Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful.

We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments.

Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials.

Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.

While epidemiological surveys worldwide have found a considerable proportion of people using mental health services not to have a diagnosis of a mental disorder, with possible implications of service overuse, other work has suggested that most people without a current diagnosis who used services exhibited other indicators of need. The aims of the present study were, using somewhat different categorisations than previous work, to investigate whether: (1) Australians without a diagnosis of a mental disorder who used mental health services had other indicators of need; and (2) how rate and frequency of service use in Australia related to level of need, then to discuss the findings in light of recent developments in Australian Mental Health Policy and other epidemiological and services research findings.

Data from the Australian National Survey of Mental Health and Wellbeing (NSMHWB) 2007 was analysed.

Most people using mental health services had evident indicators of need for mental health care (MHC), and most of those with lower evident levels of need did not make heavy use of services. Only a small proportion of individuals without any disorders or need indicators received MHC (4%). Although this latter group comprises a fair proportion of service users when extrapolating to the Australian population (16%), the vast majority of these individuals only sought brief primary-care or counselling treatment rather than consultations with psychiatrists. Access and frequency of MHC consultations were highest for people with diagnosed lifetime disorders, followed by people with no diagnosed disorders but other need indicators, and least for people with no identified need indicators. Limitations include some disorders not assessed in interview and constraints based on survey size to investigate subgroups defined, for instance, by socioeconomic advantage and disadvantage individually or by characteristics of area.

MHC for individuals with no recognised disorders or other reasonable need for such care may be occurring but if so is likely to be an area-specific phenomenon. Rather than revealing a large national pool of treatment resources being expended on the so-called ‘worried well’, the findings suggested a generally appropriate dose–response relationship between need indicators and service use. Definitive ascertainment of area-specific disparities in this national pattern would require a different survey approach. Government proposals for widespread introduction of stepped-care models that may seek to divert patients from existing treatment pathways need to be implemented with care and well informed by local data.

The standardised mean difference (SMD) is one of the most used effect sizes to indicate the effects of treatments. It indicates the difference between a treatment and comparison group after treatment has ended, in terms of standard deviations. Some meta-analyses, including several highly cited and influential ones, use the pre-post SMD, indicating the difference between baseline and post-test within one (treatment group).

In this paper, we argue that these pre-post SMDs should be avoided in meta-analyses and we describe the arguments why pre-post SMDs can result in biased outcomes.

One important reason why pre-post SMDs should be avoided is that the scores on baseline and post-test are not independent of each other. The value for the correlation should be used in the calculation of the SMD, while this value is typically not known. We used data from an ‘individual patient data’ meta-analysis of trials comparing cognitive behaviour therapy and anti-depressive medication, to show that this problem can lead to considerable errors in the estimation of the SMDs. Another even more important reason why pre-post SMDs should be avoided in meta-analyses is that they are influenced by natural processes and characteristics of the patients and settings, and these cannot be discerned from the effects of the intervention. Between-group SMDs are much better because they control for such variables and these variables only affect the between group SMD when they are related to the effects of the intervention.

We conclude that pre-post SMDs should be avoided in meta-analyses as using them probably results in biased outcomes.

Victimisation by the police is purported to be widespread in cities in the USA, but there is limited data on police–public encounters from community samples. This is partly due to an absence of measures for assessing police violence exposure from the standpoint of civilians. As such, the demographic distribution and mental health correlates of police victimisation are poorly understood. The aims of this study were to present community-based prevalence estimates of positive policing and police victimisation based on assessment with two novel measures, and to test the hypotheses that (1) exposure to police victimisation would vary across demographic groups and (2) would be associated with depression and psychological distress.

The Survey of Police–Public Encounters study surveyed adults residing in four US cities to examine the prevalence, demographic distribution and psychological correlates of police victimisation. Participants (N = 1615) completed measures of psychological distress (K-6 scale), depression (Patient Health Questionnaire 9) and two newly constructed measures of civilian-reported police–public encounters. Both measures were developed to assess police victimisation based on the WHO domains of violence, which include physical violence (with and without a weapon, assessed separately), sexual violence (inappropriate sexual contact, including public strip searches), psychological violence (e.g., threatening, intimidating, stopping without cause, or using discriminatory slurs) and neglect (police not responding when called or responding too late). The Police Practices Inventory assesses lifetime history of exposure to positive policing and police victimisation, and the Expectations of Police Practices Scale assesses the perceived likelihood of future incidents of police victimisation. Linear regression models were used to test for associations between police–public encounters and psychological distress and depression.

Psychological violence (18.6%) and police neglect (18.8%) were commonly reported in this sample and a substantial minority of respondents also reported more severe forms of violence, specifically physical (6.1%), sexual (2.8%) and physical with a weapon (3.3%). Police victimisation was more frequently reported by racial/ethnic minorities, males, transgender respondents and younger adults. Nearly all forms of victimisation (but not positive policing) were associated with psychological distress and depression in adjusted linear regression models.

Victimisation by police appears to be widespread, inequitably distributed across demographic groups and psychologically impactful. These findings suggest that public health efforts to both reduce the prevalence of police violence and to alleviate its psychological impact may be needed, particularly in disadvantaged urban communities.