Use of routinely collected electronic healthcare data for postlicensure vaccine safety signal detection: a systematic review

Abstract

BackgroundConcerns regarding adverse events following vaccination (AEFIs) are a key challenge for public confidence in vaccination. Robust postlicensure vaccine safety monitoring remains critical to detect adverse events, including those not identified in prelicensure studies, and to ensure public safety and public confidence in vaccination. We summarise the literature examined AEFI signal detection using electronic healthcare data, regarding data sources, methodological approach and statistical analysis techniques used.MethodsWe performed a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Five databases (PubMed/Medline, EMBASE, CINAHL, the Cochrane Library and Web of Science) were searched for studies on AEFIs monitoring published up to 25 September 2017. Studies were appraised for methodological quality, and results were synthesised narratively.ResultWe included 47 articles describing AEFI signal detection using electronic healthcare data. All studies involved linked diagnostic healthcare data, from the emergency department, inpatient and outpatient setting and immunisation records. Statistical analysis methodologies used included non-sequential analysis in 33 studies, group sequential analysis in two studies and 12 studies used continuous sequential analysis. Partially elapsed risk window and data accrual lags were the most cited barriers to monitor AEFIs in near real-time.ConclusionRoutinely collected electronic healthcare data are increasingly used to detect AEFI signals in near real-time. Further research is required to check the utility of non-coded complaints and encounters, such as telephone medical helpline calls, to enhance AEFI signal detection.Trial registration numberCRD42017072741

New and Increasing Rates of Adverse Events Can be Found in Unstructured Text in Electronic Health Records using the Shakespeare Method

“…Many medical care AEs occur at higher frequency in hospital critical care settings, related to complex illnesses, invasive procedures, and relatively long lists of treatments [40, 41]. In previous work, we performed a comparison of transfused to non-transfused admissions to critical care at a major teaching hospital [42] that successfully found potential blood transfusion adverse events, while addressing many published challenges (such as synonyms, overlapping meanings, and nonstandard terms) with using unstructured EHRs text [5, 11, 14, 19, 23].…”

Positive predictive value of ICD‐10 codes to detect anaphylaxis due to vaccination: A validation study

“…Passive AEFI surveillance, however, has well‐known limitations including under‐reporting and data incompleteness . Conversely, active surveillance of AEFI using routinely collected health care data has offered an opportunity to track vaccines safety in near real time . Active AEFI surveillance studies often use linked data; exposure information (vaccination) from vaccination registry are linked with prespecified outcomes of interest identified from electronic records using computerised International Classification of Diseases (ICD) diagnosis codes .…”

“…5 Active AEFI surveillance studies often use linked data; exposure information (vaccination) from vaccination registry are linked with prespecified outcomes of interest identified from electronic records using computerised International Classification of Diseases (ICD) diagnosis codes. 6 Moreover, studies also suggest that vaccine safety signals can be tracked using proxy measures of AEFI occurrences, such as postvaccination health care utilisation/medical attendance rate. 7,8 The ICD-10-CM (10th revision, clinical modification) contains diagnostic codes assigned for AEFI-related diagnoses, such as T80.5…”

“…Conversely, active surveillance of AEFI using routinely collected health care data has offered an opportunity to track vaccines safety in near real time . Active AEFI surveillance studies often use linked data; exposure information (vaccination) from vaccination registry are linked with prespecified outcomes of interest identified from electronic records using computerised International Classification of Diseases (ICD) diagnosis codes . Moreover, studies also suggest that vaccine safety signals can be tracked using proxy measures of AEFI occurrences, such as postvaccination health care utilisation/medical attendance rate …”

COVID-19 vaccine adverse reactions bring patients to emergency departments

“…Additionally, low risk reactions such as pain at injection site, redness, and local reactions also have been reported. ( 4 ) There are no studies on ED encounters related to other vaccines side effects, though anecdotally it appears uncommon. Our preliminary data raises the question of how do we best triage these complaints to ensure that high-risk diagnoses are not missed.…”