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Social determinants of health have the potential to influence mental health and addictions-related emergency department (ED) visits and the likelihood of admission to hospital. We aimed to determine how social determinants of health, individually and in combination, relate to the likelihood of hospital admission at the time of postpartum psychiatric ED visits.
Among 10 702 postpartum individuals (female based on health card) presenting to the ED for a psychiatric reason in Ontario, Canada (2008–2017), we evaluated the relation between six social determinants of health (age, neighbourhood quintile [Q, Q1 = lowest, Q5 = highest], rurality, immigrant category, Chinese or South Asian ethnicity and neighbourhood ethnic diversity) and the likelihood of hospital admission from the ED. Poisson regression models generated relative risks (RR, 95% CI) of admission for each social determinant, crude and adjusted for clinical severity (diagnosis and acuity) and other potential confounders. Generalised estimating equations were used to explore additive interaction to understand whether the likelihood of admission depended on intersections of social determinants of health.
In total, 16.0% (n = 1715) were admitted to hospital from the ED. Being young (age 19 or less v. 40 or more: RR 0.60, 95% CI 0.45–0.82), rural-dwelling (v. urban-dwelling: RR 0.75, 95% CI 0.62–0.91) and low-income (Q1 v. Q5: RR 0.81, 95% CI 0.66–0.98) were each associated with a lower likelihood of admission. Being an immigrant (non-refugee immigrant v. Canadian-born/long-term resident: RR 1.29, 95% CI 1.06–1.56), of Chinese ethnicity (v. non-Chinese/South Asian ethnicity: RR 1.88, 95% CI 1.42–2.49); and living in the most v. least ethnically diverse neighbourhoods (RR 1.24, 95% CI 1.01–1.53) were associated with a higher likelihood of admission. Only Chinese ethnicity remained significant in the fully-adjusted model (aRR 1.49, 95% CI 1.24–1.80). Additive interactions were non-significant.
For the most part, whether a postpartum ED visit resulted in admission from the ED depended primarily on the clinical severity of presentation, not on individual or intersecting social determinants of health. Being of Chinese ethnicity did increase the likelihood of admission independent of clinical severity and other measured factors; the reasons for this warrant further exploration.
Recently, artificial intelligence-powered devices have been put forward as potentially powerful tools for the improvement of mental healthcare. An important question is how these devices impact the physician-patient interaction.
Aifred is an artificial intelligence-powered clinical decision support system (CDSS) for the treatment of major depression. Here, we explore the use of a simulation centre environment in evaluating the usability of Aifred, particularly its impact on the physician–patient interaction.
Twenty psychiatry and family medicine attending staff and residents were recruited to complete a 2.5-h study at a clinical interaction simulation centre with standardised patients. Each physician had the option of using the CDSS to inform their treatment choice in three 10-min clinical scenarios with standardised patients portraying mild, moderate and severe episodes of major depression. Feasibility and acceptability data were collected through self-report questionnaires, scenario observations, interviews and standardised patient feedback.
All 20 participants completed the study. Initial results indicate that the tool was acceptable to clinicians and feasible for use during clinical encounters. Clinicians indicated a willingness to use the tool in real clinical practice, a significant degree of trust in the system's predictions to assist with treatment selection, and reported that the tool helped increase patient understanding of and trust in treatment. The simulation environment allowed for the evaluation of the tool's impact on the physician–patient interaction.
The simulation centre allowed for direct observations of clinician use and impact of the tool on the clinician–patient interaction before clinical studies. It may therefore offer a useful and important environment in the early testing of new technological tools. The present results will inform further tool development and clinician training materials.
Research in schizophrenia and pregnancy has traditionally been conducted in small samples. More recently, secondary analysis of routine healthcare data has facilitated access to data on large numbers of women with schizophrenia.
To discuss four scientific advances using data from Canada, Denmark and the UK from population-level health registers and clinical data sources.
Narrative review of research from these three countries to illustrate key advances in the area of schizophrenia and pregnancy.
Health administrative and clinical data from electronic medical records have been used to identify population-level and clinical cohorts of women with schizophrenia, and follow them longitudinally along with their children. These data have demonstrated that fertility rates in women with schizophrenia have increased over time and have enabled documentation of the course of illness in relation with pregnancy, showing the early postpartum as the time of highest risk. As a result of large sample sizes, we have been able to understand the prevalence of and risk factors for rare outcomes that would be difficult to study in clinical research. Advanced pharmaco-epidemiological methods have been used to address confounding in studies of antipsychotic medications in pregnancy, to provide data about the benefits and risks of treatment for women and their care providers.
Use of these data has advanced the field of research in schizophrenia and pregnancy. Future developments in use of electronic health records include access to richer data sources and use of modern technical advances such as machine learning and supporting team science.
Induced abortion is an indicator of access to, and quality of reproductive healthcare, but rates are relatively unknown in women with schizophrenia.
We examined whether women with schizophrenia experience increased induced abortion compared with those without schizophrenia, and identified factors associated with induced abortion risk.
In a population-based, repeated cross-sectional study (2011–2013), we compared women with and without schizophrenia in Ontario, Canada on rates of induced abortions per 1000 women and per 1000 live births. We then followed a longitudinal cohort of women with schizophrenia aged 15–44 years (n = 11 149) from 2011, using modified Poisson regression to identify risk factors for induced abortion.
Women with schizophrenia had higher abortion rates than those without schizophrenia in all years (15.5–17.5 v. 12.8–13.6 per 1000 women; largest rate ratio, 1.33; 95% CI 1.16–1.54). They also had higher abortion ratios (592–736 v. 321–341 per 1000 live births; largest rate ratio, 2.25; 95% CI 1.96–2.59). Younger age (<25 years; adjusted relative risk (aRR), 1.84; 95% CI 1.39–2.44), multiparity (aRR 2.17, 95% CI 1.66–2.83), comorbid non-psychotic mental illness (aRR 2.15, 95% CI 1.34–3.46) and substance misuse disorders (aRR 1.85, 95% CI 1.47–2.34) were associated with increased abortion risk.
These results demonstrate vulnerability related to reproductive healthcare for women with schizophrenia. Evidence-based interventions to support optimal sexual health, particularly in young women, those with psychiatric and addiction comorbidity, and women who have already had a child, are warranted.
While up to 45% of individuals with intellectual and developmental disabilities (IDD) have a comorbid psychiatric disorder, and antipsychotics are commonly prescribed, gender differences in the safety of antipsychotics have rarely been studied in this population.
To compare men and women with IDD on medical outcomes after antipsychotic initiation.
Our population-based study in Ontario, Canada, compared 1457 women and 1951 men with IDD newly initiating antipsychotic medication on risk for diabetes mellitus, hypertension, venous thromboembolism, myocardial infarction, stroke and death, with up to 4 years of follow-up.
Women were older and more medically complex at baseline. Women had higher risks for venous thromboembolism (HR 1.72, 95% CI 1.15–2.59) and death (HR 1.46, 95% CI 1.02–2.10) in crude analyses; but only thromboembolism risk was greater for women after covariate adjustment (aHR 1.58, 95% CI 1.05–2.38).
Gender should be considered in decision-making around antipsychotic medications for individuals with IDD.
Up to 13% of psychiatric patients are readmitted shortly after discharge. Interventions that ensure successful transitions to community care may play a key role in preventing early readmission.
To describe and evaluate interventions applied during the transition from in-patient to out-patient care in preventing early psychiatric readmission.
Systematic review of transitional interventions among adults admitted to hospital with mental illness where the study outcome was psychiatric readmission.
The review included 15 studies with 15 non-overlapping intervention components. Absolute risk reductions of 13.6 to 37.0% were observed in statistically significant studies. Effective intervention components were: pre- and post-discharge patient psychoeducation, structured needs assessments, medication reconciliation/education, transition managers and in-patient/out-patient provider communication. Key limitations were small sample size and risk of bias.
Many effective transitional intervention components are feasible and likely to be cost-effective. Future research can provide direction about the specific components necessary and/or sufficient for preventing early psychiatric readmission.
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