Shared decision making is a central part of the recovery paradigm and is of increasing importance in mental health service delivery. The field needs to better understand the basis on which decisions are reached regarding psychiatric treatments. Discrete choice experiments might be useful to inform the development of tools to assist shared decision making in psychiatry.

Objective-Although UK and international guidelines recommend monotherapy, antipsychotic polypharmacy in people with serious mental illness is common in clinical practice. However, empirical evidence on its effectiveness is scarce. The effectiveness of antipsychotic polypharmacy relative to monotherapy is estimated in terms of health care utilization and mortality. Methods-Primary care data from the Clinical Practice Research Datalink, hospital data from the Hospital Episodes statistics and mortality data from the Office of National Statistics were linked to compile a cohort of patients with serious mental illness in England during the period 2000-2014. The antipsychotic prescribing profile of 17,255 adults who had at least one antipsychotic drug record during the period of observation was constructed from primary care medication records. Survival analysis models were estimated to identify the effect of antipsychotic polypharmacy on the time to the first occurrence of each of three outcomes: unplanned hospital admissions (allcause), emergency department presentations, and mortality. Results-Relative to monotherapy, antipsychotic polypharmacy was not associated with increased risk of an unplanned hospital admission (HR=1.14; 95% CI=0.982-1.32), emergency department presentation (HR=0.95; 95% CI=0.80-1.14) or death (HR=1.02; 95% CI=0.76-1.37). Relative to not receiving antipsychotic medication, monotherapy was associated with a reduced

Perinatal depression and anxiety (PNDA) are an international healthcare priority, associated with significant short- and long-term problems for women, their children and families. Effective treatment is available but uptake is suboptimal: some women go untreated whilst others choose treatments without strong evidence of efficacy. Better understanding of women’s preferences for treatment is needed to facilitate uptake of effective treatment. To address this issue, a discrete choice experiment (DCE) was administered to 217 pregnant or postnatal women in Australia, who were recruited through an online research company and had similar sociodemographic characteristics to Australian data for perinatal women. The DCE investigated preferences regarding cost, treatment type, availability of childcare, modality and efficacy. Data were analysed using logit-based models accounting for preference and scale heterogeneity. Predicted probability analysis was used to explore relative attribute importance and policy change scenarios, including how these differed by women’s sociodemographic characteristics. Cost and treatment type had the greatest impact on choice, such that a policy of subsidising effective treatments was predicted to double their uptake compared with the base case. There were differences in predicted uptake associated with certain sociodemographic characteristics: for example, women with higher educational attainment were more likely to choose effective treatment. The findings suggest policy directions for decision makers whose goal is to reduce the burden of PNDA on women, their children and families.