Abstract
Background
Vaccine hesitancy remains an important public health challenge, particularly in settings where vaccine uptake may vary according to sociodemographic and contextual factors. This study aimed to assess sociodemographic and lifestyle factors associated with coronavirus disease 2019 (COVID-19) vaccination uptake among healthy individuals and patients with chronic comorbidities attending a primary health care clinic in Siedlce, Eastern Poland.
Material/Methods
A cross-sectional survey was conducted between November 2, 2022, and January 3, 2023, in a convenience sample of 458 adult primary care patients. Lifestyle was assessed using the Lifestyle Screening Questionnaire (LSQ)-Cavendish Diagnostic Questionnaire.
Results
Full COVID-19 vaccination was mainly associated with older age, higher educational attainment, and retirement status. In the adjusted model, age remained a strong predictor of vaccination uptake; participants with higher education had greater odds of full vaccination relative to those with primary or lower secondary education. Rural residence was independently associated with higher odds of full vaccination, despite a borderline association in unadjusted analyses. Several lifestyle domains differed between vaccination groups in descriptive analyses; the overall pattern did not indicate a clear independent association between lifestyle and vaccination uptake.
Conclusions
In this primary care sample from Eastern Poland, COVID-19 vaccination uptake was mainly associated with age, education, retirement status, and place of residence rather than a consistent lifestyle profile. The finding of higher vaccination uptake among rural participants contrasts with international literature and may reflect the local health care context. Vaccination strategies should consider regional and primary care-based differences in access, trust, and patient engagement.
Keywords: Attitude to Health, COVID-19, Primary Health Care, Vaccination, Vaccination Refusal
Introduction
The global rollout of coronavirus disease 2019 (COVID-19) vaccines, initiated in 2021, was an extraordinary event in the history of medicine, given both the rapid pace of vaccine development and the unprecedented scale of vaccination efforts. By September 2022, 4.92 billion people worldwide (63.1% of the global population) had been fully vaccinated against COVID-19, corresponding to 12.6 billion administered doses [1]. In Poland, 22.5 million people were fully vaccinated, representing 59.4% of the population (56.2 million doses administered) [1]. Notably, all COVID-19 vaccines approved for use in the European Union demonstrated high efficacy in protecting against severe disease, hospitalization, and death, with real-world effectiveness in the general population estimated at around 80% [2,3].
A high level of public acceptance is essential for the effectiveness of vaccination programs aimed at reducing infectious disease burden [4]. Individual reluctance to vaccinate may be influenced by sociocultural context, media exposure, public health policy, and personal decision-making processes [5]. Early studies suggested that vaccine hesitancy was partly driven by insufficient information—many individuals expressed concerns about the speed of vaccine development and possible short- and long-term adverse effects [6]. These concerns emerged in the broader context of increasing anti-vaccine sentiment, present even before the COVID-19 pandemic [7]. A systematic review of studies conducted during the pandemic further suggested that willingness to receive COVID-19 vaccination declined over time in some populations, complicating efforts to sustain vaccine uptake (particularly for booster doses) [8].
Poland has been identified as a country with relatively high levels of COVID-19 vaccine hesitancy. In June 2021, vaccine hesitancy in Poland was reportedly 40.7% and showed a negative association with trust in local government [9]. By June 2022, vaccine acceptance and hesitancy indicators in Poland remained among the least favorable internationally [10]. These findings underscore the importance of examining factors associated with vaccine uptake in the Polish context, particularly in smaller urban centers and surrounding rural communities, where social and health care-related determinants may differ from observations in large metropolitan settings.
Previous research has identified several determinants of COVID-19 vaccination behavior, including sociodemographic and economic characteristics such as age, education, and income, as well as broader attitudes toward health and prevention [4,11,12]. Lifestyle-related factors and health behaviors have also been linked to vaccination decisions [13]. However, evidence regarding the combined role of sociodemographic and lifestyle factors in real-world primary care populations from Eastern Poland remains limited.
Therefore, this study aimed to assess sociodemographic and lifestyle factors associated with COVID-19 vaccination uptake among healthy individuals and patients with chronic comorbidities attending a primary health care clinic in Siedlce, Eastern Poland.
Material and Methods
Study Design
This cross-sectional study assessed the associations of sociodemographic and lifestyle factors with COVID-19 vaccination uptake in a small city in Eastern Poland. The study was conducted in Siedlce, an important regional center serving the surrounding predominantly agricultural area. Participants were recruited from a primary health care clinic in Siedlce, Poland. Data were collected between November 2, 2022, and January 3, 2023; statistical analyses were performed between October and December 2024.
Study Population and Participant Recruitment
The study population comprised a convenience sample of adult primary care patients. Participants were classified into 2 groups: patients with chronic diseases and healthy individuals. Interviews with chronically ill patients were conducted by nurses during home visits, whereas healthy participants were interviewed by clinic reception staff during routine visits to the primary health care clinic.
Data Collection
Participation in the study was voluntary, and no financial or material incentives were offered. No external commercial or research agency was involved in participant recruitment, questionnaire administration, data entry, data analysis, or interpretation of the results. The sample was not randomly selected; it consisted of a convenience sample of 458 patients recruited from a primary health care setting.
Selected patient characteristics were obtained from the clinic’s electronic records and linked to questionnaire responses by authorized personnel using the patient identifier assigned within the health care provider’s system. Variables obtained from clinical records were limited to basic demographic and health-related information required for the analysis. The dataset used for statistical analyses was pseudonymized and did not contain direct personal identifiers, such as names or addresses.
Questionnaire and Variables
The research instrument consisted of 2 components. The first was an author-developed sociodemographic questionnaire that collected information regarding age, sex, education level, occupational status, type of work (white-collar vs blue-collar), place of residence (urban vs rural), relationship status, household size, chronic disease status, and COVID-19 vaccination status. The second component was a lifestyle assessment based on the Polish-validated adaptation of the Lifestyle Screening Questionnaire (LSQ)-Cavendish Diagnostic Questionnaire [14].
The standardized Cavendish LSQ “My Lifestyle” Diagnostic Questionnaire consists of 50 statements grouped into 10 domains covering different aspects of lifestyle and overall health. These domains include alcohol consumption, tobacco smoking, blood pressure, body weight, physical fitness, stress and internal anxiety, driving safety, relationships with others, rest and sleep, and overall life satisfaction. Items are scored dichotomously (yes = 1, no = 0). Scores for each domain are calculated as the sum of the relevant items; the total LSQ score is obtained by summing all 50 items, yielding a possible range of 0 to 50 points. Higher scores indicate a more favorable lifestyle profile and—consequently—a healthier reported lifestyle.
Ethical Considerations
The study was approved by the Medical University of Warsaw Bioethics Committee (AKBE/245/2024). Patient data were provided directly by the medical service provider. In parallel, the research team compiled a database of the survey results. The research team linked the survey data with patient data (health status and sociodemographic characteristics) and did not share the database with any third parties. The database was exclusively stored on the statistician’s hard drive and will be deleted after publication of this article. The report presents aggregated statistical summaries; therefore, the data were treated as confidential.
Statistical Analysis
Descriptive and comparative analyses were performed with full COVID-19 vaccination status as the primary outcome. For primary comparative analyses, participants who were unvaccinated and those who had received an incomplete COVID-19 vaccination course were combined into a single “not fully vaccinated” group because the partially vaccinated subgroup was small and full vaccination was considered the most clinically relevant endpoint. Categorical variables were summarized as counts and percentages, then compared between groups using the chi-square test. Continuous variables, including age and LSQ domain scores, were presented as mean ± standard deviation and compared using Welch’s 2-sample t-test.
To identify factors independently associated with full COVID-19 vaccination, binary logistic regression models were fitted and results were expressed as odds ratios (ORs) with 95% confidence intervals (CIs). First, crude ORs were separately estimated for each predictor. An adjusted model was then constructed including age, sex, education, place of residence, chronic disease status, and employment status. LSQ domains were descriptively analyzed in relation to vaccination uptake and not included in the final multivariable model. The adjusted model included key sociodemographic and clinical variables based on their substantive relevance, prior literature, and potential role as confounders of COVID-19 vaccination uptake. The model was intentionally restricted to a small set of clinically and substantively relevant covariates to reduce the risk of overfitting and collinearity. All tests were 2-sided, and P-values < 0.05 were considered statistically significant. The regression analysis was based on complete-case data and included 455 participants; 3 cases were excluded due to missing data for at least 1 variable included in the model.
Results
In total, 458 participants with available data regarding COVID-19 vaccination status were included in the descriptive analysis, of whom 311 (67.9%) were fully vaccinated and 147 (32.1%) were not fully vaccinated. Fully vaccinated participants were significantly older than those who were not fully vaccinated (53.2 ± 16.8 vs 43.5 ± 15.2 years, P < 0.001). Employment status also significantly differed between groups (P < 0.001); retired participants were more frequently represented in the fully vaccinated group. Chronic disease status was associated with vaccination uptake in the unadjusted comparison (P = 0.004), whereas sex and education were not significantly associated with vaccination status in the descriptive analysis. Place of residence (available for 455 participants due to 3 invalid entries) showed a borderline association in the unadjusted comparison (P = 0.083); a greater proportion of fully vaccinated individuals resided in rural areas relative to those who were not fully vaccinated (66.6% vs 57.6%). Details are presented in Table 1.
Table 1.
Participant characteristics and lifestyle factors according to full COVID-19 vaccination status.
| Characteristic | Total (N = 458) | Fully vaccinated (n = 311) | Not fully vaccinated (n = 147) | P-value |
|---|---|---|---|---|
| Demographic and clinical characteristics | ||||
| Age, years | 50.1 ± 16.9 | 53.2 ± 16.8 | 43.5 ± 15.2 | < 0.001 |
| Sex | 0.073 | |||
| Female | 275 (60.0%) | 196 (63.0%) | 79 (53.7%) | |
| Male | 183 (40.0%) | 115 (37.0%) | 68 (46.3%) | |
| Education | 0.461 | |||
| Primary/lower secondary | 38 (8.3%) | 24 (7.7%) | 14 (9.5%) | |
| Vocational | 120 (26.2%) | 85 (27.3%) | 35 (23.8%) | |
| Secondary | 174 (38.0%) | 112 (36.0%) | 62 (42.2%) | |
| Higher | 126 (27.5%) | 90 (28.9%) | 36 (24.5%) | |
| Employment status | < 0.001 | |||
| Working | 296 (64.6%) | 179 (57.6%) | 117 (79.6%) | |
| Retired/pensioner | 131 (28.6%) | 113 (36.3%) | 18 (12.2%) | |
| Unemployed/not working | 18 (3.9%) | 10 (3.2%) | 8 (5.4%) | |
| Student | 13 (2.8%) | 9 (2.9%) | 4 (2.7%) | |
| Chronic disease status | 0.004 | |||
| Healthy | 240 (52.4%) | 148 (47.6%) | 92 (62.6%) | |
| Chronic disease | 218 (47.6%) | 163 (52.4%) | 55 (37.4%) | |
| Place of residence | 0.083 | |||
| Urban | 165 (36.3%) | 104 (33.4%) | 61 (42.4%)* | |
| Rural | 290 (63.7%) | 207 (66.6%) | 83 (57.6%)* | |
| Lifestyle factors (LSQ domains) | ||||
| Alcohol consumption [0–5] | 3.5 ± 1.8 | 3.4 ± 1.8 | 3.7 ± 1.7 | 0.100 |
| Tobacco smoking [0–5] | 2.7 ± 1.8 | 2.7 ± 1.8 | 2.8 ± 1.8 | 0.693 |
| Blood pressure [0–5] | 2.1 ± 1.1 | 2.1 ± 1.1 | 2.1 ± 1.1 | 0.976 |
| Body weight [0–5] | 1.7 ± 1.3 | 1.6 ± 1.2 | 1.9 ± 1.3 | 0.052 |
| Physical fitness [0–5] | 1.6 ± 1.1 | 1.5 ± 1.1 | 1.8 ± 1.2 | 0.017 |
| Stress and internal anxiety [0–5] | 2.1 ± 1.4 | 2.0 ± 1.4 | 2.4 ± 1.5 | 0.015 |
| Driving safety [0–5] | 3.4 ± 1.4 | 3.4 ± 1.4 | 3.4 ± 1.4 | 0.935 |
| Relationships with others [0–5] | 2.9 ± 1.6 | 2.8 ± 1.6 | 3.1 ± 1.6 | 0.083 |
| Rest and sleep [0–5] | 2.0 ± 1.3 | 1.9 ± 1.2 | 2.2 ± 1.4 | 0.019 |
| Overall life satisfaction [0–5] | 2.2 ± 1.5 | 2.1 ± 1.5 | 2.3 ± 1.6 | 0.220 |
| LSQ total score [0–50] | 24.2 ± 9.6 | 23.5 ± 9.5 | 25.6 ± 9.5 | 0.033 |
Values are shown as n (%) for categorical variables and mean ± standard deviation for continuous variables. P-values were calculated using chi-square tests for categorical variables and Welch’s 2-sample t-tests for continuous variables. Not fully vaccinated = unvaccinated or incompletely vaccinated.
Place of residence excludes 3 invalid entries (n = 144 for not fully vaccinated group).
Abbreviation: LSQ, Lifestyle Screening Questionnaire
Several LSQ domains significantly differed according to vaccination status. Compared with participants who were not fully vaccinated, fully vaccinated participants had lower mean scores for physical fitness (1.5 ± 1.1 vs 1.8 ± 1.2, P = 0.017), stress and internal anxiety (2.0 ± 1.4 vs 2.4 ± 1.5, P = 0.015), and rest and sleep (1.9 ± 1.2 vs 2.2 ± 1.4, P = 0.019). The total LSQ score was also lower in the fully vaccinated group (23.5 ± 9.5 vs 25.6 ± 9.5, P = 0.033). The body weight domain approached statistical significance (P = 0.052); alcohol consumption, relationships with others, and overall life satisfaction did not significantly differ between groups. Details are presented in Table 1.
Crude logistic regression indicated that older age was strongly associated with greater odds of full COVID-19 vaccination (OR 1.04 per year, 95% CI: 1.03–1.05, P < 0.001). Rural residence showed a borderline association in the crude model (OR 1.46, 95% CI: 0.97–2.20, P = 0.066); it remained independently associated with higher odds of full vaccination after adjustment for age, sex, education, chronic disease, and employment status (adjusted OR [aOR] 1.76, 95% CI: 1.11–2.79, P = 0.016). In the adjusted model, age remained a significant predictor (aOR 1.05 per year, 95% CI: 1.03–1.08, P < 0.001). Higher education also was independently associated with greater odds of vaccination; the strongest effect was observed for participants with higher education relative to those with primary or lower secondary education (aOR 10.74, 95% CI: 3.70–31.21, P < 0.001). Retired participants had higher odds of full vaccination than working participants (aOR 2.32, 95% CI: 1.02–5.29, P = 0.045). Chronic disease was associated with vaccination uptake in the crude model, but this association was no longer significant after adjustment. Details are presented in Table 2.
Table 2.
Logistic regression analysis of factors associated with full COVID-19 vaccination (yes vs no/incomplete).
| Predictor | Crude OR (95% CI) | P-Value | Adjusted OR (95% CI) | P-Value |
|---|---|---|---|---|
| Age (per 1-year increase) | 1.04 (1.03 to 1.05) | < 0.001 | 1.05 (1.03 to 1.08) | < 0.001 |
| Male vs female | 0.68 (0.46 to 1.01) | 0.059 | 0.99 (0.62 to 1.57) | 0.965 |
| Education: Vocational vs primary/lower secondary | 1.42 (0.66 to 3.05) | 0.374 | 3.46 (1.36 to 8.82) | 0.009 |
| Education: Secondary vs primary/lower secondary | 1.05 (0.51 to 2.18) | 0.888 | 5.24 (2.00 to 13.70) | < 0.001 |
| Education: Higher vs primary/lower secondary | 1.46 (0.68 to 3.13) | 0.333 | 10.74 (3.70 to 31.21) | < 0.001 |
| Rural vs urban | 1.46 (0.97 to 2.20) | 0.066 | 1.76 (1.11 to 2.79) | 0.016 |
| Chronic disease vs healthy | 1.84 (1.23 to 2.75) | 0.003 | 0.80 (0.47 to 1.35) | 0.406 |
| Employment: Retired/pensioner vs working | 4.10 (2.37 to 7.11) | < 0.001 | 2.32 (1.02 to 5.29) | 0.045 |
| Employment: Unemployed/not working vs working | 0.82 (0.31 to 2.13) | 0.679 | 1.14 (0.41 to 3.19) | 0.800 |
| Employment: Student vs working | 1.47 (0.44 to 4.89) | 0.529 | 5.41 (1.29 to 22.64) | 0.021 |
The adjusted model included age, sex, education, place of residence, chronic disease status, and employment status and was based on complete-case data (N = 455). Reference categories were female, primary/lower secondary education, urban residence, healthy, and working. Abbreviations: CI, confidence interval; OR, odds ratio.
Discussion
In this primary care sample from Eastern Poland, COVID-19 vaccination uptake was mainly associated with older age, higher education, and retirement status. This pattern is broadly consistent with previous reports in which vaccination generally was more common among older and more socially advantaged groups, whereas lower educational attainment was associated with lower uptake. Polish data have also suggested that willingness to vaccinate changed over time, although age and education remained important determinants of vaccine-related decisions [15].
A particularly interesting finding in the present study was the higher likelihood of full vaccination among rural participants after adjustment for other covariates. This result differs from much of the international literature, in which rural residence has more often been associated with lower vaccination uptake. For example, data from the United States Centers for Disease Control and Prevention showed persistently lower COVID-19 vaccination coverage in rural areas than in urban areas [16]. Thus, our finding should not be generalized or interpreted as evidence of a universal rural advantage but rather as a local pattern observed in a specific primary care population from a small city and its surrounding rural communities.
A possible explanation is the health care context in which the study was conducted. Participants were recruited through a primary health care clinic; such settings may reduce some barriers that often contribute to lower vaccine uptake in rural populations. In this context, closer contact with a local primary care network may have partly offset the usual disadvantages related to access, information, or trust. This interpretation should be made cautiously, but it is consistent with the fact that our study reflects a real-world primary care population rather than a general population sample [17].
The lifestyle-related findings were more difficult to interpret. Some LSQ domains were associated with vaccination status in the descriptive analysis, but the overall pattern was not consistent and did not support a simple conclusion that a healthier lifestyle translated into greater vaccine uptake. This is notable because some previous studies revealed the opposite pattern, whereby healthier behaviors were positively associated with COVID-19 vaccination [18]. In the present study, lifestyle appeared to play a secondary role compared with age, education, and the health care setting. This role may partly reflect the structure of the LSQ itself, which combines diverse behavioral and psychosocial domains. In such a context, interpretation of individual domains may be more informative than reliance on the total score alone.
The findings should be interpreted in light of several limitations. The present study was based on a convenience sample from a single primary care setting, which limits generalizability. The cross-sectional design does not permit causal inference, and lifestyle data were self-reported. Additionally, the partially vaccinated subgroup was small and therefore was combined with the unvaccinated group in the primary analyses. Nevertheless, the study has practical value because it captures vaccination behavior in a real-world primary care population from Eastern Poland, including many participants from rural areas. In that context, the findings may be less useful as a universal model of vaccine uptake but might serve as an example of how local health care organization and patient context can shape vaccination behavior. Although the data were collected in late 2022 and early 2023, they remain relevant because they reflect vaccination behavior at a later stage of the pandemic, when decisions were shaped less by the initial vaccine rollout and more by stable patterns of trust, hesitancy, and engagement with primary care; such patterns may inform future vaccination campaigns.
Conclusions
In this primary care sample from Eastern Poland, COVID-19 vaccination uptake was mainly associated with age, education, retirement status, and place of residence rather than a consistent lifestyle profile. The finding of higher vaccination uptake among rural participants contrasts with much of the international literature and may reflect the local health care context. These findings suggest that local primary care clinics should tailor vaccination communication and outreach strategies to the specific social and geographic context of their patient populations, rather than solely relying on broad national assumptions about vaccine uptake.
Acknowledgements
An artificial intelligence (AI)-based language model was solely used for language editing and stylistic refinement during preparation of the article; it was not involved in the study design, data analysis, or interpretation of the findings.
Footnotes
Financial support: None declared
Conflict of interest: None declared
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher
Institution Where Work Was Done: Medical and Diagnostic Center, Siedlce, Poland.
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