Ravi Ancil Persad’s research while affiliated with Independent Researcher and other places

Mental illness is a predominant medical condition in Canada. Marginalized groups in the Canadian population such as those with low income, the poorly educated and ethnic minorities are susceptible to mental health disorders. Using mental health-related emergency department visits as an indicator of mental illness cases, we employ a Bayesian spatio-temporal regression model to estimate mental illness risk across the 35 public health units of Ontario, Canada from 2006 to 2017. The association between mental illness and the following marginalization-related factors: material deprivation, residential instability and ethnic concentration is also evaluated. Over the assessed period, the relative risk of mental illness ranged from 0.45 (95% CI: 0.44–0.46) to 3.29 (95% CI: 3.20–3.37). Health units with elevated levels of material deprivation and residential instability were positively associated with increased mental illness risk whilst areas with higher ethnic concentration were linked with lower risk. Findings showed that the temporal trend of risk continuously increased over the 11 year period, with health units in northern Ontario experiencing higher risk compared to southern units. The management of psychiatric disorders presents a significant challenge to the Canadian health-care system. An understanding of the geographic distribution of mental health risk across space and time can be useful for improved policy-making and public health monitoring.

Canada has one of the highest incidence rates of brain cancer in the world. This study investigates the space–time variation of brain cancer risk across Southern Ontario, Canada. A Bayesian spatio-temporal regression model is used to estimate the relative risk of brain cancer in the 12 spatial health units of Southern Ontario over a four-year period (2010–2013). This work also explores the association between brain cancer and two potential risk factors: traumatic head injury (THI) and excess body fat (EBF). Across all areal units from 2010–2013, results show that the relative risk of brain cancer ranged from 0.83 (95% credible interval (CI) 0.74–0.91) to 1.26 (95% CI 1.13–1.41). Over the years, the eastern and western health units had persistently higher risk levels compared to those in the central areas. Results suggest that areas with elevated THI rates and EBF levels were also potentially associated with higher brain cancer relative risk. Findings revealed that the mean temporal trend for cancer risk progression in the region smoothly decreased over time. Overall, 50% of the health units displayed area-specific trends which were higher than the region’s average, thus indicating a slower decrease in cancer rates for these areas in comparison to the mean trend.

This paper investigates the geographic variation of robbery risk in Toronto, Canada. A hierarchical Bayesian modeling approach is used for estimating the relative risk of robberies across Toronto’s 140 neighbourhood districts in 2017. The association between robbery risk and various socio-economic explanatory variables (i.e., business density, education and income levels) are analyzed using a Poisson-based spatial regression model. Markov Chain Monte Carlo model fitting is utilized for the estimation of relative risk and associated regression parameters. Results reveal that elevated levels of robbery risk are predominant in the eastern, north-western and southern neighbourhoods of Toronto whereas, lower risk areas are situated in the central neighbourhoods. Across all neighbourhoods, there was a geographical difference in robbery risk, ranging from 0.17 (95% CI 0.05–0.38) to 4.87 (95% CI 4.22–5.55). Education and income variables had a negative association with robberies at posterior probabilities of 96.9% and 85.5% respectively, whereas business density had a positive association with robberies at a posterior probability of 100%. Hence, neighbourhoods with higher amounts of businesses, lower education levels and lower household incomes tend to have a higher mean amount of robberies in Toronto and thus higher associated risks.