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Socio-economic and demographic variables have been identified as determinants of transmission of, and susceptibility to, COVID-19. In this study, we analyse the heterogeneous impacts of non-pharmaceutical interventions on the socio-economic and demographic (SED) variables driving COVID-19 incidence in Toronto during the 2nd, 3rd and 4th waves of the pandemic. Spatial autoregressive models were used to explore associations between COVID-19 incidence and SED variables at neighborhood scale, accounting for vaccination levels. This approach helps clarify how SED factors and vaccine coverage drive COVID-19 incidence and how non-pharmaceutical interventions (NPIs) modulate these factors at neighborhoods' level, while taking into account the pervious nature of boundaries at these scales to disease transmission due to population mobility, although without directly informing on behaviours, exposure, or vulnerability at individual level. Three distinct models were considered for each of the second, third and fourth COVID-19 waves, from late 2020 to late 2021. Associations highlighted by the models were interpreted with reference to the NPIs implemented. Level of scholarity, income, proportion of the population living alone, average number of children in families, and the proportion of the population whose mother tongue is not an official language showed significant relationships with COVID-19 incidence. Model results were different for each wave, reflecting the unequal impacts of NPIs at different time points, and for different population groups, depending on the nature of interventions and the SED determinants considered. Prioritization of population groups for testing, unequal gathering restrictions, selective closure of economic activities or work-from-home policies led to heterogenous impacts on incidence. The results highlight the unequal burden of the pandemic across populations and likely disparities in occupational exposure driven by SED factors, as well as their evolution with the implementation and lifting of NPIs. Populations with the lowest income and scholarity cumulate the highest risks of exposure and the highest risks of severe disease outcomes. Our results support the development of knowledge based public health surveillance programs integrating both non-communicable and infectious diseases cases, beyond their acute occurrences, along with their socio-economic characteristics.