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 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.

This interview by Bruce Goldman was originally published by the Stanford School of Medicine.

On May 13, the journal Science published a letter, signed by 18 scientists, stating that it was still unclear whether the virus that causes COVID-19 emerged naturally or was the result of a laboratory accident, but that neither cause could be ruled out. David Relman, MD, the Thomas C. and Joan M. Merigan Professor and professor of microbiology and immunology, spearheaded the effort.

Relman is no stranger to complicated microbial threat scenarios and illness of unclear origin. He has advised the U.S. government on emerging infectious diseases and potential biological threats. He served as vice chair of a National Academy of Sciences committee reviewing the FBI investigation of letters containing anthrax that were sent in 2001. Recently, he chaired another academy committee that assessed a cluster of poorly explained illnesses in U.S. embassy employees. He is a past president of the Infectious Diseases Society of America.

Stanford Medicine science writer Bruce Goldman asked Relman to explain what remains unknown about the coronavirus’s emergence, what we may learn and what’s at stake.

1. How might SARS-CoV-2, which causes COVID-19, have first infected humans?

Relman: We know very little about its origins. The virus’s closest known relatives were discovered in bats in Yunnan Province, China, yet the first known cases of COVID-19 were detected in Wuhan, about 1,000 miles away.

There are two general scenarios by which this virus could have made the jump to humans. First, the jump, or “spillover,” might have happened directly from an animal to a human, by means of an encounter that took place within, say, a bat-inhabited cave or mine, or closer to human dwellings — say, at an animal market. Or it could have happened indirectly, through a human encounter with some other animal to which the primary host, presumably a bat, had transmitted the virus.

Bats and other potential SARS-CoV-2 hosts are known to be shipped across China, including to Wuhan. But if there were any infected animals near or in Wuhan, they haven’t been publicly identified.

Maybe someone became infected after contact with an infected animal in or near Yunnan, and moved on to Wuhan. But then, because of the high transmissibility of this virus, you’d have expected to see other infected people at or near the site of this initial encounter, whether through similar animal exposure or because of transmission from this person.

2. What’s the other scenario?

Relman: SARS-CoV-2 could have spent some time in a laboratory before encountering humans. We know that some of the largest collections of bat coronaviruses in the world — and a vigorous research program involving the creation of “chimeric” bat coronaviruses by integrating unfamiliar coronavirus genomic sequences into other, known coronaviruses — are located in downtown Wuhan. And we know that laboratory accidents happen everywhere there are laboratories.

All scientists need to acknowledge a simple fact: Humans are fallible, and laboratory accidents happen — far more often than we care to admit. Several years ago, an investigative reporter uncovered evidence of hundreds of lab accidents across the United States involving dangerous, disease-causing microbes in academic institutions and government centers of excellence alike — including the Centers for Disease Control and Prevention and the National Institutes of Health.

SARS-CoV-2 might have been lurking in a sample collected from a bat or other infected animal, brought to a laboratory, perhaps stored in a freezer, then propagated in the laboratory as part of an effort to resurrect and study bat-associated viruses. The materials might have been discarded as a failed experiment. Or SARS-CoV-2 could have been created through commonly used laboratory techniques to study novel viruses, starting with closely related coronaviruses that have not yet been revealed to the public. Either way, SARS-CoV-2 could have easily infected an unsuspecting lab worker and then caused a mild or asymptomatic infection that was carried out of the laboratory.

3. Why is it important to understand SARS-CoV-2’s origins?

Relman: Some argue that we would be best served by focusing on countering the dire impacts of the pandemic and not diverting resources to ascertaining its origins. I agree that addressing the pandemic’s calamitous effects deserves high priority. But it’s possible and important for us to pursue both. Greater clarity about the origins will help guide efforts to prevent a next pandemic. Such prevention efforts would look very different depending on which of these scenarios proves to be the most likely.

Evidence favoring a natural spillover should prompt a wide variety of measures to minimize human contact with high-risk animal hosts. Evidence favoring a laboratory spillover should prompt intensified review and oversight of high-risk laboratory work and should strengthen efforts to improve laboratory safety. Both kinds of risk-mitigation efforts will be resource intensive, so it’s worth knowing which scenario is most likely.

4. What attempts at investigating SARS-CoV-2’s origin have been made so far, with what outcomes?

Relman: There’s a glaring paucity of data. The SARS-CoV-2 genome sequence, and those of a handful of not-so-closely-related bat coronaviruses, have been analyzed ad nauseam. But the near ancestors of SARS-CoV-2 remain missing in action. Absent that knowledge, it’s impossible to discern the origins of this virus from its genome sequence alone. SARS-CoV-2 hasn’t been reliably detected anywhere prior to the first reported cases of disease in humans in Wuhan at the end of 2019. The whole enterprise has been made even more difficult by the Chinese national authorities’ efforts to control and limit the release of public health records and data pertaining to laboratory research on coronaviruses.

In mid-2020, the World Health Organization organized an investigation into the origins of COVID-19, resulting in a fact-finding trip to Wuhan in January 2021. But the terms of reference laying out the purposes and structure of the visit made no mention of a possible laboratory-based scenario. Each investigating team member had to be individually approved by the Chinese government. And much of the data the investigators got to see was selected prior to the visit and aggregated and presented to the team by their hosts.

The recently released final report from the WHO concluded — despite the absence of dispositive evidence for either scenario — that a natural origin was “likely to very likely” and a laboratory accident “extremely unlikely.” The report dedicated only 4 of its 313 pages to the possibility of a laboratory scenario, much of it under a header entitled “conspiracy theories.” Multiple statements by one of the investigators lambasted any discussion of a laboratory origin as the work of dark conspiracy theorists. (Notably, that investigator — the only American selected to be on the team — has a pronounced conflict of interest.)

Given all this, it’s tough to give this WHO report much credibility. Its lack of objectivity and its failure to follow basic principles of scientific investigation are troubling. Fortunately, WHO’s director-general recognizes some of the shortcomings of the WHO effort and has called for a more robust investigation, as have the governments of the United States, 13 other countries and the European Union.

5. What’s key to an effective investigation of the virus’s origins?

Relman: A credible investigation should address all plausible scenarios in a deliberate manner, involve a wide variety of expertise and disciplines and follow the evidence. In order to critically evaluate other scientists’ conclusions, we must demand their original primary data and the exact methods they used — regardless of how we feel about the topic or about those whose conclusions we seek to assess. Prior assumptions or beliefs, in the absence of supporting evidence, must be set aside.

Investigators should not have any significant conflicts of interest in the outcome of the investigation, such as standing to gain or lose anything of value should the evidence point to any particular scenario.

There are myriad possible sources of valuable data and information, some of them still preserved and protected, that could make greater clarity about the origins feasible. For all of these forms of data and information, one needs proof of place and time of origin, and proof of provenance.

To understand the place and time of the first human cases, we need original records from clinical care facilities and public health institutions as well as archived clinical laboratory data and leftover clinical samples on which new analyses can be performed. One might expect to find samples of wildlife, records of animal die-offs and supply-chain documents.

Efforts to explore possible laboratory origins will require that all laboratories known to be working on coronaviruses, or collecting relevant animal or clinical samples, provide original records of experimental work, internal communications, all forms of data — especially all genetic-sequence data — and all viruses, both natural and recombinant. One might expect to find archived sequence databases and laboratory records.

Needless to say, the politicized nature of the origins issue will make a proper investigation very difficult to pull off. But this doesn’t mean that we shouldn’t try our best. Scientists are inquisitive, capable, clever, determined when motivated, and inclined to share their insights and findings. This should not be a finger-pointing exercise, nor an indictment of one country or an abdication of the important mission to discover biological threats in nature before they cause harm. Scientists are also committed to the pursuit of truth and knowledge. If we have the will, we can and will learn much more about where and how this pandemic arose.  

India is facing a mounting burden of noncommunicable diseases (NCDs) such as diabetes, cancers, and cardiovascular diseases. NCDs affect more than 20 percent of the Indian population and their prevalence is projected to expand substantially as the population aged 60 and over increases. Left unchecked, the costs of managing chronically ill and aging sectors of the population grow exponentially.

To control costs and address the growing chronic disease burden, India’s public programs must integrate curative hospital services with the most cost-effective preventive and primary interventions, argue Karen Eggleston, APARC’s deputy director and the director of the Asia Health Policy Program (AHPP), and Radhika Jain, a postdoctoral research fellow with AHPP. India must also urgently expand and improve the evidence base on economic evaluations of both preventive and curative health interventions in the country.

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In a correspondence piece published by BMC Medicine, Eggleston and Jain examine the features and limitations of a study that takes an important first step in that direction: a cost-effectiveness study of the Kerala Diabetes Prevention program (K-DPP) that adds such evidence on how to prevent diabetes cost-effectively in India and other low- and middle-income countries.

The study’s authors present a cost-effectiveness analysis of 1007 participants in the K-DPP, and their estimates indicate that K-DPP was cost-effective. Indeed, Eggleston and Jain determine that the analysis shows potential cost-effectiveness in “nudging” the participants towards a healthier lifestyle through suggestive reductions in tobacco and alcohol use and waist circumference. The results of the cost-effectiveness analysis of the K-DPP “highlight the importance of continued research on community-based promotion of healthy lifestyles,” say Eggleston and Jain.

Evidence-based approaches to chronic noncommunicable disease intervention are essential for providing cost-effective care and creating models for future programs like the K-DPP. Eggleston and Jain conclude that future studies advancing evidence-based approaches to chronic noncommunicable disease intervention — ones that cover larger and more representative populations over longer time periods — remain important for more generalizable assessments to inform policy decisions.