Results presented here provide novel insights regarding individual factors associated with COVID-19 testing hesitancy among US adults at high risk of progression to severe disease during the Omicron era. Most respondents (67.2%) indicated a low or uncertain probability of being tested for COVID-19 within the next 6 months. Non-testers were more likely than Testers to be women, aged ≥ 65 years, Non-Hispanic, White, from rural communities, and politically conservative. Compared with Testers, they were also less likely to report a high degree of knowledge regarding COVID-19–related topics, to have past experiences related to COVID-19, to report having concern about COVID-19 from a personal and public health perspective, and to have a high level of engagement with the healthcare system.

The high prevalence of COVID-19 testing hesitancy identified among survey respondents is particularly relevant in a landscape where effective treatments are available to reduce the likelihood of hospitalization and death among vulnerable individuals [11, 12]. Treatment can only be made available once SARS-CoV-2 positivity is determined. Even then, further barriers to treatment exist among individuals who test positive. In 2022, the CDC reported that out of all patients identified from a large nationwide database who were eligible to receive nirmatrelvir/ritonavir, only 28.4% had been prescribed the medication within 5 days of diagnosis [25]. This phenomenon of treatment underutilization has been corroborated by data from additional recent reports as well [26, 27]. Treatment for COVID-19 is also prescribed disparately across races and ethnicities, including among those who are high risk of progression to severe disease [28]. In our survey, 45.3% of respondents indicated a past COVID-19 diagnosis but only 21.9% of respondents indicated having received any type of treatment, despite all respondents having a high risk of progression to severe disease. As a caveat, however, it was not determined how many respondents may have been diagnosed before treatments were made available.

Many cultural and societal factors are likely to contribute to the differences in testing hesitancy across demographic groups, and the complex interactions between these factors may transcend typical social determinants of health. For instance, neither income level nor level of educational attainment was clearly associated with testing hesitancy in our survey, nor did we observe a greater likelihood of testing hesitancy among racial and ethnic minorities. We did, however, observe a greater proportion of Non-testers among those dwelling in rural compared with urban or suburban communities, which supports the need to expand access to testing sites and points of purchase for home testing kits, as has been described previously [29, 30]. Given the importance of equitable testing access and the compounding issue that those with less access to pharmacies are often the individuals at greatest risk of severe COVID-19 [30], government-funded Test-to-Treat programs were developed in 2022 as a rapid means to provide testing, prescriptions, and medications to underserved populations [29]. Although this was productive, many rural communities remain without access [29, 31]. Recent efforts by the National Institutes of Health, the Administration for Strategic Preparedness and Response, and the CDC to deploy home-based Test-to-Treat programs hold significant promise to overcome geographical barriers [32, 33], but testing hesitancy may pose an upstream challenge for broader utilization of these initiatives.

We also observed a greater prevalence of Non-testers versus Testers among female respondents, indicating that testing hesitancy may be more prominent among women compared with men in the United States. This is likely related to various social factors, which may include a consistently identified preference among women for nonpharmaceutical methods of disease prevention and management (e.g., hand-washing) compared with pharmaceutical methods (e.g., vaccination) [16, 34,35,36].

Notably, our study identified a greater prevalence of Non-testers versus Testers among those who held politically conservative attitudes, as well as among those characterized by demographic characteristics associated with political conservatism in the United States (e.g., being non-Hispanic, White, aged ≥ 50 years, and living in a rural community) [37]. This is consistent with previous studies showing that those who identify as politically conservative have increased rates of vaccine hesitancy, reduced compliance with public health guidelines regarding COVID-19, fewer concerns about the potential severity of COVID-19, and increased tolerance of societal risks [38,39,40]. Many of these factors were also independently associated with testing hesitancy in the present study as well as others [10, 16]. These trends may be due in part to the spread of COVID-19 misinformation regarding the pandemic [16, 39], although they are likely to result from an interplay of many factors contributing to testing hesitancy.

Despite clear guidance from the CDC about benefits of treatment with nirmatrelvir/ritonavir for patients at high risk for progression to severe COVID-19 [11], recent reports suggest substantial underutilization of oral antivirals [25,26,27]. This treatment hesitancy may be emblematic of testing hesitancy. Greater recognition of testing hesitancy among high-risk patients and factors associated with it can allow care providers to address upstream barriers of suboptimal treatment of COVID-19. Considering the combinations of characteristics that were and were not associated with testing hesitancy in our survey, the effectiveness of any interventions designed to improve adherence with testing guidelines may be optimized by tailoring focus and communication styles to specific patient signatures, or groupings of demographics and other characteristics, rather than focusing on a single descriptor. By emphasizing a multifaceted, evidence-based approach tailored toward the appropriate audience, national and community efforts to increase COVID-19 testing can potentially be less costly and have greater overall efficiency.

Effective strategies to reduce testing hesitancy can be implemented at the level of healthcare providers, policy makers, and local community messaging. Healthcare providers can play a pivotal role in educating patients on the value of testing for COVID-19, especially during the influenza season given the potential for co-infection [41,42,43] and the importance of differentiating between the two infections to ensure appropriate clinical intervention. Based on the findings of our survey that testing hesitancy was associated with lower healthcare system engagement, it is imperative that providers optimize the limited time they may have with their patients to ensure adequate understanding of COVID-19 symptoms, testing options, and available treatments. Additionally, our findings suggest the need to develop targeted, culturally sensitive tactics to effectively address testing hesitancy across communities. These strategies may include tailored education aligned with conservative values, influential community messengers, messages emphasizing societal as well as personal benefits, rigorous misinformation mitigation, and ongoing data-driven assessments. Expanding the existing programs to deploy localized vaccination clinics, particularly in rural areas [29], should also be a priority.

Strengths of our study included a diverse survey population, as well as the IPW methodology used to improve generalizability of the survey population to the target population of US adults with risk factors for progression to severe COVID-19 based on a large, representative data set. Although the survey population was not a perfect representation of the target population (likely due to biased sampling selection, nonresponse bias, or sampling error), characteristics were well matched after IPW.

Our study also had some important limitations. First, all questions, potential responses, and statements included in the survey were selected by the authors rather than solicited through focus groups, which may have introduced investigator bias about which factors and considerations are the most important. In particular, cognitive bias may have been introduced based upon whether questions were framed positively or negatively regarding attitudes toward COVID-19 or COVID-19 testing (framing effect, whereby respondents may be influenced by how information is presented rather than by what they are being asked [44]). Second, knowledge was assessed only by self-report; no objective measure was used to test understanding of each topic. Third, the survey population may have been impacted by survey responder bias, whereby certain demographics or other characteristics may have been associated with likelihood of accepting or declining the invitation to participate. Differences in survey response behavior across demographic groups have been identified previously, including a higher likelihood of online survey participation among women compared with men [45]. Because the survey was only available online, this also biased the sample to exclude anyone who did not have access to the internet or have the required technological skills. Some clear differences between the survey population and the target population (from IQVIA claims data) were identified, such as much greater representation of certain comorbidities (e.g., current or former smoking and/or substance abuse) among respondents; these differences may have been the result of broadly worded questions regarding comorbidities as reflected in the CDC descriptions of high-risk conditions [9], compared with the comorbidity data from the IQVIA database, which are aggregated through medical claims rather than self-report. However, IPW was used to minimize any such differences as much as possible. Finally, respondents were grouped into categories of Testers and Non-testers based solely on their reported likelihood of testing within 6 months, regardless of the potential rationale for their response. This may have resulted in an overestimation of Non-testers, and it is not clear which respondents may have been willing or unwilling to test for COVID-19 under the CDC-recommended circumstances of either experiencing symptoms or having a known exposure to a SARS-CoV-2-positive individual. To address this limitation, a follow-up manuscript is planned that will describe the circumstances under which respondents would or would not choose to get tested for COVID-19.