Validation of a screening instrument for autism spectrum disorders among primary school children in Ireland

Objectives The European Autism Information Systems Project (Posada & Ramirez, 2008) highlighted the lack of systematic and reliable data on the prevalence of autism spectrum disorders in Europe. The EAIS project designed a protocol for the study of ASD prevalence at European level to facilitate a common format for screening and diagnosing children across the EU. This is the first study to operationalise the screening phase of the protocol and validate the use of a screening instrument the Social Communication Questionnaire (SCQ: Rutter et al., 2003) as a primary screener for ASDs among national school children. Methods A study booklet completed by the parents of eligible children aged 6-11 years was returned to the teacher for collection by the study team. There were (n = 7,951) primary school children screened males 54% (n =4,268) females 46% (n = 3,683), special education school children (n = 189) males 66% (n = 125) females 34% (n = 64), in three regions: Galway, Waterford and Cork. Participation rates for parents of eligible children were 69% (n=5,457) for national schools, 36% (n=69) for special education schools. Results The distribution of SCQ total scores for the national school sample were strongly skewed towards lower scores 4.65 ± 4.75, range 0-36. The majority of children (92%) scored in the normal range (0 to11) (n = 5002), moderate (12-14) (n = 225) 4%, high (>15) score range 4% (n = 230). An optimal cut off score (>13) differentiated ASD from other diagnosis sensitivity 0.90, specificity 0.81, positive predictive value 0.43, and negative predictive value 0.98. Test re test reliability mean interval: 15 months, Pearson’s r of 0.77, df = 499, p < 0.001. Conclusions The feasibility of screening children for ASDs with the EAIS protocol, using the SCQ in a non-clinical setting of Irish primary and special schools was demonstrated

Transforming Health through Big Data: Challenges and Considerations

Modern healthcare is increasingly dependent on good data, and effective information systems, for care delivery, and to develop and evaluate health policy. The context of big data differs in significant ways from traditional types of health data, while the use of big data for epidemiology and public health is becoming more common, the use of these tools for health service planning and health policy making lags behind. A large EU funded project (titled MIDAS) that focuses on merging, analysing and visualising data from heterogeneous sources to support health policy makers work in using and accessing health data across EU countries is underway. This paper briefly describes the key challenges that must be met to access, use and make sense of this big data in healthcare, focusing on legal, governance and ethical issues. Unless these issues are dealt with, the promise of Big Data for health, will never be fulfilled

Impact Evaluation of an Emerging European Health Project – the MIDAS Model

Background: This paper describes the impact evaluation of a large big data platform initiative that is being undertaken in order to increase the probability of its success. The initiative, MIDAS (Meaningful Integration of Data Analytics and Services), is a European health-based Horizon 2020 project comprising a consortium of members from various universities, research institutions, and government agencies. Objectives: The purpose of the paper is to present a pioneering platform that will support healthcare policymakers in their decision-making by enabling greater and more efficient use of their data. The goal is to present and evaluate the results of the MIDAS project across four countries. Methods/Approach: The literature is replete with examples of worthwhile technology projects that have failed due to user resistance. In order to avoid such failure, and ensure the success of the final MIDAS platform, a detailed impact evaluation is being undertaken at timed periods of development. Results: This paper describes the impact evaluation process, outlining the use of Q-methodology and the development of a 36-item concourse using the HTMLQ system for that purpose. Conclusions: This research contributes to the overall understanding of how impact evaluation can be undertaken at timed periods during the development of an innovative technology for organisational purposes

Logic Model Early Stage Evaluation of a European Public Health Data Analytic Framework

The multi-national MIDAS (Meaningful Integration of Data Analytics and Services) project is developing a big data platform to facilitate utilisation of a wide range of health and social care data to enable integration of heterogeneous data sources, providing analytics, forecasting tools and bespoke visualisations of actionable epidemiological data. An evaluation framework starting with a logic model and semi-structured interviews using the principles of realist evaluation was developed working with end users and software developers. Parallel case studies were used to address the requirements of stakeholders at critical time points during the project. The objective was to ensure IT systems development is in line with end user requirements. Overall, the early stage interviews findings indicated the logic model is an effective framework for the evaluation of the project

Evaluating Impact of an Emerging Big Health Data Platform: A Logic Model and Q-Methodology Approach

Despite advances in technology and medical science, modern health-based projects are open to systemic failure due to many factors. These include I.T. developer’s lack of awareness with regard to end-user needs, poor communication amongst all parties concerned and inappropriate or inadequate tests of the emerging system. Other issues may be external (e.g. political and legal) such as sharing of patient data and issues surrounding consent. The goal of this paper is to take a major health-based European model in current development and explore how it addresses the needs of four institutions in four different countries, and how it will meet their respective needs. The evaluation was designed within a Logic Model, and uses the Framework approach, and Q-Methodology to assess both impact and evaluation. Data will be collected through longitudinal semi-structured interviews and Q-scoring with principal stakeholders and developers at each stage of the project. This approach, recurring interviews with the same key players in the project, will help ensure that there is mutual understanding between I.T. developers and end-users of the system. The final system is meant to provide effective health-based decision support systems for policy makers. This work is licensed under a&nbsp;Creative Commons Attribution-NonCommercial 4.0 International License.</p

Evaluating Impact of an Emerging Big Health Data Platform: A Logic Model and Q-Methodology Approach

Despite advances in technology and medical science, modern health-based projects are open to systemic failure due to many factors. These include I.T. developer’s lack of awareness with regard to end-user needs, poor communication amongst all parties concerned and inappropriate or inadequate tests of the emerging system. Other issues may be external (e.g. political and legal) such as sharing of patient data and issues surrounding consent. The goal of this paper is to take a major health-based European model in current development and explore how it addresses the needs of four institutions in four different countries, and how it will meet their respective needs. The evaluation was designed within a Logic Model, and uses the Framework approach, and Q-Methodology to assess both impact and evaluation. Data will be collected through longitudinal semi-structured interviews and Q-scoring with principal stakeholders and developers at each stage of the project. This approach, recurring interviews with the same key players in the project, will help ensure that there is mutual understanding between I.T. developers and end-users of the system. The final system is meant to provide effective health-based decision support systems for policy makers. This work is licensed under a&nbsp;Creative Commons Attribution-NonCommercial 4.0 International License.</p

Q-Method Evaluation of a European Health Data Analytic End User Framework

MIDAS (Meaningful Integration of Data Analytics and Services) project is developing a big data platform to facilitate the utilisation of a wide range of health and social care data to support better policy making. Our aim is to explore the use of Q-methodology as part of the evaluation of the implementation of the MIDAS project. Q-methodology is used to identify perspectives and viewpoints on a particular topic. In our case, we defined a concourse of statements relevant to project implementation and goals, by working from a logic model previously developed for the evaluation, and structured interviews with project participants. A 36-item concourse was delivered to participants, using the HTMLQ system. Analysis was done in the qmethod package. Participants had a range of professional backgrounds, and a range of roles in the project, including developers, end-users, policy staff, and health professionals. The q-sort is carried out at 14 months into the project, a few months before the intended first release of the software being developed. Sixteen people took part, 6 developers, 5 managers, 2 health professionals and 3 others. Three factors (distinct perspectives) were identified in the data. These were tentatively labelled ‘Technical optimism’, ‘End-user focus’ and ‘End-user optimism’. These loaded well onto individuals, and there were few consensus statements. Analysis of these factors loaded well onto individuals with a significant number of consensus statements identified. This work is licensed under a&nbsp;Creative Commons Attribution-NonCommercial 4.0 International License.</p

Autistic Adult Services Availability, Preferences, and User Experiences : Results From the Autism Spectrum Disorder in the European Union Survey

There is very little knowledge regarding autistic adult services, practices, and delivery. The study objective was to improve understanding of current services and practices for autistic adults and opportunities for improvement as part of the Autism Spectrum Disorder in the European Union (ASDEU) project. Separate survey versions were created for autistic adults, carers of autistic adults, and professionals in adult services. 2,009 persons responded to the survey and 1,085 (54%) of them completed at least one of the services sections: 469 autistic adults (65% female; 55% 50% responded "don't know"). Five of seven residential services features recommended for autistic adults were experienced byPeer reviewe

Intervention Services for Autistic Adults: An ASDEU Study of Autistic Adults, Carers, and Professionals' Experiences

The Autism Spectrum Disorders in the European Union (ASDEU) survey investigated local services' use experiences of autistic adults, carers and professionals with interventions for autistic adults. The majority of the 697 participants experienced recommended considerations prior to deciding on intervention and during the intervention plan and implementation. Psychosocial interventions were the most commonly experienced interventions, while pharmacological interventions NOT recommended for core autistic symptoms were reported by fairly large proportions of participants. Family interventions were experienced slightly more commonly by carers than adults or professionals. Less than the 26% of autistic adult responders who had experienced challenging behaviors reported receiving an intervention to change them. These results provide insights for improving gaps in service provision of interventions among autistic adults

Autistic Adult Health and Professional Perceptions of It: Evidence From the ASDEU Project

The Autism Spectrum Disorders in the European Union (ASDEU) survey investigated the knowledge and health service experiences of users and providers to generate new hypotheses and scientific investigations that would contribute to improvement in health care for autistic adults. An online survey designed for autistic adults, carers of autistic adults, and professionals in adult services was translated into 11 languages and distributed electronically by organizations and in-country adult service facilities in 2017; 522 autistic adults, 442 carers, and 113 professionals provided answers to the health questions. Professionals, the majority in non-medical services, appeared to be poorly informed about whether certain co-occurring conditions were more frequent in autistic adults than typical adults-especially some medical conditions, suicide attempts, accidents, and pain. A minority of autistic adults reported preventive health behaviors such as routine health check-ups. The majority of users and providers expressed the desire to make health care services more user-friendly for autistic adults. Among the three groups, <20% of responders knew an organization or clinician which has developed a way to monitor health, and prevent poor health, that works well for adults on the autism spectrum. The results point to means for better management of co-occurring conditions associated with autism in adulthood in order to reduce hospital admissions and potential areas of improvement in health and social services for autistic adults. Specifically, efforts should be focused on (1) professionals' education on risks for co-occurring conditions in autistic adults; (2) promoting preventive health behaviors; (3) making services user-friendly for autistic adults and their families; and (4) encouraging knowledge of good local services