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Poor physical health in severe mental illness (SMI) remains a major issue for clinical practice.
To use electronic health records of routinely collected clinical data to determine levels of screening for cardiometabolic disease and adverse health outcomes in a large sample (n = 7718) of patients with SMI, predominantly schizophrenia and bipolar disorder.
We linked data from the Glasgow Psychosis Clinical Information System (PsyCIS) to morbidity records, routine blood results and prescribing data.
There was no record of routine blood monitoring during the preceding 2 years for 16.9% of the cohort. However, monitoring was poorer for male patients, younger patients aged 16–44, those with schizophrenia, and for tests of cholesterol, triglyceride and glycosylated haemoglobin. We estimated that 8.0% of participants had diabetes and that lipids levels, and use of lipid-lowering medication, was generally high.
Electronic record linkage identified poor health screening and adverse health outcomes in this vulnerable patient group. This approach can inform the design of future interventions and health policy.
To investigate whether socioeconomic status influenced rates of depot medication prescribing, polypharmacy (more than two psychotropic medications), newer (second-generation) antipsychotic prescribing and clozapine therapy. Postcodes, Scottish Index of Multiple Deprivation (SIMD) categories and current medication status were ascertained. Patients in the most deprived SIMD groups (8–10 combined) were compared with those in the most affluent SIMD groups (1–3 combined).
Overall, 3200 patients with ICD-10 schizophrenia were identified. No clear relationship between socioeconomic status and any of the four prescribing areas was identified, although rates of depot medication use in deprived areas were slightly higher.
Contrary to our hypothesis, there was no evidence that patients with schizophrenia within NHS Greater Glasgow and Clyde who live in more deprived communities had different prescribing experiences from patients living in more affluent areas.
Prior studies have found no adverse effects of pediatric epilepsy surgery on IQ. However, empirical techniques such as regression models, designed to account for confounding factors such as practice effects and test–retest reliability and able to provide a standardized method for evaluating outcome, have not been used in studying change after pediatric epilepsy. The goal of this study was to demonstrate the regression technique while empirically measuring the effect of epilepsy surgery on IQ in a group of pediatric patients. Predictors of retest IQ (e.g., baseline IQ, retest interval, demographics, epilepsy severity) were evaluated in a control group with intractable seizures (N = 23) assessed twice with the WISC–III. The resulting equation was used to evaluate IQ changes in a second group of children who underwent epilepsy surgery (N = 22). In controls, baseline IQ was a strong predictor of retest IQ. Number of AEDs was inversely related to retest IQ. Based on the control regression, four children (18%) in the surgical sample obtained significantly higher than expected postsurgical IQ scores and one child (5%) obtained a lower than expected IQ score. This study demonstrates that regression-based techniques yield informative estimates on outcome and may be an improvement over prior methods of measuring change after pediatric epilepsy surgery. (JINS, 2003, 9, 879–886.)
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