Large-scale real-world data analysis identifies comorbidity patterns in schizophrenia
This study aimed to systematically identify comorbidity patterns associated with schizophrenia using large-scale real-world health insurance data. The cohort comprised 86 million individuals enrolled in Aetna employer-sponsored health insurance plans across the United States of America, with data collected retrospectively from 2008 to 2019. The cohort included individuals aged 15 years or older who were diagnosed with schizophrenia, as well as matched controls. Follow-up occurred continuously during each participant’s insurance coverage, with an average observation period of around six years.
Study design
Cohort
Number of participants at first data collection
86,000,000 (participants)
Age at first data collection
≥ 15 years (participants)
Participant year of birth
Varied (participants)
Participant sex
All
Representative sample at baseline?
No
Sample features
Country
Year of first data collection
2008
Primary Institutions
Harvard Medical School (HMS)
Massachusetts General Hospital (MGH)
Massachusetts Institute of Technology (MIT)
Links
No website available
Profile paper DOI
Funders
Blavatnik Center for Computational Biomedicine Award
National Institute of General Medical Sciences (NIGMS)
Ongoing?
No
Data types collected
- Secondary data
- None
- None
- Healthcare data
Engagement
Keywords
