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Uncovering Hidden Insights from the WHO Public Health Data

Way back when (at the start of 2020), Gartner invited analytics vendors to participate in analyzing the World Health Organization (WHO) population health data from around the globe.

We agreed to do the analysis and produce a 10-minute video to show what we found and how we got to those hidden insights.

My colleagues got a chance to present Oracle’s findings live at the Gartner Data & Analytics event in Sydney, Australia, in late February. We were slated to present again in London and Dallas in March. But COVID-19 reared its ugly head, all in-person events were canceled, and Gartner asked that we not publicize our findings until they decided how they might do a virtual event for vendors to demonstrate their products using this available data.

Fast forward to today. We’re now ready to show you what we found out, and how we did it. You can watch the video (10 minutes) here. This was created based on a script of questions to be answered, provided by Gartner. We followed that and went a bit further.

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A-HA! Insights from the WHO data, pre-COVID

When we reviewed the demonstration in the last few weeks, many of us had a similar response—“The world has changed so much with COVID. Will people be interested in data that doesn’t include the impacts of COVID?”

The answer, in my opinion, is an emphatic YES! The insights we found were not what we expected. Our global team working on this project represented people coming from different countries with very different healthcare systems – France, Canada, and the USA. Many of us entered the project with a set of assumptions. And most were wrong!

What follows is what we found. While most insights have implications on health policy, some are just fun observations we found in the data.

Simply spending more government money on health expenditures does not automatically translate to longer life expectancy.

  • For example, spending per capita has moderated over the last five years in Australia, New Zealand, and Europe overall, yet life expectancy has continued to rise.

  • This is not true in the US, where spending has risen steadily, but life expectancy has dropped. When comparing spending per capita against life expectancy, the US is not in the leader’s quadrant; it is in the bottom right – high spending, relatively low life expectancy.

Overall, women have a higher life expectancy than men. This is true in all geographies we analyzed but there is a bump if you reach the age of 60.

This was one assumption we all had and it was proven true. But there was a bit of a twist.

  • If a person gets to age 60, their life expectancy increases relatively substantially compared to the overall life expectancy rate. That “60 BUMP” – additional life expectancy--is higher for men than it is for women. Good news for people like me (male, over 60).

Obesity rates have continued to climb over the last several decades across all geographies.

The world population is getting fatter…In some cases, dramatically!

  • While there isn’t a strong correlation between growing obesity and life expectancy, those countries with the highest obesity rates have a slightly lower life expectancy.

Alcohol consumption varies by gender and geography. It appears the more men drink, the longer they live.

  • Australian men have a higher alcohol consumption rate than the US. Italy and France are far higher than in Australia. The male life expectancy in Italy and France is higher than in Australia and the US. So, our conclusion: the more men drink, the longer they live—at least in France and Italy!

There is some correlation between CO2 emissions/pollution and mortality rates by country

  • The more CO2 emissions a country produces, the higher the mortality rates.

Income disparity clearly influences the mortality rates of children under five years of age.

  • The higher the income share of the top 20 percent is, the higher the mortality rates under age 5 are. For example, the USA and Portugal have the highest concentrations of wealth (over 45% of the wealth is controlled by the top 20%) and also have higher mortality rates of children under five years of age. Income inequality appears to come at the price of children’s lives.

Overall life expectancy is highly correlated with a mortality rate under age 5.

  • Addressing health and accident challenges with young children under age 5 will improve the overall life expectancy of the population.

When mashing up population health data with freely available weather data, there is a clear correlation between higher level of precipitation and higher birthrates.

  • Highest levels of precipitation in, for example, New Zealand, is correlated with higher birthrates

  • When it rains, it pours (babies)—at least in New Zealand!

These findings are just the tip of the iceberg. Watch the video to hear the whole story. This was a fun exercise. And while it was done for a work event, it opened our eyes to the rich world of population health data.

Let me know your comments. You can reach me at john.hagerty@oracle.com.

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