Source: Hospital In-Patient Enquiry (HIPE) Creator: Information Unit, Department of Health
This table is derived from the HIPE (Hospital In-Patient Enquiry) data set, which records data on discharges from all publicly funded acute hospitals. Age-standardised discharge rates by county, sex and principal diagnosis are presented. These allow comparison of discharge rates between populations of different age composition, and also of discharge rates over time.
This table covers the years from 1994 to 2011. Data for 1995 to 2004 were classified using ICD-9-CM. All HIPE discharges from 2005 are now coded using ICD-10-AM (The Australian Modification of ICD-10 incorporating the Australian Classification of Health Interventions).
Although this table uses the International Shortlist for Hospital Morbidity Tabulation which provides a mapping of ICD-9 and ICD-10 codes for the diagnosis categories reported here, there are some differences in the classification of diagnoses between 1994 – 2004 and 2005-2011. This means that for certain categories of diagnoses, comparisons with previous years is difficult.
From 1st January 2006 the HIPE system has been collecting data relating to patients admitted for dialysis in dedicated dialysis units. These episodes were previously excluded from HIPE. This data is being collected in order to provide national data regarding the volume of patients receiving dialysis. The inclusion of this activity has resulted in a significant increase in ‘0000 - All Causes’, ‘2100 - Factors Influencing Health Status and Contact With Health Services’ and ‘2105 - Other Factors Influencing Health Status and Contact with Health Services’ categories for 2006. To facilitate analysis of this data, additional diagnosis categories excluding dialysis daycase activity have been included from 2006 – 2011.
The age-standardised discharge rate (SDR) for an area is the number of discharges (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.
Standardised Morbidity Ratios (SMRs) are also included. SMRs express the difference between the observed discharges in each county compared to the expected discharges if the age-specific rates of the national population applied. It is presented as a ratio. A ratio that is greater than 100 indicates that county experienced higher discharges than would be expected. A ratio of less than 100 indicates that county experienced lower discharges than would be expected. Confidence intervals for these rates are also presented.
1. SMRs and SDRs based on a small number of cases should be viewed with caution. Similarly, confidence intervals based on less than 50 cases should be viewed with caution as larger, less exact confidence intervals are produced.
2. The SMRs presented cannot be used to compare counties with each other, as each county’s population structure weights the age-specific morbidity rates differently.
3. It should be noted that persons who are hospitalised several times during the course of the year are recorded in the statistics for each episode of hospitalisation. For these and other reasons, the data should not be used as a proxy for prevalence. It provides indicators of public hospital utilisation and should be interpreted in this context.
4. Discharges from private hospitals are not included.
5. Data relating to non-residents are not included in this table.
6. For historical reasons a small number of non-acute hospitals are included in HIPE. This activity represents a small proportion of all activity in HIPE.
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.