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Inequality on the frontline: A multi-country study on gender differences in mental health among healthcare workers during the COVID-19 pandemic
- Diana Czepiel, Clare McCormack, Andréa T.C. da Silva, Dominika Seblova, Maria F. Moro, Alexandra Restrepo-Henao, Adriana M. Martínez, Oyeyemi Afolabi, Lubna Alnasser, Rubén Alvarado, Hiroki Asaoka, Olatunde Ayinde, Arin Balalian, Dinarte Ballester, Josleen A.l. Barathie, Armando Basagoitia, Djordje Basic, María S. Burrone, Mauro G. Carta, Sol Durand-Arias, Mehmet Eskin, Eduardo Fernández-Jiménez, Marcela I. F. Frey, Oye Gureje, Anna Isahakyan, Rodrigo Jaldo, Elie G. Karam, Dorra Khattech, Jutta Lindert, Gonzalo Martínez-Alés, Franco Mascayano, Roberto Mediavilla, Javier A. Narvaez Gonzalez, Aimee Nasser-Karam, Daisuke Nishi, Olusegun Olaopa, Uta Ouali, Victor Puac-Polanco, Dorian E. Ramírez, Jorge Ramírez, Eliut Rivera-Segarra, Bart P.F. Rutten, Julian Santaella-Tenorio, Jaime C. Sapag, Jana Šeblová, María T. S. Soto, Maria Tavares-Cavalcanti, Linda Valeri, Marit Sijbrandij, Ezra S. Susser, Hans W. Hoek, Els van der Ven
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- Journal:
- Cambridge Prisms: Global Mental Health / Volume 11 / 2024
- Published online by Cambridge University Press:
- 04 March 2024, e34
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- Article
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Healthcare workers (HCWs) were at increased risk for mental health problems during the COVID-19 pandemic, with prior data suggesting women may be particularly vulnerable. Our global mental health study aimed to examine factors associated with gender differences in psychological distress and depressive symptoms among HCWs during COVID-19. Across 22 countries in South America, Europe, Asia and Africa, 32,410 HCWs participated in the COVID-19 HEalth caRe wOrkErS (HEROES) study between March 2020 and February 2021. They completed the General Health Questionnaire-12, the Patient Health Questionnaire-9 and questions about pandemic-relevant exposures. Consistently across countries, women reported elevated mental health problems compared to men. Women also reported increased COVID-19-relevant stressors, including insufficient personal protective equipment and less support from colleagues, while men reported increased contact with COVID-19 patients. At the country level, HCWs in countries with higher gender inequality reported less mental health problems. Higher COVID-19 mortality rates were associated with increased psychological distress merely among women. Our findings suggest that among HCWs, women may have been disproportionately exposed to COVID-19-relevant stressors at the individual and country level. This highlights the importance of considering gender in emergency response efforts to safeguard women’s well-being and ensure healthcare system preparedness during future public health crises.
Development of a model to predict antidepressant treatment response for depression among Veterans
- Victor Puac-Polanco, Hannah N. Ziobrowski, Eric L. Ross, Howard Liu, Brett Turner, Ruifeng Cui, Lucinda B. Leung, Robert M. Bossarte, Corey Bryant, Jutta Joormann, Andrew A. Nierenberg, David W. Oslin, Wilfred R. Pigeon, Edward P. Post, Nur Hani Zainal, Alan M. Zaslavsky, Jose R. Zubizarreta, Alex Luedtke, Chris J. Kennedy, Andrea Cipriani, Toshiaki A. Furukawa, Ronald C. Kessler
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- Journal:
- Psychological Medicine / Volume 53 / Issue 11 / August 2023
- Published online by Cambridge University Press:
- 15 July 2022, pp. 5001-5011
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Background
Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA).
MethodsA 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample.
ResultsIn total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors.
ConclusionsAlthough these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.
Development of a model to predict psychotherapy response for depression among Veterans
- Hannah N. Ziobrowski, Ruifeng Cui, Eric L. Ross, Howard Liu, Victor Puac-Polanco, Brett Turner, Lucinda B. Leung, Robert M. Bossarte, Corey Bryant, Wilfred R. Pigeon, David W. Oslin, Edward P. Post, Alan M. Zaslavsky, Jose R. Zubizarreta, Andrew A. Nierenberg, Alex Luedtke, Chris J. Kennedy, Ronald C. Kessler
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- Journal:
- Psychological Medicine / Volume 53 / Issue 8 / June 2023
- Published online by Cambridge University Press:
- 11 February 2022, pp. 3591-3600
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Background
Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan.
MethodsThis prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018–2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample.
Results32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables.
ConclusionsPatients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.
Antidepressant use in low- middle- and high-income countries: a World Mental Health Surveys report
- Alan E. Kazdin, Chi-Shin Wu, Irving Hwang, Victor Puac-Polanco, Nancy A. Sampson, Ali Al-Hamzawi, Jordi Alonso, Laura Helena Andrade, Corina Benjet, José-Miguel Caldas-de-Almeida, Giovanni de Girolamo, Peter de Jonge, Silvia Florescu, Oye Gureje, Josep M. Haro, Meredith G. Harris, Elie G. Karam, Georges Karam, Viviane Kovess-Masfety, Sing Lee, John J. McGrath, Fernando Navarro-Mateu, Daisuke Nishi, Bibilola D. Oladeji, José Posada-Villa, Dan J. Stein, T. Bedirhan Üstün, Daniel V. Vigo, Zahari Zarkov, Alan M. Zaslavsky, Ronald C. Kessler, the WHO World Mental Health Survey collaborators
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- Journal:
- Psychological Medicine / Volume 53 / Issue 4 / March 2023
- Published online by Cambridge University Press:
- 23 September 2021, pp. 1583-1591
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Background
The most common treatment for major depressive disorder (MDD) is antidepressant medication (ADM). Results are reported on frequency of ADM use, reasons for use, and perceived effectiveness of use in general population surveys across 20 countries.
MethodsFace-to-face interviews with community samples totaling n = 49 919 respondents in the World Health Organization (WHO) World Mental Health (WMH) Surveys asked about ADM use anytime in the prior 12 months in conjunction with validated fully structured diagnostic interviews. Treatment questions were administered independently of diagnoses and asked of all respondents.
Results3.1% of respondents reported ADM use within the past 12 months. In high-income countries (HICs), depression (49.2%) and anxiety (36.4%) were the most common reasons for use. In low- and middle-income countries (LMICs), depression (38.4%) and sleep problems (31.9%) were the most common reasons for use. Prevalence of use was 2–4 times as high in HICs as LMICs across all examined diagnoses. Newer ADMs were proportionally used more often in HICs than LMICs. Across all conditions, ADMs were reported as very effective by 58.8% of users and somewhat effective by an additional 28.3% of users, with both proportions higher in LMICs than HICs. Neither ADM class nor reason for use was a significant predictor of perceived effectiveness.
ConclusionADMs are in widespread use and for a variety of conditions including but going beyond depression and anxiety. In a general population sample from multiple LMICs and HICs, ADMs were widely perceived to be either very or somewhat effective by the people who use them.