COVID-19 and severe mental illness in Israel: testing, infection, hospitalization, mortality and vaccination rates in a countrywide study.
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ABSTRACT:
SUBMITTER: Goldberger N
PROVIDER: S-EPMC9028900 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: Not available
| S-EPMC7906735 | biostudies-literature
Project description:BackgroundExcess mortality in severe mental illness (defined here as schizophrenia, schizoaffective disorders, and bipolar affective disorders) is well described, but little is known about this inequality in ethnic minorities. We aimed to estimate excess mortality for people with severe mental illness for five ethnic groups (white British, black Caribbean, black African, south Asian, and Irish) and to assess the association of ethnicity with mortality risk.MethodsWe conducted a longitudinal cohort study of individuals with a valid diagnosis of severe mental illness between Jan 1, 2007, and Dec 31, 2014, from the case registry of the South London and Maudsley Trust (London, UK). We linked mortality data from the UK Office for National Statistics for the general population in England and Wales to our cohort, and determined all-cause and cause-specific mortality by ethnicity, standardised by age and sex to this population in 2011. We used Cox proportional hazards regression to estimate hazard ratios and a modified Cox regression, taking into account competing risks to derive sub-hazard ratios, for the association of ethnicity with all-cause and cause-specific mortality.FindingsWe identified 18 201 individuals with a valid diagnosis of severe mental illness (median follow-up 6·36 years, IQR 3·26-9·92), of whom 1767 died. Compared with the general population, age-and-sex-standardised mortality ratios (SMRs) in people with severe mental illness were increased for a range of causes, including suicides (7·65, 95% CI 6·43-9·04), non-suicide unnatural causes (4·01, 3·34-4·78), respiratory disease (3·38, 3·04-3·74), cardiovascular disease (2·65, 2·45-2·86), and cancers (1·45, 1·32-1·60). SMRs were broadly similar in different ethnic groups with severe mental illness, although the south Asian group had a reduced SMR for cancer mortality (0·49, 0·21-0·96). Within the cohort with severe mental illness, hazard ratios for all-cause mortality and sub-hazard ratios for natural-cause and unnatural-cause mortality were lower in most ethnic minority groups relative to the white British group.InterpretationPeople with severe mental illness have excess mortality relative to the general population irrespective of ethnicity. Among those with severe mental illness, some ethnic minorities have lower mortality than the white British group, for which the reasons deserve further investigation.FundingUK Health Foundation and UK Academy of Medical Sciences.
| S-EPMC5406616 | biostudies-literature
Project description:ObjectivePsychiatric disorders have been associated with unfavourable outcome following respiratory infections. Whether this also applies to coronavirus disease 2019 (COVID-19) has been scarcely investigated.MethodsUsing the Danish administrative databases, we identified all patients with a positive real-time reverse transcription-polymerase chain reaction test for COVID-19 in Denmark up to and including 2 January 2021. Multivariable cox regression was used to calculate 30-day absolute risk and average risk ratio (ARR) for the composite end point of death from any cause and severe COVID-19 associated with psychiatric disorders, defined using both hospital diagnoses and redemption of psychotropic drugs.ResultsWe included 144,321 patients with COVID-19. Compared with patients without psychiatric disorders, the standardized ARR of the composite outcome was significantly increased for patients with severe mental illness including schizophrenia spectrum disorders 2.43 (95% confidence interval [CI], 1.79-3.07), bipolar disorder 2.11 (95% CI, 1.25-2.97), unipolar depression 1.70 (95% CI, 1.38-2.02), and for patients who redeemed psychotropic drugs 1.70 (95% CI, 1.48-1.92). No association was found for patients with other psychiatric disorders 1.13 (95% CI, 0.86-1.38). Similar results were seen with the outcomes of death or severe COVID-19. Among the different psychiatric subgroups, patients with schizophrenia spectrum disorders had the highest 30-day absolute risk for the composite outcome 3.1% (95% CI, 2.3-3.9%), death 1.2% (95% CI, 0.4-2.0%) and severe COVID-19 2.7% (95% CI, 1.9-3.6%).ConclusionSchizophrenia spectrum disorders, bipolar disorder, unipolar depression and psychotropic drug redemption are associated with unfavourable outcomes in patients with COVID-19.
| S-EPMC8250986 | biostudies-literature
Project description:Background and hypothesisPrevious studies show that people with severe mental illness (SMI) are at higher risk of COVID-19 mortality, however limited evidence exists regarding risk postvaccination. We investigated COVID-19 mortality among people with schizophrenia and other SMIs before, during and after the UK vaccine roll-out.Study designUsing the Greater Manchester (GM) Care Record to access routinely collected health data linked with death records, we plotted COVID-19 mortality rates over time in GM residents with schizophrenia/psychosis, bipolar disorder (BD), and/or recurrent major depressive disorder (MDD) from February 2020 to September 2021. Multivariable logistic regression was used to compare mortality risk (risk ratios; RRs) between people with SMI (N = 193 435) and age-sex matched controls (N = 773 734), adjusted for sociodemographic factors, preexisting comorbidities, and vaccination status.Study resultsMortality risks were significantly higher among people with SMI compared with matched controls, particularly among people with schizophrenia/psychosis (RR 3.18, CI 2.94-3.44) and/or BD (RR 2.69, CI 2.16-3.34). In adjusted models, the relative risk of COVID-19 mortality decreased, though remained significantly higher than matched controls for people with schizophrenia (RR 1.61, CI 1.45-1.79) and BD (RR 1.92, CI 1.47-2.50), but not recurrent MDD (RR 1.08, CI 0.99-1.17). People with SMI continued to show higher mortality rate ratios relative to controls throughout 2021, during vaccination roll-out.ConclusionsPeople with SMI, notably schizophrenia and BD, were at greater risk of COVID-19 mortality compared to matched controls. Despite population vaccination efforts that have prioritized people with SMI, disparities still remain in COVID-19 mortality for people with SMI.
| S-EPMC9452124 | biostudies-literature
Project description:The current study tested the Integral Model of treatment motivation (IM) in a sample of 294 outpatients with severe mental illness, using structural equation modelling. The obtained structural model was not consistent with original theory, nor was the model invariant across time and patient groups (psychotic disorders and personality disorders). The patient's perceived suitability of treatment, perceived costs of treatment and outcome expectancy were most strongly associated with motivation and treatment engagement. The model explained between 22 and 86% of variance in clinical outcomes, depending on the timing of the assessment. Currently, the IM does not constitute a robust framework for patterns through which patients become motivated to engage in treatment, but does explain substantial amounts of variance in clinical outcomes. The future potential of IM as a basis for interventions in the mental health care is discussed, including suggestions for subsequent research and potential alterations of the IM to improve its utility for application in clinical practice.
| S-EPMC6208637 | biostudies-literature
Project description:Interventions: We are assessing the impact of the National Bowel Cancer Screening Program (NBCSP) by comparing participation rates of people with and without Severe Mental Illness (SMI) and looking at health outcomes. We will be using data collected from Aug 2006 - latest available.
We are linking data using Commonwealth data (NBCSP, Medicare Benefits Schedule, Pharmaceutical Benefits Scheme, Australian Cancer Database & the National Death Index) to compare bowel cancer screening participation in people with SMI to those from the general population. We will additionally link these to the NSW cancer registry and hospital data to examine care pathways from diagnosis through treatment and end-of-life care.
We will also conduct a consultation with people with experience of SMI and Colorectal cancer and people with SMI in peer support roles who can provide their perspective on the barriers faced by people with SMI to gaining timely screening, diagnosis and optimal care.
These participants will be recruited from New South Wales consumer organisations and organisations with a strong focus on provision of peer support and advocacy. We will use a semi structured group interview schedule to facilitate discussion of a single focus group of 15 people. The group will consist of both people with lived experience who have direct experience of bowel cancer (n=10), as well as other people with lived experience who have supported people in this situation – as peer support workers or in other consumer advocacy/support roles (n=5). We will use this qualitative arm of the study to further develop our understanding of the quantitative findings. The consultation will also develop ideas on how to improve access to care.
Primary outcome(s): Participation rates in the National Bowel Cancer Screening Program assessed using data from the NBCSP.[Person-years will be calculated from entry into the study (2006) (the date of commencement of the NBCSP) or from the year that people become eligible for screening (i.e. age 50), whichever occurs later.. This is a retrospective cohort study, so all of the outcomes have already occurred and were recorded in the administrative data. This will be assessed when we receive the data.];Colonoscopy rates assessed using data from the NBCSP [Person-years will be calculated from entry into the study (2006 the date of commencement of the NBCSP), or from the year that people become eligible for screening (i.e. age 50), whichever occurs later, until colonoscopy. This is a retrospective cohort study, so all of the outcomes have already occurred and have been recorded in the administrative data.];Cancer diagnosis using data from the Australian Cancer Database[Person-years will be calculated from entry into the study (2006 the date of commencement of the NBCSP), or from the year that people become eligible for screening (i.e. age 50), whichever occurs later, until cancer diagnosis. This is a retrospective cohort study, so all of the outcomes have already occurred and have been recorded in the administrative data.]
Study Design: Purpose: Screening;Duration: Longitudinal;Selection: Case control;Timing: Both
| 2470998 | ecrin-mdr-crc
Project description:Machine learning (ML), one aspect of artificial intelligence (AI), involves computer algorithms that train themselves. They have been widely applied in the healthcare domain. However, many trained ML algorithms operate as 'black boxes', producing a prediction from input data without a clear explanation of their workings. Non-transparent predictions are of limited utility in many clinical domains, where decisions must be justifiable. Here, we apply class-contrastive counterfactual reasoning to ML to demonstrate how specific changes in inputs lead to different predictions of mortality in people with severe mental illness (SMI), a major public health challenge. We produce predictions accompanied by visual and textual explanations as to how the prediction would have differed given specific changes to the input. We apply it to routinely collected data from a mental health secondary care provider in patients with schizophrenia. Using a data structuring framework informed by clinical knowledge, we captured information on physical health, mental health, and social predisposing factors. We then trained an ML algorithm and other statistical learning techniques to predict the risk of death. The ML algorithm predicted mortality with an area under receiver operating characteristic curve (AUROC) of 0.80 (95% confidence intervals [0.78, 0.82]). We used class-contrastive analysis to produce explanations for the model predictions. We outline the scenarios in which class-contrastive analysis is likely to be successful in producing explanations for model predictions. Our aim is not to advocate for a particular model but show an application of the class-contrastive analysis technique to electronic healthcare record data for a disease of public health significance. In patients with schizophrenia, our work suggests that use or prescription of medications like antidepressants was associated with lower risk of death. Abuse of alcohol/drugs and a diagnosis of delirium were associated with higher risk of death. Our ML models highlight the role of co-morbidities in determining mortality in patients with schizophrenia and the need to manage co-morbidities in these patients. We hope that some of these bio-social factors can be targeted therapeutically by either patient-level or service-level interventions. Our approach combines clinical knowledge, health data, and statistical learning, to make predictions interpretable to clinicians using class-contrastive reasoning. This is a step towards interpretable AI in the management of patients with schizophrenia and potentially other diseases.
| S-EPMC8654849 | biostudies-literature
Project description:IntroductionSevere hyperkalemia, with potassium (K+) levels ? 6.5 mEq/L, is a potentially life-threatening electrolyte imbalance. For prompt and effective treatment, it is important to know its risk factors, clinical manifestations, and predictors of mortality.MethodsAn observational cohort study was performed at 2 medical centers. A total of 923 consecutive Korean patients were analyzed. All were 19 years of age or older and were hospitalized with severe hyperkalemia between August 2007 and July 2010; the diagnosis of severe hyperkalemia was made either at the time of admission to the hospital or during the period of hospitalization. Demographic and baseline clinical characteristics at the time of hyperkalemia diagnosis were assessed, and clinical outcomes such as in-hospital mortality were reviewed, using the institutions' electronic medical record systems.ResultsChronic kidney disease (CKD) was the most common underlying medical condition, and the most common precipitating factor of hyperkalemia was metabolic acidosis. Emergent admission was indicated in 68.6% of patients, 36.7% had electrocardiogram findings typical of hyperkalemia, 24.5% had multi-organ failure (MOF) at the time of hyperkalemia diagnosis, and 20.3% were diagnosed with severe hyperkalemia at the time of cardiac arrest. The in-hospital mortality rate was 30.7%; the rate was strongly correlated with the difference between serum K+ levels at admission and at their highest point, and with severe medical conditions such as malignancy, infection, and bleeding. Furthermore, a higher in-hospital mortality rate was significantly associated with the presence of cardiac arrest and/or MOF at the time of diagnosis, emergent admission, and intensive care unit treatment during hospitalization. More importantly, acute kidney injury (AKI) in patients with normal baseline renal function was a strong predictor of mortality, compared with AKI superimposed on CKD.ConclusionsSevere hyperkalemia occurs in various medical conditions; the precipitating factors are similarly diverse. The mortality rate is especially high in patients with severe underlying disease, coexisting medical conditions, and those with normal baseline renal function.
| S-EPMC3672605 | biostudies-literature
Project description:Severe mental illness (SMI) is a broad category that includes schizophrenia, bipolar disorder, and severe depression. Both genetic disposition and environmental exposures play important roles in the development of SMI. Multiple lines of evidence suggest that the roles of genetic and environmental factors depend on each other. Gene-environment interactions may underlie the paradox of strong environmental factors for highly heritable disorders, the low estimates of shared environmental influences in twin studies of SMI, and the heritability gap between twin and molecular heritability estimates. Sons and daughters of parents with SMI are more vulnerable to the effects of prenatal and postnatal environmental exposures, suggesting that the expression of genetic liability depends on environment. In the last decade, gene-environment interactions involving specific molecular variants in candidate genes have been identified. Replicated findings include an interaction between a polymorphism in the AKT1 gene and cannabis use in the development of psychosis and an interaction between the length polymorphism of the serotonin transporter gene and childhood maltreatment in the development of persistent depressive disorder. Bipolar disorder has been underinvestigated, with only a single study showing an interaction between a functional polymorphism in the BDNF gene and stressful life events triggering bipolar depressive episodes. The first systematic search for gene-environment interactions has found that a polymorphism in CTNNA3 may sensitize the developing brain to the pathogenic effect of cytomegalovirus in utero, leading to schizophrenia in adulthood. Strategies for genome-wide investigations will likely include coordination between epidemiological and genetic research efforts, systematic assessment of multiple environmental factors in large samples, and prioritization of genetic variants.
| S-EPMC4030208 | biostudies-literature
Project description: Not available
| S-EPMC4233116 | biostudies-literature