Alcohol and other drug treatment services in Australia annual report

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Last updated: 21 Jun 2023

Topic: Alcohol & other drug treatment services

AIHW

Australian Institute of Health and Welfare (2023) Alcohol and other drug treatment services in Australia annual report, AIHW, Australian Government, accessed 04 December 2023.

APA

Australian Institute of Health and Welfare. (2023). Alcohol and other drug treatment services in Australia annual report. Retrieved from https://www.aihw.gov.au/reports/alcohol-other-drug-treatment-services/alcohol-other-drug-treatment-services-australia

MLA

Alcohol and other drug treatment services in Australia annual report. Australian Institute of Health and Welfare, 21 June 2023, https://www.aihw.gov.au/reports/alcohol-other-drug-treatment-services/alcohol-other-drug-treatment-services-australia

Vancouver

Australian Institute of Health and Welfare. Alcohol and other drug treatment services in Australia annual report [Internet]. Canberra: Australian Institute of Health and Welfare, 2023 [cited 2023 Dec. 4]. Available from: https://www.aihw.gov.au/reports/alcohol-other-drug-treatment-services/alcohol-other-drug-treatment-services-australia

Harvard

Australian Institute of Health and Welfare (AIHW) 2023, Alcohol and other drug treatment services in Australia annual report, viewed 4 December 2023, https://www.aihw.gov.au/reports/alcohol-other-drug-treatment-services/alcohol-other-drug-treatment-services-australia

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Data and methods

For technical notes on the state and territory summaries refer to Technical notes - state and territory summaries.

Age

Age is calculated as at the start of the episode.

Data collection process

For most states and territories, the data provided for the national collection are a subset of a more detailed jurisdictional data set used for planning and policy. Figure A1 shows the processes involved in constructing the national data.

Figure A1: Alcohol and other drug treatment data collection flowchart

The diagram displays the AODTS NMDS data collection process

Drugs of concern

The AODTS NMDS contains data on drugs of concern that are coded using the ABS’s Australian Standard Classification of Drugs of Concern (ASCDC) (ABS 2011). In this report, these drugs are grouped (Table A1).

Table A1: Groupings of drugs of concern
Group	ASCDC codes	Category	Includes
Analgesics	1000–1999	Codeine
		Morphine
		Buprenorphine
		Heroin
		Methadone
		Other opioids	Oxycodone, fentanyl, pethidine
		Other analgesics	Paracetamol
Sedatives and hypnotics	2000–2999	Alcohol	Ethanol, methanol and other alcohols
		Benzodiazepines	Clonazepam, diazepam and temazepam
		Other sedatives and hypnotics	Ketamine, nitrous oxide, barbiturates and kava
Stimulants and hallucinogens	3000–3999	Amphetamines	Amphetamine, dexamphetamine and methamphetamine
		Ecstasy (MDMA)
		Cocaine
		Nicotine
		Other stimulants and hallucinogens	Volatile nitrates, ephedra alkaloids, phenethylamines, tryptamines and caffeine
Cannabinoids	7000–7199	Cannabis
Other	4000–6999 9000–9999	Other	Anabolic agents and selected hormones, antidepressants and antipsychotics, volatile solvents, diuretics and opioid antagonists
Not stated	0000–0002	Not stated

In this report, pharmaceutical drugs were grouped using 10 drug types, making up the pharmaceuticals group for the purposes of the analysis. These drugs correspond to the ASCDC codes and classifications (Table A2).

Table A2: Pharmaceutical drugs of concern, ASCDC codes and classifications
Drug category	ASCDC code	ASCDC classification (broad group and narrow group/s)	Drug description (ASCDC base level unit/s)
Codeine	1101	Analgesics Organic opiate analgesics	Codeine
Morphine	1102	Analgesics Organic opiate analgesics	Morphine
Buprenorphine	1201	Analgesics Semisynthetic opioid analgesics	Buprenorphine
Oxycodone	1203	Analgesics Semisynthetic opioid analgesics	Oxycodone
Methadone	1305	Analgesics Synthetic opioid analgesics	Methadone
Benzodiazepines	2400–2499	Sedatives and hypnotics Benzodiazepines	Benzodiazepines n.f.d., alprazolam, clonazepam, diazepam, flunitrazepam, lorazepam, nitrazepam, oxazepam, temazepam, benzodiazepines n.e.c.
Steroids	4000–4999	Anabolic agents and selected hormones Anabolic androgenic steroids Beta2 agonists Peptide hormones, mimetics and analogues Other anabolic agents and selected hormones Not further defined	Anabolic agents and selected hormones n.f.d., anabolic androgenic steroids n.f.d., boldene, dehydroepiandrosterone, fluoxymesterone, mesterolone, methandriol, methenolone, nandrolone, oxandrolone, stanozolol, testosterone, anabolic androgenic steroids n.e.c., beta2 agonists n.f.d., eformoterol, fenoterol, salbutamol, beta2 agonists n.e.c., peptide hormones, mimetics and analogues n.f.d., chorionic gonadotrophin, corticotrophin, erythropoietin, growth hormone, insulin, peptide hormones, mimetics and analogues n.e.c., other anabolic agents and selected hormones n.f.d., sulfonylurea hypoglycaemic agents, tamoxifen, thyroxine, other anabolic agents and selected hormones n.e.c.
Other opioids	1100, 1199, 1200, 1299, 1300–1304, 1306–1399	Analgesics Organic opiate analgesics Semisynthetic opioid analgesics Synthetic opioid analgesics Not further defined	Organic opiate analgesics n.f.d., organic opiate analgesics n.e.c., semisynthetic opioid analgesics n.f.d., semisynthetic opioid analgesics n.e.c., synthetic opioid analgesics n.f.d., fentanyl, fentanyl analogues, levomethadyl acetate hydrochloride, meperidine analogues, pethidine, tramadol, synthetic opioid analgesics n.e.c.
Other analgesics	0005, 1000, 1400–1499	Analgesics Non-opioid analgesics Not further defined	Analgesics n.f.d., non-opioid analgesics n.f.d., acetylsalicylic acid, paracetamol, ibuprofen, non-opioid analgesics n.e.c.
Other sedatives and hypnotics	2000, 2200–2299, 2300–2399, 2500–2599, 2900–2999	Sedatives and hypnotics Anaesthetics Barbiturates Gamma-hydroxybutyrate (GHB) type drugs and analogues Other sedatives and hypnotics	Sedatives and hypnotics n.f.d., anaesthetics n.f.d., ketamine, nitrous oxide, phencyclidine, propofol, anaesthetics n.e.c., barbiturates n.f.d., amylobarbitone, methylphenobarbitone, phenobarbitone, barbiturates n.e.c., GHB-type drugs and analogues n.f.d., GHB, gamma-butyrolactone, 1,4-butanediol, GHB-type drugs and analogues n.e.c., other sedatives and hypnotics n.f.d., chlormethiazole, kava lactones, zopclone, doxylamine, promethazine, zolpidem, other sedatives and hypnotics n.e.c.

n.f.d—not further defined; n.e.c—not elsewhere classified.

Jurisdictional notes regarding principal drug of concern:

Victoria reported comparatively high incidences of ‘Not stated drugs’ (15%) as the drug of concern. This is in part due to service providers adjusting to changes in reporting practices associated with the implementation of a new data collection system in 2019–20. In 2020–21, these drugs of concern were coded as ‘Other drugs of concern’ (14%) to realign with previous coding practices for Victoria. Victoria is working with service providers to encourage more specific reporting of drug of concern.
In Queensland, the proportion of cannabis episodes reported as the principal drug of concern is a result of the police and illicit drug court diversion programs operating in the state.
South Australia reports a high proportion of treatment episodes where amphetamines are the principal drug of concern due to the SA Police Drug Diversion Initiative (PDDI). In addition, adult cannabis offences are not included in the PDDI due to the SA Cannabis Expiation Notice legislation.

Duration

Duration is calculated in whole days, and only for closed episodes.

Population rates

In this publication, crude rates were calculated using the ABS’s estimated resident population at the midpoint of the data range: that is, rates for 2020–21 data were calculated using the estimated resident population at 31 December 2021.

The COVID-19 pandemic and the resulting Australian Government closure of the international border from 20 March 2020, caused significant disruptions to the usual Australian population trends. This report uses Australian Estimated Resident Population (ERP) estimates that reflect these disruptions.

In the year July 2020 to June 2021, the overall population growth was much smaller than the years prior and in particular, there was a relatively large decline in the population of Victoria. ABS reporting indicates these were primarily due to net-negative international migration (National, state and territory population, June 2021 | Australian Bureau of Statistics (abs.gov.au)).

Please be aware that this change in the usual population trends may complicate interpretation of statistics calculated from these ERPs. For example, rates and proportions may be greater than in previous years due to decreases in the denominator (population size) of some sub-populations.

Reason for cessation

The AODTS NMDS contains data on the reason an episode ended (reason for cessation). In this report, these reasons are grouped (Table A3), but data for the individual end reasons are available in the online supplementary tables.

A different method was used for grouping end reasons in reports released before 2014, so trend comparisons across reports should be made with caution. It is possible to compare data at the individual end reasons using the supplementary tables.

Table A3: Grouping of cessation reasons, by indicative outcome type
Outcome type	Reason for cessation
Expected/planned completion	Treatment completed
	Ceased to participate at expiation
	Ceased to participate by mutual agreement
Ended due to unplanned completion	Ceased to participate against advice
	Ceased to participate without notice
	Ceased to participate due to non-compliance
Referred to another service/change in treatment mode	Change in main treatment type
	Change in delivery setting
	Change in principal drug of concern
	Transferred to another service provider
Other	Drug court or sanctioned by court diversion service
	Imprisoned (other than drug court sanctioned)
	Died
	Other
	Not stated

Remoteness area

This report uses the ABS’s Australian Statistical Geography Standard (ASGS) Remoteness Structure 2016 (ABS 2016b) to analyse the proportion of AOD treatment agencies by remoteness area. This structure allows areas that share common characteristics of remoteness to be classified into broad geographic regions of Australia. These areas are:

Major cities
Inner regional
Outer regional
Very remote
Remote

The remoteness structure divides each state and territory into several regions based on their relative access to services.

Examples of urban centres in each remoteness area are:

Major cities: Canberra, Newcastle
Inner regional: Hobart, Bendigo
Outer regional: Cairns, Darwin
Remote: Katherine, Mount Isa
Very remote: Tennant Creek, Meekatharra.

For this report, the remoteness area of the agency was determined using the Statistical Area Level 2 (SA2) of the agency. Not all SA2 codes fit neatly within a single remoteness category, and a ratio is applied to reapportion each SA2 to the applicable remoteness categories. As a result, it is possible that the number of agencies in a particular remoteness category is not a whole number. After rounding, this can result in there being ‘<0.5%’ agencies in a remoteness area, due to the agency’s SA2 partially crossing into the remoteness area.

The Australian Statistical Geography Standard ASGS has replaced the Australian Standard Geographical Classification 2006 (ABS 2006), which was used in previous reports to calculate remoteness areas. Therefore, remoteness data for 2011–12 and previous years are not comparable with those for 2012–13 and subsequent years.

Service sectors

From 2008–09, agencies funded by the Department of Health under the Non-Government Organisation Treatment Grants Program (NGOTGP) were classified as non‑government agencies. Before this, many of these agencies were classified as government agencies. As a result, trends in service sectors of agencies should be interpreted with caution.

Source of referral: diversion

Throughout Australia, there are programs that divert people who have been apprehended or sentenced for a minor drugs offence from the criminal justice system. Many of these diversions result in clients receiving drug treatment services, who have been referred to treatment agencies as part of a drug diversion program. Since the 1980s, Australian governments have supported programs aimed at diverting from the criminal justice system people who have been apprehended or sentenced with a minor drugs offence.

In Australia, drug diversion program come in two main forms:

Police diversion occurs when an offence is first detected by a law enforcement officer. It usually applies for minor use or possession offences, often relating to cannabis, and can involve the offender being cautioned, receiving a fine and/or having to attend education or assessment sessions.
Court diversion occurs after a charge is laid. It usually applies for offences where criminal behaviour was related to drug use (for example, burglary or public order offence). Bail-based programs generally involve assessment and treatment, while pre‑ and post-sentence programs (including drug courts) tend to involve intensive treatment and are aimed at repeat offenders.

Treatment

The number of closed treatment episodes for counselling as a main treatment type has remained the most common treatment type for all clients over all collection years. Fluctuations over time in closed treatment episodes for particular treatment types may be influenced by coding practices, increased funding or changes in treatment policies/ capacity to provide specialised alcohol and other drug treatment services, which may contribute to variation in treatment types over time.

Trends

Trend data may differ from data published in previous versions of Alcohol and other drug treatment services in Australia, due to data revisions.

Imputation methodology for AOD clients

From the inception of the AODTS NMDS, data have been collected only about treatment episodes provided by AOD treatment services. An SLK was introduced into the AODTS NMDS for the 2012–13 collection to enable the number of clients receiving treatment to be counted, while continuing to ensure the privacy of these individuals receiving treatment.

An imputation strategy for the collection was developed to correct for the impact of invalid or missing SLKs on the total number of clients. This strategy takes into account several factors relating to the number of episodes per client and makes assumptions relating to spread across agencies. It also takes into consideration the likelihood that an episode with a missing SLK relates to a client that has already been counted through other episodes with a valid SLK.

To ensure an accurate representation of the AODTS client population, imputation was applied to the 2012–13, 2013–14 and 2015–16 AODTS NMDS to account for the proportion of valid SLKs being less than 95% for these years. The national rate of valid SLKs for these years was largely affected by low proportions of valid SLKs in New South Wales.

Further information on imputation methodology for AOD clients Expand

Attributing the number of clients to a set of records missing SLK

The AODTS NMDS collects information at the service record level. Service records are associated with individual clients through an SLK. There are a number of records that have missing or invalid SLK data that cannot be attributed to a client. This leads to an under‑reporting of the total number of clients using the services, because some (but not all) of the records will belong to clients who are not observed via a valid SLK.

This document describes the method of using the available data—after making several assumptions about the behaviour of the whole population—to estimate the total number of clients.

Imputation groups

Imputation groups are formed to improve the performance of the estimates. The service records were grouped according to properties that are thought to influence the behaviour of clients and the quality of SLK data, and then the imputation was performed at this imputation group level.

Possible properties used to develop groups include location, provider size (measured by number of service records) and service type. The data are also grouped according to any subpopulations that are going to be reported upon, such as jurisdiction.

The final imputation groups were formed by balancing the often-competing priorities of having homogenous groups and the need to have groups large enough to ensure that the imputation is robust.

Assumptions and approximations

Assumption 1: randomness and independence

This imputation method assumes that whichever service provider a client attends for each incidence of service is random and independent of any other incidents of service the client may have. It is further assumed that the validity or otherwise of the SLK recorded on each service record is random, and independent of both the client and the service provider with which the record is associated.

Assumption 2: distribution of the number of service records per client

This method also assumes that the distribution of the number of records per client for all clients is similar to that observed using the subset of records with valid SLKs.

Approximation 1: no client has more than 10 service records

This imputation method uses the approximation that no client has more than 10 service records.

In order to implement this approximation, any clients observed to have more than 10 service records were treated as if they had only 10, and the proportion of clients with 10 service records calculated accordingly.

Notation

The definition of the notation used in this document is as follows:

: the (unknown) total number of clients

: the imputed total number of clients

: the number of clients observed using the records with a valid SLK

: the proportion of clients with at least 1 service record with a valid SLK

: the (unknown) proportion of clients with service records

: the imputed proportion of clients with service records

: the proportion of clients with service records as observed using records with valid SLKs

: the total number of service records

: the number of service records given the total number of clients and the proportions of clients with service records, = 1,2, ... 10

: the number of service records with a valid SLK

: the number of service records with an invalid SLK

: the proportion of service records with an invalid SLK.

Methodology

Given Assumption 1 and Approximation 1, the proportion of clients who have at least 1 service record with a valid SLK is:

Now:

so it follows that the total number of clients is:

To resolve this equation for the values of the is required. These are unknown, given it is not possible to observe the whole population due to the records with invalid SLK values. This method imputes the unknown using numerical methods, then uses these values to impute .

The process starts with the distribution of number of records per client that were observed using the records with valid SLKs (). These values are then adjusted so that the following conditions are met.

Constraint 1

The sum of the imputed proportions is equal to 1. That is:

Constraint 2

The imputed proportion of clients with 1 service record is less than or equal to the observed equivalent proportion among clients with records with valid SLKs. That is:

This constraint is used because some of the clients observed to have only 1 record will, in fact, have additional records with invalid SLKs. It is unlikely that the true proportion of clients with 1 service record is higher than that observed using records with valid SLKs.

Constraint 3

The total number of service records that the imputed total number of clients and the imputed distribution of records per client imply is equal to the observed number of service records.

That is:

This constraint is used to ensure that the imputed values are consistent with the observed number of records.

Penalty function

Under Assumption 2 we want to limit how much the imputed proportions differ from the proportions observed via the records with valid SLK data. To achieve this we use a penalty function that increases as the distance between the imputed and observed proportions increases. This function is defined to be:

Using numerical methods, the are chosen such that the penalty function is minimised, subject to the 3 constraints.

The final step is to use the imputed proportions to calculate the imputed total number of clients:

The resulting number is then rounded to the nearest integer.

Discussion

This imputation technique uses available information to impute the total number of clients. The methodology takes into account the proportion of records with invalid SLK data and the distribution of the number of service records per client, as observed via the records with valid SLK data. It is apparent that the assumptions made do not hold for every client or service record. It is reasonable to expect that a client’s attendance at a service provider will be affected by location and any prior contact they had with a provider. It should also be noted that some service providers failed to collect SLK for any service record during the reference period.

Despite the known cases where Assumption 1 does not hold, it is reasonable to hope that, across the population as a whole, the assumption is a reasonable representation of the populations of clients and service records.

It is believed that the impact of Approximation 1 will be small because, given Assumption 1, the chance that a client with more than 10 service records is not observed via a record with a valid SLK is extremely small. The chance diminishes as the proportion of records with an invalid SLK decreases and across jurisdictions the highest proportion observed is about 0.3. It should also be noted that the largest proportion of clients with 10 or more service records observed in the data at the jurisdiction level was only 0.007.

There are many different penalty functions that could be used in this imputation. The function used was chosen because, compared with the other penalty functions investigated, it produced imputed proportions that were generally as close or closer to the observed proportions. It also most consistently resulted in a distribution that was similar in shape to the observed distribution of the number of records per client.