Relation of Incident Type 1 Diabetes to Recent COVID-19 Infection: Cohort Study Using e-Health Record Linkage in Scotland
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Abstract
Studies using claims databases reported that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection >30 days earlier was associated with an increase in the incidence of type 1 diabetes. Using exact dates of diabetes diagnosis from the national register in Scotland linked to virology laboratory data, we sought to replicate this finding.
RESEARCH DESIGN AND METHODS
A cohort of 1,849,411 individuals aged <35 years without diabetes, including all those in Scotland who subsequently tested positive for SARS-CoV-2, was followed from 1 March 2020 to 22 November 2021. Incident type 1 diabetes was ascertained from the national registry. Using Cox regression, we tested the association of time-updated infection with incident diabetes. Trends in incidence of type 1 diabetes in the population from 2015 through 2021 were also estimated in a generalized additive model.
RESULTS
There were 365,080 individuals who had at least one detected SARS-CoV-2 infection during follow-up and 1,074 who developed type 1 diabetes. The rate ratio for incident type 1 diabetes associated with first positive test for SARS-CoV-2 (reference category: no previous infection) was 0.86 (95% CI 0.62, 1.21) for infection >30 days earlier and 2.62 (95% CI 1.81, 3.78) for infection in the previous 30 days. However, negative and positive SARS-CoV-2 tests were more frequent in the days surrounding diabetes presentation. In those aged 0–14 years, incidence of type 1 diabetes during 2020–2021 was 20% higher than the 7-year average.
CONCLUSIONS
Type 1 diabetes incidence in children increased during the pandemic. However, the cohort analysis suggests that SARS-CoV-2 infection itself was not the cause of this increase.
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SciScore for 10.1101/2022.02.11.22270785: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics IRB: Ethics approval and data governance: Approval for use of the diabetes data was provided by the Public Benefit and Privacy Panel (https://www.informationgovernance.scot.nhs.uk/pbpphsc/) (ref. 1617-0147) and the Scotland A Multicentre Research Ethics Committee (ref 21/WS/0047).
Consent: Individual consent is not required for Public Health Scotland staff to process personal data to perform specific tasks in the public interest that fall within its statutory role.Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Cell Line Authentication Authentication: The clinical classification of type in SCI-Diabetes has previously been … SciScore for 10.1101/2022.02.11.22270785: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics IRB: Ethics approval and data governance: Approval for use of the diabetes data was provided by the Public Benefit and Privacy Panel (https://www.informationgovernance.scot.nhs.uk/pbpphsc/) (ref. 1617-0147) and the Scotland A Multicentre Research Ethics Committee (ref 21/WS/0047).
Consent: Individual consent is not required for Public Health Scotland staff to process personal data to perform specific tasks in the public interest that fall within its statutory role.Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Cell Line Authentication Authentication: The clinical classification of type in SCI-Diabetes has previously been validated against detailed prescribing and hospital admission histories; in the years 2015-2019 we reclassified the type of diabetes as type 2, monogenic or secondary in less than 2% of those aged under 16 clinically labelled as type 1 diabetes for example. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Strengths and limitations: Strengths of this study include the availability of individual level data, comprehensive national coverage of PCR tests, the inclusion of data on the level of negative testing around the time of presentation and most importantly validation of the accuracy of dates of diagnosis of type 1 diabetes in the SCI-diabetes registry against date of first hospitalization for type 1 diabetes in paediatric cases for whom the policy is to admit all newly-diagnosed cases immediately. Limitations are that because the numbers of incident cases of type 1 diabetes exposed to SARS-CoV-2 infection were relatively small, for formal modelling of the hazard ratio we had to use fairly broad categories of 0-30 and >30 days for exposure period. However the clustering of negative and positive tests around the date of diagnosis is obvious on inspection of scatter plots. Another limitation common to other studies of this question are that until mass testing was rolled out in late 2020, most cases of SARS-CoV-2 in younger people were not detected. However the cumulative incidence of infection in 5-14 year olds in the UK is estimated to have been only about 15% up to late 2020 [20], so misclassification of exposed individuals as unexposed would only slightly reduce the rate ratios for Type 1 diabetes associated with detected infection in this age group. Comparison with previous studies: Our results do not confirm the association of incident diabetes before age 18 years with SARS-...
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