Developing Health Neighbourhood Catchments for Equitable Health System Planning in Ontario, Canada

Background: In Ontario, Canada, challenges in access to primary care continue to affect patient attachment and equitable health outcomes, including in mixed urban–rural regions. The Frontenac, Lennox and Addington Ontario Health Team (FLA OHT) identified the need for a data-informed, geographically sensitive planning tool to guide local decision-making around primary care attachment and access. Understanding where populations live, their health needs, and where care is provided is central to building more integrated and equitable systems of care.

 

Approach: This work combined geo-spatial and administrative data to describe the FLA population and model local variations in attachment to primary care, including gaps in population health needs and available primary care services.

Data sources included primary care attachment and chronic disease prevalence from INSPIRE-Primary Health Care, sociodemographic indicators from the 2021 Canadian Census (social determinants of health), and the Ontario Marginalization Index. Using Statistics Canada Dissemination Blocks as the foundational geography, an enhanced two-step floating catchment area (2SFCA) method was complimented with spatial autocorrelation to identify and model spatial accessibility to care across the FLA region, and highlight "clusters" where social determinants of health are impacting access and attachment.

 

 

Population counts served as the primary access variable (demand), while drive time and provider availability were used as decay functions to model accessibility. The model incorporated the ratio of primary care providers to residents, accounting for both physician supply and practice locations. Resulting catchments were compared with the results of spatial autocorrelation and reviewed with community partners to ensure contextual validity and alignment with local service delivery realities. This collaborative co-design approach informed the process by which the FLA primary care network collaboratively plans using “Health Neighbourhoods”—data-driven primary care catchments that reflect both population need and real-world availability.

 

 

Results: Geo-spatial modeling produced distinct primary care catchments across the FLA region that highlight variations in accessibility between urban, semi-urban, and rural areas. These “Health Neighbourhoods” revealed differences in unattached patient rates, provider density, and chronic condition prevalence.

Catchment boundaries were intentionally non-contiguous to account for overlapping service areas and cross-boundary care seeking amongst patients.

Preliminary analysis demonstrates that health equity indicators—such as income, housing stability, and material deprivation—should (and do) impact resulting catchment boundaries where spatial distance alone does not equitably represent neighbourhood-level attachment and access to care. The maps provide decision-makers with actionable insights into where population needs are greatest and where interventions could have the highest impact. The model also establishes a reproducible framework that can be updated as new data become available.

 

Implications: Developing “Health Neighbourhoods” through a co-designed, geo-spatial model demonstrates the feasibility and value of integrating administrative, demographic, and equity data into local health system planning. This approach supports proactive, evidence-based decision-making on primary care attachment and service allocation. Internationally, this model offers a scalable example of how integrated care systems can use spatial analytics to promote equity and optimize resource distribution across diverse geographies. Future work will refine the model through iterative engagement with primary care partners and expand its application to other dimensions of population health planning.