Respecting Autonomy And Enabling Diversity: The Effect Of Eligibility And Enrollment On Research Data Demographics

Spector‐Bagdady, Kayte; Tang, Shengpu; Jabbour, Sarah; Price, W. Nicholson; Bracic, Ana; Creary, Melissa S.; Kheterpal, Sachin; Brummett, Chad M.; Wiens, Jenna

doi:10.1377/hlthaff.2021.01197

Cited by 19 publications

(10 citation statements)

References 32 publications

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“…Moreover, the MGI cohort itself is less diverse in terms of age, sex, race, ethnicity, and socioeconomic status than the overall clinical population at Michigan Medicine. 38 Underrepresentation of minority individuals in particular can result in non-generalizable results and exacerbate existing health inequities. 39 , 40 There is a clear need to improve enrollment of underrepresented populations beyond what is obtained in the current recruitment strategy.…”

Section: Discussionmentioning

confidence: 99%

The Michigan Genomics Initiative: A biobank linking genotypes and electronic clinical records in Michigan Medicine patients

Zawistowski¹,

Fritsche²,

Pandit³

et al. 2023

Cell Genomics

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Section: Discussionmentioning

confidence: 99%

The Michigan Genomics Initiative: A biobank linking genotypes and electronic clinical records in Michigan Medicine patients

Zawistowski¹,

Fritsche²,

Pandit³

et al. 2023

Cell Genomics

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“…The information from single‐gene experiments is continually accumulating and providing information on how individual molecules and their interactions fit into a global picture of cell and tissue regulation. While such data collection suffers from research focus biases [93] and reproducibility concerns, systems biology and network‐level approaches are gradually becoming the new standard and adopted into a number of specific use cases such as systems immunology and systems medicine. [92].…”

Section: Deriving Causality One Network At a Timementioning

confidence: 99%

Multiorgan microphysiological systems as tools to interrogate interorgan crosstalk and complex diseases

Trapečar

2021

FEBS Letters

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Metabolic and inflammatory disorders such as autoimmune and neurodegenerative diseases are increasing at alarming rates. Many of these are not tissue‐specific occurrences but complex, systemic pathologies of unknown origin for which no cure exists. Such complexity obscures causal relationships among factors regulating disease progression. Emerging technologies mimicking human physiology, such as microphysiological systems (MPSs), offer new possibilities to provide clarity in systemic metabolic and inflammatory diseases. Controlled interaction of multiple MPSs and the scalability of biological complexity in MPSs, supported by continuous multiomic monitoring, might hold the key to identifying novel relationships between interorgan crosstalk, metabolism, and immunity. In this perspective, I aim to discuss the current state of modeling multiorgan physiology and evaluate current opportunities and challenges.

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“…1,3,5,6 Disproportionate enrollment and retention compounds inequities 3,7 disproportionately conferring any potential benefits of study participation. 8 Additionally, misrepresentation impedes the external validity of the evidence base and extends to suboptimal health policies, programs, and health care services. 3 Investigators could improve the representativeness of study populations by consistently reporting participant race and ethnicity and sample representativeness.…”

Section: Introductionmentioning

confidence: 99%

“…Once enrolled, these groups often exhibit lower retention rates than their White peers . Disproportionate enrollment and retention compounds inequities disproportionately conferring any potential benefits of study participation . Additionally, misrepresentation impedes the external validity of the evidence base and extends to suboptimal health policies, programs, and health care services .…”

Section: Introductionmentioning

confidence: 99%

Use of Bland-Altman Analysis to Examine the Racial and Ethnic Representativeness of Study Populations in Community-Based Pediatric Health Research

et al. 2023

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ImportanceChildren from marginalized racial and ethnic groups are underrepresented in health research. To improve external validity and routinize race and ethnicity reporting, a specific and standardized methodology for quantifying representativeness of participant populations is needed.ObjectiveTo develop a standardized method for quantifying the racial and ethnic representativeness of study samples.Design, Setting, and ParticipantsIn this cross-sectional study, data from 7 US community-based health studies (conducted between 2003 and 2017) were retrospectively pooled to assess the school-level representativeness of enrolled samples by race and ethnicity. The sampling frame for the study was constructed using the National Center of Education Statistics Common Core of Data, which provides year-specific racial and ethnic counts by grade. Representativeness was quantified by aggregating children’s data at the school level, reported individually for Asian, Black, Hispanic or Latino, Native Hawaiian or other Pacific Islander, White, or multiple races. In this analysis, the Asian and Native Hawaiian or other Pacific Islander subgroups were combined. Data were analyzed from April 1 to June 15, 2022.ExposureCommunity-based nutritional health studies conducted with children in grades 1 to 8.Main Outcomes and MeasuresVisual comparisons of percentage expected and percentage observed of the pooled sample by race and ethnicity were performed using scatterplots and Bland-Altman plots. Spearman rank-order correlation was used to assess associations.ResultsThis study included 104 study schools (N = 5807 children) located in California, Kentucky, Massachusetts, Mississippi, and South Carolina. Bland-Altman analysis revealed notable patterns and variability in the representativeness of racial and ethnic groups. Differences in the overall representativeness of Asian or Native Hawaiian or other Pacific Islander children (0.45 percentage points [95% CI, −7.76 to 8.66]), Black children (0.12 percentage points [95% CI, −15.73 to 15.96]), and White children (−0.72 percentage points [95% CI, −23.60 to 22.16]) were negligible, but measures of spread suggested that target population demographics affected representativeness differently across groups.Conclusions and RelevanceThe results of this cross-sectional study suggest that replicating, testing, and scaling the proposed method for quantifying racial and ethnic representativeness, which uses measures of spread, could improve the transparency of race and ethnicity reporting during publication and lead to a more externally valid health evidence base. During implementation, investigators should adopt community-based research methods and allocate appropriate resources during recruitment, including a priori assessment of population demographics, as these conditions may affect racial and ethnic study enrollment differently. Prioritizing these methodological decisions could alleviate rising inequities.

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Respecting Autonomy And Enabling Diversity: The Effect Of Eligibility And Enrollment On Research Data Demographics

Cited by 19 publications

References 32 publications

The Michigan Genomics Initiative: A biobank linking genotypes and electronic clinical records in Michigan Medicine patients

The Michigan Genomics Initiative: A biobank linking genotypes and electronic clinical records in Michigan Medicine patients

Multiorgan microphysiological systems as tools to interrogate interorgan crosstalk and complex diseases

Use of Bland-Altman Analysis to Examine the Racial and Ethnic Representativeness of Study Populations in Community-Based Pediatric Health Research

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