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PMC Articles

COVID-19 Vaccination Intention and Factors Associated with Hesitance and Resistance in the Deep South: Montgomery, Alabama

PMCID: PMC9695581

PMID: 36355874

Abstract

Using COVID-19-related survey data collected from residents in the city of Montgomery, Alabama, this study assessed the prevalence of COVID-19 vaccine acceptance, hesitance, and resistance, and identified factors associated with COVID-19 vaccine hesitance and resistance. To analyze the survey data (n = 1000), a consolidation approach (machine learning modeling and multinomial logistic regression modeling) was used to identify predictors of COVID-19 vaccine hesitancy and resistance. The prevalence of vaccine acceptance, hesitancy, and resistance was 62%, 23%, and 15%, respectively. Female gender and a higher level of trust that friends and family will provide accurate information about the COVID-19 vaccine were positively associated with vaccine hesitancy. Female gender and higher trust that social media will provide accurate information about COVID-19 were positively associated with vaccine resistance. Factors positively associated with COVID-19 vaccine hesitance and resistance in the study’s geographical area are worrisome, especially given the high burden of chronic diseases and health disparities that exist in both Montgomery and the Deep South. More research is needed to elucidate COVID-19 vaccination attitudes and reasons for non-acceptance of the COVID-19 vaccine. Efforts to improve acceptance should remain a priority in this respective geographical area and across the general population.

Full Text

Coronavirus Disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), it is easily transmitted person-to-person through respiratory droplets [1,2], and it can lead to illness and hospitalization [3]. Severe COVID-19 can cause lasting lung and heart damage, respiratory failure, kidney failure, and death [4,5]. During the early phases of the COVID-19 pandemic, the United States (U.S.) reported the highest excess all-cause mortality rate in the world (19.5/100,000) and is now leading the pandemic with approximately 91 million confirmed cases, approximately 5 million COVID-related hospitalizations, and over 1 million COVID-related deaths [6,7].
COVID-19 vaccination reduces the likelihood of viral transmission, hospitalization, illness severity, and mortality associated with COVID-19 [8]. Though all three COVID-19 vaccines (Pfizer-BioNTech, Moderna, and Janssen Biotech) have been demonstrated to be safe and highly effective (up to 93% efficacy), many people are hesitant towards accepting the vaccine (Self, 2021) (FDA, 2021). In turn, the World Health Organization (WHO) declared COVID-19 vaccine hesitance and resistance a top 10 global health threat [9]. For example, in the U.S. 79% of eligible adults and children have received only one vaccine dose, only 67% have been fully vaccinated, and 48% have received one dose of the vaccine booster [7]. The U.S. is currently ranked 65th globally in terms of the percentage of those who are fully vaccinated [10].
Of particular concern is COVID-19 vaccination rates in the Deep South states such as Alabama, Louisiana, Mississippi, Georgia, and South Carolina, whose rates are trending below national vaccination rates. Currently, only 64% of the eligible population in the aforementioned Deep South states have received at least one dose, while only 54% have received two doses [11]. Lower COVID-19 vaccination rates in the Deep South are certainly cause for concern given the higher prevalence of chronic illness, and a higher concentration of African Americans (60%) when compared to other states [12,13,14].
Of the Deep South states, Alabama has reported 1.4 million COVID-19 cases and has the 8th highest case fatality rate (1.38) in the country [11]. Yet, only 60% of eligible Alabama residents have received one dose, only 50% have been fully vaccinated, and only 18% have received an additional dose of the COVID-19 vaccine [15]. Montgomery County is mostly populated by the City of Montgomery (Alabama’s state capitol) which has a large population of African Americans (60%) [16]. It should be noted COVID-19 vaccination rates in Montgomery County are slightly below the state average. Approximately 58% of Montgomery County residents received at least one vaccine dose and approximately 46% have received a second dose [15].
Increased vaccine acceptance is important in mitigating individual health, community health, and public health consequences of COVID-19, particularly in efforts to protect highly susceptible, at-risk populations (older adults and people of color) who tend to experience increased risk of exposure, more severe illness, and higher mortality rates due to pre-existing co-morbidities and other related socioeconomic factors [7,17,18]. Though several studies have published on recommendations to improve COVID-19 vaccine rates and on COVID-19 vaccine intention [19,20,21,22,23,24], more research is needed to explore COVID-19 vaccine intentions in the Deep South, especially given the low COVID-19 vaccine rates in the Deep South. Empirical data would be advantageous for use among community and public health professionals in the Deep South, who are engaged in health education and health promotion efforts to curve or reduce COVID-19 hesitance and resistance. Hence, the objectives of this study were to assess the prevalence of COVID-19 vaccine acceptance, hesitancy, and resistance, and to identify factors associated with COVID-19 vaccine hesitancy and resistance. To achieve study objectives, we conducted a secondary analysis of COVID-19-related survey data collected from residents in the city of Montgomery, Alabama.
Measures of central tendency and frequency distributions were used to characterize the sample. To robustly identify and analyze factors associated with COVID-19 vaccination intention, we adopted a consolidation approach that combines data-driven models (machine learning models) and hypothesis-driven models (regression models) [25,26,27,28,29,30]. The random forest (RF) model was developed to identify important factors that can predict individuals’ COVID-19 vaccination intention. RF model is a machine learning method for classification and regression tasks that operates by constructing a multitude of decision trees (by combining and averaging more than 100 decision tree models) at training time. Important factors associated with COVID-19 vaccination intention were selected by most decision trees. In this study, the data was split into a training set (800/1000, 80%) to develop RF models and a testing set (200/1000, 20%) to validate the performance of RF models. To ensure the rigor of prediction, we balanced the data with the in-built parameter “class_weight” of random forest classifier by setting it to “balanced” which helps us optimize the scoring for the minority class by assigning weights to the classes. Class weights were calculated as weight(i) = n_samples/(n_classes ∗ n_samples(i). Grid search with a 10-fold cross validation method was used to tune and adjust the hyperparameters in the RF model. The performance of the RF model was evaluated by testing the accuracy (0.83), F1 score (0.84), sensitivity (0.84), and specificity (0.83) (Figure 1). Based on Gini impurities of the features, a list ranking and scoring variables with important features of predicting the outcome, COVID-19 vaccination intention, was generated. Multinomial logistic regression was used to identify predictors of COVID-19 vaccine hesitancy and resistance. Python 3.7.6 and RStudio 1.3.1056 were used to develop the RF model, and Stata 12 was used to run multinomial logistic regression.
Nearly one-third (31%) of participants were 45–64 years old, and the majority were Black/African American (61%) and female (52%) (Table 1). More than one-third had an undergraduate or post-graduate degree (35%), and most did not have children <18 years old living at home (70%).
To improve the performance of the multinomial regression model, only the top 15 variables with the highest scores and any demographic variables that were not in the top 15 (gender and race), a total of 17, from the feature importance analysis were entered into the multinomial logistic regression model. A detailed ranking of all variables from the feature importance analysis can be found in Table 2, but a few examples were: level of confidence in the COVID-19 vaccine providing protection from COVID-19; level of trust in accuracy of COVID-19 vaccine information from healthcare providers; frequency of mask wearing in public places, level of COVID-19 vaccine side-effects concerns; and level of trust in accuracy of COVID-19 vaccine information from locally elected government officials.
Female gender (adjusted odds ratio [aOR] = 1.95, confidence interval [CI]: 1.02–3.73) and a higher level of trust that friends and family will provide accurate information about the COVID-19 vaccine (aOR = 1.15, CI: 1.03–2.20) were positively associated with vaccine hesitancy (Table 3). Higher age (aOR = 0.42, CI: 0.29–0.61), a higher frequency of wearing masks in public places (aOR = 0.40, CI: 0.29–0.56), a higher level of confidence that the COVID-19 vaccine would protect self and family from getting sick from COVID-19 (aOR = 0.68, CI: 0.47–0.98), and clearer and easier public health messages about COVID-19 (aOR = 0.57, CI: 0.42–0.78) were negatively associated with vaccine hesitancy.
A high proportion of participants in this study (38%) were hesitant or resistant about the COVID-19 vaccine. Similar studies conducted elsewhere have also reported high proportions of COVID-19 vaccine hesitance and resistance among adults [18,31,32,33]. High frequencies of COVID-19 vaccine hesitance and resistance in the study’s geographical area are worrisome, especially given the high burden of chronic diseases and health disparities (e.g., cardiovascular disease, diabetes, cancer, obesity) that exist in both Montgomery and the Deep South that are associated risk factors for COVID-19 infection. Wide non-willingness to accept the COVID-19 vaccine at the local level inhibits achievement of immunity at the population level. Those who are not vaccinated are not only at greater risk of acquiring COVID-19 compared to those who are vaccinated, but their risk for reinfection is more than two times higher than those who acquired COVID-19 and got vaccinated [34]. Altogether, the finding highlights a continued need for COVID-19 vaccination health education and health promotion efforts and campaigns to improve COVID-19 vaccination acceptance.
Though the current study did not ascertain why participants of female gender were more likely to have COVID-19 vaccine resistance and hesitance than participants of male gender, findings from several other studies have also demonstrated that females are more likely than males to be resistant or hesitant about the COVID-19 vaccine [24,32,35,36,37]. Similarly, those respective studies did not ascertain why females are more likely to have COVID-19 vaccine resistance and hesitance. In general, it has been noted that females more frequently express concerns about the safety of vaccines and a lack of trust in the quality and impartiality of information provided by healthcare professionals [24]. However, qualitative studies seeking to give voice to and examine concerns that females have about the COVID-19 vaccine are limited, and more qualitative studies are needed to fill this knowledge gap.
In terms of the finding that participants with high trust that social media will provide accurate information about COVID-19 had increased odds of COVID-19 vaccine resistance, it is probable that considerable amounts of misinformation widely available on social media platforms stemming from anti-vaccine efforts may be an underlying contributing factor of COVID-19 vaccine resistance [38]. It has been noted that social medial platforms have been purposefully used as “echo chambers” to circulate misinformation from unreliable or unverified sources about COVID-19 leading to COVID-19 vaccine resistance [39]. The examination of COVID-19 vaccine content on social media determined nearly 23% of content shared on social media was misleading, and that fact-based content from government health agencies was underrepresented. To combat misinformation shared on social media platforms, community health professionals and public officials are encouraged to regularly use social media to expeditiously disseminate relevant, timely, and empirically based COVID-19 information [40].
Findings also demonstrate the role and importance of COVID-19 vaccine confidence, trust in health care providers and public officials, and the frequency of mask wearing as deterrents of COVID-19 vaccine hesitance and resistance. As such, community health and public health professionals are encouraged to continue to sustain current health education and promotion efforts in these respective education-related domains about COVID-19 and the COVID-19 vaccine. Dissemination of evidence-based COVID-19 education, ideally grounded in elements of health behavior theory (e.g., the health belief model [HBM] or the Theory of Planned Behavior [TBP]), has the potential to address and target attitudes, thoughts, perceptions, and social influences that reinforce non-acceptance of the COVID-19 vaccine [18,41,42].