Use of social media increases the risk of anxiety depression globally: results from 113 countries

Use of social media increases the risk of anxiety depression globally: results from 113 countries | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Use of social media increases the risk of anxiety depression globally: results from 113 countries Chhabi Lal Ranabhat, Jason W Marion This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4664537/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Generalized anxiety and depression is the initial stage of mental illness and reflects a mood disorder reflected in sadness, hopelessness, nervousness, and worry. There are different factors associated with general anxiety and depression (GAD). Together with basic demographic and economic factors, we observed the use of social media by GAD. The aim of this study is to explore globally the influence of social media on self-reported general anxiety and depression. Methodology We chose different factors associated with anxiety and depression affected for more than 2 weeks from the 2020 Wellcome Global Monitor from the available secondary data. The samples from each country are nationally representative of the resident population aged 15 and older with access to a phone in 113 countries. The research design process was completed by leading researchers and subject experts; cognitive testing was conducted in ten countries to ensure questions could be understood across countries and by various demographic groups; and pilot tests were conducted in 10 countries. Independent variables were demographic variables: age, gender, economics, education, employment status, belief factors, and trends in social media use. Univariate variables were presented in frequency and percentage; bivariate analysis was performed with cross-tabulation using the chi square test; and logistic regression was used among significant variables by adjusted odds ratios and 95% CI as multivariate analysis. Results The prevalence of self-reported generalized anxiety depression (GAD) was 20% out of 119,234 in 113 countries. More than 38.27% were between the ages of 30 and 49, with 51% being male, more than half having completed high school, and 27% falling into the 4th and 5th quintiles (rich group). Similarly, more than 63.4% were employed, 81.3% believed in science, 42.3% believed in traditional healers, and 81.5% used social media, which was significantly associated with self-reported GAD. Adjusted odds ratio (aOR) showed that young age (15–30) years 1.24 times, females 1.21 times, elementary and primary education 1.34 times, the poorest twenty 1.39 times, and those who use social media several times an hour are more likely to be GAD with reference to early old age, males, higher education, the richest twenty, and those who did not use social media in the past 30 days. At the same time, the older age group (65+), those who trusted science and traditional healers and did not use social media in the past 30 days, were less likely to be GAD. Conclusions: There is an increasing risk of GAD worldwide, and young adults and females are more vulnerable. Excessive use of social media is a challenging and risky factor. Introduction Mental health is an increasingly vital issue in the 21st century. There are multiple impacts attributed to mental illness which was impacting at least 12% of the world's population in 2019 1 . As of 2019, 15% of the total years lived with disability in the world were due to mental disorders, which increased the global burden of disease rank attributable to mental health from the 12th position in 1990 to the 7th position in 2019[ 1 ]. General Anxiety and Depression (GAD), one of the most common mental disorders, is contributing to severe mental illness like schizophrenia, personality disorder, compulsive disorder etc. Globally, 4% (301 million) of people suffered from anxiety depression in 2019 with higher frequencies being observed in higher resourced countries with greater social media usage [ 2 ]. There are several risk factors associated with GAD, which are psychological, socio-economic, and lifestyle, and the most important is the use of social media [ 3 ]. The use of social media greatly impacts mental health. It enhances connection, increases self-esteem, and improves a sense of belonging [ 4 ]. But at the same time, it can also lead to tremendous stress, pressure to compare oneself to others, and increased sadness and isolation. There are 5.04 billion social media users around the world as of January 2024, equating to 62.3% of the total global population [ 5 ]. For understanding the plausibility for social media contributing to anxiety depression, Petryszak (1980) [ 6 ] summarized theoretical aspects of human nature in 'Sociological Theory and Human Nature.' Using influential writings by Pareto, Mosca, and others, Petryszak explained that humans need continuous interaction, influential figures and heroes, attractions in nature, and guidance for direction from the community [ 6 ]. The introvert theory of 'Human Psychology' from 1924 also explains that persons do not want to share everything, every time and with every person [ 7 ]. As social media rose in the 2000s and was distributed globally, governments, businesses, high-profile persons, and the most affluent started communication. While social media became a powerful tool for increasing humanitarian support, providing warnings and education, social media also has been linked to increasing cybercrime, diminishing personal privacy, and increasing risks for mental disorders and various types of conflict in society [ 8 , 9 ]. Accordingly, there has been a simultaneous increase in social media usage and mental disorder frequency over time. Given the simultaneous rise and population size impacted, there has been considerable research regarding social media usage on mental health. Positive and negative impacts of social media have been described. Fundamentally, social media links people, providing a platform for personal bonding, sharing new information and personal entertainment materials, which has enabled improvements in aspects of mental health [ 10 ]. Additionally, while general anxiety and depression are linked to loneliness and/or lack of communication, hope and entertainment, these conditions can manifest into severe mental disorder(s) [ 11 ]. Recent research carried out during the COVID-19 pandemic confirms that many turned to social media to seek both emotional and practical social support [ 12 ], and the use of social media was positively associated with subjective wellbeing [ 13 ]. Social media use during COVID-19 improved subjective happiness and self-rated mental health [ 14 , 15 ] and reduced stress[ 16 ] for many individuals. Conversely, there are also social media use-related risk factors that have been identified for developing a mental health disease, specifically, the duration, frequency, and number of social media platforms being used [ 17 ]. Adverse effects linked to increased social media usage include college students reporting declines in their academic performance, [ 18 ] which could increase the likelihood of downstream mental health effects and/or be indicative of an upstream mental health impact from social media usage. Beyond the use of social media, there are several known risk factors associated with anxiety and depression. Firstly, the prevalence of anxiety depression is higher among older aged persons, [ 19 ] influenced by older persons having increased vulnerability to diseases and disability, isolation from family, insufficient wealth to maintain a standard of living, or a combination of these factors, among others. A study carried out in China during the COVID period in which movement freedoms were restricted, found that the severity of depression symptoms were decreased with increases in age resilience, but were increased if unemployed, feeling less adapted, and being more stressed. The severity of anxiety symptoms were decreased among those with higher education and greater resilience [ 20 ]. Their findings offer clues for further research not able to be addressed because of their small sample size, and limited representation of a diverse set of populations from other countries. Given the rise in social media, research cannot be limited to only specific communities, countries and cultures, and it is therefore appropriate to inform essential global efforts aimed at the careful utilization of social media for informing policy or guidance in a manner that is scientific, practical, regulative, collaborative and fosters or promotes health. For contributing knowledge, we accessed the free online database from Wellcome Trust [ 21 ]. Included are data which were taken from 113 countries using an appropriate representative sample from each country. Such data can yield findings that can act as a backbone for policy considerations globally, regionally, and nationally. Furthermore, this research is significant for policy makers, researchers, academicians and general readers, who are interested in mitigating the potential risk of anxiety depression that can or has already impacted the physical health, disability status, life expectancy, and health span of individuals and families, as well as the social and economic climates for countries. Accordingy, the aim of this study is to scrutinize the factors related to anxiety depression considering the application of social media. Methodology Secondary data were accessed and obtained from Wellcome Global Monitor 2020: Mental Health [ 21 ]. It is the largest survey in the world of how people consider and cope with anxiety and depression and explores the perceived role of science to find new solutions. Data collection tool The 2020 Wellcome Global Monitor questionnaire was developed as a tool using a careful research and design process informed by a discussion series with leading researchers and subject matter experts, cognitive testing in ten countries to ensure questions could be understood across countries and various demographic groups, and pilot tested in 10 countries [ 21 ]. The questionnaire was then translated into the major conversational languages of each country and checked by an independent third party for quality assurance. Sampling and data collection Due to COVID 19 pandemic, face-to-face interview methods were changed into telephone interviews. The samples from each country are nationally representative of the resident population aged 15 and older with access to a phone (either landline or mobile); however, the inability to conduct in-person interviews reduced population coverage in many low-income countries. Variables Outcome/dependent variable The outcome variable of interest was self-reported Anxiety and Depression. The outcome condition is defined as being so extreme that feelings of anxiety and depression impacted their ability to function as they normally would for two weeks or more – a definition drawn from the World Health Organization (WHO). Specifically, the survey asked, “Have you ever been so anxious or depressed that you could not continue your regular daily activities as you normally would for two weeks or longer?” Independent observations were not made, and the outcome relied on each respondent’s self-reported feelings of anxiety or depression as defined in the survey. Independent variables Informed by background literature, we selected independent variables of study interest. Common independent variables included were the demographic variables pertaining to age and gender as well as the economic, education and employment status of respondents. Furthermore, we selected the trust-related variables; specifically, trust in science and trust in traditional healers that has linked with neuroanatomy [ 22 ]. We also selected variables to assess associations described in the introduction section related to social media use (frequency and duration), such as social media use in times per hour, as well as use after regular hours, occasional use per day, once per day, a few times per month, etc. Data verification and analysis We downloaded the data and survey questionnaires from Wellcome Global Monitor 2020: Mental Health web page. We listed the variables related to title, variable categories and codes. We carefully checked the data errors like outliers, alphabetical letters/terms, coding errors etc. Similarly, we analyzed our interested variables with an order of univariate, bivariate and multivariate chronology. After verification and re-verification, the data were exported for analysis with the Statistical Package for the Social Sciences IBM SPSS Statistics V22.0. Data Analysis steps First, univariate analyses were computed for obtaining frequencies and percentages. In the second phase, Chi-square tests were computed. In the third step, simple and multivariable logistic regression and odds ratio was performed using the covariates that were statistically significant ( p < 0.05) from the bivariate analyses. The logistic regression data were interpreted with unadjusted and adjusted odds ratios, their respective 95% confidence intervals, and p-values with the significance level of p < 0.05. Validity and Reliability There are very few questions about the validity and reliability of Wellcome trust data which was used in this study. After database formation, we performed more than two rounds of crosschecking particularly the outliers and proportion of missing values. Likewise, the normality of the data was verified by the observation of histograms and consistency of data was checked by Cronbach's alpha for appropriate variables. Ethical consideration The data were obtained as open access data available for everyone, including researchers and policy makers, for maximizing the impact from their data collection. The Wellcome Trust, United Kingdom, has the sole authority of and accountability of data use and the protection of the study participants. There was no identifiable information in the data. The Wellcome Trust has more information regarding their commitment to the responsible conduct of research on their website 1 Results We used the open access dataset available from Welcome Global Monitor 2020. The were taken from 113 countries with a total sample size of 119,234 individuals. A representative sample size was taken from those countries. The prevalence of self-reported Generalized Anxiety Depression (GAD) was 20%. For general demographics, by age, more than one-third (38.27%) of participants were from 30–49 years. Males represented 51% (70790), over half of participants had graduated high school, and 27% of participants were from the from the 5th quintile group (wealthiest group). Additionally, more than one-third of participants were employed full-time. A significant majority (81.3%) believed in science and 42.3% believed in traditional healers. Lastly, 81.5% used social media in the previous 30 days (Table 1 ). Table 1 further explores the association among different predictors with outcome (Anxiety/ Depression). There is an association between age, sex, education, income, individual trust and different patterns of using social media with Anxiety/Depression (p < 0.001). The frequency of reporting anxiety/depression was greater among persons in lower income groups, younger age populations, females, and persons using social media most frequently (p < 0.001). Likewise, persons who use social media every hour and after some hours had a significantly higher prevalence of anxiety/depression than those not using that pattern. Interestingly, persons who used social media once a day had a significantly lower prevalence of Anxiety/Depression relative to more than once a day (p < 0.001) Table 1 Situation of demographic, trust and practice with anxiety and depression Independent Variables/Covariates Categories Dependent Variable /Outcome ~ General Anxiety Depression) P value No (%) Yes (%) Age 15–29 39,188 (33.6) 8,889 (22.7) < 0.001 30–49 44,380 (38.0) 9,471 (21.3) 50–64 19,954 (17.1) 3,705 (15.4) 65+ 12,673 (10.9) 1,803 (14.2) DK/Refused 615 (0.5) 103 (16.8) Sex Male 59,478. (50.9) 11,312 (19.0) < 0.001 Female 57,332 (49.1) 12,659 (22.1) Education Elementary or less (8 years or less) 15,361 (13.1) 3,464 (22.6) < 0.001 Secondary (8–15 years) 64,042 (54.8) 13,846 (21.6) Tertiary (16 + years) 36,736 (31.4) 6,469 (17.6) DK/Refused 671 (0.6) 192 (28.6) Household Income Poorest 20% 17,371 (15.0) 4307 (18.5) < 0.001 Second 20% 19,359 (16.7) 4251 (18.3) Middle 20% 22,141 (19.1) 4388 (18.9) Fourth 20% 25,320 (21.9) 4710 (20.2) Richest 20% 31,622 (27.3) 5554 (23.9) Employment Employed 74,268 (63.6) 15,213 (20.5) 0.675 Unemployed 42,532 (36.4) 8,756 (20.6) Trust in Science Yes 95,198 (81.5) 19,056 (20.0) < 0.001 No 14,833 (12.7) 3,476 (23.4) DK/Refused 6,819 (5.8) 1,439 (21.1) Trust in traditional healer Yes 49,523 (42.4) 9,644 (19.5) < 0.001 No 59,676 (51.1) 12,897 (21.6) DK/Refused 7,611 (6.5) 1,430 (18.8) Used social media in Past 30 Days Yes 95,495 (81.8) 20,209 (21.2) < 0.001 No 21,133 (18.1) 3,726 (17.6) DK/Refused 182 (0.2) 36 (19.8) Use of social media in different time pattern No use in past 30 days 21,133 (18.1) 3,726 (17.6) < 0.001 Less Frequently 5,150 (4.4) 1,281 (24.9) A few days a week 7,485 (6.4) 1,900 (25.4) Once a day 12,877 (11.0) 2,554 (19.8) Several times a day 44,764 (38.3) 8,777 (19.6) Almost every hour 11,198 (9.6) 2,383 (21.3) Several times an hour 13,516 (11.6) 3,229 (23.9) DK/Refused 687 (0.6) 121 (17.6) Note Non response rate in each variable was about 0.6% which is negligible. Comparison of odds ratio and 95% CI among predictors and outcome After bivariate analysis, we used regression in those variables having statistically significant. Age, sex education, income quintile, trust and social media utilization pattern were statistical significant and we further analyzed putting one reference group. Age between 15 to 29 1.24 (1.19–1.30) *** and 30 to 49 were more likely but 65 + was less likely to be GAD with reference to 50–64 years age. Likewise, females were more likely 1.21 (1.18–1.26) *** to be GAD with reference to male. Education with Elementary or less (1.34 (1.27–1.41) *** and Secondary 1.21 (1.16.- 1.25) *** were more likely to be GAD with reference to Tertiary education. With reference to 4th quintile group, poorest − 20% (1.39; 1.32–1.47) ***, second- 20% (1.20; 1.14–1.26) *** and middle 20 – (1.06 ;1.01–1.11) ** is more likely to be GAD but richest − 20 (0.96 ;0.92–1.00) is less likely to be GAD. Similarly, respondents who trust in Science 0.92 (0.88–0.96) *** and Traditional Healers 0.87 (0.84–0.90) were less likely to be GAD with reference to those do not trust. Use of social media was observed risk factor to be GAD than those not used. Less frequently 1.45 (1.34–1.57) ***, a few days in week 1.50 (1.40–1.60) ***, once a day 1.17 (1.10–1.24) ***, several times a day1.14 (1.09–1.20) ***, almost every hour 1.22 (1.14–1.30) *** and several time an hour1.43 (1.34–1.51) were more likely to be GAD with reference to those not used social media within past 30 days (Table 2 ). Table 2 Comparison of odds ratio and 95% CI with independent variables with Anxiety and Depression Covariate Category Unadjusted odds ratio (95% CI) Adjusted odds ratio (95% CI) Age 15–29 1.29 (1.23–1.34) *** 1.24 (1.19–1.30) *** 30–49 1.19 (1.14–1.24) *** 1.19 (1.14–1.25) *** 50–64 Ref Ref 65+ 0.73 (0.68–0.77) *** 0.76 (0.71–0.81) *** Sex Male Ref Ref Female 1.21 (1.18–1.25) *** 1.21 (1.18–1.26) *** Education Elementary or less (8 years or less) 1.36 (1.30–1.43) *** 1.34 (1.27–1.41) *** Secondary (8–15 years) 1.29 (1.25–1.33) *** 1.21 (1.16.- 1.25) *** Tertiary (16 + years) Ref Ref Income quintile Poorest 20% 1.45 (1.38–1.52) *** 1.39 (1.32–1.47) *** Second 20% 1.23 (1.17–1.28) *** 1.20 (1.14–1.26) *** Middle 20% 1.08 (1.03–1.13) ** 1.06 (1.01–1.11) ** Fourth 20% Ref Ref Richest 20% 0.93 (0.89–0.97) *** 0.96 (0.92–1.00) Trust in Science No Ref Ref Yes 0.82 (0.79–0.85) *** 0.92 (0.88–0.96) *** Trust in traditional healer No Ref Ref Yes 0.88 (0.85–0.90) *** 0.87 (0.84–0.90) *** Use of social media in different time pattern No use in past 30 days Ref Ref Less Frequently 1.55 (1.44–1.66) *** 1.45 (1.34–1.57) *** A few days a week 1.59 (1.49–1.69) *** 1.50 (1.40–1.60) *** Once a day 1.16 (1.09–1.22) *** 1.17 (1.10–1.24) *** Several times a day 1.14 (1.09–1.19) *** 1.14 (1.09–1.20) *** Almost every hour 1.26 (1.19–1.38) *** 1.22 (1.14–1.30) *** Several times an hour 1.47 (1.39–1.55) *** 1.43 (1.34–1.51) *** Note: ** p < 0.01, ***p < 0.001 Discussion This study carried out 113 countries, all continents, representative samples from each country, using telephone interview, age after 15 years, equal proportion to male and female. We analyzed the demographic, trust related and application of social media related variables. Use of social media is very popular and making easier life style and at the same time it has multiple impact in human life and becoming a threat too. The nature of social media is providing recent information/news, interesting, amazing and hidden information so that every people have been attracted and addicted. As a result, social media users have been isolated from social interaction and a mental illness called anxiety depression has been developed [ 23 , 24 ]. The generalized anxiety disorder was significantly higher after 50 years age, female, trusted in science and traditional healers and used the social media in past 30 days. The respondents, who used social media once a day or less frequent were low prevalence of GAD than who used every hour and sooner. Our study shows the overall prevalence of GAD was 20%, young age (15–30) year 1.24 times, female 1.21times, elementary/primary education 1.34 times, poorest twenty 1.39 times and who use social media several times an hour more likely to be GAD with reference to early old age, male, higher education, richest twenty and those who did not use social media in past 30 days. At the same time, old age group (65+), those trust in science and traditional healers and did not use social media in past 30 days less likely to be GAD. Borwin Bandelow &Sophie Michaelis (2015) found that prevalences of anxiety depression within 12 months and life time were 21% and 34% respectively [ 25 ]. In the USA, the government official report shows that Over 15% of adults experienced symptoms of anxiety that were either mild, moderate, or severe in the past 2 weeks and women are more likely to be anxiety than man [ 26 ]. Data from 23 community surveys in 21 countries of the World Mental Health (WMH) surveys in 2018 showed that 10% had anxiety disorder within 12 months[ 27 ]. Community survey in Italy was performed by Antonio Preti et.al in 2021 and findings showed that overall lifetime prevalence in the sample was 2.3%, with a markedly higher frequency in women [ 28 ]. A study by Quanman Li (2020) revels that the prevalence of GAD was more in older age than young age [ 29 ]. A study by Karin Hammarberg et.al 2020 in Australia published in BMJ showed that women were more likely than men to have clinically significant symptoms of depression [ 30 ]. Study by Sally Adams et.al 2022 reveled that the young adult and male are more likely to be anxiety depression symptoms [ 31 ]. A study using the 2005–2016 National Health and Nutrition Examination Survey (NHANES) USA 2022 concluded that young adults (35–49) and low poverty income ratio are more likely to be depressive symptoms in comparison early old age people, with old age group and low and medium poverty income ratio [ 32 ]. Our study shows, respondents who trust in Science and Traditional Healers were less likely to be GAD reference with those not trust. We could not find relative comparison with previous research. At the peak time of COVID pandemic, a study showed that there was a significant role of belief process in pandemic depression and anxiety [ 33 ]. A pathway analysis showed that belief process including emotional belief was associated with anxiety disorder [ 34 ]. A traditional healing process is fundamentally the belief system and those respondents are more likely to anxiety disorder than those do not. Likewise, people with GAD were equally consulted with psychiatric doctors and traditional healers [ 35 ] and who did spiritual practice and alternative medicine had better self-perceived health than those did not [ 36 ]. There are not consistent findings regarding the association between use of social media and mental illness like Anxiety and Depression. Social media has been widely used as an important source of health information, particularly during public health crises. So, a study by Brailovskaia et al. (2022) found that COVID-19-related social media use is positively related to the psychological burden of the pandemic [ 37 ]. John A. Naslund et.al 2020 concluded that the role of social media as a potentially viable intervention platform for offering support to persons with mental disorders, promoting engagement and retention in care, and enhancing existing mental health services, however the caution should be warranted [ 38 ]. A systematic review by Abderrahman M Khalaf 2020, revels that ethical social media use can expand opportunities for connection and conversation, as well as boost self-esteem, promote health, and gain access to critical medical information and mitigate the anxiety and depression in starting phase but excessive use increase the risk of information misleading, confusion and enhance the severity of GAD [ 39 ]. Study by Vannucci A et.al concluded that use of social media among teenagers and young people appears to be predictive of depressive symptoms and low offline social support from both family and peers [ 40 ]. A study by Michelle O’Reilly − 2020 concluded that social media used by adolescent increase the mood and anxiety disorders, as a platform for cyberbullying and the use of social media itself was often framed as a kind of ‘addiction’[ 41 ] which finding is perfectly matching with our findings. Conclusion This study explored the different factors associated with General Anxiety Depression globally. Young adults, female, lower educational attainment, poor economic status and excessive use of social media are high risk factors for GAD and trust in science and traditional healers are less likely to be GAD. Ethical and careful use of social media could be beneficial but high frequently use of social media is an addiction and increase the risk of GAD. On the other hand, the content and unverified information could be great risk for mental illness. There were different obstacles to get the information during the COVID 19 peak time. Being a telephone interview, there might be possibility of information and communication bias during the data collection time. Based on our findings, more specific studies are recommended by age, situation of public health emergencies, employment group education groups etc. Declarations Acknowledgement We are grateful with Welcome Trust who provided the assess the data for further analysis. We provide sincere thanks to the respondents who provided the research response during the COVID 19 peak time. Competing interests The authors declare no competing interests. Funding No funding. Availability of data and materials The data is free access online can be reached in this link https://wellcome.org/reports/wellcome-global-monitor-mental-health/2020. Authors contribution CR conceptualized, designed, prepared, reviewed, and led the article. JM verified the results thoroughly reviewed and updated the article. Author affiliations CR is associated with Eastern Scientific LLC, 316 N. Estill Ave. Richmond, KY 40475, USA, and Planetary Health Research Centre (PHRC), Nagarjun - 01, Kathmandu, Nepal. 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The lifetime prevalence and impact of generalized anxiety disorders in an epidemiologic Italian National Survey carried out by clinicians by means of semi-structured interviews. BMC Psychiatry. 2021;21(1):48. Li Q, Miao Y, Zeng X, Tarimo CS, Wu C, Wu J. Prevalence and factors for anxiety during the coronavirus disease 2019 (COVID-19) epidemic among the teachers in China. J Affect Disord. 2020;277:153–8. Hammarberg K, Tran T. Sex and age differences in clinically significant symptoms of depression and anxiety among people in Australia in the first month of COVID-19 restrictions: a national survey. 2020, 10(11):e042696. Adams SH, Schaub JP, Nagata JM, Park MJ, Brindis CD, Irwin CE Jr. Young adult anxiety or depressive symptoms and mental health service utilization during the COVID-19 pandemic. J Adolesc Health. 2022;70(6):985–8. Zare H, Meyerson NS, Nwankwo CA, Thorpe RJ. How Income and Income Inequality Drive Depressive Symptoms in U.S. Adults, Does Sex Matter: 2005–2016. Int J Environ Res Public Health. 2022;19(10):6227. Milman E, Lee SA, Neimeyer RA, Mathis AA, Jobe MC. Modeling pandemic depression and anxiety: The mediational role of core beliefs and meaning making. J Affect Disorders Rep. 2020;2:100023. Johnston TE, Petrova K, Mehta A, Gross JJ, McEvoy P, Preece DA. The role of emotion beliefs in depression, anxiety, and stress. Australian Psychol:1–10. Schoonover J, Lipkin S, Javid M, Rosen A, Solanki M, Shah S, Katz CL. Perceptions of Traditional Healing for Mental Illness in Rural Gujarat. Annals Global Health. 2014;80(2):96–102. Ranabhat CL, Kim C-B, Park M-B, Bajgai J. Impact of spiritual behavior on self-reported illness: a cross-sectional study among women in the Kailali district of Nepal. J Lifestyle Med. 2018;8(1):23. Brailovskaia J, Miragall M, Margraf J, Herrero R, Baños RM. The relationship between social media use, anxiety and burden caused by coronavirus (COVID-19) in Spain. Curr Psychol. 2022;41(10):7441–7. Naslund JA, Bondre A, Torous J, Aschbrenner KA. Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice. J Technol Behav Sci. 2020;5(3):245–57. Khalaf AM, Alubied AA, Khalaf AM, Rifaey AA. The Impact of Social Media on the Mental Health of Adolescents and Young Adults: A Systematic Review. Cureus. 2023;15(8):e42990. Vannucci A, McCauley Ohannessian C. Social media use subgroups differentially predict psychosocial well-being during early adolescence. J Youth Adolesc. 2019;48:1469–93. O’Reilly M, Dogra N, Whiteman N, Hughes J, Eruyar S, Reilly P. Is social media bad for mental health and wellbeing? Exploring the perspectives of adolescents. Clin Child Psychol Psychiatry. 2018;23(4):601–13. Footnotes https://wellcome.org/grant-funding/guidance/responsible-conduct-research Additional Declarations No competing interests reported. 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Estill Ave","correspondingAuthor":true,"prefix":"","firstName":"Chhabi","middleName":"Lal","lastName":"Ranabhat","suffix":""},{"id":328168161,"identity":"edc6066a-43e4-490a-931b-68d987a217a5","order_by":1,"name":"Jason W Marion","email":"","orcid":"","institution":"Eastern Kentucky University","correspondingAuthor":false,"prefix":"","firstName":"Jason","middleName":"W","lastName":"Marion","suffix":""}],"badges":[],"createdAt":"2024-07-01 00:29:31","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4664537/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4664537/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64021836,"identity":"b683dc9a-67d3-452c-bed9-fc66d0ac41da","added_by":"auto","created_at":"2024-09-05 06:32:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":494374,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4664537/v1/6cbe5b2e-8c33-4065-bd87-3398c45de681.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Use of social media increases the risk of anxiety depression globally: results from 113 countries","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMental health is an increasingly vital issue in the 21st century. There are multiple impacts attributed to mental illness which was impacting at least 12% of the world's population in 2019\u003csup\u003e1\u003c/sup\u003e. As of 2019, 15% of the total years lived with disability in the world were due to mental disorders, which increased the global burden of disease rank attributable to mental health from the 12th position in 1990 to the 7th position in 2019[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. General Anxiety and Depression (GAD), one of the most common mental disorders, is contributing to severe mental illness like schizophrenia, personality disorder, compulsive disorder etc. Globally, 4% (301\u0026nbsp;million) of people suffered from anxiety depression in 2019 with higher frequencies being observed in higher resourced countries with greater social media usage [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. There are several risk factors associated with GAD, which are psychological, socio-economic, and lifestyle, and the most important is the use of social media [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe use of social media greatly impacts mental health. It enhances connection, increases self-esteem, and improves a sense of belonging [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. But at the same time, it can also lead to tremendous stress, pressure to compare oneself to others, and increased sadness and isolation. There are 5.04\u0026nbsp;billion social media users around the world as of January 2024, equating to 62.3% of the total global population [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. For understanding the plausibility for social media contributing to anxiety depression, Petryszak (1980) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] summarized theoretical aspects of human nature in 'Sociological Theory and Human Nature.' Using influential writings by Pareto, Mosca, and others, Petryszak explained that humans need continuous interaction, influential figures and heroes, attractions in nature, and guidance for direction from the community [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The introvert theory of 'Human Psychology' from 1924 also explains that persons do not want to share everything, every time and with every person [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. As social media rose in the 2000s and was distributed globally, governments, businesses, high-profile persons, and the most affluent started communication. While social media became a powerful tool for increasing humanitarian support, providing warnings and education, social media also has been linked to increasing cybercrime, diminishing personal privacy, and increasing risks for mental disorders and various types of conflict in society [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Accordingly, there has been a simultaneous increase in social media usage and mental disorder frequency over time.\u003c/p\u003e \u003cp\u003eGiven the simultaneous rise and population size impacted, there has been considerable research regarding social media usage on mental health. Positive and negative impacts of social media have been described. Fundamentally, social media links people, providing a platform for personal bonding, sharing new information and personal entertainment materials, which has enabled improvements in aspects of mental health [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Additionally, while general anxiety and depression are linked to loneliness and/or lack of communication, hope and entertainment, these conditions can manifest into severe mental disorder(s) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Recent research carried out during the COVID-19 pandemic confirms that many turned to social media to seek both emotional and practical social support [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and the use of social media was positively associated with subjective wellbeing [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Social media use during COVID-19 improved subjective happiness and self-rated mental health [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and reduced stress[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] for many individuals. Conversely, there are also social media use-related risk factors that have been identified for developing a mental health disease, specifically, the duration, frequency, and number of social media platforms being used [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Adverse effects linked to increased social media usage include college students reporting declines in their academic performance, [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] which could increase the likelihood of downstream mental health effects and/or be indicative of an upstream mental health impact from social media usage.\u003c/p\u003e \u003cp\u003eBeyond the use of social media, there are several known risk factors associated with anxiety and depression. Firstly, the prevalence of anxiety depression is higher among older aged persons, [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] influenced by older persons having increased vulnerability to diseases and disability, isolation from family, insufficient wealth to maintain a standard of living, or a combination of these factors, among others. A study carried out in China during the COVID period in which movement freedoms were restricted, found that the severity of depression symptoms were decreased with increases in age resilience, but were increased if unemployed, feeling less adapted, and being more stressed. The severity of anxiety symptoms were decreased among those with higher education and greater resilience [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Their findings offer clues for further research not able to be addressed because of their small sample size, and limited representation of a diverse set of populations from other countries.\u003c/p\u003e \u003cp\u003eGiven the rise in social media, research cannot be limited to only specific communities, countries and cultures, and it is therefore appropriate to inform essential global efforts aimed at the careful utilization of social media for informing policy or guidance in a manner that is scientific, practical, regulative, collaborative and fosters or promotes health. For contributing knowledge, we accessed the free online database from Wellcome Trust [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Included are data which were taken from 113 countries using an appropriate representative sample from each country. Such data can yield findings that can act as a backbone for policy considerations globally, regionally, and nationally. Furthermore, this research is significant for policy makers, researchers, academicians and general readers, who are interested in mitigating the potential risk of anxiety depression that can or has already impacted the physical health, disability status, life expectancy, and health span of individuals and families, as well as the social and economic climates for countries. Accordingy, the aim of this study is to scrutinize the factors related to anxiety depression considering the application of social media.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eSecondary data were accessed and obtained from Wellcome Global Monitor 2020: Mental Health [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. It is the largest survey in the world of how people consider and cope with anxiety and depression and explores the perceived role of science to find new solutions.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection tool\u003c/h2\u003e \u003cp\u003eThe 2020 Wellcome Global Monitor questionnaire was developed as a tool using a careful research and design process informed by a discussion series with leading researchers and subject matter experts, cognitive testing in ten countries to ensure questions could be understood across countries and various demographic groups, and pilot tested in 10 countries [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The questionnaire was then translated into the major conversational languages of each country and checked by an independent third party for quality assurance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSampling and data collection\u003c/h2\u003e \u003cp\u003eDue to COVID 19 pandemic, face-to-face interview methods were changed into telephone interviews. The samples from each country are nationally representative of the resident population aged 15 and older with access to a phone (either landline or mobile); however, the inability to conduct in-person interviews reduced population coverage in many low-income countries.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eVariables\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eOutcome/dependent variable\u003c/h2\u003e \u003cp\u003eThe outcome variable of interest was self-reported Anxiety and Depression. The outcome condition is defined as being so extreme that feelings of anxiety and depression impacted their ability to function as they normally would for two weeks or more \u0026ndash; a definition drawn from the World Health Organization (WHO). Specifically, the survey asked, \u0026ldquo;Have you ever been so anxious or depressed that you could not continue your regular daily activities as you normally would for two weeks or longer?\u0026rdquo; Independent observations were not made, and the outcome relied on each respondent\u0026rsquo;s self-reported feelings of anxiety or depression as defined in the survey.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eIndependent variables\u003c/h2\u003e \u003cp\u003eInformed by background literature, we selected independent variables of study interest. Common independent variables included were the demographic variables pertaining to age and gender as well as the economic, education and employment status of respondents. Furthermore, we selected the trust-related variables; specifically, trust in science and trust in traditional healers that has linked with neuroanatomy [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. We also selected variables to assess associations described in the \u003cspan refid=\"Sec1\" class=\"InternalRef\"\u003eintroduction\u003c/span\u003e section related to social media use (frequency and duration), such as social media use in times per hour, as well as use after regular hours, occasional use per day, once per day, a few times per month, etc.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData verification and analysis\u003c/h2\u003e \u003cp\u003eWe downloaded the data and survey questionnaires from \u003cem\u003eWellcome Global Monitor 2020: Mental Health\u003c/em\u003e web page. We listed the variables related to title, variable categories and codes. We carefully checked the data errors like outliers, alphabetical letters/terms, coding errors etc. Similarly, we analyzed our interested variables with an order of univariate, bivariate and multivariate chronology. After verification and re-verification, the data were exported for analysis with the Statistical Package for the Social Sciences IBM SPSS Statistics V22.0.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis steps\u003c/h2\u003e \u003cp\u003eFirst, univariate analyses were computed for obtaining frequencies and percentages. In the second phase, Chi-square tests were computed. In the third step, simple and multivariable logistic regression and odds ratio was performed using the covariates that were statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) from the bivariate analyses. The logistic regression data were interpreted with unadjusted and adjusted odds ratios, their respective 95% confidence intervals, and p-values with the significance level of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eValidity and Reliability\u003c/h2\u003e \u003cp\u003eThere are very few questions about the validity and reliability of Wellcome trust data which was used in this study. After database formation, we performed more than two rounds of crosschecking particularly the outliers and proportion of missing values. Likewise, the normality of the data was verified by the observation of histograms and consistency of data was checked by Cronbach's alpha for appropriate variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEthical consideration\u003c/h2\u003e \u003cp\u003eThe data were obtained as open access data available for everyone, including researchers and policy makers, for maximizing the impact from their data collection. The Wellcome Trust, United Kingdom, has the sole authority of and accountability of data use and the protection of the study participants. There was no identifiable information in the data. The Wellcome Trust has more information regarding their commitment to the responsible conduct of research on their website\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eWe used the open access dataset available from Welcome Global Monitor 2020. The were taken from 113 countries with a total sample size of 119,234 individuals. A representative sample size was taken from those countries. The prevalence of self-reported Generalized Anxiety Depression (GAD) was 20%. For general demographics, by age, more than one-third (38.27%) of participants were from 30\u0026ndash;49 years. Males represented 51% (70790), over half of participants had graduated high school, and 27% of participants were from the from the 5th quintile group (wealthiest group). Additionally, more than one-third of participants were employed full-time. A significant majority (81.3%) believed in science and 42.3% believed in traditional healers. Lastly, 81.5% used social media in the previous 30 days (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e further explores the association among different predictors with outcome (Anxiety/ Depression). There is an association between age, sex, education, income, individual trust and different patterns of using social media with Anxiety/Depression (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The frequency of reporting anxiety/depression was greater among persons in lower income groups, younger age populations, females, and persons using social media most frequently (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Likewise, persons who use social media every hour and after some hours had a significantly higher prevalence of anxiety/depression than those not using that pattern. Interestingly, persons who used social media once a day had a significantly lower prevalence of Anxiety/Depression relative to more than once a day (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSituation of demographic, trust and practice with anxiety and depression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIndependent Variables/Covariates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eDependent Variable /Outcome\u0026thinsp;~\u0026thinsp;General Anxiety Depression)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39,188 (33.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,889 (22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44,380 (38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,471 (21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19,954 (17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,705 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,673 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,803 (14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDK/Refused\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e615 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103 (16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59,478. (50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11,312 (19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57,332 (49.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,659 (22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElementary or less (8 years or less)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15,361 (13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,464 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary (8\u0026ndash;15 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64,042 (54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13,846 (21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTertiary (16\u0026thinsp;+\u0026thinsp;years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36,736 (31.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,469 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDK/Refused\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e671 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e192 (28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eHousehold Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoorest 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17,371 (15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4307 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecond 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19,359 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4251 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22,141 (19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4388 (18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFourth 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25,320 (21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4710 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRichest 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31,622 (27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5554 (23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEmployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74,268 (63.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15,213 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42,532 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,756 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTrust in Science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95,198 (81.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19,056 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14,833 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,476 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDK/Refused\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,819 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,439 (21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTrust in traditional healer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49,523 (42.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,644 (19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59,676 (51.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,897 (21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDK/Refused\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,611 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,430 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eUsed social media in Past 30 Days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95,495 (81.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20,209 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21,133 (18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,726 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDK/Refused\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eUse of social media in different time pattern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo use in past 30 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21,133 (18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,726 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess Frequently\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,150 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,281 (24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA few days a week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,485 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,900 (25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOnce a day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,877 (11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,554 (19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeveral times a day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44,764 (38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,777 (19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlmost every hour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,198 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,383 (21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeveral times an hour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,516 (11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,229 (23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDK/Refused\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e687 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eNon response rate in each variable was about 0.6% which is negligible.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eComparison of odds ratio and 95% CI among predictors and outcome\u003c/h2\u003e \u003cp\u003eAfter bivariate analysis, we used regression in those variables having statistically significant. Age, sex education, income quintile, trust and social media utilization pattern were statistical significant and we further analyzed putting one reference group. Age between 15 to 29 1.24 (1.19\u0026ndash;1.30) *** and 30 to 49 were more likely but 65\u0026thinsp;+\u0026thinsp;was less likely to be GAD with reference to 50\u0026ndash;64 years age. Likewise, females were more likely 1.21 (1.18\u0026ndash;1.26) *** to be GAD with reference to male. Education with Elementary or less (1.34 (1.27\u0026ndash;1.41) *** and Secondary 1.21 (1.16.- 1.25) *** were more likely to be GAD with reference to Tertiary education. With reference to 4th quintile group, poorest \u0026minus;\u0026thinsp;20% (1.39; 1.32\u0026ndash;1.47) ***, second- 20% (1.20; 1.14\u0026ndash;1.26) *** and middle 20 \u0026ndash; (1.06 ;1.01\u0026ndash;1.11) ** is more likely to be GAD but richest \u0026minus;\u0026thinsp;20 (0.96 ;0.92\u0026ndash;1.00) is less likely to be GAD. Similarly, respondents who trust in Science 0.92 (0.88\u0026ndash;0.96) *** and Traditional Healers 0.87 (0.84\u0026ndash;0.90) were less likely to be GAD with reference to those do not trust. Use of social media was observed risk factor to be GAD than those not used. Less frequently 1.45 (1.34\u0026ndash;1.57) ***, a few days in week 1.50 (1.40\u0026ndash;1.60) ***, once a day 1.17 (1.10\u0026ndash;1.24) ***, several times a day1.14 (1.09\u0026ndash;1.20) ***, almost every hour 1.22 (1.14\u0026ndash;1.30) *** and several time an hour1.43 (1.34\u0026ndash;1.51) were more likely to be GAD with reference to those not used social media within past 30 days (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of odds ratio and 95% CI with independent variables with Anxiety and Depression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCovariate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUnadjusted odds ratio (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted odds ratio\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e(95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29 (1.23\u0026ndash;1.34) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24 (1.19\u0026ndash;1.30) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19 (1.14\u0026ndash;1.24) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19 (1.14\u0026ndash;1.25) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73 (0.68\u0026ndash;0.77) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76 (0.71\u0026ndash;0.81) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21 (1.18\u0026ndash;1.25) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.21 (1.18\u0026ndash;1.26) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElementary or less (8 years or less)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.36 (1.30\u0026ndash;1.43) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.34 (1.27\u0026ndash;1.41) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary (8\u0026ndash;15 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29 (1.25\u0026ndash;1.33) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.21 (1.16.- 1.25) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTertiary (16\u0026thinsp;+\u0026thinsp;years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eIncome quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoorest 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.45 (1.38\u0026ndash;1.52) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.39 (1.32\u0026ndash;1.47) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecond 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23 (1.17\u0026ndash;1.28) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.20 (1.14\u0026ndash;1.26) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08 (1.03\u0026ndash;1.13) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 (1.01\u0026ndash;1.11) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFourth 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRichest 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93 (0.89\u0026ndash;0.97) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96 (0.92\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTrust in Science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.82 (0.79\u0026ndash;0.85) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92 (0.88\u0026ndash;0.96) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTrust in traditional healer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88 (0.85\u0026ndash;0.90) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87 (0.84\u0026ndash;0.90) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eUse of social media in different time pattern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo use in past 30 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess Frequently\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.55 (1.44\u0026ndash;1.66) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.45 (1.34\u0026ndash;1.57) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA few days a week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.59 (1.49\u0026ndash;1.69) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.50 (1.40\u0026ndash;1.60) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOnce a day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16 (1.09\u0026ndash;1.22) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17 (1.10\u0026ndash;1.24) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeveral times a day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.14 (1.09\u0026ndash;1.19) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14 (1.09\u0026ndash;1.20) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlmost every hour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26 (1.19\u0026ndash;1.38) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22 (1.14\u0026ndash;1.30) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeveral times an hour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.47 (1.39\u0026ndash;1.55) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.43 (1.34\u0026ndash;1.51) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNote: ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study carried out 113 countries, all continents, representative samples from each country, using telephone interview, age after 15 years, equal proportion to male and female. We analyzed the demographic, trust related and application of social media related variables. Use of social media is very popular and making easier life style and at the same time it has multiple impact in human life and becoming a threat too. The nature of social media is providing recent information/news, interesting, amazing and hidden information so that every people have been attracted and addicted. As a result, social media users have been isolated from social interaction and a mental illness called anxiety depression has been developed [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The generalized anxiety disorder was significantly higher after 50 years age, female, trusted in science and traditional healers and used the social media in past 30 days. The respondents, who used social media once a day or less frequent were low prevalence of GAD than who used every hour and sooner.\u003c/p\u003e \u003cp\u003eOur study shows the overall prevalence of GAD was 20%, young age (15\u0026ndash;30) year 1.24 times, female 1.21times, elementary/primary education 1.34 times, poorest twenty 1.39 times and who use social media several times an hour more likely to be GAD with reference to early old age, male, higher education, richest twenty and those who did not use social media in past 30 days. At the same time, old age group (65+), those trust in science and traditional healers and did not use social media in past 30 days less likely to be GAD. Borwin Bandelow \u0026amp;Sophie Michaelis (2015) found that prevalences of anxiety depression within 12 months and life time were 21% and 34% respectively [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In the USA, the government official report shows that Over 15% of adults experienced symptoms of anxiety that were either mild, moderate, or severe in the past 2 weeks and women are more likely to be anxiety than man [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Data from 23 community surveys in 21 countries of the World Mental Health (WMH) surveys in 2018 showed that 10% had anxiety disorder within 12 months[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Community survey in Italy was performed by Antonio Preti et.al in 2021 and findings showed that overall lifetime prevalence in the sample was 2.3%, with a markedly higher frequency in women [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. A study by Quanman Li (2020) revels that the prevalence of GAD was more in older age than young age [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A study by Karin Hammarberg et.al 2020 in Australia published in BMJ showed that women were more likely than men to have clinically significant symptoms of depression [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Study by Sally Adams et.al 2022 reveled that the young adult and male are more likely to be anxiety depression symptoms [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. A study using the 2005\u0026ndash;2016 National Health and Nutrition Examination Survey (NHANES) USA 2022 concluded that young adults (35\u0026ndash;49) and low poverty income ratio are more likely to be depressive symptoms in comparison early old age people, with old age group and low and medium poverty income ratio [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study shows, respondents who trust in Science and Traditional Healers were less likely to be GAD reference with those not trust. We could not find relative comparison with previous research. At the peak time of COVID pandemic, a study showed that there was a significant role of belief process in pandemic depression and anxiety [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. A pathway analysis showed that belief process including emotional belief was associated with anxiety disorder [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. A traditional healing process is fundamentally the belief system and those respondents are more likely to anxiety disorder than those do not. Likewise, people with GAD were equally consulted with psychiatric doctors and traditional healers [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and who did spiritual practice and alternative medicine had better self-perceived health than those did not [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere are not consistent findings regarding the association between use of social media and mental illness like Anxiety and Depression. Social media has been widely used as an important source of health information, particularly during public health crises. So, a study by Brailovskaia et al. (2022) found that COVID-19-related social media use is positively related to the psychological burden of the pandemic [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. John A. Naslund et.al 2020 concluded that the role of social media as a potentially viable intervention platform for offering support to persons with mental disorders, promoting engagement and retention in care, and enhancing existing mental health services, however the caution should be warranted [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. A systematic review by Abderrahman M Khalaf 2020, revels that ethical social media use can expand opportunities for connection and conversation, as well as boost self-esteem, promote health, and gain access to critical medical information and mitigate the anxiety and depression in starting phase but excessive use increase the risk of information misleading, confusion and enhance the severity of GAD [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Study by Vannucci A et.al concluded that use of social media among teenagers and young people appears to be predictive of depressive symptoms and low offline social support from both family and peers [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. A study by Michelle O\u0026rsquo;Reilly \u0026minus;\u0026thinsp;2020 concluded that social media used by adolescent increase the mood and anxiety disorders, as a platform for cyberbullying and the use of social media itself was often framed as a kind of \u0026lsquo;addiction\u0026rsquo;[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] which finding is perfectly matching with our findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study explored the different factors associated with General Anxiety Depression globally. Young adults, female, lower educational attainment, poor economic status and excessive use of social media are high risk factors for GAD and trust in science and traditional healers are less likely to be GAD. Ethical and careful use of social media could be beneficial but high frequently use of social media is an addiction and increase the risk of GAD. On the other hand, the content and unverified information could be great risk for mental illness. There were different obstacles to get the information during the COVID 19 peak time. Being a telephone interview, there might be possibility of information and communication bias during the data collection time. Based on our findings, more specific studies are recommended by age, situation of public health emergencies, employment group education groups etc.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgement\u003c/p\u003e\n\u003cp\u003eWe are grateful with Welcome Trust who provided the assess the data for further analysis. We provide sincere thanks to the respondents who provided the research response during the COVID 19 peak time.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eNo funding.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe data is free access online can be reached in this link https://wellcome.org/reports/wellcome-global-monitor-mental-health/2020.\u003c/p\u003e\n\u003cp\u003eAuthors contribution\u003c/p\u003e\n\u003cp\u003eCR conceptualized, designed, prepared, reviewed, and led the article. JM verified the results thoroughly reviewed and updated the article.\u003c/p\u003e\n\u003cp\u003eAuthor affiliations\u003c/p\u003e\n\u003cp\u003eCR\u003csup\u003e\u0026nbsp;\u003c/sup\u003eis associated with Eastern Scientific LLC, 316 N. Estill Ave. Richmond, KY 40475, USA, and Planetary Health Research Centre (PHRC), Nagarjun - 01, Kathmandu, Nepal. JM is associated with Eastern Scientific LLC, 316 N. Estill Ave. Richmond, KY 40475, USA and Department of Environmental Health Science, Eastern Kentucky University, Richmond, KY, USA.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArias D, Saxena S, Verguet S. Quantifying the global burden of mental disorders and their economic value. EClinicalMedicine 2022, 54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJavaid SF, Hashim IJ, Hashim MJ, Stip E, Samad MA, Ahbabi AA. Epidemiology of anxiety disorders: global burden and sociodemographic associations. 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Int J Mental Health Addict. 2023;21(1):96\u0026ndash;110.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHou Y, Xiong D, Jiang T, Song L, Wang Q. Social media addiction: Its impact, mediation, and intervention. Cyberpsychology: J psychosocial Res cyberspace 2019, 13(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBandelow B, Michaelis S. Epidemiology of anxiety disorders in the 21st century. Dialog Clin Neurosci. 2015;17(3):327\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSymptoms of generalized anxiety disorder among adults: United States, 2019: US Department of Health and Human Services, Centers for Disease Control and \u0026amp;#8230.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlonso J, Liu Z, Evans-Lacko S, Sadikova E, Sampson N, Chatterji S, Abdulmalik J, Aguilar‐Gaxiola S, Al‐Hamzawi A, Andrade LH. Treatment gap for anxiety disorders is global: Results of the World Mental Health Surveys in 21 countries. Depress Anxiety. 2018;35(3):195\u0026ndash;208.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePreti A, Demontis R, Cossu G, Kalcev G, Cabras F, Moro MF, Romano F, Balestrieri M, Caraci F, Dell\u0026rsquo;Osso L, et al. The lifetime prevalence and impact of generalized anxiety disorders in an epidemiologic Italian National Survey carried out by clinicians by means of semi-structured interviews. BMC Psychiatry. 2021;21(1):48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Q, Miao Y, Zeng X, Tarimo CS, Wu C, Wu J. Prevalence and factors for anxiety during the coronavirus disease 2019 (COVID-19) epidemic among the teachers in China. J Affect Disord. 2020;277:153\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHammarberg K, Tran T. Sex and age differences in clinically significant symptoms of depression and anxiety among people in Australia in the first month of COVID-19 restrictions: a national survey. 2020, 10(11):e042696.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdams SH, Schaub JP, Nagata JM, Park MJ, Brindis CD, Irwin CE Jr. Young adult anxiety or depressive symptoms and mental health service utilization during the COVID-19 pandemic. J Adolesc Health. 2022;70(6):985\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZare H, Meyerson NS, Nwankwo CA, Thorpe RJ. How Income and Income Inequality Drive Depressive Symptoms in U.S. Adults, Does Sex Matter: 2005\u0026ndash;2016. Int J Environ Res Public Health. 2022;19(10):6227.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMilman E, Lee SA, Neimeyer RA, Mathis AA, Jobe MC. Modeling pandemic depression and anxiety: The mediational role of core beliefs and meaning making. J Affect Disorders Rep. 2020;2:100023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnston TE, Petrova K, Mehta A, Gross JJ, McEvoy P, Preece DA. The role of emotion beliefs in depression, anxiety, and stress. Australian Psychol:1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchoonover J, Lipkin S, Javid M, Rosen A, Solanki M, Shah S, Katz CL. Perceptions of Traditional Healing for Mental Illness in Rural Gujarat. Annals Global Health. 2014;80(2):96\u0026ndash;102.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRanabhat CL, Kim C-B, Park M-B, Bajgai J. Impact of spiritual behavior on self-reported illness: a cross-sectional study among women in the Kailali district of Nepal. J Lifestyle Med. 2018;8(1):23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrailovskaia J, Miragall M, Margraf J, Herrero R, Ba\u0026ntilde;os RM. The relationship between social media use, anxiety and burden caused by coronavirus (COVID-19) in Spain. Curr Psychol. 2022;41(10):7441\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaslund JA, Bondre A, Torous J, Aschbrenner KA. Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice. J Technol Behav Sci. 2020;5(3):245\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhalaf AM, Alubied AA, Khalaf AM, Rifaey AA. The Impact of Social Media on the Mental Health of Adolescents and Young Adults: A Systematic Review. Cureus. 2023;15(8):e42990.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVannucci A, McCauley Ohannessian C. Social media use subgroups differentially predict psychosocial well-being during early adolescence. J Youth Adolesc. 2019;48:1469\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO\u0026rsquo;Reilly M, Dogra N, Whiteman N, Hughes J, Eruyar S, Reilly P. Is social media bad for mental health and wellbeing? Exploring the perspectives of adolescents. Clin Child Psychol Psychiatry. 2018;23(4):601\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wellcome.org/grant-funding/guidance/responsible-conduct-research\u003c/span\u003e\u003cspan address=\"https://wellcome.org/grant-funding/guidance/responsible-conduct-research\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4664537/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4664537/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGeneralized anxiety and depression is the initial stage of mental illness and reflects a mood disorder reflected in sadness, hopelessness, nervousness, and worry. There are different factors associated with general anxiety and depression (GAD). Together with basic demographic and economic factors, we observed the use of social media by GAD. The aim of this study is to explore globally the influence of social media on self-reported general anxiety and depression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe chose different factors associated with anxiety and depression affected for more than 2 weeks from the 2020 Wellcome Global Monitor from the available secondary data. The samples from each country are nationally representative of the resident population aged 15 and older with access to a phone in 113 countries. The research design process was completed by leading researchers and subject experts; cognitive testing was conducted in ten countries to ensure questions could be understood across countries and by various demographic groups; and pilot tests were conducted in 10 countries. Independent variables were demographic variables: age, gender, economics, education, employment status, belief factors, and trends in social media use. Univariate variables were presented in frequency and percentage; bivariate analysis was performed with cross-tabulation using the chi square test; and logistic regression was used among significant variables by adjusted odds ratios and 95% CI as multivariate analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe prevalence of self-reported generalized anxiety depression (GAD) was 20% out of 119,234 in 113 countries. More than 38.27% were between the ages of 30 and 49, with 51% being male, more than half having completed high school, and 27% falling into the 4th and 5th quintiles (rich group). Similarly, more than 63.4% were employed, 81.3% believed in science, 42.3% believed in traditional healers, and 81.5% used social media, which was significantly associated with self-reported GAD. Adjusted odds ratio (aOR) showed that young age (15–30) years 1.24 times, females 1.21 times, elementary and primary education 1.34 times, the poorest twenty 1.39 times, and those who use social media several times an hour are more likely to be GAD with reference to early old age, males, higher education, the richest twenty, and those who did not use social media in the past 30 days. At the same time, the older age group (65+), those who trusted science and traditional healers and did not use social media in the past 30 days, were less likely to be GAD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e There is an increasing risk of GAD worldwide, and young adults and females are more vulnerable. Excessive use of social media is a challenging and risky factor.\u003c/p\u003e","manuscriptTitle":"Use of social media increases the risk of anxiety depression globally: results from 113 countries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-24 11:08:19","doi":"10.21203/rs.3.rs-4664537/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7fd0cb66-8e4d-411c-b3eb-78661c99590f","owner":[],"postedDate":"July 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-05T06:24:07+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-24 11:08:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4664537","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4664537","identity":"rs-4664537","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}