A novel way to quantify schizophrenia symptoms in clinical trials.

BACKGROUND: A major problem in quantifying symptoms of schizophrenia is establishing a reliable distinction between enduring and dynamic aspects of psychopathology. This is critical for accurate diagnosis, monitoring and evaluating treatment effects in both clinical practice and trials. MATERIALS AND METHODS: We applied Generalizability Theory, a robust novel method to distinguish between dynamic and stable aspects of schizophrenia symptoms in the widely used Positive and Negative Symptom Scale (PANSS) using a longitudinal measurement design. The sample included 107 patients with chronic schizophrenia assessed using the PANSS at five time points over a 24-week period during a multi-site clinical trial of N-Acetylcysteine as an add-on to maintenance medication for the treatment of chronic schizophrenia. RESULTS: The original PANSS and its three subscales demonstrated good reliability and generalizability of scores (G = 0.77-0.93) across sample population and occasions making them suitable for assessment of psychosis risks and long-lasting change following a treatment, while subscales of the five-factor models appeared less reliable. The most enduring symptoms represented by the PANSS were poor attention, delusions, blunted affect and poor rapport. More dynamic symptoms with 40%-50% of variance explained by patient transient state including grandiosity, preoccupation, somatic concerns, guilt feeling and hallucinatory behaviour. CONCLUSIONS: Identified dynamic symptoms are more amendable to change and should be the primary target of interventions aiming at effectively treating schizophrenia. Separating out the dynamic symptoms would increase assay sensitivity in trials, reduce the signal to noise ratio and increase the potential to detect the effects of novel therapies in clinical trials.

Wellbeing measures for workers: a systematic review and methodological quality appraisal.

Introduction: Increasing attention on workplace wellbeing and growth in workplace wellbeing interventions has highlighted the need to measure workers' wellbeing. This systematic review sought to identify the most valid and reliable published measure/s of wellbeing for workers developed between 2010 to 2020. Methods: Electronic databases Health and Psychosocial Instruments, APA PsycInfo, and Scopus were searched. Key search terms included variations of [wellbeing OR "well-being"] AND [employee* OR worker* OR staff OR personnel]. Studies and properties of wellbeing measures were then appraised using Consensus-based Standards for the selection of health Measurement Instruments. Results: Eighteen articles reported development of new wellbeing instruments and eleven undertook a psychometric validation of an existing wellbeing instrument in a specific country, language, or context. Generation and pilot testing of items for the 18 newly developed instruments were largely rated 'Inadequate'; only two were rated as 'Very Good'. None of the studies reported measurement properties of responsiveness, criterion validity, or content validity. The three instruments with the greatest number of positively rated measurement properties were the Personal Growth and Development Scale, The University of Tokyo Occupational Mental Health well-being 24 scale, and the Employee Well-being scale. However, none of these newly developed worker wellbeing instruments met the criteria for adequate instrument design. Discussion: This review provides researchers and clinicians a synthesis of information to help inform appropriate instrument selection in measurement of workers' wellbeing. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=79044, identifier: PROSPERO, CRD42018079044.

Predictors of Life Satisfaction in New Zealand: Analysis of a National Dataset.

The study aim was to determine prevalence and predictors of life satisfaction in New Zealand. In this observational cross-sectional study, a sample of 10,799 participants from NZ were drawn from the Gallup World Poll from 2006 to 2017. Data were analysed using regression analysis and ANOVA. Prevalence of life satisfaction across time varied little from a high of 7.61 (SD = 1.6) in 2007 to a low of 7.23 (SD = 1.73) in 2011 (range 0-10). Satisfaction with standards of living predicted life satisfaction regardless of age or gender. For males across all age groups and females up to age 40 years, positive experiences and satisfaction with household income were important predictors. Being married was an important predictor for males over 40 years and feeling satisfied with their current city was important for females across all ages and for men under 40. The levels of life satisfaction changed over time, possibly due to major national events. Satisfaction with standards of living was found to predict life satisfaction regardless of age or gender. These results provide a path for policy focus towards increased life satisfaction.

Strengthening Community End-of-Life Care through Implementing Measurement-Based Palliative Care.

The increasing demand for palliative care in New Zealand presents a potential threat to the quality of service delivery. One strategy to overcome this is through the implementation of valid and reliable patient-reported outcome measures. This mixed-methods study aimed to (1) implement measurement-based palliative care (MBPC) in a community palliative care service in Auckland, New Zealand; (2) evaluate the clinical utility of MBPC perceived by clinicians; (3) describe patient characteristics as measured by the Integrated Palliative Care Outcome Scale (IPOS), the Australasian Modified Karnofsky Performance Scale (AKPS), and Phase of Illness (POI); and (4) evaluate the internal consistency of the IPOS. Participants were over 18 years of age from a community outpatient palliative care service. In a phased approach to implementation, healthcare staff were educated on each instrument used for patient assessment. Uptake and internal consistency were evaluated through descriptive statistics. An interpretive descriptive methodology was used to explore the clinical utility of MBPC through semi-structured interviews with seven clinical staff members. Individual patient assessments (n = 1507) were undertaken predominantly on admission, with decreasing frequency as patients advanced through to the terminal phase of their care. Mean total IPOS scores were 17.97 (SD = 10.39, α = 0.78). The POI showed that 65% of patients were in the stable phase, 20% were in the unstable phase, 9% were in the deteriorating phase, and 2% were in the terminal phase. Clinicians reported that MBPC facilitated holistic and comprehensive assessments, as well as the development of a common interdisciplinary language. Clinicians expressed discomfort using the psychosocial and spiritual items. Measurement-based palliative care was only partially implemented but it was valued by staff and perceived to increase the quality of service delivery. Future research should determine the optimal timing of assessments, cultural responsivity for Maori and Pacific patients, and the role of MBPC in decision support for clinicians.

Intelligent Palliative Care Based on Patient-Reported Outcome Measures.

CONTEXT: The growth of patient reported outcome measures data in palliative care provides an opportunity for machine learning to identify patterns in patient responses signifying different phases of illness. OBJECTIVES: The study will explore if machine learning and network analysis can identify phases in patient palliative status through symptoms reported on the Integrated Palliative Care Outcome Scale (IPOS). METHODS: A partly cross-sectional and partially longitudinal observational study was undertaken using the Australasian Karnofsky Performance Scale (AKPS); Integrated Palliative Care Outcome Scale (IPOS); Phase of Illness (POI). Patient palliative records (n = 1507, 65% stable, 20% unstable, 9% deteriorating, 2% terminal) from 804 adult patients enrolled in a New Zealand palliative care service were analysed using a combination of statistical, machine learning and network analysis techniques. RESULTS: Data from IPOS showed considerable variation with phase. Also, network analysis showed clear associations between items by phase. Six machine learning techniques identified the most important variables for predicting possible transition between phases of illness. Network analysis for all patients showed that Poor Appetite and Loss of Energy were central IPOS items, with Loss of Energy linked to Drowsiness, Shortness of Breath and Lack of Mobility on the one hand, and Poor Appetite linked to Nausea, Vomiting, Constipation and Sore and Dry Mouth on the other. CONCLUSION: These preliminary results, when coupled with the latest technological developments in mobile apps and wearable technology, could point the way to increased use of digital therapeutics in continuous palliative care monitoring.

A Rasch Analysis of the Integrated Palliative Care Outcome Scale.

CONTEXT: Accurate assessment of a patient's palliative care needs is essential for the timely provision of treatment and support. The Integrated Palliative Care Outcome Scale (IPOS) is an ordinal measure possessing acceptable psychometric properties, but its ability to discriminate precisely between individual symptom levels has not been rigorously investigated. OBJECTIVES: The study aimed to conduct Rasch analysis of the IPOS to evaluate and enhance precision of the instrument. METHODS: Responses of 300 community-dwelling palliative care patients were subjected to Rasch analysis using the partial credit model. RESULTS: Initial analysis supported the use of the Rasch model and acceptable reliability (person separation index = 0.77) was observed; however, unsatisfactory model fit was found. Local dependency between items was resolved through the creation of super-items, which increased model fit, reliability (person separation index = 0.80), and unidimensionality. There were no misfitting super-items or differential item functioning by age, rater, sex, or ethnicity. The IPOS showed satisfactory coverage of symptoms within the present clinical sample, with the ability to assess higher severity patients. CONCLUSION: The modified IPOS showed excellent reliability for a clinical measure in assessing the overall palliative care needs of a patient. The provided ordinal-to-interval conversion table accounts for unique contribution of each symptom to the overall symptom burden and easy to use without the need to modify the original IPOS format.