The Adverse Events of Influenza A (H1N1) Vaccination and Its Risk Factors in Healthcare Personnel in 18 Military Healthcare Units in Korea

Adverse event detection by integrating twitter data and VAERS

BACKGROUND

Vaccine has been one of the most successful public health interventions to date. However, vaccines are pharmaceutical products that carry risks so that many adverse events (AEs) are reported after receiving vaccines. Traditional adverse event reporting systems suffer from several crucial challenges including poor timeliness. This motivates increasing social media-based detection systems, which demonstrate successful capability to capture timely and prevalent disease information. Despite these advantages, social media-based AE detection suffers from serious challenges such as labor-intensive labeling and class imbalance of the training data.

RESULTS

To tackle both challenges from traditional reporting systems and social media, we exploit their complementary strength and develop a combinatorial classification approach by integrating Twitter data and the Vaccine Adverse Event Reporting System (VAERS) information aiming to identify potential AEs after influenza vaccine. Specifically, we combine formal reports which have accurately predefined labels with social media data to reduce the cost of manual labeling; in order to combat the class imbalance problem, a max-rule based multi-instance learning method is proposed to bias positive users. Various experiments were conducted to validate our model compared with other baselines. We observed that (1) multi-instance learning methods outperformed baselines when only Twitter data were used; (2) formal reports helped improve the performance metrics of our multi-instance learning methods consistently while affecting the performance of other baselines negatively; (3) the effect of formal reports was more obvious when the training size was smaller. Case studies show that our model labeled users and tweets accurately.

CONCLUSIONS

We have developed a framework to detect vaccine AEs by combining formal reports with social media data. We demonstrate the power of formal reports on the performance improvement of AE detection when the amount of social media data was small. Various experiments and case studies show the effectiveness of our model.

Interaction of obesity and infections

There is evidence that certain infections may induce obesity. Obese persons may also have more severe infections and have compromised response to therapies. The objective of this study is to review the available literature identifying infections that potentially contribute to greater body mass index (BMI) and differential responses of overweight and obese persons to infections. A systematic literature review of human studies examining associations between infections and weight gain, differential susceptibility, severity, and response to prevention and treatment of infection according to BMI status (January 1980-July 2014) was conducted. Three hundred and forty-three studies were eligible for inclusion. Evidence indicated that viral infection by human adenovirus Ad36 and antibiotic eradication of Helicobacter pylori were followed by weight gain. People who were overweight or obese had higher susceptibility to developing post-surgical infections, H1N1 influenza and periodontal disease. More severe infections tended to be present in people with a larger BMI. People with a higher BMI had a reduced response to vaccinations and antimicrobial drugs. Higher doses of antibiotics were more effective in obese patients. Infections may influence BMI, and BMI status may influence response to certain infections, as well as to preventive and treatment measures. These observations have potential clinical implications.

Risk factors associated with anaphylaxis and other allergic-like events following receipt of 2009 monovalent AS03-adjuvanted pandemic influenza vaccine in Quebec, Canada

INTRODUCTION

In Quebec, Canada, receipt of the 2009 AS03-adjuvanted pandemic H1N1 vaccine was associated with increased risk of anaphylaxis and other allergic-like events (ALE), especially among women of childbearing age. In response to this safety signal, a case-control study was conducted to identify potential risk factors.

METHODS

A total of 435 ALE (50 anaphylaxis) occurring <24h following pandemic vaccination were compared to 849 age-gender matched controls randomly selected from the provincial Pandemic Influenza Vaccination Registry. More than 60 potential risk factors were evaluated through phone interviews and included demographic information, medical history, medication use or acute respiratory illnesses (ARI) concurrent with vaccination and other risk factors associated with general allergy. Odds ratios (ORs) with 95% confidence intervals were estimated with unconditional logistic regression.

RESULTS

Factors associated with increased risk of anaphylaxis included concurrent ARI (18% cases vs. 4% controls, ORadj 7.67, 95%CI: 3.04-13.37), food allergy (26% cases vs. 4% controls, ORadj 3.84, 95%CI: 1.51-9.74) and vaccination during the first four weeks of the campaign (66% cases vs. 50% controls, ORadj 2.16, 95%CI: 1.10-4.25) whereas alcohol exposure (≥1 drink/week) was associated with reduced risk (29% cases vs. 42% controls, ORadj 0.26, 95%CI: 0.13-0.57). These factors were also significantly associated with any ALE but the strength of association was weaker. Allergy to components found in the vaccine (e.g., egg, thimerosal) was infrequent and did not significantly differ between cases and controls.

CONCLUSION

Increased anaphylaxis and other allergic-like events observed in association with AS03-adjuvanted pandemic H1N1 vaccine remain mostly unexplained despite extensive risk factor review. However, prior to mass vaccination with similar formulations this safety signal warrants further consideration and better understanding. In particular, the predominance among women of childbearing age may be a clue to underlying biological or hormonal influences on adverse immunological responses to vaccine.