The association of medical resource utilization with physical morbidity and premature mortality among patients with schizophrenia: An historical prospective population cohort study

Nationwide Evaluation of Quality of Care Indicators for Individuals with Severe Mental Illness and Diabetes Mellitus, Following Israel's Mental Health Reform

Diabetes Mellitus (DM) is more common among individuals with severe mental illness (SMI). We aimed to assess quality-of-care-indicators in individuals with SMI following the 2015 Israel's Mental-Health-reform. We analyzed yearly changes in 2015-2019 of quality-of-care-measures and intermediate-DM-outcomes, with adjustment for gender, age-group, and socioeconomic status (SES) and compared individuals with SMI to the general adult population. Adults with SMI had higher prevalences of DM (odds ratio (OR) = 1.64; 95% confidence intervals (CI): 1.61-1.67) and obesity (OR = 2.11; 95% CI: 2.08-2.13), compared to the general population. DM prevalence, DM control, and obesity rates increased over the years in this population. In 2019, HbA1c testing was marginally lower (OR = 0.88; 95% CI: 0.83-0.94) and uncontrolled DM (HbA1c > 9%) slightly more common among patients with SMI (OR = 1.22; 95% CI: 1.14-1.30), control worsened by decreasing SES. After adjustment, uncontrolled DM (adj. OR = 1.02; 95% CI: 0.96-1.09) was not associated with SMI. Cardio-metabolic morbidity among patients with SMI may be related to high prevalences of obesity and DM rather than poor DM control. Effective screening for metabolic diseases in this population and social reforms are required.

Exploring the Association between Schizophrenia and Cardiovascular Diseases: Insights into the Role of Sigma 1 Receptor

Contemporary society is characterized by rapid changes. Various epidemiological, political and economic crises represent a burden to mental health of nowadays population, which may at least partially explain the increasing incidence of mental disorders, including schizophrenia. Schizophrenia is associated with premature mortality by at least 13-15 years. The leading cause of premature mortality in schizophrenia patients is high incidence of cardiovascular diseases. The specific-cause mortality risk for cardiovascular diseases in schizophrenia patients is more than twice higher as compared to the general population. Several factors are discussed as the factor of cardiovascular diseases development. Intensive efforts to identify possible link between schizophrenia and cardiovascular diseases are made. It seems that sigma 1 receptor may represent such link. By modulation of the activity of several neurotransmitter systems, including dopamine, glutamate, and GABA, sigma 1 receptor might play a role in pathophysiology of schizophrenia. Moreover, significant roles of sigma 1 receptor in cardiovascular system have been repeatedly reported. The detailed role of sigma 1 receptor in both schizophrenia and cardiovascular disorders development however remains unclear. The article presents an overview of current knowledge about the association between schizophrenia and cardiovascular diseases and proposes possible explanations with special emphasis on the role of the sigma 1 receptor.

Relationship between efficacy and common metabolic parameters in first-treatment drug-naïve patients with early non-response schizophrenia: a retrospective study

BACKGROUND

Comorbid metabolic disorders in patients with schizophrenia are very common. Patients with schizophrenia who respond to therapy early are often strongly predictive of better treatment outcomes. However, the differences in short-term metabolic markers between early responders and early non-responders in schizophrenia are unclear.

METHODS

143 first-treatment drug-naïve schizophrenia patients were included in this study and were given a single antipsychotic medication for 6 weeks after admission. After 2 weeks, the sample was divided into an early response group and an early non-response group based on psychopathological changes. For the study endpoints, we depicted the change curves of psychopathology in both subgroups and compared the differences between the two groups in terms of remission rates and multiple metabolic parameters.

RESULTS

The early non-response had 73 cases (51.05%) in the 2nd week. In the 6th week, the remission rate was significantly higher in the early response group than in the early non-response group (30,42.86% vs. 8,10.96%); the body weight, body mass index, blood creatinine, blood uric acid, total cholesterol, triglyceride, low-density lipoprotein, fasting blood glucose, and prolactin of the enrolled samples were significantly increased, and high-density lipoprotein was significantly decreased. ANOVAs revealed a significant effect of treatment time on abdominal circumference, blood uric acid, total cholesterol, triglyceride, high-density lipoprotein, low-density lipoprotein, fasting blood glucose and prolactin, and a significant negative effect of early non-response to treatment on abdominal circumference, blood creatinine, triglyceride, fasting blood glucose.

CONCLUSIONS

Schizophrenia patients with early non-response had lower rates of short-term remission and more extensive and severe abnormal metabolic indicators. In clinical practice, patients with early non-response should be given a targeted management strategy, antipsychotic drugs should be switched on time, and active and effective interventions for their metabolic disorders should be given.

People with serious mental illness are at higher risk for acute care hospitalization in Israel, 2000-2019

BACKGROUND

People with severe mental disorders have higher mortality rates and more chronic physical conditions than the general population. Recent reforms in the Israeli mental health system included reducing the number of psychiatric hospital beds ("Structural Reform"), establishing community- based rehabilitation services ("Rehabilitation Reform"), and the transfer of governmental responsibility to the Health Maintenance Organizations (HMOs) ("Insurance Reform"). We examined how these changes have impacted the physical health of people with severe mental illness as reflected in acute care hospitalizations.

METHODS

Data from the National Psychiatric Case Register were linked with data from the National Hospital Discharges Database for 2000-2019. Acute care discharges from public hospitals were identified for people who had a psychiatric hospitalization with a diagnosis of severe mental illness (SMI, ICD-10 codes F10-F69 or F90-F99) within the preceding 5 years. The discharge rate of SMI patients was compared to that of the total population by age, diagnosis group, and period of hospitalization. Total and age-standardized discharge ratios (SDR) were calculated, using indirect standardization.

RESULTS

The SDR for total acute care hospitalizations showed that discharge rates in 2016-2019 were 2.7 times higher for the SMI population than expected from the total population. The highest SDR was for external causes (5.7), followed by respiratory diseases (4.4), infectious diseases (3.9), skin diseases (3.7) and diabetes (3.3). The lowest SDR was for cancer (1.6). The total discharge rate ratio was lowest at ages 65-74 (2.2) and highest at ages 45-54 (3.2). The SDR was lowest for females at ages 25-34 (2.1) and for males at ages 18-24 (2.3). SDRs increased over the study period for all diagnoses. This increasing trend slowed at the end of the period, and between 2012-2015 and 2016-2019 there was a small decrease for skin and liver diseases, the SDR was stable for cancer and the increase was smaller for respiratory, infectious and circulatory diseases and diabetes.

CONCLUSION

This study showed higher hospitalization rates in people with SMI compared to the total population. These differences increased between 2000 and 2019 following the opening of alternative services in the community, possibly due to a higher likelihood of psychiatric hospitalization only for those with more severe mental disease. We recommend that general practitioners and mental health professionals in the community be made aware of the essential importance of good physical healthcare, and collaborate on health promotion and disease prevention in the SMI population.

Comparison of Machine Learning Algorithms in the Prediction of Hospitalized Patients with Schizophrenia

New computational methods have emerged through science and technology to support the diagnosis of mental health disorders. Predictive models developed from machine learning algorithms can identify disorders such as schizophrenia and support clinical decision making. This research aims to compare the performance of machine learning algorithms: Decision Tree, AdaBoost, Random Forest, Naïve Bayes, Support Vector Machine, and k-Nearest Neighbor in the prediction of hospitalized patients with schizophrenia. The data set used in the study contains a total of 11,884 electronic admission records corresponding to 6933 patients with various mental health disorders; these records belong to the acute units of 11 public hospitals in a region of Spain. Of the total, 5968 records correspond to patients diagnosed with schizophrenia (3002 patients) and 5916 records correspond to patients with other mental health disorders (3931 patients). The results recommend Random Forest with the best accuracy of 72.7%. Furthermore, this algorithm presents 79.6%, 72.8%, 72.7%, and 72.7% for AUC, precision, F1-Score, and recall, respectively. The results obtained suggest that the use of machine learning algorithms can classify hospitalized patients with schizophrenia in this population and help in the hospital management of this type of disorder, to reduce the costs associated with hospitalization.