Evidence map›Paper›PMID 40438998›Full record

ArticleHealth services research2025

Benchmarking the Transformed Medicaid Statistical Information System Analytic Files for analysis of opioid use disorder treatment.

Mah Afroze Chughtai, Joo Yeon Kim, Julie M Donohue, Haiden A Huskamp, Ateev Mehrotra, Michael L Barnett

Abstract read
In one paragraph

Article in Health services research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

6 authors.

Mah Afroze ChughtaiDepartment of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.ORCID https://orcid.org/0009-0005-4215-9356
Joo Yeon KimDepartment of Health Policy and Management, School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Julie M DonohueDepartment of Health Policy and Management, School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.ORCID https://orcid.org/0000-0003-2418-6017
Haiden A HuskampDepartment of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA.
Ateev MehrotraDepartment of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA.ORCID https://orcid.org/0000-0003-2223-1582
Michael L BarnettDepartment of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

Funding

Telemedicine for Treatment of Opioid Use Disorder: NIDA Summer Research Internship ProgramR01DA048533 · NIDA · HARVARD MEDICAL SCHOOL · PI Haiden A Huskamp, Ateev Mehrotra · 2019 to 2026
$5.6M
Examining the quality of opioid use disorder treatment in a Medicaid research networkR01DA048029 · NIDA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI DONOHUE, JULIE MARIE · 2019 to 2021
$3.5M
NIDA NIH HHS P30 DA03577NIDA NIH HHS R01 DA048029NIDA NIH HHS R01 DA048533
6 · The paper itself

Abstract

objectiveTo evaluate the data consistency and quality of Medicaid claims data on opioid use disorder (OUD) treatment, benchmarking the Transformed Medicaid Statistical Information System Analytic Files (TAF) against metrics reported by the Medicaid Outcomes Distributed Research Network (MODRN), which analyzed data obtained directly from 11 state Medicaid agencies. DATA SOURCES AND STUDY

settingThe primary data source was TAF claims for the years 2017 and 2018, limited to non-dual, full-benefit Medicaid beneficiaries aged 12-64 in one of the 11 states in our study sample. STUDY

designUsing TAF data, we replicated performance on the following five OUD quality metrics reported by MODRN: number of enrollees receiving any OUD medication, receipt of behavioral health counseling, completion of urine drug screens, receipt of any opioid analgesic fill, and receipt of any benzodiazepine fill. DATA COLLECTION/EXTRACTION

methodsAccess to TAF data was facilitated through the Chronic Conditions Warehouse. PRINCIPAL

findingsThere were 11% fewer Medicaid enrollees with OUD in TAF compared to MODRN (912,478 vs. 1,034,412). Patient characteristics were largely similar across the two datasets, with the exception of more missing race information in TAF (20.9% vs. 7.1%). Across the 11 states, performance on the six quality measures was similar. For example, the rate of use of any OUD medication in 2018 was 57.1% in MODRN and 58.6% in TAF. However, there were important discrepancies in the TAF data in individual states for single years.

conclusionsTAF data may undercount patients with OUD, but otherwise exhibited consistency with MODRN benchmarks, suggesting suitability of TAF for research on OUD treatment. Our results highlight several data quality issues with TAF that researchers who use these data should be aware of, including reporting of race and ethnicity.

Indexed as

BenchmarkingInformation SystemsMedicaidOpioid-Related DisordersAdolescentAdultAnalgesics, OpioidChildFemaleHumansMaleMiddle AgedUnited StatesYoung AdultAnalgesics, Opioidhealth policy/politics/law/regulationMedicaidsubstance abuse: alcohol/chemical dependency/tobacco

Identifiers

PMID40438998
PMCPMC12120527

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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.