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INEGRATED DATABASES in Cancer-Related Health Services Research

INEGRATED DATABASES in Cancer-Related Health Services Research. Data in silos. Clinical Assessment Data. Cancer Data. Adminis- trative Data (Medicare Medicaid). Death Certificates Data. Health and Retirement Study. Cancer-related HSR.

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INEGRATED DATABASES in Cancer-Related Health Services Research

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  1. INEGRATED DATABASESinCancer-Related Health Services Research

  2. Data in silos.. Clinical Assessment Data Cancer Data Adminis- trative Data (Medicare Medicaid) Death Certificates Data Health and Retirement Study

  3. Cancer-related HSR • SEER-Medicare files (article by Potosky et al.) • Tumor specific data + use of health services (claims data) • SEER-Medicare files include all cancer cases + 5% non-cancer sample of Medicare beneficiaries • Multiple uses of data!!

  4. Description of the OCISS (Ohio Cancer Incidence Surveillance System) • OCISS: Mandatory reporting of all incident cases of cancer (except insitu cervical, squamous cell and basal cell carcinoma), since January, 1992 • Relevant data elements include: • Patient demographics • Patient residence at the time of diagnosis • Type of cancer • Date of cancer at diagnosis • Cancer stage • Surgical treatment

  5. Developing the linked OCISS and Medicaid • Linked database to mirror the SEER Medicare files at the Federal level, enabling the development of longitudinal records at the patient level to study patterns of enrollment in Medicaid and use of health services. • Patient unique identifier in Medicaid to link enrollment and claims data across different time spans and service types. • Linkage algorithm using patient identifiers: • Patient first and last name • Date of birth • Social security number • Project approved by the Institutional Review Board at the Ohio Department of Health and by the Ohio Department of Job and Family Services

  6. Why data linkage? • Interest in studying cancer-related outcomes in a subgroup of the population (in this case, vulnerable/economically disadvantaged populations) • Existing datasets unable to address current analytic needs • Medicaid beneficiaries are • More likely than others to be diagnosed at later stages of cancer • Less likely than others to receive adequate cancer treatment and follow-up => Disadvantage in survival and quality of life

  7. Some of the variables of relevance: Demographics Tumor site Tumor type Date of cancer diagnosis Cancer stage Cancer treatment Utility: Surveillance Monitoring cancer incidence in demographic subgroups of the population Effectiveness of cancer prevention/control programs The use of existing data sets – separately: The Ohio Cancer Incidence Surveillance System (OCISS)

  8. Some of the variables of relevance: Demographics Enrollment history Data on use of health services Preventive services Treatment, follow-up care, palliative care Utility: Ability to study health care access and outcomes Use of services in the presence of a certain condition Comparison of treatment patterns to care guidelines Measure of disease burden to the Medicaid program The use of existing data sets – separately: Medicaid enrollment and claims files

  9. OCISS: Medicaid status Enrollment history to study cancer prevention issues in greater detail  not possible to study the effectiveness of a health plan in cancer prevention/control Use of health services to study cancer treatment, surveillance, end-of-life care Inability to measure various aspects of disease burden Medicaid: Incident status of cancer Cancer site Date of cancer diagnosis Cancer stage Data that could provide a basis of comparison (in this case, the non-Medicaid population)  KEY elements in studying cancer-related disparities Inability to obtain pertinent data when used separately:

  10. Proposed solution to study cancer-related outcomes in the Medicaid population: Data linkage

  11. Identify Medicaid Status in the OCISS file  Analyze disparities in cancer-related outcomes (e.g., stage, treatment) Non-Medicaid Medicaid: Cancer patients NOT identified as Medicaid beneficiaries Cancer patients also identified as Medicaid beneficiaries Compare cancer-related outcomes by Medicaid status

  12. 2) Create a record incorporating data on pre-cancer and post-cancer history of enrollment and health care use data specific to Medicaid beneficiaries Cancer Diagnosis (OCISS) • Post-cancer-diagnosis, adjusting for cancer stage, comorbid conditions • Treatment • Follow-up care • Palliative/end-of-life care • Pre-cancer-diagnosis • Enrollment history • Use of preventive services

  13. Project Overview: The Development of the linked Ohio Cancer Incidence Surveillance System (OCISS) and Medicaid files • Assess the quality and utility of Medicaid claims data in studying cancer-related issues in health services research • Study the effectiveness of Medicaid in cancer prevention and control • Study disparities in cancer-related outcomes by Medicaid status and other patient characteristics • Assess the burden of cancer to the Medicaid program • Explore opportunities to estimate OCISS completeness rates

  14. Project 1: Assessing the effectiveness of Medicaid in Breast and Cervical Cancer Prevention* *Koroukian SM. Journal of Public Health Management and Practice: JPHMP, 2003; 9(4): 306-314.

  15. Methods • Data Sources: • Linked OCISS and Medicaid enrollment files, 1996-1998 • Study Variables: • Date of cancer diagnosis • Stage of cancer at diagnosis (SEER summary stage) • In situ • Local • Regional • Distant • Timing of enrollment in Medicaid in relation to cancer diagnosis

  16. Stratified analysis • Stage at diagnosis • Distant vs. all other • Medicaid status and timing of enrollment in Medicaid in relation to cancer diagnosis • Non-Medicaid • Medicaid PERI-DIAGNOSIS GROUP, 2 MONTHS PRIOR, UPON, OR 2 MONTHS AFTER CANCER DX PRE-DIAGNOSIS GROUP, >= 3 MONTHS PRIOR TO CANCER DX POST-DIAGNOSIS, 3 MONTHS AFTER CANCER DX

  17. Percent women with breast and cervical cancer presenting with distant metastases at the time of diagnosis: Medicaid versus non-Medicaid

  18. Percent Medicaid participants with breast and cervical cancer presenting with distant metastases at the time of diagnosis, by length of participation in the Medicaid program % Diagnosed with Distant Metastases Medicaid, 9-12 Months Pre-Diagnosis Medicaid, 6-8 Months Pre-Diagnosis Medicaid, 3-5 Months Pre-Diagnosis Post-Diagnosis Non-Medicaid Peri-Diagnosis Months of participation in Medicaid

  19. Distant Stage at Diagnosisa: Multivariate Logistic Regression Analysis by Cancer Site a Distant versus all other stages † Reference Group * CI: Confidence Interval ** In Situ cases of cervical cancer were not reported to OCISS in most of the study period, therefore not included in the analysis Rates adjusted for patient age and race

  20. THE CANCER-AGING LINKED DATABASE Koroukian SM. Linking the Ohio Cancer Incidence System with Medicare, Medicaid, and clinical data from home health care and long term care assessment instruments: Paving the way for new research endeavors in geriatric oncology.Journal of Registry Management, 2008; 35(4):156-165.

  21. Sub-Analysis: focus on the OCISS-OASIS files Records for patients identified with incident cases of breast, colorectal, and prostate cancer (geocoded) • Data linked on a year-by-year basis using social security number and gender • Incident cases of breast, prostate, and colorectal cancer diagnosed in years 1999-2001 • To obtain BASELINE DATA, limit the patient population to those admitted to home health care in the 30 days before or after cancer diagnosis Home Health Outcomes& Assessment Information System (OASIS)

  22. Distribution of OASIS patients by age

  23. Percent patients in home health care % of total cancer patients N=13,710 N=16,251 N=14,838

  24. Percent patients in home health care, by age group (all cancer sites) % of total cancer patients Age Groups

  25. Prevalence of comorbidities in the study population, by cancer site % of Total

  26. Prevalence of comorbid conditions in the study population, by cancer site (reported only for conditions with prevalence > 5%) % of Total

  27. Prevalence of geriatric syndromes in the study population, by cancer site % of Total

  28. Prevalence of geriatric syndromes in the study population, by cancer site (reported only for conditions with prevalence > 2%) % of Total

  29. Prevalence of functional limitations in the study population, by cancer site % of Total

  30. BREAST CANCER PATIENTS Functional Limitations Comorbidity 69 (6.9%) 35 (3.5%) 285 (28.3%) 117 (11.6%) 28 (2.8%) 134 (13.3%) 67 (6.7%) Geriatric Syndromes Patients with no Functional Limitations, no Comorbidity, and no Geriatric Syndromes: 272 (27.2%) Koroukian SM, Madigan E, Murray P. Comorbidity, disability, and geriatric syndromes in elderly cancer patients receiving home health care.Journal of Clinical Oncology, 2006; 24(15):2304-10.

  31. PROSTATE CANCER PATIENTS Functional Limitations Comorbidity 43 (12.5%) 46 (13.3%) 63 (18.3%) 83 (24.1%) 19 (5.5%) 56 (16.2%) 24 (7.0%) Geriatric Syndromes Patients with no Functional Limitations, no Comorbidity, and no Geriatric Syndromes: 46 (13.3%) Koroukian SM, Madigan E, Murray P. Comorbidity, disability, and geriatric syndromes in elderly cancer patients receiving home health care.Journal of Clinical Oncology, 2006; 24(15):2304-10.

  32. COLORECTAL CANCER PATIENTS Functional Limitations Comorbidity 143 (4.9%) 56 (4.2%) 346 (25.8%) 211 (15.8%) 63 (4.7%) 220 (16.4%) 94 (7.0%) Geriatric Syndromes Patients with no Functional Limitations, no Comorbidity, and no Geriatric Syndromes: 188 (14.0%) Koroukian SM, Madigan E, Murray P. Comorbidity, disability, and geriatric syndromes in elderly cancer patients receiving home health care.Journal of Clinical Oncology, 2006; 24(15):2304-10.

  33. Study: Comorbidities, Functional Limitations, and Geriatric Syndromes in Relation to Treatment and Survival Patterns Among Elders with Colorectal Cancer Koroukian et al., J Gerontol A Biol Sci Med Sci. 2010 Mar;65(3):322-9. Epub 2009 Dec 16.

  34. Study Aim • Determine the independent effects of Comorbidity, Functional Limitations, and Geriatric Syndromes relative to receipt of cancer treatment and survival in colorectal cancer patients admitted to home health care shortly before or after cancer diagnosis.

  35. Study Hypothesis • Functional limitations and geriatric syndromes are associated with treatment and survival independently of age and comorbidities.

  36. Study using the OCISS-OASIS-Medicare files Records for patients identified with incident cases of colorectal cancer Ohio Death Certificate Files Medicare enrollment and claims files Home Health Outcomes& Assessment Information System (OASIS)

  37. Log-Rank p=0.5306

  38. Log-Rank p<.0001

  39. Log-Rank p<.0001

  40. Log-Rank p=0.0039

  41. Log-Rank p=0.1199

  42. Log-Rank p=0.0020

  43. Adjusted odds ratios (AOR) and 95% confidence interval (CI) for receipt of definitive treatment home health care patients with loco-regional breast or colorectal cancer • Model adjusts for age, race, sex, marital status, and cancer stage (local vs. regional) • Definitive treatment = • Breast cancer: • Local stage: Mastectomy OR Lumpectomy + radiation therapy • Regional stage: Treatment for local stage + chemotherapy • Colorectal cancer: • Local stage: Colon resection • Regional stage: Colon Resection+ chemotherapy

  44. Adjusted hazard ratios and 95% confidence interval for home health care patients with loco-regional breast or colorectal cancer Model adjusts for age, race, sex, marital status, cancer stage (local vs. regional), and definitive treatment

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