Cardiovascular diseases (CVDs) are the leading cause of death and disability around the world. The purpose of this thesis is to investigate the impact of socio-environmental determinants of CVDs at the neighbourhood scale in order to inform actionable interventions, which may lead to large-scale reductions in preventable CVDs. Drawing on 2411 surveys carried out in Toronto, Canada, this thesis employs multilevel models to estimate the magnitude of socio-environmental influences on the risk of CVD while adjusting for individual-level risk factors. To advance current research methodology, strategies and innovations were developed to 1) improve the characterization of neighbourhoods by empirically testing a full range of socio-environmental influences; 2) account for non-residential exposures by including a combined analysis of work and home contexts; 3) account for variations in the duration of exposure through the use of time-weighted models; 4) deal with problem of spatial data aggregation by developing and testing a novel method of neighbourhood zone design, and 5) account for the spatial scales of different socio-environmental determinants by modeling at multiple scales. The thesis demonstrated that land use decisions are inextricably public health decisions. It found that living in neighbourhoods with inadequate access to food stores and areas for physical activity, burdened by violent crimes and fast food restaurants, and over-dependent on automobiles (leading to air pollution), with a high level of noise may significantly increase the risk of CVDs, over and above individual-level risks. The thesis also found that working in neighbourhoods that are socio-economically disadvantaged or have high-traffic may significantly increase CVD risk. The thesis developed and demonstrated novel methods to reduce the measurement error of neighbourhood exposures through 1) the use of “amoeba buffers” to improve neighbourhood zone design to better reflect participants’ local neighbourhoods and 2) the use of duration of exposure weights to adjust for individual differences in the time spent across different contexts. Finally, it found that the significance of socio-environmental factors depends on the scale of data aggregation; thus, investigation of multiple scales may be required to identify the relevant scale that matches the specific contextual factor in future research.

Cardiovascular diseases (CVDs) are the leading cause of death and disability around the world. The purpose of this thesis is to investigate the impact of socio-environmental determinants of CVDs at the neighbourhood scale in order to inform actionable interventions, which may lead to large-scale reductions in preventable CVDs. Drawing on 2411 surveys carried out in Toronto, Canada, this thesis employs multilevel models to estimate the magnitude of socio-environmental influences on the risk of CVD while adjusting for individual-level risk factors. To advance current research methodology, strategies and innovations were developed to 1) improve the characterization of neighbourhoods by empirically testing a full range of socio-environmental influences; 2) account for non-residential exposures by including a combined analysis of work and home contexts; 3) account for variations in the duration of exposure through the use of time-weighted models; 4) deal with problem of spatial data aggregation by developing and testing a novel method of neighbourhood zone design, and 5) account for the spatial scales of different socio-environmental determinants by modeling at multiple scales. The thesis demonstrated that land use decisions are inextricably public health decisions. It found that living in neighbourhoods with inadequate access to food stores and areas for physical activity, burdened by violent crimes and fast food restaurants, and over-dependent on automobiles (leading to air pollution), with a high level of noise may significantly increase the risk of CVDs, over and above individual-level risks. The thesis also found that working in neighbourhoods that are socio-economically disadvantaged or have high-traffic may significantly increase CVD risk. The thesis developed and demonstrated novel methods to reduce the measurement error of neighbourhood exposures through 1) the use of “amoeba buffers” to improve neighbourhood zone design to better reflect participants’ local neighbourhoods and 2) the use of duration of exposure weights to adjust for individual differences in the time spent across different contexts. Finally, it found that the significance of socio-environmental factors depends on the scale of data aggregation; thus, investigation of multiple scales may be required to identify the relevant scale that matches the specific contextual factor in future research.

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