Original Article

Lower Intent to Comply with COVID-19 Public Health Recommendations Correlates to Higher Disease Burden in Following 30 Days

Authors: Robert P. Lennon, MD, JD, Aleksandra E. Zgierska, MD, PhD, Erin L. Miller, BS, Bethany Snyder, MPH, Aparna Keshaviah, ScM, Xindi C. Hu, ScD, Hanzhi Zhou, PhD, Lauren Jodi Van Scoy, MD

Abstract

Objectives: We sought to determine whether self-reported intent to comply with public health recommendations correlates with future coronavirus disease 2019 (COVID-19) disease burden.

Methods: A cross-sectional, online survey of US adults, recruited by snowball sampling, from April 9 to July 12, 2020. Primary measurements were participant survey responses about their intent to comply with public health recommendations. Each participant’s intent to comply was compared with his or her local COVID-19 case trajectory, measured as the 7-day rolling median percentage change in COVID-19 confirmed cases within participants’ 3-digit ZIP code area, using public county-level data, 30 days after participants completed the survey.

Results: After applying raking techniques, the 10,650-participant sample was representative of US adults with respect to age, sex, race, and ethnicity. Intent to comply varied significantly by state and sex. Lower reported intent to comply was associated with higher COVID-19 case increases during the following 30 days. For every 3% increase in intent to comply with public health recommendations, which could be achieved by improving average compliance by a single point for a single item, we estimate a 9% reduction in new COVID-19 cases during the subsequent 30 days.

Conclusions: Self-reported intent to comply with public health recommendations may be used to predict COVID-19 disease burden. Measuring compliance intention offers an inexpensive, readily available method of predicting disease burden that can also identify populations most in need of public health education aimed at behavior change.
Posted in: Infectious Disease136

Full Article

Having trouble viewing the article content below? Click here to open it directly.

Images

Download Image

Download Image

Download Image

Download Image

References

1. Ioannidis JPA, Cripps S, Tanner MA. Forecasting for COVID-19 has failed. Int J Forecast 2020; DOI: 10.1016/j.ijforecast.2020.08.004.
 
2. Clason L. Lack of national COVID testing strategy drives confusion. Washington, DC: Roll call; 2021. Available at: https://www.rollcall.com/ 2021/04/20/lack-of-national-covid-testing-strategy-drives-confusion/. Accessed October 25, 2021.
 
3. Daughton CG. Wastewater surveillance for population-wide Covid-19: the present and future. Sci Total Environ 2020;736:139631.
 
4. Reiner RC, Barber RM, Collins JK, et al. Modeling COVID-19 scenarios for the United States. Nat Med 2020;27:94–105.
 
5. Frieden TR. Six components necessary for effective public health program implementation. Am J Public Health 2014;104:17–22.
 
6. Van Scoy LJ, Miller EL, Snyder B, et al. Knowledge, perceptions, and preferred information sources related to COVID-19 among central Pennsylvania adults early in the pandemic: results of a mixed methods cross sectional survey. Ann Fam Med 2021;19:293–301.
 
7. Lennon RP, Sakya SM, Miller EL, et al. Public Intent to Comply with COVID19 Public Health Recommendations. Health Lit Res Pract 2020;4:e161–e165.
 
8. Adler NE, Epel ES, Castellazzo G, et al. Relationship of subjective and objective social status with psychological and physiological functioning: preliminary data in healthy white women. Health Psychol 2000;19:586–592.
 
9. Deming WE, Stephan FF. On a least squares adjustment of a sampled frequency table when the expected marginal totals are known. Ann Math Statist 1940;11:427–444.
 
10. Deville J-C, Särndal C-E. Calibration estimators in survey sampling. J Am Stat Assoc 1992;87:376–382.
 
11. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.
 
12. von Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet 2007;370:1453–1457.
 
13. Lennon RP, Sakya SM, Miller EL, et al. Public intent to comply with COVID-19 public health recommendations. Health Lit Res Pract 2020; 4:e161–e165.
 
14. Czeisler MÉ, Tynan MA, Howard ME, et al. Public attitudes, behaviors, and beliefs related to COVID-19, stay-at-home orders, nonessential business closures, and public health guidance—United States, New York City, and Los Angeles, May 5–12, 2020. MMWR Morb Wkly Rep 2020;69:751–758.
 
15. Thompson RN. Epidemiological models are important tools for guiding COVID-19 interventions. BMC Med 2020;18:152.
 
16. Wynants L, Van Calster B, Bonten MMJ, et al. Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal. BMJ 2020;369:m1328.