Defining treatment intensity using the AODTS NMDS

This report examines the treatment patterns of subsets of clients based on their treatment intensity, where intensive treatment was conceptualised as treatment that took place across many episodes over a long period of time. This required the development of set criteria for classifying any given series of treatment episodes.

From this goal, 2 broad criteria were developed:

  1. treatment across multiple years
  2. treatment across many episodes.

Each of these criteria required specific values to determine the treatment cohort. Before establishing these treatment criteria, episodes of treatment for another person’s alcohol and other drug (AOD) use, or episodes with the main treatment type of ‘assessment only’ were removed. The goal was specifically to examine people accessing treatment for their own AOD use, and the cohort of interest was people who had received direct AOD treatment over set periods of time, rather than many assessment episodes.

Criteria 1: Treatment across multiple years

To define treatment across multiple years, the smallest possible number of collection periods over which treatment could be considered long-term was identified as a threshold.

Treatment was considered long-term where a person has received treatment over 3 or more collection periods (aligning with financial years), with treatment episodes ending more than a year apart.

Receiving treatment in 2 collection periods or less was not considered sufficient; for example, a client could be counted in 2 separate collection periods if they received 2 episodes in May and August of the same calendar year.

Criteria 2: Treatment across multiple episodes

To define treatment across multiple episodes, the smallest possible number of episodes over which treatment could be considered long-term was identified as a threshold.

Treatment was considered long-term where a client received 11 episodes or more. This threshold was selected as it captures the top quartile of episodes received per client.

Note that this is an adjustment from the methodology previously used in previous AIHW treatment cohort analysis, due to the additional years included in the study period for this report (AIHW 2021).

Defining the treatment cohorts

Using the above criterion, the treatment cohorts were defined as follows in Table 5. Figure 12 illustrates examples of client pathways through treatment that would be assigned to each cohort.

Clients were excluded from treatment intensity cohort analysis if they:

  • were referred from another AOD treatment service for their initial episode between 1 July 2013 and 30 June 2014
  • received their first recorded closed treatment episode between 1 July 2019 and 30 June 2021.

These criteria ensure that the initial cohort received treatment for their own drug use and did not receive AOD treatment in the 12 months before 1 July 2013. They also ensured that there was enough time for clients to have received treatment in 3 or more collection periods. However, it is important to note that clients may have received treatment before 1 July 2013, and/or continued to receive treatment beyond 30 June 2021. Services accessed in these periods are outside the scope of this report.

Table 5: Definition of AODTS NMDS treatment intensity cohorts

Treatment cohort

Number of collection periods

Number of episodes


< 3

< 11



< 11


> 3

> 11

Figure 12: Examples of client pathways through specialist AOD treatment, by treatment intensity cohort

This interactive data visualisation illustrates the timing and number of episodes in 3 different examples of specialist AOD treatment, which are defined as intensive, recurring and non-recurring. 

Logistic modelling of jurisdictional differences

States and territories take different approaches to treatment, both in terms of the mix of treatment types offered across treatment services, and how episode data from treatment episodes are recorded. One potential consequence of these differences is that clients in some jurisdictions may, on average, record more or fewer distinct treatment episodes. This in turn means that treatment may be more likely to be categorised as intensive in some jurisdictions than in others.

The AIHW has previously undertaken logistic regression modelling to collection period data between 2014–15 and 2019–20 to determine whether these jurisdictional differences affect national results by treatment cohort (AIHW 2021). To ensure accurate representation of the alcohol and other drugs treatment service client population, a similar logistic regression model was applied to AODTS collection period data between 2013–14 and 2020–21.

Modelling was undertaken to determine whether known treatment and reporting differences between states and territories were influencing the national results of clients receiving intensive, recurring and non-recurring treatment.

For example, while 23% of the clients in the AODTS NMDS recorded treatment in Victoria, 44% of clients receiving intensive treatment were based in Victoria (Table 6). However, it is important to note that Victorian data is not directly comparable with data for other jurisdictions as every treatment type provided is reported as a separate episode, regardless of whether it is a main or additional treatment type. This results in greater numbers of episodes compared to other states and territories, where main and additional treatment types are recorded under a single episode (AIHW 2022).

Table 6: Proportion of clients receiving intensive, recurring and non-recurring treatment by state and territory



Intensive treatment (%)

Recurring treatment (%)

Non-recurring treatment (%)

All clients (%)














































Differences between jurisdictions may affect the generalisability of the results. For example, if clients receiving AOD treatment in one state tend to be younger, and that state is more likely to have clients who received intensive treatment, then there may appear to be a relationship between age and intensive treatment that is purely caused by jurisdictional differences.

Modelling other client and treatment differences

To ensure differences between states and territories are not causing the differences between clients receiving intensive, recurring and non-recurring treatment in this report, a logistic model was applied. This model allows for exploration of the association between personal and treatment-level characteristics, while controlling for potential confounding effects between them. By controlling for state and territory in this model, it is possible to examine whether the other variables were still associated with intensive or recurring treatment.

The likelihood of a client receiving intensive or recurring treatment was modelled against the characteristics of the first two episodes that they received in the study period of 2013–14 and 2018–19 (noting that clients who first entered treatment from 2019–20 onwards are excluded, due to not meeting the minimum requirement of appearing in at least 3 collection periods).

After controlling for state and territory differences, the likelihood of a client receiving intensive or recurring treatment generally aligned with the results prior to controlling for state and territory differences.