Effects of Vaccination Day Routine Activities on Influenza Vaccine Efficacy and Vaccination-Induced Adverse Reaction Incidence: A Cohort Study

Health behaviors are largely not predictive of adverse events following influenza vaccination

BACKGROUND

Identifying health behaviors associated with adverse events following immunization (AEFI) could identify intervention targets for AEFI prevention.

METHODS

University employees receiving an influenza vaccination (n = 1301) completed a series of online surveys for health behaviors including sleep, exercise, dietary intake, and smoking habits, and emotional state (baseline), and for indications of AEFI (three days post-vaccination) and influenza-like illness (ILI) symptoms (fortnightly follow-up for 4 months).

RESULTS

29.9% of participants reported an AEFI and 46.0% reported experiencing ILI during follow-up. Multivariate logistic regression revealed usual sleep duration was associated with AEFI (odds ratio 1.20, 95% confidence interval 1.03-1.41), increasing with each hour of sleep. ILI was associated with reporting AEFI (1.70, 1.24-2.33), increasing BMI (1.03, 1.00-1.06) and survey response frequency (1.13, 1.04-1.22), and decreased with better usual sleep quality (0.96, 0.92-1.00) and with increasing age (0.98, 0.96-1.00). Sex stratification revealed no significant predictors of AEFI for either sex; in women, experiencing AEFI increased likelihood of ILI (1.88, 1.25-2.85) and in men, survey completion frequency increased ILI likelihood (1.19, 1.05-1.36).

CONCLUSIONS

Our study suggests modifying health behaviors would not alter AEFI risk and reactogenicity may signal weaker immunogenicity but confirmation through objective measures is warranted.

Association between Adverse Reactions to the First and Second Doses of COVID-19 Vaccine

This study investigated the frequency of adverse reactions to COVID-19 vaccines in Japan and the impact of first-dose adverse reactions on second-dose adverse reactions. Individuals who received an mRNA COVID-19 vaccine at our center in March or April 2021 were included. Data were collected using questionnaires. The main factors were age (<40, 40−59, and >60 years), sex, underlying disease, and first-dose adverse reaction. The primary outcomes were incidence of local and systemic adverse reactions (ARs) attributable to the vaccine. Logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs). Among 671 participants, 90% experienced local or systemic ARs. An AR to the first dose was associated with a significantly increased risk of an AR to the second dose (OR: 49.63, 95% CI: 21.96−112.16). ARs were less common among men than among women (OR: 0.36, 95% CI: 0.17−0.76). Local ARs were less common among those aged 60 years or older (OR: 0.35, 95% CI: 0.18−0.66), whereas systemic ARs were more common among those aged under 40 years. Information on ARs to the first dose is important for healthcare providers and recipients when making vaccination decisions.

Effects of Exercise and Physical Activity Levels on Vaccination Efficacy: A Systematic Review and Meta-Analysis

Objective: We examined whether different intensities of exercise and/or physical activity (PA) levels affected and/or associated with vaccination efficacy. Methods: A systematic review and meta-analysis was conducted and registered with PROSPERO (CRD42021230108). The PubMed, EMBASE, Cochrane Library (trials), SportDiscus, and CINAHL databases were searched up to January 2022. Results: In total, 38 eligible studies were included. Chronic exercise increased influenza antibodies (standardized mean difference (SMD) = 0.49, confidence interval (CI) = 0.25−0.73, Z = 3.95, I2 = 90%, p < 0.01), which was mainly driven by aerobic exercise (SMD = 0.39, CI = 0.19−0.58, Z = 3.96, I2 = 77%, p < 0.01) as opposed to combined (aerobic + resistance; p = 0.07) or other exercise types (i.e., taiji and qigong, unspecified; p > 0.05). PA levels positively affected antibodies in response to influenza vaccination (SMD = 0.18, CI = 0.02−0.34, Z = 2.21, I2 = 76%, p = 0.03), which was mainly driven by high PA levels compared to moderate PA levels (Chi2 = 10.35, I2 = 90.3%, p < 0.01). Physically active individuals developed influenza antibodies in response to vaccination in >4 weeks (SMD = 0.64, CI = 0.30−0.98, Z = 3.72, I2 = 83%, p < 0.01) as opposed to <4 weeks (p > 0.05; Chi2 = 13.40, I2 = 92.5%, p < 0.01) post vaccination. Conclusion: Chronic aerobic exercise or high PA levels increased influenza antibodies in humans more than vaccinated individuals with no participation in exercise/PA. The evidence regarding the effects of exercise/PA levels on antibodies in response to vaccines other than influenza is extremely limited.

Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors

Background: The unprecedented global spread of coronavirus disease 2019 (COVID-19) has imposed huge challenges on the healthcare facilities, and impacted every aspect of life. This has led to the development of several vaccines against COVID-19 within one year. This study aimed to assess the attitudes and the side effects among Arab communities after receiving a COVID-19 vaccine and use of machine learning (ML) tools to predict post-vaccination side effects based on predisposing factors. Methods: An online-based multinational survey was carried out via social media platforms from 14 June to 31 August 2021, targeting individuals who received at least one dose of a COVID-19 vaccine from 22 Arab countries. Descriptive statistics, correlation, and chi-square tests were used to analyze the data. Moreover, extensive ML tools were utilized to predict 30 post vaccination adverse effects and their severity based on 15 predisposing factors. The importance of distinct predisposing factors in predicting particular side effects was determined using global feature importance employing gradient boost as AutoML. Results: A total of 10,064 participants from 19 Arab countries were included in this study. Around 56% were female and 59% were aged from 20 to 39 years old. A high rate of vaccine hesitancy (51%) was reported among participants. Almost 88% of the participants were vaccinated with one of three COVID-19 vaccines, including Pfizer-BioNTech (52.8%), AstraZeneca (20.7%), and Sinopharm (14.2%). About 72% of participants experienced post-vaccination side effects. This study reports statistically significant associations (p < 0.01) between various predisposing factors and post-vaccinations side effects. In terms of predicting post-vaccination side effects, gradient boost, random forest, and XGBoost outperformed other ML methods. The most important predisposing factors for predicting certain side effects (i.e., tiredness, fever, headache, injection site pain and swelling, myalgia, and sleepiness and laziness) were revealed to be the number of doses, gender, type of vaccine, age, and hesitancy to receive a COVID-19 vaccine. Conclusions: The reported side effects following COVID-19 vaccination among Arab populations are usually non-life-threatening; flu-like symptoms and injection site pain. Certain predisposing factors have greater weight and importance as input data in predicting post-vaccination side effects. Based on the most significant input data, ML can also be used to predict these side effects; people with certain predicted side effects may require additional medical attention, or possibly hospitalization.