AODstats provides the ability to track trends of acute harms at the community level, and help inform policy and strategies to intervene and minimise the impact or spread of these harms.
This information provides a convenient, interactive, statistical resource for policy planners, drug service providers, health professionals and other key stakeholders interested in the harms relating to alcohol and other drug use in Victoria.
For further information on the analysis, please see the methods document.
- Data indicator: Ambulance
- Data Source: The Ambo Project – Turning Point.
- Details of data analysis: Ambulance data are extracted from NASS, the National Ambulance Surveillance System. Data includes alcohol and other drug-related events attended by ambulance paramedics in Victoria. The original clinical data from Ambulance Victoria is provided to Turning Point, where specialist project staff manually code events for alcohol and other drug involvement. Data is based on the location of the ambulance event.
- Numbers: cell sizes less than 5 are obfuscated in line with ethics and data custodian requirements. Some other categorical data may also be obfuscated if a category can be calculated by subtracting any remaining categories from the total.
- Rates: rates are crude rates, which can allow for adjustment of population sizes across different areas, however these do not adjust for certain demographic attributes (specifically age and sex). The advantage to using crude rates is particularly important from a policy perspective, to understand what is influencing the rates. For example, it is important for policy and services to be aware if an area has more men and younger people.
- Population estimates: ABS estimated resident population (ERP) on age, sex and statistical local areas are used throughout AODstats based on calendar year of data. For financial year datasets the earliest year is used (e.g. 2012/13, 2012 ERP is used)
There are limitations to using administrative data for purposes other than what it was originally intended when collected. This includes:
- Incomplete or missing data and inadequate coding.
- Crude rates are used, which do not allow for certain demographic attributes (age and gender) to be compared accurately across areas, and also rates based on small numbers can produce unstable results.