Abstract
Background
Measles is a serious infectious disease that contributes significantly to the burden of disease in many developing countries. In most developed nations, such as Canada, endemic transmission of measles has been declared eliminated thanks to rigorous vaccination programs, but isolated outbreaks of the disease continue to happen. Therefore, a thorough understanding of the factors contributing to these outbreaks is necessary.
Objectives
There were two main objectives of this thesis. The first objective was to assess the geospatial distribution of reported measles cases in Ontario with a goal of identifying clusters of reported measles. For this objective, the main hypothesis was that measles cases would not be randomly distributed across Ontario and instead would cluster in certain regions. The second objective was to explore some of the factors that may be associated with measles clusters. For this objective, the main hypothesis was that the proportion of immigrants, population density, low-income prevalence and education level would be associated with measles clusters.
Methods
The first objective was achieved through a thorough geospatial analysis using SaTScan and R. Individual forward sortation areas were used as the spatial unit of analysis. The analysis leveraged data from multiple sources: 2016 Census data, Ontario measles cases data from iPHIS from 2008 to 2019, a shapefile of all forward sortation areas in Canada from Statistics Canada and centroid coordinates of forward sortation areas that were obtained using web scrapping techniques on the geolocation service of Natural Resources Canada. The maximal window size of the geospatial analysis was chosen using the maximum clustering heterogeneous set-proportion technique. The geospatial analysis was run with 99,999 Monte Carlo repetitions under a Poisson distribution using the purely spatial analysis. The Ontario population from the 2016 Census was used as the population at risk. Any cluster with a p ≤ 0.05 was deemed statistically significant. The second objective was achieved through a case-control study: Forward sortation areas that were within statistically significant measles clusters were considered as cases and the rest of the forward sortation areas were considered as controls. Demographic data necessary to assess the factors of interest were extracted from the 2016 Census. A univariable logistic regression model was run to compute the odds ratio and test the association between the factors of interest and measles clusters. 95% confidence intervals were computed for each odds ratio. Data-curation techniques and data analysis were performed in R 4.0.4.
Results
From 2008 through 2019, 178 measles cases were identified. 82% of cases lacked necessary vaccination or vaccination records against measles, 35% of cases were linked to traveling outside of Ontario, 20% of cases reported being in contact with a known case, and 72% of cases were less than 5 years old or older than 21. Ten measles clusters were identified of which six were deemed statistically significant. These six significant clusters represented 7% of the population at risk but contained nearly 40% of all reported measles cases between 2008 and 2019. Measles clusters had a strong association with the proportion of immigrants living within them, population density and prevalence of low-income. No association was found between education level and measles clusters.
Conclusion
The results indicate that most measles cases in Ontario are unvaccinated or lack proof of vaccination; arise through secondary transmission within the province; arise from undetected transmission; and are adults or infants. Additionally, it is possible to see that the risk of reported measles cases is not randomly distributed across the province, but instead measles cases tend to cluster in certain regions. Such clusters tend to be characterized by specific population-level factors that may be contributing to the risk of reported measles. Targeted and equitable interventions are needed as we continue on the path to eradication.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/43013 |
| Date | 13 December 2021 |
| Creators | Miron-Celis, Marcel |
| Contributors | Smith?, Stacey |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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