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