This paper is in the following e-collection/theme issue:

Cross-Sectional Studies in Public Health (289) JMIR Theme Issue: COVID-19 Special Issue (2461) Outbreak and Pandemic Preparedness and Management (1743) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1520) Infectious Diseases (non-STD/STI) (1592) JMH Theme Issue: COVID-19 and Mental Health: Impact and Interventions (386) Depression and Mood Disorders; Suicide Prevention (1392) Intentional Self-Harm (101) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1565) Mass Media/Social Media Communication and Campaigns (466) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1050) Infodemiology and Infoveillance (1369)

Published on in Vol 8, No 1 (2022): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/32140, first published .
Patterns of Suicide Ideation Across Eight Countries in Four Continents During the COVID-19 Pandemic Era: Repeated Cross-sectional Study

Patterns of Suicide Ideation Across Eight Countries in Four Continents During the COVID-19 Pandemic Era: Repeated Cross-sectional Study

Patterns of Suicide Ideation Across Eight Countries in Four Continents During the COVID-19 Pandemic Era: Repeated Cross-sectional Study

Authors of this article:

Philip J Schluter1, 2 Author Orcid Image ;   Mélissa Généreux3 Author Orcid Image ;   Kevin KC Hung4 Author Orcid Image ;   Elsa Landaverde3 Author Orcid Image ;   Ronald P Law5 Author Orcid Image ;   Catherine Pui Yin Mok6 Author Orcid Image ;   Virginia Murray7 Author Orcid Image ;   Tracey O'Sullivan8 Author Orcid Image ;   Zeeshan Qadar9 Author Orcid Image ;   Mathieu Roy10 Author Orcid Image
Article Authors Cited by (15) Tweetations (4) Metrics

    Original Paper

    1School of Health Sciences, University of Canterbury - Te Whare Wananga o Waitaha, Christchurch, New Zealand

    2Primary Care Clinical Unit, School of Clinical Medicine, The University of Queensland, Brisbane, Australia

    3Department of Community Health Sciences, Faculté de Médecine et des Sciences de la Santé, Université de Sherbrooke, Sherbrooke, QC, Canada

    4Accident and Emergency Medicine Academic Unit, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China

    5Department of Health, Manila, Philippines

    6The Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China

    7United Kingdom Health Security Agency, London, United Kingdom

    8Interdisciplinary School of Health Sciences, Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada

    9National Collaborating Centre for Infectious Diseases, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada

    10Department of Family Medicine & Emergency Medicine, Faculté de Médecine et des Sciences de la Santé, Université de Sherbrooke, Sherbrooke, QC, Canada

    Corresponding Author:

    Philip J Schluter, PhD

    School of Health Sciences

    University of Canterbury - Te Whare Wananga o Waitaha

    Private Bag 4800

    Christchurch, 8140

    New Zealand

    Phone: 64 275106239

    Email: philip.schluter@canterbury.ac.nz


    Background: The COVID-19 pandemic and countries’ response measures have had a globally significant mental health impact. This mental health burden has also been fueled by an infodemic: an information overload that includes misinformation and disinformation. Suicide, the worst mental health outcome, is a serious public health problem that can be prevented with timely, evidence-based, and often low-cost interventions. Suicide ideation, one important risk factor for suicide, is thus important to measure and monitor, as are the factors that may impact on it.

    Objective: This investigation had 2 primary aims: (1) to estimate and compare country-specific prevalence of suicide ideation at 2 different time points, overall and by gender and age groups, and (2) to investigate the influence of sociodemographic and infodemic variables on suicide ideation.

    Methods: A repeated, online, 8-country (Canada, the United States, England, Switzerland, Belgium, Hong Kong, Philippines, and New Zealand), cross-sectional study was undertaken with adults aged ≥18 years, with measurement wave 1 conducted from May 29, 2020 to June 12, 2020 and measurement wave 2 conducted November 6-18, 2021. Self-reported suicide ideation was derived from item 9 of the Patient Health Questionnaire-9 (PHQ-9). Age-standardized suicide ideation rates were reported, a binomial regression model was used to estimate suicide ideation indication rates for each country and measurement wave, and logistic regression models were then employed to relate sociodemographic, pandemic, and infodemic variables to suicide ideation.

    Results: The final sample totaled 17,833 adults: 8806 (49.4%) from measurement wave 1 and 9027 (50.6%) from wave 2. Overall, 24.2% (2131/8806) and 27.5% (2486/9027) of participants reported suicide ideation at measurement waves 1 and 2, respectively, a difference that was significant (P<.001). Considerable variability was observed in suicide ideation age-standardized rates between countries, ranging from 15.6% in Belgium (wave 1) to 42.9% in Hong Kong (wave 2). Frequent social media usage was associated with increased suicide ideation at wave 2 (adjusted odds ratio [AOR] 1.47, 95% CI 1.25-1.72; P<.001) but not wave 1 (AOR 1.11, 95% CI 0.96-1.23; P=.16). However, having a weaker sense of coherence (SOC; AOR 3.80, 95% CI 3.18-4.55 at wave 1 and AOR 4.39, 95% CI 3.66-5.27 at wave 2; both P<.001) had the largest overall effect size.

    Conclusions: Suicide ideation is prevalent and significantly increasing over time in this COVID-19 pandemic era, with considerable variability between countries. Younger adults and those residing in Hong Kong carried disproportionately higher rates. Social media appears to have an increasingly detrimental association with suicide ideation, although having a stronger SOC had a larger protective effect. Policies and promotion of SOC, together with disseminating health information that explicitly tackles the infodemic’s misinformation and disinformation, may importantly reduce the rising mental health morbidity and mortality triggered by this pandemic.

    JMIR Public Health Surveill 2022;8(1):e32140

    doi:10.2196/32140

    Keywords

    pandemic; infodemic; psychosocial impacts; sense of coherence; suicide ideation; epidemiology; suicide; pattern; COVID-19; cross-sectional; mental health; misinformation; risk; prevalence; gender; age; sociodemographic 


    Since the first known case was identified in Wuhan, China, the COVID-19 pandemic has led to globally significant physical and mental health sequelae [1,2] and extraordinary financial costs [3]. Inconsistent, continually evolving, and often swiftly implemented international and national response measures aimed at preventing the spread of COVID-19 have impacted all facets of society. Responses, while varied, commonly included stringent control measures such as lockdown and isolation periods, quarantine, restricted social gatherings and physical distancing, school and workplace closures, and domestic and international travel curtailments. The scale of global economic disruption from the COVID-19 pandemic has been unprecedented, resulting in countless business failures and job losses [3], despite multiple stimulus packages aimed at limiting the human and economic impacts of the pandemic [4]. Fear, anxiety, uncertainty, fatigue, together with the social and economic effects of the virus and associated countermeasures, have directly contributed to increased mental health burden [2,3]. This burden is unequally shared, disproportionately affecting vulnerable groups including young adults, students, ethnic minorities, and adults in socially or economically precarious situations [5,6]. In an effort to mitigate this mental health burden, many governments around the world have also implemented additional mental health support and financial measures [2].

    The mental health burden of the COVID-19 pandemic has also been fueled by an infodemic—a rapid and far-reaching information overload, which includes misinformation and disinformation, that can serve to undermine or stymie public health responses [7-9]. The negative influence of excessive media exposure on mental health is receiving increasing attention and recognition [8-11], although its impact across the myriad of mental health and well-being domains has yet to be fully understood. In addition to national and international efforts aimed at readdressing this infodemic, such as a joint statement by the World Health Organization, the United Nations, and the United Nations Children’s Fund among others [7], it has been opined that its effect can be buffered by individuals’ and communities’ psychological resources. Family functioning, social support, social participation, trust in agencies including health care institutions, and sense of coherence (SOC) are considered to be important resistance resources [7,9,12,13]. SOC develops over the life course, and those with a stronger SOC are able to understand, handle, and make sense of a stressful situation [9,12]. This likely increases individuals’ capacities to use resistance resources to more effectively deal with the COVID-19 pandemic and associated circumstances [9,12,13]. On a population level, the infodemic is considered a major threat, with its promotion of noncompliance with public health measures; this reduces the effectiveness of these measures and ultimately enables the virus to continue to thrive [7]. On an individual level, it adds to confusion and strains mental health, already exacerbated by the pandemic. Therefore, it is essential to understand the importance of the infodemic, together with the factors and their role in mediating its effect.

    Despite many national and international efforts, the global mental health burden of the COVID-19 pandemic is heavy [8], appears to be worsening [9], and is unequally shared within and between countries [8,9]. As a result, negative psychiatric and psychological responses are likely to be more prevalent, leading to poor mental health outcomes including, in the most severe cases, suicide. However, early findings from high-income and upper-middle-income countries suggest that the COVID-19 pandemic has not been associated with increases in population-level suicide rates [2]. Whether these findings remain true for lower-income countries or over longer timeframes, as the pandemic and associated global response measures continue, is open to conjecture and warrants future investigation. One mechanism for investigation is the monitoring of suicide ideation—a broad term used to describe a range of contemplations, wishes, and preoccupations with death [14]—an important risk factor for suicide [15]. Such monitoring is critical not only in alerting and informing governments and mental health agencies of a looming public health crisis but also to avert this already noted global issue with the aid of appropriate planning and prevention [16].

    At the beginning of the pandemic, a cross-sectional convenience sampling study of suicide ideation among the general population across 10 countries between March 24, 2020 and April 30, 2020 (25,053 participants; 22.7% male) revealed significant differences between countries and among participants who were of younger age, male, married, and with various health beliefs [17]. That study included adults aged ≥18 years from Hong Kong (n=11,368), Brazil (n=8375), China (n=956), the United Kingdom (n=845), Turkey (n=782), the United States (n=717), the Republic of Korea (n=658), Canada (n=508), Philippines (n=454), and Macau (n=186); overall suicide ideation within the previous 2 weeks was indicated by 15.7% of participants, derived from item 9 of the Patient Health Questionnaire-9 (PHQ-9) [18]. Suicide ideation ranged from 7.6% of participants in Brazil and the United Kingdom to 24.9% of participants in the Philippines [17]. However, the reliance on convenience sampling limited the external validity of these findings, and further tracking over time would provide much needed epidemiological information.

    Using an 8-country, repeated-measure, cross-sectional study design, based on representative samples of adults and including several previously surveyed countries with the same PHQ-9 measurement instrument, this investigation had 2 primary aims: (1) to estimate and compare country-specific prevalence of suicide ideation at 2 different time points, overall and by gender and age groups, and (2) to investigate the influence of sociodemographic and infodemic variables on suicide ideation. In this paper, we contextualized findings with those published elsewhere to strengthen our understanding of suicide ideation across nations and people, to grasp the effect of the infodemic, and to provide empirical evidence that will ultimately be used to save lives.


    Study Design

    This was a repeated, 8-country, cross-sectional study, with measurement wave 1 conducted from May 29, 2020 to June 12, 2020 and measurement wave 2 conducted during November 6-18, 2020.

    Participants

    Study participants included adults aged ≥18 years residing in 1 of 8 countries from 4 continents (Canada, the United States, England, Switzerland, Belgium, Hong Kong, Philippines, and New Zealand) at the time of surveying.

    Primary Measure

    Our primary measure, self-reported suicide ideation, was derived from item 9 of the PHQ-9 [18]. The PHQ-9 asks: “Over the past 2 weeks, how often have you been bothered by the following problems?”, with item 9 asking “Thoughts that you would be better off dead or hurting yourself in some way.” Response options include (0) not at all, (1) several days, (2) over half the days, and (3) nearly every day. Suicide ideation responses were dichotomized into indicated (combining responses 1 through 3) and otherwise (response 0) categories. The PHQ-9 is available in multiple languages and has excellent internal consistency (Cronbach α=.89) and test-retest reliability (r=0.84) among primary care participants [18].

    Sociodemographic, Pandemic, and Infodemic Variables

    A detailed account of these variables and their definitions appears elsewhere [8]. In brief, gender identity was elicited with the following response options: male, female, another gender identity, I don't know/I prefer not to answer. Age in years was asked, with responses collapsed into the following groupings: 18-24, 25-34, 35-44, 45-54, 55-64, 65-74, and ≥75 years. The usual household composition was defined as living alone, living with others including children, or living with others but without children; being an essential worker (eg, health care and social services, law enforcement, emergency services, provider of essential goods, educational institution) was indicated by a “yes” response. Those who worked in health care and social services were further partitioned from the other essential workers. The overarching goal of this interdisciplinary and international research project was to better understand how risk information is delivered and communicated by authorities and media and how it is received, understood, and used by the public. The perceived factors and threats caused by COVID-19 that are directly related to self were investigated, together with sources and trust in information [19]. Table 1 gives the names, descriptions, and response options of all utilized pandemic- and infodemic-related variables included in the survey. At both measurement waves, the questionnaire was validated by the project collaborators, then translated and made available in the English, French, German, Italian, and Chinese languages [8].

    Table 1. Names, descriptions, and response options for the considered variables influenced by the pandemic.
    NameDescriptionsResponse options
    Self-isolation/quarantineHaving experienced self-isolation/quarantine, mandatory or voluntaryYes because of symptoms or diagnosis of COVID-19, yes for other reasons, no
    Financial lossesHaving experienced financial losses of any kind due to COVID-19Yes, no, unsure/ unknown
    Threat perceived for oneself and/or familyLevel of threat posed by the COVID-19 perceived for oneself and/or the familyVery low, low, moderate, high, very high
    Threat perceived for country and/or worldLevel of threat posed by COVID-19 perceived for the country and/or the worldVery low, low, moderate, high, very high
    Being a victim of stigmaBeing a victim of stigma or discrimination due to COVID-19Yes, no, decline to answer
    Level of information about COVID-19Level would you rank your level of information about COVID-1910-point scale: 1, very low level; 1-8, otherwise; 9-10, high level; 10, very high level
    Trust in authorities scoreLevel at which you would rank your level of trust in (1) scientists, doctors, and health experts; (2) national health organizations; (3) global health organizations; (4) governmentEach response rated on a 10-point scale: 1, very low level; 10, very high level; 4 scores summed, and partitioned into approximate quartiles based on measurement wave 1-response distributions
    Internet-based social media as a regular source of informationExtent that social networks (eg, Facebook, Twitter, Instagram, other networks) are used to inform yourself about COVID-19Mainly/always, often, sometimes, not much/never
    Friends/family/co-workers as a regular source of informationExtent that friends/family/co-workers are used to inform yourself about COVID-19Mainly/always, often, sometimes, not much/never
    Sense of coherenceMeasured using the 3-item Sense of Coherence (SOC-3) instrument [20,21], corresponding to comprehensibility, manageability, and meaningfulness; participants were asked (1) Do you usually see a solution to problems and difficulties that other people find hopeless? (2) Do you usually feel that your daily life is a source of personal satisfaction? (3) Do you usually feel that the things that happen to you in your daily life are hard to understand?Each with response options: no (0), yes - sometimes (1), yes - usually (2); question (3) was reverse scored; then, the 3 scores were summed and dichotomized using the threshold: weaker (summed score of 0-4) or stronger (summed score of 5-6).

    Procedure

    A detailed description of the procedure and analyses involving these survey data to answer different research questions appears elsewhere [8,9]. Two polling firms, in collaboration with international partners, undertook recruitment and data collection using an online platform. Participants were randomly recruited from online panels using multiple sources, including traditional and mobile telephone methodologies (through a call center), social media (through Facebook and Instagram), and offline methods (through partner programs and campaigns such as friend recommendations). Quota sampling was used to ensure recruitment and representation of hard-to-reach groups. Once contacted and eligibility confirmed, a full explanation of the study purpose, methods of data management, and assurance of confidentiality was provided to potential participants prior to their agreement to participate in the online study. The survey was designed to take approximately 20 minutes to complete.

    Selection of countries for inclusion was based on ensuring global continent diversity within a constrained budget and capturing different demographics, health systems and policies, and COVID-19 burdens and responses. Moreover, it was deemed necessary to invite country-specific lead investigators to provide context and ensure the survey was culturally fit-for-purpose. The core team came together from multiple existing professional connections, including the World Health Organization Thematic Platform for Health Emergency and Disaster Risk Management Research Network.

    The implemented quota sampling was tailored for each country and based on that country’s latest available census-derived population demographics. Strata comprised of age groups (18-24 years, 25-34 years, 35-44 years, 45-54 years, 55-64 years, ≥65 years), gender (female, male), and region (which was country-specific; eg, for Canada: Ontario, Québec, British Columbia, Alberta, Manitoba/ Saskatchewan, Atlantic provinces). A 70% minimum recruitment of the estimated stratum numbers for each characteristic (age, gender, and region) was targeted in order to ensure the best possible representation in the sample. The collected data were then weighted by the population demographic distributions to reach the final representative sample.

    For each country, a minimum sample target was set at 1000 adults, except for Canada (the host country of the lead investigators), which was set at 1500. Common to broad-based, multipurpose epidemiology studies, a number of core principles and pragmatic considerations were invoked in selecting these sample sizes. They include (1) a largely balanced sample size for each country so that investigations of differences between countries has maximal statistical power, (2) the power to detect differences in proportions of ≥10% or a relative risk of ≥1.2 exceeds 80% at the 2-tailed α=.05 within each country (these detectable differences are moderate to large and likely to be of clinical or meaningful significance), and (3) to maximize the number of different countries able to participate within a constrained budget.

    Statistical Analysis

    Reporting of study findings was informed by the STROBE (STrengthening the Reporting of OBservational studies in Epidemiology) guidelines [22]. Participant numbers and demographics by countries and measurement waves were initially described and compared using Pearson design-based F tests, which also accounted for sample weightings. Next, the crude overall PHQ-9 response distributions by measurement wave were described and compared using Pearson design-based F tests. Age-standardized suicide ideation rates across countries and measurement waves were then determined and compared. Direct standardization was employed, with the reference population drawn from the combined wave 1 and wave 2 samples and then stratified by age groups. This derived reference population was preferred, rather than adopting any standard population, due to the particular mix of lower- and higher-income countries included within this study. Age-specific observed rates stratified by age groups were then derived for each country and measurement wave separately, and the weighted average of the stratum-specific rates, together with measures of their variability, relative to the reference population was calculated. Analysis of variance was used to compare rates between countries for each measurement wave, and 2-sample Student t tests were employed to test the mean difference in age-standardized rates between measurement waves by country.

    Treating countries as fixed effects, a binomial regression model with an identity link function was then used to estimate and compare rates of suicide ideation indication by gender and age groups for each country and measurement wave. The identity link function was chosen as multivariable-adjusted prevalences and their differences were of primary reporting interest [23]. In this model, gender, age group, country, and measurement wave main effects were initially considered together with all their 2-factor interaction term combinations. Backward stepwise elimination of nonsignificant interaction terms followed, determined by sequentially removing nonsignificant interaction terms yielding the highest Ward type III ꭓ2P value, to derive the final model. The main effects and interaction terms in this final model represented a baseline combination of variables.

    Binomial regression models with log link function were next considered to relate sociodemographic, pandemic, and infodemic variables to suicide ideation. However, despite using different maximization techniques (such as maximum likelihood optimization and iterated, reweighted least-squares optimization of the deviance) and starting value searches, some of these models failed to converge, which is a widely recognized issue [23]. Instead, more stable logistic models were employed. For the crude analysis, each variable and its interaction with measurement wave were added and investigated in a regression model that also included the baseline combination of variables. Finally, an adjusted analysis was conducted, whereby all considered sociodemographic, pandemic, and infodemic variables together with their interaction by measurement wave were simultaneously included in a regression model that also contained the baseline combination of variables. No main effect nor interaction term variable selection was undertaken for this adjusted analysis. A direct evaluation of the final model fit was unable to be conducted as most diagnostics are unavailable when the data include survey sampling weights. Instead, an indirect evaluation was conducted whereby the final model was rerun without these weights. The Hosmer-Lemeshow goodness-of-fit test was conducted using the conventionally employed 10 partitions [24]. This was followed by an area under the receiver operating characteristic curve (AUC) analysis. AUC is frequently employed as a summary measure of a model’s predictive accuracy [25]. Adopting the recommendations of Hosmer and Lemeshow [24], an AUC of .5 suggests no discrimination, .7-.8 is considered acceptable, .8-.9 is considered excellent, and more than .9 is considered outstanding. All analyses were conducted using Stata SE version 16.0 (StataCorp, College Station, TX), accommodating the survey sampling weights and employing robust variance estimators. A 2-tailed α=.05 defined significance.

    Ethics

    This study sits within a broader program of research funded by the Canadian Institutes of Health Research, reviewed and approved by the Research Ethics Board of the Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l’Estrie—Center hospitalier universitaire de Sherbrooke (CHUS; Human Ethics Committee [HEC] ref: 2020-3674). Informed consent was obtained from all participants before their participation, and the collection of information was carried out confidentially. Participants were able to withdraw at any time without penalty or need for explanation. The data sets did not carry any personally identifiable information. The study complied with the ethical standards for human experimentation as established by the Helsinki Declaration and Canada’s HEC. All methods and reporting were performed in accordance with the HEC’s relevant guidelines and regulations.


    Participants

    The final sample totaled 17,833 adults: 8806 (49.4%) from measurement wave 1 and 9027 (50.6%) from wave 2. Overall, 51.6% (9204/17,833) were female, and 49.2% (8769/17,833) were aged between 18 years and 44 years. The sample numbers and weighted distribution (%) of participants’ demographic characteristics by country and measurement wave appear in Table 2. Significant differences between countries were observed for age groups at measurement wave 1 (P<.001) and wave 2 (P<.001) but not for gender (P=.68 and P=.70 for measurement waves 1 and 2, respectively). Consistent with global demographic patterns, Filipino participants were younger than their non-Filipino counterparts.

    Table 2. Participant numbers and weighted distribution (%) of their demographic characteristics by country and measurement waves 1 (surveyed between May 29, 2020 and June 12, 2020) and 2 (surveyed November 6-18, 2020).
    CountryGendera, n (%)Age (years), n (%)

    		FemaleMale18-2425-3435-4445-5455-6465-74≥75
    Canada

    		Wave 1 (n=1501)723 (48.4)772 (51.6)163 (10.9)247 (16.4)243 (16.2)269 (17.9)262 (17.5)246 (16.4)72 (4.8)

    		Wave 2 (n=2004)963 (48.3)1031 (51.7)218 (10.9)329 (16.4)324 (16.2)359 (17.9)350 (17.5)340 (17.0)84 (4.2)
    United States

    		Wave 1 (n=1065)516 (48.5)548 (51.5)59 (5.5)226 (21.2)191 (17.9)204 (19.1)189 (17.8)146 (13.7)50 (4.7)

    		Wave 2 (n=1003)478 (48.1)517 (51.9)81 (8.0)187 (18.7)180 (17.9)192 (19.1)178 (17.8)71 (7.1)114 (11.4)
    England

    		Wave 1 (n=1041)508 (48.8)532 (51.2)116 (11.1)181 (17.4)170 (16.3)186 (17.9)151 (14.5)190 (18.3)47 (4.5)

    		Wave 2 (n=1000)487 (48.8)511 (51.2)111 (11.1)174 (17.4)163 (16.3)179 (17.9)145 (14.5)192 (19.2)35 (3.5)
    Belgium

    		Wave 1 (n=1015)494 (48.6)521 (51.4)63 (6.2)208 (20.5)139 (13.7)210 (20.7)171 (16.9)186 (18.3)37 (3.7)

    		Wave 2 (n=1014)489 (48.4)520 (51.6)57 (5.6)215 (21.2)118 (11.6)228 (22.5)161 (15.9)197 (19.4)38 (3.7)
    Switzerland

    		Wave 1 (n=1002)478 (47.7)523 (52.3)95 (9.5)144 (14.4)138 (13.8)177 (17.6)239 (23.9)160 (16.0)48 (4.8)

    		Wave 2 (n=1000)477 (47.8)522 (52.2)95 (9.5)144 (14.4)138 (13.8)176 (17.6)171 (17.1)226 (22.6)49 (4.9)
    Hong Kong

    		Wave 1 (n=1140)513 (45.1)626 (54.9)108 (9.5)196 (17.2)206 (18.1)218 (19.1)202 (17.7)200 (17.5)10 (0.9)

    		Wave 2 (n=1002)451 (45.0)550 (55.0)95 (9.5)172 (17.2)181 (18.1)192 (19.1)177 (17.7)171 (17.1)13 (1.3)
    Philippines

    		Wave 1 (n=1041)510 (49.4)522 (50.6)224 (21.6)260 (25.0)209 (20.1)162 (15.5)106 (10.2)63 (6.1)17 (1.6)

    		Wave 2 (n=1003)489 (49.3)503 (50.7)216 (21.6)251 (25.0)201 (20.1)156 (15.5)126 (12.5)47 (4.7)6 (0.6)
    New Zealand

    		Wave 1 (n=1001)484 (48.6)512 (51.4)122 (12.2)184 (18.4)163 (16.3)175 (17.5)157 (15.7)149 (14.9)50 (5.0)

    		Wave 2 (n=1001)484 (48.6)513 (51.4)122 (12.2)184 (18.4)163 (16.3)175 (17.5)157 (15.7)138 (13.8)61 (6.1)

    a25 participants at the measurement wave 1 and 42 participants at measurement wave 2 did not identify as being female (F) or male (M) or preferred not to answer this question, so had their gender set to missing.

    Suicide Ideation

    Overall Rates

    At measurement wave 1, 75.8% (6674.9/8806) of participants had no thoughts of being better off dead or hurting themselves in some way over the previous 2 weeks, whereas 14.7% (1247.9/8806) had these thoughts on several days, 6.4% (565.9/8806) had these thoughts over half the days, and 3.6% (317.3/8806) reported having these thoughts nearly every day. Approximately 5 months later, at the second measurement wave, 72.5% (6541.3/9027) of participants had no thoughts of being better off dead or hurting themselves in some way over the previous 2 weeks, 15.3% (1379.3/9027) had these thoughts on several days, 7.8% (706.6/9027) had these thoughts over half the days, and 4.4% (399.8/9027) reported having these thoughts nearly every day. These response distributions were significantly different between measurement waves (P<.001), with a 3.3% decrease in those having no thoughts of death of harm over the previous 2 weeks at measurement wave 2 compared with wave 1 and concomitantly, a 2.2% increase in those reporting having these thoughts nearly every day or over half the days.

    Age-Standardized Rates

    Figure 1 presents the age-standardized rates of the dichotomized suicide ideation variable across countries for measurement waves 1 and 2. Table S1 in Multimedia Appendix 1 gives the combined total age distribution of participants over measurement waves and countries used as the reference population for these age standardization calculations. Perusal of Figure 1 suggests that important differences in self-reported suicide ideation exist between countries and measurement waves. Analysis of variance confirmed this, with significant differences in age-standardized rates found between countries at measurement wave 1 (P<.001) and wave 2 (P<.001). Rates in Hong Kong participants, for instance, were significantly higher than all other countries at both measurement waves (all P<.001, except for P=.006 when compared with England participants at measure wave 1). When comparing age-standardized rates of suicide ideation between measurement waves 1 and wave 2, significant increases were observed for participants in Canada (mean difference .067, 95% CI .041-.094; P<.001), Belgium (mean difference .052, 95% CI .017-.087; P=.004), Hong Kong (mean difference .071, 95% CI .029-.113; P<.001), and New Zealand (mean difference .041, 95% CI .006-.076; P=.02) but not for participants in the United States (P=.75), England (P=.07), Switzerland (P=.86), or the Philippines (P=.08).

    ‎
    Figure 1. Age-standardized rates and associated 95% CIs of suicide ideation by country for measurement waves 1 (surveyed between May 29, 2020 and June 12, 2020) and 2 (surveyed November 6-18, 2020).
    View this figure
    Age- and Gender-Adjusted Comparison of Suicide Ideation Between Countries

    As a result of the relatively small number of participants within the ≥75-year age group (see Table 1 and Multimedia Appendix 1, Table S1), they were combined with those aged 65-74 years to form the ≥65-year age category used henceforth. Initially, the statistical model of the binary measure of suicide ideation included the main effects of gender, age group, country, and measurement wave, together with all 2-factor interactions. However, the measurement wave × gender (step 1, P=.46) and measurement wave × country (step 2, P=.12) interaction terms were nonsignificant and were thus removed, leaving a model that included gender (P=.44), age group (P<.001), country (P<.001), measurement wave (P=.003), age group × gender (P<.001), country × gender (P=.003), age group × country (P<.001), and age group × measurement wave (P=.001). Due to its significant interactions with age group and country, the gender main effect was retained within the final model, despite not having a significant P value. Figure 2 and Table S2 in Multimedia Appendix 1 provide the estimated proportions of suicide ideation indication, together with associated 95% CIs, derived from this final model. These estimated proportions ranged from .040 (95% CI .010-.069) in measurement wave 1 and .030 (95% CI .001-.059) in wave 2 among women aged ≥65 years from Switzerland to .623 (95% CI .555-.690) in wave 1 and .692 (95% CI .618-.765) in wave 2 among women aged 18-24 years from England; see Table S2 in Multimedia Appendix 1.

    ‎
    Figure 2. Estimated proportion of participants self-reporting suicide ideation by country and measurement waves 1 and 2 stratified by age group, derived from the binomial regression model including age group, gender, country, measurement wave, and the measurement wave × age group, country × age group, country × gender, and age group × gender interactions. Females are denoted by red, males by blue, measurement wave 1 with hollow circles, and measurement wave 2 with solid circles. BEL: Belgium; CAN: Canada; ENG: England; HK: Hong Kong; NZ: New Zealand; PHL: Philippines; SWI: Switzerland; USA: United States.
    View this figure

    Overall, among women, those aged 18-24 years had the highest estimated proportion of indications at both measurement waves (mean .389 and .458, respectively) and the greatest increase between measurement waves (mean change .069). Both the proportion of indications and the difference between measurement waves dampened with increasing age group. For men, those aged 25-34 years had the highest estimated indication proportion at both measurement waves (mean .414 and .468, respectively), somewhat higher than for those aged 18-24 years (mean .398 and .467, respectively). However, similar to female participants, the greatest increase in suicide ideation indications between measurement waves (mean change .069) occurred for those aged 18-24 years, and the proportion of indications and difference between measurement waves dampened with increasing age group; see Figure 2 and Table S2 in Multimedia Appendix 1. Notable in Figure 2 and Table S2 in Multimedia Appendix 1 are the relatively high proportions of participants experiencing suicide ideation from Hong Kong across all age groups, especially among those aged 45-54 years or ≥65 years.

    Factors Affecting Suicide Ideation

    The weighted frequency distributions of the potential risk and protective COVID-19–related factors for suicide ideation indication by measurement wave are presented in Table S3 in Multimedia Appendix 1. Logistic regression–estimated crude (OR) and adjusted (AOR) odds ratios and accompanying 95% CIs of suicide ideation associated with these factors by measurement wave appear in Table 3. The crude ORs were adjusted by gender, age group, country, and measurement wave main effects together with the age group × gender, country × gender, age group × country, and age group × measurement wave interaction terms identified in the previous section. In these analyses, both the main effect and interaction by measurement wave terms were significant for variables corresponding to self-isolation/quarantine (P<.001 and P=.02, respectively), financial losses (P<.001 and P=.003, respectively), and threat perceived for oneself and/or family (P<.001 and P=.008, respectively). However, significant main effect and nonsignificant interactions by measurement wave terms were identified for variables corresponding to being an essential worker (P<.001 and P=.66, respectively), being a victim of stigma (P<.001 and P=.09, respectively), trust in authorities score (P<.001 and P=.49, respectively), internet-based social media as a regular source of information (P<.001 and P=.13, respectively), friends/family/co-worker as a regular source of information (P<.001 and P=.33, respectively), and SOC (P<.001 and P=.24, respectively). This implies that these variables have a significant relationship with suicide ideation, which did not change between measurement waves. For the remaining variables, no significant relationships were observed.

    Table 3. Estimated crude and adjusted odds ratios (ORs) and associated 95% CIs of suicide ideation associated with potential risk and protective COVID-19 related factors by measurement wave for the logistic model that included the entire sample over both measurement waves.
    FactorsCrude OR (95% CI)aAdjusted OR (95% CI)b

    		Wave 1Wave 2Wave 1Wave 2
    Household composition

    		Alone1 (reference)1 (reference)1 (reference)1 (reference)

    		With children0.93 (0.78-1.11)1.02 (0.85-1.23)1.03 (0.83-1.27)1.05 (0.85-1.29)

    		With others0.89 (0.75-1.06)0.88 (0.74-1.05)0.98 (0.81-1.20)0.94 (0.77-1.13)
    Essential worker

    		No1 (reference)1 (reference)1 (reference)1 (reference)

    		Yes: health1.72 (1.45-2.04)1.60 (1.33-1.91)1.60 (1.31-1.96)1.36 (1.10-1.69)

    		Yes: other1.43 (1.24-1.65)1.32 (1.14-1.52)1.29 (1.09-1.53)1.22 (1.03-1.44)
    Self-isolation/quarantine

    		No1 (reference)1 (reference)1 (reference)1 (reference)

    		Yes: case/symptom-free1.16 (1.01-1.32)1.42 (1.24-1.63)1.08 (0.93-1.26)1.23 (1.05-1.44)

    		Yes: case or symptoms3.16 (2.64-3.77)2.84 (2.42-3.34)2.39 (1.95-2.93)1.91 (1.58-2.32)
    Financial losses

    		No1 (reference)1 (reference)1 (reference)1 (reference)

    		Yes1.39 (1.23-1.57)1.82 (1.62-2.06)1.09 (0.95-1.25)1.40 (1.22-1.60)

    		Unsure/unknown1.95 (1.56-2.43)2.85 (2.15-3.78)1.79 (1.30-2.47)2.42 (1.50-3.91)
    Threat perceived for oneself and/or family

    		High1.84 (1.64-2.07)1.47 (1.31-1.66)1.66 (1.44-1.90)1.31 (1.14-1.51)

    		Otherwise1 (reference)1 (reference)1 (reference)1 (reference)
    Threat perceived for country and/or world

    		High1.00 (0.88-1.13)0.91 (0.80-1.04)0.85 (0.73-0.98)0.82 (0.70-0.96)

    		Otherwise1 (reference)1 (reference)1 (reference)1 (reference)
    Being a victim of stigma

    		No1 (reference)1 (reference)1 (reference)1 (reference)

    		Yes3.34 (2.88-3.87)3.83 (3.27-4.49)2.58 (2.17-3.06)2.74 (2.26-3.31)

    		Decline to answer1.64 (1.37-1.95)2.24 (1.73-2.89)1.15 (0.90-1.45)1.23 (0.78-1.95)
    Level of information about COVID-19

    		High1 (reference)1 (reference)1 (reference)1 (reference)

    		Otherwise0.96 (0.85-1.08)0.98 (0.86-1.11)0.91 (0.79-1.06)0.97 (0.83-1.13)
    Trust in authorities score

    		Q1 (low)1.39 (1.19-1.63)1.39 (1.19-1.63)1.56 (1.29-1.89)1.40 (1.15-1.71)

    		Q21.15 (0.98-1.36)1.27 (1.08-1.50)1.29 (1.06-1.57)1.34 (1.10-1.63)

    		Q30.96 (0.81-1.13)1.11 (0.95-1.31)1.06 (0.87-1.28)1.09 (0.90-1.32)

    		Q4 (high)1 (reference)1 (reference)1 (reference)1 (reference)
    Internet-based social mediaas a regular source of information

    		Often/always1.35 (1.19-1.52)1.54 (1.35-1.75)1.11 (0.96-1.30)1.47 (1.25-1.72)

    		Sometimes/never1 (reference)1 (reference)1 (reference)1 (reference)
    Friends/family/co-workers as a regular source of information

    		Often/always1.23 (1.10-1.38)1.14 (1.01-1.28)1.06 (0.92-1.23)0.96 (0.83-1.11)

    		Sometimes/never1 (reference)1 (reference)1 (reference)1 (reference)
    Sense of coherence

    		Stronger (5-6)1 (reference)1 (reference)1 (reference)1 (reference)

    		Weaker (0-4)3.96 (3.36-4.67)4.56 (3.85-5.40)3.80 (3.18-4.55)4.39 (3.66-5.27)

    aAdjusted for gender, age group, country, measurement wave, age group × gender, country × gender, age group × country, and age group × measurement wave.

    bAdjusted for all variables included within this table, together with their interaction by measurement wave and the main effect and interactions terms included within the crude analysis.

    When considered together in the adjusted analysis, the patterns of associations remained the same except that “friends/family/co-worker as a regular source of information” was no longer significant (main effect P=.38; interaction P=.32), “threat perceived for country and/or world” had a significant main effect (P=.03) but nonsignificant interaction (P=.80), and “internet-based social media as a regular source of information” had a nonsignificant main effect (P=.16) but significant interaction (P=.01), so that frequent users of social media were associated with significantly higher suicide ideation than infrequent users at measurement wave 2 (P<.001). Based on the magnitude of the z score, having a weaker SOC was associated with the highest likelihood of suicide ideation among the factors considered (AOR 3.80, 95% CI 3.18-4.55; P<.001 at wave 1, and AOR 4.39, 95% CI 3.66-5.27; P<.001 at wave 2), followed by identifying as being a victim of stigma (AOR 2.58, 95% CI 2.17-3.06; P<.001 at wave 1 and AOR 2.74, 95% CI 2.26-3.31; P<.001 at wave 2). Hong Kong participants were the most likely to report a weaker SOC. At measurement wave 1, 90.0% (1025.9/1140) of Hong Kong participants reported a weaker SOC compared with 66.9% (5130.4/7666) of participants in the other countries, while for measurement wave 2, the percentages were 90.3% (905.0/1002) and 67.3% (5402.7/8025), respectively.

    In the adjusted model, re-analyzed without survey sampling weights, the Hosmer-Lemeshow goodness-of-fit was not significant (P=.38), and the AUC was .803 (95% CI .795-.810), which is on the cusp between acceptable and excellent. This indirect evidence suggests that the final model has adequate fit.


    Principal Findings

    Suicide ideation is prevalent and increased significantly over time in the COVID-19 pandemic. Overall, 24.2% and 27.5% of adults aged ≥18 years reported at least one such thought at measurement waves 1 and 2, respectively. This is substantially higher than the 15.5% reported from a study undertaken approximately 2 months earlier than measurement wave 1 [17]. However, like the previous study, there was considerable variability between countries in the age-standardized rates of suicide ideation at measurement wave 1, ranging from 15.6% (95% CI 13.3%-17.9%) in Belgium to 35.8% (95% CI 33.0%-39.7%) in Hong Kong, and measurement wave 2, ranging from 20.8% (95% CI 18.2%-23.5%) in Belgium to 42.9% (95% CI 39.8%-46.1%) in Hong Kong. This significant increase in overall rates between studies likely reflects people’s significant psychological deterioration over time, as observed within this study; the different composition of countries investigated; and the fundamentally different sampling strategies. Reliance on convenience sampling, particularly in surveys of mental health (where individuals with existing or severe mental illness are less likely to participate), is prone to substantial bias [26].

    Hong Kong adults had demonstratively higher rates of suicide ideation than their counterparts in the comparator countries. This may reflect the cumulative effects of COVID-19 together with the political instability and social unrest of that time, which included the 2019-2020 Hong Kong protests [27]. These protests and the accompanying social unrest may have also directly contributed to the Hong Kong participants having consistently lower SOC levels (the greatest protective factor, over and above age and gender, against suicide ideation observed in this study) compared with the other investigated countries. Although the lower SOC levels may also be cultural, or even simply arise from different understandings of the questions in different cultures, it behooves further investigation.

    Previously, a population-based prospective cohort of adults aged ≥18 years identified a major mental health burden during a time of social unrest in Hong Kong, although suicide ideation was identified in only 4.3% of their respondents using the same PHQ-9 instrument [28]. Also using the PHQ-9 item 9, another population-representative sample of Hong Kong residents aged ≥15 years conducted in July 2019 reported that 9.1% of their participants had suicide ideation [29]. These rates are significantly less than the 22.0% reported in a multinational study of suicide ideation [17] and the age-standardized rate of 35.8% reported here. Although the Hong Kong rate of suicide increased with the severe acute respiratory syndrome (SARS) outbreak in 2003, particularly among older adults, it has stabilized since to around 13.0/100,000 people per year [30]. This 2019 age-standardized rate for Hong Kong is higher than that in Canada or New Zealand, both estimated at 10.3/100,000 people per year, but less than that in the United States or Belgium, estimated at 14.5/100,000 and 13.9/100,000 people per year, respectively [30,31]. Thus, it could be argued that suicide and suicide ideation in Hong Kong may reflect culturally traditional patterns rather than a result of lower SOC [32].

    Both age and gender were associated with suicide ideation. Apart from Hong Kong participants, rates decreased as age groups increased, a finding consistent with the literature [14,33]. Among those aged 18-24 years, 55-64 years, or ≥65 years, women in England, Belgium, and the Philippines had higher estimated suicide ideation rates than men. Interestingly, according to the Worldometer [34], cumulative COVID-19 death rates for England (60.6 and 115.6 per 100,000 in June 2020 and November 2021, respectively) and Belgium (82.0 and 125.1 per 100,000 in June 2020 and November 2021, respectively) were highest among the countries investigated here, although the Philippines ranked sixth among the 8 countries. Women may have been differentially impacted or burdened by the relatively high mortality rates in England and Belgium, and the cultural expectations of Filipino women could contribute to these observations.

    In all other countries and age groups, the reverse pattern was observed, with men having higher estimated rates than women. Arguably, apart from those aged 25-34 years, these gender differences were relatively small compared to the variations across age groups, countries, and measurement waves—suggesting that this extraordinary pandemic effect transcends previously documented gender differences. It is noteworthy that the reported results from the general population 2015 National Survey on Drug Use and Health in the United States showed similar variability between gender across age groups [33]. Strikingly and perhaps unsurprisingly, the factor with the most protective effect against suicide ideation, over age and gender, was having a stronger SOC. An increased SOC has previously been associated with lower rates of suicide ideation and attempts of suicide [35-37]. It also has been shown to be associated with lower rates of common psychopathological symptoms in this pandemic [8,9] and thus arguably, is an important underestimated resource in minimizing the COVID-19 psychosocial impact [8]. Health promotion strategies that strengthen SOC may provide a useful protective mechanism to assuage people’s mental health burdens [38].

    Although having a smaller effect size, another key finding was the rise and significance of internet-based social media as a regular source of information associated with suicide ideation in the adjusted analyses. In the first measurement wave, those who often or always used social networks as a regular source of information had an AOR of 1.11 (95% CI 0.96-1.30) compared with those who sometimes or never used social networks, a nonsignificant difference (P=.16). However, by measurement wave 2, the AOR had increased to 1.47 (95% CI 1.25-1.72), a difference that was significant (P<.001). Although social media can have a crucial role in disseminating health information and tackling infodemics and misinformation [39], frequent exposure to social media has been associated with mental health problems during the COVID-19 outbreak [40]. Interestingly, the deterioration in mental health associated with frequent social media use over the course of this pandemic has also been previously observed in 2 studies from China [40,41].

    Strengths and Limitations

    Although having salient strengths, such as the relatively large sample size, the timeliness of the recruitment and analysis, the spread of participants across 8 countries and 4 continents, and the repeated nature of the survey using consistent and psychometrically robust instruments, this study also has limitations. Arguably, the greatest potential weakness is the sampling mechanism and associated unmeasurable nonsampling bias. The employed sampling frame is more opaque than traditional or conventionally used frames. Participants were nonetheless randomly recruited from panels developed using multiple online and offline sources. Moreover, quota targeting sampling together with survey sampling weights were used to ensure approximately representative samples. In designing, attracting funding for, securing ethics for, and implementing this study, there was a pragmatic requirement to maximize expedited data collection, sample frame availability, cost effectiveness, and international reach while minimizing nonsampling bias. The selected approach sought to optimize these requirements and yield reliable and robust research data. However, despite considerable efforts undertaken to derive representative samples, some population groups may be underrepresented, including people without internet access or those with lower literacy levels [26]. In addition, people living with existing disabilities (including mental illness) are less likely to participate in online surveys compared with those without such disabilities and illnesses [26,42]. Although sampling weightings were adopted to ameliorate this effect, these adjustments may miss crucial elements of bias and cannot account for groups not included within the surveys.

    Another potential weakness is the repeated cross-sectional rather than longitudinal study design, which negates any causal assertions. Moreover, individual participant changes over time cannot be investigated. However, assuming the sampling strategy and uptake remain consistent, valid population-level trend analyses can be conducted [43]. A careful statistical approach was employed here, which sought to disentangle systematic patterns from sampling variability, to investigate population-level, time-varying changes between measurement waves. Furthermore, ORs were reported rather than prevalence ratios (PRs). As suicide ideation was relatively common in this study, these ORs may overestimate their respective PRs and should not be interpreted as measures of relative risk [44]. Binomial regression models estimating PRs were initially entertained, but convergence issues arose. Instead, the more stable logistic models were employed without issue [23]. However, regardless of the regression model ultimately employed, the reported results may suffer from residual or unmeasured confounding effects [45]. For example, questions on the availability of face masks and protective clothing and the market flooding of ineffective counterfeit versions in Hong Kong [46] were not asked but are likely to contribute to poor mental health and suicide ideation of its people. Unmeasured confounding variables can result in substantial bias in the estimated exposure-outcome AOR, particularly if it is uncorrelated with the measured explanatory variables. Study replication using different suites of variables is needed to understand its effect. Another potential limitation is the PHQ-9 item 9 measure itself. It has been widely used and endorsed as a single measure to assess the prevalence of suicide ideation in research studies [47]. However, this stance is not uniformly shared, with some regarding it as an insufficient assessment tool for suicide risk and suicide ideation [48]. The lack of a universally accepted consistent definition of suicide ideation leads to ongoing challenges for researchers and others [14] and makes direct study comparisons difficult.

    Conclusion

    Globally, the COVID-19 pandemic era represents an extraordinary time for all societies, presenting extraordinary challenges and posing extraordinary and worsening mental health burdens on people. The high and increased rates of suicide ideation reported by participants across 8 countries in 4 continents reflect the cumulative effects of this pandemic and its associated burdens. Young adults and those in Hong Kong, in particular, have been deeply affected. SOC appears to be a potent protective force. A health promotion approach using a salutogenic orientation aimed at strengthening SOC may offer a new perspective for reducing suicide ideation. Moreover, with social media appearing to have an increasingly negative influence, it is critical for countries and health agencies to squarely redress rampant misinformation and disinformation communications. Suicide ideation is an important mental health indictor and risk factor for completed suicides. Policies and promotion of SOC, together with dissemination of health information that explicitly tackles the infodemic’s misinformation and disinformation, may importantly contribute to reducing the rising mental health morbidity and mortality triggered by this pandemic.

    Acknowledgments

    This research was funded by a Canadian Institute of Health Research Operating Grant (OV7-170635). The funder had no involvement nor role in the review or approval of this research. The international research team wishes to thank all the collaborators and knowledge users on this project who contributed significantly to its enrichment.

    Authors' Contributions

    PJS, MG, KKCH, EL, RL, CPYM, VM, TO, ZQ, and MR were involved in the study conceptualization, design, and implementation. MG (principal investigator) obtained the funding. PJS led the data analysis and drafting of the manuscript. All authors contributed to manuscript revisions prior to submission and during the review process. All authors have read and agreed to the published version of the manuscript.

    Conflicts of Interest

    None declared.

    ‎
    Multimedia Appendix 1

    Tables S1, S2, and S3.

    PDF File (Adobe PDF File), 666 KB
    1. Kontis V, Bennett JE, Rashid T, Parks RM, Pearson-Stuttard J, Guillot M, et al. Magnitude, demographics and dynamics of the effect of the first wave of the COVID-19 pandemic on all-cause mortality in 21 industrialized countries. Nat Med 2020 Dec 14;26(12):1919-1928. [CrossRef] [Medline]
    2. Pirkis J, John A, Shin S, DelPozo-Banos M, Arya V, Analuisa-Aguilar P, et al. Suicide trends in the early months of the COVID-19 pandemic: an interrupted time-series analysis of preliminary data from 21 countries. The Lancet Psychiatry 2021 Jul;8(7):579-588. [CrossRef]
    3. Cutler DM, Summers LH. The COVID-19 Pandemic and the $16 Trillion Virus. JAMA 2020 Oct 20;324(15):1495-1496 [FREE Full text] [CrossRef] [Medline]
    4. Policy Responses to COVID-19. International Monetary Fund.   URL: https://www.imf.org/en/Topics/imf-and-covid19/Policy-Responses-to-COVID-19#N [accessed 2021-05-21]
    5. Allwood L, Bell A. Covid-19: Understanding Inequalities in Mental Health during the Pandemic. Centre for Mental Health. 2020.   URL: https:/​/www.​centreformentalhealth.org.uk/​sites/​default/​files/​2020-06/​CentreforMentalHealth_CovidInequalities_0.​pdf [accessed 2021-12-04]
    6. Shadmi E, Chen Y, Dourado I, Faran-Perach I, Furler J, Hangoma P, et al. Health equity and COVID-19: global perspectives. Int J Equity Health 2020 Jun 26;19(1):104 [FREE Full text] [CrossRef] [Medline]
    7. Managing the COVID-19 infodemic: Promoting healthy behaviours and mitigating the harm from misinformation and disinformation. World Health Organization. 2020 Sep 23.   URL: https://tinyurl.com/54rm4fpz [accessed 2021-05-21]
    8. Généreux M, Schluter PJ, Hung KK, Wong CS, Pui Yin Mok C, O'Sullivan T, et al. One Virus, Four Continents, Eight Countries: An Interdisciplinary and International Study on the Psychosocial Impacts of the COVID-19 Pandemic among Adults. Int J Environ Res Public Health 2020 Nov 13;17(22):8390 [FREE Full text] [CrossRef] [Medline]
    9. Généreux M, Schluter PJ, Landaverde E, Hung KK, Wong CS, Mok CPY, et al. The Evolution in Anxiety and Depression with the Progression of the Pandemic in Adult Populations from Eight Countries and Four Continents. Int J Environ Res Public Health 2021 May 01;18(9):4845 [FREE Full text] [CrossRef] [Medline]
    10. Bendau A, Petzold MB, Pyrkosch L, Mascarell Maricic L, Betzler F, Rogoll J, et al. Associations between COVID-19 related media consumption and symptoms of anxiety, depression and COVID-19 related fear in the general population in Germany. Eur Arch Psychiatry Clin Neurosci 2021 Mar;271(2):283-291 [FREE Full text] [CrossRef] [Medline]
    11. Wang C, Pan R, Wan X, Tan Y, Xu L, McIntyre RS, et al. A longitudinal study on the mental health of general population during the COVID-19 epidemic in China. Brain Behav Immun 2020 Jul;87:40-48 [FREE Full text] [CrossRef] [Medline]
    12. Barni D, Danioni F, Canzi E, Ferrari L, Ranieri S, Lanz M, et al. Facing the COVID-19 Pandemic: The Role of Sense of Coherence. Front Psychol 2020 Nov 6;11:578440 [FREE Full text] [CrossRef] [Medline]
    13. Dymecka J, Gerymski R, Machnik-Czerwik A. How does stress affect life satisfaction during the COVID-19 pandemic? Moderated mediation analysis of sense of coherence and fear of coronavirus. Psychol Health Med 2021 Mar 31:1-9. [CrossRef] [Medline]
    14. Harmer B, Lee S, Duong T, Saadabadi A. Suicidal Ideation. In: StatPearls. Treasure Island, FL: StatPearls Publishing; 2021.
    15. Simon GE, Rutter CM, Peterson D, Oliver M, Whiteside U, Operskalski B, et al. Does response on the PHQ-9 Depression Questionnaire predict subsequent suicide attempt or suicide death? Psychiatr Serv 2013 Dec 01;64(12):1195-1202 [FREE Full text] [CrossRef] [Medline]
    16. Preventing Suicide: A Global Imperative. World Health Organization. 2014 Aug 17.   URL: https://www.who.int/publications/i/item/9789241564779 [accessed 2021-12-04]
    17. Cheung T, Lam SC, Lee PH, Xiang YT, Yip PSF, International Research Collaboration on COVID-19. Global Imperative of Suicidal Ideation in 10 Countries Amid the COVID-19 Pandemic. Front Psychiatry 2020 Jan 13;11:588781 [FREE Full text] [CrossRef] [Medline]
    18. Kroenke K, Spitzer RL, Williams JBW. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med 2001 Sep;16(9):606-613 [FREE Full text] [CrossRef] [Medline]
    19. Généreux M, David M, O'Sullivan T, Carignan M, Blouin-Genest G, Champagne-Poirier O, et al. Communication strategies and media discourses in the age of COVID-19: an urgent need for action. Health Promot Int 2021 Aug 30;36(4):1178-1185 [FREE Full text] [CrossRef] [Medline]
    20. Lundberg O, Peck MN. A simplified way of measuring sense of coherence. Eur J Public Health 1995;5(1):56-59. [CrossRef]
    21. Chiesi F, Bonacchi A, Primi C, Toccafondi A, Miccinesi G. Are Three Items Sufficient to Measure Sense of Coherence? European Journal of Psychological Assessment 2018 Jul;34(4):229-237. [CrossRef]
    22. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. The Lancet 2007 Oct;370(9596):1453-1457. [CrossRef]
    23. Spiegelman D, Hertzmark E. Easy SAS calculations for risk or prevalence ratios and differences. Am J Epidemiol 2005 Aug 01;162(3):199-200. [CrossRef] [Medline]
    24. Hosmer DW, Lemeshow S. Applied Logistic Regression, 2nd ed. New York, NY: Wiley; 2000.
    25. Fan J, Upadhye S, Worster A. Understanding receiver operating characteristic (ROC) curves. CJEM 2006 Jan 21;8(1):19-20. [CrossRef] [Medline]
    26. Pierce M, McManus S, Jessop C, John A, Hotopf M, Ford T, et al. Says who? The significance of sampling in mental health surveys during COVID-19. The Lancet Psychiatry 2020 Jul;7(7):567-568. [CrossRef]
    27. Hui VT. Beijing's Hard and Soft Repression in Hong Kong. Orbis 2020;64(2):289-311 [FREE Full text] [CrossRef] [Medline]
    28. Ni MY, Yao XI, Leung KSM, Yau C, Leung CMC, Lun P, et al. Depression and post-traumatic stress during major social unrest in Hong Kong: a 10-year prospective cohort study. Lancet 2020 Jan 25;395(10220):273-284. [CrossRef] [Medline]
    29. Hou WK, Hall BJ, Liang L, Li TW, Liu H, Galea S. Probable depression and suicidal ideation in Hong Kong amid massive civil unrest. Ann Epidemiol 2021 Feb;54:45-51. [CrossRef] [Medline]
    30. 1981-2019 Hong Kong Suicide Statistics. Centre for Suicide Research and Prevention. 2019.   URL: https://csrp.hku.hk/statistics/ [accessed 2021-07-14]
    31. Suicide Rate Estimates, Age-standardized Estimates by Country. World Health Organization.   URL: https://apps.who.int/gho/data/view.main.MHSUICIDEASDRv?lang=en [accessed 2021-07-14]
    32. Lee S, Tsang A, Li X, Phillips M, Kleinman A. Attitudes toward suicide among Chinese people in Hong Kong. Suicide Life Threat Behav 2007 Oct;37(5):565-575. [CrossRef] [Medline]
    33. Piscopo K, Lipari R, Cooney J, Glasheen C. Suicidal Thoughts And Behavior Among Adults: Results From The 2015 National Survey On Drug Use And Health. Substance Abuse and Mental Health Services Administration. 2016 Sep 15.   URL: https://www.samhsa.gov/data/sites/default/files/NSDUH-DR-FFR3-2015/NSDUH-DR-FFR3-2015.pdf [accessed 2021-07-07]
    34. COVID-19 Coronavirus Pandemic. Worldometer.   URL: https://www.worldometers.info/coronavirus/ [accessed 2021-07-13]
    35. Sjöström N, Hetta J, Waern M. Sense of coherence and suicidality in suicide attempters: a prospective study. J Psychiatr Ment Health Nurs 2012 Feb;19(1):62-69. [CrossRef] [Medline]
    36. Ristkari T, Sourander A, Helenius H, Nikolakaros G, Salanterä S, Multimäki P, et al. Sense of coherence among Finnish young men--a cross-sectional study at military call-up. Nord J Psychiatry 2005 Jul 12;59(6):473-480. [CrossRef] [Medline]
    37. Edwards MJ, Holden RR. Coping, meaning in life, and suicidal manifestations: examining gender differences. J Clin Psychol 2001 Dec;57(12):1517-1534. [CrossRef] [Medline]
    38. Super S, Wagemakers MAE, Picavet HSJ, Verkooijen KT, Koelen MA. Strengthening sense of coherence: opportunities for theory building in health promotion. Health Promot Int 2016 Dec 23;31(4):869-878. [CrossRef] [Medline]
    39. Tsao S, Chen H, Tisseverasinghe T, Yang Y, Li L, Butt ZA. What social media told us in the time of COVID-19: a scoping review. The Lancet Digital Health 2021 Mar;3(3):e175-e194. [CrossRef]
    40. Gao J, Zheng P, Jia Y, Chen H, Mao Y, Chen S, et al. Mental health problems and social media exposure during COVID-19 outbreak. PLoS One 2020 Apr 16;15(4):e0231924 [FREE Full text] [CrossRef] [Medline]
    41. Li S, Wang Y, Xue J, Zhao N, Zhu T. The Impact of COVID-19 Epidemic Declaration on Psychological Consequences: A Study on Active Weibo Users. Int J Environ Res Public Health 2020 Mar 19;17(6):2032 [FREE Full text] [CrossRef] [Medline]
    42. Internet users, UK: 2020. Office for National Statistics. 2021 Apr 06.   URL: https://www.ons.gov.uk/businessindustryandtrade/itandinternetindustry/bulletins/internetusers/2020 [accessed 2021-06-28]
    43. Steel D. Repeated cross-sectional design. In: Layrakas PJ, editor. Encyclopedia of Survey Research Methods. Thousand Oaks, CA: SAGE Publications; 2008:714-716.
    44. Webb P, Bain C, Page A. Essential Epidemiology: An Introduction for Students and Health Professionals, 4th edition. Cambridge, United Kingdom: Cambridge University Press; 2020.
    45. Fewell Z, Davey Smith G, Sterne JAC. The impact of residual and unmeasured confounding in epidemiologic studies: a simulation study. Am J Epidemiol 2007 Sep 15;166(6):646-655. [CrossRef] [Medline]
    46. Lam SC, Suen LKP, Cheung TCC. Global risk to the community and clinical setting: Flocking of fake masks and protective gears during the COVID-19 pandemic. Am J Infect Control 2020 Aug;48(8):964-965 [FREE Full text] [CrossRef] [Medline]
    47. Rossom RC, Coleman KJ, Ahmedani BK, Beck A, Johnson E, Oliver M, et al. Suicidal ideation reported on the PHQ9 and risk of suicidal behavior across age groups. J Affect Disord 2017 Jun;215:77-84 [FREE Full text] [CrossRef] [Medline]
    48. Na PJ, Yaramala SR, Kim JA, Kim H, Goes FS, Zandi PP, et al. The PHQ-9 Item 9 based screening for suicide risk: a validation study of the Patient Health Questionnaire (PHQ)-9 Item 9 with the Columbia Suicide Severity Rating Scale (C-SSRS). J Affect Disord 2018 May;232:34-40. [CrossRef] [Medline]

    ‎
    AOR: adjusted odds ratio
    AUC: area under the receiver operating characteristic curve
    CHUS: Center hospitalier universitaire de Sherbrooke
    CIUSSS: Centre intégré universitaire de santé et de services sociaux
    HEC: Human Ethics Committee
    OR: odds ratio
    PHQ-9: Patient Health Questionnaire-9
    PR: prevalence ratio
    SARS: severe acute respiratory syndrome
    SOC: sense of coherence
    STROBE: STrengthening the Reporting of OBservational studies in Epidemiology

    Edited by T Sanchez; submitted 15.07.21; peer-reviewed by C Leung, S Lam; comments to author 21.09.21; revised version received 10.10.21; accepted 28.10.21; published 17.01.22

    Copyright

    ©Philip J Schluter, Mélissa Généreux, Kevin KC Hung, Elsa Landaverde, Ronald P Law, Catherine Pui Yin Mok, Virginia Murray, Tracey O'Sullivan, Zeeshan Qadar, Mathieu Roy. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 17.01.2022.

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