Today, we’re gleaning life expectancy information and insights for some selected countries. We break it up by gender and try to analyze the underlying meaning behind the numbers. These countries are mostly selected due to the selection made for my earlier post about Global Health and Safety; then I added a few more and removed some from that list to keep the dataset and chart within a palatable size. For the Global Health and Safety Stats blog post, see the link at the bottom of this post. Let’s take a closer look at life expectancy around the world!
Life expectancy in selected countries
Columns: Overall shows the combined average of male and female longevity in years. F-M (years) shows the longevity gap between female and male in years. F-M (%) shows the longevity gap between female and male in percentage (e.g. 8.0 means the females live 8% longer than males in that country overall). Rank (Overall) shows the ranking of a country’s overall longevity (both sexes) in the list of selected country (e.g. 1.0 means, it’s ranked #1 or has the highest overall longevity). Table is sorted in descending order by Overall life expectancy.
Facts and Observations
Practically, we already know that women in general live longer than men. Across the board in this list we see women live longer than men at varying degrees by countries.
The fact is, there isn’t any country where males have a higher overall life expectancy than females.
This is due to the fact that women generally have biological advantages that contribute to longer life expectancy, such as lower risk of heart disease and certain cancers.
Factors that affect the men’s longevity other than biological include: social/cultural, economic, and healthcare factors; and combination thereof.
For example: Healthcare Access: Countries with better healthcare systems and more accessible medical care tend to have smaller gender gaps. In countries with limited healthcare, the gap can be larger due to higher male mortality from preventable diseases.
Lifestyle and Behavior: Differences in lifestyle choices, such as smoking, alcohol consumption, and
occupational hazards, can affect life expectancy. Men often engage in riskier behaviors, leading to higher mortality rates.
Cultural Norms: Cultural attitudes towards gender roles and health can influence life expectancy.
In some cultures, men may face more pressure to engage in high risk activities or may be less likely to seek medical help.
Social and Economic Factors: Economic stability, education, and social support systems play a crucial role. In countries with higher economic inequality and social stress, the gender gap can be more pronounced.
Life expectancy chart in selected countries (both genders):
Click on the chart below to see larger sized chart. A popup window will open that can be further resized as desired. Darker blue regions on the map indicate longer life expectancy while lighter blue regions indicate lower life expectancy.
Analysis
Hong Kong and Japan’s high life expectancies are attributed to several key factors:
Healthy diet: Their traditional diet, rich in vegetables, fish, and fermented foods, is low in saturated fats and high in nutrients.
Active Lifestyle: Regular physical activity, including walking, gardening, and participation in community activities.
Strong Social Connections: Social cohesion and strong community.
Healthcare System: Their healthcare systems are highly accessible and efficient, with a focus on preventive care.
Low Smoking Rates: Smoking rates in Japan are relatively low compared to other countries.
Some studies suggest that genetic predispositions may also play a role in the longevity of the Japanese and Hong Kong population. Additionally, Hong Kong has high public health standards resulting in effective public health policies that help prevent diseases or their spread.
Takeaway
Ukraine’s female longevity is about average across the countries discussed.
When it comes to males, Brazil’s male longevity is about average across the countries discussed.
Analysis
Zimbabwe’s life expectancy is among the lowest in the world due to a combination of economic challenges, political instability, and health crises.
Zimbabwe has faced severe economic issues, including hyperinflation, unemployment, and poverty, which have limited access to basic necessities and healthcare.
Also, its political turmoil and governance issues have disrupted healthcare services and social programs, affecting overall public health.
Zimbabwe has experienced significant health challenges, including the HIV/AIDS epidemic, which has had a profound impact on life expectancy.
Malnutrition and lack of clean water, also contribute to the lower life expectancy.
Analysis
Russia has a very high gender gap in life expectancy due to a combination of high male mortality from alcohol-related diseases, smoking, and occupational hazards.
India’s lower gap may be influenced by different social and cultural factors.
Analyzing the data collected, we find that USA falls in the 3rd quartile among the 23 countries listed. The 3rd quartile contains the group of countries with some of the highest life expectancies in this list; and 1st quartile consists of countries with some of the lowest life expectancy.
USA falls in 3rd quartile for overall (both sexes) longevity, for females, and for males. That means USA’s life expectancy is higher than 75% of the countries in this list.
Ranking is expressed differently where rank 1 is the best (longest life expectancy), rank 23 being the worst. USA ranks 7th. However, when considering all the countries in the world (as opposed to just this selected list), the USA ranks around 38th in the world in life expectancy. The top spots are typically occupied by countries like Monaco, Japan, and Hong Kong, with life expectancies well above 80 years.
For the curious minds
In case you’re interested, the data collected was around each country and its female and male life expectancies. From there we can extract the difference in years and calculate the gender gaps in percentage. Using statistical methods, we find the quartiles, ranks, minimum, and maximum values to find trends. The table is then automatically color-coded by values and data bars applied in specific columns to readily express the patterns in the table. The subsequent analysis sections use a combination of XLOOKUP, CONCAT, simple math for means, and nested IF combined with statistical functions for quartiles, ranks and such to extract all the accurate values from the main table dynamically on demand. The map chart is a dynamic chart (based on the underlying table data) created using Bing map and Excel’s charting features. Finally, for UI convenience, there’s a little custom HTML used on the WordPress platform. At any rate, I hope you found this fascinating.
If you’re interested in learning some of the powerful features of Excel, Python, PowerBI, etc., I welcome you to explore this blog site for additional posts on a wide range of STEM topics. You can also easily search for word(s) of your interest across my site by clicking on the magnifying glass icon from the top menu bar.
Take good care of yourself and your loved ones! See you next time.
Data sources: worldometers.info + various others. Data as of 2024.
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