Most at‐risk individuals will not develop a mental disorder: the limited predictive strength of risk factors
Authors: Pim Cuijpers, Filip Smit, Toshi A. Furukawa
Journal: World Psychiatry (2021)

One major problem of preventive psychiatry is the limited predictive strength of all known risk factors for mental disorders, meaning that most of the individuals who are judged to be at risk have only a small chance of developing a mental disorder within the next period of their lives. Fusar-Poli et al1 have produced an excellent overview of the current state of preventive psychiatry, and they refer to this problem several times. However, we think this is a key issue that deserves more exploration, because it can also give directions for how the prevention field can move forward. The problem of the low predictive strength of risk factors is partly related to the different priorities of epidemiological research and prevention science. In epidemiological research, the relative risk (RR) or the odds ratio (OR) is often the main indicator describing the strength of the association between a risk factor and a health outcome. However, these indicators only have limited value for prevention science. For example, if the incidence of a mental disorder in the next year is 1% of the population, and the RR of a group at risk is 4, that means that 4% of this high-risk group will develop the disorder instead of only 1% in the general population. Epidemiological researchers usually stop when they find a (significant) RR of 4, because this indicates a clear high-risk group. However, this is not enough for prevention science. A preventive intervention for a group with 4% risk (instead of 1%) still means that almost all people with this risk factor (96%) will not develop the disorder. Suppose that a preventive intervention can reduce this risk from 4% to 2%. That means that, of the 100 high-risk participants in the intervention, 96 would not develop the disorder anyway and, of the 4 who would, only two will benefit from the preventive effect. This is neither cost-effective nor ethical. Unfortunately, even though high RRs and ORs are often found in epidemiological research, almost all risk factors in mental health suffer from a low predictive strength. Having a parent with a depressive disorder is often given as an example of a group with an exceptionally high risk. One study even indicates that 50% of these children will develop a depression by the age of 202, which is much larger than any other risk factor for mental disorders. But, from the perspective of preventive interventions, even such an elevated incidence rate is still problematic. Suppose that the development of depression starts at the age of 12 and is evenly divided over the subsequent 8 years. This means that every year still only about 6% of these children will develop depression. Offering a preventive intervention to a group in which 94% will not develop the disorder in the following year is still problematic. Screening for high-risk groups has com­parable problems. For example, testing positive for high risk for psychosis has been found to be associated with a 6% lifetime risk of actually developing psychosis3. This means that 94% of those who score positive will not develop psychosis in their lifetime, and it can be disputed whether preventive interventions should be considered in these cases4. So, from the perspective of preventive interventions, RRs and ORs are clearly not sufficient as indicators of risk. An absolute risk of developing a disorder within a reasonable time frame would be a better indicator. In addition, we need to take the prevalence of the risk factor in the population into account (exposure prevalence), because that indicates the size of the population that will have to be given the intervention. For example, it is known that women have a higher chance of developing a depressive disorder, but intervening in half of the population is simply not feasible nor cost-efficient (apart from all ethical issues). On the other hand, an intervention in a small group (i.e., with a small exposure prevalence) and a high risk may be useful for the individual participants, but it will not have a large impact on the incidence of a disorder in the general population. This implies that, from the perspective of preventive interventions, we need to identify a population with a modest prevalence (because otherwise the cost of intervening is too high), but this population should be responsible for as many new cases as possible, meaning that the absolute risk is as high as possible in this group. Finally, preventive interventions should reduce the incidence of the disorder in the population as much as possible. From this perspective, the weak predictive power of most risk indicators is also problematic, because the lower the incidence rate in the population, the larger randomized trials need to be, in order to have sufficient statistical power to be able to show a significant reduction of the incidence5. For example, if we were able to identify a high-risk group with 25% incidence in the next year and we had an intervention that is capable to reduce the incidence to 17%, we would need a trial of about 1,000 participants (assuming an alpha of 0.05, 80% power and 20% attrition)5. How can this problem of the low predictive power of most risk factors be solved? One possible solution is to focus on combinations of risk factors, that identify groups that are as small as possible but are at the same time responsible for as many incident cases as possible. For example, in one study among older adults, we found that those with sub-threshold depression, functional limitations, a small social network and female gender were 8% of the population, but they explained 24% of the new incident cases of depression6. A related solution is to develop prediction tools to identify individuals with a much increased risk for developing mental disorders. The PredictD method has been studied in several large European epidemiological studies7. A comparable method has been developed in the US8. Based on well-established predictors for the development of depression, these methods calculate the exact personal risk to develop a depressive disorder in the coming year. Unfortunately, these methods do not solve the problem of the low specificity of known risk factors1. However, the digitalization of our societies and the progress in epidemiology has resulted in large datasets which may improve such approaches with machine learning techniques. In addition to the identification of high-risk groups with greater certainty, we also need better interventions. The impact of preventive interventions not only depends on the absolute risk in the target group, but also on their ability to reduce that risk. Some strategies may strengthen the effects of interventions. For example, by focusing on multiple disorders instead of only one, the absolute risk in the target group may be higher and the effects could be demonstrated easier in prevention trials9. Stepped care approaches, in which at-risk people are followed over time, may also improve outcome, although that has not been confirmed in all studies. We conclude that the predictive strength of most risk factors for the development of mental disorders is low and the identification of populations at ultra-high risk is key to the further development of preventive psy­chiatry.