Development of a Synthetic Population Model for Assessing Excess Risk for Cardiovascular Disease Death

Krauland, Mary G.; Frankeny, Robert J; Lewis, Joshua B; Brink, LuAnn L.; Hulsey, Eric; Roberts, Mark S.; Hacker, Karen

doi:10.1001/jamanetworkopen.2020.15047

Cited by 9 publications

(15 citation statements)

References 33 publications

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“…The synthetic population approaches have shown to be effective in epidemic simulation applications and in deriving synthetic population from the country-wide census for privacy protection of the individuals (Wickramasinghe et al, 2020). This approach also has vast policy implications, because by using synthetic populations, policymakers can evaluate the city-scale built environment policies (He et al, 2020), analyze the risk for cardiovascular disease (Krauland et al, 2020), and evaluate electricity consumption in a neighborhood (van Dam et al, 2017).…”

Section: Johnsonmentioning

confidence: 99%

Exploring city digital twins as policy tools: A task-based approach to generating synthetic data on urban mobility

Papyshev

Yarime

2021

Data & Policy

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This article discusses the technology of city digital twins (CDTs) and its potential applications in the policymaking context. The article analyzes the history of the development of the concept of digital twins and how it is now being adopted on a city-scale. One of the most advanced projects in the field—Virtual Singapore—is discussed in detail to determine the scope of its potential domains of application and highlight challenges associated with it. Concerns related to data privacy, availability, and its applicability for predictive simulations are analyzed, and potential usage of synthetic data is proposed as a way to address these challenges. The authors argue that despite the abundance of urban data, the historical data are not always applicable for predictions about the events for which there does not exist any data, as well as discuss the potential privacy challenges of the usage of micro-level individual mobility data in CDTs. A task-based approach to urban mobility data generation is proposed in the last section of the article. This approach suggests that city authorities can establish services responsible for asking people to conduct certain activities in an urban environment in order to create data for possible policy interventions for which there does not exist useful historical data. This approach can help in addressing the challenges associated with the availability of data without raising privacy concerns, as the data generated through this approach will not represent any real individual in society.

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Section: Johnsonmentioning

confidence: 99%

Exploring city digital twins as policy tools: A task-based approach to generating synthetic data on urban mobility

Papyshev

Yarime

2021

Data & Policy

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“…Table 2 highlights the confusion matrix results for the three methods that were evaluated as a part of this analysis. 1 This result is different from the 2018 FYSAS statewide value (0.137) because the dataset was split in half for the purposes of model building. The models are compared with the result from the half of the dataset that they were tested on.…”

Section: Estimating Electronic Vaping Usage and Relevant Predictors Among Youth In Floridamentioning

confidence: 86%

“…Examples include predicting the effect of new cancer screening on the US population, identifying obesity hotspots (i.e., geographic areas with unusually high prevalence of high BMI individuals), and predicting the effect of interventions aimed to reduce opioid-related deaths. [1][2][3][4][5][6] In each case, predictive models need to use data on population demographics and geographic locations, along with the natural history of the disease, administrative data, etc. We can thus formulate the main question as follows:…”

Section: Introductionmentioning

confidence: 99%

Universal Mechanism to Link Multiple Datasets with Synthetic Populations for Clinical and Epidemiological Research

Bobashev

Hadley

Karr³

et al. 2021

Preprint

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Many modern predictive models require knowledge acquired from multiple datasets, yet data linkage can be a substantial challenge. Datasets exist in a wide variety of formats and linking them directly is often infeasible or restricted by privacy requirements. One solution is to map variables from different datasets onto a synthetic population, producing a dataset that contains information from multiple sources sufficient for reliable statistical inference with quantifiable uncertainty. This approach is universal because it is applicable to a broad range of datasets and has good potential for privacy protection.We consider datasets with information about individuals and describe three methods for building linked synthetic data: resampling, modeling predictors independently, and modeling predictors sequentially. We apply these methods to the prediction of the prevalence of Florida youth vaping by state, county, and census tract using the 2018 Florida Youth Substance Abuse Survey (FYSAS) and the 5-Year American Community Survey (ACS). We find that resampling and sequential modeling most closely approximate the 2018 survey results, and that the sequential model captures more variability. We find that the order of predicting the variables in sequential modeling does not substantially impact the outcome.

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“…26 Importantly, risk evaluations of synthetic data use reveal that no direct connections between a generated data point and the real person are possible and that synthetic data (SD) are regarded as low-risk in practice. 27,28 Some study results show that synthetic data have high validity and can be used as a proxy in clinical trials, 29 in neuroimaging studies, 30 or RWD 27,[31][32][33][34][35] and do not lead to an increased bias, however, systematic and scoping reviews are still lacking. Utility metrics serve as a way to assess the consistency between RWD and synthetic data, as well as validate and measure utility.…”

Section: Privacy and Utilitymentioning

confidence: 99%

A Case for Synthetic Data in Regulatory Decision‐Making in Europe

Alloza,

Knox,

Raad

et al. 2023

Clin Pharma and Therapeutics

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Regulators are faced with many challenges surrounding health data usage, including privacy, fragmentation, validity, and generalizability, especially in the European Union (EU), for which synthetic data may provide innovative solutions. Synthetic data, defined as data artificially generated rather than captured in the real world, are increasingly being used for healthcare research purposes as a proxy to real‐world data (RWD). Currently, there are barriers particularly challenging in Europe, where sharing patient's data is strictly regulated, costly, and time consuming, causing delays in evidence generation and regulatory approvals. Recent initiatives are encouraging the use of synthetic data in regulatory decision‐making and health technology assessment to overcome these challenges, but synthetic data have still to overcome realistic obstacles before their adoption by researchers and regulators in Europe. Thus, the emerging use of RWD and synthetic data by pharmaceutical and medical device industries calls regulatory bodies to provide a framework for proper evidence generation and informed regulatory decision‐making. As the provision of data becomes more ubiquitous in scientific research, so will innovations in artificial intelligence, machine learning, and generation of synthetic data, making the exploration and intricacies of this topic all the more important and timely.In this review, we discuss the potential merits and challenges of synthetic data in the context of decision‐making in the European regulatory environment. We explore the current uses of synthetic data and ongoing initiatives, the value of synthetic data for regulatory purposes, and realistic barriers to the adoption of synthetic data in healthcare.

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Development of a Synthetic Population Model for Assessing Excess Risk for Cardiovascular Disease Death

Abstract: This decision analytical model describes the use of a semisynthetic population to identify the distribution of excess cardiovascular death risk and its correlation with social and biological risk factors.

Cited by 9 publications

References 33 publications

Exploring city digital twins as policy tools: A task-based approach to generating synthetic data on urban mobility

Exploring city digital twins as policy tools: A task-based approach to generating synthetic data on urban mobility

Universal Mechanism to Link Multiple Datasets with Synthetic Populations for Clinical and Epidemiological Research

A Case for Synthetic Data in Regulatory Decision‐Making in Europe

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