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Health Services Data: The Ontario Cancer Registry (a Unique, Linked, and Automated Population-Based Registry)

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Part of the Health Services Research book series (HEALTHSR)

Abstract

Since its creation in 1964, the Ontario Cancer Registry (OCR) has been an important source of high-quality information on cancer incidence and mortality. As a population-based registry, the OCR can be used to assess the provincial burden of cancer, track the progress of cancer control programs, identify health disparities among subpopulations, plan and improve healthcare, perform health services research, verify clinical guideline adherence, evaluate screening effectiveness, and much more. With over one third of Canadians residing in Ontario, the OCR is the nation’s largest provincial cancer registry and a major contributor to the Canadian Cancer Registry. In 2015 alone, the OCR collected data on an estimated 83,000 malignant cases.

Through its active participation in Canadian, North American, and international standard setting bodies, the OCR adopts the latest methods for registry data collection and reporting. The OCR is created entirely from records generated for purposes other than cancer registration. These records include pathology reports, treatment-level activity, hospital discharges, surgery data, and death certificates. Electronic records are linked at the person level and then “resolved” into incident cases of cancer using a unique computerized medical logic. Recent technological updates to the OCR have further modernized the registry and prepared it for future developments in the field of cancer registration.

This chapter describes the evolution of the OCR, its basic processes and components of automation, data elements, data quality measures, linkage processes, and other aspects of the registry that make it of particular interest to health services researchers and more broadly to the healthcare and public health community.

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Abbreviations

AJCC:

American Joint Committee on Cancer

ALR:

Activity Level Reporting

CCO:

Cancer Care Ontario

CIHI:

Canadian Institute for Health Information

CS:

Collaborative Stage

DAD:

CIHI’s Discharge Abstract Database

DCO:

Death certificate only

DSA:

Data sharing agreement

eCC:

Electronic Cancer Checklist

EDW:

Enterprise Data Warehouse

EDW-OCR:

Enterprise Data Warehouse based OCR

eMaRC:

Electronic Mapping, Reporting, and Coding Plus

ePath:

Electronic pathology data collection system

IACR:

International Association of Cancer Registries

IARC:

International Agency for Research on Cancer

ICBP:

International Cancer Benchmarking Partnership

ICD:

International Classification of Diseases

ICD-O:

International Classification of Diseases for Oncology

MPH:

Multiple Primary and Histology

NAACCR:

North American Association of Central Cancer Registries

NACRS:

CIHI’s National Ambulatory Care Reporting System

OCR:

Ontario Cancer Registry

OCRIS:

Ontario Cancer Registry Information System

OCTRF:

Ontario Cancer Treatment and Research Foundation

RCC:

Regional Cancer Center

SEER:

Surveillance, Epidemiology, and End Results program

SSF:

Site-specific factors

TNM:

Tumor Node Metastasis staging

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Correspondence to Prithwish De .

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Technical Appendix

Technical Appendix

Synoptic pathology reports are an integral component of the EDW and feed the Pathology Data Mart, which is needed for CS integration (Fig. 9). Synoptic pathology reports from the Pathology Data Mart, OCR case files and CS abstracts are utilized by the Registry Plus service to drive CS integration and populate the CS data mart (see section “Cancer Stage at Diagnosis” for more information on CS and its processes). Registry Plus is a suite of publicly available free software programs for collecting and processing cancer registry data (Centers for Disease Control and Prevention 2015).

Fig. 9
figure 9

Diagram of pathology-driven processes at CCO. LIS laboratory information system, EPR electronic patient record, CS Collaborative Stage

ePath, eMaRC, and ASTAIRE

All pathology reports are handled through CCO’s ePath electronic pathology reporting system. receives, processes and stores pathology reports, connecting the diagnostic laboratories to the OCR. ePath is comprised of several major subsystems, including the Electonic Mapping, Reporting, and Coding (eMaRC) and the Automated Synoptic Template Analysis Interface and Rule Engine (ASTAIRE).

CCO eMaRC is a subcomponent of the ePath system, which processes and stores pathology reports received in HL7 messaging format. CCO eMaRC automatically filters cancer vs non-cancer reports and non-reportable reports and provides partial automation for numerous ICD-O-3 diagnoses codes, collaborative staging elements, and creates NAACCR compatible abstract records. The system also merges multiple reports for a single patient so as to prevent the creation of extra cases in the OCR.

A data quality assessment tool, ASTAIRE ensures that synoptic data is compliant with the College of American Pathologists standards. ASTAIRE is made up of three components: GINGER, FRED, and ADELE. Combined, GINGER and FRED ensure that synoptic data is sufficiently complete and in line with current eCC versions. ADELE then cleans data so that may be admitted to the EDW.

In the interest of privacy and efficiency, data handled through ePath is coded in Health Level Seven V2 format, which is a secure method of data transmission designed to protect sensitive health information. This data contains three main elements: patient ID (PID), observation report ID (OBR), and observations (OBX). Patient ID contains personal and identifiable information, such as a patient’s name, sex, and address. The observation report ID pertains to the pathology report and provides information regarding the pathologist, surgeon, referrals, and specimen collection. The observations data element conveys information regarding the clinical diagnosis, clinical history, gross pathology, submitted tissues, and full diagnosis.

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Prodhan, S., King, M.J., De, P., Gilbert, J. (2019). Health Services Data: The Ontario Cancer Registry (a Unique, Linked, and Automated Population-Based Registry). In: Levy, A., Goring, S., Gatsonis, C., Sobolev, B., van Ginneken, E., Busse, R. (eds) Health Services Evaluation. Health Services Research. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-8715-3_18

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  • DOI: https://doi.org/10.1007/978-1-4939-8715-3_18

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