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Record W4386116565 · doi:10.1111/add.16323

Commentary on Socias <i>et al</i>.: It is time to be serious about AUD treatment disparities

2023· letter· en· W4386116565 on OpenAlexaboutno aff
Katherine J. Karriker‐Jaffe, Kara M. Bensley

Bibliographic record

VenueAddiction · 2023
Typeletter
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
FundersNational Institute on Alcohol Abuse and AlcoholismNational Institutes of Health
KeywordsAlcohol use disorderContext (archaeology)Equity (law)Government (linguistics)MedicineDemographyGeographyFamily medicinePolitical scienceSociologyAlcohol

Abstract

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Despite full coverage of medications to treat alcohol use disorder (AUD) by the provincial government of British Columbia, Canada, disparities in access for patients in rural and economically deprived areas remain. An equity-based approach to telemedicine may help to bridge existing service gaps so we can be serious about eliminating AUD treatment disparities. The study by Socias et al. [1] uses administrative health records for 2015–19 from British Columbia (BC), Canada, to describe the alcohol use disorder (AUD) care cascade, ranging from first diagnosis to treatment engagement, with a focus on medications for AUD (MAUD). Greater than one-third (36.9%) of their sample of more than 7000 people with AUD received MAUD at some point during the study period. One unique aspect of this study is that the BC provincial health authority fully covers the cost of MAUD [2]. Readers from localities that do not have the same explicit and guaranteed coverage of MAUD may find the rates of MAUD initiation to be high as a result of this supportive policy context. For example, a nationally representative survey of US adults from 2019 showed that only 1.6% of people with AUD received MAUD [3]. The reported rates from BC are also higher than other Canadian studies [4, 5]. However, although the high rates MAUD in this sample of people with moderate-to-severe AUD are noteworthy, despite full coverage of MAUD by the provincial government, there are still clear geographical disparities in access for rural patients and people in more economically deprived areas. This is not unique to BC. In many contexts, MAUD are not universally available from all providers. In the United States, data from the 2017 National Drug Abuse Treatment System Survey documented that just 16% of specialty substance use disorder treatment programs offered oral naltrexone for AUD, and even fewer programs offered the other approved MAUD [6]. US survey data from 2019 showed that people with AUD living in large metropolitan areas were more likely to report using MAUD than people living in smaller cities or rural areas [3]. Within the US Veterans Health Administration health-care system, receipt of alcohol medication among those with AUD is also low overall (below 5%), and there are significant disparities, with racial and ethnic minorities [7] and those in rural areas [8] less likely to receive MAUD. A Swedish population registry study documented relatively high rates of MAUD (53.7%), but also found that people living in economically deprived areas and those with other disadvantaged statuses (including people with lower levels of education and immigrants born outside Sweden) were significantly less likely to receive MAUD, despite the universal health-care system that focuses upon care for marginalized groups [9]. These disparities exist both at the provider and system levels. There are differences in MAUD prescribing by physician type [10], and qualitative studies suggest that this may be due to differences in provider education as well as persisting negative beliefs about MAUD [11]. At the systems level, publicly funded treatment programs—those programs that serve economically disadvantaged clients in the US—have been less likely to prescribe MAUD than privately funded treatment programs [12]. Further, since 1995, the proportion of US treatment programs that offer MAUD has actually decreased over time [6]. We need an equity-based approach to implementing evidence-based pharmacological treatments for AUD that reduces the long-standing existing disparities, with explicit attention to patients from rural areas and economically disadvantaged communities. Increased coverage of these medications, as seen in BC and Sweden, may increase utilization of MAUD, but without an explicit equity focus we are unlikely to reduce disparities. Recent US policy changes on Medicaid expansion and Medicaid coverage of MAUD for low-income patients (as reviewed in Abraham et al. [6]) provide a good start for reaching people in disadvantaged communities. Telemedicine also may help to bridge provider shortages in rural and economically deprived areas. A recent pilot study suggests that linking primary care providers to addiction medicine specialists via video consultations can increase uptake of MAUD by patients [13]. Efforts to reduce disparities in access to buprenorphine for opioid use disorder (OUD) (as reviewed in Abraham et al. [6]) could serve as a model for AUD care. During the COVID-19 pandemic, US federal laws changed to allow patients to initiate buprenorphine treatment via telemedicine, and some studies suggest that this approach can be successful for engaging patients with low incomes (those covered by Medicare/Medicaid) to initiate care for OUD [14]. Although some barriers to the application of telemedicine to AUD treatment remain [15], recent increases in telemedicine use for alcohol and drug treatment [16] are promising. In conjunction with action to reduce disparities in access to broadband internet and video-enabled cellular telephones, telemedicine may help to bridge existing service gaps so that we can be serious about eliminating AUD treatment disparities. Katherine J Karriker-Jaffe: Conceptualization (equal); writing—original draft (equal). Kara Bensley: Conceptualization (equal); writing—original draft (equal). Preparation of this commentary was supported by grants from the US National Institutes of Health National Institute on Alcohol Abuse and Alcoholism (R01AA029812 and P50AA005595). The content and opinions are those of authors and do not reflect official positions of NIH or NIAAA. The authors have no conflicts of interest to declare. There are no data used in this commentary.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.113
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.005

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.291
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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