The authors have declared that no competing interests exist.
Since January 2021, Indonesia has administered a nationwide COVID-19 vaccination. This study examined vaccine intention and identified reasons for vaccine hesitancy in the capital city of Jakarta. This is a cross-sectional online survey using the Health Belief Model (HBM) to assess vaccine intent predictors and describe reasons for hesitancy among Jakarta residents. Among 11,611 respondents, 92.99% (10.797) would like to get vaccinated. This study indicated that all HBM constructs predict vaccine intention (P< 0.05). Those with a high score of perceived susceptibility to the COVID-19 vaccine were significantly predicted vaccine hesitancy (OR = 0.18, 95% CI: 0.16–0.21). Perceived higher benefits of COVID-19 vaccine (OR = 2.91, 95% CI: 2.57–3.28), perceived severity of COVID-19 disease (OR: 1.41, 95% CI: 1.24–1.60), and perceived susceptibility of the current pandemic (OR = 1.21, 95% CI: 1.06–1.38) were significantly predicted vaccination intend. Needle fears, halal concerns, vaccine side effects, and the perception that vaccines could not protect against COVID-19 disease emerged as reasons why a small portion of the respondents (n = 814, 7.23%) are hesitant to get vaccinated. This study demonstrated a high COVID-19 vaccine intention and highlighted the reasons for vaccine refusal, including needle fears, susceptibility to vaccine efficacy, halal issues, and concern about vaccine side effects. The current findings on COVID-19 vaccination show that the government and policymakers should take all necessary steps to remove vaccine hesitancy by increasing awareness of vaccine efficacy and benefit interventions.
As of May 2021, Indonesia was one of the countries with the highest number of novel Coronavirus Diseases 2019 (COVID-19) cases with the lowest testing rate in Southeast Asia [
By the end of May 2021, Indonesia has reached 27 million shots of the COVID-19 vaccine. The Indonesian government has issued a regulation as a legal basis for vaccinating the Indonesian population prioritizing health care workers, older people, public servants, those with preexisting medical conditions, and those who live in areas with high transmission of COVID-19 [
As the capital city of Indonesia, with a population of around 10.56 million, Jakarta has been the epicenter of COVID-19 in Indonesia since the beginning of March 2020 [
In the Southeast Asia region and worldwide, studies have been conducted to examine the intention of a vaccine against COVID-19. A previous study showed that COVID-19 vaccine uptake in Indonesia was influenced by the effectiveness of the vaccines [
To pursue our goal, we applied the Health Belief Model (HBM) as the core framework in our study. As one of the most widely applied theories in health behaviors [
Perceived susceptibility refers to a belief about the possibility of getting a condition. This study addressed individuals’ beliefs about getting impacted by two conditions: the COVID-19 pandemics and the vaccine. Within this construct, we studied individuals’ perception of vaccine side effects, whether or not the vaccine could protect against infection, and halal concerns about the vaccine that may hinder individuals from getting vaccination against coronavirus infection. Perceived severity refers to feelings about the seriousness of having the COVID-19 disease. In a broader sense, we included severity related to social and financial consequences such as reduced income, loss of jobs, restricted family and social interactions. Moreover, as information and access to vaccination centers were found to be obstacles for some individuals [
This cross-sectional study was performed from 30 April to 15 May 2021. Quota sampling was used to analyze data collected from the proportion of gender represented across the five districts (West Jakarta, South Jakarta, East Jakarta, North Jakarta, and Central Jakarta) in the capital city, Jakarta. Data were collected through a web-based anonymous survey using a Qualtrics-based online questionnaire. The Jakarta Administration Bureau facilitated the distribution of questionnaires to the Jakarta population through JAKI, an application for administrative information for Jakarta residents. Inclusion criteria were that the respondents were Jakarta residents who were more than 18 years of age and with internet access, while those who work in Jakarta but live on the outskirts of the city were excluded from the study. The questionnaire was pilot tested and validated by local experts prior to the administration of the survey.
A 45-item structured questionnaire was developed to assess the study objectives. The survey consisted of questions that assessed demographic background (8 questions), health status and COVID-19 experience (3 questions), and HBM constructs (28 questions). A 5-point Likert scale (1 = strongly disagree to 5 = strongly agree) was used for the HBM portion of the questionnaire groups. Eight demographic variables were collected: gender, age, occupancy and whether the respondent works in the health area, their role in the local community, estimated monthly income, education level completed, and religious belief. Three questions assessed the comorbidities of the respondents and whether respondents and their families have existed or been diagnosed with COVID-19 (Yes/No). The survey was anonymous and contained no identifiable respondent information.
This study was approved by the Faculty of Psychology, Universitas Indonesia Research Ethics Committee in April 2021. Approval code: 039/FPsi.Komite Etik/PDP.04.00/2021/. The survey was conducted online. Informed consent was obtained before the respondent began participating in the study. Informed consent was documented on a digital platform. This study did not include minors.
Descriptive statistics (mean, standard deviation, frequency) were obtained for all variables. The HBM-based statements were grouped according to their constructs (perceived susceptibility to the COVID-19 pandemic and the vaccine, perceived severity of COVID-19 disease, perceived barriers to vaccination, and perceived specific vaccine benefits, and cue to action). Cronbach’s alpha was calculated for the constructs; see supplemental materials for the detailed values. Spearman’s rho and Pearson Chi-Square correlation were used to assess the correlation between HBM construct and (1) demographic variables; (2) health status and COVID-19 experience variables. A logistic regression model was applied to examine HBM factors that significantly predicted COVID-19 vaccine intent and refusal. Additional regression test was done to study if COVID-19 health experience variables significantly predicted vaccination intention. All statistical analyses were performed using the IBM SPSS 26 software. A P-value of less than 0.05 (95 percent of confidence interval) was considered statistically significant in this study.
A total of 11,611 participants completed the survey. The study received proportional gender-based responses from all five districts within Jakarta province. As shown in
Variable | Category (N = 11611) | n | (%) |
---|---|---|---|
Sex | Male | 5844 | 50.33 |
Female | 5767 | 49.67 | |
Age | 18–20 years old | 170 | 1.46 |
>20–30 years old | 1174 | 10.11 | |
>30–40 years old | 2327 | 20.04 | |
>40–50 years old | 3288 | 28.32 | |
>50–60 years old | 2347 | 20.21 | |
>60 years old | 2305 | 19.85 | |
Health-related jobs | Yes | 1378 | 11.87 |
No | 10233 | 88.13 | |
Occupation | Student | 197 | 1.70 |
Housewife | 4017 | 34.60 | |
Educational Staff | 275 | 2.37 | |
Doctor/midwife/nurse/other health workers | 99 | 0.85 | |
Day laborer (on-line driver, street trader, etc) | 1136 | 9.78 | |
Military/Police | 59 | 0.51 | |
Business owner | 671 | 5.78 | |
State worker | 252 | 2.17 | |
Private worker | 2134 | 18.38 | |
NGO worker | 44 | 0.38 | |
Artist | 39 | 0.34 | |
Unemployed | 1256 | 10.82 | |
Other | 1432 | 12.33 | |
Role in local community | Youth leader | 1411 | 12.15 |
Woman leader | 1690 | 14.56 | |
Religious leader | 328 | 2.82 | |
Senior citizen | 3083 | 26.55 | |
N/A | 5099 | 43.92 | |
Monthly income (Indonesian Rupiah) | < Rp. 2.500.000 | 6233 | 53.68 |
IDR 2.500.001- IDR 5.000.000 | 3697 | 31.84 | |
IDR 5.000.001- IDR7.500.000 | 694 | 5.98 | |
IDR 7.500.001-IDR 10.000.000 | 324 | 2.79 | |
IDR 10.000.001-IDR 12.500.000 | 139 | 1.20 | |
IDR. 12.500.001-IDR 15.000.000 | 116 | 1.00 | |
> IDR 15.000.000 | 408 | 3.51 | |
Religion | Buddha | 214 | 1.84 |
Hindu | 45 | 0.39 | |
Islam | 10168 | 87.57 | |
Catholic | 468 | 4.03 | |
Christian | 583 | 5.02 | |
Indigenous Beliefs | 14 | 0.12 | |
Other | 19 | 0.16 | |
Do not answer | 100 | 0.86 | |
Education | Not finished elementary/Middle/High school | 2704 | 23.29 |
High School | 6119 | 52.70 | |
Diploma/College/Post Graduate | 2788 | 24.01 | |
Are currently being or had previously diagnosed with COVID-19 | Yes | 612 | 5.27 |
No | 10999 | 94.73 | |
Have family members who are currently being or had previously diagnosed with COVID-19 | Yes | 834 | 7.182 |
No | 10777 | 92.82 | |
Comorbidities | No | 8221 | 70.80 |
3390 | 29.20 | ||
Cardiovascular Disease (CVD) | 270 | 2.33 | |
Asthma | 290 | 2.50 | |
Kidney Disease | 40 | 0.34 | |
Diabetes Mellitus | 532 | 4.58 | |
Hypertension | 1355 | 11.67 | |
Autoimun | 31 | 0.27 | |
Other | 473 | 4.07 | |
Do not know | 833 | 7.17 |
More than half of the respondents (62,45%) received their first dose of the COVID-19 vaccine. Only a small portion (29.2%) reported having chronic diseases. The majority of respondents (94.73%) and their families (52.79%) were not being and had not previously been diagnosed with COVID-19.
The survey revealed that only a small portion of the respondents was unwilling to get vaccinated (n = 814, 7.01%) and identified five factors describing such hesitancy. Almost two percent (1.73%) or 201 respondents showed a strong agreement of being afraid of needle injection, 2.5% (n = 290) strongly agreed that the available COVID-19 vaccine is not halal, 3.49% (n = 405) strongly agreed that the available vaccine does not provide protection from COVID-19 infection, and 3.62% (n = 420) were concerned about the vaccine side effects. In addition, 279 respondents (2.4%) expressed their concern that they were not included in the targeted vaccination population.
Correlation coefficient analyses were used to examine the relationship between demographic variables and the HBM constructs and COVID-19 experience variables (
Demographic variables | Perceived susceptibility of COVID-19 pandemics | Perceived susceptibility of COVID19 vaccine | Perceived severity of COVID-19 disease | Perceived barriers to COVID-19 vaccine | Perceived benefits of COVID-19 Vaccine | ||
---|---|---|---|---|---|---|---|
Spearman’s rho | Age | Correlation Coefficient | 0.022 |
-0.133 |
0.004 | -0.039 |
0.046 |
Sig. (2-tailed) | |||||||
Monthly income (Indonesian Rupiah) | Correlation Coefficient | 0.028 |
-0.120 |
0.045 |
-0.137 |
0.052 |
|
Sig. (2-tailed) | |||||||
Highest Education level | Correlation Coefficient | 0.053 |
-0.102 |
0.094 |
-0.205 |
0.03 | |
Sig. (2-tailed) | |||||||
Pearson Chi-Square | Sex | Contingency Coefficient | 0.068 |
0.106 |
0.084 |
0.047 |
0.057 |
Sig. (2-tailed) | |||||||
Health-related jobs | Contingency Coefficient | 0.069 |
0.044 |
0.070 |
0.107 |
0.05 |
|
Sig. (2-tailed) | |||||||
Occupation | Contingency Coefficient | 0.096 |
0.146 |
0.114 |
0.139 |
0.101 |
|
Sig. (2-tailed) | |||||||
Role in Local community | Contingency Coefficient | 0.067 |
0.118 |
0.085 |
0.075 |
0.104 |
|
Sig. (2-tailed) | |||||||
Religion | Contingency Coefficient | 0.125 |
0.173 |
0.119 |
0.107 |
0.103 |
|
Sig. (2-tailed) | |||||||
Pearson Chi-Square | Are currently being or had previously diagnosed with COVID-19 | Contingency Coefficient | 0.033 |
0.021 | 0.017 | 0.120 |
0.012 |
Sig. (2-tailed) | |||||||
Family members are currently being or had previously diagnosed with COVID-19 | Contingency Coefficient | 0.022 | 0.021 | 0.042 |
0.022 | 0.021 | |
Sig. (2-tailed) | |||||||
Comorbidities | Contingency Coefficient | 0.051 |
0.163 |
0.048 |
0.023 | 0.06 |
|
Sig. (2-tailed) | |||||||
COVID-19 Vaccination status | Contingency Coefficient | 0.037 |
0.272 |
0.073 |
0.128 |
0.139 |
|
Sig. (2-tailed) | |||||||
Family consent to get vaccinated | Contingency Coefficient | 0.065 |
0.364 |
0.075 |
0.130 |
0.282 |
|
Sig. (2-tailed) | |||||||
Vaccination willingness | Contingency Coefficient | 0.060 |
0.374 |
0.073 |
0.118 |
0.282 |
|
Sig. (2-tailed) |
*. Correlation is significant at the 0.05 level (2-tailed).
This study indicated that all HBM construct predicts vaccine intention (P < 0.05) as described in
Explanatory variable | β | Standard Error | P-Value | Wald Test | Odds Ratio OR | 95% CI of OR | |
---|---|---|---|---|---|---|---|
Lower | Upper | ||||||
Perceived susceptibility to COVID-19 pandemic | 0.2 | 0.07 | 0.004 | 9.31 | 1.21 | 1.06 | 1.38 |
Perceived susceptibility to COVID-19 vaccine | -1.71 | 0.07 | 0 | 686.5 | 0.18 | 0.16 | 0.21 |
Perceived severity of the COVID-19 disease | 0.34 | 0.06 | 0 | 30.46 | 1.41 | 1.24 | 1.6 |
Perceived barriers to vaccination | -0.15 | 0.06 | 0.006 | 8.78 | 0.85 | 0.77 | 0.96 |
Perceived benefits of COVID-19 vaccine | 1.07 | 0.06 | 0 | 289.49 | 2.91 | 2.57 | 3.28 |
Constant | 2.47 | 0.23 | 0 | 121.57 | 13 | ||
Overall Model Evaluation | Likelihood Ratio Test | 1686.46 | 5 | 0 | |||
Goodness of Fit Test | Hosmer and Lemeshow Test | 8.48 | 8 | 0.38 |
Explanatory variable | β | Standard Error | P-Value | Wald Test | Odds Ratio OR | 95% CI of OR | |
---|---|---|---|---|---|---|---|
Lower | Upper | ||||||
Family members who are currently or have been diagnosed with COVID-19 (Yes) | 0.40 | 0.16 | 6.21 | 1.50 | 1.09 | 2.05 | |
Underlying medical condition (comorbidities) (Yes) | -0.680 | 0.07 | 85.75 | 1.98 | 1.72 | 2.29 | |
Constant | 2.12 | 0.06 | 1374.20 | 8.32 | |||
Overall Model Evaluation | Likelihood Ratio Test | 87.691 | 2 | 0 | |||
Goodness of Fit Test | Hosmer and Lemeshow Test | 0.19 | 1 | 0.662 |
The present study examined vaccine intention and described reasons for vaccine hesitancy vis-a-vis vaccine acceptance among Jakarta residents. This was conducted during the first phase of the COVID-19 vaccination rollout in Jakarta in which COVID-19 cases and deaths were the highest in the nation. By 30 April 2021, at the start of the present study, Jakarta recorded 408,620 cases, of which 6,733 died from COVID-19 [
At the time of the study, vaccination priority was given to health care workers, older citizens, and those who work in public service areas [
Consistent with the previous studies [
However, although only a small portion of the respondents (n = 814, 7.01%) was unwilling to uptake the COVID-19 vaccine, scrutinizing the reasons for vaccine hesitancy helps better understand the barriers and formulate recommendations, especially communication to address the obstacles. Addressing vaccination barriers in Jakarta, the COVID-19 epicenter of Indonesia, is critical to ensure most of its population is protected by vaccines. This present study excavated five reasons as such barriers to vaccine hesitancy. One of the barriers this study revealed is needle fears or being afraid of injection among 201 respondents (1.73%). This is not surprising because the previous study demonstrated that some Indonesian adults are afraid to inject a needle into the body [
Moreover, consistent with Baraniuk [
In addition to the halal issue, we found 405 or 3.49% of respondents perceived that the available vaccine could not protect against COVID-19 disease. This finding has been consistent with the most recent study that assesses vaccine acceptance. Harapan et al. [
Furthermore, although our findings suggest that people’s beliefs or perceptions about the susceptibility and severity of current COVID-19 pandemics and vaccines, including perceived benefits and technical barriers to access vaccines, were determinants of vaccine intent or refusal, greater attention should be emphasized to the perceived vaccine susceptibility (B = -1.72, P < 0.05) and the benefit of the vaccine to protect against COVID-19 (B = 1.023, P < 0.05). In this study, these two variables significantly provided major contributions to predicting vaccine intention and refusal compared to the other HBM variables. Again, these findings highlighted the pivotal role of removing barriers to halal issues and fears of needle injection. Moreover, the effectiveness of vaccines was one of the essential drivers for vaccine uptake [
Lastly, this study indicated that self and family health conditions significantly predicted vaccine intention. Those who have comorbidities were less likely to get vaccinated compared to those who have no comorbidities (OR = 0.50, 95% CI: 0.44–0.58, P < 0.05). These findings are in line with studies conducted elsewhere: in Northern Italy [
This study demonstrated a high COVID-19 vaccine intention (n = 10,797, 92.77%). Four major factors have been identified as predictors of such high uptake, i.e., perceived COVID-19 disease susceptibility (OR = 1.34, P = 0.00), the technical barrier to access vaccination (OR = 0.58, P = 0.00), family members who were currently being or previously had diagnosed with COVID-19 (OR = 1.42, P = 0.03), and self-comorbidities (OR = 1.89, P = 0.00). Additionally, this study underscored the importance of identifying reasons for vaccine refusal. Needle fears, susceptibility to vaccine efficacy, halal issues, concern about vaccine side effects and comorbidities, and not being included in the vaccination targeted group were indicated as barriers to vaccine uptake. Although only accounted for by a small number of respondents, it is plausible to address these specific barriers, given that Jakarta always had the highest COVID-19 cases and deaths. This study suggests that education on vaccine efficacy and benefit interventions, which encompasses removing vaccine hesitancy, is critically needed to promote vaccine uptake. Lastly, there is a need for further similar studies in the same population that might provide a comprehensive picture of vaccination intentions and barriers.
This study has two limitations. First, we used a simple stratification of the sample based on the sample’s gender proportion. However, the quota sampling employed could lead to sampling bias because the sample has not been chosen using random selection. The generalizability of the survey results may be impacted by how we distributed the online questionnaire. Second, the Jakarta administration team helped us to disseminate the questionnaire using an application for Jakarta residents. As a result, it might not reach people with no internet and no smartphone access, thus affecting data representation.
PGPH-D-22-00144
Using a Health Belief Model to Assess COVID-19 Vaccine Intention and Hesitancy in Jakarta, Indonesia
PLOS Global Public Health
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Reviewer #1: Congratulations to the research team for their thoughtful manuscript. The statistical analysis has been accurately done and presented and the findings either provides new information or consistent with other findings.
My only specific comment is the need to standardize how numbers are captured in the manuscript. For instance: the use of commas or periods in numbers/figures wasn't consistent such as IDR 10.000.001-IDR 12.500.000 (use of period in table 1 for income) or in the discussion you notice a number captured as 1,906,096 (with commas) or 11611 in table 1 (without period or commas). Same for P-values: where decimal is used (P< .05) but in the tables you see P-values reflected as 0,006 (with commas). In the discussion you have for instance n= 4,652, 40.06% (decimals used) but in the tables you see percentages using commas (not decimals for consistency). The lack of consistency is my challenge.
Recommendation: In as much as how the numbers have been reflected by the authors is not wrong, there is need for standardization and uniformity in the use of periods and commas in the tables and in the main write up to make it easier for the reader to follow. e.g either use P<0.05 or P<0,05 for consistency or 1,100,000 or 1.100.000 for consistency and not a mixture of the two formats.
Reviewer #2: Thank you for inviting me to review " Using a Health Belief Model to Assess COVID-19 Vaccine Intention and Hesitancy in Jakarta, Indonesia"
Comments # 1: Abstract
Line number 43: Please add 0 (zero) before writing a decimal number and add a space between CI:2.57
After line 51, the authors should give a conclusion or solution-related line that beautifies the abstract part. The author can mention something like the following line. For example, the current findings on COVID-19 vaccination show that the government and policymakers should take all necessary steps to ensure the effectiveness of the vaccination program
Comments # 2: Introduction
Line number 87, 88: Correct the statement of only 1.178.243 persons (39.3%) received the first dose out of 3.000.689 targets. This indicates an inaccurate number, please correct the right number as 1,178,243 persons and 3,000,689 targets in place of 1.178.243 persons and 3.000.689 targets
The author should include some references that have summarized vaccine acceptance and vaccine hesitancy. The author also ought to include some studies that have been conducted in developing countries like Asia and developed countries to support this manuscript
Comments # 3: Methods
Study participants and survey design
Line number121: What is the name of the 5 districts? Please included
Line number 125: What are the exclusion criteria of this survey? What were the reason for only adding inclusion criteria?
Instruments:
Line number 130: What types of questionnaires were used in this study? Was it structured, or semi-structured or other types?
Statistical analysis
Why is not mentioned confidence interval (CI) in the statistical analysis section
Comments # 4: Results
Line number 168: use space between 2.5million
Table 1 Headings section: Please write n instead of count and % in lieu of Percentage (%)
Correct the proper percentage of table 1 in each percentage row. This column shows the inaccurate percentage and uses the full stop (.) symbol in place of a comma (,)
It is not essential to show the total number of respondents for each categorical row. It can be shown in the table 1 headline as
variable category Overall respondent N= 11611
n %
Sex Male 5844 50.33
Remove highlights in the rows of (Religion Do not answer 100)
Use single space in the row of (Have family members who are currently being or had previously diagnosed with COVID-19 No 10777 92,80). This row contains double space
Table 2
Similarly, it is not essential to show the total number of respondents for each categorical row. It can be written as like as table 1.
Why are not Spearman's rho and Pearson Chi-Square test analysis show different rows in a similar column? This analysis should be shown different rows in each column.
Line number 199: Write this sentence properly. Table 2 details the relationship between whether respondents and their families were being or had previously been diagnosed with COVID-19, respondents’ comorbidities, and the HBM construct
Table 3:
Include coefficient symbol instead of
Use full stop (.) in place of comma (,)
Check Table 3 last rows and correct this
Line number 236: Above line number 236, insert this table in proper format and use a proper Chi-Square symbol.
Table 4:
Include coefficient symbol in lieu of
Use full stop (.) instead of comma (,)
Check the whole Table 4. It seems to miss information in this table.
Line number 242: Below the line number 236, insert this table in proper format and use a proper Chi-Square symbol.
Comments # 5: Discussions
Line number 251: Insert this reference in similar format.
The author showed that 92.99% (10,797) would like to get vaccinated. But there is no sufficient explanation for it. The author also didn’t explain the overall world scenario of COVID-19 vaccine acceptance and hesitancy. So, the author should give some recent references to the related studies that clear the manuscript.
Line number 268: Address mathematical notation (%) in this line, we found nearly 12 percent
In lines 269, 270, and 271, this sentence doesn’t sound right and it should be written correctly.
Overall comments:
There are some grammatical errors as well as improving the English language. It is noticed that the structure of many sentences is misleading. Please revise the manuscript correctly, avoiding grammatical errors.
Please add 0 (zero) before writing a decimal number
Use full stop (.) in place of comma (,) for all table components were using a numerical number
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Using a Health Belief Model to Assess COVID-19 Vaccine Intention and Hesitancy in Jakarta, Indonesia
PGPH-D-22-00144R1
Dear Dr Amir,
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Reviewer #1: Yes
Reviewer #2: Yes
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6. Review Comments to the Author
Reviewer #1: Congratulations for the efforts made in revising the manuscript based on the feedback.
Reviewer #2: (No Response)
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