The study was led by PhD student Maria Östman of the University of Oslo and colleagues, who exploited one of the fastest and most complete vaccine introductions in Europe as a natural experiment. When Norway added the monovalent Rotarix vaccine to its national childhood immunisation programme in September 2014, uptake among infants reached 92.8 percent within a short period. That near-total and abrupt shift created a clean dividing line in the population: children born before the introduction had essentially no rotavirus vaccination in infancy, while those born after it were overwhelmingly vaccinated. Such a sharp exposure boundary is rare in epidemiology, and it allowed the researchers to compare diabetes incidence across birth cohorts with unusual clarity.

Rotavirus is a highly common virus transmitted by the faecal–oral route and a leading cause of severe gastroenteritis in young children, particularly those under five years of age. Immunity builds with each infection, so repeat infections tend to be milder, but first infections can be dangerous. Rotavirus vaccines, which are oral live-attenuated products made from a laboratory-weakened virus, have dramatically reduced hospitalisation and mortality from severe rotavirus disease worldwide. Because viral infections have long been suspected of influencing the risk of type 1 diabetes — an autoimmune disease in which the immune system destroys the insulin-producing beta cells of the pancreas — researchers have debated whether rotavirus vaccination might either raise or lower diabetes risk. Previous studies in other countries have produced conflicting results, with some suggesting a protective effect and others finding no association.

To resolve the question in the Norwegian setting, the researchers followed all children born in Norway between 2007 and 2019, a total of 740,744 children, from the age of six months to five years. Crucially, they ensured that the length of follow-up was identical for children born before and after the vaccine’s introduction, eliminating a common source of bias in cohort comparisons. Data collection continued until 31 December 2024. Among the full cohort, 846 children were diagnosed with type 1 diabetes before their fifth birthday. The researchers then applied an interrupted time series analysis, a statistical technique that uses the observed pre-vaccination trend to predict what incidence would have been without the intervention, and compares that prediction with the actual observed trend afterwards.

The main analysis showed a relative increase of 11 percent in type 1 diabetes incidence among children born after the vaccine’s introduction compared with the predicted trend. In a separate adjusted analysis, the relative hazard ratio for children fully vaccinated with two doses of the monovalent vaccine, compared with unvaccinated children, was 1.17 — an apparent elevation that, however, did not reach statistical significance. The authors summarised their central finding bluntly: the study found no evidence for a reduced risk of type 1 diabetes after the introduction of rotavirus vaccination in infancy, but instead found that incidence before age five increased after vaccination was introduced. Taken at face value, that combination of results could have fuelled concerns about the vaccine’s safety.

It is here that the study’s secondary analyses become decisive. To test whether the observed increase was genuinely attributable to the rotavirus vaccine, the researchers examined what happened to diabetes incidence after other, unrelated changes to the Norwegian immunisation programme. These served as negative controls: there is no biological or epidemiological evidence that the introduction of the hepatitis B vaccine in 2016, or the switch from the 7-valent to the 13-valent pneumococcal vaccine in 2011, would affect type 1 diabetes risk. If the association seen after the rotavirus rollout reflected a true vaccine effect, similar associations should not appear around these unrelated programme changes. Yet they did. The incidence of type 1 diabetes among children born after the hepatitis B vaccine’s introduction rose by a magnitude similar to that seen after the rotavirus rollout, and the same pattern followed the pneumococcal vaccine switch.

The authors interpret this convergence as strong evidence that the initial 11 percent finding is probably not related to the rotavirus vaccine at all, but rather to other, as yet unknown changes in the environment affecting Norwegian children. Type 1 diabetes incidence has generally been increasing over recent decades, with year-to-year fluctuation, and the researchers emphasise that they do not believe any of the changes in the national vaccination programme, individually or in combination, explain the rise. As they put it, despite their best efforts to design the study to remove the influence of other unknown factors, the identical pattern appearing around every programme change — including changes hypothesised to have opposite or null effects — points to unidentified environmental factors operating across the period.

The team also systematically considered alternative explanations for the observed increase. The COVID-19 pandemic, which was followed in many countries by a temporary surge in paediatric type 1 diabetes diagnoses, was one candidate; Norway itself experienced a temporary increase in incidence in 2022 followed by a decrease. However, a recent study of Swedish and Norwegian adolescents found no increase in type 1 diabetes incidence after COVID-19 infection, weakening that hypothesis. The researchers also examined a change in Norwegian prenatal care guidelines in June 2018, which began recommending measurement of serum ferritin concentration in the first trimester of pregnancy to determine the need for iron supplementation, replacing the less sensitive measurement of haemoglobin at week 30. Because high iron levels have been suspected of association with type 1 diabetes, the change warranted scrutiny. The authors concluded it was unlikely to be relevant, noting that iron levels are usually low in pregnancy and that only children born in 2019 within the study window were affected by the new recommendations.

The authors are careful about the limits of their conclusions. They state that they do not believe rotavirus vaccination increases the risk of type 1 diabetes, but they also argue that additional studies of rotavirus vaccination and type 1 diabetes are needed in different settings and with different vaccines before any definitive conclusion is drawn. This matters because vaccine formulations differ across countries: Norway and the United Kingdom use the monovalent Rotarix vaccine alone, Germany and Italy use both Rotarix and the pentavalent RotaTeq vaccine, while Finland and Iceland implemented RotaTeq alone. Replication across these different exposure profiles would help determine whether the Norwegian findings hold elsewhere or are artefacts of local circumstances. The authors also stress the broader need for research into other environmental factors that could be associated with type 1 diabetes, given that the disease’s incidence continues to climb in many populations without a fully established cause.

For the public, the practical message is one of reassurance grounded in methodological rigour. Rotavirus vaccination has efficiently reduced hospitalisation and mortality from severe rotavirus infections in children, and the Norwegian data, once subjected to negative-control scrutiny, do not support the idea that the vaccine drives diabetes risk in early childhood. The study instead illustrates a subtle pitfall of interrupted time series designs: when a long-running upward trend in a disease coincides with a policy change, the change can appear causal even when it is not. By testing the same pattern against interventions with no plausible link to diabetes, the Oslo team showed that the 11 percent rise was more plausibly a marker of some other environmental shift affecting children born from September 2014 onward. The authors declare no conflict of interest, and their work stands as a model of how vaccine safety signals should be interrogated — thoroughly, transparently, and with controls designed to catch spurious associations before they harden into public fear.

Subject of Research: Association between rotavirus vaccination and type 1 diabetes incidence in Norwegian children

Article Title: Nationwide study in Norway shows a relative 11% increase in incidence of type 1 diabetes in children aged 0.5 to 5 years after nationwide introduction of rotavirus vaccine, but secondary analyses suggest vaccine probably not the cause

Article References: Nationwide study in Norway shows a relative 11% increase in incidence of type 1 diabetes in children aged 0.5 to 5 years after nationwide introduction of rotavirus vaccine, but secondary analyses suggest vaccine probably not the cause. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: type 1 diabetes, rotavirus vaccine, Rotarix, Norway, EASD, Diabetologia, interrupted time series, childhood immunisation, epidemiology, negative controls, autoimmune disease, vaccine safety

The central premise of the study is deceptively simple. A negative control outcome is a condition that shares the patterns of healthcare use, testing and diagnosis with the outcome of interest but is not causally affected by the exposure being studied. In vaccine effectiveness research, if vaccinated and unvaccinated people truly differ only in their vaccination status, a condition that the vaccine cannot prevent should show no association with vaccination. Any apparent protective effect on such a condition signals that unmeasured confounding, selection bias or temporal artifacts are contaminating the analysis. Researchers can then use the size of this spurious effect to recalibrate, or empirically adjust, their estimates for the real outcome. The catch, as the new work demonstrates, is that the validity of the negative control is an assumption, and assumptions about control outcomes deserve the same scrutiny as assumptions about the exposure itself.

In COVID-19 research, a widely proposed negative control has been the documented SARS-CoV-2 infection occurring within the first days after vaccination. The reasoning goes that biological protection cannot yet have developed in that window, so any difference in infection rates between newly vaccinated and unvaccinated individuals must reflect bias rather than vaccine effect. That logic, however, embeds a second assumption: that the biases operating in the immediate post-vaccination window resemble those operating later in follow-up. The team behind the new study suspected that the immediate window carries its own idiosyncratic distortions, driven by behavior changes around the vaccination visit itself, and that a control validated in that window may be grossly miscalibrated for later time periods.

To test this, the investigators designed what epidemiologists call a target trial emulation, an attempt to mirror the design of a hypothetical randomized trial using observational data. They drew on PEDSnet, a pediatric clinical data network spanning institutions including Children’s Hospital of Philadelphia, Cincinnati Children’s Hospital Medical Center, Children’s Hospital Colorado, Lurie Children’s Hospital of Chicago, Nationwide Children’s Hospital, Nemours Children’s Hospital, Seattle Children’s Hospital and Stanford Children’s Health. The study window ran from January 1 to November 16, 2022, and the population comprised adolescents aged 12 to 20 years who had no prior documented SARS-CoV-2 infection and no prior COVID-19 vaccination. Vaccinated patients were matched one to one to unvaccinated patients using propensity scores, a technique that balances measured characteristics such as demographics, comorbidities and healthcare utilization between the two groups.

The key analytical move was to compare two outcomes side by side across two distinct time windows. The first outcome was documented SARS-CoV-2 infection, the outcome of genuine interest for vaccine effectiveness. The second was infection with other respiratory pathogens, principally influenza and respiratory syncytial virus, or RSV. These other respiratory infections share a great deal with COVID-19: similar clinical presentations, similar testing practices and similar patterns of seeking care when a child falls ill. Crucially, however, COVID-19 vaccination has no biological mechanism to prevent them. That makes them theoretically ideal negative controls, since any observed association with vaccination should be pure bias. The researchers estimated hazard ratios separately for days 1 through 14 after vaccination and for day 15 onward, and then confronted these estimates with an empirical null distribution constructed from a set of prespecified health events that vaccination cannot plausibly influence.

The results were striking and, in places, cautionary. In the first 14 days after vaccination, the hazard ratio for documented SARS-CoV-2 infection was 0.33, with a 95 percent confidence interval of 0.24 to 0.47, suggesting that vaccinated adolescents appeared 67 percent less likely to be diagnosed with COVID-19 than their matched unvaccinated peers. That might look like remarkably rapid vaccine protection. But the negative control told a different story. Over the same window, the hazard ratio for other respiratory infections was 0.30, with a confidence interval of 0.14 to 0.63. Since the vaccine cannot protect against influenza or RSV, an apparent 70 percent reduction in those infections could only be bias. Both estimates deviated significantly from the empirical null distribution, with p values below 0.001, indicating that the immediate post-vaccination period is saturated with spurious protective associations.

The picture changed substantially after day 15. The hazard ratio for documented SARS-CoV-2 infection settled at 0.32, with a confidence interval of 0.27 to 0.36, a figure broadly consistent with the genuine protection expected from vaccination during that period of the pandemic. Meanwhile the hazard ratio for other respiratory infections rose to 0.68, with a confidence interval of 0.60 to 0.77. Although still numerically below one, this estimate no longer differed significantly from the empirical null distribution, with a p value of 0.142. In other words, once the immediate post-vaccination window was excluded, the biases that had inflated apparent protection largely dissipated, and the negative control behaved the way theory says a valid control should. The same control outcome that flagged massive distortion in days 1 through 14 was reasonably well calibrated for later follow-up.

The mechanistic interpretation offered by the authors centers on behavioral and temporal artifacts surrounding the vaccination encounter itself. A vaccination visit is not a neutral event. People who feel ill tend to postpone vaccination, creating a healthy-vaccinee effect in the days immediately after the shot. Vaccination may prompt contact with the health system in ways that alter testing for coincidental infections, or it may coincide with periods when families are unusually cautious about exposing children to respiratory pathogens. Conversely, the act of attending a clinic can itself lead to exposure or to diagnostic workups that would not otherwise occur. These time-dependent distortions are strongest immediately after vaccination and fade with longer follow-up, which is exactly the pattern the empirical calibration revealed. A negative control borrowed from the early window therefore imports the very biases it is supposed to measure, miscalibrating the analysis for later periods.

The implications extend well beyond COVID-19. Vaccine effectiveness studies for influenza, RSV and future pandemic vaccines routinely rely on negative controls to detect and correct residual bias, and many adopt short post-vaccination windows as convenient controls without systematically validating them. The study shows that negative control validity is not a fixed property of an outcome but depends jointly on the choice of outcome and the time at risk. A control that is valid for a 60-day hazard ratio may be badly invalid for a 14-day hazard ratio, and vice versa. The authors argue that researchers should evaluate control outcomes within each time-at-risk window used in the analysis, comparing estimates against an empirical null built from multiple unrelated health events rather than relying on a single assumed-null outcome. Where a control deviates from the null, empirical calibration can rescale the confidence interval of the main estimate, yielding inference that is honest about the uncertainty introduced by unmeasured confounding.

The work also illustrates a broader shift in how large-scale observational evidence is being made trustworthy. Rather than treating bias detection as an afterthought, the framework embeds diagnostics into the analysis pipeline itself, leveraging the scale of networks like PEDSnet to construct empirical null distributions from real data. With funding from the National Institutes of Health and the Patient-Centered Outcomes Research Institute, and institutional review board approval from the University of Pennsylvania, the study exemplifies a rigorous, open-access approach to a problem that shapes public confidence in vaccines. As real-world evidence increasingly informs regulatory and clinical decisions, the message is clear: the tools that guard against misleading results must themselves be tested, because an unvalidated negative control can be just as misleading as no control at all.

Subject of Research: Validation of negative control outcomes for detecting and calibrating residual bias in real-world COVID-19 vaccine effectiveness studies among adolescents

Article Title: Assessing residual bias in real-world vaccine effectiveness studies: validating negative controls to avoid misleading results

Article References: Zhang, B., Wu, Q., Zhou, T., Zhang, D., Tong, J., Lei, Y., Chu, H., Wang, L., Ostropolets, A., Qiu, Y., Lu, Y., Ryan, P. B., Morris, J. S., Schuemie, M. J., Forrest, C. B., Hripcsak, G., & Chen, Y. (2026). Assessing residual bias in real-world vaccine effectiveness studies: validating negative controls to avoid misleading results. BMC Medicine. https://doi.org/10.1186/s12916-026-05260-6

Image Credits: AI Generated

DOI: 10.1186/s12916-026-05260-6

Keywords: vaccine effectiveness, negative controls, residual bias, target trial emulation, electronic health records, PEDSnet, empirical calibration, SARS-CoV-2, COVID-19 vaccination, observational studies, confounding, adolescents