Expanding SEER Reporting with Comorbidity Data Colorectal Cancer HORNBROOK

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Chronic Illness and Multimorbidity

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  • Total number tumor records sent to HTR = 10,389, representing 9,591 unique KPH HRNs.
  • We had 73% exact matches, and 17% based on 24 combinations of fewer matching variables where the omitted key variables did not agree. Even with both parties holding patient identifiers (name, address, DOB, gender, etc., matching KPH records to HTR records is a significant challenge.
  • This slide shows that we still have unmatched cases at each end of the data transfer even with fuzzy matching.Both the unmatched cases and fuzzy data linkages are sources of measurement error.
  • Expanding SEER Reporting with Comorbidity Data Colorectal Cancer HORNBROOK

    1. 1. Adding Comorbidity Data to the Hawai‘i SEER Registry for Kaiser Permanente Hawai‘i Members HMO Research Network Annual Meeting April 30, 2012 Mark C. Hornbrook, PhD and Joan Holup, MA The Center for Health Research, Kaiser Permanente Marsha E. Reichman, PhD, MPH The Food and Drug Administration Marc T. Goodman, PhD, MPH Cancer Research Center of Hawai„i, University of Hawai„i Robin Yabroff, PhD DCCPS, National Cancer Institute© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
    2. 2. Research Site© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
    3. 3. Project Team  CHR Hawai„i  Aileen Uchida, MPH  Mark M. Schmidt  SEER Hawai„i (Hawai„i Tumor Registry, Cancer Research Center of Hawai„i)  Michael Green, CTR  Alan Y. Mogi, CDP  IMS Inc.  Jennifer Stevens© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
    4. 4. Funding and IRB  National Cancer Institute  IRB approvals  KP Hawai„i  KP Northwest delegated to KPH  University of Hawai„i© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
    5. 5. Why?  A cancer registry focuses exclusively on malignancies, which can generate an incomplete picture of the patient‟s health state  New developments in health informatics make it feasible and affordable to extract and transfer comorbidity data to a disease registry  Cancer can be examined within the context of other significant health problems© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
    6. 6. Comorbidities  Comorbidities may influence medical decisionmaking  “Numerous biologic ties between cancer and comorbidity exist, one example being an association of diabetes with an increased risk of disease recurrence and mortality in the setting of colon cancer.” Pal SK, Hurria A. Impact of age, sex, and comorbidity on cancer therapy and disease progression. J Clin Oncol. 2010 Sep 10;28(26):4086-93. Epub 2010 Jul 19.  Need to understand the role of comorbidities on cancer treatment and outcomes to personalize care and derive optimal benefit© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
    7. 7. Aims—SEER  Develop and test pathway for up-loading comorbidity data on KPH cancer patients to the Hawai„i SEER Registry  To illustrate the utility of comorbidity data to a cancer or other disease registry© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
    8. 8. Challenges  IRB issues  Data linkage issues© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
    9. 9. Human Subjects Protection Issues  Univ. of Hawai„i Committee on Human Studies, IRB for Hawai„i SEER (Hawaii Tumor Registry)  Approved receipt of PHI and comorbidity data from KPH  Did not approve sending PHI for KPH cancer cases not recorded by KPH for their members  KPH IRB  Since KPH is already sending PHI to HTR, approval provided to resubmit PHI (for linking purposes) with additional diagnosis and medication comorbidity variables  Did not approve releasing PHI for patients NOT reported to the Hawai„i Tumor Registry© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
    10. 10. Phase I: PHI File—KPH to HTR  Match and reconcile KPH records to HTR records for cancer diagnosed between January 1, 2000 and December 31, 2008  Tabulate KPH comorbidities by tumor to match with HTR tumor records© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
    11. 11. KPH Tumor Records Sent to HTR, 2000-2008 1400 1200 1,276 1,201 1,224 1,179 1,134 1,132 1,090 1,173 1000 980 800 600 400 200 0 2000 2001 2002 2003 2004 2005 2006 2007 2008© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
    12. 12. Number of Matches: KPH to HTR Matching Keys: Site, Histology, Behavior, Laterality, Date of Diagnosis 8000 7000 73% 6000 5000 4000 6935 3000 17% 2000 1000 2611 0 Perfect Fuzzy© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
    13. 13. KPH Tumor Records Matched to HTR Using Dx Date, Name, Birth Date, and Other PHI, 2000-2008 100% 94% 94% 94% 92% 89% 91% 92% 89% 92% 92% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 2000 2001 2002 2003 2004 2005 2006 2007 2008 All Years© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
    14. 14. Matching Tumor Records 100% 50 41 59 72 84 150 134 155 62 63 67 172 90% 86 128 112 93 135 97 80% 142 205 219 265 287 70% 381 388 313 411 60% 50% 40% 776 866 846 30% 739 786 783 692 769 678 20% 10% 0% 1 2000 2 2001 3 2002 4 2003 5 2004 6 2005 7 2006 8 2007 9 2008 Unmatched, on HTR file, not KPH Unmatched, on KPH file, not HTR Fuzzy Match (KPH to HTR) Perfect Match (KPH to HTR)© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
    15. 15. Comorbidity Measurement Cancer Diagnosed Comorbidity Assessment Period Treatment  12 months 1 month One-month gap between comorbidity assessment period and date of cancer diagnosis reduces influence of cancer-related “rule-out” diagnoses on comorbidity measure. 1© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
    16. 16. Charlson-Deyo Comorbidity Classes  Myocardial infarction  Rheumatoid arthritis  Congestive heart disease  Peptic ulcer disease  Peripheral vascular  Mild liver disease disease  Diabetes  Cerebrovascular disease  Diabetes complications  Dementia  Paralysis  Chronic obstructive  Renal disease pulmonary disease  Severe liver disease  Rheumatoid arthritis© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
    17. 17. Presence of Comorbid Diagnoses byCancer Site Total # of % of Tumors with Cancer Site Tumors Comorbidity All Sites 9,546 37% Respiratory & Intra-thoracic 1,133 54% Colon & Rectum 1,110 40% Prostate 1,080 37% Breast 1,832 25%© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
    18. 18. Frequency of Comorbid Diagnoses Total Number of Tumors 9,546 100% Number of Tumors with C-D Comorbidities 3,503 37% Total C-D Comorbidities 6,018 100% Serious Chronic Conditions Number Percent Diabetes Mellitus 1,578 26% Chronic Obstructive Pulmonary Disease 1,487 25% Complications of Diabetes Mellitus 585 10% Renal Disease 486 8% Cerebrovascular Disease 458 8% 36% Congestive Heart Disease 413 7% Myocardial Infarction 269 4% Peripheral Vascular Disease 280 5%© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
    19. 19. Cancers of Digestive Organs # of Tumors # of with # of Comorbidities Digestive Organs Tumors Comorbidities Comorbidities per TumorAll Digestive System sites 1,110 448 804 1.8Pelvic/Sigmoid/Sigmoid Flexure(C187) 260 90 152 1.7Rectum (C209) 259 85 154 1.8Ascending/Right (C182) 140 77 153 2.0Cecum (C180) 121 47 81 1.7Rectosigmoid/Colon andRectum (C199) 109 38 55 1.4Descending/Left (C186) 63 40 81 2.0Transverse (C184) 62 28 55 2.0Hepatic Flexure (C183) 39 21 34 1.6Splenic Flexure (C185) 37 16 32 2.0Appendix (C181) 10 2 2 1.0Colon, NOS (C189) 7 3 4 1.3© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
    20. 20. Comorbidities of Digestive System Cancers Total Number of Digestive Organ Tumors 1,843 Number of Digestive Organ Tumors with C-D Comorbidities 807 C-D Comorbidities Number Diabetes Mellitus 417 Chronic Obstructive Pulmonary Disease 274 Complications of Diabetes Mellitus 152 Renal Disease 105 Cerebrovascular Disease 104 Congestive Heart Disease 101 Myocardial Infarction 72 Peripheral Vascular Disease 67 Peptic Ulcer Disease 52 Mild Liver Disease 50 Dementia 25 Severe Liver Disease 17 Rheumatoid Arthritis 14 Paralysis 7 Total C-D Comorbidities© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH 1,450
    21. 21. Implications  SEER registries should have comorbidity data  Reporting laws may need amending  Matching algorithms should include two-way sharing of linkage files to identify and resolve linkage errors  Matching by patient attributes only is not sufficient  Matching must include tumor attributes  Site, histology, behavior, laterality, date of diagnosis (year/month)© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
    22. 22. © 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH

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