Applying Shared Decision Making to the Process of Medication Deprescribing in Schizophrenia.

Deprescribing, the identification and discontinuation of medications that are no longer indicated or that cause adverse effects that outweigh clinical benefit, relies on the integration of clinical expertise and patient values using shared decision making (SDM). This case series describes the application of SDM to the process of deprescribing in patients with serious mental illness, illustrating the ways in which SDM builds a therapeutic alliance between patient, psychiatrist, family members, and other health care professionals to collaboratively develop treatment plans.

Bayesian emulation and history matching of JUNE.

We analyze JUNE: a detailed model of COVID-19 transmission with high spatial and demographic resolution, developed as part of the RAMP initiative. JUNE requires substantial computational resources to evaluate, making model calibration and general uncertainty analysis extremely challenging. We describe and employ the uncertainty quantification approaches of Bayes linear emulation and history matching to mimic JUNE and to perform a global parameter search, hence identifying regions of parameter space that produce acceptable matches to observed data, and demonstrating the capability of such methods. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

History matching of a complex epidemiological model of human immunodeficiency virus transmission by using variance emulation.

Complex stochastic models are commonplace in epidemiology, but their utility depends on their calibration to empirical data. History matching is a (pre)calibration method that has been applied successfully to complex deterministic models. In this work, we adapt history matching to stochastic models, by emulating the variance in the model outputs, and therefore accounting for its dependence on the model's input values. The method proposed is applied to a real complex epidemiological model of human immunodeficiency virus in Uganda with 22 inputs and 18 outputs, and is found to increase the efficiency of history matching, requiring 70% of the time and 43% fewer simulator evaluations compared with a previous variant of the method. The insight gained into the structure of the human immunodeficiency virus model, and the constraints placed on it, are then discussed.

How well do farmers know their badgers? Relating farmer knowledge to ecological survey data.

Knowledge of badger distribution is important for the management of bovine tuberculosis. At the farm level, typically the only information on badger activity available is from the farmers themselves. This study compares how well farmer perceptions of badger activity match data obtained from ecological surveys. Farmer estimates of numbers of badger setts (burrows) surrounding their farms were generally correlated with field survey results, but tended to be underestimates. Farmers correctly recorded 50 per cent of setts recorded in surveys, with larger setts and active setts more likely to be correctly recorded. Badger visits to farm buildings and yards were also monitored using surveillance cameras. The majority of farmers were aware of badger visits to their farm buildings, but in 22 per cent of cases farmers were not aware of badger visits. At the farm level, knowledge of badger activity will be useful in informing vets and animal health professionals of the potential risks of disease transmission, and hence directing management interventions. However, the tendency to underestimate activity, combined with a lack of detailed knowledge of sett locations, means that farmer estimates of badger activity should be interpreted with caution and in isolation may not be sufficient to inform management interventions.