Factors associated with receiving an initial COVID-19 vaccine among Alaskan residents: results from an online cross-sectional survey

ABSTRACT

We conducted an online survey of Alaskan adults between May and June, 2022 to identify factors associated with vaccine hesitancy. Of the 948 respondents, approximately 80% reported having received a COVID-19 vaccine. The factors significantly associated with ever receiving COVID-19 vaccine include perceived risk of harm if not vaccinated (PRH), the vaccination status of the respondent’s social network (SNW), gender, and education. For each point increase in PRH score, there was more than three times the odds of having ever been vaccinated (OR = 3.42, p < 0.001); and for every point increase in SNW score, there was more than two times the odds of having ever been vaccinated (OR = 2.15, p < 0.001). Males had more than four times the odds of having ever been vaccinated compared to females (OR = 4.55, p < 0.001). Those with a college degree (OR = 2.80, p < 0.05) had greater odds of ever being vaccinated compared to their counterparts. Findings from this sample suggest that, among Alaskans, ever receiving a COVID-19 vaccine is associated with having a majority of their close social networks who have received COVID-19 vaccine and perceiving that not obtaining a COVID-19 vaccine can pose greater risk of harm to themselves than not getting vaccinated. Practical implications of these findings are provided.

KEYWORDS:

• COVID-19
• vaccine
• vaccine hesitancy
• immunization
• Alaska

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Correction

Introduction

Vaccination is a key component of preventive medicine and public health [1]. The United States (U.S.) has some of the highest rates of COVID-19 mortality compared to peer nations and the lowest rates of COVID-19 vaccination [2]. During the Delta and Omicron waves, the U.S. reported 370,298 COVID-19 deaths (112 per 100, 000) while 63% of its population had received two doses of COVID-19 vaccine [2]. In comparison, the COVID-19 mortality rates among peer nations during this period were lower while their population vaccination rates were higher: Australia (19.2/100,000, 76%); Canada (30.4/100,000, 77%); France (42.2/100,000, 74%); and the United Kingdom (59/100,000, 71%) [2]. If the U.S. had the COVID-19 mortality rate of Canada, for example, 269,401 American lives could have been saved during this period [2].

Similarly, within the U.S., researchers have also observed wide differences in COVID-19 mortality and vaccination rates. By January 2022, the top 10 most vaccinated U.S. states saw a combined average of 73% of their populations receive at least two doses of COVID-19 vaccine and experienced a COVID-19 mortality rate of 74/100,000 [2]. Meanwhile, the 10 least vaccinated states saw a combined average of only 52% of their populations receive at least two doses of vaccine and experienced almost double the mortality rate (146/100,000) [2]. In Alaska, as of January 2022, the proportion of Alaskans who had received two doses of COVID-19 vaccine was 54%; by November 2022 that proportion had only increased to 57% [3]. The COVID-19 mortality rate for Alaskans during the Delta wave was 137.9/100,000 and this surged even higher during the Omicron wave [3–5]. Explanations for the low uptake of COVID-19 vaccination among Alaskans remains unknown.

In the circumpolar north, public reporting and rates of COVID-19 vaccination with at least the primary vaccination series vary widely by country. As of August 2022, Norway [6] reported 90.9% of their population ages 18 or older had completed the primary vaccination series (i.e. the initial one or two-dose series, not including any boosters); Canada’s circumpolar territories of Yukon [7], Northwest Territories [8], and Nunavut [9] reported primary series completion (ages 5+) of 86.8%, 92.9%, and 94.6%, respectively. As of December 2022, Sweden [10] reported 86.4% primary series completion, Finland [11] reported 87.1%, and Russia [12] 67.5%. No data has been reported on COVID-19 vaccination rates of Greenland residents since March 2022, when it sat at approximately 68% of all eligible residents [13]. As of 31 January 2023, 66.3% of Alaskan adults had completed their primary vaccination series; however, at the time of our study (May 2022), only 65% of Alaskans [14] (including those reported by the Department of Defense and Veterans Administration) aged 5 and over had completed their initial two-dose series. Regionally, we observed persistent differences in COVID-19 vaccine uptake with the City and Borough of Juneau/Southeast Alaska, the Yukon-Kuskokwim Delta region, the Northwest Region and the Municipality of Anchorage reporting a higher proportion of the population as fully vaccinated and the Fairbanks-North Star Borough, the Kenai Peninsula Borough and Matanuska-Susitna Borough reporting a consistently lower proportion of the population as fully vaccinated [3].

The public health benefits of vaccination on individual and population health outcomes are well-established [1,15,16]. Yet, over the past several decades rates of vaccine hesitancy, defined as the delay in acceptance or refusal of vaccination, has increased to a level that the World Health Organization declared it one of the top 10 threats to global health in 2019 [17,18]. While it has long been recognised that vaccine hesitancy is multicausal [17,19–24], few investigations have attempted to include the role of vaccine misinformation on vaccine acceptance and more specifically COVID-19 misinformation on vaccine uptake among Alaskan residents [25–29]. Previous interventions to increase vaccine acceptance have often focused on improving communication between patients and providers or developing targeted social marketing campaigns to improve demand [30–32]. While factors that influence vaccine hesitancy among Alaska Native and American Indian populations have been explored [33–36], the characteristics and determinants of vaccine hesitancy among the Alaskan population as a whole have only recently become an area of focus in the peer-reviewed literature [37,38]. Until we can better understand the interrelated factors that contribute to vaccine hesitancy, limitations to vaccine uptake may persist, thereby placing communities at risk from vaccine preventable diseases. In this study, we sought to identify individual (i.e. knowledge, attitudes, and beliefs) and interpersonal-level (i.e. social networks) factors associated with acceptance of COVID-19 vaccination, as well as the role of misinformation in the decision to vaccinate. Our results were intended to help to identify factors that influence vaccine hesitancy among Alaskans and inform immunisation promotion planning.

Methods

Design, sampling and data collection

We used a cross-sectional study design, with data collected through an online, self-administered survey using the Qualtrics platform. Selection criteria for this study included those a) residing in Alaska and b) those who are at least 18 years old. A third-party vendor, Alaska Survey Research (ASR), was responsible for the participant recruitment and data collection. In recruiting study participants, ASR utilized their list of mobile phone numbers for Alaskan residents. The participants were randomly chosen from this list and invited them to participate via text message. This process continued until the desired minimum sample size was achieved. Note that we designed the sampling to reflect regional demographics with oversampling in the Matanuska-Susitna Valley, Kenai Peninsula, and Fairbanks given lower COVID-19 vaccination uptake in those regions. As an incentive for participation, study participants were invited to enroll in a random drawing for gift cards upon completion of the survey. Although the participant recruitment was random, the study sample represents a convenient sample of Alaskans with cell phones. The University of Alaska Anchorage Institutional Review Board reviewed and approved the protocols of this study (UAA IRB#:1848498). The survey was conducted from May to June 2022, when vaccination for children under five and the COVID-19 Omicron booster had not yet been made available in the U.S.

Variables

The main dependent variable for this study was a binary (0,1) for whether survey respondents ever received an approved vaccination for protection against COVID-19 including one or more shots from a multi-shot vaccine series (e.g. those from Moderna or Pfizer) or a single shot from a one-shot vaccine (e.g. from Johnson & Johnson). Six independent variables were tested for association with ever receiving COVID-19 vaccine. These variables are composite indices of several survey questions asked throughout the survey and are based on key concepts of health behaviour theories, including the Health Belief Model [39], Social Cognitive Theory [40], and Behaviour Change Model [41]. Taken together, these theories suggest that behaviour, such as the decision to get vaccinated, may be shaped by one’s knowledge, attitudes, beliefs, social environment, and/or personal abilities and motivations, respectively. To explore these constructs, we constructed crude independent index variables including: Belief in COVID Misinformation (BCM); COVID Knowledge (CKN); Perceived Risk of Harm if not vaccinated (PRH); how much of the respondents’ Social Network had been vaccinated against COVID (SNW); Motivation for self, family, and friends not to get infected by COVID (MOT); and Personal Ability to Vaccinate (PAV). Each of these composite indices have a reliability coefficient of α > 0.70, indicating good internal consistency [42]. Moreover, factor analysis for all indices (except COVID Knowledge [CKN]) showed unidimensionality (i.e. the Eigenvalue for the first factor was significantly larger than the next factor and accounting for a large proportion of total variance) [43]. We did not run factor analysis for COVID Knowledge because it was meant to test the respondents’ knowledge of various COVID-19 facts and not a measure of a single psychological or cognitive construct. Details of how each of these independent variables were compiled and scored can be found in Table 1.

Table 1. List of independent variables and scoring details.

Variables	Scoring
Belief in COVID Misinformation (BCM)	Score range: 0–8; High Score = High belief in COVID misinformation Cronbach’s α: 0.82 Factor Analysis: Component 1 Eigenvalue = 5.34, Component 2 Eigenvalue = 0.69, first factor accounts for 67% of the variance.
8 Items (1 point each): Agree that the COVID-19 vaccine reduces fertility Agree that the COVID-19 vaccine alters DNA Agree that Ivermectin treats COVID-19 Agree that hospitals overcount COVID deaths Agree that the COVID-19 vaccine tracks people Agree that SARSCOV2 virus was released from a laboratory in China Doesn’t agree that the COVID-19 vaccine is effective Doesn’t agree that the COVID-19 vaccine is safe
COVID Knowledge (CKN)	Score range: 0–7; High Score = High knowledge of COVID facts. Cronbach’s α: 0.74
7-Items (1 point each): Agree that the COVID-19 vaccine is recommended even if I’ve already had COVID Agree that the COVID-19 vaccine is free for everyone eligible Agree that washing hands can prevent COVID Agree that the COVID-19 vaccine is recommended for children 5+ Agree that the COVID-19 vaccine is recommended for pregnant people Agree that the COVID-19 vaccine has long-term health effects Agree that masks reduce COVID spread
Perceived Risk of Harm (PRH)	Score range: 0–4; High Score = High perceived risk of harm if not vaccinated Cronbach’s α: 0.89 Factor Analysis: Component 1 Eigenvalue = 3.03, Component 2 Eigenvalue = 0.68, first factor accounts for 76% of the variance.
4-Items (1 point each): Know of someone getting sick from COVID (not vaxxed) Agree that risk of harm of the COVID-19 vaccine not enough to warrant getting vaccinated Agree that family’s risk of harm from COVID not enough to warrant getting vaccinated Disagree that risk of harm from getting vaccinated higher than risk from getting COVID
Motivation not to get infected by COVID (MOT)	Score range: 0–4; High Score = High motivation not for self, family, and friends to get infected by COVID Cronbach’s α: 0.83 Factor Analysis: Component 1 Eigenvalue = 5.34, Component 2 Eigenvalue = 0.69, first factor accounts for 67% of the variance.
4-Items (1 point each): Passionate about not becoming infected by COVID Personal goal not to be ever infected by COVID Personal goal not to have family become infected by COVID Personal goal not to spread COVID to friends and family
Personal ability to get vaccinated (PAV)	Score range: 0–6; High Score = High self-efficacy to get vaccinated Cronbach’s α: 0.76 Factor Analysis: Component 1 Eigenvalue = 2.78, Component 2 Eigenvalue = 0.92, first factor accounts for 46% of the variance.
6-Items (1 point each): Know where to get the COVID-19 vaccine Able to use phone/internet to find the COVID-19 vaccine site Able to travel to place to get the COVID-19 vaccine Able to take time to get the COVID-19 vaccine Places where to get the COVID-19 vaccine open during available times the COVID-19 vaccine sites are easy to reach
Social Network Vaccinated (SNW)	Score range: 0–12; High Score = Most or all of respondents’ social network are vaccinated Cronbach’s α: 0.70 Factor Analysis: Component 1 Eigenvalue = 1.90, Component 2 Eigenvalue = 0.78, first factor accounts for 63% of the variance.
3-Items (4 points each): All of immediate family vaccinated All of closest friends vaccinated All of community members vaccinated

Analysis

Both bivariate and multivariate analyses were conducted. Bivariate analysis involved comparing mean scores of each of the independent index variables and demographic characteristics between those who had received at least one COVID-19 vaccination and those who had not. Independent sample’s t-test was used to assess significant differences in mean scores for each of these variables. For multivariate analysis, we used a binomial logistic regression with all the independent variables testing for its direct effects on the dependent variable and controlling for demographic characteristics, including sex, age, education, race, annual household income, marital status, and political ideology. Analyses were conducted using SPSS v.28.

Results

Sample characteristics

A total of 948 respondents met the conditions for participation in the survey, i.e. they completed the consent, were 18 years or older and resided in Alaska. More than half of respondents were female (57%), married (56%), and had an annual household income of $80,000 or more (54%). Average age of respondents was 50.4 years. Almost 70% were white, 12% were Alaska Native/American Indian, and the rest were of mixed or other race/ethnicity. We aggregated the various racial/ethnic minority groups and those with mixed race/ethnicity into one category for our main regression analysis because each had fewer than 100 observations, which could lead to unstable estimates when comparing between groups. In terms of education, approximately half of the respondents did not have a college degree. Regarding political ideology, 35% identified as liberal, 37% moderate, and 29% conservative. The respondents were from eight regions in Alaska, with 38% from Anchorage, 17% from the Interior, 13% from the Matanuska-Susitna region, 12% from the Gulf Coast, 10% from the Southeast, 6% from the Southwest, and 5% from the Northern region. Note that in our main regression analysis, we combined Southeast, Southwest, and Northern regions into the “Other” category because each had fewer than 100 observations. Regarding COVID-19 vaccine status, approximately 80% of the respondents reported receiving at least one dose of COVID-19 vaccine. Overall, around 5% or less had missing cases for each of these demographic characteristics. Table 2 provides details about the sample characteristics juxtaposed with relevant population data on these characteristics for the state of Alaska. Though our sample was geographically well balanced, it is not fully representative of Alaskans across other demographic characteristics. Compared to statewide data, our respondents are about 10% points more likely to be female, 20% points more likely to have a college education or better, are 11% points more likely to identify as ideologically liberal, and have a slightly lower proportion of those identifying as Alaska Native or other racial/ethnic minority group than the general population. Note that we could not directly compare the age of our sample with the Alaska population because our study was limited to those who are 18 years old and above.

Table 2. Sample characteristics.

Demographic Characteristics			Alaska Population ^[Citation1]
Sex	Frequency	Percent or Mean (S.D.)	Percent
Male	388	42.6%	52.4%
Female	523	57.4%	47.6%
Education
< College degree	479	50.7%	69.4%
≥ College degree	465	49.3%	30.60%
Race/Ethnicity
Alaska Native/American Indian	115	12.1%	15.20%
Mixed and other racial/ethnic minorities ^[2]	159	16.8%	20.30%
Whites	639	67.4%	64.50%
Age
Mean	948	50.41 (16.42) ^[Citation3]	See note ^[4]
Annual Household Income
< $80,000	411	45.8%	See note ^[5]
≥ $80,000	486	54.2%
Marital Status
Not Married	531	44.0%	49.00%
Married	417	56.0%	51.00%
Political Ideology ^[6]
Liberal	318	34.5%	23.00%
Moderate	340	36.8%	37.00%
Conservative	265	28.7%	34.00%
Alaska Region
Anchorage	357	37.9%	39.70%
Gulf Coast	110	11.7%	11.10%
Interior	159	16.9%	14.00%
Matanuska-Susitna	120	12.7%	14.60%
Other (Northern, Southeast, Southwest) ^[7]	197	20.8%	20.60%
Ever Received COVID-19 Vaccine
Yes	754	80.2%	66.3% ^[8]
No	186	19.8%	33.70%

Note: ¹Demographic data on sex, education, race, age, annual household income, and marital status comes from the U.S. Census 2020 data, which was accessed on June 20, 2023 at https://www.census.gov/library/stories/state-by-state/alaska-population-change-between-census-decade.html. Alaska population is 733,395.

²Included in this category were Blacks/African Americans, Asians, Native Hawaiians or Pacific Islanders, and those who identified as having multiple races/ethnicities. Each of these groups had less than 100 cases, thus they were aggregated.

³Study sample included only those 18 years old and above.

⁴Median age of Alaskans is 34.6 years, which includes children.

⁵Median annual household income of Alaskans is $80,287.

⁶Data on political ideology is not available in the U.S. Census. The statistics provided here is based on the national survey conducted by Pew Research conducted in 2014 (see: https://www.pewresearch.org/religion/religious-landscape-study/state/alaska/political-ideology/).

⁷This category included the Northern, Southeast, and Southwest Alaska regions. They were aggregated due to having less than 100 cases each.

⁸State data on COVID-19 vaccination comes from Alaska Department of Health, accessed on May 18, 2022 at https://content.govdelivery.com/accounts/AKDHSS/bulletins/3186532%20COVID–19%20DATA%20SUMMARY%20%E2%80%93%20May%2018,%202022.

Factors associated with ever receiving COVID-19 vaccine

Table 3 shows a series of unadjusted bivariate analyses comparing those who have ever received a COVID-19 vaccination to those who have not. A significantly greater proportion of those who identified as male, had at least a college degree, were white, had an annual household income of more than $80,000, were married, and had a liberal political ideology have ever received COVID-19 vaccine compared to their counterparts. Those who had ever received a COVID-19 vaccine had a higher mean age than those who had not. The Alaska regions with significantly higher proportions of having ever received COVID-19 vaccine were Anchorage and the combination of the Southwest, Southeast, and Northern regions, while the lowest was the Matanuska-Susitna region. With regard to socio-cognitive factors, we investigated, those who had ever received a COVID-19 vaccine had a significantly lower mean Belief in COVID Misinformation but higher mean COVID Knowledge, Perceived Risk of Harm, Motivation for self, family, and friends not to get infected by COVID, Perceived Ability to Vaccinate, and Social Network, compared to those who had not received a shot.

Table 3. Comparisons of those who have ever received covid-19 vaccine versus those who have not.

	Unvaccinated	Vaccinated	p-value [1]
Sample Characteristics	Percent or Mean (S.D.)	Percent or Mean (S.D.)
Belief in COVID-19 Misinformation Score, n = 926	4.04 (1.87)	1.53 (1.73)	<0.001
COVID Knowledge Score, n = 940	3.39 (1.89)	5.63 (1.53)	<0.001
Perceived Risk of Harm, n = 914	0.65 (0.95)	3.36 (1.13)	<0.001
Motivation not to get infected by COVID-19, n = 916	1.67 (1.21)	2.61 (1.17)	<0.001
Personal ability to get vaccinated, n = 933	5.37 (1.34)	5.79 (0.74)	<0.001
Social Network Vaccinated, n = 940	4.79 (2.27)	9.10 (1.62)	<0.001
Sex
Male, n = 336	17.1%38.8%	82.943.6%	0.26
Female, n = 518	20.1%	79.9%
Age
Age (continuous), n = 940	44.03 (15.40)	52.03 (16.26)	<0.001
Education
< College degree, n = 475	29.174.2%	70.944.9%	<0.001
≥ College degree, n = 461	10.4%	89.6%
Race/Ethnicity
Alaska Native/American Indian, n = 115	25.216.5%	74.811.8%	<0.01
Mixed and other racial/ethnic minorities, n = 159	26.423.9%	73.616.0%
Whites, n = 623	16.6%	83.4%
Income
< $80,000 income/year, n = 411	25.359.1%	74.742.9%	<0.001
≥ $80,000 income/year, n = 481	15.0%	85.0%
Marital Status
Not married, n = 415	25.356.5%	74.741.1%	<0.001
Married, n = 525	15.443.5%	84.658.9%
Political Ideology			<0.001
Liberal, n = 318	4.4%	95.6%
Moderate, n = 337	18.7%	81.3%
Conservative, n = 261	38.3%	61.7%
Alaska Region			<0.001
Anchorage, n = 352	17.0%	83.0%
Gulf Coast, n = 109	26.6%	73.4%
Interior, n = 157	20.4%	79.6%
Matanuska-Susitna, n = 120	32.5%	67.5%
Other (Northern Southeast, and Southwest), n = 197	13.2%	86.8%

ⁱNote that mean values were calculated for the continuous variables and tested for significant differences between groups using student’s t-test, while percentages were calculated for the categorical variables and tested for significant differences between groups using chi-square test.

In our multivariate logistic regression, among the six independent variables, only Perceived Risk of Harm and Social Network were associated with having ever received a COVID-19 vaccine (see Table 4). For each point increase in Perceived Risk of Harm, there was more than three times the odds of having ever been vaccinated (OR = 3.42, p < 0.001); and for every point increase in SNW score, there was more than two times the odds of having ever been vaccinated (OR = 2.15, p < 0.001). Regarding demographic variables, sex and education were associated with having ever been vaccinated. Males (OR = 4.55, p < 0.001) and those with a college degree (OR = 2.80, p < 0.05) had greater odds of having ever been vaccinated compared to their counterparts. The logistic regression model had a good fit with a non-significant Hosmer-Lemeshow Chi-Square Test finding (X [2] = 2.11, 8 df, p > 0.05). No multicollinearity was found between the independent variables. Note that the total sample used for the logistic regression was truncated to 777 (about 18% missing from the original N = 948) due to small non-response to various survey questions as shown in Table 3.

Table 4. Logistic regression analysis identifying factors associated with ever receiving covid-19 vaccine, n = 777¹.

Variables	Odds Ratio (OR): Vaccinated	95% Confidence Interval
Independent Variables
COVID-19 Misinformation Score	0.92	0.73–1.17
COVID-19Knowledge Score	1.17	0.92–1.46
Perceived Risk of Harm	3.42***	2.39–4.90
Motivation not to get infected by COVID-19	0.94	0.67–1.32
Personal ability to get vaccinated	1.32	0.90–1.94
Social Network Vaccinated	2.15***	1.74–2.66
Demographic Variables
Sex
Male (vs. Female)	4.55***	1.95–10.63
Age
Age (continuous)	1.02	0.99–1.05
Race/Ethnicity
Alaska Native/American Indian (vs. Whites)	1.56	0.49–5.01
Mixed and other racial/ethnic minorities (vs. Whites)	0.74	0.29–1.91
Education
≥ College degree (vs. <College degree)	2.80*	1.18–6.63
Annual Household Income
≥ $80,000 income/year (vs. < $80,000 income/year)	0.94	0.39–2.27
Marital Status
Married (vs. Not Married)	1.98	0.88–4.49
Political Ideology
Liberal (vs. Conservative)	0.94	0.26–3.34
Moderate (vs. Conservative)	1.40	0.58–3.36
Alaska Region
Gulf Coast (vs. Anchorage)	0.50	0.17–1.48
Interior (vs. Anchorage)	0.94	0.33–2.69
Matanuska-Susitna (vs. Anchorage)	0.79	0.23–2.70
Northern (vs. Anchorage)	1.94	0.61–6.10
Constant
?0 = −10.23	0.00***
Hosmer-Lemeshow Test: X [2] = 2.11, 8 df, p > 0.05). Nagelkerke R^2 = 0.80; Cox & Snell R^2 = 0.48

Note. ¹Sample was truncated to n = 777 because of small non-response to various survey questions (see Table 3).

*p < 0.05, **p < 0.01, ***p < 0.001.

Discussion

Based on the findings from this study, decisions regarding initial adoption of the COVID-19 vaccine appear to be associated with social pressure and perceived risk of harm from the virus. When individuals perceive many in their social network were vaccinated against COVID-19, they were more likely to be vaccinated themselves. Moreover, individuals were more likely to be vaccinated when they feel that they or their family members were at greater risk for harm if they themselves are not vaccinated. These associations held even after controlling for demographic factors and other indices.

COVID-19 vaccination and COVID-19 in general have been subject to misinformation and disinformation both in the U.S. and internationally [25,28,29]. They have also been heavily politicised [27]. Previous national studies have even shown that COVID-19 vaccine hesitancy and aversion to COVID-19 preventative behaviours and practices (e.g. masking) were prevalent among viewers of conservative news sources [26] and/or those who identify as Republican as their political party [44]. However, we find that the potential effects of these social and informational influences do not appear to be associated with the initial decision to receive vaccines among Alaskan adults sampled in our survey. We intend to explore the effects of these influences on subsequent decisions to get boosted in future studies.

Demographic characteristics that had a significant independent association with ever being vaccinated include sex and education. We find that men are more likely than women to have ever been vaccinated against COVID-19. Surprisingly, we also do not find an association between age and initial vaccination. We do not have empirical evidence to explain these findings on gender and age, though we suspect that this may be driven in part by employment-based vaccine mandates [45,46] for which we cannot control since our survey lacked questions on employment or type of occupation. Nonetheless, our finding on gender is consistent with the recent study conducted by Zintel et al. [47], which found that women had lower COVID-19 vaccine intentions than men based on their meta-analysis of 46 studies with a cumulative total sample of N = 141,550. We also find that those who had at least a college degree were more likely to have ever been vaccinated than their counterparts. This finding is also consistent with other studies conducted in the U.S [48,49]. However, the reason for this association has not been thoroughly investigated in the literature. Finally, we expected that racial/ethnic minorities would be more likely to be vaccine hesitant (compared to whites) given that previous studies [49,50] have shown this and that this marginalised group have institutional and interpersonal distrust driven by negative healthcare experiences and historical trauma due to past biomedical research abuses [51]. While our unadjusted bivariate analysis showed significantly lower COVID-19 vaccination among racial/ethnic minorities than whites, this association did not hold in our logistic regression analysis. Future studies should further investigate this discrepancy, particularly identifying potential mediating or moderating factors to the relationship of race/ethnicity and vaccine hesitancy.

Limitations

The study has several limitations. First, we examine factors for receipt of any vaccine for COVID-19. We do not examine subsequent decisions to receive boosters, where additional variation in the outcome may reveal different trends. Second, we used a cross-sectional study design and do not have repeated measures for our respondent sample over time. Third, the survey was collected via self-report; thus, some of its questions, particularly related to vaccine practices and beliefs, may be subject to social desirability bias. Fourth, we do not explore the timing of vaccine receipt (e.g. early adoption vs. later adoption). We cannot assess the concomitant influence of societal factors including waves of mis- and dis-information about vaccines that have changed over time since the start of the pandemic vis-à-vis when respondents received their initial shots. Fifth, no information is available regarding underlying reasons for receiving a vaccine (e.g. self-selection vs. compliance with any institutional mandates). These factors could help to better address the question of whether any vaccine receipt was due to socio-cognitive or motivational pathways, or simply the result of employer policy among specific sub-groups. Another limitation in our study is that we grouped racial/ethnic minority groups (except Alaska Natives/American Indians) and those with mixed race/ethnicity into one category given the small number of each of these groups in our sample. We understand that each racial/ethnic minority group is unique in terms of their culture, beliefs, and practices. Given our sample, we were underpowered to investigate whether specific racial/ethnic minority groups were more likely to receive COVID-19 vaccine than others. Similarly, we grouped three regions of Alaska (Southeast, Southwest, and Northern) into one category (“Other”) also because of the small sample size for each region. Like race/ethnicity, each of these regions in Alaska has a unique cultural, population, and geographic make-up, which can contribute to vaccine beliefs and practices. Unfortunately, we were not able to fully investigate these differences due to this limitation. Finally, the findings of the survey are not necessarily generalisable to the entire Alaska population. Data were collected using convenience sampling, and those who participated in the survey had mobile phones and internet access, which are not uniformly available throughout the state. We also do not have information about those who opted out of the survey.

Conclusions

Over 2 years into the COVID-19 pandemic, findings from our sample of Alaskans suggest that, after controlling for other factors, ever being vaccinated against COVID-19 is least likely among women compared to men and least likely among those with less than a college degree compared to those with at least a college degree. We also find that ever-receiving COVID-19 vaccine is associated with the perception that a majority of one’s close social network has been vaccinated, and that not obtaining a COVID-19 vaccination poses greater risk of harm to one’s self and family than getting vaccinated. While belief in COVID-19 misinformation (including vaccine misinformation) did not have an independent association with ever receiving a vaccine, we speculate that misinformation may nonetheless be associated with perceptions of risk of harm, especially as it relates to the decision to receive subsequent vaccines and boosters. Future studies should further investigate this relationship, especially among unvaccinated subgroups. In terms of practical implications, study findings suggest that public health practitioners and agencies should consider educational and promotional efforts, particularly targeted among females and those with less than a college degree, that can help shape perceptions among these target groups utilising both social pressure, by emphasising that peer groups have taken similar precautions like vaccination, and leveraging risk of harm from not vaccinating – specifically, that not receiving a vaccine can put ones’ self and family at increased risk of harm. Though our findings do not find an association between initial vaccination and how we defined misinformation in our survey, it is nonetheless critical for interventions to directly address COVID-19 related myths that may minimise the seriousness of risk from COVID-19 infection, minimise the effectiveness of COVID-19 vaccines, and/or heighten fear of potential harm and side effects of COVID-19 vaccination.

Acknowledgments

The research team thanks the Alaska Survey Research for assistance in the data collection. Additionally, the team thanks Dr. Joy Mapaye (UAA Journalism and Public Communications) and Ms. Rebecca Van Wyck (UAA Institute for Social and Economic Research) for providing research support and consultation.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data associated with this study may be shared with investigator and institutional approval.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

This article was originally published with errors, which have now been corrected in the online version. Please see Correction (http://dx.doi.org/10.1080/03650340.2023.2205681).

Additional information

Funding

Garcia, Meyer, and Edwards were funded by the Alaska Department of Health. Cameron was funded by the National Heart Lung and Blood Institute (K12HL138037) and the Yale Scholars in Implementation Science.