Source: Injury Observatory for Britain and Ireland (IOBI)
Creator: Injury Observatory for Britain and Ireland (IOBI)

"Number, rate and age-standardised rate of emergency hospital admissions for all serious injury in 2011 by age, sex and the following regions: Republic of Ireland Northern Ireland Scotland Wales England Numbers and rates are based on official hospital statistics from each region. All regions use International Classification of Disease (ICD) version 10 for hospital discharges in 2011. Only emergency inpatient hospital spells with an ICD 10 code for serious injury (in any diagnostic position) were included. A hospital spell is an unbroken period of time that a person spends as an inpatient in a hospital. The person may change consultant and/or specialty during a spell but is counted only once. See http://www.injuryobservatory.net/analysis-of-inpatient-admissions-data-for-serious-injury/ for more details including the list of ICD 10 codes for serious injury."

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.

For example,

• 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.