Pandemic fatigue.

Lessons learned from the pandemic by strategy & the”pandemic fatigue factor”

Change in Reported Adherence to Nonpharmaceutical Interventions During the COVID-19 Pandemic, April-November 2020

JAMA. Published online January 22, 2021. doi:10.1001/jama.2021.0286

Matthew A. Crane, BS1; Kenneth M. Shermock, PharmD, PhD2; Saad B. Omer, MBBS, MPH, PhD3; et al

KEY MESSAGES:

  • This study found a decrease in reported adherence to NPIs overall and to most individual NPIs during the pandemic, irrespective of geography.
  • The increase in reported mask wearing aligns with other national surveys of self-reported mask use, and may reflect improved public health messaging

Nonpharmaceutical interventions (NPIs) have been used to mitigate the effects of the coronavirus disease 2019 (COVID-19) pandemic.

Reports describe an increasing attitude of apathy or resistance toward adherence to NPIs, termed pandemic fatigue.1 To better describe this phenomenon in the US, we used national surveillance data to analyze reporting of adherence to protective behaviors identified as NPIs.

Methods

We analyzed survey responses from 16 waves of the Coronavirus Tracking Survey (CTS) completed between April 1, 2020, and November 24, 2020. CTS participants are recruited from the Understanding America Study (UAS), an ongoing panel of US residents from marketing data on all household addresses conducted by the University of Southern California Center for Economic and Social Research. Households without access to the internet are provided internet-connected tablets, with responses weighted for national representativeness.2 CTS respondents consented to participation via the UAS website. Data collection was approved by the University of Southern California Institutional Review Board.

Every 14 days, each participant was asked to complete a wave of the CTS within the next 14 days. We constructed an NPI adherence index from 16 evidence-based protective behaviors that were included in all survey waves and susceptible to pandemic fatigue (Supplement).1 The index sums the number of behaviors reported in the week prior to survey completion (Figure 1), ranging from to 0 to 100; higher scores indicate better adherence.

We report the index by week of survey completion and percentage of participants who were adherent to each behavior. Responses were adjusted for sociodemographic factors of the survey week, age, sex, race/ethnicity, education, household income, and 7-day mean of daily new COVID-19 cases in the respondent’s state on the survey date.3 Weighted linear regression (for the NPI adherence index) and logistic regression (for behaviors) based on predictive margins were performed. A robust sandwich estimator was used to allow arbitrary correlations within participating households. We performed t and z tests to determine significant differences between adherence in the first and final survey weeks, defined as 2-sided P < .05. Analyses were conducted in Stata, version 14.0 (StataCorp).

Results

Ninety-two percent of UAS panelists consented to participation in the CTS; 97% of participants completed the first wave of the survey and 80% completed the last wave. The analysis involved 7705 participants.

The national NPI adherence index decreased substantially from 70.0 in early April, reaching a plateau in the high 50s in June (Figure 2). In late November, an increase to 60.1 in the final survey week remained significantly below the starting level in early April (P < .001). All US Census regions experienced decreases in the NPI adherence index from early April to late November, from 70.0 to 60.5 in the South, 71.5 to 62.2 in the West, 70.8 to 62.4 in the Northeast, and 70.3 to 54.4 in the Midwest (all P < .001). The NPI adherence index in the final survey week was significantly lower in the Midwest than in the South (P = .003), West (P < .001), and Northeast (P = .001).

Reported protective behaviors that had the largest decreases in weighted and adjusted adherence from early April to late November 2020 were remaining in residence except for essential activities or exercise (79.6% [95% CI, 77.2%-81.9%] to 41.1% [95% CI, 38.2%-44.0%]), having no close contact with non–household members (63.5% [95% CI, 60.7%-66.3%] to 37.8% [95% CI, 35.1%-40.5%]), not having visitors over (80.3% [95% CI, 77.9%-82.7%] to 57.6% [95% CI, 54.6%-60.5%]), and avoiding eating at restaurants (87.3% [95% CI, 85.4%-89.3%] to 65.8% [95% CI, 63.0%-68.6%]) (all P < .001) (Figure 1). Reported wearing of a mask or other face covering showed a significant increase among participants (39.2% [95% CI, 36.3%-42.1%] to 88.6% [95% CI, 86.6%-90.6%]) (P < .001).

Discussion

This study found a decrease in reported adherence to NPIs overall and to most individual NPIs during the pandemic, irrespective of geography. The increase in reported mask wearing aligns with other national surveys of self-reported mask use, and may reflect improved public health messaging.4

Strategic approaches to combating pandemic fatigue have been proposed, such as precision in government mandates and consistent communication from authorities.1,5 Additional research is necessary to understand the differential effect of NPIs in reducing COVID-19 transmission and to inform where policy interventions and public health messaging may be most effective.6 Study limitations include a reliance on self-reported behaviors, which may not reflect actual behaviors, as well as the use of an adherence index that has not been validated.

Section Editor: Jody W. Zylke, MD, Deputy Editor.

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Article Information

Corresponding Author: John A. Romley, PhD, Leonard D. Schaeffer Center for Health Policy and Economics, 635 Downey Way, Los Angeles, CA 90089–3333 (romley@usc.edu).

Accepted for Publication: January 11, 2021.

Published Online: January 22, 2021. doi:10.1001/jama.2021.0286

Author Contributions: Dr Romley had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.
Acquisition, analysis, or interpretation of data: Crane, Omer, Romley.
Drafting of the manuscript: Crane, Shermock.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Shermock, Romley.
Administrative, technical, or material support: Shermock, Omer, Romley.
Supervision: Romley.

Conflict of Interest Disclosures: None reported.

Funding/Support: The Coronavirus Tracking Survey has been supported by the Bill and Melinda Gates Foundation, the National Institute on Aging (grant 5U01AG054580–03), and the National Science Foundation (grant 2028683).

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The project described in this article relies on data from survey(s) administered by the Understanding America Study, which is maintained by CESR at the University of Southern California. The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of the University of Southern California or the Understanding America Study.

Additional Contributions: We thank Arie Kapteyn, PhD; Tania Gutsche, BA; Daniel Bennett, PhD; and Htay-Wah Saw, PhD (University of Southern California Center for Economic and Social Research), for developing and fielding the Coronavirus Tracking Survey, sharing the results with the research community, and providing technical assistance to the study team. None of these individuals received compensation for their contribution.

References

  1. Pandemic Fatigue: Reinvigorating the Public to Prevent COVID-19. World Health Organization; 2020. Accessed December 23, 2020. https://apps.who.int/iris/bitstream/handle/10665/335820/WHO-EURO-2020-1160-40906-55390-eng.pdf
  2. Welcome to the Understanding America Study. University of South California. Accessed December 23, 2020. https://uasdata.usc.edu/index.php
  3. Dong E , Du H , Gardner L . An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis. 2020;20(5):533–534. doi:10.1016/S1473–3099(20)30120–1PubMedGoogle ScholarCrossref
  4. Hutchins HJ , Wolff B , Leeb R , et al. COVID-19 mitigation behaviors by age group: United States, April-June 2020. MMWR Morb Mortal Wkly Rep. 2020;69(43):1584–1590. doi:10.15585/mmwr.mm6943e4PubMedGoogle ScholarCrossref
  5. Encouraging Adoption of Protective Behaviors to Mitigate the Spread of COVID-19: Strategies for Behavior Change. National Academies of Sciences, Engineering, and Medicine; 2020.
  6. Fisher KA , Tenforde MW , Feldstein LR , et al. Community and close contact exposures associated with COVID-19 among symptomatic adults ≥18 years in 11 outpatient health care facilities: United States, July 2020. MMWR Morb Mortal Wkly Rep. 2020;69(36):1258–1264. doi:10.15585/mmwr.mm6936a5PubMedGoogle ScholarCrossref

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Joaquim Cardoso @ BCG

Joaquim Cardoso @ BCG

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Senior Advisor for Health Care Strategy to BCG — Boston Consulting Group