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Table 3 Summary of papers and classification of modelling approaches, timeframe and perspectives in costs and benefits

From: A systematic review of modelling approaches in economic evaluations of health interventions for drug and alcohol problems

Authors/year of study and summary

Country

Year of study

Analytic method

Study participants

Modelling approach

Time frame

Perspective on costs and benefits

Barbosa et al. (2010) [41] have used a cohort based probabilistic lifetime Markov model where alcohol consumption and drinking history are used for classifying patients into 4 Markov states. One year cycle length was used for the model. The main outcomes were QALYs and lifetime costs.

The U.K

2010

CEA

Males who are seeking alcohol treatment

Markov

Life time

Health care sector

Barnett et al. (2001) [8] have developed a dynamic compartmental model to estimate the effect of adding buprenorphine maintenance therapy to the US healthcare system. The model divides population into mutually exclusive groups (“compartments”) based on HIV status and drug use status. Transitions between these compartments were modelled as a system of non-linear differential equations. Current healthcare costs and outcomes with the adoption of buprenorphine under different scenarios were compared.

The U.S

2001

CEA

Current population of methadone treatment participants in the U.S health care system

Markov

10 years

Health care sector

Coffin and Sullivan (2013) [42] employs integrated cohort based Markov and decision analytic model to assess cost-effectiveness of distributing naloxone to heroin users in the U.S over lifetime. In the model, heroin users enter the model in ‘Heroin use’ state and can make transitions to ‘discontinue and relapse’, ‘overdose’, or ‘death by other reasons’ state. The ‘overdose’ state triggers a decision tree based model based on naloxone distribution to assess whether an individual will survive or die after overdose.

The U.S

2013

CEA

Hypothetical 21-year-old novice U.S. heroin user

Markov

Life time

Societal

Downs and Klein (1995) [28] developed cost-effectiveness model based on decision trees for adolescent population (15-19 years). The intervention is screening visits for alcohol abuse and unsafe sexual activity.

The U.S

1995

CEA

Adolescents aged 15 to 19 years

Decision trees

5 years

Societal

Magnus et al. (2012) [39] modelled economic and health gains on the basis of an absolute change in alcohol consumption. They modelled population simulation model to determine lifetime benefits of a reduction in per capita alcohol consumption from 2008 Australian adult cohort (aged ≥ 15 years). It considers workforce production gains model, household production and leisure time model, and health sector cost estimates for economic benefit evaluation. This study aims to evaluate the benefits of reduction of alcohol use thanks to a hypothetical intervention.

Australia

2012

N/A

The 2008 Australian population

Aggregate model

Lifetime

Societal

Navarro et al. (2011) [29] developed a decision tree based model to assess outcomes and costs of GP-delivered intervention for alcohol misuse. Nine difference scenarios with incremental increase in screening, brief intervention, or in the combination of screening and brief intervention were compared to current practice.

Australia

2011

CEA

Risky drinkers in 10 rural communities in New South Wales, Australia

Decision trees

1 year

Health care sector

Purshouse et al. (2013) [30] developed a health economic model combining the healthcare resource requirements for alcohol screening and brief intervention with an epidemiological model of relationships between alcohol consumption and health harms.

England

2013

CEA

Risky drinkers who are screened through GP’s visits

Decision trees

30 years

Health care sector

Sheerin et al. (2004) [40] used Markov model to model cohorts of injecting drug users, changes in their health states and effects of methadone maintenance therapy and anti-viral therapy on morbidity and mortality.

New Zealand

2004

CEA

Injecting drug users (IDUs)

Markov

Lifetime

Health care sector

Wammes et al. (2012) [59] used Asian epidemic model and resource needs model to evaluate the long term preventive impact of expanding methadone maintenance therapy in West Java. In this model, population is divided into 8 compartments and individuals move from one compartment to another based on a transition probabilities.

Indonesia

2012

CEA

Injecting drug users (IDUs)

Markov

20 years

Societal

Tran et al. (2012) [32] developed a simulated decision tree based model to represent HIV-positive drug user’s transition to 4 health state within one year horizon. Each of the states had services cost and health outcomes which were used for cost and benefit assessments.

Vietnam

2012

CEA

HIV-positive drug users

Decision trees

1 year

Health care sector

Zaric et al. (2000) [43] developed a dynamic compartmental model to assess the effects of increased methadone maintenance capacity on healthcare costs and survival (QALYs) for HIV epidemic in a population aged 18-44years. Population is divided into 9 subgroups based on risk group and HIV infection status. Size of each compartment over time is modelled with the help of set of non-linear differential equations.

The U.S

2000

CEA

The population of adults, aged 18 to 44

Markov

10 years

Health care sector

Tariq et al. (2009) [44] used a RIVM model to conduct a CEA of screening and brief intervention for alcohol in primary care targeting at reisk drinkers; outcomes were ICER, costs and QALY.

The Netherlands

2009

CEA

Risky drinkers aged between 20 and 65 who visit the GP yearly (50 %)

Markov

80 years

Health care sector

van den Berg et al. (2008) [45] used chronic disease model (CDM) to estimate the cost effectiveness of an alcohol tax increase from a health care perspective in the Netherlands; the outcomes were QALYs and LYS and health care costs

The Netherlands

2008

CEA

Current Dutch population

Markov

100 years

Health care sector

Vickerman et al. (2012) [36] used a system of differential equations to examine the impact on Hepatitis C of scaling up OST and needle syringe programs;

The U.K

2012

CEA

Injecting Drug Users (IDUs)

System dynamics

20 years

Health care sector

Nosyk et al. (2012) [46] used a semi Markov cohort model to assess the increemental cost effectiveness of methadone versus diacetylmorphine in a cohort who had multiple failures of OST ; used data from the North American Opiate Medication Initiative trial; Outcomes used were QALYs and social costs (treatment, HIV, crime, calculated an ICER)

Canada

2012

CEA

Injective drug users (IDUs)

Markov

Life time

Societal

Zaric and Brandeau (2001) [37] used an epidemic model to determine optimal allocation of HIV prevention funds. Three types of programs NSP (1), methadone (2), and condoms (3). Outcomes were QALYs gained; and the investment portfolio that maximises the number of HIV cases averted

The U.S

2001

Resource allocation framework

a population of injection drug users (IDUs) and non-IDUs

System dynamics

3 years

Health care sector

Mortimer and Segal (2005) [47] used a time dependent state-transition model to compare complementary and competing interventions for prevention or treatment of alcohol misuse and dependence; compares usual care with interventions. Assesses proportions of patients drinking beyond specified threshold, at 6,12 months follow-up; costs; cost utility; used QALY league tables

Australia

2005

CEA

Problem alcohol drinkers

Markov

Life time

Health care sector

Palmer et al. (2000) [48] uses a Markov model to explore the long term clinical and economic outcomes of alcohol maintenance with counselling or counselling plus accamprosate. Discounted and non-discounted LE and life time costs, incremental cost effectiveness; uses abstinence.

Germany

2000

CEA

Problem alcohol drinkers

Markov

Life time

Health care sector

Zaric et al. (2000) [49] uses a dynamic compartmental model of HIV to assess the cost effectiveness of MMT as a method of preventing HIV infection; the outcomes of the model are discounted LYS and QALYs and discounted health care and treatment costs

The U.S

2000

CEA

Injective drug users (IDUs)

Markov

10 years

Health care sector

Adi et al. (2007) [31] investigates the clinical effectiveness and cost effectiveness of naltrexone for relapse prevention in detoxified opioid dependent persons compared to psychosocial support.

The U.K

2007

CEA

Injective drug users (IDUs)

Decision trees

1 year

Societal

Barnett (1999) [50] examined cost effectiveness of methadone compared to standard care among cohort of 25 years old heroin users in the U.S.

The U.S

1999

CEA

Injective drug users (IDUs)

Markov

Life time

Health care sector

Bayoumi (2008) [51] examined cost effectiveness of medically supervised injecting centre; compared situation with supervised injecting centre to no injecting centre but with needle syringe programs.

Canada

2008

CEA

Injection drug users and persons infected with HIV and hepatitis C virus

Markov

10 years

Health care sector

Alistar et al. (2011) [60] have developed a dynamic compartment model of a population of IDUs on methadone substitution therapy, IDUs injecting opiates and non-IDUs in order to evaluate the effectiveness and cost effectiveness of expanding methadone substitution therapy to IDUs, increasing access to ART, or both. The outcome measures are the cost-effectiveness and QALYs.

Ukraine

2011

CEA

A population of non-IDUs, IDUs who inject opiates, and IDUs in MMT, adding an oral PrEP program (tenofovir/emtricitabine, 49 % susceptibility reduction) for uninfected IDUs

Markov

20 years

Health care sector

Kapoor et al. (2009) [52] examine cost-effectiveness of various screening strategies for unhealthy alcohol use with % Carbohydrate Deficient Transferrin using a Markov model.

The U.S

2009

CEA

Adult men and women (ages 18 to 100 years) in primary care

Markov

Life time

Health care sector

Schackman et al. (2015) [62] evaluate the cost-effectiveness of long-term office-based buprenorphine/naloxone treatment for clinically stable opioid-dependent patients compared to no treatment.

The U.S

2012

CEA

Cohort of clinically stable opioid-dependent individuals who have already completed 6 months of office-based buprenorphine/naloxone treatment

Markov

2 year

Health care sector

Tran et al (2012) [33] analyse the cost-effectiveness and budget impact of the methadone maintenance treatment (MMT) programme in HIV prevention and treatment among injection drug users (DUs) in Vietnam.

Vietnam

2012

CEA

injection drug users (DUs)

Decision trees

1 year

Health-care sector

Zarkin (2012) [3] builds a Discrete event simulation to estimate the net societal benefits of diverting eligible poisoners to community based treatment in the U.S.

The U.S

2012

CBA

A cohort of individuals who are incarcerated in the state prison system in the United States

Discrete event simulation

Life time

Societal

Zarkin et al. (2005) [2] estimate net societal benefits of providing methadone treatment in the U.S using Monte Carlo simulation model.

The U.S

2005

CBA

The general population aged 18–60 (a percentage is heroin users)

Individual-based microsimulation

Life time

Societal

Rydell et al. (1994) [61] presents a model that estimates the relative cost-effectiveness of four cocaine-control programs: three "supply control" programs (source-country control, interdiction, and domestic enforcement) and a "demand control" program (treating heavy users).

The U.S

1996

CEA

The market includes the supply and demand of cocaine

Aggregate model

15 years

Societal

Cartwright (2000) [34] estimates the benefits of reduced cocaine consumption in terms of reduced societal costs resulting from the introduction of a medication for cocaine dependence with a small incremental treatment effect.

The U.S

2000

CBA

Heavy cocaine users

Decision trees

1 year

Societal

Ciketic et al. (2015) [53] evaluates the cost-effectiveness of counselling as a treatment option for illicit MA use compared with no treatment option.

Australia

2015

CEA

Individuals recruited into Methamphetamine Treatment Evaluation Study (MATES)

Decision trees

3 years

Societal

Alistar et al. (2014) [54] estimated the effectiveness and cost effectiveness of strategies for using oral PrEP in various combinations with methadone maintenance treatment (MMT) and antiretroviral treatment (ART) in Ukraine, a representative case for mixed HIV epidemics.

Ukraine

2014

CEA

A population of non-IDUs, IDUs who inject opiates, and IDUs in MMT, adding an oral PrEP program (tenofovir/emtricitabine, 49 % susceptibility reduction) for uninfected IDUs.

Markov

20 years

Health care sector

Angus et al. (2014) [55] adapt the Sheffield Alcohol Policy Model to evaluate a programme of screening and brief interventions (SBI) in Italy. Results are reported as Incremental Cost-Effectiveness Ratios (ICERs) of SBI programmes versus a ‘do-nothing’ scenario.

Italy

2014

CEA

General population who visit GPs

Decision trees

30 years

Societal

Jackson et al. (2015) [56] estimate the cost-effectiveness of injectable extended release naltrexone (XR-NTX) compared to methadone maintenance and buprenorphine maintenance treatment (MMT and BMT respectively) for adult males enrolled in treatment for opioid dependence in the United States from the perspective of state-level addiction treatment payers.

The U.S

2015

CEA

Adult males enrolled in treatment for opioid dependence

Markov

6 months

Health care sector

Laramee et al (2014) [57] investigate whether nalmefene combined with psychosocial support is cost-effective compared with psychosocial support alone for reducing alcohol consumption in alcohol-dependent patients with high/very high drinking risk levels (DRLs) as defined by the WHO, and to evaluate the public health benefit of reducing harmful alcohol-attributable diseases, injuries and deaths.

The U.K (England and Wales)

2014

CEA

The licensed population for nalmefene

Markov

5 years

Health care sector

Schackman et al (2015) [62] evaluate the cost-effectiveness of rapid hepatitis C virus (HCV) and simultaneous HCV/HIV antibody testing in substance abuse treatment programs.

The U.S

2014

CEA

Opioid users in substance abuse treatment programs

Decision trees

Life time

Health care sector

Thanh et al (2014) [63] used a decision analytic modeling technique to estimate the incremental cost–effectiveness ratio and the net monetary benefit of the Parent–Child Assistance Program (P-CAP) within the Alberta Fetal Alcohol Spectrum Disorder Service Networks in Canada.

Canada

2015

CEA

Women who abuse substances (e.g. alcohol and/or drugs) and are pregnant

Decision trees

3 years

Health care sector

Braithwaite et al (2014) [58] estimate the portion of HIV infections attributable to unhealthy alcohol use and to evaluate the impact of hypothetical interventions directed at unhealthy alcohol use on HIV infections and deaths.

Kenya

2014

CEA

The Kenyan population

System dynamics

20 years

Health care sector