The COVID-19 pandemic caused widespread disruption and increased concerns about mental health. One area of particular concern is social anxiety disorder (SAD), as the lockdowns and restrictions have made it difficult for people to engage in social activities. Studies show inconsistency in the prevalence rates reported in different studies, making it difficult to draw a clear picture of the situation. Thus, the study, first attempt to the best of our knowledge, investigates the overall prevalence of SAD during the COVID-19 pandemic. Following PRISMA 2020 guidelines, four academic databases including PubMed, Scopus, Web of Science, and ProQuest, in addition to hand search of reference list of included studies, yielded 338 initial studies, from which 16 were included in the systematic review and meta-analysis. The study found that prevalence of SAD during pandemic was 28% [95% CI: 0.12 to 0.50; I2: 99.6% [based on 16 included studies with 13,961 individuals. After removing seven studies identified as outliers, the pooled prevalence was found to be 34% [95% CI: 0.23 to 0.47; I 2 of 93.1%]. No publication bias was detected. Sub-Group analysis suggested that only the prevalence differed significantly for WHO region sub-group. Additionally, moderator analysis was performed on study-level data, such as age, gender, and risk of bias but no significant changes were found. As the prevalence of SAD was considerable during Covid-19pandemic, mental health support during these challenging times is needed.
This is a preview of subscription content, log in via an institution to check access.
Subscribe and save.
Price includes VAT (Russian Federation)
Instant access to the full article PDF.
Rent this article via DeepDyve
Institutional subscriptions
Guaranteed.
Aderka, I. M., McLean, C. P., Huppert, J. D., Davidson, J. R. T., & Foa, E. B. (2013). Fear, avoidance and physiological symptoms during cognitive-behavioral therapy for social anxiety disorder. Behaviour Research and Therapy, 51 (7), 352–358. https://doi.org/10.1016/j.brat.2013.03.007
Article PubMed PubMed Central Google Scholar
Agordzo, M. P., Ofosuhene-Mensah, J. K., Krafona, K., & Ekem-Ferguson, G. (2021). Social Anxiety and Self-Compassion in Persons with Alcohol Use Disorders in Ghana. International Journal of Research and Innovation in Social Science, 05 (01), 563–568. https://doi.org/10.47772/IJRISS.2021.5128
Article Google Scholar
Alrazeeni, D. (2021). Public anxiety during coronavirus-19 disease (COVID-19) in Saudi Arabia: Indication for a psychological assistance intervention. International Journal of Advanced and Applied Sciences, 8 (4), 12–16. https://doi.org/10.21833/IJAAS.2021.04.002
Alvi, T., Kumar, D., & Tabak, B. A. (2022). Social anxiety and behavioral assessments of social cognition: A systematic review. Journal of Affective Disorders, 311 , 17–30. https://doi.org/10.1016/J.JAD.2022.04.130
Ambusaidi, A., Al-Huseini, S., Alshaqsi, H., AlGhafri, M., Chan, M.-F., Al-Sibani, N., Al-Adawi, S., & Qoronfleh, M. W. (2022). The Prevalence and Sociodemographic Correlates of Social Anxiety Disorder: A Focused National Survey. Chronic Stress, 6 , 247054702210812. https://doi.org/10.1177/24705470221081215
APA. (2013). APA - DSM - Diagnostic and Statistical Manual of Mental Disorders . https://www.appi.org/dsm
Google Scholar
Armour, C., McGlinchey, E., Butter, S., McAloney-Kocaman, K., & McPherson, K. E. (2021). The COVID-19 Psychological Wellbeing Study: Understanding the Longitudinal Psychosocial Impact of the COVID-19 Pandemic in the UK; a Methodological Overview Paper. Journal of Psychopathology and Behavioral Assessment, 43 (1), 174–190. https://doi.org/10.1007/S10862-020-09841-4
Article PubMed Google Scholar
Australian Bureau of Statistics National Study of Mental Health and Wellbeing. (2023). National Study of Mental Health and Wellbeing, 2020–2022| Australian Bureau of Statistics . https://www.abs.gov.au/statistics/health/mental-health/national-study-mental-health-and-wellbeing/latest-release
Barnett, M. D., Maciel, I. V., Johnson, D. M., & Ciepluch, I. (2021). Social Anxiety and Perceived Social Support: Gender Differences and the Mediating Role of Communication Styles. Psychological Reports, 124 (1), 70–87. https://doi.org/10.1177/0033294119900975
Biglbauer, S., & Korajlija, A. L. (2020). Social connections, depression and anxiety. Socijalna Psihijatrija, 48 (4), 404–425. https://doi.org/10.24869/SPSIH.2020.404
Bijl, R. V., De Graaf, R., Ravelli, A., Smit, F., & Vollebergh, W. A. M. (2002). Gender and age-specific first incidence of DSM-III-R psychiatric disorders in the general population: Results from the Netherlands Mental Health Survey and Incidence Study (NEMESIS). Social Psychiatry and Psychiatric Epidemiology, 37 (8), 372–379. https://doi.org/10.1007/s00127-002-0566-3
Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2021). Introduction to meta-analysis . John Wiley & Sons. https://books.google.com/books?hl=en&lr=&id=pdQnEAAAQBAJ&oi=fnd&pg=PP1&dq=Introduction+to+Meta-Analysis.+John+Wiley+%26+Sons.&ots=WFUnAUgNR1&sig=WGt_g7G1XuRzNNGTlxav1xcIYOQ
Buckner, J. D., Abarno, C. N., Lewis, E. M., Zvolensky, M. J., & Garey, L. (2021). Increases in distress during stay-at-home mandates During the COVID-19 pandemic: A longitudinal study. Psychiatry Research, 298 . https://doi.org/10.1016/J.PSYCHRES.2021.113821
Burkle, F. M., Jr. (2019). Challenges of global public health emergencies: Development of a health-crisis management framework. The Tohoku Journal of Experimental Medicine, 249 (1), 33–41.
Campion, J., Javed, A., Sartorius, N., & Marmot, M. (2020). Addressing the public mental health challenge of COVID-19. The Lancet. Psychiatry, 7 (8), 657.
Caruana, E. J., Roman, M., Hernández-Sánchez, J., & Solli, P. (2015). Longitudinal Studies. Journal of Thoracic Disease, 7 (11), E537. https://doi.org/10.3978/j.issn.2072-1439.2015.10.63
Cucinotta, D., & Vanelli, M. (2020). WHO Declares COVID-19 a Pandemic. Acta Bio-Medica: Atenei Parmensis, 91 (1), 157–160. https://doi.org/10.23750/abm.v91i1.9397
Davidson, J. R., Hughes, D. L., George, L. K., & Blazer, D. G. (1993). The epidemiology of social phobia: Findings from the Duke Epidemiological Catchment Area Study. Psychological Medicine, 23 (3), 709–718.
de Vries Robbé, M., & Willis, G. M. (2017). Assessment of protective factors in clinical practice. Aggression and Violent Behavior, 32 , 55–63. https://doi.org/10.1016/j.avb.2016.12.006
Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5 (1), 1–4.
Fawwaz, A. N., Prihadi, K. D., & Purwaningtyas, E. K. (2022). Studying online from home and social anxiety among university students: The role of societal and interpersonal mattering. International Journal of Evaluation and Research in Education, 11 (3), 1338–1345. https://doi.org/10.11591/IJERE.V11I3.23464
Fehm, L., Pelissolo, A., Furmark, T., & Wittchen, H.-U. (2005). Size and burden of social phobia in Europe. European Neuropsychopharmacology, 15 (4), 453–462.
Field, A. P., & Gillett, R. (2010). How to do a meta-analysis. British Journal of Mathematical and Statistical Psychology, 63 (3), 665–694. https://doi.org/10.1348/000711010X502733
Harrer, M., Cuijpers, P., Furukawa, T. A., & Ebert, D. D. (2021). Doing Meta-Analysis with R. Chapman and Hall/CRC . https://doi.org/10.1201/9781003107347
Higgins, J. P., Thompson, S. G., Deeks, J. J., & Altman, D. G. (2003). Measuring Inconsistency in Meta-Analyses. Bmj, 327 (7414), 557–560.
PubMed Google Scholar
Hofmann, S. G., Anu Asnaani, M. A., & Hinton, D. E. (2010). Cultural aspects in social anxiety and social anxiety disorder. Depression and Anxiety, 27 (12), 1117–1127. https://doi.org/10.1002/da.20759
Huremović, D. (Ed.). (2019). Psychiatry of Pandemics: A Mental Health Response to Infection Outbreak . Springer International Publishing. https://doi.org/10.1007/978-3-030-15346-5
Itani, M. H., Eltannir, E., Tinawi, H., Daher, D., Eltannir, A., & Moukarzel, A. A. (2021). Severe Social Anxiety Among Adolescents During COVID-19 Lockdown. Journal of Patient Experience, 8 , 23743735211038384. https://doi.org/10.1177/23743735211038386
Kaur, A., Ani, A., Sharma, A., & Kumari, V. (2021). Nomophobia and social interaction anxiety among university students. International Journal of Africa Nursing Sciences, 15 . https://doi.org/10.1016/J.IJANS.2021.100352
Kessler, R. C., Petukhova, M., Sampson, N. A., Zaslavsky, A. M., Wittchen, H., Ronald Kessler, C. C., Wiley Online Library, in. (2012). Twelve-month and lifetime prevalence and lifetime morbid risk of anxiety and mood disorders in the United States. International Journal of Methods in Psychiatric Research Int. J. Methods Psychiatr. Res, 21 (3), 169–184. https://doi.org/10.1002/mpr.1359
Kessler, R. C. (2003). The impairments caused by social phobia in the general population: Implications for intervention. Acta Psychiatrica Scandinavica, 108 (s417), 19–27. https://doi.org/10.1034/j.1600-0447.108.s417.2.x
Keynejad, R. C., Dua, T., Barbui, C., & Thornicroft, G. (2018). WHO Mental Health Gap Action Programme (mhGAP) Intervention Guide: A systematic review of evidence from low and middle-income countries. BMJ Ment Health, 21 (1), 30–34.
Kim, H., Rackoff, G. N., Fitzsimmons-Craft, E. E., Shin, K. E., Zainal, N. H., Schwob, J. T., Eisenberg, D., Wilfley, D. E., Taylor, C. B., & Newman, M. G. (2022). College Mental Health Before and During the COVID-19 Pandemic: Results From a Nationwide Survey. Cognitive Therapy and Research, 46 (1). https://doi.org/10.1007/S10608-021-10241-5
Kok, A. A. L., Pan, K. Y., Rius-Ottenheim, N., Jörg, F., Eikelenboom, M., Horsfall, M., Luteijn, R., van Oppen, P., Rhebergen, D., Schoevers, R. A., Giltay, E. J., & Penninx, B. W. J. H. (2022). Mental health and perceived impact during the first Covid-19 pandemic year: A longitudinal study in Dutch case-control cohorts of persons with and without depressive, anxiety, and obsessive-compulsive disorders. Journal of Affective Disorders, 305 , 85–93. https://doi.org/10.1016/J.JAD.2022.02.056
Krajewska-Kułak, E., Kułak-Bejda, A., Kułak, W., Bejda, G., Łukaszuk, C., Waszkiewicz, N., Cybulski, M., Guzowski, A., Fiłon, J., Aniśko, P., & Popławska, M. (2022). Well-Being at Home During Forced Quarantine Amid the COVID-19 Pandemic. Frontiers in Psychiatry, 13 . https://doi.org/10.3389/FPSYT.2022.846122
Leary, M. R. (1990). Responses to Social Exclusion: Social Anxiety, Jealousy, Loneliness, Depression, and Low Self-Esteem. Journal of Social and Clinical Psychology, 9 (2), 221–229. https://doi.org/10.1521/jscp.1990.9.2.221
Li, D. J., Chou, L. S., Chou, F. H. C., Hsu, S. T., Hsieh, K. Y., Wu, H. C., Kao, W. T., Lin, G. G., Chen, W. J., & Huang, J. J. (2021). COVID-related psychological distress fully mediates the association from social impact to sleep disturbance among patients with chronic schizophrenia. Scientific Reports, 11 (1). https://doi.org/10.1038/S41598-021-96022-2
Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ, 339 . https://doi.org/10.1136/BMJ.B2700
Makarova, E. A., Makarova, E. L., & Mishchenko, V. I. (2021). Phenomena of loneliness and fear caused by the mass media threat in the situation of COVID-19 pandemic. International Journal of Media and Information Literacy, 6 (1), 146–155. https://doi.org/10.13187/IJMIL.2021.1.146
McEnery, C., Lim, M. H., Tremain, H., Knowles, A., & Alvarez-Jimenez, M. (2019). Prevalence rate of social anxiety disorder in individuals with a psychotic disorder: A systematic review and meta-analysis. Schizophrenia Research, 208 , 25–33. https://doi.org/10.1016/j.schres.2019.01.045
Miyazaki, Y., Kamatani, M., & Kawahara, J. I. (2021). The influence of social anxiety, trait anxiety, and perceived vulnerability to disease on the frequency of face mask wearing. Shinrigaku Kenkyu, 92 (5), 339–349. https://doi.org/10.4992/JJPSY.92.20063
Moitra, E., Herbert, J. D., & Forman, E. M. (2008). Behavioral avoidance mediates the relationship between anxiety and depressive symptoms among social anxiety disorder patients. Journal of Anxiety Disorders, 22 (7), 1205–1213. https://doi.org/10.1016/j.janxdis.2008.01.002
Nadkarni, A., Hanlon, C., & Patel, V. (2023). Mental Health Care Models in Low-and Middle-Income Countries. In A. Tasman, M. B. Riba, R. D. Alarcón, C. A. Alfonso, S. Kanba, D. M. Ndetei, C. H. Ng, T. G. Schulze, & D. Lecic-Tosevski (Eds.), Tasman’s Psychiatry (pp. 1–47). Springer International Publishing. https://doi.org/10.1007/978-3-030-42825-9_156-1
Chapter Google Scholar
Patel, A., Knapp, M., Henderson, J., & Baldwin, D. (2002). The economic consequences of social phobia. Journal of Affective Disorders, 68 (2–3), 221–233.
Patel, V., & Prince, M. (2010). Global mental health: A new global health field comes of age. JAMA, 303 (19), 1976–1977.
Perugi, G., Simonini, E., Savino, M., Mengali, F., Cassano, G. B., & Akiskal, H. S. (1990). Primary and secondary social phobia: Psychopathologic and familial differentiations. Comprehensive Psychiatry, 31 (3), 245–252.
Pragholapati, A. (2020). Covid-19 Impact on Students (pp. 1–6). https://doi.org/10.35542/OSF.IO/895ED
Book Google Scholar
Quittkat, H. L., Düsing, R., Holtmann, F. J., Buhlmann, U., Svaldi, J., & Vocks, S. (2020). Perceived Impact of Covid-19 Across Different Mental Disorders: A Study on Disorder-Specific Symptoms, Psychosocial Stress and Behavior. Frontiers in Psychology, 11 . https://doi.org/10.3389/fpsyg.2020.586246
Rapee, R. M., Sanderson, W. C., & Barlow, D. H. (1988). Social phobia features across the DSM-III-R anxiety disorders. Journal of Psychopathology and Behavioral Assessment, 10 (3), 287–299. https://doi.org/10.1007/BF00962552
Reinelt, E., Aldinger, M., Stopsack, M., Schwahn, C., John, U., Baumeister, S. E., Grabe, H. J., & Barnow, S. (2014). High social support buffers the effects of 5-HTTLPR genotypes within social anxiety disorder. European Archives of Psychiatry and Clinical Neuroscience, 264 (5), 433–439. https://doi.org/10.1007/s00406-013-0481-5
Ruscio, A. M., Brown, T. A., Chiu, W. T., Sareen, J., Stein, M. B., & Kessler, R. C. (2008). Social fears and social phobia in the USA: Results from the National Comorbidity Survey Replication. Psychological Medicine, 38 (1), 15–28.
Salehian, R., Jolfaei, A. G., Naserbakht, M., & Abdi, M. (2021). Posttraumatic stress symptoms and general mental health problems during the covid-19 pandemic in iran: Aweb-based cross-sectional survey. Iranian Journal of Psychiatry and Behavioral Sciences, 15 (3). https://doi.org/10.5812/IJPBS.114432
Shengbo, L., Fiaz, M., Mughal, Y. H., Wisetsri, W., Ullah, I., Ren, D., Kiran, A., & Kumar Kesari, K. (2022). Impact of Dark Triad on Anxiety Disorder: Parallel Mediation Analysis During Pandemic. Frontiers in Psychology, 13 , 914328. https://doi.org/10.3389/fpsyg.2022.914328
Stanbouly, D., & Chuang, S. K. (2021). What are the Psychosocial Consequences of Chronic Mask-Wearing in the COVID-19 Pandemic? Journal of Oral and Maxillofacial Surgery, 79 (9), 1815–1816. https://doi.org/10.1016/J.JOMS.2021.04.014
Suhail, A., Dar, K. A., & Iqbal, N. (2022). COVID-19 related fear and mental health in Indian sample: The buffering effect of support system. Current Psychology, 41 (1), 480–491. https://doi.org/10.1007/S12144-021-01694-8
The ESEMeD, MHEDEA 2000 Investigators. (2004). Prevalence of mental disorders in Europe: Results from the European Study of the Epidemiology of Mental Disorders (ESEMeD) project. Acta Psychiatrica Scandinavica, 109 (s420), 21–27. https://doi.org/10.1111/j.1600-0047.2004.00327.x
Torabi, F., Hashemi, M., Zamanian, M., Shayganfard, M., & Yousefichaijan, P. (2023). The Prevalence of Social Anxiety in Children with Chronic Functional Constipation. Journal of Comprehensive Pediatrics, 14 (1). https://doi.org/10.5812/compreped-132384
Tundo, A., Betro’, S., & Necci, R. (2021). What is the impact of covid-19 pandemic on patients with pre-existing mood or anxiety disorder? An observational prospective study. Medicina (Lithuania), 57 (4). https://doi.org/10.3390/MEDICINA57040304
Vriends, N., Becker, E. S., Meyer, A. H., & Margraf, J. (2011). Incidence of DSM-IV social phobia in a community sample of young German women. German Journal of Psychiatry, 14 (2), 80–90.
Vriends, N., Bolt, O. C., & Kunz, S. M. (2014). Social anxiety disorder, a lifelong disorder? A review of the spontaneous remission and its predictors. Acta Psychiatrica Scandinavica, 130 (2), 109–122. https://doi.org/10.1111/acps.12249
Wagner, R., Silove, D., Marnane, C., & Rouen, D. (2006). Delays in referral of patients with social phobia, panic disorder and generalized anxiety disorder attending a specialist anxiety clinic. Journal of Anxiety Disorders, 20 (3), 363–371.
Wang, C., Pan, R., Wan, X., Tan, Y., Xu, L., Ho, C. S., & Ho, R. C. (2020). Immediate Psychological Responses and Associated Factors during the Initial Stage of the 2019 Coronavirus Disease (COVID-19) Epidemic among the General Population in China. International Journal of Environmental Research and Public Health 2020, Vol. 17, Page 1729, 17 (5), 1729. https://doi.org/10.3390/IJERPH17051729
Weiller, E., Bisserbe, J.-C., Boyer, P., Lepine, J.-P., & Lecrubier, Y. (1996). Social phobia in general health care: An unrecognised undertreated disabling disorder. The British Journal of Psychiatry, 168 (2), 169–174.
WHO. (2022). Mental disorders . Https://Www.Who.Int/News-Room/Fact-Sheets/Detail/Mental-Disorders.
WHO World Mental Health Survey Collaborators, Stein, D. J., Lim, C. C. W., Roest, A. M., De Jonge, P., Aguilar-Gaxiola, S., Al-Hamzawi, A., Alonso, J., Benjet, C., Bromet, E. J., Bruffaerts, R., De Girolamo, G., Florescu, S., Gureje, O., Haro, J. M., Harris, M. G., He, Y., Hinkov, H., Horiguchi, I., & Scott, K. M. (2017). The cross-national epidemiology of social anxiety disorder: Data from the World Mental Health Survey Initiative. BMC Medicine, 15 (1), 143. https://doi.org/10.1186/s12916-017-0889-2
Article PubMed Central Google Scholar
Yun, P., Xiaohong, H., Zhongping, Y., & Zhujun, Z. (2021). Family Function, Loneliness, Emotion Regulation, and Hope in Secondary Vocational School Students: A Moderated Mediation Model. Frontiers in Public Health, 9 , 722276. https://doi.org/10.3389/fpubh.2021.722276
Zheng, V., & Fung, A. Y. H. (2021). COVID-19, social anxiety, and economic-political crisis in Hong Kong: A public opinion survey’s perspective. The Routledge Handbook of Public Health and the Community , 150–170. https://doi.org/10.4324/9781003119111-15
Download references
Authors and affiliations.
Department of Psychology, Jamia Millia Islamia, New Delhi, India
Mohammad Hashim, Meena Osmany & Naved Iqbal
Social Determinants of Health Research Center, Research Institute for Prevention of Non-Communicable Diseases, Qazvin University of Medical Sciences, Qazvin, Iran
Zainab Alimoradi
Department of Psychology, Government Degree College Baramulla, Jammu and Kashmir, India
Kaiser Ahmad Dar
You can also search for this author in PubMed Google Scholar
MH is the corresponding author and guarantor of the research. He and NI proposed the idea of the research, wrote the first draft, and analysed the result. MO and KAD contributed to data collection and verification. ZA supervised systematic review and meta-analysis portions and suggested appropriate changes. NI supervised the whole process. All 5 members contributed to the critical evaluation of the final manuscript.
Correspondence to Mohammad Hashim .
Ethics approval, consent to participate, consent for publication, conflicts of interest/competing interests, additional information, publisher’s note.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary material 1, rights and permissions.
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Reprints and permissions
Hashim, M., Osmany, M., Alimoradi, Z. et al. Prevalence of social anxiety during COVID-19 pandemic: A systematic review and meta-analysis. Curr Psychol (2024). https://doi.org/10.1007/s12144-024-06617-x
Download citation
Accepted : 23 August 2024
Published : 29 August 2024
DOI : https://doi.org/10.1007/s12144-024-06617-x
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative
Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.
Scientific Reports volume 14 , Article number: 20121 ( 2024 ) Cite this article
Metrics details
The COVID-19 pandemic has had a catastrophic impact on public health, extending to the food system and people's livelihoods worldwide, including Bangladesh. This study aimed to ascertain the COVID-19 pandemic impacts on livelihood assets in the North-Western areas (Rajshahi and Rangpur) of Bangladesh. Primary data were collected from 320 farmers engaged in high-value agriculture using a multistage sampling method. The data were analysed using first-order structural equation modelling. The findings reveal a significant impact (p < 0.01) of the pandemic on all livelihood assets in Bangladesh. Notably, human assets exhibited the highest impact, with a coefficient of 0.740, followed sequentially by financial (0.709), social (0.684), natural (0.600), physical (0.542), and psychological (0.537) assets. Government-imposed lockdowns and mobility restrictions were identified as the major causes of the pandemic's negative effects on livelihoods, which included lost income, rising food prices, decreased purchasing power, inadequate access to food and medical supplies, increased social insecurity, and a rise in depression, worry, and anxiety among farmers. The effects of COVID-19 and associated policy measures on the livelihoods of high-value crop farmers have reversed substantial economic and nutritional advances gained over the previous decade. This study suggests attention to the sustainable livelihoods of farmers through direct cash transfer and input incentive programs to minimize their vulnerability to a pandemic like COVID-19 or any other crisis in the future.
The SARS-CoV-2 virus, which causes COVID-19, emerged as a threat to public health around the world, and on March 11, 2020, it was declared a worldwide pandemic by the World Health Organization 1 . In Bangladesh, the disease was first detected on March 7, 2020 2 . Bangladesh experienced its 1st phase of lockdown in March–May 2020, drastically disrupted food value chains by restricting the movement of people and commodities. This distribution led to growing rates of food loss and waste, supply chain disruption, and declining product demand 2 . Globally, food insecurity rose due to the disruption of supply chains, resulting in prices and production costs 3 .
The COVID-19 pandemic presents an opportunity to study a severe shock to food systems and underscores the importance of access to livelihood assets in buffering against such shocks. Livelihood assets refer to the resources and capabilities that individuals and communities possess, influencing their ability to cope with stresses and shocks and to recover and maintain their livelihoods sustainably 4 , 5 . People in developing nations rely on a variety of resources, including capital and assets, to support their daily lives. Five subsistence assets—natural, physical, financial, human, and social—are used to classify livelihood assets 6 . Psychological factors were added based on the COVID-19 pandemic situation. These assets play an important role in the survival of sustainable rural and urban livelihoods 7 . Not all shocks are anticipated to have the same impact on assets and outcomes related to livelihoods, but shocks such as the pandemic can undermine some or all assets and have a detrimental impact on livelihoods 8 .
The COVID-19 has profoundly affected various aspects of life globally. Previous outbreaks, such as Middle East respiratory syndrome (MERS), severe acute respiratory syndrome (SARS), and Ebola, have been extensively studied and have shown to significantly disrupt agricultural labor and output 9 , 10 , 11 . The substantial impacts of COVID-19 on agriculture underscore the importance of a sustainable livelihood strategy that considers different capital assets—natural, economic, financial, human, and social 12 , 13 , 14 , 15 , 16 , 17 . The multidimensional effects of the epidemic on employment, food availability, and market dynamics have resulted in significant food insecurity for daily wage workers 18 , 19 , 20 . Due to COVID-19, there have been substantial disruptions in agricultural productivity and food value chains, as identified in prior research. Due to labor shortages, transportation restrictions, and difficulties in obtaining agricultural inputs have led to increased production costs 21 , 22 . Farmers also encountered challenges in harvesting crops and transporting products to the market, resulted in higher food transportation costs 23 . Labor shortages, further impeded productivity and market access was restricted for sellers and purchasers due to travel restrictions 24 , 25 , 26 , 27 . Furthermore, financial challenges faced by farmers were exacerbated by a decrease in consumer demand for perishables, price increases, and reduced earnings among informal laborers 28 , 29 .
This study focuses on farmers engaged in the production of high-value agriculture, such as vegetables. In general, agricultural products that are eaten either fresh or processed and have a substantially higher value (per weight or unit) in the market are considered high-value agriculture 30 . In developing countries like Bangladesh, high-value agricultural practices are important to achieve the sustainable development goals (SDG), particularly SDG 2.3, which describes 2030 as a doubling of the agricultural productivity and incomes of small-scale food producers 31 . Previous studies have examined the impact of COVID-19 on rural livelihood, food safety, dietary diversity, and food security 18 , 32 , 33 , 34 , 35 , 36 , 37 . However, no systematic study has been found concerning how the pandemic impacted the livelihood assets of farmers engaged in high-value agriculture.
The study was conducted in four districts of the North-Western (NW) region of Bangladesh, a region with considerable agricultural significance and vulnerability to natural and socioeconomic challenges. The decision to focus on this region was based on its diverse livelihoods, potential for policy impact, and the need to address existing research gaps. Farmers in this region continuously fight against natural disasters, illiteracy, and other development problems 38 . This study offers several novel aspects. Unlike past research focusing on individual aspects of livelihood and farming systems individually, particularly either economic well-being or social aspects 32 , 36 , 37 , 39 , 40 , this study examines the impact of COVID-19 on six dimensions of livelihood assets (financial, social, human, natural, physical, and psychological factors) combined, providing more robust findings. Additionally, this study specifically focuses on high-value crops, which are crucial for commercial farming, whereas other studies broadly focus on farming systems. Conducted in 2022, the study generates more robust, longer-term findings compared to studies conducted immediately after the outbreak of COVID-19 in 2020 2 , 18 , 19 , 32 , 33 , 34 , 35 , 36 , 39 , 40 , 41 , 42 . Finally, the use of Structural Equation Modeling (SEM) is employed in this study for research hypotheses testing since it is more robust than other studies using the OLS model or qualitative approaches 43 . The key methodological strength of this study is the application of first-order SEM to evaluate the impact of COVID-19 on livelihood assets. SEM enables the analysis of complex relationships between observed and latent variables, facilitating a more nuanced comprehension of the interrelations and impacts of various dimensions of livelihood assets during the pandemic.
As the global food systems have been significantly disrupted by the COVID-19 pandemic, which has resulted in intensified food insecurity, it is essential to comprehend the effects to develop strategies that will improve the sustainability and resilience of agricultural systems and livelihoods, particularly in developing countries such as Bangladesh. The study offers a comprehensive evaluation of the pandemic's effects on six dimensions of livelihood assets among high-value crop farmers, providing critical insights for policymakers to develop evidence-based recommendations for targeted interventions. This research provides critical insights into the multifaceted impacts of the COVID-19 pandemic on high-value crop producers in Bangladesh, thereby facilitating the creation of more sustainable and resilient food systems. The findings are especially pertinent for policymakers, as they offer evidence-based recommendations for targeted interventions that can improve the resilience of rural communities and ensure food security. This study contributes valuable knowledge to the existing body of research by addressing a research gap on the pandemic's impact on high-value crop producers.
The paper is divided into six sections. The literature review is presented in Section " Literature review ", followed by the methodology in Section " Methodology ". Section " Results " presents the results, whereas Section " Discussion " presents the discussion. Conclusions and policy recommendations are presented in the final section.
In Bangladesh, approximately 16.2 million farm households, predominantly smallholders (with 0.05–2.49 acres of land), engage in commercial vegetable production, utilizing approximately 2.63% of the total cultivable land 44 . While the immediate consequences of COVID-19 were widely felt 21 , on-farm challenges also emerged. Assessment of agricultural inputs became more difficult, leading to increased production costs alongside labor shortages and transportation hurdles. Farmers experienced obstacles in harvesting crops or transporting goods to markets due to mobility restrictions 22 . The reduced number of vehicles on the road contributed to heightened food transportation costs 23 . Labor shortages further hampered agricultural productivity, while travel restrictions constrained access to markets for both sellers and buyers 24 . Although there was a surplus of physical labor due to the return of migrant workers from other countries and unemployed urban workers to rural areas, restrictions on the movement of migrant workers resulted in labor shortage 25 , 26 , 27 .
In addition to production issues, farmers encountered market challenges stemming from a decrease in consumer demand for goods, especially perishables. High-value agricultural products such as fruits and vegetables, meat, fish, milk, and eggs, which typically have strong income elasticities, experienced substantial declines in demand due to reduced earnings among non-salaried informal workers and price hikes, particularly in metropolitan areas 28 . As consumer demand dwindled, supply disruptions persisted, forcing farmers to sell below cost and leading to significant financial hardships 29 .
These production disruptions are not unique to the COVID-19 pandemic but have been observed in both emerging and industrialized nations during previous epidemics. Diseases like Ebola, MERS, and SARS primarily disrupted food systems in the regions where they occurred 9 . By affecting agricultural labor forces and hindering other input factors 10 , these diseases significantly reduced agricultural production 11 . Similarly, COVID-19 has had a profound impact on the agricultural production industry, which serves as the cornerstone of the food system.
The sustainable livelihoods approach offers a framework for understanding the purpose, significance, and dimensions of human development 12 . It encompasses various forms of capital, including natural, economic, financial, human, and social, all of which contribute to sustainable livelihoods. Natural capital refers to the ownership or shared management of natural resources such as climate, soil fertility, and water sources, which are essential for production 13 . Human capital encompasses all human potential that enables individuals to pursue various livelihood activities and achieve communal objectives 14 . Physical capital comprises infrastructure and means of production necessary to support livelihoods 15 . Social capital emerges from social organizations and encompasses characteristics like trust, norms, and collaboration, which can strengthen society by promoting coordination and cooperation for various benefits 16 . Financial assets indicate access to different resources, particularly savings and loans 17 . Both direct loans and savings serve as forms of productive capital that can be converted into other types of capital or utilized for immediate consumption.
COVID-19 had a profound impact on rural livelihoods, manifesting in several ways 19 , 20 . For example, many individuals lost their jobs due to regulations on social interactions, self-imposed isolation, and travel restrictions. Additionally, panic buying resulted in unpredictable food supplies. The significant disruptions to the agriculture industry led to severe food shortages, lower wages and significant food insecurity among Bangladesh's daily wage workers, who comprise one-third of the labor force 18 . Labour shortages affected agricultural production, while travel restrictions constrained access to markets for both buyers and sellers 24 . Consequently, prices for agricultural products initially surged in local marketplaces due to a lack of consumers and dealers before subsequently plummeting, particularly for perishable goods like vegetables and fish 42 .
Previous research indicates that COVID-19 significantly disrupted households’ ability to access adequate food, with 82.5% of respondents expressing concerns about food security, rising costs, and disruptions in local markets 36 . The pandemic also had adverse effects on agricultural production, sales, prices, and income, with over 80% of farms experiencing sales declines and 20% faced severe losses, while 90% reported price reductions 39 . The vulnerability of households to the COVID-19 outbreak encompasses social, economic, human, physical, and psychological dimensions, which significantly impact their resilience 40 . The diverse impacts of shocks on rural households highlight the disparities in their capital assets and subsistence strategies, influencing their ability to recover from market or natural shocks 45 . For rural households with limited access to natural resources, procuring food and accumulating other assets becomes challenging, exacerbating vulnerabilities 46 . Furthermore, the trauma experienced during crises can lead people to rely more heavily on their social networks for support 47 . Shocks such as epidemics can severely impact various livelihood assets (financial, social, human, physical, and natural assets), as evidenced by the negative effects of Ebola on home crop production in Liberia, exacerbating food insecurity 48 . While previous studies have addressed the impacts of epidemics and COVID-19 on individual aspects of livelihoods and farming systems, our research offers a comprehensive perspective by examining the combined vulnerability of six dimensions of livelihood assets (financial, social, human, physical, natural, and psychological assets). By including psychological assets our study acknowledges the holistic nature of livelihood vulnerability, recognizing that mental well-being influences and interacts with traditional livelihood assets. This comprehensive approach enables a more accurate assessment of the multifaceted impacts of COVID-19 on people's lives and livelihoods.
The selection of the NW region of Bangladesh for this study was deliberate, considering its significant cultivation of high-value crops and prevalent poverty conditions, particularly exacerbated during the COVID-19 pandemic. Rural livelihoods in these regions rely heavily on high-value agriculture, making them vital study areas. Additionally, the NW region is susceptible to natural disasters, further complicating the socio-economic landscape. The pandemic exacerbated existing vulnerabilities, pushing households deeper into poverty 49 . The study focused on four districts within the NW region: Dinajpur, Rangpur, Bogura, and Pabna (see Fig. 1 ) chosen in collaboration with the Department of Agricultural Extension (DAE).
Study area. The authors used ArcGIS 10.8 ( https://www.arcgis.com/index.html ) to produce the map, employing the administrative shapefile of Bangladesh in the process. Shapefile republished from the Bangladesh Agricultural Research Council (BARC) database ( http://maps.barcapps.gov.bd/index.php ) under a CC BY license, with permission from Computer and GIS unit, BARC, original copyright 2014.
The mWater surveyor app was used to conduct direct interviews using a structured questionnaire for the collection of primary data. A total of 320 farmers, 80 from each district, were surveyed from eight upazilas in four districts of the North-West region, employing a multistage sampling technique (Table 1 ). Initially, the selection of the four districts in our study was based on their prominence in high-value crop farming within the North-West region. In this study, two Upazilas were purposively selected from each district to capture the geographic and socioeconomic diversity within the districts, ensuring a comprehensive representation of different agricultural practices and livelihood conditions. Subsequently, farmers were randomly selected by drawing numbers from a compiled list of high-value vegetable growers, provided by the Sub-Assistant Agricultural Officer of the DAE from the respective agricultural blocks. An equal sample size of 40 participants per Upazila was adopted to ensure statistical consistency and enable reliable comparative analysis across Upazilas, thereby enhancing the robustness of our findings.
The total sample size was determined using the following formula 50 .
where p is the predicted proportion of respondents and n is the sample size, the p-value of 0.50 was utilized to obtain the greatest number of respondents. The acceptable margin of error is represented by e, which is equal to 0.06, and Z stands for standard error for a 95% confidence level. So, the sample size would be,
Although the estimator suggested a sample size of 266, we selected to survey 320 farming households from four districts in Bangladesh to reduce the margin of error by an additional 20%. Farmers from the study area were selected using simple random sampling. These individuals, known as high-value agricultural producers, specialize in cultivating crops such as brinjal, pointed gourd, beans, cabbage, cauliflower, tomato, carrot, and bottle gourd.
The survey was conducted between May and June 2022, relying on respondents’ memories for data for 2019 and 2022. This sample had been used previously somewhere else. A structured questionnaire was designed (as detailed in Supplementary file 2 ), covering demographic characteristics, livelihood assets, and psychological factors of high-value crop farming. Equal weight was given to each of the six livelihood diversification options when selecting responses. The questionnaire underwent pre-testing by the authors before being finalized. The study did not use the pre-tested data in the analysis. Data collection was conducted via face-to-face interviews using the finalised questionnaire, which was exported to the mWater portal, a web-based digital data collection tool.
In this study, we examine the impact of COVID-19 on the assets of high-value crop farmers in Bangladesh's NW region using a reflecting model. Reflective models are applicable when indicators represent underlying latent constructs, meaning changes in the latent variable are mirrored in changes in the indicators 51 . The financial, social, physical, human, natural, and psychological resources examined in this study are considered reflective, as they are expected to adapt to changes in the underlying latent concept of livelihood impact.
The collected primary data were used to assess the impact of COVID-19 on livelihood assets. First-order partial least square structural equation modelling (PLS-SEM) was applied to determine these impacts using SmartPLS 4 software 52 . Structural equation modelling is a hypothesis testing method that evaluates whether the indicators accurately measure latent variables. As latent variables cannot be directly measured, they are inferred from the observable. Due to its flexibility in modelling complex interactions without making rigid assumptions about data distribution, PLS-SEM is well suited for this purpose. It is particularly useful for analyzing data from small samples providing valid findings 43 .
Figure 2 presents the conceptual model linking the relationship between six livelihood factors and COVID-19 impacts. Twenty-seven statements were constructed to define six livelihood assets (see Appendix Table 1 ). The scales and attributes were derived from previous research 5 , and these were tailored to the context of vulnerable livelihood assets in Bangladesh. Farmers responses to these statements were collected using a five-point Likert scale (1 = very low to 5 = very high). COVID-19 was treated as a dependent variable, categorised as 0 = before COVID-19 and 1 = during COVID-19. The study identified five categories of assets: natural, physical, financial, human, and social 6 . These assets play a vital role in survival strategies for rural and urban livelihoods 7 . Additionally, psychological assets, such as fear of infection, social tensions, and depression, were considered due to the pandemic's profound effect on mental health and well-being. The capacity of farmers to manage tension and anxiety became an integral component of their overall vulnerability. Psychological vulnerability (defined as PhAV) was added based on the COVID-19 pandemic when anxiety, worry, and depression were high among the farmers 53 . Financial assets were chosen as indicators to capture the pandemic's economic effects. Farmers were expected to experience income loss, increased food prices diminished purchasing power, and unemployment as a result of the pandemic. Financial asset vulnerabilities (FAV) indicate income loss, decreased purchasing power, increased food prices, unemployment, poverty, and inequality. Social resources that people utilize to support their livelihoods are referred to as social property because they are part of a network of social ties between individuals or groups 54 . Farmers frequently communicate face-to-face with friends and family members to demonstrate their skills and knowledge of agricultural operations 55 . Social assets were chosen as indicators due to their involvement in crisis resilience 56 . It was anticipated that the pandemic's impact on social interactions, trust in information sources, and social insecurity would hinder farmers' ability to respond to the crisis' challenges. Social asset vulnerability (SAV) encompasses trust among individuals, social solidarity, trust in media information, changes in traditions and customs, and social insecurity. Given the threat posed by the pandemic to public health, assessing the vulnerability of human assets becomes crucial. Anticipated outcomes of the pandemic, such as the closure of educational institutions, limited access to medical services, and psychological distress, could significantly impact high-value crop producers. Human assets vulnerability (HAV) is therefore characterised by factors such as the closure of educational institutions, inadequate access to medical staff, and insufficient health information and counselling services. Physical assets typically encompass essential amenities and infrastructure supporting agricultural production and livelihoods such as tractors, water supply canals, and roads. Disruptions in the supply chain caused by the COVID-19 pandemic have limited access to vital agricultural inputs and equipment. Understanding the pandemic’s impact on agricultural productivity necessitates a thorough evaluation of physical assets vulnerability. (PAV), which includes factors like inadequate access to pharmaceutical items, limited availability of disinfectants and detergents, and a shortage of reliable resources providing information about COVID-19 treatment. Natural assets refer to the natural properties relied upon for survival and progress. Disruptions to farming operations, brought about by the pandemic, are of utmost importance, as they can significantly affect farmers' ability to sustain themselves. Natural assets vulnerability (NAV) encompasses delays in agricultural activities, underutilization of natural and recreational resources, decreased agricultural outputs, and farmers’ hesitancy to plan crop production 55 , 57 . Detailed descriptions of these assets are provided in Appendix Table 1 .
A conceptual model.
To estimate the impacts of COVID-19 on different livelihood assets, we constructed and estimated the following six equations (Eqs. 1 – 6 ),
These conceptual equations illustrate our aim to estimate the impacts of COVID-19 on various livelihood assets. The following six hypotheses were formulated to support the above six equations:
H 1 = The COVID-19 has a substantial impact on the vulnerability of financial assets.
H 2 = The COVID-19 has a substantial impact on the vulnerability of social assets.
H 3 = The COVID-19 has a substantial impact on the vulnerability of human assets.
H 4 = The COVID-19 has a substantial impact on the vulnerability of physical assets.
H 5 = The COVID-19 has a substantial impact on the vulnerability of natural assets.
H 6 = The COVID-19 has a substantial impact on the vulnerability of psychological assets.
As a result, three steps were taken to ensure the precision of the measurement model: (1) Model dependability and validity, (2) Uni-dimensionality, and (3) Diagnostic analysis, all of which were applied to the effect of COVID-19 on means of subsistence.
In assessing reliability, indicators are evaluated based on their consistency in measuring a particular component. When the construct explains more than 50% of the variation of the indicator, as is the case when loading is above 0.60, the indicator is said to have a satisfactory level of dependability 58 . When evaluating reliability, a higher score is better. Reliability levels that are “acceptable to good” are explained by results between 0.70 and 0.95. Next, the average extracted variance was used to determine the convergent validity (AVE). The AVE must be 0.50 or greater to be considered valid, meaning that the construct must account for (at least) 50% of the variance of its elements. Assessing the discriminant validity is the final stage. Finally, discriminant validity is assessed using Fornell–Larcker's criterion, which examines correlations between constructs. The suggested threshold is a value of the Fornell–Larcker criterion of 0.90.
This study received approval from the Research Ethics Committee of Bangladesh Agricultural University, Mymensingh, Bangladesh (BAU-REC-2022-102) on April 20, 2022. The study was performed in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants and the questionnaires were anonymized to protect their privacy. Participants were also given the option to decline participation in the survey if they chose to do so.
Farmers' socio-demographic characteristics are explained in Table 2 . The average age of the respondents during COVID-19 was 44.26 years. The mean household size was around five members. About 80% of farmers had various levels of literacy, ranging from primary to upper education. The results also demonstrate a decrease in the average total income of agricultural households during the pandemic. Specifically, the average income from vegetable cultivation decreased by 6,477 Taka, leading to reductions in expenditure. To cope with the income loss, farmers reduced their meals, resulting in an average reduction of 2,197 Taka in monthly household expenditure.
Each latent variable was operationalized through several indicators (as detailed in Appendix Table 1 ). Financial and social assets were represented by five indicators each; while natural assets had three indicators. Physical, human assets and psychological factors were represented by four indicators. The relationships between the latent variables and their respective indicators are presented in Table 3 .
The findings revealed that the scores assigned to all assessed indicators of financial asset vulnerability ranged above three, indicating high to very high response. The pandemic had a significant income and purchasing power, leading to increased food prices, and reduced employment opportunities, and decreased incomes. Consequently, farmers had to reduce their expenditures by rationing basic needs such as food. The analysis also revealed a notable level of financial stress, as individuals had to ration food to accommodate other necessities.
An inverse relationship was found to exist between farmers’ investments in social assets and vulnerability. Some highlighted outcomes included social distrust, particularly within communities and towards national information resources, erosion of social cohesion and solidarity, as well as heightened social vulnerability. Many of these changes were attributed to social distancing measures and restrictions on gatherings, which hindered relationships that typically offer social support.
Overall, human assets remained more vulnerable in specific areas, including education and health sectors. School closings disrupted children’s education and hindered the long-term development of human capital. Additionally, inadequate access to medical services and health information complicated households' ability to manage their health during the pandemic. Health concerns and mobility limitations also reduced labour availability, leading to lower agricultural output.
Farmers faced significant challenges in obtaining essential goods and/or services such as disinfectants, sanitary products, and medications. Delays in planting and harvesting were common due to labour shortages and movement restrictions. Market access was also restricted, affected the physical transportation of goods, which further strained physical resources.
Another example of the vulnerability of natural assets was seen in the postponement of the farming seasons, resulting in reduced production and efficiency. Farmers hesitated to invest in the next planting season due to uncertainty. Input constraints and limited extension services, crucial for managing natural resources, resulted in inefficient use of the resources.
The study also demonstrated that farmers experienced adverse effects on their psychological health due to the COVID-19 outbreak. The uncertainty and health risks posed by the epidemic increased the prevalence of depression and anxiety. Farmers grappled with the social and economic consequences, which led to heightened social tensions and frustrations. Coping strategies, including reduced food intake and increased reliance on social support networks, were employed to deal with the stress of revenue loss and changed behaviours.
The structure of factors was assessed using CFA, which relied on factor loadings to test the validity of the factors. The threshold values for combined reliability, Cronbach’s α coefficient, and average extracted variance for each structure in the intended model were greater than 0.60, 0.70, and 0.50, respectively. The reliability and validity of all the latent variables are shown in Table 4 .
We found that the factor loading values of SAV3 and HAV1 were less than 0.70. Given that the factor loadings did not exceed the cut-off, this suggests that these two factors were invalid. Besides, the coefficient of determination (CR) and Cronbach α were used to determine the latent constructs’ reliability. Thus, the measurement model findings show that for all the latent variables, the least value of Cronbach's alpha and CR was larger than 0.70. Furthermore, all AVE values were above 0.50, indicating convergent validity. After excluding the two invalid factors (SAV3 and HAV1), the convergent validity and reliability were re-estimated.
Since all the factor loadings exceeded the cut-off, we concluded that none of the factors were invalid (Table 4 ). Therefore, the results of the measurement model indicate that all the minimum values of Cronbach’s α and CR were greater than 0.70, suggesting that all the constructs were statistically reliable. The relationship between the variables is instead determined using convergent validity 59 . Convergent validity was assessed using the same study, which gave the AVE threshold of 0.50. As the lowest validity was determined to be 0.561, which exceeded 0.50, the results suggest that all the latent constructs have acceptable convergent validity.
The predictive value of the model was assessed using the R-square value. The R-square values indicate that the variance in COVID-19 explained 49.6%, 46.6%, 54.2%, 29.3%, 35.8%, and 28.6% in financial, social, human, physical, natural, and psychological assets vulnerability, respectively.
The Fornell–Larcker criterion was applied to evaluate discriminant validity by establishing the degree to which one latent concept is distinguishable from the other. The study claimed that 0.90 is the highest figure appropriate in this case. Consequently, none of the connections had a value greater than 0.90 (Table 5 ), showing that no violation of the discriminant validity assumption occurred.
The study utilised bootstrapping (5,000 iterations), a resampling approach, to assess the importance of each component in explaining the others. The path coefficient results are presented in Table 6 .
We find that the impact of COVID-19 on all livelihood assets was statistically significant at 1% (Table 6 ). Figure 1 in the appendix presents the outcomes of the path model. All latent variables were significantly affected by COVID-19 (financial assets, social assets, human assets, physical assets, natural assets, and psychological factors). The p-values and standardized regression coefficients are presented in the numbers on the path relationships. The p-values were less than 0.01 for all latent variables, indicating significance at the 1% level. On the other hand, the values from latent variables to indicators imply the relationship between indicators and latent variables. For all the indicators, the p-values were less than 0.01, indicating significance at the 1% level. This suggests that all the indicators were related to the latent variables.
We discovered that COVID-19 had the greatest effect on financial assets (coefficient = 0.709; p-value < 0.01). The positive effect indicates that income and purchasing power were reduced by the COVID-19 pandemic due to inflated food prices, decreased employment opportunities, and increased costs of rural households. Additionally, COVID-19 had a statistically significant and positive effect on social assets (coefficient = 0.684, p-value < 0.01), indicating that due to the COVID-19 pandemic, people's trust in each other declined, social solidarity diminished, and the level of social insecurity increased. The lowest impact of COVID-19 was on human assets (coefficient = 0.740, p-value < 0.01), which implies the COVID-19 pandemic forced educational institutions to close, there was a lack of adequate health information and a lack of adequate medical staff. COVID-19 affected physical assets significantly and positively (coefficient = 0.542, p-value < 0.01), showing that the COVID-19 pandemic reduced sufficient access to pharmaceutical items, disinfectants, sanitary detergents, and reliable medical information. Furthermore, COVID-19 had a statistically significant and favourable influence on natural assets (coefficient = 0.600, p-value < 0.01), indicating that due to the COVID-19 pandemic, farming activities (fertilization, harvesting etc.) were delayed, agricultural output decreased, and farmers were reluctant to plan to grow their crops. COVID-19 also had a significant and positive impact on psychological factors (coefficient = 0.537, p-value < 0.01), indicating that due to the COVID-19 pandemic, farmers were worried about getting COVID-19, social tensions were high, and depression and disappointment increased. The impact of COVID-19 was less on physical assets and psychological assets compared to other assets.
The impact of COVID-19 on assets used for sustaining livelihoods has been thoroughly examined. According to the path analysis, COVID-19 had a substantial influence on all categories, including financial, social, physical, human, natural, and psychological assets. This is consistent with most earlier studies, which have also observed a strong influence of COVID-19 on rural livelihoods 5 . Figure 3 illustrates the significant impact of COVID-19 on the assets supporting the livelihoods of farmers engaged in high-value agriculture in the NW regions of Bangladesh.
TreeMap illustrates the consequences of COVID-19 on six livelihood domains. Note: The number in the figure shows the percentage of farmers who ‘agreed’ to ‘strongly agreed’ with the statement.
The COVID-19 pandemic has been found to have a statistically significant and considerable effect on the vulnerability of financial assets within the high-value crop farming sector. According to our findings, 91.3% of farmers believed that the COVID-19 pandemic had decreased income and purchasing power in rural households. Similar results were reported by Kundu et al. 34 . Additionally, around 78% of farmers concurred that the pandemic had led to an increase in food prices 60 . This rise in food costs forced many farmers to go without eating, contributing to widespread malnutrition. Moreover, approximately, 77.5% of farmers stated that rural household employment had decreased due to the pandemic, a trend confirmed by Mandal et al. 61 . Many rural residents feared that poverty and inequality would worsen if the pandemic persisted, with 86.3% of farmers holding this view 62 . Additionally, about 71% of farmers believed that the COVID-19 epidemic was responsible for rising living expenses 63 . These findings highlight the tangible negative impact of the pandemic on the economic dimensions of the farmers’ livelihoods. Farmers faced a decline in income, a reduction in their ability to purchase goods and services, and an increased susceptibility to financial risks, attributed to factors such as escalation of food prices and disruptions in the economy.
School dropout became a significant challenge in Bangladesh during the COVID-19 pandemic due to various factors, including limited internet access in rural areas, a lack of electronic devices, high costs of internet, early marriage and maternal age, prolonged closures of educational institutions, and inadequate teacher preparation for online learning 64 . Over 95% of farmers agreed that the pandemic had contributed to increased school dropout rates due to limited access to educational facilities 65 . Additionally, 70.6% of farmers agreed that the scarcity of medical personnel and lack of healthcare information in rural areas had heightened the risk of COVID-19 infection 66 . These findings suggest that the pandemic had significant implications for both the physical and mental well-being of farmers. The closure of educational institutions, limited access to medical services, and heightened psychological distress underscored the diverse impact on human and psychological resources.
The COVID-19 pandemic has significantly affected social assets, with evidence pointing to decreased social solidarity (60%), reduced social trust (59.4%), and increased social insecurity (48.4%), findings that align with those of De Vos 9 . Furthermore, a majority of farmers (80.9%) reported heightened vulnerability to psychological disorders, such as anxiety, stress, and disappointment 67 . These observations highlight the profound impact of the pandemic on the social fabric and mental health of farmers. Challenges in maintaining social bonds, accessing reliable information sources, and managing escalating social tensions and feelings of insecurity have become prevalent. This underscores the societal and psychological consequences of the pandemic on individuals' livelihoods.
Approximately 26.6% of farmers acknowledged that various agricultural activities, such as harvesting and fertilizing, were disrupted during the COVID-19 pandemic 68 . Additionally, around 30% of farmers agreed that the rural community struggled to fully comply with quarantine and health standards due to existing facilities and physical layout constraints 69 . As a result, agricultural production declined (23.5% responses), and farmers hesitated to plan future crop cultivation (30.4% responses) 70 . These findings underscore the pandemic’s adverse effects on agricultural productivity, as farmers encountered difficulties accessing essential inputs and infrastructure 37 . This highlights the pressing challenges in safeguarding physical and natural assets amid the crisis.
Our results align with previous research highlighting the significant impact of COVID-19 on rural livelihoods. Consistent with Kundu et al. 34 we observed a decrease in income and purchasing power among rural households. Similarly, our findings of rising food prices and resulting malnutrition corroborate those of Rabbi et al. 60 . However, our study identifies distinct causal factors contributing to these impacts. Disruptions of supply chains and reduced labor availability due to mobility restrictions directly affected agricultural productivity and income. Moreover, financial vulnerabilities were compounded by issues with market access, highlighting the interdependent nature of these factors. By focusing on high-value crop farmers, our study provides new insights into specific vulnerabilities within this subgroup, despite the overall consistency with existing research.
In a nutshell, the COVID-19 pandemic has had a profound impact on the ability of individuals to maintain their livelihoods, ranging from significant to extreme. The confirmation of all hypotheses underscores the extensive influence of the pandemic on various aspects of high-value crop farmers' livelihoods. Given the heterogeneous nature of these impacts, policymakers must be mindful and develop pro-poor strategies to enhance crisis-resilience capacity, particularly targeting the most vulnerable farm households in Bangladesh.
While it is true that the government of Bangladesh has taken proactive measures to address the challenges posed by COVID-19 in the agricultural sector 2 , 71 , it is important to note that the situation remains dynamic. Ongoing research can play a vital role in shaping policy-making in several ways. The broad impact of COVID-19 on farmers’ livelihoods highlights the need for comprehensive, multifaceted policy interventions. By addressing the specific vulnerabilities and underlying causes identified in this study, policymakers can bolster the resilience of rural livelihoods against future crises.
Firstly, our current research provides a comprehensive and in-depth analysis of the impact of COVID-19 on livelihood assets, specifically focusing on high-value crop farmers. By quantifying the extent of the pandemic's impact on various assets related to vulnerability, such as human, financial, social, natural, physical, and psychological, our study offers a nuanced understanding of the lingering effects that may not have been fully addressed yet. Secondly, while the government has prioritized the cultivation and export of high-value vegetables 42 , 72 , our research can identify gaps in these policies and shed light on potential vulnerabilities that might still exist within the sector. For instance, our findings highlight the significance of different asset categories, with financial assets being the most impacted. This emphasizes the need for targeted interventions and support mechanisms, such as access to low-interest loans or financial aid, which can further strengthen the resilience of high-value crop farmers. On the other hand, to enhance the accessibility of healthcare and education services in rural areas, policymakers must prioritize investments in digital infrastructure. The healthcare infrastructure must be fortified to mitigate the psychological distress and health risks that producers encounter. Local governance structures and community-based organizations should be instrumental in the development of trust and social cohesion. Furthermore, our research underscores the role of mobility restrictions and lockdowns as factors affecting livelihoods. As these measures could potentially recur in response to various shocks, including new variants or future pandemics, our study offers insights into strategies that can minimize disruptions. Proposing the establishment of a well-structured online marketplace for agricultural products and exploring labor-efficient farming techniques could mitigate the negative consequences of movement restrictions.
In summary, while initial policy responses have been implemented, our current research contributes by providing a comprehensive analysis of the multifaceted impact of COVID-19 on high-value crop farmers' livelihoods. By identifying areas of vulnerability and proposing targeted strategies to enhance resilience, our findings can assist policymakers in refining and adapting their approach to ensure the long-term sustainability of this vital sector in the face of evolving challenges.
The study's findings revealed a significant impact of COVID-19 on all categories of assets crucial for sustaining livelihoods. The pandemic and associated governmental restrictions notably affected rural Bangladeshi livelihoods, primarily stemming from lockdowns, mobility limitations, and the repercussions of lost income, rising food prices, and diminished purchasing power. Farm households in a developing country like Bangladesh encounter multifaceted challenges. The unpredictable nature of the COVID-19 situation led to major disruptions in production and marketing activities, income reduction, increase in food prices, and job losses among high-value crop farmers, exacerbating long-term vulnerability.
The impact of COVID-19 on financial assets has been profound, creating economic pressure and disrupted the livelihood conditions of farmers. Urgent policy considerations are essential for their recovery. The interdependence of economic, institutional, and social ties within food systems underscores the need for comprehensive interventions. Movement restrictions during the pandemic severely curtailed farmers' access to markets, necessitating the development of a robust online marketplace to mitigate such disruptions, especially considering the perishable nature of agricultural commodities. To address the decrease in both farm and off-farm income and the rise in family expenditure, farmers require easy access to low-interest loans. Government input assistance programs should prioritise agribusiness production, incorporating labor-saving farming techniques and productivity-boosting technologies. Access to food, both physically and financially, is crucial, particularly during public health emergencies. This study underscores the importance of expanding direct cash transfer and food assistance programs and allocating resources to remove barriers to accessing food and other necessities, both in the present and the future.
While this study provides insights into the impact of the COVID-19 pandemic on farming households, it is important to acknowledge its limitations. One significant limitation is the reliance on respondents’ memory due to the lack of written documentation regarding income, expenditure, and savings. This reliance on the recall method introduces potential recall bias and may affect the accuracy of the data collected. Additionally, the focus on high-value agricultural practices may limit the generalizability of findings to other social strata within farming communities. Furthermore, the study’s cross-sectional design presents challenges in drawing definitive conclusions about changes in livelihoods over time. Further research could benefit from longitudinal studies to track changes in high-value crop farming livelihood activities more accurately. Moreover, comparative analyses across different socio-economic strata would enhance our understanding of the differential impacts of the pandemic and the effectiveness of various policy interventions and adaptation strategies. This would provide valuable insights for policymakers seeking to mitigate the pandemic's effects on farming communities in developing countries.
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
Cucinotta, D. & Vanelli, M. WHO declares COVID-19 a pandemic. Acta Biomed. 91 , 157–160 (2020).
PubMed PubMed Central Google Scholar
Islam, M. T., Talukder, A. K., Siddiqui, M. N. & Islam, T. Tackling the COVID-19 pandemic: The Bangladesh perspective. J. Public Health Res. 9 , 389–397 (2020).
Google Scholar
Paslakis, G., Dimitropoulos, G. & Katzman, D. K. A call to action to address COVID-19-induced global food insecurity to prevent hunger, malnutrition, and eating pathology. Nutr. Rev. 79 , 114–116 (2021).
PubMed Google Scholar
Dehghani-Pour, M., Barati, A. A., Azadi, H. & Scheffran, J. Revealing the role of livelihood assets in livelihood strategies: Towards enhancing conservation and livelihood development in the Hara Biosphere Reserve. Iran. Ecol. Indic. 94 , 336–347 (2018).
Yazdanpanah, M. et al. The impact of livelihood assets on the food security of farmers in southern Iran during the Covid-19 pandemic. Int. J. Environ. Res. Public Health 18 , 5310 (2021).
DFID. Sustainable Livelihoods Guidance Sheets, section 2 .1. Department for International Development (DFID). (1999).
Mphande, F. A. Infectious diseases and rural livelihood in developing countries. Infect. Diseas. Rural Livelihood Dev. Count. https://doi.org/10.1007/978-981-10-0428-5 (2016).
Article Google Scholar
Tsegaye, T. G. et al. Impact of the West African Ebola epidemic on agricultural production and rural welfare evidence from Liberia. PLoS Negl. Trop. Dis. 12 , 1–17 (2018).
De Vos, J. The effect of COVID-19 and subsequent social distancing on travel behavior. Transp. Res. Interdiscip. Perspect. 5 , 100121 (2020).
Gong, H., Hassink, R., Tan, J. & Huang, D. Regional resilience in times of a pandemic crisis: The case of COVID-19 in China. Tijdschr. voor Econ. en Soc. Geogr. 111 , 497–512 (2020).
Lopez, A. D., Mathers, C. D., Ezzati, M., Jamison, D. T. & Murray, C. J. Global and regional burden of disease and risk factors, 2001: Systematic analysis of population health data. Lancet 367 , 1747–1757 (2006).
Maas, L. T. The effect of social capital on governance and sustainable livelihood of coastal city community Medan. Procedia Soc. Behav. Sci. 211 , 718–722 (2015).
Bhaduri, S., Sinha, K. M. & Knorringa, P. Frugality and cross-sectoral policymaking for food security. NJAS Wageningen J. Life Sci. 84 , 72–79 (2018).
DFID. Human Rights for Poor People . (2000).
Hussein, K. Livelihoods approaches compared: A multi-agency review of current practice. October 1–61 (2002).
Bakarbessy, D. Business independence in social capital review for Asar fish business communities in Laha village, Teluk Ambon district, Ambon city. J. BADATI Ilmu Sos. Hum. 5 , 126–136 (2021).
Ellis, F. Rural livelihood diversity in developing countries: Evidence and policy implications (Overseas Dev. Institute, 1999).
Mottaleb, K. A., Mainuddin, M. & Sonobe, T. COVID-19 induced economic loss and ensuring food security for vulnerable groups: Policy implications from Bangladesh. PLoS One 15 , e0240709 (2020).
CAS PubMed PubMed Central Google Scholar
Kar, S. K. et al. COVID-19 pandemic and addiction: Current problems and future concerns. Asian J. Psychiatr. 51 , 102064 (2020).
Nicola, M. et al. The socio-economic implications of the coronavirus pandemic (COVID-19): A review. Int. J. Surg. 78 , 185–193 (2020).
Reardon, T., Bellmare, M. F. & Zilberman, D. How COVID-19 may disrupt food supply chains in developing countries. COVID-19 Glob. Food Secur. 78–80 (2020).
Zhang, S. et al. COVID-19 containment: China provides important lessons for global response. Front. Med. 14 , 215–219 (2020).
HLPE. Impacts of COVID-19 on food security and nutrition: developing effective policy responses to address the hunger and malnutrition pandemic. HLPE Issues Pap. 1–24 (2021).
Zabir, A. A. et al. COVID-19 and food supply in Bangladesh: A review. South Asian J. Soc. Stud. Econ. 10 , 15–23 (2021).
Kabir, H., Maple, M. & Usher, K. The impact of COVID-19 on Bangladeshi readymade garment (RMG) workers. J. Public Heal. (United Kingdom) 43 , 47–52 (2021).
Karim, M. R., Islam, M. T. & Talukder, B. COVID-19′s impacts on migrant workers from Bangladesh: In search of policy intervention. World Dev. 136 , 105123 (2020).
Kumar, P. & Kumar Singh, R. Strategic framework for developing resilience in Agri-Food Supply Chains during COVID 19 pandemic. Int. J. Logist. Res. Appl. 0 , 1–24 (2021).
Abhishek, et al. India’s food system in the time of covid-19. Econ. Polit. Wkly. 55 , 12–14 (2020).
Meuwissen, M. P. M. et al. Impact of Covid-19 on farming systems in Europe through the lens of resilience thinking. Agric. Syst. 191 , 103152 (2021).
Swinnen, J. F. M., Colen, L. & Maertens, M. Constraints to smallholder participation in high-value agriculture in West Africa. in Rebuilding West Africa’s Food Potential (ed. Elbehri, A.) 289–313 (FAO/IFAD, 2013).
Pingali, P. & Plavšić, M. Hunger and environmental goals for Asia: Synergies and trade-offs among the SDGs. Environ. Challenges 7 , 100491 (2022).
Gatto, M. & Islam, A. H. M. S. Impacts of COVID-19 on rural livelihoods in Bangladesh: Evidence using panel data. PLoS One 16 , e0259264 (2021).
Hall, B. Coronavirus and the Implications for Food Systems and Policy. Agrilinks https://agrilinks.org/post/coronavirus-and-implications-food-systems-and-policy (2020).
Kundu, S. et al. Determinants of household food security and dietary diversity during the COVID-19 pandemic in Bangladesh. Public Health Nutr. 24 , 1079–1087 (2021).
Laborde, D., Martin, W., Swinnen, J. & Vos, R. COVID-19 risks to global food security. Science 369 , 500–502 (2020).
ADS CAS PubMed Google Scholar
Middendorf, B. J. et al. Smallholder farmer perceptions about the impact of COVID-19 on agriculture and livelihoods in Senegal. Agric. Syst. 190 , 103108 (2021).
Dixon, J. M. et al. Response and resilience of Asian agrifood systems to COVID-19: An assessment across twenty-five countries and four regional farming and food systems. Agric. Syst. 193 , 103168 (2021).
Mardy, T., Uddin, M. N., Sarker, M. A., Roy, D. & Dunn, E. S. Assessing coping strategies in response to drought: A micro level study in the North-West region of Bangladesh. Climate 6 , 23 (2018).
Harris, J., Depenbusch, L., Pal, A. A., Nair, R. M. & Ramasamy, S. Food system disruption: Initial livelihood and dietary effects of COVID-19 on vegetable producers in India. Food Secur. 12 , 841–851 (2020).
Pakravan-Charvadeh, M. R. et al. The short-term effects of COVID-19 outbreak on dietary diversity and food security status of Iranian households (A case study in Tehran province). J. Clean. Prod. 281 , 124537 (2021).
CAS PubMed Google Scholar
Guido, Z., Knudson, C. & Rhiney, K. Will COVID-19 be one shock too many for smallholder coffee livelihoods?. World Dev. 136 , 105172 (2020).
Alam, G. M. M. & Khatun, M. N. Impact of COVID-19 on vegetable supply chain and food security: Empirical evidence from Bangladesh. PLoS One 16 , 1–12 (2021).
Sharma, P. N. & Kim, K. Model selection in information systems research using partial least squares based structural equation modeling. in International Conference on Interaction Sciences (2012).
Weinberger, K. & Genova-II, C. Vegetable production in Bangladesh: Commercialization and rural livelihoods . (2005).
Chiwaula, L. & Waibel, H. The role of shocks and risks for the livelihoods of small scale fishing communities of Hadejia-Nguru Wetlands in Nigeria. Proc. Ger. Dev. Econ. Conf. No. 3, (2009).
Mbiba, M., Collinson, M., Hunter, L. & Twine, W. Social capital is subordinate to natural capital in buffering rural livelihoods from negative shocks: Insights from rural South Africa. J. Rural Stud. 65 , 12–21 (2019).
Berhanu, W. Recurrent Shocks, Poverty Traps and the Degradation of the Social Capital Base of Pastoralism: A Case Study from Southern Ethiopia . CEGA Working Paper Series No. AfD-0916. Center of Evaluation for Global Action. University of California, Berkeley (2009).
Gatiso, T. T. et al. The impact of the Ebola virus disease (EVD) epidemic on agricultural production and livelihoods in Liberia. PLoS Negl. Trop. Dis. 12 , e0006580 (2018).
Mainuddin, M. et al. Irrigated agriculture in the northwest region of Bangladesh. Canberra, Aust. … (2019).
Kothari, C. R. Research methodology : Methods and techniques . (New Age International (P) Limited, 2004).
Simonetto, A. Formative and reflective models: State of the art. Electron. J. Appl. Stat. Anal. 5 , 452–457 (2012).
MathSciNet Google Scholar
Ringle, C. M., Wende, S. & Becker, J.-M. SmartPLS 4. Oststeinbek: SmartPLS. (2022).
Thompson, R. et al. Mental health and substance use of farmers in Canada during COVID-19. Int. J. Environ. Res. Public Health 19 , 13566 (2022).
Carney, D. et al. Livelihoods appriaches compared: A brief comparison of the livelihoods approaches of the UK Department for International Development (DFID), CARE, Oxfam and the United Nations Development Programme (UNDP) . (1999).
Kuang, F., Jin, J., He, R., Ning, J. & Wan, X. Farmers’ livelihood risks, livelihood assets and adaptation strategies in Rugao City, China. J. Environ. Manage. 264 , 110463 (2020).
Busic-Sontic, A. & Schubert, R. Social resilience indicators for pandemic crises. Disasters https://doi.org/10.1111/disa.12610 (2023).
Article PubMed Google Scholar
Baffoe, G. & Matsuda, H. An empirical assessment of rural livelihood assets from gender perspective: Evidence from Ghana. Sustain. Sci. 13 , 815–828 (2018).
Brown, T. A. Confirmatory factor analysis for applied research . (A Division of Guilford Publications, Inc. 72 Spring Street, New York, NY 10012, 2015).
Wong, K. K. K. Mastering partial least squares structural equation modeling (PLS-SEM) with SmartPLS in 38 hours. Syria Stud. 7 , 37–72 (2015).
Rabbi, M. F., Oláh, J., Popp, J., Máté, D. & Kovács, S. Food security and the covid-19 crisis from a consumer buying behaviour perspective—The case of Bangladesh. Foods 10 , 1–20 (2021).
Mandal, S. C. et al. The impact of the COVID-19 pandemic on fish consumption and household food security in Dhaka city, Bangladesh. Glob. Food Sec. 29 , 100526 (2021).
Patel, J. A. et al. Poverty, inequality and COVID-19: The forgotten vulnerable. Public Health 183 , 110–111 (2020).
Diwambuena, J., Musimwa, I. & Tsasa, J.-P. K. Covid-19 and cost of living in developing countries*. Pre print (2020) https://doi.org/10.21203/rs.3.rs-100217/v1 License.
Sarker, M. R., Rouf Sarkar, M. A., Alam, M. J., Begum, I. A. & Bhandari, H. Systems thinking on the gendered impacts of COVID-19 in Bangladesh: A systematic review. Heliyon 9 , e13773 (2023).
Dutta, S. & Smita, M. K. The Impact of COVID-19 pandemic on tertiary education in Bangladesh: Students’ perspectives. Open J. Soc. Sci. 08 , 53–68 (2020).
Ahmed, S. A. K. S. et al. Impact of the societal response to covid-19 on access to healthcare for non-COVID-19 health issues in slum communities of Bangladesh, Kenya, Nigeria and Pakistan: Results of pre-COVID and COVID-19 lockdown stakeholder engagements. BMJ Glob. Heal. 5 , 1–17 (2020).
CAS Google Scholar
Rudenstine, S. et al. Examining the role of material and social assets on mental health in the context of COVID-19 among an urban public university sample. Psychol. Trauma Theory Res. Pract. Policy https://doi.org/10.1037/tra0001307 (2022).
Gu, H. Y. & Wang, C. W. Impacts of the COVID-19 pandemic on vegetable production and countermeasures from an agricultural insurance perspective. J. Integr. Agric. 19 , 2866–2876 (2020).
Dubey, S. et al. Psychosocial impact of COVID-19. Diabetes Metab. Syndr. Clin. Res. Rev. 14 , 779–788 (2020).
Boughton, D. et al. Impacts of COVID-19 on agricultural production and food systems in late transforming Southeast Asia: The case of Myanmar. Agric. Syst. 188 , 103026 (2021).
Talukder, B., VanLoon, G. W., Hipel, K. W. & Orbinski, J. COVID-19’s implications on agri-food systems and human health in Bangladesh. Curr. Res. Environ. Sustain. 3 , 100033 (2021).
Alam, M. J. et al. The impact of the COVID-19 pandemic on vegetable farmers in Bangladesh. Cogent Food Agric. 9 , 2214432 (2023).
Download references
The authors acknowledge the International Food Policy Research Institute (2022X020.BAU) and the Australian Centre for International Agricultural Research (ACIAR). The authors also wish to express their gratitude to Md Shajedur Rahaman, Senior Scientific Officer, Agricultural Economics Division, Bangladesh Rice Research Institute, for his assistance in generating the study area map.
Authors and affiliations.
Department of Agribusiness and Marketing, Bangladesh Agricultural University, Mymensingh, 2202, Bangladesh
Umme Salma, Mohammad Jahangir Alam, Tamanna Mastura & Md. Salauddin Palash
Department of Agricultural Economics, Bangladesh Agricultural University, Mymensingh, 2202, Bangladesh
Ismat Ara Begum
School of Economics, Zhongnan University of Economics and Law, Wuhan, 430073, China
Md Abdur Rouf Sarkar
Agricultural Economics Division, Bangladesh Rice Research Institute, Gazipur, 1701, Bangladesh
School of Agriculture, Food and Wine, The University of Adelaide, Adelaide, 5005, Australia
Tamara Jackson
Department of Agribusiness and Agricultural Economics, University of Arkansas, Fayetteville, AR, 72701, USA
Andrew M. McKenzie
Development Strategies and Governance Unit, International Food Policy Research Institute, New Delhi, 110012, India
Avinash Kishore
You can also search for this author in PubMed Google Scholar
U.S.: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper. M.J.A.; I.A.B.: Conceived and designed the experiments; Analyzed and interpreted the data; Wrote the paper. M.A.R.S.; T.J.; T.M.: Conceived and designed the experiments; Contributed reagents, materials, analysis tools, or data; Wrote the paper. M.S.P.; A.M.M.; A.K.: Analyzed and interpreted the data; Wrote the paper.
Correspondence to Mohammad Jahangir Alam .
Competing interests.
The authors declare no competing interests.
Publisher's note.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information 1., supplementary information 2., rights and permissions.
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .
Reprints and permissions
Cite this article.
Salma, U., Alam, M.J., Begum, I.A. et al. The impact of COVID-19 on livelihood assets: a case study of high-value crop farmers in North-West Bangladesh. Sci Rep 14 , 20121 (2024). https://doi.org/10.1038/s41598-024-71242-4
Download citation
Received : 17 February 2024
Accepted : 26 August 2024
Published : 29 August 2024
DOI : https://doi.org/10.1038/s41598-024-71242-4
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative
By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.
Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.
IMAGES
VIDEO
COMMENTS
CASE STUDY Mike (social anxiety) Case Study Details. Mike is a 20 year-old who reports to you that he feels depressed and is experiencing a significant amount of stress about school, noting that he'll "probably flunk out." He spends much of his day in his dorm room playing video games and has a hard time identifying what, if anything, is ...
Social anxiety disorder (SAD), also known as social phobia, is one of the most common anxiety disorders. ... In our case study, the duration of each session lasted 50 minutes and we gave 20 sessions of individual therapy to our client over a period of 5 months, thus trying to tailor our client's needs and requirements for treatment. ...
Treatment of Social Anxiety Disorder: A Case Study of an 11-Year-Old ...
This case study deals with Sara, a 37-year-old social phobic woman who suffered from a primary fear of blushing as well as comorbid disorders, including obsessive-compulsive disorder, generalized anxiety disorder and spider phobia. The client was treated in an intensive, one-week group cognitive-behavioral therapy program in an educational university clinic in Aarhus, Denmark.
Social Phobia/Anxiety Case Study: Jim. Jim was a nice looking man in his mid-30's. He could trace his shyness to boyhood and his social anxiety to his teenage years. He had married a girl he knew well from high school and had almost no other dating history. He and his wife, Lesley, had three children, two girls and a boy.
Sara, A Social Phobia Client with Sudden Change After Exposure Exercises in Intensive Cognitive-Behavior Group Therapy: A Case-Based Analysis of Mechanisms of Change. VICKI L. JENSEN a, ESBEN HOUGAARD a,c, & DANIEL B. FISHMAN b. Department of Psychology and Behavioural Sciences, University of Aarhus, Denmark.
Analysis and Insights. Sarah's case study offers valuable insights into the treatment of social anxiety disorder and highlights several key factors contributing to her success: 1. Comprehensive approach: The combination of psychotherapy, medication, and exposure techniques addressed multiple aspects of Sarah's anxiety.
Guided by the cognitive behavior therapy (CBT) (Beck, 2020) framework, the therapist approached Gi's case with special attention to the interpretations, assumptions, and beliefs that seemed to maintain his social anxiety.The way Gi saw himself and others' views of him seemed to be informed by a deeply held belief that "I am incompetent" and an expectation that others are critical and ...
This aligns with the pre- to post-intervention progress shown by Josie (using the Reliable Change Index), particularly in measures assessing acceptance, psychological inflexibility, and self-reported social anxiety. Josie's case study gives preliminary evidence of the acceptability and feasibility of the ACT@TeenSAD, making it a helpful tool ...
The 2013 updated NICE guideline, Social Anxiety Disorder: Recognition, Assessment and Treatment, has been critically reviewed and applied to a case study. The guideline is intended to provide evidence-based best practice advice for providers on how to recognize, complete assessments of, and treat social anxiety disorder.
Abstract. A case report is presented of Penny, aged 28, who was referred to the psychology pathway in the chronic pain service after reporting feeling anxious and low during a physiotherapy appointment for neck pain. An initial assessment highlighted Penny experienced anxiety in social situations and had a pervasive low mood stemming from her ...
Social anxiety disorder (previously termed 'social phobia') was formally recognised as a separate phobic disorder in the mid-1960s (Marks & Gelder, 1965). The term 'social anxiety disorder' reflects current understanding, including in diagnostic manuals, and is used throughout the guideline. As set out in the International Classification of Diseases, 10th Revision (ICD-10) (World ...
Social anxiety disorder (SAD), also known as social phobia, is one of the most common anxiety disorders and has been shown to be effectively treated using cognitive-behavioral therapy (CBT). ... The cognitive-behavioral treatment of social phobia. Clinical Case Studies. 2004; 3:124-146. [Google Scholar] Gould RA, Buckminster S, Pollack MH ...
After his first therapy session, Williams began his road to recovery. "After I was diagnosed with social anxiety disorder, I felt immense relief because it meant that there was a name for my suffering. I wasn't crazy or weird, like I thought for so many years," said Williams. "As part of my treatment program, my physician prescribed an ...
Emotional and behavioral symptoms. Signs and symptoms of social anxiety disorder can include constant: Fear of situations in which you may be judged negatively. Worry about embarrassing or humiliating yourself. Intense fear of interacting or talking with strangers. Fear that others will notice that you look anxious.
Abstract. Chapter 8 covers the treatment of Social Anxiety Disorder (SAD), and includes information about the condition, epidemiological considerations, the case study, assessment strategy and case formulation, intervention model and course of treatment, strategies for handling homework non-compliance, handling poor attendance and relapse, relapse prevention, post-treatment assessment, basic ...
Social Anxiety Disorder (SAD) is defined as an intense fear or anxiety associated with exposure to social scrutiny in past, current, or future social situations. Social situations might involve interactions (e.g., having a conversation with someone familiar or a stranger), being observed (e.g., while eating or writing), or performing in front ...
Counselling Case Study: Social Anxiety. Sasha is a 60 year old woman who has recently retired from a career in teaching. Working for many years in a secondary school environment, Sasha was confident, motivated and dedicated to her work, but at the same time looking forward to retirement so she and her husband could travel and spend more time ...
Case Study 1: Overcoming Social Anxiety. Case Study 2: Triumphing Over Panic Attacks. Case Study 3: Mastering Generalised Anxiety. Common Themes and Strategies. The Role of Resilience. Inspiring Others. Anxiety is a formidable adversary in the everyday life of many people in the UK. According to statistics, around 6% of people are diagnosed ...
Sir, Social phobias come under the category of phobic anxiety disorders and are centered around a fear of scrutiny by other people, usually leading to avoidance of social situations.[] Although social anxiety has been found to be the most common comorbidity in people with psychosis,[] social phobia or anxiety per se is a neurotic disorder in which the patient usually maintains an adequate ...
Introduction. Anxiety disorders affect as many as 30% of children and adolescents and contribute to social and academic dysfunction. These disorders or their temperamental precursors1 are often evident in early childhood, with 10% of children ages 2-5 already exhibiting anxiety disorders.2 Anxiety symptoms in toddlerhood3 and preschool age4 show moderate persistence and map on to the ...
CASE STUDY A CASE STUDY OF SOCIAL PHOBIA: SELF-PERCEPTION OF BEING UGLY Saliha Anjum GC University, Lahore This case study discusses the case of a 21 years old young man referred be-cause of his problem of social anxiety and strong feelings of ugliness. The cli-ent fulfilled the DSM-IV-TR criteria of Social Phobia. His family background
Hannah, an anxious child. This article presents a case study of an anxious child, and highlights some common symptoms for parents and teachers to be watchful for. The case study involves a fictitious identity; any resemblance to a real person is completely coincidental. Hannah (not a real person) was a 10-year-old girl from a close, supportive ...
The COVID-19 pandemic caused widespread disruption and increased concerns about mental health. One area of particular concern is social anxiety disorder (SAD), as the lockdowns and restrictions have made it difficult for people to engage in social activities. Studies show inconsistency in the prevalence rates reported in different studies, making it difficult to draw a clear picture of the ...
Alopecia areata (AA), or hair loss on the scalp and body, affects approximately 1 in 1,000 people. Psychological distress has been hypothesized as a determining factor of AA. Researchers evaluating psychological disorders in the development and course of AA have found equivocal results. Furthermore, few studies have evaluated parental distress in relation to childhood AA. Case studies have ...
The uncertainty and health risks posed by the epidemic increased the prevalence of depression and anxiety. Farmers grappled with the social and economic consequences, which led to heightened ...