Introduction
“Cash can’t purchase happiness.” “You’ll be able to’t decide a guide by its cowl.” “An apple a day retains the physician away.”
You’ve most likely heard these sayings a number of occasions, however do they really maintain up after we have a look at the information? On this article sequence, I need to take fashionable myths/sayings and put them to the check utilizing real-world information.
We’d verify some surprising truths, or debunk some fashionable beliefs. Hopefully, in both case we are going to acquire new insights into the world round us.
The speculation
“An apple a day retains the physician away”: is there any actual proof to help this?
If the parable is true, we must always count on a detrimental correlation between apple consumption per capita and physician visits per capita . So, the extra apples a rustic consumes, the less physician visits folks ought to want.
Let’s look into the information and see what the numbers actually say.
Testing the connection between apple consumption and physician visits
Let’s begin with a easy correlation examine between apple consumption per capita and physician visits per capita.
Information sources
The info comes from:
Since information availability varies by yr, 2017 was chosen because it offered probably the most full when it comes to variety of nations. Nevertheless, the outcomes are constant throughout different years.
Visualizing the connection
To visualise whether or not increased apple consumption is related to fewer physician visits, we begin by a scatter plot with a regression line.
The regression plot reveals a very slim detrimental correlation, that means that in nations the place folks eat extra apples, there’s a barely noticeable tendency to have decrease physician visits.
Sadly, the development is so weak that it can’t be thought of significant.
OLS regression
To check this relationship statistically, we run a linear regression (OLS), the place physician visits per capita is the dependent variable and apple consumption per capita is the impartial variable.
The outcomes verify what the scatterplot urged:
- The coefficient for apple consumption is -0.0107, that means that even when there’s an impact, it’s very small.
- The p-value is 0.860 (86%), way over the usual significance threshold of 5%.
- The R² worth is nearly zero, that means apple consumption explains nearly none of the variation in physician visits.
This doesn’t strictly imply that there is no such thing as a relationship, however relatively that we can not show one with the accessible information. It’s doable that any actual impact is just too small to detect, that different components we didn’t embody play a bigger function, or that the information merely doesn’t mirror the connection effectively.
Controlling for confounders
Are we carried out? Not fairly. To this point, we’ve solely checked for a direct relationship between apple consumption and physician visits.
As already talked about, many different components might be influencing each variables, probably hiding a real relationship or creating a man-made one.
If we contemplate this causal graph:
We’re assuming that apple consumption straight impacts physician visits. Nevertheless, different hidden components is likely to be at play. If we don’t account for them, we threat failing to detect an actual relationship if one exists.
A well known instance the place confounder variables are on show comes from a research by Messerli (2012), which discovered an fascinating correlation between chocolate consumption per capita and the variety of Nobel laureates.
So, would beginning to eat a variety of chocolate assist us win a Nobel Prize? In all probability not. The possible clarification was that GDP per capita was a confounder. That signifies that richer nations are inclined to have each increased chocolate consumption and extra Nobel Prize winners. The noticed relationship wasn’t causal however relatively as a result of a hidden (confounding) issue.
The identical factor might be occurring in our case. There is likely to be confounding variables that affect each apple consumption and physician visits, making it tough to see an actual relationship if one exists.
Two key confounders to contemplate are GDP per capita and median age. Wealthier nations have higher healthcare techniques and completely different dietary patterns, and older populations have a tendency to go to medical doctors extra typically and will have completely different consuming habits.
To regulate for this, we alter our mannequin by introducing these confounders:
Information sources
The info comes from:
OLS regression (with confounders)
After controlling for GDP per capita and median age, we run a a number of regression to check whether or not apple consumption has any significant impact on physician visits.
The outcomes verify what we noticed earlier:
- The coefficient for apple consumption stays very small(-0.0100), that means any potential impact is negligible.
- The p-value (85.5%) remains to be extraordinarily excessive, removed from statistical significance.
- We nonetheless can not reject the null speculation, that means we now have no sturdy proof to help the concept consuming extra apples results in fewer physician visits.
Similar as earlier than, this doesn’t essentially imply that no relationship exists, however relatively that we can not show one utilizing the accessible information. It might nonetheless be doable that the true impact is just too small to detect or that there are but different components we didn’t embody.
One fascinating commentary, nonetheless, is that GDP per capita additionally reveals no important relationship with physician visits, as its p-value is 0.668 (66.8%), indicating that we couldn’t discover within the information that wealth explains variations in healthcare utilization.
However, median age seems to be strongly related to physician visits, with a p-value of 0.001 (0.1%) and a optimistic coefficient (0.4952). This means that older populations have a tendency to go to medical doctors extra continuously, which is definitely not likely shocking if we give it some thought!
So whereas we discover no help for the apple fantasy, the information does reveal an fascinating relationship between getting old and healthcare utilization.
Median age → Physician visits
The outcomes from the OLS regression confirmed a robust relationship between median age and physician visits, and the visualization under confirms this development.
There’s a clear upward development, indicating that nations with older populations are inclined to have extra physician visits per capita.
Since we’re solely median age and physician visits right here, one might argue that GDP per capita is likely to be a confounder, influencing each. Nevertheless, the earlier OLS regression demonstrated that even when GDP was included within the mannequin, this relationship remained sturdy and statistically important.
This means that median age is a key consider explaining variations in physician visits throughout nations, impartial of GDP.
GDP → Apple consumption
Whereas in a roundabout way associated to physician visits, an fascinating secondary discovering emerges when wanting on the relationship between GDP per capita and apple consumption.
One doable clarification is that wealthier nations have higher entry to recent merchandise. One other chance is that local weather and geography play a task, so it might be that many high-GDP nations are positioned in areas with sturdy apple manufacturing, making apples extra accessible and inexpensive.
In fact, different components might be influencing this relationship, however we gained’t dig deeper right here.
The scatterplot reveals a optimistic correlation: as GDP per capita will increase, apple consumption additionally tends to rise. Nevertheless, in comparison with median age and physician visits, this development is weaker, with extra variation within the information.
The OLS confirms the connection: with a 0.2257 coefficient for GDP per capita, we will estimate a rise of round 0.23 kg in apple consumption per capita for every improve of $1,000 in GDP per capita.
The three.8% p-value permits us to reject the null speculation. So the connection is statistically important. Nevertheless, the R² worth (0.145) is comparatively low, so whereas GDP explains some variation in apple consumption, many different components possible contribute.
Conclusion
The saying goes:
“An apple a day retains the physician away,”
However after placing this fantasy to the check with real-world information, the outcomes appear not in step with this saying. Throughout a number of years, the outcomes had been constant: no significant relationship between apple consumption and physician visits emerged, even after controlling for confounders. Evidently apples alone aren’t sufficient to maintain the physician away.
Nevertheless, this doesn’t fully disprove the concept consuming extra apples might scale back physician visits. Observational information, irrespective of how effectively we management for confounders, can by no means absolutely show or disprove causality.
To get a extra statistically correct reply, and to rule out all doable confounders at a degree of granularity that might be actionable for a person, we would want to conduct an A/B check.
In such an experiment, individuals could be randomly assigned to 2 teams, for instance one consuming a set quantity of apples every day and the opposite avoiding apples. By evaluating physician visits over time amongst these two teams, we might decide if any distinction between them come up, offering stronger proof of a causal impact.
For apparent causes, I selected to not go that route. Hiring a bunch of individuals could be costly, and ethically forcing folks to keep away from apples for science is unquestionably questionable.
Nevertheless, we did discover some fascinating patterns. The strongest predictor of physician visits wasn’t apple consumption, however median age: the older a rustic’s inhabitants, the extra typically folks see a health care provider.
In the meantime, GDP confirmed a light connection to apple consumption, probably as a result of wealthier nations have higher entry to recent produce, or as a result of apple-growing areas are usually extra developed.
So, whereas we will’t verify the unique fantasy, we will provide a much less poetic, however data-backed model:
“A younger age retains the physician away.”
When you loved this evaluation and need to join, you will discover me on LinkedIn.
The complete evaluation is on the market on this notebook on GitHub.
Information Sources
Fruit Consumption: Meals and Agriculture Group of the United Nations (2023) — with main processing by Our World in Information. “Per capita consumption of apples — FAO” [dataset]. Meals and Agriculture Group of the United Nations, “Meals Balances: Meals Balances (-2013, outdated methodology and inhabitants)”; Meals and Agriculture Group of the United Nations, “Meals Balances: Meals Balances (2010-)” [original data]. Licensed below CC BY 4.0.
Physician Visits: OECD (2024), Consultations, URL (accessed on January 22, 2025). Licensed below CC BY 4.0.
GDP per Capita: World Financial institution (2025) — with minor processing by Our World in Information. “GDP per capita — World Financial institution — In fixed 2021 worldwide $” [dataset]. World Financial institution, “World Financial institution World Improvement Indicators” [original data]. Retrieved January 31, 2025 from https://ourworldindata.org/grapher/gdp-per-capita-worldbank. Licensed below CC BY 4.0.
Median Age: UN, World Inhabitants Prospects (2024) — processed by Our World in Information. “Median age, medium projection — UN WPP” [dataset]. United Nations, “World Inhabitants Prospects” [original data]. Licensed below CC BY 4.0.
All photographs, except in any other case famous, are by the creator.
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