Abstract
BackgroundElectronic health records (EHR) data can be used to understand population level quality of care especially when supplemented with patient reported data. However, survey non-response can result in biased population estimates. As a case study, we demonstrate that EHR and survey data can be combined to estimate primary care population prescription treatment status for migraine stratified by migraine disability, without and with adjustment for survey non-response bias. We selected disability as it is associated with survey participation and patterns of prescribing for migraine.MethodsA stratified random sample of Sutter Health adult primary care (PC) patients completed a digital survey about headache, migraine, and migraine related disability. The survey data from respondents with migraine were combined with their EHR data to estimate the proportion who had prescription orders for acute or preventive migraine treatments. Separate proportions were also estimated for those with mild disability (denoted “mild migraine”) versus moderate to severe disability (denoted mod-severe migraine) without and with correction, using the inverse propensity weighting method, for non-response bias. We hypothesized that correction for non-response bias would result in smaller differences in proportions who had a treatment order by migraine disability status.ResultsThe response rate among 28,268 patients was 8.2%. Among survey respondents, 37.2% had an acute treatment order and 16.8% had a preventive treatment order. The response bias corrected proportions were 26.2% and 11.6%, respectively, and these estimates did not differ from the total source population estimates (i.e., 26.4% for acute treatments, 12.0% for preventive treatments), validating the correction method. Acute treatment orders proportions were 32.3% for mild migraine versus 37.3% for mod-severe migraine and preventive treatment order proportions were 12.0% for mild migraine and 17.7% for mod-severe migraine. The response bias corrected proportions for acute treatments were 24.8% for mild migraine and 26.6% for mod-severe migraine and the proportions for preventive treatment were 8.1% for mild migraine and 12.0% for mod-severe migraine.ConclusionsIn this study, we combined survey data with EHR data to better understand treatment needs among patients diagnosed with migraine. Migraine-related disability is directly related to preventive treatment orders but less so for acute treatments. Estimates of treatment status by self-reported disability status were substantially over-estimated among those with moderate to severe migraine-related disability without correction for non-response bias.
Highlights
Electronic health records (EHR) data can be used to understand population level quality of care especially when supplemented with patient reported data
We present migraine as a use-case to demonstrate that the combined use of EHR data and survey data facilitates a better understanding of population health needs and overcomes the response bias common to traditional population-based surveys
Survey data indicate that variation in use of acute and preventive medications is directly linked to migraine-related disability and to associated comorbidities [5,6,7, 12, 14]
Summary
Electronic health records (EHR) data can be used to understand population level quality of care especially when supplemented with patient reported data. We demonstrate that EHR and survey data can be combined to estimate primary care population prescription treatment status for migraine stratified by migraine disability, without and with adjustment for survey non-response bias. We present migraine as a use-case to demonstrate that the combined use of EHR data and survey data facilitates a better understanding of population health needs and overcomes the response bias common to traditional population-based surveys. Survey data for migraine are prone to non-response and reporting biases in ways that directly influence estimates of migraine severity and prescription medications use whether a survey is done within a healthcare system or in the general population [15,16,17,18,19,20]. The validity of self-report varies by some of these same factors, as education and SES levels influence ability to interpret questions and response options [19,20,21]
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