Evidence map›Paper›PMID 36692881›Full record

ArticleJAMA network open2023

Medical Costs of Substance Use Disorders in the US Employer-Sponsored Insurance Population.

Mengyao Li, Cora Peterson, Likang Xu, Christina A Mikosz, Feijun Luo

Open access · goldAbstract read
In one paragraph

Article in JAMA network open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
12.7field-weighted citation impact, top 1% of its field
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

23 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.

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  10. Economic Impact of an Employer-Sponsored Integrated Digital Intervention Targeting Substance Use Disorders: Return on Investment Analysis.Telemedicine journal and e-health : the official journal of the American Telemedicine Association · 2025
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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 at 2 institutions in 1 country.

Mengyao LiNational Center for Injury Prevention and Control, US Centers for Disease Control and Prevention, Atlanta, Georgia.
Cora PetersonNational Center for Injury Prevention and Control, US Centers for Disease Control and Prevention, Atlanta, Georgia.
Likang XuNational Center for Injury Prevention and Control, US Centers for Disease Control and Prevention, Atlanta, Georgia.
Christina A MikoszNational Center for Injury Prevention and Control, US Centers for Disease Control and Prevention, Atlanta, Georgia.
Feijun LuoNational Center for Chronic Disease Prevention and Health Promotion, US Centers for Disease Control and Prevention, Atlanta, Georgia.
Centers for Disease Control and Prevention · USNational Center for Injury Prevention and Control · US

Funding

Intramural CDC HHS CC999999
6 · The paper itself

Abstract

Importance: Direct costs of substance use disorders (SUDs) in the United States are incurred primarily among the working-age population. Quantifying the medical cost of SUDs in the employer-sponsored insurance (ESI) population can improve understanding of how SUDs are affecting workplaces and inform decision-making on the value of prevention strategies. Objective: To estimate the annual attributable medical cost of SUDs in the ESI population from the health care payer perspective. Design, Setting, and Participants: In this economic evaluation, Merative MarketScan 2018 databases were weighted to represent the non-Medicare eligible ESI population. Regression and mathematical modeling of medical expenditures controlled for insurance enrollee demographic, clinical, and insurance factors to compare enrollees with and without an SUD diagnosis to identify the annual attributable medical cost of SUDs. Data analysis was conducted from January to March 2022. Exposures: International Statistical Classification of Diseases, Tenth Revision, Clinical Modification SUD diagnoses on inpatient or outpatient medical records according to Clinical Classifications Software categories (alcohol-, cannabis-, hallucinogen-, inhalant-, opioid-, sedative-, stimulant-, and other substance-related disorders). Main Outcomes and Measures: Annual SUD medical cost in the ESI population overall and by substance type (eg, alcohol). Number of enrollees with an SUD diagnosis and the annual mean cost per affected enrollee of SUD diagnosis (any and by substance type) are also reported. Results: Among 162 million ESI enrollees, 2.3 million (1.4%) had an SUD diagnosis in 2018. The regression analysis sample included 210 225 individuals with an SUD diagnosis (121 357 [57.7%] male individuals; 68 325 [32.5%] aged 25-44 years) and 1 049 539 individuals with no SUD diagnosis. The mean annual medical cost attributable to SUD diagnosis per affected enrollee was $15 640 (95% CI, $15 340-$15 940), and the total annual medical cost in the ESI population was $35.3 billion (2018 USD). Alcohol use disorder ($10.2 billion) and opioid use disorder ($7.3 billion) were the most costly. Conclusions and Relevance: In this economic evaluation of medical expenditures in the ESI population, the per-person and total medical costs of SUDs were substantial. Strategies to support employees and their health insurance dependents to prevent and treat SUDs can be considered in terms of potentially offsetting the existing high medical cost of SUDs. Medical expenditures for SUDs represent the minimum direct cost that employers and health insurers face because not all people with SUDs have a diagnosis, and costs related to absenteeism, presenteeism, job retention, and mortality are not addressed.

Indexed as

AlcoholismSubstance-Related DisordersFemaleHealth ExpendituresHumansInsurance, HealthMaleOutpatientsUnited States

Identifiers

PMID36692881
PMCPMC9972180
OpenAlexW4317871469

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.