This editorial proposes a shift in emphasis in the field of mental health epidemiology in conflict-affected settings. After a brief summary of the nature of contemporary armed conflicts, we consider the current and potential roles that epidemiology can play with regard to: (1) establishing the burden of mental disorders; (2) identifying risk and protective factors; and (3) intervention research. We advocate for improved methodological rigor; more attention to mixed methods approaches and multi-level longitudinal research; inclusion of the determinants of mental health beyond conflict-related violence; and consideration of a wider array of mental health outcomes. We particularly highlight the importance of expanding interest to epidemiological research that advances prevention and promotion interventions (e.g., in the early childhood period), in order to fill the gap between epidemiology and mental health practice in conflict-affected settings.

Having first introduced the pragmatic health care trial, the discussion then focuses on a selected list of technical problems that are important for the design, analysis and inference from such trials. The first is lack of independence of participants’ outcomes do to clustering either arising from a cluster randomized design or to the way treatment is delivered (therapist and group effects). The second and third concern the implications of non-adherence to treatment and subsequent loss to follow-up, particularly, when non-adherence is associated with missing outcome data. Finally, it is argued that pragmatism and a desire for a scientific explanation should not be regarded as mutually exclusive.

In the light of the recent publication of the DSM-5, there is renewed debate about the relative merit of categorical diagnosis, as laid down in Diagnostic and Statistical Manual of Mental Disorders (DSM) and International Classification of Diseases (ICD) diagnostic manuals. Issues such as validity, usefulness and acceptability of the diagnoses in this manual are increasingly debated. Several alternative possibilities have been suggested including: (i) the introduction of truly cross-cutting dimensional measures, that would facilitate dynamic multidimensional formulations of psychopathology, (ii) the Research Domain Criteria, that may facilitate biological research but move away from clinical symptoms, (iii) a system of personalized diagnosis based on psychopathology as a network of symptoms and contexts, and (iv) enhanced focus on motor alterations, other than catatonia, as a possible additional informative dimension of diagnosis in psychiatry, particularly as a possible marker of underlying neurodevelopmental alterations. We suggest that novel systems of diagnosis are likely to rely more on continuous monitoring of diagnostically relevant information in daily life, complementing retrospective symptom criteria in DSM and ICD. Patients and their families are likely to benefit from these projects, as novel models of diagnosis based on daily life information may be linked more strongly to treatment needs and prognosis.

The importance of measuring the quality of mental health care is widely recognized. A number of factors should be considered when constructing mental health quality indicators including the aspects of care to be measured; translation of quality measurement concepts into indicators that can be measured; pilot-testing, analysis and display of measures; and maintaining effectiveness of performance measures and policies over time. The impetus to measure quality in mental health care may be dampened by the innumerable challenges inherent in this worthwhile endeavour. In particular, many countries lack adequate quality measurement infrastructure. Challenges may be overcome to a certain extent by international collaboration. While cross-country co-operation can also introduce additional complexities; its benefits usually outweigh the costs. Quality indicators can have many uses but of utmost importance is that quality measurement in mental health care subsequently results in quality improvements.

The quality of routine mental health care is not optimal, it can vary greatly from region to region and among providers; in many occasions, it does not correspond to the standards of evidence-based mental health. To bridge this gap, the promotion of a systematic use of the information available for quality assurance would be most helpful, but measuring the quality of mental health care is particularly challenging. Quality measurement can play a key role in transforming health care systems, and the routine measurement of quality, using clinical indicators derived from evidence-based practice guidelines, is an important step to this end. In Italy, the use of clinical indicators is still sporadic: over the last 5 years only three projects have been aimed at analysing, in a structured way, the quality of care in severe mental illness, and two of these were led by the Italian Society of Psychiatric Epidemiology. Not only in Italy but also at global level there is an urgent need for the implementation of mental health information systems that could lead to a substantial improvement in information technology. Once this has been achieved, a common set of clinical indicators, agreed upon at the regional and national level and useful for benchmarking and for comparing mental health services, could be defined. Finally, using the implementation strategies, a system of quality improvement at both regional and local levels will be built.

Names matter! Schizophrenia has negative associations which impede individual recovery and induce societal and self-stigmatization. Alternatives have been proposed and are worthy of debate; changes made in Japan have generally been considered successful. The group of ‘schizophrenia and other psychoses’ could be further differentiated based on the major social factors identified, i.e. drug misuse and the effects of severe childhood trauma. The use of appropriate International Classification of Diseases (ICD) coding and definitions could usefully differentiate these groups – the former is a drug-induced psychosis and the latter frequently presents as comorbid schizophrenia and borderline personality disorder (often attracting a diagnosis of schizoaffective disorder). The current established differentiation between early onset (‘stress-sensitive’ – ‘Kraepelinian’ schizophrenia) and later onset (DSM5 delusional disorder, i.e. with ‘non-bizarreness’ criterion removed) psychosis may also be worthy of further investigation to establish validity and reliability. Psychosocially descriptive terms have been found to be more acceptable to patients and perceived as less stigmatizing by others. Subgroups of psychosis with greater homogeneity would benefit research, clinical and therapeutic practice and public understanding, attitudes and behaviour.

This Editorial addresses the crucial issue of which research methodology is most suited for capturing the complexity of psychosocial interventions conducted in ‘real world’ mental health settings. It first examines conventional randomized controlled trial (RCT) methodology and critically appraises its strengths and weaknesses. It then considers the specificity of mental health care treatments and defines the term ‘complex’ intervention and its implications for RCT design. The salient features of pragmatic RCTs aimed at generating evidence of psychosocial intervention effectiveness are then described. Subsequently, the conceptualization of pragmatic RCTs, and of their further developments – which we propose to call ‘new generation’ pragmatic trials – in the broader routine mental health service context, is explored. Helpful tools for planning pragmatic RCTs, such as the CONSORT extension for pragmatic trials, and the PRECIS tool are also examined. We then discuss some practical challenges that are involved in the design and implementation of pragmatic trials based on our own experience in conducting the GET UP PIANO Trial. Lastly, we speculate on the ways in which current ideas on the purpose, scope and ethics of mental health care research may determine further challenges for clinical research and evidence-based practice.

The development of ICD11 and DSM5 was seen as an opportunity to harmonize the two major classification systems for mental disorders. The proposed ICD11 and DSM5 diagnostic criteria for PTSD are markedly different. The implications of this remain to be seen, but have the potential to cause confusion to PTSD sufferers, clinicians, researchers and others impacted on by the condition.

According to a recently published population study conducted in France, exposure to benzodiazepines may be associated with an approximately 50% increase in the risk of dementia in the elderly. However, the clinical interpretation of this finding raised some concerns. A causal link between benzodiazepine use and diagnosis of dementia may be real, but it is nevertheless possible that the increased risk might be due to other confounding factors. In this article, the main strengths and weaknesses of this study are briefly analysed, including the possibility of reverse causation. Implications for research and current practice are discussed.

Activation likelihood estimation (ALE) meta-analyses allow investigators to integrate the results of multiple neuroimaging studies, potentially yielding novel results that may not have been evident in the individual studies. Here, we provide a brief, introductory description of ALE methods for readers without extensive expertise in neuroimaging.

In psychiatry, the use of rating scales as measures of outcome in clinical trials allows us to generate continuous outcome data, where each individual's outcome is measured in numbers. Continuous outcomes can be divided into two categories, such as improved and not improved, or may be kept continuous. This article briefly presents the main advantages and disadvantages of these two approaches, which are commonly employed in the analyses of rating scale scores in clinical trials and systematic reviews.

Randomized controlled trials (RCTs), typically, randomize participants to one of two intervention groups. It has been shown, however, that about 25% of RCTs published in the scientific literature randomize participants to three or more treatment groups. These studies are called ‘multi-arm’ trials: there may be, for instance, two or more experimental intervention groups with a common control group, or two control intervention groups such as a placebo group and a standard treatment group. A special case of multi-arm studies are factorial trials, which address two or more intervention comparisons carried out simultaneously, using four or more intervention groups.