Evidence map›Paper›PMID 42085674›Full record

ArticleJMIR human factors2026

Feasibility and Acceptability of a Prevention-Focused Screener for Perinatal Depression Risk: Mixed Methods Cohort Study.

Tamar Krishnamurti, Samantha Rodriguez, Leah Cope, Lara Lemon, Priya Gopalan, Cara Nikolajski, Hyagriv Simhan, Kelly Williams

Abstract read
In one paragraph

Article in JMIR human factors, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Tamar KrishnamurtiGeneral Internal Medicine, University of Pittsburgh School of Medicine, 200 Meyran Avenue, Parkvale Building, Suite 300, Pittsburgh, PA, 15213, United States, 1 412-383-5556.ORCID 0000-0002-3416-2230
Samantha RodriguezGeneral Internal Medicine, University of Pittsburgh School of Medicine, 200 Meyran Avenue, Parkvale Building, Suite 300, Pittsburgh, PA, 15213, United States, 1 412-383-5556.ORCID 0009-0006-1058-8014
Leah CopeUPMC Center for High Value Healthcare, Pittsburgh, PA, United States.ORCID 0009-0000-3804-7523
Lara LemonObstetrics, Gynecology, and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States.ORCID 0000-0001-6806-7787
Priya GopalanDepartment of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States.ORCID 0000-0001-8403-8847
Cara NikolajskiUPMC Center for High Value Healthcare, Pittsburgh, PA, United States.ORCID 0000-0002-4922-6663
Hyagriv SimhanObstetrics, Gynecology, and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States.ORCID 0000-0002-4267-2089
Kelly WilliamsUPMC Center for High Value Healthcare, Pittsburgh, PA, United States.ORCID 0000-0003-0217-5800

Funding

SCH: Machine learning for personalized preventative intervention in perinatal depressionR01MH139097 · NIMH · CARNEGIE-MELLON UNIVERSITY · PI Tamar Krishnamurti, Bryan Wilder · 2024 to 2026
$787k
Peripartum Depression Prevention: Algorithmic Identification and Digital SolutionsR34MH130950 · NIMH · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI KRISHNAMURTI, TAMAR · 2022 to 2024
$536k
NIMH NIH HHS R01 MH139097NIMH NIH HHS R34 MH130950
6 · The paper itself

Abstract

Background: More than 20% of perinatal women experience depression, with suicide being a leading cause of maternal death in the United States. Professional societies emphasize the need to identify those at risk of developing perinatal depression to better target preventive care delivery during pregnancy. Objective: We evaluated receptivity to a machine learning-based predictive screener designed to identify women in the first trimester of pregnancy who were asymptomatic but were at risk for developing moderate to severe depression symptoms later in pregnancy. Methods: Our participants were adult pregnant women with negative first-trimester depression (Patient Health Questionnaire-9) screens at 1 of 4 obstetric practices. Of the 810 women who were clinically eligible, 787 were successfully contacted via their patient portal. Of these, 289 (36.7%) viewed the screener and 255 (88.2%) completed the 6-question predictive screener. In total, 51 (20%) were identified by the screener as being at risk for developing perinatal depression. Participants were asked a series of follow-up questions regarding the acceptability of the predictive screener and desired preventive resources. Chi-square tests were used to compare demographic characteristics, perceived benefits and concerns, and desired resources between those identified as at risk for depression and those who were not. Differences in acceptability ratings between the two risk groups were determined using nonparametric Mann-Whitney U tests. Results: On a 5-point Likert scale of agreement, participants found the screener questions easy to complete (median score 5, IQR 5-5) and felt comfortable sharing their answers with their obstetric care providers (median 5, IQR 4-5). Key perceived benefits of completing the screener included opportunities to seek preventive care (75/255, 29.4%) and to receive education on depression risk (66/255, 25.9%). Primary concerns about knowing one's risk of future depression included worrying about developing depression (90/255, 35.3%) and a lack of prevention opportunities (39/255, 15.3%). Desired preventive resources included counseling (197/255, 77.3%), mind-body interventions (166/255, 65.1%) such as exercise, and prenatal classes or support groups (81/255, 31.8%). Conclusions: Participants found the screener acceptable and felt comfortable receiving it through their patient portal. Specific preventive care options were commonly endorsed, several of which are scalable and evidence based. A minority of participants voiced addressable concerns about knowing their risk of developing depression in the future.

Indexed as

DepressionMass ScreeningPatient Acceptance of Health CarePregnancy ComplicationsAdultCohort StudiesFeasibility StudiesFemaleHumansPregnancyRisk AssessmentSurveys and Questionnairesdepressionmachine learningmental healthpatient-centered carepostpartumprediction algorithmpredictive screeningpregnancypreventive care

Identifiers

PMID42085674
PMCPMC13143196

What OpenQuestion holds

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Registered trials

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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.