This study is an interrupted time series analysis (ITS) analysing the impact of the 1972 change in minimal school leaving age from 15 to 16 on myopia using the UK Biobank. It compares the time trends in the average level of mean spherical equivalent refractive error before and after the introduction of this policy on the 1st of September 1972. This report has been written using the STROBE Checklist.
The UKB is a large cohort study of around half a million British citizens. Recruitment occurred around the UK between 2006 and 2010 [17, 18]. A full description of the study design, participants and quality control (QC) methods have been described in detail previously. UK Biobank received ethical approval from the Research Ethics Committee (REC reference for UK Biobank is 11/NW/0382), and all participants provided written informed consent.
To be enrolled into the UK Biobank, participants had to be aged between 40 and 69. All participants attended a baseline assessment at one of 22 assessment centres. While at the centre participants provided information via questionnaires and interviews on demographics, lifestyle, cognitive and psychological measures, health status, as well as anthropometric and biomarker measures. Other measures have subsequently been made at follow-ups, which are generally online (data available at www.ukbiobank.ac.uk).
We included all UKB participants who provided information on the outcome(s) of interest. To avoid any contamination or misclassification effects, we additionally excluded participants born in 1957 from the regression models.
The primary outcome of this study is mean spherical equivalent refractive error. Other variables were used as negative controls and included: the log odds of reporting the primary outcome, and confounding noted in the prior literature (sex, polygenic risk score of myopia, deprivation) [1, 5, 6]. All variables were created by aggregating the data stratified by birth year.
Year of birth (UKB ID: 34) and sex (UKB ID: 31) were recorded at baseline using a mixture of self-report and NHS central registry data.
Age of completing full time education (UKB ID: 845) was asked through a touchscreen question “At what age did you complete your continuous full-time education” at the UK Biobank assessment centre. Answers which were greater than the participants age, or less than five were removed, and participants who put greater than 40 were asked to double check they had put the right number of years down.
Myopia (UKB ID: 20261) was measured using the mean spherical equivalent refractive error, averaged between the eyes. This information was collected at a UKB assessment centre, and calculated using a standardised and publicly available protocol (https://biobank.ndph.ox.ac.uk/showcase/showcase/docs/Visualacuity.pdf). The odds of reporting the primary outcome were then calculated as the ratio of the number of participants in the UKB who had provided information for this item over the number who had not.
The polygenic risk score (PGRS) of myopia was created using summary data from the Pickrell et al.  GWAS of myopia, extracted from the EBI GWAS catalogue (https://www.ebi.ac.uk/gwas/efotraits/HP_0000545). This study was conducted using 106086 self-reported cases and 85757 controls who had answered the question “Have you ever been diagnosed by a doctor with near-sightedness (near objects are clear, far objects are blurry)?” for the company 23andMe. The PGRS was then created using the MR-Base GRS extension.
The number of participants included in this study was determined by the number of participants in the UKB who met the inclusion criteria.
Quantitative variables were by default assumed to be linear, with non-linear terms being added if they improved model fit. Binary variables were converted to log odds and then treated as quantitative variables.
The time trends were modelled used a segmented ARMA regression model. Each model included a variable for time, a dummy variable for observations occurring after the intervention, and an interaction term between these two variables. Autocorrelation was explored by examining the residuals of an OLS regression, and the ACF, PACF plots, as well as using the Durbin-Watson test.
To check that the intervention did impact on years of education, we ran a sensitivity analysis using years of fulltime education as the outcome. ITS assumes that there is no change in confounding or selection effects at the time of the intervention’s introduction. To examine this assumption, we conducted falsification tests by re-running the analyses using the confounders (Myopia PGRS, sex, and deprivation) previously identified in the literature and the odds of reporting the primary outcome as negative control outcomes. All GLS analyses were conducted using the NLME package in R 4.0.2.[23, 24]. The standard error of predicted points as calculated using the AICcmodavg package.
Because the actual effect of the intervention will not be a one year change in the number of years in school, due to some participants, we used an instrumental variable Wald Ratio to recover the causal effect of a one year increase in education on myopia, using the TwoSampleMR R package. As a sensitivity analyses we calculated the E-value of the point estimate and lower 95% Confidence interval using the E-Value package.