TY - JOUR
T1 - Use of routinely collected electronic healthcare data for postlicensure vaccine safety signal detection
T2 - A systematic review
AU - Mesfin, Yonatan Moges
AU - Cheng, Allen
AU - Lawrie, Jock
AU - Buttery, Jim
PY - 2019/7
Y1 - 2019/7
N2 - Background Concerns 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. Methods We 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. Result We 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. Conclusion Routinely 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 number CRD42017072741
AB - Background Concerns 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. Methods We 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. Result We 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. Conclusion Routinely 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 number CRD42017072741
KW - adverse event following immunization
KW - electronic healthcare records
KW - post-licensure safety surveillance
KW - systematic review
UR - http://www.scopus.com/inward/record.url?scp=85068822244&partnerID=8YFLogxK
U2 - 10.1136/bmjgh-2018-001065
DO - 10.1136/bmjgh-2018-001065
M3 - Review Article
C2 - 31354969
AN - SCOPUS:85068822244
SN - 2059-7908
VL - 4
JO - BMJ Global Health
JF - BMJ Global Health
IS - 4
M1 - e001065
ER -