'Why Should I Take the COVID-19 Vaccine after Recovering from the Disease?' A Mixed-methods Study of Correlates of COVID-19 Vaccine Acceptability among Health Workers in Northern Nigeria.
Zubairu IliyasuMuhammad R GarbaAuwalu U GajidaTaiwo G AmoleAmina A UmarHadiza M AbdullahiAminatu A KwakuHamisu M SalihuMuktar H AliyuPublished in: Pathogens and global health (2021)
We assessed the acceptability of COVID-19 vaccine, predictors, and reasons for vaccine hesitancy among clinical and non-clinical staff at a tertiary hospital in Kano, northern Nigeria.Using a mixed-methods design, structured questionnaires were administered to 284 hospital staff, followed by 20 in-depth interviews with a purposive sub-sample. Logistic regression and the framework approach were used to analyze the data.Only 24.3% ( n = 69) of the respondents were willing to accept the COVID-19 vaccine. Acceptance was lower among females (Adjusted Odds Ratio (aOR) = 0.37, 95% Confidence Interval (95%CI): 0.18-0.77 (male vs. female), nurses/midwives (aOR = 0.41, 95%CI:0.13-0.60, physicians vs. nurses/midwives), persons not tested for COVID-19 (aOR = 0.32, 95%CI 0.13-0.79) (no vs. yes) and those who perceived themselves to be at low risk of COVID-19 (aOR = 0.47, 95%CI,0.21-0.89, low vs. high). In contrast, vaccine acceptance was higher among more experienced workers (aOR = 2.28, 95%CI:1.16-8.55, ≥10 vs. <5 years). Vaccine acceptance was also higher among persons who did not worry about vaccine efficacy (aOR = 2.35, 95%CI:1.18-6.54, no vs. yes), or about vaccine safety (aOR = 1.76, 95%CI: 1.16-5.09, no vs. yes), side effects (aOR = 1.85, 95%CI:1.17-5.04, no vs. yes), or rumors (aOR = 2.55, 95%CI:1.25-5.20, no vs. yes). The top four reasons for vaccine hesitancy included distrust, inadequate information, fear of long-term effects, and infertility-related rumors.Concerted efforts are required to build COVID-19 vaccine confidence among health workers in Kano, Nigeria.Our findings can help guide implementation of COVID-19 vaccination in similar settings.
Keyphrases
- coronavirus disease
- sars cov
- healthcare
- mental health
- primary care
- public health
- respiratory syndrome coronavirus
- machine learning
- randomized controlled trial
- computed tomography
- clinical trial
- depressive symptoms
- physical activity
- health information
- metabolic syndrome
- adipose tissue
- deep learning
- insulin resistance
- prefrontal cortex