Dataset Information

Recruiting under-represented populations into psychiatric research: Results from the help for hoarding study.

ABSTRACT: This study compares the effectiveness of approaches used to recruit a diverse sample for a randomized clinical trial for Hoarding Disorder (HD) in the San Francisco Bay Area. Of the 632 individuals who inquired about the study, 313 were randomized and 231 completed treatment. Most participants heard about the study via flyering (N?=?161), followed by advocacy groups (N?=?113), word of mouth (N?=?84), health care professionals (N?=?78), online (N?=?68), and media (N?=?11). However, those that heard about the study via advertising methods, such as flyers, were less likely to complete the study, p?=?.01, while those recruited via advocacy groups were most likely to be randomized, p?=?.03. No source proved more effective in recruiting underrepresented groups such as men, p?=?.60; non-whites, p?=?.49; or Hispanics, p?=?.97. Advertising recruited the youngest individuals, p?

SUBMITTER: Martin AM

PROVIDER: S-EPMC6240798 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Recruiting under-represented populations into psychiatric research: Results from the help for hoarding study.

Martin Anna M AM Zakrzewski Jessica J JJ Chou Chia-Ying CY Uhm Soo Y SY Gause R Michael RM Chan Joanne J Eckfield Monika M Salazar Mark M Vigil Ofilio O Bain David D Stark Sandra J SJ Mackin R Scott RS Vega Eduardo E Delucchi Kevin L KL Tsoh Janice Y JY Mathews Carol A CA

Contemporary clinical trials communications 20181109

This study compares the effectiveness of approaches used to recruit a diverse sample for a randomized clinical trial for Hoarding Disorder (HD) in the San Francisco Bay Area. Of the 632 individuals who inquired about the study, 313 were randomized and 231 completed treatment. Most participants heard about the study via flyering (N = 161), followed by advocacy groups (N = 113), word of mouth (N = 84), health care professionals (N = 78), online (N = 68), and media (N = 11). However, those that hea ...[more]

PMID: 30480164

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Project description:ObjectivesIndividuals under-recruited in diabetes research studies include those not seen at endocrinology centres and those from rural, low socioeconomic and/or under-represented racial/ethnic groups. The purpose of this descriptive analysis is to detail recruitment and retention efforts of Project ECHO Diabetes clinical sites affiliated with Stanford University and University of Florida.DesignProspective collection of participant engagement and qualitative analysis of barriers and facilitators of research engagement within Project ECHO Diabetes, a virtual tele-education programme for healthcare providers in the management of individuals with insulin-requiring diabetes.SettingData were collected at the patient level, provider level and clinic level between 1 May 2021 and 31 July 2022.ParticipantsParticipants and study personnel were recruited from 33 Project ECHO Diabetes sites in California and Florida.OutcomesWe report study completion rates for participants recruited into 33 Project ECHO Diabetes sites. Using barrier analysis, a methodology designed for the real-time assessment of interventions and system processes to identify barriers and facilitators, study personnel identified significant barriers to recruitment and retention and mapped them to actionable solutions.ResultsIn total, 872 participants (California n=495, Florida n=377) were recruited with differing recruitment rates by site (California=52.7%, Florida=21.5%). Barrier analysis identified lack of trust, unreliable contact information, communication issues and institutional review board (IRB) requirements as key recruitment barriers. Culturally congruent staff, community health centre (CHC) support, adequate funding and consent process flexibility were solutions to address recruitment challenges. Barriers to retention were inconsistent postal access, haemoglobin A1c kit collection challenges, COVID-19 pandemic and broadband/connectivity issues. Additional funding supporting research staff and analogue communication methods were identified as solutions address barriers to retention.ConclusionsFunded partnerships with CHCs, trusted by their local communities, were key in our recruitment and retention strategies. IRB consent process flexibility reduced barriers to recruitment. Recruiting historically under-represented populations is feasible with funding aimed to address structural barriers to research participation.

Project description:Neuromuscular diseases (NMDs) affect ∼15 million people globally. In high income settings DNA-based diagnosis has transformed care pathways and led to gene-specific therapies. However, most affected families are in low-to-middle income countries (LMICs) with limited access to DNA-based diagnosis. Most (86%) published genetic data is derived from European ancestry. This marked genetic data inequality hampers understanding of genetic diversity and hinders accurate genetic diagnosis in all income settings. We developed a cloud-based transcontinental partnership to build diverse, deeply-phenotyped and genetically characterized cohorts to improve genetic architecture knowledge, and potentially advance diagnosis and clinical management. We connected 18 centres in Brazil, India, South Africa, Turkey, Zambia, Netherlands and the UK. We co-developed a cloud-based data solution and trained 17 international neurology fellows in clinical genomic data interpretation. Single gene and whole exome data were analysed via a bespoke bioinformatics pipeline and reviewed alongside clinical and phenotypic data in global webinars to inform genetic outcome decisions. We recruited 6001 participants in the first 43 months. Initial genetic analyses 'solved' or 'possibly solved' ∼56% probands overall. In-depth genetic data review of the four commonest clinical categories (limb girdle muscular dystrophy, inherited peripheral neuropathies, congenital myopathy/muscular dystrophies and Duchenne/Becker muscular dystrophy) delivered a ∼59% 'solved' and ∼13% 'possibly solved' outcome. Almost 29% of disease causing variants were novel, increasing diverse pathogenic variant knowledge. Unsolved participants represent a new discovery cohort. The dataset provides a large resource from under-represented populations for genetic and translational research. In conclusion, we established a remote transcontinental partnership to assess genetic architecture of NMDs across diverse populations. It supported DNA-based diagnosis, potentially enabling genetic counselling, care pathways and eligibility for gene-specific trials. Similar virtual partnerships could be adopted by other areas of global genomic neurological practice to reduce genetic data inequality and benefit patients globally.

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Project description:Recent studies have demonstrated that polygenic risk scores (PRS) trained on multi-ancestry data can improve prediction accuracy in groups historically underrepresented in genomic studies, but the availability of linked health and genetic data from large-scale diverse cohorts representative of a wide spectrum of human diversity remains limited. To address this need, the All of Us research program (AoU) generated whole-genome sequences of 245,388 individuals who collectively reflect the diversity of the USA. Leveraging this resource and another widely-used population-scale biobank, the UK Biobank (UKB) with a half million participants, we developed PRS trained on multi-ancestry and multi-biobank data with up to ~750,000 participants for 32 common, complex traits and diseases across a range of genetic architectures. We then compared effects of ancestry, PRS methodology, and genetic architecture on PRS accuracy across a held out subset of ancestrally diverse AoU participants. Due to the more heterogeneous study design of AoU, we found lower heritability on average compared to UKB (0.075 vs 0.165), which limited the maximal achievable PRS accuracy in AoU. Overall, we found that the increased diversity of AoU significantly improved PRS performance in some participants in AoU, especially underrepresented individuals, across multiple phenotypes. Notably, maximizing sample size by combining discovery data across AoU and UKB is not the optimal approach for predicting some phenotypes in African ancestry populations; rather, using data from only AoU for these traits resulted in the greatest accuracy. This was especially true for less polygenic traits with large ancestry-enriched effects, such as neutrophil count (R 2: 0.055 vs. 0.035 using AoU vs. cross-biobank meta-analysis, respectively, because of e.g. DARC). Lastly, we calculated individual-level PRS accuracies rather than grouping by continental ancestry, a critical step towards interpretability in precision medicine. Individualized PRS accuracy decays linearly as a function of ancestry divergence, but the slope was smaller using multi-ancestry GWAS compared to using European GWAS. Our results highlight the potential of biobanks with more balanced representations of human diversity to facilitate more accurate PRS for the individuals least represented in genomic studies.

Dataset Information

Recruiting under-represented populations into psychiatric research: Results from the help for hoarding study.

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Recruiting under-represented populations into psychiatric research: Results from the help for hoarding study.

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