Abstract
Risk prediction algorithms have long been used in health research and practice (e.g., in prediction of cardiovascular disease, diabetes, etc.) However, similar tools have not been developed for mental health problems, despite extensive research on risk factors. For example, for psychotic disorders, attempts to sum environmental risk are rare, usually unsystematic and dictated by available data. In light of this, we sought to develop a valid, easy to use measure of the total environmental risk for psychotic disorders, which can be used in research and clinical practice.
We first reviewed the literature to identify well-replicated and validated environmental risk factors for psychosis and, then, used the largest available meta-analyses to derive current best estimates of risk. We devised a method of scoring individuals based on the level of exposure to each risk factor, using odds ratios from the meta-analyses, to produce an Environmental Risk Score (ERS).
Six risk factors (ethnic minority status, urbanicity, high paternal age, obstetric complications, cannabis use, and childhood adversity) were used to generate the ERS. A distribution for different levels of risk based on permuted data showed that most of population would be at low/moderate risk with a small minority at increased environmental risk for psychosis.
This is the first systematic approach to develop an aggregate measure of environmental risk for psychoses. This can be used as a continuous measure of liability to disease or transformed to a relative risk. Its predictive ability will improve with the collection of additional, population specific data.
