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

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

Disclosures

South Med J. 2021;114(12):744-750. 

In This Article

Abstract and Introduction

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.

Introduction

Efforts to forecast coronavirus disease 2019 (COVID-19) case burden have been largely unsuccessful, and draconian measures predicated on flawed models have negatively affected economic and health issues beyond COVID-19.[1] Widespread gaps in diagnostic testing have impeded population sampling as an efficient, reliable means to predict disease burden.[2] Complex predictive techniques for population testing (eg, wastewater surveillance) show promise in providing predictive insights by mapping population disease burden,[3] but require further methodological validation.

As COVID-19 data have increased, modeling efforts have improved; however, even the most sophisticated modeling remains limited by its requirements of implicit assumptions about human behavior, and the need to identify specific compliance parameters to test the model. For example, Reiner et al recently demonstrated the power of universal mask wearing using state-level US data and five susceptible-exposed-infectious-recovered models.[4] They make a compelling case for the effectiveness of 95% mask use in public to ameliorate projected increases in case counts as public health–mandated restrictions are removed. Although their modeling is arguably the best published to date with a mean absolute percent error (MAPE) nearly 40% lower than the average COVID-19 model, it still has a MAPE of >20%.[4] Also, their predetermined mask usage rates are 95% and 85%,[4] which are markedly higher than the observed rate of 59% in US residents (https://covid19.healthdata.org). Furthermore, although we often think of the requirement of a "limited number of high-priority, evidence-based interventions" necessary for effective public health programs[5] as a limiting factor, in this case it is an expansive one. Any one of the COVID-19 mitigation strategies may, with 95% adherence, show good results in a model. Effective public health policy on mitigation strategies should consider the effect of improvements across them all, and be able to demonstrate the impact of even modest improvements in compliance.

To help guide public health policy regarding infection control policies, we show the correlation between self-reported intent to comply with several public health recommendations and local COVID-19 disease burden within the following 30 days based on survey results from a national, demographically representative sample of adults in the United States and their actual, local COVID-19 case rates.

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