Improving Public Health Requires Inclusion of Underrepresented Populations in Research.
Ontology highlight
ABSTRACT:
SUBMITTER: Spong CY
PROVIDER: S-EPMC10082651 | biostudies-literature | 2018 Jan
REPOSITORIES: biostudies-literature
Publications
Improving Public Health Requires Inclusion of Underrepresented Populations in Research.
Spong Catherine Y CY Bianchi Diana W DW
JAMA 20180101 4
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Project description:BackgroundHistorically, groups that are most susceptible to health and healthcare disparities have been underrepresented in medical research. It is imperative to explore approaches that can facilitate the recruitment of underrepresented individuals into research studies.MethodsTwo approaches, hospital and community-based recruitment (CBR), were developed and implemented over 36 months to study the genetics of hereditary breast cancer and associated cancers in Alabama, a medically underserved state with double the national percentage of self-identifying African Americans, establishing the Alabama Hereditary Cancer Cohort.ResultsOverall, 242 individuals enrolled. This included 84 cancer probands through hospital recruitment, as well as 76 probands and 82 family members through CBR. Eighty-one percent of the study participants' counties of residence are completely medically underserved. Furthermore, African Americans represent 26% of the hospital probands compared to 49% and 70% of the probands and family members who, respectively, enrolled through CBR.ConclusionAlthough both recruitment mechanisms were instrumental, the unique trust building, educational, and traveling components of CBR facilitated the enrollment of African Americans resulting in large families for genetic analyses. The ultimate goal is to gain insight from these rudimentary efforts in order to expand recruitment and accrue a unique resource for cancer genetics research.
| S-EPMC6160710 | biostudies-literature
Project description:BackgroundHuman genetics research lacks diversity; over 80% of genome-wide association studies have been conducted on individuals of European ancestry. In addition to limiting insights regarding disease mechanisms, disproportionate representation can create disparities preventing equitable implementation of personalized medicine.ObjectiveThis systematic review provides an overview of research involving Parkinson's disease (PD) genetics in underrepresented populations (URP) and sets a baseline to measure the future impact of current efforts in those populations.MethodsWe searched PubMed and EMBASE until October 2021 using search strings for "PD," "genetics," the main "URP," and and the countries in Latin America, Caribbean, Africa, Asia, and Oceania (excluding Australia and New Zealand). Inclusion criteria were original studies, written in English, reporting genetic results on PD from non-European populations. Two levels of independent reviewers identified and extracted information.ResultsWe observed imbalances in PD genetic studies among URPs. Asian participants from Greater China were described in the majority of the articles published (57%), but other populations were less well studied; for example, Blacks were represented in just 4.0% of the publications. Also, although idiopathic PD was more studied than monogenic forms of the disease, most studies analyzed a limited number of genetic variants. We identified just nine studies using a genome-wide approach published up to 2021, including URPs.ConclusionThis review provides insight into the significant lack of population diversity in PD research highlighting the immediate need for better representation. The Global Parkinson's Genetics Program (GP2) and similar initiatives aim to impact research in URPs, and the early metrics presented here can be used to measure progress in the field of PD genetics in the future. © 2022 International Parkinson and Movement Disorder Society.
| S-EPMC10360137 | biostudies-literature
Project description:Genetic risk modeling for dementia offers significant benefits, but studies based on real-world data, particularly for underrepresented populations, are limited. We employ an Elastic Net model for dementia risk prediction using single-nucleotide polymorphisms prioritized by functional genomic data from multiple neurodegenerative disease genome-wide association studies. We compare this model with APOE and polygenic risk score models across genetic ancestry groups (Hispanic Latino American sample: 610 patients with 126 cases; African American sample: 440 patients with 84 cases; East Asian American sample: 673 patients with 75 cases), using electronic health records from UCLA Health for discovery and the All of Us cohort for validation. Our model significantly outperforms other models across multiple ancestries, improving the area-under-precision-recall curve by 31-84% (Wilcoxon signed-rank test p-value <0.05) and the area-under-the-receiver-operating characteristic by 11-17% (DeLong test p-value <0.05) compared to the APOE and the polygenic risk score models. We identify shared and ancestry-specific risk genes and biological pathways, reinforcing and adding to existing knowledge. Our study highlights the benefits of integrating functional mapping, multiple neurodegenerative diseases, and machine learning for genetic risk models in diverse populations. Our findings hold potential for refining precision medicine strategies in dementia diagnosis.
| S-EPMC11345412 | biostudies-literature
Project description:BackgroundGenetic risk modeling for dementia offers significant benefits, but studies based on real-world data, particularly for underrepresented populations, are limited.MethodsWe employed an Elastic Net model for dementia risk prediction using single-nucleotide polymorphisms prioritized by functional genomic data from multiple neurodegenerative disease genome-wide association studies. We compared this model with APOE and polygenic risk score models across genetic ancestry groups, using electronic health records from UCLA Health for discovery and All of Us cohort for validation.ResultsOur model significantly outperforms other models across multiple ancestries, improving the area-under-precision-recall curve by 21-61% and the area-under-the-receiver-operating characteristic by 10-21% compared to the APOEand the polygenic risk score models. We identified shared and ancestry-specific risk genes and biological pathways, reinforcing and adding to existing knowledge.ConclusionsOur study highlights benefits of integrating functional mapping, multiple neurodegenerative diseases, and machine learning for genetic risk models in diverse populations. Our findings hold potential for refining precision medicine strategies in dementia diagnosis.
| S-EPMC10896371 | biostudies-literature
Project description:BackgroundGenetic risk modeling for dementia offers significant benefits, but studies based on real-world data, particularly for underrepresented populations, are limited.MethodsWe employed an Elastic Net model for dementia risk prediction using single-nucleotide polymorphisms prioritized by functional genomic data from multiple neurodegenerative disease genome-wide association studies. We compared this model with APOE and polygenic risk score models across genetic ancestry groups, using electronic health records from UCLA Health for discovery and All of Us cohort for validation.ResultsOur model significantly outperforms other models across multiple ancestries, improving the area-under-precision-recall curve by 21-61% and the area-under-the-receiver-operating characteristic by 10-21% compared to the APOE and the polygenic risk score models. We identified shared and ancestry-specific risk genes and biological pathways, reinforcing and adding to existing knowledge.ConclusionsOur study highlights benefits of integrating functional mapping, multiple neurodegenerative diseases, and machine learning for genetic risk models in diverse populations. Our findings hold potential for refining precision medicine strategies in dementia diagnosis.
| S-EPMC10871463 | biostudies-literature
Project description:The purpose of this article is to provide an overview of the role of nurse scientists in -omics-based research and to promote discussion around the conduct of -omics-based nursing research in minority communities. Nurses are advocates, educators, practitioners, scientists, and researchers, and are crucial to the design and successful implementation of -omics studies, particularly including minority communities. The contribution of nursing in this area of research is crucial to reducing health disparities.In this article, challenges in the conduct of -omics-based research in minority communities are discussed, and recommendations for improving diversity among nurse scientists, study participants, and utilization of training and continuing education programs in -omics are provided.Many opportunities exist for nurses to increase their knowledge in -omics and to continue to build the ranks of nurse scientists as leaders in -omics-based research. In order to work successfully with communities of color, nurse scientists must advocate for participation in the Precision Medicine Initiative, improve representation of nurse faculty of color, and increase utilization of training programs in -omics and lead such initiatives.All nursing care has the potential to be affected by the era of -omics and precision health. By taking an inclusive approach to diversity in nursing and -omics research, nurses will be well placed to be leaders in reducing health disparities through research, practice, and education.
| S-EPMC5935491 | biostudies-other
Project description:BackgroundTo increase engagement of historically underrepresented groups in health sciences research, we created the North Carolina Diversity and Inclusion Pathway Program (NC-DIPP). This year-long, paid internship provides undergraduate and graduate students from 2 historically Black colleges and universities an opportunity to gain real-world experience under the mentorship of expert faculty.MethodsTo evaluate the early experiences with the NC-DIPP program, we conducted semi-structured interviews with interns and program leaders. Faculty mentors completed an online questionnaire to describe their experiences to date. A thematic approach was used to analyze the findings.ResultsIn March-April 2023, 7 of 8 interns (88%), 6 of 11 mentors (54%), and 4 of 4 program leaders (100%) participated in various evaluation components. Overall, respondents agreed about the importance of programs like NC-DIPP, which further engage historically underrepresented groups in the health sciences. Interns had positive feedback about the internship, including real-world work experience, connections to experienced mentors, and early career planning. On a scale of 1 (poor) to 10 (excellent), interns rated their experience as a median of 8.3 (range: 4.5-10.0). Mentors had favorable but slightly lower scores (median: 7.0, range: 5.0-8.0). Areas for improvement were noted, including clearer expectations, improved logistical support, and central engagement of interns across projects.ConclusionsThis early evaluation of NC-DIPP was generally favorable across all stakeholder groups. By providing a long-term experience in health science research, such programs can contribute to work experience, career planning, and professional networking.
| S-EPMC11253698 | biostudies-literature
Project description:IntroductionAlzheimer's disease and related dementias (ADRD) disproportionately impact racial and ethnic minority and socioeconomically disadvantaged adults. Yet, these populations are significantly underrepresented in research.MethodsWe systematically reviewed the literature for published reports describing recruitment and retention of individuals from underrepresented backgrounds in ADRD research or underrepresented participants' perspectives regarding ADRD research participation. Relevant evidence was synthesized and evaluated for quality.ResultsWe identified 22 eligible studies. Seven studies focused on recruitment/retention approaches, all of which included multifaceted efforts and at least one community outreach component. There was considerable heterogeneity in approaches used, specific activities and strategies, outcome measurement, and conclusions regarding effectiveness. Despite limited use of prospective evaluation strategies, most authors reported improvements in diverse representation in ADRD cohorts. Studies evaluating participant views focused largely on predetermined explanations of participation including attitudes, barriers/facilitators, education, trust, and religiosity. Across all studies, the strength of evidence was low.DiscussionOverall, the quantity and quality of available evidence to inform best practices in recruitment, retention, and inclusion of underrepresented populations in ADRD research are low. Further efforts to systematically evaluate the success of existing and emergent approaches will require improved methodological standards and uniform measures for evaluating recruitment, participation, and inclusivity.
| S-EPMC6944728 | biostudies-literature
Project description:BackgroundOhio, the catchment area of The Ohio State University Comprehensive Cancer Center (OSUCCC), includes diverse populations with different cancer profiles. As part of the National Cancer Institute (NCI)-funded initiative to conduct population health assessments in cancer center catchment areas, the OSUCCC surveyed residents, focusing on factors contributing to cancer disparities in Ohio populations.MethodsTwo sampling strategies were used: (i) probability sampling of mailing lists and (ii) convenience sampling at community events, coupled with phone/in-person/web surveys. Survey items were chosen along multilevel framework constructs, used in concert with other funded NCI-Designated Cancer Centers. Multivariable logistic regression models investigated predictors associated with health behaviors, cancer beliefs, knowledge, and screening.ResultsThe sample of 1,005 respondents were white (46.6%), African American (24.7%), Hispanic (13.7%), Somali (7.6%), and Asian (7.5%). A total of 216 respondents were Appalachian. Variations in cancer attitudes, knowledge, and behaviors were noted by racial/ethnic and geographic group. Multivariable models identified individuals with less financial security as less likely to exercise or be within guidelines for screening, but more likely to smoke and have a poor diet. At the community-level, measures of poverty were highest in Appalachia, whereas children in female-headed households were greater in urban minority areas.ConclusionsThis population health assessment reinforced the diversity of the OSUCCC catchment area. These populations are ripe for implementation science strategies, focusing in communities and clinics that serve vulnerable populations.ImpactUnderstanding attitudes, knowledge, and behaviors of this population can assist tailoring outreach and research strategies to lessen the cancer burden.
| S-EPMC6724198 | biostudies-literature
Project description:Prior research highlights that rural populations have been historically underrepresented/excluded from clinical research. The primary objective of this study was to describe the inclusion of rural populations within our research enterprise using Clinical Research Management System demographic information at a large academic medical center in the Southeast. This was a cross-sectional study using participant demographic information for all protocols entered into our Clinical Research Management System between May 2018 and March 2021. Descriptive statistics were used to analyze the representation of rural and non-rural participants and demographic breakdown by age, sex, race, and ethnicity for our entire enterprise and at the state level. We also compared Material Community Deprivation Index levels between urban and rural participants. Results indicated that 19% of the research population was classified as rural and 81% as non-rural for our entire sample, and 17.5% rural and 82.5% urban for our state-level sample. There were significant differences in race, sex, and age between rural and non-rural participants and Material Community Deprivation Indices between rural and non-rural participants. Lessons learned and recommendations for increasing the inclusion of rural populations in research are discussed.
| S-EPMC11294188 | biostudies-literature