Recovery beyond clinical improvement - Recovery outcomes measured for people with bipolar disorder between 1980 and 2020.

Background: Outcome measurement in bipolar disorder (BD) traditionally focused on clinical improvement without considering other domains. Improvement trajectories in clinical and social-functional domains are different and can simultaneously appear in one while not in other domains. Measuring personal recovery (PR) has become a priority internationally.

Psychological factors in personal and clinical recovery in bipolar disorder.

Background: Research into bipolar disorder (BD) has primarily focused upon clinical recovery (CR), i.e. symptom reduction, and overlooked personally meaningful recovery outcomes emphasized by service users.

Idiopathic focal eosinophilic enteritis (IFEE), an emerging cause of abdominal pain in horses: the effect of age, time and geographical location on risk.

Background: Idiopathic focal eosinophilic enteritis (IFEE) is an emerging cause of abdominal pain (colic) in horses that frequently requires surgical intervention to prevent death. The epidemiology of IFEE is poorly understood and it is difficult to diagnose pre-operatively. The aetiology of this condition and methods of possible prevention are currently unknown.

Bayesian partitioning for mapping disease risk using a matched case-control approach to confounding.

Disease maps are useful for exploring geographical heterogeneity in health outcomes. Typically interest lies in unearthing atypical regions after adjusting for known confounders. This paper presents a Bayesian partitioning approach for analyses when individual-level matching has been used to control confounding.

Bayesian partitioning for modeling and mapping spatial case-control data.

Methods for modeling and mapping spatial variation in disease risk continue to motivate much research. In particular, spatial analyses provide a useful tool for exploring geographical heterogeneity in health outcomes, and consequently can yield clues as to disease etiology, direct public health management, and generate research hypotheses. This article presents a Bayesian partitioning approach for the analysis of individual level geo-referenced health data.