Mapping the intersection of demographics, behaviour, and government response to the COVID-19 pandemic

doi:10.21203/rs.3.rs-5027153/v1

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Mapping the intersection of demographics, behaviour, and government response to the COVID-19 pandemic

https://doi.org/10.21203/rs.3.rs-5027153/v1

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During the early phase of the COVID-19 pandemic (March 2020-August 2021), the Province of Ontario enacted restrictions and recommendations that changed over time. We assessed individual and systemic factors shaping behaviours and contributing to exposure risk during this time. Participants across all age groups and health statuses adapted their behaviours in response to evolving regulations but older individuals and those with pre-existing conditions had the largest change in behaviour and when they developed COVID-19 or other influenza-like illnesses, had the most severe symptoms. Our analysis underscores the responsiveness of exposure risk to government-imposed restrictions, with increased stringency coinciding with decreased exposure risk among participants. These findings collectively emphasize the interplay between demographic factors and government interventions in shaping individual behaviours over the course of the pandemic. Understanding these dynamics is crucial for informing targeted interventions and mitigating the impact of future pandemics.

COVID-19 has directly and indirectly led to deaths¹, overburdened and disrupted health care systems^2,3, and negatively impacted national economies⁴. Governments employed a variety of non-pharmaceutical interventions (NPIs) to reduce infection rates⁵. These NPIs included workplace and school closures, stay-at-home-orders, and restrictions on gathering sizes⁶. Overall, these measures were effective in reducing mobility and COVID-19 transmission^{7, 8,9}; however, adherence to NPIs varied by country, sex, and other factors such as age and pre-existing health conditions^10–13. For example, in the first few months of the COVID-19 pandemic, women in the United States demonstrated greater adherence to physical distancing measures than men^{10, 11}, although men indicated greater willingness to isolate for a longer period¹⁰. Furthermore, older individuals practiced more physical distancing than younger ones^{10, 11, 12} and individuals with pre-existing health conditions were also more likely to practice physical distancing than healthy individuals^{11, 13}.

Adherence to NPIs also varied over the course of the pandemic. In 124 countries, during the period from March to December 2020, compliance with physical distancing restrictions decreased overall, although minor rebounds in compliance were observed near the end of this period¹¹. Decreased adherence to government restrictions over time has been attributed to ‘pandemic fatigue’¹¹. This term is used to describe the gradual decline in motivation to practice behaviours prescribed to protect oneself and others against COVID-19 due to a complex interaction of emotions, experiences, and diminishing perception of risk associated with COVID-19^11,14. In addition to pandemic fatigue, vaccination or previous infection with SARS-CoV-2 may also reduce individuals’ perceived risk of COVID-19¹⁵. Surveys conducted by health officials in the Netherlands in September 2021 found that in comparison to the non-vaccinated population, a greater proportion of vaccinated individuals reported a decrease in compliance with protective measures¹⁶.

In this study, we surveyed a cohort of community-dwelling adults in Ontario, Canada over approximately 16 months in 2020 and 2021 to determine the effects of government restrictions on individuals’ physical distancing behaviour.

Participant demographics

Overall, 362 participants consented to the study. One participant did not report their age and was excluded. Data from 348 participants who completed both a baseline questionnaire and at least one monthly exposure risk survey were included in subsequent reporting and analyses. There were significant age-associated differences in participant demographics (Table 1). A greater proportion of older participants (≥ 60 years of age) were male, white, and retired or did not have access to paid sick leave. Participants with pre-existing conditions were older and a greater proportion were either retired or unemployed. The most prevalent pre-existing conditions in this cohort were asthma (reported by 42% of participants with a pre-existing condition), autoimmune disorders (37%), and hypertension (25%). Older participants were more likely to have interstitial lung disease (ILD) and/or idiopathic pulmonary fibrosis (IPF), an auto-immune disorder, a bone and/or joint disorder, diabetes, heart disease, hypertension, high cholesterol, a history of cancer, or a thyroid disorder.

Reported illnesses

Half of participants (n = 175; 50.3%) reported at least one illness over the course of the study. In total, 631 illness reports were completed for 406 unique illnesses. Age impacted the number of illnesses reported per participant (main effect p = 0.0002) with older participants reporting fewer illnesses (mean 0.75 illnesses per participant) than either younger (mean 1.66 illnesses per participant; ≥60 vs. ≤39, p = 0.0003) or middle-aged participants (mean 1.34 illnesses per participant; ≥60 vs. 40–59, p = 0.014).

Reported cases of COVID-19 (n = 9) accounted for 2.2% of all illnesses reported (Fig. 1a). The first COVID-19 infection was reported in February 2020 and the last in May 2021. During the early pandemic (February-June 2020) provincial diagnostic testing was only available to those who were hospitalized, had a confirmed exposure with an infected person, or had travelled to an area with active infections. Rapid antigen tests were not available to the general public during this study. Consequently, the participants who reported COVID-19 infections were told by a health care professional that they had COVID-19 without a nasopharyngeal PCR or rapid antigen test. Influenza-like illnesses (ILIs; n = 45) accounted for 11.1% of all illness reports.

Both age and health-status impacted the severity of reported illnesses. Older adults reported more severe symptoms (≥ 60 vs. ≤39, p = 0.034; main effect of age group, p = 0.029; Fig. 1c) and more days of missed activities due to illness (≥ 60 vs. ≤39, p = 0.018; main effect of age group, p = 0.023; Fig. 1e) compared to younger adults. Individuals with pre-existing conditions also reported more severe symptoms (p = 0.014; Fig. 1d) and more days of missed activities due to illness (p = 0.020; Fig. 1f) compared to healthy individuals.

Behavioural changes with pandemic declaration

After the start of the pandemic, participants changed their social behaviour resulting in lower exposure risk scores across all age groups (pre vs post, p < 0.0001 for each group; Fig. 2a) and health statuses (p < 0.0001; Fig. 2c) at baseline compared to recalled behaviours from before the start of the pandemic. The magnitudes of these decreases were greatest in younger individuals (≤ 39 vs. 40–59, p = 0.025; ≤39 vs. ≥60, p < 0.0001; Fig. 2b) and those without pre-existing conditions (p = 0.0009; Fig. 2d) indicating that these groups changed their behaviour more in response to the onset of the pandemic. This may be due to higher exposure risk scores within these groups before the pandemic, and therefore more opportunity to decrease exposure risk by changing social behaviours (interaction of timepoint (pre-post) and age-group, p < 0.0001; interaction of timepoint and health-status, p = 0.0009) such that all groups reduced their exposure risk as much as possible at the onset of the pandemic.

Exposure risk was responsive to public health measures

Throughout the pandemic, participants modified their social behaviours in response to government restrictions. As the stringency index increased, participants’ exposure risk decreased (Fig. 3a). The Government of Ontario introduced the most stringent restrictions in January 2021 and again in April-May 2021, which coincided with the lowest exposure risk in calculated exposure risk across our cohort. Similarly, the lowest stringency indices were seen in September-October 2020 and July-August 2021 which coincided with peaks in participant exposure risk (Fig. 3a).

The relationship between exposure risk and stringency index was confirmed by a Poisson family generalized linear mixed model (GLMM) used to assess which factors influenced changes in total exposure risk over the course of the pandemic (Fig. 3b, Supplemental Fig. 1). The impact of these factors on individual components contributing to overall exposure risk was also assessed (Supplemental Fig. 2). Increasing government restrictions (stringency index) were associated with a decrease in total exposure risk (estimate = -0.21; p = 0.0213) Government restrictions also had the broadest impact across all categories contributing to total exposure risk. Increasing government restrictions had the largest effect on social gatherings (estimate = -0.65, p < 0.0001), but also were associated with decreases in risk due to working outside the home (estimate = -0.11, p < 0.0001), public transportation use (estimate = -0.21, p = 0.0023), receiving care (estimate = -0.18, p < 0.0001), visiting care facilities (estimate = -0.13, p = 0.0025), and volunteering (estimate = -0.30, p = 0.025). The only category not significantly impacted by government restrictions was handwashing frequency (p = 0.75). Overall, these data show that government restrictions had a broad and significant impact on reducing exposure risk.

Exposure risk was also impacted by demographic factors. Handwashing was most strongly impacted by sex, with female participants reporting higher handwashing frequency (i.e. lower handwashing exposure risk, estimate = -0.81, p < 0.0001). Female participants also had lower total exposure risk (estimate = -0.18, p = 0.017), despite increases in the component categories social gathering (estimate = 0.20, p = 0.045) and receiving care (estimate = 0.54, p < 0.0001). Increasing age was associated with the largest decrease in total exposure risk (estimate = -0.27; p < 0.0001). While older participants had greater care-associated exposure risk (receiving care, estimate = 0.20, p = 0.021) their risk due to social gatherings was lower (estimate = -0.22, p < 0.001). The strongest association with increasing age was decreased work-associated exposure risk (estimate = -2.37, p < 0.0001). Work-associated exposure risk was higher among healthy participants (estimate = 0.91, p = 0.017) and was the largest contributor to increased total exposure risk among healthy participants compared to those with pre-existing conditions (estimate = 0.18, p = 0.021). These data suggest that while behavioural choices contribute, the impact of demographic factors on total exposure risk was largely driven by systemic factors such as a return to in-person work.

Changes to exposure risk over the course of the pandemic further support the role of systemic factors over individual behavioural choices. Exposure risk increased over the course of the pandemic (pandemic week, estimate = 0.06, p < 0.0001). Over the course of the pandemic (pandemic week), the effect of government restrictions on reducing exposure risk became less pronounced, such that exposure risk increased over time even when the stringency index was high (stringency index:pandemic week, estimate = 0.09, p < 0.0001), consistent with “pandemic fatigue”. However, this increased in exposure risk over the course of the pandemic appears to be driven by increases to receiving care (estimate = 0.27, p < 0.0001) and working outside the home (estimate = 0.21, p = 0.00088) with a smaller increase to social gathering (estimate = 0.10, p = 0.0020) and concurrent increases in handwashing frequency (decreased handwashing-associated risk, estimate = -0.17, p < 0.0001). Although we hypothesized that vaccination might be associated with reduced perceived risk of COVID-19 infection, the data show no effect of vaccination (combined first or second dose) on total exposure risk (estimate = -0.005, p = 0.94). Among the component categories vaccination was only associated with decreased social gathering-associated exposure risk (estimate = -0.40, p = 0.017). These data emphasize that systemic factors (e.g. the return to in-person work) predominantly drive total exposure risk, with individual behaviours (e.g. social gatherings) playing a comparatively smaller role.

Incidence of COVID-19 infection

We aimed to determine whether exposure risk was associated with COVID-19 infection. We assessed COVID-19 infection by self-report and by serostatus. Dried blood spots were collected between June and November 2021 (n = 219). At this time 32% of participants (n = 70) had received one vaccine dose, 63% (n = 138) had received two doses, and 5% of participants (n = 11) had an unknown vaccination status (Table 3). 6 participants with PCR confirmed SARS-CoV-2 infections had antibodies for spike, RBD and NP SARS-CoV-2 proteins, which is consistent with having had COVID-19. Three additional participants who did not report having had any symptoms or an infection but were found to have anti-Spike, anti-RBD and anti-NP antibodies and were therefore presumed to have had asymptomatic infections.

Table 3

Participant COVID-19 Vaccine Status and Serostatus Determined by Anti-Spike and Anti-NP antibodies.
	Serostatus Consistent with COVID Infection† n = 9	Serostatus Consistent with Vaccination ꬸ *-n = 177	Indeterminant Serostatus‡ n = 17	Negative Serostatus n = 16
Vaccine Status
Single Dose	4	46	14	7
Double Dose	4	126	3	4
Unknown	1	5	0	5
† The presence of anti-NP antibodies and either or both anti-Spike and anti-RBD was considered evidence of infection. ꬸ The presence of anti-Spike and anti-RBD antibodies with absence of anti-NP antibodies were attributed to vaccination. ‡Positivity to a single antigen (either anti-NP, -Spike, -RBD)

In response to the COVID-19 pandemic government restrictions were employed in Ontario, Canada to reduce infection rates. This study sheds light on several critical aspects of the COVID-19 pandemic’s impact on a cohort of community-dwelling adults in Ontario, Canada. We observed significant associations between participant demographics and reported illnesses, with older individuals and those with pre-existing conditions experiencing more severe illness symptoms and greater associated activity limitations. Moreover, our findings highlight the substantial behavioural changes triggered by the pandemic declaration, with participants across all age groups and health statuses adopting lower exposure risk behaviours. Furthermore, our analysis underscores the responsiveness of exposure risk to government-imposed restrictions, with increased stringency coinciding with decreased exposure risk among participants^{6, 21}. Notably, the observed decline in adherence to these measures over time suggests the presence of "pandemic fatigue," with diminishing perceived risk associated with COVID-19. These findings collectively emphasize the nuanced interplay between demographic factors, government interventions, and individual behaviours in shaping the trajectory of the pandemic within our community.

The incidence of communicable infections during the early stages of the COVID-19 pandemic was very low due to social distancing behaviours²⁸. In pre-pandemic times, older adults had, on average, 1 upper respiratory tract infection a year²⁹ and healthy, younger adults had 2–3/year, proportionate to their number of social contacts³⁰. We found that only half of all respondents reported any illness (communicable or non-communicable) over the study period, with younger adults (≤ 39) reporting 1.66 illnesses, middle-aged adults (40–69) reporting 1.34, and older adults (≥ 60) reporting 0.75 on average. Of these, the vast majority were not upper respiratory tract infections suggesting government restrictions reduced the incidence of infection during the study period. When participants did report an illness, we found that older adults and individuals with pre-existing health conditions reported more severe symptoms. COVID-19 infections (as detected by serology and/or symptom reports) were low and consequently we were not able to attribute infection to differences in exposure risk; however, all the COVID-19 infections were in younger adults (< 60 years), consistent with local epidemiology²¹.

Consistent with previous reports^22,23, we found that compliance with government restrictions was high and individual exposure risk was low during this period. We found that across all demographics, changes to pre-pandemic behaviours significantly lowered individuals’ exposure risk early in the pandemic; however, over time, government restrictions had a diminishing effect on exposure risk. Waning compliance to restrictions, even if the number of infections remained high, has been reported elsewhere²⁴. As an example, the majority of UK adults surveyed from April 2020 to February 2021 maintained high levels of compliance during the two COVID-19 waves during that period²⁵. However, 15% of participants reported reduced levels of compliance during the second wave²⁵. This may indicate that individuals experiencing repeated cycles of government restrictions become less compliant over time. Contrary to our initial hypothesis, we found that vaccination status did not significantly alter exposure risk among participants. The association between vaccination status and adherence to social distancing measures and other restrictions is inconsistent in other reports ^26,27.

Demographic factors (e.g., age and sex) are known to impact compliance with government restrictions: women and older adults have reported higher levels of compliance with recommended and mandated health measures^{10, 11, 12}, consistent with our finding of reduced exposure risk associated with these populations. We also report a significantly lower exposure risk in participants with pre-existing health conditions known to increase the risk of severe COVID-19 infection (e.g., chronic respiratory disease). Conflicting data exist on the effect of health status on physical distancing behaviour. Coroiu et al. (2020) observed from March to April 2020 that individuals with pre-existing conditions in countries with police-enforced isolation measures were more likely to interact with family members outside of their households¹². In contrast, Hills and Eraso (2021) observed that in May 2020 in the UK, a country with relatively less strict physical distancing measures¹², individuals with a pre-existing condition were more likely to adhere to physical distancing measures¹³. Potential explanations for these contrasting results may be an increased sense of isolation in countries with strictly enforced measures¹² or more comprehensive at-home public services in the UK¹³, which may have decreased and increased compliance, respectively.

Non-pharmaceutical interventions, including masking, have substantially reduced the number of cases, hospitalizations, and deaths due to COVID-19 in Canada^31,32. Understanding the factors associated with compliance may inform implementation in future public health emergencies.

Participant recruitment

Research participants were recruited from the greater Hamilton area (Ontario, Canada) between May 2020 and January 2021 and were surveyed until August 2021. Participants had previously participated in pre-pandemic research studies at McMaster University where they had provided serum and had agreed to be contacted for future research studies. All protocols were approved by the Hamilton Integrated Research Ethics Board (#10757).

Participants completed questionnaires online or via phone call with a research coordinator. A baseline questionnaire was used to gather information on sex, age, employment, household demographics, social distancing practices, and health status (Table 1). Weekly questionnaires recorded any new illness-related symptoms and every 4th week this questionnaire included additional questions on social distancing practices and behaviours associated with exposure risk. Up to 78 weekly diaries were completed by each participant over the duration of this study.

Participant health status

Participant health status was determined using data from the baseline questionnaire. ‘Pre-existing conditions’ status was assigned to participants who reported having one or more of the following conditions: lung disorder, type 1 or type 2 diabetes, history of cancer, organ failure, autoimmune disease, bone and joint disorders, gastrointestinal disorder, thyroid, or heart disease. ‘Healthy’ status was assigned to participants who either reported no health conditions or any of the following: skin conditions/dermatoses, neurological conditions (i.e., migraines, epilepsy), mood disorders, hypertension, or high cholesterol (Table 2).

Exposure Risk Score

Exposure risk was calculated for each participant using seven categories of exposure risk behaviours: attendance at social events, receiving care (hospital, rehabilitation centres, dialysis units), visiting or volunteering at care facilities (rehabilitation centres, long term care facilities), public transportation use, hours working outside of the home, hours volunteering outside of the home, and handwashing frequency. Within each category, responses to individual questions (see Appendix A) were summed and a category score from 0–5 calculated, with a higher score reflecting an increased exposure risk. Total ‘Exposure Risk’ was then calculated by summing the scores of these seven categories, with 35 being the highest attainable score. Pre- and post-pandemic declaration exposure risk were assessed using the baseline questionnaire from behaviours over the previous 2-weeks (post) and recalled behaviours before the pandemic (pre).

Illness Severity

Participants reporting illness within the previous week were asked to rate their symptoms (see Appendix B) as mild (score of 1), moderate (score of 2), or severe (score of 3). Reports of illness beginning within 7 days of the end of a previous illness were manually reviewed and combined into one incident of illness where appropriate as determined by symptomatology. The total severity score was calculated by summing severity scores across all symptoms, with a possible range of 0–57. A total score of 0–5 was deemed a mild illness (e.g., symptoms that were easily managed without needing to take time off work or requiring medical attention), 6–10 a moderate illness, and > 10 for a severe illness (e.g., time off work, needed medical attention).

Classification of Illnesses

Reported illnesses were categorized as ‘COVID-19’, ‘influenza-like illness (ILI)’, or ‘other symptoms’ (Table S1 lists illnesses in each category). Confirmation of COVID-19 infection by PCR testing at a provincially licensed lab or diagnosis by a healthcare professional was self-reported. ‘ILI’ included influenza and pneumonia diagnosed by medical professionals and undiagnosed ILI where participants reported both cough and fever/feverishness in accordance with WHO¹⁸ and Public Health Agency of Canada (PHAC)^18,19 definitions. ‘Other symptoms’ included all other reports including non-infectious diseases.

Measurement of anti-SARS-CoV-2 antibodies

A dried blood spot (DBS) sample was collected from participants between June 2021 and November 2021. SARS-CoV-2 antibody levels in DBS were quantified by enzyme-linked immunosorbent assay (ELISA) as outlined in Colwill et al. (2022)¹⁷. Determination of antibodies to SARS-CoV-2 spike trimer (Spike), receptor-binding domain of the spike trimer (RBD), and nucleocapsid protein (NP) were performed with raw counts normalized to a synthetic antibody reference curve included in each assay plate. Positivity thresholds were determined using receiver operating characteristic (ROC) curve analyses from pre-pandemic samples and PCR confirmed COVID infections¹⁷.

The detection of anti-NP antibodies in addition to anti-Spike or anti-RBD antibodies was considered evidence of SARS-CoV-2 infection. Participants with detectable antibodies to a single target protein (e.g., only RBD, Spike, or NP) were assigned an ‘indeterminate’ serostatus. Participants with no detectable antibodies were considered seronegative. The absence of anti-NP antibodies and positivity for anti-Spike and anti-RBD antibodies was attributed to vaccination.

Data Sources

The stringency of public health measures in Ontario aimed to limit exposure risk (government restrictions) is represented by the Stringency Index data collected from the Oxford COVID-19 Government Response Tracker (OxGCRT) Github repository March 2022²⁰.

Statistical analysis

Statistical analyses were performed in R. Linear modelling and linear mixed models were performed using the lmerTest (RRID:SCR_015656) and lme4 (RRID: SCR_015654) packages. Post-hoc multiple comparisons were performed using the emmeans package (RRID:SCR_018734). Generalized linear mixed models were performed using the glmmTMB package. Scaled and centered values for the following variables: stringency index, pandemic week, age. Raw values were used for all other variables. Model fit validation was performed using the DHARMa package (RRID: SCR_022136). Details of each statistical analysis are described in the respective figure legend.

Funding: This study was funded by the W. Garfield Weston Family Foundation. DMEB is supported by a Canada Research Chair in Aging & Immunity. MS is supported by a Canada Research Chair in Interdisciplinary Microbiome Research. PDM is supported by the Canada Research Chair in Computational Statistics.

Author Contributions:

Conceptualization: DMEB, MS, AM, BC, AJ, MJL, ML, NH, KA, SAA

Methodology: PM, AM, DMEB, KK, BC

Investigation: DMEB, MS, AK, AC, ED

Visualization: KK, AC, AK, ED

Supervision: DMEB, MS, AM, BC

Writing – original draft: KK, AC, AK, ED, DMEB

Writing- review & Editing: All authors

Competing interests: The authors declare that they have no competing interests.

Data and code availability

All original code has been deposited on GitHub (https://github.com/kennek6/Kennedyetal2024/) and is publicly available as of the date of publication. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

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Tables 1 and 2 are available in the Supplementary Files section.

No competing interests reported.

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Mapping the intersection of demographics, behaviour, and government response to the COVID-19 pandemic

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Version 1

Abstract

Figures

Introduction

Results

Participant demographics

Reported illnesses

Behavioural changes with pandemic declaration

Exposure risk was responsive to public health measures

Incidence of COVID-19 infection

Discussion

Methods

Participant recruitment

Participant health status

Exposure Risk Score

Illness Severity

Classification of Illnesses

Measurement of anti-SARS-CoV-2 antibodies

Data Sources

Statistical analysis

Declarations

Data and code availability

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1