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Learn more: PMC Disclaimer | PMC Copyright Notice Hum Vaccin Immunother . 2026 Apr 13;22(1):2657121. doi: 10.1080/21645515.2026.2657121 Search in PMC Search in PubMed View in NLM Catalog Add to search Cognitive factors associated with willingness to receive three important vaccines among older adults: A protection motivation theory perspective Xueqing Peng Xueqing Peng a Department of Immunization Planning, Chengdu Center for Disease Control and Prevention (Chengdu Institute of Health Supervision), Chengdu, China Conceptualization, Methodology, Project administration, Investigation, Writing – original draft, Writing – review & editing Find articles by Xueqing Peng a, ✉ , Rongna Huang Rongna Huang a Department of Immunization Planning, Chengdu Center for Disease Control and Prevention (Chengdu Institute of Health Supervision), Chengdu, China Conceptualization, Methodology, Project administration, Writing – review & editing Find articles by Rongna Huang a , Anita Nyarkoa Walker Anita Nyarkoa Walker b School of Public Health, Nanjing Medical University, Nanjing, China Resources, Writing – original draft, Writing – review & editing Find articles by Anita Nyarkoa Walker b , Liangzhi Zhang Liangzhi Zhang a Department of Immunization Planning, Chengdu Center for Disease Control and Prevention (Chengdu Institute of Health Supervision), Chengdu, China Conceptualization, Methodology, Writing – review & editing Find articles by Liangzhi Zhang a , Yunqi Miao Yunqi Miao a Department of Immunization Planning, Chengdu Center for Disease Control and Prevention (Chengdu Institute of Health Supervision), Chengdu, China Investigation, Software, Writing – review & editing Find articles by Yunqi Miao a , Qingsong Yu Qingsong Yu a Department of Immunization Planning, Chengdu Center for Disease Control and Prevention (Chengdu Institute of Health Supervision), Chengdu, China Investigation, Resources, Writing – review & editing Find articles by Qingsong Yu a , Lei Li Lei Li a Department of Immunization Planning, Chengdu Center for Disease Control and Prevention (Chengdu Institute of Health Supervision), Chengdu, China Conceptualization, Methodology, Project administration, Supervision, Writing – review & editing Find articles by Lei Li a, ✉ Author information Article notes Copyright and License information a Department of Immunization Planning, Chengdu Center for Disease Control and Prevention (Chengdu Institute of Health Supervision), Chengdu, China b School of Public Health, Nanjing Medical University, Nanjing, China ✉ CONTACT Xueqing Peng [email protected] ✉ Lei Li [email protected] Department of Immunization Planning, Chengdu Center for Disease Control and Prevention (Chengdu Institute of Health Supervision), 10 Longxiang Road, Wuhou District, Chengdu 610041, China. Roles Xueqing Peng : Conceptualization, Methodology, Project administration, Investigation, Writing – original draft, Writing – review & editing Rongna Huang : Conceptualization, Methodology, Project administration, Writing – review & editing Anita Nyarkoa Walker : Resources, Writing – original draft, Writing – review & editing Liangzhi Zhang : Conceptualization, Methodology, Writing – review & editing Yunqi Miao : Investigation, Software, Writing – review & editing Qingsong Yu : Investigation, Resources, Writing – review & editing Lei Li : Conceptualization, Methodology, Project administration, Supervision, Writing – review & editing Received 2026 Jan 31; Accepted 2026 Apr 4; Collection date 2026. © 2026 The Author(s). Published with license by Taylor & Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( http://creativecommons.org/licenses/by-nc/4.0/ ), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. PMC Copyright notice PMCID: PMC13078240 PMID: 41974623 ABSTRACT Vaccines against influenza, pneumococcal pneumonia, and herpes zoster are pivotal for infectious disease control in older adults, yet vaccination coverage remains suboptimal. Guided by Protection Motivation Theory (PMT), this cross-sectional study identified cognitive factors of overall willingness toward the three important vaccines among adults aged ≥60 y, to inform population-level intervention strategies. A representative sample of 420 older adults was recruited from Chengdu, China, via multistage stratified sampling between August and October 2024. Partial least squares structural equation modeling was applied to analyze associations between PMT cognitive constructs, health status, and overall vaccination willingness. Results showed moderate overall willingness to receive the three vaccines (16.7 ± 5.0, range: 5–25), with PMT explaining 57.6% of its variance (R 2 = 0.576). Four PMT cognitive factors significantly predicted vaccination willingness: three had positive associations – perceived vulnerability (b = 0.128, p < .01), self-efficacy (b = 0.396, p < .001), and response efficacy (b = 0.289, p < .001) – with self-efficacy being the strongest predictor; whereas response cost (b = −0.102, p < .01) exerted a negative effect. Health status moderated the perceived severity → vaccination willingness pathway (diff b = −0.189, p < .05); specifically, perceived severity positively correlated with willingness among older adults with poor health (b = 0.143, p < .05). Findings suggest that, except among older adults with poor, physical health tailored public health intervention policies to boost older adults’ overall willingness for the three key vaccines should prioritize targeting vaccination-related beliefs (self-efficacy, benefits, costs, and risk of infection) rather than disease severity. KEYWORDS: Older adults, vaccination willingness, cognitive factors, protection motivation theory, structural equation modeling Introduction The global population is aging, and China is no exception. According to China’s seventh national census, 18.7% of the population is aged 60 y or older, 1 and by 2035 the country is projected to experience severe population aging. 2 In this context, infectious diseases such as influenza, pneumococcal pneumonia, and herpes zoster warrant particular attention, as they are major causes of morbidity and mortality among older adults. 3 These diseases contribute to the loss of healthy life years and impose a substantial burden on society. Vaccination is a crucial strategy for maintaining the health and enhancing quality of life in this population. Three vaccines – namely influenza, pneumococcal pneumonia, and herpes zoster – are strongly recommended for older adults because they are at higher risk of infection, complications, hospitalization, and mortality. 4 In high-income countries, uptake varies considerably. For instance, in the United Kingdom, influenza vaccine coverage among adults aged 65 y and older ranged from 72.0% to 84.2% between 2018 and 2024 5 ; in the United States, pneumococcal pneumonia vaccine coverage among adults aged 65 y and older is 69.0% 6 ; and in Australia, 46.9% of individuals aged 50 y and older have received the herpes zoster vaccine. 7 Compared with high-income countries, China has been relatively slow in recommending or promoting vaccination among older adults. Currently, all three vaccines are non-National Immunization Program (non-EPI, non-cost-exempt) vaccines, available nationwide and accessible voluntarily on a self-paid basis by older adults. However, data from China’s National Immunization Program Information System indicate that vaccination coverage among older adults remains extremely low. In 2022, the estimated coverage rates among individuals aged 60 y and older were 3.75% for influenza vaccine, 3.23% for pneumococcal pneumonia vaccine, and 0.1% for herpes zoster vaccine. 8 These figures highlight a substantial gap in coverage compared with developed countries. Despite the absence of these three vaccines from China’s national EPI, regional governments have implemented targeted vaccination policies to improve coverage rates. Concerted efforts have been made to enhance service accessibility, such as maintaining sufficient vaccine supply, providing door-to-door vaccination services for elderly populations, and implementing fee-reduction policies. 8–10 These measures have facilitated easier access to these vaccines for older adults. Nevertheless, access to vaccine services alone is insufficient, as low vaccination willingness and hesitancy are major factors underlying limited uptake. As Griffin et al. 11 have shown, willingness to be vaccinated is the strongest predictor of actual vaccination behavior. Consequently, enhancing vaccination willingness among older adults is essential for improving overall vaccine coverage. Vaccination willingness among older adults is influenced by a combination of psychological and cognitive factors, as well as external or physical factors, which together shape decision-making and behavior. Noteworthily, psychological and cognitive factors act as direct determinants (proximal determinants), guiding how individuals assess risks and benefits associated with vaccination. 12 External factors – such as personal disease history or medical recommendations – affect vaccination decisions indirectly, operating through these cognitive processes; for example, poor health status may evoke risk perception, 13 while recommendations may guide behavior when interpreted as trust in healthcare providers. 14 , 15 Understanding the psychological and cognitive mechanisms underlying vaccination willingness is therefore essential for designing interventions aimed at improving vaccine uptake among older adults. Most existing studies have examined individual cognitive factors in isolation, 16 , 17 providing limited understanding of vaccination willingness. Although several theories, including the Health Belief Model (HBM), Theory of Planned Behavior (TPB), Knowledge-Attitude-Practice (KAP) model, 3Cs (Confidence-Complacency-Convenience) model, and WHO’s Behavioral and Social Drivers (BeSD) framework have been applied to systematically investigate the multifaceted determinants of vaccination willingness, 18–22 each has key limitations for our specific research aims. KAP’s linear structure cannot quantify predictor weights. Although HBM includes self-efficacy, it does not systematically integrate it with response efficacy and cost into a unified coping appraisal. TPB lacks native health threat appraisal and vaccine-specific efficacy/cost prioritization. While the 3Cs model provides a useful categorization of barriers, and the BeSD framework offers a comprehensive tool for measuring multidimensional drivers, both are primarily descriptive and diagnostic in nature, focusing on identifying barriers and facilitators without specifying a priori causal pathways – a core requirement for our theory-driven confirmatory structural equation modeling approach. We therefore selected the Protection Motivation Theory (PMT) to address these gaps. PMT’s testable dual-process structure (threat/coping appraisal) captures risk and competence perception, incorporates response cost as a barrier, and quantifies predictor weights. Its focus on protection motivation as the proximal determinant of behavior aligns directly with our outcome of interest – vaccination willingness. PMT, originally developed by Rogers et al., 23 is a widely used social cognitive framework for understanding how individuals are motivated to adopt protective behaviors in response to health threats. According to PMT, the decision to engage in a protective behavior – such as vaccination – is driven by two interrelated cognitive processes: threat appraisal and coping appraisal. Threat appraisal evaluates the perceived severity of and personal vulnerability to a health threat (e.g., contracting influenza, pneumococcal pneumonia, or herpes zoster). Coping appraisal assesses the individual’s belief in the effectiveness of the recommended protective behavior (response efficacy), their confidence in successfully completing the behavior (self-efficacy), and the perceived barriers or costs associated with it (response cost). Together, these cognitive appraisals shape protection motivation, which is most commonly operationalized as behavioral intention (i.e., vaccination willingness in this study). In public health practice, influenza, pneumococcal pneumonia, and herpes zoster vaccines are routinely jointly recommended as core preventive measures for older adults. 24 We hypothesize that PMT framework can effectively explain and predict older adults’ overall willingness to receive these three vaccines. Existing PMT-based vaccine acceptance studies are narrow in scope, focusing mainly on COVID-19 vaccine 11 or human papillomavirus vaccine, 25 and the general population 26 or specific patient groups. 24 To our knowledge, few studies have applied PMT to unpack the cognitive pathways of older adults’ multi-vaccine willingness. Furthermore, individual backgrounds can subtly influence personal cognition. 27 For example, Ye and Zheng found that urban residents with better self-rated health status perceived lower infectious diseases risk. 28 This finding indicates that health-related cognition may differ among individuals with varying health statuses, yet this critical boundary condition has not been systematically examined in prior multi-vaccine willingness research among older adults. Therefore, this study employs the PMT to investigate universal cognitive drivers of older adults’ overall willingness to receive the above three vaccines (examined as a single composite outcome). Specifically, this study aims to: (1) analyze the cognitive mechanisms underlying this willingness based on the PMT; and (2) test the moderating effect of health status on the associations between PMT cognitive factors and vaccination willingness. Methods Study design and participants A cross-sectional survey was conducted in Chengdu, China, from August to October 2024, to assess the willingness of older adults to receive three vaccines – influenza, 23-valent polysaccharide pneumococcal pneumonia, and recombinant zoster – as a single composite construct. Chengdu is located in western China. It is one of the most economically and medically advanced cities in China, covering an area of approximately 14,335 km 2 . As of December 2024, the resident population reached 21.474 million, with a per capita GDP of 109,669 CNY. A multi-stage stratified sampling method was employed to select participants. Based on the geographical location and economic level divisions, Chengdu city was divided into three areas: central urban, suburban, and rural. At the initial stage, one district or county was randomly selected from each area. Then, one community street and one township were randomly selected from each county/district as survey units. At each survey site, trained investigators first conducted on-site screening to verify whether residents met the inclusion criteria (age ≥ 60 y; residing in Chengdu ≥ 6 months; capable of understanding or reading and writing, and able to complete the questionnaire). Eligible residents were invited to participate via face-to-face communication, during which investigators explained the study purpose, procedures, confidentiality, and voluntary nature of participation. A total of 472 eligible residents were approached, with 440 agreeing to participate (participation rate: 93.22%). Refusal reasons included lack of interest (n = 21) and time constraints (n = 11). Written informed consent was obtained from all enrolled participants prior to the face-to-face questionnaire survey. Each survey participant required 10–15 minutes to complete the questionnaire (in Chinese) independently under the guidance of a survey administrator. Upon completion of the survey, participants were given a small gift (e.g. tissues or towels) as a compensation. Three investigators, all college students, assisted with the data collection. Prior to conducting the survey, all the survey administrators undertook unified training to understand the questionnaire content and standardized screening procedures. The sample size was determined using the 10-times rule for questionnaire items. 29 With 30 items, the initial minimum required sample size was 300. This estimate was then adjusted to account for a 10% potential invalid response rate, resulting in a minimum required sample size of 333. A total of 440 individuals voluntarily participated in the survey. After excluding 20 questionnaires with clear logical errors the final sample comprised 420 valid responses, yielding a valid response rate of 95.45%. This study was approved by the Ethics Review Committee of the Chengdu Center for Disease Control and Prevention (No. 2024022). Measurements The questionnaire was designed by the researchers. The research team drafted the initial document based on extensive research on vaccine acceptance 11 , 30–32 and a comprehensive understanding of the operational definitions of each dimension of the PMT. The instrument was then reviewed by three senior experts in immunization planning and health-related behavioral science and pretested in a pilot survey among 30 older adults (consistent with our formal study population) to evaluate the clarity and relevance of all items. Following these steps, the draft was finalized. The measurement content is as follows: ① General Information Questionnaire. This section encompassed a range of sociodemographic characteristics, including age, gender, education, marital status, and annual household income. In addition, the question “In general, how would you rate your health?” was used to assess the self-rated health status of respondents. The answers to this question were rated on a five-point Likert-type scale with the following categories: excellent, very good, good, fair, and poor. ② PMT-based questionnaire. This section was composed of five subscales, all measured using a composite approach that treated the three vaccines and corresponding diseases as a unified concept (not measured separately for each disease/vaccine). Each subscale comprised five items and used a five-point Likert-type scale ranging from 1 (completely disagree) to 5 (completely agree). Reverse-coded items were recoded before summation. Each subscale’s total score was calculated by summing corresponding items (ranging from 5 to 25), with higher scores indicating stronger corresponding cognition. Specifically, the perceived severity subscale assessed respondents’ perceptions of the potential consequences of contracting influenza, pneumococcal pneumonia, or herpes zoster as a unified set of diseases (e.g., “If I contract vaccine-preventable diseases (influenza, pneumococcal pneumonia, or herpes zoster), it will significantly impact my health”). The perceived vulnerability subscale measured respondents’ perceived likelihood of contracting the three aforementioned infectious diseases as an integrated group (e.g., “Not getting vaccinated against the influenza, pneumococcal pneumonia, or herpes zoster increases my risk of getting these diseases”). The response efficacy subscale measured the perceived benefits from receiving the three vaccines against these diseases (e.g., “If I get vaccinated against the influenza, pneumococcal pneumonia, or herpes zoster, it can lower my chances of getting those illnesses”). The self-efficacy subscale measured respondents’ confidence in their ability to obtain the three aforementioned vaccines (e.g., “I believe I can overcome any obstacles in getting these vaccines”). The response cost subscale measured the obstacles that respondents perceived they would have to overcome to receive the three vaccines (e.g., “Getting these vaccines may cause mild adverse reactions, which could disrupt my daily life”). The Cronbach’s alpha value for each subscale ranged from 0.694 to 0.949. For the entire scale, the Cronbach’s alpha was 0.812, the Kaiser–Meyer–Olkin (KMO) value was 0.868, and test of Bartlett’s sphericity was significant ( p < .001). ③ Vaccination willingness questionnaire. This section was used to measure participants’ overall future-oriented willingness toward the three vaccines as a single composite construct (rather than willingness to receive any single vaccine separately), for example: “I will get these vaccines when they are needed.” All participants completed the same five items, regardless of prior vaccination history, with items designed to capture general vaccination attitude, recommendation intention, and future uptake willingness (not only first-dose intention). Items were scored on a five-point Likert-type scale (1 = completely disagree to 5 = completely agree), with a total score ranging from 5 to 25 (higher scores indicating stronger vaccination willingness). The Cronbach’s alpha was 0.947, the KMO value was 0.867, and test of Bartlett’s sphericity was significant ( p < .001). A supplementary file shows the questionnaire in more detail (see Table S1). Statistical analysis Descriptive statistics were used to describe sociodemographic characteristics, PMT cognitive factors, and overall vaccination willingness. The Shapiro-Wilk test was employed to assess the normality of the PMT cognitive factors and vaccination willingness. Spearman correlation analysis was conducted to examine the relationships between the constructs of the PMT and vaccination willingness. Since normality testing indicated that the data for each PMT construct and overall vaccination intention were not normally distributed, we chose the partial least squares structural equation modeling (PLS-SEM) for structural equation modeling. 33 In the PLS-SEM analysis, the initial steps involved the calculation of factor loadings, Cronbach’s α, composite reliability (CR), and average variance extracted (AVE) values to assess the reliability of the measurement model. In addition, the Fornell-Larcker criteria and Heterotrait-Monotrait ratio of correlations (HTMT) were calculated to assess the validity of the measurement model. Subsequently, bootstrapping algorithms (5000 times) were conducted to ascertain the significance of the path coefficients in the structural model. Meanwhile, the R 2 , Q 2 , and goodness of fit (GoF) values were calculated to evaluate the validity of the structural model. Finally, we categorized self-rated health status as “good” (excellent, very good, good) or “poor” (fair, poor), and performed a multigroup analysis to assess the moderating effects of health status (0 = poor; 1 = good). All reference criteria for evaluation metrics in the SEM analysis were based on the recommendations proposed by Hair, Urbach, Henseler, and Cohen et al . 34–37 To verify statistical power, we conducted a post hoc sensitivity analysis using the inverse square root method (α = 0.05, power = 0.80). The minimum detectable path coefficients were 0.1213 (full sample group), 0.1629 (good health group), and 0.1818 (poor health group). All significant paths exceeded or approached these thresholds, indicating sufficient power. Data analysis was performed using R 4.5.0, with a two-sided significance level of α = 0.05. Results Descriptive statistics In Table 1 , the 420 participants included in the analysis had an average age of 68.3 (SD = 6.5, range: 60–92) y, with 60.7% being female and 39.3% male. Among them, 37.60% had completed primary school; 74.50% were married and living with a spouse; 58.30% had an average annual household income below 50,000(CNY). Participants showed moderate overall willingness to be vaccinated these three vaccines (16.7 ± 5.0). Detailed PMT subscale scores are presented in Table 1 . Table 1. Descriptive statistics of participants (N = 420). Variables N Percentage/Mean (SD) Age, y 68.3(6.5) Gender Female 255 60.7 Male 165 39.3 Education attainment Unlearned 64 15.2 Primary school 158 37.6 Junior high school/Technical secondary school 136 32.4 High School/Technical School 41 9.8 Junior college/undergraduate and above 21 5.0 Marriage Married 313 74.5 Widowed 94 22.4 Divorced 12 2.9 Single 1 0.2 Annual household income, CNY <50,000 245 58.3 50,000–100,000 112 26.7 100,001–150,000 44 10.5 >150,000 19 4.5 Health status Excellent 16 3.8 Very good 74 17.6 Good 143 34.0 Fair 155 36.9 Poor 32 7.6 PMT constructs Perceived severity 15.8(3.8) Perceived vulnerability 16.0(3.3) Response efficacy 18.4(4.6) Response cost 14.1(3.9) Self-efficacy 18.2(4.4) Willingness to be vaccinated 16.7(5.0) Open in a new tab Bivariate correlation analysis among key variables Table 2 showed the correlation between overall vaccination willingness and various constructs of PMT. The results indicated that overall vaccination willingness positively correlated with perceived vulnerability (r s = 0.472), response efficacy (r s = 0.689), and self-efficacy (r s = 0.714), and negatively correlated with response cost (r s = −0.356), all p < .01. No statistically significant correlation with perceived severity was observed. Table 2. Bivariate relationship between overall vaccination willingness and PMT constructs. Constructs 1 2 3 4 5 6 1 Perceived severity – 0.234** 0.067 0.083 0.059 0.078 2 Perceived vulnerability – 0.467** −0.246** 0.427** 0.472** 3 Response efficacy – −0.290** 0.776** 0.689** 4 Response cost – −0.308** −0.356** 5 Self-efficacy – 0.714** 6 Willingness to be vaccinated – Open in a new tab ** p < .01. Measurement model results Table 3 presented the reliability and convergence validity of each construct’s measurement model. The factor loading for each item ranged from 0.600 to 0.948, Cronbach’s alpha ranged from 0.804 to 0.950, CR ranged from 0.861 to 0.950, and AVE values were higher than 0.500, indicating that the measurement models demonstrated good internal consistency. Table 3. Reliability and convergence validity of the measurement model. Constructs Item labels Mean SD Loadings Cronbach’s ɑ CR AVE Perceived severity (Se) Se1 3.23 1.24 0.940 0.804 0.916 0.721 Se2 3.11 1.19 0.948 Se3 3.42 1.09 0.618 Se4 3.08 1.12 N/A Se5 2.96 1.05 N/A Perceived vulnerability (Vul) Vul1 3.19 1.17 0.934 0.916 0.921 0.856 Vul2 3.24 1.13 0.930 Vul3 3.05 1.20 0.912 Vul4 3.04 1.11 N/A Vul5 3.42 1.08 N/A Response Efficacy (ReE) ReE1 3.74 0.97 0.909 0.950 0.950 0.833 ReE2 3.60 1.04 0.925 ReE3 3.74 0.97 0.933 ReE4 3.61 1.05 0.893 ReE5 3.75 0.97 0.901 Response Cost (ReC) ReC1 3.30 1.10 0.600 0.814 0.861 0.573 ReC2 2.61 0.99 0.753 ReC3 2.75 1.03 0.805 ReC4 2.75 1.09 0.773 ReC5 2.67 0.95 0.834 Self-efficacy (SeE) SeE1 3.49 1.03 0.851 0.906 0.916 0.730 SeE2 3.55 1.06 0.906 SeE3 3.87 0.97 0.726 SeE4 3.57 1.05 0.897 SeE5 3.71 1.02 0.880 Willingness to be vaccinated Willingness1 3.60 1.07 0.895 0.947 0.947 0.825 Willingness2 3.41 1.15 0.885 Willingness3 3.29 1.11 0.940 Willingness4 3.24 1.10 0.912 Willingness5 3.17 1.08 0.907 Open in a new tab Note: N/A refers to these indicators were deleted due to low loadings in order to achieve AVE > 0.5. Table 4 showed the discriminant validity of the measurement model. AVE root values for each construct exceeded the correlation coefficients between constructs in the same row or column, and HTMT values between constructs were all less than 0.85, indicating good discriminant validity. Table 4. Differential validity of the measurement model. Fornell-Larcker ReC ReE Se SeE Vul Willingness ReC 0.757 ReE −0.297 0.912 Se 0.090 0.156 0.849 SeE −0.310 0.786 0.118 0.855 Vul −0.371 0.441 0.115 0.378 0.925 Willingness −0.353 0.694 0.148 0.709 0.448 0.908 HTMT ReC ReE Se SeE Vul Willingness ReC ReE 0.309 Se 0.170 0.195 SeE 0.328 0.844 0.165 Vul 0.403 0.470 0.133 0.409 Willingness 0.381 0.731 0.160 0.761 0.479 Open in a new tab Note: Bold diagonal values of the Fornell-Larcker matrix represent the square roots of the AVE for each latent construct. Structural model results Table 5 showed the results of the structural model. The R 2 value for the overall explanatory power of the model on overall vaccination willingness was 0.576, and the Q 2 was 0.471, indicating predictive relevance (Q 2 > 0). The f 2 values indicated that vaccination willingness was moderately to strongly explained by perceived vulnerability, response efficacy, response cost, and self-efficacy, whereas perceived severity had weaker explanatory power. The GoF value demonstrated a high overall model fit. The path coefficient for perceived severity was not significant ( p = .171); vulnerability (b = 0.128, p < .01), response efficacy (b = 0.289, p < .001), and self-efficacy (b = 0.396, p < .001) exerted positive effects on overall vaccination willingness, while response cost (b = −0.102, p < .01) exerted a negative effect on it. Table 5. Structural model results. Path Relationships b β t p f 2 R 2 Q 2 GoF Se → Willingness 0.050 0.048 1.370 .171 0.006 0.576 0.471 0.660 Vul → Willingness 0.128 0.119 3.034 .002 0.029 ReE → Willingness 0.289 0.317 5.188 <.001 0.071 ReC → Willingness −0.102 −0.132 2.651 .008 0.020 SeE → Willingness 0.396 0.448 6.922 <.001 0.140 Open in a new tab Note: b = standardized coefficient; β = unstandardized coefficient. Multigroup analysis results Figure 1 displayed the results of multi-group analysis of health status. The R 2 values for the good- and poor-health groups were 0.622 and 0.561, respectively. Figure 1. Open in a new tab Path relationship diagram (group comparison by health status). Note: Structural model with standardized estimates for good/poor health status groups. The dashed line yield significant moderating effect of health status. ns = no significant, * p < .05, ** p < .01, *** p < .001. The results indicated that health status moderates the relationship between perceived severity and vaccination willingness (diff b = −0.189, Comparison p < .05). Specifically, perceived severity positively influences overall vaccination willingness in the poor health group (b = 0.143, p < .05), whereas it does not exert such an effect in the good health group. Furthermore, the impact of vulnerability on the overall vaccination willingness was not significant for the poor health group. Similarly, response cost does not have a significant impact in the good health group. The effects of other factors remain stable in both groups. Discussion Based on the PMT, this study investigated the cognitive factors related to older adults’ willingness to receive the three vaccines, as well as the moderating role of health status in these associations. The findings of the model validation process demonstrated that the PMT exhibited a substantial explanatory capacity for older adults’ vaccination willingness, thereby providing further validation of the efficacy of PMT in interpreting and predicting vaccination acceptance. 26 While the efficacy of the PMT core cognitive factors varied, this study yielded novel and potentially significant information that may inform the design of targeted vaccination intervention strategies for older adults. According to Rogers’ perspective, threat assessment is dependent upon an individual’s perception of threats which triggers fear arousal and, subsequently, protective motivation. 23 This typically involves evaluating both the severity of the threat and one’s vulnerability to it. In the present study, perceived vulnerability was the sole factor significantly associated with vaccination willingness among older adults. Concurrent conclusions have been reached in the field of vaccination research 11 , 25 and in other behavioral research domains, including smoking behavior 38 and respiratory infection preventive behaviors. 39 The findings of the present study indicated that, in both correlation analysis and SEM analysis, perceived severity did not demonstrate a statistically significant association with older adults’ overall vaccination willingness, contrary to Rogers’ findings. This overall non-significant result aligns conceptually with “complacency” in the 3Cs model, yet PMT’s threat appraisal goes further by distinguishing severity and vulnerability, revealing that severity only matters for older adults in poor health. However, extant research on vaccination willingness has also yielded analogous results. For instance, Xiao et al. found that perceived severity did not significantly influence adults’ intention to receive the COVID-19 vaccine. 40 Based on the findings of this study, we explored potential underlying reasons, including cognitive bias and underestimation of disease severity. As indicated in previous reports, middle-aged and elderly populations tend to underestimate the risks and harms associated with common infectious diseases, such as influenza, pneumococcal pneumonia, and herpes zoster. 41 In this study, the respondents’ severity perception levels were also found to be medium to low. This cognitive bias, or underestimation, may explain the lack of observed association between perceived severity and vaccination behavior. 42 Coping assessment is an individual’s evaluation of the effectiveness and perceived costs of engaging in protective behavior, which is usually composed of response efficacy, self-efficacy and response cost cognition. 23 The present study also found that these three cognitive factors had a significant impact on the older adults’ overall vaccination willingness. As expected, the findings of this study demonstrated that higher self-efficacy beliefs significantly increased older adults’ willingness of receiving vaccinations, with this factor exhibiting the strongest predictive capacity. Gao et al.’s research also showed that self-efficacy contributed most to explaining vaccine hesitancy among middle-aged and older adults (aged ≥ 40 y) regarding herpes zoster vaccines. 43 Self-efficacy is an important concept in numerous academic domains, serving as a pivotal mechanism that elucidates individual behavioral motivation. The findings of this study further corroborate this perspective. Furthermore, we discovered that perceived efficacy can positively influence older adults vaccination willingness. Greater recognition of the benefits or effectiveness of healthy behaviors increases the likelihood of adopting protective behaviors or intentions. 44 A study conducted in Iran that employed PMT to predict adults’ willingness to be vaccinated against COVID-19 reached the same conclusion. 26 Behavioral change frequently necessitates a certain degree of personal sacrifice (energy, time or money, etc.), which can impede the adoption of the desired behavior. Our study also found that perceived response costs were negatively correlated with older adults’ willingness to be vaccinated, a relationship that has been reported in many similar articles. 45 , 46 The present study’s multi-group analysis of respondents with different health conditions yielded several noteworthy findings. Among individuals with poor health, perceptions of disease severity positively influenced their willingness to receive vaccinations. In contrast, this relationship was not observed among those in good health. This phenomenon can be attributed to the heightened awareness of disease in individuals with poor health, 47 , 48 who may possess a more profound understanding of its implications due to their firsthand experience or heightened concerns regarding its consequences, such as hospitalization, financial burden, or loss of function. 49–51 This may also explain why vulnerability perception was not significantly associated with overall vaccination willingness among older adults experiencing poor health. These individuals prioritize the potential consequences of contracting a disease over the probability of infection. The study also found that the perception of response costs did not affect the willingness to be vaccinated among those in good health. One potential explanation for this disparity is that these individuals tend to have more stable physical functions, along with enhanced mobility and economic stability. 52 Consequently, they may be less susceptible to the perception of reaction costs, such as short-term adverse effects of vaccines, temporal inconvenience, or financial disbursements, hence diminishes the effect of response costs on their vaccination decisions. This finding clarifies the boundary condition of PMT’s threat appraisal process in older adults’ multi-vaccine decision-making, fills a research gap in the heterogeneous effects of PMT constructs across health statuses, and provides stratified intervention evidence: disease severity messaging only drives vaccination willingness among older adults with poor health. Overall, findings from this research indicated vulnerability (from the threat assessment process) and response efficacy, self-efficacy, and response cost (from the coping assessment process) collectively determine the formation of vaccination willingness among older adults. Furthermore, the magnitude of the effects of each cognitive factor also differs. Among them, the effect produced by the coping assessment exceeds that of the threat assessment. This finding indicates that subsequent interventions aimed at enhancing older adults’ willingness to receive vaccinations can be informed by PMT. Critically, health status moderates the severity – willingness relationship, with important implications for intervention design. For healthy older adults, interventions should prioritize strengthening coping appraisal (self-efficacy, benefits, cost reduction). For those in poor health, emphasizing disease severity is also effective, as they are more responsive to threat-based messaging. This tailored approach aligns with health communication principles and could enhance vaccination promotion efforts. These stratified recommendations are directly actionable for public health practitioners designing tailored vaccination campaigns for older adults with different health profiles. Limitation The interpretation and application of the results of this study should also take into account the following limitations: First, this cross-sectional, self-report study precludes definitive causal inference and may be subject to response and non-response bias. Second, treating overall willingness to receive the three vaccines as a single composite construct may obscure vaccine-specific differences. Additionally, this composite measure was not formally tested for criterion validity against an established external standard. However, given the commonalities in psychological decision-making across disease risks, 53 a holistic approach helps identify shared facilitators and barriers, supporting the relevance of our conclusions. Future research should examine willingness for each vaccine separately and incorporate more rigorous scale development procedures, including criterion validity testing, to further validate the composite construct. Third, sociodemographic factors and broader social-environmental influences (e.g., healthcare access) were not directly modeled. Future studies should integrate these variables to complement the PMT’s cognitive focus and enhance explanatory power. Fourth, single-city findings have limited generalizability; off-peak sampling may bias perceptions, warranting multi-region studies. Fifth, uniform willingness scale use across participants with and without prior vaccination history may introduce item interpretation heterogeneity; stratified measurement tools for different vaccination histories are recommended for future research. Finally, this study concentrated on vaccination willingness rather than actual behavior. While willingness is a strong predictor of uptake, 11 discrepancies may exist between these outcomes. Further research on actual vaccination behavior in the future may yield more meaningful evidence. Conclusions The PMT is a theoretically sound model for interpreting and predicting vaccine uptake among older adults. Within this framework, self-efficacy, response efficacy, response cost, and perceived vulnerability have emerged as key cognitive predictors. Critically, health status acts as a significant boundary condition: perceived severity only predicts willingness among older adults with poor health. Accordingly, interventions should prioritize addressing vaccination-related beliefs, including the self-efficacy, perceived benefits, potential costs, and the risk of infection, rather than focusing solely on disease severity. However, for older adults with compromised health, intervention measures should also consider the severity of the disease. Supplementary Material Supplementary_Material.docx KHVI_A_2657121_SM6565.docx (43.1KB, docx) Acknowledgments We would like to thank all the respondents for their cooperation. Biographies Xueqing Peng , Master of Public Health, is currently a member of the Department of Immunization Planning of Chengdu Center for Disease Control and Prevention (Chengdu Institute of Health Supervision), responsible for immunization planning related work. Peng has primarily focused on the surveillance and management of vaccine-preventable diseases. She has led or participated in multiple research projects and has published over 10 scientific papers to date. Peng’s research interests encompass vaccine-preventable disease control, vaccine hesitancy, and health education. Rongna Huang is a Chief Physician in the Immunization Division of the Chengdu Center for Disease Control and Prevention (Chengdu Institute of Health Supervision), with over 30 y of experience in immunization-related work. Her research focuses primarily on the control of vaccine-preventable diseases and health education. Anita Nyarkoa Walker is a doctoral candidate at the School of Public Health, Nanjing Medical University. Her research interests focus on understanding the determinants of health-related behaviors and health outcomes. Liangzhi Zhang , Chief Physician of the Immunization Division at the Chengdu Center for Disease Control and Prevention (Chengdu Institute of Health Supervision), has dedicated over 20 y to infectious disease control and has published numerous academic papers. Yunqi Miao is a physician in the Immunization Division of the Chengdu Center for Disease Control and Prevention (Chengdu Institute of Health Supervision). Her research interests focus on understanding people’s attitudes toward vaccines and their vaccination behaviors. Qingsong Yu is a physician in the Immunization Division of the Chengdu Center for Disease Control and Prevention(Chengdu Institute of Health Supervision), specializing in the control of vaccine-preventable diseases. Lei Li , Master of Epidemiology and Health Statistics, Chief Physician, is currently the Chief of the Department of Immunization Planning of Chengdu Center for Disease Control and Prevention (Chengdu Institute of Health Supervision), responsible for the work of the planned immunization department of the center. Li, who has more than ten years of experience, specializes in the field of immunization planning and vaccine preventable disease monitoring. Li has led or participated in a number of research projects and has published more than 10 scientific papers so far, making important contributions to this field. Funding Statement This study was supported by a research project from the Chengdu Center for Disease Control and Prevention [No. 20240206]. Disclosure statement No potential conflict of interest was reported by the author(s). Data availability statement The data that support the findings of the present study are available from the corresponding author upon reasonable request. Ethics statement Ethical approval for this study was conducted by the Ethics Committee of the Chengdu Center for Disease Control and Prevention (No. 2024022). The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. All participants were informed about the purpose of the study, assured of confidentiality, and provided written consent prior to participation. 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