Healthcare Spending Per Capita by Country GDP

Data Exploration Journal

Brandon Hill

Dataset: 

GDP per capita and health consumption spending per capita, 2020 (U.S. dollars, PPP adjusted)

Goal: 

I want to compare healthcare expenditure in the US to healthcare expenditure in other countries. I’m particularly interested in seeing if there is an expenditure trend when compared to countries with universal healthcare programs. 

The data I downloaded comes from the visualization that is paired with this story

Based on this chart alone, it seems that there is a pretty consistent trend for how GDP per capita relates to healthcare spending per capita. It makes sense that as people steadily make more money, they increase their healthcare expenditure at a pretty similar rate. However, there are two pretty clear outliers that I will be looking into. The US spending on healthcare is pretty far above the line for their GDP, and Ireland is pretty far below the line. 

I also want to see what is included in the calculation of healthcare expenditure. Is government expenditure included in the total? Or is this data just representative of the out-of-pocket expenditure on healthcare? 

Process 

The first thing I did, was to throw the dataset into Tableau to see if I could replicate the chart above. The axis were easy enough, but I wasn’t entirely sure how to get that line representing the ratio. I tried this calculated field: (SUM([GDP Per Capita])/COUNT([Country]))/(SUM([Health Spending Per Capita])/COUNT([Country])) and got this when adding it to my chart: 

I don’t think this is quite was I was going for, but it still produces a pretty interesting visualization. I believe that the red end of the spectrum seems to show countries with a below-average ratio of GDP to Healthcare expenditure per capita, and the green end of the spectrum shows countries with a higher than average ratio. If I’m correct, this visualization actually reveals something that the above chart did not — the higher the GDP per capita, the ratio of expenditure seems to taper off. Ireland, on the far right, is still an outlier to the trend, but the US follows the trend with a dramatic uptick. 

The rest of the story that is the source of this data goes on to talk about the trend in increasing healthcare expenditure in wealthy countries. While most wealthy countries are increasing their healthcare expenditure, the US expenditure is rising at a faster rate. 

However, expenditure alone, even as a ratio of GDP, doesn’t tell us everything we need to know about a country’s healthcare. Maybe we can justify the US’s extremely high expenditure if the US healthcare is much more effective. Let’s see what happens when we compare the life expectancy of these countries. 

I found the data for life expectancy by birth for the period 2015-2020 here. This period was also included in the above story’s tracking of the increasing healthcare expenditure in wealthy nations. I envision that it would be possible to create a calculated field that would depict how the ratio of GDP/Healthcare Expenditure per capita compares to a given country’s life expectancy. It’s worth noting that the dataset on life expectancy includes a breakdown by male and female and that women on average live longer than men everywhere in the world but for the purpose of this analysis, I’ll be using life expectancy as an average of both genders since the expenditure data I have is not broken down by gender. Also, by listing the data only by male and female, the dataset is missing life expectancy for intergender and transgender people. 

I used this calculated field to hopefully get the result I’m looking for: [Ratio of Average GDP and Expenditure per capita]/[Years (Both Sexes)]. This calculated field returned the error message, “cannot mix aggregate and non-aggregate arguments with this function.” 

 Furthermore, for some reason even after editing the field in my second data set to “United States,” I cannot get Tableau to connect that data for the United States between the two data sets. I think this is where I have to call it a night.  

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