Integrating Community-Level Data Resources for Precision Medicine Research

Bush, William S.; Crawford, Dana C.; Briggs, Farren; Freedman, Darcy A.; Sloan, Chantel D.

doi:10.1142/9789813235533_0057

Cited by 3 publications

(3 citation statements)

References 30 publications

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“…Data efforts related to diversity should align well with current data sharing norms, such as FAIR (findable, accessible, interoperable, reusable[51]) and evolving standards (GA4GH, https://www.ga4gh.org). The use of social and environmental data in electronic health records[52] and community level data[53] will enhance the evidence base for understanding health differences within and between populations and ability to identify high risk populations[54]. The current limited availability of geographic, social and behavioral data in genomic research adversely impacts the ability to address social and behavioral determinants of health and appropriately incorporate genomic medicine in diverse communities.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Diversity to Reduce Health Information Disparities and Build an Evidence Base foR Genomic Medicine

2018

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Advances in genomic medicine are arising from efforts to build a national learning healthcare system (LHS) and large-scale precision medicine studies. However, the underlying evidence base lacks sufficient data from populations historically underrepresented in biomedical research. Although the literature on health and healthcare disparities is extensive, disparities in the availability and quality of health information about diverse and underrepresented populations are less well characterized. This Perspective describes scientific and ethical benefits to incorporating health information from diverse and underrepresented populations in the LHS, resulting in a more robust and generalizable LHS. Near-term recommendations for incorporating diversity into the evidence base for genomic medicine are proposed, even as the groundwork for national and international efforts is underway.

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Section: Introductionmentioning

confidence: 99%

Enhancing Diversity to Reduce Health Information Disparities and Build an Evidence Base foR Genomic Medicine

2018

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“…Zip codes, census blocks, and geocoded addresses can be linked to various public repositories of community-level environmental data, such as walkability maps, air pollution monitors, and food desert maps (King & Clarke, 2015;Pike et al, 2017;Xie, Greenblatt, Levy, & Himes, 2017). These Geographic Information Systems (GIS) have the potential to help identify patterns or associations between EHR conditions or disease status and exposures related to the physical, built, and social environments of patients (Bush, Crawford, Briggs, Freedman, & Sloan, 2018;Casey, Schwartz, Stewart, & Adler, 2016).…”

Section: Future Trends In Ehr Data Collectionmentioning

confidence: 99%

Using Electronic Health Records To Generate Phenotypes For Research

Pendergrass¹,

Crawford

2018

CP Human Genetics

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Electronic health records contain patient-level data collected during and for clinical care. Data within the electronic health record include diagnostic billing codes, procedure codes, vital signs, laboratory test results, clinical imaging, and physician notes. With repeated clinic visits, these data are longitudinal, providing important information on disease development, progression, and response to treatment or intervention strategies. The near universal adoption of electronic health records nationally has the potential to provide population-scale real-world clinical data accessible for biomedical research including genetic association studies. For this research potential to be realized, high quality research-grade variables must be extracted from these clinical data warehouses. We describe here common and emerging electronic phenotyping approaches applied to electronic health records as well as current limitations of both the approaches and the biases associated with these clinically-collected data that impact their use in research.

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“…A spatial appreciation continues to grow within the health sector, ranging from the addition of geographic locations in research to needs assessments (e.g., Health Impact Assessments, Community Health Improvement Plans, etc. ), to spatially guided precision medicine [14]. Common requests between clinicians and researchers include tasks such as mapping patient locations, or finding distances between cases and the nearest clinic.…”

Section: Introductionmentioning

confidence: 99%

Addressing the data guardian and geospatial scientist collaborator dilemma: how to share health records for spatial analysis while maintaining patient confidentiality

Ajayakumar

Curtis

2019

Int J Health Geogr

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BackgroundThe utility of being able to spatially analyze health care data in near-real time is a growing need. However, this potential is often limited by the level of in-house geospatial expertise. One solution is to form collaborative partnerships between the health and geoscience sectors. A challenge in achieving this is how to share data outside of a host institution’s protection protocols without violating patient confidentiality, and while still maintaining locational geographic integrity. Geomasking techniques have been previously championed as a solution, though these still largely remain an unavailable option to institutions with limited geospatial expertise. This paper elaborates on the design, implementation, and testing of a new geomasking tool Privy, which is designed to be a simple yet efficient mechanism for health practitioners to share health data with geospatial scientists while maintaining an acceptable level of confidentiality. The basic premise of Privy is to move the important coordinates to a different geography, perform the analysis, and then return the resulting hotspot outputs to the original landscape.ResultsWe show that by transporting coordinates through a combination of random translations and rotations, Privy is able to preserve location connectivity among spatial point data. Our experiments with typical analytical scenarios including spatial point pattern analysis and density analysis shows that, along with protecting spatial privacy, Privy maintains the spatial integrity of data which reduces information loss created due to data augmentation.ConclusionThe results from this study suggests that along with developing new mathematical techniques to augment geospatial health data for preserving confidentiality, simple yet efficient software solutions can be developed to enable collaborative research among custodians of medical and health data records and GIS experts. We have achieved this by developing Privy, a tool which is already being used in real-world situations to address the spatial confidentiality dilemma.

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Integrating Community-Level Data Resources for Precision Medicine Research

Cited by 3 publications

References 30 publications

Enhancing Diversity to Reduce Health Information Disparities and Build an Evidence Base foR Genomic Medicine

Enhancing Diversity to Reduce Health Information Disparities and Build an Evidence Base foR Genomic Medicine

Using Electronic Health Records To Generate Phenotypes For Research

Addressing the data guardian and geospatial scientist collaborator dilemma: how to share health records for spatial analysis while maintaining patient confidentiality

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