Marfan syndrome (MFS) is a multisystemic, autosomal dominant connective tissue disorder that occurs de novo in 25%. In many families, parent and child(ren) are affected, which may increase distress in parents. To assess distress, 42 mothers (29% MFS) and 25 fathers (60% MFS) of 43 affected children, completed the validated screening‐questionnaire Distress thermometer for parents of a chronically ill child, including questions on overall distress (score 0–10; ≥4 denoting “clinical distress”) and everyday problems (score 0–36). Data were compared to 1,134 control‐group‐parents of healthy children. Mothers reported significantly less overall distress (2, 1–4 vs. 3, 1–6; p = .049; r = −.07) and total everyday problems (3, 0–6 vs. 4, 1–8; p = .03; r = −.08) compared to control‐group‐mothers. Mothers without MFS reported significantly less overall distress compared to mothers with MFS, both of a child with MFS (1, 0–4 vs. 3.5, 2–5; p = .039; r = −.17). No significant differences were found between the father‐groups, nor between the group of healthy parents of an affected child living together with an affected partner compared to control‐group‐parents. No differences in percentages of clinical distress were reported between mothers and control‐group‐mothers (33 vs. 42%); fathers and control‐group‐fathers (28 vs. 32%); nor between the other groups. Distress was not associated with the children's MFS characteristics. Concluding, parents of a child with MFS did not show more clinical distress compared to parents of healthy children. However, clinical distress was reported in approximately one‐third and may increase in case of acute medical complications. We advise monitoring distress in parents of a child with MFS to provide targeted support.
Background: Adverse Childhood Experiences (ACEs) are an overlooked risk factor for behavioural, mental and physical health disparities in children with intellectual disabilities (ID) and borderline intellectual functioning (BIF). Aims: To gain insight into the presence of the 10 original Wave II ACEs and family context risk variables in a convenience sample of children with ID and BIF in Dutch residential care. Methods and procedures: 134 case-files of children with ID (n = 82) and BIF (n = 52) were analysed quantitatively. Outcomes and results: 81.7 % of the children with ID experienced at least 1 ACE, as did 92.3 % of the children with BIF. The average number of ACEs in children with ID was 2.02 (range 0???? 8) and in children with BIF 2.88 (range 0???? 7). About 20 % of the children with moderate and mild ID experienced 4 ACEs or more. Many of their families faced multiple and complex problems (ID: 69.5 %; BIF 86.5 %). Multiple regression analysis indicated an association between family context risk variables and the number of ACEs in children. Conclusions and implications: The prevalence of ACEs in children with ID and BIF appears to be considerably high. ACEs awareness in clinical practice is vital to help mitigate negative outcomes.
Several interventions have been developed to support families living with parental mental illness (PMI). Recent evidence suggests that programmes with whole-family components may have greater positive effects for families, thereby also reducing costs to health and social care systems. This review aimed to identify whole-family interventions, their common characteristics, effectiveness and acceptability. A systematic review was conducted according to PRISMA 2020 guidelines. A literature search was conducted in ASSIA, CINAHL, Embase, Medline, and PsycINFO in January 2021 and updated in August 2022. We double screened 3914 abstracts and 212 papers according to pre-set inclusion and exclusion criteria. The Mixed Methods Appraisal Tool was used for quality assessment. Quantitative and qualitative data were extracted and synthesised. Randomised-control trial data on child and parent mental health outcomes were analysed separately in random-effects meta-analyses. The protocol, extracted data, and meta-data are accessible via the Open Science Framework (https://osf.io/9uxgp/). Data from 66 reports—based on 41 independent studies and referring to 30 different interventions—were included. Findings indicated small intervention effects for all outcomes including children’s and parents’ mental health (dc = −0.017, −027; dp = −0.14, −0.16) and family outcomes. Qualitative evidence suggested that most families experienced whole-family interventions as positive, highlighting specific components as helpful, including whole-family components, speaking about mental illness, and the benefits of group settings. Our findings highlight the lack of high-quality studies. The present review fills an important gap in the literature by summarising the evidence for whole-family interventions. There is a lack of robust evidence coupled with a great need in families affected by PMI which could be addressed by whole-family interventions. We recommend the involvement of families in the further development of these interventions and their evaluation.
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Significant Others, family care, substance abuse, addiction, substance use disorder, Concerned significant others of a person with substance use disorder face psychological, social and financial problems caused by the subtance abuse of their loved one. Tradionally health care orginizations focus on the person with substance use disorder and pay less attention to their concerned significant other. In the Netherlands there is less information available about concerned significant others of persons with substance abuse. To develop a family care aproach for the significant other it's necessary to provide insight in the charasteristics of the concerned significant others of persons with substance use disorder.
Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
