Use of telephone helpline data for syndromic surveillance of adverse events following immunization in Australia: A retrospective study, 2009 to 2017

Active surveillance of immunization adverse effects: a multicentre, open-label, three-arm randomized uncontrolled trial in Ethiopia

BACKGROUND

Participant-centred active adverse event following immunization (AEFI) surveillance can offer real-time vaccine safety data and help in signal detection. This study aimed to evaluate the effectiveness of participant-centred active adverse events (AEs) surveillance following measles immunization in Gedeo Zone health facilities in Ethiopia.

METHODS

An open-label, multicentred, three-arm randomized uncontrolled trial was conducted from 1 June to 21 October 2023. After assessing enrolment eligibility, the study participants were randomized into three groups (short message service [SMS], telephone interview, and diary card). They were expected to report AEs in children 1 week after receiving immunization. Binary and multivariable logistic regression and χ2 tests were used to analyse the data.

RESULTS

Among the 396 participants randomized into the three groups, 80.8% (320 participants) reported back about their children's AE status. Participants in the telephone interview group exhibited a substantially superior response rate (93.2% of 132 participants; p<0.00001) compared with the SMS (71.2%) and diary card (78%) groups. The likelihood of reporting the status of AEs experienced by children was lower by 77% (adjusted odds ratio 0.23 [95% confidence interval 0.1 to 0.52], p-value <0.00001) in the diary card group compared with the telephone interview group.

CONCLUSIONS

In this study, a telephone interview was found to be the best method for AEFI reporting. Participant-centred active AE surveillance could potentially permit more rapid identification of emerging safety signals. Trial registration:  https://clinicaltrials.gov/ct2/show/NCT05803538.

Information systems for vaccine safety surveillance

Immunization implementation in the community relies upon post-licensure vaccine safety surveillance to maintain safe vaccination programs and to detect rare AEFI not observed in clinical trials. The increasing availability of electronic health-care related data and correspondence from both health-related providers and internet-based media has revolutionized health-care information. Many and varied forms of health information related to adverse event following immunization (AEFI) are potentially suitable for vaccine safety surveillance. The utilization of these media ranges from more efficient use of electronic spontaneous reporting, automated solicited surveillance methods, screening various electronic health record types, and the utilization of natural language processing techniques to scan enormous amounts of internet-based data for AEFI mentions. Each of these surveillance types have advantages and disadvantages and are often complementary to each other. Most are "hypothesis generating," detecting potential safety signals, where some, such as vaccine safety datalinking, may also serve as "hypothesis testing" to help verify and investigate those potential signals.

Vaccine adverse event mentions in social media: Mining the language of Twitter conversations (Preprint)

Background

Traditional monitoring for adverse events following immunization (AEFI) relies on various established reporting systems, where there is inevitable lag between an AEFI occurring and its potential reporting and subsequent processing of reports. AEFI safety signal detection strives to detect AEFI as early as possible, ideally close to real time. Monitoring social media data holds promise as a resource for this.

Objective

The primary aim of this study is to investigate the utility of monitoring social media for gaining early insights into vaccine safety issues, by extracting vaccine adverse event mentions (VAEMs) from Twitter, using natural language processing techniques. The secondary aims are to document the natural language processing techniques used and identify the most effective of them for identifying tweets that contain VAEM, with a view to define an approach that might be applicable to other similar social media surveillance tasks.

Methods

A VAEM-Mine method was developed that combines topic modeling with classification techniques to extract maximal VAEM posts from a vaccine-related Twitter stream, with high degree of confidence. The approach does not require a targeted search for specific vaccine reaction–indicative words, but instead, identifies VAEM posts according to their language structure.

Results

The VAEM-Mine method isolated 8992 VAEMs from 811,010 vaccine-related Twitter posts and achieved an F₁ score of 0.91 in the classification phase.

Conclusions

Social media can assist with the detection of vaccine safety signals as a valuable complementary source for monitoring mentions of vaccine adverse events. A social media–based VAEM data stream can be assessed for changes to detect possible emerging vaccine safety signals, helping to address the well-recognized limitations of passive reporting systems, including lack of timeliness and underreporting.

Post-vaccination healthcare attendance rate as a proxy measure for syndromic surveillance of adverse events following immunisation

OBJECTIVE

This study explored whether all-cause healthcare attendance rate post-vaccination could detect the two historical influenza safety episodes occurring in 2010 and 2015 using a large de-identified general practitioner (GP) consultations dataset.

METHODS

A retrospective observational cohort study was conducted using GP consultation data routinely collected from 2008 to 2017 in Victoria, Australia. Post-vaccination GP consultation rates were monitored, over a 22-week surveillance period each year that aligned with each year's influenza vaccination season, using the Observed minus Expected (O-E) and the Log-Likelihood Ratio (LLR) CUSUM charts. Days 1-7 post-vaccination were considered as the risk period. The LLR CUSUM was designed to detect both a 50% and two-fold rise in the odds of the baseline post-vaccination GP consultation rates.

RESULTS

Over the 10-year study period, more than 1.5 million seasonal influenza vaccines doses were administered to 295,091 persons. Overall, 1.29% had a GP consultation within one week of vaccination, but 98.53% of the consultations occurred in days 1-3 post-vaccination. The LLR CUSUM chart detected significant increases in the weekly rates of post-vaccination GP consultation in 2010 in children aged under ten years and in 2015 in adults aged 19-64 years. These increases were aligned by week, but one week earlier and by age category, with the historical adverse events following immunisation (AEFI) signals occurring in 2010 and 2015. However, in the absence of historical AEFI signals, increased rates of post-vaccination GP consultations were identified in three of the eight influenza vaccination years.

CONCLUSION

The crude post-vaccination healthcare attendance rate has the potential to offer a sensitive proxy to monitor vaccine safety signal. Implications for public health: Vaccine safety monitoring using syndromic indicator has the potential to augment the existing surveillance systems as part of an integrated vaccine safety monitoring approach.