Identifying and definitional attributes | |
Metadata item type: | Indicator |
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Indicator type: | Indicator |
Short name: | Diabetes complications hospitalisations, 2014-15 to 2017-18 |
METEOR identifier: | 724543 |
Registration status: | Australian Commission on Safety and Quality in Health Care, Standard 27/04/2021 |
Description: | Number of potentially preventable hospitalisations for diabetes complications, per 100,000 people, all ages, age and sex standardised. |
Indicator set: | |
Data quality statement: | National Healthcare Agreement: PI 18-Selected potentially preventable hospitalisations, 2018 QS Health!, Standard 30/01/2018 |
Collection and usage attributes | ||||||||||||||||||||||||
Population group age from: | All ages | |||||||||||||||||||||||
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Computation description: | Inclusion codes, description and additional requirements
Exclusion codes and description
Presented as a number of hospitalisations per 100,000 people. Rates are directly age- and sex- standardised, to the 30 June 2001 Australian population, using 5-year age groups: 0-4, 5-9, … , 80-84, 85 and over. Indigenous and other Australian rates are directly age and sex standardised, to the 2001 Australian population, using 5-year age groups: 0-4, 5-9, … , 60–64, 65 and over. Population estimates at 31 December are calculated as an average of the 30 June population estimates before and after the relevant December to derive a midpoint (31 December) population estimate. For more information about age-standardisation in general see glossary item Age-standardised rate. Analysis by Statistical Area Level 3 (SA3) 2016 is based on:
Suppress data (number and rate) if at least one of the following conditions are met:
Age and sex standardised rates are also suppressed where the denominator for at least one of the age and sex groups used to calculate the rate is below 30 and results of sensitivity analysis indicate that the rates are volatile. However, for SA3 data, if the volatility of the rate is not found to have a material impact on its decile, the rate is published with caution. For more information about the sensitivity analysis, see the Technical supplement of the Fourth Atlas. Consequential suppression may be applied to preserve confidentialised data. | |||||||||||||||||||||||
Computation: | 100,000 × (Numerator ÷ Denominator) | |||||||||||||||||||||||
Numerator: | The number of diabetes complications hospitalisations, 2014-15 to 2017-18 | |||||||||||||||||||||||
Numerator data elements: |
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Denominator: | Total population as at 31 December 2014, 31 December 2015, 31 December 2016 and 31 December 2017. | |||||||||||||||||||||||
Denominator data elements: |
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Disaggregation: |
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Disaggregation data elements: |
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Representational attributes | ||||||||||||||||||||||||
Representation class: | Rate | |||||||||||||||||||||||
Data type: | Integer | |||||||||||||||||||||||
Unit of measure: | Episode | |||||||||||||||||||||||
Format: | N[NNNN] | |||||||||||||||||||||||
Data source attributes | ||||||||||||||||||||||||
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Accountability attributes | ||||||||||||||||||||||||
Methodology: | Statistical Area Level 3 (SA3s) are geographic areas defined in the ABS Australian Statistical Geography Standard (ASGS). The aim of SA3s is to create a standard framework for the analysis of ABS data at the regional level through clustering groups of SA2s that have similar regional characteristics. There are 340 spatial SA3s covering the whole of Australia without gaps or overlaps. They are designed to provide a regional breakdown of Australia. SA3s generally have a population of between 30,000 and 130,000 people. There are approximately 78 with fewer than 30,000 people and 46 with more than 130,000 as at 30 June 2016. The Other Territories of Jervis Bay, Cocos (Keeling) Islands, Christmas Island and Norfolk Island are each represetned by a SA3 in the 2016 ASGS. For further information see the ABS publication, Population by Age and Sex, Regions of Australia, 2016. The scope of the NHMD is episodes of care for admitted patients in all public and private acute and psychiatric hospitals, free-standing day hospital facilities and alcohol and drug treatment centres in Australia. Hospitals operated by the Australian Defence Force, corrections authorities and in Australia’s off-shore territories are not in scope, but some are included.
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Reporting requirements: | Australian Commission on Safety and Quality in Health Care Australian Atlas of Healthcare Variation 2021 | |||||||||||||||||||||||
Organisation responsible for providing data: | Australian Institute of Health and Welfare | |||||||||||||||||||||||
Accountability: | Australian Commission on Safety and Quality in Health Care | |||||||||||||||||||||||
Release date: | 28/04/2021 | |||||||||||||||||||||||
Source and reference attributes | ||||||||||||||||||||||||
Submitting organisation: | Australian Commission on Safety and Quality in Health Care | |||||||||||||||||||||||
Reference documents: | For more information about potentially preventable hospitalisations see: National Healthcare Agreement: PI 18–Selected potentially preventable hospitalisations, 2019 Australian Atlas of Healthcare Variation, 2017: Technical Supplement | |||||||||||||||||||||||
Relational attributes | ||||||||||||||||||||||||
Related metadata references: | See also ABS Estimated resident population (total population), QS Health!, Standard 08/06/2011 See also ABS Indigenous experimental estimates and projections, QS Health!, Standard 08/06/2011 Indigenous, Standard 11/09/2012 See also Australian Atlas of Healthcare Variation: Number of potentially preventable hospitalisations - diabetes complications, per 100,000 people, 2014–15 Australian Commission on Safety and Quality in Health Care, Standard 07/06/2017 See also Data quality statement: Admitted Patient Care 2015-16 AIHW Data Quality Statements, Standard 27/11/2019 See also Data quality statement: Admitted Patient Care 2016-17 AIHW Data Quality Statements, Standard 27/11/2019 See also Data quality statement: Admitted Patient Care 2017-18 AIHW Data Quality Statements, Standard 27/11/2019 See also Data quality statement: National Hospital Morbidity Database 2014–15 AIHW Data Quality Statements, Standard 31/08/2016 |