A prediction model to estimate completeness of electronic physician claims databases
Objectives: Electronic physician claims databases are widely used for chronic disease research and surveillance, but quality of the data may vary with a number of physician characteristics, including payment method. The objectives were to develop a prediction model for the number of prevalent diabet...
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ftqueensland:oai:eprints.qut.edu.au:245595 2024-02-11T10:05:58+01:00 A prediction model to estimate completeness of electronic physician claims databases Lix, Lisa M. Yao, Xue Kephart, George Quan, Hude Smith, Mark Kuwornu, John Paul Manoharan, Nitharsana Kouokam, Wilfrid Sikdar, Khokan 2015-07 https://eprints.qut.edu.au/245595/ unknown BMJ Publishing Group doi:10.1136/bmjopen-2014-006858 Lix, Lisa M., Yao, Xue, Kephart, George, Quan, Hude, Smith, Mark, Kuwornu, John Paul, Manoharan, Nitharsana, Kouokam, Wilfrid, & Sikdar, Khokan (2015) A prediction model to estimate completeness of electronic physician claims databases. BMJ Open, 5(8), Article number: e006858. https://eprints.qut.edu.au/245595/ Consult author(s) regarding copyright matters This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au BMJ Open Contribution to Journal 2015 ftqueensland https://doi.org/10.1136/bmjopen-2014-006858 2024-01-22T23:25:08Z Objectives: Electronic physician claims databases are widely used for chronic disease research and surveillance, but quality of the data may vary with a number of physician characteristics, including payment method. The objectives were to develop a prediction model for the number of prevalent diabetes cases in fee-for-service (FFS) electronic physician claims databases and apply it to estimate cases among non-FFS (NFFS) physicians, for whom claims data are often incomplete. Design: A retrospective observational cohort design was adopted. Setting: Data from the Canadian province of Newfoundland and Labrador were used to construct the prediction model and data from the province of Manitoba were used to externally validate the model. Participants: A cohort of diagnosed diabetes cases was ascertained from physician claims, insured resident registry and hospitalisation records. A cohort of FFS physicians who were responsible for the diagnosis was ascertained from physician claims and registry data. Primary and secondary outcome measures: A generalised linear model with a ? distribution was used to model the number of diabetes cases per FFS physician as a function of physician characteristics. The expected number of diabetes cases per NFFS physician was estimated. Results: The diabetes case cohort consisted of 31 714 individuals; the mean cases per FFS physician was 75.5 (median=49.0). Sex and years since specialty licensure were significantly associated (p<0.05) with the number of cases per physician. Applying the prediction model to NFFS physician registry data resulted in an estimate of 18 546 cases; only 411 were observed in claims data. The model demonstrated face validity in an independent data set. Conclusions: Comparing observed and predicted disease cases is a useful and generalisable approach to assess the quality of electronic databases for population-based research and surveillance. Article in Journal/Newspaper Newfoundland Queensland University of Technology: QUT ePrints Newfoundland BMJ Open 5 8 e006858 |
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Queensland University of Technology: QUT ePrints |
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ftqueensland |
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description |
Objectives: Electronic physician claims databases are widely used for chronic disease research and surveillance, but quality of the data may vary with a number of physician characteristics, including payment method. The objectives were to develop a prediction model for the number of prevalent diabetes cases in fee-for-service (FFS) electronic physician claims databases and apply it to estimate cases among non-FFS (NFFS) physicians, for whom claims data are often incomplete. Design: A retrospective observational cohort design was adopted. Setting: Data from the Canadian province of Newfoundland and Labrador were used to construct the prediction model and data from the province of Manitoba were used to externally validate the model. Participants: A cohort of diagnosed diabetes cases was ascertained from physician claims, insured resident registry and hospitalisation records. A cohort of FFS physicians who were responsible for the diagnosis was ascertained from physician claims and registry data. Primary and secondary outcome measures: A generalised linear model with a ? distribution was used to model the number of diabetes cases per FFS physician as a function of physician characteristics. The expected number of diabetes cases per NFFS physician was estimated. Results: The diabetes case cohort consisted of 31 714 individuals; the mean cases per FFS physician was 75.5 (median=49.0). Sex and years since specialty licensure were significantly associated (p<0.05) with the number of cases per physician. Applying the prediction model to NFFS physician registry data resulted in an estimate of 18 546 cases; only 411 were observed in claims data. The model demonstrated face validity in an independent data set. Conclusions: Comparing observed and predicted disease cases is a useful and generalisable approach to assess the quality of electronic databases for population-based research and surveillance. |
format |
Article in Journal/Newspaper |
author |
Lix, Lisa M. Yao, Xue Kephart, George Quan, Hude Smith, Mark Kuwornu, John Paul Manoharan, Nitharsana Kouokam, Wilfrid Sikdar, Khokan |
spellingShingle |
Lix, Lisa M. Yao, Xue Kephart, George Quan, Hude Smith, Mark Kuwornu, John Paul Manoharan, Nitharsana Kouokam, Wilfrid Sikdar, Khokan A prediction model to estimate completeness of electronic physician claims databases |
author_facet |
Lix, Lisa M. Yao, Xue Kephart, George Quan, Hude Smith, Mark Kuwornu, John Paul Manoharan, Nitharsana Kouokam, Wilfrid Sikdar, Khokan |
author_sort |
Lix, Lisa M. |
title |
A prediction model to estimate completeness of electronic physician claims databases |
title_short |
A prediction model to estimate completeness of electronic physician claims databases |
title_full |
A prediction model to estimate completeness of electronic physician claims databases |
title_fullStr |
A prediction model to estimate completeness of electronic physician claims databases |
title_full_unstemmed |
A prediction model to estimate completeness of electronic physician claims databases |
title_sort |
prediction model to estimate completeness of electronic physician claims databases |
publisher |
BMJ Publishing Group |
publishDate |
2015 |
url |
https://eprints.qut.edu.au/245595/ |
geographic |
Newfoundland |
geographic_facet |
Newfoundland |
genre |
Newfoundland |
genre_facet |
Newfoundland |
op_source |
BMJ Open |
op_relation |
doi:10.1136/bmjopen-2014-006858 Lix, Lisa M., Yao, Xue, Kephart, George, Quan, Hude, Smith, Mark, Kuwornu, John Paul, Manoharan, Nitharsana, Kouokam, Wilfrid, & Sikdar, Khokan (2015) A prediction model to estimate completeness of electronic physician claims databases. BMJ Open, 5(8), Article number: e006858. https://eprints.qut.edu.au/245595/ |
op_rights |
Consult author(s) regarding copyright matters This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au |
op_doi |
https://doi.org/10.1136/bmjopen-2014-006858 |
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BMJ Open |
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5 |
container_issue |
8 |
container_start_page |
e006858 |
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