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000157064 1001_ $$00000-0001-6973-887X$$aZhong, Charlie$$b0
000157064 245__ $$aAssessing Cancer Treatment Information Using Medicare and Hospital Discharge Data among Women with Non-Hodgkin Lymphoma in a Los Angeles County Case-Control Study.
000157064 260__ $$aPhiladelphia, Pa.$$bAACR$$c2020
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000157064 520__ $$aWe assessed the ability to supplement existing epidemiologic/etiologic studies with data on treatment and clinical outcomes by linking to publicly available cancer registry and administrative databases.Medical records were retrieved and abstracted for cases enrolled in a Los Angeles County case-control study of non-Hodgkin lymphoma (NHL). Cases were linked to the Los Angeles County cancer registry (CSP), the California state hospitalization discharge database (OSHPD), and the SEER-Medicare database. We assessed sensitivity, specificity, and positive predictive value (PPV) of cancer treatment in linked databases, compared with medical record abstraction.We successfully retrieved medical records for 918 of 1,004 participating NHL cases and abstracted treatment for 698. We linked 59% of cases (96% of cases >65 years old) to SEER-Medicare and 96% to OSHPD. Chemotherapy was the most common treatment and best captured, with the highest sensitivity in SEER-Medicare (80%) and CSP (74%); combining all three data sources together increased sensitivity (92%), at reduced specificity (56%). Sensitivity for radiotherapy was moderate: 77% with aggregated data. Sensitivity of BMT was low in the CSP (42%), but high for the administrative databases, especially OSHPD (98%). Sensitivity for surgery reached 83% when considering all three datasets in aggregate, but PPV was 60%. In general, sensitivity and PPV for chronic lymphocytic leukemia/small lymphocytic lymphoma were low.Chemotherapy was accurately captured by all data sources. Hospitalization data yielded the highest performance values for BMTs. Performance measures for radiotherapy and surgery were moderate.Various administrative databases can supplement epidemiologic studies, depending on treatment type and NHL subtype of interest.
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000157064 7001_ $$0P:(DE-He78)fd17a8dbf8d08ea5bb656dfef7398215$$aSeibold, Petra$$b1$$udkfz
000157064 7001_ $$aChao, Chun R$$b2
000157064 7001_ $$00000-0003-3415-8247$$aCozen, Wendy$$b3
000157064 7001_ $$aSong, Joo Y$$b4
000157064 7001_ $$aWeisenburger, Dennis$$b5
000157064 7001_ $$00000-0002-7692-6518$$aBernstein, Leslie$$b6
000157064 7001_ $$aWang, Sophia S$$b7
000157064 773__ $$0PERI:(DE-600)2036781-8$$a10.1158/1055-9965.EPI-19-1504$$gVol. 29, no. 5, p. 936 - 941$$n5$$p936 - 941$$tCancer epidemiology, biomarkers & prevention$$v29$$x1538-7755$$y2020
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000157064 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)fd17a8dbf8d08ea5bb656dfef7398215$$aDeutsches Krebsforschungszentrum$$b1$$kDKFZ
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000157064 9141_ $$y2020
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