Indicators of Data Quality at the Cancer Registry Zurich and Zug in Switzerland

Abstract

Data quality is an important issue in cancer registration. This paper provides a comprehensive overview of the four main data quality indicators (comparability, validity, timeliness, and completeness) for the Cancer Registry Zurich and Zug (Switzerland). We extracted all malignant cancer cases (excluding non-melanoma skin cancer) diagnosed between 1980 and 2014 in the canton of Zurich. Methods included the proportion of morphologically verified cases (MV%), the proportion of DCN and DCO cases (2009–2014), cases with primary site uncertain (PSU%), the stability of incidence rates over time, age-specific incidence rates for childhood cancer, and mortality:incidence (MI) ratios. The DCO rate decreased from 6.4% in 1997 to 0.8% in 2014 and was <5% since 2000. MV% was 95.5% in 2014. PSU% was <3% over the whole period. The incidence rate of all tumours increased over time with site-specific fluctuations. The overall M:I ratio decreased from 0.58 in 1980 to 0.37 in 2014. Overall, data quality of the Cancer Registry Zurich and Zug was acceptable according to the methods presented in this review. Most indicators improved over time with low DCO rates, high MV%, low PSU%, relatively low M:I ratios and age-specific incidence of childhood cancer within reference ranges.

Risk for Invasive Cancers in Women With Breast Cancer In Situ: Results From a Population Not Covered by Organized Mammographic Screening

“…The data were obtained from the Cancer Registry of the Cantons of Zurich, Zug, Schaffhausen, and Schwyz. A recent publication demonstrated that the data quality from the Cancer Registry was acceptable based on four widely used data quality indicators (comparability, validity, timeliness, and completeness) ( 15 ). The Cancer Registry started recording cases for the Cantons of Zug, Schaffhausen, and Schwyz later (2011, 2020, and 2020, respectively), so the present analyses only focus on data from the Canton of Zurich.…”

Geographical variation in malignant and benign/borderline brain and CNS tumor incidence: a comparison between a high-income and a middle-income country

“…Each year, about 120 incident malignant brain/CNS tumors and about 200 benign tumors are registered in the canton of Zurich. The registry receives notifications (patient information, tumor characteristics) from pathology and hematology laboratories, hospitals, and physicians as well as death certificates from the Swiss Federal Statistical Office [7]. A completeness of 93.4% for brain/CNS tumors three years after diagnosis was estimated for Zurich [8], which is above the international level for satisfactory completeness of 90%.…”

Analysis of Open Health Data Quality Using Data Object-Driven Approach to Data Quality Evaluation: Insights From a Latvian Context

“…Moreover, despite analysed data were "closed", identified data quality problems are common for "open" data, too. The list of researches discussing quality of health(care) data in many countries such as New Zealand - (Kerr et al, 2007), (Raghupathi, 2014), Denmark - (Schmidt et al, 2015), Brazil - (Oliveira, 2016), USA, UK and Japan - (Yi, 2019), Switzerland - (Wanner et al, 2018), Sweden - (Tomic, 2015), Colombia -(Prieto Rodríguez, 2018), comes to the one conclusionhealth(care) data have data quality problems (with the sole exception of Norway - (Larsen, 2009)).…”

“…28 of 136 detected empty values may not be considered as data quality issues, however, while there are no any notes from the data publisher regarding the nullability of these parameters, there is no certainty, that there are no any problems there, as empty values may have different interpretations. The issue of completeness is almost "traditional" and it occurs not only in the case of open data (Oliveira, 2016;Yi, 2019) but also in "closed" data (Schmidt et al, 2015;Tomic, 2015) in many sectors and countries (Wanner et al, 2018). However, some researches demonstrate a high level of data completeness can be achieved -Cancer Registry of Iceland (Sigurdardottir, 2012) and Norway (Larsen, 2009) (however, both are examples of "closed" data).…”