Sociodemographic Characteristics Associated with Higher Numbers of COVID-19 Cases: A Neighbourhood Level Study in Ottawa, Ontario

  •  Josephine Etowa    
  •  Bishwajit Ghose    
  •  Yuji Sano    
  •  Ilene Hyman    
  •  Hindia Mohamoud    
  •  Ikenna Mbagwu    
  •  Charles Dabone    


INTRODUCATION: The complex interplay of the social conditions that one lives in and functions greatly influences the exposure to a certain health condition and the ability to recover from it. Reports on COVID-19 diagnosis, hospitalization and fatality reveal a clear sociodemographic divide with the marginalized communities bearing a disproportionately higher burden of the outcomes. The objectives of this study was to explore the neighbourhoods characteristics associated with higher COVID-19 rates in Ottawa by employing a social determinants of health framework that identifies critical intersections that impact of multi-level determinants of health.

METHODOLOGY: This study is based on data on 98 neighbourhoods in Ottawa that were collected from the Ottawa Neighbourhood study (ONS). The outcome variable was COVID-19 rate between March 09, 2020 - January 31, 2021. The independent variables were potentially vulnerable groups, Socioeconomic, neighbourhood, and demographic factors. We used descriptive and logistic regression methods to analyse the data.

RESULTS: The neighbourhoods that had relatively high number of rates COVID-19 cases were Hunt Club Park (909), Manor Park (831), Greenboro East (739), Overbrook McArthur (641), and Hunt Club Upper-Blossom Park (619); and the ones with lowest rates were Laurentian (44), Old Ottawa South (31), Richmond (22), Riverside South Leitrim (19), Wateridge Village (5), and Westboro (3). Multivariate regression analysis showed that neighborhood characteristics such as percentage of community health center within a 50m walk, higher population density, higher percentage of people taking public transportation to work, higher percentage of people with no high school diploma, perceived walkability score, low income prevalence, higher percentage of unsuitable housing,  households with multiple family,  refugee population, new comer during 2011-16, single parent family, and higher percentage of living alone were associated higher odds of above average COVID-19 rates.

CONCLUSION: These findings reflect a greater likelihood of COVID-19 infection in the neighbourhoods with poorer socioeconomic and living conditions such as low income, unhealthy housing, and higher population density. Addressing the challenges among the disadvantaged communities may help reduce the higher vulnerability and promote health conditions.

This work is licensed under a Creative Commons Attribution 4.0 License.