How to measure temporal changes in care pathways for chronic diseases using health care registry data.

ABSTRACT:

Background

Disease trajectories for chronic diseases can span over several decades, with several time-dependent factors affecting treatment decisions. Thus, there is a need for long-term predictions of disease trajectories to inform patients and healthcare professionals on the long-term outcomes and provide information on the need of future health care. Here, we propose a state transition model to describe and predict disease trajectories up to 25 years after diagnosis in men with prostate cancer (PCa), as a proof of principle.

Methods

States, state transitions, and transition probabilities were identified and estimated in Prostate Cancer data Base of Sweden (PCBaSe^Traject), using nationwide population-based data from 118,743 men diagnosed with PCa. A state transi

SUBMITTER: Ventimiglia E 

PROVIDER: S-EPMC6543619 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature