A Lifetime Individual Sampling Model for Heroin Use and Treatment Evaluation in Australia

A Lifetime Individual Sampling Model for Heroin Use and Treatment

Evaluation in Australia

Nagesh ShuklaVan HoangMarian ShahananAlison RitterVu Lam CaoPascal Perez

September 2014

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• Australian federal and state governments spend about AUD 1.7b pa in prevention, treatment, harm reduction and law enforcement to combat illicit drugs.

• There is an increasing pressure from both the government and the public to know – whether the current spending is optimal; and/or – what needs to change to increase the benefits of spending.

• This is particularly important for complicated policies where there are many external costs and benefits, and as such; there are diverse views about the value of the projects.

• The aim of this study is to – assess the net social benefit of current heroin treatment strategies, and – compare different combinations of treatment alternatives through modelled scenarios

Introduction

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• Initial Population with over 97,000 heroin users and heroin abstainers.

• Each individual is transitioned from one health state to others using predefined (individual based) state transition probabilities.

• Time step is defined as the length of stay in each state, individually driven.

• Population is evolved and added a sub-population of new drug initiators each year.

• Net Social Benefit is computed based on the outcomes of the simulation model.

• Main data sources:– Australia Treatment Outcome Study (ATO) Dataset– MIX Study Dataset– National Opioid Pharmacotherapy Statistic Annual

Data (NOPSAD)– Alcohol and Other Drug Treatment Services National

Minimum Data Set (AODTS-NMDS treatment data)

Conceptual model

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Model Components

Initial Population States

Transition Time

State Transitions

Costs and Outcomes

Net Social Benefit

• Is estimated as the current NSW heroin using population.

• Over 97,000 heroin users and heroin abstainers.

• Each year, a sub-population of new initiators is added to include new drug users.

• Each individual in the initial population has the characteristics as: – Age: starting with 18 to 60 years spread – Gender: male or female– State: current state– Opioid use history– Incarceration history– Treatment history

• The initial population is evolved over the lifetime

Model Components (cont.)Initial Population

• Model has a set of mutually exclusive states which are:

– large enough to capture the complexity of the treatment process and

– low enough to ensure the resulting model is tractable and does not overburden the model with very detailed and specific data requirements.

• There are 2 main types of states:– Drug use state: S1, S2, S3– Treatment states: S4, S5, S6

• The model also considers 3 important locations (stages) in the drug using individual’s trajectory:– In Community: S1 to S6– In Prison: S8, S9, S10– Death Stage: S11, S12

• There is only 1 treatment state in the prison stage due to the insufficient in-prison treatment data.

Model Components (cont.)States

State Name StageAbstinence (S1) COMMUNITY

Irregular Use (S2) COMMUNITY

No Treatment & Use (S3) COMMUNITY

Withdrawal (S4) COMMUNITY

Residential Rehabilitation (S5) COMMUNITY

Pharmacotherapy (OTP) (S6) COMMUNITY

Counselling Only (S7) COMMUNITY

Abstinence (S8) PRISON

No Treatment & Use (S9) PRISON

Treatment (S10) PRISON

Drug Related Death or 60+ Years Old (S11) DEATH

Non-Drug Related Death (S12) DEATH

• Is heterogeneous ‘time to transition’ for each individual in the model based on his/her attributes such as age, sex, treatment history, and state.

• Is defined as the length of stay (LOS) in each state, individually driven, stratified by age, sex, history.

• Free from traditional fixed time steps for individual movements across states as using continuous function for individual’s length of stay determination

Model Components (cont.)Transition Time

• After finishing assigned LOS in a state, individuals transition to other states based on transition probability functions dependent upon the individuals’ attributes.

• There are 2 types of transition functions in the model:– An equation: empirically derived, specifies the probability based on individual’s characteristics and

history of the transition. – A probability distribution of the likelihood of transition: empirically derived from summary data,

based on a known distribution of an event

• Once a distribution function is established, Monte Carlo sampling is used to choose transition probabilities.

Model Components (cont.)State Transition

• During running through cycles, the model will accrue costs and outcomes (also referred to as rewards) within each cycle.

• Main categories of costs in the model:– Treatment costs: per days and transition– Crime costs: including social costs, penalty, and police costs– Life-years: saved, or lost– Other health care utilization (i.e. hospital, emergency department visits, and treatment for specific

diseases such as Hepatitis B and C)– Economic impact on family burden event

• Main categories of benefits in the model:– Earnings due to returning to work after successful treatments– Cost-savings to the government and society due to successful treatments (e.g. reduction of crime and health care utilization).

Model Components (cont.)Cost & Outcomes

• Once the costs and benefits have been calculated, the criterion for assessing the overall efficiency of an intervention is the Net Social Benefit (NSB).

– are benefits in year t,– are costs in year t,– r is the discount rate, and – T is the duration in years under consideration.

• The NSB is the sum of the present value of all benefits minus the sum of the present value of all costs.

• A policy is potentially worthwhile if NSB is > 0.

Model Components (cont.)Net Social Benefit

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Model Architecture

Java (Eclipse)

State Transition Algorithm

Cost/Benefit Estimation

Population Generator

PostgreSQL (Output Data)

PostgreSQL (Source Data)

Java Swing

JDBC

Graphic User Interface

PostgreSQL (Intermediate Data)

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• An initial prototype simulation model has built, that creates the initial heroin using population, new heroin initiators, and transitions to different states.

• Developing user interface to support users to interact with the model to design and run different scenarios.

• In the process of feeding the model with validated transition functions, per unit/event costs, and benefits.

• The final step in the modelling will be to validate whether the model is consistent with heroin user career trajectory.

Work In Progress Results

User interface

Baseline Summary

Prof. Pascal PerezResearch Director| SMART Infrastructure FacilityUniversity of Wollongong NSW 2522 P: +61 2 4252 8238 | F: +61 2 4298 1489 | M: +61 432 435 192E: pascal_perez@uow.edu.au | W: http://smart.uow.edu.au/staff/UOW114981.html

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