First-onset major depression during the COVID-19 pandemic: A predictive machine learning model

Daniela Caldirola, Silvia Daccò, Francesco Cuniberti, Massimiliano Grassi, Alessandra Alciati, Tatiana Torti, Giampaolo Perna, Daniela Caldirola, Silvia Daccò, Francesco Cuniberti, Massimiliano Grassi, Alessandra Alciati, Tatiana Torti, Giampaolo Perna

Abstract

Background: This study longitudinally evaluated first-onset major depression rates during the pandemic in Italian adults without any current clinician-diagnosed psychiatric disorder and created a predictive machine learning model (MLM) to evaluate subsequent independent samples.

Methods: An online, self-reported survey was released during two pandemic periods (May to June and September to October 2020). Provisional diagnoses of major depressive disorder (PMDD) were determined using a diagnostic algorithm based on the DSM criteria of the Patient Health Questionnaire-9 to maximize specificity. Gradient-boosted decision trees and the SHapley Additive exPlanations technique created the MLM and estimated each variable's predictive contribution.

Results: There were 3532 participants in the study. The final sample included 633 participants in the first wave (FW) survey and 290 in the second (SW). First-onset PMDD was found in 7.4% of FW participants and 7.2% of the SW. The final MLM, trained on the FW, displayed a sensitivity of 76.5% and a specificity of 77.8% when tested on the SW. The main factors identified in the MLM were low resilience, being an undergraduate student, being stressed by pandemic-related conditions, and low satisfaction with usual sleep before the pandemic and support from relatives. Current smoking and taking medication for medical conditions also contributed, albeit to a lesser extent.

Limitations: Small sample size; self-report assessment; data covering 2020 only.

Conclusions: Rates of first-onset PMDD among Italians during the first phases of the pandemic were considerable. Our MLM displayed a good predictive performance, suggesting potential goals for depression-preventive interventions during public health crises.

Keywords: COVID-19; Depression; First-onset; General population; Machine learning; Predictive model.

Conflict of interest statement

The authors report no conflicts of interest in this work.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Copyright © 2022 Elsevier B.V. All rights reserved.

Figures

Fig. 1
Fig. 1
Flow diagram of the participant selection process for the aim of the study.
Fig. 2
Fig. 2
Variables included in the final ML predictive model and average of the absolute SHAP values for each variable, ordered by their relevance to the model (train dataset, first wave). The larger the absolute SHAP value of a certain variable, the larger the contribution of that variable in determining that prediction in a specific case. Specifically, a higher risk of first-onset provisional diagnosis of major depressive disorder (PMDD) was associated with higher agreement with “BRS-item 6”; higher levels of “Being scared of transmitting COVID-19”; higher disagreement with “BRS-item 3”; lower levels of “satisfaction with the usual sleep before the pandemic”; higher levels of “Being stressed by pandemic-related restrictions on activities and personal movement ”; being an undergraduate student (“Employment status”); higher disagreement with “perception of being supported..”; having continued or started smoking (“Smoking habit during the pandemic”); yes (“current medications for medical diseases”); and yes (“Having experienced a loved one's hospitalization”). ML: machine learning; SHAP: SHapley Additive exPlanations technique.
Fig. 3
Fig. 3
Variables included in the final ML predictive model and average of the absolute SHAP values for each variable, ordered by their relevance to the model (test dataset, second wave). The larger the absolute SHAP value of a certain variable, the larger the contribution of that variable in determining that prediction in a specific case. Specifically, a higher risk of first-onset provisional diagnosis of major depressive disorder (PMDD) was associated with higher agreement with “BRS-item 6”; higher levels of “Being scared of transmitting COVID-19”; being an undergraduate student (“Employment status”); higher disagreement with “BRS-item 3”; higher levels of “Being stressed by pandemic-related restrictions on activities and personal movement ”; lower levels of “satisfaction with the usual sleep before the pandemic”; higher disagreement with “perception of being supported..”; having continued or started smoking (“Smoking habit during the pandemic”); yes (“current medications for medical diseases”); and yes (“Having experienced a loved one's hospitalization”) ML: machine learning; SHAP: SHapley Additive exPlanations technique.
Fig. 4
Fig. 4
Levels of the variables plotted against the associated SHAP values in the second wave. SHAP: SHapley Additive exPlanations technique.

References

    1. Adu M., Wallace L., Lartey K., Arthur J., Oteng K., Dwomoh S., Owusu-Antwi R., Larsen-Reindorf R., Agyapong V. Prevalence and correlates of likely major depressive disorder among the adult population in Ghana during the COVID-19 pandemic. Int. J. Environ. Res. Public Health. 2021;18 doi: 10.3390/IJERPH18137106.
    1. Akeman E., Kirlic N., Clausen A.N., Cosgrove K.T., McDermott T.J., Cromer L.D., Paulus M.P., Yeh H.W., Aupperle R.L. A pragmatic clinical trial examining the impact of a resilience program on college student mental health. Depress. Anxiety. 2020;37:202–213. doi: 10.1002/DA.22969.
    1. American Psychiatric Association . 5th ed. American Psychiatric Association; Arlington, VA: 2013. Diagnostic and Statistical Manual of Mental Disorders.
    1. Arpino B., Pasqualini M. Effects of pandemic on feelings of depression in Italy: the role of age, gender, and individual experiences during the first lockdown. Front. Psychol. 2021;12 doi: 10.3389/FPSYG.2021.660628.
    1. Awan H.A., Najmuddin Diwan M., Aamir A., Ali M., Di Giannantonio M., Ullah I., Shoib S., De Berardis D. SARS-CoV-2 and the brain: what do we know about the causality of ’Cognitive COVID? J. Clin. Med. 2021;10 doi: 10.3390/JCM10153441.
    1. Awan S., Diwan M.N., Aamir A., Allahuddin Z., Irfan M., Carano A., Vellante F., Ventriglio A., Fornaro M., Valchera A., Pettorruso M., Martinotti G., Di Giannantonio M., Ullah I., De Berardis D. Suicide in healthcare workers: determinants, challenges, and the impact of COVID-19. Front. Psychiatry. 2022;12 doi: 10.3389/FPSYT.2021.792925.
    1. Ayed N., Toner S., Priebe S. Conceptualizing resilience in adult mental health literature: a systematic review and narrative synthesis. Psychol. Psychother. 2019;92:299–341. doi: 10.1111/PAPT.12185.
    1. Baglioni C., Battagliese G., Feige B., Spiegelhalder K., Nissen C., Voderholzer U., Lombardo C., Riemann D. Insomnia as a predictor of depression: a meta-analytic evaluation of longitudinal epidemiological studies. J. Affect. Disord. 2011;135:10–19. doi: 10.1016/J.JAD.2011.01.011.
    1. Blackmon B.J., Lee J., Cochran D.M., Kar B., Rehner T.A., Baker A.M. Adapting to life after hurricane Katrina and the Deepwater horizon oil spill: an examination of psychological resilience and depression on the Mississippi Gulf Coast. Soc. Work Public Health. 2017;32:65–76. doi: 10.1080/19371918.2016.1188746.
    1. Bruno G., Panzeri A., Granziol U., Alivernini F., Chirico A., Galli F., Lucidi F., Spoto A., Vidotto G., Bertamini M. The italian COVID-19 psychological research consortium (IT C19PRC): general overview and replication of the UK study. J. Clin. Med. 2020;10:52. doi: 10.3390/jcm10010052.
    1. Byrne E.M., Timmerman A., Wray N.R., Agerbo E. Sleep disorders and risk of incident depression: a population case-control study. Twin Res. Hum. Genet. 2019;22:140–146. doi: 10.1017/THG.2019.22.
    1. Caldirola D., Cuniberti F., Daccò S., Grassi M., Torti T., Perna G. Predicting new-onset psychiatric disorders throughout the COVID-19 pandemic: a machine learning approach. J. Neuropsychiatry Clin. Neurosci. 2022 doi: 10.1176/appi.neuropsych.21060148.
    1. Cénat J.M., Blais-Rochette C., Kokou-Kpolou C.K., Noorishad P.G., Mukunzi J.N., McIntee S.E., Dalexis R.D., Goulet M.A., Labelle R.P. Prevalence of symptoms of depression, anxiety, insomnia, posttraumatic stress disorder, and psychological distress among populations affected by the COVID-19 pandemic: a systematic review and meta-analysis. Psychiatry Res. 2021 doi: 10.1016/j.psychres.2020.113599.
    1. Conceição V., Rothes I., Gusmão R. The association between changes in the university educational setting and peer relationships: effects in students’ depressive symptoms during the COVID-19 pandemic. Front. Psychiatry. 2021;12 doi: 10.3389/FPSYT.2021.783776.
    1. De Berardis D. How concerned should we be about neurotropism of SARS-Cov-2? A brief clinical consideration of the possible psychiatric implications. CNS Spectr. 2020 doi: 10.1017/S1092852920002175.
    1. De Berardis D., Fornaro M., Ventriglio A., Valchera A., Vellante F., Pettorruso M., Martinotti G., Fraticelli S., Di Giannantonio M. Trazodone add-on in COVID-19-related selective serotonin reuptake inhibitor-resistant post-traumatic stress disorder in healthcare workers: two case reports. Clin. Psychopharmacol. Neurosci. 2021;19:780–785. doi: 10.9758/CPN.2021.19.4.780.
    1. Dogan-Sander E., Kohls E., Baldofski S., Rummel-Kluge C. More depressive symptoms, alcohol and drug consumption: increase in mental health symptoms among university students after one year of the COVID-19 pandemic. Front. Psychiatry. 2021;12 doi: 10.3389/FPSYT.2021.790974.
    1. Eder S., Steyrl D., Stefanczyk M., Pieniak M., Martínez Molina J., Pešout O., Binter J., Smela P., Scharnowski F., Nicholson A. Predicting fear and perceived health during the COVID-19 pandemic using machine learning: a cross-national longitudinal study. PLoS One. 2021;16 doi: 10.1371/JOURNAL.PONE.0247997.
    1. Ettman C.K., Abdalla S.M., Cohen G.H., Sampson L., Vivier P.M., Galea S. Prevalence of depression symptoms in US adults before and during the COVID-19 pandemic. JAMA Netw. Open. 2020;3 doi: 10.1001/jamanetworkopen.2020.19686.
    1. Fang H., Tu S., Sheng J., Shao A. Depression in sleep disturbance: a review on a bidirectional relationship, mechanisms and treatment. J. Cell. Mol. Med. 2019;23:2324–2332. doi: 10.1111/JCMM.14170.
    1. Flesia L., Monaro M., Mazza C., Fietta V., Colicino E., Segatto B., Roma P. Predicting perceived stress related to the Covid-19 outbreak through stable psychological traits and machine learning models. J. Clin. Med. 2020;9:3350. doi: 10.3390/jcm9103350.
    1. Friedman J.H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 2001;29:1189–1232. doi: 10.1214/aos/1013203451.
    1. GBD 2019 Mental Disorders C. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry. 2022 doi: 10.1016/S2215-0366(21)00395-3.
    1. Ge F., Zhang D., Wu L., Mu H. Predicting psychological state among chinese undergraduate students in the covid-19 epidemic: a longitudinal study using a machine learning. Neuropsychiatr. Dis. Treat. 2020;16:2111–2118. doi: 10.2147/NDT.S262004.
    1. Gilman S.E., Trinh N.-H., Smoller J.W., Fava M., Murphy J.M., Breslau J. Psychosocial stressors and the prognosis of major depression: a test of Axis IV. Psychol. Med. 2013;43:303. doi: 10.1017/S0033291712001080.
    1. Girolamo G., Polidori G., Morosini P., Scarpino V., Reda V., Serra G., Mazzi F., Alonso J., Vilagut G., Visonà G., Falsirollo F., Rossi A., Warner R. Prevalence of common mental disorders in Italy. Soc. Psychiatry Psychiatr. Epidemiol. 2006;41:853–861. doi: 10.1007/s00127-006-0097-4.
    1. Goldmann E., Galea S. Mental health consequences of disasters. Annu. Rev. Public Health. 2014;35:169–183. doi: 10.1146/ANNUREV-PUBLHEALTH-032013-182435.
    1. González-Sanguino C., Ausín B., Castellanos M.Á., Saiz J., López-Gómez A., Ugidos C., Muñoz M. Mental health consequences during the initial stage of the 2020 coronavirus pandemic (COVID-19) in Spain. Brain Behav. Immun. 2020;87:172–176. doi: 10.1016/J.BBI.2020.05.040.
    1. Guse J., Heinen I., Mohr S., Bergelt C. Understanding mental burden and factors associated with study worries among undergraduate medical students during the COVID-19 pandemic. Front. Psychol. 2021;12 doi: 10.3389/FPSYG.2021.734264.
    1. Hahad O., Daiber A., Michal M., Kuntic M., Lieb K., Beutel M., Münzel T. Smoking and neuropsychiatric disease-associations and underlying mechanisms. Int. J. Mol. Sci. 2021;22 doi: 10.3390/IJMS22147272.
    1. Hao F., Tan W., Jiang L., Zhang L., Zhao X., Zou Y., Hu Y., Luo X., Jiang X., McIntyre R.S., Tran B., Sun J., Zhang Z., Ho R., Ho C., Tam W. Do psychiatric patients experience more psychiatric symptoms during COVID-19 pandemic and lockdown? A case-control study with service and research implications for immunopsychiatry. Brain Behav. Immun. 2020;87:100–106. doi: 10.1016/j.bbi.2020.04.069.
    1. He C., Levis B., Riehm K.E., Saadat N., Levis A.W., Azar M., Rice D.B., Krishnan A., Wu Y., Sun Y., Imran M., Boruff J., Cuijpers P., Gilbody S., Ioannidis J.P.A., Kloda L.A., McMillan D., Patten S.B., Shrier I., Ziegelstein R.C., Akena D.H., Arroll B., Ayalon L., Baradaran H.R., Baron M., Beraldi A., Bombardier C.H., Butterworth P., Carter G., Chagas M.H.N., Chan J.C.N., Cholera R., Clover K., Conwell Y., De Man-Van Ginkel J.M., Fann J.R., Fischer F.H., Fung D., Gelaye B., Goodyear-Smith F., Greeno C.G., Hall B.J., Harrison P.A., Härter M., Hegerl U., Hides L., Hobfoll S.E., Hudson M., Hyphantis T.N., Inagaki M., Ismail K., Jetté N., Khamseh M.E., Kiely K.M., Kwan Y., Lamers F., Liu S.I., Lotrakul M., Loureiro S.R., Löwe B., Marsh L., McGuire A., Mohd-Sidik S., Munhoz T.N., Muramatsu K., Osório F.L., Patel V., Pence B.W., Persoons P., Picardi A., Reuter K., Rooney A.G., Da Silva Dos Santos I.S., Shaaban J., Sidebottom A., Simning A., Stafford L., Sung S., Tan P.L.L., Turner A., Van Weert H.C.P.M., White J., Whooley M.A., Winkley K., Yamada M., Thombs B.D., Benedetti A. The accuracy of the patient health questionnaire-9 algorithm for screening to detect major depression: an individual participant data meta-analysis. Psychother. Psychosom. 2020;89:25–37. doi: 10.1159/000502294.
    1. Kageyama K., Iwasaki Y., Daimon M. Hypothalamic regulation of corticotropin-releasing factor under stress and stress resilience. Int. J. Mol. Sci. 2021;22 doi: 10.3390/IJMS222212242.
    1. Kalisch R., Cramer A.O.J., Binder H., Fritz J., Leertouwer Ij, Lunansky G., Meyer B., Timmer J., Veer I.M., van Harmelen A.L. Deconstructing and reconstructing resilience: a dynamic network approach. Perspect. Psychol. Sci. 2019;14:765–777. doi: 10.1177/1745691619855637.
    1. Lakhan R., Agrawal A., Sharma M. Prevalence of depression, anxiety, and stress during COVID-19 pandemic. J. Neurosci. Rural Pract. 2020;11:519–525. doi: 10.1055/S-0040-1716442.
    1. Landi G., Pakenham K.I., Boccolini G., Grandi S., Tossani E. Health anxiety and mental health outcome during COVID-19 lockdown in Italy: the mediating and moderating roles of psychological flexibility. Front. Psychol. 2020;11 doi: 10.3389/fpsyg.2020.02195.
    1. Lei L., Huang X., Zhang S., Yang J., Yang L., Xu M. Comparison of prevalence and associated factors of anxiety and depression among people affected by versus people unaffected by quarantine during the COVID-19 epidemic in southwestern China. Med. Sci. Monit. 2020;26 doi: 10.12659/MSM.924609.
    1. Lenzo V., Quattropani M.C., Musetti A., Zenesini C., Freda M.F., Lemmo D., Vegni E., Borghi L., Plazzi G., Castelnuovo G., Cattivelli R., Saita E., Franceschini C. Resilience contributes to low emotional impact of the COVID-19 outbreak among the general population in Italy. Front. Psychol. 2020;11 doi: 10.3389/fpsyg.2020.576485.
    1. Li L., Wu C., Gan Y., Qu X., Lu Z. Insomnia and the risk of depression: a meta-analysis of prospective cohort studies. BMC Psychiatry. 2016;16 doi: 10.1186/S12888-016-1075-3.
    1. Li Y., Wang A., Wu Y., Han N., Huang H. Impact of the COVID-19 pandemic on the mental health of college students: a systematic review and meta-analysis. Front. Psychol. 2021;12 doi: 10.3389/FPSYG.2021.669119.
    1. Liu C., Stevens C., Conrad R., Hahm H. Evidence for elevated psychiatric distress, poor sleep, and quality of life concerns during the COVID-19 pandemic among U.S. Young adults with suspected and reported psychiatric diagnoses. Psychiatry Res. 2020;292 doi: 10.1016/J.PSYCHRES.2020.113345.
    1. Liu C., Zhang E., Wong G., Hyun S., Hahm H. Factors associated with depression, anxiety, and PTSD symptomatology during the COVID-19 pandemic: clinical implications for U.S. Young adult mental health. Psychiatry Res. 2020;290 doi: 10.1016/J.PSYCHRES.2020.113172.
    1. Lundberg S.M., Lee S.-I. In: Advances in Neural Information Processing Systems. Guyon I., Luxburg U.V., Bengio S., Wallach H., Fergus R., Vishwanathan S., Garnett R., editors. Curran Associates Inc; 2017. A unified approach to interpreting model predictions.
    1. Luo M., Guo L., Yu M., Wang H. The psychological and mental impact of coronavirus disease 2019 (COVID-19) on medical staff and general public – a systematic review and meta-analysis. Psychiatry Res. 2020 doi: 10.1016/j.psychres.2020.113190.
    1. Matos Fialho P.M., Spatafora F., Kühne L., Busse H., Helmer S.M., Zeeb H., Stock C., Wendt C., Pischke C.R. Perceptions of study conditions and depressive symptoms during the COVID-19 pandemic among university students in Germany: results of the international COVID-19 student well-being study. 2021;9 doi: 10.3389/FPUBH.2021.674665.
    1. Mazza C., Ricci E., Biondi S., Colasanti M., Ferracuti S., Napoli C., Roma P. A nationwide survey of psychological distress among italian people during the covid-19 pandemic: immediate psychological responses and associated factors. Int. J. Environ. Res. Public Health. 2020;17 doi: 10.3390/ijerph17093165.
    1. McLafferty M., Brown N., McHugh R., Ward C., Stevenson A., McBride L., Brady J., Bjourson A.J., O’Neill S.M., Walsh C.P., Murray E.K. Depression, anxiety and suicidal behaviour among college students: comparisons pre-COVID-19 and during the pandemic. Psychiatry Res. Commun. 2021;1 doi: 10.1016/J.PSYCOM.2021.100012.
    1. Mencacci C., Salvi V. Expected effects of COVID-19 outbreak on depression incidence in Italy. J. Affect. Disord. 2021;278:66–67. doi: 10.1016/J.JAD.2020.09.043.
    1. Meng X., Brunet A., Turecki G., Liu A., D’Arcy C., Caron J. Risk factor modifications and depression incidence: a 4-year longitudinal Canadian cohort of the Montreal catchment area study. BMJ Open. 2017;7 doi: 10.1136/BMJOPEN-2016-015156.
    1. National Centre for Disease Prevention and Health Promotion (CNaPPS) 2019. Depressione 2016-2019 - The Italian Behavioral Risk Factor Surveillance System (PASSI)
    1. Nutt D.J., Wilson S., Paterson L. Sleep disorders as core symptoms of depression. Dialogues Clin. Neurosci. 2008;10:329–336. doi: 10.31887/DCNS.2008.10.3/DNUTT.
    1. Olagoke A.A., Olagoke O.O., Hughes A.M. Exposure to coronavirus news on mainstream media: the role of risk perceptions and depression. Br. J. Health Psychol. 2020;25:865–874. doi: 10.1111/BJHP.12427.
    1. Orrù G., Monaro M., Conversano C., Gemignani A., Sartori G. Machine learning in psychometrics and psychological research. Front. Psychol. 2020;10 doi: 10.3389/fpsyg.2019.02970.
    1. Osofsky H.J., Osofsky J.D., Hansel T.C. Deepwater horizon oil spill: mental health effects on residents in heavily affected areas. Disaster Med. Public Health Prep. 2011;5:280–286. doi: 10.1001/DMP.2011.85.
    1. Osservatorio Nazionale sulla Salute nelle Regioni Italiane . 2019. Rapporto Osservasalute 2019.
    1. Perna G., Grassi M., Caldirola D., Nemeroff C.B. The revolution of personalized psychiatry: will technology make it happen sooner? Psychol. Med. 2018 doi: 10.1017/S0033291717002859.
    1. Perna G., Riva A., Defillo A., Sangiorgio E., Nobile M., Caldirola D. Heart rate variability: can it serve as a marker of mental health resilience? J. Affect. Disord. 2020;263:754–761. doi: 10.1016/j.jad.2019.10.017.
    1. Pirro F., Caldarola G., Chiricozzi A., Tambone S., Mariani M., Calabrese L., D’Urso D.F., De Simone C., Peris K. The impact of COVID-19 pandemic in a cohort of Italian psoriatic patients treated with biological therapies. J. Dermatol. Treat. 2020 doi: 10.1080/09546634.2020.1800578.
    1. Prout T.A., Zilcha-Mano S., Aafjes-van Doorn K., Békés V., Christman-Cohen I., Whistler K., Kui T., Di Giuseppe M. Identifying predictors of psychological distress during COVID-19: a machine learning approach. Front. Psychol. 2020;11 doi: 10.3389/fpsyg.2020.586202.
    1. Roeckner A.R., Oliver K.I., Lebois L.A.M., van Rooij S.J.H., Stevens J.S. Neural contributors to trauma resilience: a review of longitudinal neuroimaging studies. Transl. Psychiatry. 2021;11 doi: 10.1038/S41398-021-01633-Y.
    1. Roma P., Monaro M., Colasanti M., Ricci E., Biondi S., Domenico A.Di, Verrocchio M.C., Napoli C., Ferracuti S., Mazza C. A 2-month follow-up study of psychological distress among Italian people during the COVID-19 lockdown. Int. J. Environ. Res. Public Health. 2020;17:1–12. doi: 10.3390/IJERPH17218180.
    1. Roohi E., Jaafari N., Hashemian F. On inflammatory hypothesis of depression: what is the role of IL-6 in the middle of the chaos? J. Neuroinflammation. 2021;18 doi: 10.1186/S12974-021-02100-7.
    1. Rossi R., Socci V., Talevi D., Mensi S., Niolu C., Pacitti F., Di Marco A., Rossi A., Siracusano A., Di Lorenzo G. COVID-19 pandemic and lockdown measures impact on mental health among the general population in Italy. Front. Psychiatry. 2020;11 doi: 10.3389/fpsyt.2020.00790.
    1. Salari N., Hosseinian-Far A., Jalali R., Vaisi-Raygani A., Rasoulpoor Shna, Mohammadi M., Rasoulpoor Shabnam, Khaledi-Paveh B. Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: a systematic review and meta-analysis. Glob. Health. 2020;16:57. doi: 10.1186/s12992-020-00589-w.
    1. Santomauro D.F., Mantilla Herrera A.M., Shadid J., Zheng P., Ashbaugh C., Pigott D.M., Abbafati C., Adolph C., Amlag J.O., Aravkin A.Y., Bang-Jensen B.L., Bertolacci G.J., Bloom S.S., Castellano R., Castro E., Chakrabarti S., Chattopadhyay J., Cogen R.M., Collins J.K., Dai X., Dangel W.J., Dapper C., Deen A., Erickson M., Ewald S.B., Flaxman A.D., Frostad J.J., Fullman N., Giles J.R., Giref A.Z., Guo G., He J., Helak M., Hulland E.N., Idrisov B., Lindstrom A., Linebarger E., Lotufo P.A., Lozano R., Magistro B., Malta D.C., Månsson J.C., Marinho F., Mokdad A.H., Monasta L., Naik P., Nomura S., O’Halloran J.K., Ostroff S.M., Pasovic M., Penberthy L., Reiner R.C., Jr., Reinke G., Ribeiro A.L.P., Sholokhov A., Sorensen R.J.D., Varavikova E., Vo A.T., Walcott R., Watson S., Wiysonge C.S., Zigler B., Hay S.I., Vos T., Murray C.J.L., Whiteford H.A., Ferrari A.J. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. Lancet (London, England) 2021;398:1700–1712. doi: 10.1016/S0140-6736(21)02143-7.
    1. Shenesey J.W., Langhinrichsen-Rohling J. Perceived resilience: examining impacts of the Deepwater horizon oil spill one-year post-spill. Psychol. Trauma. 2015;7:252–258. doi: 10.1037/A0035182.
    1. Smith B.W., Dalen J., Wiggins K., Tooley E., Christopher P., Bernard J. The brief resilience scale: assessing the ability to bounce back. Int. J. Behav. Med. 2008;15:194–200. doi: 10.1080/10705500802222972.
    1. Solomou I., Constantinidou F. Prevalence and predictors of anxiety and depression symptoms during the COVID-19 pandemic and compliance with precautionary measures: age and sex matter. Int. J. Environ. Res. Public Health. 2020;17:1–19. doi: 10.3390/IJERPH17144924.
    1. Song S., Yang X., Yang H., Zhou P., Ma H., Teng C., Chen H., Ou H., Li J., Mathews C.A., Nutley S., Liu N., Zhang X., Zhang N. Psychological resilience as a protective factor for depression and anxiety among the public during the outbreak of COVID-19. Front. Psychol. 2021;11 doi: 10.3389/FPSYG.2020.618509.
    1. Spitzer R.L., Kroenke K., Williams J.B.W. Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. J. Am. Med. Assoc. 1999;282:1737–1744. doi: 10.1001/jama.282.18.1737.
    1. Thom R., Silbersweig D.A., Boland R.J. Major depressive disorder in medical illness: a review of assessment, prevalence, and treatment options. Psychosom. Med. 2019;81:246–255. doi: 10.1097/PSY.0000000000000678.
    1. Vindegaard N., Benros M.E. COVID-19 pandemic and mental health consequences: systematic review of the current evidence. Brain Behav. Immun. 2020 doi: 10.1016/j.bbi.2020.05.048.
    1. Wang J.L., Sareen J., Patten S., Bolton J., Schmitz N., Birney A. A prediction algorithm for first onset of major depression in the general population: development and validation. J. Epidemiol. Community Health. 2014;68:418–424. doi: 10.1136/JECH-2013-202845.
    1. Wardenaar K., Riese H., Giltay E., Eikelenboom M., van Hemert A., Beekman A., Penninx B., Schoevers R. Common and specific determinants of 9-year depression and anxiety course-trajectories: a machine-learning investigation in the Netherlands study of depression and anxiety (NESDA) J. Affect. Disord. 2021;293:295–304. doi: 10.1016/J.JAD.2021.06.029.
    1. Wu T., Jia X., Shi H., Niu J., Yin X., Xie J., Wang X., Ph D., Jia X., Ph D., Shi H., Ph D., Niu J., Yin X., Xie J., Wang X., Ph D. Prevalence of mental health problems during the COVID-19 pandemic: a systematic review and meta-analysis. J. Affect. Disord. 2021;281:91–98. doi: 10.1016/j.jad.2020.11.117.

Source: PubMed

Sponsorer og samarbejdspartnere

Medicinske tilstande

Narkotikainterventioner

3
Abonner