Health Beliefs Model and Vaccine Acceptance: Insights from the COVID-19 Pandemic among Non-Medical Personnel in  Mwanza, Tanzania

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MOSHA, TITUS ROBERT LEEYIO, ROBERT KITAMBO, EVELINE THOBIAS KONJE This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6783060/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Jan, 2026 Read the published version in BMC Public Health → Version 1 posted 14 You are reading this latest preprint version Abstract Background Without vaccine, attaining herd immunity remains a global challenge. While medical personnel have been a focus for vaccine promotion and improving immunization, non-medical personnel (NMPs) working in hospital settings remain unconsidered and understudied in Tanzania. Given their routine interaction with patients, NMPs can play a key role as agents in the spread of nosocomial infections. This study aimed to assess vaccine acceptance level in this vulnerable, predisposed population using the health belief model (HBM) as a guiding frame to obtain insights from the COVID-19 vaccine in Tanzania. Methods A total of 203 purposively selected NMPs from three health facilities in Mwanza City were involved in a cross-sectional study. Descriptive statistics and multivariate analysis were performed, results were presented, and the associations were reported using adjusted odds ratios from logistic regression with 95% confidence intervals at p < 0.05. Results The overall COVID-19 vaccine acceptance was 16.75% despite 62.07% of NMPs indicating willingness to vaccinate. Hesitancy was prevalent at 65% primarily due to lack of information (19.21%), fear of side effects (17.73%) and concerns about vaccine safety (16.25%). Perceived threats (aOR: 9.89, p = 0.01), perceived barriers (aOR: 8.83, p = 0.02) and cues to action (aOR: 1.45, p = 0.05) were significantly associated factors of vaccine acceptance among NMPs. Conclusion Vaccine acceptance among NMPs remains low and the HBM provides a robust framework for understanding vaccine-related behavior. This underscores the need to address personal attitudes and mobilize effective cues to action to improve vaccine acceptance in hospital-based populations and the general population. Acceptance Vaccine Tanzania Non-Medical Personnel Figures Figure 1 Figure 2 Figure 3 Background Without universal vaccine coverage, attaining herd immunity becomes a challenge in containing epidemics. Mathematical models have shown that if the vaccine is 80% effective, then coverage must be at least 75% to control any existing pandemic (1). The Health Belief Model (HBM) has been widely adopted as a framework to explain individual perceptions and attitudes on vaccine acceptance among populations (2-4). The theory holds that health-related behavior depends on a combination of several factors: perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, and self-efficacy (5). It empowers researchers to explain and predict health-promoting behavior in terms of patterns of belief by addressing the association between health behaviors and an individual’s acceptance of health services (6). As of December 2023, approximately 32% of the total population in the African Region had completed the primary COVID-19 vaccination series. Among healthcare workers, the coverage was higher, with 48% having completed the primary series. This suggests that the vaccination coverage among non-medical personnel (NMPs) was slightly below the regional average of 32% (7). Similarly, as of June 2023, 54% of Tanzanians were fully vaccinated as compared to only 3.2% vaccinated in the general population in January 2022 (8). More than 34% of those who were vaccinated did not know which vaccine they received (9). In contrast, the lowest vaccine acceptance rates were reported in Tanzania compared to other LMICs (10, 11), signifying a potential need to assess personal and belief influences in vaccine acceptance. Furthermore, studies have identified demographic, socioeconomic, individual behavior, and belief factors linked to varying vaccine acceptance levels (12-14). Studies in Tanzania have reported an 18.5% COVID-19 vaccine coverage among healthcare workers with a 29% acceptance level (10). Similarly, elsewhere in Tanzania, 70.5% of healthcare workers were vaccinated against SARS-CoV-2 with an 81.4% acceptance level and 62.5% hesitancy level (15). Limited information exists on vaccine coverage and acceptance levels among NMPs who work in hospital settings. NMPs interact regularly with patients, potentially influencing infection control dynamics. Moreover, studies indicate a lower vaccine acceptance level among NMPs than other groups working in similar environments (76.98% versus 56.19%) (16). Protecting NMPs from infections plays a key role in controlling nosocomial transmission, and increasing the proportion of immunized NMP through vaccination would be a viable measure to avoid nosocomial infections (16). Therefore, we hypothesize a similar lower acceptance among NMPs in Tanzania, and therefore, we adopt the HBM as a tool to assess vaccine acceptance with an illustration of the COVID-19 vaccine. Materials and Methods Aim, design and settings of the study A cross-sectional study was conducted in Mwanza City in three hospital facilities (health center, regional referral hospital, and district hospital) purposively selected to get insights on vaccine acceptance using the COVID-19 vaccine and the Health Belief Model (HBM) as a guiding framework. The study was conducted between April to May 2023. Three hospital facilities were considered in the study to have the inclusion of all levels of participants in the study. Study Population Non-medical personnel refer to anyone working in a medical institution and providing non-medical services, such as a receptionist, cashier, and laundry attendant. In this study, 203 non-medical personnel were purposely chosen for the study from three health facilities within the Mwanza region, which are health center, district hospital and regional referral hospital. Inclusion and Exclusion Criteria All NMPs aged 18 years and above who were present at the health facility at the time of the study were eligible for the study. All personnel who were on leave, absent from work for official or non-official reasons, as well as those with ill were excluded from the study. The study also excluded all visiting and temporarily employed NMPs who were present at study sites during data collection period. Data Collection and Analysis Procedure A structured questionnaire was developed that consisted of key variables such as age, sex, and educational level. Lifestyle behavior information such as smoking status, history of chronic disease, type of chronic disease, history of hepatitis B vaccine, level of health facility, and questions on health belief model (HBM) constructs. A self-administered data collection approach was utilized after distributing a structured questionnaire among NMPs who were available at their workplace. A complete study questionnaire is available online: https://docs.google.com/document/d/1ZBLLASWwc0WJyloGUxltAhAiE2Cp5mq0/edit?usp=drive_link&ouid=115764617992089467467&rtpof=true&sd=true . Well-trained research assistants collected all questionnaires immediately after filling and for a few participants, the collection was done at the end of their shift. Statistical Analysis All data was encoded in Microsoft Excel 2021 and exported to Stata 15; descriptive statistics, such as frequency and percentages, were used to describe the proportion of participants in each category. Logistic regression was used to assess the association between variables, where the strength of the association was interpreted using an odd ratio and 95% confidence interval at a statistically significant p-value < 0.05. Results were presented in words, graphs, and tables. Results The study included 203 NMPs from three hospital levels in Mwanza City. More than half were males (110, 54.19%), and most were aged 20-30 (90, 44.33%). Two-thirds (66.50%) of NMPs worked in the health centers, and almost three-quarters of the participants had tertiary education (147, 72.41%). Only 17 (8.37%) NMPs reported having a chronic disease, of which 41.18% of the 17 participants were suffering from asthma. Generally, 11.33% of the NMPs reported having a smoking habit, and only 56.65% performed regular exercise. Additionally, in Table 1, among all included NMPs, more than half had received the hepatitis B vaccine (116, 57.14%). Table 1: Participants’ characteristics Characteristic Category N = 203 Percentage Age (Years) 20 – 30 90 44.33 31 – 40 54 26.60 Above 40 59 29.06 Sex Male 110 54.19 Female 93 45.81 Education Level Tertiary 147 72.41 Primary and Secondary 56 27.59 Level of Health Facility Health Centre 135 66.50 District Hospital 30 14.78 Regional Referral Hospital 38 18.72 Smoking Habit Yes 23 11.33 No 180 88.67 Exercise Practice Yes 115 56.65 No 88 43.35 Hepatitis B Vaccine Yes 116 57.14 No 87 42.86 Chronic Disease Yes 17 8.37 No 186 91.63 Name of Chronic Disease Asthma 7 41.18 Others 10 58.82 COVID-19 Symptoms in 1 st , 2 nd and 3 rd Phase Yes 65 32.02 No 138 67.98 Among 203 NMPs, the vaccine acceptance level was 32.51%, while 42.86% reported low hesitance and 15.76% were strongly hesitant, and 8.87% were undecided about accepting or hesitating about the vaccine Figure 1. Concerning a self-assessment on vaccine acceptance and exposure among NMPs, 62.07% indicated willingness to receive the COVID-19 vaccine, whereas 37.93% reported they would not. On the other hand, actual uptake remains low—only 16.75% of respondents had ever received a COVID-19 vaccine, while a substantial 83.25% had never been vaccinated. Additionally, just 8.90% of NMPs had a confirmed history of COVID-19 infection, as seen in Figure 2. Self-Reported Barrier to Vaccine Acceptance Table 2 shows the decision on vaccine acceptance among NMPs; key reasons for low vaccine acceptance included lack of information (19.21%), fear of side effects (17.73%), and concerns that the vaccine was rushed or insufficiently tested (16.25%). Smaller proportions did not believe in the vaccine's necessity (6.90%) or effectiveness (2.46%). Table 2: Self-Reported Barriers to COVID-19 Vaccine Uptake (N=203) Barriers n % The vaccine has been rushed or has not been adequately tested 33 16.25 I fear side effects from the vaccine 36 17.73 I lack information about the vaccine 39 19.21 I don’t believe in the necessity of the vaccine 14 6.90 I don’t believe in the effectiveness of the vaccine and its protectiveness 5 2.46 Responses to HBM Constructs Figure 3 presents the distribution of responses to HBM constructs related to COVID-19 vaccination, highlighting significant variations in perception and behavioral determinants. Perceived susceptibility was acknowledged by approximately 50% of respondents, while 30% were uncertain and 20% disagreed, suggesting a gap in risk awareness. Perceived severity had the highest agreement at around 70%, with 25% uncertainty and 5% disagreement, reinforcing the notion that most NMPs recognize COVID-19 as a serious health threat. 55% of non-medical personnel agreed on the perceived benefits of vaccination, while 40% remained uncertain on the perceived barriers of the vaccine. At least 80% of the non-medical personnel confirmed health motivation as a key element influencing vaccine acceptance. Overall, COVID-19 vaccine acceptance was 16.75% (95% C.I. 11.61%-21.89%) among NMPs. NMPs aged above 40 years demonstrated higher odds of acceptance compared to those aged 20–30 years (aOR = 1.58, 95% CI: 0.60–4.15, 0.35). Although females had higher odds of acceptance compared to males (aOR = 1.63, 95% CI: 0.70–3.76, 0.26), this association was not statistically significant. NMPs with primary and secondary education level were 12% less likely to vaccinate compared to NMPs with tertiary level. This was also not statistically significant in the multivariate analysis, (aOR: 0.88, 95% CI: 0.34-2.27, 0.79), table 3. Regarding the level of health facility in table 3; NMPs from referral hospitals were significantly more likely to accept vaccination compared to those from health centers (aOR = 2.57, 95% CI: 0.95–6.95, 0.06), while those from district hospitals showed no significant difference in vaccine acceptance compared to those in health center (aOR = 0.69, 95% CI: 0.19–2.52, 0.58). Smoking habit was not significantly associated with vaccine acceptance (aOR = 1.37, 95% CI: 0.42–4.51, 0.60). Similarly, NMPs who engaged in regular physical activity were not significantly associated with vaccine acceptance compared to those who did not engage, (aOR = 0.60, 95% CI: 0.26–1.41, 0.24. Further in Table 3, NMPs who had vaccinated with hepatitis B had more than twice the odds of vaccine acceptance compared to NMPs who had not vaccinated for hepatitis B, (aOR = 2.26, 95% CI: 0.93–5.51, 0.07). The presence of a chronic disease was also strongly associated with increased vaccine acceptance where NMPs who had chronic diseases such as asthma were three times more likely to accept vaccine compared to NMPs who had no chronic disease, (aOR = 3.72, 95% CI: 1.18–11.76, 0.03). Non-medical personnel who reported experiencing COVID-19 symptoms during any of the pandemic’s first three phases were significantly more likely to accept vaccine compared to those who did not report such symptoms (aOR = 2.40, 95% CI: 1.04–5.54, 0.04). Table 3: Factors associated with uptake of the COVID-19 vaccine Uptake Factors Acceptance Univariate Analysis Multivariate Analysis n = 34 (%) cOR 95% C.I. P-value aOR 95% C.I. P-value Age (Years) 20-30 31-40 Above 40 14 (41.18) 9 (26.47) 11 (32.35) 1 1.09 1.24 0.44 – 2.71 0.52 – 2.97 0.86 0.62 1 1.07 1.58 0.39 – 2.92 0.60 – 4.15 0.89 0.35 Sex Male Female 15 (44.12) 19 (55.88) 1 1.63 0.77 – 3.42 0.20 1 1.63 0.70 – 3.76 0.26 Education Level Tertiary Primary and Secondary 24 (70.59) 10 (29.41) 1 1.11 0.49 – 2.51 0.79 1 0.88 0.34 – 2.27 0.79 Level of Health Facility Health Centre District Hospital Referral Hospital 19 (55.88) 4 (11.76) 11 (32.35) 1 0.94 2.49 0.29 – 2.93 1.06 – 5.83 0.92 0.04 1 0.69 2.57 0.19 – 2.52 0.95 – 6.95 0.58 0.06 Smoking Habit No Yes 28 (82.35) 6 (17.65) 1 1.92 0.70 – 5.28 0.21 1 1.37 0.42 – 4.51 0.60 Exercise Practice No Yes 21 (61.76) 13 (38.24) 1 0.41 0.19 – 0.87 0.02 1 0.60 0.26 – 1.41 0.24 Hepatitis B Vaccine No Yes 9 (26.47) 25 (73.53) 1 2.38 1.05 – 5.40 0.04 1 2.26 0.93 – 5.51 0.07 Chronic Disease No Yes 26 (76.47) 8 (23.53) 1 5.47 1.94 – 15.46 <0.00 1 3.72 1.18 – 11.76 0.03 COVID-19 symptoms in 1 st , 2 nd , 3 rd phase No Yes 18 (52.94) 16 (47.06) 1 2.18 1.03 – 4.61 0.04 1 2.40 1.04 – 5.54 0.04 Health Belief Constructs Health belief constructs were regressed to assess their influence on vaccine acceptance in table 4. Age was not significantly associated with vaccination in the modified logistic regression of the HBM construct. Females were 4 times more likely to vaccinate than males when assessed with HMB constructs (aOR: 4.93, 95% CI: 1.05-23.14, 0.04). NMPs were 11 times more likely to vaccinate if they perceived higher susceptibility and severe threats of COVID-19 than those who perceived lower threats (aOR = 11.65, 95% CI: 2.30–58.98, 0.00). NMPs who agreed on the benefits of vaccination were three times more likely to accept the vaccine; however, on the modified model, the construct was not statistically significant (aOR=3.13, 95% CI: 0.36-26.88, 0.30). On the other hand, NMPs who agreed on cues to action as causative of vaccination were 5 times more likely to vaccinate than those who disagreed (aOR: 5.80, 95% CI: 0.88-38.11, 0.07). Cues to action were relatively significant at the near borderline. NMPs on their self-efficacy as personal behavioral promotion for vaccination were 85% more likely to vaccinate, despite not being statistically significant (aOR=1.85, 95% CI: 0.24-14.39, 0.60), table 4. Table 4: Health Belief Model Constructs and COVID-19 Vaccine Uptake HBM Construct Vaccinated, n (%) aOR 95% C.I. p-value Age 20 – 30 Years 31 – 41 Years Above 41 Years 14 (15.56) 9 (16.67) 11 (18.64) 1 0.96 1.32 0.19 – 4.89 0.27 – 6.48 0.96 0.74 Sex Male Female 15 (13.64) 19 (20.43) 1 4.93 1.05 – 23.14 0.04 Perceived Threats (Susceptibility & Severity) Low/No Threats High Threats 3 (2.21) 31 (46.27) 1 11.65 2.30 – 58.98 0.00 Perceived Benefits Disagree Agree 2 (2.00) 32 (31.07) 1 3.13 0.36 – 26.88 0.30 Perceived Barriers Disagree Agree 5 (3.45) 29 (50.00) 1 12.25 2.43 – 61.78 0.00 Cues to Actions Disagree Agree 4 (2.76) 30 (51.72) 1 5.80 0.88 – 38.11 0.07 Perceived Self-Efficacy Disagree Agree 3 (3.70) 31 (25.41) 1 1.85 0.24 – 14.39 0.60 Discussion This study explored vaccine acceptance among non-medical personnel in Mwanza, Tanzania, using the Health Belief Model and insights from the COVID-19 pandemic as a case illustration. Vaccine acceptance level among NMPs was 16.75%, with a 65% hesitancy level. The majority of the participants reported lacking information on the vaccine and fearing side effects that may result from the vaccine (19.21% versus 17.73%). In this study, only 8.90% of NMPs were COVID-19 positive at the time of the data collection. These findings underscore a critical gap in vaccine coverage among NMPs and highlight the urgent need for context-specific risk communication and behavioral interventions to improve vaccine confidence, particularly among those demonstrating resistance and uncertainty. Since the HBM goes with personal conviction, vaccine acceptability carries much weight, and hence high proportion of uninfected individuals may also contribute to perceived threats, further impacting vaccine acceptance among NMPs. While limited information exists on vaccine acceptance among non-medical personnel as compared to their counterparts, medical personnel, vaccine acceptance reported in this study is relatively lower compared to a study in Cameron that reported a 31.21% among the general population ( 17 ). Acceptance in this study is also much lower than the global acceptance of 60.8% and 67.8%, respectively ( 18 , 19 ). This could be attributed to government measures in creating awareness towards the uptake of the vaccine, late arrival of the vaccines, and limited published articles ( 20 ). For instance, 47% of non-medical personnel were willing to receive the vaccine in Lebanon ( 21 ). Similar reasons for lower acceptance reported in this study have also been reported worldwide ( 10 , 17 , 18 , 20 , 22 ). Similar concerns highlighted in this study included fear of side effects, doubts about vaccine safety, personal beliefs, and skepticism regarding the vaccine's effectiveness in preventing COVID-19. Results in this study suggest that, despite other modifying factors such as age, sex, education, and underlying comorbidities, HBM can be useful in understanding vaccine acceptance in the general population. Findings are consistent with a Malaysian study that used the HBM to assess willingness to receive the COVID-19 vaccine ( 4 ). Perceive susceptibility, severity, and benefits of compelled vaccination in the general population. Both perceived severity and susceptibility form health threats, such as worrying about being infected and infecting the family. Our study aligns with findings from other studies, which demonstrate that perceived severity and perceived susceptibility exert a stronger positive influence on vaccine acceptance than other constructs of the HBM ( 2 , 23 , 24 ). This implies that NMPs who believe they are at risk of getting COVID-19 are more likely to vaccinate compared to those who believe they are not at risk. Moreover, NMPs who perceive COVID-19 as a severe comorbidity are more likely to accept vaccination compared to their counterparts. On the other hand, cues to action as well as self-efficacy are prompted by perceived susceptibility, severity and benefits and thus trigger individuals to accept the vaccine. In this study, among participants who agreed with cues to action, 13.8% reported having chronic diseases such as asthma compared to only 6.2% who disagreed. This confirms that individuals with chronic conditions may be more likely to accept vaccination. This study coincides with research from Lebanon, confirming that individuals are more motivated to accept vaccines if both internal or external cues to action prompt vaccination ( 21 , 25 ). Results are also consistent with findings from the USA that observed cues to action are inversely associated with vaccine hesitancy. Barriers such as worries about side effects of the vaccine and accessibility were associated with vaccine acceptance. While in this study, perceived barriers were significantly associated with vaccine acceptance, the results contradict findings from Morocco, which observed a negative association with intention to vaccinate ( 23 ). This is because in this study, individuals who acknowledged more barriers were informed of the risks of the COVID-19 vaccine. This awareness might paradoxically prompt higher vaccine acceptance. In this study, among NMPs who acknowledged perceived threats, more than 50.7% agreed on perceived barriers. Research in Australia found that individuals perceiving themselves as being at high risk had increased confidence in the vaccine, hence the likelihood of accepting the vaccine ( 26 ). Likewise, of all who agreed on perceived barriers, more than 58.6% agreed on perceived threats to vaccine acceptance in the present study. In Tanzania, the safety, efficacy, and side effects of the COVID-19 vaccine have been largely confirmed ( 27 ). Perceived benefits and perceived self-efficacy both increased the odds of vaccine acceptance, though not statistically significantly in this study. The insignificance of the results could be due to the low response in the questionnaire items. Moreover, increased odds of vaccine acceptance are in line with a study in Hong Kong, which reported perceived benefits as being highly correlated with vaccine acceptance ( 28 ). Findings in this study underscore the critical need to incorporate the HBM, which plays a big role in individual behavioral change. HBM also influences policy making by providing guidelines for health promotion and disease prevention programs by improving interventions such as vaccines to improve coverage to attain herd immunity in hospital settings, given that such NMPs are at high risk of spreading infectious diseases such as COVID-19. We hypothesize a low vaccine acceptance among a highly vulnerable population in Tanzania; however, we adopted a cross-sectional study design, which might fail to explain the cause of low acceptance among such a high-risk population. Moreover, the data used in this study were limited to three health facilities, which accounted for 203 participants. A bigger proportion of NMPs might not have been covered in this study. Still, due to the random sampling technique and inclusion of all levels of health facilities, we believe the findings in this study are generalizable to the public and can influence policy formulation. We acknowledge changes in public perception towards COVID-19, which might vary from the data collection date to the time of writing this study, however, this study utilized COVID-19 as an illustration to foster vaccine acceptance. Therefore, we believe the results of this study can be widely used to foster higher vaccine coverage in the general community. Conclusions Vaccine acceptance among NMPs is still low, and the vulnerable population is still understudied, both in Africa and in Tanzania specifically. Although a higher proportion was willing to vaccinate, personal beliefs, perceived barriers, doubt about the efficiency of the vaccine, and a lack of precise information on the essence of the vaccines remain major hindrances. This present study recommends the inclusion of non-medical personnel in vaccine campaigns, the involvement of NMPs in addressing vaccine efficacy to the general population, and incorporating NMP champions in advocacy. Abbreviations NMPs Non-Medical Personnel HBM Health Belief Model CUHAS Catholic University of Health and Allied Sciences BMC Bugando Medical Centre LMICs Low Middle-Income Countries Declarations Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki, its later amendments, ethical standards, and was approved by the joint Catholic University of Health and Allied Sciences / Bugando Medical Centre (CUHAS/BMC) research ethics and review committee (CREC) and was given ethical clearance number CREC_2570/2023 on April 3rd, 2023 and a clearance certificate number CRECU/2570/2023 issued on 26 th May 2025. Informed assent and consent were obtained from all study participants. Consent for publication Not Applicable. Availability of data and materials The dataset used in this study is available upon request from the corresponding author. Competing Interest The authors declare no competing interests. Funding No funding, grants or other support was received. Author Contributions ETK. and GWM.: Conceptualization. ETK, GWM, and TRL: Methodology. GWM: Data collection. TRL, ETK: statistical analysis. ETK, and TRL: writing—original draft preparation, writing—review and editing, visualization: ETK, RK, and TR; ETK: supervision, ETK All authors have read and agreed to the published version of the manuscript. Acknowledgments: We acknowledge the Catholic University of Health and Allied Sciences, Mwanza, Tanzania, for approving the study and for financial support to cover article processing charges. Sincere gratitude to the administrative management of health facilities involved in this study for allowing data collection in their respective institution and for administrative support. We appreciate all study participants who voluntarily agreed to participate in the study. We appreciate the contributions of Mosha, GW, Leeyio TR, Kitambo, R, and Konje, ET for their involvement in preparing this manuscript. References Bartsch SM, O'shea KJ, Ferguson MC, Bottazzi ME, Wedlock PT, Strych U, et al. Vaccine efficacy needed for a COVID-19 coronavirus vaccine to prevent or stop an epidemic as the sole intervention. Am J Prev Med. 2020;59(4):493–503. Getachew T, Lami M, Eyeberu A, Balis B, Debella A, Eshetu B, et al. Acceptance of COVID-19 vaccine and associated factors among health care workers at public hospitals in Eastern Ethiopia using the health belief model. Front Public Health. 2022;10:957721. Chen H, Li X, Gao J, Liu X, Mao Y, Wang R, et al. Health belief model perspective on the control of COVID-19 vaccine hesitancy and the promotion of vaccination in China: web-based cross-sectional study. J Med Internet Res. 2021;23(9):e29329. Wong LP, Alias H, Wong P-F, Lee HY, AbuBakar S. The use of the health belief model to assess predictors of intent to receive the COVID-19 vaccine and willingness to pay. Hum vaccines immunotherapeutics. 2020;16(9):2204–14. Alyafei A, Easton-Carr R. The health belief model of behavior change. StatPearls [Internet]: StatPearls Publishing; 2024. Jose R, Narendran M, Bindu A, Beevi N, Benny P. Public perception and preparedness for the pandemic COVID 19: a health belief model approach. Clin Epidemiol global health. 2021;9:41–6. Doshi RH. COVID-19 Vaccination Coverage—World Health Organization African Region, 2021–2023. MMWR Morbidity and Mortality Weekly Report. 2024;73. WHO. Taking a ride to vaccinate Tanzania's nomadic communities against COVID-19 Dar es Salaam: WHO. 2023 [updated 30/6/2023; cited 2025 4/4/2025]. Available from: https://www.afro.who.int/photo-story/taking-ride-vaccinate-tanzanias-nomadic-communities-against-covid-19 Msuya SE, Manongi RN, Jonas N, Mtei M, Amour C, Mgongo MB, et al. COVID-19 vaccine uptake and associated factors in Sub-Saharan Africa: evidence from a community-based survey in Tanzania. Vaccines. 2023;11(2):465. Konje ET, Basinda N, Kapesa A, Mugassa S, Nyawale HA, Mirambo MM, et al. The coverage and acceptance spectrum of COVID-19 vaccines among healthcare professionals in western Tanzania: what can we learn from this pandemic? Vaccines. 2022;10(9):1429. Kebede A, Kanwagi R, Dibaba A, Kalam MA, Davis T, Larson H. Determinants of COVID-19 vaccine acceptance in six lower-and middle-income countries. 2021. Lazarus JV, Ratzan SC, Palayew A, Gostin LO, Larson HJ, Rabin K, et al. A global survey of potential acceptance of a COVID-19 vaccine. Nat Med. 2021;27(2):225–8. Bhopal S, Nielsen M. Vaccine hesitancy in low-and middle-income countries: potential implications for the COVID-19 response. Arch Dis Child. 2021;106(2):113–4. Xiao X, Wong RM. Vaccine hesitancy and perceived behavioral control: A meta-analysis. Vaccine. 2020;38(33):5131–8. Kessy SJ, Wei T, Zhou Y, Zhang WX, Alwy Al-Beity FM, Zhang SS et al. Vaccination willingness, vaccine hesitancy, and estimated coverage of SARS‐CoV‐2 vaccine among healthcare workers in Tanzania: A call for action. Immunity, Inflammation and Disease. 2023;11(12):e1126. Wang M-W, Wen W, Wang N, Zhou M-Y, Wang C-y, Ni J, et al. COVID-19 vaccination acceptance among healthcare workers and non-healthcare workers in China: a survey. Front public health. 2021;9:709056. Cheuyem FZL, Amani A, Nkodo ICA, Boukeng LBK, Edzamba MF, Nouko A, et al. COVID-19 vaccine acceptance and hesitancy in Cameroon: a systematic review and meta-analysis. BMC Public Health. 2025;25(1):1035. Wang Q, Hu S, Du F, Zang S, Xing Y, Qu Z, et al. Mapping global acceptance and uptake of COVID-19 vaccination: A systematic review and meta-analysis. Commun Med. 2022;2(1):113. Mengistu DA, Demmu YM, Asefa YA. Global COVID-19 vaccine acceptance rate: Systematic review and meta-analysis. Front public health. 2022;10:1044193. Gudina EK, Muro FJ, Kyala NJ, Melaku T, Sørensen JB, Meyrowitsch DW, et al. Understanding the COVID-19 vaccine uptake, acceptance, and hesitancy in Ethiopia and Tanzania: a scoping review. Front Public Health. 2024;12:1422673. Youssef D, Abou-Abass L, Issa O, Youssef J, Hassan H. Unlocking the keys to COVID-19 vaccine acceptance: insights from healthcare workers and the general population. Discover Social Sci Health. 2024;4(1):1–19. Bahreini R, Sardareh M, Arab-Zozani M. A scoping review of COVID-19 vaccine hesitancy: refusal rate, associated factors, and strategies to reduce. Front Public Health. 2024;12:1382849. Berni I, Menouni A, Filali Zegzouti Y, Kestemont M-P, Godderis L, El Jaafari S. Factors associated with COVID-19 vaccine acceptance in Morocco: applying the health belief model. Vaccines. 2022;10(5):784. Yu Y, Lau JT, She R, Chen X, Li L, Li L, et al. Prevalence and associated factors of intention of COVID-19 vaccination among healthcare workers in China: application of the Health Belief Model. Hum Vaccines Immunotherapeutics. 2021;17(9):2894–902. Hopman C, Riphagen-Dalhuisen J, Looijmans-van den Akker I, Frijstein G, Van der Geest-Blankert A, Danhof-Pont M, et al. Determination of factors required to increase uptake of influenza vaccination among hospital-based healthcare workers. J Hosp Infect. 2011;77(4):327–31. Enticott J, Gill JS, Bacon SL, Lavoie KL, Epstein DS, Dawadi S, et al. Attitudes towards vaccines and intention to vaccinate against COVID-19: a cross-sectional analysis—implications for public health communications in Australia. BMJ open. 2022;12(1):e057127. Mtei M, Mboya IB, Mgongo M, Manongi R, Amour C, Bilakwate JS, et al. Confidence in COVID-19 vaccine effectiveness and safety and its effect on vaccine uptake in Tanzania: a community-based cross-sectional study. Hum Vaccines Immunotherapeutics. 2023;19(1):2191576. Wong MC, Wong EL, Huang J, Cheung AW, Law K, Chong MK, et al. Acceptance of the COVID-19 vaccine based on the health belief model: A population-based survey in Hong Kong. Vaccine. 2021;39(7):1148–56. Additional Declarations No competing interests reported. Supplementary Files QuestionnaireEnglishVersionHBM.pdf Cite Share Download PDF Status: Published Journal Publication published 26 Jan, 2026 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 12 Sep, 2025 Reviews received at journal 11 Sep, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviews received at journal 03 Sep, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviews received at journal 29 Jul, 2025 Reviewers agreed at journal 18 Jul, 2025 Reviewers agreed at journal 27 Jun, 2025 Reviewers agreed at journal 26 Jun, 2025 Reviewers invited by journal 23 Jun, 2025 Editor assigned by journal 19 Jun, 2025 Editor invited by journal 17 Jun, 2025 Submission checks completed at journal 17 Jun, 2025 First submitted to journal 17 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6783060","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":476850407,"identity":"41bb4ffb-d55c-454c-8a30-d718ec094184","order_by":0,"name":"GODFREY W. MOSHA","email":"","orcid":"","institution":"Catholic University of Health and Allied Sciences","correspondingAuthor":false,"prefix":"","firstName":"GODFREY","middleName":"W.","lastName":"MOSHA","suffix":""},{"id":476850408,"identity":"ef619cd3-5e84-4115-a4e5-5f0595acede9","order_by":1,"name":"TITUS ROBERT LEEYIO","email":"","orcid":"","institution":"Catholic University of Health and Allied Sciences","correspondingAuthor":false,"prefix":"","firstName":"TITUS","middleName":"ROBERT","lastName":"LEEYIO","suffix":""},{"id":476850411,"identity":"4aaf7386-20d5-48a9-9ab3-4f97d3d8e116","order_by":2,"name":"ROBERT KITAMBO","email":"","orcid":"","institution":"Catholic University of Health and Allied Sciences","correspondingAuthor":false,"prefix":"","firstName":"ROBERT","middleName":"","lastName":"KITAMBO","suffix":""},{"id":476850413,"identity":"7269ac87-076e-4e65-8253-03ecbb8192eb","order_by":3,"name":"EVELINE THOBIAS KONJE","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYDACHiCWYLCR42dgYCNJS5qxZANJWhgYDiduOECsFn6ew8ceWFQwGxvfSH724EMFgzy/2AH8WiR729INJM6wyZndSDM3nHGGwXDm7AT8WgzO85hJSLbxGJvdSDCT5m1jSDC4TUCLPVjLP4nEzTPSvxGnxYC3B6ilwSBxg0QOkbZInDmWJiFxLMFY4sybMskZZyQI+4W/J/mYtETNfzn+9vRtEh8qbOT5pQloAQFmCRApAFYpQVg5CDB+ANt3gDjVo2AUjIJRMPIAAM87PUWX4rDkAAAAAElFTkSuQmCC","orcid":"","institution":"Catholic University of Health and Allied Sciences","correspondingAuthor":true,"prefix":"","firstName":"EVELINE","middleName":"THOBIAS","lastName":"KONJE","suffix":""}],"badges":[],"createdAt":"2025-05-30 09:08:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6783060/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6783060/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-026-26319-2","type":"published","date":"2026-01-26T15:59:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85634434,"identity":"35c309d0-fbca-4aae-b1a5-b1f6e543b7f2","added_by":"auto","created_at":"2025-06-30 05:31:05","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":85270,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVaccine Acceptance Level\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6783060/v1/6fb109bbc1010c38fc075bca.jpg"},{"id":85634417,"identity":"0cf64c60-15b0-4ca9-bf96-dadb0a44cb0e","added_by":"auto","created_at":"2025-06-30 05:31:04","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":201660,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSelf-assessment on willingness to Vaccinate\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6783060/v1/afc3503ced4efc7e97112cf5.jpg"},{"id":85634409,"identity":"e1525f6c-8d66-4fd8-9b07-bd1035052f9b","added_by":"auto","created_at":"2025-06-30 05:31:03","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":150030,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProportion of responses to HBM Constructs\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6783060/v1/b57926205390b09bda065bbf.jpg"},{"id":101690541,"identity":"437798ca-33e8-4165-ab7d-78133abb4c76","added_by":"auto","created_at":"2026-02-02 16:05:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1359832,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6783060/v1/cdcc265a-54e9-4328-bd1b-75b38a5e110d.pdf"},{"id":85634407,"identity":"93879c8f-7f5a-4114-aca0-5ae8edf6127e","added_by":"auto","created_at":"2025-06-30 05:31:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":233984,"visible":true,"origin":"","legend":"","description":"","filename":"QuestionnaireEnglishVersionHBM.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6783060/v1/3df8e2d27be64bac9e8662d9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Health Beliefs Model and Vaccine Acceptance: Insights from the COVID-19 Pandemic among Non-Medical Personnel in Mwanza, Tanzania","fulltext":[{"header":"Background","content":"\u003cp\u003eWithout universal vaccine coverage, attaining herd immunity becomes a challenge in containing epidemics. Mathematical models have shown that if the vaccine is 80% effective, then coverage must be at least 75% to control any existing pandemic (1). The Health Belief Model (HBM) has been widely adopted as a framework to explain individual perceptions and attitudes on vaccine acceptance among populations (2-4). The theory holds that health-related behavior depends on a combination of several factors: perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, and self-efficacy (5). It empowers researchers to explain and predict health-promoting behavior in terms of patterns of belief by addressing the association between health behaviors and an individual\u0026rsquo;s acceptance of health services (6). As of December 2023, approximately 32% of the total population in the African Region had completed the primary COVID-19 vaccination series. Among healthcare workers, the coverage was higher, with 48% having completed the primary series. This suggests that the vaccination coverage among non-medical personnel (NMPs) was slightly below the regional average of 32% (7). Similarly, as of June 2023, 54% of Tanzanians were fully vaccinated as compared to only 3.2% vaccinated in the general population in January 2022 (8). More than 34% of those who were vaccinated did not know which vaccine they received (9). In contrast, the lowest vaccine acceptance rates were reported in Tanzania compared to other LMICs (10, 11), signifying a potential need to assess personal and belief influences in vaccine acceptance. Furthermore, studies have identified demographic, socioeconomic, individual behavior, and belief factors linked to varying vaccine acceptance levels (12-14). Studies in Tanzania have reported an 18.5% COVID-19 vaccine coverage among healthcare workers with a 29% acceptance level (10). Similarly, elsewhere in Tanzania, 70.5% of healthcare workers were vaccinated against SARS-CoV-2 with an 81.4% acceptance level and 62.5% hesitancy level (15). Limited information exists on vaccine coverage and acceptance levels among NMPs who work in hospital settings. NMPs interact regularly with patients, potentially influencing infection control dynamics. Moreover, studies indicate a lower vaccine acceptance level among NMPs than other groups working in similar environments (76.98% versus 56.19%) (16). Protecting NMPs from infections plays a key role in controlling nosocomial transmission, and increasing the proportion of immunized NMP through vaccination would be a viable measure to avoid nosocomial infections (16). Therefore, we hypothesize a similar lower acceptance among NMPs in Tanzania, and therefore, we adopt the HBM as a tool to assess vaccine acceptance with an illustration of the COVID-19 vaccine.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eAim, design and settings of the study\u003c/h2\u003e \u003cp\u003e A cross-sectional study was conducted in Mwanza City in three hospital facilities (health center, regional referral hospital, and district hospital) purposively selected to get insights on vaccine acceptance using the COVID-19 vaccine and the Health Belief Model (HBM) as a guiding framework. The study was conducted between April to May 2023. Three hospital facilities were considered in the study to have the inclusion of all levels of participants in the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eNon-medical personnel refer to anyone working in a medical institution and providing non-medical services, such as a receptionist, cashier, and laundry attendant. In this study, 203 non-medical personnel were purposely chosen for the study from three health facilities within the Mwanza region, which are health center, district hospital and regional referral hospital.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInclusion and Exclusion Criteria\u003c/h3\u003e\n\u003cp\u003eAll NMPs aged 18 years and above who were present at the health facility at the time of the study were eligible for the study. All personnel who were on leave, absent from work for official or non-official reasons, as well as those with ill were excluded from the study. The study also excluded all visiting and temporarily employed NMPs who were present at study sites during data collection period.\u003c/p\u003e\n\u003ch3\u003eData Collection and Analysis Procedure\u003c/h3\u003e\n\u003cp\u003eA structured questionnaire was developed that consisted of key variables such as age, sex, and educational level. Lifestyle behavior information such as smoking status, history of chronic disease, type of chronic disease, history of hepatitis B vaccine, level of health facility, and questions on health belief model (HBM) constructs. A self-administered data collection approach was utilized after distributing a structured questionnaire among NMPs who were available at their workplace. A complete study questionnaire is available online: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://docs.google.com/document/d/1ZBLLASWwc0WJyloGUxltAhAiE2Cp5mq0/edit?usp=drive_link\u0026amp;ouid=115764617992089467467\u0026amp;rtpof=true\u0026amp;sd=true\u003c/span\u003e\u003cspan address=\"https://docs.google.com/document/d/1ZBLLASWwc0WJyloGUxltAhAiE2Cp5mq0/edit?usp=drive_link\u0026amp;ouid=115764617992089467467\u0026amp;rtpof=true\u0026amp;sd=true\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Well-trained research assistants collected all questionnaires immediately after filling and for a few participants, the collection was done at the end of their shift.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll data was encoded in Microsoft Excel 2021 and exported to Stata 15; descriptive statistics, such as frequency and percentages, were used to describe the proportion of participants in each category. Logistic regression was used to assess the association between variables, where the strength of the association was interpreted using an odd ratio and 95% confidence interval at a statistically significant p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Results were presented in words, graphs, and tables.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe study included 203 NMPs from three hospital levels in Mwanza City. More than half were males (110, 54.19%), and most were aged 20-30 (90, 44.33%). Two-thirds (66.50%) of NMPs worked in the health centers, and almost three-quarters of the participants had tertiary education (147, 72.41%). Only 17 (8.37%) NMPs reported having a chronic disease, of which 41.18% of the 17 participants were suffering from asthma. Generally, 11.33% of the NMPs reported having a smoking habit, and only 56.65% performed regular exercise. Additionally, in Table 1, among all included NMPs, more than half had received the hepatitis B vaccine (116, 57.14%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Participants\u0026rsquo; characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"80%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN = 203\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003eAge (Years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e20 \u0026ndash; 30\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e44.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e31 \u0026ndash; 40\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e26.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eAbove 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e29.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e54.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e45.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003eEducation Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eTertiary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e72.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003ePrimary and Secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e27.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003eLevel of Health Facility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eHealth Centre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e66.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eDistrict Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e14.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eRegional Referral Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e18.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003eSmoking Habit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e11.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e88.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003eExercise Practice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e56.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e43.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003eHepatitis B Vaccine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e57.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e42.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003eChronic Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e8.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e91.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003eName of Chronic Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eAsthma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e41.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e58.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003eCOVID-19 Symptoms in 1\u003csup\u003est\u003c/sup\u003e, 2\u003csup\u003end\u003c/sup\u003e and 3\u003csup\u003erd\u003c/sup\u003e Phase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e32.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e67.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAmong 203 NMPs, the vaccine acceptance level was 32.51%, while 42.86% reported low hesitance and 15.76% were strongly hesitant, and 8.87% were undecided about accepting or hesitating about the vaccine Figure 1.\u003c/p\u003e\n\u003cp\u003eConcerning a self-assessment on vaccine acceptance and exposure among NMPs, 62.07% indicated willingness to receive the COVID-19 vaccine, whereas 37.93% reported they would not. On the other hand, actual uptake remains low\u0026mdash;only 16.75% of respondents had ever received a COVID-19 vaccine, while a substantial 83.25% had never been vaccinated. Additionally, just 8.90% of NMPs had a confirmed history of COVID-19 infection, as seen in Figure 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelf-Reported Barrier to Vaccine Acceptance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 shows the decision on vaccine acceptance among NMPs; key reasons for low vaccine acceptance included lack of information (19.21%), fear of side effects (17.73%), and concerns that the vaccine was rushed or insufficiently tested (16.25%). Smaller proportions did not believe in the vaccine\u0026apos;s necessity (6.90%) or effectiveness (2.46%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Self-Reported Barriers to COVID-19 Vaccine Uptake (N=203)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"552\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 468px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBarriers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 468px;\"\u003e\n \u003cp\u003eThe vaccine has been rushed or has not been adequately tested\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e16.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 468px;\"\u003e\n \u003cp\u003eI fear side effects from the vaccine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e17.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 468px;\"\u003e\n \u003cp\u003eI lack information about the vaccine\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e19.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 468px;\"\u003e\n \u003cp\u003eI don\u0026rsquo;t believe in the necessity of the vaccine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e6.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 468px;\"\u003e\n \u003cp\u003eI don\u0026rsquo;t believe in the effectiveness of the vaccine and its protectiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eResponses to HBM Constructs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 3 presents the distribution of responses to HBM constructs related to COVID-19 vaccination, highlighting significant variations in perception and behavioral determinants. Perceived susceptibility was acknowledged by approximately 50% of respondents, while 30% were uncertain and 20% disagreed, suggesting a gap in risk awareness. Perceived severity had the highest agreement at around 70%, with 25% uncertainty and 5% disagreement, reinforcing the notion that most NMPs recognize COVID-19 as a serious health threat. 55% of non-medical personnel agreed on the perceived benefits of vaccination, while 40% remained uncertain on the perceived barriers of the vaccine. At least 80% of the non-medical personnel confirmed health motivation as a key element influencing vaccine acceptance.\u003c/p\u003e\n\u003cp\u003eOverall, COVID-19 vaccine acceptance was 16.75% (95% C.I. 11.61%-21.89%) among NMPs. NMPs aged above 40 years demonstrated higher odds of acceptance compared to those aged 20\u0026ndash;30 years (aOR = 1.58, 95% CI: 0.60\u0026ndash;4.15, 0.35). Although females had higher odds of acceptance compared to males (aOR = 1.63, 95% CI: 0.70\u0026ndash;3.76, 0.26), this association was not statistically significant. NMPs with primary and secondary education level were 12% less likely to vaccinate compared to NMPs with tertiary level. This was also not statistically significant in the multivariate analysis, (aOR: 0.88, 95% CI: 0.34-2.27, 0.79), table 3.\u003c/p\u003e\n\u003cp\u003eRegarding the level of health facility in table 3; NMPs from referral hospitals were significantly more likely to accept vaccination compared to those from health centers (aOR = 2.57, 95% CI: 0.95\u0026ndash;6.95, 0.06), while those from district hospitals showed no significant difference in vaccine acceptance compared to those in health center (aOR = 0.69, 95% CI: 0.19\u0026ndash;2.52, 0.58). Smoking habit was not significantly associated with vaccine acceptance (aOR = 1.37, 95% CI: 0.42\u0026ndash;4.51, 0.60). Similarly, NMPs who engaged in regular physical activity were not significantly associated with vaccine acceptance compared to those who did not engage, (aOR = 0.60, 95% CI: 0.26\u0026ndash;1.41, 0.24.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurther in Table 3, NMPs who had vaccinated with hepatitis B had more than twice the odds of vaccine acceptance compared to NMPs who had not vaccinated for hepatitis B, (aOR = 2.26, 95% CI: 0.93\u0026ndash;5.51, 0.07). The presence of a chronic disease was also strongly associated with increased vaccine acceptance where NMPs who had chronic diseases such as asthma were three times more likely to accept vaccine compared to NMPs who had no chronic disease, (aOR = 3.72, 95% CI: 1.18\u0026ndash;11.76, 0.03). Non-medical personnel who reported experiencing COVID-19 symptoms during any of the pandemic\u0026rsquo;s first three phases were significantly more likely to accept vaccine compared to those who did not report such symptoms (aOR = 2.40, 95% CI: 1.04\u0026ndash;5.54, 0.04).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Factors associated with uptake of the COVID-19 vaccine Uptake\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"right\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"107%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eFactors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eAcceptance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003eUnivariate Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eMultivariate Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cem\u003en = 34 (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cem\u003ecOR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003e95% C.I.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cem\u003eaOR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003e95% C.I.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eAge (Years)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u003cem\u003e20-30\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 31-40\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Above 40\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14 (41.18)\u003c/p\u003e\n \u003cp\u003e9 (26.47)\u003c/p\u003e\n \u003cp\u003e11 (32.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.44 \u0026ndash; 2.71\u003c/p\u003e\n \u003cp\u003e0.52 \u0026ndash; 2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.39 \u0026ndash; 2.92\u003c/p\u003e\n \u003cp\u003e0.60 \u0026ndash; 4.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u003cem\u003eMale\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Female\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15 (44.12)\u003c/p\u003e\n \u003cp\u003e19 (55.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.77 \u0026ndash; 3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.70 \u0026ndash; 3.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eEducation Level\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cem\u003eTertiary \u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Primary and Secondary\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e24 (70.59)\u003c/p\u003e\n \u003cp\u003e10 (29.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.49 \u0026ndash; 2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.34 \u0026ndash; 2.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eLevel of Health Facility\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u003cem\u003eHealth Centre\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;District Hospital\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Referral Hospital\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e19 (55.88)\u003c/p\u003e\n \u003cp\u003e4 (11.76)\u003c/p\u003e\n \u003cp\u003e11 (32.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003cp\u003e2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.29 \u0026ndash; 2.93\u003c/p\u003e\n \u003cp\u003e1.06 \u0026ndash; 5.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003cp\u003e2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.19 \u0026ndash; 2.52\u003c/p\u003e\n \u003cp\u003e0.95 \u0026ndash; 6.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eSmoking Habit\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Yes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e28 (82.35)\u003c/p\u003e\n \u003cp\u003e6 (17.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.70 \u0026ndash; 5.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.42 \u0026ndash; 4.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eExercise Practice\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; No\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Yes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e21 (61.76)\u003c/p\u003e\n \u003cp\u003e13 (38.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.19 \u0026ndash; 0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.26 \u0026ndash; 1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eHepatitis B Vaccine\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Yes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9 (26.47)\u003c/p\u003e\n \u003cp\u003e25 (73.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.05 \u0026ndash; 5.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.93 \u0026ndash; 5.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eChronic Disease\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Yes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e26 (76.47)\u003c/p\u003e\n \u003cp\u003e8 (23.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e5.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.94 \u0026ndash; 15.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.18 \u0026ndash; 11.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eCOVID-19 symptoms in 1\u003csup\u003est\u003c/sup\u003e, 2\u003csup\u003end\u003c/sup\u003e, 3\u003csup\u003erd\u003c/sup\u003e phase\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; No\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Yes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e18 (52.94)\u003c/p\u003e\n \u003cp\u003e16 (47.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.03 \u0026ndash; 4.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.04 \u0026ndash; 5.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eHealth Belief Constructs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHealth belief constructs were regressed to assess their influence on vaccine acceptance in table 4. Age was not significantly associated with vaccination in the modified logistic regression of the HBM construct. Females were 4 times more likely to vaccinate than males when assessed with HMB constructs (aOR: 4.93, 95% CI: 1.05-23.14, 0.04). NMPs were 11 times more likely to vaccinate if they perceived higher susceptibility and severe threats of COVID-19 than those who perceived lower threats (aOR = 11.65, 95% CI: 2.30\u0026ndash;58.98, 0.00). NMPs who agreed on the benefits of vaccination were three times more likely to accept the vaccine; however, on the modified model, the construct was not statistically significant (aOR=3.13, 95% CI: 0.36-26.88, 0.30). On the other hand, NMPs who agreed on cues to action as causative of vaccination were 5 times more likely to vaccinate than those who disagreed (aOR: 5.80, 95% CI: 0.88-38.11, 0.07). Cues to action were relatively significant at the near borderline. NMPs on their self-efficacy as personal behavioral promotion for vaccination were 85% more likely to vaccinate, despite not being statistically significant (aOR=1.85, 95% CI: 0.24-14.39, 0.60), table 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Health Belief Model Constructs and COVID-19 Vaccine Uptake\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHBM Construct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVaccinated, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95% C.I.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cem\u003e20 \u0026ndash; 30 Years\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;31 \u0026ndash; 41 Years\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Above 41 Years\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14 (15.56)\u003c/p\u003e\n \u003cp\u003e9 (16.67)\u003c/p\u003e\n \u003cp\u003e11 (18.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.19 \u0026ndash; 4.89\u003c/p\u003e\n \u003cp\u003e0.27 \u0026ndash; 6.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSex\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cem\u003eMale\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;Female\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15 (13.64)\u003c/p\u003e\n \u003cp\u003e19 (20.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e4.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.05 \u0026ndash; 23.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePerceived Threats (Susceptibility \u0026amp; Severity)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Low/No Threats\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;High Threats\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3 (2.21)\u003c/p\u003e\n \u003cp\u003e31 (46.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e11.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.30 \u0026ndash; 58.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePerceived Benefits\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cem\u003eDisagree\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Agree\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2 (2.00)\u003c/p\u003e\n \u003cp\u003e32 (31.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.36 \u0026ndash; 26.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePerceived Barriers\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cem\u003eDisagree\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Agree\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5 (3.45)\u003c/p\u003e\n \u003cp\u003e29 (50.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e12.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.43 \u0026ndash; 61.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCues to Actions\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cem\u003eDisagree\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Agree\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4 (2.76)\u003c/p\u003e\n \u003cp\u003e30 (51.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e5.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.88 \u0026ndash; 38.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePerceived Self-Efficacy\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cem\u003eDisagree\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Agree\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3 (3.70)\u003c/p\u003e\n \u003cp\u003e31 (25.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.24 \u0026ndash; 14.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study explored vaccine acceptance among non-medical personnel in Mwanza, Tanzania, using the Health Belief Model and insights from the COVID-19 pandemic as a case illustration. Vaccine acceptance level among NMPs was 16.75%, with a 65% hesitancy level. The majority of the participants reported lacking information on the vaccine and fearing side effects that may result from the vaccine (19.21% versus 17.73%). In this study, only 8.90% of NMPs were COVID-19 positive at the time of the data collection. These findings underscore a critical gap in vaccine coverage among NMPs and highlight the urgent need for context-specific risk communication and behavioral interventions to improve vaccine confidence, particularly among those demonstrating resistance and uncertainty. Since the HBM goes with personal conviction, vaccine acceptability carries much weight, and hence high proportion of uninfected individuals may also contribute to perceived threats, further impacting vaccine acceptance among NMPs.\u003c/p\u003e \u003cp\u003eWhile limited information exists on vaccine acceptance among non-medical personnel as compared to their counterparts, medical personnel, vaccine acceptance reported in this study is relatively lower compared to a study in Cameron that reported a 31.21% among the general population (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Acceptance in this study is also much lower than the global acceptance of 60.8% and 67.8%, respectively (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This could be attributed to government measures in creating awareness towards the uptake of the vaccine, late arrival of the vaccines, and limited published articles (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). For instance, 47% of non-medical personnel were willing to receive the vaccine in Lebanon (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Similar reasons for lower acceptance reported in this study have also been reported worldwide (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Similar concerns highlighted in this study included fear of side effects, doubts about vaccine safety, personal beliefs, and skepticism regarding the vaccine's effectiveness in preventing COVID-19. Results in this study suggest that, despite other modifying factors such as age, sex, education, and underlying comorbidities, HBM can be useful in understanding vaccine acceptance in the general population. Findings are consistent with a Malaysian study that used the HBM to assess willingness to receive the COVID-19 vaccine (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePerceive susceptibility, severity, and benefits of compelled vaccination in the general population. Both perceived severity and susceptibility form health threats, such as worrying about being infected and infecting the family. Our study aligns with findings from other studies, which demonstrate that perceived severity and perceived susceptibility exert a stronger positive influence on vaccine acceptance than other constructs of the HBM (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). This implies that NMPs who believe they are at risk of getting COVID-19 are more likely to vaccinate compared to those who believe they are not at risk. Moreover, NMPs who perceive COVID-19 as a severe comorbidity are more likely to accept vaccination compared to their counterparts.\u003c/p\u003e \u003cp\u003eOn the other hand, cues to action as well as self-efficacy are prompted by perceived susceptibility, severity and benefits and thus trigger individuals to accept the vaccine. In this study, among participants who agreed with cues to action, 13.8% reported having chronic diseases such as asthma compared to only 6.2% who disagreed. This confirms that individuals with chronic conditions may be more likely to accept vaccination. This study coincides with research from Lebanon, confirming that individuals are more motivated to accept vaccines if both internal or external cues to action prompt vaccination (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Results are also consistent with findings from the USA that observed cues to action are inversely associated with vaccine hesitancy.\u003c/p\u003e \u003cp\u003eBarriers such as worries about side effects of the vaccine and accessibility were associated with vaccine acceptance. While in this study, perceived barriers were significantly associated with vaccine acceptance, the results contradict findings from Morocco, which observed a negative association with intention to vaccinate (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). This is because in this study, individuals who acknowledged more barriers were informed of the risks of the COVID-19 vaccine. This awareness might paradoxically prompt higher vaccine acceptance. In this study, among NMPs who acknowledged perceived threats, more than 50.7% agreed on perceived barriers. Research in Australia found that individuals perceiving themselves as being at high risk had increased confidence in the vaccine, hence the likelihood of accepting the vaccine (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Likewise, of all who agreed on perceived barriers, more than 58.6% agreed on perceived threats to vaccine acceptance in the present study. In Tanzania, the safety, efficacy, and side effects of the COVID-19 vaccine have been largely confirmed (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePerceived benefits and perceived self-efficacy both increased the odds of vaccine acceptance, though not statistically significantly in this study. The insignificance of the results could be due to the low response in the questionnaire items. Moreover, increased odds of vaccine acceptance are in line with a study in Hong Kong, which reported perceived benefits as being highly correlated with vaccine acceptance (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Findings in this study underscore the critical need to incorporate the HBM, which plays a big role in individual behavioral change. HBM also influences policy making by providing guidelines for health promotion and disease prevention programs by improving interventions such as vaccines to improve coverage to attain herd immunity in hospital settings, given that such NMPs are at high risk of spreading infectious diseases such as COVID-19.\u003c/p\u003e \u003cp\u003eWe hypothesize a low vaccine acceptance among a highly vulnerable population in Tanzania; however, we adopted a cross-sectional study design, which might fail to explain the cause of low acceptance among such a high-risk population. Moreover, the data used in this study were limited to three health facilities, which accounted for 203 participants. A bigger proportion of NMPs might not have been covered in this study. Still, due to the random sampling technique and inclusion of all levels of health facilities, we believe the findings in this study are generalizable to the public and can influence policy formulation. We acknowledge changes in public perception towards COVID-19, which might vary from the data collection date to the time of writing this study, however, this study utilized COVID-19 as an illustration to foster vaccine acceptance. Therefore, we believe the results of this study can be widely used to foster higher vaccine coverage in the general community.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eVaccine acceptance among NMPs is still low, and the vulnerable population is still understudied, both in Africa and in Tanzania specifically. Although a higher proportion was willing to vaccinate, personal beliefs, perceived barriers, doubt about the efficiency of the vaccine, and a lack of precise information on the essence of the vaccines remain major hindrances. This present study recommends the inclusion of non-medical personnel in vaccine campaigns, the involvement of NMPs in addressing vaccine efficacy to the general population, and incorporating NMP champions in advocacy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNMPs\u0026nbsp; \u0026nbsp;\u0026nbsp;Non-Medical Personnel\u003c/p\u003e\n\u003cp\u003eHBM\u0026nbsp; \u0026nbsp; \u0026nbsp;Health Belief Model\u003c/p\u003e\n\u003cp\u003eCUHAS\u0026nbsp;\u0026nbsp;Catholic University of Health and Allied Sciences\u003c/p\u003e\n\u003cp\u003eBMC\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Bugando Medical Centre\u003c/p\u003e\n\u003cp\u003eLMICs \u0026nbsp; Low Middle-Income Countries\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki, its later amendments, ethical standards, and was approved by the joint Catholic University of Health and Allied Sciences / Bugando Medical Centre (CUHAS/BMC) research ethics and review committee (CREC) and was given ethical clearance number CREC_2570/2023 on April 3rd, 2023 and a clearance certificate number CRECU/2570/2023 issued on 26\u003csup\u003eth\u003c/sup\u003e May 2025. Informed assent and consent were obtained from all study participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset used in this study is available upon request from the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding, grants or other support was received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eETK. and GWM.: Conceptualization. ETK, GWM, and TRL: Methodology. GWM: Data collection. TRL, ETK: statistical analysis. \u0026nbsp;ETK, and TRL: writing—original draft preparation, writing—review and editing, visualization: ETK, RK, and TR; ETK: supervision, ETK \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe acknowledge the Catholic University of Health and Allied Sciences, Mwanza, Tanzania, for approving the study and for financial support to cover article processing charges. Sincere gratitude to the administrative management of health facilities involved in this study for allowing data collection in their respective institution and for administrative support. We appreciate all study participants who voluntarily agreed to participate in the study. We appreciate the contributions of Mosha, GW, Leeyio TR, Kitambo, R, and Konje, ET for their involvement in preparing this manuscript.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBartsch SM, O'shea KJ, Ferguson MC, Bottazzi ME, Wedlock PT, Strych U, et al. Vaccine efficacy needed for a COVID-19 coronavirus vaccine to prevent or stop an epidemic as the sole intervention. Am J Prev Med. 2020;59(4):493\u0026ndash;503.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGetachew T, Lami M, Eyeberu A, Balis B, Debella A, Eshetu B, et al. Acceptance of COVID-19 vaccine and associated factors among health care workers at public hospitals in Eastern Ethiopia using the health belief model. Front Public Health. 2022;10:957721.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen H, Li X, Gao J, Liu X, Mao Y, Wang R, et al. Health belief model perspective on the control of COVID-19 vaccine hesitancy and the promotion of vaccination in China: web-based cross-sectional study. J Med Internet Res. 2021;23(9):e29329.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong LP, Alias H, Wong P-F, Lee HY, AbuBakar S. The use of the health belief model to assess predictors of intent to receive the COVID-19 vaccine and willingness to pay. Hum vaccines immunotherapeutics. 2020;16(9):2204\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlyafei A, Easton-Carr R. The health belief model of behavior change. StatPearls [Internet]: StatPearls Publishing; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJose R, Narendran M, Bindu A, Beevi N, Benny P. Public perception and preparedness for the pandemic COVID 19: a health belief model approach. Clin Epidemiol global health. 2021;9:41\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoshi RH. COVID-19 Vaccination Coverage\u0026mdash;World Health Organization African Region, 2021\u0026ndash;2023. MMWR Morbidity and Mortality Weekly Report. 2024;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. Taking a ride to vaccinate Tanzania's nomadic communities against COVID-19 Dar es Salaam: WHO. 2023 [updated 30/6/2023; cited 2025 4/4/2025]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.afro.who.int/photo-story/taking-ride-vaccinate-tanzanias-nomadic-communities-against-covid-19\u003c/span\u003e\u003cspan address=\"https://www.afro.who.int/photo-story/taking-ride-vaccinate-tanzanias-nomadic-communities-against-covid-19\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMsuya SE, Manongi RN, Jonas N, Mtei M, Amour C, Mgongo MB, et al. COVID-19 vaccine uptake and associated factors in Sub-Saharan Africa: evidence from a community-based survey in Tanzania. Vaccines. 2023;11(2):465.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKonje ET, Basinda N, Kapesa A, Mugassa S, Nyawale HA, Mirambo MM, et al. The coverage and acceptance spectrum of COVID-19 vaccines among healthcare professionals in western Tanzania: what can we learn from this pandemic? Vaccines. 2022;10(9):1429.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKebede A, Kanwagi R, Dibaba A, Kalam MA, Davis T, Larson H. Determinants of COVID-19 vaccine acceptance in six lower-and middle-income countries. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLazarus JV, Ratzan SC, Palayew A, Gostin LO, Larson HJ, Rabin K, et al. A global survey of potential acceptance of a COVID-19 vaccine. Nat Med. 2021;27(2):225\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhopal S, Nielsen M. Vaccine hesitancy in low-and middle-income countries: potential implications for the COVID-19 response. Arch Dis Child. 2021;106(2):113\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao X, Wong RM. Vaccine hesitancy and perceived behavioral control: A meta-analysis. Vaccine. 2020;38(33):5131\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKessy SJ, Wei T, Zhou Y, Zhang WX, Alwy Al-Beity FM, Zhang SS et al. Vaccination willingness, vaccine hesitancy, and estimated coverage of SARS‐CoV‐2 vaccine among healthcare workers in Tanzania: A call for action. Immunity, Inflammation and Disease. 2023;11(12):e1126.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang M-W, Wen W, Wang N, Zhou M-Y, Wang C-y, Ni J, et al. COVID-19 vaccination acceptance among healthcare workers and non-healthcare workers in China: a survey. Front public health. 2021;9:709056.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheuyem FZL, Amani A, Nkodo ICA, Boukeng LBK, Edzamba MF, Nouko A, et al. COVID-19 vaccine acceptance and hesitancy in Cameroon: a systematic review and meta-analysis. BMC Public Health. 2025;25(1):1035.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Q, Hu S, Du F, Zang S, Xing Y, Qu Z, et al. Mapping global acceptance and uptake of COVID-19 vaccination: A systematic review and meta-analysis. Commun Med. 2022;2(1):113.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMengistu DA, Demmu YM, Asefa YA. Global COVID-19 vaccine acceptance rate: Systematic review and meta-analysis. Front public health. 2022;10:1044193.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGudina EK, Muro FJ, Kyala NJ, Melaku T, S\u0026oslash;rensen JB, Meyrowitsch DW, et al. Understanding the COVID-19 vaccine uptake, acceptance, and hesitancy in Ethiopia and Tanzania: a scoping review. Front Public Health. 2024;12:1422673.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoussef D, Abou-Abass L, Issa O, Youssef J, Hassan H. Unlocking the keys to COVID-19 vaccine acceptance: insights from healthcare workers and the general population. Discover Social Sci Health. 2024;4(1):1\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBahreini R, Sardareh M, Arab-Zozani M. A scoping review of COVID-19 vaccine hesitancy: refusal rate, associated factors, and strategies to reduce. Front Public Health. 2024;12:1382849.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerni I, Menouni A, Filali Zegzouti Y, Kestemont M-P, Godderis L, El Jaafari S. Factors associated with COVID-19 vaccine acceptance in Morocco: applying the health belief model. Vaccines. 2022;10(5):784.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu Y, Lau JT, She R, Chen X, Li L, Li L, et al. Prevalence and associated factors of intention of COVID-19 vaccination among healthcare workers in China: application of the Health Belief Model. Hum Vaccines Immunotherapeutics. 2021;17(9):2894\u0026ndash;902.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHopman C, Riphagen-Dalhuisen J, Looijmans-van den Akker I, Frijstein G, Van der Geest-Blankert A, Danhof-Pont M, et al. Determination of factors required to increase uptake of influenza vaccination among hospital-based healthcare workers. J Hosp Infect. 2011;77(4):327\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEnticott J, Gill JS, Bacon SL, Lavoie KL, Epstein DS, Dawadi S, et al. Attitudes towards vaccines and intention to vaccinate against COVID-19: a cross-sectional analysis\u0026mdash;implications for public health communications in Australia. BMJ open. 2022;12(1):e057127.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMtei M, Mboya IB, Mgongo M, Manongi R, Amour C, Bilakwate JS, et al. Confidence in COVID-19 vaccine effectiveness and safety and its effect on vaccine uptake in Tanzania: a community-based cross-sectional study. Hum Vaccines Immunotherapeutics. 2023;19(1):2191576.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong MC, Wong EL, Huang J, Cheung AW, Law K, Chong MK, et al. Acceptance of the COVID-19 vaccine based on the health belief model: A population-based survey in Hong Kong. Vaccine. 2021;39(7):1148\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Acceptance, Vaccine, Tanzania, Non-Medical Personnel","lastPublishedDoi":"10.21203/rs.3.rs-6783060/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6783060/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWithout vaccine, attaining herd immunity remains a global challenge. While medical personnel have been a focus for vaccine promotion and improving immunization, non-medical personnel (NMPs) working in hospital settings remain unconsidered and understudied in Tanzania. Given their routine interaction with patients, NMPs can play a key role as agents in the spread of nosocomial infections. This study aimed to assess vaccine acceptance level in this vulnerable, predisposed population using the health belief model (HBM) as a guiding frame to obtain insights from the COVID-19 vaccine in Tanzania.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA total of 203 purposively selected NMPs from three health facilities in Mwanza City were involved in a cross-sectional study. Descriptive statistics and multivariate analysis were performed, results were presented, and the associations were reported using adjusted odds ratios from logistic regression with 95% confidence intervals at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe overall COVID-19 vaccine acceptance was 16.75% despite 62.07% of NMPs indicating willingness to vaccinate. Hesitancy was prevalent at 65% primarily due to lack of information (19.21%), fear of side effects (17.73%) and concerns about vaccine safety (16.25%). Perceived threats (aOR: 9.89, p\u0026thinsp;=\u0026thinsp;0.01), perceived barriers (aOR: 8.83, p\u0026thinsp;=\u0026thinsp;0.02) and cues to action (aOR: 1.45, p\u0026thinsp;=\u0026thinsp;0.05) were significantly associated factors of vaccine acceptance among NMPs.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eVaccine acceptance among NMPs remains low and the HBM provides a robust framework for understanding vaccine-related behavior. This underscores the need to address personal attitudes and mobilize effective cues to action to improve vaccine acceptance in hospital-based populations and the general population.\u003c/p\u003e","manuscriptTitle":"Health Beliefs Model and Vaccine Acceptance: Insights from the COVID-19 Pandemic among Non-Medical Personnel in Mwanza, Tanzania","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-30 05:30:08","doi":"10.21203/rs.3.rs-6783060/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-12T08:08:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-11T11:42:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"298394192600055291326671437557508012964","date":"2025-09-03T18:17:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-03T15:13:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"229353089567806138011107856212335707556","date":"2025-09-03T15:02:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-29T19:30:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"39101756773416637997865056582348670619","date":"2025-07-18T18:22:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245767282515984988174196992059543773262","date":"2025-06-27T11:12:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249091820545457405397017051674421715818","date":"2025-06-26T11:46:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-23T07:04:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-19T07:00:48+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-17T06:31:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-17T06:01:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-06-17T05:57:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e0d4a95c-e83d-449a-961e-2e10325919d0","owner":[],"postedDate":"June 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T16:01:36+00:00","versionOfRecord":{"articleIdentity":"rs-6783060","link":"https://doi.org/10.1186/s12889-026-26319-2","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2026-01-26 15:59:04","publishedOnDateReadable":"January 26th, 2026"},"versionCreatedAt":"2025-06-30 05:30:08","video":"","vorDoi":"10.1186/s12889-026-26319-2","vorDoiUrl":"https://doi.org/10.1186/s12889-026-26319-2","workflowStages":[]},"version":"v1","identity":"rs-6783060","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6783060","identity":"rs-6783060","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}