Assessing the Comparability of EMR Information to Patient Registry and Health Claims Data

Transcription

1 Assessing the Comparability of EMR Information to Patient Registry and Health Claims Data F.S. Mowat, 1 E.C. Lau, 1 M.A. Kelsh, 2* J.C. Legg, 2 N.M. Engel-Nitz, 3 H.N. Watson, 1 H.L. Collins, 2 R.J. Nordyke, 4,5** and J.L. Whyte 2 1 Exponent, Inc., Menlo Park, CA 94025; 2 Amgen, Inc., Thousand Oaks, CA 91320; 3 OPTUMInsight, Eden Prairie, MN 55344; 4 PriceSpective LLC, El Segundo, CA 90245; 5 UCLA School of Public Health, Los Angeles, CA * Previously at Exponent at time of study. ** Previously at Amgen at time of study. Presented at the 27 th Annual Meeting of ICPE, August 17,

2 Disclosure Statement This research was funded by Amgen, Inc. Four of the authors own Amgen stock (JCL, HLC, JLW, RJN) 2

3 Background EMRs are used increasingly for health research In 2011, FDA issued draft guidance for best practices in using EMRs in pharmacoepidemiologic studies EMRs offer different data elements not typically available in disease registries, claims records, and prescription claims databases Availability of data depends on the completeness, design, type, and quality of data entry into the EMR EMRs can be valuable resources for epidemiologic and comparative effectiveness research 3

4 Objectives To compare an oncology EMR database with other sources of U.S. cancer data SEER Medicare (for patients >65 years) Commercial claims (for patients <65 years) Data derived from 14 million persons insured under commercial health plans To assess the limitations in generalizing EMR oncology data in population-based cancer research Clinical oncology evaluations Epidemiologic analyses Comparative effectiveness research 4

5 Methods Compared data from 2006, the most recent year available for all databases at the time of analysis Primary tumor sites evaluated in newly diagnosed patients: Breast cancer (male & female) Lung/bronchus cancer Prostate cancer Colorectal cancer Head & neck cancer Non-Hodgkin s lymphoma Other remaining primary tumor sites were combined into Other cancer group 5

6 Factors Compared and Evaluated Demographic (EMR, SEER, Medicare, commercial claims) Age at diagnosis (10-year groupings), sex, and race Tumor stage (EMR, SEER) Text fields not analyzed Ambulatory treatment (clinic- or office-based) Chemotherapy, biologics, and hormones Identified using HCPCS codes Pharmacy claims and oral chemotherapy were not evaluated 6

7 Specifics of EMR Oncology Database The Oncology Services Comprehensive Electronic Records (OSCER) data warehouse is a proprietary database of EMRs 52 outpatient oncology/hematology practice groups (15 hospitalaffiliated and 37 community office-based) at 145 sites in 27 states Initially formed by merging two EMR systems (Varian and IMPAC) Upon analysis of EMR: ~70% of data for stage were missing ~40% of data for race were missing <2% of data missing for age and sex 7

8 Data Analysis Focused on descriptive comparisons, rather than conventional hypothesis testing Used non-parametric Cohen s effect size (w) to evaluate importance of observed differences w is a qualitative measure that is independent of sample size Differences are evaluated as small (w=0.1), modest (w=0.3), and large (w=0.5) In this study, these differences were approximately <5% (small), 5 15% (modest), and >15% (large) Data imputation was performed using hot-deck method for missing data See Lau et al. [2011] ICPE poster 8

9 Description, Characteristics, and Inclusion Criteria EMR SEER Medicare Commercial claims Identification of Tumor Types Identification of Patients/Facilities ICD-9-CM Excluded: Tumors identified solely by V-codes Codes indicating uncertain behavior Sarcoma histology ICD-O-3 Included in situ or malignant tumors Excluded patients with unconfirmed diagnosis or sarcoma histology Diagnosed in 2006 Age >20 years At least two visits within 6 months of cancer diagnosis Excluded: Facilities only administering radiation therapy Clinics with administrative information only for diagnosis date Diagnosed in 2006 Age > 20 years No prior diagnosis of cancer Excluded if information solely from death certificates or autopsy records ICD-9-CM (Part A, Part B, outpatient records) Excluded: Tumors identified solely by V-codes Claims from laboratories, diagnostic testing centers, and diagnostic tests Unspecified or benign tumors ICD-9-CM Excluded claims from laboratories, diagnostic testing centers, and diagnostic tests Diagnosed in 2006 Age 66 years At least two claims with same diagnosis within ~6 months No prior diagnosis of cancer within 12 months Diagnosed in 2006 Age 20 age < 65 years Covered by a health plan 12 months prior to first cancer claim At least two claims with cancer diagnosis 42 days apart Excluded claims with rule out codes No prior use of ambulatory treatment 9

10 Distribution of Tumor Sites in Oncology EMR and Comparison Databases EMR (32,357 patient records) SEER (330,887 patient records) Medicare (60,255 patient records) Commercial claims (16,427 patient records) 10

11 Geographic Distribution of Oncology EMR and SEER Patients (2006) 11

12 Evaluation of Treatment Patterns Proportion of treated patients in EMR was higher than in commercial claims Patients in the EMR were more likely to receive biologics for most tumor sites Much larger percentage of elderly EMR prostate cancer patients received chemotherapy compared to those in Medicare (44% versus 5%) May be due to the fact that prostate cancer is often treated by urologists 12

13 Percent of Treated Patients Receiving Ambulatory Chemotherapy, Biologics, or Hormones by Tumor Site 13

14 Summary of Results Sex and 10-year age distributions by tumor site were similar (<5% difference) Small to modest relative differences observed for: Race (<15%) Most likely due to geographic differences in patients included in each database Distribution of tumor types (<15%) Prostate cancer noticeably under-represented in EMR Larger differences observed for ambulatory treatment therapy (<30%) Overall, Cohen s Effect Size indicated small to medium relative differences in demographic and clinical factors across all four databases 14

15 Limitations Timeliness of the data and availability of SEER (2006) Inherent patient selection criteria in the four databases Differences in operational definitions used to extract data Criteria used to identify new cancer patients (rather than recurrent disease) Only outpatient clinics were evaluated in current analysis Text fields in EMR were not evaluated Missing data 15

16 Discussion Our study indicated that several factors should be considered when using EMRs for oncology research: Geography Evaluation of stage and race Specialization of medical facilities Definitions and data vocabulary Type of EMR Missing data posed a challenge in our EMR data Imputation could aid in minimizing data loss See ICPE poster by Lau et al. (2011) Specialty EMRs can provide detailed clinical data that are extremely important in conducting epidemiologic and outcomes research 16

17 Acknowledgements The authors thank: Jon Fryzek and Cynthia O Malley at Amgen for their input regarding the epidemiology of various tumor types and aid in the initial stages of this project Duane Steffey for his aid in developing and applying imputation procedures SDI Health LLC for provision of de-identified EMR data Jane Sullivan and Gabriel Gomez Rey at OPTUMInsight for programming and statistical analysis of commercial claims data 17