Combining patient reported outcomes and EHR data to understand population level treatment needs: correcting for selection bias in the migraine signature study
Abstract Background Electronic 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 dat...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2021-12-01
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| Series: | Journal of Patient-Reported Outcomes |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s41687-021-00401-2 |
| _version_ | 1819095615370428416 |
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| author | Walter F. Stewart Xiaowei Yan Alice Pressman Alice Jacobson Shruti Vaidya Victoria Chia Dawn C. Buse Richard B. Lipton |
| author_facet | Walter F. Stewart Xiaowei Yan Alice Pressman Alice Jacobson Shruti Vaidya Victoria Chia Dawn C. Buse Richard B. Lipton |
| author_sort | Walter F. Stewart |
| collection | DOAJ |
| description | Abstract Background Electronic 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. Methods A 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. Results The 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. Conclusions In 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. |
| first_indexed | 2024-12-21T23:46:07Z |
| format | Article |
| id | doaj.art-9a6280b240e544f4af91138824025b62 |
| institution | Directory Open Access Journal |
| issn | 2509-8020 |
| language | English |
| last_indexed | 2024-12-21T23:46:07Z |
| publishDate | 2021-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Patient-Reported Outcomes |
| spelling | doaj.art-9a6280b240e544f4af91138824025b622022-12-21T18:46:05ZengSpringerOpenJournal of Patient-Reported Outcomes2509-80202021-12-015111310.1186/s41687-021-00401-2Combining patient reported outcomes and EHR data to understand population level treatment needs: correcting for selection bias in the migraine signature studyWalter F. Stewart0Xiaowei Yan1Alice Pressman2Alice Jacobson3Shruti Vaidya4Victoria Chia5Dawn C. Buse6Richard B. Lipton7Medcurio Inc.Sutter Health Center for Health Systems ResearchSutter Health Center for Health Systems ResearchSutter Health Center for Health Systems ResearchSutter Health Center for Health Systems ResearchAmgen Inc.Montefiore Medical CenterMontefiore Medical CenterAbstract Background Electronic 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. Methods A 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. Results The 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. Conclusions In 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.https://doi.org/10.1186/s41687-021-00401-2Non-response biasElectronic health recordsMigraine disabilityPrescription medications |
| spellingShingle | Walter F. Stewart Xiaowei Yan Alice Pressman Alice Jacobson Shruti Vaidya Victoria Chia Dawn C. Buse Richard B. Lipton Combining patient reported outcomes and EHR data to understand population level treatment needs: correcting for selection bias in the migraine signature study Journal of Patient-Reported Outcomes Non-response bias Electronic health records Migraine disability Prescription medications |
| title | Combining patient reported outcomes and EHR data to understand population level treatment needs: correcting for selection bias in the migraine signature study |
| title_full | Combining patient reported outcomes and EHR data to understand population level treatment needs: correcting for selection bias in the migraine signature study |
| title_fullStr | Combining patient reported outcomes and EHR data to understand population level treatment needs: correcting for selection bias in the migraine signature study |
| title_full_unstemmed | Combining patient reported outcomes and EHR data to understand population level treatment needs: correcting for selection bias in the migraine signature study |
| title_short | Combining patient reported outcomes and EHR data to understand population level treatment needs: correcting for selection bias in the migraine signature study |
| title_sort | combining patient reported outcomes and ehr data to understand population level treatment needs correcting for selection bias in the migraine signature study |
| topic | Non-response bias Electronic health records Migraine disability Prescription medications |
| url | https://doi.org/10.1186/s41687-021-00401-2 |
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