| Literature DB >> 34138823 |
Maria L Pacella-LaBarbara1, Yunseo Linda Park, P Daniel Patterson, Ankur Doshi, Maria Koenig Guyette, Ambrose H Wong, Bernard P Chang, Brian P Suffoletto.
Abstract
OBJECTIVE: Vaccine hesitancy limits population protection from SARS-CoV (coronavirus disease [COVID-19]). Vaccine hesitancy among healthcare workers (HCW) could put patients and coworkers at risk.Entities:
Mesh:
Substances:
Year: 2021 PMID: 34138823 PMCID: PMC8478093 DOI: 10.1097/JOM.0000000000002298
Source DB: PubMed Journal: J Occup Environ Med ISSN: 1076-2752 Impact factor: 2.162
Percentage of Participants Rating Agreement With 9-item Perceived COVID-19 Vulnerability Instrument
| Perceived COVID-19 Vulnerability ( | Agree or Strongly Agree |
| My job puts me at great risk | 384 (83.5%) |
| I feel more stress at work | 320 (69.6%) |
| I am afraid of falling ill with COVID-19 | 185 (40.2%) |
| I have little control over whether I get infected or not | 146 (31.7%) |
| I have little chance of survival if I were to get COVID-19 | 17 (3.7%) |
| I think of resigning because of COVID-19 | 39 (8.5%) |
| I am afraid I will pass COVID-19 to others | 301 (65.4%) |
| My family and friends are worried they might get infected through me | 204 (44.4%) |
| People avoid my family because of my work | 125 (27.2%) |
Bivariate Differences in Vaccine Intent/Uptake by Demographics and Related Factors
| Demographics | Full Sample ( | No Intent/Unvaccinated ( | Intent/Vaccinated ( | OR (95% Confidence Interval) | |
| Age | 41.01 (13.29) | 38.9 (12.4) | 41.6 (13.4) | 0.08 | 1.02 (0.99, 1.03) |
| Range 18–75 | |||||
| Missing | 15 (3.2%) | ||||
| Sex | 0.01 | ||||
| Male | 241 (50.7%) | 39 (39.8%) | 202 (53.9%) | REF | |
| Female | 229 (48.2%) | 59 (60.2%) | 170 (45.3%) | 0.56 (0.35, 0.87) | |
| Other (prefer not to answer; third gender) | 3 (0.6%) | ||||
| Missing | 2 (0.4%) | ||||
| Race | 0.24 | ||||
| White | 450 (94.7%) | 91 (92.9%) | 359 (95.7%) | REF | |
| Nonwhite (Black, Hispanic, Asian, multiracial, other) | 23 (4.8%) | 7 (7.1%) | 16 (4.3%) | 0.58 (0.23, 1.45) | |
| Missing | 2 (0.4%) | ||||
| Marital status | 0.85 | ||||
| Single (including divorced, widowed, separated) | 151 (31.8%) | 32 (32.7%) | 114 (31.6%) | REF | |
| Married/living with significant other | 323 (68%) | 66 (67.3%) | 257 (68.4%) | 0.96 (0.59, 1.53) | |
| Missing | 1 (0.2%) | ||||
| Job role | ∗0.01 | ∗12.95 | |||
| Doctor and Mid-Level Provider | 38 (8.0%) | 0 (0%) | 38 (10.1%) | ||
| Nurse and Patient Care Technicians | 70 (14.7%) | 19 (19.4%) | 51 (13.5%) | ||
| Emergency Medical Services | 315 (66.3%) | 65 (66.4%) | 250 (66.3%) | ||
| Other | 52 (10.9%) | 14 (14.3%) | 38 (10.1%) | ||
| Education | 0.01 | ||||
| No degree | 150 (31.6%) | 34 (34.5%) | 116 (31.3%) | REF | |
| Degree | 255 (53.7%) | 59 (60.2%) | 196 (53.0%) | 0.97 (0.60, 1.57) | |
| Advanced/professional degree | 63 (13.3%) | 5 (5.1%) | 58 (15.7%) | 3.40 (1.26, 9.15) | |
| Other | 7 (1.5%) | 7 (1.9%) | |||
| Direct care to patients with COVID-19 or suspected COVID-19 | 0.96 | ||||
| Yes | 425 (89.5%) | 88 (89.8%) | 337 (89.6%) | REF | |
| No | 49 (10.3%) | 10 (10.2%) | 39 (10.4%) | 0.98 (0.47, 2.04) | |
| Missing | 1 (0.2%) | ||||
| COVID-19 testing status† | 0.02 | ||||
| Received a positive test | 80 (16.8%) | 24 (24.5%) | 56 (14.8%) | 0.54 (0.31, 0.92) | |
| Negative test or not tested for COVID-19 | 395 (83.2%) | 74 (75.5%) | 321 (85.2%) | REF | |
| Underlying health condition | 0.03 | ||||
| Yes | 139 (29.3%) | 20 (20.6%) | 119 (31.8%) | 1.80 (1.05, 3.08) | |
| No | 332 (69.9%) | 77 (79.4%) | 255 (68.2%) | REF | |
| Prefer not to answer | 4 (0.8%) | ||||
| Social Media (eg, Twitter, Facebook, Instagram, Reddit, Tumblr, etc) | 3.54 (1.24) | 3.29 (1.31) | 3.61 (1.22) | 0.02 | 1.23 (1.03, 1.47) |
Due to small numbers and a 0 in the no intent/unvaccinated category for Doctors, a logistic regression could not be conducted. These numbers represent the Pearson's Chi-Square and corresponding P value, suggesting differences in vaccine uptake/intent between job roles.
COVID-19 testing status numbers are greater than 475 because HCWs could have received both a negative and positive test since the beginning of the pandemic; those who endorsed both test results were included in the positive category for final analysis. The outcome was coded as 0 = no intent/unvaccinated and 1 = intent/vaccinated. OR = odds ratio. The “other” and “missing” categories were excluded from all logistic regression analyses given small numbers, and therefore do not have an associated OR. Education was defined as follows: no degree program (eg, high school or some college); degree program (eg, trade, vocational or technical school; associates degree; college degree); advanced degree (eg, postgraduate degree—PhD, DSc, etc or professional degree—MD, DO, DNP, etc). Underlying health condition was specified as high blood pressure, heart disease, lung disease, kidney disease, etc.
Summary of Logistic Regression Analysis Demonstrating Odds of Vaccine Intent/Uptake
|
| SE |
| OR | 95% CI | ||
| Outcome: Vaccine intent/uptake ( | ||||||
| Variables | 0.000 | |||||
| Age | 0.01 | 0.01 | 1.32 | 1.01 | 0.99, 1.03 | 0.187 |
| Female sex | −0.59 | 0.25 | −2.33 | 0.34 | 0.34, 0.91 | 0.020 |
| Education: degree | −0.01 | 0.26 | −0.03 | 0.99 | 0.59, 1.65 | 0.977 |
| Advanced degree | 1.26 | 0.57 | 2.22 | 3.53 | 1.16, 10.77 | 0.026 |
| Health condition | 0.32 | 0.30 | 1.07 | 1.38 | 0.76, 2.50 | 0.286 |
| Positive Infection History | −0.60 | 0.29 | −2.04 | 0.55 | 0.31, 0.98 | 0.041 |
| Social media exposure | 0.20 | 0.10 | 1.92 | 1.22 | 1.00, 2.90 | 0.054 |
| COVID-19 vulnerability | 0.69 | 0.19 | 3.61 | 1.99 | 1.37, 2.90 | 0.000 |
The outcome was coded as 0 = no intent/unvaccinated and 1 = intent/vaccinated. OR = odds ratio. Sex was coded dichotomously such that 0 = male and 1 = female; three HCWs who preferred not to answer or selected third gender were not included in this model due to small numbers. Education was coded such that 0 = no degree program; 1 = degree program and 2 = advanced degree program; 7 HCWs with “other” responses were not included in this model due to small numbers. Positive Infection History was coded such that 0 = no infection history and/or did not get tested for COVID-19 and 1 = tested positive for COVID-19. Age was analyzed as a continuous variable; subjects provided their age in response to the question “How old are you?” HCWs = healthcare workers.
Summary of Linear Regression Analysis Demonstrating Perceived COVID-19 Vulnerability Adjusted for Confounding
|
| SE |
| 95% CI | ||
| Outcome: COVID-19 vulnerability ( | |||||
| Model | 0.002 | ||||
| Age | −0.01 | 0.00 | −2.06 | −0.01, −0.00 | 0.040 |
| Female sex | 0.07 | 0.06 | 1.12 | −0.06, 0.20 | 0.263 |
| Education: degree | −0.10 | 0.07 | −1.35 | −0.24, 0.04 | 0.178 |
| Advanced degree | −0.13 | 0.11 | −1.25 | −0.35, 0.08 | 0.211 |
| Health condition | 0.26 | 0.07 | 3.39 | 0.11, 0.40 | 0.001 |
| Positive Infection History | 0.08 | 0.08 | 0.91 | −0.09, 0.25 | 0.361 |
| Social media exposure | 0.05 | 0.03 | 1.69 | −0.01, 0.10 | 0.091 |
Sex was coded dichotomously such that 0 = male and 1 = female; three HCWs who preferred not to answer or selected third gender were not included in this model due to small numbers. Education was coded such that 0 = no degree program; 1 = degree program and 2 = advanced degree program; 7 HCWs with “other” responses were not included in this model due to small numbers. Positive Infection History was coded such that 0 = no infection history and/or did not get tested for COVID-19 and 1 = tested positive for COVID-19. Age was analyzed as a continuous variable; subjects provided their age in response to the question “How old are you?” HCWs = healthcare workers.