Reevaluating the relationship between COVID-19 and type 1 diabetes mellitus: Methodological considerations

Abstract

A recent nationwide cohort study reported an increased incidence and altered seasonality of type 1 diabetes mellitus (T1DM) during the coronavirus disease 2019 (COVID-19) pandemic. The study found that new-onset T1DM cases were significantly higher during the pandemic than in prior years, and the typical winter peak in T1DM diagnoses was blunted. This occurred alongside markedly reduced circulation of other respiratory viruses under lockdown measures. Carmon et al noted weak positive correlations between T1DM incidence and certain viruses (e.g., influenza and respiratory syncytial virus), suggesting that reduced exposure to common infections - and possibly severe acute respiratory syndrome coronavirus 2 infection itself - might have contributed to the rise in T1DM. To highlight key methodological limitations of that study, which may affect the interpretation of the findings. We reviewed the study design and data of Carmon et al and discussed potential biases, including ecological inference, confounding factors, delayed diagnoses, lack of COVID-19-stratified analysis, and biases in viral surveillance data, supported by recent literature. The association observed by Carmon et al is at risk of ecological fallacy due to the absence of individual infection linkage. Uncontrolled confounders (healthcare access, socioeconomic changes) and not stratifying by COVID-19 infection status limit causal inference. Pandemic-related diagnostic delays likely inflated apparent T1DM incidence, as evidenced by higher rates of diabetic ketoacidosis in new cases. Biases in virological testing data (reduced testing and non-representative sampling) complicate conclusions about “reduced” viral circulation. The pandemic’s impact on T1DM incidence is important but requires cautious interpretation. Future studies should employ individual-level analyses, adjust for confounders, distinguish true incidence increases from diagnostic delays, stratify by infection status, and use comprehensive viral exposure data to draw more robust conclusions.

Keywords: Type 1 diabetes mellitus; COVID-19; Incidence; Seasonality; Epidemiology; Viral infections; Bias

Core Tip: Carmon et al’s study suggests a link between the coronavirus disease 2019 (COVID-19) pandemic and rising type 1 diabetes mellitus cases. This letter provides a constructive critique of their methodology. The authors point out that ecological analysis without individual infection data can be misleading, unaddressed confounders and lack of COVID-19 stratification weaken causal inference, and pandemic-related diagnostic delays (reflected in higher diabetic ketoacidosis rates) may have inflated case counts. The authors also caution that biases in viral surveillance data complicate the interpretation of “reduced” non-COVID infections. Addressing these issues with more granular data and analyses will improve future research on the type 1 diabetes mellitus-COVID relationship.