医学
队列
背景(考古学)
队列研究
癌症
医疗保健
人口
公共卫生
流行病学
混淆
家庭医学
老年学
环境卫生
内科学
病理
古生物学
经济
生物
经济增长
作者
Elinor Nemlander,Eliya Abedi,Per Ljungman,Jan Hasselström,Axel C. Carlsson,Andreas Rosenblad
标识
DOI:10.1007/s10654-024-01192-8
摘要
Abstract The Stockholm Early Detection of Cancer Study (STEADY-CAN) cohort was established to investigate strategies for early cancer detection in a population-based context within Stockholm County, the capital region of Sweden. Utilising real-world data to explore cancer-related healthcare patterns and outcomes, the cohort links extensive clinical and laboratory data from both inpatient and outpatient care in the region. The dataset includes demographic information, detailed diagnostic codes, laboratory results, prescribed medications, and healthcare utilisation data. Since its inception, STEADY-CAN has collected longitudinal data on 2,732,005 individuals aged ≥ 18 years old living in or having access to health care in Stockholm County during the years 2011–2021. Focusing on cancer, the cohort includes 140,042 (5.1%) individuals with incident cancer and a control group of 2,591,963 (94.9%) cancer-free individuals. The cohort’s diverse adult population enables robust analyses of early symptom detection, incidental findings, and the impact of comorbidities on cancer diagnoses. Utilizing the wide range of available laboratory data and clinical variables allow for advanced statistical analyses and adjustments for important confounding factors. The cohort’s primary focus is to improve understanding of the early diagnostic phase of cancer, offering a crucial resource for studying cancer detection in clinical practice. Its comprehensive data collection provides unique opportunities for research into comorbidities and cancer outcomes, making the cohort a useful resource for ongoing cancer surveillance and public health strategies. The present study gives a detailed description of the rationale for creating the STEADY-CAN cohort, its design, the data collection procedure, and baseline characteristics of collected data.
科研通智能强力驱动
Strongly Powered by AbleSci AI