Public willingness to participate in personalized health research and biobanking: A large-scale Swiss survey

This paper reports survey findings on the Swiss public’s willingness, attitudes, and concerns regarding personalized health research participation by providing health information and biological material. The survey reached a sample of 15,106 Swiss residents, from which we received 5,156 responses (34.1% response rate). The majority of respondents were aware of research using human biological samples (71.0%) and held a positive opinion towards this type of research (62.4%). Of all respondents, 53.6% indicated that they would be willing to participate in a personalized health research project. Willingness to participate was higher in younger, higher educated, non-religious respondents with a background in the health sector. Respondents were more willing to provide ‘traditional’ types of health data, such as health questionnaires, blood or biological samples, as opposed to social media or app-related data. All respondents valued the return of individual research results, including risk for diseases for which no treatment is available. Our findings highlight that alongside general positive attitudes towards personalized health research using data and samples, respondents have concerns about data privacy and re-use. Concerns included potential discrimination, confidentiality breaches, and misuse of data for commercial or marketing purposes. The findings of this large-scale survey can inform Swiss research institutions and assist policymakers with adjusting practices and developing policies to better meet the needs and preferences of the public. Efforts in this direction could focus on research initiatives engaging in transparent communication, education, and engagement activities, to increase public understanding and insight into data sharing activities, and ultimately strengthen personalized health research efforts.

Download Full-text

A systematic literature review of Native American and Pacific Islanders’ perspectives on health data privacy in the United States

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocaa235 ◽

2020 ◽

Vol 27 (12) ◽

pp. 1987-1998

Author(s):

Riley Taitingfong ◽

Cinnamon S Bloss ◽

Cynthia Triplett ◽

Julie Cakici ◽

Nanibaa’ Garrison ◽

...

Keyword(s):

Native American ◽

Literature Review ◽

Systematic Literature Review ◽

Alaska Native ◽

Health Research ◽

Data Privacy ◽

Indigenous Communities ◽

The United States ◽

Health Data ◽

Willingness To Participate

Abstract Background Privacy-related concerns can prevent equitable participation in health research by US Indigenous communities. However, studies focused on these communities' views regarding health data privacy, including systematic reviews, are lacking. Methods We conducted a systematic literature review analyzing empirical, US-based studies involving American Indian/Alaska Native (AI/AN) and Native Hawaiian or other Pacific Islander (NHPI) perspectives on health data privacy, which we define as the practice of maintaining the security and confidentiality of an individual’s personal health records and/or biological samples (including data derived from biological specimens, such as personal genetic information), as well as the secure and approved use of those data. Results Twenty-one studies involving 3234 AI/AN and NHPI participants were eligible for review. The results of this review suggest that concerns about the privacy of health data are both prevalent and complex in AI/AN and NHPI communities. Many respondents raised concerns about the potential for misuse of their health data, including discrimination or stigma, confidentiality breaches, and undesirable or unknown uses of biological specimens. Conclusions Participants cited a variety of individual and community-level concerns about the privacy of their health data, and indicated that these deter their willingness to participate in health research. Future investigations should explore in more depth which health data privacy concerns are most salient to specific AI/AN and NHPI communities, and identify the practices that will make the collection and use of health data more trustworthy and transparent for participants.

Download Full-text

4382 All IN for Health: Promoting good health and engaging a health research volunteer community in the Hoosier state

Journal of Clinical and Translational Science ◽

10.1017/cts.2020.190 ◽

2020 ◽

Vol 4 (s1) ◽

pp. 53-53

Author(s):

Jessica Hall ◽

Christine Drury ◽

Carmel Egan

Keyword(s):

Rural Communities ◽

Health Research ◽

Online Community ◽

Large Scale ◽

Research Participation ◽

Good Health ◽

The State ◽

List Type ◽

Research Literacy ◽

Research Volunteers

OBJECTIVES/GOALS: To improve and expand health and research literacy throughout Indiana by sharing health-focused resources and research outcomes.To encourage and increase health research participation throughout Indiana by promoting health research opportunities, including clinical studies.METHODS/STUDY POPULATION: Discover and understand community concerns and barriers to good health and clinical research participation by providing a platform for individuals and communities to share their voices.Educate Indiana residents on the importance of participating in health research.Engage with the community to meet them where they are (online) and continue to build relationships throughout the state.Promote healthy living for Indiana residents by sharing health education and resources from existing state health organizations and initiatives.Develop and maintain the largest statewide database of research volunteers.RESULTS/ANTICIPATED RESULTS: The anticipated results from this program include engagement of all populations and all communities throughout the state in conversation and education around good health and health research, as well as participation in health research across the CTSI’s partner organizations. Large-scale growth is expected in both the online community and consented volunteer registry is expected to include and engage racially and ethnically diverse populations, as well as special health populations, such as representatives of rural communities, aged, rare disease survivors, and transgender individuals.DISCUSSION/SIGNIFICANCE OF IMPACT: Thorough this work, the Indiana CTSI has developed a unique program, educating the public about health research and opportunities to participate, while simultaneously supporting research departments with marketing promotion of their efforts, and a ready statewide volunteer community.

Download Full-text

Improving community participation in clinical and translational research: CTSA Sentinel Network proof of concept study

Journal of Clinical and Translational Science ◽

10.1017/cts.2020.21 ◽

2020 ◽

Vol 4 (4) ◽

pp. 323-330

Author(s):

Deepthi S. Varma ◽

Alvin H. Strelnick ◽

Nancy Bennett ◽

Patricia Piechowski ◽

Sergio Aguilar-Gaxiola ◽

...

Keyword(s):

Health Research ◽

Minority Groups ◽

Research Participation ◽

Albert Einstein College ◽

Community Outreach ◽

Willingness To Participate ◽

Community Members ◽

Sentinel Network ◽

The University ◽

Research Studies

AbstractBackground:Research participation by members of racial or ethnic minority groups continues to be less than optimum resulting in difficulties to generalization of research findings. Community-engaged research that relies on a community health worker (CHW) model has been found effective in building trust in the community, thereby motivating people to participate in health research. The Sentinel Network study aimed at testing the feasibility of utilizing the CHW model to link community members to appropriate health research studies at each of the research sites.Methods:The study was conducted at six Clinical and Translational Science Award institutions (N = 2371) across the country; 733 (30.9%) of the participants were from the University of Florida, 525 (22.0%) were from Washington University in St. Louis, 421 (17.8%) were from the University of California, Davis, 288 (12.1%) were from the University of Michigan, Ann Arbor, 250 (10.5%) were from Rochester, and 154 (6.5%) from Albert Einstein College of Medicine. Trained CHWs from each of these sites conducted regular community outreach where they administered a Health Needs Assessment, provided medical and social referrals, and linked to eligible research studies at each of those sites. A 30-day follow-up assessment was developed to track utilization of services satisfaction with the services and research study participation.Results:A large majority of people, especially African Americans, expressed willingness to participate in research studies. The top two health concerns reported by participants were hypertension and diabetes.Conclusion:Findings on the rate of navigation and enrollment in research from this study indicate the effectiveness of a hybrid CHW service and research model of directly engaging community members to encourage people to participate in research.

Download Full-text

Big Data Analytics & Artificial Intelligence in Healthcare

10.21203/rs.3.rs-150303/v1 ◽

2021 ◽

Author(s):

PRANJAL KUMAR ◽

Siddhartha Chauhan

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Large Scale ◽

Health Sector ◽

Big Data Analytics ◽

Personal Data ◽

Related Data ◽

Large Scale Data ◽

Ethical Policy ◽

Health Related

Abstract Big data analysis and Artificial Intelligence have received significant attention recently in creating more opportunities in the health sector for aggregating or collecting large-scale data. Today, our genomes and microbiomes can be sequenced i.e., all information exchanged between physicians and patients in Electronic Health Records (EHR) can be collected and traced at least theoretically. Social media and mobile devices today obviously provide many health-related data regarding activity, diets, social contacts, and so on. However, it is increasingly difficult to use this information to answer health questions and, in particular, because the data comes from various domains and lives in different infrastructures and of course it also is very variable quality. The massive collection and aggregation of personal data come with a number of ethical policy, methodological, technological challenges. It should be acknowledged that large-scale clinical evidence remains to confirm the promise of Big Data and Artificial Intelligence (AI) in health care. This paper explores the complexities of big data & artificial intelligence in healthcare as well as the benefits and prospects.

Download Full-text

Secure multiparty computation for privacy-preserving drug discovery

Bioinformatics ◽

10.1093/bioinformatics/btaa038 ◽

2020 ◽

Vol 36 (9) ◽

pp. 2872-2880 ◽

Cited By ~ 4

Author(s):

Rong Ma ◽

Yi Li ◽

Chenxing Li ◽

Fangping Wan ◽

Hailin Hu ◽

...

Keyword(s):

Drug Discovery ◽

Data Privacy ◽

Large Scale ◽

Quantitative Structure Activity Relationship ◽

Privacy Preserving ◽

Secure Multiparty Computation ◽

Supplementary Information ◽

Supporting Evidence ◽

Multiparty Computation ◽

Related Data

Abstract Motivation Quantitative structure–activity relationship (QSAR) and drug–target interaction (DTI) prediction are both commonly used in drug discovery. Collaboration among pharmaceutical institutions can lead to better performance in both QSAR and DTI prediction. However, the drug-related data privacy and intellectual property issues have become a noticeable hindrance for inter-institutional collaboration in drug discovery. Results We have developed two novel algorithms under secure multiparty computation (MPC), including QSARMPC and DTIMPC, which enable pharmaceutical institutions to achieve high-quality collaboration to advance drug discovery without divulging private drug-related information. QSARMPC, a neural network model under MPC, displays good scalability and performance and is feasible for privacy-preserving collaboration on large-scale QSAR prediction. DTIMPC integrates drug-related heterogeneous network data and accurately predicts novel DTIs, while keeping the drug information confidential. Under several experimental settings that reflect the situations in real drug discovery scenarios, we have demonstrated that DTIMPC possesses significant performance improvement over the baseline methods, generates novel DTI predictions with supporting evidence from the literature and shows the feasible scalability to handle growing DTI data. All these results indicate that QSARMPC and DTIMPC can provide practically useful tools for advancing privacy-preserving drug discovery. Availability and implementation The source codes of QSARMPC and DTIMPC are available on the GitHub: https://github.com/rongma6/QSARMPC_DTIMPC.git. Supplementary information Supplementary data are available at Bioinformatics online.

Download Full-text

Asian Americans are less willing than other racial groups to participate in health research

Journal of Clinical and Translational Science ◽

10.1017/cts.2019.372 ◽

2019 ◽

Vol 3 (2-3) ◽

pp. 90-96

Author(s):

Yiyang Liu ◽

Amy Elliott ◽

Hal Strelnick ◽

Sergio Aguilar-Gaxiola ◽

Linda B. Cottler

Keyword(s):

Ethnic Groups ◽

Asian Americans ◽

Health Research ◽

Research Participation ◽

Lower Amount ◽

Racial Groups ◽

Willingness To Participate ◽

Community Members ◽

Study Results ◽

Racial Ethnic

AbstractBackground:Asian Americans constitute 5% of the U.S. population. Their willingness to participate in research is important to examine because it influences participation rates and the representativeness of study results.Methods:A total of 17,339 community members participated from six diverse Clinical and Translational Award (CTSA) sites. Community members were asked about their willingness to volunteer for eight different types of health research, their expectation of monetary compensation for research participation, their trust in research and researchers, their preferred language to receive health information, and their socio-demographic background. We examined Asian Americans’ willingness to participate in various types of health research studies and compared their perceptions with other racial/ethnic groups (i.e., Asian n = 485; African-American n = 9516; Hispanic/Latino n = 1889; Caucasian n = 4760; and other minority n = 689).Results:Compared to all other racial/ethnic groups, Asian Americans were less willing to participate in all eight types of health research. However, Asian Americans reported a lower amount of fair compensation for research participation than African-Americans and Hispanics/Latinos but were as likely to trust researchers as all other racial/ethnic groups.Conclusion:Asian Americans are less willing to participate in health research than other racial/ethnic groups, and this difference is not due to dissatisfaction with research compensation or lower trust in researchers. Lack of trust in research and language barriers should be addressed to improve representativeness and generalizability of all populations in research.

Download Full-text

Computer Analyses in Preventive Health Research

Methods of Information in Medicine ◽

10.1055/s-0038-1636287 ◽

1967 ◽

Vol 06 (01) ◽

pp. 8-14 ◽

Cited By ~ 11

Author(s):

M. F. Collen

Keyword(s):

Medical Research ◽

Preventive Medicine ◽

Health Research ◽

Large Scale ◽

Preventive Health ◽

New Era ◽

Large Numbers ◽

Normal Test ◽

Computer Analyses

The utilization of an automated multitest laboratory as a data acquisition center and of a computer for trie data processing and analysis permits large scale preventive medical research previously not feasible. Normal test values are easily generated for the particular population studied. Long-term epidemiological research on large numbers of persons becomes practical. It is our belief that the advent of automation and computers has introduced a new era of preventive medicine.

Download Full-text

Quality Control of Data in a Large-Scale Cancer Register Program

Methods of Information in Medicine ◽

10.1055/s-0038-1636339 ◽

1966 ◽

Vol 05 (02) ◽

pp. 67-74 ◽

Cited By ~ 5

Author(s):

W. I. Lourie ◽

W. Haenszeland

Keyword(s):

United States ◽

Quality Control ◽

Cancer Patients ◽

Large Scale ◽

The United States ◽

Newly Diagnosed ◽

Related Data ◽

Cancer Register ◽

Independent Review ◽

End Results

Quality control of data collected in the United States by the Cancer End Results Program utilizing punchcards prepared by participating registries in accordance with a Uniform Punchcard Code is discussed. Existing arrangements decentralize responsibility for editing and related data processing to the local registries with centralization of tabulating and statistical services in the End Results Section, National Cancer Institute. The most recent deck of punchcards represented over 600,000 cancer patients; approximately 50,000 newly diagnosed cases are added annually.Mechanical editing and inspection of punchcards and field audits are the principal tools for quality control. Mechanical editing of the punchcards includes testing for blank entries and detection of in-admissable or inconsistent codes. Highly improbable codes are subjected to special scrutiny. Field audits include the drawing of a 1-10 percent random sample of punchcards submitted by a registry; the charts are .then reabstracted and recoded by a NCI staff member and differences between the punchcard and the results of independent review are noted.

Download Full-text

Federated Learning in a Medical Context: A Systematic Literature Review

ACM Transactions on Internet Technology ◽

10.1145/3412357 ◽

2021 ◽

Vol 21 (2) ◽

pp. 1-31

Author(s):

Bjarne Pfitzner ◽

Nico Steckhan ◽

Bert Arnrich

Keyword(s):

Machine Learning ◽

Literature Review ◽

Systematic Literature Review ◽

Data Privacy ◽

Research Area ◽

Learning Models ◽

Related Data ◽

Private Data ◽

Large Databases ◽

Machine Learning Models

Data privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients’ anonymity. However, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area, and many domains have been identified for the application of those methods. This systematic literature review provides an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets.

Download Full-text

Biobanking for Neurodegenerative Diseases: Challenge for Translational Research and Data Privacy

The Neuroscientist ◽

10.1177/10738584211036693 ◽

2021 ◽

pp. 107385842110366

Author(s):

Emilia Giannella ◽

Valentino Notarangelo ◽

Caterina Motta ◽

Giulia Sancesario

Keyword(s):

Neurodegenerative Diseases ◽

Translational Research ◽

Data Protection ◽

Biological Samples ◽

Data Privacy ◽

Neurological Diseases ◽

Great Effort ◽

General Data Protection Regulation ◽

In Silico Studies

Biobanking has emerged as a strategic challenge to promote knowledge on neurological diseases, by the application of translational research. Due to the inaccessibility of the central nervous system, the advent of biobanks, as structure collecting biospecimens and associated data, are essential to turn experimental results into clinical practice. Findings from basic research, omics sciences, and in silico studies, definitely require validation in clinically well-defined cohorts of patients, even more valuable when longitudinal, or including preclinical and asymptomatic individuals. Finally, collecting biological samples requires a great effort to guarantee respect for transparency and protection of sensitive data of patients and donors. Since the European General Data Protection Regulation 2016/679 has been approved, concerns about the use of data in biomedical research have emerged. In this narrative review, we focus on the essential role of biobanking for translational research on neurodegenerative diseases. Moreover, we address considerations for biological samples and data collection, the importance of standardization in the preanalytical phase, data protection (ethical and legal) and the role of donors in improving research in this field.

Download Full-text