Schizophrenia is not the only field to face such challenges. Researchers involved in pharmacological/therapeutic studies in which the number of outcome events is very small (or where the time to outcome is very long) are faced with similar problems. Examples include trials of new treatments for HIV infection, for stroke prevention or for chronic diseases such as diabetes. In these situations, one viable solution is to assess the impact of the intervention using surrogate endpoints (SEs): i.e. biomarkers that are more convenient to measure and, as they are linked to the outcome of interest, can be used as a substitute for it.
In this paper, we argue that to evaluate the impact of universal prevention strategies on the incidence of schizophrenia, a similar approach, at the population level, could be useful.
The need for validated surrogate endpoints at the population level
This approach has already been used (although not explicitly) in the evaluation of universal prevention strategies in other mental health domains. For example, Shochet et al.  evaluated the efficacy of preventive measures for depression in secondary school students by measuring levels of depressive symptomatology and hopelessness. However, because the use of those SEs has not been preceded by a convincing evaluation of their link with the measure of interest, in this case clinical depression, the results of those studies have been considered with scepticism. For example, in a meta-analysis on prevention measures for depression, Cuijpers et al.  explicitly excluded all studies that used outcome measures other than a diagnosis of depression. This suggests that before using SEs for prevention studies, at the population level, an explicit and rigorous approach similar to that used in the development and validation of SEs for clinical trials, needs to be applied.
Definitions and general framework for SEs from clinical trials
A clinical trial measures the effect of a therapeutic intervention on a target, which reflects the principal outcome we are interested in, i.e. patient survival, function or well-being. This target is referred to as a clinical endpoint (CE).
Surrogate endpoints are biomarkers that predict the effect of a therapeutic intervention on the CE, and could thus be used as substitutes when the CE is difficult or impractical to measure (Fig. 1). Sometimes the SE, although not the central outcome measure (i.e. the CE), also reflect a real clinical benefit. To signify this added quality, such SEs are called intermediate clinical endpoints (ICE).
An example of an SE is the use of CD4 counts, instead of survival rates (CE) in people with HIV infection, in clinical trials of antiretroviral drugs. Another possible SE in this case could be the number of opportunistic infections. Because this later measure also reflects a clinical benefit (fewer opportunistic infections being in itself a desirable outcome), it could be considered as an ICE.
Surrogate endpoints provide some tangible benefits for the development of therapeutic interventions. A good SE will save research time and money and, more importantly, could save lives or avoid suffering by speeding up the process of treatment approval. However, before using a SE, a thorough evaluation of the proposed SE is needed .
Surrogate endpoints validation
Two characteristics are essential for a SE, both of them relative to the CE: surrogacy (i.e. SEs have to predict the effect of the treatment on CE) and convenience (i.e. be more easy to measure than the CE).
In the first stages, putative SEs are chosen based on their plausibility i.e. they are reasonably likely, based on existing scientific evidence to predict the CE. Most arguments at this stage come from epidemiological studies that suggest a statistical relationship between the SE and CE in basic conditions, i.e. before any therapeutic intervention, or from pathophysiological studies, or causal models that suggest that the SE is in the disease’s pathway. A measure will be used as a putative SE, keeping in mind the risk of false results, until definite demonstration of its validity, i.e. proof of its surrogacy.
As pointed by Lesko and Atkinson , the level of stringency required for a SE depends on its intended use and although validated SEs are needed when they are used for final approval of a new treatment, putative SEs can be useful in the first phases of drug development, for example to provide proof of concept.
Since SEs are used to record treatment effects with less expenditure and at a faster pace, they must also be more convenient to measure than the CE. Boissel et al.  argued that the most important characteristic a convenient SE must possess is frequency, i.e. it should occur more often than the corresponding CE, thus limiting the time needed to assess the outcome. A particularly favourable case is the use of quantitative, continuous variables as SE of binary CE, which enhances statistical power and reduces time needed to perform trials .
Finally, to be useful for assessing the effect of a treatment, the measures used as SEs must be modifiable  and reliable .
We suggest that conceptually similar criteria may be useful for identifying putative SEs at population level, for public health interventions.
However, translation of concepts from the individual to the population level has to be done with caution. Epidemiologists are well aware of the risk of serious errors when inferences are not confined at the level of observation individual vs. population—the so-called atomistic fallacy. A similar situation could arise when considering that good SEs for clinical trials would automatically make good SEs for population interventions. Thus, for any SE suggestions have to be made and arguments examined at the level (individual vs. population) of interest.