Explaining Neighbourhood Disparities in Cardiovascular Disease in Hamilton, Ontario, Canada

Abstract

Introduction: In 2010, Hamilton’s local newspaper published a series of articles highlighting the inequities in numerous health outcomes across the city, with cardiovascular disease (CVD) Emergency Room (ER) visits among them (DeLuca et al., 2012). In Canada CVD is the second leading cause of death and the leading cause of hospitalizations, however, previous research has demonstrated that CVD risk may vary geographically (Chu et al., 2019). Furthermore, health analyses are rarely conducted in ways accessible to non-scientific communities. Given inequities in the distribution of COVID-19 across Hamilton communities, the need for population level analysis of comorbidities and risk factors is of heightened importance. The aim of this study is to identify the underlying factors that explain neighbourhood differences in the rate of CVD across Hamilton, Ontario. Methods: Census Tract (CT) aggregated Ontario Marginalization (ON-Marg) values and health data from Cancer Care Ontario were used. The material deprivation dimension of ON-Marg includes information on income, employment, education, and lone parent households, which have previously been linked to increased CVD risk, while the dependency dimension represents those who do not have income from employment which captures older age and disability (Matheson & van Ingen, 2018). Factor analysis was performed to identify underlying factors that account for common variance. Spatial associations were analyzed using choropleth maps as well as measures of both global spatial autocorrelation (i.e., global Moran’s I) and local indicators of association (i.e., local Moran’s I). Contiguity was based on rook-weights (sharing a common boundary). Exploratory ordinary least squares (OLS) regression was performed to understand which indicators may explain geographic variation in CVD ER visits. Linear regression assumptions were assessed by testing the residuals for heteroskedasticity using the Koenker test, spatial autocorrelation using Global Moran’s I, and normality using the Kolmogorov-Smirnov test. Results: Initial analysis from 2016 and 2017 revealed that the rate of CVD ER visits in Hamilton is spatially autocorrelated (global Moran’s I score of 0.516 (p<0.001) and ranged from less than 3 in 1000 to over 30 in 1000 people per year. For regression analysis, factor scores for material deprivation and dependency domains of the well validated ON-Marg Index were used together with the percentage of patients with no family physician. OLS regression using the four regressors resulted in a statistically significant model (F=65.94, p<0.001) that explains about 65% of the variability in CVD ER visits in Hamilton (R2 = 0.653). Residuals were tested for heteroscedasticity (Koenker = 4.33, p=0.363), autocorrelation (global Moran’s I = 0.042, p = 0.367) and normality (Kolmogorov-Smirnov = 0.072, p = 0.079). Discussion: This information can help inform neighbourhood-level public health interventions and broader policy decisions to help address local CVD disparities. Patterns of high CVD and poorer socioeconomic conditions also correspond with COVID-19 disparities and support the need for greater neighbourhood-level research of both infectious and chronic conditions. Furthermore, geographic outputs of this work also provide visual and interactive neighbourhood-level health information accessible to non-scientific audiences that may support community-centred health and social action.