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Galina Shapiro, Maxim Bez, Tomer Talmy, Josef Daniel Shakargy, Ariel Furer, Erez Karp, David Segal, SARS-CoV-2 Vaccine Acceptance Disparity Among Israeli Defense Forces Personnel, Military Medicine, Volume 188, Issue 7-8, July/August 2023, Pages e2592–e2597, https://doi.org/10.1093/milmed/usac122
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ABSTRACT
Promoting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine acceptance and uptake became necessary to achieve a high vaccination rate and subsequently herd immunity. Although the Israeli population has been largely acceptant of the SARS-CoV-2 vaccine, vaccine hesitancy has remained a major concern, especially in younger adults. We hypothesized that young adults who refused SARS-CoV-2 vaccination differed from those who have been adherent and could be characterized. Studying this specific population and recognizing individuals within this group who might be more probable to refuse vaccination can enable to target measures to further promote vaccination acceptance.
We conducted a cross-sectional comparison in a study population comprised of 17,435 Israeli Defense Forces (IDF) personnel who were SARS-CoV-2 vaccine eligible. This group included 14,834 vaccinated and 2,601 nonvaccinated individuals. Patient characteristics including occupational parameters, demographic features, psychotechnical grading (an intelligence assessment score), education level, and medical background were collected.
The median age was 20.57 years and almost 80% were males. At the time of data collection, most individuals (85.1%, n = 14,834) have been vaccinated. Officers and noncommissioned officers were more likely to be vaccinated compared with regular soldiers (96%, and 90.2% vs. 83.3% respectively, P < .001), as well as combat battalions stationed personnel compared to their peers in rear and administrative units (89.4% vs. 78.4%, P < .001). Socioeconomic clusters were also associated with vaccination adherence, with 92.9% vs. 79.5% vaccination rates in the highest and lowest clusters, respectively (P < .001). Younger age, no previous immigration status, higher education level, and higher psychotechnical grades were also found associated with an increased likelihood of being vaccinated (P < .001).
In a large cohort of enlisted IDF personnel, disparity in SARS-CoV-2 vaccine adherence was found to be related to multiple socioeconomic, educational, and military service-related variables. Although some differences were substantial, others were small and of questionable public health significance. Acknowledging these differences may enable community leaders, health care providers, and administrators to target specific populations in order to further promote SARS-CoV-2 vaccination acceptance.
INTRODUCTION
Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in late 2019 in China, spread rapidly, and was declared a global pandemic by the World Health Organization on March 2020.1 The clinical spectrum of COVID-19 ranged from asymptomatic individuals to a critical disease complicated by acute respiratory distress syndrome,2 thromboembolic events,3 and encephalopathy.4 As of December 2021, over 268 million people have been infected by SARS-CoV-2 globally with over 5.2 million deaths.5 Effective vaccines were urgently needed.
Three vaccines, manufactured by Pfizer, Moderna, and AstraZeneca, have been approved for emergency use in Europe6 by June 2021, bringing hope of pandemic containment. This, in turn, shifted the focus to promoting vaccine acceptance and uptake to achieve the high vaccination rate needed for herd immunity.7 Unfortunately, although some researchers reported high vaccination rates,8 early reports indicated that SARS-CoV-2 vaccine hesitancy and resistance rates were found to be as high as 31% to 49%.9–11 According to some reports, vaccine acceptance was found to correlate with trust levels in governmental information,11 socioeconomic status,12 higher education, higher income,9 age above 60 years,13 and male gender.13,14
Even in countries that led the global effort to achieve herd immunity,15 vaccine hesitancy remained a major concern, especially in younger individuals.12 Formal reports on the vaccination rates among military personnel worldwide have been thus far scarce.16 Various reports have implied significant hesitancy17 for initiating vaccination, with vaccination rates as low as 27%,16 while others presented impressive adherence rates of 80% to 90%.8,18 Although SARS-CoV-2 vaccination in the Israeli Defense Forces (IDF) was never mandatory, some military medical organizations did consider obligatory vaccination.19 Aiming to better understand factors associated with vaccination hesitancy among military personnel, we conducted a cross-sectional study and focused our investigation on a population of over 17,000 individuals under active military duty in Israel, a country where SARS-CoV-2 vaccines have been widely accessible, and who have been under the medical supervision of a single health organization. We hypothesized that vaccine hesitancy would be associated with occupational, demographic, socioeconomic, health-related, and intellectual characteristics.
METHODS
Study Population
This was a cross-sectional study of individuals who served in the IDF and have been under the authors’ medical responsibility. All individuals had been medically screened pre-enlistment and were found to be free of significant comorbidities. Upon enlistment, they became medically insured by the IDF medical corps. Accordingly, the study population was a specific subset of individuals—generally healthy young adults of primarily Jewish ancestry, who were mostly nonextreme orthodox males. This group was representative of the IDF population8,20 and was similar in many features to the enlisted population in other military organizations.
The SARS-CoV-2 vaccination campaign for the IDF population was initiated on December 27, 2020, in parallel with the civilian population. The vaccines have been offered free of charge and were widely accessible.8 Significant logistical, communicational, and informative efforts have been conducted to maximize vaccine compliance, as has been published elsewhere,8 although SARS-CoV-2 vaccination was never mandatory. During the study timeframe, only individuals who had not previously tested positive for SARS-CoV-2 were allowed and encouraged to be vaccinated against the virus. A total of 17,435 individuals on active duty who served in military units that were under the authors’ medical supervision have been included in the study. Participants were categorized into two groups based on their SARS-CoV-2 vaccination status on March 1, 2021 (have or have not been vaccinated during the vaccination campaign’s first 2 months), representative of their adherence toward vaccination.
Occupational, Sociodemographic, Health-Related, and Intellectual Characteristics
We screened electronic human resources and medical records to collect occupational, sociodemographic, and medical variables of each participant. Data included age, sex, marital status, highest available education type, country of birth, military rank, current military service type (combat units or rear and administration units), medical status, and psychotechnical grading. Before enlistment, potential IDF recruits underwent a battery of skill assessment examinations, culminating in a psychotechnical grading score ranging from 10 to 90.21 A higher psychotechnical grade correlated to a higher IQ level. Although this score has been used for many years in the IDF and has been previously reported in medical literature,21,22 data on the grading methodology and the distribution in the general population are classified and could not be presented. The military medical profile is a medical scoring scale that represents the medical fitness of an individual. The score was defined by comorbidities and functional competency and has been previously used for research.21,22 The components of this score and the specific effect of each pathology, as well as the general military population medical profile distribution, are discrete. For the current analysis, medical profiles have been clustered into two categories based on medical fitness (yes/no) to combat infantry roles, which would represent near-perfect medical status vs. possible comorbidities. Age was categorized into four groups (quartiles) and handled as a categorical variable. We also collected the socioeconomic clusters and geographic peripherality clusters using the place of residence of each individual and the classification method of the Israeli Central Bureau of statistics.23 Geographic peripherality represented the extent to which the area of residence of an individual was rural or far from the main urban centers in Israel. The vaccinated and nonvaccinated groups were compared to look for differences in all the above-mentioned characteristics. The study was approved by the IDF medical corps institutional review board.
Statistical Analysis
Data collection and analysis were conducted using SPSS 28.0 (IBM, Chicago, IL). We conducted a preliminary univariate analysis with Fisher’s exact test, Mann–Whitney U test, and Chi-squared test. Following a multicollinearity assessment with a variance of inflation factor (VIF) calculation, we performed a binary logistic regression analysis, where the vaccination status was modeled against independent variables that have been found associated with vaccine acceptance on the preliminary univariate analysis. A P-value of <.05 was considered statistically significant.
RESULTS
A total of 17,435 individuals were included in this study. The median age was 20.57 years (range 18-61 years, interquartile range = 1.898) and almost 80% were males. At the time of data collection, most individuals (85.1%, n = 14,834) were vaccinated (Table I).
Sociodemographic and Occupational Characteristics of the Vaccinated and Nonvaccinated Cohorts Among Israeli Defense Forces Personnel
Variable . | Vaccinated . | Nonvaccinated . | Total . | P-value/PRa (95% CI) . |
---|---|---|---|---|
Individuals | n = 14,834 (85.1%) | n = 2,601 (14.9%) | n = 17,435 (100%) | |
Age (median, range, interquartile range)a | 20.539, 18.245 to 56.764, 1.805 | 20.846, 18.331 to 60.926, 2.317 | 20.572, 18.245 to 60.926, 1.898 | <.001 |
Age Q1 (n, %) | 3,768 (86.3%) | 597 (13.7%) | 4,365 (100%) | 1.068 (1.049 to .089) |
Age Q2 (n, %) | 3,813 (87.4%) | 549 (12.6%) | 4,362 (100%) | 1.082 (1.062 to 1.102) |
Age Q3 (n, %) | 3,727 (85.8%) | 617 (14.2%) | 4,344 (100%) | 1.062 (1.042 to 1.082) |
Age Q4 (n, %) | 3,526 (80.8%) | 838 (19.2%) | 4,364 (100%) | 1 |
Sex | .047 | |||
Male (n, %) | 11,576 (84.8%) | 2,075 (15.2%) | 13,651 (100%) | .985 (.971-.999) |
Female (n, %) | 3,258 (86.1%) | 52 (13.9%) | 3,784 (100%) | 1 |
Born in Israel (n, %) | <0.001 | |||
Yes (n, %) | 13,486 (85.5%) | 2,280 (14.5%) | 15,766 (100%) | 1.059 (1.034 to 1.085) |
No (n, %) | 1,348 (80.8%) | 321 (19.2%) | 1,669 (100%) | 1 |
Marital status | .084 | |||
Single (n, %) | 13,665 (85%) | 2,419 (15%) | 16,084 (100%) | .902 (.84 to .965) |
Married (n, %) | 1,120 (86.2%) | 179 (13.8%) | 1,299 (100%) | .915 (.853 to .982) |
Divorced (n, %) | 49 (94.2%) | 3 (5.8%) | 52 (100%) | 1 |
Education | <.001 | |||
Below high school (n, %) | 27 (75%) | 9 (25%) | 36 (100%) | 1 |
High school (n, %) | 13,750 (84.5%) | 2,516 (15.5%) | 16,266 (100%) | 1.127 (.933 to 1.361) |
Certification studies (n, %) | 489 (92.3%) | 41 (7.7%) | 530 (100%) | 1.23 (1.017 to 1.488) |
Academic degree (n, %) | 568 (94%) | 35 (5.8%) | 603 (100%) | 1.256 (1.039 to 1.518) |
Rank | <.001 | |||
Regular soldier (n, %) | 12,058 (83.3%) | 2,415 (16.7%) | 14,473 (100%) | 1 |
Noncommissioned officer (n, %) | 1,064 (90.2%) | 115 (9.8%) | 1,179 (100%) | 1.083 (1.062 to 1.105) |
Officer (n, %) | 1,712 (96%) | 71 (4%) | 1,783 (100%) | 1.152 (1.139 to 1.166) |
Stationed in a combat battalion | <.001 | |||
Yes (n, %) | 9,485 (89.4%) | 1,127 (1.6%) | 10,612 (100%) | 1.14 (1.124 to 1.156) |
No (n, %) | 5,349 (78.4%) | 1474 (21.6%) | 6,823 (100%) | 1 |
Socioeconomic (SE) cluster: lowest (1) to highest (5) SE level | <.001 | |||
SE 1 | 1,054 (79.5%) | 271 (20.5%) | 1,325 (100%) | 1 |
SE 2 | 2,472 (81.3%) | 567 (18.7%) | 3,039 (100%) | 1.023 (.99 to 1.056) |
SE 3 | 5,664 (84.2%) | 1,061 (15.8%) | 6,725 (100%) | 1.059 (1.028 to 1.09) |
SE 4 | 4,993 (88.4%) | 652 (11.5%) | 5,645 (100%) | 1.112 (1.08 to 1.145) |
SE 5 | 651 (92.9%) | 50 (7.1%) | 701 (100%) | 1.167 (1.128 to 1.208) |
Geographic peripherality (GP) clustered from most central (1) to peripheral (5) | <.001 | |||
GP cluster 1 (n, %) | 2,508 (83.9%) | 482 (16.1%) | 2,990 (100%) | .99 (.969 to 1.013) |
GP cluster 2 (n, %) | 2,138 (85.2%) | 371 (14.8%) | 2,509 (100%) | 1.006 (.984 to 1.029) |
GP cluster 3 (n, %) | 3,478 (83.8%) | 673 (16.2%) | 4,151 (100%) | .989 (.969 to 1.01) |
GP cluster 4 (n, %) | 4,308 (87%) | 614 (13%) | 4,949 (100%) | 1.028 (1.008 to 1.047) |
GP cluster 5 (n, %) | 2,402 (84.7%) | 434 (15.3%) | 2,836 (100%) | 1 |
Urban living | <.001 | |||
Yes (n, %) | 9,319 (83.8%) | 1,802 (16.2%) | .959 (.947 to .971) | |
No (n, %) | 5,515 (87.3%) | 799 (12.7%) | 1 | |
Infantry-level medical fitness | <.001 | |||
Yes (n, %) | 11,424 (85.7%) | 1,906 (14.3%) | 13,330 (100%) | 1.14 (1.124 to 1.156) |
No (n, %) | 3,410 (83.1%) | 695 (16.9%) | 4,105 (100%) | 1 |
Psychotechnical grading (PTG) | <.001 | |||
PTG clusters 1 (lowest) to 10 (highest) respectively, (%)b | 75.3, 75.7, 79.6, 82.9, 86.2, 100, 0, 87, 87.6, 88, 86.7, 85.1 | 24.7, 24.3, 20.4, 17.1, 13.8, 0, 100, 13, 12.4, 12, 13.3, 14.9 |
Variable . | Vaccinated . | Nonvaccinated . | Total . | P-value/PRa (95% CI) . |
---|---|---|---|---|
Individuals | n = 14,834 (85.1%) | n = 2,601 (14.9%) | n = 17,435 (100%) | |
Age (median, range, interquartile range)a | 20.539, 18.245 to 56.764, 1.805 | 20.846, 18.331 to 60.926, 2.317 | 20.572, 18.245 to 60.926, 1.898 | <.001 |
Age Q1 (n, %) | 3,768 (86.3%) | 597 (13.7%) | 4,365 (100%) | 1.068 (1.049 to .089) |
Age Q2 (n, %) | 3,813 (87.4%) | 549 (12.6%) | 4,362 (100%) | 1.082 (1.062 to 1.102) |
Age Q3 (n, %) | 3,727 (85.8%) | 617 (14.2%) | 4,344 (100%) | 1.062 (1.042 to 1.082) |
Age Q4 (n, %) | 3,526 (80.8%) | 838 (19.2%) | 4,364 (100%) | 1 |
Sex | .047 | |||
Male (n, %) | 11,576 (84.8%) | 2,075 (15.2%) | 13,651 (100%) | .985 (.971-.999) |
Female (n, %) | 3,258 (86.1%) | 52 (13.9%) | 3,784 (100%) | 1 |
Born in Israel (n, %) | <0.001 | |||
Yes (n, %) | 13,486 (85.5%) | 2,280 (14.5%) | 15,766 (100%) | 1.059 (1.034 to 1.085) |
No (n, %) | 1,348 (80.8%) | 321 (19.2%) | 1,669 (100%) | 1 |
Marital status | .084 | |||
Single (n, %) | 13,665 (85%) | 2,419 (15%) | 16,084 (100%) | .902 (.84 to .965) |
Married (n, %) | 1,120 (86.2%) | 179 (13.8%) | 1,299 (100%) | .915 (.853 to .982) |
Divorced (n, %) | 49 (94.2%) | 3 (5.8%) | 52 (100%) | 1 |
Education | <.001 | |||
Below high school (n, %) | 27 (75%) | 9 (25%) | 36 (100%) | 1 |
High school (n, %) | 13,750 (84.5%) | 2,516 (15.5%) | 16,266 (100%) | 1.127 (.933 to 1.361) |
Certification studies (n, %) | 489 (92.3%) | 41 (7.7%) | 530 (100%) | 1.23 (1.017 to 1.488) |
Academic degree (n, %) | 568 (94%) | 35 (5.8%) | 603 (100%) | 1.256 (1.039 to 1.518) |
Rank | <.001 | |||
Regular soldier (n, %) | 12,058 (83.3%) | 2,415 (16.7%) | 14,473 (100%) | 1 |
Noncommissioned officer (n, %) | 1,064 (90.2%) | 115 (9.8%) | 1,179 (100%) | 1.083 (1.062 to 1.105) |
Officer (n, %) | 1,712 (96%) | 71 (4%) | 1,783 (100%) | 1.152 (1.139 to 1.166) |
Stationed in a combat battalion | <.001 | |||
Yes (n, %) | 9,485 (89.4%) | 1,127 (1.6%) | 10,612 (100%) | 1.14 (1.124 to 1.156) |
No (n, %) | 5,349 (78.4%) | 1474 (21.6%) | 6,823 (100%) | 1 |
Socioeconomic (SE) cluster: lowest (1) to highest (5) SE level | <.001 | |||
SE 1 | 1,054 (79.5%) | 271 (20.5%) | 1,325 (100%) | 1 |
SE 2 | 2,472 (81.3%) | 567 (18.7%) | 3,039 (100%) | 1.023 (.99 to 1.056) |
SE 3 | 5,664 (84.2%) | 1,061 (15.8%) | 6,725 (100%) | 1.059 (1.028 to 1.09) |
SE 4 | 4,993 (88.4%) | 652 (11.5%) | 5,645 (100%) | 1.112 (1.08 to 1.145) |
SE 5 | 651 (92.9%) | 50 (7.1%) | 701 (100%) | 1.167 (1.128 to 1.208) |
Geographic peripherality (GP) clustered from most central (1) to peripheral (5) | <.001 | |||
GP cluster 1 (n, %) | 2,508 (83.9%) | 482 (16.1%) | 2,990 (100%) | .99 (.969 to 1.013) |
GP cluster 2 (n, %) | 2,138 (85.2%) | 371 (14.8%) | 2,509 (100%) | 1.006 (.984 to 1.029) |
GP cluster 3 (n, %) | 3,478 (83.8%) | 673 (16.2%) | 4,151 (100%) | .989 (.969 to 1.01) |
GP cluster 4 (n, %) | 4,308 (87%) | 614 (13%) | 4,949 (100%) | 1.028 (1.008 to 1.047) |
GP cluster 5 (n, %) | 2,402 (84.7%) | 434 (15.3%) | 2,836 (100%) | 1 |
Urban living | <.001 | |||
Yes (n, %) | 9,319 (83.8%) | 1,802 (16.2%) | .959 (.947 to .971) | |
No (n, %) | 5,515 (87.3%) | 799 (12.7%) | 1 | |
Infantry-level medical fitness | <.001 | |||
Yes (n, %) | 11,424 (85.7%) | 1,906 (14.3%) | 13,330 (100%) | 1.14 (1.124 to 1.156) |
No (n, %) | 3,410 (83.1%) | 695 (16.9%) | 4,105 (100%) | 1 |
Psychotechnical grading (PTG) | <.001 | |||
PTG clusters 1 (lowest) to 10 (highest) respectively, (%)b | 75.3, 75.7, 79.6, 82.9, 86.2, 100, 0, 87, 87.6, 88, 86.7, 85.1 | 24.7, 24.3, 20.4, 17.1, 13.8, 0, 100, 13, 12.4, 12, 13.3, 14.9 |
Abbreviations: PR, prevalence ratio; CI, confidence interval. a Ages were grouped into 4 quartiles. bSpecifications on psychotechnical grading could not be published because of IDF information security restrictions.
Sociodemographic and Occupational Characteristics of the Vaccinated and Nonvaccinated Cohorts Among Israeli Defense Forces Personnel
Variable . | Vaccinated . | Nonvaccinated . | Total . | P-value/PRa (95% CI) . |
---|---|---|---|---|
Individuals | n = 14,834 (85.1%) | n = 2,601 (14.9%) | n = 17,435 (100%) | |
Age (median, range, interquartile range)a | 20.539, 18.245 to 56.764, 1.805 | 20.846, 18.331 to 60.926, 2.317 | 20.572, 18.245 to 60.926, 1.898 | <.001 |
Age Q1 (n, %) | 3,768 (86.3%) | 597 (13.7%) | 4,365 (100%) | 1.068 (1.049 to .089) |
Age Q2 (n, %) | 3,813 (87.4%) | 549 (12.6%) | 4,362 (100%) | 1.082 (1.062 to 1.102) |
Age Q3 (n, %) | 3,727 (85.8%) | 617 (14.2%) | 4,344 (100%) | 1.062 (1.042 to 1.082) |
Age Q4 (n, %) | 3,526 (80.8%) | 838 (19.2%) | 4,364 (100%) | 1 |
Sex | .047 | |||
Male (n, %) | 11,576 (84.8%) | 2,075 (15.2%) | 13,651 (100%) | .985 (.971-.999) |
Female (n, %) | 3,258 (86.1%) | 52 (13.9%) | 3,784 (100%) | 1 |
Born in Israel (n, %) | <0.001 | |||
Yes (n, %) | 13,486 (85.5%) | 2,280 (14.5%) | 15,766 (100%) | 1.059 (1.034 to 1.085) |
No (n, %) | 1,348 (80.8%) | 321 (19.2%) | 1,669 (100%) | 1 |
Marital status | .084 | |||
Single (n, %) | 13,665 (85%) | 2,419 (15%) | 16,084 (100%) | .902 (.84 to .965) |
Married (n, %) | 1,120 (86.2%) | 179 (13.8%) | 1,299 (100%) | .915 (.853 to .982) |
Divorced (n, %) | 49 (94.2%) | 3 (5.8%) | 52 (100%) | 1 |
Education | <.001 | |||
Below high school (n, %) | 27 (75%) | 9 (25%) | 36 (100%) | 1 |
High school (n, %) | 13,750 (84.5%) | 2,516 (15.5%) | 16,266 (100%) | 1.127 (.933 to 1.361) |
Certification studies (n, %) | 489 (92.3%) | 41 (7.7%) | 530 (100%) | 1.23 (1.017 to 1.488) |
Academic degree (n, %) | 568 (94%) | 35 (5.8%) | 603 (100%) | 1.256 (1.039 to 1.518) |
Rank | <.001 | |||
Regular soldier (n, %) | 12,058 (83.3%) | 2,415 (16.7%) | 14,473 (100%) | 1 |
Noncommissioned officer (n, %) | 1,064 (90.2%) | 115 (9.8%) | 1,179 (100%) | 1.083 (1.062 to 1.105) |
Officer (n, %) | 1,712 (96%) | 71 (4%) | 1,783 (100%) | 1.152 (1.139 to 1.166) |
Stationed in a combat battalion | <.001 | |||
Yes (n, %) | 9,485 (89.4%) | 1,127 (1.6%) | 10,612 (100%) | 1.14 (1.124 to 1.156) |
No (n, %) | 5,349 (78.4%) | 1474 (21.6%) | 6,823 (100%) | 1 |
Socioeconomic (SE) cluster: lowest (1) to highest (5) SE level | <.001 | |||
SE 1 | 1,054 (79.5%) | 271 (20.5%) | 1,325 (100%) | 1 |
SE 2 | 2,472 (81.3%) | 567 (18.7%) | 3,039 (100%) | 1.023 (.99 to 1.056) |
SE 3 | 5,664 (84.2%) | 1,061 (15.8%) | 6,725 (100%) | 1.059 (1.028 to 1.09) |
SE 4 | 4,993 (88.4%) | 652 (11.5%) | 5,645 (100%) | 1.112 (1.08 to 1.145) |
SE 5 | 651 (92.9%) | 50 (7.1%) | 701 (100%) | 1.167 (1.128 to 1.208) |
Geographic peripherality (GP) clustered from most central (1) to peripheral (5) | <.001 | |||
GP cluster 1 (n, %) | 2,508 (83.9%) | 482 (16.1%) | 2,990 (100%) | .99 (.969 to 1.013) |
GP cluster 2 (n, %) | 2,138 (85.2%) | 371 (14.8%) | 2,509 (100%) | 1.006 (.984 to 1.029) |
GP cluster 3 (n, %) | 3,478 (83.8%) | 673 (16.2%) | 4,151 (100%) | .989 (.969 to 1.01) |
GP cluster 4 (n, %) | 4,308 (87%) | 614 (13%) | 4,949 (100%) | 1.028 (1.008 to 1.047) |
GP cluster 5 (n, %) | 2,402 (84.7%) | 434 (15.3%) | 2,836 (100%) | 1 |
Urban living | <.001 | |||
Yes (n, %) | 9,319 (83.8%) | 1,802 (16.2%) | .959 (.947 to .971) | |
No (n, %) | 5,515 (87.3%) | 799 (12.7%) | 1 | |
Infantry-level medical fitness | <.001 | |||
Yes (n, %) | 11,424 (85.7%) | 1,906 (14.3%) | 13,330 (100%) | 1.14 (1.124 to 1.156) |
No (n, %) | 3,410 (83.1%) | 695 (16.9%) | 4,105 (100%) | 1 |
Psychotechnical grading (PTG) | <.001 | |||
PTG clusters 1 (lowest) to 10 (highest) respectively, (%)b | 75.3, 75.7, 79.6, 82.9, 86.2, 100, 0, 87, 87.6, 88, 86.7, 85.1 | 24.7, 24.3, 20.4, 17.1, 13.8, 0, 100, 13, 12.4, 12, 13.3, 14.9 |
Variable . | Vaccinated . | Nonvaccinated . | Total . | P-value/PRa (95% CI) . |
---|---|---|---|---|
Individuals | n = 14,834 (85.1%) | n = 2,601 (14.9%) | n = 17,435 (100%) | |
Age (median, range, interquartile range)a | 20.539, 18.245 to 56.764, 1.805 | 20.846, 18.331 to 60.926, 2.317 | 20.572, 18.245 to 60.926, 1.898 | <.001 |
Age Q1 (n, %) | 3,768 (86.3%) | 597 (13.7%) | 4,365 (100%) | 1.068 (1.049 to .089) |
Age Q2 (n, %) | 3,813 (87.4%) | 549 (12.6%) | 4,362 (100%) | 1.082 (1.062 to 1.102) |
Age Q3 (n, %) | 3,727 (85.8%) | 617 (14.2%) | 4,344 (100%) | 1.062 (1.042 to 1.082) |
Age Q4 (n, %) | 3,526 (80.8%) | 838 (19.2%) | 4,364 (100%) | 1 |
Sex | .047 | |||
Male (n, %) | 11,576 (84.8%) | 2,075 (15.2%) | 13,651 (100%) | .985 (.971-.999) |
Female (n, %) | 3,258 (86.1%) | 52 (13.9%) | 3,784 (100%) | 1 |
Born in Israel (n, %) | <0.001 | |||
Yes (n, %) | 13,486 (85.5%) | 2,280 (14.5%) | 15,766 (100%) | 1.059 (1.034 to 1.085) |
No (n, %) | 1,348 (80.8%) | 321 (19.2%) | 1,669 (100%) | 1 |
Marital status | .084 | |||
Single (n, %) | 13,665 (85%) | 2,419 (15%) | 16,084 (100%) | .902 (.84 to .965) |
Married (n, %) | 1,120 (86.2%) | 179 (13.8%) | 1,299 (100%) | .915 (.853 to .982) |
Divorced (n, %) | 49 (94.2%) | 3 (5.8%) | 52 (100%) | 1 |
Education | <.001 | |||
Below high school (n, %) | 27 (75%) | 9 (25%) | 36 (100%) | 1 |
High school (n, %) | 13,750 (84.5%) | 2,516 (15.5%) | 16,266 (100%) | 1.127 (.933 to 1.361) |
Certification studies (n, %) | 489 (92.3%) | 41 (7.7%) | 530 (100%) | 1.23 (1.017 to 1.488) |
Academic degree (n, %) | 568 (94%) | 35 (5.8%) | 603 (100%) | 1.256 (1.039 to 1.518) |
Rank | <.001 | |||
Regular soldier (n, %) | 12,058 (83.3%) | 2,415 (16.7%) | 14,473 (100%) | 1 |
Noncommissioned officer (n, %) | 1,064 (90.2%) | 115 (9.8%) | 1,179 (100%) | 1.083 (1.062 to 1.105) |
Officer (n, %) | 1,712 (96%) | 71 (4%) | 1,783 (100%) | 1.152 (1.139 to 1.166) |
Stationed in a combat battalion | <.001 | |||
Yes (n, %) | 9,485 (89.4%) | 1,127 (1.6%) | 10,612 (100%) | 1.14 (1.124 to 1.156) |
No (n, %) | 5,349 (78.4%) | 1474 (21.6%) | 6,823 (100%) | 1 |
Socioeconomic (SE) cluster: lowest (1) to highest (5) SE level | <.001 | |||
SE 1 | 1,054 (79.5%) | 271 (20.5%) | 1,325 (100%) | 1 |
SE 2 | 2,472 (81.3%) | 567 (18.7%) | 3,039 (100%) | 1.023 (.99 to 1.056) |
SE 3 | 5,664 (84.2%) | 1,061 (15.8%) | 6,725 (100%) | 1.059 (1.028 to 1.09) |
SE 4 | 4,993 (88.4%) | 652 (11.5%) | 5,645 (100%) | 1.112 (1.08 to 1.145) |
SE 5 | 651 (92.9%) | 50 (7.1%) | 701 (100%) | 1.167 (1.128 to 1.208) |
Geographic peripherality (GP) clustered from most central (1) to peripheral (5) | <.001 | |||
GP cluster 1 (n, %) | 2,508 (83.9%) | 482 (16.1%) | 2,990 (100%) | .99 (.969 to 1.013) |
GP cluster 2 (n, %) | 2,138 (85.2%) | 371 (14.8%) | 2,509 (100%) | 1.006 (.984 to 1.029) |
GP cluster 3 (n, %) | 3,478 (83.8%) | 673 (16.2%) | 4,151 (100%) | .989 (.969 to 1.01) |
GP cluster 4 (n, %) | 4,308 (87%) | 614 (13%) | 4,949 (100%) | 1.028 (1.008 to 1.047) |
GP cluster 5 (n, %) | 2,402 (84.7%) | 434 (15.3%) | 2,836 (100%) | 1 |
Urban living | <.001 | |||
Yes (n, %) | 9,319 (83.8%) | 1,802 (16.2%) | .959 (.947 to .971) | |
No (n, %) | 5,515 (87.3%) | 799 (12.7%) | 1 | |
Infantry-level medical fitness | <.001 | |||
Yes (n, %) | 11,424 (85.7%) | 1,906 (14.3%) | 13,330 (100%) | 1.14 (1.124 to 1.156) |
No (n, %) | 3,410 (83.1%) | 695 (16.9%) | 4,105 (100%) | 1 |
Psychotechnical grading (PTG) | <.001 | |||
PTG clusters 1 (lowest) to 10 (highest) respectively, (%)b | 75.3, 75.7, 79.6, 82.9, 86.2, 100, 0, 87, 87.6, 88, 86.7, 85.1 | 24.7, 24.3, 20.4, 17.1, 13.8, 0, 100, 13, 12.4, 12, 13.3, 14.9 |
Abbreviations: PR, prevalence ratio; CI, confidence interval. a Ages were grouped into 4 quartiles. bSpecifications on psychotechnical grading could not be published because of IDF information security restrictions.
A univariate analysis (Table I) revealed that the nonvaccinated group was slightly younger (P < .001, Table I) and that female individuals were more probable to be vaccinated (P = .047). Adherence to vaccination was also found to be associated with no previous immigration status, a higher education level and psychotechnical grading, a higher military rank, being stationed in combat battalions, medical fitness for infantry (health status), higher socioeconomic level, and nonurban living (P < .05 for all, Table I). Although in some variables, differences were substantial (for example, vaccination rates among officers were 96% compared with 83.3% among regular soldiers), other statistically significant differences represented very small actual differences (for example, 84.8% vaccination rates among male individuals compared with 86.1% among female individuals.
A multivariate logistic regression was performed to ascertain the effects of sex, age, socioeconomic cluster, urban living, geographic peripherality or urban living, military rank, battalion stationing, education level, sociotechnical grading, and medical fitness on the likelihood that military personnel had accepted vaccination against SARS-CoV-2 (Table II). A VIF lower than 2 was found for all variables above, which indicated a low risk for multicollinearity. The logistic regression model was statistically significant, χ2(4) = 16.365, P = .003. The model explained 13.7% (Nagelkerke R2) of the variance in vaccine acceptance and correctly classified 85.5% of cases. Officers and noncommissioned officers, respectively, were 10.7 (95% CI, 7.994-14.322) and 5.891 (95% CI, 4.553-7.621) times more likely to be vaccinated compared with regular soldiers, as well as combat battalion stationed personnel who were 2.616 (95% CI, 2.373-2.884) times more likely to be vaccinated (Table II). Younger age, no previous immigration status, higher socioeconomical cluster, geographic peripherality, higher education level, and higher psychotechnical grades have also been found associated with an increased likelihood of being vaccinated (Table II),
A Multivariable Logistic Regression Modeling SARS-CoV-2 Vaccination Adherence Among Israeli Defense Forces Military Personnel (Vaccinated or Not) Against Sociodemographic and Occupational Variables
variable . | P-value . | OR . | 95% CI . |
---|---|---|---|
Sex (male) | .065 | 1.116 | 0.993 to 1.254 |
Age. Fourth quartile (Q4, Oldest) used as reference | <.001 | ||
Age Q1 | 3.701 | 2.87 to 3.796 | |
Age Q2 | 3.559 | 3.097 to 4.091 | |
Age Q3 | 2.782 | 2.436 to 3.178 | |
Previous immigration status (yes) | .039 | 0.862 | 0.748 to 0.993 |
Socioeconomic (SE) level. SE cluster 1 (lowest) used as reference | P < .001 | ||
SE cluster 2 | 1.01 | 0.837 to 1.219 | |
SE cluster 3 | 1.249 | 1.051 to 1.484 | |
SE cluster 4 | 1.511 | 1.269 to 1.799 | |
SE cluster 5 | 2.211 | 1.656 to 3.208 | |
Geographic peripherality (GP). GP cluster 5, most peripheral used as reference | P = .02 | ||
GP cluster 1 | 1.239 | 1.025 to 1.497 | |
GP cluster 2 | 1.323 | 1.091 to 1.605 | |
GP cluster 3 | 1.09 | 0.925 to 1.283 | |
GP cluster 4 | 1.182 | 1.02 to 1.37 | |
Urban living (yes) | .112 | 0.913 | 0.816 to 1.021 |
Health impairment (infantry-level medical fitness as reference) | .43 | 1.043 | 0.929 to 1.159 |
Psychotechnical grade (continuous) | <.001 | 1.008 | 1.006 to 1.011 |
Education (high school level used as reference) | P = .022 | ||
Certification studies | 1.742 | 1.186 to 2.558 | |
University degree | 1.256 | 0.838 to 1.884 | |
Less than high school | 0.67 | 0.306 to 1.469 | |
Military rank (regular soldiers used as reference) | P < .001 | ||
Noncommissioned officer | 5.891 | 4.553 to 7.621 | |
Officer | 10.7 | 7.994 to 14.322 | |
Stationed in combat battalions (yes) | <.001 | 2.616 | 2.373 to 2.884 |
constant | 0.505 |
variable . | P-value . | OR . | 95% CI . |
---|---|---|---|
Sex (male) | .065 | 1.116 | 0.993 to 1.254 |
Age. Fourth quartile (Q4, Oldest) used as reference | <.001 | ||
Age Q1 | 3.701 | 2.87 to 3.796 | |
Age Q2 | 3.559 | 3.097 to 4.091 | |
Age Q3 | 2.782 | 2.436 to 3.178 | |
Previous immigration status (yes) | .039 | 0.862 | 0.748 to 0.993 |
Socioeconomic (SE) level. SE cluster 1 (lowest) used as reference | P < .001 | ||
SE cluster 2 | 1.01 | 0.837 to 1.219 | |
SE cluster 3 | 1.249 | 1.051 to 1.484 | |
SE cluster 4 | 1.511 | 1.269 to 1.799 | |
SE cluster 5 | 2.211 | 1.656 to 3.208 | |
Geographic peripherality (GP). GP cluster 5, most peripheral used as reference | P = .02 | ||
GP cluster 1 | 1.239 | 1.025 to 1.497 | |
GP cluster 2 | 1.323 | 1.091 to 1.605 | |
GP cluster 3 | 1.09 | 0.925 to 1.283 | |
GP cluster 4 | 1.182 | 1.02 to 1.37 | |
Urban living (yes) | .112 | 0.913 | 0.816 to 1.021 |
Health impairment (infantry-level medical fitness as reference) | .43 | 1.043 | 0.929 to 1.159 |
Psychotechnical grade (continuous) | <.001 | 1.008 | 1.006 to 1.011 |
Education (high school level used as reference) | P = .022 | ||
Certification studies | 1.742 | 1.186 to 2.558 | |
University degree | 1.256 | 0.838 to 1.884 | |
Less than high school | 0.67 | 0.306 to 1.469 | |
Military rank (regular soldiers used as reference) | P < .001 | ||
Noncommissioned officer | 5.891 | 4.553 to 7.621 | |
Officer | 10.7 | 7.994 to 14.322 | |
Stationed in combat battalions (yes) | <.001 | 2.616 | 2.373 to 2.884 |
constant | 0.505 |
Abbreviations: OR, odds ratio; CI, confidence interval.
A Multivariable Logistic Regression Modeling SARS-CoV-2 Vaccination Adherence Among Israeli Defense Forces Military Personnel (Vaccinated or Not) Against Sociodemographic and Occupational Variables
variable . | P-value . | OR . | 95% CI . |
---|---|---|---|
Sex (male) | .065 | 1.116 | 0.993 to 1.254 |
Age. Fourth quartile (Q4, Oldest) used as reference | <.001 | ||
Age Q1 | 3.701 | 2.87 to 3.796 | |
Age Q2 | 3.559 | 3.097 to 4.091 | |
Age Q3 | 2.782 | 2.436 to 3.178 | |
Previous immigration status (yes) | .039 | 0.862 | 0.748 to 0.993 |
Socioeconomic (SE) level. SE cluster 1 (lowest) used as reference | P < .001 | ||
SE cluster 2 | 1.01 | 0.837 to 1.219 | |
SE cluster 3 | 1.249 | 1.051 to 1.484 | |
SE cluster 4 | 1.511 | 1.269 to 1.799 | |
SE cluster 5 | 2.211 | 1.656 to 3.208 | |
Geographic peripherality (GP). GP cluster 5, most peripheral used as reference | P = .02 | ||
GP cluster 1 | 1.239 | 1.025 to 1.497 | |
GP cluster 2 | 1.323 | 1.091 to 1.605 | |
GP cluster 3 | 1.09 | 0.925 to 1.283 | |
GP cluster 4 | 1.182 | 1.02 to 1.37 | |
Urban living (yes) | .112 | 0.913 | 0.816 to 1.021 |
Health impairment (infantry-level medical fitness as reference) | .43 | 1.043 | 0.929 to 1.159 |
Psychotechnical grade (continuous) | <.001 | 1.008 | 1.006 to 1.011 |
Education (high school level used as reference) | P = .022 | ||
Certification studies | 1.742 | 1.186 to 2.558 | |
University degree | 1.256 | 0.838 to 1.884 | |
Less than high school | 0.67 | 0.306 to 1.469 | |
Military rank (regular soldiers used as reference) | P < .001 | ||
Noncommissioned officer | 5.891 | 4.553 to 7.621 | |
Officer | 10.7 | 7.994 to 14.322 | |
Stationed in combat battalions (yes) | <.001 | 2.616 | 2.373 to 2.884 |
constant | 0.505 |
variable . | P-value . | OR . | 95% CI . |
---|---|---|---|
Sex (male) | .065 | 1.116 | 0.993 to 1.254 |
Age. Fourth quartile (Q4, Oldest) used as reference | <.001 | ||
Age Q1 | 3.701 | 2.87 to 3.796 | |
Age Q2 | 3.559 | 3.097 to 4.091 | |
Age Q3 | 2.782 | 2.436 to 3.178 | |
Previous immigration status (yes) | .039 | 0.862 | 0.748 to 0.993 |
Socioeconomic (SE) level. SE cluster 1 (lowest) used as reference | P < .001 | ||
SE cluster 2 | 1.01 | 0.837 to 1.219 | |
SE cluster 3 | 1.249 | 1.051 to 1.484 | |
SE cluster 4 | 1.511 | 1.269 to 1.799 | |
SE cluster 5 | 2.211 | 1.656 to 3.208 | |
Geographic peripherality (GP). GP cluster 5, most peripheral used as reference | P = .02 | ||
GP cluster 1 | 1.239 | 1.025 to 1.497 | |
GP cluster 2 | 1.323 | 1.091 to 1.605 | |
GP cluster 3 | 1.09 | 0.925 to 1.283 | |
GP cluster 4 | 1.182 | 1.02 to 1.37 | |
Urban living (yes) | .112 | 0.913 | 0.816 to 1.021 |
Health impairment (infantry-level medical fitness as reference) | .43 | 1.043 | 0.929 to 1.159 |
Psychotechnical grade (continuous) | <.001 | 1.008 | 1.006 to 1.011 |
Education (high school level used as reference) | P = .022 | ||
Certification studies | 1.742 | 1.186 to 2.558 | |
University degree | 1.256 | 0.838 to 1.884 | |
Less than high school | 0.67 | 0.306 to 1.469 | |
Military rank (regular soldiers used as reference) | P < .001 | ||
Noncommissioned officer | 5.891 | 4.553 to 7.621 | |
Officer | 10.7 | 7.994 to 14.322 | |
Stationed in combat battalions (yes) | <.001 | 2.616 | 2.373 to 2.884 |
constant | 0.505 |
Abbreviations: OR, odds ratio; CI, confidence interval.
DISCUSSION
This study is one of the first evaluations of the actual, rather than the anticipated, SARS-CoV-2 vaccine acceptance rates among a large specific group of military personnel. In this study, a very high vaccination rate was found, reaching 85.1% within a 2-month vaccination campaign. Historically, the ability of vaccination campaigns to affect public health has relied vastly on the adherence of the general population to be vaccinated.24,25 The rapid development and availability of a SARS-CoV-2 vaccine had the potential to end the COVID-19 pandemic. Similar to other vaccination campaigns,24,25 we recognized that adherence to SARS-CoV-2 vaccination might have been affected by several parameters. In this research, we found that vaccination acceptance was associated with occupational, sociodemographic, and intellectual determinants. Officers and noncommissioned officers were more likely to be vaccinated, compared to regular soldiers. This was also true for combat battalion stationed personnel and for individuals with a higher level of education, higher sociotechnical gradings, and individuals of higher socioeconomic clusters. Although some other factors, such as age, sex, and geographic peripherality have also been found associated with vaccination adherence, the actual differences were small, and of limited practical significance (Tables I and II).
Our results support previous publications regarding the associations between vaccine hesitancy and sociodemographic characteristics including a lower socioeconomic status,26,27 a lower education level,26,28 and previous immigrant status.29 Nevertheless, we were not able to reproduce a clear association between a lower vaccination adherence and residing outside large cities, as has been previously reported.9 In the current study, this association was statistically significant but too small to represent actual differences. Impaired baseline health status was found both in this study and in a previous study9 to be associated with vaccine hesitancy, although this association has been inconclusive in current literature.30 It should be noted that the current study included a largely healthy population of young adults who have been found fit for military service, and our definition of medical fitness or impairment might be interpreted differently in other settings. Although female sex has been previously associated with vaccine hesitancy,31–34 this association was opposite and negligible in the current study. This could be attributed to the male predominancy that limited sex-related comparisons. Alternatively, this could have also implied a less-significant sex-related vaccine hesitancy in this age group.
Age has been previously found to influence the adherence to SARS-CoV-2 vaccination, although findings have been thus far inconsistent. Most studies have found more SARS-CoV-2 vaccine hesitancy in younger individuals compared with the elderly.26,35,36 When the elderly were excluded, SARS-CoV-2 vaccine hesitancy was found to be age dependent with an inverted U-shaped relationship.31 The current study population has been comprised (mostly) of young adults aged around 21 years old who have been found to be highly adherent to SARS-CoV-2 vaccination. This high vaccine acceptance level was obtained following a thorough multidisciplinary promotion campaign, absent of any mandates or requirements for vaccination. The IDF did not offer extraordinary incentives for being vaccinated that could not be implemented elsewhere.8 Of note, highly obligated personnel, such as officers and noncommissioned officers, as well as soldiers who served in combat battalions, have been exceptionally adherent to SARS-CoV-2 vaccination (Tables I, II). This finding could be attributed to either a sense of devotion to the military organization and its agenda, an aim to maintain fit and active, occupational restrictions on nonvaccinated personnel, social expectations, a wish to set an example, or other reasons.
Our study had several limitations. It had the basic limitations of being a retrospective study. It was impossible to infer on any causation presented in this study because of its retrospective nature. The study population consisted of enlisted military personnel, who were a homogeneous group of mostly healthy Israeli Jewish men and women of similar ages under a mandatory draft law (as opposed to other military organizations). This represented a very specific population that was healthier and younger than the general population. This population might have also been under a survival bias (healthy worker effect), although most individuals were regular soldiers on a mandatory service. Accordingly, generalization on other groups of individuals should be made with caution. This study relied on a large population. Although this often increases power, it also increases the chance of finding statistically significant differences that were not necessarily significant in terms of public health. Accordingly, we find importance in appreciating the actual differences and not merely relying on statistical significance. Future research can focus on vaccinated and nonvaccinated individuals and look for factors that have been important in their decision regarding vaccination. It would be beneficial to learn how specific measures in the vaccination promotion campaign and educational activity affected vaccine acceptance. Prospective trials can assess how promotion interventions were able to affect vaccination rates among groups of individuals who have been primarily suggested to present low adherence rates.
In a large cohort of enlisted IDF personnel, the disparity in vaccine adherence was found to be related to multiple socioeconomic, educational, and military service-related variables, although some differences were small and of questionable public health significance. Acknowledging these differences may enable community leaders, health care providers, and administrators to target specific populations in order to further promote SARS-CoV-2 vaccination acceptance.
ACKNOWLEDGMENTS
The authors acknowledge all medical professionals who have taken part in the SARS-CoV-2 vaccination campaign in the IDF.
FUNDING
No funding was provided or utilized for this study.
CONFLICT OF INTEREST STATEMENT
The authors of this study report no conflicts of interest.
REFERENCES
World Health Organization.
Ritchie H, Mathieu E, Rodés-Guirao L, et al:
Author notes
contributed equally to the work.
- coronavirus
- adult
- demography
- educational status
- emigration and immigration
- health personnel
- intelligence
- military personnel
- peer group
- socioeconomic factors
- vaccination
- vaccines
- knowledge acquisition
- public health medicine
- severe acute respiratory syndrome
- young adult
- health disparity
- community
- herd immunity
- soldiers
- higher education
- vaccination refusal
- sars-cov-2
- covid-19 vaccines
- vaccination hesitancy