Natural experiment on neuroinflammatory disease incidence and infection links pre- and post-COVID-19

Abstract

Infections are recognized triggers for several neuroinflammatory disorders. The coronavirus disease 2019 (COVID-19) pandemic's non-pharmaceutical interventions sharply curtailed pathogen exposure, creating a natural experiment to test infection-disease links. Using Japan's National Claims Database, we first validated the nationwide decline with two strictly infection-dependent conditions-epidemic keratoconjunctivitis and influenza-associated encephalopathy-whose monthly incidences fell by >60% after April 2020. Next, we applied an interrupted time series design, a causal-inference method for longitudinal data, to nine immune-mediated inflammatory diseases. Unsupervised clustering of model-derived level and slope changes identified three data-driven clusters. The first cluster, comprising Guillain-Barré syndrome and acute disseminated encephalomyelitis, showed large, statistically significant level reductions (P < 0.001), particularly in women, consistent with infection-susceptible pathophysiology. The second cluster, including myasthenia gravis and optic neuritis, exhibited transient declines followed by significant positive post-intervention slopes (P < 0.001), suggesting deferred diagnosis, treatment interruption or immune rebound. The third cluster, consisting of sarcoidosis, neuromyelitis optica, multiple sclerosis, Vogt-Koyanagi-Harada disease and Behçet's disease, remained stable, suggesting limited or complex infectious links. These data-driven trajectories mirror clinical pathophysiology and demonstrate that reduced pathogen exposure affects neuroinflammatory disease onset to varying degrees. This framework supports infection-related risk stratification, preventive strategies and continuity planning in neuroimmunology practice.

Keywords: COVID-19; incidence; infection; interrupted time-series analysis; natural experiment; neuroinflammatory disease.