Evidence map›Paper›PMID 33253263›Full record

ArticlePloS one2020

Examining trends in substance use disorder capacity and service delivery by Health Resources and Services Administration-funded health centers: A time series regression analysis.

Nadereh Pourat, Brenna O'Masta, Xiao Chen, Connie Lu, Weihao Zhou, Marlon Daniel, Hank Hoang, Alek Sripipatana

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
0.5field-weighted citation impact, top 27% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
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

8 authors at 3 institutions in 1 country.

Nadereh PouratCenter for Health Policy Research, University of California, Los Angeles (UCLA), Los Angeles, CA, United States of America.ORCID 0000-0001-5118-1188
Brenna O'MastaCenter for Health Policy Research, University of California, Los Angeles (UCLA), Los Angeles, CA, United States of America.
Xiao ChenCenter for Health Policy Research, University of California, Los Angeles (UCLA), Los Angeles, CA, United States of America.
Connie LuCenter for Health Policy Research, University of California, Los Angeles (UCLA), Los Angeles, CA, United States of America.
Weihao ZhouCenter for Health Policy Research, University of California, Los Angeles (UCLA), Los Angeles, CA, United States of America.
Marlon DanielCenter for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration. All work related to this manuscript was completed as an employee of Bureau of Primary Health Care, Health Resources and Services Administration, Rockville, MD, United States of America.
Hank HoangBureau of Primary Health Care, Health Resources and Services Administration, U.S. Department of Health and Human Services, Rockville, MD, United States of America.ORCID 0000-0001-8538-8515
Alek SripipatanaBureau of Primary Health Care, Health Resources and Services Administration, U.S. Department of Health and Human Services, Rockville, MD, United States of America.ORCID 0000-0002-3078-0111
University of California, Los Angeles · USHealth Resources and Services Administration · USSubstance Abuse and Mental Health Services Administration · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe opioid epidemic and subsequent mortality is a national concern in the U.S. The burden of this problem is disproportionately high among low-income and uninsured populations who are more likely to experience unmet need for substance use services. We assessed the impact of two Health Resources and Services Administration (HRSA) substance use disorder (SUD) service capacity grants on SUD staffing and service use in HRSA -funded health centers (HCs). METHODS AND

findingsWe conducted cross-sectional analyses of the Uniform Data System (UDS) from 2010 to 2017 to assess HC (n = 1,341) trends in capacity measured by supply of SUD and medication-assisted treatment (MAT) providers, utilization of SUD and MAT services, and panel size and visit ratio measured by the number of patients seen and visits delivered by SUD and MAT providers. We merged mortality and national survey data to incorporate SUD mortality and SUD treatment services availability, respectively. From 2010 to 2015, 20% of HC organizations had any SUD staff, had an average of one full-time equivalent SUD employee, and did not report an increase in SUD patients or SUD services. SUD capacity grew significantly in 2016 (43%) and 2017 (22%). MAT capacity growth was measured only in 2016 and 2017 and grew by 29% between those years. Receipt of both supplementary grants increased the probability of any SUD capacity by 35% (95% CI: 26%, 44%) and service use, but decreased the probability of SUD visit ratio by 680 visits (95% CI: -1,013, -347), compared to not receiving grants.

conclusionsThe significant growth in HC specialized SUD capacity is likely due to supplemental SUD-specific HRSA grants and may vary by structure of grants. Expanding SUD capacity in HCs is an important step in increasing SUD access for low income and uninsured populations broadly and for patients of these organizations.

Indexed as

United States Health Resources and Services AdministrationCross-Sectional StudiesHealth Services AccessibilityHumansRegression AnalysisSubstance Abuse Treatment CentersSubstance-Related DisordersUnited States

Identifiers

PMID33253263
PMCPMC7703936
OpenAlexW3108303224

What OpenQuestion holds

Textmetadata
LicenceCC0
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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.