Depression prevalence using the HADS-D compared to SCID major depression classification: An individual participant data meta-analysis
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 – de...
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Gespeichert in:
| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of psychosomatic research - Amsterdam [u.a.] : Elsevier Science, 1956, 139 |
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| Übergeordnetes Werk: |
volume:139 |
| DOI / URN: |
10.1016/j.jpsychores.2020.110256 |
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ELV005121361 |
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| 024 | 7 | |a 10.1016/j.jpsychores.2020.110256 |2 doi | |
| 035 | |a (DE-627)ELV005121361 | ||
| 035 | |a (ELSEVIER)S0022-3999(20)30818-7 | ||
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| 100 | 1 | |a Brehaut, Eliana |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Depression prevalence using the HADS-D compared to SCID major depression classification: An individual participant data meta-analysis |
| 264 | 1 | |c 2020 | |
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a 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. | ||
| 650 | 4 | |a Depression | |
| 650 | 4 | |a Hospital Anxiety and Depression Scale | |
| 650 | 4 | |a Individual participant data | |
| 650 | 4 | |a Meta-analysis | |
| 650 | 4 | |a Screening tools | |
| 700 | 1 | |a Neupane, Dipika |e verfasserin |4 aut | |
| 700 | 1 | |a Levis, Brooke |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Yin |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Ying |e verfasserin |4 aut | |
| 700 | 1 | |a Krishnan, Ankur |e verfasserin |4 aut | |
| 700 | 1 | |a He, Chen |e verfasserin |4 aut | |
| 700 | 1 | |a Bhandari, Parash Mani |e verfasserin |4 aut | |
| 700 | 1 | |a Negeri, Zelalem |e verfasserin |4 aut | |
| 700 | 1 | |a Riehm, Kira E. |e verfasserin |4 aut | |
| 700 | 1 | |a Rice, Danielle B. |e verfasserin |4 aut | |
| 700 | 1 | |a Azar, Marleine |e verfasserin |4 aut | |
| 700 | 1 | |a Yan, Xin Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Imran, Mahrukh |e verfasserin |4 aut | |
| 700 | 1 | |a Chiovitti, Matthew J. |e verfasserin |4 aut | |
| 700 | 1 | |a Saadat, Nazanin |e verfasserin |4 aut | |
| 700 | 1 | |a Cuijpers, Pim |e verfasserin |4 aut | |
| 700 | 1 | |a Ioannidis, John P.A. |e verfasserin |4 aut | |
| 700 | 1 | |a Markham, Sarah |e verfasserin |4 aut | |
| 700 | 1 | |a Patten, Scott B. |e verfasserin |4 aut | |
| 700 | 1 | |a Ziegelstein, Roy C. |e verfasserin |4 aut | |
| 700 | 1 | |a Henry, Melissa |e verfasserin |4 aut | |
| 700 | 1 | |a Ismail, Zahinoor |e verfasserin |4 aut | |
| 700 | 1 | |a Loiselle, Carmen G. |e verfasserin |4 aut | |
| 700 | 1 | |a Mitchell, Nicholas D. |e verfasserin |4 aut | |
| 700 | 1 | |a Tonelli, Marcello |e verfasserin |4 aut | |
| 700 | 1 | |a Boruff, Jill T. |e verfasserin |4 aut | |
| 700 | 1 | |a Kloda, Lorie A. |e verfasserin |4 aut | |
| 700 | 1 | |a Beraldi, Anna |e verfasserin |4 aut | |
| 700 | 1 | |a Braeken, Anna P.B.M. |e verfasserin |4 aut | |
| 700 | 1 | |a Carter, Gregory |e verfasserin |4 aut | |
| 700 | 1 | |a Clover, Kerrie |e verfasserin |4 aut | |
| 700 | 1 | |a Conroy, Ronán M. |e verfasserin |4 aut | |
| 700 | 1 | |a Cukor, Daniel |e verfasserin |4 aut | |
| 700 | 1 | |a da Rocha e Silva, Carlos E. |e verfasserin |4 aut | |
| 700 | 1 | |a De Souza, Jennifer |e verfasserin |4 aut | |
| 700 | 1 | |a Downing, Marina G. |e verfasserin |4 aut | |
| 700 | 1 | |a Feinstein, Anthony |e verfasserin |4 aut | |
| 700 | 1 | |a Ferentinos, Panagiotis P. |e verfasserin |4 aut | |
| 700 | 1 | |a Fischer, Felix H. |e verfasserin |4 aut | |
| 700 | 1 | |a Flint, Alastair J. |e verfasserin |4 aut | |
| 700 | 1 | |a Fujimori, Maiko |e verfasserin |4 aut | |
| 700 | 1 | |a Gallagher, Pamela |e verfasserin |4 aut | |
| 700 | 1 | |a Goebel, Simone |e verfasserin |4 aut | |
| 700 | 1 | |a Jetté, Nathalie |e verfasserin |4 aut | |
| 700 | 1 | |a Julião, Miguel |e verfasserin |4 aut | |
| 700 | 1 | |a Keller, Monika |e verfasserin |4 aut | |
| 700 | 1 | |a Kjærgaard, Marie |e verfasserin |4 aut | |
| 700 | 1 | |a Love, Anthony W. |e verfasserin |4 aut | |
| 700 | 1 | |a Löwe, Bernd |e verfasserin |4 aut | |
| 700 | 1 | |a Martin-Santos, Rocio |e verfasserin |4 aut | |
| 700 | 1 | |a Michopoulos, Ioannis |e verfasserin |4 aut | |
| 700 | 1 | |a Navines, Ricard |e verfasserin |4 aut | |
| 700 | 1 | |a O’Rourke, Suzanne J. |e verfasserin |4 aut | |
| 700 | 1 | |a Öztürk, Ahmet |e verfasserin |4 aut | |
| 700 | 1 | |a Pintor, Luis |e verfasserin |4 aut | |
| 700 | 1 | |a Ponsford, Jennie L. |e verfasserin |4 aut | |
| 700 | 1 | |a Rooney, Alasdair G. |e verfasserin |4 aut | |
| 700 | 1 | |a Sánchez-González, Roberto |e verfasserin |4 aut | |
| 700 | 1 | |a Schwarzbold, Marcelo L. |e verfasserin |4 aut | |
| 700 | 1 | |a Sharpe, Michael |e verfasserin |4 aut | |
| 700 | 1 | |a Simard, Sébastien |e verfasserin |4 aut | |
| 700 | 1 | |a Singer, Susanne |e verfasserin |4 aut | |
| 700 | 1 | |a Stone, Jon |e verfasserin |4 aut | |
| 700 | 1 | |a Tung, Ka-Yee |e verfasserin |4 aut | |
| 700 | 1 | |a Turner, Alyna |e verfasserin |4 aut | |
| 700 | 1 | |a Walker, Jane |e verfasserin |4 aut | |
| 700 | 1 | |a Walterfang, Mark |e verfasserin |4 aut | |
| 700 | 1 | |a White, Jennifer |e verfasserin |4 aut | |
| 700 | 1 | |a Benedetti, Andrea |e verfasserin |4 aut | |
| 700 | 1 | |a Thombs, Brett D. |e verfasserin |4 aut | |
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10.1016/j.jpsychores.2020.110256 doi (DE-627)ELV005121361 (ELSEVIER)S0022-3999(20)30818-7 DE-627 ger DE-627 rda eng 610 DE-600 44.91 bkl 77.70 bkl 77.84 bkl Brehaut, Eliana verfasserin aut Depression prevalence using the HADS-D compared to SCID major depression classification: An individual participant data meta-analysis 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Depression Hospital Anxiety and Depression Scale Individual participant data Meta-analysis Screening tools Neupane, Dipika verfasserin aut Levis, Brooke verfasserin aut Wu, Yin verfasserin aut Sun, Ying verfasserin aut Krishnan, Ankur verfasserin aut He, Chen verfasserin aut Bhandari, Parash Mani verfasserin aut Negeri, Zelalem verfasserin aut Riehm, Kira E. verfasserin aut Rice, Danielle B. verfasserin aut Azar, Marleine verfasserin aut Yan, Xin Wei verfasserin aut Imran, Mahrukh verfasserin aut Chiovitti, Matthew J. verfasserin aut Saadat, Nazanin verfasserin aut Cuijpers, Pim verfasserin aut Ioannidis, John P.A. verfasserin aut Markham, Sarah verfasserin aut Patten, Scott B. verfasserin aut Ziegelstein, Roy C. verfasserin aut Henry, Melissa verfasserin aut Ismail, Zahinoor verfasserin aut Loiselle, Carmen G. verfasserin aut Mitchell, Nicholas D. verfasserin aut Tonelli, Marcello verfasserin aut Boruff, Jill T. verfasserin aut Kloda, Lorie A. verfasserin aut Beraldi, Anna verfasserin aut Braeken, Anna P.B.M. verfasserin aut Carter, Gregory verfasserin aut Clover, Kerrie verfasserin aut Conroy, Ronán M. verfasserin aut Cukor, Daniel verfasserin aut da Rocha e Silva, Carlos E. verfasserin aut De Souza, Jennifer verfasserin aut Downing, Marina G. verfasserin aut Feinstein, Anthony verfasserin aut Ferentinos, Panagiotis P. verfasserin aut Fischer, Felix H. verfasserin aut Flint, Alastair J. verfasserin aut Fujimori, Maiko verfasserin aut Gallagher, Pamela verfasserin aut Goebel, Simone verfasserin aut Jetté, Nathalie verfasserin aut Julião, Miguel verfasserin aut Keller, Monika verfasserin aut Kjærgaard, Marie verfasserin aut Love, Anthony W. verfasserin aut Löwe, Bernd verfasserin aut Martin-Santos, Rocio verfasserin aut Michopoulos, Ioannis verfasserin aut Navines, Ricard verfasserin aut O’Rourke, Suzanne J. verfasserin aut Öztürk, Ahmet verfasserin aut Pintor, Luis verfasserin aut Ponsford, Jennie L. verfasserin aut Rooney, Alasdair G. verfasserin aut Sánchez-González, Roberto verfasserin aut Schwarzbold, Marcelo L. verfasserin aut Sharpe, Michael verfasserin aut Simard, Sébastien verfasserin aut Singer, Susanne verfasserin aut Stone, Jon verfasserin aut Tung, Ka-Yee verfasserin aut Turner, Alyna verfasserin aut Walker, Jane verfasserin aut Walterfang, Mark verfasserin aut White, Jennifer verfasserin aut Benedetti, Andrea verfasserin aut Thombs, Brett D. verfasserin aut Enthalten in Journal of psychosomatic research Amsterdam [u.a.] : Elsevier Science, 1956 139 Online-Ressource (DE-627)306661128 (DE-600)1500642-6 (DE-576)081986726 1879-1360 nnns volume:139 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.91 Psychiatrie Psychopathologie 77.70 Klinische Psychologie 77.84 Krisenbewältigung AR 139 |
| spelling |
10.1016/j.jpsychores.2020.110256 doi (DE-627)ELV005121361 (ELSEVIER)S0022-3999(20)30818-7 DE-627 ger DE-627 rda eng 610 DE-600 44.91 bkl 77.70 bkl 77.84 bkl Brehaut, Eliana verfasserin aut Depression prevalence using the HADS-D compared to SCID major depression classification: An individual participant data meta-analysis 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Depression Hospital Anxiety and Depression Scale Individual participant data Meta-analysis Screening tools Neupane, Dipika verfasserin aut Levis, Brooke verfasserin aut Wu, Yin verfasserin aut Sun, Ying verfasserin aut Krishnan, Ankur verfasserin aut He, Chen verfasserin aut Bhandari, Parash Mani verfasserin aut Negeri, Zelalem verfasserin aut Riehm, Kira E. verfasserin aut Rice, Danielle B. verfasserin aut Azar, Marleine verfasserin aut Yan, Xin Wei verfasserin aut Imran, Mahrukh verfasserin aut Chiovitti, Matthew J. verfasserin aut Saadat, Nazanin verfasserin aut Cuijpers, Pim verfasserin aut Ioannidis, John P.A. verfasserin aut Markham, Sarah verfasserin aut Patten, Scott B. verfasserin aut Ziegelstein, Roy C. verfasserin aut Henry, Melissa verfasserin aut Ismail, Zahinoor verfasserin aut Loiselle, Carmen G. verfasserin aut Mitchell, Nicholas D. verfasserin aut Tonelli, Marcello verfasserin aut Boruff, Jill T. verfasserin aut Kloda, Lorie A. verfasserin aut Beraldi, Anna verfasserin aut Braeken, Anna P.B.M. verfasserin aut Carter, Gregory verfasserin aut Clover, Kerrie verfasserin aut Conroy, Ronán M. verfasserin aut Cukor, Daniel verfasserin aut da Rocha e Silva, Carlos E. verfasserin aut De Souza, Jennifer verfasserin aut Downing, Marina G. verfasserin aut Feinstein, Anthony verfasserin aut Ferentinos, Panagiotis P. verfasserin aut Fischer, Felix H. verfasserin aut Flint, Alastair J. verfasserin aut Fujimori, Maiko verfasserin aut Gallagher, Pamela verfasserin aut Goebel, Simone verfasserin aut Jetté, Nathalie verfasserin aut Julião, Miguel verfasserin aut Keller, Monika verfasserin aut Kjærgaard, Marie verfasserin aut Love, Anthony W. verfasserin aut Löwe, Bernd verfasserin aut Martin-Santos, Rocio verfasserin aut Michopoulos, Ioannis verfasserin aut Navines, Ricard verfasserin aut O’Rourke, Suzanne J. verfasserin aut Öztürk, Ahmet verfasserin aut Pintor, Luis verfasserin aut Ponsford, Jennie L. verfasserin aut Rooney, Alasdair G. verfasserin aut Sánchez-González, Roberto verfasserin aut Schwarzbold, Marcelo L. verfasserin aut Sharpe, Michael verfasserin aut Simard, Sébastien verfasserin aut Singer, Susanne verfasserin aut Stone, Jon verfasserin aut Tung, Ka-Yee verfasserin aut Turner, Alyna verfasserin aut Walker, Jane verfasserin aut Walterfang, Mark verfasserin aut White, Jennifer verfasserin aut Benedetti, Andrea verfasserin aut Thombs, Brett D. verfasserin aut Enthalten in Journal of psychosomatic research Amsterdam [u.a.] : Elsevier Science, 1956 139 Online-Ressource (DE-627)306661128 (DE-600)1500642-6 (DE-576)081986726 1879-1360 nnns volume:139 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.91 Psychiatrie Psychopathologie 77.70 Klinische Psychologie 77.84 Krisenbewältigung AR 139 |
| allfields_unstemmed |
10.1016/j.jpsychores.2020.110256 doi (DE-627)ELV005121361 (ELSEVIER)S0022-3999(20)30818-7 DE-627 ger DE-627 rda eng 610 DE-600 44.91 bkl 77.70 bkl 77.84 bkl Brehaut, Eliana verfasserin aut Depression prevalence using the HADS-D compared to SCID major depression classification: An individual participant data meta-analysis 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Depression Hospital Anxiety and Depression Scale Individual participant data Meta-analysis Screening tools Neupane, Dipika verfasserin aut Levis, Brooke verfasserin aut Wu, Yin verfasserin aut Sun, Ying verfasserin aut Krishnan, Ankur verfasserin aut He, Chen verfasserin aut Bhandari, Parash Mani verfasserin aut Negeri, Zelalem verfasserin aut Riehm, Kira E. verfasserin aut Rice, Danielle B. verfasserin aut Azar, Marleine verfasserin aut Yan, Xin Wei verfasserin aut Imran, Mahrukh verfasserin aut Chiovitti, Matthew J. verfasserin aut Saadat, Nazanin verfasserin aut Cuijpers, Pim verfasserin aut Ioannidis, John P.A. verfasserin aut Markham, Sarah verfasserin aut Patten, Scott B. verfasserin aut Ziegelstein, Roy C. verfasserin aut Henry, Melissa verfasserin aut Ismail, Zahinoor verfasserin aut Loiselle, Carmen G. verfasserin aut Mitchell, Nicholas D. verfasserin aut Tonelli, Marcello verfasserin aut Boruff, Jill T. verfasserin aut Kloda, Lorie A. verfasserin aut Beraldi, Anna verfasserin aut Braeken, Anna P.B.M. verfasserin aut Carter, Gregory verfasserin aut Clover, Kerrie verfasserin aut Conroy, Ronán M. verfasserin aut Cukor, Daniel verfasserin aut da Rocha e Silva, Carlos E. verfasserin aut De Souza, Jennifer verfasserin aut Downing, Marina G. verfasserin aut Feinstein, Anthony verfasserin aut Ferentinos, Panagiotis P. verfasserin aut Fischer, Felix H. verfasserin aut Flint, Alastair J. verfasserin aut Fujimori, Maiko verfasserin aut Gallagher, Pamela verfasserin aut Goebel, Simone verfasserin aut Jetté, Nathalie verfasserin aut Julião, Miguel verfasserin aut Keller, Monika verfasserin aut Kjærgaard, Marie verfasserin aut Love, Anthony W. verfasserin aut Löwe, Bernd verfasserin aut Martin-Santos, Rocio verfasserin aut Michopoulos, Ioannis verfasserin aut Navines, Ricard verfasserin aut O’Rourke, Suzanne J. verfasserin aut Öztürk, Ahmet verfasserin aut Pintor, Luis verfasserin aut Ponsford, Jennie L. verfasserin aut Rooney, Alasdair G. verfasserin aut Sánchez-González, Roberto verfasserin aut Schwarzbold, Marcelo L. verfasserin aut Sharpe, Michael verfasserin aut Simard, Sébastien verfasserin aut Singer, Susanne verfasserin aut Stone, Jon verfasserin aut Tung, Ka-Yee verfasserin aut Turner, Alyna verfasserin aut Walker, Jane verfasserin aut Walterfang, Mark verfasserin aut White, Jennifer verfasserin aut Benedetti, Andrea verfasserin aut Thombs, Brett D. verfasserin aut Enthalten in Journal of psychosomatic research Amsterdam [u.a.] : Elsevier Science, 1956 139 Online-Ressource (DE-627)306661128 (DE-600)1500642-6 (DE-576)081986726 1879-1360 nnns volume:139 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.91 Psychiatrie Psychopathologie 77.70 Klinische Psychologie 77.84 Krisenbewältigung AR 139 |
| allfieldsGer |
10.1016/j.jpsychores.2020.110256 doi (DE-627)ELV005121361 (ELSEVIER)S0022-3999(20)30818-7 DE-627 ger DE-627 rda eng 610 DE-600 44.91 bkl 77.70 bkl 77.84 bkl Brehaut, Eliana verfasserin aut Depression prevalence using the HADS-D compared to SCID major depression classification: An individual participant data meta-analysis 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Depression Hospital Anxiety and Depression Scale Individual participant data Meta-analysis Screening tools Neupane, Dipika verfasserin aut Levis, Brooke verfasserin aut Wu, Yin verfasserin aut Sun, Ying verfasserin aut Krishnan, Ankur verfasserin aut He, Chen verfasserin aut Bhandari, Parash Mani verfasserin aut Negeri, Zelalem verfasserin aut Riehm, Kira E. verfasserin aut Rice, Danielle B. verfasserin aut Azar, Marleine verfasserin aut Yan, Xin Wei verfasserin aut Imran, Mahrukh verfasserin aut Chiovitti, Matthew J. verfasserin aut Saadat, Nazanin verfasserin aut Cuijpers, Pim verfasserin aut Ioannidis, John P.A. verfasserin aut Markham, Sarah verfasserin aut Patten, Scott B. verfasserin aut Ziegelstein, Roy C. verfasserin aut Henry, Melissa verfasserin aut Ismail, Zahinoor verfasserin aut Loiselle, Carmen G. verfasserin aut Mitchell, Nicholas D. verfasserin aut Tonelli, Marcello verfasserin aut Boruff, Jill T. verfasserin aut Kloda, Lorie A. verfasserin aut Beraldi, Anna verfasserin aut Braeken, Anna P.B.M. verfasserin aut Carter, Gregory verfasserin aut Clover, Kerrie verfasserin aut Conroy, Ronán M. verfasserin aut Cukor, Daniel verfasserin aut da Rocha e Silva, Carlos E. verfasserin aut De Souza, Jennifer verfasserin aut Downing, Marina G. verfasserin aut Feinstein, Anthony verfasserin aut Ferentinos, Panagiotis P. verfasserin aut Fischer, Felix H. verfasserin aut Flint, Alastair J. verfasserin aut Fujimori, Maiko verfasserin aut Gallagher, Pamela verfasserin aut Goebel, Simone verfasserin aut Jetté, Nathalie verfasserin aut Julião, Miguel verfasserin aut Keller, Monika verfasserin aut Kjærgaard, Marie verfasserin aut Love, Anthony W. verfasserin aut Löwe, Bernd verfasserin aut Martin-Santos, Rocio verfasserin aut Michopoulos, Ioannis verfasserin aut Navines, Ricard verfasserin aut O’Rourke, Suzanne J. verfasserin aut Öztürk, Ahmet verfasserin aut Pintor, Luis verfasserin aut Ponsford, Jennie L. verfasserin aut Rooney, Alasdair G. verfasserin aut Sánchez-González, Roberto verfasserin aut Schwarzbold, Marcelo L. verfasserin aut Sharpe, Michael verfasserin aut Simard, Sébastien verfasserin aut Singer, Susanne verfasserin aut Stone, Jon verfasserin aut Tung, Ka-Yee verfasserin aut Turner, Alyna verfasserin aut Walker, Jane verfasserin aut Walterfang, Mark verfasserin aut White, Jennifer verfasserin aut Benedetti, Andrea verfasserin aut Thombs, Brett D. verfasserin aut Enthalten in Journal of psychosomatic research Amsterdam [u.a.] : Elsevier Science, 1956 139 Online-Ressource (DE-627)306661128 (DE-600)1500642-6 (DE-576)081986726 1879-1360 nnns volume:139 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.91 Psychiatrie Psychopathologie 77.70 Klinische Psychologie 77.84 Krisenbewältigung AR 139 |
| allfieldsSound |
10.1016/j.jpsychores.2020.110256 doi (DE-627)ELV005121361 (ELSEVIER)S0022-3999(20)30818-7 DE-627 ger DE-627 rda eng 610 DE-600 44.91 bkl 77.70 bkl 77.84 bkl Brehaut, Eliana verfasserin aut Depression prevalence using the HADS-D compared to SCID major depression classification: An individual participant data meta-analysis 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Depression Hospital Anxiety and Depression Scale Individual participant data Meta-analysis Screening tools Neupane, Dipika verfasserin aut Levis, Brooke verfasserin aut Wu, Yin verfasserin aut Sun, Ying verfasserin aut Krishnan, Ankur verfasserin aut He, Chen verfasserin aut Bhandari, Parash Mani verfasserin aut Negeri, Zelalem verfasserin aut Riehm, Kira E. verfasserin aut Rice, Danielle B. verfasserin aut Azar, Marleine verfasserin aut Yan, Xin Wei verfasserin aut Imran, Mahrukh verfasserin aut Chiovitti, Matthew J. verfasserin aut Saadat, Nazanin verfasserin aut Cuijpers, Pim verfasserin aut Ioannidis, John P.A. verfasserin aut Markham, Sarah verfasserin aut Patten, Scott B. verfasserin aut Ziegelstein, Roy C. verfasserin aut Henry, Melissa verfasserin aut Ismail, Zahinoor verfasserin aut Loiselle, Carmen G. verfasserin aut Mitchell, Nicholas D. verfasserin aut Tonelli, Marcello verfasserin aut Boruff, Jill T. verfasserin aut Kloda, Lorie A. verfasserin aut Beraldi, Anna verfasserin aut Braeken, Anna P.B.M. verfasserin aut Carter, Gregory verfasserin aut Clover, Kerrie verfasserin aut Conroy, Ronán M. verfasserin aut Cukor, Daniel verfasserin aut da Rocha e Silva, Carlos E. verfasserin aut De Souza, Jennifer verfasserin aut Downing, Marina G. verfasserin aut Feinstein, Anthony verfasserin aut Ferentinos, Panagiotis P. verfasserin aut Fischer, Felix H. verfasserin aut Flint, Alastair J. verfasserin aut Fujimori, Maiko verfasserin aut Gallagher, Pamela verfasserin aut Goebel, Simone verfasserin aut Jetté, Nathalie verfasserin aut Julião, Miguel verfasserin aut Keller, Monika verfasserin aut Kjærgaard, Marie verfasserin aut Love, Anthony W. verfasserin aut Löwe, Bernd verfasserin aut Martin-Santos, Rocio verfasserin aut Michopoulos, Ioannis verfasserin aut Navines, Ricard verfasserin aut O’Rourke, Suzanne J. verfasserin aut Öztürk, Ahmet verfasserin aut Pintor, Luis verfasserin aut Ponsford, Jennie L. verfasserin aut Rooney, Alasdair G. verfasserin aut Sánchez-González, Roberto verfasserin aut Schwarzbold, Marcelo L. verfasserin aut Sharpe, Michael verfasserin aut Simard, Sébastien verfasserin aut Singer, Susanne verfasserin aut Stone, Jon verfasserin aut Tung, Ka-Yee verfasserin aut Turner, Alyna verfasserin aut Walker, Jane verfasserin aut Walterfang, Mark verfasserin aut White, Jennifer verfasserin aut Benedetti, Andrea verfasserin aut Thombs, Brett D. verfasserin aut Enthalten in Journal of psychosomatic research Amsterdam [u.a.] : Elsevier Science, 1956 139 Online-Ressource (DE-627)306661128 (DE-600)1500642-6 (DE-576)081986726 1879-1360 nnns volume:139 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.91 Psychiatrie Psychopathologie 77.70 Klinische Psychologie 77.84 Krisenbewältigung AR 139 |
| language |
English |
| source |
Enthalten in Journal of psychosomatic research 139 volume:139 |
| sourceStr |
Enthalten in Journal of psychosomatic research 139 volume:139 |
| format_phy_str_mv |
Article |
| bklname |
Psychiatrie Psychopathologie Klinische Psychologie Krisenbewältigung |
| institution |
findex.gbv.de |
| topic_facet |
Depression Hospital Anxiety and Depression Scale Individual participant data Meta-analysis Screening tools |
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610 |
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false |
| container_title |
Journal of psychosomatic research |
| authorswithroles_txt_mv |
Brehaut, Eliana @@aut@@ Neupane, Dipika @@aut@@ Levis, Brooke @@aut@@ Wu, Yin @@aut@@ Sun, Ying @@aut@@ Krishnan, Ankur @@aut@@ He, Chen @@aut@@ Bhandari, Parash Mani @@aut@@ Negeri, Zelalem @@aut@@ Riehm, Kira E. @@aut@@ Rice, Danielle B. @@aut@@ Azar, Marleine @@aut@@ Yan, Xin Wei @@aut@@ Imran, Mahrukh @@aut@@ Chiovitti, Matthew J. @@aut@@ Saadat, Nazanin @@aut@@ Cuijpers, Pim @@aut@@ Ioannidis, John P.A. @@aut@@ Markham, Sarah @@aut@@ Patten, Scott B. @@aut@@ Ziegelstein, Roy C. @@aut@@ Henry, Melissa @@aut@@ Ismail, Zahinoor @@aut@@ Loiselle, Carmen G. @@aut@@ Mitchell, Nicholas D. @@aut@@ Tonelli, Marcello @@aut@@ Boruff, Jill T. @@aut@@ Kloda, Lorie A. @@aut@@ Beraldi, Anna @@aut@@ Braeken, Anna P.B.M. @@aut@@ Carter, Gregory @@aut@@ Clover, Kerrie @@aut@@ Conroy, Ronán M. @@aut@@ Cukor, Daniel @@aut@@ da Rocha e Silva, Carlos E. @@aut@@ De Souza, Jennifer @@aut@@ Downing, Marina G. @@aut@@ Feinstein, Anthony @@aut@@ Ferentinos, Panagiotis P. @@aut@@ Fischer, Felix H. @@aut@@ Flint, Alastair J. @@aut@@ Fujimori, Maiko @@aut@@ Gallagher, Pamela @@aut@@ Goebel, Simone @@aut@@ Jetté, Nathalie @@aut@@ Julião, Miguel @@aut@@ Keller, Monika @@aut@@ Kjærgaard, Marie @@aut@@ Love, Anthony W. @@aut@@ Löwe, Bernd @@aut@@ Martin-Santos, Rocio @@aut@@ Michopoulos, Ioannis @@aut@@ Navines, Ricard @@aut@@ O’Rourke, Suzanne J. @@aut@@ Öztürk, Ahmet @@aut@@ Pintor, Luis @@aut@@ Ponsford, Jennie L. @@aut@@ Rooney, Alasdair G. @@aut@@ Sánchez-González, Roberto @@aut@@ Schwarzbold, Marcelo L. @@aut@@ Sharpe, Michael @@aut@@ Simard, Sébastien @@aut@@ Singer, Susanne @@aut@@ Stone, Jon @@aut@@ Tung, Ka-Yee @@aut@@ Turner, Alyna @@aut@@ Walker, Jane @@aut@@ Walterfang, Mark @@aut@@ White, Jennifer @@aut@@ Benedetti, Andrea @@aut@@ Thombs, Brett D. @@aut@@ |
| publishDateDaySort_date |
2020-01-01T00:00:00Z |
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306661128 |
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3610 |
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ELV005121361 |
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englisch |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV005121361</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524155513.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230503s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jpsychores.2020.110256</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV005121361</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0022-3999(20)30818-7</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.91</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">77.70</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">77.84</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Brehaut, Eliana</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Depression prevalence using the HADS-D compared to SCID major depression classification: An individual participant data meta-analysis</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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. 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Brehaut, Eliana Neupane, Dipika Levis, Brooke Wu, Yin Sun, Ying Krishnan, Ankur He, Chen Bhandari, Parash Mani Negeri, Zelalem Riehm, Kira E. Rice, Danielle B. Azar, Marleine Yan, Xin Wei Imran, Mahrukh Chiovitti, Matthew J. Saadat, Nazanin Cuijpers, Pim Ioannidis, John P.A. Markham, Sarah Patten, Scott B. Ziegelstein, Roy C. Henry, Melissa Ismail, Zahinoor Loiselle, Carmen G. Mitchell, Nicholas D. Tonelli, Marcello Boruff, Jill T. Kloda, Lorie A. Beraldi, Anna Braeken, Anna P.B.M. Carter, Gregory Clover, Kerrie Conroy, Ronán M. Cukor, Daniel da Rocha e Silva, Carlos E. De Souza, Jennifer Downing, Marina G. Feinstein, Anthony Ferentinos, Panagiotis P. Fischer, Felix H. Flint, Alastair J. Fujimori, Maiko Gallagher, Pamela Goebel, Simone Jetté, Nathalie Julião, Miguel Keller, Monika Kjærgaard, Marie Love, Anthony W. Löwe, Bernd Martin-Santos, Rocio Michopoulos, Ioannis Navines, Ricard O’Rourke, Suzanne J. Öztürk, Ahmet Pintor, Luis Ponsford, Jennie L. Rooney, Alasdair G. Sánchez-González, Roberto Schwarzbold, Marcelo L. Sharpe, Michael Simard, Sébastien Singer, Susanne Stone, Jon Tung, Ka-Yee Turner, Alyna Walker, Jane Walterfang, Mark White, Jennifer Benedetti, Andrea Thombs, Brett D. |
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depression prevalence using the hads-d compared to scid major depression classification: an individual participant data meta-analysis |
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Depression prevalence using the HADS-D compared to SCID major depression classification: An individual participant data meta-analysis |
| 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. |
| abstractGer |
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. |
| abstract_unstemmed |
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. |
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Depression prevalence using the HADS-D compared to SCID major depression classification: An individual participant data meta-analysis |
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Neupane, Dipika Levis, Brooke Wu, Yin Sun, Ying Krishnan, Ankur He, Chen Bhandari, Parash Mani Negeri, Zelalem Riehm, Kira E. Rice, Danielle B. Azar, Marleine Yan, Xin Wei Imran, Mahrukh Chiovitti, Matthew J. Saadat, Nazanin Cuijpers, Pim Ioannidis, John P.A. Markham, Sarah Patten, Scott B. Ziegelstein, Roy C. Henry, Melissa Ismail, Zahinoor Loiselle, Carmen G. Mitchell, Nicholas D. Tonelli, Marcello Boruff, Jill T. Kloda, Lorie A. Beraldi, Anna Braeken, Anna P.B.M. Carter, Gregory Clover, Kerrie Conroy, Ronán M. Cukor, Daniel da Rocha e Silva, Carlos E. De Souza, Jennifer Downing, Marina G. Feinstein, Anthony Ferentinos, Panagiotis P. Fischer, Felix H. Flint, Alastair J. Fujimori, Maiko Gallagher, Pamela Goebel, Simone Jetté, Nathalie Julião, Miguel Keller, Monika Kjærgaard, Marie Love, Anthony W. Löwe, Bernd Martin-Santos, Rocio Michopoulos, Ioannis Navines, Ricard O’Rourke, Suzanne J. Öztürk, Ahmet Pintor, Luis Ponsford, Jennie L. Rooney, Alasdair G. Sánchez-González, Roberto Schwarzbold, Marcelo L. Sharpe, Michael Simard, Sébastien Singer, Susanne Stone, Jon Tung, Ka-Yee Turner, Alyna Walker, Jane Walterfang, Mark White, Jennifer Benedetti, Andrea Thombs, Brett D. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV005121361</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524155513.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230503s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jpsychores.2020.110256</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV005121361</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0022-3999(20)30818-7</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.91</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">77.70</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">77.84</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Brehaut, Eliana</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Depression prevalence using the HADS-D compared to SCID major depression classification: An individual participant data meta-analysis</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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. 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|
| score |
7.40096 |