Degree of coverage and data quality for the Norwegian Registry for Lymphoid Malignancies

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Degree of coverage and completeness

All patients diagnosed with lymphoid malignancies should be included in the quality registry. That is, ICD 10 C81–C86, C88, C90, C91 and C96.

It is important for the Norwegian Lymphoid Malignancy Registry that the positive trend of increased interest in reporting among hospital management and health personnel continues. Not all health institutions still have good enough routines for following up reporting to the Cancer Registry.

The Cancer Registry's basic registry contains information on 98.6 per cent of all patients diagnosed with lymphoid malignancy. In the 2021 report, the register was able to show a coverage rate of over 70 per cent for the first time. The coverage ratio (for reporting on investigations) in 2022 is 72 per cent. 

The Cancer Registry of Norway has its own reporting team that will work continuously to improve coverage. The reporting team works well, but we are constantly working to improve coverage. Results from this year's report show a stable increase in reporting in recent years, with post-registered reports. Figure 5.1 in the annual report shows that all analyses with data from the report on investigation in 2021 now have a coverage rate of 80 per cent.

Read more about coverage and data quality in the Annual report for lymphoid malignancies 2021 (Norwegian only)

Data quality

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