When initiating and testing the effectiveness of indicated prevention, it is essential to first select a group of people who could benefit from the intervention. Furthermore, we need to select a risk profile. Third, there needs to be an intervention that addresses these risk factors in an effective and cost-effective way. This can be reached by answering the following three questions:

The strength of the association between risk factors and the onset of depression is often expressed as an odds ratio signifying the difference in probability to develop the disorder between those with and those without a certain risk factor (or combination of factors). However, for an individual, the only number that really counts is the absolute risk that he or she will actually develop a depression within a certain time period (e.g., expressed in a percentage). Pragmatic (cost-benefits) as well as ethical reasons also require that indicated prevention studies target those who are at substantial risk of developing the disease. Those are the people that may benefit most from a preventive intervention, and their baseline risk to get the disorder makes it worthwhile. The downside is that people who do not view themselves as ill are made aware of the risk that they could develop a disorder. They are then encouraged to change their behavior or lifestyle or to follow an indicated prevention program for a disease that they possibly will never develop. The costs of this, expressed in the strain and concern for the individual, but also financially, have to be weighted against the benefits [19].

From the public health perspective it is important to know what the potential health benefits would be if the harmful effect of certain risk factors could be removed. What health benefits would arise from this, at which efforts and costs? To measure this the population attributive fraction (PAF) can be used. The PAF is expressed in a percentage and demonstrates the decrease of the percentage of incidences (number of new cases) when the harmful effects of the targeted risk factors are fully taken away. For public health it would be more effective to design an intervention targeted at a risk factor with a high PAF than a low PAF. When a risk factor with a high PAF can be treated, or if people would be allowed to adequately deal with the negative effects of the risk factor, this would provide a meaningful public health gain [44]. An example of a study examining these associations is Smit et al., which shows that persons are at a high risk of depression when they experience symptoms of anxiety, functional impairments, two or more chronic illnesses, and either a low attained educational level or below average levels of mastery, while living without a partner [21] These risk factors are responsible for 48.7 % of the incidence of depression, indicating that large health gains can be generated if the effects of these risk factors could be contained. Interestingly, no more than 8.3 % of the older population has these characteristics, suggesting that this can be done efficiently.

An intervention needs to be effective in order to be implemented; this means that it has to show a statistically significant difference with placebo or other treatment. Secondly, it needs to be effective; it needs to prove its benefits also in real life (“everyday care”) circumstances. Thirdly, it needs to be efficient. The measure to address this is the Number Needed to Be Treated (NNT). The NNT expresses how many people need to be treated to prevent the onset of one new case with the disorder; the lower the number, the more efficient the intervention [45].

To summarize, an indicated preventative intervention would ideally be targeted at a relatively small group of people with a high, absolute chance of developing the disease, and a risk profile that is responsible for a high PAF. Furthermore, there needs to be an intervention that is both effective and efficient.

A practical example of this approach is provided in a paper by Schoevers et al. [44]. In a large sample of community living elderly people, two models for selective (people at elevated risk) and indicated (those with subsyndromal depressive symptoms) prevention were compared. The goal was to identify groups in primary care with a high vulnerability for depression using easily identifiable criteria (e.g., gender, education, disability, widowed) that can be used in primary care as a screener to identify patients at elevated risk of developing a depression. The results showed that indicated prevention, which includes identifying subsyndromal depressive symptoms as the primary risk indicator, was the best way to identify groups who were at a high risk of developing depression [44]. People who had depressive symptoms had a risk of almost 30 % and accounted for 24.6 % of new cases at follow-up. In terms of selective prevention, the study demonstrated that elderly people who were recently widowed were at great risk of developing depression, and having a chronic medical condition increased this risk. The Schoevers et al. study showed that indicated prevention is the preferred option when detecting large groups of subjects at high risk of developing depression. However, compared to selected prevention, indicated prevention does require extra effort in screening for subsyndromal depression [44] (Figs. 2.1 and 2.2).

It is also of interest to note the relation between absolute risk, PAF, and NNT. Figure 2.3 shows graphically how a more detailed and specific description of the target group results in a higher absolute risk, a lower NNT, and also a lower PAF. This is helpful in determining the costs and benefits of interventions aiming at more specific or broader subgroups in the population.

Over time, universal and selected intervention services could be the most effective ways of reducing the incidence of the disease in the population. Unfortunately very large samples are required to demonstrate reductions in universal or selected interventions [46]. The incidence of major depression in the general population is around 1.7 % per year [47]. If a preventive intervention would be able to reduce the incidence by 22 % compared to the control group (to 1.3 %), as was found in a meta-analysis of preventive interventions [28], both experimental and control group would need at least 17,253 participants. However, if the preventive intervention would be made twice as effective (so reducing the incidence by 44 %), you would only need 3,933 participants per condition, which is still an impressive number [5]. If the incidence rate is higher in the target population, which is usually the case in selective and even more so in indicated prevention, the number of participants needed to prove an effect is much smaller [5]. This shows that, even though universal interventions may be effective, its effect is harder to prove than that of indicated prevention. Hundreds of studies have been conducted examining the effectiveness of prevention programs targeted at mental health [46]. Meta-analyses have demonstrated that prevention of psychiatric illnesses among adults is possible. Perhaps in line with the above, the strongest effects of prevention were observed with depressive illnesses and indicated preventive prevention [16, 46].

It is also important to examine the specific risk factors and their association with depression onset. Several risk factors such as gender, socioeconomic status, trauma, and family history are known to contribute to major depression [5]. These so-called lifetime risk factors are useful in identifying groups for selected interventions but not for identifying participants for indicated prevention trials since the specificity of those well-known risk factors is low for predicting short-term risks [5]. Muñoz et al. also stress the importance of distinguishing between risk factors that can be modified and those that function primarily as markers. Family and personal history cannot be modified while stressful life events can. Having a past history of depressive episodes is a high-risk marker of new major depressive episodes. Those individuals who have had a major depressive episode but currently do not meet criteria for a depression could be at risk for a recurrence or relapse [5]. High levels of depressive symptoms are known to be short-term predictors of major depression [48]. When selecting groups at risk for preventive interventions, these aspects need to be taken into account.