Beyond the "Hype" on the association between metabolic syndrome and atypical antipsychotics - The confounding effects of cohort, typical antipsychotics, severe mental illness, comedications, and comorbid substance use

Journal of Clinical Psychopharmacology (Impact Factor: 3.76). 05/2008; 28(2):125-31. DOI: 10.1097/JCP.0b013e318166f533
Source: PubMed
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    ABSTRACT: Introduction: Although a range of adverse effects of antipsychotic medication are well documented, less attention has been paid to the issue of reduced life expectancy.Method: The medical literature was searched to identify studies assessing severe somatic side‐effects of long‐term antipsychotic treatment with a possible impact on mortality, and studies evaluating antipsychotic‐associated brain structure changes.Results: Short‐term cardiac and long‐term metabolic side effects increasing cardiovascular risk exert a negative influence on mortality in people diagnosed with schizophrenia. Three out of five studies examining antipsychotic dosage and higher mortality showed a significant association for one or more antipsychotics. Two out of four found negative effects of antipsychotic polypharmacy on life‐expectancy. One large historical cohort study found an association between longer duration of cumulative use and lower mortality, whereas other prospective studies found no effect. There is evidence for frontal grey matter reduction which seems to be accelerated by antipsychotic treatment, and may depend on cumulative doses. The amount of brain volume changes varies between individuals and with type and duration of antipsychotic treatment.Conclusion: Antipsychotics should be used more selectively, for shorter durations and with lowest possible effective dose. Greater use of psychosocial interventions that have been proven effective should be an integral part of facilitating reductions in frequency, dosage and duration of antipsycotics.
    Psychosis 02/2010; 2(1-1):50-69. DOI:10.1080/17522430903501999
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    ABSTRACT: This study investigated concordance between self-perceived and measured weight status for persons with serious mental illness. A total of 586 mental health clients assessed their weight as underweight, normal, overweight, or obese. The agreement between these self-assessments and the same categories based on measured body mass index was related to gender, ethnicity, education, age, and psychiatric diagnosis. Three hundred consumers (51%) underestimated their weight (they thought they weighed less than they did); only 35 (6%) overestimated it. In logistic regression analyses, gender, education, and psychiatric diagnosis showed significant effects on accuracy of self-perception, but ethnicity and age did not. People with serious mental illness are more likely than others to have weight problems, which contribute to higher rates of morbidity and mortality. However, they also tend to underestimate their weight. This gap between reality and self-perception must be addressed.
    Psychiatric services (Washington, D.C.) 01/2013; 64(1):91-3. DOI:10.1176/ · 2.81 Impact Factor
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    ABSTRACT: Although randomised controlled trials are regarded as the gold standard for treatments efficacy, evidence from observational studies remains relevant. To address the problem of possible confounding in these studies, investigators must employ analysis methods that adjust for confounders and lead to an unbiased estimation of the treatment effect. In this paper, the authors describe two relevant statistical methods. The first method represents the classical approach consisting of a multiple regression model including the effects of treatment and covariates. This approach considers the relation between prognostic factors and the outcome variable as a relevant criterion for adjustment. The second method is based on the propensity score, and focuses on the relation between prognostic factors and treatment assignment. These approaches were applied to a cohort of 183 French schizophrenic patients who were followed for a 2-year period (from 1998 to 2000). The probability of relapse according to antipsychotic treatment exposure was modelled using Cox regression models with the two statistical methods. Goodness-of-fit criteria were used to compare the modelling approaches. This study demonstrates that the propensity score, a predicted probability, has an important balancing property that underscores its value in strengthening the results of nonrandomised observational studies.
    Community Mental Health Journal 04/2014; 50(6). DOI:10.1007/s10597-014-9723-x · 1.03 Impact Factor