Enhancing Healthcare Access in Saskatchewan Using GIS
A spatial analysis approach to measure healthcare facilitates access and connection of vulnerable communities to essential health services.
Canadian Health Facilities Waiting Time
Health equity is widely recognized as one of the most pressing challenges in Canadian public health, largely due to ineffective geographic access to healthcare, particularly in sparsely populated regions. Convenient access to hospitals, clinics, pharmacies, and specialized healthcare providers has been consistently associated with improved health outcomes and enhanced well-being among the population. However, generating a report or visual presentation of current healthcare facilities' efficiency spatially is a difficult task for health professionals and health policymakers.
Here's how GIS can be utilized to measure access to healthcare in numerous ways. This study will employ ArcGIS tools to gauge network adequacy, thereby offering a more detailed and insightful understanding of healthcare accessibility in Saskatchewan. The objective is to address the challenge of enhancing healthcare accessibility for vulnerable populations in Saskatchewan through spatial analysis. This approach entails assessing various factors, including distance, drive time, and demographic distribution. These assessments will facilitate the formulation of strategic recommendations aimed at optimizing healthcare facility placement and enhancing overall accessibility throughout the province.
Health Facilities in Saskatchewan
Spatial Analysis methods used
Methodology encompasses hotspot analysis, drive time, drive distance features, OD matrix features, and spatial statistics. These techniques facilitate the identification of areas with high concentrations of vulnerable populations and the assessment of travel burdens to healthcare facilities.
Vulnerable Population Calculation
The vulnerable population is calculated based on age bands: 65 and above for senior citizens, and below 5 for children. Instead of calculating solely based on the total vulnerable population, which may disproportionately represent densely populated areas, the vulnerable population rate is determined by dividing the total vulnerable population by the total population of each location. This approach ensures a more accurate representation of hotspot distribution across various areas based on their population density.
Hotspot analysis
Below is a map illustrating hotspot and cold spot areas of vulnerable populations. Red dots indicate regions with a high concentration of vulnerable populations, followed by orange, yellow, and green dots representing decreasing levels of vulnerability, respectively. The green dots denote areas with fewer vulnerable populations, identified as cold spots.
It is evident from this analysis that the eastern region of Saskatchewan has a higher concentration of vulnerable population. is one among those hotspot regions. The Hotspot spatial analysis tool was employed to calculate these hotspot regions.
Utilizing drive distance from hotspots to healthcare facilities
The map provided below represents the distances from hotspot regions to healthcare facilities. The pink lines indicate the drive distances, showing the routes that residents of each area take to access their closest services.
Drive time service area analysis
Next, drive-time distances were applied to create service areas for each healthcare facility. The light green, yellow and pink shaded areas represent service areas around the hospitals.
Drive distance service area analysis
The map below illustrates the travel burden between residential areas across Saskatchewan and healthcare facilities. The green lines depict the drive distance from residential areas to the nearest health facilities. Light green indicates the shortest distance, while darker shades of green represent longer routes.
Implications and Future Directions
This type of analysis can assist health officials in enhancing public health network adequacy by evaluating potential new facility locations to identify which would provide the greatest benefit to their communities. Furthermore, in the event of medical emergencies, facilities can be strategically installed or distributed most efficiently, including the deployment of mobile health units, the establishment of care homes, and seasonal campaigns such as those aimed at addressing heat waves.
Since demographics are dynamic and vulnerable populations tend to change, the calculation of the vulnerable population rate has been automated using the provided Python code. Additionally, the automation of vulnerable population rate calculation streamlines data processing, ensuring ongoing relevance and adaptability.
import arcpy
import math
layer_name = r"D:\Saskpopulation\Saskpopulation\Default.gdb\PopulatedCentre_Project_Final_1"
try:
# Start an edit session
edit = arcpy.da.Editor(arcpy.env.workspace)
edit.startEditing(False, True)
# Start an edit operation
edit.startOperation()
field_types = {field.name: field.type for field in arcpy.ListFields(layer_name)}
expected_field_types = {"Vulnerable_Population": ["Integer", "Double"], "FiveYearPopulationTotals_ECYPTAPOP": ["Integer", "Double"], "Vulnerable_Population_Rate": ["Double"]}
for field_name, expected_types in expected_field_types.items():
if field_name not in field_types:
raise ValueError(f"Field '{field_name}' does not exist in the layer.")
field_type = field_types[field_name]
if field_type not in expected_types:
raise ValueError(f"Field '{field_name}' has an unexpected data type: {field_type}. Expected: {', '.join(expected_types)}.")
# Calculate the vulnerable population rate using field calculations with error handling
expression = "0 if !Vulnerable_Population! is None or !FiveYearPopulationTotals_ECYPTAPOP! is None or !FiveYearPopulationTotals_ECYPTAPOP! == 0 else math.ceil((!Vulnerable_Population! / !FiveYearPopulationTotals_ECYPTAPOP!) * 100)"
arcpy.CalculateField_management(r"D:\Saskpopulation\Saskpopulation\Default.gdb\PopulatedCentre_Project_Final_1", "Vulnerable_Population_Rate", expression, "PYTHON3")
# Stop the edit operation
edit.stopOperation()
# Stop the edit session and save changes
edit.stopEditing(True)
print("Calculation completed successfully.")
except arcpy.ExecuteError:
print("An arcpy error occurred. Check the ArcGIS logs for details.")
except Exception as e:
print(f"An error occurred: {str(e)}")
if edit.isEditing:
edit.stopOperation()
edit.stopEditing(False)Webapp Link
To facilitate an interactive map experience, a web app has been included for this purpose.
ArcGIS Web Application
In conclusion, the integration of GIS into healthcare access assessment unveils novel opportunities for targeted interventions and equitable healthcare delivery in Saskatchewan.
acknowledgement
Saskatchewan Polytechnic Supervisor: Ryan Galbraith , M.Sc., GISP
Saskatchewan Polytechnic Instructor: Leila Benmerrouche , B.A Tech.
