Optimal cut-offs of depression screening tools during the COVID-19 pandemic: a systematic review
Background Studies have reported an increase in the prevalence of depression during the COVID-19 pandemic. The accuracy of screening tools may change with the prevalence and distribution of a disease in a population or sample: the “Spectrum Effect”. Methods First, we selected commonly used screening tools and developed search strategies for the inclusion of original studies during the pandemic. Second, we searched PsycINFO, EMBASE, and MEDLINE from March 2020 to September 2022 to obtain original studies that investigated the accuracy of depression screening tools during the pandemic. We then searched these databases to identify meta-analyses summarizing the accuracy of these tools conducted before the pandemic and compared the optimal cut-offs for depression screening tools during the pandemic with those before. Result Four original studies evaluating the optimal cut-offs for four screening tools (Beck Depression Inventory [BDI-II], Hospital Anxiety and Depression Scale-Depression [HADS-D], Patient Health Questionnaire-9 [PHQ-9], and Geriatric Depression Scale-4 [GDS-4]) were published during the pandemic. Four meta-analyses summarizing these tools before the pandemic. We found that the optimal cut-off of BDI-II was 14 during the pandemic (23.8% depression prevalence, screening patients with Type 2 diabetes) and 14.5 before the pandemic (17.6% depression prevalence, screening psychiatric, primary care, and healthy populations); HADS-D was 10 during the pandemic (23.8% depression prevalence, screening patients with type 2 diabetes) and 7 before the pandemic (15.0% depression prevalence, screening medically ill patients); PHQ-9 was 11 during the pandemic (14.5% depression prevalence, screening university students) and 8 before the pandemic (10.9% depression prevalence, screening the unrestricted population), and GDS-4 was 1.8 during the pandemic (29.0% depression prevalence, screening adults seen in a memory clinic setting) and 3 before the pandemic (18.5% depression prevalence, screening older adults). Conclusion The optimal cut-off for different screening tools may be sensitive to changes in study populations and reference standards. And potential spectrum effects that should be considered in post-COVID time which aiming to improve diagnostic accuracy..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:23 |
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| Enthalten in: |
BMC psychiatry - 23(2023), 1 vom: 19. Dez. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhou, Jieru [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
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| Anmerkungen: |
© The Author(s) 2023 |
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| doi: |
10.1186/s12888-023-05455-8 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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SPR054135761 |
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| 520 | |a Background Studies have reported an increase in the prevalence of depression during the COVID-19 pandemic. The accuracy of screening tools may change with the prevalence and distribution of a disease in a population or sample: the “Spectrum Effect”. Methods First, we selected commonly used screening tools and developed search strategies for the inclusion of original studies during the pandemic. Second, we searched PsycINFO, EMBASE, and MEDLINE from March 2020 to September 2022 to obtain original studies that investigated the accuracy of depression screening tools during the pandemic. We then searched these databases to identify meta-analyses summarizing the accuracy of these tools conducted before the pandemic and compared the optimal cut-offs for depression screening tools during the pandemic with those before. Result Four original studies evaluating the optimal cut-offs for four screening tools (Beck Depression Inventory [BDI-II], Hospital Anxiety and Depression Scale-Depression [HADS-D], Patient Health Questionnaire-9 [PHQ-9], and Geriatric Depression Scale-4 [GDS-4]) were published during the pandemic. Four meta-analyses summarizing these tools before the pandemic. We found that the optimal cut-off of BDI-II was 14 during the pandemic (23.8% depression prevalence, screening patients with Type 2 diabetes) and 14.5 before the pandemic (17.6% depression prevalence, screening psychiatric, primary care, and healthy populations); HADS-D was 10 during the pandemic (23.8% depression prevalence, screening patients with type 2 diabetes) and 7 before the pandemic (15.0% depression prevalence, screening medically ill patients); PHQ-9 was 11 during the pandemic (14.5% depression prevalence, screening university students) and 8 before the pandemic (10.9% depression prevalence, screening the unrestricted population), and GDS-4 was 1.8 during the pandemic (29.0% depression prevalence, screening adults seen in a memory clinic setting) and 3 before the pandemic (18.5% depression prevalence, screening older adults). Conclusion The optimal cut-off for different screening tools may be sensitive to changes in study populations and reference standards. And potential spectrum effects that should be considered in post-COVID time which aiming to improve diagnostic accuracy. | ||
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| 700 | 1 | |a Radojčić, Maja R. |4 aut | |
| 700 | 1 | |a Ashton-James, Claire E. |4 aut | |
| 700 | 1 | |a Yang, Hanqiao |4 aut | |
| 700 | 1 | |a Chen, Ziyi |4 aut | |
| 700 | 1 | |a Wang, Ruijia |4 aut | |
| 700 | 1 | |a Yang, Ying |4 aut | |
| 700 | 1 | |a Si, Jinhua |4 aut | |
| 700 | 1 | |a Yao, Liang |4 aut | |
| 700 | 1 | |a Li, Ge |4 aut | |
| 700 | 1 | |a Chen, Lingxiao |4 aut | |
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