The Intellectual Disability Supplement to the Irish Longitudinal Study on Ageing (IDS-TILDA) Wave 2, loneliness consequences supporting data, 2014

This study is the first of its kind in Europe, and the only study able to directly compare the ageing of people with an intellectual disability with the general ageing population. The underpinning values of IDS-TILDA are inclusion, choice, empowerment, person centred, the promotion of people with intellectual disability, the promotion of best practice and to contribute to the lives of people with intellectual disability.<br> <br> The objectives of IDS-TILDA are:<br> <ul> <li> to understand the health characteristics of people ageing with an intellectual disability;</li> <li>to examine the service needs and health service utilization of people ageing with an intellectual disability;</li> <li>to identify disparities in the health status of adults with an intellectual disability as compared to The Irish Longitudinal Study on Ageing’s (TILDA) findings for the general population; and</li> <li>to support evidence-informed policies, practices and evaluation.</li> </ul> This research looks at the consequences of loneliness in terms of physical and psychological reactions and coping mechanisms through the five pathway social-environmental model to investigate the consequences of loneliness.

The article sought to answer the questions: how do older people with an ID physically react to loneliness?; and do lonely people with an ID demonstrate the use of specific coping mechanisms?<br> In adherence with the principals of open science (Health Research Board 2021, Trinity College Dublin, 2020) this data has been deposited to support the submission of a paper deposited with the HRB on 16th October 2021 titled “An exploration of the consequences of, and coping with loneliness in an ageing intellectual disability population” by Dr Andrew Wormald, Professor Mary McCarron and Professor Philip McCallion. <br> This data is extracted from the second wave of the IDS-TILDA study and utilises variables from different sections of the dataset.