Statistics Canada’s modelling of life expectancy and healthy life expectancy using 2019 and 2020 figures shows wide variations between municipalities. But the 14-year gap between the highest- and lowest-ranking municipalities does not predict what would happen to an individual who moved from one municipality to another.
Our MyChoice team reviewed Statistics Canada’s municipal life-expectancy and health-adjusted life-expectancy study, as well as the Financial Consumer Agency of Canada’s guide to factors that affect insurance needs and cost, to see what the life-expectancy gaps actually look like.
Can Where You Live Shorten Your Life by 14 Years?
Technically, the study estimated a municipal gap of up to 13.9 years. However, it does not mean that moving from one municipality to another will change an individual’s life expectancy by that amount.
Other factors like health, income level, education, employment, housing, behaviours, age, and access to health care can impact your life expectancy in a major way.
How Statistics Canada Estimates Municipal Life Expectancy
Life expectancy (LE) is the number of years that a newborn could be expected to live. Health-adjusted life expectancy (HALE) is the number of years that a newborn could be expected to live in good health.
The share of life in good health is the number of healthy years expressed as a percentage of expected years of life (HALE/LE). HALE accounts for both mortality and quality of life, or health.
These indicators do not predict a person’s life expectancy, and the study was not designed to create individual insurance-risk scores. Similarly, HALE does not predict when someone will be affected by a chronic condition, become ill or stop working. These tools model LE and HALE at the municipal level.
Where Major Canadian Cities Sit on the Life Expectancy Curve
| Municipality | Women LE | Men LE | Women HALE | Men HALE |
|---|---|---|---|---|
| Saint John | 81.0 | 75.8 | 65.5 | 62.9 |
| Winnipeg | 83.2 | 78.5 | 69.5 | 67.2 |
| Edmonton | 84.2 | 79.3 | 70.0 | 67.4 |
| Montréal | 84.8 | 80.5 | 74.1 | 71.8 |
| Toronto | 86.8 | 81.5 | 73.7 | 70.8 |
Statistics Canada says the median among the included census subdivisions is 84.1 for women and 79.6 for men. Edmonton and Hamilton are examples of cities close to the study’s municipal median for women, while Gatineau and Kitchener are close to the median for men.
This table isn’t a best-to-worst ranking. It shows selected examples from the study.
Read More: Read How Rising Life Expectancy Can Affect Life Insurance
Education Had the Strongest Correlation Among the Four Characteristics Studied
The values in the following tables are Kendall rank correlations. Positive values indicate that municipalities that rank higher for the characteristic also tend to rank higher for the health outcome. Negative values indicate that municipal rankings go in opposite directions. Values closer to zero indicate a weaker association.
Suppose a characteristic had a Kendall rank correlation with life expectancy of 0.24. That number doesn’t mean that an increase in the characteristic leads to an increase of 0.24 years of life expectancy. It indicates that communities ranked higher for the characteristic also tend to rank higher for life expectancy, in this case across all 1,227 communities in the study.
Correlation With Life Expectancy at Birth
| Municipal characteristic | Women | Men |
|---|---|---|
| % with postsecondary education | 0.24 | 0.27 |
| Population size | 0.12 | 0.12 |
| Average after-tax income | 0.11 | 0.11 |
| Population density | −0.04 | −0.04 |
Of the four characteristics compared, postsecondary education has the strongest positive correlation with life expectancy. However, the table doesn’t indicate how much any one factor might affect lifespan, nor does it control for other influences.
Correlation With Healthy Life Expectancy at Birth
| Municipal characteristic | Women | Men |
|---|---|---|
| % with postsecondary education | 0.35 | 0.36 |
| Population size | 0.10 | 0.11 |
| Income | 0.03 | 0.04 |
| Population density | 0.01 | 0.01 |
While education correlates more strongly with HALE than LE, income and density are close to zero. We cannot infer from this table that income and density do not affect health in all circumstances.
The Estimated Gaps Were Larger for Men
The estimated 5th-to-95th-percentile LE and HALE gaps were larger for men: 13.9 years for LE and 13.9 years for HALE, compared with 12.5 years for women’s LE and 13.0 years for women’s HALE.
These estimates do not explain why the gap was larger for men. They only show the spread of modelled LE and HALE estimates by municipality.
What the Findings Do and Do Not Mean for Life Insurance
LE and HALE estimates for your municipality won’t tell you whether you’re eligible for life insurance or how much it will cost. Depending on the product and applicable law, insurers consider other factors such as your age, health history, tobacco use and the amount of coverage sought when deciding whether to provide coverage and how much it will cost.
The study could prompt you to review your household’s financial situation, which can help you make life insurance choices. Think about:
- how much income or caregiving others would lose if you died;
- how long savings and workplace benefits would provide support during illness or disability;
- debts and other costs that would be left behind, including education costs and final tax liabilities; and
- the insurance you already have and when it expires.
Read More: See How Life Insurance Coverage Falls Short Across Canada