the aggregate density of skilled health workers and density of nursing and midwifery personnel, was significantly associated with better levels of several health outcomes, including maternal mortality ratio, under-five mortality rate, infant mortality rate, and neonatal mortality rate, and was significantly correlated with a lower level of COVID-19 excess deaths per 100K people, though not robust to weighting by population.

The low density of the health workforce, especially in relatively low-income countries, can be a major barrier to improving these health outcomes and achieving health-related Sustainable Development Goals (SDGs); however, improving the density of the health workforce alone is far from enough to achieve these goals. Our study suggests that investment in health workforce should be an integral part of strategies to achieve health-related SDGs, and that achieving non-health SDGs related to poverty alleviation and expansion of female education are complementary to achieving both sets of goals, especially for those low- and middle-income countries. In light of the strains on the health workforce during the current COVID-19 pandemic, more attention should be paid to health workforce to strengthen health system resilience and long-term improvement in health outcomes.

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Publication Type
Journal Articles
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Journal Publisher
Social Indicators Research
Authors
Karen Eggleston
Jinlin Liu
Shorenstein APARC Encina Hall E301 Stanford University
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Visiting Scholar at APARC, 2021-2022
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Ph.D

Dr. Cynthia Chen joined the Walter H. Shorenstein Asia-Pacific Research Center (APARC) as visiting scholar with the Asia Health Policy Program during the 2022 winter and spring quarters. She is an Assistant Professor at the National University of Singapore (NUS). Her current research focuses on the well-being and older adults, healthcare financing, and the economics of ageing. She is interested in how demographic, economic and social changes can affect the burden of care, financing needs and optimal resource allocation in the future. Her research has been supported by the Singapore’s Ministry of Health, Ministry of Education, the US National Institutes of Aging, and the Thai Health Promotion Foundation among others. To date, she has published more than 45 internationally peer-reviewed journals on societal ageing, the burden of chronic diseases, and cost-effectiveness research. Dr. Chen obtained her Ph.D. in Public Health, Masters and BSc in Statistics from NUS.

Authors
Noa Ronkin
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News
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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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Modeling Health Outcomes Among East Asian Populations

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 CHIME outperformed the widely used United Kingdom Prospective Diabetes Study Outcomes Model 2 (UKPDS-OM2) and Risk Equations for Complications of type 2 Diabetes (RECODe) models on real-world data.
Karen Eggleston et al

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.

Towards Reducing the Disease Burden of Diabetes

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.

Read More

A parent holds a child waiting to be given an infusion at an area hospital in China.
News

In China, Better Financial Coverage Increases Health Care Access and Utilization

Research evidence from China’s Tongxiang county by Karen Eggleston and colleagues indicates that enhanced financial coverage for catastrophic medical expenditures increased health care access and expenditures among resident insurance beneficiaries while decreasing out-of-pocket spending as a portion of total spending.
cover link In China, Better Financial Coverage Increases Health Care Access and Utilization
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News

Robotics and the Future of Work: Lessons from Nursing Homes in Japan

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.
cover link Robotics and the Future of Work: Lessons from Nursing Homes in Japan
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Subtitle

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.

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This paper describes the qualitative results of the mixed-methods study by Eggleston and her colleagues. For the quantitative results of the study, read the April 2021 paper in the journal BMC Public Health. Also, watch and read our full story and interview with Eggleston.

Objective

People with chronic conditions are known to be vulnerable to the COVID-19 pandemic. This study aims to describe patients’ lived experiences, challenges faced by people with chronic conditions, their coping strategies, and the social and economic impacts of the COVID-19 pandemic.
 

Design, Setting, and participants

We conducted a qualitative study using a syndemic framework to understand the patients’ experiences of chronic disease care, challenges faced during the lockdown, their coping strategies and mitigators during the COVID-19 pandemic in the context of socioecological and biological factors. A diverse sample of 41 participants with chronic conditions (hypertension, diabetes, stroke, and cardiovascular diseases) from four sites (Delhi, Haryana, Vizag, and Chennai) in India participated in semistructured interviews. All interviews were audio-recorded, transcribed, translated, anonymized and coded using MAXQDA software. We used the framework method to qualitatively analyze the COVID-19 pandemic impacts on health, social and economic well-being.
 

Results

Participant experiences during the COVID-19 pandemic were categorized into four themes: challenges faced during the lockdown, experiences of the participants diagnosed with COVID-19, preventive measures taken, and lessons learned during the COVID-19 pandemic. A subgroup of participants faced difficulties in accessing healthcare while a few reported using teleconsultations. Most participants reported the adverse economic impact of the pandemic which led to higher reporting of anxiety and stress. Participants who tested COVID-19 positive reported experiencing discrimination and stigma from neighbors. All participants reported taking essential preventive measures.
 

Conclusion

People with chronic conditions experienced a confluence (reciprocal effect) of COVID-19 pandemic and chronic diseases in the context of difficulty in accessing healthcare, sedentary lifestyle, and increased stress and anxiety. Patients’ lived experiences during the pandemic provide important insights to inform effective transition to a mixed realm of online consultations and ‘distanced’ physical clinic visits.

 

Karen Eggleston 4X4

Karen Eggleston, PhD

Senior Fellow at FSI, Director of the Asia Health Policy Program at Shorenstein Asia-Pacific Research Center
Full Biography
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Publication Type
Journal Articles
Publication Date
Subtitle
A Qualitative Study
Journal Publisher
BMJ Open
Authors
Kavita Singh
Aprajita Kaushik
Leslie Johnson
Suganthi Jaganathan
Prashant Jarhyan
Mohan Deepa
Sandra Kong
Nikhil Srinivasapura Venkateshmurthy
Dimple Kondal
Sailesh Mohan
Ranjit Mohan Anjana
Mohammed K Ali
Nikhil Tandon
K M Venkat Narayan
Viswanathan Mohan
Karen Eggleston
Dorairaj Prabhakaran1
Number
2021;11:e048926
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This paper describes and analyzes the quantitative results of the mixed-methods study by Eggleston and her colleagues. For the qualitative results of the study, read the June 2021 paper in the journal BMJ Open. Also, watch and read our full story and interview with Eggleston.

Background

People with chronic conditions are disproportionately prone to be affected by the COVID-19 pandemic but there are limited data documenting this. We aimed to assess the health, psychosocial and economic impacts of the COVID-19 pandemic on people with chronic conditions in India.

Methods

Between July 29, to September 12, 2020, we telephonically surveyed adults (n = 2335) with chronic conditions across four sites in India. Data on participants’ demographic, socio-economic status, comorbidities, access to health care, treatment satisfaction, self-care behaviors, employment, and income were collected using pre-tested questionnaires. We performed multivariable logistic regression analysis to examine the factors associated with difficulty in accessing medicines and worsening of diabetes or hypertension symptoms. Further, a diverse sample of 40 participants completed qualitative interviews that focused on eliciting patient’s experiences during the COVID-19 lockdowns and data analyzed using thematic analysis.

Results

One thousand seven hundred thirty-four individuals completed the survey (response rate = 74%). The mean (SD) age of respondents was 57.8 years (11.3) and 50% were men. During the COVID-19 lockdowns in India, 83% of participants reported difficulty in accessing healthcare, 17% faced difficulties in accessing medicines, 59% reported loss of income, 38% lost jobs, and 28% reduced fruit and vegetable consumption. In the final-adjusted regression model, rural residence (OR, 95%CI: 4.01,2.90–5.53), having diabetes (2.42, 1.81–3.25) and hypertension (1.70,1.27–2.27), and loss of income (2.30,1.62–3.26) were significantly associated with difficulty in accessing medicines. Further, difficulties in accessing medicines (3.67,2.52–5.35), and job loss (1.90,1.25–2.89) were associated with worsening of diabetes or hypertension symptoms. Qualitative data suggest most participants experienced psychosocial distress due to loss of job or income and had difficulties in accessing in-patient services.

Conclusion

People with chronic conditions, particularly among poor, rural, and marginalized populations, have experienced difficulties in accessing healthcare and been severely affected both socially and financially by the COVID-19 pandemic.

Dr. Karen Eggleston

Karen Eggleston, PhD

Senior Fellow at FSI, Director of the Asia Health Policy Program at Shorenstein Asia-Pacific Research Center
Full Biography
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Publication Type
Journal Articles
Publication Date
Subtitle
A Mixed Methods Study
Journal Publisher
BMC Public Health
Authors
Kavita Singh
Dimple Kondal
Sailesh Mohan
Suganthi Jaganathan
Mohan Deepa
Nikhil Srinivasapura Venkateshmurthy
Prashant Jarhyan
Ranjit Mohan Anjana
K. M. Venkat Narayan
Viswanathan Mohan
Nikhil Tandon
Mohammed K. Ali
Dorairaj Prabhakaran
Karen Eggleston
Number
685
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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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Register in advance for this meeting:
https://stanford.zoom.us/meeting/register/tJYpcO2ppzooGNdbf8o1OxXNUWd3rukNEb7i 

After registering, you will receive a confirmation email containing information about joining the meeting.

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Stanford Medicine Innovation Professor
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PhD, MPP

Alyce Adams is a Professor of Epidemiology and Population Health in the Stanford School of Medicine, as well as Associate Director for Health Equity and Community Engagement in the Stanford Cancer Institute. Focusing on racial and socioeconomic disparities in chronic disease treatment outcomes, Dr. Adams' interdisciplinary research seeks to evaluate the impact of changes in drug coverage policy on access to essential medications, understand the drivers of disparities in treatment adherence among insured populations, and test strategies for maximizing the benefits of treatment outcomes while minimizing harms through informed decision-making. Prior to joining Stanford School of Medicine, Dr. Adams was Associate Director for Health Care Delivery and Policy and a Research Scientist at the Kaiser Permanente Division of Research, as well as a Professor at the Bernard J. Tyson Kaiser Permanente School of Medicine. From 2000 to 2008, she was an Assistant Professor in the Department of Population Medicine (formerly Ambulatory Care and Prevention) at Harvard Medical School and Harvard Pilgrim Health care. She received her PhD in Health Policy and an MPP in Social Policy from Harvard University. She is Vice Chair of the Board of Directors for AcademyHealth and a former recipient of the John M. Eisenberg Excellence in Mentoring Award from Agency for Healthcare Research and Quality and an invited lecturer on racial disparities in health care in the 2014/2015 National Institute of Mental Health Director’s Innovation Speaker Series.

Professor, Epidemiology and Population Health
Professor, Health Policy
Professor, Pediatrics (by courtesy)
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