Evidence map›Paper›PMID 36461893›Full record

ReviewInternational journal of methods in psychiatric research2023

'Optimal' cutoff selection in studies of depression screening tool accuracy using the PHQ-9, EPDS, or HADS-D: A meta-research study.

Eliana Brehaut, Dipika Neupane, Brooke Levis, Yin Wu, Ying Sun, John P A Ioannidis, Sarah Markham, Pim Cuijpers, Scott B Patten, Andrea Benedetti and 1 more

Abstract readReview
In one paragraph

Review in International journal of methods in psychiatric research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 3 pooled it
–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

13 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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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

11 authors.

Eliana BrehautLady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, Canada.
Dipika NeupaneLady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, Canada.
Brooke LevisLady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, Canada.ORCID 0000-0002-4310-3689
Yin WuLady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, Canada.
Ying SunLady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, Canada.
John P A IoannidisDepartment of Medicine, Department of Epidemiology and Population Health, Department of Biomedical Data Science, Department of Statistics, and Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, USA.
Sarah MarkhamDepartment of Biostatistics and Health Informatics, King's College London, London, UK.
Pim CuijpersDepartment of Clinical, Neuro and Developmental Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands.ORCID 0000-0001-5497-2743
Scott B PattenDepartment of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada.
Andrea BenedettiDepartment of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec, Canada.ORCID 0000-0002-8314-9497
Brett D ThombsLady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, Canada.ORCID 0000-0002-5644-8432

Funding

CIHR DA5-170278CIHR KRS-134297CIHR KRS-140994CIHR KRS-144045CIHR PBB-175358CIHR PBB-175359CIHR PCG-155468CIHR PJT-162206CIHR PJT-178167
6 · The paper itself

Abstract

objectivesOptimal cutoff thresholds are selected to separate 'positive' from 'negative' screening results. We evaluated how depression screening tool studies select optimal cutoffs.

methodsWe included studies from previously conducted meta-analyses of Patient Health Questionnaire-9, Edinburgh Postnatal Depression Scale, or Hospital Anxiety and Depression Scale-Depression accuracy. Outcomes included whether an optimal cutoff was selected, method used, recommendations made, and reporting guideline and protocol citation.

resultsOf 212 included studies, 172 (81%) attempted to identify an optimal cutoff, and 147 of these 172 (85%) reported one or more methods. Methods were heterogeneous with Youden's J (N = 35, 23%) most common. Only 23 of 147 (16%) studies described a rationale for their method. Rationales focused on balancing sensitivity and specificity without describing why desirable. 131 of 172 studies (76%) identified an optimal cutoff other than the standard; most did not make use recommendations (N = 56; 43%) or recommended using a non-standard cutoff (N = 53; 40%). Only 4 studies cited a reporting guideline, and 4 described a protocol with optimal cutoff selection methods, but none used the protocol method in the published study.

conclusionsResearch is needed to guide how selection of cutoffs for depression screening tools can be standardized and reflect clinical considerations.

Indexed as

Depression, PostpartumPatient Health QuestionnaireDepressionFemaleHumansMass ScreeningPsychiatric Status Rating ScalesSensitivity and SpecificitySurveys and Questionnairesedinburgh postnatal depression scalehospital anxiety and depression scalemajor depressionoptimal cutoff selectionpatient health questionnaire-9screening

Identifiers

PMID36461893
PMCPMC10485315

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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.