Introduction: Pertussis, commonly known as whooping cough, is a bacterial respiratory tract infection caused by Bordetella pertussis. Pertussis affects more than 48 million people worldwide annually, most of whom are under the age of 5.
Hypothesis & Objectives: The hypothesis being investigated is that pertussis incidence, between 2005 and 2016, is not equally distributed across public health units in southern Ontario. We aim to identify disease cluster locations and associate geospatial fluctuations in incidence rates with putative risk factors.
Materials and Methods: Data was sourced from Public Health Ontario on pertussis incidence in southern Ontario for all ages, specifically for each public health unit’s geographical area. A choropleth map was generated using data smoothed by empirical Bayesian estimation in a spatial analysis context. Following the creation of an incidence map for southern Ontario, the spatial scan test was applied to elucidate the existence of any disease clusters at a public health unit level. Moran’s I was used to determine whether there was evidence of any spatial dependence in pertussis incidence. Finally, putative risk factors were assessed in Poisson regression models and spatial Poisson regression models as potential predictor variables.
Results and Discussion: The flexible spatial scan test identified three spatial clusters where incidence rates of pertussis were higher than expected. A spatial Poisson regression model was fit that included predictor variables of socioeconomic status and population density. For every 100 people/km2 increase in population density there was a significant 6% increase in pertussis incidence (p=0.03). Interestingly, vaccination rates were not found to be predictive of pertussis incidence nor did the variable improve the model. This epidemiological study identifies where pertussis incidence is clustered and what variables it is associated with, both of which are valuable for public health purposes and as a reference for future research into pertussis.
Does a Gibbs sampler approach to spatial Poisson regression models outperform a single site MH sampler?
