The geographic distribution of un-immunized children in Ontario, Canada: Hotspot detection using Bayesian spatial analysis

Abstract

BACKGROUND: In Ontario, Canada, little is currently known about the extent to which un-immunized children may cluster geographically. Our objectives were to: describe the geographic distribution of fully un-immunized children; identify geographic clusters (hotspots) of un-immunized children; and to characterize the contribution of spatial effects and covariates on hotspots, where found. METHODS: Our analytic cohort consisted of Ontario students aged 7-17 years in the 2016-2017 school year. We defined students as un-immunized if they had zero doses of any vaccine and a non-medical exemption recorded in Ontario's registry. We calculated unadjusted proportions of un-immunized students by Census Subdivision (CSD) and then used a sequential approach to identify hotspots starting first with hotspot identification at the CSD level and then probed identified hotspots further by Dissemination Area (DA) and including covariates. Hotspots were identified using the Besag-York-Mollie Bayesian spatial model and were defined as areas with >95% probability of having two times the proportion of un-immunized students, relative to the province overall. RESULTS: We identified 15,208 (0.94%) un-immunized children within our cohort consisting of more than 1.61 million students. Unadjusted proportions of un-immunized students varied greatly by geography, ranging from 0% to 21.5% by CSD. We identified 16 hotspot CSDs which clustered in five distinct areas, all of which were located in southern Ontario. The contribution of covariates and spatial effects on the risk of having un-immunized students varied greatly across hotspot areas. CONCLUSIONS: Although the provincial proportion (0.94%) of un-immunized students is small, geographical clustering of such students is evident in Ontario and in some areas presents an important risk for future outbreaks. Further qualitative work within these hotspot areas would be a helpful next step to better characterize the factors associated with vaccine refusal in these communities.

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