Evidence map›Paper›PMID 29685872›Full record

Trial reportJournal of medical Internet research2018

Comparative Effectiveness of a Technology-Facilitated Depression Care Management Model in Safety-Net Primary Care Patients With Type 2 Diabetes: 6-Month Outcomes of a Large Clinical Trial.

Shinyi Wu, Kathleen Ell, Haomiao Jin, Irene Vidyanti, Chih-Ping Chou, Pey-Jiuan Lee, Sandra Gross-Schulman, Laura Myerchin Sklaroff, David Belson, Arthur M Nezu and 20 more

Registry-linked trialOpen access · goldAbstract readClinical TrialComparative Study
In one paragraph

Trial report in Journal of medical Internet research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04070456 (Addressing Social Determinants of Health to Reduce Physician Burnout), which is not on this map. Cited by 17 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 4 pooled it
3.3field-weighted citation impact, top 7% 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.

NCT04070456 nacompletednot on this mapstarted 2019, after this paper: background citation

Addressing Social Determinants of Health to Reduce Physician Burnout

TypeinterventionalSponsorWake Forest University Health SciencesRan2019 to 2021Enrolled34ConditionsBurnout, ProfessionalArmsTablet-based SDH tool
3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 4 syntheses or guidelines pooled it, 32 citations in OpenAlex.

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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

30 authors at 6 institutions in 1 country.

Shinyi WuSuzanne Dworak-Peck School of Social Work, University of Southern California, Los Angeles, CA, United States.ORCID 0000-0002-6624-3385
Kathleen EllSuzanne Dworak-Peck School of Social Work, University of Southern California, Los Angeles, CA, United States.ORCID 0000-0001-8519-4905
Haomiao JinSuzanne Dworak-Peck School of Social Work, University of Southern California, Los Angeles, CA, United States.ORCID 0000-0001-8908-1959
Irene VidyantiDaniel J. Epstein Department of Industrial and Systems Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States.ORCID 0000-0001-7232-2867
Chih-Ping ChouKeck School of Medicine, University of Southern California, Los Angeles, CA, United States.ORCID 0000-0003-2457-2276
Pey-Jiuan LeeSuzanne Dworak-Peck School of Social Work, University of Southern California, Los Angeles, CA, United States.ORCID 0000-0001-6163-2325
Sandra Gross-SchulmanLos Angeles County Department of Health Services, Los Angeles, CA, United States.ORCID 0000-0001-9904-2064
Laura Myerchin SklaroffLos Angeles County Department of Health Services, Los Angeles, CA, United States.ORCID 0000-0002-9588-1933
David BelsonDaniel J. Epstein Department of Industrial and Systems Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States.ORCID 0000-0002-9137-0917
Arthur M NezuDepartment of Psychology, Drexel University, Philadelphia, PA, United States.ORCID 0000-0002-1651-2102
Joel HaySchaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles, CA, United States.ORCID 0000-0002-4467-9997
Chien-Ju WangLos Angeles County Department of Health Services, Los Angeles, CA, United States.ORCID 0000-0003-2914-5871
Geoffrey ScheibLos Angeles County Department of Health Services, Los Angeles, CA, United States.ORCID 0000-0001-7285-9293
Paul Di CapuaCaremore Medical Group, East Haven, CT, United States.ORCID 0000-0003-1583-2232
Caitlin HawkinsDaniel J. Epstein Department of Industrial and Systems Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States.ORCID 0000-0003-2858-9905
Pai LiuDaniel J. Epstein Department of Industrial and Systems Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States.ORCID 0000-0003-1029-4973
Magaly RamirezDaniel J. Epstein Department of Industrial and Systems Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States.ORCID 0000-0001-5063-1028
Brian W WuKeck School of Medicine, University of Southern California, Los Angeles, CA, United States.ORCID 0000-0002-5418-5245
Mark RichmanDepartment of Emergency Medicine, Northwell Health Long Island Jewish Medical Center, New Hyde Park, NY, United States.ORCID 0000-0001-8792-6129
Caitlin MyersLos Angeles County Department of Health Services, Los Angeles, CA, United States.ORCID 0000-0002-4157-6108
Davin AgustinesLos Angeles County Department of Health Services, Los Angeles, CA, United States.ORCID 0000-0001-6269-5483
Robert DasherLos Angeles County Department of Health Services, Los Angeles, CA, United States.ORCID 0000-0003-3989-7188
Alex KopelowiczLos Angeles County Department of Health Services, Los Angeles, CA, United States.ORCID 0000-0002-1728-4105
Joseph AllevatoLos Angeles County Department of Health Services, Los Angeles, CA, United States.ORCID 0000-0001-9342-4731
Mike RoybalLos Angeles County Department of Health Services, Los Angeles, CA, United States.ORCID 0000-0003-0042-579X
Eli IppDavid Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States.ORCID 0000-0003-1588-0146
Uzma HaiderHarbor-UCLA Medical Center, University of California Los Angeles, Los Angeles, CA, United States.ORCID 0000-0002-1467-5231
Sharon GrahamLos Angeles County Department of Health Services, Los Angeles, CA, United States.ORCID 0000-0001-7215-7814
Vahid MahabadiLos Angeles County Department of Health Services, Los Angeles, CA, United States.ORCID 0000-0002-7461-6559
Jeffrey GutermanLos Angeles County Department of Health Services, Los Angeles, CA, United States.ORCID 0000-0003-3290-9262
Los Angeles County Department of Health Services · USUniversity of Southern California · USUniversity of California, Los Angeles · USDrexel University · USFlorida International University · USNorthwell Health · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundComorbid depression is a significant challenge for safety-net primary care systems. Team-based collaborative depression care is effective, but complex system factors in safety-net organizations impede adoption and result in persistent disparities in outcomes. Diabetes-Depression Care-management Adoption Trial (DCAT) evaluated whether depression care could be significantly improved by harnessing information and communication technologies to automate routine screening and monitoring of patient symptoms and treatment adherence and allow timely communication with providers.

objectiveThe aim of this study was to compare 6-month outcomes of a technology-facilitated care model with a usual care model and a supported care model that involved team-based collaborative depression care for safety-net primary care adult patients with type 2 diabetes.

methodsDCAT is a translational study in collaboration with Los Angeles County Department of Health Services, the second largest safety-net care system in the United States. A comparative effectiveness study with quasi-experimental design was conducted in three groups of adult patients with type 2 diabetes to compare three delivery models: usual care, supported care, and technology-facilitated care. Six-month outcomes included depression and diabetes care measures and patient-reported outcomes. Comparative treatment effects were estimated by linear or logistic regression models that used generalized propensity scores to adjust for sampling bias inherent in the nonrandomized design.

resultsDCAT enrolled 1406 patients (484 in usual care, 480 in supported care, and 442 in technology-facilitated care), most of whom were Hispanic or Latino and female. Compared with usual care, both the supported care and technology-facilitated care groups were associated with significant reduction in depressive symptoms measured by scores on the 9-item Patient Health Questionnaire (least squares estimate, LSE: usual care=6.35, supported care=5.05, technology-facilitated care=5.16; P value: supported care vs usual care=.02, technology-facilitated care vs usual care=.02); decreased prevalence of major depression (odds ratio, OR: supported care vs usual care=0.45, technology-facilitated care vs usual care=0.33; P value: supported care vs usual care=.02, technology-facilitated care vs usual care=.007); and reduced functional disability as measured by Sheehan Disability Scale scores (LSE: usual care=3.21, supported care=2.61, technology-facilitated care=2.59; P value: supported care vs usual care=.04, technology-facilitated care vs usual care=.03). Technology-facilitated care was significantly associated with depression remission (technology-facilitated care vs usual care: OR=2.98, P=.04); increased satisfaction with care for emotional problems among depressed patients (LSE: usual care=3.20, technology-facilitated care=3.70; P=.05); reduced total cholesterol level (LSE: usual care=176.40, technology-facilitated care=160.46; P=.01); improved satisfaction with diabetes care (LSE: usual care=4.01, technology-facilitated care=4.20; P=.05); and increased odds of taking an glycated hemoglobin test (technology-facilitated care vs usual care: OR=3.40, P<.001).

conclusionsBoth the technology-facilitated care and supported care delivery models showed potential to improve 6-month depression and functional disability outcomes. The technology-facilitated care model has a greater likelihood to improve depression remission, patient satisfaction, and diabetes care quality.

Indexed as

ComorbidityDepressionDiabetes Mellitus, Type 2FemaleHumansMalePatient Reported Outcome MeasuresPrimary Health CareQuality of Health CareTime Factorscomparative effectiveness researchdepressiondiabetes mellitusdisease managementhealth information technologypatient reported outcome measurespopulation healthprimary carepropensity scoretelemedicine

Identifiers

PMID29685872
PMCPMC5938593
OpenAlexW2799710177

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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.