A New Validated Approach for Identifying Childhood Immunizations in Electronic Health Records in the United Kingdom.

BACKGROUND: Routinely collected electronic health records (EHR) offer a valuable opportunity to carry out research on immunization uptake, effectiveness, and safety, using large and representative samples of the population. In contrast to other drugs, vaccines do not require electronic prescription in many settings, which may lead to ambiguous coding of vaccination status and timing. METHODOLOGY: We propose a comprehensive algorithm to identifying childhood immunizations in routinely collected EHR. In order to deal with ambiguous coding, over-recording, and backdating in EHR, we suggest an approach combining a wide range of medical codes in combination to identify vaccination events and using appropriate wash-out periods and quality checks. We illustrate this approach on a cohort of children born between 2006 and 2014 followed up to the age of five in the Clinical Practice Research Datalink (CPRD) Aurum, a UK primary care dataset of EHR, and validate the results against national estimates of vaccine coverage by NHS Digital and Public Health England. RESULTS: Our algorithm reproduced estimates of vaccination coverage, which are comparable to official national estimates and allows to approximate the age at vaccination. Electronic prescription data only do not cover vaccination events sufficiently. CONCLUSION: Our new proposed method could be used to provide a more accurate estimation of vaccination coverage and timing of vaccination for researchers and policymakers using EHR. As with all observational research using real-world data, it is important that researchers understand the context of the used dataset used and the clinical practice of recording.

NHS national data opt-outs: trends and potential consequences for health data research.

BACKGROUND: The English NHS data opt-out allows people to prevent use of their health data for purposes other than direct care. In 2021, the number of opt-outs increased in response to government-led proposals to create a centralised pseudonymised primary care record database. AIM: To describe the potential impact of NHS national data opt-outs in 2021 on health data research. DESIGN & SETTING: We conducted a descriptive analysis of opt-outs using publicly available data and the potential consequences on research are discussed. METHOD: Trends in opt-outs in England were described by age, sex, and region. Using a hypothetical study, we explored statistical and epidemiological implications of opt-outs. RESULTS: During the lead up to a key government-led deadline for registering opt-outs (from 31 May 2021-30 June 2021), 1 339 862 national data opt-outs were recorded; increasing the percentage of opt-outs in England from 2.77% to 4.97% of the population. Among females, percentage opt-outs increased by 83% (from 3.02% to 5.53%) compared with 76% in males (from 2.51% to 4.41%). Across age groups, the highest relative increase was among people aged 40-49 years, which rose from 2.89% to 6.04%. Considerable geographical variation was not clearly related to deprivation. Key research consequences of opt-outs include reductions in sample size and unpredictable distortion of observed measures of the frequency of health events or associations between these events. CONCLUSION: Opt-out rates varied by age, sex, and place. The impact of this and variation by other characteristics on research is not quantifiable. Potential effects of opt-outs on research and consequences for health policies based on this research must be considered when creating future opt-out solutions.