Integrating community-level data resources for precision medicine research

Precision Medicine focuses on collecting and using individual-level data to improve healthcare outcomes. To date, research efforts have been motivated by molecular-scale measurements, such as incorporating genomic data into clinical use. In many cases however, environmental, social, and economic factors are much more predictive of health outcomes, yet are not systematically used in clinical practice due to the difficulties in measurement and quantification. Advances in both the availability of electronic health information, environmental exposure data, and the more systematic use of geo-coding now provide ways to systematically assess community-level indicators of health, and link these factors to electronic health records for evaluating their influence on disease outcomes. In this workshop, we discuss new electronic sources of community-level data, and provide insight into their utility and validity when compared with gold-standard data collection approaches.

[1]  D. Kivlahan,et al.  Quality Concerns with Routine Alcohol Screening in VA Clinical Settings , 2011, Journal of General Internal Medicine.

[2]  C. Sloan,et al.  Can Extreme Air Pollution Events Provide a Window into Incident Asthma? , 2016, American journal of respiratory and critical care medicine.

[3]  K. J. Johansen Taber,et al.  Pharmacogenomic knowledge gaps and educational resource needs among physicians in selected specialties , 2014, Pharmacogenomics and personalized medicine.

[4]  Christopher G. Chute,et al.  Technical Brief: Mayo Clinic NLP System for Patient Smoking Status Identification , 2008, J. Am. Medical Informatics Assoc..

[5]  T. Glass,et al.  Community socioeconomic deprivation and obesity trajectories in children using electronic health records , 2014, Obesity.

[6]  Guthrie S Birkhead,et al.  Successes and Continued Challenges of Electronic Health Records for Chronic Disease Surveillance. , 2017, American journal of public health.

[7]  Ernest Hilsenrath,et al.  Satellite Data of Atmospheric Pollution for U.S. Air Quality Applications: Examples of Applications, Summary of Data End-User Resources, Answers to FAQs, and Common Mistakes to Avoid , 2014 .

[8]  Jessica G. Burke,et al.  The Development of a Standardized Neighborhood Deprivation Index , 2006, Journal of Urban Health.

[9]  Philip R. O. Payne,et al.  Community-level determinants of obesity: harnessing the power of electronic health records for retrospective data analysis , 2014, BMC Medical Informatics and Decision Making.

[10]  Vandana Sundaram,et al.  The American Journal of Public Health. , 1945, American journal of public health and the nation's health.

[11]  Amartya Sen,et al.  Health: perception versus observation , 2002, BMJ : British Medical Journal.

[12]  F. Collins,et al.  A new initiative on precision medicine. , 2015, The New England journal of medicine.

[13]  Darcy A. Freedman,et al.  Do GIS-derived measures of fast food retailers convey perceived fast food opportunities? Implications for food environment assessment. , 2017, Annals of epidemiology.

[14]  Alaa M Althubaiti,et al.  Information bias in health research: definition, pitfalls, and adjustment methods , 2016, Journal of multidisciplinary healthcare.

[15]  Brian E Dixon,et al.  Incorporating Geospatial Capacity within Clinical Data Systems to Address Social Determinants of Health , 2011, Public health reports.

[16]  Nicole A. Restrepo,et al.  Development and Performance of Text-Mining Algorithms to Extract Socioeconomic Status from De-Identified Electronic Health Records , 2017, PSB.

[17]  Teri E. Klein,et al.  Incorporation of Pharmacogenomics into Routine Clinical Practice: the Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline Development Process , 2014, Current drug metabolism.

[18]  W. Bush,et al.  Extracting Country-of-Origin from Electronic Health Records for Gene- Environment Studies as Part of the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) Study , 2017, AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science.

[19]  D. Roblin,et al.  Validation of a Neighborhood SES Index in a Managed Care Organization , 2013, Medical care.

[20]  S. Melly,et al.  Food environments and childhood weight status: effects of neighborhood median income. , 2015, Childhood obesity.

[21]  Pingsheng Wu,et al.  The impact of temperature and relative humidity on spatiotemporal patterns of infant bronchiolitis epidemics in the contiguous United States , 2017, Health & place.

[22]  Rachel Gold,et al.  "Community vital signs": incorporating geocoded social determinants into electronic records to promote patient and population health , 2016, J. Am. Medical Informatics Assoc..

[23]  Melissa A. Basford,et al.  Genetic variation among 82 pharmacogenes: The PGRNseq data from the eMERGE network , 2016, Clinical pharmacology and therapeutics.

[24]  J. Fishman Geospatial analysis of preventable emergency department visits in Chicago, IL , 2015 .

[25]  Urs A. Meyer,et al.  Pharmacogenetics – five decades of therapeutic lessons from genetic diversity , 2004, Nature Reviews Genetics.

[26]  Darcy A. Freedman,et al.  Geographic measures of retail food outlets and perceived availability of healthy foods in neighbourhoods , 2015, Public Health Nutrition.

[27]  B. Himes,et al.  Enhancing Electronic Health Record Data with Geospatial Information , 2017, AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science.

[28]  Erwan Bocher,et al.  An overview on current free and open source desktop GIS developments , 2009, Int. J. Geogr. Inf. Sci..

[29]  Es Chen,et al.  An Analysis of Free-Text Alcohol Use Documentation in the Electronic Health Record , 2014, Applied Clinical Informatics.

[30]  Denise L. Anthony,et al.  The double-edged sword of electronic health records: implications for patient disclosure , 2015, J. Am. Medical Informatics Assoc..

[31]  Steven J. Melly,et al.  Characteristics of Walkable Built Environments and BMI z-Scores in Children: Evidence from a Large Electronic Health Record Database , 2014, Environmental health perspectives.

[32]  S. Woolf,et al.  Giving everyone the health of the educated: an examination of whether social change would save more lives than medical advances. , 2007, American journal of public health.

[33]  Ken Kleinman,et al.  Effects of Proximity to Supermarkets on a Randomized Trial Studying Interventions for Obesity. , 2016, American journal of public health.

[34]  Rachel Gold,et al.  Perspectives in Primary Care: A Conceptual Framework and Path for Integrating Social Determinants of Health Into Primary Care Practice , 2016, Annals of Family Medicine.

[35]  Suzette J. Bielinski,et al.  Design and Anticipated Outcomes of the eMERGE-PGx Project: A Multi-Center Pilot for Pre-Emptive Pharmacogenomics in Electronic Health Record Systems , 2014, Clinical pharmacology and therapeutics.

[36]  E. Clayton,et al.  Operational Implementation of Prospective Genotyping for Personalized Medicine: The Design of the Vanderbilt PREDICT Project , 2012, Clinical pharmacology and therapeutics.