Preparing for the Next Pandemic: Exploring Predictors of Vaccine Uptake in American Cities

Abstract

Purpose

COVID-19 devastated communities globally, with the United States leading in fatalities despite extensive control efforts. In 2021, COVID-19 vaccines emerged as the most promising solution, with an efficacy rate exceeding 95% after full vaccination. However, herd immunity remains an elusive target with only 70% of the U.S. population fully vaccinated. This study investigates whether prior vaccination and medical history significantly predict COVID-19 vaccine acceptance and uptake.

Design

Using data collected from the 66^th largest U.S. city after the widespread deployment of COVID-19 vaccines and boosters, we employed logistic regression to test whether an individual’s history of vaccination and prior medical engagement positively influence COVID-19 vaccine-related decisions.

Setting

Cross-sectional U.S. survey from July-October 2022 (N = 397) and November 2023-February 2024 (N = 429).

Subjects

Adult Residents of the City of Newark, New Jersey (Aged 18+).

Measures

Newark Community Health Survey (2022, 2023).

Results

Individuals with a history of preventive care, such as attending at least one non-emergency medical visit in the past year, receiving either the pneumococcal or influenza vaccine within the past year, strongly predicted COVID-19 vaccine acceptance. These predictors also demonstrated a strong positive association with the completion of the primary vaccine series and a higher overall total vaccine uptake.

Conclusion

Future vaccine campaigns should prioritize “low engagement” populations who lack both recent routine medical visits and a history of previous vaccinations.

Keywords

COVID-19 vaccine hesitancy vaccine acceptability health behavior

Introduction

Celebrated as one of the most significant scientific breakthroughs of the 20^th century, vaccines have a remarkable track record of preventing infectious diseases, reducing mortality and morbidity, and eradicating devastating illnesses (eg, smallpox, polio, measles).^1-6 Continuing this legacy, COVID-19 vaccines demonstrated impressive efficacy rates (exceeding 95% after full vaccination), and are poised to be remembered as another historic medical breakthrough.^7,8 However, vaccine hesitancy, particularly when it progresses to refusal, significantly hampers public health efforts towards herd immunity.^9,10

Defined as “the delay in acceptance or refusal of vaccination despite the availability of vaccination services,”^{9, p.56} vaccine hesitancy poses a global threat.¹¹ In the case of COVID-19, a significant portion of the U.S. population refused vaccination due to safety concerns.^12,13 This resulted in a significant shortfall in coverage, jeopardizing the goal of herd immunity, which requires an estimated 70% to 80% vaccination rate.^14,15 While extensive research has explored vaccine hesitancy, a critical gap remains in identifying reliable predictors of COVID-19 vaccine uptake. Additionally, much of this literature predates the introduction of COVID-19 boosters. Therefore, we examined the association between prior vaccine uptake and non-emergency medical visits as predictors of COVID-19 vaccine acceptance and uptake, using data from New Jersey, one of the most impacted U.S. states (ranking 10^th in COVID-19 confirmed cases and 11^th in deaths).¹⁶

Literature Review

Decades before the pandemic, Chapman and Coups (1999) published one of the earliest studies on vaccine acceptability, demonstrating that past vaccination behavior shapes individuals’ risk-benefit analysis of future vaccines.¹⁷ In the study, employees who received the influenza vaccine in the previous year were 85% more likely to receive the vaccine in the subsequent year, compared to only 17% among employees without a prior influenza vaccination.¹⁶ Since then, scholars have investigated additional factors influencing vaccine acceptance and uptake.^3,10,18-20 However, research conducted outside of a pandemic limits our understanding of how a public health crisis influences vaccine acceptance, particularly in subgroups not previously considered in the existing literature.²¹ Furthermore, most COVID-specific studies predate the availability of COVID-19 vaccines and boosters, limiting our ability to assess if intentions translated into action.

To this end, this study focuses on Newark, New Jersey, a city with significant social and economic vulnerability, to test whether medical and vaccine history strongly predicted SARS-CoV-2 vaccine uptake. The City of Newark has a higher proportion of Black and Hispanic residents, foreign-born, and uninsured individuals than state and national averages; subgroups that bore the brunt of the pandemic.²² Beyond these disparities, the City of Newark accounted for nearly 40% of COVID-19-related deaths in Essex County and 4% in the state.^16,23 Given the complex interplay of influencing factors (eg, race, socioeconomic status, health literacy), Newark, New Jersey offered a unique setting to test the study’s hypotheses.

Medical History

According to the Health Belief Model (HBM), the strongest predictors of vaccine acceptance are an individual’s perception of their susceptibility to a disease, the severity of that disease, and a physician’s recommendation.^17,19,20,24 Building on this argument, the 5C Model provides a more nuanced framework with five core drivers: confidence, constraints, complacency, calculation, and collective responsibility.^3,25 High confidence and a strong sense of collective responsibility generally predict high vaccine acceptance, while structural constraints and complacency often lead to hesitancy. Therefore, we expect individuals with at least one non-emergency medical visit in the last year to exhibit higher COVID-19 vaccine acceptance and uptake. Conversely, we expect individuals who could not afford a medical visit to exhibit lower vaccine acceptance and uptake, as financial barriers reduce exposure to a physician’s guidance.

• Hypothesis 1: Individuals who had at least one medical visit in the past year will exhibit higher COVID-19 vaccine acceptance and uptake rates.

• Hypothesis 2: Individuals who could not afford a medical visit in the past year will exhibit lower COVID-19 vaccine acceptance and uptake rates.

Vaccine History

Nearly 10% of the U.S. population exhibits firm anti-vaccine views, with an additional 40% categorized as vaccine hesitant (having concerns but remaining open to some vaccines).¹³ Indeed, despite ranking 1^st in COVID-19-related deaths, the U.S. ranked 79^th in the percentage of its total population who are fully vaccinated.^26,27 Given that past vaccine behavior predicts future acceptance,^10,28 and recent research demonstrates that prior influenza and pneumococcal vaccinations are associated with a lower risk of COVID-19 infection, hospitalization, and death,^29-31 we hypothesize that individuals who received influenza or pneumococcal vaccines will exhibit significantly higher COVID-19 vaccine acceptance and uptake.

• Hypothesis 3: Individuals who received the influenza vaccine in the past year will exhibit higher COVID-19 vaccine acceptance and uptake rates.

• Hypothesis 4: Individuals who received the pneumococcal vaccine will exhibit higher COVID-19 vaccine acceptance and uptake rates.

Data and Methods

Data Collection

Data for this study were gathered using two waves of the Newark Community Health Survey administered to adult residents in Newark, New Jersey. The survey is part of a public health initiative led by the City of Newark and funded by the U.S. Department of Health and Human Services (HHS). The study protocol was reviewed and approved by the Rutgers University Institutional Review Board (Study ID: Pro2020001641). Prior to participation, respondents were presented with an online informed consent form detailing the study’s purpose, procedures, potential risks and benefits, and participant rights, enabling them to make an informed decision. Adhering to IRB guidelines, we ensured participant anonymity and obtained informed consent from all participants. The datasets were stored in password-protected systems with restricted access, and data will be retained indefinitely in accordance with institutional data management policies.

The survey used an address-based sample (ABS) provided by Marketing Systems Group (MSG), supplemented with an online panel to reach Newark households. MSG’s ABS sampling frame was developed from the U.S. Postal Service Computerized Delivery Sequence File (CDSF), which provides near-complete coverage of residential addresses in Newark, New Jersey. A mail-to-web data collection mode was implemented, whereby invitation letters were mailed to randomly selected households. The first wave was conducted between July and August 2022, and the second wave between January and February 2024 (with fieldwork timing influenced by contracting delays and year-end holidays). Each mailed letter included a QR code and a short URL directing recipients to a self-administered online questionnaire hosted on the Qualtrics platform. Due to low response rates to the mailed invitations (approximately 1 percent), a nonprobability online market research panel was deployed to supplement the ABS sample. Panel respondents were recruited by MSG through the Cint platform in October 2022 for Wave 1 and from November to December 2023 for Wave 2.

Across both waves, the survey yielded 826 completed responses: 397 surveys in Wave 1 (161 ABS and 236 online panel) collected between July and October 2022, and 429 surveys in Wave 2 (83 ABS and 346 online panel) collected between November 2023 and February 2024. The combined dataset integrates both probability-based ABS respondents and nonprobability panel respondents to enhance overall sample size and analytic power.

Measurement

Outcome Variables

We used a binary approach to assess vaccination intention, ensuring a clear differentiation between acceptance and non-acceptance. Participants were asked, “Which of the following best describes your intent to take the recommended COVID vaccinations?” Responses were coded as 0 (no plan to vaccinate) and 1 (vaccinated or intending to vaccinate).

We measured vaccine uptake using primary series completion and total doses received. Primary series completion refers to completing the initial two doses, whereas total doses received captures the cumulative uptake, including boosters. We used two questions to create these variables. First, “Have you had a COVID-19 vaccination?” Responses were coded 0 for “No” and 1 for “Yes.” Second, “How many COVID-19 vaccinations have you received?” Responses ranged from 1 to 4 or more. Combining these questions, we created the variables: total doses received, ranging from 0 (unvaccinated) to 4 or more doses; and primary series completion, with individuals reporting two or more doses coded as 1 and those reporting one dose or less coded as 0.

Predictor Variables

We operationalized vaccine history as receipt of influenza and pneumococcal vaccinations (0 for “No” and 1 for “Yes”). Similarly, medical history was assessed using healthcare affordability and non-emergency doctor visits. Non-emergency visits assessed general engagement with healthcare services in the past year (0 for none, 1 for any). Healthcare affordability was operationalized as a financial barrier to healthcare access. (1 for “Yes,” 0 for “No”). We also included control variables such as age, gender, race, employment status, education, and income (see Appendix for question wording and response categories).

Analysis

Since the first two dependent variables, vaccination intention and primary series completion, are dichotomous, this study estimates these models using logit regression, which is appropriate for binary outcomes. The third dependent variable, total doses received, is measured as an ordered categorical indicator of the number of vaccine doses obtained. Therefore, an ordered logit model is employed to account for the ordinal structure of this outcome. The proportional odds (parallel lines) assumption is assessed using the Brant test. The global test is statistically significant (χ² = 72.78, P < .001 for the Vaccination History model; χ² = 63.95, P < .001 for the Medical History model), indicating some departure from the proportional odds assumption. However, violations are confined to a limited subset of covariates, including Age, White race, and survey wave, while the remaining predictors satisfy the assumption.

To address potential violations of the proportional odds assumption, generalized ordered logit models were also estimated, allowing non-parallel effects for variables exhibiting significant constraint violations. However, the fully generalized specification yielded unstable predicted probabilities, as there were 68 in-sample cases producing predicted probabilities below zero. This instability is attributable to the sparse distribution of the outcome variable, whereby only 8.6% of respondents received a single dose of vaccination, the lowest ordered category, resulting in insufficient cell counts to support fully unconstrained parameter estimation.

Given the localized nature of the violations and substantive similarity of estimates, the ordered logit model is retained for parsimony. Findings derived from this model should nonetheless be interpreted with appropriate caution, acknowledging the possibility that the proportional odds assumption may not hold uniformly across all outcome thresholds.

All models include survey-wave and data-source fixed effects to account for systematic differences across the two survey waves and between ABS and panel respondents, including potential variations in sample composition, timing, or contextual factors. Collinearity and overfitting risks were assessed using Variance Inflation Factors (VIFs). VIFs above four warrant investigation; those exceeding ten indicate serious multicollinearity. Our analysis yielded mean VIFs of 1.84 and 1.96, suggesting no significant multicollinearity concerns.

Results

As presented in Table 1, 71.1% of Newark’s adult respondents reported that they had either completed the primary vaccine series or had intentions to do so. Among the vaccinated group, 69.0% received two doses or more while 8.6% received only one dose. Notably, several subgroups fell short of the 70% herd immunity coverage threshold due to under-vaccination or lack of vaccination, including males (66.5%), individuals aged 18-44 (69.6%), and African Americans (62.3%). The reasons reported for hesitancy or refusal, in order of frequency, were: a general distrust of vaccines, concerns about side effects, the belief that COVID-19 is exaggerated or that the vaccine is too new, a distrust of the U.S. government and healthcare system, difficulty in accessing the COVID-19 vaccine, concerns about cost, and religious reasons.

Table 1.

City of Newark Demographic and Vaccination Intentions

Respondent demographic	Sample		Intention to take recommended COVID-19 vaccination doses (Est. Pop.)		Doses of COVID-19 vaccination received
Respondent demographic	Sample	Percent	Do not plan to receive all doses	Already/plan to receive all doses	Not vaccinated	One dose	Two doses	Three doses	Four doses or more
Gender
Male	504	61%	33.5%	66.5%	25.4%	7.6%	31.4%	27.8%	7.8%
Female	322	39%	21.7%	78.3%	17.7%	10.2%	32.3%	28.6%	11.2%
Age
18-44	543	66%	30.4%	69.6%	27.3%	8.8%	34.4%	25.8%	3.7%
45-64	222	27%	27.0%	73.0%	14.4%	9.5%	28.4%	33.3%	14.4%
65 or older	61	7%	21.7%	78.3%	8.3%	3.3%	20.0%	30.0%	38.3%
Race & ethnicity
White	146	18%	13.7%	86.3%	14.4%	8.2%	30.1%	40.4%	6.8%
African American	392	47%	37.7%	62.3%	29.7%	9.7%	30.2%	23.0%	7.4%
Hispanic/Latino	235	28%	26.4%	73.6%	17.9%	8.1%	33.2%	28.9%	11.9%
Asian or other	53	6%	17.0%	83.0%	11.3%	3.8%	41.5%	28.3%	15.1%
Total	826	100%	28.9%	71.1%	22.4%	8.6%	31.8%	28.1%	9.1%

The Effects of Vaccination History

We found a positive correlation between vaccine history and COVID-19 vaccine uptake (Table 2). Individuals who received the influenza vaccine within the past 12 months were more than four times as likely to report intentions to receive the COVID-19 vaccine (OR = 4.14, P < 0.01) and over twice as likely to have completed the primary series (OR = 2.68, P < 0.01). In addition, prior flu vaccination was associated with a 164% increase in overall vaccine uptake (IRR = 2.64, P < 0.01). Similarly, recipients of the pneumococcal vaccine were nearly twice as likely to report intentions to receive all recommended COVID-19 doses (OR = 1.97, P < 0.01) and just over twice as likely to have completed the primary series (OR = 2.04, P < 0.01). Furthermore, pneumococcal vaccination was associated with a 76% increase in the rate of total COVID-19 vaccine uptake (IRR = 1.76, P < 0.01).

Table 2.

Vaccination History Regression Results

Variables	Vaccination intention	Primary series completion	Total doses received
Influenza vaccine (12 months)	4.14^*** [2.78, 6.18]	2.68^*** [1.85, 3.88]	2.64^*** [1.98, 3.51]
Pneumococcal vaccine	1.97^*** [1.32, 2.96]	2.04^*** [1.38, 3.02]	1.76^*** [1.33, 2.32]
Employment status	1.18 [0.81, 1.71]	1.66^*** [1.17, 2.37]	1.27 [0.95, 1.70]
Age	1.23 [0.89, 1.70]	1.72^*** [1.26, 2.35]	2.03^*** [1.58, 2.62]
Gender	1.57^** [1.10, 2.24]	1.07 [0.76, 1.50]	1.15 [0.88, 1.51]
Black	0.36^** [0.15, 0.82]	0.30^*** [0.12, 0.74]	0.46^*** [0.26, 0.83]
Hispanic	0.61 [0.26, 1.42]	0.55 [0.22, 1.39]	0.85 [0.48, 1.52]
White	1.22 [0.47, 3.16]	0.47 [0.18, 1.26]	0.75 [0.40, 1.41]
Education	0.95 [0.79, 1.15]	1.21^* [1.00, 1.47]	1.17^** [1.01, 1.37]
Income	1.05 [0.98, 1.13]	1.05 [0.98, 1.13]	1.06^** [1.00, 1.12]
Source = survey 2022	1.40 [0.72, 2.71]	1.97^** [1.02, 3.82]	1.91^** [1.11, 3.27]
Method = ABS	0.85 [0.54, 1.35]	1.33 [0.86, 2.06]	2.03^*** [1.44, 2.86]
Observations	823	823	823
Pseudo R²	0.16	0.15	0.11

95% Confidence Interval in square brackets. ***P < 0.01, **P < 0.05, *P < 0.1.

The Effects of Medical History

We found a positive but mixed correlation between medical history and COVID-19 vaccine uptake (Table 3). Individuals with at least one non-emergency medical visit in the past 12 months were more than twice as likely to report intentions to receive the COVID-19 vaccine (OR = 2.19, P < 0.01) and 66% more likely to have completed the primary series (OR = 1.66, P < 0.05). Furthermore, recent medical engagement was associated with a 55% increase in overall vaccine uptake (IRR = 1.55, P < 0.05). However, the inability to afford a medical visit showed no significant association with vaccine outcomes, despite a slight, non-significant tendency toward lower primary series completion and cumulative uptake.

Table 3.

Medical History Regression Results

Variables	Vaccination intention	Primary series completion	Total doses received
Attended at least one medical visit	2.19^*** [1.47, 3.28]	1.66^** [1.10, 2.50]	1.55^** [1.07, 2.25]
Unable to afford a medical visit	1.08 [0.75, 1.54]	0.86 [0.61, 1.22]	0.82 [0.62, 1.10]
Employment status	0.98 [0.69, 1.40]	1.45^** [1.03, 2.04]	1.13 [0.85, 1.51]
Age	1.33^* [0.98, 1.82]	1.85^*** [1.36, 2.52]	2.16^*** [1.68, 2.79]
Gender	1.79^*** [1.27, 2.53]	1.23 [0.89, 1.71]	1.31^** [1.01, 1.70]
Black	0.28^*** [0.12, 0.64]	0.24^*** [0.10, 0.54]	0.38^*** [0.22, 0.65]
Hispanic	0.54 [0.23, 1.24]	0.50 [0.21, 1.17]	0.80 [0.47, 1.36]
White	1.00 [0.40, 2.51]	0.43^* [0.17, 1.05]	0.72 [0.40, 1.28]
Education	1.00 [0.83, 1.19]	1.22^** [1.01, 1.47]	1.18^** [1.01, 1.37]
Income	1.08^** [1.01, 1.15]	1.07^** [1.00, 1.14]	1.08^*** [1.02, 1.15]
Source = survey 2022	1.51 [0.79, 2.89]	1.91^** [1.00, 3.65]	1.91^** [1.10, 3.31]
Method = ABS	0.82 [0.53, 1.28]	1.21 [0.78, 1.87]	1.72^*** [1.23, 2.40]
Observations	824	825	825
Pseudo R²	0.09	0.10	0.07

95% Confidence Interval in square brackets. ***P < 0.01, **P < 0.05, *P < 0.1.

Discussion

This study investigated the influence of medical and vaccination history on COVID-19 vaccine acceptance and uptake to inform public health strategies. Focusing on New Jersey, one of the largest cities in the United States,²² we observed a significant relationship between medical and vaccination history and COVID-19 vaccine uptake. Supporting H3, recent influenza vaccination was positively associated with vaccination intent, primary series completion, and overall uptake. Similarly, supporting H4, pneumococcal vaccination was positively associated with higher likelihoods of primary series completion, intention to receive all recommended doses, and total vaccine uptake. We additionally found strong support for Hypothesis 1. Having at least one non-emergency medical visit within the previous 12 months was positively associated with both COVID-19 vaccine intent and primary series completion. However, we found no support for Hypothesis 2. The inability to afford healthcare services did not correlate with lower vaccination intentions or uptake, suggesting that the U.S. government may have successfully addressed financial cost as a barrier to accessing COVID-19 vaccines.

These findings offer three critical insights that advance existing literature. First, our findings show that recent or COVID-19-adjacent vaccinations alone do not explain COVID-19 vaccine uptake. We found that adherence to the long-established pneumococcal vaccine was significantly associated with COVID-19 vaccine intent and uptake. This suggests that adherence patterns for a long-standing vaccine (pneumococcal) may transfer to novel and even politically charged vaccines (such as COVID-19), particularly during a pandemic. Our findings support Chapman and Coups’ (1999) conclusion that previous vaccination is a strong predictor of future acceptance, as evidenced by the high rates of cumulative and booster uptake in our study. Nevertheless, each new vaccine appears to restart the evaluation process, with the recency of past vaccinations playing a significant role in its continued acceptance.

Second, although our survey did not ask whether COVID-19 was discussed during medical encounters, we observed a positive and significant correlation between having at least one non-emergency medical visit and acceptance and uptake of the COVID-19 vaccine. Indeed, people who attend non-emergency medical visits tend to practice preventive care and are more likely to adhere to science-based solutions. Regardless of the full extent of this effect, the results indicate that a non-emergency medical visit may offset the complex, contextual factors that hinder adherence to healthcare guidelines prevalent in urban settings. The results also strongly indicate the urgent need for greater intervention to address the damaging effects of healthcare employees spreading vaccine myths. Given the growing documentation of COVID-19 vaccine skepticism among healthcare personnel, future efforts must prioritize aligning provider values for COVID-19 vaccines with those of established immunizations (eg, influenza and pneumococcal). If contradictory, these views can have a severe effect on patient behavior, transforming acceptance into hesitancy or accelerating hesitancy into refusal.

Third, the findings demonstrate that vaccine hesitancy exists on a spectrum from low to high reluctance. For instance, data from the first survey wave alone reveal that 34.0% of unvaccinated individuals indicated a definite refusal, whereas 23.4% were unsure about receiving the COVID-19 vaccine (Appendix B). This suggests that public health interventions, when addressing hesitancy rather than refusal, should be tailored to the specific situational or informational drivers of hesitancy within the unvaccinated group. Based on the identified reasons for vaccine hesitancy, we propose a four-factor framework (Table 4) to guide future efforts to address vaccine hesitancy and refusal.

Table 4.

A Four-Factor Framework to Understand and Address Vaccine Hesitancy

Concerns regarding vaccine safety	Concerns regarding vaccine necessity	Concerns regarding vaccine effectiveness	Concerns regarding vaccine affordability
• Novelty of vaccines	• Low-health literacy	• Vaccine’s effectiveness in preventing infection, hospitalization, and death	• Lack of financial resources
• The rapid development of vaccines	• General distrust of vaccines	• Duration of immunity	• Uninsured and underinsured populations
• Low public trust in the U.S. Government and healthcare institutions (inconsistent messages)	• Anti-authority behaviors	• Long-term effectiveness
• Conspiracy theories and misinformation	• Personal beliefs
• Long-term side effects	• Preference for natural immunity or alternative methods
• Negative past experiences	• Preference for natural immunity or alternative methods

Concerns Regarding Vaccine Safety and Necessity

Safety concerns were the primary reason for COVID-19 vaccine hesitancy.³² The novelty and rapid development of SARS-CoV-2 vaccines raised concerns about the adequacy of testing prior to distribution.^10,12,18,28 These safety concerns were intensified by pre-existing low trust in government, science, and pharmaceutical entities. Furthermore, inconsistent messaging from public and healthcare officials, such as nurses refusing vaccination, compounded this resistance and diminished institutional credibility. In addition, conspiracy theories alleging a pre-planned pandemic or vaccine-induced infection significantly fueled hesitancy.^10,33 In other cases, long-term adverse effects, particularly for vulnerable populations, and prior negative vaccination experiences contributed to hesitancy, refusal, or avoidance.⁹ COVID-19 vaccine hesitancy was also driven by a range of individual beliefs regarding necessity, including low health literacy, needle phobia, anti-authority views, the prioritization of philosophical or religious beliefs, and the conviction that asymptomatic infection provided adequate protection.^{4,10,12,15,20,32.}

In light of our findings, we argue that to better meet the unique needs of diverse populations, public health programs should be stratified by healthcare involvement patterns. For individuals without recent medical visits, officials should clearly outline the vaccine development process, testing, and safety profiles. Communication should emphasize that the benefits of COVID-19 vaccination substantially outweigh the potential risks,³⁴ while acknowledging that rapid deployment was balanced with the understanding that future improvements might be necessary. Furthermore, public health agencies should deploy dedicated teams to counter misinformation on social media, targeting both naive users (including uninsured and underinsured populations) and influential figures whose reach can amplify inaccuracies. In addition, while vaccine mandates may provide short-term boosts,^13,39 the results underscore the importance of institutional trust and shared responsibility in maintaining effective public health outreach efforts.^35,36,37

Conversely, for individuals with a history of medical engagement or prior vaccination, the focus should shift toward reinforcing institutional trust. Given the pervasive influence of misinformation on health-related decisions,²⁵ ongoing education for healthcare professionals regarding vaccine safety and necessity is essential.^9,19 Empowering clinicians with the latest evidence may mitigate the influence of misinformation during patient encounters. Furthermore, communication should consistently emphasize that the COVID-19 vaccine, mirroring the safety profiles of other established vaccines, is a scientifically robust and safe pathway toward achieving population-level immunity.

Concerns Regarding Vaccine Effectiveness and Affordability

Effectiveness concerns center around three critical questions: First, do COVID-19 vaccines effectively prevent infection, hospitalization, and death?^12,14,18,21 Second, what is the duration of vaccine-induced immunity?^4,24 Third, what is the long-term effectiveness of these vaccines?¹¹ In addition, healthcare or vaccine costs presented significant financial burdens, further hindering vaccine acceptance.^1,12,32 For example, Reiter et al (2020) showed that cost is a major constraint, with 65% of participants preferring a vaccine priced at $50 or less, and 30% indicating they would rather pay nothing. This demonstrates that cost is a separate barrier distinct from concerns about effectiveness, necessity, or safety. Therefore, to prevent logistical and financial factors from becoming major barriers to access, public and healthcare officials should prioritize providing affordable, preferably free, vaccines. Furthermore, to increase COVID-19 vaccine acceptance among risk-calculating groups questioning vaccine effectiveness, officials should emphasize the rigorous testing and stringent safety standards to which all vaccines are held.³⁸ Public health officials are also encouraged to highlight how newer vaccines, including the COVID-19 vaccine, build on this legacy by discussing the similarities and differences between the COVID-19 vaccine and other commonly administered vaccines.

Implications and Future Research

What are the broader implications of these findings beyond the specific case presented? Collectively, this study demonstrates that adherence to public health measures and a history of preventive care are intrinsically linked. Compliance is not merely a response to the sudden introduction of a new vaccine or the imposition of mandates. Rather, it is rooted in a pre-existing belief system that views science and medicine as instruments for collective well-being. This means that people who follow health guidelines tend to have a history of following other health recommendations. These positive experiences foster a deeper trust in science, increasing their willingness to embrace new health measures.

This points to a two-fold strategy for public health practitioners in future practice. First, healthcare professionals must ensure that patient interactions are consistently positive, particularly when introducing medical innovations. Transparency about the capabilities and limitations of medical interventions is also vital, because once trust is broken, people may fall into a pattern of avoiding preventive care and seeking medical help only when necessary. Second, health communication must shift from a “reactive” crisis-based model to a more proactive approach to information sharing. Relying on emergency periods or a global crisis to discuss the value of vaccination is counterproductive. Instead, health officials should leverage the four-factor framework to develop continuous educational curricula that bridge the gap between established medical practices and novel technologies. This can effectively reduce general distrust, address hesitancy, and encourage questions that help overcome refusal or skepticism. This is grounded in the fact that, when vaccine education is tied exclusively to a crisis, it can appear as though the incentives are detached from the public’s best interests.

Finally, it is critical to identify the unique reasons for vaccine acceptance and refusal. As previous research and this study demonstrate, past behaviors shape future vaccine intake. Therefore, future research should explore the underlying drivers of vaccine refusal, hesitancy, and acceptance. Understanding these distinct motivations is a prerequisite for effectively expanding the number of people who practice preventive care in U.S. cities or abroad.

Conclusion

This study addresses a critical question regarding COVID-19 vaccine acceptance and uptake: Does an individual’s prior medical and vaccination history influence future vaccine-related decisions? Drawing on the principles of the HBM and the 5C Model, we found that individuals with at least one non-emergency medical visit in the last year, and those who received the pneumococcal and influenza vaccines, were significantly more likely to report intention to receive the COVID-19 vaccine, complete the primary series, and report a higher overall total vaccine uptake. This suggests that vaccine adherence patterns for long-standing inoculations can be transferred to novel and politically charged vaccines, demonstrating that vaccine acceptance is not solely tied to recent or disease-related inoculations (eg, flu), and that recent, non-emergency medical visits can significantly improve COVID-19 vaccine acceptance. Therefore, we argue that future vaccination campaigns should target and prioritize individuals with less recent medical engagement and a history of past vaccinations, as these are strong predictors of hesitancy. We also provide a four-factor framework to guide future efforts in addressing COVID-19 vaccine hesitancy and refusal in American cities.

However, to fully contextualize these findings and the resulting framework, several limitations must be addressed. First, this study is limited by its geographic focus on a single U.S. city, which may restrict the generalizability of the findings to other regions. Second, the study’s cross-sectional design precludes the establishment of definitive temporal ordering or causal inferences. Third, the use of self-reported data is subject to inherent recall and social desirability biases, as well as self-selection into the survey sample. Finally, the use of a mixed sampling design that combines address-based sampling (ABS) with a nonprobability online panel may introduce selection bias, and the low ABS response rate (approximately 1 percent) raises concerns about nonresponse bias. Considered collectively, these limitations suggest that the results should be interpreted as evidence of robust associations rather than definitive causal effects. Future research should address these limitations by employing longitudinal designs, strengthening probability-based sampling, and incorporating validated or administrative data sources to improve both causal inference and measurement accuracy.

So What?

What is Already Known About This Topic?

The literature demonstrates that vaccine hesitancy significantly impedes progress toward achieving COVID-19 herd immunity, and that vaccine acceptance is influenced by a complex interplay of factors, including an individual’s sense of collective responsibility, past vaccine behaviors (specifically, the flu vaccine), and medical intervention.

What Does This Article Add?

The study demonstrates that vaccine uptake is not merely driven by recent or disease-adjacent inoculations (such as the flu vaccine). Adherence patterns established through long-standing vaccination habits can transfer to novel and politically charged vaccines, particularly during a pandemic. It also provides evidence that recent non-emergency medical visits can significantly increase acceptance and uptake of the COVID-19 vaccine. Furthermore, this study introduces a four-factor framework to guide targeted efforts in mitigating vaccine hesitancy and refusal.

What are the Implications for Health Promotion or Research?

Vaccine interventions should move beyond a one-size-fits-all approach. To maximize impact, future vaccine campaigns should prioritize individuals who lack recent routine medical visits and current immunization records, as past vaccinations significantly predict COVID-19 vaccine uptake, and clinical encounters may offer a critical opportunity to initiate a cognitive shift toward vaccine acceptance.

Footnotes

ORCID iD

Kasny Damas

Ethical Considerations

This study was approved by the Rutgers University Institutional Review Board (Study ID: 2022000255).

Consent to Participate

Informed consent was obtained from all individual participants included in the study.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the funding for this research project was provided by the U.S. Department of Health and Human Services. Office of Minority Health. 1 CPIMP211242-01-00.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Appendix

References

Wang

Zhang

, et al. Effect of vaccine efficacy on vaccination behavior with adaptive perception. Appl Math Comput. 2024;469:1-11. doi:10.1016/j.amc.2024.128543.

Ebrahimi

Johnson

Ebling

, et al. Risk, trust, and flawed assumptions: vaccine hesitancy during the COVID-19 pandemic. Front Public Health. 2021;9:1-8. doi:10.3389/fpubh.2021.700213.

Yasmin

Najeeb

Moeed

, et al. COVID-19 vaccine hesitancy in the United States: a systematic review. Front Public Health. 2021;9:1-17. doi:10.31235/osf.io/r9xs7.

Dhama

Sharun

Tiwari

, et al. COVID-19 vaccine hesitancy – reasons and solutions to achieve a successful global vaccination campaign to tackle the ongoing pandemic. Hum Vaccines Immunother. 2021;17:3495-3499. doi:10.1080/21645515.2021.1926183.

Hudson

Montelpare

. Predictors of vaccine hesitancy: implications for COVID-19 public health messaging. Int J Environ Res Publ Health. 2021;18(15):3-14. doi:10.3390/ijerph18158054.

Corace

Garber

. When knowledge is not enough: changing behavior to change vaccination results. Hum Vaccines Immunother. 2014;10:2623-2624. doi:10.4161/21645515.2014.970076.

Savoia

Piltch-Loeb

Goldberg

, et al. Predictors of COVID-19 vaccine hesitancy: socio-demographics, co-morbidity, and past experience of racial discrimination. Vaccines. 2021;9(7):1-13. doi:10.3390/vaccines9070767.

Razai

Osama

McKechnie

Majeed

. Covid-19 vaccine hesitancy among ethnic minority groups: Tackling the reasons for hesitancy requires engagement, understanding, and trust. BMJ. 2021;372:n513. doi:10.1136/bmj.n513

Rittle

. COVID-19 vaccine hesitancy and how to address it. Workplace Health Saf. 2022;70:56-62. doi:10.1177/21650799211073525.

10.

Romate

Rajkumar

Gopi

, et al. What contributes to COVID-19 vaccine hesitancy? A systematic review of the psychological factors associated with COVID-19 vaccine hesitancy. Vaccines. 2022;10:1-21. doi:10.3390/vaccines10111777.

11.

Bunch

. A tale of two crises: addressing Covid-19 vaccine hesitancy as promoting racial justice. HEC Forum. 2021;33:143-154. doi:10.1007/s10730-021-09440-0.

12.

Khubchandani

Sharma

Price

Wiblishauser

Sharma

Webb

. COVID-19 vaccination hesitancy in the United States: a rapid national assessment. J Community Health. 2021;46:270-277. doi:10.1007/s10900-020-00958-x.

13.

Thunström

Ashworth

Finnoff

Newbold

. Hesitancy toward a COVID-19 vaccine. EcoHealth. 2021;18:44-60. doi:10.1007/s10393-021-01524-0.

14.

Kafadar

Tekeli

Jones

Stephan

Dening

. Determinants for COVID-19 vaccine hesitancy in the general population: a systematic review of reviews. J Public Health. 2023;31:1829-1845. doi:10.1007/s10389-022-01753-9

15.

Gerretsen

Kim

Caravaggio

, et al. Individual determinants of COVID-19 vaccine hesitancy. PLoS One. 2021;16:1-14. doi:10.1371/journal.pone.0258462.

16.

Menifield

Damas

Chen

, et al. Health literacy and patient compliance: implications for public health interventions from a COVID-19 study. J Health Hum Serv Adm. 2024;47(3-4):104-115. doi:10.1177/10793739241303043.

17.

Troiano

Nardi

. Vaccine hesitancy in the era of COVID-19. Public Health. 2021;194:245-251. doi:10.1016/j.puhe.2021.02.025.

18.

Joshi

Kaur

Grover

Nash

El-Mohandes

. Predictors of COVID-19 vaccine acceptance, intention, and hesitancy: a scoping review. Front Public Health. 2021;9:1-20. doi:10.3389/fpubh.2021.698111.

19.

Reiter

Pennell

Katz

. Acceptability of a COVID-19 vaccine among adults in the United States: how many people would get vaccinated? Vaccine. 2020;38:6500-6507. doi:10.1016/j.vaccine.2020.08.043.

20.

Soares

Rocha

Moniz

, et al. Factors associated with COVID-19 vaccine hesitancy. Vaccines. 2021;9:1-14. doi:10.3390/vaccines9030300.

21.

U.S. Census . United States Census Bureau. Retrieved November 2023, from City and Town Population Totals: 2020–2022; 2023. https://www.census.gov/data/tables/time-series/demo/popest/2020s-total-cities-and-towns.html#v2022

22.

Chapman

Coups

. Predictors of influenza vaccine acceptance among healthy adults. Prev Med. 1999;29:249-262. doi:10.1006/pmed.1999.0535.

23.

County of Essex New Jersey . County Directory. Retrieved November 2023, from Essex County Coronavirus Cases; 2023. https://essexcountynj.org/covid-19-municipality/

24.

Rutten

Zhu

Leppin

, et al. Evidence-based strategies for clinical organizations to address COVID-19 vaccine hesitancy. Mayo Clin Proc. 2021;96:699-707. doi:10.1016/j.mayocp.2020.12.024.

25.

Wiysonge

Ndwandwe

Ryan

, et al.

Vaccine hesitancy in the era of COVID-19: could lessons from the past help in divining the future?

Hum Vaccines Immunother. 2022;18(1):1-3. doi:10.1080/21645515.2021.1893062.

26.

USA FACTS . What’s the Nation’s Progress on Vaccinations?. Retrieved from US Coronavirus Vaccine Tracker; 2025. https://usafacts.org/visualizations/covid-vaccine-tracker-states/

27.

WHO . Percentage of Total Population Vaccinated with at Least One Dose of a COVID-19 Vaccine. Retrieved from World Health Organization; 2023. https://data.who.int/dashboards/covid19/vaccines

28.

Dror

Eisenbach

Taiber

, et al. Vaccine hesitancy: the next challenge in the fight against COVID-19. Eur J Epidemiol. 2020;35:775-779. doi:10.1007/s10654-020-00671-y.

29.

Heuvel

Lv.

Paget

Dückers

Caini

. The impact of influenza and pneumococcal vaccination on antibiotic use: an updated systematic review and meta-analysis. Antimicrob Resist Infect Control. 2023;12(1):1-15. doi:10.1186/s13756-023-01272-6.

30.

Kapoula

Vennou

Bagos

. Influenza and pneumococcal vaccination and the risk of COVID-19: a systematic review and meta-analysis. Diagnostics. 2022;12:1-21. doi:10.3390/diagnostics12123086.

31.

Ser

Cho

. Promising expectations for pneumococcal vaccination during COVID-19. Vaccines. 2021;9(12):1-16. doi:10.3390/vaccines9121507.

32.

Callaghan

Moghtaderi

Lueck

, et al. Correlates and disparities of COVID-19 vaccine hesitancy. SSRN Journal. 2020;1-20. doi:10.2139/ssrn.3667971

33.

Kwok

K-K

Wei

Tang

Wong

Lee

. Influenza vaccine uptake, COVID-19 vaccination intention and vaccine hesitancy among nurses: a survey. Int J Nurs Stud. 2021;114:1-9. doi:10.1016/j.ijnurstu.2020.103854

34.

World Health Organization (WHO) . WHO COVID-19 Dashboard. Retrieved from Number of COVID-19 Deaths Reported to WHO (Cumulative Total); 2024. https://data.who.int/dashboards/covid19/deaths?n=c

35.

Zhou

Shen

. Racial influences, social media usage, and vaccine hesitancy: a national survey across vaccine topics. J Racial Ethn Health Dispar. 2025;13:1271-1283. doi:10.1007/s40615-025-02332-w.

36.

Padamsee

Bond

Dixon

, et al. Changes in COVID-19 vaccine hesitancy among black and white individuals in the US. JAMA Netw Open. 2022;5:1-12. doi:10.1001/jamanetworkopen.2021.44470.

37.

Harber

Vila

. Fevered reasoning: how heightened distress and lowered resources relate to COVID-19 beliefs. J Soc Polit Psychol. 2023;11(1):247-271. doi:10.5964/jspp.9267.

38.

Perry

Mizer

Wynn

Kruczek

. Countering COVID-19 vaccine hesitancy. The Southwest Respiratory and Critical Care Chronicles. 2020;8(36):32-46. doi:10.12746/swrccc.v8i36.749.

39.

Lin

Parker

Pejavara

Smith

. “I would never push a vaccine on you”: a qualitative study of social norms and pressure in vaccine behavior in the U.S. Vaccines. 2022;10(9):1402. doi:10.3390/vaccines10091402.