Source: Central Statistics Office Creator: Information Unit, Department of Health
Age-standardised mortality rates by county, sex and cause are presented. These allow comparison of mortality rates between populations of different age composition, and also of mortality rates over time. The age-standardised rate for an area is the number of deaths (per 100,000) that would occur if that area had the same age structure as the WHO European Standard Population and the local age-specific rates for that area applied. Confidence intervals for these rates are also presented.
The data cover the years from 1980 to 2012. For 2012, year of registration data are used; for all previous years, statistics are based on year of occurrence.
A total of 74 causes of death categories are reported. These are ordered according to the Eurostat 65 Cause of Death shortlist, along with 9 additional national categories.
The classification system used for data up to and including 2006 is ICD9. From 2007, ICD10 is used. Caution should be exercised in comparing data up to 2006 with data from 2007 onwards.
Standardised Mortality Ratios (SMRs) are also included. SMRs express the difference between the mortality experience of each county as it would be if that county had experienced the age-specific rates of the national population. It is presented as a ratio. A ratio more than 100 indicates that county experienced higher mortality than would be expected. A ratio lower than 100 indicates that county experienced lower mortality than would be expected.
Confidence intervals for these ratios are also presented.
1. Confidence intervals based on less than 50 cases should be viewed with caution as larger, less exact confidence intervals are produced. Rates showing wide confidence intervals should be viewed with caution.
2. The SMRs presented cannot be used to compare counties with each other, as each county’s population structure weights the age-specific death rates differently.
3. SMRs and confidence intervals based on a small number of cases should be viewed with caution.
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.