Context: Little is known about the proportion of the Australian population using alcohol or other drugs who may seek treatment. There is a need to have some additional estimates of population morbidity which reflect harms associated with use. Objective: To determine Australian population rates of publicly funded community based specialised alcohol and other drug treatment and in-patient hospital care by those 'at risk', by drug type, sex and age. Design and setting: The design is secondary data analysis of publicly available datasets. We use the latest available complete data on Australian general population incidence of alcohol, cannabis amphetamines and ecstasy use (2007 National Drug Strategy House hold Survey) and nationally collected administrative data on publicly funded specialised alcohol and other drug treatment services (2006-2007 Alcohol and Other Drug Treatment Services National Minimum Dataset) and public hospitals (2006-2007 National Hospital Morbidity Minimum Dataset) to calculate rates of drug treatment and in-patient hospital care per 1000 Australians. 'At risk' for alcohol is defined as being at risk of short term harm, as defined by the National Health and Medical Research Council (2001). 'At risk' for illicit drugs is defined as those exposed to potential harm through at least weekly use of cannabis, amphetamines and ecstasy use. Results: Risky alcohol consumption followed by recent cannabis use appears to lead to most harm. Greater harm seems to be experienced by males rather than females. Younger adults (15-19 years) and older adults (40+ years) seem also to experience the highest rates of harm. Conclusions: It is possible to derive population estimates of harms associated with licit and illicit drugs use. Treatment rates vary across drug type, gender and age. Alcohol and cannabis are the substances whose use leads to the greatest demand for services. Ecstasy appears to generate few presentations for treatment. Publicly available data can be used to estimate harms associated with the use of particular substances. Such estimates are best interpreted in the light of other ways of estimating harms.
- Drug treatment
- Population datasets