Making Visible the Invisible: Why Disability-Disaggregated Data is Vital to “Leave No-One Behind”

Abstract

People with disability make up approximately 15% of the world’s population and are, therefore, a major focus of the ‘leave no-one behind’ agenda. It is well known that people with disabilities face exclusion, particularly in low-income contexts, where 80% of people with disability live. Understanding the detail and causes of exclusion is crucial to achieving inclusion, but this cannot be done without good quality, comprehensive data. Against the background of the Convention for the Rights of Persons with Disabilities in 2006, and the advent of 2015’s 2030 Agenda for Sustainable Development there has never been a better time for the drive towards equality of inclusion for people with disability. Governments have laid out targets across seventeen Sustainable Development Goals (SDGs), with explicit references to people with disability. Good quality comprehensive disability data, however, is essential to measuring progress towards these targets and goals, and ultimately their success. It is commonly assumed that there is a lack of disability data, and development actors tend to attribute lack of data as the reason for failing to proactively plan for the inclusion of people with disabilities within their programming. However, it is an incorrect assumption that there is a lack of disability data. There is now a growing amount of disability data available. Disability, however, is a notoriously complex phenomenon, with definitions of disability varying across contexts, as well as variations in methodologies that are employed to measure it. Therefore, the body of disability data that does exist is not comprehensive, is often of low quality, and is lacking in comparability. The need for comprehensive, high quality disability data is an urgent priority bringing together a number of disability actors, with a concerted response underway. We argue here that enough data does exist and can be easily disaggregated as demonstrated by Leonard Cheshire’s Disability Data Portal and other studies using the Washington Group Question Sets developed by the Washington Group on Disability Statistics. Disaggregated data can improve planning and budgeting for reasonable accommodation to realise the human rights of people with disabilities. We know from existing evidence that disability data has the potential to drive improvements, allowing the monitoring and evaluation so essential to the success of the 2030 agenda of ‘leaving no-one behind’.

A Synthesis of Findings from ‘Rapid Assessments’ of Disability and the COVID-19 Pandemic: Implications for Response and Disability-Inclusive Data Collection

“…The findings reported here were broadly consistent with the existing evidence demonstrating how the right to health, livelihood, education, and all other aspects of life is denied to many people with disabilities, especially in low- and middle-income countries pre-pandemic [ 26 ] and extended that knowledge to the unique context of the COVID-19 global pandemic, national preparedness, and responses. Our findings also highlighted the potential value but current challenges in conducting early assessments of the impacts of health emergencies generally.…”

Direct participation of people with communication disabilities in research on poverty and disabilities in low and middle income countries: A critical review

“…Even inclusive research and data collection processes do not automatically translate into policy and practice–in some contexts people with disability may be over-researched but remain marginalised [ 88 ]. However the mandate to collect accurate and representative disability data remains important in order to monitor inequalities, with the next step then to ensure the data generated is used to meet the needs of people who may be marginalised [ 89 ]. Accountability in relation to data collection is complex and multifaceted.…”

The Potential of an Artificial Intelligence for Disability Advocacy: The WikiDisability Project

“…While reports about people with disabilities much of the available data (e.g., reports, raw data, case laws) is scattered [1]; the lack of availability of disability data has been identified as a major challenge hindering continuous disability rights monitoring [2,3] and exposing systemic discrimination [4]. Health informatics [5], machine learning [6], particularly Natural Language Processing (NLP) [7], can enable users to search for data [8][9][10] and find semantic similarities within disparate documents [11,12].…”