This is the first report on the association between trauma exposure and depression from the Advancing Understanding of RecOvery afteR traumA(AURORA) multisite longitudinal study of adverse post-traumatic neuropsychiatric sequelae (APNS) among participants seeking emergency department (ED) treatment in the aftermath of a traumatic life experience.

We focus on participants presenting at EDs after a motor vehicle collision (MVC), which characterizes most AURORA participants, and examine associations of participant socio-demographics and MVC characteristics with 8-week depression as mediated through peritraumatic symptoms and 2-week depression.

Eight-week depression prevalence was relatively high (27.8%) and associated with several MVC characteristics (being passenger v. driver; injuries to other people). Peritraumatic distress was associated with 2-week but not 8-week depression. Most of these associations held when controlling for peritraumatic symptoms and, to a lesser degree, depressive symptoms at 2-weeks post-trauma.

These observations, coupled with substantial variation in the relative strength of the mediating pathways across predictors, raises the possibility of diverse and potentially complex underlying biological and psychological processes that remain to be elucidated in more in-depth analyses of the rich and evolving AURORA database to find new targets for intervention and new tools for risk-based stratification following trauma exposure.

Interventions and services for people with mental health problems can have broad remits: they are often designed to treat people with a variety of diagnoses. Furthermore, addressing mental health problems can have long-term implications for economic, social, and health outcomes. This represents a challenge for health technology assessment, for which long-term trial data can be lacking. In this review, we sought to identify how analysts have tackled this problem. We reviewed the methods used to extrapolate costs and outcomes for the purpose of economic evaluation where long-term trial data are not available.

We conducted a systematic review of the medical and economic literature evaluating long-term costs and outcomes for mental health interventions and services designed to treat or prevent more than two mental health conditions. We searched key databases including MEDLINE, Embase, PsycINFO, CINAHL, and EconLit. Two authors independently screened citations. Articles were excluded if they reported within-trial analyses or employed a time horizon of less than 5 years.

The search identified 829 unique records. No papers could be included in the review.

This review highlights the lack of research and understanding available to inform the appraisal of broad mental health interventions. In light of our findings, we consider the reasons for this lack of information and review relevant literature on the subject. Potential barriers to research in this context include: (i) challenges in understanding the value of broad mental health services, such as the mental and physical health nexus, intersectoral costs and benefits, and interpersonal impacts, (ii) methodological difficulties, such as data availability, patient heterogeneity, and the challenge of extrapolation, and (iii) parity of esteem. We make recommendations for resolving this problem with regard to funding, data collection, modelling methods, and outcome measurement.

There is growing evidence that many people attending annual screening for diabetic retinopathy in the United Kingdom (UK) are at low risk of developing the disease. This has led to new policy statements. However, the basis on which to establish a risk-based individualized variable-recall screening program has not yet been determined. We present a methodology for using information on an individual's risk factors to improve the allocation of resources within a screening program.

We developed a patient-level state-transition model to evaluate the cost-effectiveness of risk-based screening for diabetic retinopathy in the UK. The model incorporated a recently developed risk calculation engine that predicts an individual's risk of disease onset, and allocated individuals to alternative screening recall periods according to this level of risk. Using the findings, we demonstrate a means of estimating: (i) a threshold level of risk, above which individuals should be invited to screening, and (ii) the optimum screening recall period for an individual, based on the expected cost-effectiveness of screening and treatment.

The cost-effectiveness analysis demonstrated that standardized screening (current practice) is the least cost-effective program. Individualized screening can improve outcomes at a reduced cost. We found it feasible – though computationally expensive – to incorporate a risk calculation engine into a decision model in Microsoft Excel. In an optimized screening program, the majority or patients would be invited to attend screening at least two years after a negative screening result.

Individualized risk-based screening is likely to be cost-effective in the context of diabetic eye disease in the UK. It is expected that risk calculation engines will be developed in other disease areas in the future, and used to allocate screening and treatment at the individual level. It is important that researchers develop robust methods for combining risk calculation engines into decision analytic models and health technology assessment more broadly.