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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
Examining interactions between risk factors for psychosis.
Stanley Zammit1, Glyn Lewis, Christina Dalman
1Department of Psychological Medicine, Cardiff University, UK. zammits@cardiff.ac.uk
The British Journal of Psychiatry : the Journal of Mental Science
|September 3, 2010
Summary
Investigating psychosis risk factors revealed that multiplicative models better explain their combined effects than additive models. While interactions were observed, their clinical implications remain limited.
Area of Science:
- Psychiatry
- Epidemiology
- Genetics
Background:
- Complex diseases often involve multiple risk factors.
- Co-exposure to risk factors may have synergistic effects on disease development.
Purpose of the Study:
- To determine if risk factors for psychosis exhibit greater than additive relationships.
- To compare additive and multiplicative models for assessing joint risk factor effects.
Main Methods:
- A large cohort study of 50,053 Swedish conscripts.
- Data included IQ, cannabis use, and psychiatric history.
- Follow-up for non-affective psychosis admissions over 27 years.
- Statistical interaction models (additive and multiplicative) were applied.
Main Results:
- Evidence of interaction was found for 8 of 10 risk factor combinations under additive models.
- Only 1 combination showed interaction under multiplicative models.
- Multiplicative models provided a more adequate description of joint effects.
Conclusions:
- Multiplicative models are more suitable for describing the combined impact of psychosis risk factors.
- The observed interactions, while statistically significant, have limited implications for current understanding or treatment.
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