Degree of coverage and data quality

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Degree of coverage and completeness

All doctors who provide health care to patients with cancer are obliged to report to the Cancer Registry. This includes reporting to the Sarcoma Registry.

All patients with sarcoma in Norway must be included in the registry. Sarcoma can occur in any location and organ and accounts for about 1 percent of all diagnosed cancers.

Degree of coverage refers to the proportion of patients registered in the Cancer Registry of Norway in 2022 who have a clinical report. For example, the coverage rate for clinical assessment reports is calculated as the proportion of all cases diagnosed in 2022 where an assessment report has been received and registered.

The Cancer Registry's basic registry contains information on 98.6 per cent of all sarcoma patients. The coverage ratio (for reporting on investigations) for 2021 is 66.7 per cent.

Read more about coverage and data quality in the Annual Report 2022 Sarcoma (Norwegain only)

Data quality

The data quality for the entire group of patients with sarcoma is considered to be very good because the Cancer Registry of Norway makes a specific assessment of all pathology results from the laboratories. Information on incidence, survival and the basis for diagnosis is considered almost complete.

Quality assurance of data is done as an integral part of the coding and registration process. In addition, the following examples help to ensure data quality in the Cancer Registry:

  • Several independent sources report information
  • The information is reported at several points in the course of the disease
  • The employees have unique expertise in coding cancer cases according to the Cancer Registry's own code book and international coding systems
  • IT systems have rules and barriers for illogical combinations, incorrect information and more
  • The Cancer Registry of Norway conducts analyses and control runs that reveal inconsistency in the data
  • Data extraction for researchers makes it possible to check a smaller data set of information that can reveal individual errors (e.g. incorrect entry of hospital codes) or systematic differences due to different interpretations of coding systems and rules