Evidence map›Paper›PMID 32408135›Full record

Observational studyDrug and alcohol dependence2020

Identifying optimal level-of-care placement decisions for adolescent substance use treatment.

Denis Agniel, Daniel Almirall, Q Burkhart, Sean Grant, Sarah B Hunter, Eric R Pedersen, Rajeev Ramchand, Beth Ann Griffin

Open access · greenAbstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Drug and alcohol dependence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.5field-weighted citation impact, top 10% 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, 8 citations in OpenAlex.

  1. Deeply Tailored Adaptive Interventions to Reduce College Student Drinking: a Real-World Application of Q-Learning for SMART Studies.Prevention science : the official journal of the Society for Prevention Research · 2022
    Trial
  2. Review
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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

8 authors at 2 institutions in 1 country.

Denis AgnielRAND Corporation, 1776 Main Street, Santa Monica, CA 90401, USA; Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck St., Boston, MA 02115, USA. Electronic address: Denis_Agniel@rand.org.
Daniel AlmirallInstitute for Social Research, University of Michigan, 426 Thompson Street, Ann Arbor, MI 48104-2321, USA.
Q BurkhartRAND Corporation, 1776 Main Street, Santa Monica, CA 90401, USA.
Sean GrantRAND Corporation, 1776 Main Street, Santa Monica, CA 90401, USA; Department of Social & Behavioral Sciences, Indiana University Richard M. Fairbanks School of Public Health, 1050 Wishard Blvd, RG 6046, Indianapolis, IN 46202, USA.
Sarah B HunterRAND Corporation, 1776 Main Street, Santa Monica, CA 90401, USA.
Eric R PedersenRAND Corporation, 1776 Main Street, Santa Monica, CA 90401, USA.
Rajeev RamchandRAND Corporation, 1200 South Hayes Street, Arlington, VA 22202-5050, USA.
Beth Ann GriffinRAND Corporation, 1200 South Hayes Street, Arlington, VA 22202-5050, USA.
RAND Corporation · USUniversity of Michigan · US

Funding

Novel Methods for Intensive Longitudinal Data in SMART Studies of Drug Abuse and HIVR01DA039901 · NIDA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ALMIRALL, DANIEL, NAHUM-SHANI, INBAL BILLIE · 2015 to 2024
$5.4M
The Causal Effect of Community-Based Treatment for YouthsR01DA015697 · NIDA · RAND CORPORATION · PI GRIFFIN, BETH ANN · 2003 to 2018
$2.8M
NIDA NIH HHS R01 DA015697NIDA NIH HHS R01 DA039901
6 · The paper itself

Abstract

backgroundAdolescents respond differentially to substance use treatment based on their individual needs and goals. Providers may benefit from guidance (via decision rules) for personalizing aspects of treatment, such as level-of-care (LOC) placements, like choosing between outpatient or inpatient care. The field lacks an empirically-supported foundation to inform the development of an adaptive LOC-placement protocol. This work begins to build the evidence base for adaptive protocols by estimating them from a large observational dataset.

methodsWe estimated two-stage LOC-placement protocols adapted to individual adolescent characteristics collected from the Global Appraisal of Individual Needs assessment tool (n = 10,131 adolescents). We used a modified version of Q-learning, a regression-based method for estimating personalized treatment rules over time, to estimate four protocols, each targeting a potentially distinct treatment goal: one primary outcome (a composite of ten positive treatment outcomes) and three secondary (substance frequency, substance problems, and emotional problems). We compared the adaptive protocols to non-adaptive protocols using an independent dataset.

resultsIntensive outpatient was recommended for all adolescents at intake for the primary outcome, while low-risk adolescents were recommended for no further treatment at followup while higher-risk patients were recommended to inpatient. Our adaptive protocols outperformed static protocols by an average of 0.4 standard deviations (95 % confidence interval 0.2-0.6) of the primary outcome.

conclusionsAdaptive protocols provide a simple one-to-one guide between adolescents' needs and recommended treatment which can be used as decision support for clinicians making LOC-placement decisions.

Indexed as

AdolescentAdolescent BehaviorAmbulatory CareClinical Decision-MakingFemaleFollow-Up StudiesHospitalizationHumansInpatientsLongitudinal StudiesOutpatientsSubstance-Related DisordersAdaptive methodsAdolescent substance useClinical placement guidelinesDynamic treatment regimesObservational data

Identifiers

PMID32408135
PMCPMC7293956
OpenAlexW3021967166

What OpenQuestion holds

Textmetadata
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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.