Building a microsimulation model of heroin use careers in Australia

Heroin is an illegal drug that is widely abused. It is estimated that there are around 16.5 million heroin users worldwide (a 0.4% population prevalence rate). This does not include pharmaceutical opioid misuse, which is growing at a very fast rate (United Nations Office on Drugs and Crime, 2015). Australia has one of the higher rates of heroin use internationally and is estimated to be around 1% of the general population (Australian Institute of Health and Welfare [AIHW], 2013a). In Australia, heroin is purchased on the black market and usually injected. A person dependent on heroin will inject three to four times a day, and will experience withdrawal symptoms if they reduce or cease using heroin. The costs to society of heroin use and dependence are substantial and include both healthcare costs and the costs associated with crime. The social costs of illicit drugs has been estimated to be AUD$8,189 billion (Collins & Lapsley, 2008) in Australia with similar per capita estimates from other nations (European Monitoring Centre on Drugs and Drug Addition [EMCDDA], 2008, 2011, 2014). Given the size of the economic burden, it is not surprising that governments across the globe expend considerable funds in trying to better manage illicit drugs. In Australia, the federal and state governments spend about AUD$1.7 billion per annum in prevention, treatment, harm reduction and law enforcement to combat illicit drugs (Ritter, McLeod, & Shanahan, 2013). Of the total spending, the majority was allocated to law enforcement, leaving a small proportion to prevention, treatment and harm reduction. There is increasing pressure from both the government and the public to know whether the current spending is optimal or what needs to change to increase the benefits of spending. This is particularly important for complicated social problems, such as heroin use, where there are many external costs.

In light of both the high social cost associated with illicit drugs and the extent of government expenditure, it is vital that resources are invested in the best possible way. While existing research has demonstrated the efficacy and relative cost-effectiveness of heroin treatments notably pharmacotherapy maintenance such as methadone and buprenorphine (Barnett, 1999; Connock et al., 2007) there has to date been no analysis of the net social benefit of providing heroin treatment interventions over the life course. There is a pressing need to evaluate the existing combinations of heroin treatment interventions over a heroin using career, and explore the best combinations that provide the highest net social benefits.

The aim of this paper is to detail the development of a microsimulation model (MSM) for heroin use which can be used to assess the net social benefit of current heroin treatment strategies, such that the final model can be used to compare different combinations of treatment alternatives through modelled scenarios. This will lead to better informed policy decisions about the mix and type of treatments.

The critical methodological issue is the choice of modelling approach. The model needs to capture recurring events over time, such as the common pattern of cycling in and out of heroin treatment (Bell, Burrell, Indig, & Gilmour, 2006) as well as reflect the heterogeneity amongst people who use heroin and their alternative trajectories. The chosen model is MSM. MSM depicts events and outcomes at the level of the individual. The MSM enables ‘memory’ for each individual of such things as the length of heroin use, past treatments and incarcerations.

Heroin use careers with recurring events, such as abstinence, crime, incarceration, and treatment over time, can be modelled using an approach broadly classified as state transition models (STM). As per Siebert et al. (2012), a typical STM consist of a set of mutually exclusive and collectively exhaustive health states where individuals can transition among health states based on predefined transition probabilities. STMs are flexible and transparent computer-based decision making models which include both Markov model cohort simulation and individual-based microsimulation. Cohort-based Markov model is one of the predominantly used STM in the substance abuse field (Schackman, Leff, Polsky, Moore, & Fiellin, 2012). Cohort-based Markov models are relatively simple to develop when the number of health states is not large. These models are restricted by the Markovian assumption, where transition probabilities do not depend on individual history or memory (i.e. past health states or state duration) (Siebert et al., 2012). In the case of heroin use, it is recognised that an individual’s history of incarceration, treatment, and length of time in treatment has an effect on the state transition for that individual (Hser, Evans, Huang, & Anglin, 2004; Zhang, Friedmann, & Gerstein, 2003). Therefore, MSMs, which are not restricted by the Markovian assumptions and number of health states, are more appropriate for modelling heroin use careers. MSM reduces the number of health states required as it simulates an individual’s history by using tracker variables.

MSMs better represent the heterogeneity among individuals in complex modelling scenarios such as illicit drug use. MSMs can model individual characteristics, treatment and heroin use history as continuous variables, whereby future transitions depend on current and past individual history. MSM has been widely used to evaluate health policies and other social and economic policies in many countries. It serves as an effective tool for policy evaluation, decision making and allocation of scarce financial resources. Many large MSM models have been built in Australia and overseas (Briggs & Sculpher, 1998; Harding, Keegan, & Kelly, 2010; Karnon, 2003; Ringel, Eibner, Girosi, Cordova, & McGlynn, 2010; Rutter, Zaslavsky, & Feuer, 2011; Zucchelli, Jones, & Rice, 2012). However, there are very few MSM models that have been built to specifically evaluate illicit drug treatment policies (Hoang et al., 2016). It is noted that it is important to build an MSM specifically for a country/state due to the differences in health care financing structure, costs and benefits, as well as the substantial difference in the availability of treatment methods and drug use.

There are two existing MSM in our content domain, both from the USA. The RAND Marijuana micro simulation model simulates the use of marijuana over the life course in the USA (Paddock, Kilmer, Caulkins, Booth, & Pacula, 2012). The model follows a cohort of 12 year olds representative of the United States population in 2004, and its focus is on the epidemiology of marijuana use. The second model, by Zarkin et al. (2005) models the costs and benefits of methadone treatment related to heroin use, criminal activity, labour market participation and health care utilization. This model follows 1,000,000 individuals from 18 to 60 year olds who are representative of the United States population. Despite taking into account the life-course perspective of illicit drug use, these two existing models are limited by only containing a few states of drug use, and in the case of Zarkin et al. (2005) a focus on a single treatment (methadone maintenance) and a number of simplifications about internal and external costs and benefits. Our goal was to develop a detailed model of heroin careers for one Australian jurisdiction, i.e. New South Wales (NSW) which enabled assessment of the net social benefit of treatment and the opportunity to simulate alternate treatment scenarios such as different combinations of treatment modalities over the lifetime of heroin use.

This paper proceeds as follows: we provide an overview of the model and its components, (Section 2), then detail the parameterisation and data sources for each of the model components (Section 3). Section 4 provides details of the cost elements within the model, followed by the verification and validation results (Section 5). The paper concludes with a discussion of both the strengths and limitations of the approach taken here as well as considering the future applications of the model.

The MSM model for heroin use careers takes the population of individuals who have ever used heroin (previously and currently) for one Australian state (NSW) including those who used heroin previously and are currently abstinent, those in treatment subgroups as well as those in prison. Each individual in the initial population is characterised by his/her age, gender, treatment history (number of episodes and duration of treatment), human immunodeficiency virus (HIV) and hepatitis C (HCV) status. Each individual evolves over his/her simulated lifetime and eventually transitions from one state to another one.

There are nine mutually exclusive states, which represent the major modalities of a heroin use career: heroin use, in treatment, abstaining, in prison. Three drug-using states (in absence of treatment), are represented in the model: i) abstinence, ii) irregular use, and iii) regular/dependent use. For the community treatment states, four mutually exclusive states were included: i) withdrawal from heroin (detoxification), ii) residential rehabilitation (RR), iii) Opioid Substitution Treatment (OST), and iv) counselling only. There are two prison states: in prison without treatment; and in prison and in receipt of OST. Exit from the model occurs if an individual is still alive at age 60, or if death from drug related or non-drug related causes occurs prior to age 60.

The population of individuals are transitioned from one state to another using predefined (individually based) state transition probabilities. In this model, an approach which provides heterogeneous ‘time to transition’ for each individual is used. The length of stay (LOS) in each treatment, heroin use and prison episode is drawn from LOS distributions. LOS was chosen because it is highly predictive of subsequent drug use and treatment outcomes (Hser et al., 2004; Zhang et al., 2003).

Each state is associated with costs (and benefits) which depend on both fixed costs (such as the cost of one day in treatment) and variable costs, such as the LOS for any one individual. The costs and benefits that were valued in this model were: (i) heroin treatment costs; (ii) other health care utilisation; (iii) treatment for specific diseases such as HIV, HCV; and (iv) crime and criminal justice system costs (police, courts, prisons and social costs of crime). Benefits include: earnings due to individuals returning to work after successful treatments; and value of treatment to the family.

The costs and benefits included in this study were:

• Costs

  • Heroin treatment

  • Other healthcare utilization

  • HIV/AIDs (aquired immunodeficiency syndrome) & HCV treatment

• Criminal justice system (police, courts, prison, social cost of crime) benefits

  • Productivity

  • Life years saved (value of life year)

  • Value to family of treatment

Verification is the process of determining whether a computer program-based simulation model perform as it is intended, whereas validation refers to the process of determining whether the conceptual model (instead of computer program-based simulation model) accurately represents the real world system under study (Sargent, Kuhl, Steiger, Armstrong, & Joines, 2005).

The simulation model was written in Java using the Eclipse IDE. The computer program was written following a modular approach (object orient programming) for model components. Several verification tests were defined for each of the modules to verify the intermediate simulation outputs. The logic for transition of individuals within the conceptual model was matched with the output from the simulation model using Eclipse IDE debugger. To verify that the conceptual logic was correctly coded in the computer program, we randomly selected 100 individuals in the simulation model and traced their behaviour and matched that against conceptual logic. The transition summary from the model for these 100 individuals was manually compared against the expected transition summary. The transitions for these individuals matched the conceptual logic implemented in the computer program. In this situation, we verified the simulation response by running a simplified version of the simulation program with a known analytical solution. A simplified version of the model was run according to the changed parameters, i.e., age was fixed to 20 years, treatment history of individuals were assumed to be 1 episode each of S4 – S7, LOS for all individuals was assumed to be 360 days, initial population was considered to be 100,000, mortality rates for HIV, Hep-C was assumed to be zero, and initial population was distributed equally from state 1 to 10 (i.e. 10,000 each). The analytical solution obtained by using the above mentioned parameters in the transition functions was then used to compare with the simplified simulation model.

The MSM model described in this paper simulates a range of treatment services in the community and prison, and uses a large variety of datasets from illicit drug use surveys and published literature. Traditionally, simulation models in the field of illicit drug use have been either short-term, or focussed on single treatment options and with a lack of consideration for individual histories and attributes.

MSM are increasingly being used in health-care decision-making. These models have a better ability to represent the heterogeneity that is required in complex modelling scenarios such as illicit drug use. Individuals in these models accommodate their histories to influence the probabilities of the next transition without creating too large a number of health states. The model development described herein followed good modelling practice described in Karnon et al. (2012). The model has been verified internally by extensive logic reviews with domain experts and by analysing the simulation response by running a simplified version of the simulation program with known analytical solutions. All of these were consistent with expected modelling parameters and assumptions. Various features of the model were validated against external datasets and published data in this field, which were not used as an input for model parameterization. The model output seems coherent and does not significantly diverge from external datasets.

The proposed model has, unsurprisingly, a number of limitations. In real-life, there are more than nine possible states for heroin use, including some other, relatively minor treatment types, such as injectable heroin. It is not possible to represent every possible treatment state in the model: the data are simply not available and the model itself would become intractable. Additionally, there are gradations in heroin use. Our model somewhat crudely provides for three heroin use options: abstinence, irregular use or dependent use. While these three capture the broad array of heroin use, within each of the three, there can be much heterogeneity; for example our abstinence state (S1) includes both, those who are temporarily abstaining for a brief period of time, and those who have ‘recovered’ and will never resume heroin use. The parameterisation of LOS by individual characteristics overcomes this to some extent. In the prison states only one treatment type was considered (OST) for individuals receiving treatment in prison. This was due to the paucity of datasets informing different type of treatments received by drug using individuals. There was a lack of past research data to drive the mortality probabilities beyond those determined by state and LOS, but this is an important area for further work. Finally, the individual level model required a significant amount of data for modelling various aspects such as individual history, LOS, incarceration, mortality, and transitions to community. Moreover, the computational time required to run the model is generally higher than the simplified cohort based compartmental models.

Having developed a verified and valid model of heroin use and its transitions for one Australian state (NSW), the deployment of the model for policy simulations can now commence. While there are many possible simulations, the most obvious initial ones to undertake are: increasing the availability/accessibility of treatment (for example through increasing the probability of inflow into treatment); and improving treatment outcomes (for example through increasing LOS). The main outcome of policy interest from these simulations is to compare the Net Social Benefit under the current status quo (that is the basecase model results) with the Net Social Benefit produced by the alternate scenarios. We hope that the policy simulations will provide economic data of relevance and value to current decision-makers.

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Author details

1. Alison Ritter

   Drug Policy Modelling Program, National Drug and Alcohol Research Centre, University of New South Wales, Australia

   For correspondence

   alison.ritter@unsw.edu.au

2. Nagesh Shukla

   SMART Infrastructure Facility, University of Wollongong, Australia

   For correspondence

   nshukla@uow.edu.au

3. Marian Shanahan

   Drug Policy Modelling Program, National Drug and Alcohol Research Centre, University of New South Wales, Australia

   For correspondence

   m.shanahan@unsw.edu.au

4. Phuong Van Hoang

   Drug Policy Modelling Program, National Drug and Alcohol Research Centre, University of New South Wales, Australia

   For correspondence

   phuong_hoang80@hotmail.com

5. Vu Lam Cao

   SMART Infrastructure Facility, University of Wollongong, Australia

   For correspondence

   vlcao@uow.edu.au

6. Pascal Perez

   SMART Infrastructure Facility, University of Wollongong, Australia

   For correspondence

   pascal@uow.edu.au

7. Michael Farrell

   Drug Policy Modelling Program, National Drug and Alcohol Research Centre, University of New South Wales, Australia

   For correspondence

   m.farrell@unsw.edu.au