Geographical distribution and the impact of socio-environmental indicators on incidence of Mpox in Ontario, Canada - Chigozie Louisa J. Ugwu; Ali Asgary; Jianhong Wu; Jude Dzevela Kong; Nicola Luigi Bragazzi; James Orbinski; Woldegebriel Assefa Woldegerima (PLoS ONE, 2025)
Background Ontario, being one of Canada’s largest provinces, has been central to the high incidence of human Mpox. Research is scarce on how socio-environmental factors influence Mpox incidences. This study seeks to explore potential geographical correlations and the relationship between indicators of social marginalization and Mpox incidence rate in Ontario. Methodology We used surveillance data on confirmed human Mpox cases from May 1, 2022, to March 31, 2024, extracted from the Public Health Ontario website for this study. Spatial autocorrelation of Mpox incidence was investigated using spatial methods including Moran’s Index, Getis–Ord Gi*statistic, and spatial Poisson scan statistic. Following this, we adopted a generalized Poisson regression (GPR) model to estimate the incidence rate ratios (IRRs) based on the association between Ontario PHU-level marginalization and Mpox incidence, while adjusting for age and sex. The goodness-of-fit of the models was assessed using the Log Likelihood (LL), Akaike Information Criterion (AIC), Akaike’s Information Criterion corrected (AICc), and the Bayesian Information Criterion (BIC). Results Our analysis revealed significant localized spatial heterogeneity in Mpox incidence across Ontario. Statistically significant local clusters of Mpox cases were identified in Toronto ( R R = 11.34 , L L = 511.97 ), Ottawa ( R R = 3.21 , L L = 19.88 ), and a secondary cluster, overlapping Hamilton PHU with nine local districts ( R R = 2.07 , L L = 9.64 ), all with p − v a l u e < 0.05 . The incidence rate of Mpox was statistically significantly associated with a higher proportion of ethnic concentration (racialized groups, migrants, or visible minorities) I R R = 9.478 ; 95 % C I = 5.062 − 17.747 , gender I R R = 5.150 ; 95 % C I = 1.159 − 8.324 and higher residential instability I R R = 14.112 ; 95 % C I = 6.596 − 30.189 . Conclusion We identified major Mpox hotspots in Toronto. According to our model results, the high incidence rate may be influenced by the greater population of internal migrants and younger individuals. Based on these insights, we recommend targeted interventions in the high-risk neighborhoods. Efforts to improve Mpox diagnosis and promote health equity among socioeconomically vulnerable populations, including racial and ethnic minorities, should be implemented.