Short summary

The aim of the study is to identify relevant data available in Danish registers (determined using the International Classification of Diseases and Related Health Problems, 10th Revision [ICD-10] code, ATC codes and contact with general practitioner) for inclusion in a predictive model (Cancer Risk Assesment Model [CRAM]) for automated case finding of individuals at risk of any type of cancer (except nonmelanoma skin cancer) in the Danish population. 

Rationale

The early stage identification of individuals with increased risk of having cancer is an important challenge for all health care systems. Efforts have been numerous including campaigns, screening and in the latter period focus has been on using healthcare data for risk assessment models of cancer. Denmark has a long tradition for collecting a vast amount of healthcare data which have been described in articles in Science entitled "When an entire country is a cohort" and "The epidemiologist's dream: Denmark". However, these data has yet to bee used for cancer prediction tools to be applied in a clinical setting. 

It is evident that screening and diagnosis based on doctors timely referring patients with symptom will mean that a substantial proportion of cancer patients are diagnosed on an early stage of their diseases. However, in order to increase early stage diagnosing, other approaches may add to screening and adequate symptom-based referral. Hence, improving the pre-diagnostic precision in cancer risk stratification is warranted. 

The Danish healthcare data has the possibility to test whether models based on a vast amount of different data can be the offspring for effective methods of risk assessments tools of having early stage cancer . Consequently, our overall intention was to develop and validate a risk prediction model for quantifying the probability of being diagnosed with cancer and for automated case finding of individuals at risk of cancer. 

Description of the cohort

This study includes the total population of Denmark on January 1st 2017

Data and biological material

Register data 

Collaborating researchers and departments

Odense Patient data Explorative Network, Odense University Hospital

• Associate Professor, Senior researcher, PhD  Katrine Hass Rubin 
• Statistician, Senior researcher, PhD. Sören Möller 
• Register Datamanager, PhD, MHS, Nana Hylding

Research Unit of General Practice, Department of Public Health

• Professor, PhD, GP Jens Sønderggard
• Statistician, PhD, Sonja Wehberg