Incorporating a location-based socioeconomic index into a de-identified i2b2 clinical data warehouse

• Published in: Journal of the American Medical Informatics Association : JAMIA Vol. 26; no. 4; pp. 286 - 293
• Main Authors: Gardner, Bret J, Pedersen, Jay G, Campbell, Mary E, McClay, James C
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01-04-2019
• Subjects: Adolescent; Adult; Aged; Aged, 80 and over; Censuses; Child; Child, Preschool; Data Anonymization; Data Warehousing; Electronic Health Records; Emergency Service, Hospital - statistics & numerical data; Female; Geographic Information Systems; Geographic Mapping; Humans; Infant; Infant, Newborn; Logistic Models; Male; Middle Aged; Nebraska; Research and Applications; Social Class; Social Determinants of Health; Socioeconomic Factors; United States; Young Adult; United States; Nebraska; i2b2; social determinants of health; American Community Survey (ACS); socioeconomic status; census
• DOI: 10.1093/jamia/ocy172
• EISSN: 1527-974X
• ISSN: 1527-974X; 1067-5027

Abstract
Objective
Clinical research data warehouses are largely populated from information extracted from electronic health records (EHRs). While these data provide information about a patient’s medications, laboratory results, diagnoses, and history, her social, economic, and environmental determinants of health are also major contributing factors in readmission, morbidity, and mortality and are often absent or unstructured in the EHR. Details about a patient’s socioeconomic status may be found in the U.S. census. To facilitate researching the impacts of socioeconomic status on health outcomes, clinical and socioeconomic data must be linked in a repository in a fashion that supports seamless interrogation of these diverse data elements. This study demonstrates a method for linking clinical and location-based data and querying these data in a de-identified data warehouse using Informatics for Integrating Biology and the Bedside.
Materials and Methods
Patient data were extracted from the EHR at Nebraska Medicine. Socioeconomic variables originated from the 2011-2015 five-year block group estimates from the American Community Survey. Data querying was performed using Informatics for Integrating Biology and the Bedside. All location-based data were truncated to prevent identification of a location with a population <20 000 individuals.
Results
We successfully linked location-based and clinical data in a de-identified data warehouse and demonstrated its utility with a sample use case.
Discussion
With location-based data available for querying, research investigating the impact of socioeconomic context on health outcomes is possible. Efforts to improve geocoding can readily be incorporated into this model.
Conclusion
This study demonstrates a means for incorporating and querying census data in a de-identified clinical data warehouse.