Living with Disability

This series of articles will explore a number of long-term health conditions and disabilities, including how the condition presents itself, management strategies and the role of the healthcare assistant. This article, the sixteenth in the series, will discuss hypotension and hypertension, including causes of each and management strategies.

This study examines a peer leadership approach by a disability representative organisation in Australia, designed to enhance individualised support for members and improve systemic advocacy efforts. Our research objective was to explore the structure, roles, leadership qualities, and benefits of disability-led peer support groups as part of a broader participatory action research program examining disability leadership and its application in Disability Inclusive Disaster Risk Reduction (DIDRR). Structured interviews were conducted with 14 peer leaders from established groups, with reflexive thematic analysis applied to interpret the data. The research identified transformational leadership qualities within the peer support model, including a shared purpose, mentoring, and supportive connections. Leaders fostered inclusivity, facilitating information sharing and community bonding. Notably, the deliberate use of curiosity and joint exploration emerged as a key method for building confidence among less-heard members, turning the disability representative organisation into a dynamic learning hub that integrates grassroots insights to enhance disability-inclusive programs and advocacy strategies. The study highlights the effectiveness of transformational leadership in disability peer support groups, offering insights into peer leadership dynamics and its potential to advance DIDRR. Further research could expand its application, transforming disaster preparedness and response strategies to be more inclusive and effective.

The implementation of the framework offered by the international classification of functioning disability and health (ICF) is recognized as a necessary step in the process of school inclusion for pupils with disabilities. This step may be greatly eased by informatics tools that guide users without requiring extensive familiarity with the ICF. Here we describe the development and testing of PETRA, a specific digital tool that supports the personalization of the educational path for students with certified disabilities in Italy. The tool foresees the active participation of the family and the student himself in his own training path and guides the user through sequential steps: observation, planning, realization, validation, The usability and efficiency of the tool was tested in a representative set of schools in the Trento province. Using this tool, each student is considered in his uniqueness and the educational and didactic interventions are calibrated to its specificities (including the oftenunrecognized potentialities,) and the context in which it lives. The tool was well received, showed its efficiency in each of the steps promising to offer substantial support in improving school inclusion and empowerment of students with disabilities.

<bold>Introduction:</bold> Alpha-1 Antitrypsin Deficiency (AATD)-associated panniculitis is a rare inflammatory condition characterized by painful subcutaneous plaques or nodules, often accompanied by ulceration or oily discharge. Despite its clinical and emotional burden, limited data exist on the lived experiences of individuals with this condition. <bold>Methods:</bold> An international online survey was conducted between April and July 2024, targeting individuals with AATD-associated panniculitis. The survey, co-designed with an affected individual and multi-disciplinary specialists, included 32 questions on demographics, diagnostic journey, life impact, and treatments. Responses were analysed using descriptive statistics and thematic analysis. <bold>Results:</bold> 41 responses were included in the analysis (68% female; mean age 52.3 years). Participants reported lesions at diverse sites, affecting the lower and upper limbs, followed by the trunk, buttocks, and genitalia. 61% of participants reported being misdiagnosed which negatively affected their mental health. More than half of respondents ‘strongly agreed’ or ‘agreed’ that living with alpha-1 panniculitis had made them anxious. Treatments varied, and augmentation therapy was identified as the subjectively the most effective. Open-ended responses revealed gaps in healthcare professionals’ awareness and highlighted the need for better mental health support. <bold>Conclusion:</bold> AATD-associated panniculitis significantly impacts physical, emotional, and social well-being. Addressing gaps in diagnosis and treatment, increasing healthcare providers awareness, and adopting multidisciplinary approaches are essential to improve individuals’ outcomes and quality of life.

ObjectivesTo describe adult care and support patterns across Wales, examining variations by socio-demographic characteristics, impairments and health conditions, and deprivation. This study aims to identify disparities in care provision across individuals and areas to inform equitable service delivery and improve outcomes for adults receiving local authority (LA) care and support. MethodsThe Adults Receiving Care and Support (ARCS) census became a mandatory data return to the Welsh Government in April 2023. The CARE Lab is a data partnership between ADR Wales, Welsh Government, Swansea University, London School of Economics, Disability Wales, Social Care Wales and Cardiff University, integrating data from adult social care, children’s social care, health care and the 2021 Census. This initiative advances the understanding of adults receiving LA care and support, enabling detailed analysis of socio-demographic factors and inequalities. The findings will support service planning, policy evaluation, and strategies to reduce disparities in care provision across Wales. ResultsWe will present highlights related to the initial 2023/24 ARCS standalone data collection, offering a detailed profile of adults with care and support plans. Analysis includes demographic breakdowns, intersections of socio-demographic factors, and variations in care durations. Additionally, it examines whether individuals had active care or support on the census date. Insights reveal patterns of care provision and identify areas of potential inequality or unmet need. These findings serve as a baseline for future comparisons and analyses, supporting targeted interventions to address disparities in adult social care services, and enable planning for future research for the CARE Lab and other researchers interested in the ARCS data, and suggestions towards the future primary data collection of the ARCS data. ConclusionThis study will be the first UK study to explore adult social care at a national level using individual-level population-scale linked data sources. Findings will inform future data collection improvements, provide actionable insights for policy and practice, and offer an early snapshot of the population receiving LA care and support.

The Down to 10 Days campaign was an advocacy initiative aimed at reducing long delays in housing approvals under Australia's National Disability Insurance Scheme (NDIS), a federal self-directed funding program for people with disability. Delays in securing appropriate housing often resulted in prolonged hospital stays or inappropriate placements, significantly impacting individuals' well-being and independence. The campaign, led by a coalition of disability and advocacy organizations, sought to streamline NDIS approval processes, targeting a reduction in decision times to 10 days. A process evaluation systematically examines a program or campaign's implementation, assessing whether activities were delivered as intended and identifying factors influencing success. It is particularly valuable in advocacy, offering insights into campaign effectiveness, stakeholder engagement, and short- and long-term outcomes. A process evaluation utilizing Kotter's eight-Step Change Model, incorporating document analysis (n = 42) and semi-structured interviews with key stakeholders (n = 6), was undertaken to assess the campaign's implementation and effectiveness. The use of clear messaging, targeted political advocacy, and public engagement strategies contributed to the campaign's reach and influence. Key short-term successes included heightened awareness, improved government transparency regarding NDIS decision timelines, and commitments to process improvements. This evaluation underscores the importance of strategic advocacy, coalition-building, and evidence-based messaging in driving policy change. The findings provide valuable insights for future campaigns seeking to enhance disability services and social policy reforms.

ABSTRACTBackgroundPeople with a learning disability from ethnic minorities experience barriers in their access to healthcare services and poorer health outcomes. This study aimed to explore the factors that contribute to these barriers and to better understand how they can be reduced.MethodsTwenty ‘experts by experience’ took part in experience‐based co‐design workshops to ascertain how lived experiences related to findings from a co‐designed scoping review. Audio recordings were transcribed and analysed using framework analysis.FindingsSelf‐advocates and carers experienced discrimination within a range of healthcare settings; however, recognising and understanding the source was difficult. Many people reported a lack of reasonable adjustments or support services that considered their ethnic identities. Participants also experienced isolation and stigma within their own family and community networks. Access to healthcare was adversely affected by COVID‐19. Many people were unaware of the Learning Disability Register and there was insufficient support when transitioning between healthcare services.ConclusionsServices can be improved by effective communication, continuity of care and an enhanced understanding of the experiences of learning disability across different ethnic groups. Understanding an individual's needs and preferences from the first point of contact is important for the allocation of resources.

Testimonial of Kerri Joffe

In today’s society, with fast-growing case rates, medical expenditures, social implications, and lengthy waiting periods after the first screening, there is a need for early screening that is both simple and effective for children who may be at risk for autism spectrum disorder (ASD). This is of utmost significance in light of the significant rise in the case rates of ASDs, as well as the associated medical expenses and social effects, in the contemporary world. In this study, utilizing methods from machine learning, a system was constructed, which was effective in obtaining high performance for identifying the early indicators of ASD in children. The study was carried out by the authors of this paper. The purpose of this research is to categorize ASD data in order to give a fast, easily available, and simple method for supporting the early identification of ASD. It was suggested to use machine learning methods, such as k-nearest neighbors, linear discriminant analysis, the support vector machine (SVM) method, and the random forests (RF) method, to divide populations into those who have ASD and those who do not have it. These machine learning algorithms were examined and tested using standard data collected from the machine learning repository, which contains two classes: normal and autism. The dataset was split into a training portion of 80% and a testing portion of 20%. In their separate testing, both the SVM and RF algorithms achieved a level of accuracy that was exceptional (100%). In addition, the sensitivity analysis method was used to estimate the amount of inaccuracy that would be present between the values that were intended to be achieved and the values that were predicted. The findings of the sensitivity analysis revealed that both SVM and RF had an R 2 = 100% in both the phases. When the results obtained were compared with those of the current systems, it was found that the suggested algorithms performed better than that of existing systems. It is very important to diagnose ASD as early as possible. The machine learning algorithms obtained a high level of accuracy in the diagnosis of ASD. When it comes to the categorization of ASD data, the SVM and RF approaches exhibit the best results among the two different classification approaches.