Using linked administrative data to aid the handling of non-response and restore sample representativeness in cohort studies: the 1958 national child development study and hospital episode statistics data.

BACKGROUND: There is growing interest in whether linked administrative data have the potential to aid analyses subject to missing data in cohort studies. METHODS: Using linked 1958 National Child Development Study (NCDS; British cohort born in 1958, n = 18,558) and Hospital Episode Statistics (HES) data, we applied a LASSO variable selection approach to identify HES variables which are predictive of non-response at the age 55 sweep of NCDS. We then included these variables as auxiliary variables in multiple imputation (MI) analyses to explore the extent to which they helped restore sample representativeness of the respondents together with the imputed non-respondents in terms of early life variables (father's social class at birth, cognitive ability at age 7) and relative to external population benchmarks (educational qualifications and marital status at age 55). RESULTS: We identified 10 HES variables that were predictive of non-response at age 55 in NCDS. For example, cohort members who had been treated for adult mental illness had more than 70% greater odds of bring non-respondents (odds ratio 1.73; 95% confidence interval 1.17, 2.51). Inclusion of these HES variables in MI analyses only helped to restore sample representativeness to a limited extent. Furthermore, there was essentially no additional gain in sample representativeness relative to analyses using only previously identified survey predictors of non-response (i.e. NCDS rather than HES variables). CONCLUSIONS: Inclusion of HES variables only aided missing data handling in NCDS to a limited extent. However, these findings may not generalise to other analyses, cohorts or linked administrative datasets. This work provides a demonstration of the use of linked administrative data for the handling of missing cohort data which we hope will act as template for others.

Examining the quality and population representativeness of linked survey and administrative data: guidance and illustration using linked 1958 National Child Development Study and Hospital Episode Statistics data.

Introduction: Recent years have seen an increase in linkages between survey and administrative data. It is important to evaluate the quality of such data linkages to discern the likely reliability of ensuing research. Evaluation of linkage quality and bias can be conducted using different approaches, but many of these are not possible when there is a separation of processes for linkage and analysis to help preserve privacy, as is typically the case in the UK (and elsewhere). Objectives: We aimed to describe a suite of generalisable methods to evaluate linkage quality and population representativeness of linked survey and administrative data which remain tractable when users of the linked data are not party to the linkage process itself. We emphasise issues particular to longitudinal survey data throughout. Methods: Our proposed approaches cover several areas: i) Linkage rates, ii) Selection into response, linkage consent and successful linkage, iii) Linkage quality, and iv) Linked data population representativeness. We illustrate these methods using a recent linkage between the 1958 National Child Development Study (NCDS; a cohort following an initial 17,415 people born in Great Britain in a single week of 1958) and Hospital Episode Statistics (HES) databases (containing important information regarding admissions, accident and emergency attendances and outpatient appointments at NHS hospitals in England). Results: Our illustrative analyses suggest that the linkage quality of the NCDS-HES data is high and that the linked sample maintains an excellent level of population representativeness with respect to the single dimension we assessed. Conclusions: Through this work we hope to encourage providers and users of linked data resources to undertake and publish thorough evaluations. We further hope that providing illustrative analyses using linked NCDS-HES data will improve the quality and transparency of research using this particular linked data resource.

How do childhood ADHD symptoms affect labour market outcomes?

Inattention, hyperactivity and impulsivity are the main symptoms of ADHD, which affects up to one in ten European and North American children. Existing research shows these symptoms are associated with school exclusion and poor academic performance during childhood and adolescence. Using the British Cohort Study (n = 17,196 people born in April 1970), this is the first study of relationships between ADHD symptoms measured during childhood (age 10) and labour market outcomes measured beyond early adulthood (ages 26-46); and the first to explore the role of childhood circumstances (at birth) and academic performance (ages 10 and 26) in explaining those relationships. A one standard deviation increase in childhood symptoms reduced employment by up to two percentage points and pay by up to four percentage points. Differences in academic performance at age 10 accounted for almost half the observed variation in employment outcomes, indicating a possible role for educational interventions in schools.

How does arthritis affect employment? Longitudinal evidence on 18,000 British adults with arthritis compared to matched controls.

INTRODUCTION: One in ten working age people in the UK live with arthritis or a similar condition affecting their joints. This impacts their quality of life, including through their work. But little is known about how arthritis affects labour market outcomes and the types of people most likely to be affected. METHODS: Data from three population-representative household panel surveys (BHPS, ELSA, UKHLS) collected in 2001-2019 was harmonised. Propensity score matching was used to match 18,014 UK adults aged 18-80 who have arthritis with comparable adults without arthritis. The relationship between arthritis and employment, and earnings and work hours conditional on employment, were assessed using multilevel regression modelling. Heterogeneity in these relationships were assessed by age, gender, degree-level education status, NS-SEC job classification and employer type. RESULTS: On average, arthritis was associated with a 3 percentage point reduction in the probability of employment. The effect size varied over people's life course and was larger amongst females, people without a degree, and those in routine or intermediate occupations (when compared to those in professional occupations) or working for small private companies (when compared to large private companies and non-private employers). Our models predict, for instance, that arthritis is associated with an 11 percentage point reduction in the probability of employment among 50-year-old women without a degree. This contrasts with a 5 percentage point reduction among 50-year-old men without a degree. If employed, men with a degree earned less if they had arthritis, whereas others (including women with a degree and men without a degree) had similar earnings regardless of their arthritis status. Those in professional occupations with arthritis also earnt less, especially if they were women aged over 40, with indications that this was driven by reduced work hours. CONCLUSION: Policy interventions to support people with arthritis who wish to remain in work might be designed with people in routine work in mind, and targeted at those working in smaller private firms. More research on the cost-effectiveness of those interventions is needed.