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COVID-19 Self-Testing Preferences Linked to Political Perspectives: Social Determinants in the U.S. Pandemic

Published:November 02, 2022DOI:https://doi.org/10.1016/j.amepre.2022.09.024

      INTRODUCTION

      Throughout the pandemic, partisanship has been strongly associated with coronavirus disease 2019 (COVID-19) vaccination status and other pandemic behaviors, such as mask wearing.,
      • Liu R
      • Li GM.
      Hesitancy in the time of coronavirus: temporal, spatial, and sociodemographic variations in COVID-19 vaccine hesitancy.
      It is unknown whether political perspectives are also associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing preferences. This research letter examines whether political perspectives in the U.S. are associated with a preference for self-testing at home versus testing in clinical settings.

      METHODS

      Qualtrics hosted a web-based survey. The authors hired Prolific, in December 2021, to enroll a nonprobability sample of U.S. adults (aged ≥18 years) who were unvaccinated for COVID-19 because this population has higher COVID-19 mortality and morbidity risks. Participants reported sociodemographic characteristics and indicated their political perspectives using 3 items, each with 5-point, standardized scales measuring political party identification, favorability toward President Biden, and favorability toward former President Trump. These 3 political items that were highly correlated were aggregated into 1 variable with categories we labeled strong left to strong right, with a neutral middle point (Figure 1).
      Figure 1
      Figure 1Politics linked to preferences for COVID-19 self-testing (over testing in clinical settings). Note: Preference for at-home self-testing over clinical-setting testing is expressed in terms of OR using strong left as the reference group, after adjusting for age, ethnicity, and gender. The points represent the OR, and the bars represent the 95% CIs. The x-axis of this figure is an aggregate measure created by summing the 3 highly correlated political items. For this aggregate measure, the scaled opinions of President Biden were reverse coded.
      Participants were instructed to imagine that they decided to test for COVID-19 and to select a single testing option representing their testing preference. A randomly ordered list was provided that included clinical, home, and worksite testing options. These methods of measuring preference for a hypothetical scenario have shown predictive validity for future behavior.
      • Chang JB
      • Lusk JL
      • Norwood FB.
      How closely to do hypothetical surveys and laboratory experiments predict field behavior?.
      • Hainmueller J
      • Hangartner D
      • Yamamoto T.
      Validating vignette and conjoint survey experiments against real-world behavior.
      • Natter M
      • Feurstein M.
      Real world performance of choice-based conjoint models.
      • Vossler CA
      • Kerkvliet J.
      A criterion validity test of the contingent valuation method: comparing hypothetical and actual voting behavior for a public referendum.
      • Levitt SD
      • List JA.
      What do laboratory experiments measuring social preferences reveal about the real world?.
      In a multivariable logistic regression analysis, testing preference was modeled as a binary outcome comprising home versus clinical setting. Those who preferred testing at work, which may not be mutually exclusive with self-testing or clinical testing, were excluded. The model was adjusted for age, gender, and ethnicity, which, on the basis of chi-square tests, were related to testing preference. A chi-square test for trend was conducted to test the relationship between the political spectrum and the proportion preferring self-testing.

      RESULTS

      Only 143 respondents (3.2%) preferred testing at work. Otherwise, a significant majority of respondents (3,084/4,299; 71.7%) preferred testing in a clinical setting (Appendix Table 1, available online). The adjusted odds of self-testing preference were higher among the political right (OR=1.57, 95% CI=1.25, 1.97 for strong right and OR=1.36, 95% CI=1.08, 1.72 for lean right) and the political center groups (OR=1.28, 95% CI=1.00, 1.65) than among those on the left of the political spectrum (Figure 1). In sensitivity analysis, the findings did not change after adding education and income as covariates (data not shown). Using the categories of the political spectrum defined as strong left, left, center, right, and strong right, the proportion with self-testing preferences increased with chi-square test for trend p=0.06.

      DISCUSSION

      Those with right and center politics were more likely to prefer self-testing than those with left politics. Given such preferences, the availability of self-testing kits could be prioritized in conservative counties, where most adults and youth have been unvaccinated.,
      • Liu R
      • Li GM.
      Hesitancy in the time of coronavirus: temporal, spatial, and sociodemographic variations in COVID-19 vaccine hesitancy.
      Relatedly, communities that prefer self-testing may experience underreporting of COVID-19 cases. Although most clinic-based test results are reported to public health authorities, many rapid self-test results are not.
      • Kasakove S.
      As at-home tests surge, doubts rise about accuracy of public covid counts.

      Limitations

      This study has notable limitations. In particular, it was designed to examine preferences and not actual testing behavior, which can be influenced by other factors, such as access.
      • Jamil MS
      • Eshun-Wilson I
      • Witzel TC
      • et al.
      Examining the effects of HIV self-testing compared to standard HIV testing services in the general population: a systematic review and meta-analysis.
      For example, the availability of self-testing kits can vary. However, when the preferred option is available, it is reasonable to expect that preferences elicited through scenario-based methods are related to future behavior and show external validity.
      • Chang JB
      • Lusk JL
      • Norwood FB.
      How closely to do hypothetical surveys and laboratory experiments predict field behavior?.
      • Levitt SD
      • List JA.
      What do laboratory experiments measuring social preferences reveal about the real world?.
      Additional research is needed to understand the specific beliefs driving political differences in testing preferences. This study focused on a high-risk population, and the HIV self-testing literature has shown that a similar approach can help to determine which service delivery models are effective for key populations.
      • Jamil MS
      • Eshun-Wilson I
      • Witzel TC
      • et al.
      Examining the effects of HIV self-testing compared to standard HIV testing services in the general population: a systematic review and meta-analysis.
      Before concluding whether the current results are generalizable to other populations, additional research would be needed.

      CONCLUSIONS

      The associations reported in this study support the growing scientific effort to identify social determinants of health disparities. In this study, partisanship had stronger associations with outcomes than several traditional sociodemographic measures, but such tests have been relatively rare.
      • Liu R
      • Li GM.
      Hesitancy in the time of coronavirus: temporal, spatial, and sociodemographic variations in COVID-19 vaccine hesitancy.
      ,
      • Muntaner C
      • Borrell C
      • Ng E
      • et al.
      Politics, welfare regimes, and population health: controversies and evidence.
      The authors are not aware of research that is similar to this study: the self-testing literature has focused on HIV, and to the authors’ knowledge, it has not investigated political beliefs.
      • Jamil MS
      • Eshun-Wilson I
      • Witzel TC
      • et al.
      Examining the effects of HIV self-testing compared to standard HIV testing services in the general population: a systematic review and meta-analysis.
      Brief political measures can be implemented more widely to examine how limited public health resources may be allocated most effectively.

      CRediT AUTHOR STATEMENT

      Jess Fishman: Conceptualization, Funding acquisition, Investigation, Methodology, Supervision, Writing - original draft, Writing - review and editing. Cedric H. Bien-Gund: Funding acquisition, Visualization, Writing - review and editing. Gregory P. Bisson: Supervision, Visualization, Writing - review and editing. Yeonsoo Baik: Data curation, Formal analysis, Methodology, Software, Validation, Visualization, Writing - review and editing.

      ACKNOWLEDGMENTS

      This research was supported by funding from the NIH R01HL151292-01S1.
      No financial disclosures were reported by the authors of this paper.

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