Sources of potential bias when combining routine data linkage and a national survey of secondary school-aged children: a record linkage study

Abstract

Background: Linking survey data to administrative records requires informed participant consent. When linkage includes child data, this includes parental and child consent. Little is known of the potential impacts of introducing consent to data linkage on response rates and biases in school-based surveys. This paper assessed: i) the impact on overall parental consent rates and sample representativeness when consent for linkage was introduced and ii) the quality of identifiable data provided to facilitate linkage. Methods: Including an option for data linkage was piloted in a sub-sample of schools participating in the Student Health and Wellbeing survey, a national survey of adolescents in Wales, UK. Schools agreeing to participate were randomized 2:1 to receive versus not receive the data linkage question. Survey responses from consenting students were anonymised and linked to routine datasets (e.g. general practice, inpatient, and outpatient records). Parental withdrawal rates were calculated for linkage and non-linkage samples. Multilevel logistic regression models were used to compare characteristics between: i) consenters and non-consenters; ii) successfully and unsuccessfully linked students; and iii) the linked cohort and peers within the general population, with additional comparisons of mental health diagnoses and health service contacts. Results: The sub-sample comprised 64 eligible schools (out of 193), with data linkage piloted in 39. Parental consent was comparable across linkage and non-linkage schools. 48.7% (n = 9232) of students consented to data linkage. Modelling showed these students were more likely to be younger, more affluent, have higher positive mental wellbeing, and report fewer risk-related behaviours compared to non-consenters. Overall, 69.8% of consenting students were successfully linked, with higher rates of success among younger students. The linked cohort had lower rates of mental health diagnoses (5.8% vs. 8.8%) and specialist contacts (5.2% vs. 7.7%) than general population peers.

Self‐harm, in‐person bullying and cyberbullying in secondary school‐aged children: A data linkage study in Wales

“…We could simultaneously assess psychological factors, self‐harm and other physical/mental health related conditions using validated measures or codes to ensure validity and reliability. To circumvent selection/collider biases from using nonrepresentative samples (Griffith et al, 2020; Morgan et al, 2020), we applied IPW adjustment to the SHRN cohort by nesting it within a population‐based cohort (Griffith et al, 2020). We were able to analyse the associations of in‐person bullying victimization at school and cyberbullying victimization on future self‐harm separately and at the same time adjusted for other known risk factors.…”

“…To alleviate potential biases of the SAIL‐linked SHRN cohort (Griffith et al, 2020; Morgan et al, 2020), we weighted the SHRN cohort by inverse probability weighting (IPW) based on propensity scores (PS) (Seaman & White, 2013). We first identified individuals from SAIL who were not from the survey sample and aged 11–16 years at the date of survey and then combined them with the SHRN cohort to form a large cohort representing the 11–16 years olds population of Wales (Figure 1).…”

Public opinion on sharing data from health services for clinical and research purposes without explicit consent: an anonymous online survey in the UK

“…Participants supported a central system for patients to control the uses of their data, and likewise a single NHS mechanism to sign up for active research participation. There is a trade-off between the scientific desirability of everyone contributing de-identified data, including to avoid bias,76 77 and the desirability of individual control over data use 78. As we suggest below, a reasonable balance might be a central system to opt out from identifiable clinical use, identifiable (§251) research use, or de-identified research use of one’s data, and to opt in for participatory research.…”

An Introduction to Probabilistic Record Linkage with a Focus on Linkage Processing for WTC Registries

“…In other contexts, researchers cannot access the large datasets that can be used for social and health-related research due to concerns around privacy and confidentiality. Current research topics related to these concerns revolve around privacy-preserving record linkage and understanding the bias introduced by the requirement for informed consent [ 26 , 27 ]. We turn to further discussion of the practical considerations around record linkage next.…”