The challenges in collating disability data
Collating national data disaggregated by disability to create a global snapshot is not a straightforward task. In fact, it highlights many of the challenges that remain regarding the disability data landscape more broadly. However, it is encouraging to note that there is a substantial amount of data.
It is important to understand the challenges and limitations around the collated data. A number of important caveats must be applied to interpretation and comparison of the data on this portal.
Availability of data: In some cases, disability disaggregated data was not available in the chosen countries for the indicators. For example, a number of countries did not have disability disaggregated data for the selected indicators on violence and technology. This limits the extent to which meaningful conclusions can be drawn.
Date of data: Many of the available datasets that include disability disaggregated data are from surveys and censuses are not up to date, and may not reflect the situation in 2022.
Ability to compare: Data sets presented on the portal are not directly comparable, as data is drawn from different data sources (e.g. census or survey), uses different methodologies to measure disability, and covers different time periods. Practical issues around interviewer training and question translation also have an impact on the robustness and comparability of data within surveys.
Verification: Due to the limited timeframe for preparing this analysis ahead of the Summit, the data calculations included on this portal have not been verified by Country Governments or National Statistics Offices. As this is an on-going project, Leonard Cheshire would welcome input from National Governments, National Statistics Offices or others who would like to further discuss verification of the data.
Methodological issues: The analysis of findings shows a range of quality of data and in those instances where data collection methodologies were unclear, the data set was excluded from this portal.
It is important that the methodology for data collection is well understood so that analysis can be used appropriately.