Development and Validation of a Large Synthetic Cohort for the Study of Cardiovascular Health Across the Life Span

Health economic evaluation of trans-tibial prosthetic suspension systems: a protocol for a pilot using an observational study and synthetic cohort

BACKGROUND

Health Economic Evaluations (HEEs) provide the necessary evidence of cost-benefit to inform policy and investment decisions. No HEEs have quantified the cost-benefit of passive suction (PS) vs vacuum assisted suction (VAS) suspension for trans-tibial prosthesis users. There are methodological challenges to conducting HEE in prosthetics given the benefit measures are not focused on the things most important to prosthesis users and funders, and the required time horizons are lengthy. To address these challenges, we propose a pilot study using two PROMIS instruments to measure benefits and trial the use of a Synthetic Cohort Method, to quantify the cost-effectiveness and cost-utility of PS and VAS suspension for people living with trans-tibial amputation.

METHODS

An observational study will measure the costs and benefits of PS and VAS suspension for trans-tibial prosthesis users using a Synthetic Cohort Method, a technique used in epidemiological modelling of life-time risks. Each intervention will include 3 sub-groups, representing prosthesis users in the first, second, or third year of the intervention since fitting. A prosthetic payor perspective will be taken, with data collected over a 1-year period and synthesised to reflect the costs and benefits over a 3-year time horizon. Benefits will be measured using two PROMIS instruments reported to best measure the benefits most important to prosthesis users and funders. Costs will be calculated from actual billable costs to the funder. Costs and benefits will be discounted at 4%. Cost-effectiveness and cost-utility will be calculated using the incremental costs and incremental benefits, with results presented as incremental cost-effectiveness and incremental cost-utility ratios. Bootstrapping will be undertaken to assess uncertainty, and discounting will be analysed through a one-way sensitivity analysis.

DISCUSSION

This pilot will make a novel contribution by trailing the use of a Synthetic Cohort Method to reduce the lengthy time horizons required in prosthetic HEE. The HEE will use a two-pronged approach whereby cost-utility and cost-effectiveness are simultaneously evaluated using the PROMIS instruments to inform a wide range of policy and investment decisions. Additionally, this pilot will be the first HEE of suction suspension systems for people with transtibial amputation and will therefore make an important contribution to the prosthetic evidence base.

Overcoming Data Gaps in Life Course Epidemiology by Matching Across Cohorts

Life course epidemiology is hampered by the absence of large studies with exposures and outcomes measured at different life stages in the same individuals. We describe when the effect of an exposure ( A ) on an outcome ( Y ) in a target population is identifiable in a combined ("synthetic") cohort created by pooling an early-life cohort including measures of A with a late-life cohort including measures of Y . We enumerate causal assumptions needed for unbiased effect estimation in the synthetic cohort and illustrate by simulating target populations under four causal models. From each target population, we randomly sampled early- and late-life cohorts and created a synthetic cohort by matching individuals from the two cohorts based on mediators and confounders. We estimated the effect of A on Y in the synthetic cohort, varying matching variables, the match ratio, and the strength of association between matching variables and A . Finally, we compared bias in the synthetic cohort estimates when matching variables did not d-separate A and Y to the bias expected in the original cohort. When the set of matching variables includes all variables d-connecting exposure and outcome (i.e., variables blocking all backdoor and front-door pathways), the synthetic cohort yields unbiased effect estimates. Even when matching variables did not fully account for confounders, the synthetic cohort estimate was sometimes less biased than comparable estimates in the original cohort. Methods based on merging cohorts may hasten the evaluation of early- and mid-life determinants of late-life health but rely on available measures of both confounders and mediators.

Aging, Race, and Health Disparities: Recommendations From the Research Centers Collaborative Network

Racial disparities in adverse health outcomes with aging have been well described. Yet, much of the research focuses on racial comparisons, with relatively less attention to the identification of underlying mechanisms. To address these gaps, the Research Centers Collaborative Network held a workshop on aging, race, and health disparities to identify research priorities and inform the investigation, implementation, and dissemination of strategies to mitigate disparities in healthy aging. This article provides a summary of the key recommendations and highlights the need for research that builds a strong evidence base with both clinical and policy implications. Successful execution of these recommendations will require a concerted effort to increase participation of underrepresented groups in research through community engagement and partnerships. In addition, resources to support and promote the training and development of health disparities researchers will be critical in making health equity a shared responsibility for all major stakeholders.

Five-Year Cumulative Cardiovascular Health and Clinical Events in Patients With Chronic Kidney Disease: The CRIC Study

BACKGROUND

Higher cardiovascular health (CVH) score is associated with lower risks of cardiovascular disease (CVD) and mortality in the general population. However, it is unclear whether cumulative CVH is associated with CVD, end-stage kidney disease (ESKD), and death in patients with chronic kidney disease.

METHODS AND RESULTS

Among individuals from the prospective CRIC (Chronic Renal Insufficiency Cohort) Study, we used the percentage of the maximum possible CVH score attained from baseline to the year 5 visit to calculate cumulative CVH score. Multivariable-adjusted Cox proportional hazards regression was used to investigate the associations of cumulative CVH with risks of adjudicated CVD (myocardial infarction, stroke, and heart failure), ESKD, and all-cause mortality. A total of 3939 participants (mean age, 57.7 years; 54.9% men) were included. The mean (SD) cumulative CVH score attained during 5 years was 55.5% (12.3%). Over a subsequent median 10.2-year follow-up, 597 participants developed CVD, 656 had ESKD, and 1324 died. A higher cumulative CVH score was significantly associated with lower risks of CVD, ESKD, and mortality, independent of the CVH score at year 5. Multivariable-adjusted hazard ratios and 95% CIs per 10% higher cumulative CVH score during 5 years were 0.81 (0.69-0.95) for CVD, 0.82 (0.70-0.97) for ESKD, and 0.80 (0.72-0.89) for mortality.

CONCLUSIONS

Among patients with chronic kidney disease stages 2 to 4, a better CVH status maintained throughout 5 years is associated with lower risks of CVD, ESKD, and all-cause mortality. The findings support the need for interventions to maintain ideal CVH status for prevention of adverse outcomes in the population with chronic kidney disease.

Longitudinal clustering of Life's Essential 8 health metrics: application of a novel unsupervised learning method in the CARDIA study

OBJECTIVE

Changes in cardiovascular health (CVH) during the life course are associated with future cardiovascular disease (CVD). Longitudinal clustering analysis using subgraph augmented non-negative matrix factorization (SANMF) could create phenotypic risk profiles of clustered CVH metrics.

MATERIALS AND METHODS

Life's Essential 8 (LE8) variables, demographics, and CVD events were queried over 15 ears in 5060 CARDIA participants with 18 years of subsequent follow-up. LE8 subgraphs were mined and a SANMF algorithm was applied to cluster frequently occurring subgraphs. K-fold cross-validation and diagnostics were performed to determine cluster assignment. Cox proportional hazard models were fit for future CV event risk and logistic regression was performed for cluster phenotyping.

RESULTS

The cohort (54.6% female, 48.7% White) produced 3 clusters of CVH metrics: Healthy & Late Obesity (HLO) (29.0%), Healthy & Intermediate Sleep (HIS) (43.2%), and Unhealthy (27.8%). HLO had 5 ideal LE8 metrics between ages 18 and 39 years, until BMI increased at 40. HIS had 7 ideal LE8 metrics, except sleep. Unhealthy had poor levels of sleep, smoking, and diet but ideal glucose. Race and employment were significantly different by cluster (P < .001) but not sex (P = .734). For 301 incident CV events, multivariable hazard ratios (HRs) for HIS and Unhealthy were 0.73 (0.53-1.00, P = .052) and 2.00 (1.50-2.68, P < .001), respectively versus HLO. A 15-year event survival was 97.0% (HIS), 96.3% (HLO), and 90.4% (Unhealthy, P < .001).

DISCUSSION AND CONCLUSION

SANMF of LE8 metrics identified 3 unique clusters of CVH behavior patterns. Clustering of longitudinal LE8 variables via SANMF is a robust tool for phenotypic risk assessment for future adverse cardiovascular events.

"We adjusted for race": now what? A systematic review of utilization and reporting of race in American Journal of Epidemiology and Epidemiology, 2020-2021

Race is a social construct, commonly used in epidemiologic research to adjust for confounding. However, adjustment of race may mask racial disparities, thereby perpetuating structural racism. We conducted a systematic review of articles published in Epidemiology and American Journal of Epidemiology between 2020 and 2021 to (1) understand how race, ethnicity, and similar social constructs were operationalized, used, and reported; and (2) characterize good and poor practices of utilization and reporting of race data on the basis of the extent to which they reveal or mask systemic racism. Original research articles were considered for full review and data extraction if race data were used in the study analysis. We extracted how race was categorized, used-as a descriptor, confounder, or for effect measure modification (EMM)-and reported if the authors discussed racial disparities and systemic bias-related mechanisms responsible for perpetuating the disparities. Of the 561 articles, 299 had race data available and 192 (34.2%) used race data in analyses. Among the 160 US-based studies, 81 different racial categorizations were used. Race was most often used as a confounder (52%), followed by effect measure modifier (33%), and descriptive variable (12%). Fewer than 1 in 4 articles (22.9%) exhibited good practices (EMM along with discussing disparities and mechanisms), 63.5% of the articles exhibited poor practices (confounding only or not discussing mechanisms), and 13.5% were considered neither poor nor good practices. We discuss implications and provide 13 recommendations for operationalization, utilization, and reporting of race in epidemiologic and public health research.

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

Regulators are faced with many challenges surrounding health data usage, including privacy, fragmentation, validity, and generalizability, especially in the European Union, 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.