February 1, 2016

Electronic Health Records to Evaluate and Account for Non-response Bias: A Survey of Patients Using Chronic Opioid Therapy

By Susan Shortreed, Michael Von Korff, Stephen Thielke, Linda LeResche, Kathleen Saunders, Dori Rosenberg and Judith Turner

EHR_non_response

Background: In observational studies concerning drug use and misuse, persons misusing drugs may be less likely to respond to surveys. However, little is known about differences in drug use and drug misuse risk factors between survey respondents and nonrespondents.

Methods: Using electronic health record (EHR) data, we compared respondents and nonrespondents of a telephone survey of middle-aged and older chronic opioid therapy patients to assess predictors of interview nonresponse. We compared general patient characteristics, specific opioid misuse risk factors, and patterns of opioid use associated with increased risk of opioid misuse. Inverse probability weights were calculated to account for nonresponse bias by EHR-measured covariates. EHR-measured covariate distributions for the full sample (nonrespondents and respondents), the unweighted respondent sample, and the inverse probability weighted respondent sample are reported. We present weighted and unweighted prevalence of self-reported opioid misuse risk factors.

Results: Among 2489 potentially eligible patients, 1477 (59.3%) completed interviews. Response rates differed with age (45- 54 years, 51.8%; 55-64 years, 58.7%; 65-74 years, 67.9%; and 75 years or older, 59.9%). Tobacco users had lower response rates than did nonusers (53.5% versus 60.9%). Charlson comorbidity score was also related to response rates. Individuals with a Charlson score of 2 had the highest response rate at 65.6%; response rates were lower among patients with the lowest (the patients with the fewest health conditions had response rates of 56.7-60.0%) and the highest Charlson scores (patients with the most health conditions had response rates of 52.2-56.0%). These bivariate relationships persisted in adjusted multivariable logistic regression models predicting survey response. Response rates of persons with and without specific opioid misuse risk factors were similar (e.g., 58.7% for persons with substance abuse diagnoses, 59.4% for those without). Opioid use patterns associated with opioid misuse did not predict response rates (e.g., 60.6% versus 59.2% for those receiving versus not receiving opioids from 3 or more physicians outside their primary care clinic). Very few patient characteristics predicted non-response; thus, inverse probability weights accounting for nonresponse had little impact on the distributions of EHR-measured covariates or self-reported measures related to opioid use and misuse.

Conclusions: Response rates differed by characteristics that predict nonresponse in general health surveys (age, tobacco use), but did not appear to differ by specific patient or drug use risk factors for prescription opioid misuse among middle- and older-aged chronic opioid therapy patients. When observational studies are conducted in health plan populations, electronic health records may be used to evaluate nonresponse bias and to adjust for variables predicting interview nonresponse, complementing other research uses of EHR data in observational studies.


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