Evidence map›Paper›PMID 41807076›Full record

ArticleAddiction (Abingdon, England)2026

Cost-effectiveness of contingency management for methamphetamine use disorder: A model-based analysis.

Gary L Qian, Yupeng Chen, Richard A Rawson, Keith Humphreys, Margaret L Brandeau

Abstract read
In one paragraph

Article in Addiction (Abingdon, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Gary L QianDepartment of Management Science and Engineering, Stanford University, Stanford, CA, USA.ORCID https://orcid.org/0000-0003-3044-4107
Yupeng ChenDepartment of Management Science and Engineering, Stanford University, Stanford, CA, USA.ORCID https://orcid.org/0009-0008-7564-7067
Richard A RawsonDepartment of Psychiatry, University of California at Los Angeles, Los Angeles, CA, USA.
Keith HumphreysDepartment of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.ORCID https://orcid.org/0000-0003-0694-5761
Margaret L BrandeauDepartment of Management Science and Engineering, Stanford University, Stanford, CA, USA.ORCID https://orcid.org/0000-0001-9331-8920

Funding

Making Better Decisions: Policy Modeling for AIDS and Drug AbuseR01DA015612 · NIDA · STANFORD UNIVERSITY · PI OWENS, DOUGLAS K · 2002 to 2017
$9.4M
Making Better Decisions: Policy Modeling for AIDS and Drug AbuseR37DA015612 · NIDA · STANFORD UNIVERSITY · PI DOUGLAS K OWENS · 2019 to 2026
$6.3M
NIDA NIH HHS R01 DA015612NIDA NIH HHS R37 DA015612NIDA NIH HHS R37-DA15612
6 · The paper itself

Abstract

BACKGROUND AND

aimsThe United States (US) is experiencing a surge in methamphetamine use. Treatment options for methamphetamine use disorder (MethUD) focus on behavior change, particularly via contingency management (CM), where individuals receive rewards for submitting drug-free urine samples or for other positive behaviors such as attending treatment sessions. Little is known about the cost-effectiveness of CM for treating MethUD, including the January 2025 decision by the US federal government to increase the maximum annual CM incentive ten-fold to $750 per patient. This study aimed to assess the cost-effectiveness of CM for individuals with MethUD in the US. DESIGN, SETTING AND

participantsWe developed a microsimulation model of methamphetamine use behavior among individuals with MethUD to assess the effectiveness and cost-effectiveness of CM for treating MethUD. We modeled methamphetamine use states and psychiatric and cardiovascular comorbidities. We considered a 12-week and 24-week CM program, with a maximum $750 incentive. We simulated the model in weekly time steps over the lifetime of a cohort of 10 000 individuals with MethUD, under the status quo (no treatment) and with the CM program. MEASUREMENTS: Number of deaths over one year and lifetime per person healthcare costs (healthcare sector perspective) and quality-adjusted life years (QALYs) experienced.

findingsWith no treatment, 274 overdose deaths and 305 total deaths occurred in the cohort over 1 year. Individuals experienced 11.37 lifetime QALYs and incurred $216 320 in lifetime healthcare costs. With a 12-week CM program, an estimated 117 deaths were prevented over 1 year, with a net gain of 0.70 lifetime QALYs per person and incremental cost of $6850 compared with no treatment, yielding an incremental cost-effectiveness ratio (ICER) of $9830/QALY gained [95% credible interval (CR) = $8100-$11 400]. With a 24-week program, 153 deaths were prevented over 1 year, with a net gain of 0.81 lifetime QALYs per person and incremental cost of $10 000, yielding an ICER of $12 312/QALY gained (95% CR = $10 400-$14 100). Even under the pessimistic assumption of no lasting behavior change after CM program completion, the programs cost less than $130 000/QALY gained. Threshold analysis suggests that at a $50 000 willingness to pay, the 24-week program would be cost-effective even if the maximum incentive were $2491.

conclusionsModelling shows that contingency management appears to be a highly cost-effective intervention for treating methamphetamine use disorder, even with conservative assumptions and a $750 incentive cap. When cost impacts in the criminal justice and child welfare systems are included, such programs are likely cost-saving.

Indexed as

Amphetamine-Related DisordersBehavior TherapyMethamphetamineAdultCost-Benefit AnalysisCost-Effectiveness AnalysisFemaleHumansMaleMotivationQuality-Adjusted Life YearsUnited StatesMethamphetaminebehavioral treatmentcontingency managementcost‐effectiveness analysismental healthmethamphetamine use disorderstimulant use management

Identifiers

PMID41807076
PMCPMC13357890

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.