Abstract
Background:
This study aimed to identify the predictors of the intention to receive the COVID-19 vaccine among Iranian health care workers (HCWs) based on the Theory of Planned Behavior (TPB).
Methods:
The study was a descriptive cross-sectional study that was conducted on 473 personnel working in hospitals of Hamadan, in May 2021 and before COVID-19 vaccination on hospital staff. The multi-stage sampling method was used for choosing participants. The survey included socio-demographic, questions related to TPB dimensions, and intention to receive a COVID-19 vaccine. Qualitative and quantitative data analyses were performed using the Chi-square test and T-test, respectively. Predictors of COVID-19 vaccination intention were determined using the logistic regression model.
Results:
Seventy percent of 361 eligible respondents stated their willingness to receive COVID-19 vaccine. The participants with the intention to receive COVID-19 vaccine had higher scores of attitude (7.25 ± 3.92 vs. 4.40 ± 5.14) and norm (3.04 ± 2.92 vs. -0.5 ± 3.18) (P < 0.001). Having an underlying disease and being married were significantly associated with the intention to receive COVID-19 vaccine (P < 0.05). Higher attitude and norm scores as a construct of the TPB were associated with an increase in intention to receive COVID-19 vaccine.
Conclusions:
The results of this study showed that the vaccination intention was affected by social, demographic, health, and behavioral features, such as age, marital status, underlying diseases, subjective norms, and attitude. Therefore, age groups below 50, single people, and those with no underlying diseases were eligible to be the target of interventional programs.
Keywords: Hospital staff, theory of planned behavior (TPB), vaccine
INTRODUCTION
Infecting millions of people and killing thousands of individuals, the COVID-19 pandemic is rapidly spreading throughout the world.[1] Up to April 14th, 2021, Iran had at an alarming rate of 2,118,212 confirmed laboratory cases and 65,055 COVID19-related mortalities.[2] However, the disease has had a more serious impact on the economy related to the Iran Sanctions.[3] Considering the lack of effective treatment, vaccination for the prevention of COVID-19 infection could be assumed as the best way to end the pandemic.[4]
World health organization (WHO) has identified health care workers (HCWs) as a priority group for vaccination.[5] About 14% of COVID-19 cases reported to WHO are among HCWs.[6] Hence, HCWs are potential victims and carriers of the disease.[7] Prevention of COVID-19 in HCWs is of paramount importance. This is because HCWs are at high risk of infection, their place of residence and place of work is usually distant, and they are in close contact to patients with suspected or confirmed infection. On the other hand, it is crucial to protect HCWs against COVID-19 in order to prevent under-resourced and overwhelmed healthcare systems.[8] Therefore, “Iranian HCWs” were considered a priority target group for COVID-19 vaccination.
While the Iran Ministry of Health has initiated a vaccine dissemination process among HCWs to control the pandemic, surveys show that many of these employees are unwilling to receive their vaccine.[9] Therefore, it is extremely important to understand the intentions, motivations, and barriers affecting HCWs decision to get vaccinated against COVID-19. This knowledge can help policymakers to develop effective interventional programs to increase COVID-19 vaccination acceptance among HCWs.[4] Previous studies showed that social, behavioral, and psychological factors can affect the rate of COVID-19 vaccine acceptance.[3] In this regard, attitudes, subjective norms, and self-efficacy include the most important factors affecting the intention to vaccinate.[4,10,11]
Given the crucial role of the theory of planned behavior (TPB) as a value expectation model, it includes attitudes, subjective norms, and perceived behavioral controls to realize intent and behavior. TPB’s role to understand factors affecting the reception of vaccines has been strongly validated in previous studies.[4,10,11] Numerous studies showed that TPB constructs describe 66% of the variance in intention to receive a COVID-19 vaccine.[11] Studies also show that TPB has a superior prediction ability, compared to other models.[12] Therefore, the present study aimed to identify the predictors of the intention to receive COVID-19 vaccine among the hospital staff of the Hamadan University of Medical Sciences based on the TPB.
MATERIALS AND METHODS
Study design and setting
This was a descriptive cross-sectional study designed on 473 people working at Hamadan hospitals, which are affiliated with Hamadan University of Medical Sciences. The study was conducted in May 2021, before hospital staff vaccination program against COVID-19.
Sample size, sampling, and setting
The sample size was determined based on the following equation: (z21-α/2) p (1-p)/d2. Gagneux-Brunon et al.[13] reported that the proportion of hospital staff who did not intend to receive the vaccine was 23% with a 95% confidence interval. The required sample size was obtained as 445. Considering the possibility of distorted and incomplete questionnaires, in this research, 473 hospital staff were included in the study.
The multi-stage (stratified- random) sampling method was used to collect data. About 15% of Shahid Beheshti, Sina, Farshchian, Fatemieh, and Besat hospital staff were enrolled in the study using stratified sampling (Stratum 1). Then, the devoted sample to each hospital was chosen proportional to their staff position (treatment staff, service staff, and administrative staff) (Stratum 2), and finally allocated sample sizes to each job category were selected using random sampling.
Inclusion criteria were organizational affiliation with Hamadan University of Medical Sciences and willingness to participate in the study.
Ethical consideration
This study was approved by the Ethics Committee of the Hamadan University of Medical Sciences (Ethics code No.: IR.UMSHA.REC.1399.1083). The questionnaire was anonymous and informed consent was obtained from the participants in the survey. Participants acknowledged that their participation in the study was voluntary.
Measures
The data collection tool included a questionnaire consisting of two parts including demographic information of participants and constructs of the TPB, which was completed as a self-report method by the study participants. Questionnaires used by previous studies[4,10,12] were used to design and construct the TPB scale.
Demographics data
Demographic data questions covered age, sex, marital status, work history, degree, occupation, history of COVID-19 morbidity in the participants and their relatives, and history of COVID-19 related mortality in friends and relatives.
Validity of theory planned behavior
Content validity was conducted to evaluate the degree that the instrument covers the putative content. Content validity of the TPB scale was approved using the perspectives of 10 specialists in the field of health education, health promotion, and community dentistry through determining content validity ratio (CVR) and content validity index (CVI).
Subsequently, CVR and CVI were calculated as 0.760 and 0.82, respectively. The face validity of the questionnaire was assessed by 30 hospital staff whose characteristics were similar to the target study sample. The reliability of the questionnaire was assessed by calculating the internal reliability (alpha greater than 0.70) [Table 1]. The TPB part of the questionnaire consisted of 26 questions, which covered four constructs described in Table 1.
Table 1.
TPB questionnaire about intention to vaccinate against COVID-19 epidemic in health staff
| Measures | Questions (N) | Cronbach Alpha (>0.7)a | Examples | Scale |
|---|---|---|---|---|
| Attitude | 12 | 0.71 | How much you feel that getting vaccinated for COVID-19 is: "Negative" to "Positive" | 5-point Likert Scale from -2 (Negative) to+2 (Positive) |
| Subjective Norms | 10 | 0.81 | Most people who are important to me think that I should get COVID-19 vaccine | 5-point Likert Scale from -2 (completely disagree) to+2 (completely agree) |
| Perceived Behavioral Control | 3 | 0.73 | If I am offered a vaccine against COVID-19, I'm sure I'll be vaccinated, and this decision is entirely up to me. | 5-point Likert Scale from -2 (completely disagree) to+2 (completely agree) |
| Behavioral Intention | 1 | - | I intend to get the COVID-19 vaccine | binary variable (1 - intends to get vaccinated, 0 - does not intend to get vaccinated) |
Statistical analyses
Descriptive statistics were reported. Categorical data analysis was conducted using the Chi-square test and quantitative data analysis was performed using T-test. Predictors of COVID-19 vaccination intention were determined using a logistic regression model. All statistical analyses were conducted in Stata software version 14 with a significant level of less than 5%.
RESULTS
Of 473 participants, 328 (69.4%) were female and 222 (46.93%) were in the 35–50 years age group; 84.57% were treatment staff and 13.95% of them had underlying diseases; 28.12% of them had a previous history of COVID-19 morbidity [Table 2]; 361 (76.32%) of participants had the intention to receive the COVID-19 vaccine. Having an underlying disease (P = 0.03) and being married (P = 0.006) were significantly associated with the intention to receive COVID-19 vaccine [Table 2].
Table 2.
Associations between respondents' demographic characteristics and intention to receive COVID-19 vaccine
| Variable | n (%) | Intention n (%) | No intention n (%) | P |
|---|---|---|---|---|
| Total | 473 | 361 (76.32) | 112 (23.68) | |
| Gender | ||||
| Male | 145 (30.66) | 109 (75.17) | 36 (24.83) | 0.7 |
| Female | 328 (69.4) | 252 (76.83) | 76 (23.17) | |
| Age group (year) | ||||
| 20-34.9 | 198 (41.86) | 156 (78.79) | 42 (21.21) | 0.08 |
| 35-49.9 | 222 (46.93) | 160 (72.07) | 62 (27.93) | |
| 50+ | 53 (11.21) | 45 (84.91) | 8 (15.09) | |
| Education | ||||
| Less than diploma | 14 (2.96) | 8 (57.14) | 6 (42.86) | 0.21 |
| Diploma | 59 (12.47) | 44 (74.58) | 15 (25.42) | |
| Academic | 400 (84.57) | 309 (77.25) | 91 (22.75) | |
| Job | ||||
| Treatment staff | 354 (74.84) | 274 (77.4) | 80 (22.60) | 0.1 |
| Service staff | 27 (5.71) | 16 (59.26) | 11 (40.74) | |
| Administrative staff | 92 (19.45) | 71 (77.17) | 21 (22.83) | |
| Marital status | ||||
| Single | 149 (31.50) | 102 (68.46) | 47 (31.54) | 0.006 |
| Married | 324 (68.50) | 259 (79.94) | 65 (20.06) | |
| Underlying disease | ||||
| Yes | 66 (13.95) | 57 (86.36) | 9 (13.64) | 0.039 |
| No | 407 (86.05) | 304 (74.69) | 103 (25.31) | |
| Previous COVID-19 morbidity | ||||
| Yes | 133 (28.12) | 107 (80.45) | 26 (19.55) | 0.19 |
| No | 340 (71.88) | 254 (74.71) | 86 (25.29) | |
| COVID-19 morbidity in relatives | ||||
| Yes | 356 (75.26) | 276 (77.53) | 80 (22.47) | 0.28 |
| No | 117 (24.74) | 85 (72.65) | 32 (27.35) |
The participants with the intention to receive COVID-19 vaccine had higher scores of attitude (7.25 ± 3.92 vs. 4.40 ± 5.14, P < 0.001) and subjective norm (3.04 ± 2.92 vs. -0.5 ± 3.18, P < 0.001) [Table 3].
Table 3.
Relation between constructs of the TPB variables and the intention to get vaccinated against COVID-19
| Variable | Intention Mean (SD) | Not intention Mean (SD) | P |
|---|---|---|---|
| Attitude | 7.25±3.92 | 4.40±5.14 | <0.001 |
| Subjective Norm | 3.04±2.92 | -0.5±3.18 | <0.001 |
| Perceived Behavioral Control | 0.14±2.36 | -0.09±1.99 | 0.34 |
After adjusting for other variables, married personnel had a 1.99 fold higher intention to receive COVID-19 vaccine (OR = 1.99, 95% CI: 1.13, 3.54, P = 0.018). Additionally, those with the underlying disease had a 2.97 fold higher intention to receive COVID-19 vaccine (OR = 2.97, 95% CI: 1.23, 7.15, P = 0.016). Higher attitude and subjective norm scores as a construct of the TPB were associated with an increase in intention to receive COVID-19 vaccine [Table 4].
Table 4.
Univariable and multivariable regression analysis predictors of intention to get vaccinated against COVID-19
| Variables | Crude model |
Adjusted model |
||
|---|---|---|---|---|
| OR (95% CI) | P | OR (95% CI) | P | |
| Gender | ||||
| Male | 1 | 1 | ||
| Female | 1.095 (0.69, 1.73) | 0.39 | 1.49 (0.85, 2.63) | 0.17 |
| Age group (year) | ||||
| 20-34.9 | 1 | 1 | ||
| 35-49.9 | 0.69 (0.44, 1.09) | 0.11 | 0.39 (0.22, 0.70) | 0.002 |
| 50+ | 1.51 (0.66, 3.46) | 0.32 | 0.99 (0.36, 2.74) | 0.99 |
| Education | ||||
| Less than diploma | 1 | 1 | ||
| Diploma | 2.2 (0.66, 7.38) | 0.20 | 2.48 (0.54, 11.29) | 0.24 |
| Academic | 2.55 (0.86, 7.53) | 0.09 | 2.99 (0.73, 12.32) | 0.13 |
| Job | ||||
| Service staff | 1 | |||
| Treatment staff | 2.35 (1.05, 5.28) | 0.038 | - | - |
| Administrative staff | 2.32 (0.94, 5.77) | 0.69 | - | - |
| Marital status | ||||
| Single | 1 | 1 | ||
| Married | 1.83 (1.18, 2.85) | 0.007 | 1.99 (1.13, 3.54) | 0.018 |
| Underlying disease | ||||
| No | 1 | 1 | ||
| Yes | 2.15 (1.03, 4.49) | 0.042 | 2.97 (1.23, 7.15) | 0.016 |
| Previous COVID-19 morbidity | ||||
| No | 1 | 1 | ||
| Yes | 1.39 (0.85, 2.28) | 0.19 | 1.74 (0.94, 3.23) | 0.077 |
| COVID-19 morbidity in relatives | ||||
| No | 1 | - | - | |
| Yes | 1.3 (0.81, 2.09) | 0.28 | - | |
| Attitude | 1.16 (1.1, 1.23) | <0.001 | 1.1 (1.04, 1.17) | 0.002 |
| Subjective Norm | 1.47 (1.35, 1.61) | <0.001 | 1.46 (1.33, 1.61) | <0.001 |
| Perceived Behavioral Control | 1.05 (0.95, 1.15) | 0.34 | - | - |
DISCUSSION
The present study evaluated the level of HCWs’ intention to receive COVID-19 vaccine and its related psychological and demographic determinants. Confirming the previous studies, our results showed that 76.32% of the participants had the intention to receive the COVID-19 vaccine.[4,10,14] These promising results showed that extensive vaccination is the key to controlling the COVID-19 pandemic.[15] About 24% of the participants had no intention to receive the vaccine. Because an immunization rate of 70%–90% is required to create herd immunity against COVID-19 and given the fact that immunity created by vaccination against COVID-19 may not last long compared with immunity to other diseases,[15] there might be a need for planning and implementing an intervention that could effectively promote vaccination, especially among vaccine hesitancy group. This is alarming since HCWs are at a higher risk of COVID-19 infection and mortality; therefore, they are the priority target group for vaccination.
Similar to previous studies,[10,14] our findings revealed that participants aged 50 years and higher were most likely to receive the vaccine. This higher intention in the mentioned age group could be due to their related high-risk group.[10]
In addition, the results were indicative of a higher intention to receive vaccines among married people and those with underlying diseases. On the contrary, a lower vaccination intention was observed among participants under 50 years and other demographic groups (e.g., academics), which is consistent with other studies.[16,17]
According to studies, the Construct model showed 50% of the variance in COVID-19 vaccine intention. This model is a useful framework for interventional design in order to encourage HCWs to get vaccinated. According to the results, COVID-19 vaccination intention significantly affected the participants' subjective norms. In fact, people would be encouraged to receive the COVID-19 vaccine when their relatives, close family members, and other significant people persuaded them to do so. In addition, a positive attitude toward COVID-19 vaccination significantly predicted HCWs' intention to get vaccinated. According to the results of the current research, HCWs had a positive attitude toward COVID-19 vaccination. They considered the vaccination to be favorable to themselves and their community, which is consistent with the results of our study.[4,18] Yet, the rapid development of vaccines, especially compared to previous vaccines, may lead to increased vaccine safety concerns.[4,18] Therefore, public health activities aimed at increasing vaccination rates should address the benefits of COVID-19 vaccine in the community and also maintain a high level of transparency about vaccine safety.[4] Similar to another study,[4] the participants in the present study believed that they could prevent COVID-19 spread by allowing them to get vaccinated, and they were free to decide whether to get vaccinated or not.
According to the results, there was no significant relationship between perceived behavioral control and COVID-19 vaccination intention, which might be due to the fact that the vaccine was provided to HCWs for free, they were able to get vaccinated at work, and it was completely up to them to get vaccinated or not. Therefore, limited variance in perceived behavioral control may prevent its association with vaccination intent. Ostensibly, providing vaccines at a reasonable or no cost and providing them at work may help increase the rate of vaccination, especially in people who do not intend to get vaccinated. Furthermore, public health intervention programs should focus more on increasing the understanding of the benefits of vaccination in order to affect people's attitudes. In terms of subjective norms, efforts should be made to inspire people to share their positive thoughts and experiences about COVID-19 vaccination with friends and relatives. One of the major problems of the present study was the analysis of data based on a cross-sectional study, which did not determine the causal relationship between the model construct and intention. Therefore, the effect of other similar variables on COVID-19 vaccine administration cannot be excluded. The second limitation was a collection of self-reported data.
CONCLUSION
In the present study, behavioral changes were analyzed, which is often a good predictor. In other words, their entrance into the analysis process will hide the effects of social cognitive variables. The present research assessed HCWs’ intention to receive COVID-19 vaccination and determined the social, demographic, health, and behavioral predictors of this intention based on the TPB model. According to the results, the significance of designing and implementing intervention programs is to address participants who do not intend to receive the COVID-19 vaccine. In addition, while most HCWs intended to get vaccinated, the vaccination intention was affected by social, demographic, health, and behavioral features, such as age, marital status, underlying diseases, subjective norms, and attitude. Therefore, the under-50s age group, single people, and those with no underlying diseases should be the target of interventional programs. Moreover, public health intervention programs should focus more on improving people's attitudes. In this regard, the Iran ministry of health should invest more in information campaigns, do its best to make vaccines available at work for free or at a reasonable price, and encourage people to share their positive thoughts and experiences about COVID-19 vaccination.
Financial support and sponsorship
This study was supported by the Hamadan University of Medical Sciences (Grant Number: 14000110175). The funder had no role in the design of the study; the collection, analysis, and interpretation of the data; the writing of the manuscript; or the decision to submit the manuscript for publication. The content is solely the responsibility of the authors.
Conflict of Interest
There are no conflicts of interest.
REFERENCES
- 1.Organization WH. World Health Organization Coronavirus Disease (COVID-19) 2020 [Google Scholar]
- 2.Winanti A, Satino S, Bakhtiar HS. The roles of governments in implementing large-scale social restrictions to accelerate Covid-19 handling. Journal of Community Positive Practices. 2022;1:77–85. [Google Scholar]
- 3.Taherinia MHA. Economic consequences of Covid-19 disease on the iranian economy; with an emphasis on employment. Quart J Nurs Manag (IJNV) 2020;9:43–58. [Google Scholar]
- 4.Chu H, Liu S. Integrating health behavior theories to predict American's intention to receive a COVID-19 vaccine. Patient Educ Couns. 2021;104:1878–86. doi: 10.1016/j.pec.2021.02.031. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Organization WH. WHO SAGE Values Framework For The Allocation And Prioritization of COVID-19 Vaccination, 14 September 2020. World Health Organization; 2020 [Google Scholar]
- 6.Zu ZY, Jiang MD, Xu PP, Chen W, Ni QQ, Lu GM, et al. Coronavirus disease 2019 (COVID-19): A perspective from China. Radiology. 2020;296:E15–25. doi: 10.1148/radiol.2020200490. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Schwierzeck V, König JC, Kühn J, Mellmann A, Correa-Martínez CL, Omran H, et al. First reported nosocomial outbreak of severe acute respiratory syndrome coronavirus 2 in a pediatric dialysis unit. Clin Infect Dis. 2021;72:265–70. doi: 10.1093/cid/ciaa491. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Bashirian S, Khazaie S, Barati M, Jenabi E, Soltanian A, Shirahmadi S, et al. Covid-19 prevention behaviors among health staff: Data from a large survey in the west of iran. J Res Health Sci. 2021;21:e00509. doi: 10.34172/jrhs.2021.43. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Ali AO, Sanaz RKK. Estimating willingness to pay for the Covid-19 vaccine using the conditional valuation method. Payesh. 2021;20:223–36. [Google Scholar]
- 10.Shmueli L. Predicting intention to receive COVID-19 vaccine among the general population using the health belief model and the theory of planned behavior model. BMC Public Health. 2021;21:1–13. doi: 10.1186/s12889-021-10816-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Guidry JP, Laestadius LI, Vraga EK, Miller CA, Perrin PB, Burton CW, et al. Willingness to get the COVID-19 vaccine with and without emergency use authorization. Am J Infect Control. 2021;49:137–42. doi: 10.1016/j.ajic.2020.11.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Thoma V, Weiss-Cohen L, Filkuková P, Ayton P. Cognitive Predictors of Precautionary Behavior During the COVID-19 Pandemic. Frontiers in Psychology. 2021:12. doi: 10.3389/fpsyg.2021.589800. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Gagneux-Brunon A, Detoc M, Bruel S, Tardy B, Rozaire O, Frappe P, et al. Intention to get vaccinations against COVID-19 in French healthcare workers during the first pandemic wave: A cross-sectional survey. J Hosp Infect. 2021;108:168–73. doi: 10.1016/j.jhin.2020.11.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Wang K, Wong ELY, Ho KF, Cheung AWL, Chan EYY, Yeoh EK, et al. Intention of nurses to accept coronavirus disease 2019 vaccination and change of intention to accept seasonal influenza vaccination during the coronavirus disease 2019 pandemic: A cross-sectional survey. Vaccine. 2020;38:7049–56. doi: 10.1016/j.vaccine.2020.09.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Control CfD, Prevention. Ten great public health achievements--United States, 2001-2010. MMWR Morb Mortal Wkly Rep. 2011;60:619–23. [PubMed] [Google Scholar]
- 16.Fisher KA, Bloomstone SJ, Walder J, Crawford S, Fouayzi H, Mazor KM. Attitudes toward a potential SARS-CoV-2 vaccine: A survey of US adults. Ann Intern Med. 2020;173:964–73. doi: 10.7326/M20-3569. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Neumann-Böhme S, Varghese NE, Sabat I, Barros PP, Brouwer W, van Exel J, et al. Once we have it, will we use it? A European survey on willingness to be vaccinated against COVID-19. Eur J Health Econ. 2020;21:977–82. doi: 10.1007/s10198-020-01208-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Prasetyo YT, Castillo AM, Salonga LJ, Sia JA, Seneta JA. Factors affecting perceived effectiveness of COVID-19 prevention measures among Filipinos during enhanced community quarantine in Luzon, Philippines: Integrating protection motivation theory and extended theory of planned behavior. Int J Infect Dis. 2020;99:312–23. doi: 10.1016/j.ijid.2020.07.074. [DOI] [PMC free article] [PubMed] [Google Scholar]
