Measuring quality of life in mental health: Are we asking the right questions?

Measuring quality-adjusted-life years using generic preference-based quality of life measures is common practice when evaluating health interventions. However, there are concerns that measures in common use, such as the EQ-5D and SF-6D, focus overly on physical health and therefore may not be appropriate for measuring quality of life for people with mental health problems. The aim of this research was to identify the domains of quality of life that are important to people with mental health problems in order to assess the content validity of these generic measures. Qualitative semi-structured interviews were conducted with 19 people, recruited from UK mental health services, with a broad range of mental health problems at varying levels of severity. This complemented a previous systematic review and thematic synthesis of qualitative studies on the same topic. Seven domains important to quality of life for people with mental health problems were identified: well-being and ill-being; relationships and a sense of belonging; activity; self-perception; autonomy, hope and hopelessness; and physical health. These were consistent with the systematic review, with the addition of physical health as a domain, and revealed a differing emphasis on the positive and negative aspects of quality of life according to the severity of the mental health problems. We conclude that the content of existing generic preference-based measures of health do not cover this domain space well. Additionally, because people may experience substantial improvements in their quality of life without registering on the positive end of a quality of life scale, it is important that the full spectrum of negative through to positive aspects of each domain are included in any quality of life measure

In silico toxicology protocols

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information