Source: Disability Database Unit, Health Research Board Creator: Disability Database Unit, Health Research Board
The Health Research Board (HRB) is the lead agency supporting and funding health research in Ireland. They have a core role in maintaining health information systems and conducting research linked to national health priorities. The Disability Databases Unit manages two national service-planning databases for people with disabilities on behalf of the Department of Health and Children: the National Intellectual Disability Database (NIDD), established in 1995, and the National Physical and Sensory Disability Database (NPSDD), established in 2002. These databases inform decision making in relation to the planning of specialised health and personal social services for people with intellectual, physical or sensory disabilities.
The National Physical and Sensory Disability Database was set up in 2002 to provide information on the service needs of people with a physical/sensory disability to the Department, the Health Service Executive and voluntary organisations.
There are over 27,000 persons registered on the database. The database provides valuable information to those involved with the planning and delivery of services.
Information is key to decision making in relation to resource allocation, it provides a foundation for the development of policies and supports research projects. For data to be of a practical value in informing policy and decision making, it must be reliable, timely, relevant, sufficient, focussed and presented appropriately. The Disability Database Unit in the Health Research Board in conjunction with the Health Service Executive and voluntary providers of services is committed to improving the quality of the databases and identifying the information needs of all stakeholders in the disability sector.
Using this data:
Interpret these data cautiously. As a general rule, this website does not include confidence limits in its charts and maps. It aims to provide visual tools that allow you to explore an underling table or dataset. If you find something that you think is important, we strongly urge you to explore it more rigorously – consulting an experienced data analyst if appropriate – before taking any action based on that finding.
1. Statistical precision
Indicator values are prone to statistical error (the difference between an estimated value and the true value). The statistical error associated with an indicator depends on the population subgroup (e.g. the population of a county or LGD) that it refers to. Such differences in levels of statistical error can distort what we see in maps and charts. They can make some relationships involving indicators and attributes appear “real” (practically meaningful or statistically significant) when they are in fact spurious; other relationships that are “real” can be masked. These differences in statistical error can even distort the shape of plots or the colour patterns we see in maps.
Many indicator values estimates are derived from sample surveys, and different sample sizes from different population subgroups will lead to different levels of precision in the indicator values for these subgroups.
Different population subgroups have different population sizes which means that rate estimates for these subgroups will also have different confidence limits.
The true value of a percentage or a rate can influence the level of statistical error of any estimate.
2. Scales and legends
The scales used on chart axes and in can also distort our perceptions:
The range of values allowed on chart axes can accentuate relationships making them appear more “real” than they actually are.
The radial arms of spider plots of scaled data show the position of the value (in a population subgroup) relative to the minimum and maximum values of that indicator. Because these minimum and maximum depend on the indicator, relative positions of different population subgroups on different radial arms are not directly comparable.
The cut-off values used to determine the colours to shade areas of a map are default selections and do not necessarily represent meaningful values of the indicator. Areas with very similar data values can be shown with different shades. You should always note the actual values.