Identifying incident cancer cases in dispensing claims
Introduction Dispensing claims are used commonly as proxy measures in pharmacoepidemiological studies; however, their validity is often untested. Objectives To assess the performance of a proxy for identifying cancer cases based on the dispensing of anticancer medicines and estimate the misclassi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Swansea University
2020-03-01
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| Series: | International Journal of Population Data Science |
| Subjects: | |
| Online Access: | https://ijpds.org/article/view/1152 |
| _version_ | 1827614981654315008 |
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| author | Benjamin Daniels Hanna E Tervonen Sallie-Anne Pearson |
| author_facet | Benjamin Daniels Hanna E Tervonen Sallie-Anne Pearson |
| author_sort | Benjamin Daniels |
| collection | DOAJ |
| description | Introduction
Dispensing claims are used commonly as proxy measures in pharmacoepidemiological studies; however, their validity is often untested.
Objectives
To assess the performance of a proxy for identifying cancer cases based on the dispensing of anticancer medicines and estimate the misclassification of cancer status and potential for bias researchers may encounter when using this proxy.
Methods
We conducted our validation study using Department of Veterans’ Affairs (DVA) client data linked with the New South Wales (NSW) Cancer Registry and Repatriation Pharmaceutical Benefits Scheme data. We included DVA clients aged ≥65 years residing in NSW between July 2004 and December 2012. We matched clients with a cancer diagnosis to clients without a diagnosis based on demographic characteristics and available observation time. We used dispensing claims for anticancer medicines dispensed between July 2004 and December 2013 as a proxy to identify clients with cancer and calculated sensitivity, specificity, positive predictive values and negative predictive values compared with cancer registrations (gold standard), overall and by cancer site. We illustrated misclassification by the proxy in a cohort of people initiating opioid therapy. Using the proxy, we excluded people with cancer from the cohort, in an attempt to delineate people potentially using opioids for cancer rather than chronic non-cancer pain.
Results
We identified 15,679 new cancer diagnoses in 14,112 DVA clients from the cancer registry and 62,663 clients without a diagnosis. Sensitivity of the proxy based on dispensing claims was 30% for all cancers and around 20% for specific cancers (range: 10-67%). Specificity was above 90% for all cancers. The dispensing proxy correctly identified 26% of people with a cancer diagnosis who initiated opioid therapy and failed to identify 74% those with a cancer diagnosis; the proxy was most robust for clients with breast cancer where 61% were correctly identified by proxy.
Conclusions
Dispensings of anticancer medicines as a proxy for people with a cancer diagnosis performed poorly. Excluding patients with evidence of anticancer medicine use from cohort studies may result removal of a disproportionate number of women with breast cancer. Researchers excluding or otherwise using anticancer medicine dispensing to identify people with cancer in pharmacoepidemiological studies should acknowledge the potential biases introduced to their findings. |
| first_indexed | 2024-03-09T09:03:39Z |
| format | Article |
| id | doaj.art-f41f41e823f647b6954dd73e21eed5d6 |
| institution | Directory Open Access Journal |
| issn | 2399-4908 |
| language | English |
| last_indexed | 2024-03-09T09:03:39Z |
| publishDate | 2020-03-01 |
| publisher | Swansea University |
| record_format | Article |
| series | International Journal of Population Data Science |
| spelling | doaj.art-f41f41e823f647b6954dd73e21eed5d62023-12-02T10:56:34ZengSwansea UniversityInternational Journal of Population Data Science2399-49082020-03-015110.23889/ijpds.v5i1.1152Identifying incident cancer cases in dispensing claimsBenjamin Daniels0Hanna E Tervonen1Sallie-Anne Pearson2Medicines Policy Research Unit, Centre for Big Data Research in Health, University of New South WalesMedicines Policy Research Unit, Centre for Big Data Research in Health, University of New South WalesMedicines Policy Research Unit, Centre for Big Data Research in Health, University of New South WalesIntroduction Dispensing claims are used commonly as proxy measures in pharmacoepidemiological studies; however, their validity is often untested. Objectives To assess the performance of a proxy for identifying cancer cases based on the dispensing of anticancer medicines and estimate the misclassification of cancer status and potential for bias researchers may encounter when using this proxy. Methods We conducted our validation study using Department of Veterans’ Affairs (DVA) client data linked with the New South Wales (NSW) Cancer Registry and Repatriation Pharmaceutical Benefits Scheme data. We included DVA clients aged ≥65 years residing in NSW between July 2004 and December 2012. We matched clients with a cancer diagnosis to clients without a diagnosis based on demographic characteristics and available observation time. We used dispensing claims for anticancer medicines dispensed between July 2004 and December 2013 as a proxy to identify clients with cancer and calculated sensitivity, specificity, positive predictive values and negative predictive values compared with cancer registrations (gold standard), overall and by cancer site. We illustrated misclassification by the proxy in a cohort of people initiating opioid therapy. Using the proxy, we excluded people with cancer from the cohort, in an attempt to delineate people potentially using opioids for cancer rather than chronic non-cancer pain. Results We identified 15,679 new cancer diagnoses in 14,112 DVA clients from the cancer registry and 62,663 clients without a diagnosis. Sensitivity of the proxy based on dispensing claims was 30% for all cancers and around 20% for specific cancers (range: 10-67%). Specificity was above 90% for all cancers. The dispensing proxy correctly identified 26% of people with a cancer diagnosis who initiated opioid therapy and failed to identify 74% those with a cancer diagnosis; the proxy was most robust for clients with breast cancer where 61% were correctly identified by proxy. Conclusions Dispensings of anticancer medicines as a proxy for people with a cancer diagnosis performed poorly. Excluding patients with evidence of anticancer medicine use from cohort studies may result removal of a disproportionate number of women with breast cancer. Researchers excluding or otherwise using anticancer medicine dispensing to identify people with cancer in pharmacoepidemiological studies should acknowledge the potential biases introduced to their findings.https://ijpds.org/article/view/1152Pharmacoepidemiology, Neoplasms, Sensitivity, Specificity, Positive predictive value, Negative predictive value, Validation studies |
| spellingShingle | Benjamin Daniels Hanna E Tervonen Sallie-Anne Pearson Identifying incident cancer cases in dispensing claims International Journal of Population Data Science Pharmacoepidemiology, Neoplasms, Sensitivity, Specificity, Positive predictive value, Negative predictive value, Validation studies |
| title | Identifying incident cancer cases in dispensing claims |
| title_full | Identifying incident cancer cases in dispensing claims |
| title_fullStr | Identifying incident cancer cases in dispensing claims |
| title_full_unstemmed | Identifying incident cancer cases in dispensing claims |
| title_short | Identifying incident cancer cases in dispensing claims |
| title_sort | identifying incident cancer cases in dispensing claims |
| topic | Pharmacoepidemiology, Neoplasms, Sensitivity, Specificity, Positive predictive value, Negative predictive value, Validation studies |
| url | https://ijpds.org/article/view/1152 |
| work_keys_str_mv | AT benjamindaniels identifyingincidentcancercasesindispensingclaims AT hannaetervonen identifyingincidentcancercasesindispensingclaims AT sallieannepearson identifyingincidentcancercasesindispensingclaims |