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Monitoring and prevalence rates of metabolic syndrome in military veterans with serious mental illness.

ABSTRACT: BACKGROUND: Cardiovascular disease is the leading cause of mortality among patients with serious mental illness (SMI) and the prevalence of metabolic syndrome--a constellation of cardiovascular risk factors--is significantly higher in these patients than in the general population. Metabolic monitoring among patients using second generation antipsychotics (SGAs)--a risk factor for metabolic syndrome--has been shown to be inadequate despite the release of several guidelines. However, patients with SMI have several factors independent of medication use that predispose them to a higher prevalence of metabolic syndrome. Our study therefore examines monitoring and prevalence of metabolic syndrome in patients with SMI, including those not using SGAs. METHODS AND FINDINGS: We retrospectively identified all patients treated at a Veterans Affairs Medical Center with diagnoses of schizophrenia, schizoaffective disorder or bipolar disorder during 2005-2006 and obtained demographic and clinical data. Incomplete monitoring of metabolic syndrome was defined as being unable to determine the status of at least one of the syndrome components. Of the 1,401 patients included (bipolar disorder: 822; schizophrenia: 222; and schizoaffective disorder: 357), 21.4% were incompletely monitored. Only 54.8% of patients who were not prescribed SGAs and did not have previous diagnoses of hypertension or hypercholesterolemia were monitored for all metabolic syndrome components compared to 92.4% of patients who had all three of these characteristics. Among patients monitored for metabolic syndrome completely, age-adjusted prevalence of the syndrome was 48.4%, with no significant difference between the three psychiatric groups. CONCLUSIONS: Only one half of patients with SMI not using SGAs or previously diagnosed with hypertension and hypercholesterolemia were completely monitored for metabolic syndrome components compared to greater than 90% of those with these characteristics. With the high prevalence of metabolic syndrome seen in this population, there appears to be a need to intensify efforts to reduce this monitoring gap.

SUBMITTER: Khatana SA

PROVIDER: S-EPMC3082569 | biostudies-other | 2011

REPOSITORIES: biostudies-other

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Project description:BackgroundPatients with serious mental illness (i.e., SMI; bipolar disorder, major depressive disorder, and schizophrenia) are at increased risk of readmission, yet little is known about the extent to which readmission rates among these patients vary across hospitals. The purpose of this study was to examine the variation across hospitals in readmissions for patients with SMI and differences in the characteristics of hospitals with the highest and lowest adjusted readmission rates.MethodsWe conducted a cross-sectional analysis of pooled inpatient claims from 2013-2016. Mixed logit models with hospital random effects were used to estimate the hospital-level variance. The sample included patients with SMI from a 5% sample of fee-for-service Medicare beneficiaries.ResultsWe identified 2066 hospitals with at least 30 index admissions for Medicare beneficiaries with SMI. In multivariate analyses, factors most strongly associated with increased risk of readmission included substance use disorder (OR 2.311; p < 0.001) and end stage renal disease (OR 2.024; p < 0.001). Unadjusted readmission for hospitals at the 5th and 95th percentiles of performance were 7.05% and 15.24%, respectively, constituting an 8.2% difference. Adjusting for patient and community characteristics reduced the spread in readmission rates between the 5th and 95th percentiles of hospitals by 1.0% (i.e. to 7.2%). Hospitals in the lowest vs. highest quintiles of adjusted readmission rates were more likely to be teaching hospitals (11.1% vs. 16.7%; p < 0.05) and located in the South (37.7% vs. 40.4%) or Midwest (19.8% vs. 30.0%; p < 0.001 for region differences).ConclusionsThere is substantial hospital-level variation in readmission rates among patients with serious mental illness, even after adjusting for patient and community characteristics. This has implications for policy guiding investment in hospital-based services and community resources, to improve transitions of care for patients with SMI.

Project description:BackgroundSerious mental illnesses (SMI) are common, disabling, and challenging to treat, requiring years of monitoring and treatment adjustments. Stress or reduced medication adherence can lead to rapid worsening of symptoms and behaviors. Illness exacerbations and relapses generally occur with little or no clinician awareness in real time, leaving limited opportunity to modify treatments. Previous research suggests that passive mobile sensing may be beneficial for individuals with SMI by helping them monitor mental health status and behaviors, and quickly detect worsening mental health for prompt assessment and intervention. However, there is too little research on its feasibility and acceptability and the extent to which passive data can predict changes in behaviors or symptoms.ObjectiveThe aim of this research is to study the feasibility, acceptability, and safety of passive mobile sensing for tracking behaviors and symptoms of patients in treatment for SMI, as well as developing analytics that use passive data to predict changes in behaviors and symptoms.MethodsA mobile app monitors and transmits passive mobile sensor and phone utilization data, which is used to track activity, sociability, and sleep in patients with SMI. The study consists of a user-centered design phase and a mobile sensing phase. In the design phase, focus groups, interviews, and usability testing inform further app development. In the mobile sensing phase, passive mobile sensing occurs with participants engaging in weekly assessments for 9 months. Three- and nine-month interviews study the perceptions of passive mobile sensing and ease of app use. Clinician interviews before and after the mobile sensing phase study the usefulness and feasibility of app utilization in clinical care. Predictive analytic models are built, trained, and selected, and make use of machine learning methods. Models use sensor and phone utilization data to predict behavioral changes and symptoms.ResultsThe study started in October 2020. It has received institutional review board approval. The user-centered design phase, consisting of focus groups, usability testing, and preintervention clinician interviews, was completed in June 2021. Recruitment and enrollment for the mobile sensing phase began in October 2021.ConclusionsFindings may inform the development of passive sensing apps and self-tracking in patients with SMI, and integration into care to improve assessment, treatment, and patient outcomes.Trial registrationClinicalTrials.gov NCT05023252; https://clinicaltrials.gov/ct2/show/NCT05023252.International registered report identifier (irrid)DERR1-10.2196/39010.

Project description:BackgroundPeople with a serious mental illness are more likely to smoke more and to be more dependent smokers than the general population. This may be due to a wide range of factors that could include a common aetiology to both smoking and the illness, self medication, smoking to alleviate adverse effects of medications, boredom in the existing environment, or a combination of these factors. It is important to undertake this review to facilitate improvements in both the health and safety of people with serious mental illness who smoke, and to reduce the overall burden of costs (both financial and health) to the smoker and, eventually, to the taxpayer.ObjectivesTo review the effects of smoking cessation advice for people with serious mental illness.Search methodsWe searched the Cochrane Schizophrenia Group Specialized Trials Register up to 2 April 2015, which is based on regular searches of CENTRAL, BIOSIS, PubMed, MEDLINE, EMBASE, CINAHL, PsycINFO, and trial registries. We also undertook unsystematic searches of a sample of the component databases (BNI, CINHAL, EMBASE, MEDLINE, and PsycINFO), up to 2 April 2015, and searched references of all identified studiesSelection criteriaWe planned to include all randomised controlled trials (RCTs) that focussed on smoking cessation advice versus standard care or comparing smoking cessation advice with other more focussed methods of delivering care or information.Data collection and analysisThe review authors (PK, AC, and DB) independently screened search results but did not identify any trials that fulfilled the inclusion criteria of this review.Main resultsWe did not identify any RCTs that evaluated advice regarding smoking cessation for people with serious mental illness. The excluded studies illustrate that randomisation of packages of care relevant to smokers with serious mental illness is possible.Authors' conclusionsPeople with serious mental illness are more likely to smoke than the general population. Yet we could not find any high quality evidence to guide the smoking cessation advice healthcare professionals pass onto service users. This is an area where trials are possible and needed.

Project description:BackgroundRecent attention has highlighted the common occurrence and health consequences of military sexual trauma (MST) in younger women veterans. However, almost nothing is known about MST in older veterans.ObjectiveTo describe MST among older women veterans, including prevalence and common comorbidities.DesignCross-sectional observational study, using data from national Department of Veterans Affairs medical records.ParticipantsPopulation-based sample of women Veterans aged 55+ with at least one documented MST screen response and at least one clinical encounter in fiscal years 2005-2015.Main measuresMST screen: medical diagnoses (diabetes, hypertension, hyperlipidemia, myocardial infarction, cerebrovascular disease, congestive heart failure, obesity, chronic pain conditions, back pain, dementia, insomnia, sleep apnea, menopause symptoms) and mental health diagnoses (anxiety, depression, posttraumatic stress disorder, tobacco use, alcohol use disorder, substance use disorder, opioid use disorder, suicidal ideation) from International Classification of Diseases, Ninth Revision Clinical Modification codes in the medical record.Key resultsIn this cohort of older women veterans (n?=?70,864, mean age 65.8?±?10.4 years), 13% had a positive MST screen. In multivariable regression analyses adjusted for age, race/ethnicity, and marital status, MST was strongly associated with most mental health diagnoses, particularly posttraumatic stress disorder (OR 7.25, 95% CI 6.84-7.68), depression (OR 2.39, 95% CI 2.28-2.50), and suicidal ideation (OR 2.42, 95% CI 2.08-2.82). MST was also associated with multiple medical conditions, particularly sleep disorders (insomnia OR 1.61, 95% CI 1.43-1.82; sleep apnea OR 1.48, 95% CI 1.37-1.61) and pain (chronic pain OR 1.58, 95% CI 1.50-1.67; back pain OR 1.40, 95% CI 1.34-1.47).ConclusionsA history of MST is common among older women veterans and associated with a range of medical and mental health diagnoses. These findings call attention to the need for additional research in this understudied population, and the importance of trauma-informed care approaches for women across the lifespan.

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Monitoring and prevalence rates of metabolic syndrome in military veterans with serious mental illness.

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