Models Estimating the Effects of Changes in the Concentration of Specific Climatic Pollutants and Temperature on the Prevalence of Acute Ophthalmic Inflammatory Conditions: A Retrospective Cross-Sectional Observational Study.

BACKGROUND: Air pollution is a major environmental health risk linked to systemic and ocular diseases, including conjunctivitis, keratitis, and dry eye disease. Pollutants such as particulate matter, nitrogen oxides, and diesel exhaust particles have been associated with an increased prevalence of acute ophthalmic inflammatory conditions (AOICs). Identifying key pollutants may help reduce the prevalence of AOICs. AIM: The aim of this study was to investigate the relationship between atmospheric pollution and AOICs by identifying pollutants with the most consistent impact on AOIC prevalence, developing predictive models to estimate their composite effect, and assessing model accuracy. METHODS: This retrospective study analyzed cases of AOICs from the Eye Emergency Department in Katowice (2011-2019). Diagnoses were classified into subgroups using ICD-10 codes and ophthalmologic evaluation. Annual prevalence was correlated with average pollutant levels and temperature via linear and multiple regression. Significant associations informed predictive models, assessed for accuracy using limits of agreement (±1.96 × SD), with p < 0.05 as the significance threshold. RESULTS: Between 2011 and 2019, over 60,000 cases of AOICs were identified among nearly 95,000 emergency visits. The most frequent diagnoses included conjunctivitis (46.8%), keratitis (26.4%), and blepharitis (14.1%). Multiple regression analyses revealed that the prevalence of several AOIC subtypes was significantly associated with mean annual concentrations of specific pollutants and/or temperature. CONCLUSION: Formaldehyde, CO, SO2, PM2.5, and PM10 were significantly linked to inflammatory ocular conditions, with stronger associations after excluding noninflammatory cases and conjunctivitis. These findings highlight the role of environmental monitoring in predicting or reducing serious eye inflammation, supporting further large-scale research.