Planning for community well-being: Prioritizing and identifying local neighbourhood attributes of belonging.
Objectives Local neighbourhoods have great potential to foster community belonging which can improve health and well-being. In partnership with the City of Toronto, we prioritize and assess the importance of physical and social environmental attributes to community belonging. Using new data linkage...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Swansea University
2022-08-01
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| Series: | International Journal of Population Data Science |
| Subjects: | |
| Online Access: | https://ijpds.org/article/view/2060 |
| _version_ | 1797427142198296576 |
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| author | Sarah Mah Lori Diemert Scott McKean Sarah Collier Laura Rosella |
| author_facet | Sarah Mah Lori Diemert Scott McKean Sarah Collier Laura Rosella |
| author_sort | Sarah Mah |
| collection | DOAJ |
| description |
Objectives
Local neighbourhoods have great potential to foster community belonging which can improve health and well-being. In partnership with the City of Toronto, we prioritize and assess the importance of physical and social environmental attributes to community belonging. Using new data linkages, we contribute to Toronto’s community safety and well-being plan, SafeTO.
Approach
We leverage an individual-level record linkage of respondents from multiple cycles of the Canadian Community Health Survey (CCHS, 2000 to 2017), the Canadian Vital Statistics Death Database (CVSD), and the Discharge Abstract Database (DAD) from the Centre for Population Health Data at Statistics Canada. Environmental data sources include the Census, administrative data, and open-source data. Using postal code information from the CCHS, we connected each respondent with their neighbourhood’s attributes, which include but are not limited to proximity to amenities (such as childcare, libraries and public transit), the Canadian Active Living Environment measure, green space, and air quality.
Results
Of the 74,000 CCHS respondents from the Toronto census metropolitan area (representing a population of 4 to 5 million annually), an overall rate of 86% agreed to link and share their data, which renders an estimated linked sample of approximately 63,600 respondents. Across the entire linkage, 54% of respondents are linked to a record in the DAD between 1999/00 and 2017/18, and 10% are linked to a death record in the CVSD between 2000 and 2017. In consultation with municipal agencies and community stakeholders, we will prioritize the environmental attributes that are most relevant to community belonging and well-being, and comprehensively model the relationship between these attributes, community belonging and downstream health outcomes using multivariable regression and time-to-event models.
Conclusion
This on-going work contributes to the development of innovative approaches for using multi-sector data to inform decision making, as per the goals of SafeTO. Results from the analyses will be used to identify environmental attributes potentially important for community belonging and will support municipal city planning and resource allocation.
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| first_indexed | 2024-03-09T08:40:38Z |
| format | Article |
| id | doaj.art-2b5fcd8153bc43719e360dc0910beec6 |
| institution | Directory Open Access Journal |
| issn | 2399-4908 |
| language | English |
| last_indexed | 2024-03-09T08:40:38Z |
| publishDate | 2022-08-01 |
| publisher | Swansea University |
| record_format | Article |
| series | International Journal of Population Data Science |
| spelling | doaj.art-2b5fcd8153bc43719e360dc0910beec62023-12-02T17:02:08ZengSwansea UniversityInternational Journal of Population Data Science2399-49082022-08-017310.23889/ijpds.v7i3.2060Planning for community well-being: Prioritizing and identifying local neighbourhood attributes of belonging.Sarah Mah0Lori Diemert1Scott McKean2Sarah Collier3Laura Rosella4Dalla Lana of Public HealthDalla Lana of Public HealthCity of TorontoToronto Public HealthDalla Lana School of Public Health Objectives Local neighbourhoods have great potential to foster community belonging which can improve health and well-being. In partnership with the City of Toronto, we prioritize and assess the importance of physical and social environmental attributes to community belonging. Using new data linkages, we contribute to Toronto’s community safety and well-being plan, SafeTO. Approach We leverage an individual-level record linkage of respondents from multiple cycles of the Canadian Community Health Survey (CCHS, 2000 to 2017), the Canadian Vital Statistics Death Database (CVSD), and the Discharge Abstract Database (DAD) from the Centre for Population Health Data at Statistics Canada. Environmental data sources include the Census, administrative data, and open-source data. Using postal code information from the CCHS, we connected each respondent with their neighbourhood’s attributes, which include but are not limited to proximity to amenities (such as childcare, libraries and public transit), the Canadian Active Living Environment measure, green space, and air quality. Results Of the 74,000 CCHS respondents from the Toronto census metropolitan area (representing a population of 4 to 5 million annually), an overall rate of 86% agreed to link and share their data, which renders an estimated linked sample of approximately 63,600 respondents. Across the entire linkage, 54% of respondents are linked to a record in the DAD between 1999/00 and 2017/18, and 10% are linked to a death record in the CVSD between 2000 and 2017. In consultation with municipal agencies and community stakeholders, we will prioritize the environmental attributes that are most relevant to community belonging and well-being, and comprehensively model the relationship between these attributes, community belonging and downstream health outcomes using multivariable regression and time-to-event models. Conclusion This on-going work contributes to the development of innovative approaches for using multi-sector data to inform decision making, as per the goals of SafeTO. Results from the analyses will be used to identify environmental attributes potentially important for community belonging and will support municipal city planning and resource allocation. https://ijpds.org/article/view/2060multi-sector data linkagecommunity belongingpopulation healthcommunity well-beingneighbourhood health |
| spellingShingle | Sarah Mah Lori Diemert Scott McKean Sarah Collier Laura Rosella Planning for community well-being: Prioritizing and identifying local neighbourhood attributes of belonging. International Journal of Population Data Science multi-sector data linkage community belonging population health community well-being neighbourhood health |
| title | Planning for community well-being: Prioritizing and identifying local neighbourhood attributes of belonging. |
| title_full | Planning for community well-being: Prioritizing and identifying local neighbourhood attributes of belonging. |
| title_fullStr | Planning for community well-being: Prioritizing and identifying local neighbourhood attributes of belonging. |
| title_full_unstemmed | Planning for community well-being: Prioritizing and identifying local neighbourhood attributes of belonging. |
| title_short | Planning for community well-being: Prioritizing and identifying local neighbourhood attributes of belonging. |
| title_sort | planning for community well being prioritizing and identifying local neighbourhood attributes of belonging |
| topic | multi-sector data linkage community belonging population health community well-being neighbourhood health |
| url | https://ijpds.org/article/view/2060 |
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