Are active children and young people at increased risk of injuries resulting in hospital admission or accident and emergency department attendance? Analysis of linked cohort and electronic hospital records in Wales and Scotland

Potential for advances in data linkage and data science to support injury prevention research

The recent COVID-19 pandemic stimulated unprecedented linkage of datasets worldwide, and while injury is endemic rather than pandemic, there is much to be learned by the injury prevention community from the data science approaches taken to respond to the pandemic to support research into the primary, secondary and tertiary prevention of injuries. The use of routinely collected data to produce real-world evidence, as an alternative to clinical trials, has been gaining in popularity as the availability and quality of digital health platforms grow and the linkage landscape, and the analytics required to make best use of linked and unstructured data, is rapidly evolving. Capitalising on existing data sources, innovative linkage and advanced analytic approaches provides the opportunity to undertake novel injury prevention research and generate new knowledge, while avoiding data waste and additional burden to participants. We provide a tangible, but not exhaustive, list of examples showing the breadth and value of data linkage, along with the emerging capabilities of natural language processing techniques to enhance injury research. To optimise data science approaches to injury prevention, injury researchers in this area need to share methods, code, models and tools to improve consistence and efficiencies in this field. Increased collaboration between injury prevention researchers and data scientists working on population data linkage systems has much to offer this field of research.

Sex Differences in the Relation Between Supervision and Injury Risk Across Motor Development Stages: Transitioning From Infancy Into Toddlerhood

Rationale

Addressing a notable gap in research on injuries during infancy, this longitudinal study examined sex differences in the relationship between parents’ typical levels of supervision and infants’ injuries across motor development stages.

Method

Parents were recruited and completed biweekly phone calls about their infant’s motor skills. Once the infant was able to sit up independently, then a home visit was scheduled. Applying a participant-event monitoring method, parents were taught to complete diary forms (injury, supervision), which they started doing once the child could move from their seated location on the floor in some way (e.g., roll, crawl). Recordings continued until a month after the child could walk independently. Data (injury, supervision) were averaged within each motor development stage (low, high), and associations across stages were examined.

Results

Model testing indicated that supervision level moderated the relation between injury rate across motor development stages, but the strength of this association varied by sex of the child. More intense supervision predicted lower injury rates for girls more so than for boys.

Conclusions

Although the emergence of motor milestones has been associated with increased risk of injury during infancy, the current findings indicate that greater supervision can reduce this risk. However, supervision alone is not as effective to moderate injury risk for boys as it is for girls. Thus, for boys, additional strategies (e.g., hazard removal) may also be warranted to maximize reduction in their risk of injury as they acquire increasing motor skills.

Linking cohort-based data with electronic health records: a proof-of-concept methodological study in Hong Kong

OBJECTIVES

Data linkage of cohort-based data and electronic health records (EHRs) has been practised in many countries, but in Hong Kong there is still a lack of such research. To expand the use of multisource data, we aimed to identify a feasible way of linking two cohorts with EHRs in Hong Kong.

METHODS

Participants in the 'Children of 1997' birth cohort and the Chinese Early Development Instrument (CEDI) cohort were separated into several batches. The Hong Kong Identity Card Numbers (HKIDs) of each batch were then uploaded to the Hong Kong Clinical Data Analysis and Reporting System (CDARS) to retrieve EHRs. Within the same batch, each participant has a unique combination of date of birth and sex which can then be used for exact matching, as no HKID will be returned from CDARS. Raw data collected for the two cohorts were checked for the mismatched cases. After the matching, we conducted a simple descriptive analysis of attention deficit hyperactivity disorder (ADHD) information collected in the CEDI cohort via the Strengths and Weaknesses of ADHD Symptoms and Normal Behaviour Scale (SWAN) and EHRs.

RESULTS

In total, 3473 and 910 HKIDs in the birth cohort and CEDI cohort were separated into 44 and 5 batches, respectively, and then submitted to the CDARS, with 100% and 97% being valid HKIDs respectively. The match rates were confirmed to be 100% and 99.75% after checking the cohort data. From our illustration using the ADHD information in the CEDI cohort, 36 (4.47%) individuals had ADHD-Combined score over the clinical cut-off in the SWAN survey, and 68 (8.31%) individuals had ADHD records in EHRs.

CONCLUSIONS

Using date of birth and sex as identifiable variables, we were able to link the cohort data and EHRs with high match rates. This method will assist in the generation of databases for future multidisciplinary research using both cohort data and EHRs.