COVID-19 vaccination and mask wearing behaviors in the United States, August 2020 - June 2021

Using an integrated social cognition model to identify the determinants of QR code check-in compliance behaviors in the COVID-19 pandemic

In Australia, checking in while entering venues was a legal requirement during the COVID-19 pandemic to track potential infection sites. This two-wave correlational study used an integrated theory of planned behavior model including moral norms, anticipated regret, and habit to predict check-in compliance in a sample of 181 Victorians (Mean Age = 41.88, 56.4% female) and 162 Queenslanders (Mean Age = 43.26, 47.5% female). Habit and intention predicted behavior, while perceived behavioral control did not. Intention was predicted by baseline habit, attitude, subjective norm, and moral norm in the Victorian sample, while only baseline habit and moral norm predicted intention in the Queensland sample. This study has potential implications for reviewing previous strategies and for future pandemic preparedness, both by identifying the drivers of infection control compliance, and through the discussion of how differences in effects between states may be linked to each state's experience of the pandemic (e.g. infection rates, lockdown length).

Predictors of nirmatrelvir-ritonavir receipt among COVID-19 patients in a large US health system

A clear understanding of real-world uptake of nirmatrelvir-ritonavir for treatment of SARS-CoV-2 can inform treatment allocation strategies and improve interpretation of effectiveness studies. We used data from a large US healthcare system to describe nirmatrelvir-ritonavir dispenses among all SARS-CoV-2 positive patients aged ≥ 12 years meeting recommended National Institutes of Health treatment eligibility criteria for the study period between 1 January and 31 December, 2022. Overall, 10.9% (N = 34,791/319,900) of treatment eligible patients with SARS-CoV-2 infections received nirmatrelvir-ritonavir over the study period. Although uptake of nirmatrelvir-ritonavir increased over time, by the end of 2022, less than a quarter of treatment eligible patients with SARS-CoV-2 infections had received nirmatrelvir-ritonavir. Across patient demographics, treatment was generally consistent with tiered treatment guidelines, with dispenses concentrated among patients aged ≥ 65 years (14,706/63,921; 23.0%), and with multiple comorbidities (10,989/54,431; 20.1%). However, neighborhoods of lower socioeconomic status (upper third of neighborhood deprivation index [NDI]) had between 12% (95% CI: 7-18%) and 28% (25-32%) lower odds of treatment dispense over the time periods studied compared to the lower third of NDI distribution, even after accounting for demographic and clinical characteristics. A limited chart review (N = 40) confirmed that in some cases a decision not to treat was appropriate and aligned with national guidelines to use clinical judgement on a case-by-case basis. There is a need to enhance patient and provider awareness on the availability and benefits of nirmatrelvir-ritonavir for the treatment of COVID-19 illness.

The effect of COVID-19 infection on patients with rheumatic diseases in China

OBJECTIVES

At the end of 2022, the COVID-19 outbreak erupted in China, and BA.5.2 or BF.7 subtypes of Omicron novel variations were implicated in more than 90% of the cases. We created a real-world questionnaire survey to better understand how this new variant pandemic was affecting rheumatic patients in China.

METHODS

During the COVID-19 outbreak in China, the subjects of this study were rheumatic patients and non-rheumatic individuals (control group), who were matched for sex and age. Professional physicians carefully questioned the participants before administering a questionnaire as part of the study. This study focused on the general baseline characteristics, clinical symptoms and treatment after COVID-19 infection, and the target populations' awareness of COVID-19.

RESULTS

The study included 1130 participants, of whom 572 were assigned to the rheumatic group and 558 to the control group. The percentage of vaccinated controls was significantly higher than that of rheumatic patients (90.1% vs. 62.8%, p < 0.001), while the rate of COVID-19 infection was not significantly different between the two groups (82.3% vs. 86.6%, p = 0.051). Patients with rheumatic disease experienced substantially more days of fever following infection (2.87 ± 3.42 vs. 2.18 ± 1.65, p = 0.002) compared to individuals in the control group. The rheumatic patients had a greater prevalence of cough (67.1% vs. 54.0%, p < 0.001), somnipathy (13.8% vs. 6.0%, p < 0.001), and conjunctivitis/ophthalmodynia (5.3% vs. 2.1%, p = 0.008), while dry throat/throat pain/weakness (49.9% vs. 59.4%, p = 0.003), myalgia/osteodynia (33.3% vs. 41.8%, p = 0.003), and dyspnea (14.0% vs. 25.3%, p < 0.001) were more likely to occur in non-rheumatic group after infection. Human immunoglobulin (2.1% vs. 0.2%, p = 0.006), glucocorticoids (19.5% vs. 1.6%, p < 0.001), oxygen support (6.8% vs. 2.1%, p < 0.001), and traditional Chinese medicine (21.9% vs. 16.6%, p = 0.037) were all more frequently used by rheumatic patients with COVID-19 infection. People in the control group were more confused about whether to use masks in following social activities after contracting COVID-19 (14.7% vs. 7.6%, p = 0.001). In the control group, more individuals than patients with rheumatic disease (25.1% vs. 13.4%, p < 0.001) expressed an interest to receive the vaccine again. After being exposed to COVID-19, the majority of rheumatic patients (66.9%) reported no discernible change, only 29.1% reported a worsening of their symptoms, and the remaining 4% indicated an improvement.

CONCLUSIONS

After the COVID-19 outbreak in China, the proportion of patients with rheumatic diseases infected with the virus was similar to that of normal individuals. But the clinical symptoms, follow-up treatment requirements, and awareness of the COVID-19 among rheumatic patients were distinct from those among non-rheumatic patients, necessitating the use of individualized diagnosis and treatment plans as well as health advice by medical professionals in clinical work. Key Points • Despite there were different comorbidities and vaccination rates, the rate of COVID-19 infection in patients with rheumatic disease was similar to that of normal individuals. • After COVID-19 infection, rheumatic patients and normal controls had different clinical symptoms and drug usage. • After being exposed to COVID-19, the majority of rheumatic patients felt no significant change in the primary disease, while the normal controls was more likely to accept a new vaccine injection and confused about whether to use masks in following social activities.

Interplay of Digital Proximity App Use and SARS-CoV-2 Vaccine Uptake in Switzerland: Analysis of Two Population-Based Cohort Studies

Objectives: Our study aims to evaluate developments in vaccine uptake and digital proximity tracing app use in a localized context of the SARS-CoV-2 pandemic. Methods: We report findings from two population-based longitudinal cohorts in Switzerland from January to December 2021. Failure time analyses and Cox proportional hazards regression models were conducted to assess vaccine uptake and digital proximity tracing app (SwissCovid) uninstalling outcomes. Results: We observed a dichotomy of individuals who did not use the SwissCovid app and did not get vaccinated, and who used the SwissCovid app and got vaccinated during the study period. Increased vaccine uptake was observed with SwissCovid app use (aHR, 1.51; 95% CI: 1.40-1.62 [CI-DFU]; aHR, 1.79; 95% CI: 1.62-1.99 [CSM]) compared to SwissCovid app non-use. Decreased SwissCovid uninstallation risk was observed for participants who got vaccinated (aHR, 0.55; 95% CI: 0.38-0.81 [CI-DFU]; aHR, 0.45; 95% CI: 0.27-0.78 [CSM]) compared to participants who did not get vaccinated. Conclusion: In evolving epidemic contexts, these findings underscore the need for communication strategies as well as flexible digital proximity tracing app adjustments that accommodate different preventive measures and their anticipated interactions.

Invited Commentary: To Make Long-Term Gains Against Infection Inequity, Infectious Disease Epidemiology Needs to Develop a More Sociological Imagination

In a recent article in the Journal, Noppert et al. (Am J Epidemiol. 2023;192(3):475-482) articulated in detail the mechanisms connecting high-level "fundamental social causes" of health inequity to inequitable infectious disease outcomes, including infection, severe disease, and death. In this commentary, we argue that while intensive focus on intervening mechanisms is welcome and necessary, it cannot occur in isolation from examination of the way that fundamental social causes-including racism, socioeconomic inequity, and social stigma-sustain infection inequities even when intervening mechanisms are addressed. We build on the taxonomy of intervening mechanisms laid out by Noppert et al. to create a road map for strengthening the connection between fundamental cause theory and infectious disease epidemiology and discuss its implications for future research and intervention.

Spatiotemporal Trends in Self-Reported Mask-Wearing Behavior in the United States: Analysis of a Large Cross-sectional Survey

BACKGROUND

Face mask wearing has been identified as an effective strategy to prevent the transmission of SARS-CoV-2, yet mask mandates were never imposed nationally in the United States. This decision resulted in a patchwork of local policies and varying compliance, potentially generating heterogeneities in the local trajectories of COVID-19 in the United States. Although numerous studies have investigated the patterns and predictors of masking behavior nationally, most suffer from survey biases and none have been able to characterize mask wearing at fine spatial scales across the United States through different phases of the pandemic.

OBJECTIVE

Urgently needed is a debiased spatiotemporal characterization of mask-wearing behavior in the United States. This information is critical to further assess the effectiveness of masking, evaluate the drivers of transmission at different time points during the pandemic, and guide future public health decisions through, for example, forecasting disease surges.

METHODS

We analyzed spatiotemporal masking patterns in over 8 million behavioral survey responses from across the United States, starting in September 2020 through May 2021. We adjusted for sample size and representation using binomial regression models and survey raking, respectively, to produce county-level monthly estimates of masking behavior. We additionally debiased self-reported masking estimates using bias measures derived by comparing vaccination data from the same survey to official records at the county level. Lastly, we evaluated whether individuals' perceptions of their social environment can serve as a less biased form of behavioral surveillance than self-reported data.

RESULTS

We found that county-level masking behavior was spatially heterogeneous along an urban-rural gradient, with mask wearing peaking in winter 2021 and declining sharply through May 2021. Our results identified regions where targeted public health efforts could have been most effective and suggest that individuals' frequency of mask wearing may be influenced by national guidance and disease prevalence. We validated our bias correction approach by comparing debiased self-reported mask-wearing estimates with community-reported estimates, after addressing issues of a small sample size and representation. Self-reported behavior estimates were especially prone to social desirability and nonresponse biases, and our findings demonstrated that these biases can be reduced if individuals are asked to report on community rather than self behaviors.

CONCLUSIONS

Our work highlights the importance of characterizing public health behaviors at fine spatiotemporal scales to capture heterogeneities that may drive outbreak trajectories. Our findings also emphasize the need for a standardized approach to incorporating behavioral big data into public health response efforts. Even large surveys are prone to bias; thus, we advocate for a social sensing approach to behavioral surveillance to enable more accurate estimates of health behaviors. Finally, we invite the public health and behavioral research communities to use our publicly available estimates to consider how bias-corrected behavioral estimates may improve our understanding of protective behaviors during crises and their impact on disease dynamics.