ArticlePsychiatric services (Washington, D.C.)2018
Predicting Barriers to Treatment for Depression in a U.S. National Sample: A Cross-Sectional, Proof-of-Concept Study.
Article in Psychiatric services (Washington, D.C.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled 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.
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.
Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Efficacy of stand-alone digital mental health applications for anxiety and depression: A meta-analysis of randomized controlled trials.Journal of psychiatric research · 2023Pooled it
- Evaluating Commercially Available Mobile Apps for Depression Self-Management.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Pooled it
- Efficacy of a Cognitive Behavioral Therapy-Based Online Self-Help Group for Depression and Suicide Ideation: Randomized Controlled Trial.JMIR mHealth and uHealth · 2026Trial
- Framing Effects of Cognitive Behavioural Therapy for Depression on Perceptions of Believability, Acceptability, and Credibility.International journal of environmental research and public health · 2023Trial
- The Seeking Mental Health Care model: prediction of help-seeking for depressive symptoms by stigma and mental illness representations.BMC public health · 2023Trial
- Identifying Evidence-Based Strategies in a Digital Mental Health Intervention for Depression: Qualitative Content Analysis.Journal of medical Internet research · 2026Article
- Facilitators and Barriers to Digital Self-Management in Older Adults With Depression: COM-B and Theoretical Domain Framework Qualitative Study.JMIR aging · 2026Article
- Article
- Provider and patient barriers and facilitators to integration of digital mental health applications in routine clinical care.Journal of affective disorders · 2024Article
- Rewriting the Script: The Need for Effective Education to Address Racial Disparities in Transcranial Magnetic Stimulation Uptake in BIPOC Communities.Neuroethics · 2024Article
- Depression, anxiety, and stress among university students in Selangor, Malaysia during COVID-19 pandemics and their associated factors.PloS one · 2023Article
- Public mental health during and after the SARS-CoV-2 pandemic: Opportunities for interventionFrontiers in psychology · 2023Article
- Predictors of Dropout in a Digital Intervention for the Prevention and Treatment of Depression in Patients With Chronic Back Pain: Secondary Analysis of Two Randomized Controlled Trials.Journal of medical Internet research · 2022Article
- Comparative Efficacy of Exercise Training and Conventional Psychotherapies for Adult Depression: A Network Meta-Analysis.Commonhealth (Philadelphia, Pa.) · 2022Article
- Predicting Changes in Depression Severity Using the PSYCHE-D (Prediction of Severity Change-Depression) Model Involving Person-Generated Health Data: Longitudinal Case-Control Observational Study.JMIR mHealth and uHealth · 2022Observational
- Effects of decision aids for depression treatment in adults: systematic review.BJPsych bulletin · 2022Review
- Feasibility, engagement, and preliminary clinical outcomes of a digital biodata-driven intervention for anxiety and depression.Frontiers in digital health · 2022Article
- A pilot study of participatory and rapid implementation approaches to increase depression screening in primary care.BMC family practice · 2021Article
- Cluster Analysis of Care Pathways in Adults with Major Depressive Disorder with Acute Suicidal Ideation or Behavior in the USA.PharmacoEconomics · 2021Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
objectiveEven though safe and effective treatments for depression are available, many individuals with a diagnosis of depression do not obtain treatment. This study aimed to develop a tool to identify persons who might not initiate treatment among those who acknowledge a need.
methodsData were aggregated from the 2008-2014 U.S. National Survey on Drug Use and Health (N=391,753), including 20,785 adults given a diagnosis of depression by a health care provider in the 12 months before the survey. Machine learning was applied to self-report survey items to develop strategies for identifying individuals who might not get needed treatment.
resultsA derivation cohort aggregated between 2008 and 2013 was used to develop a model that identified the 30.6% of individuals with depression who reported needing but not getting treatment. When applied to independent responses from the 2014 cohort, the model identified 72% of those who did not initiate treatment (p<.01), with a balanced accuracy that was also significantly above chance (71%, p<.01). For individuals who did not get treatment, the model predicted 10 (out of 15) reasons that they endorsed as barriers to treatment, with balanced accuracies between 53% and 65% (p<.05 for all).
conclusionsConsiderable work is needed to improve follow-up and retention rates after the critical initial meeting in which a patient is given a diagnosis of depression. Routinely collected information about patients with depression could identify those at risk of not obtaining needed treatment, which may inform the development and implementation of interventions to reduce the prevalence of untreated depression.
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Registered trials
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.