Truce:Outcomes and mechanisms of change of a seven-week acceptance and commitment therapy programs for young people whose parent has cancer.

Truce is an Acceptance and Commitment Therapy group program for young people who have a parent with cancer. In a pragmatic controlled trial, we compared Truce with a wait-list condition to assess its effect on unmet needs and distress. We also investigated how process variables—mindfulness, cognitive inflexibility, family functioning, and life events—might influence outcomes. Participants' unmet needs improved over time (β^ = −5.01, SE = 16.48, p = 0.036, effect size = 0.42), and those improvements were greater for the intervention group compared to controls (β^ = −5.03, SE = 2.41, p = 0.040, effect size = 0.29). There was no evidence of a significant program benefit for distress. For the intervention group, greater improvements in unmet needs were associated with higher baseline distress (t = 2.36, df = 47, p = 0.022), and being less mindful at baseline (t = 2.07, df = 47, p = 0.044). No significant mediators were identified. For the control group only, experiencing negative/mixed life events related to cancer was a significant moderator of improvement (t = −2.36, df = 33, p = 0.024). Truce appears to offer therapeutic benefits to young people who have a parent with cancer, over and above the expected adjustment to the situation over time. The program seems to buffer the impact of negative cancer-related life events on participants’ well-being, but the mechanisms of change remain unclear

Optimal model complexity for terrestrial carbon cycle prediction

The terrestrial carbon cycle plays a critical role in modulating the interactions of climate with the Earth system, but different models often make vastly different predictions of its behavior. Efforts to reduce model uncertainty have commonly focused on model structure, namely by introducing additional processes and increasing structural complexity. However, the extent to which increased structural complexity can directly improve predictive skill is unclear. While adding processes may improve realism, the resulting models are often encumbered by a greater number of poorly determined or over-generalized parameters. To guide efficient model development, here we map the theoretical relationship between model complexity and predictive skill. To do so, we developed 16 structurally distinct carbon cycle models spanning an axis of complexity and incorporated them into a model–data fusion system. We calibrated each model at six globally distributed eddy covariance sites with long observation time series and under 42 data scenarios that resulted in different degrees of parameter uncertainty. For each combination of site, data scenario, and model, we then predicted net ecosystem exchange (NEE) and leaf area index (LAI) for validation against independent local site data. Though the maximum model complexity we evaluated is lower than most traditional terrestrial biosphere models, the complexity range we explored provides universal insight into the inter-relationship between structural uncertainty, parametric uncertainty, and model forecast skill. Specifically, increased complexity only improves forecast skill if parameters are adequately informed (e.g., when NEE observations are used for calibration). Otherwise, increased complexity can degrade skill and an intermediate-complexity model is optimal. This finding remains consistent regardless of whether NEE or LAI is predicted. Our COMPLexity EXperiment (COMPLEX) highlights the importance of robust observation-based parameterization for land surface modeling and suggests that data characterizing net carbon fluxes will be key to improving decadal predictions of high-dimensional terrestrial biosphere models.</p

Associations between depressive symptoms and disease progression in older patients with chronic kidney disease: results of the EQUAL study

Background Depressive symptoms are associated with adverse clinical outcomes in patients with end-stage kidney disease; however, few small studies have examined this association in patients with earlier phases of chronic kidney disease (CKD). We studied associations between baseline depressive symptoms and clinical outcomes in older patients with advanced CKD and examined whether these associations differed depending on sex. Methods CKD patients (&gt;= 65 years; estimated glomerular filtration rate &lt;= 20 mL/min/1.73 m(2)) were included from a European multicentre prospective cohort between 2012 and 2019. Depressive symptoms were measured by the five-item Mental Health Inventory (cut-off &lt;= 70; 0-100 scale). Cox proportional hazard analysis was used to study associations between depressive symptoms and time to dialysis initiation, all-cause mortality and these outcomes combined. A joint model was used to study the association between depressive symptoms and kidney function over time. Analyses were adjusted for potential baseline confounders. Results Overall kidney function decline in 1326 patients was -0.12 mL/min/1.73 m(2)/month. A total of 515 patients showed depressive symptoms. No significant association was found between depressive symptoms and kidney function over time (P = 0.08). Unlike women, men with depressive symptoms had an increased mortality rate compared with those without symptoms [adjusted hazard ratio 1.41 (95% confidence interval 1.03-1.93)]. Depressive symptoms were not significantly associated with a higher hazard of dialysis initiation, or with the combined outcome (i.e. dialysis initiation and all-cause mortality). Conclusions There was no significant association between depressive symptoms at baseline and decline in kidney function over time in older patients with advanced CKD. Depressive symptoms at baseline were associated with a higher mortality rate in men

Truce : feasibility and acceptability of an Acceptance and Commitment Therapy-based intervention for adolescents and young adults impacted by parental cancer

For adolescents and young adults (AYAs, 12–24 years), a parental cancer diagnosis can disrupt critical developmental processes and family relationships, negatively impacting wellbeing. However, few supportive interventions are available to affected offspring. This paper reports the feasibility and acceptability of Truce, an Acceptance and Commitment Therapy (ACT)-based weekly program for AYAs impacted by parental cancer. A multi-method, multi-informant approach was used, with data drawn from facilitator, AYA and parent/caregiver feedback collected after each session and at the end of the program. Truce was delivered with high fidelity to the program manual and high participant interest. AYAs and parents/caregivers reported perceived benefits of participation around therapeutic teachings, peer connection, and parental participation. While subsequent work will determine whether Truce has significant psychosocial benefits for participants, findings are a promising indicator of the potential for ACT-based group interventions to support AYAs affected by parental cancer

Development of the COVID-19 Real-Time Information System for Preparedness and Epidemic Response (CRISPER), Australia

Accurate and current information has been highlighted across the globe as a critical requirement for the COVID-19 pandemic response. To address this need, many interactive dashboards providing a range of different information about COVID-19 have been developed. A similar tool in Australia containing current information about COVID-19 could assist general practitioners and public health responders in their pandemic response efforts. The COVID-19 Real-time Information System for Preparedness and Epidemic Response (CRISPER) has been developed to provide accurate and spatially explicit real-time information for COVID-19 cases, deaths, testing and contact tracing locations in Australia. Developed based on feedback from key users and stakeholders, the system comprises three main components: (1) a data engine; (2) data visualization and interactive mapping tools; and (3) an automated alert system. This system provides integrated data from multiple sources in one platform which optimizes information sharing with public health responders, primary health care practitioners and the general public.Emma Field, Amalie Dyda, Michael Hewett, Haotian Weng, Jingjing Shi, Stephanie Curtis, Charlee Law, Lisa McHugh, Meru Sheel, Jess Moore, Luis Furuya-Kanamori, Priyanka Pillai, Paul Konings, Michael Purcell, Nigel Stocks, Graham Williams, and Colleen L. La

Design and recruitment of a large-scale cohort study on prevalence, risk factors and impact evaluation of post-COVID-19 condition and its wider long-term social, mental, and physical health impact:The PRIME post-COVID study

BACKGROUND: Persistent symptoms, described as long COVID or post-COVID-19 condition, pose a potential public health problem. Here, the design and recruitment of the PRIME post-COVID study is described. PRIME post-COVID is a large-scale population-based observational study that aims to improve understanding of the occurrence, risk factors, social, physical, mental, emotional, and socioeconomic impact of post-COVID-19 condition. METHODS: An observational open cohort study was set up, with retrospective and prospective assessments on various health-conditions and health-factors (medical, demographic, social, and behavioral) based on a public health COVID-19 test and by self-report (using online questionnaires in Dutch language). Invited for participation were, as recorded in a public health registry, adults (18 years and older) who were tested for COVID-19 and had a valid Polymerase Chain Reaction (PCR) positive or negative test result, and email address. In November 2021, 61,655 individuals were invited by email to participate, these included all eligible adults who tested PCR positive between 1 June 2020 and 1 November 2021, and a sample of adults who tested negative (2:1), comparable in distribution of age, sex, municipality of residence and year-quarter of testing. New recruitment periods are planned as well. Participants are followed over time by regular follow-up measurements. Data are analyzed using the appropriate data-analyses methods. DISCUSSION: The PRIME post-COVID study will provide insights into various health-related aspects of post-COVID-19 condition in the context of various stages of the COVID-19 pandemic. Results will inform practical guidance for society, clinical and public health practice for the prevention and care for long-term impact of COVID-19. TRIAL REGISTRATION CLINICALTRIALSGOV IDENTIFIER: NCT05128695

Table_1_Design and recruitment of a large-scale cohort study on prevalence, risk factors and impact evaluation of post-COVID-19 condition and its wider long-term social, mental, and physical health impact: The PRIME post-COVID study.DOCX

BackgroundPersistent symptoms, described as long COVID or post-COVID-19 condition, pose a potential public health problem. Here, the design and recruitment of the PRIME post-COVID study is described. PRIME post-COVID is a large-scale population-based observational study that aims to improve understanding of the occurrence, risk factors, social, physical, mental, emotional, and socioeconomic impact of post-COVID-19 condition.MethodsAn observational open cohort study was set up, with retrospective and prospective assessments on various health-conditions and health-factors (medical, demographic, social, and behavioral) based on a public health COVID-19 test and by self-report (using online questionnaires in Dutch language). Invited for participation were, as recorded in a public health registry, adults (18 years and older) who were tested for COVID-19 and had a valid Polymerase Chain Reaction (PCR) positive or negative test result, and email address. In November 2021, 61,655 individuals were invited by email to participate, these included all eligible adults who tested PCR positive between 1 June 2020 and 1 November 2021, and a sample of adults who tested negative (2:1), comparable in distribution of age, sex, municipality of residence and year-quarter of testing. New recruitment periods are planned as well. Participants are followed over time by regular follow-up measurements. Data are analyzed using the appropriate data-analyses methods.DiscussionThe PRIME post-COVID study will provide insights into various health-related aspects of post-COVID-19 condition in the context of various stages of the COVID-19 pandemic. Results will inform practical guidance for society, clinical and public health practice for the prevention and care for long-term impact of COVID-19.Trial registration ClinicalTrials.gov identifierNCT05128695.</p