Results & Conclusion

Results & Discussion

Objective 1: Develop Vulnerability Indices Using Pre-Determined Dissemination Drea-Level Factors of Exposures, Sensitivity, and Adaptive Capacity.

PART 1 - Factor Analysis on Exposure, Sensitivity and Adaptive Capacity Indicators

1. Exposure indicators

Scores of factor 1 and factor 2 of each DA were added up to generate the exposure dimension. Figure 13 shows the map for exposure dimension in Edmonton. Categories are the result of the quantile classification method on the factor scores.

Figure 13. Map showing the exposure dimension in Edmonton. Light red indicated a low level of exposure. Medium red indicated a medium level of exposure. Dark red indicated a high level of exposure. Blank areas are the ones without data.

2. Sensitivity indicators

Scores of factor 1 and factor 2 of each DA were added up to generate the sensitivity dimension. Figure 14 shows the map for the sensitivity dimension in Edmonton. Categories are the result of the quantile classification method on the factor scores.

Figure 14. Map showing the sensitivity dimension in Edmonton. Light red indicated a low level of sensitivity. Medium red indicated a medium level of sensitivity. Dark red indicated a high level of sensitivity. Blank areas are the ones without data.

3. Adaptive capacity indicators

Scores of factor 1 and factor 2 of each DA were added up to generate the adaptive capacity dimension. Figure 15 shows the map for the adaptive capacity dimension in Edmonton. Categories are the result of the quantile classification method on the factor scores.

Figure 15. Map showing the adaptive capacity dimension in Edmonton. Light red indicated a high level of sensitivity. Medium red indicated a medium level of sensitivity. Dark red indicated a low level of sensitivity. Blank areas are the ones without data.

PART 2 - Generateing Vulnerability Index

The vulnerability index was generated using exposure dimension (added up scores from factors 1 and 2), sensitivity dimension (added up scores from factors 1 and 2), and adaptivity capacity dimension (added up scores from factors 1 and 2), using the below formula:

Vulnerability = (Exposure + Sensitivity) - Adaptive Capacity

Figure 16 shows the map for the overall vulnerability of each DA in Edmonton. Categories are the result of the quantile classification method on the factor scores.

Figure 16. Map showing the vulnerability of each dissemination area in Edmonton. Light green indicated a low level of vulnerability. Medium green indicated a medium level of vulnerability. Dark green indicated a high level of vulnerability. Blank areas are the ones without data.

Objective 2: To Assess if the Vulnerable Index is a Significant Predictor of Health Events Incidence.

Negative binomial regression was performed to explore the relationship between the generated vulnerability index from objective one and each health outcome (respiratory diseases, cardiovascular diseases, mental health issues and injury events).
Results showed that the generated vulnerability index was significantly associated with a higher incidence of all health outcomes, indicating that the vulnerability index is a significant predictor of health events (Table 10).
- For each unit increase in the vulnerability index, the area has 1.19 times more incidence of respiratory disease events, 1,06 times more incidence of cardiovascular disease events, 1.12 times more incidence of mental health events, and 1.13 times more incidence of injury events.

Table 10. Models presenting the association between the generated vulnerbility index and each health events

Conclusion

In conclusion, this study mapped the levels of exposure, sensitivity, and adaptive capacity dimensions in Edmonton. The vulnerability index of each dissemination area in Edmonton was also generated and mapped. This study further found that the generated vulnerability index was significantly associated with incidences of different health events, including respiratory diseases, cardiovascular diseases, mental health issues and injuries.

Evidence from this study will help to inform a wide variety of climate change and healthy aging initiatives (e.g., urban planning, infrastructure, household readiness, emergency services, transportation) to protect the well-being of Edmontonians. Fundamentally, the findings and maps from this study can contribute to the development of a municipal knowledge base that assists in the prioritization and development of local initiatives aimed at reducing climate change vulnerability in Edmonton.

This study also has potential limitations. The unit of analysis for this study was the Dissemination Area (DA), which is the smallest geographical unit for which Canadian census data is available and, on average, represents 400-700 persons. Edmonton, Alberta, has a total of 1,193 DAs; however, analyses in this study were limited to only 901 (76%) DAs due to a lack of people residing in these areas and missing data from independent variables. Furthermore, the indicators used in this study were already aggregated to the DA level, or derived by using proximity to the centroid of each DA. Therefore, these indicators may not represent the actual measures for a given individual residence (ecological fallacy).

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