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The United State of American Healthcare Costs

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When we hear about Medicare for All, proponents of the policy are talking methods of paying to take care of our health. One of the reasons that Medicare for All is being put forth is that healthcare in the U.S. is so expensive that it’s one of the major reasons people regularly report avoiding the doctor’s office. It seems like every year, the cost of healthcare goes up, and I’m here to tell you: you’re not imagining it. Every single year, we are paying more and more to take care of ourselves, and it’s projected to get even worse over the next ten years if we don’t do something. 

While putting this project together, I asked:

What are Americans more likely to be paying out of pocket for? How much of insurance is private versus public versus paid for by “third party providers”? Are any healthcare costs projected to fall instead of rising?

My audience for these visualizations consists of Americans who are concerned about the cost of healthcare. As healthcare costs rise, more people will likely have to pay attention to their expenditures, and I hope that being able to see these projections could help them prepare for an uncertain future in managing their healthcare costs, or even put them in a position to lobby Congress for regulation of the health and/or pharmaceutical industries in general.

I made a total of 5 visualizations for this project, which collectively tell a story about where our healthcare costs are going. All visualizations, due to the data, contain historical estimates from 2011-2017 and projections from 2018-2027. The first visualization is a simple shape chart, showing an estimate of how much we have spent on healthcare on a yearly basis from 2011-2017, and then the projection of what 2018-2027 will look like. We go from spending around 2 trillion in 2011 to 5 trillion in 2027, and these numbers have been deflated. The second and third visualizations are kind of paired—they both show a breakdown of where all that money comes from, but the area chart shows it without numbers while the heat map table shows it with. The area chart also performs the function of showing which paying options (insurance/non-insurance) will pay less and which will pay more, which is less easy to see in the table. However, the heat map table does the job of asking you to confront the amount of zeroes in each of the categories, which the area chart was not quite built for.

In my second set of visualizations, I broke down what the money was being spent on, first in a tree map showing out of pocket payments, and then in an infographic showing overall what we are spending on. The tree map can be filtered out by year, and uses percentages as opposed to numbers so that the viewer’s mind can more readily grasp the parts of the whole. The tooltips then show the actual amount in billions of dollars. The infographic uses the “medical id” symbol and represents amount spent/projected to be spent by size. This is particularly useful when sorting the fields, showing overall what is spent and providing a useful measure against the out of pocket visualization—Americans don’t usually pay for hospital costs out of pocket, but they account for the most spending. The second most in both visualizations is physician care, showing that our doctor’s visits are largely paid out of our own funds.

The data itself was fairly clean, and I was able to consolidate multiple datasets into two main datasets: one consolidated different sectors of healthcare (physician services, hospital care, etc) divided by the type of medical payment (insurance types versus out of pocket) and the other consolidated demographic data, population data, and breakdowns by federal and state government expenditures. I ended up using the first dataset mostly, because the categorical breakdown was narrower in scope and more interesting than the federal/state government. 

Regarding design choices, I wanted to play with custom shapes and palettes for this project, so my first visualization uses money bag icons, and my second uses a custom palette. I tried to separate color choices by dashboard/story beat: green palette for the money, red palette for categorical medical breakdown. The two colors are contrasting as well, establishing a visual dichotomy. A lot of my data represents parts of a whole, which really tempted me to make a pie chart, and I almost caved, but in the end, it just felt too reductive.

In terms of limitations, I was attempting to make an infographic visualization regarding what one person on average pays for medical care in the US, but I couldn’t get the calculated field right. Additionally, I was looking into making a waffle chart, but what I had in mind was too complicated for Tableau.

For next steps, I would like to create a story section about individual breakdown—it’s a little dangerous, but I think it would punctuate the prior two sections by really showing how much money trillions of dollars breaks down to per person, and then making the point that some people don’t have medical debt, so that number is actually higher. Considering the data from 2011 says that healthcare spending per capita was around $8,000, I really feel like this section is necessary.

Homeless Outreach in NYC: 311 Reports About New York’s Vulnerable Populations

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Over the past few years, the De Blasio administration has taken a special interest in addressing New York City’s homeless population via outreach methods,

Over the past few years, the De Blasio administration has taken a special interest in addressing New York City’s homeless population via outreach methods, namely in the Home-Stat project, which aims to encourage New Yorkers to report any homeless individuals they might see on the streets to 311, so that an outreach team can attempt to assist the individual. 

In working with the 311 data, I asked the following two-part question:

Over a five year period, what boroughs have had the biggest changes in homeless assistance reporting, and have more people been accepting assistance on a yearly basis, or has there been a relatively static trend?

The following visualizations aim to help New Yorkers understand if it is helpful to dispatch an outreach team to address homelessness in the city, as well as the boroughs in which we do the most reporting. Additionally, they will help the DHS understand what times of year they should recruit for their mobile outreach teams, and when they should do more preparatory work since the reporting is not as robust.

Figure 1, “311 Requests for Homeless Person Assistance by Borough, Sept. 2014 – Sept. 2019” shows the monthly amount of reports made to 311 by New Yorkers in each of the five boroughs. From 2014-early 2016, there was a slow upward trend in reporting, which then skyrocketed in March 2016. 

Though we haven’t seen a boom in reporting like that since, the amount of reporting is still much higher than it was pre-2016. Additionally, less reporting tends to happen in the winter months–from around December to March, while most of the reports tend to happen in summer months, usually in August. 

Despite—or perhaps because of—Manhattan having the least amount of space, most reporting happens in Manhattan overall. The population density in Manhattan might be one explanation for these thousands of reports over the five year period, but other factors might contribute.

In Figure 2, “Yearly Reports on NYC’s Homeless Population by Resolution”, there are several resolution descriptions provided by the DHS with regard to the 311 complaints—my research question focuses on those who accepted assistance, and using the heat map to show the trend of numbers, you can see that there was a significant drop of assistance acceptance after 2017. However, in context, if you turn your attention to the row right beneath it, there was a significant increase in 2018 in resolutions that end right at the point of offering assistance. 

Since each individual report only gets one resolution, it’s not likely that the number is enveloped in the “accepted assistance” row or the “did not accept assistance” row.

Additionally, much like in Figure 1, you can see the spike in reporting in 2016—along with the significant drop in years afterward. Most of those reports from Figure 1 concluded that the individual could not be found.

The other graph, Figure 1, was much less finicky—I decided to go for a stacked bar chart with a monthly breakdown rather than a yearly breakdown because the trends are much more visible over the months, and you can see how they shift with the boroughs, which was an essential part of my research question. Additionally, you can very clearly see the summer months versus the winter months, which ended up being an interesting point I hadn’t considered in my research question.

Working with the data, it became apparent to me that the values I thought were most useful and fascinating were also the most messy. It was definitely a challenge to find the right way to visualize that data in particular. I started with a line graph for what ended up becoming Figure 2, but because the data was so messy, I concluded that no one would be able to read the graph, so I went for a more legible visualization. I was hesitant about the heat map because it is a little more difficult to show trends—colors are not the greatest way to do so—but because the resolution descriptions were inherently strings, everything got very cramped and cluttered if I chose any other way.

With more time to clean up the data, it would be great if I could aggregate the resolution descriptions that were input improperly. Additionally, after aggregating the resolution descriptions, I would make the graphs integrate a little better—breaking the heat map up by months instead of years. I also wanted to compare this data with the data on homeless encampments—some of these cases were referred to the NYPD, and I wonder how they interact with that data. Preferably, I would like to have one graph that accomplishes something big instead of two that accomplish two small, distantly related parts of the data.