Association between Mental Disorders and Subsequent Medical Conditions.

BACKGROUND: Persons with mental disorders are at a higher risk than the general population for the subsequent development of certain medical conditions. METHODS: We used a population-based cohort from Danish national registries that included data on more than 5.9 million persons born in Denmark from 1900 through 2015 and followed them from 2000 through 2016, for a total of 83.9 million person-years. We assessed 10 broad types of mental disorders and 9 broad categories of medical conditions (which encompassed 31 specific conditions). We used Cox regression models to calculate overall hazard ratios and time-dependent hazard ratios for pairs of mental disorders and medical conditions, after adjustment for age, sex, calendar time, and previous mental disorders. Absolute risks were estimated with the use of competing-risks survival analyses. RESULTS: A total of 698,874 of 5,940,299 persons (11.8%) were identified as having a mental disorder. The median age of the total population was 32.1 years at entry into the cohort and 48.7 years at the time of the last follow-up. Persons with a mental disorder had a higher risk than those without such disorders with respect to 76 of 90 pairs of mental disorders and medical conditions. The median hazard ratio for an association between a mental disorder and a medical condition was 1.37. The lowest hazard ratio was 0.82 for organic mental disorders and the broad category of cancer (95% confidence interval [CI], 0.80 to 0.84), and the highest was 3.62 for eating disorders and urogenital conditions (95% CI, 3.11 to 4.22). Several specific pairs showed a reduced risk (e.g., schizophrenia and musculoskeletal conditions). Risks varied according to the time since the diagnosis of a mental disorder. The absolute risk of a medical condition within 15 years after a mental disorder was diagnosed varied from 0.6% for a urogenital condition among persons with a developmental disorder to 54.1% for a circulatory disorder among those with an organic mental disorder. CONCLUSIONS: Most mental disorders were associated with an increased risk of a subsequent medical condition; hazard ratios ranged from 0.82 to 3.62 and varied according to the time since the diagnosis of the mental disorder. (Funded by the Danish National Research Foundation and others; COMO-GMC ClinicalTrials.gov number, NCT03847753.).

The co-occurrence of common mental and physical disorders within Australian families: a national population-based study.

OBJECTIVE: Because comorbidity between mental and physical disorders is commonly found in patients, it would be expected that this pattern would also be reflected at the family level. During a recent population-based survey of common mental disorders, respondents were asked about the presence of selected mental and physical disorders in their relatives. The aim of this research was to describe the within-family co-occurrence of selected common physical and mental disorders in a population-based sample. METHODS: Subjects were drawn from the Australian National Survey of Mental Health and Wellbeing 2007. A modified version of the World Mental Health Survey Initiative of the Composite International Diagnostic Interview (WMH-CIDI 3.0, henceforth CIDI) was used to identify lifetime-ever common psychiatric disorders (anxiety disorders, depression, drug or alcohol disorders). The respondents were asked if any of their relatives had one of a list of psychiatric (anxiety, bipolar disorder, depression, drug or alcohol problem, schizophrenia) or general physical disorders (cancer, heart problems, intellectual disability, memory problems). We examined the relationship between the variables of interest using logistic regression, adjusting for potential confounding factors. RESULTS: Compared to otherwise-well respondents, those who had a CIDI diagnosis of major depressive disorders, anxiety disorders, or drug or alcohol abuse/dependence were significantly more likely to have first-degree relatives with (a) the same diagnosis as the respondent, (b) other mental disorders not identified in the respondent, and (c) a broad range of general physical conditions. CONCLUSIONS: Individuals with common mental disorders report greater familial co-occurrence for a range of mental and physical disorders. When eliciting family histories, clinicians should remain mindful that both mental and physical disorders can co-occur within families.

Bioelectric impedance is a better indicator of obesity in men with schizophrenia than body mass index.

Body mass index (BMI) is commonly used as an indicator of obesity, although in both clinical and research settings the use of bioelectric impedance analysis (BIA) is commonplace. The purpose of this study was to examine the relationship between BMI, BIA and percentage body fat to determine whether either is a superior indicator of obesity in men with schizophrenia. The reference method of deuterium dilution was used to measure total body water and, subsequently, percentage body fat in 31 men with schizophrenia. Comparisons with the classification of body fat using BMI and BIA were made. The correlation between percentage body fat and BMI was 0.64 whereas the correlation between percentage body fat and BIA was 0.90. The sensitivity and specificity in distinguishing between obese and overweight participants was 0.55 and 0.80 for BMI and 0.86 and 0.75 for BIA. BIA proved to be a better indicator of obesity than BMI. BMI misclassified a large proportion of men with schizophrenia as overweight when they had excess adiposity of sufficient magnitude to be considered as obese. Because of the widespread use of BMI as an indicator of obesity among people with schizophrenia, the level of obesity among men with schizophrenia may be in excess of that previously indicated.

Benchmarking Australia's mental health services: is it possible and useful?

BACKGROUND: Benchmarking of performance indicators in the mental health field is gaining currency in Australia as a strategy for improving service quality. AIM: To engage mental health service providers in the collection and evaluation of performance data. METHODS: Three separate rounds of data collection involving high secure, extended treatment, and medium secure services were carried out between 2003 and 2005. Twenty-five core indicators were identified and these were used to assess service inputs, processes, outputs and outcomes. RESULTS: Differences in casemix, clinical practice and local business rules gave rise to variation in service performance. The benchmarking exercise led to the implementation of quality improvement initiatives. CONCLUSIONS: It is possible and useful to collect and evaluate performance data for mental health services. While services appear similar enough to benchmark, information related to both casemix and service characteristics needs to be included in benchmarking data to understand the factors that produce differences in service performance.

Resting energy expenditure is lower than predicted in people taking atypical antipsychotic medication.

Resting energy expenditure (REE) is lower than predicted in persons taking atypical antipsychotic medication, and weight management is a significant clinical challenge for some of them. However, to date there have been no published guidelines to assist clinicians in choosing appropriate prediction equations to estimate energy expenditure in persons taking atypical antipsychotic medications. The objectives of this study were to measure REE in a group of men taking the atypical antipsychotic clozapine and to determine whether REE can be accurately predicted for this population using previously published regression equations. REE was measured using indirect calorimetry via a ventilated hood on eight men who had completed at least 6 months of treatment with clozapine. Comparisons between measured REE and predicted REE using five different equations were undertaken. The commonly-used Harris-Benedict and Schofield equations systematically overestimated REE. Predictions of REE from other equations were too variable for clinical use. When estimating energy requirements as part of a weight-management program in men who have been taking clozapine for 6 months, predictions of REE from the equations of Harris-Benedict and Schofield should be reduced by 280 kcal/day.

Comparison of clinical body composition methods in people taking weight-inducing atypical antipsychotic medications.

The purpose of this study was to compare the accuracy of clinical methods to estimate body fat (%BF) in people who take weight-inducing atypical antipsychotic medications. Forty-seven people (35 males, 12 females) with previously diagnosed psychotic illness who had been taking atypical antipsychotic medications for more than 6 months took part in this study. Percentage body fat was estimated using bioelectrical impedance analysis (BIA) and anthropometry from previously published prediction equations and compared with that measured using the deuterium dilution technique which served as the criterion measure. Bland-Altman analyses were used to assess the agreement between measures. In the males, %BF determined using BIA with the Lukaski equation was the only clinical method with mean differences that were not significant from criterion values. While in the females, %BF determined from BMI was the only method that was significantly different from the criterion values. All of the methods of estimating %BF except Watson equations provided consistent estimates across the weight range. Therefore, this study suggests that in a group of people who predominantly had schizophrenia and were taking atypical antipsychotic medications, BIA using the equation of Lukaski was the best indicator of %BF, although on an individual basis the accuracy was poor. BMI underestimated %BF to a greater significant extent than BIA. The use of BIA rather than BMI may provide a better indicator of adiposity in people who take weight inducing antipsychotic medications.

Energy expenditure and physical activity in clozapine use: implications for weight management.

OBJECTIVE: The management of atypical antipsychotic-induced weight gain is a significant challenge for people with mental illness. Fundamental research into energy metabolism in people taking atypical antipsychotic medication has been neglected. The current study of men with schizophrenia taking clozapine aimed to measure total energy expenditure (TEE) and energy expended on physical activity--activity energy expenditure (AEE) and to consider the clinical implications of the findings. METHOD: The well-established reference method of doubly labelled water (DLW) was used to measure TEE and AEE in men with schizophrenia who had been taking clozapine for more than 6 months. Resting energy expenditure was determined using indirect calorimetry. RESULTS: The TEE was 2511+/-606 kcal day-1 which was significantly different to World Health Organization recommendations (more than 20% lower). The Physical activity level (PAL) was 1.39+/-0.27 confirming the sedentary nature of people with schizophrenia who take clozapine. CONCLUSIONS: The findings support the need for weight management strategies for people with schizophrenia who take clozapine to focus on the enhancement of energy expenditure by increasing physical activity and reducing inactivity or sedentary behaviours, rather than relying primarily on strategies to reduce energy intake.