Impact of the COVID-19 pandemic on anxiety and depression symptoms of young people
in the global south: evidence from a four-country cohort study
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Abstract
Objective
To provide evidence on the effect of the COVID-19 pandemic on the mental health of
young people who grew up in poverty in low/middle-income countries (LMICs).
Design
A phone survey administered between August and October 2020 to participants of a population-based
longitudinal cohort study established in 2002 comprising two cohorts born in 1994–1995
and 2001–2002 in Ethiopia, India (Andhra Pradesh and Telangana), Peru and Vietnam.
We use logistic regressions to examine associations between mental health and pandemic-related
stressors, structural factors (gender, age), and lifelong protective/risk factors
(parent and peer relationship, wealth, long-term health problems, past emotional problems,
subjective well-being) measured at younger ages.
Setting
A geographically diverse, poverty-focused sample, also reaching those without mobile
phones or internet access.
Participants
10 496 individuals were approached; 9730 participated. Overall, 8988 individuals were
included in this study; 4610 (51%) men and 4378 (49%) women. Non-inclusion was due
to non-location or missing data.
Main outcome measures
Symptoms consistent with at least mild anxiety or depression were measured by Generalized
Anxiety Disorder-7 (≥5) or Patient Health Questionnaire-8 (≥5).
Results
Rates of symptoms of at least mild anxiety (depression) were highest in Peru at 41%
(32%) (95% CI 38.63% to 43.12%; (29.49–33.74)), and lowest in Vietnam at 9% (9%) (95% CI
8.16% to 10.58%; (8.33–10.77)), mirroring COVID-19 mortality rates. Women were most
affected in all countries except Ethiopia. Pandemic-related stressors such as health
risks/expenses, economic adversity, food insecurity, and educational or employment
disruption were risk factors for anxiety and depression, though showed varying levels
of importance across countries. Prior parent/peer relationships were protective factors,
while long-term health or emotional problems were risk factors.
Conclusion
Pandemic-related health, economic and social stress present significant risks to the
mental health of young people in LMICs where mental health support is limited, but
urgently needed to prevent long-term consequences.
Generalized anxiety disorder (GAD) is one of the most common mental disorders; however, there is no brief clinical measure for assessing GAD. The objective of this study was to develop a brief self-report scale to identify probable cases of GAD and evaluate its reliability and validity. A criterion-standard study was performed in 15 primary care clinics in the United States from November 2004 through June 2005. Of a total of 2740 adult patients completing a study questionnaire, 965 patients had a telephone interview with a mental health professional within 1 week. For criterion and construct validity, GAD self-report scale diagnoses were compared with independent diagnoses made by mental health professionals; functional status measures; disability days; and health care use. A 7-item anxiety scale (GAD-7) had good reliability, as well as criterion, construct, factorial, and procedural validity. A cut point was identified that optimized sensitivity (89%) and specificity (82%). Increasing scores on the scale were strongly associated with multiple domains of functional impairment (all 6 Medical Outcomes Study Short-Form General Health Survey scales and disability days). Although GAD and depression symptoms frequently co-occurred, factor analysis confirmed them as distinct dimensions. Moreover, GAD and depression symptoms had differing but independent effects on functional impairment and disability. There was good agreement between self-report and interviewer-administered versions of the scale. The GAD-7 is a valid and efficient tool for screening for GAD and assessing its severity in clinical practice and research.
In December, 2019, a local outbreak of pneumonia of initially unknown cause was detected in Wuhan (Hubei, China), and was quickly determined to be caused by a novel coronavirus, 1 namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak has since spread to every province of mainland China as well as 27 other countries and regions, with more than 70 000 confirmed cases as of Feb 17, 2020. 2 In response to this ongoing public health emergency, we developed an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualise and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository, along with the feature layers of the dashboard, which are now included in the Esri Living Atlas. The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable; therefore, on Feb 1, we adopted a semi-automated living data stream strategy. Our primary data source is DXY, an online platform run by members of the Chinese medical community, which aggregates local media and government reports to provide cumulative totals of COVID-19 cases in near real time at the province level in China and at the country level otherwise. Every 15 min, the cumulative case counts are updated from DXY for all provinces in China and for other affected countries and regions. For countries and regions outside mainland China (including Hong Kong, Macau, and Taiwan), we found DXY cumulative case counts to frequently lag behind other sources; we therefore manually update these case numbers throughout the day when new cases are identified. To identify new cases, we monitor various Twitter feeds, online news services, and direct communication sent through the dashboard. Before manually updating the dashboard, we confirm the case numbers with regional and local health departments, including the respective centres for disease control and prevention (CDC) of China, Taiwan, and Europe, the Hong Kong Department of Health, the Macau Government, and WHO, as well as city-level and state-level health authorities. For city-level case reports in the USA, Australia, and Canada, which we began reporting on Feb 1, we rely on the US CDC, the government of Canada, the Australian Government Department of Health, and various state or territory health authorities. All manual updates (for countries and regions outside mainland China) are coordinated by a team at Johns Hopkins University. The case data reported on the dashboard aligns with the daily Chinese CDC 3 and WHO situation reports 2 for within and outside of mainland China, respectively (figure ). Furthermore, the dashboard is particularly effective at capturing the timing of the first reported case of COVID-19 in new countries or regions (appendix). With the exception of Australia, Hong Kong, and Italy, the CSSE at Johns Hopkins University has reported newly infected countries ahead of WHO, with Hong Kong and Italy reported within hours of the corresponding WHO situation report. Figure Comparison of COVID-19 case reporting from different sources Daily cumulative case numbers (starting Jan 22, 2020) reported by the Johns Hopkins University Center for Systems Science and Engineering (CSSE), WHO situation reports, and the Chinese Center for Disease Control and Prevention (Chinese CDC) for within (A) and outside (B) mainland China. Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks. We believe our efforts are crucial to help inform modelling efforts and control measures during the earliest stages of the outbreak.
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History
Date
received
: 01
February
2021
Date
revision received
: 18
March
2021
Date
accepted
: 23
March
2021
Funding
Funded by: FundRef http://dx.doi.org/10.13039/501100000278, Department for International Development, UK Government;