Excess resource use and costs of physical comorbidities in individuals with mental health disorders: A systematic literature review and meta-analysis.

Individuals with mental health disorders (MHDs) have worse physical health than the general population, utilise healthcare resources more frequently and intensively, incurring higher costs. We provide a first comprehensive overview and quantitative synthesis of literature on the magnitude of excess resource use and costs for those with MHDs and comorbid physical health conditions (PHCs). This systematic review (PROSPERO CRD42017075319) searched studies comparing resource use or costs of individuals with MHDs and comorbid PHCs versus individuals without comorbid conditions published between 2007 and 2021. We conducted narrative and quantitative syntheses, using random-effects meta-analyses to explore ranges of excess resource use and costs across care segments, comparing to MHD only, PHC only, or general population controls (GPC). Of 20,075 records, 228 and 100 were eligible for narrative and quantitative syntheses, respectively. Most studies were from the US, covered depression or schizophrenia, reporting endocrine/metabolic or circulatory comorbidities. Frequently investigated healthcare segments were inpatient, outpatient, emergency care and medications. Evidence on lost productivity, long-term and informal care was rare. Substantial differences exist between MHDs, with depressive disorder tending towards lower average excess resource use and cost estimates, while excess resource use ranges between +6% to +320% and excess costs between +14% to +614%. PHCs are major drivers of resource use and costs for individuals with MHDs, affecting care segments differently. Significant physical health gains and cost savings are potentially achievable through prevention, earlier identification, management and treatment, using more integrated care approaches. Current international evidence, however, is heterogeneous with limited geographical representativeness and comparability.

A Network-Based Mixed Methods Approach to Analyze Current Perspectives on Personalized Oncological Medicine in Austria.

Personalized medicine (PM) to tailor healthcare (HC) to the individual, is a promising but challenging concept. So far, no study exists investigating stakeholders' perspectives on PM in oncology in Austria potentially hindering implementation, which was the aim of this study. We performed semi-structured interviews among experts (n = 14) and cancer patients (n = 2) of the Vienna General Hospital and the Medical University of Vienna and analyzed them by a mixed methods network theoretical approach. Study results show a great variety of topics addressed by the interviewees. Clear differences in the topic selection between patients and experts could be observed. Patient-doctor relationship was the most prominent theme among experts, whereas HC systems and public health in PM was more relevant for the patients. Although promising new molecular pathology methods were explicitly mentioned, the experts believed that their practical implementation and the implementation of PM in standard care will take a long time in Austria. A variety of concerns regarding PM were mentioned by the experts, including communication issues and knowledge gaps. Besides important insights into the current situation of PM in Austria, the study has shown that network theory is a powerful tool for analyzing qualitative interview data.

Soft sensor for monitoring biomass subpopulations in mammalian cell culture processes.

OBJECTIVES: Biomass subpopulations in mammalian cell culture processes cause impurities and influence productivity, which requires this critical process parameter to be monitored in real-time. RESULTS: For this reason, a novel soft sensor concept for estimating viable, dead and lysed cell concentration was developed, based on the robust and cheap in situ measurements of permittivity and turbidity in combination with a simple model. It could be shown that the turbidity measurements contain information about all investigated biomass subpopulations. The novelty of the developed soft sensor is the real-time estimation of lysed cell concentration, which is directly correlated to process-related impurities such as DNA and host cell protein in the supernatant. Based on data generated by two fed-batch processes the developed soft sensor is described and discussed. CONCLUSIONS: The presented soft sensor concept provides a tool for viable, dead and lysed cell concentration estimation in real-time with adequate accuracy and enables further applications with respect to process optimization and control.