IntroductionLinkage of administrative data for universal state education and National Health Service (NHS) hospital care would enable research into the inter-relationships between education and health for all children in England. ObjectivesWe aim to describe the linkage process and evaluate the uality of linkage of four one-year birth cohorts within the National Pupil Database (NPD) and Hospital Episode Statistics (HES). MethodsWe used multi-step deterministic linkage algorithms to link longitudinal records from state schools to the chronology of records in the NHS Personal Demographics Service (PDS; linkage stage 1), and HES (linkage stage 2). We calculated linkage rates and compared pupil characteristics in linked and unlinked samples for each stage of linkage and each cohort (1990/91, 1996/97, 1999/00, and 2004/05). ResultsOf the 2,287,671 pupil records, 2,174,601 (95%) linked to HES. Linkage rates improved over time (92% in 1990/91 to 99% in 2004/05). Ethnic minority pupils and those living in more deprived areas were less likely to be matched to hospital records, but differences in pupil characteristics between linked and unlinked samples were moderate to small. ConclusionWe linked nearly all pupils to at least one hospital record. The high coverage of the linkage represents a unique opportunity for wide-scale analyses across the domains of health and education. However, missed links disproportionately affected ethnic minorities or those living in the poorest neighbourhoods: selection bias could be mitigated by increasing the quality and completeness of identifiers recorded in administrative data or the application of statistical methods that account for missed links. Highlights • Longitudinal administrative records for all children attending state school and acute hospital services in England have been used for research for more than two decades, but lack of a shared unique identifier has limited scope for linkage between these databases. • We applied multi-step deterministic linkage algorithms to 4 one-year cohorts of children born 1 September-31 August in 1990/91, 1996/97, 1999/00 and 2004/05. In stage 1, full names, date of birth, and postcode histories from education data in the National Pupil Database were linked to the NHS Personal Demographic Service. In stage 2, NHS number, postcode, date of birth and sex were linked to hospital records in Hospital Episode Statistics. • Between 92% and 99% of school pupils linked to at least one hospital record. Ethnic minority pupils and pupils who were living in the most deprived areas were least likely to link. Ethnic minority pupils were less likely than white children to link at the first step in both algorithms. • Bias due to linkage errors could lead to an underestimate of the health needs in disadvantaged groups. Improved data quality, more sensitive linkage algorithms, and/or statistical methods that account for missed links in analyses, should be considered to reduce linkage bias.
e339 processes, including routine reporting by disaggregated ethnic subgroups, would allow ethnic biases to be accounted for by statistical methods, and considered when assessing the validity of analyses and interpreting results. Data providers need to continually improve data quality and linkage methods (eg, through training of patient-facing staff in recording data for ethnic minorities, more inclusive data capture systems, and more flexible linkage algorithms). For example, we recently showed that, when linking administrative health and education records, relaxing requirements for exact matching on name improved linkage rates for ethnic minorities, although they remained disproportionately low. 5 Crucially, echoing Knight and colleagues, 1 we must all strive for greater diversity in the data linkage community, and more meaningful engagement with ethnic minorities to increase understanding of data linkage and address their concerns.We declare no competing interests.
Introduction We aimed to generate evidence about child development measured through school attainment and provision of special educational needs (SEN) across the spectrum of gestational age, including for children born early term and >41 weeks of gestation, with and without chronic health conditions. Methods We used a national linked dataset of hospital and education records of children born in England between 1 September 2004 and 31 August 2005. We evaluated school attainment at Key Stage 1 (KS1; age 7) and Key Stage 2 (KS2; age 11) and any SEN by age 11. We stratified analyses by chronic health conditions up to age 2, and size-for-gestation, and calculated population attributable fractions (PAF). Results Of 306 717 children, 5.8% were born <37 weeks gestation and 7.0% had a chronic condition. The percentage of children not achieving the expected level at KS1 increased from 7.6% at 41 weeks, to 50.0% at 24 weeks of gestation. A similar pattern was seen at KS2. SEN ranged from 29.0% at 41 weeks to 82.6% at 24 weeks. Children born early term (37–38 weeks of gestation) had poorer outcomes than those born at 40 weeks; 3.2% of children with SEN were attributable to having a chronic condition compared with 2.0% attributable to preterm birth. Conclusions Children born with early identified chronic conditions contribute more to the burden of poor school outcomes than preterm birth. Evaluation is needed of how early health characteristics can be used to improve preparation for education, before and at entry to school.
We use longitudinal data across a key developmental period, spanning much of childhood and adolescence (age 5 to 17, years 2006–2018) from the UK Millennium Cohort Study, a nationally representative study with an initial sample of just over 19,000. We first examine the extent to which inequalities in overweight, obesity, BMI and body fat over this period are consistent with the evolution of inequalities in health behaviours, including exercise and healthy diet markers (i.e., skipping breakfast) (n = 7,220). We next study the links between SES, health behaviours and adiposity (BMI, body fat), using rich models that account for the influence of a range of unobserved factors that are fixed over time. In this way, we improve on existing estimates measuring the relationship between SES and health behaviours on the one hand and adiposity on the other. The advantage of the individual fixed effects models is that they exploit within-individual changes over time to help mitigate biases due to unobserved fixed characteristics (n = 6,883). We observe stark income inequalities in BMI and body fat in childhood (age 5), which have further widened by age 17. Inequalities in obesity, physical activity, and skipping breakfast are observed to widen from age 7 onwards. Ordinary Least Square estimates reveal the previously documented SES gradient in adiposity, which is reduced slightly once health behaviours including breakfast consumption and physical activity are accounted for. The main substantive change in estimates comes from the fixed effects specification. Here we observe mixed findings on the SES associations, with a positive association between income and adiposity and a negative association with wealth. The role of health behaviours is attenuated but they remain important, particularly for body fat.
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