Depression prevalence based on the Edinburgh Postnatal Depression Scale compared to Structured Clinical Interview for DSM DIsorders classification : Systematic review and individual participant data meta-analysis

Objectives Estimates of depression prevalence in pregnancy and postpartum are based on the Edinburgh Postnatal Depression Scale (EPDS) more than on any other method. We aimed to determine if any EPDS cutoff can accurately and consistently estimate depression prevalence in individual studies. Methods We analyzed datasets that compared EPDS scores to Structured Clinical Interview for DSM (SCID) major depression status. Random-effects meta-analysis was used to compare prevalence with EPDS cutoffs versus the SCID. Results Seven thousand three hundred and fifteen participants (1017 SCID major depression) from 29 primary studies were included. For EPDS cutoffs used to estimate prevalence in recent studies (>= 9 to >= 14), pooled prevalence estimates ranged from 27.8% (95% CI: 22.0%-34.5%) for EPDS >= 9 to 9.0% (95% CI: 6.8%-11.9%) for EPDS >= 14; pooled SCID major depression prevalence was 9.0% (95% CI: 6.5%-12.3%). EPDS >= 14 provided pooled prevalence closest to SCID-based prevalence but differed from SCID prevalence in individual studies by a mean absolute difference of 5.1% (95% prediction interval: -13.7%, 12.3%). Conclusion EPDS >= 14 approximated SCID-based prevalence overall, but considerable heterogeneity in individual studies is a barrier to using it for prevalence estimation

Probability of major depression classification based on the SCID, CIDI, and MINI diagnostic interviews:A synthesis of three individual participant data meta-analyses

Introduction: Three previous individual participant data meta-analyses (IPDMAs) reported that, compared to the Structured Clinical Interview for the DSM (SCID), alternative reference standards, primarily the Composite International Diagnostic Interview (CIDI) and the Mini International Neuropsychiatric Interview (MINI), tended to misclassify major depression status, when controlling for depression symptom severity. However, there was an important lack of precision in the results. Objective: To compare the odds of the major depression classification based on the SCID, CIDI, and MINI. Methods: We included and standardized data from 3 IPDMA databases. For each IPDMA, separately, we fitted binomial generalized linear mixed models to compare the adjusted odds ratios (aORs) of major depression classification, controlling for symptom severity and characteristics of participants, and the interaction between interview and symptom severity. Next, we synthesized results using a DerSimonian-Laird random-effects meta-analysis. Results: In total, 69,405 participants (7,574 [11%] with major depression) from 212 studies were included. Controlling for symptom severity and participant characteristics, the MINI (74 studies; 25,749 participants) classified major depression more often than the SCID (108 studies; 21,953 participants; aOR 1.46; 95% confidence interval [CI] 1.11-1.92]). Classification odds for the CIDI (30 studies; 21,703 participants) and the SCID did not differ overall (aOR 1.19; 95% CI 0.79-1.75); however, as screening scores increased, the aOR increased less for the CIDI than the SCID (interaction aOR 0.64; 95% CI 0.52-0.80). Conclusions: Compared to the SCID, the MINI classified major depression more often. The odds of the depression classification with the CIDI increased less as symptom levels increased. Interpretation of research that uses diagnostic interviews to classify depression should consider the interview characteristics

Data-driven methods distort optimal cutoffs and accuracy estimates of depression screening tools: a simulation study using individual participant data

Objective: To evaluate, across multiple sample sizes, the degree that data-driven methods result in (1) optimal cutoffs different from population optimal cutoff and (2) bias in accuracy estimates. Study design and setting: A total of 1,000 samples of sample size 100, 200, 500 and 1,000 each were randomly drawn to simulate studies of different sample sizes from a database (n = 13,255) synthesized to assess Edinburgh Postnatal Depression Scale (EPDS) screening accuracy. Optimal cutoffs were selected by maximizing Youden's J (sensitivity+specificity–1). Optimal cutoffs and accuracy estimates in simulated samples were compared to population values. Results: Optimal cutoffs in simulated samples ranged from ≥ 5 to ≥ 17 for n = 100, ≥ 6 to ≥ 16 for n = 200, ≥ 6 to ≥ 14 for n = 500, and ≥ 8 to ≥ 13 for n = 1,000. Percentage of simulated samples identifying the population optimal cutoff (≥ 11) was 30% for n = 100, 35% for n = 200, 53% for n = 500, and 71% for n = 1,000. Mean overestimation of sensitivity and underestimation of specificity were 6.5 percentage point (pp) and -1.3 pp for n = 100, 4.2 pp and -1.1 pp for n = 200, 1.8 pp and -1.0 pp for n = 500, and 1.4 pp and -1.0 pp for n = 1,000. Conclusions: Small accuracy studies may identify inaccurate optimal cutoff and overstate accuracy estimates with data-driven methods.</p

Depression prevalence based on the Edinburgh Postnatal Depression Scale compared to Structured Clinical Interview for DSM DIsorders classification: Systematic review and individual participant data meta-analysis

OBJECTIVES:Estimates of depression prevalence in pregnancy and postpartum are based on the Edinburgh Postnatal Depression Scale (EPDS) more than on any other method. We aimed to determine if any EPDS cutoff can accurately and consistently estimate depression prevalence in individual studies. METHODS:We analyzed datasets that compared EPDS scores to Structured Clinical Interview for DSM (SCID) major depression status. Random-effects meta-analysis was used to compare prevalence with EPDS cutoffs versus the SCID. RESULTS:Seven thousand three hundred and fifteen participants (1017 SCID major depression) from 29 primary studies were included. For EPDS cutoffs used to estimate prevalence in recent studies (≥9 to ≥14), pooled prevalence estimates ranged from 27.8% (95% CI: 22.0%-34.5%) for EPDS ≥ 9 to 9.0% (95% CI: 6.8%-11.9%) for EPDS ≥ 14; pooled SCID major depression prevalence was 9.0% (95% CI: 6.5%-12.3%). EPDS ≥14 provided pooled prevalence closest to SCID-based prevalence but differed from SCID prevalence in individual studies by a mean absolute difference of 5.1% (95% prediction interval: -13.7%, 12.3%). CONCLUSION:EPDS ≥14 approximated SCID-based prevalence overall, but considerable heterogeneity in individual studies is a barrier to using it for prevalence estimation

Depression prevalence based on the Edinburgh Postnatal Depression Scale compared to Structured Clinical Interview for DSM DIsorders classification: systematic review and individual participant data meta‐analysis

Objectives: Estimates of depression prevalence in pregnancy and postpartum are based on the Edinburgh Postnatal Depression Scale (EPDS) more than on any other method. We aimed to determine if any EPDS cutoff can accurately and consistently estimate depression prevalence in individual studies. Methods: We analyzed datasets that compared EPDS scores to Structured Clinical Interview for DSM (SCID) major depression status. Random‐effects meta‐analysis was used to compare prevalence with EPDS cutoffs versus the SCID. Results: Seven thousand three hundred and fifteen participants (1017 SCID major depression) from 29 primary studies were included. For EPDS cutoffs used to estimate prevalence in recent studies (≥9 to ≥14), pooled prevalence estimates ranged from 27.8% (95% CI: 22.0%–34.5%) for EPDS ≥ 9 to 9.0% (95% CI: 6.8%–11.9%) for EPDS ≥ 14; pooled SCID major depression prevalence was 9.0% (95% CI: 6.5%–12.3%). EPDS ≥14 provided pooled prevalence closest to SCID‐based prevalence but differed from SCID prevalence in individual studies by a mean absolute difference of 5.1% (95% prediction interval: 13.7%, 12.3%). Conclusion: EPDS ≥14 approximated SCID‐based prevalence overall, but considerable heterogeneity in individual studies is a barrier to using it for prevalence estimation.This study was funded by the Canadian Institutes of Health Research (CIHR, KRS‐140994). Ms. Lyubenova was supported by the Mitacs Globalink Research Internship Program. Ms. Neupane was supported by G.R. Caverhill Fellowship from the Faculty of Medicine, McGill University. Drs. Levis and Wu were supported by Fonds de recherche du Québec‐Santé (FRQS) Postdoctoral Training Fellowships. Mr. Bhandari was supported by a studentship from the Research Institute of the McGill University Health Centre. Ms. Rice was supported by a Vanier Canada Graduate Scholarship. Ms. Azar was supported by a FRQS Masters Training Award. The primary study by Barnes et al. was supported by a grant from the Health Foundation (1665/608). The primary study by Beck et al. was supported by the Patrick and Catherine Weldon Donaghue Medical Research Foundation and the University of Connecticut Research Foundation. The primary study by Helle et al. was supported by the Werner Otto Foundation, the Kroschke Foundation, and the Feindt Foundation. Prof. Robertas Bunevicius, MD, PhD (1958‐2016) was Principal Investigator of the primary study by Bunevicius et al., but passed away and was unable to participate in this project. The primary study by Chaudron et al. was supported by a grant from the National Institute of Mental Health (grant K23 MH64476). The primary study by Tissot et al. was supported by the Swiss National Science Foundation (grant 32003B 125493). The primary study by Tendais et al. was supported under the project POCI/SAU‐ESP/56397/2004 by the Operational Program Science and Innovation 2010 (POCI 2010) of the Community Support Board III and by the European Community Fund FEDER. The primary study by Garcia‐Esteve et al. was supported by grant 7/98 from the Ministerio de Trabajo y Asuntos Sociales, Women's Institute, Spain. The primary study by Howard et al. was supported by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (Grant Reference Numbers RP‐PG‐1210‐12002 and RP‐DG‐1108‐10012) and by the South London Clinical Research Network. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. The primary study by Phillips et al. was supported by a scholarship from the National Health and Medical and Research Council (NHMRC). The primary study by Nakić Radoš et al. was supported by the Croatian Ministry of Science, Education, and Sports (134‐0000000‐2421). The primary study by Navarro et al. was supported by grant 13/00 from the Ministry of Work and Social Affairs, Institute of Women, Spain. The primary study by Pawlby et al. was supported by a Medical Research Council UK Project Grant (number G89292999N). The primary study by Quispel et al. was supported by Stichting Achmea Gezondheid (grant number z‐282). Dr. Robertson‐Blackmore was supported by a Young Investigator Award from the Brain and Behavior Research Foundation and NIMH grant K23MH080290. The primary study by Rochat et al. was supported by grants from the University of Oxford (HQ5035), the Tuixen Foundation (9940), the Wellcome Trust (082384/Z/07/Z and 071571), and the American Psychological Association. Dr. Rochat receives salary support from a Wellcome Trust Intermediate Fellowship (211374/Z/18/Z). The primary study by Prenoveau et al. was supported by The Wellcome Trust (grant number 071571). The primary study by Stewart et al. was supported by Professor Francis Creed's Journal of Psychosomatic Research Editorship fund (BA00457) administered through University of Manchester. The primary study by Tandon et al. was funded by the Thomas Wilson Sanitarium. The primary study by Tran et al. was supported by the Myer Foundation who funded the study under its Beyond Australia scheme. Dr. Tran was supported by an early career fellowship from the Australian National Health and Medical Research Council. The primary study by Vega‐Dienstmaier et al. was supported by Tejada Family Foundation, Inc, and Peruvian‐American Endowment, Inc. Drs. Benedetti and Thombs were supported by FRQS researcher salary awards

Depression prevalence based on the Edinburgh Postnatal Depression Scale compared to Structured Clinical Interview for DSM DIsorders classification: Systematic review and individual participant data meta-analysis

Objectives Estimates of depression prevalence in pregnancy and postpartum are based on the Edinburgh Postnatal Depression Scale (EPDS) more than on any other method. We aimed to determine if any EPDS cutoff can accurately and consistently estimate depression prevalence in individual studies. Methods We analyzed datasets that compared EPDS scores to Structured Clinical Interview for DSM (SCID) major depression status. Random-effects meta-analysis was used to compare prevalence with EPDS cutoffs versus the SCID. Results Seven thousand three hundred and fifteen participants (1017 SCID major depression) from 29 primary studies were included. For EPDS cutoffs used to estimate prevalence in recent studies (&gt;= 9 to &gt;= 14), pooled prevalence estimates ranged from 27.8% (95% CI: 22.0%-34.5%) for EPDS &gt;= 9 to 9.0% (95% CI: 6.8%-11.9%) for EPDS &gt;= 14; pooled SCID major depression prevalence was 9.0% (95% CI: 6.5%-12.3%). EPDS &gt;= 14 provided pooled prevalence closest to SCID-based prevalence but differed from SCID prevalence in individual studies by a mean absolute difference of 5.1% (95% prediction interval: -13.7%, 12.3%). Conclusion EPDS &gt;= 14 approximated SCID-based prevalence overall, but considerable heterogeneity in individual studies is a barrier to using it for prevalence estimation

Probability of major depression classification based on the SCID, CIDI, and MINI diagnostic interviews: A synthesis of three individual participant data meta-analyses

Introduction: Three previous individual participant data meta-analyses (IPDMAs) reported that, compared to the Structured Clinical Interview for the DSM (SCID), alternative reference standards, primarily the Composite International Diagnostic Interview (CIDI) and the Mini International Neuropsychiatric Interview (MINI), tended to misclassify major depression status, when controlling for depression symptom severity. However, there was an important lack of precision in the results. Objective: To compare the odds of the major depression classification based on the SCID, CIDI, and MINI. Methods: We included and standardized data from 3 IPDMA databases. For each IPDMA, separately, we fitted binomial generalized linear mixed models to compare the adjusted odds ratios (aORs) of major depression classification, controlling for symptom severity and characteristics of participants, and the interaction between interview and symptom severity. Next, we synthesized results using a DerSimonian-Laird random-effects meta-analysis. Results: In total, 69,405 participants (7,574 [11%] with major depression) from 212 studies were included. Controlling for symptom severity and participant characteristics, the MINI (74 studies; 25,749 participants) classified major depression more often than the SCID (108 studies; 21,953 participants; aOR 1.46; 95% confidence interval [CI] 1.11-1.92]). Classification odds for the CIDI (30 studies; 21,703 participants) and the SCID did not differ overall (aOR 1.19; 95% CI 0.79-1.75); however, as screening scores increased, the aOR increased less for the CIDI than the SCID (interaction aOR 0.64; 95% CI 0.52-0.80). Conclusions: Compared to the SCID, the MINI classified major depression more often. The odds of the depression classification with the CIDI increased less as symptom levels increased. Interpretation of research that uses diagnostic interviews to classify depression should consider the interview characteristics.</p