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1. Comparison of the Accuracy of the 7-Item HADS Depression Subscale and 14-Item Total HADS for Screening for Major Depression: A Systematic Review and Individual Participant Data Meta-Analysis

Author

Wu, Y, Levis, B, Daray, FM, Ioannidis, JPA, Patten, SB, Cuijpers, P, Ziegelstein, RC, Gilbody, S, Fischer, FH, Fan, S, Sun, Y, He, C, Krishnan, A, Neupane, D, Bhandari, PM, Negeri, Z, Riehm, KE, Rice, DB, Azar, M, Yan, XW, Imran, M, Chiovitti, MJ, Boruff, JT, McMillan, D, Kloda, LA, Markham, S, Henry, M, Ismail, Z, Loiselle, CG, Mitchell, ND, Al-Adawi, S, Beck, KR, Beraldi, A, Bernstein, CN, Boye, B, Buel-Drabe, N, Bunevicius, A, Can, C, Carter, G, Chen, C-K, Cheung, G, Clover, K, Conroy, RM, Costa-Requena, G, Cukor, D, Dabscheck, E, De Souza, J, Downing, M, Feinstein, A, Ferentinos, PP, Flint, AJ, Gallagher, P, Gandy, M, Grassi, L, Haerter, M, Hernando, A, Jackson, ML, Jenewein, J, Jette, N, Juliao, M, Kjaergaard, M, Kohler, S, Konig, H-H, Krishna, LKR, Lee, Y, Loebner, M, Loosman, WL, Love, AW, Loewe, B, Malt, UF, Marrie, RA, Massardo, L, Matsuoka, Y, Mehnert, A, Michopoulos, I, Misery, L, Nelson, CJ, Ng, CG, O'Donnell, ML, O'Rourke, SJ, Ozturk, A, Pabst, A, Pasco, JA, Peceliuniene, J, Pintor, L, Ponsford, JL, Pulido, F, Quinn, TJ, Reme, SE, Reuter, K, Riedel-Heller, SG, Rooney, AG, Sanchez-Gonzalez, R, Saracino, RM, Schellekens, MPJ, Scherer, M, Schwarzbold, ML, Cankorur, VS, Sharpe, L, Sharpe, M, Simard, S, Singer, S, Stafford, L, Stone, J, Strobe, NA, Sultan, S, Teixeira, AL, Tiringer, I, Turner, A, Walker, J, Walterfang, M, Wang, L-J, Weyerer, SB, White, J, Wiese, B, Williams, LJ, Wong, L-Y, Benedetti, A, Thombsi, BD, Wu, Y, Levis, B, Daray, FM, Ioannidis, JPA, Patten, SB, Cuijpers, P, Ziegelstein, RC, Gilbody, S, Fischer, FH, Fan, S, Sun, Y, He, C, Krishnan, A, Neupane, D, Bhandari, PM, Negeri, Z, Riehm, KE, Rice, DB, Azar, M, Yan, XW, Imran, M, Chiovitti, MJ, Boruff, JT, McMillan, D, Kloda, LA, Markham, S, Henry, M, Ismail, Z, Loiselle, CG, Mitchell, ND, Al-Adawi, S, Beck, KR, Beraldi, A, Bernstein, CN, Boye, B, Buel-Drabe, N, Bunevicius, A, Can, C, Carter, G, Chen, C-K, Cheung, G, Clover, K, Conroy, RM, Costa-Requena, G, Cukor, D, Dabscheck, E, De Souza, J, Downing, M, Feinstein, A, Ferentinos, PP, Flint, AJ, Gallagher, P, Gandy, M, Grassi, L, Haerter, M, Hernando, A, Jackson, ML, Jenewein, J, Jette, N, Juliao, M, Kjaergaard, M, Kohler, S, Konig, H-H, Krishna, LKR, Lee, Y, Loebner, M, Loosman, WL, Love, AW, Loewe, B, Malt, UF, Marrie, RA, Massardo, L, Matsuoka, Y, Mehnert, A, Michopoulos, I, Misery, L, Nelson, CJ, Ng, CG, O'Donnell, ML, O'Rourke, SJ, Ozturk, A, Pabst, A, Pasco, JA, Peceliuniene, J, Pintor, L, Ponsford, JL, Pulido, F, Quinn, TJ, Reme, SE, Reuter, K, Riedel-Heller, SG, Rooney, AG, Sanchez-Gonzalez, R, Saracino, RM, Schellekens, MPJ, Scherer, M, Schwarzbold, ML, Cankorur, VS, Sharpe, L, Sharpe, M, Simard, S, Singer, S, Stafford, L, Stone, J, Strobe, NA, Sultan, S, Teixeira, AL, Tiringer, I, Turner, A, Walker, J, Walterfang, M, Wang, L-J, Weyerer, SB, White, J, Wiese, B, Williams, LJ, Wong, L-Y, Benedetti, A, and Thombsi, BD

Abstract

The seven-item Hospital Anxiety and Depression Scale Depression subscale (HADS-D) and the total score of the 14-item HADS (HADS-T) are both used for major depression screening. Compared to the HADS-D, the HADS-T includes anxiety items and requires more time to complete. We compared the screening accuracy of the HADS-D and HADS-T for major depression detection. We conducted an individual participant data meta-analysis and fit bivariate random effects models to assess diagnostic accuracy among participants with both HADS-D and HADS-T scores. We identified optimal cutoffs, estimated sensitivity and specificity with 95% confidence intervals, and compared screening accuracy across paired cutoffs via two-stage and individual-level models. We used a 0.05 equivalence margin to assess equivalency in sensitivity and specificity. 20,700 participants (2,285 major depression cases) from 98 studies were included. Cutoffs of ≥7 for the HADS-D (sensitivity 0.79 [0.75, 0.83], specificity 0.78 [0.75, 0.80]) and ≥15 for the HADS-T (sensitivity 0.79 [0.76, 0.82], specificity 0.81 [0.78, 0.83]) minimized the distance to the top-left corner of the receiver operating characteristic curve. Across all sets of paired cutoffs evaluated, differences of sensitivity between HADS-T and HADS-D ranged from -0.05 to 0.01 (0.00 at paired optimal cutoffs), and differences of specificity were within 0.03 for all cutoffs (0.02-0.03). The pattern was similar among outpatients, although the HADS-T was slightly (not nonequivalently) more specific among inpatients. The accuracy of HADS-T was equivalent to the HADS-D for detecting major depression. In most settings, the shorter HADS-D would be preferred. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Published
2023

2. Selective cutoff reporting in studies of the accuracy of the Patient Health Questionnaire-9 and Edinburgh Postnatal Depression Scale: Comparison of results based on published cutoffs versus all cutoffs using individual participant data meta-analysis

Author

Neupane, D, Levis, B, Bhandari, PM, Thombs, BD, Benedetti, A, Sun, Y, He, C, Wu, Y, Krishnan, A, Negeri, Z, Imran, M, Rice, DB, Riehm, KE, Saadat, N, Azar, M, Sanchez, TA, Chiovitti, MJ, Levis, AW, Boruff, JT, Cuijpers, P, Gilbody, S, Ioannidis, JPA, Kloda, LA, Patten, SB, Shrier, I, Ziegelstein, RC, Comeau, L, Mitchell, ND, Tonelli, M, Vigod, SN, Akena, DH, Alvarado, R, Arroll, B, Bakare, MO, Baradaran, HR, Beck, CT, Bombardier, CH, Bunevicius, A, Carter, G, Chagas, MH, Chaudron, LH, Cholera, R, Clover, K, Conwell, Y, Castro e Couto, T, de Man-van Ginkel, JM, Delgadillo, J, Fann, JR, Favez, N, Fung, D, Garcia-Esteve, L, Gelaye, B, Goodyear-Smith, F, Hyphantis, T, Inagaki, M, Ismail, K, Jetté, N, Khalifa, DS, Khamseh, ME, Kohlhoff, J, Kozinszky, Z, Kusminskas, L, Liu, SI, Lotrakul, M, Loureiro, SR, Löwe, B, Sidik, SM, Nakić Radoš, S, Osório, FL, Pawlby, SJ, Pence, BW, Rochat, TJ, Rooney, AG, Sharp, DJ, Stafford, L, Su, KP, Sung, SC, Tadinac, M, Darius Tandon, S, Thiagayson, P, Töreki, A, Torres-Giménez, A, Turner, Alyna, van der Feltz-Cornelis, CM, Vega-Dienstmaier, JM, Vöhringer, PA, White, J, Whooley, MA, Winkley, K, Yamada, M, Neupane, D, Levis, B, Bhandari, PM, Thombs, BD, Benedetti, A, Sun, Y, He, C, Wu, Y, Krishnan, A, Negeri, Z, Imran, M, Rice, DB, Riehm, KE, Saadat, N, Azar, M, Sanchez, TA, Chiovitti, MJ, Levis, AW, Boruff, JT, Cuijpers, P, Gilbody, S, Ioannidis, JPA, Kloda, LA, Patten, SB, Shrier, I, Ziegelstein, RC, Comeau, L, Mitchell, ND, Tonelli, M, Vigod, SN, Akena, DH, Alvarado, R, Arroll, B, Bakare, MO, Baradaran, HR, Beck, CT, Bombardier, CH, Bunevicius, A, Carter, G, Chagas, MH, Chaudron, LH, Cholera, R, Clover, K, Conwell, Y, Castro e Couto, T, de Man-van Ginkel, JM, Delgadillo, J, Fann, JR, Favez, N, Fung, D, Garcia-Esteve, L, Gelaye, B, Goodyear-Smith, F, Hyphantis, T, Inagaki, M, Ismail, K, Jetté, N, Khalifa, DS, Khamseh, ME, Kohlhoff, J, Kozinszky, Z, Kusminskas, L, Liu, SI, Lotrakul, M, Loureiro, SR, Löwe, B, Sidik, SM, Nakić Radoš, S, Osório, FL, Pawlby, SJ, Pence, BW, Rochat, TJ, Rooney, AG, Sharp, DJ, Stafford, L, Su, KP, Sung, SC, Tadinac, M, Darius Tandon, S, Thiagayson, P, Töreki, A, Torres-Giménez, A, Turner, Alyna, van der Feltz-Cornelis, CM, Vega-Dienstmaier, JM, Vöhringer, PA, White, J, Whooley, MA, Winkley, K, and Yamada, M

Abstract

Objectives: Selectively reported results from only well-performing cutoffs in diagnostic accuracy studies may bias estimates in meta-analyses. We investigated cutoff reporting patterns for the Patient Health Questionnaire-9 (PHQ-9; standard cutoff 10) and Edinburgh Postnatal Depression Scale (EPDS; no standard cutoff, commonly used 10–13) and compared accuracy estimates based on published cutoffs versus all cutoffs. Methods: We conducted bivariate random effects meta-analyses using individual participant data to compare accuracy from published versus all cutoffs. Results: For the PHQ-9 (30 studies, N = 11,773), published results underestimated sensitivity for cutoffs below 10 (median difference: −0.06) and overestimated for cutoffs above 10 (median difference: 0.07). EPDS (19 studies, N = 3637) sensitivity estimates from published results were similar for cutoffs below 10 (median difference: 0.00) but higher for cutoffs above 13 (median difference: 0.14). Specificity estimates from published and all cutoffs were similar for both tools. The mean cutoff of all reported cutoffs in PHQ-9 studies with optimal cutoff below 10 was 8.8 compared to 11.8 for those with optimal cutoffs above 10. Mean for EPDS studies with optimal cutoffs below 10 was 9.9 compared to 11.8 for those with optimal cutoffs greater than 10. Conclusion: Selective cutoff reporting was more pronounced for the PHQ-9 than EPDS.

Published
2021

3. External validation of a shortened screening tool using individual participant data meta-analysis: A case study of the Patient Health Questionnaire-Dep-4.

Author

Harel, D, Levis, B, Sun, Y, Fischer, F, Ioannidis, JPA, Cuijpers, P, Patten, SB, Ziegelstein, RC, Markham, S, Benedetti, A, Thombs, BD, DEPRESsion Screening Data DEPRESSD PHQ Collaboration, Harel, D, Levis, B, Sun, Y, Fischer, F, Ioannidis, JPA, Cuijpers, P, Patten, SB, Ziegelstein, RC, Markham, S, Benedetti, A, Thombs, BD, and DEPRESsion Screening Data DEPRESSD PHQ Collaboration

Abstract

Shortened versions of self-reported questionnaires may be used to reduce respondent burden. When shortened screening tools are used, it is desirable to maintain equivalent diagnostic accuracy to full-length forms. This manuscript presents a case study that illustrates how external data and individual participant data meta-analysis can be used to assess the equivalence in diagnostic accuracy between a shortened and full-length form. This case study compares the Patient Health Questionnaire-9 (PHQ-9) and a 4-item shortened version (PHQ-Dep-4) that was previously developed using optimal test assembly methods. Using a large database of 75 primary studies (34,698 participants, 3,392 major depression cases), we evaluated whether the PHQ-Dep-4 cutoff of ≥ 4 maintained equivalent diagnostic accuracy to a PHQ-9 cutoff of ≥ 10. Using this external validation dataset, a PHQ-Dep-4 cutoff of ≥ 4 maximized the sum of sensitivity and specificity, with a sensitivity of 0.88 (95% CI 0.81, 0.93), 0.68 (95% CI 0.56, 0.78), and 0.80 (95% CI 0.73, 0.85) for the semi-structured, fully structured, and MINI reference standard categories, respectively, and a specificity of 0.79 (95% CI 0.74, 0.83), 0.85 (95% CI 0.78, 0.90), and 0.83 (95% CI 0.80, 0.86) for the semi-structured, fully structured, and MINI reference standard categories, respectively. While equivalence with a PHQ-9 cutoff of ≥ 10 was not established, we found the sensitivity of the PHQ-Dep-4 to be non-inferior to that of the PHQ-9, and the specificity of the PHQ-Dep-4 to be marginally smaller than the PHQ-9.

Published
2021

4. The Accuracy of the Patient Health Questionnaire-9 Algorithm for Screening to Detect Major Depression: An Individual Participant Data Meta-Analysis

Author

He, C, Levis, B, Riehm, KE, Saadat, N, Levis, AW, Azar, M, Rice, DB, Krishnan, A, Wu, Y, Sun, Y, Imran, M, Boruff, J, Cuijpers, P, Gilbody, S, Ioannidis, JPA, Kloda, LA, McMillan, D, Patten, SB, Shrier, I, Ziegelstein, RC, Turner, Alyna, He, C, Levis, B, Riehm, KE, Saadat, N, Levis, AW, Azar, M, Rice, DB, Krishnan, A, Wu, Y, Sun, Y, Imran, M, Boruff, J, Cuijpers, P, Gilbody, S, Ioannidis, JPA, Kloda, LA, McMillan, D, Patten, SB, Shrier, I, Ziegelstein, RC, and Turner, Alyna

Published
2020

5. Probability of major depression diagnostic classification based on the SCID, CIDI and MINI diagnostic interviews controlling for Hospital Anxiety and Depression Scale - Depression subscale scores: An individual participant data meta-analysis of 73 primary studies

Author

Wu, Y, Levis, B, Sun, Y, Krishnan, A, He, C, Riehm, KE, Rice, DB, Azar, M, Yan, XW, Neupane, D, Bhandari, PM, Imran, M, Chiovitti, MJ, Saadat, N, Boruff, JT, Cuijpers, P, Gilbody, S, McMillan, D, Ioannidis, JPA, Kloda, LA, Patten, SB, Shrier, I, Ziegelstein, RC, Henry, M, Ismail, Z, Loiselle, CG, Mitchell, ND, Tonelli, M, Al-Adawi, S, Beraldi, A, Braeken, APBM, Bueel-Drabe, N, Bunevicius, A, Carter, G, Chen, C-K, Cheung, G, Clover, K, Conroy, RM, Cukor, D, Rocha e Silva, CE, Dabscheck, E, Daray, FM, Douven, E, Downing, MG, Feinstein, A, Ferentinos, PP, Fischer, FH, Flint, AJ, Fujimori, M, Gallagher, P, Gandy, M, Goebel, S, Grassi, L, Haerter, M, Jenewein, J, Jette, N, Juliao, M, Kim, J-M, Kim, S-W, Kjaergaard, M, Kohler, S, Loosman, WL, Loewe, B, Martin-Santos, R, Massardo, L, Matsuoka, Y, Mehnert, A, Michopoulos, I, Misery, L, Navines, R, O'Donnell, ML, Ozturk, A, Peceliuniene, J, Pintor, L, Ponsford, JL, Quinn, TJ, Reme, SE, Reuter, K, Rooney, AG, Sanchez-Gonzalez, R, Schwarzbold, ML, Cankorur, VS, Shaaban, J, Sharpe, L, Sharpe, M, Simard, S, Singer, S, Stafford, L, Stone, J, Sultan, S, Teixeira, AL, Tiringer, I, Turner, A, Walker, J, Walterfang, M, Wang, L-J, White, J, Wong, DK, Benedetti, A, Thombs, BD, Wu, Y, Levis, B, Sun, Y, Krishnan, A, He, C, Riehm, KE, Rice, DB, Azar, M, Yan, XW, Neupane, D, Bhandari, PM, Imran, M, Chiovitti, MJ, Saadat, N, Boruff, JT, Cuijpers, P, Gilbody, S, McMillan, D, Ioannidis, JPA, Kloda, LA, Patten, SB, Shrier, I, Ziegelstein, RC, Henry, M, Ismail, Z, Loiselle, CG, Mitchell, ND, Tonelli, M, Al-Adawi, S, Beraldi, A, Braeken, APBM, Bueel-Drabe, N, Bunevicius, A, Carter, G, Chen, C-K, Cheung, G, Clover, K, Conroy, RM, Cukor, D, Rocha e Silva, CE, Dabscheck, E, Daray, FM, Douven, E, Downing, MG, Feinstein, A, Ferentinos, PP, Fischer, FH, Flint, AJ, Fujimori, M, Gallagher, P, Gandy, M, Goebel, S, Grassi, L, Haerter, M, Jenewein, J, Jette, N, Juliao, M, Kim, J-M, Kim, S-W, Kjaergaard, M, Kohler, S, Loosman, WL, Loewe, B, Martin-Santos, R, Massardo, L, Matsuoka, Y, Mehnert, A, Michopoulos, I, Misery, L, Navines, R, O'Donnell, ML, Ozturk, A, Peceliuniene, J, Pintor, L, Ponsford, JL, Quinn, TJ, Reme, SE, Reuter, K, Rooney, AG, Sanchez-Gonzalez, R, Schwarzbold, ML, Cankorur, VS, Shaaban, J, Sharpe, L, Sharpe, M, Simard, S, Singer, S, Stafford, L, Stone, J, Sultan, S, Teixeira, AL, Tiringer, I, Turner, A, Walker, J, Walterfang, M, Wang, L-J, White, J, Wong, DK, Benedetti, A, and Thombs, BD

Abstract

OBJECTIVE: Two previous individual participant data meta-analyses (IPDMAs) found that different diagnostic interviews classify different proportions of people as having major depression overall or by symptom levels. We compared the odds of major depression classification across diagnostic interviews among studies that administered the Depression subscale of the Hospital Anxiety and Depression Scale (HADS-D). METHODS: Data accrued for an IPDMA on HADS-D diagnostic accuracy were analysed. We fit binomial generalized linear mixed models to compare odds of major depression classification for the Structured Clinical Interview for DSM (SCID), Composite International Diagnostic Interview (CIDI), and Mini International Neuropsychiatric Interview (MINI), controlling for HADS-D scores and participant characteristics with and without an interaction term between interview and HADS-D scores. RESULTS: There were 15,856 participants (1942 [12%] with major depression) from 73 studies, including 15,335 (97%) non-psychiatric medical patients, 164 (1%) partners of medical patients, and 357 (2%) healthy adults. The MINI (27 studies, 7345 participants, 1066 major depression cases) classified participants as having major depression more often than the CIDI (10 studies, 3023 participants, 269 cases) (adjusted odds ratio [aOR] = 1.70 (0.84, 3.43)) and the semi-structured SCID (36 studies, 5488 participants, 607 cases) (aOR = 1.52 (1.01, 2.30)). The odds ratio for major depression classification with the CIDI was less likely to increase as HADS-D scores increased than for the SCID (interaction aOR = 0.92 (0.88, 0.96)). CONCLUSION: Compared to the SCID, the MINI may diagnose more participants as having major depression, and the CIDI may be less responsive to symptom severity.

Published
2020

6. Depression prevalence using the HADS-D compared to SCID major depression classification: An individual participant data meta-analysis

Author

Brehaut, E, Neupane, D, Levis, B, Wu, Y, Sun, Y, Krishnan, A, He, C, Bhandari, PM, Negeri, Z, Riehm, KE, Rice, DB, Azar, M, Yan, XW, Imran, M, Chiovitti, MJ, Saadat, N, Cuijpers, P, Ioannidis, JPA, Markham, S, Patten, SB, Ziegelstein, RC, Henry, M, Ismail, Z, Loiselle, CG, Mitchell, ND, Tonelli, M, Boruff, JT, Kloda, LA, Beraldi, A, Braeken, APBM, Carter, G, Clover, K, Conroy, RM, Cukor, D, da Rocha E Silva, CE, De Souza, J, Downing, MG, Feinstein, A, Ferentinos, PP, Fischer, FH, Flint, AJ, Fujimori, M, Gallagher, P, Goebel, S, Jette, N, Juliao, M, Keller, M, Kjaergaard, M, Love, AW, Loewe, B, Martin-Santos, R, Michopoulos, I, Navines, R, O'Rourke, SJ, Ozturk, A, Pintor, L, Ponsford, JL, Rooney, AG, Sanchez-Gonzalez, R, Schwarzbold, ML, Sharpe, M, Simard, S, Singer, S, Stone, J, Tung, K-Y, Turner, A, Walker, J, Walterfang, M, White, J, Benedetti, A, Thombs, BD, Brehaut, E, Neupane, D, Levis, B, Wu, Y, Sun, Y, Krishnan, A, He, C, Bhandari, PM, Negeri, Z, Riehm, KE, Rice, DB, Azar, M, Yan, XW, Imran, M, Chiovitti, MJ, Saadat, N, Cuijpers, P, Ioannidis, JPA, Markham, S, Patten, SB, Ziegelstein, RC, Henry, M, Ismail, Z, Loiselle, CG, Mitchell, ND, Tonelli, M, Boruff, JT, Kloda, LA, Beraldi, A, Braeken, APBM, Carter, G, Clover, K, Conroy, RM, Cukor, D, da Rocha E Silva, CE, De Souza, J, Downing, MG, Feinstein, A, Ferentinos, PP, Fischer, FH, Flint, AJ, Fujimori, M, Gallagher, P, Goebel, S, Jette, N, Juliao, M, Keller, M, Kjaergaard, M, Love, AW, Loewe, B, Martin-Santos, R, Michopoulos, I, Navines, R, O'Rourke, SJ, Ozturk, A, Pintor, L, Ponsford, JL, Rooney, AG, Sanchez-Gonzalez, R, Schwarzbold, ML, Sharpe, M, Simard, S, Singer, S, Stone, J, Tung, K-Y, Turner, A, Walker, J, Walterfang, M, White, J, Benedetti, A, and Thombs, BD

Abstract

OBJECTIVES: Validated diagnostic interviews are required to classify depression status and estimate prevalence of disorder, but screening tools are often used instead. We used individual participant data meta-analysis to compare prevalence based on standard Hospital Anxiety and Depression Scale - depression subscale (HADS-D) cutoffs of ≥8 and ≥11 versus Structured Clinical Interview for DSM (SCID) major depression and determined if an alternative HADS-D cutoff could more accurately estimate prevalence. METHODS: We searched Medline, Medline In-Process & Other Non-Indexed Citations via Ovid, PsycINFO, and Web of Science (inception-July 11, 2016) for studies comparing HADS-D scores to SCID major depression status. Pooled prevalence and pooled differences in prevalence for HADS-D cutoffs versus SCID major depression were estimated. RESULTS: 6005 participants (689 SCID major depression cases) from 41 primary studies were included. Pooled prevalence was 24.5% (95% Confidence Interval (CI): 20.5%, 29.0%) for HADS-D ≥8, 10.7% (95% CI: 8.3%, 13.8%) for HADS-D ≥11, and 11.6% (95% CI: 9.2%, 14.6%) for SCID major depression. HADS-D ≥11 was closest to SCID major depression prevalence, but the 95% prediction interval for the difference that could be expected for HADS-D ≥11 versus SCID in a new study was -21.1% to 19.5%. CONCLUSIONS: HADS-D ≥8 substantially overestimates depression prevalence. Of all possible cutoff thresholds, HADS-D ≥11 was closest to the SCID, but there was substantial heterogeneity in the difference between HADS-D ≥11 and SCID-based estimates. HADS-D should not be used as a substitute for a validated diagnostic interview.

Published
2020

7. Equivalency of the diagnostic accuracy of the PHQ-8 and PHQ-9: a systematic review and individual participant data meta-analysis.

Author

Wu, Y, Levis, B, Riehm, KE, Saadat, N, Levis, AW, Azar, M, Rice, DB, Boruff, J, Cuijpers, P, Gilbody, S, Ioannidis, JPA, Kloda, LA, McMillan, D, Patten, SB, Shrier, I, Ziegelstein, RC, Akena, DH, Arroll, B, Ayalon, L, Baradaran, HR, Baron, M, Bombardier, CH, Butterworth, P, Carter, G, Chagas, MH, Chan, JCN, Cholera, R, Conwell, Y, de Man-van Ginkel, JM, Fann, JR, Fischer, FH, Fung, D, Gelaye, B, Goodyear-Smith, F, Greeno, CG, Hall, BJ, Harrison, PA, Härter, M, Hegerl, U, Hides, L, Hobfoll, SE, Hudson, M, Hyphantis, T, Inagaki, MD, Jetté, N, Khamseh, ME, Kiely, KM, Kwan, Y, Lamers, F, Liu, S-I, Lotrakul, M, Loureiro, SR, Löwe, B, McGuire, A, Mohd-Sidik, S, Munhoz, TN, Muramatsu, K, Osório, FL, Patel, V, Pence, BW, Persoons, P, Picardi, A, Reuter, K, Rooney, AG, Santos, IS, Shaaban, J, Sidebottom, A, Simning, A, Stafford, MD, Sung, S, Tan, PLL, Turner, A, van Weert, HC, White, J, Whooley, MA, Winkley, K, Yamada, M, Benedetti, A, Thombs, BD, Wu, Y, Levis, B, Riehm, KE, Saadat, N, Levis, AW, Azar, M, Rice, DB, Boruff, J, Cuijpers, P, Gilbody, S, Ioannidis, JPA, Kloda, LA, McMillan, D, Patten, SB, Shrier, I, Ziegelstein, RC, Akena, DH, Arroll, B, Ayalon, L, Baradaran, HR, Baron, M, Bombardier, CH, Butterworth, P, Carter, G, Chagas, MH, Chan, JCN, Cholera, R, Conwell, Y, de Man-van Ginkel, JM, Fann, JR, Fischer, FH, Fung, D, Gelaye, B, Goodyear-Smith, F, Greeno, CG, Hall, BJ, Harrison, PA, Härter, M, Hegerl, U, Hides, L, Hobfoll, SE, Hudson, M, Hyphantis, T, Inagaki, MD, Jetté, N, Khamseh, ME, Kiely, KM, Kwan, Y, Lamers, F, Liu, S-I, Lotrakul, M, Loureiro, SR, Löwe, B, McGuire, A, Mohd-Sidik, S, Munhoz, TN, Muramatsu, K, Osório, FL, Patel, V, Pence, BW, Persoons, P, Picardi, A, Reuter, K, Rooney, AG, Santos, IS, Shaaban, J, Sidebottom, A, Simning, A, Stafford, MD, Sung, S, Tan, PLL, Turner, A, van Weert, HC, White, J, Whooley, MA, Winkley, K, Yamada, M, Benedetti, A, and Thombs, BD

Abstract

BACKGROUND: Item 9 of the Patient Health Questionnaire-9 (PHQ-9) queries about thoughts of death and self-harm, but not suicidality. Although it is sometimes used to assess suicide risk, most positive responses are not associated with suicidality. The PHQ-8, which omits Item 9, is thus increasingly used in research. We assessed equivalency of total score correlations and the diagnostic accuracy to detect major depression of the PHQ-8 and PHQ-9. METHODS: We conducted an individual patient data meta-analysis. We fit bivariate random-effects models to assess diagnostic accuracy. RESULTS: 16 742 participants (2097 major depression cases) from 54 studies were included. The correlation between PHQ-8 and PHQ-9 scores was 0.996 (95% confidence interval 0.996 to 0.996). The standard cutoff score of 10 for the PHQ-9 maximized sensitivity + specificity for the PHQ-8 among studies that used a semi-structured diagnostic interview reference standard (N = 27). At cutoff 10, the PHQ-8 was less sensitive by 0.02 (-0.06 to 0.00) and more specific by 0.01 (0.00 to 0.01) among those studies (N = 27), with similar results for studies that used other types of interviews (N = 27). For all 54 primary studies combined, across all cutoffs, the PHQ-8 was less sensitive than the PHQ-9 by 0.00 to 0.05 (0.03 at cutoff 10), and specificity was within 0.01 for all cutoffs (0.00 to 0.01). CONCLUSIONS: PHQ-8 and PHQ-9 total scores were similar. Sensitivity may be minimally reduced with the PHQ-8, but specificity is similar.

Published
2020

8. Estimating the sample mean and standard deviation from commonly reported quantiles in meta-analysis

Author

McGrath, S, Zhao, X, Steele, R, Thombs, BD, Benedetti, A, Levis, B, Riehm, KE, Saadat, N, Levis, AW, Azar, M, Rice, DB, Sun, Y, Krishnan, A, He, C, Wu, Y, Bhandari, PM, Neupane, D, Imran, M, Boruff, J, Cuijpers, P, Gilbody, S, Ioannidis, JPA, Kloda, LA, McMillan, D, Patten, SB, Shrier, I, Ziegelstein, RC, Akena, DH, Arroll, B, Ayalon, L, Baradaran, HR, Baron, M, Beraldi, A, Bombardier, CH, Butterworth, P, Carter, G, Chagas, MH, Chan, JCN, Cholera, R, Chowdhary, N, Clover, K, Conwell, Y, Ginkel, JMDM-V, Delgadillo, J, Fann, JR, Fischer, FH, Fischler, B, Fung, D, Gelaye, B, Goodyear-Smith, F, Greeno, CG, Hall, BJ, Harrison, PA, Harter, M, Hegerl, U, Hides, L, Hobfoll, SE, Hudson, M, Hyphantis, T, Inagaki, M, Ismail, K, Jette, N, Khamseh, ME, Kiely, KM, Kwan, Y, Lamers, F, Liu, S-I, Lotrakul, M, Loureiro, SR, Loewe, B, Marsh, L, McGuire, A, Sidik, SM, Munhoz, TN, Muramatsu, K, Osorio, FL, Patel, V, Pence, BW, Persoons, P, Picardi, A, Reuter, K, Rooney, AG, Santos, IS, Shaaban, J, Sidebottom, A, Simning, A, Stafford, L, Sung, SC, Tan, PLL, Turner, A, van der Feltz-Cornelis, CM, van Weert, HC, Vohringer, PA, White, J, Whooley, MA, Winkley, K, Yamada, M, Zhang, Y, McGrath, S, Zhao, X, Steele, R, Thombs, BD, Benedetti, A, Levis, B, Riehm, KE, Saadat, N, Levis, AW, Azar, M, Rice, DB, Sun, Y, Krishnan, A, He, C, Wu, Y, Bhandari, PM, Neupane, D, Imran, M, Boruff, J, Cuijpers, P, Gilbody, S, Ioannidis, JPA, Kloda, LA, McMillan, D, Patten, SB, Shrier, I, Ziegelstein, RC, Akena, DH, Arroll, B, Ayalon, L, Baradaran, HR, Baron, M, Beraldi, A, Bombardier, CH, Butterworth, P, Carter, G, Chagas, MH, Chan, JCN, Cholera, R, Chowdhary, N, Clover, K, Conwell, Y, Ginkel, JMDM-V, Delgadillo, J, Fann, JR, Fischer, FH, Fischler, B, Fung, D, Gelaye, B, Goodyear-Smith, F, Greeno, CG, Hall, BJ, Harrison, PA, Harter, M, Hegerl, U, Hides, L, Hobfoll, SE, Hudson, M, Hyphantis, T, Inagaki, M, Ismail, K, Jette, N, Khamseh, ME, Kiely, KM, Kwan, Y, Lamers, F, Liu, S-I, Lotrakul, M, Loureiro, SR, Loewe, B, Marsh, L, McGuire, A, Sidik, SM, Munhoz, TN, Muramatsu, K, Osorio, FL, Patel, V, Pence, BW, Persoons, P, Picardi, A, Reuter, K, Rooney, AG, Santos, IS, Shaaban, J, Sidebottom, A, Simning, A, Stafford, L, Sung, SC, Tan, PLL, Turner, A, van der Feltz-Cornelis, CM, van Weert, HC, Vohringer, PA, White, J, Whooley, MA, Winkley, K, Yamada, M, and Zhang, Y

Abstract

Researchers increasingly use meta-analysis to synthesize the results of several studies in order to estimate a common effect. When the outcome variable is continuous, standard meta-analytic approaches assume that the primary studies report the sample mean and standard deviation of the outcome. However, when the outcome is skewed, authors sometimes summarize the data by reporting the sample median and one or both of (i) the minimum and maximum values and (ii) the first and third quartiles, but do not report the mean or standard deviation. To include these studies in meta-analysis, several methods have been developed to estimate the sample mean and standard deviation from the reported summary data. A major limitation of these widely used methods is that they assume that the outcome distribution is normal, which is unlikely to be tenable for studies reporting medians. We propose two novel approaches to estimate the sample mean and standard deviation when data are suspected to be non-normal. Our simulation results and empirical assessments show that the proposed methods often perform better than the existing methods when applied to non-normal data.

Published
2020

9. Patient Health Questionnaire-9 scores do not accurately estimate depression prevalence: individual participant data meta-analysis.

Author

Levis, B, Benedetti, A, Ioannidis, JPA, Sun, Y, Negeri, Z, He, C, Wu, Y, Krishnan, A, Bhandari, PM, Neupane, D, Imran, M, Rice, DB, Riehm, KE, Saadat, N, Azar, M, Boruff, J, Cuijpers, P, Gilbody, S, Kloda, LA, McMillan, D, Patten, SB, Shrier, I, Ziegelstein, RC, Alamri, SH, Amtmann, D, Ayalon, L, Baradaran, HR, Beraldi, A, Bernstein, CN, Bhana, A, Bombardier, CH, Carter, G, Chagas, MH, Chibanda, D, Clover, K, Conwell, Y, Diez-Quevedo, C, Fann, JR, Fischer, FH, Gholizadeh, L, Gibson, LJ, Green, EP, Greeno, CG, Hall, BJ, Haroz, EE, Ismail, K, Jetté, N, Khamseh, ME, Kwan, Y, Lara, MA, Liu, S-I, Loureiro, SR, Löwe, B, Marrie, RA, Marsh, L, McGuire, A, Muramatsu, K, Navarrete, L, Osório, FL, Petersen, I, Picardi, A, Pugh, SL, Quinn, TJ, Rooney, AG, Shinn, EH, Sidebottom, A, Spangenberg, L, Tan, PLL, Taylor-Rowan, M, Turner, A, van Weert, HC, Vöhringer, PA, Wagner, LI, White, J, Winkley, K, Thombs, BD, Levis, B, Benedetti, A, Ioannidis, JPA, Sun, Y, Negeri, Z, He, C, Wu, Y, Krishnan, A, Bhandari, PM, Neupane, D, Imran, M, Rice, DB, Riehm, KE, Saadat, N, Azar, M, Boruff, J, Cuijpers, P, Gilbody, S, Kloda, LA, McMillan, D, Patten, SB, Shrier, I, Ziegelstein, RC, Alamri, SH, Amtmann, D, Ayalon, L, Baradaran, HR, Beraldi, A, Bernstein, CN, Bhana, A, Bombardier, CH, Carter, G, Chagas, MH, Chibanda, D, Clover, K, Conwell, Y, Diez-Quevedo, C, Fann, JR, Fischer, FH, Gholizadeh, L, Gibson, LJ, Green, EP, Greeno, CG, Hall, BJ, Haroz, EE, Ismail, K, Jetté, N, Khamseh, ME, Kwan, Y, Lara, MA, Liu, S-I, Loureiro, SR, Löwe, B, Marrie, RA, Marsh, L, McGuire, A, Muramatsu, K, Navarrete, L, Osório, FL, Petersen, I, Picardi, A, Pugh, SL, Quinn, TJ, Rooney, AG, Shinn, EH, Sidebottom, A, Spangenberg, L, Tan, PLL, Taylor-Rowan, M, Turner, A, van Weert, HC, Vöhringer, PA, Wagner, LI, White, J, Winkley, K, and Thombs, BD

Abstract

OBJECTIVES: Depression symptom questionnaires are not for diagnostic classification. Patient Health Questionnaire-9 (PHQ-9) scores ≥10 are nonetheless often used to estimate depression prevalence. We compared PHQ-9 ≥10 prevalence to Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders (SCID) major depression prevalence and assessed whether an alternative PHQ-9 cutoff could more accurately estimate prevalence. STUDY DESIGN AND SETTING: Individual participant data meta-analysis of datasets comparing PHQ-9 scores to SCID major depression status. RESULTS: A total of 9,242 participants (1,389 SCID major depression cases) from 44 primary studies were included. Pooled PHQ-9 ≥10 prevalence was 24.6% (95% confidence interval [CI]: 20.8%, 28.9%); pooled SCID major depression prevalence was 12.1% (95% CI: 9.6%, 15.2%); and pooled difference was 11.9% (95% CI: 9.3%, 14.6%). The mean study-level PHQ-9 ≥10 to SCID-based prevalence ratio was 2.5 times. PHQ-9 ≥14 and the PHQ-9 diagnostic algorithm provided prevalence closest to SCID major depression prevalence, but study-level prevalence differed from SCID-based prevalence by an average absolute difference of 4.8% for PHQ-9 ≥14 (95% prediction interval: -13.6%, 14.5%) and 5.6% for the PHQ-9 diagnostic algorithm (95% prediction interval: -16.4%, 15.0%). CONCLUSION: PHQ-9 ≥10 substantially overestimates depression prevalence. There is too much heterogeneity to correct statistically in individual studies.

Published
2020

10. Comparison of major depression diagnostic classification probability using the SCID, CIDI, and MINI diagnostic interviews among women in pregnancy or postpartum: An individual participant data meta-analysis

Author

Levis, B, McMillan, D, Sun, Y, He, C, Rice, DB, Krishnan, A, Wu, Y, Azar, M, Sanchez, TA, Chiovitti, MJ, Bhandari, PM, Neupane, D, Saadat, N, Riehm, KE, Imran, M, Boruff, JT, Cuijpers, P, Gilbody, S, Ioannidis, JPA, Kloda, LA, Patten, SB, Shrier, I, Ziegelstein, RC, Comeau, L, Mitchell, ND, Tonelli, M, Vigod, SN, Aceti, F, Alvarado, R, Alvarado-Esquivel, C, Bakare, MO, Barnes, J, Beck, CT, Bindt, C, Boyce, PM, Bunevicius, A, Couto, TCE, Chaudron, LH, Correa, H, de Figueiredo, FP, Eapen, V, Fernandes, M, Figueiredo, B, Fisher, JRW, Garcia-Esteve, L, Giardinelli, L, Helle, N, Howard, LM, Khalifa, DS, Kohlhoff, J, Kusminskas, L, Kozinszky, Z, Lelli, L, Leonardou, AA, Lewis, BA, Maes, M, Meuti, V, Nakić Radoš, S, Navarro García, P, Nishi, D, Okitundu Luwa E-Andjafono, D, Robertson-Blackmore, E, Rochat, TJ, Rowe, HJ, Siu, BWM, Skalkidou, A, Stein, A, Stewart, RC, Su, KP, Sundström-Poromaa, I, Tadinac, M, Tandon, SD, Tendais, I, Thiagayson, P, Töreki, A, Torres-Giménez, A, Tran, TD, Trevillion, K, Turner, K, Vega-Dienstmaier, JM, Wynter, K, Yonkers, KA, Benedetti, A, Thombs, BD, Levis, B, McMillan, D, Sun, Y, He, C, Rice, DB, Krishnan, A, Wu, Y, Azar, M, Sanchez, TA, Chiovitti, MJ, Bhandari, PM, Neupane, D, Saadat, N, Riehm, KE, Imran, M, Boruff, JT, Cuijpers, P, Gilbody, S, Ioannidis, JPA, Kloda, LA, Patten, SB, Shrier, I, Ziegelstein, RC, Comeau, L, Mitchell, ND, Tonelli, M, Vigod, SN, Aceti, F, Alvarado, R, Alvarado-Esquivel, C, Bakare, MO, Barnes, J, Beck, CT, Bindt, C, Boyce, PM, Bunevicius, A, Couto, TCE, Chaudron, LH, Correa, H, de Figueiredo, FP, Eapen, V, Fernandes, M, Figueiredo, B, Fisher, JRW, Garcia-Esteve, L, Giardinelli, L, Helle, N, Howard, LM, Khalifa, DS, Kohlhoff, J, Kusminskas, L, Kozinszky, Z, Lelli, L, Leonardou, AA, Lewis, BA, Maes, M, Meuti, V, Nakić Radoš, S, Navarro García, P, Nishi, D, Okitundu Luwa E-Andjafono, D, Robertson-Blackmore, E, Rochat, TJ, Rowe, HJ, Siu, BWM, Skalkidou, A, Stein, A, Stewart, RC, Su, KP, Sundström-Poromaa, I, Tadinac, M, Tandon, SD, Tendais, I, Thiagayson, P, Töreki, A, Torres-Giménez, A, Tran, TD, Trevillion, K, Turner, K, Vega-Dienstmaier, JM, Wynter, K, Yonkers, KA, Benedetti, A, and Thombs, BD

Abstract

Objectives: A previous individual participant data meta-analysis (IPDMA) identified differences in major depression classification rates between different diagnostic interviews, controlling for depressive symptoms on the basis of the Patient Health Questionnaire-9. We aimed to determine whether similar results would be seen in a different population, using studies that administered the Edinburgh Postnatal Depression Scale (EPDS) in pregnancy or postpartum. Methods: Data accrued for an EPDS diagnostic accuracy IPDMA were analysed. Binomial generalised linear mixed models were fit to compare depression classification odds for the Mini International Neuropsychiatric Interview (MINI), Composite International Diagnostic Interview (CIDI), and Structured Clinical Interview for DSM (SCID), controlling for EPDS scores and participant characteristics. Results: Among fully structured interviews, the MINI (15 studies, 2,532 participants, 342 major depression cases) classified depression more often than the CIDI (3 studies, 2,948 participants, 194 major depression cases; adjusted odds ratio [aOR] = 3.72, 95% confidence interval [CI] [1.21, 11.43]). Compared with the semistructured SCID (28 studies, 7,403 participants, 1,027 major depression cases), odds with the CIDI (interaction aOR = 0.88, 95% CI [0.85, 0.92]) and MINI (interaction aOR = 0.95, 95% CI [0.92, 0.99]) increased less as EPDS scores increased. Conclusion: Different interviews may not classify major depression equivalently.

Published
2019

11. Erratum to:Methods for evaluating medical tests and biomarkers

Author

Gopalakrishna, G, Langendam, M, Scholten, R, Bossuyt, P, Leeflang, M, Noel-Storr, A, Thomas, J, Marshall, I, Wallace, B, Whiting, P, Davenport, C, GopalaKrishna, G, De Salis, I, Mallett, S, Wolff, R, Riley, R, Westwood, M, Kleinen, J, Collins, G, Reitsma, H, Moons, K, Zapf, A, Hoyer, A, Kramer, K, Kuss, O, Ensor, J, Deeks, JJ, Martin, EC, Riley, RD, Rücker, G, Steinhauser, S, Schumacher, M, Snell, K, Willis, B, Debray, T, Deeks, J, Di Ruffano, LF, Taylor-Phillips, S, Hyde, C, Taylor, SA, Batnagar, G, STREAMLINE COLON Investigators, STREAMLINE LUNG Investigators, METRIC Investigators, Seedat, F, Clarke, A, Byron, S, Nixon, F, Albrow, R, Walker, T, Deakin, C, Zhelev, Z, Hunt, H, Yang, Y, Abel, L, Buchanan, J, Fanshawe, T, Shinkins, B, Wynants, L, Verbakel, J, Van Huffel, S, Timmerman, D, Van Calster, B, Zwinderman, A, Oke, J, O'Sullivan, J, Perera, R, Nicholson, B, Bromley, HL, Roberts, TE, Francis, A, Petrie, D, Mann, GB, Malottki, K, Smith, H, Billingham, L, Sitch, A, Gerke, O, Holm-Vilstrup, M, Segtnan, EA, Halekoh, U, Høilund-Carlsen, PF, Francq, BG, Dinnes, J, Parkes, J, Gregory, W, Hewison, J, Altman, D, Rosenberg, W, Selby, P, Asselineau, J, Perez, P, Paye, A, Bessede, E, Proust-Lima, C, Naaktgeboren, C, De Groot, J, Rutjes, A, Reitsma, J, Ogundimu, E, Cook, J, Le Manach, Y, Vergouwe, Y, Pajouheshnia, R, Groenwold, R, Peelen, L, Nieboer, D, De Cock, B, Pencina, MJ, Steyerberg, EW, Cooper, J, Parsons, N, Stinton, C, Smith, S, Dickens, A, Jordan, R, Enocson, A, Fitzmaurice, D, Adab, P, Boachie, C, Vidmar, G, Freeman, K, Connock, M, Court, R, Moons, C, Harris, J, Mumford, A, Plummer, Z, Lee, K, Reeves, B, Rogers, C, Verheyden, V, Angelini, GD, Murphy, GJ, Huddy, J, Ni, M, Good, K, Cooke, G, Hanna, G, Ma, J, Moons, KGMC, De Groot, JAH, Altman, DG, Reitsma, JB, Collins, GS, Moons, KGM, Kamarudin, AN, Kolamunnage-Dona, R, Cox, T, Borsci, S, Pérez, T, Pardo, MC, Candela-Toha, A, Muriel, A, Zamora, J, Sanghera, S, Mohiuddin, S, Martin, R, Donovan, J, Coast, J, Seo, MK, Cairns, J, Mitchell, E, Smith, A, Wright, J, Hall, P, Messenger, M, Calder, N, Wickramasekera, N, Vinall-Collier, K, Lewington, A, Damen, J, Cairns, D, Hutchinson, M, Sturgeon, C, Mitchel, L, Kift, R, Christakoudi, S, Rungall, M, Mobillo, P, Montero, R, Tsui, T-L, Kon, SP, Tucker, B, Sacks, S, Farmer, C, Strom, T, Chowdhury, P, Rebollo-Mesa, I, Hernandez-Fuentes, M, Damen, JAAG, Debray, TPA, Heus, P, Hooft, L, Scholten, RJPM, Schuit, E, Tzoulaki, I, Lassale, CM, Siontis, GCM, Chiocchia, V, Roberts, C, Schlüssel, MM, Gerry, S, Black, JA, Van der Schouw, YT, Peelen, LM, Spence, G, McCartney, D, Van den Bruel, A, Lasserson, D, Hayward, G, Vach, W, De Jong, A, Burggraaff, C, Hoekstra, O, Zijlstra, J, De Vet, H, Graziadio, S, Allen, J, Johnston, L, O'Leary, R, Power, M, Johnson, L, Waters, R, Simpson, J, Fanshawe, TR, Phillips, P, Plumb, A, Helbren, E, Halligan, S, Gale, A, Sekula, P, Sauerbrei, W, Forman, JR, Dutton, SJ, Takwoingi, Y, Hensor, EM, Nichols, TE, Kempf, E, Porcher, R, De Beyer, J, Hopewell, S, Dennis, J, Shields, B, Jones, A, Henley, W, Pearson, E, Hattersley, A, MASTERMIND consortium, Scheibler, F, Rummer, A, Sturtz, S, Großelfinger, R, Banister, K, Ramsay, C, Azuara-Blanco, A, Burr, J, Kumarasamy, M, Bourne, R, Uchegbu, I, Murphy, J, Carter, A, Marti, J, Eatock, J, Robotham, J, Dudareva, M, Gilchrist, M, Holmes, A, Monaghan, P, Lord, S, StJohn, A, Sandberg, S, Cobbaert, C, Lennartz, L, Verhagen-Kamerbeek, W, Ebert, C, Horvath, A, Test Evaluation Working Group of the European Federation of Clinical Chemistry and Laboratory Medicine, Jenniskens, K, Peters, J, Grigore, B, Ukoumunne, O, Levis, B, Benedetti, A, Levis, AW, Ioannidis, JPA, Shrier, I, Cuijpers, P, Gilbody, S, Kloda, LA, McMillan, D, Patten, SB, Steele, RJ, Ziegelstein, RC, Bombardier, CH, Osório, FDL, Fann, JR, Gjerdingen, D, Lamers, F, Lotrakul, M, Loureiro, SR, Löwe, B, Shaaban, J, Stafford, L, Van Weert, HCPM, Whooley, MA, Williams, LS, Wittkampf, KA, Yeung, AS, Thombs, BD, Cooper, C, Nieto, T, Smith, C, Tucker, O, Dretzke, J, Beggs, A, Rai, N, Bayliss, S, Stevens, S, Mallet, S, Sundar, S, Hall, E, Porta, N, Estelles, DL, De Bono, J, CTC-STOP protocol development group, and National Institute for Health Research

Subjects
medicine.medical_specialty, Astrophysics::High Energy Astrophysical Phenomena, MEDLINE, 030204 cardiovascular system & hematology, BTC (Bristol Trials Centre), MASTERMIND consortium, 03 medical and health sciences, 0302 clinical medicine, medicine, 030212 general & internal medicine, Intensive care medicine, CTC-STOP protocol development group, lcsh:R5-920, business.industry, Test Evaluation Working Group of the European Federation of Clinical Chemistry and Laboratory Medicine, Published Erratum, STREAMLINE COLON Investigators, 3. Good health, STREAMLINE LUNG Investigators, Centre for Surgical Research, Family medicine, METRIC Investigators, High Energy Physics::Experiment, Erratum, business, lcsh:Medicine (General)
Abstract

[This corrects the article DOI: 10.1186/s41512-016-0001-y.].

Published
2017

12. Erratum to: Methods for evaluating medical tests and biomarkers.

Author

Gopalakrishna, G, Langendam, M, Scholten, R, Bossuyt, P, Leeflang, M, Noel-Storr, A, Thomas, J, Marshall, I, Wallace, B, Whiting, P, Davenport, C, GopalaKrishna, G, de Salis, I, Mallett, S, Wolff, R, Riley, R, Westwood, M, Kleinen, J, Collins, G, Reitsma, H, Moons, K, Zapf, A, Hoyer, A, Kramer, K, Kuss, O, Ensor, J, Deeks, JJ, Martin, EC, Riley, RD, Rücker, G, Steinhauser, S, Schumacher, M, Snell, K, Willis, B, Debray, T, Deeks, J, di Ruffano, LF, Taylor-Phillips, S, Hyde, C, Taylor, SA, Batnagar, G, STREAMLINE COLON Investigators, STREAMLINE LUNG Investigators, METRIC Investigators, Di Ruffano, LF, Seedat, F, Clarke, A, Byron, S, Nixon, F, Albrow, R, Walker, T, Deakin, C, Zhelev, Z, Hunt, H, Yang, Y, Abel, L, Buchanan, J, Fanshawe, T, Shinkins, B, Wynants, L, Verbakel, J, Van Huffel, S, Timmerman, D, Van Calster, B, Zwinderman, A, Oke, J, O'Sullivan, J, Perera, R, Nicholson, B, Bromley, HL, Roberts, TE, Francis, A, Petrie, D, Mann, GB, Malottki, K, Smith, H, Billingham, L, Sitch, A, Gerke, O, Holm-Vilstrup, M, Segtnan, EA, Halekoh, U, Høilund-Carlsen, PF, Francq, BG, Dinnes, J, Parkes, J, Gregory, W, Hewison, J, Altman, D, Rosenberg, W, Selby, P, Asselineau, J, Perez, P, Paye, A, Bessede, E, Proust-Lima, C, Naaktgeboren, C, de Groot, J, Rutjes, A, Reitsma, J, Ogundimu, E, Cook, J, Le Manach, Y, Vergouwe, Y, Pajouheshnia, R, Groenwold, R, Peelen, L, Nieboer, D, De Cock, B, Pencina, MJ, Steyerberg, EW, Cooper, J, Parsons, N, Stinton, C, Smith, S, Dickens, A, Jordan, R, Enocson, A, Fitzmaurice, D, Adab, P, Boachie, C, Vidmar, G, Freeman, K, Connock, M, Court, R, Moons, C, Harris, J, Mumford, A, Plummer, Z, Lee, K, Reeves, B, Rogers, C, Verheyden, V, Angelini, GD, Murphy, GJ, Huddy, J, Ni, M, Good, K, Cooke, G, Hanna, G, Ma, J, Moons, KGMC, de Groot, JAH, Altman, DG, Reitsma, JB, Collins, GS, Moons, KGM, Kamarudin, AN, Kolamunnage-Dona, R, Cox, T, Borsci, S, Pérez, T, Pardo, MC, Candela-Toha, A, Muriel, A, Zamora, J, Sanghera, S, Mohiuddin, S, Martin, R, Donovan, J, Coast, J, Seo, MK, Cairns, J, Mitchell, E, Smith, A, Wright, J, Hall, P, Messenger, M, Calder, N, Wickramasekera, N, Vinall-Collier, K, Lewington, A, Damen, J, Cairns, D, Hutchinson, M, Sturgeon, C, Mitchel, L, Kift, R, Christakoudi, S, Rungall, M, Mobillo, P, Montero, R, Tsui, T-L, Kon, SP, Tucker, B, Sacks, S, Farmer, C, Strom, T, Chowdhury, P, Rebollo-Mesa, I, Hernandez-Fuentes, M, Damen, JAAG, Debray, TPA, Heus, P, Hooft, L, Scholten, RJPM, Schuit, E, Tzoulaki, I, Lassale, CM, Siontis, GCM, Chiocchia, V, Roberts, C, Schlüssel, MM, Gerry, S, Black, JA, van der Schouw, YT, Peelen, LM, Spence, G, McCartney, D, van den Bruel, A, Lasserson, D, Hayward, G, Vach, W, de Jong, A, Burggraaff, C, Hoekstra, O, Zijlstra, J, de Vet, H, Graziadio, S, Allen, J, Johnston, L, O'Leary, R, Power, M, Johnson, L, Waters, R, Simpson, J, Fanshawe, TR, Phillips, P, Plumb, A, Helbren, E, Halligan, S, Gale, A, Sekula, P, Sauerbrei, W, Forman, JR, Dutton, SJ, Takwoingi, Y, Hensor, EM, Nichols, TE, Kempf, E, Porcher, R, de Beyer, J, Hopewell, S, Dennis, J, Shields, B, Jones, A, Henley, W, Pearson, E, Hattersley, A, MASTERMIND consortium, Scheibler, F, Rummer, A, Sturtz, S, Großelfinger, R, Banister, K, Ramsay, C, Azuara-Blanco, A, Burr, J, Kumarasamy, M, Bourne, R, Uchegbu, I, Murphy, J, Carter, A, Marti, J, Eatock, J, Robotham, J, Dudareva, M, Gilchrist, M, Holmes, A, Monaghan, P, Lord, S, StJohn, A, Sandberg, S, Cobbaert, C, Lennartz, L, Verhagen-Kamerbeek, W, Ebert, C, Horvath, A, Test Evaluation Working Group of the European Federation of Clinical Chemistry and Laboratory Medicine, Jenniskens, K, Peters, J, Grigore, B, Ukoumunne, O, Levis, B, Benedetti, A, Levis, AW, Ioannidis, JPA, Shrier, I, Cuijpers, P, Gilbody, S, Kloda, LA, McMillan, D, Patten, SB, Steele, RJ, Ziegelstein, RC, Bombardier, CH, Osório, FDL, Fann, JR, Gjerdingen, D, Lamers, F, Lotrakul, M, Loureiro, SR, Löwe, B, Shaaban, J, Stafford, L, van Weert, HCPM, Whooley, MA, Williams, LS, Wittkampf, KA, Yeung, AS, Thombs, BD, Cooper, C, Nieto, T, Smith, C, Tucker, O, Dretzke, J, Beggs, A, Rai, N, Bayliss, S, Stevens, S, Mallet, S, Sundar, S, Hall, E, Porta, N, Estelles, DL, de Bono, J, CTC-STOP protocol development group, Gopalakrishna, G, Langendam, M, Scholten, R, Bossuyt, P, Leeflang, M, Noel-Storr, A, Thomas, J, Marshall, I, Wallace, B, Whiting, P, Davenport, C, GopalaKrishna, G, de Salis, I, Mallett, S, Wolff, R, Riley, R, Westwood, M, Kleinen, J, Collins, G, Reitsma, H, Moons, K, Zapf, A, Hoyer, A, Kramer, K, Kuss, O, Ensor, J, Deeks, JJ, Martin, EC, Riley, RD, Rücker, G, Steinhauser, S, Schumacher, M, Snell, K, Willis, B, Debray, T, Deeks, J, di Ruffano, LF, Taylor-Phillips, S, Hyde, C, Taylor, SA, Batnagar, G, STREAMLINE COLON Investigators, STREAMLINE LUNG Investigators, METRIC Investigators, Di Ruffano, LF, Seedat, F, Clarke, A, Byron, S, Nixon, F, Albrow, R, Walker, T, Deakin, C, Zhelev, Z, Hunt, H, Yang, Y, Abel, L, Buchanan, J, Fanshawe, T, Shinkins, B, Wynants, L, Verbakel, J, Van Huffel, S, Timmerman, D, Van Calster, B, Zwinderman, A, Oke, J, O'Sullivan, J, Perera, R, Nicholson, B, Bromley, HL, Roberts, TE, Francis, A, Petrie, D, Mann, GB, Malottki, K, Smith, H, Billingham, L, Sitch, A, Gerke, O, Holm-Vilstrup, M, Segtnan, EA, Halekoh, U, Høilund-Carlsen, PF, Francq, BG, Dinnes, J, Parkes, J, Gregory, W, Hewison, J, Altman, D, Rosenberg, W, Selby, P, Asselineau, J, Perez, P, Paye, A, Bessede, E, Proust-Lima, C, Naaktgeboren, C, de Groot, J, Rutjes, A, Reitsma, J, Ogundimu, E, Cook, J, Le Manach, Y, Vergouwe, Y, Pajouheshnia, R, Groenwold, R, Peelen, L, Nieboer, D, De Cock, B, Pencina, MJ, Steyerberg, EW, Cooper, J, Parsons, N, Stinton, C, Smith, S, Dickens, A, Jordan, R, Enocson, A, Fitzmaurice, D, Adab, P, Boachie, C, Vidmar, G, Freeman, K, Connock, M, Court, R, Moons, C, Harris, J, Mumford, A, Plummer, Z, Lee, K, Reeves, B, Rogers, C, Verheyden, V, Angelini, GD, Murphy, GJ, Huddy, J, Ni, M, Good, K, Cooke, G, Hanna, G, Ma, J, Moons, KGMC, de Groot, JAH, Altman, DG, Reitsma, JB, Collins, GS, Moons, KGM, Kamarudin, AN, Kolamunnage-Dona, R, Cox, T, Borsci, S, Pérez, T, Pardo, MC, Candela-Toha, A, Muriel, A, Zamora, J, Sanghera, S, Mohiuddin, S, Martin, R, Donovan, J, Coast, J, Seo, MK, Cairns, J, Mitchell, E, Smith, A, Wright, J, Hall, P, Messenger, M, Calder, N, Wickramasekera, N, Vinall-Collier, K, Lewington, A, Damen, J, Cairns, D, Hutchinson, M, Sturgeon, C, Mitchel, L, Kift, R, Christakoudi, S, Rungall, M, Mobillo, P, Montero, R, Tsui, T-L, Kon, SP, Tucker, B, Sacks, S, Farmer, C, Strom, T, Chowdhury, P, Rebollo-Mesa, I, Hernandez-Fuentes, M, Damen, JAAG, Debray, TPA, Heus, P, Hooft, L, Scholten, RJPM, Schuit, E, Tzoulaki, I, Lassale, CM, Siontis, GCM, Chiocchia, V, Roberts, C, Schlüssel, MM, Gerry, S, Black, JA, van der Schouw, YT, Peelen, LM, Spence, G, McCartney, D, van den Bruel, A, Lasserson, D, Hayward, G, Vach, W, de Jong, A, Burggraaff, C, Hoekstra, O, Zijlstra, J, de Vet, H, Graziadio, S, Allen, J, Johnston, L, O'Leary, R, Power, M, Johnson, L, Waters, R, Simpson, J, Fanshawe, TR, Phillips, P, Plumb, A, Helbren, E, Halligan, S, Gale, A, Sekula, P, Sauerbrei, W, Forman, JR, Dutton, SJ, Takwoingi, Y, Hensor, EM, Nichols, TE, Kempf, E, Porcher, R, de Beyer, J, Hopewell, S, Dennis, J, Shields, B, Jones, A, Henley, W, Pearson, E, Hattersley, A, MASTERMIND consortium, Scheibler, F, Rummer, A, Sturtz, S, Großelfinger, R, Banister, K, Ramsay, C, Azuara-Blanco, A, Burr, J, Kumarasamy, M, Bourne, R, Uchegbu, I, Murphy, J, Carter, A, Marti, J, Eatock, J, Robotham, J, Dudareva, M, Gilchrist, M, Holmes, A, Monaghan, P, Lord, S, StJohn, A, Sandberg, S, Cobbaert, C, Lennartz, L, Verhagen-Kamerbeek, W, Ebert, C, Horvath, A, Test Evaluation Working Group of the European Federation of Clinical Chemistry and Laboratory Medicine, Jenniskens, K, Peters, J, Grigore, B, Ukoumunne, O, Levis, B, Benedetti, A, Levis, AW, Ioannidis, JPA, Shrier, I, Cuijpers, P, Gilbody, S, Kloda, LA, McMillan, D, Patten, SB, Steele, RJ, Ziegelstein, RC, Bombardier, CH, Osório, FDL, Fann, JR, Gjerdingen, D, Lamers, F, Lotrakul, M, Loureiro, SR, Löwe, B, Shaaban, J, Stafford, L, van Weert, HCPM, Whooley, MA, Williams, LS, Wittkampf, KA, Yeung, AS, Thombs, BD, Cooper, C, Nieto, T, Smith, C, Tucker, O, Dretzke, J, Beggs, A, Rai, N, Bayliss, S, Stevens, S, Mallet, S, Sundar, S, Hall, E, Porta, N, Estelles, DL, de Bono, J, and CTC-STOP protocol development group

Abstract

[This corrects the article DOI: 10.1186/s41512-016-0001-y.].

Published
2017

17. Routine screening for depression in patients with coronary heart disease never mind.

Author

Ziegelstein RC, Thombs BD, Coyne JC, de Jonge P, Ziegelstein, Roy C, Thombs, Brett D, Coyne, James C, and de Jonge, Peter

Abstract

A recent Science Advisory from the American Heart Association (AHA) recommended routine screening of all patients with coronary heart disease (CHD) for depression. The authors of the advisory noted that the high prevalence of depression in patients with CHD supports this strategy. A systematic review of the evidence on depression screening and treatment in CHD patients published soon after the AHA advisory found that screening tools for major depression are reasonably accurate among patients with CHD, but that the majority of patients who screen positive will not have major depression; that depression treatment in CHD patients only accounts for a small amount of variance in depression symptom change scores; and that there is no evidence that screening for depression improves CHD outcomes. We call for the AHA to reassess their recommendations in light of this systematic review and considering the potential impact of their document on clinical practice. [ABSTRACT FROM AUTHOR]

Published
2009
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19. Depression screening and patient outcomes in cardiovascular care: a systematic review.

Author

Thombs BD, de Jonge P, Coyne JC, Whooley MA, Frasure-Smith N, Mitchell AJ, Zuidersma M, Eze-Nliam C, Lima BB, Smith CG, Soderlund K, Ziegelstein RC, Thombs, Brett D, de Jonge, Peter, Coyne, James C, Whooley, Mary A, Frasure-Smith, Nancy, Mitchell, Alex J, Zuidersma, Marij, and Eze-Nliam, Chete

Abstract

Context: Several practice guidelines recommend that depression be evaluated and treated in patients with cardiovascular disease, but the potential benefits of this are unclear. Objective: To evaluate the potential benefits of depression screening in patients with cardiovascular disease by assessing (1) the accuracy of depression screening instruments; (2) the effect of depression treatment on depression and cardiac outcomes; and (3) the effect of screening on depression and cardiac outcomes in patients in cardiovascular care settings. Data Sources: MEDLINE, PsycINFO, CINAHL, EMBASE, ISI, SCOPUS, and Cochrane databases from inception to May 1, 2008; manual journal searches; reference list reviews; and citation tracking of included articles. Study Selection: We included articles in any language about patients in cardiovascular care settings that (1) compared a screening instrument to a valid major depressive disorder criterion standard; (2) compared depression treatment with placebo or usual care in a randomized controlled trial; or (3) assessed the effect of screening on depression identification and treatment rates, depression, or cardiac outcomes. Data Extraction: Methodological characteristics and outcomes were extracted by 2 investigators. Results: We identified 11 studies about screening accuracy, 6 depression treatment trials, but no studies that evaluated the effects of screening on depression or cardiovascular outcomes. In studies that tested depression screening instruments using a priori-defined cutoff scores, sensitivity ranged from 39% to 100% (median, 84%) and specificity ranged from 58% to 94% (median, 79%). Depression treatment with medication or cognitive behavioral therapy resulted in modest reductions in depressive symptoms (effect size, 0.20-0.38; r(2), 1%-4%). There was no evidence that depression treatment improved cardiac outcomes. Among patients with depression and history of myocardial infarction in the ENRICHD trial, there was no difference in event-free survival between participants treated with cognitive behavioral therapy supplemented by an antidepressant vs usual care (75.5% vs 74.7%, respectively). Conclusions: Depression treatment with medication or cognitive behavioral therapy in patients with cardiovascular disease is associated with modest improvement in depressive symptoms but no improvement in cardiac outcomes. No clinical trials have assessed whether screening for depression improves depressive symptoms or cardiac outcomes in patients with cardiovascular disease. [ABSTRACT FROM AUTHOR]

Published
2008
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23. Even minimal symptoms of depression increase mortality risk after acute myocardial infarction.

Author

Bush DE, Ziegelstein RC, Tayback M, Richter D, Stevens S, Zahalsky H, Fauerbach JA, Bush, D E, Ziegelstein, R C, Tayback, M, Richter, D, Stevens, S, Zahalsky, H, and Fauerbach, J A

Abstract

Mild to moderate levels of depressive symptoms as characterized by Beck Depression Inventory (BDI) scores of > or =10 are associated with decreased survival after acute myocardial infarction (AMI). We investigated whether lower levels of depressive symptoms are also associated with increased mortality risk after AMI. We prospectively studied 285 patients with AMI who survived to discharge for evidence, at the time of hospitalization, of a DSM-IIIR mood disorder (using a structured clinical interview) and for symptoms of depression (using the BDI). The overall mortality rate at 4 months was 6.7%. Multiple logistic regression (chi-square 35.79, p < or =0.001) revealed that the independent predictors of mortality were: age > or =65 years, left ventricular ejection fraction <35%, diabetes mellitus, and any depression (DSM-IIIR mood disorder or BDI > or =10) present at the time of AMI. Among patients > or =65 years old with left ventricular ejection fraction <35%, the 4-month mortality was 12%. However, in this same group, those with any depression at the time of AMI had a 4-month mortality of 50% (relative risk 4.1, p = 0.01). Among patients aged > or =65 years, the mortality according to BDI scale grouping 0 to 3, 4 to 9, and 10+ was 2.6%, 17.1%, and 23.3%, respectively (p <0.002). Highest mortality rates were observed in patients with most severe depressive symptoms. However, compared with those without depression, higher mortality was also observed at very low levels of depressive symptoms (BDI 4 to 9) not generally considered clinically significant and below the level usually considered predictive of increased post-AMI mortality. [ABSTRACT FROM AUTHOR]

Published
2001
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42. Comparison of Patient Health Questionnaire-9, Edinburgh Postnatal Depression Scale and Hospital Anxiety and Depression - Depression subscale scores by administration mode: An individual participant data differential item functioning meta-analysis.

Author

Harel D, Wu Y, Levis B, Fan S, Sun Y, Xu M, Rice DB, Boruff J, Markham S, Ioannidis JPA, Takwoingi Y, Patten SB, Ziegelstein RC, Cuijpers P, Gilbody S, Vigod S, Akena D, Benedetti A, and Thombs BD

Subjects
Humans, Female, Patient Reported Outcome Measures, Psychometrics, Depression diagnosis, Depression psychology, Adult, Depression, Postpartum diagnosis, Depression, Postpartum psychology, Surveys and Questionnaires standards, Patient Health Questionnaire, Psychiatric Status Rating Scales standards
Abstract

Administration mode of patient-reported outcome measures (PROMs) may influence responses. We assessed if Patient Health Questionnaire-9 (PHQ-9), Edinburgh Postnatal Depression Scale (EPDS) and Hospital Anxiety and Depression Scale - Depression subscale (HADS-D) item responses and scores were associated with administration mode. We compared (1) self-administration versus interview-administration; within self-administration (2) research or medical setting versus private; and (3) pen-and-paper versus electronic; and within interview-administration (4) in-person versus phone. We analysed individual participant data meta-analysis datasets with item-level data for the PHQ-9 (N = 34,529), EPDS (N = 16,813), and HADS-D (N = 16,768). We used multiple indicator multiple cause models to assess differential item functioning (DIF) by administration mode. We found statistically significant DIF for most items on all measures due to large samples, but influence on total scores was negligible. In 10 comparisons conducted across the PHQ-9, EPDS, and HADS-D, Pearson's correlations and intraclass correlation coefficients between latent depression symptom scores from models that did or did not account for DIF were between 0.995 and 1.000. Total PHQ-9, EPDS, and HADS-D scores did not differ materially across administration modes. Researcher and clinicians who evaluate depression symptoms with these questionnaires can select administration methods based on patient preferences, feasibility, or cost., Competing Interests: Declaration of competing interest All authors have completed the ICJME uniform disclosure form. Dr. Vigod declares that she receives royalties from UpToDate, outside the submitted work. Dr. Beck CT declares that she receives royalties for her Postpartum Depression Screening Scale published by Western Psychological Services. Dr. Bernstein declares that he has consulted to Abbvie Canada, Amgen Canada, Bristol Myers Squibb Canada, Eli Lilly, Ferring Canada, JAMP Pharmaceuticals, Janssen Canada, Pendopharm Canada, Pfizer Canada, Sandoz Canada, Takeda Canada. He has also received unrestricted educational grants from Abbvie Canada, Bristol Myers Squibb Canada, Eli Lilly, Ferring Canada, Organon Canada, Janssen Canada, Pfizer Canada, and Takeda Canada; as well as been on speaker's bureau of Abbvie Canada, Janssen Canada, Takeda Canada and Pfizer Canada, all outside the submitted work. Dr. Chan J CN is a steering committee member and/or consultant of Astra Zeneca, Bayer, Lilly, MSD and Pfizer. She has received sponsorships and honorarium for giving lectures and providing consultancy and her affiliated institution has received research grants from these companies. Dr. Chan LF declares personal fees and non-financial support from Otsuka, Lundbeck, and Johnson and Johnson; and non-financial support from Ortho-McNeil-Janssen, and Menarini, outside the submitted work. Dr. Hegerl declares that within the last three years, he was an advisory board member for Janssen and received a research grant from Medice, all outside the submitted work. Dr. Howard declares that she has received personal fees from NICE Scientific Advice, outside the submitted work. Dr. Inagaki declares that he has received personal fees from Meiji, Mochida, Takeda, Novartis, Yoshitomi, Pfizer, Eisai, Otsuka, MSD, Sumitomo Dainippon, Janssen, and Eli Lilly, all outside of the submitted work. Dr. Marrie declares that she is a co-investigator on a study funded in part by Biogen Idec and Roche (no funds to her), outside the submitted work. Dr. Pugh declares that she received salary support from Pfizer-Astella and Millennium, outside the submitted work. Dr. Sultan declares funding from Sanofi-Aventis Corporation, during conduct of the primary study. Dr. Sundström-Poromaa declares that she has served on advisory boards and acted as invited speaker at scientific meetings for MSD, Novo Nordisk, Bayer Health Care, and Lundbeck A/S. Dr. Lynne Wagner declares that she receives personal fees from Celgene, outside the submitted work. Dr. Yonkers declares that she receives royalties from UpTodate, outside the submitted work. All other authors declare no support from any organization for the submitted work; no financial relationships with any organizations that might have an interest in the submitted work in the previous 3 years; and no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Published
2024
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43. Disparities in Medical School Clerkship Grades Associated With Sex, Race, and Ethnicity: A Person-Centered Approach.

Author

Perez Mejias P, Lara G, Duran A, Musci R, Hueppchen NA, Ziegelstein RC, and Lipsett PA

Subjects
Humans, Male, Female, Students, Medical statistics & numerical data, Sex Factors, Schools, Medical statistics & numerical data, United States, Logistic Models, Adult, Clinical Clerkship statistics & numerical data, Ethnicity statistics & numerical data, Racial Groups statistics & numerical data, Educational Measurement statistics & numerical data, Educational Measurement methods
Abstract

Purpose: To determine whether students' self-reported race/ethnicity and sex were associated with grades earned in 7 core clerkships. A person-centered approach was used to group students based on observed clerkship grade patterns. Predictors of group membership and predictive bias by race/ethnicity and sex were investigated., Method: Using data from 6 medical student cohorts at Johns Hopkins University School of Medicine (JHUSOM), latent class analysis was used to classify students based on clerkship grades. Multinomial logistic regression was employed to investigate if preclerkship measures and student demographic characteristics predicted clerkship performance-level groups. Marginal effects for United States Medical Licensing Exam (USMLE) Step 1 scores were obtained to assess the predictive validity of the test on group membership by race/ethnicity and sex. Predictive bias was examined by comparing multinomial logistic regression prediction errors across racial/ethnic groups., Results: Three clerkship performance-level groups emerged from the data: low, middle, and high. Significant predictors of group membership were race/ethnicity, sex, and USMLE Step 1 scores. Black or African American students were more likely (odds ratio [OR] = 4.26) to be low performers than White students. Black or African American (OR = 0.08) and Asian students (OR = 0.41) were less likely to be high performers than White students. Female students (OR = 2.51) were more likely to be high performers than male students. Patterns of prediction errors observed across racial/ethnic groups showed predictive bias when using USMLE Step 1 scores to predict clerkship performance-level groups., Conclusions: Disparities in clerkship grades associated with race/ethnicity were found among JHUSOM students, which persisted after controlling for USMLE Step 1 scores, sex, and other preclerkship performance measures. Differential predictive validity of USMLE Step 1 exam scores and systematic error predictions by race/ethnicity show predictive bias when using USMLE Step 1 scores to predict clerkship performance across racial/ethnic groups., (Copyright © 2024 the Association of American Medical Colleges.)

Published
2024
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44. Depression prevalence of the Geriatric Depression Scale-15 was compared to Structured Clinical Interview for DSM using individual participant data meta-analysis.

Author

Parsons M, Qiu L, Levis B, Fan S, Sun Y, Amiri LSN, Harel D, Markham S, Vigod SN, Ziegelstein RC, Wu Y, Boruff JT, Cuijpers P, Gilbody S, Patten SB, Benedetti A, and Thombs BD

Subjects
Humans, Prevalence, Aged, Female, Male, Geriatric Assessment methods, Aged, 80 and over, Depressive Disorder, Major epidemiology, Depressive Disorder, Major diagnosis, Depression epidemiology, Depression diagnosis, Diagnostic and Statistical Manual of Mental Disorders, Psychiatric Status Rating Scales
Abstract

Depression questionnaire cutoffs are calibrated for screening accuracy and not to assess prevalence, but the Geriatric Depression Scale (GDS-15) is often used to estimate diagnostic prevalence among older adults, most commonly with scores of ≥ 5. We conducted an individual participant data meta-analysis to compare depression prevalence based on GDS-15 ≥ 5 to Structured Clinical Interview for Diagnostic and Statistical Manual (SCID) diagnoses and assessed whether an alternative cutoff could be more accurate. We used generalized linear mixed models to estimate prevalence. Data from 14 studies (3602 participants, 434 SCID major depression) were included. Pooled GDS-15 ≥ 5 prevalence was 34.2% (95% confidence interval [CI] 27.5-41.6%), and pooled SCID prevalence was 14.8% (95% CI 10.0-21.5%; difference of 17.6%, 95% CI 11.6-23.6%). GDS-15 ≥ 8 provided the closest estimate to SCID with mean difference of - 0.3% (95% prediction interval - 17.0-16.5%). Prevalence estimate differences were not associated with study or participant characteristics. In sum, GDS-15 ≥ 5 substantially overestimated depression prevalence. A cutoff of ≥ 8 was accurate overall, but heterogeneity was too high for implementation in practice. Validated diagnostic interviews should be used to estimate major depression prevalence among older adults., (© 2024. The Author(s).)

Published
2024
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45. Individual Patient Data Meta-Analysis Estimates the Minimal Detectable Change of the Geriatric Depression Scale-15.

Author

González-Domínguez NP, Wu Y, Fan S, Levis B, Sun Y, Gilbody S, Ioannidis JPA, Harel D, Vigod SN, Markham S, Ziegelstein RC, Cuijpers P, Patten SB, Boruff JT, Thombs BD, and Benedetti A

Abstract

Objective: To use individual participant data meta-analysis (IPDMA) to estimate the minimal detectable change (MDC) of the Geriatric Depression Scale-15 (GDS-15) and to examine whether MDC may differ based on participant characteristics and study-level variables., Study Design and Setting: This was a secondary analysis of data from an IPDMA on the depression screening accuracy of the GDS. Datasets from studies published in any language were eligible for the present study if they included GDS-15 scores for participants aged 60 or older. MDC of the GDS-15 was estimated via random-effects meta-analysis using 2.77 (MDC95) and 1.41 (MDC67) standard errors of measurement (SEM). Subgroup analyses were used to evaluate differences in MDC by participant age and sex. Meta-regression was conducted to assess for differences based on study-level variables, including mean age, proportion male, proportion with major depression, and recruitment setting., Results: 5,876 participants (mean age 76 years, 40% male, 11% with major depression) from 21 studies were included. The MDC95 was 3.81 points (95% confidence interval [CI] 3.59, 4.04), and MDC67 was 1.95 (95% CI 1.83, 2.03). The difference in MDC95 was 0.26 points (95% CI 0.04, 0.48) between ≥ 80-year-olds and < 80-year-olds; MDC95 was similar for females and males (0.05, 95% CI -0.12, 0.22). The MDC95 increased by 0.29 points (95% CI 0.17, 0.41) per 10% increase in proportion of participants with major depression; mean age had a small association (0.04 points, 95% CI 0.00 to 0.09) with MDC95, but sex and recruitment setting were not significantly associated., Conclusions: The MDC95 was 3.81 points and MDC67 was 1.95 points. MDC95 increased with the proportion of participants with major depression. Results can be used to evaluate individual changes in depression symptoms and as a threshold for assessing minimal clinical important difference estimates., (Copyright © 2024. Published by Elsevier Inc.)

Published
2024
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47. The Johns Hopkins Physician-Scientist Training Program to Enhance Institutional Retention and Entry Into Academic Positions: An Evaluation of Program Effectiveness and Outcomes.

Author

Rincon-Torroella J, Feghali J, Antar A, Azad TD, Rosen A, Ziegelstein RC, Rothman PB, Burns KH, Bienstock JL, Cox AL, and Bettegowda C

Abstract

Purpose: The Johns Hopkins Physician-Scientist Training Program (PSTP) was implemented to overcome well-documented challenges in training and retaining physician-scientists by providing physician-scientist pathway training for residents and clinical fellows. The program's core tenets include monthly seminars, individualized feedback on project proposals, access to mentors, and institutional funding opportunities. This study evaluated the effectiveness and outcomes of the PTSP and provides a framework for replication., Method: A query of institutional demographic data and bibliometric variables of the PSTP participants (2017-2020) at a single academic medical center was conducted in 2021. In addition, a voluntary survey collected personal and program evaluation information., Results: Of 145 PSTP scholars, 59 (41%) were women, and 41 (31%), 8 (6%), and 6 (5%) of scholars self-identified as Asian, Hispanic, and Black, respectively. Thirty-three (23%) scholars received PSTP research support or career development microgrants. Of 66 PSTP graduates, 29 (44%) remained at Johns Hopkins as clinical fellows or faculty. Of 48 PSTP graduates in a post-training position, 42 (88%) were in academia, with the majority, 29 (76%), holding the rank of assistant professor. Fifty-nine of 140 available participants responded to the survey (42% response rate). The top-cited reason for joining the PSTP was exposure to mentors and administration (50/58 respondents, 86%), followed by seeking scholarly opportunities (37/58 respondents, 64%). Most scholars intended to continue a career as a physician-scientist., Conclusions: The PSTP provides internal research support and institutional oversight. Although establishing close mentor-mentee relationships requires individualized approaches, the PSTP provided structured academic pathways that enhanced participating scholars' ability to apply for grants and jobs. The vast majority continued their careers as physician-scientists after training. In light of the national evidence of a "leaky physician-scientist pipeline," programs such as the PSTP can be critical to entry into early academic career positions and institutional retention., (Copyright © 2024 the Association of American Medical Colleges.)

Published
2024
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48. Medical School Rankings: Time to End or Time to Amend?

Author

Gabrielson AT and Ziegelstein RC

Subjects
Humans, Students, Research Report, Schools, Medical, Education, Medical
Abstract

Abstract: The recent decisions of several medical schools to no longer participate in the rankings published annually by U.S. News & World Report have added greater visibility to the issues surrounding medical school rankings than ever before. While these announcements garnered significant attention in academic medicine and from the lay public, the authors believe these events are unlikely to eliminate rankings in medical education. In this article, the authors outline the potential harms of rankings in higher education, provide a rationale for why they will likely endure despite recent scrutiny, and offer suggestions for prospective students and the academic medical community to engage with rankings in a more productive way. Criticisms of medical school rankings have been noted for decades, including their use of subjective information and data with questionable relevance to the quality of students' education or the outcomes of their educational experience, methodological concerns, and the potential harms of ranking systems (such as schools diverting their focus and resources away from initiatives that benefit students to improve their ranking). At the same time, rankings are ubiquitous in today's culture and are powerful in human decision-making, so there is reason to believe that medical school rankings may weather the current storm. Given these concerns, the authors suggest students continue to use a variety of resources to obtain information about medical schools and consider how each school fits with their own educational needs. The authors also encourage medical schools, medical education organizations, and governing medical bodies to suggest different metrics that reflect quality in medical education and that are of importance to applicants., (Copyright © 2023 the Association of American Medical Colleges.)

Published
2024
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49. Depression Screening Tool Accuracy Individual Participant Data Meta-analyses: Data Contribution was Associated with Multiple Factors.

Author

Wu Y, Sun Y, Liu Y, Levis B, Krishnan A, He C, Neupane D, Patten SB, Cuijpers P, Ziegelstein RC, Benedetti A, and Thombs BD

Abstract

Objective: To examine the proportion of eligible primary studies that contributed data, study characteristics associated with data contribution, and reasons for non-contribution using diagnostic test accuracy individual participant data meta-analysis (IPDMA) datasets from the DEPRESsion Screening Data project., Study Design and Setting: We reviewed dataset contributions from four IPDMAs. A multivariable logistic regression model was fitted to evaluate study factors associated with data contribution., Results: Of 456 eligible studies from four included IPDMAs, 295 (65%) contributed data. More recent year of publication and higher journal impact factor were associated with greater odds of data contribution. Studies conducted in Europe (excluding the United Kingdom), Oceania, Canada, the Middle East, Africa, Central or South America (reference=the United States), recruited in inpatient care or non-medical settings (reference=outpatient), reported screening accuracy results, or drew negative conclusions (reference=positive conclusions) were more likely to contribute data. Studies of the Geriatric Depression Scale (reference=the Patient Health Questionnaire) or lacking funding information were negatively associated with data contribution. Over 80% of non-contributions were due to authors being unreachable or data being unavailable., Conclusion: The study identified factors associated with data contribution that may support future research to promote data contribution to IPDMAs., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Published
2023
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