An Existential-Humanistic View of Personality Change: Co-Occurring Changes with Psychological Well-Being in a 10 Year Cohort Study

Increasingly, psychological research has indicated that an individual’s personality changes across the lifespan. We aim to better understand personality change by examining if personality change is linked to striving towards fulfilment, as suggested by existential–humanistic theories of personality dynamics. Using the Wisconsin Longitudinal Study, a cohort of 4,733 mid-life individuals across 10years, we show that personality change was significantly associated with change in existential well-being, represented by psychological well-being (PWB). Moreover, personality change was more strongly related to change in PWB than changes in other well-being indicators such as depression, hostility and life satisfaction. Personality changed to a similar degree and explained greater variation in our well-being measures than changes in socioeconomic variables. The findings indicate personality change is necessary for the holistic development of an individual, supporting a greater need to understand personality change and increasing room for use of personality measures as indicators of well-being and policy making

Comparing indices of relative deprivation using behavioural evidence

What measure of relative deprivation best predicts health? While numerous indices of relative deprivation exist, few studies have compared how well different measures account for empirical data. Hounkpatin et al. (2016) demonstrated that the relative ranked position of an individual i's income within a comparison group (their relative rank) was a better predictor of i's health than i's relative deprivation as assessed by the widely-used Yitzhaki index. In their commentary, Stark and Jakubek (2020) argue that both relative rank and relative deprivation may matter, and they develop a composite index. Here we identify some issues with their composite index, develop an alternative based on behavioural evidence, and test the various indices against data. Although almost all existing indices assume that the significance of an income y to an individual with income y (y >y ) will be some increasing function of the difference between y and y , we find that the influence of j's income on i's health is actually a reducing function of (y -y ). This finding - that less significance is assigned to distant higher incomes than to near higher incomes - is consistent with the well-established idea that we compare ourselves primarily to similar others. [Abstract copyright: Copyright © 2020 Elsevier Ltd. All rights reserved.

Modeling bivariate change in individual differences: prospective associations between personality and life satisfaction

A number of structural equation models have been developed to examine change in 1 variable or the longitudinal association between 2 variables. The most common of these are the latent growth model, the autoregressive cross-lagged model, the autoregressive latent trajectory model, and the latent change score model. The authors first overview each of these models through evaluating their different assumptions surrounding the nature of change and how these assumptions may result in different data interpretations. They then, to elucidate these issues in an empirical example, examine the longitudinal association between personality traits and life satisfaction. In a representative Dutch sample (N = 8,320), with participants providing data on both personality and life satisfaction measures every 2 years over an 8-year period, the authors reproduce findings from previous research. However, some of the structural equation models overviewed have not previously been applied to the personality-life satisfaction relation. The extended empirical examination suggests intraindividual changes in life satisfaction predict subsequent intraindividual changes in personality traits. The availability of data sets with 3 or more assessment waves allows the application of more advanced structural equation models such as the autoregressive latent trajectory or the extended latent change score model, which accounts for the complex dynamic nature of change processes and allows stronger inferences on the nature of the association between variables. However, the choice of model should be determined by theories of change processes in the variables being studied

Prevalence of chronic kidney disease in adults in England: comparison of nationally representative cross-sectional surveys from 2003 to 2016

Objectives: To identify recent trends in chronic kidney disease (CKD) prevalence in England and explore their association with changes in sociodemographic, behavioural and clinical factors. Design: Pooled cross-sectional analysis.Setting: Health Survey for England 2003, 2009/2010 combined, and 2016.Participants: 17,663 individuals (aged 16+) living in private households.Primary and secondary outcome measures: Prevalence of estimated glomerular filtration rate (eGFR

Patients’ and kidney care team’s perspectives of treatment burden and capacity in older people with chronic kidney disease: a qualitative study

Objective: Chronic kidney disease (CKD) is often a multimorbid condition and progression to more severe disease is commonly associated with increased management requirements, including lifestyle change, more medication, and greater clinician involvement. This study explored patients’ and kidney care team’s perspectives of the nature and extent of this workload (treatment burden) and factors that support capacity (the ability to manage health) for older individuals with CKD. Design: Qualitative semi-structured interview and focus group study Setting and Participants: Adults (aged 60+) with pre-dialysis CKD stages G3-5 (identified in two general practitioner surgeries and two renal clinics) and a multi-professional secondary kidney care team in the United Kingdom.Results: 29 individuals and 10 kidney team members were recruited. Treatment burden themes were: (a) understanding CKD, its treatment and consequences, (b) adhering to treatments and management, and (c) interacting with others (e.g.: clinicians) in the management of CKD. Capacity themes were: (a) personal attributes (e.g. optimism, pragmatism), (b) support network (family/friends, service providers), (c) financial capacity, environment (e.g.: geographical distance to unit) and life responsibilities (e.g.: caring for others). Patients reported poor provision of CKD information and lack of choice in treatment, whereas kidney care team members discussed health literacy issues. Patients reported having to withdraw from social activities and loss of employment due to CKD, which further impacted their capacity. Conclusion: Improved understanding of and measures to reduce the treatment burden (e.g. clear information, simplified medication, joined up care, free parking) associated with CKD in individuals as well as assessment of their capacity and interventions to improve capacity (social care, psychological support) will likely improve patient experience and their engagement with kidney care services

Post-consultation acute respiratory tract infection recovery: a latent class informed analysis of individual patient data from randomised controlled trials and observational studies

Background: There is a lack of evidence regarding post-consultation symptom trajectories for patients with respiratory tract infections (RTIs) and whether patient characteristics can be used to predict illness duration. Aim: To describe symptom trajectories in patients with RTIs, assess baseline characteristics and adverse events associated with trajectories. Design and setting: 9103 adults and children from 12 primary care studies. Method: Individual patient data latent class-informed regression analysis of randomised controlled trials and observational cohort studies. Post-consultation symptom trajectory (severity and duration), re-consultation with same or worsening illness and hospitalisation were assessed. Results: 90% of participants recovered from all symptoms by 28 days, regardless of antibiotic strategy. For studies of RTI with cough as a dominant symptom (n=5314), four trajectories were identified: ‘rapid[6]’ (90% of participants recovered within 6 days)’ in 52.0%; ‘intermediate[10]’ (28.9%); ‘slow progressive improvement[27]’ (12.5%); and ‘slow initial high symptom burden[27]’ (6.6%). Older age (OR: (95% CI): 2.57 (1.72-3.85), higher presenting illness baseline severity (OR) (95% CIs): 1.51 (1.12-2.03)); presence of lung disease (OR (95% CI): 1.78 (1.44-2.21)); above median illness duration prior to consultation (OR (95% CI): 1.99 (1.68-2.37)) were associated with slower recovery (>10 days) compared to faster recovery (≤10 days). Re-consultations and hospitalisations were respectively higher in those with slower recovery (ORs: 2.15 (1.78-2.60) and 7.42 (3.49-15.78)). Conclusion: Older patients presenting with more severe, longer pre-consultation symptoms, and chronic lung disease should be advised they are more likely to experience longer post-consultation illness durations, and that recovery rates are similar with and without antibiotics

Development and Validation of Population Clusters for Integrating Health and Social Care: Protocol for a Mixed Methods Study in Multiple Long-Term Conditions (Cluster-Artificial Intelligence for Multiple Long-Term Conditions)

Background: Multiple long-term health conditions (multimorbidity) (MLTC-M) are increasingly prevalent and associated with high rates of morbidity, mortality, and health care expenditure. Strategies to address this have primarily focused on the biological aspects of disease, but MLTC-M also result from and are associated with additional psychosocial, economic, and environmental barriers. A shift toward more personalized, holistic, and integrated care could be effective. This could be made more efficient by identifying groups of populations based on their health and social needs. In turn, these will contribute to evidence-based solutions supporting delivery of interventions tailored to address the needs pertinent to each cluster. Evidence is needed on how to generate clusters based on health and social needs and quantify the impact of clusters on long-term health and costs. Objective: We intend to develop and validate population clusters that consider determinants of health and social care needs for people with MLTC-M using data-driven machine learning (ML) methods compared to expert-driven approaches within primary care national databases, followed by evaluation of cluster trajectories and their association with health outcomes and costs. Methods: The mixed methods program of work with parallel work streams include the following: (1) qualitative semistructured interview studies exploring patient, caregiver, and professional views on clinical and socioeconomic factors influencing experiences of living with or seeking care in MLTC-M; (2) modified Delphi with relevant stakeholders to generate variables on health and social (wider) determinants and to examine the feasibility of including these variables within existing primary care databases; and (3) cohort study with expert-driven segmentation, alongside data-driven algorithms. Outputs will be compared, clusters characterized, and trajectories over time examined to quantify associations with mortality, additional long-term conditions, worsening frailty, disease severity, and 10-year health and social care costs. Results: The study will commence in October 2021 and is expected to be completed by October 2023. Conclusions: By studying MLTC-M clusters, we will assess how more personalized care can be developed, how accurate costs can be provided, and how to better understand the personal and medical profiles and environment of individuals within each cluster. Integrated care that considers “whole persons” and their environment is essential in addressing the complex, diverse, and individual needs of people living with MLTC-M

Acute kidney injury in the UK:a replication cohort study of the variation across three regional populations

Objectives A rapid growth in the reported rates of acute kidney injury (AKI) has led to calls for greater attention and greater resources for improving care. However, the reported incidence of AKI also varies more than tenfold between previous studies. Some of this variation is likely to stem from methodological heterogeneity. This study explores the extent of cross-population variation in AKI incidence after minimising heterogeneity. Design Population-based cohort study analysing data from electronic health records from three regions in the UK through shared analysis code and harmonised methodology. Setting Three populations from Scotland, Wales and England covering three time periods: Grampian 2003, 2007 and 2012; Swansea 2007; and Salford 2012. Participants All residents in each region, aged 15 years or older. Main outcome measures Population incidence of AKI and AKI phenotype (severity, recovery, recurrence). Determined using shared biochemistry-based AKI episode code and standardised by age and sex. Results Respectively, crude AKI rates (per 10 000/year) were 131, 138, 139, 151 and 124 (p=0.095), and after standardisation for age and sex: 147, 151, 146, 146 and 142 (p=0.257) for Grampian 2003, 2007 and 2012; Swansea 2007; and Salford 2012. The pattern of variation in crude rates was robust to any modifications of the AKI definition. Across all populations and time periods, AKI rates increased substantially with age from ∼20 to ∼550 per 10 000/year among those aged <40 and ≥70 years. Conclusion When harmonised methods are used and age and sex differences are accounted for, a similar high burden of AKI is consistently observed across different populations and time periods (∼150 per 10 000/year). There are particularly high rates of AKI among older people. Policy-makers should be careful not draw simplistic assumptions about variation in AKI rates based on comparisons that are not rigorous in methodological terms

Delayed antibiotic prescribing for respiratory tract infections: protocol of an individual patient data meta-analysis

Introduction Delayed prescribing can be a useful strategy to reduce antibiotic prescribing, but it is not clear for whom delayed prescribing might be effective. This protocol outlines an individual patient data (IPD) meta-analysis of randomised controlled trials (RCTs) and observational cohort studies to explore the overall effect of delayed prescribing and identify key patient characteristics that are associated with efficacy of delayed prescribing. Methods and analysis A systematic search of the databases Cochrane Central Register of Controlled Trials, Ovid MEDLINE, Ovid Embase, EBSCO CINAHL Plus and Web of Science was conducted to identify relevant studies from inception to October 2017. Outcomes of interest include duration of illness, severity of illness, complication, reconsultation and patient satisfaction. Study authors of eligible papers will be contacted and invited to contribute raw IPD data. IPD data will be checked against published data, harmonised and aggregated to create one large IPD database. Multilevel regression will be performed to explore interaction effects between treatment allocation and patient characteristics. The economic evaluation will be conducted based on IPD from the combined trial and observational studies to estimate the differences in costs and effectiveness for delayed prescribing compared with normal practice. A decision model will be developed to assess potential savings and cost-effectiveness in terms of reduced antibiotic usage of delayed prescribing and quality-adjusted life years. Ethics and dissemination Ethical approval was obtained from the University of Southampton Faculty of Medicine Research Ethics Committee (Reference number: 30068). Findings of this study will be published in peer-reviewed academic journals as well as General Practice trade journals and will be presented at national and international conferences. The results will have important public health implications, shaping the way in which antibiotics are prescribed in the future and to whom delayed prescriptions are issued