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. 2023 Mar 21;323:115865. doi: 10.1016/j.socscimed.2023.115865

Feeling too isolated to be vaccinated? The contributing role of subjective interpersonal isolation factors towards COVID-19 vaccine hesitancy and resistance

Madhwa S Galgali a,∗, Peter J Helm a,b, Jamie Arndt a
PMCID: PMC10029341  PMID: 36963211

Abstract

Objective

The present research examines how different forms of subjective isolation predict COVID-19 vaccine hesitancy and resistance with two online studies conducted in the U.S.

Methods

Study 1 (n = 695), conducted before COVID-19 vaccines were available, tested if different forms of subjective isolation predicted lower trust in potential COVID-19 vaccines. Study 2 (n = 674), conducted almost a year after COVID-19 vaccines were available, tested if different forms of subjective isolation predicted not being vaccinated.

Results

In Study 1, existential isolation and alienation predicted lower trust in potential COVID-19 vaccines, while loneliness did not. In Study 2, existential isolation and alienation, but not loneliness, predicted not getting vaccinated.

Conclusion

Existential isolation and alienation are associated with negative attitudes and behavior towards vaccines and may contribute to decreased participation in public health-related behaviors.

Keywords: 5/8): Existential Isolation, Alienation, Loneliness, Mistrust in COVID-19 vaccines, COVID-19 vaccine hesitancy

1. Introduction

Interpersonal relationships have a crucial influence on health decisions (Umberson et al., 2010). Yet the extent to which varying forms of subjective interpersonal isolation influence vaccine intake is currently unknown. Such insights are especially important given the lower rates of COVID-19 vaccine intake in the U.S. (Shah et al., 2021) and the impact the COVID-19 pandemic had on people's sense of disconnection from others.

1.1. Role of subjective interpersonal isolation in vaccine hesitancy and resistance

Psychological dimensions that contribute to anti vaccine attitudes and non vaccination status are: a) vaccine hesitancy (feelings of uncertainty and indecision about whether to get vaccinated) and vaccine resistance (opposition to getting vaccinated) (Edwards et al., 2021). Although various ideological, cognitive, demographic, and personality traits contribute to anti vaccine attitudes (Edwards et al., 2021), research has yet to explore how varying forms of subjective interpersonal isolation might contribute to anti vaccine attitudes.

Subjective interpersonal isolation refers to perceived disconnection from others and includes loneliness (perceived discrepancy between desired and current social relationships; Peplau and Pearlman, 1982), alienation (feelings of marginalization and social disenfranchisement; Travis, 1993), and existential isolation (EI; perceptions that nobody else shares one's experiences; Pinel et al., 2017). These forms of subjective interpersonal isolation are related but distinct (e.g., Helm et al., 2019a), and thus may be differentially associated with anti-vaccine attitudes.

The negative association between loneliness and health related behavior is well documented (e.g., Hacihasanoglu Asilar et al., 2020). Regarding COVID-19 behaviors, loneliness is associated with lower preventive behaviors such as social distancing and hygiene related behaviors (Stickley et al., 2021). In the present study, we expected loneliness to be associated with anti vaccine attitudes because of its relation to interpersonal trust and paranoia. Those with higher loneliness tend to report lower interpersonal trust (Lieberz et al., 2021), which predicts COVID-19 vaccine hesitancy (Freeman et al., 2020). Furthermore, loneliness is associated with increased paranoia (Lamster et al., 2017), which predicts vaccine mistrust (Suthaharan et al., 2021).

Research also suggests a connection between EI and anti vaccine attitudes. Chronic EI is theorized to be associated with withdrawal like behaviors because people with higher EI should be more likely to resign themselves to feeling separate from others and incorporate such feelings into their self-concepts (Helm et al., 2019a). In addition, higher EI predicts less satisfaction with health experiences and lower trust in mental health professionals (Constantino et al., 2019), greater death-related cognitions (Helm et al., 2019b) which can contribute to health and physician avoidance (Arndt et al., 2007), and lower engagement with COVID-19 preventative behaviors (Jimenez et al., 2020). Pinel et al. (2022) also found those with higher EI make more uncertain social judgments, suggesting elevated EI should predict less confidence in personal assessments of vaccine attitudes. Taken together, elevated EI should predict less interest in vaccines because of its associations with withdrawing from social connections (social connectivity is an important predictor of health behaviors; Umberson et al., 2010), less satisfaction with past health experiences, and less confidence in health recommendations.

Feelings of alienation should also predict anti vaccine attitudes because alienation tends to be associated with populist attitudes (Elchardus and Spruyt, 2016) and lower trust in institutions (Achterberg et al., 2017), which both predict greater mistrust of vaccines and non vaccination status (Edwards et al., 2021; Murphy et al., 2021).

2. Study 1

Study 1 was conducted online from 18th November 2020 to 9th December 2020 as part of a larger project. The Pfizer-BioNTech COVID-19 Vaccine was approved on December 11th, 2020 (Food and Drug Administration, 2021). Both the current and subsequent study was approved by the Institutional Review Board at the University of Missouri (#2031523 & #2085842 respectively). Data and code for all the studies have been made available at https://osf.io/2r9e5/.

2.1. Method

2.1.1. Participants

Participants were recruited from Amazon Mechanical Turk (n = 397) and an undergraduate research pool at the University of Missouri (n = 356). Those who failed attention checks (n = 53) or had missing data (n = 5) were excluded. Little's completely missing at random test (MCAR; Little, 1988) found missing data were unrelated to the variables of interest (ps > .15), with one exception (see supplemental online materials (SOM)). As per multiple imputations using the fully conditional specification technique (FCS) (Lee and Carlin, 2010), results did not differ when using the FCS technique or listwise deletion. Finally, a sensitivity analysis using G*Power (Faul et al., 2009) for a single regression coefficient with the current sample size, excluding cases with missing values, revealed the data were sufficiently powered to detect significant effects (f 2 = 0.011, see SOM) (Thabane et al., 2013), thus listwise deletion was used for missing data.

The final sample (N = 694, M age = 31.09, SD = 15.23) included 57.6% females, 41.8% males, and 0.4% other. Most participants identified as Caucasian (80.8%), 9.7% as African American, 5.8% as Asian/Pacific Islander, 3.2% as other, 0.1% as American Indian/Native Alaskan, and 0.1% as Native Hawaiian/Pacific Islander.

Students reported trusting vaccines more (M = 3.58) than the Mturk sample (M = 3.41), t (688.89) = 2.05, p = .040. However, no higher order interactions between sample and isolation variables emerged (ps > .07); thus samples were combined, and sample source was treated as a covariate (see SOM for analyses).

2.1.2. Materials and procedure

Complete information on measures is presented in SOM. Participants completed the 3-Item Loneliness scale (Hughes et al., 2004; α = 0.86), the 14-item Margins of Society Alienation scale (Travis, 1993; α = 0.81), and the 6-item Existential Isolation Scale (Pinel et al., 2017; α = 0.85). The Vaccination Attitudes Examination scale (Martin and Petrie, 2017) was adapted to measure trust toward the beneficial effects of getting vaccinated for COVID-19 (α = 0.91).

2.2. Results

SOM provides zero-order correlations between primary variables. Loneliness and alienation were highly correlated, yet collinearity diagnostics revealed no predictors exceeded the recommended variance inflation factor (VIF) value of 5 (all VIFs <3) (James et al., 2013).

All predictors were mean centered in a multiple regression: isolation variables in Step 1, and relevant covariates in Step 2 (see Table 1 ). Step 1 revealed a significant main effect of EI predicting lower trust in COVID-19 vaccines, but not of alienation or loneliness. In Step 2, both EI and alienation were significant predictors, while loneliness remained non significant (see Table 1). According to the observed f 2 at each step, the study was well powered to reliably detect significant effects of EI, but not alienation and loneliness.

Table 1.

Regression results for Study 1 predictors of COVID-19 vaccine trust.

Predictor β t p f2
Step 1
Existential Isolation −.190 −4.697 <.001 .048
Alienation −.084 −1.502 .134 .004
Loneliness .029 .549 .583 .000
Step 2
Existential Isolation −.191 −4.711 <.001 .052
Alienation −.140 −2.470 .014 .003
Loneliness .060 1.141 .254 .000
Pseudo Profound Bullshit Receptivity −.068 −1.656 .098 .002
Need for Uniqueness −.028 −.697 .486 .000
Cognitive Reflection Test .074 1.937 .053 .012
Social Media Use .109 2.593 .010 .002
Political Orientation −.199 −5.337 <.001 .040
Sample .126 2.247 .025 .008
Age .057 .989 .323 .001
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Note. Sample coded as (1 = student; 0 = Mturk); Step 1 R2adj = 0.045, p < .001; Step 2 R2adj = 0.095, p < .001.

Communality analyses (Nimon et al., 2008) indicated that, in Step 1, EI explained the highest percentage of unique variance (3.04%), followed by alienation (0.31%) and loneliness (0.04%). In Step 2, political orientation uniquely explained the highest percentage of variance (3.72%) followed by EI (2.9%), with each of the other covariates uniquely accounting for less than 1% (see Table S3 in SOM).

Overall, there were small-medium effect sizes for EI and alienation (Lovakov and Agadullina, 2021).

3. Study 2

While Study 1 was a prospective study conducted when COVID-19 vaccines were still under development, Study 2 was conducted between Jan. 24th to Jan. 25th, 2022, approximately 13 months after COVID-19 vaccines became available in the U.S. and thus allowed examination of actual vaccination status. Study 2 also included different covariates than Study 1 to further assess the potentially unique contributions of interpersonal isolation on vaccination behavior. All hypotheses and materials were preregistered https://osf.io/2r9e5/.

3.1. Method

3.1.1. Participants

Participants were from Amazon's Mturk (N = 715). Eligibility was limited to participants with at least a 90% approval rate, who did not participate in Study 1, and who resided within the U.S. Those who failed attention checks or carelessly responded (e.g., indicated agreement with “I work fourteen months in a year.” (Huang et al., 2015)) were excluded (n = 41). Two participants failed to report political orientation and were excluded. In the final sample (N = 672, M age = 40.22, SD = 12.19), 63.5% identified as female, 35.4% as male, and 1% as other. Additionally, 81.1% identified as Caucasian, 11.5% as African American, 7.1% as Asian/Pacific Islander, 1.9% as American Indian/Native Alaskan, and 1.9% as some other racial combination.

Sensitivity analysis using G*Power (Faul et al., 2009) revealed the data could detect an odds ratio of 0.87 or below at 80% power at Step 1, and an odds ratio of 0.82 or below at 80% power at Step 2 (see SOM). Observed effect sizes for variables of interest revealed that, in both steps, data were sufficiently powered to detect effects for EI and alienation.

3.1.2. Materials and procedure

See SOM for information on scales administered. Scales were presented in random order, except vaccination status questions were presented last. Isolation variables included the same loneliness (but with a 7-point scale; α = 0.90) and EI scales (α = 0.90) from Study 1. To avoid the high correlation with loneliness in Study 1, alienation was measured using the 12-item Perceived Anomie Scale (Teymoori et al., 2016) (α = 0.87).

Assessment of COVID-19 vaccination status used a single item: “Have you had at least one shot of a COVID-19 vaccine?” with response options “Yes” or “No.”

3.2. Results

A binary logistic multiple regression tested whether subjective isolation predicted COVID-19 vaccination status. Mean centered isolation variables were entered in Step 1 and mean centered covariates were added to Step 2 (see Table 2 ).

Table 2.

Logistic regression results predicting having received a COVID-19 vaccine.

Predictor B S.E. Wald p Exp(B)
Step 1
(Intercept) 1.189 .094 160.816 <.001 3.282
Existential Isolation −.124 .083 2.243 .134 .884
Alienation −.373 .104 12.976 <.001 .688
Loneliness .023 .056 .174 .676 1.023
Step 2
Existential Isolation −.202 .092 4.783 .029 .817
Alienation −.245 .124 3.891 .049 .782
Loneliness .011 .084 .016 .990 1.011
Cognitive Perceptual .022 .106 .045 .833 1.023
Social Anxiety .036 .070 .261 .612 1.036
Interpersonal .127 .109 1.371 .242 1.136
Dangerous World Beliefs −.117 .157 .550 .458 .890
Anxious Attachment −.123 .103 1.426 .230 .883
Avoidant Attachment −.057 .108 .279 .597 .945
Political Orientation −.461 .063 53.866 <.001 .631
Age .034 .009 13.810 <.001 1.034
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Note. Step 1: Nagelkerke R2 = 0.048; Step 2: Nagelkerke R2=0.206.

Step 1 revealed a main effect of alienation predicting a lower likelihood of having a COVID-19 vaccine dose (p < .001). For every one point increase in alienation, the probability of being unvaccinated increased by 31.3%. Neither EI nor loneliness was significant.

Step 2 revealed EI and alienation significantly predicted being unvaccinated. For every one point increase in EI, the probability of being unvaccinated increased by approximately 18.3%. For every one point increase in feelings of alienation, the probability of being unvaccinated increased by approximately 21.7%. Loneliness did not predict vaccination status.

Communality analysis revealed that in step 1, alienation explained the highest percentage of unique variance (1.9%), followed by EI (0.34%) and loneliness (0.03%). In step 2, political orientation explained the highest percentage of unique variance (8.24%) followed by participant age (1.57%). Among the isolation variables, EI explained the highest amount of unique variance (0.61%) followed by alienation (0.43%) and a negligible amount by loneliness (<0.05%) (see Table S5 in the SOM).

Effect sizes revealed small effects of isolation (Chen et al., 2010).

4. General discussion

Although people during the pandemic endured interpersonal isolation, few studies have investigated how such feelings contributed to vaccination attitudes and behavior. Study 1 was conducted before COVID-19 vaccines were available and found that EI and alienation, but not loneliness, predicted greater mistrust towards COVID-19 vaccines with small to medium effect sizes. Study 2 corroborated these findings. About a year after the COVID-19 vaccines became available to the public, EI and alienation, but not loneliness, predicted being unvaccinated against COVID-19. Effect sizes suggest a small effect of EI and alienation on vaccination status. Although effect sizes in the present studies were small to moderate, small effect sizes can be meaningful, especially when cumulative, and can have practical significance when predicting real-life outcomes (Funder and Ozer, 2019).

4.1. Implications

The differential contributions of types of subjective isolation on anti vaccine attitudes and behavior highlight the need to account for subjective isolation when assessing public sentiment towards vaccines and when designing vaccine compliance interventions.

These issues may be especially important when considering underrepresented groups.

Mistrust in vaccines tends to be more prevalent among minoritized groups (e.g., lower socioeconomic status, and less education; Ndugga et al., 2022); these individuals are also more likely to feel EI (Pinel et al., 2022) and be alienated from societal structures (Mitchell et al., 2021). Additionally, people from racially minoritized backgrounds are more likely to face discrimination when accessing local services (The RSA, 2021) potentially further exacerbating feelings of EI and alienation. Thus, these are critical factors to understand when considering engagement in public health behavior (e.g., vaccine intake).

4.2. Limitations and future directions

Although the current conceptualization posits subjective isolation impacts vaccine attitudes, vaccine hesitancy may (also) lead to feelings of EI and alienation. Longitudinal and/or experimental designs are needed to justify causal inferences. Additionally, these studies would benefit from examining potential mechanisms through which these processes work. The sample characteristics in the present study (e.g., majority white and female, conducted in the U.S.) also limit generalizability to other cultural contexts and demographics.

Finally, the present studies did not measure health conditions that could be associated with increased subjective isolation and contribute to vaccine hesitancy. For instance, vaccine hesitancy is higher among those who were HIV positive and those with cancer (e.g., Barrière et al., 2021; Bogart et al., 2021). Such people are also more likely to experience intense feelings of interpersonal isolation, especially when their health conditions are associated with stigma, potentially further exacerbating feelings of EI and alienation (Audet et al., 2013). Hence it is possible one's prior health conditions may explain the relationship between subjective isolation and anti vaccine attitudes.

5. Conclusion

Two studies tested the extent to which subjective interpersonal isolation is associated with vaccine hesitancy and resistance. Before COVID-19 vaccines were available, EI and alienation, but not loneliness, predicted greater mistrust towards COVID-19 vaccines. Almost a year after vaccines became available, EI and alienation, but not loneliness, predicted being unvaccinated against COVID-19. These relationships persisted even after controlling for relevant covariates, pointing to the importance of subjective isolation for understanding vaccination decisions.

Credit statement

All authors contributed to the study conceptualization and writing of this manuscript. Madhwa S. Galgali and Peter J. Helm contributed to data collection. Madhwa S. Galgali contributed to data analysis and data preparation.

Declaration of competing interest

The authors declare that there are no known conflicts of interest and there was no financial support received for this work.

Handling Editor: M Hagger

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.socscimed.2023.115865.

Appendix A. Supplementary data

The following is the Supplementary data to this article:

Multimedia component 1
mmc1.docx (210.3KB, docx)

Data availability

The data has been made open on OSF: https://osf.io/2r9e5/?view_only=169589d2166f41f9933d7e18b6728a4a

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Multimedia component 1
mmc1.docx (210.3KB, docx)

Data Availability Statement

The data has been made open on OSF: https://osf.io/2r9e5/?view_only=169589d2166f41f9933d7e18b6728a4a

Articles from Social Science & Medicine (1982) are provided here courtesy of Elsevier

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