Strengthening Research through Data Sharing

Warren, Elizabeth

doi:10.1056/nejmp1607282

Cited by 105 publications

(72 citation statements)

References 3 publications

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“…Encourage data sharing to benefit under-resourced minority-serving institutions and underrepresented minority researchers by research intensive institutions (Challenge III) ties Act (Public Law 96-516), the gap in educational attainment separating underrepresented minorities from Whites and Asians remains wide in mathematics, statistics, and computer sciences. 82 Both underrepresenta-…”

Section: Resultsmentioning

confidence: 99%

“…Additionally, the medical research community was urged to consider novel approaches to share data including non-positive findings. 81 Although this issue is not specific to health disparities research, minority scientists especially those in under-resourced institutions, are more likely to experience such barriers and may benefit more from open data policies.…”

Section: Challenge Iii: Data Access and Sharingmentioning

confidence: 99%

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Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century

Zhang

Pérez‐Stable²,

Bourne³

et al. 2017

Ethn Dis

163

138

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Addressing minority health and health disparities has been a missing piece of the puzzle in Big Data science. This article focuses on three priority opportunities that Big Data science may offer to the reduction of health and health care disparities. One opportunity is to incorporate standardized information on demographic and social determinants in electronic health records in order to target ways to improve quality of care for the most disadvantaged populations over time. A second opportunity is to enhance public health surveillance by linking geographical variables and social determinants of health for geographically defined populations to clinical data and health outcomes. Third and most importantly, Big Data science may lead to a better understanding of the etiology of health disparities and understanding of minority health in order to guide intervention development. However, the promise of Big Data needs to be considered in light of significant challenges that threaten to widen health disparities. Care must be taken to incorporate diverse populations to realize the potential benefits. Specific recommendations include investing in data collection on small sample populations, building a diverse workforce pipeline for data science, actively seeking to reduce digital divides, developing novel ways to assure digital data privacy for small populations, and promoting widespread data sharing to benefit under-resourced minority-serving institutions and minority researchers. With deliberate efforts, Big Data presents a dramatic opportunity for reducing health disparities but without active engagement, it risks further widening them.Ethn.Dis;2017;27(2):95-106; doi:10.18865/ed.27.2.95.

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Section: Resultsmentioning

confidence: 99%

Section: Challenge Iii: Data Access and Sharingmentioning

confidence: 99%

Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century

Zhang

Pérez‐Stable²,

Bourne³

et al. 2017

Ethn Dis

163

138

View full text Add to dashboard Cite

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“…In addition, the predominant presence of European patients (due to the origin of the project in the EULAR-SjS Task Force Group) could limit the generalisation of the results, due to the small size of some ethnic subpopulations, such as black African-American patients. With respect to the study design, although studies comparing relative frequencies of clinical features should, ideally, be population-based, our study was designed according to a ‘Data Sharing’ approach, which is currently considered an alternative way of international scientific collaboration, especially in diseases with a low prevalence 26. Since the participant centres are mainly tertiary university centres that are considered the referral centre in their corresponding cities (and in most cases, in their countries), the magnitude of the selection bias may vary between the 20 countries involved in the study and this could have an impact on the results (online supplementary figure S2 summarises the size of each cohort classified per city), as may differing medical practices across regions (availability of diagnostic tests included in the 2002 criteria); in fact, we found a negative correlation between the percentage of biopsied patients and the percentage of Ro/La-positive patients in each centre (R=−0.55) (see online supplementary figure S3).…”

Section: Discussionmentioning

confidence: 99%

Influence of geolocation and ethnicity on the phenotypic expression of primary Sjögren's syndrome at diagnosis in 8310 patients: a cross-sectional study from the Big Data Sjögren Project Consortium

et al. 2016

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“…Simultaneously, it has been recognized that the scientific community needs new approaches to make its work sustainable during times of both decreased funding and increased demand for timely and actionable outcomes of research programs. One potential solution to both of these challenges is the adoption of open science models that allow: 1) increased transparency of data and methods, which promotes research reproducibility and rigor [1][2][3][4]; and 2) cumulative efficiencies wherein research tools and the output of research are combined to accelerate the delivery of new knowledge [5][6][7]. For the purposes of this manuscript, we provide the following working definition for open science:…”

Section: Introductionmentioning

confidence: 99%

Democratizing Health Data for Translational Research

et al. 2017

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There is an expanding and intensive focus on the accessibility, reproducibility, and rigor of basic, clinical, and translational research. This focus complements the need to identify sustainable ways to generate actionable research results that improve human health. The principles and practices of open science offer a promising path to address both issues by facilitating: 1) increased transparency of data and methods which promotes research reproducibility and rigor; and 2) cumulative efficiencies wherein research tools and the output of research are combined to accelerate the delivery of new knowledge. While great strides have been in made in terms of enabling the open science paradigm in the biological sciences, progress in sharing of patient-derived health data has been more moderate. This lack of widespread access to common and well characterized health data is a substantial impediment to the timely, efficient, and multi-disciplinary conduct of translational research, particularly in those instances where hypotheses spanning multiple scales (from molecules to patients to populations) are being developed and tested. To address such challenges, we review current best practices and lessons learned, and explore the need for policy changes and technical innovation that can enhance the sharing of health data for translational research.

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Strengthening Research through Data Sharing

Cited by 105 publications

References 3 publications

Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century

Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century

Influence of geolocation and ethnicity on the phenotypic expression of primary Sjögren's syndrome at diagnosis in 8310 patients: a cross-sectional study from the Big Data Sjögren Project Consortium

Democratizing Health Data for Translational Research

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