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J Am Med Inform Assoc. 2019 Mar 1;26(3):219-227. doi: 10.1093/jamia/ocy164.

Enrichment sampling for a multi-site patient survey using electronic health records and census data.

Author information

1
Department of Radiology, Institute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts, USA.
2
Department of Pediatrics, University of Louisville, Louisville, Kentucky, USA.
3
Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA.
4
Center for Biomedical Ethics and Society, Vanderbilt University, Nashville, Tennessee, USA.
5
Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
6
Division of Genetics and Genomics, Boston Children's Hospital, Boston, Massachusetts, USA.
7
Department of Population Health Science and Policy, Ichan School of Medicine at Mt. Sinai, New York, New York, USA.
8
Department of Genome Sciences, University of Washington, Seattle, Washington, USA.
9
Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA.
10
Division of Genomic Medicine, National Human Genome Research Institute, Bethesda, Maryland, USA.
11
Department of Family Medicine and Biobehavioral Health, University of Minnesota Medical School, Duluth, Minnesota, USA.
12
Biomedical Ethics Program, Mayo Clinic, Rochester, Minnesota, USA.
13
Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA.
14
Division of Human Genetics, Cincinnati Children's Hospital, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA.
15
Department of Information Technology, Mayo Clinic, Rochester, Minnesota, USA.
16
Vanderbilt Epidemiology Center, Vanderbilt University, Nashville, Tennessee, USA.
17
Center for Genetic Medicine, Northwestern University, Chicago, Illinois, USA.
18
Division of Geriatric Medicine, Vanderbilt University, Nashville, Tennessee, USA.
19
Genomic Medicine Institute, Geisinger, Danville, Pennsylvania, USA.
20
Department of Biostatistics, Vanderbilt University, Nashville, Tennessee, USA.

Abstract

Objective:

We describe a stratified sampling design that combines electronic health records (EHRs) and United States Census (USC) data to construct the sampling frame and an algorithm to enrich the sample with individuals belonging to rarer strata.

Materials and Methods:

This design was developed for a multi-site survey that sought to examine patient concerns about and barriers to participating in research studies, especially among under-studied populations (eg, minorities, low educational attainment). We defined sampling strata by cross-tabulating several socio-demographic variables obtained from EHR and augmented with census-block-level USC data. We oversampled rarer and historically underrepresented subpopulations.

Results:

The sampling strategy, which included USC-supplemented EHR data, led to a far more diverse sample than would have been expected under random sampling (eg, 3-, 8-, 7-, and 12-fold increase in African Americans, Asians, Hispanics and those with less than a high school degree, respectively). We observed that our EHR data tended to misclassify minority races more often than majority races, and that non-majority races, Latino ethnicity, younger adult age, lower education, and urban/suburban living were each associated with lower response rates to the mailed surveys.

Discussion:

We observed substantial enrichment from rarer subpopulations. The magnitude of the enrichment depends on the accuracy of the variables that define the sampling strata and the overall response rate.

Conclusion:

EHR and USC data may be used to define sampling strata that in turn may be used to enrich the final study sample. This design may be of particular interest for studies of rarer and understudied populations.

PMID:
30590688
PMCID:
PMC6351976
[Available on 2019-12-27]
DOI:
10.1093/jamia/ocy164

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