There are significant gaps in knowledge regarding the epidemiology of epilepsy in dementia and vice versa. Accurate estimates are needed to inform public health policy and prevention, and to understand health resource needs for these populations.

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ObjectiveTo use clinically informed machine learning to derive prediction models for early and late premature death in epilepsy.MethodsThis was a population‐based primary care observational cohort study. All patients meeting a case definition for incident epilepsy in the Health Improvement Network database for inclusive years 2000‐2012 were included. A modified Delphi process identified 30 potential risk factors. Outcome was early (within 4 years of epilepsy diagnosis) and late (4 years or more from diagnosis) mortality. We used regularized logistic regression, support vector machines, Gaussian naive Bayes, and random forest classifiers to predict outcomes. We assessed model calibration, discrimination, and generalizability using the Brier score, mean area under the receiver operating characteristic curve (AUC) derived from stratified fivefold cross‐validation, plotted calibration curves, and extracted measures of association where possible.ResultsWe identified 10 499 presumed incident cases from 11 194 182 patients. All models performed comparably well following stratified fivefold cross‐validation, with AUCs ranging from 0.73 to 0.81 and from 0.71 to 0.79 for early and late death, respectively. In addition to comorbid disease, social habits (alcoholism odds ratio [OR] for early death = 1.54, 95% confidence interval [CI] = 1.12‐2.11 and OR for late death = 2.62, 95% CI = 1.66‐4.16) and treatment patterns (OR for early death when no antiseizure medication [ASM] was prescribed at baseline = 1.33, 95% CI = 1.07‐1.64 and OR for late death after receipt of enzyme‐inducing ASM at baseline = 1.32, 95% CI = 1.04‐1.66) were significantly associated with increased risk of premature death. Baseline ASM polytherapy (OR = 0.55, 95% CI = 0.36‐0.85) was associated with reduced risk of early death.SignificanceClinically informed models using routine electronic medical records can be used to predict early and late mortality in epilepsy, with moderate to high accuracy and evidence of generalizability. Medical, social, and treatment‐related risk factors, such as delayed ASM prescription and baseline prescription of enzyme‐inducing ASMs, were important predictors.

Caregivers of persons with dementia and depression experience adverse effects associated with their role. The aim of this scoping review was to identify the challenges faced by caregivers of people with dementia and depression, along with interventions to support them. The MEDLINE®, Embase and PsycINFO databases were searched using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. Grey literature was assessed using the Canadian Agency for Drugs and Technologies in Health’s Gray Matter tool. The population consisted of caregivers of people with dementia and depression; the concept was to identify the negative impacts that caregivers experience and whether there are interventions to reduce them; the context was any study design targeting family or friends who were caregivers. A total of 12,835 citations were identified; 139 studies were included. Dementia and depression have variable impacts on outcomes experienced by caregivers, including burden/strain (n = 52), depression (n = 27), distress (n = 53), quality of life (n = 5) and health/well-being (n = 9). Pharmacological and non-pharmacological interventions have mixed effects. This study is important considering that depression in people with dementia is associated with caregiver distress. The use of a variety of non-pharmacological interventions could be beneficial to the latter.