In the United Kingdom, the month of January has been less than stellar for the National Health Service, the NHS, as surgeries have been cancelled or delayed, patients left waiting in corridors, and a shortage of staff to cope with higher-than-usual demand.
But another problem is the shortage of hospital beds, which compounds problems elsewhere in hospitals and health services. The Guardian did a nice job last week of capturing the state of bed capacity in some hospitals. Overall, the piece uses line charts and scatter plots to tell the story, but this screenshot in particular is a lovely small multiples set that shows how even with surge capacity, the beds in orange, many hospitals are running at near 100% capacity.
While I am still looking for a graphic about Zimbabwe, I also want to cover the tax reform plans as they are being discussed visually. But then the Senate went and threw a spanner into the works by incorporating a repeal of Obamacare’s individual mandate. “What is that?”, some of you may ask, especially those not from the States. It is the requirement that everyone have health insurance and it comes with tax penalties if you fail to have coverage.
Thankfully the New York Times put together a piece explaining how the mandate is needed to keep premiums low. Consequently, removing it will actually only increase the premiums paid by the poor, sick, and elderly. The piece does this through illustrations accompanying the text.
Overall the piece does a nice job of pairing graphics and text to explain just why the mandate, so reviled by some quarters, is so essential to the overall system.
I know I have said it before, but I like the increasing number of graphics-led articles published by Politico. Many policy and politics stories are driven—or should be driven—by data. But, myself included, we cannot hit it out of the park at every plate appearance. And that is what we have from Politico today, actually last week.
The graphic focuses on the healthcare industry and its need for a larger labour force in coming years as the baby boomers continue to age and start to retire. If their own doctors retire along with them, who will be their new doctors?
But there are two components of the graphic on which I want to focus. The first is the projection of the number of registered nurses (RNs) in 2024 compared to a 2014 baseline.
The story focuses on the future condition, but that colour is set to the lighter green thus drawing the reader’s eyes to the 2014 data point. Flipping those two colours would shift the focus of the chart to the 2024 timeframe, which would better match the text above.
Then we have the design decision to include a line chart for the growth rate, presumably total, for each category of RN from 2014 to 2024. The problem is that the chart itself does not sit on any baseline. While I do not care for the dual axis chart, that format at least keeps an axis legend on the right side of the chart. (You still have the problem of implying certain things based on what scale you choose to use relative to the first data series.) Here, because there is no chart lines associated with the growth data, I wonder if a table below the x-axis labels would be more efficient? Home health care, a very small category, will have the highest growth (a small change from a small base will beat the same small change or even slightly bigger changes from a far larger base) but the eye has the furthest to travel to reach the 61% number from the top of the bars or the labelling.
The other component I wanted to discuss is the scatter plot that compares the number of jobs to their average salary.
But this is a bubble chart, not a scatter plot, and so we have a third variable encoded in the size of the dot/bubble. The first thing I looked for was a scale for the size of the circles. What magnitude is the RN circle vs. the Personal Care Aides circle? There is none, but unfortunately that seems to be a common practice with bubble chart. But after failing to find that, I noticed that the circles decrease in size from right to left. That was when I looked to the legend and saw the y-axis in numbers of jobs and the x-axis in average salary. But then the circles are sized in proportion to the average salary of each profession to the other. In other words, the circles are basically re-plotting the x-axis. The physical therapist circle should be roughly twice as large, by area, than the vocational nurses. But we can also just see by the x-axis coordinates. The bubble chart-ness of the chart is unnecessary and the data could be told more clearly by stripping that away and making a straight-up scatter plot where all the circles are sized the same.
Credit for the piece goes to Christina Animashaun.
I meant to post this yesterday, but accidentally saved it as a draft. So let’s try this again.
Yesterday the New York Times published a print piece that explored how the Cassidy-Graham bill would change the healthcare system. This would, of course, be another attempt to repeal and replace Obamacare. And like previous efforts, this bill would do real damage to the aim of covering individuals. We know the dollar amounts in terms of changes to aid given to states, but in terms of the numbers of people likely to lose their coverage, that would have to wait for a CBO score.
The graphic makes really nice use of the tall vertical space afforded by two columns. (You can kind of see this too in the online version of the article.) At the beginning of the article, above the title even, are two maps that locate the states with the biggest funding gains and cuts. I wonder if the two maps could have been combined into one or if a small table, like in the online version, would have worked better. The map does not read well in the print version as the non-highlighted states are very faint.
The designer chose to repeatedly use the same chart, but highlight different states based on different conditions. This makes the small multiples that appear below the big version useful despite their small size. Any question about the particular length can be referenced in the big chart at the top.
With the exception of the maps at the top of the piece, this was a great piece that used its space on the page very well.
In this piece from the Guardian, we have one of my favourite types of charts. But, the piece begins with a chart I wonder about. We have a timeline of countries creating universal healthcare coverage, according to the WHO definition—of which there are only 32 countries. But we then plot their 2016 population regardless of when the country established the system. It honestly took me a few minutes to figure out what the chart was trying to communicate.
However, we do get one of my favourite charts: the scatter plot over time. And in it we look at the correlation between spending on healthcare compared to life expectancy. And, as I revealed in the spoiler, for all the money we spend on healthcare—it is not leading to longer lives as it broadly does throughout the world. And care as you might want to blame Obamacare, the data makes clear this problem began in the 1980s.
And of course Obamacare is why the Guardian published this piece since this is the week of the Vote-a-rama that we expect to see Thursday night. The Republicans will basically be holding an open floor to vote on anything and everything that can get some measure of repeal and/or replace 50 votes. And to wrap the piece, the Guardian concludes with a simple line chart showing the number of uninsured out to 2026. To nobody’s surprise, all the plans put forward leave tens of millions uncovered.
It is a fantastic piece that is well worth the read, especially because it compares the systems used by a number of countries. (That is largely the text bit that we do not cover here at Coffeespoons.) I found the piece very informative.
Credit for the piece goes to the Guardian graphics department.
Well as of last night, we are having yet another vote on AHCA, better known as Trumpcare. I will not get into the details of the changes, but basically it can be summed up as waivers for Obamacare regulations. And as of last night, $8 billion over five years to cover those at high-risk. What about after five years? What if, as experts say, that sum is insufficient and it runs out before five years are up?
This is still a bad bill.
But thankfully we have FiveThirtyEight who looked at support before the Upton amendment—the $8 billion bit—and found that the bill could still fail because of a lack of moderate support.
The basic premise is this: In order to get the conservative Freedom Caucus, which scuppered the bill a few weeks ago, on side Ryan et al. had to make the bill more conservative. They likely had to make it cover fewer people at a higher cost. I say likely because Ryan is not sending this to the Congressional Budget Office (CBO) to score the bill, something typically done to see how much it costs and whether it might work. Problem is, by making the bill more conservative, they push away moderate Republicans. Yes, Virginia, they do exist.
Today’s question is whether an $8 billion throw-in will buy in enough moderate votes.
We are going to have a busy week this week. From the CBO release on Trumpcare costs and coverage to the elections in the Netherlands. Oh, and it might snow a wee bit here in Philadelphia and the East Coast. So let’s dive straight into today’s post, an article all the way from the West Coast and the LA Times.
It looks at a comparison between Trumpcare and Obamacare.
The clearest takeaway is that they are using some pretty good colours here. Because purple.
But in all seriousness, the takeaway from this graphic is that Trumpcare as proposed will cost more for the poor and the elderly. And it will cost especially more for those who live in rural and more isolated areas. And that basically comes down to the fact that Trumpcare will not factor in the local cost of insurance, which generally costs more in non-urban areas.
But for the fullest understanding of the differences, you should read the full piece as it offers a point-by-point comparison.
Credit for the piece goes to Noam N. Levey and Kyle Kim.
Well, we are one day away now. And I’ve been saving this piece from the New York Times for today. They call it simply 2016 in Charts, but parts of it look further back while other parts try to look ahead to new policies. But all of it is well done.
I chose the below set of bar charts depicting deaths by terrorism to show how well the designers paid attention to their content and its placement. Look how the scale for each chart matches up so that the total can fit neatly to the left, along with the totals for the United States, Canada, and the EU. What it goes to show you is best summarised by the author, whom I quote “those 63 [American] deaths, while tragic, are about the same as the number of Americans killed annually by lawn mowers.”
I propose a War on Lawn Mowers.
The rest of the piece goes on to talk about the economy—it’s doing well; healthcare—not perfect, but reasonably well; stock market—also well; proposed tax cuts—good for the already wealthy; proposed spending—bad for public debt; and other things.
The commonality is that the charts work really well for communicating the stories. And it does all through a simple, limited, and consistent palette.
If you have heard enough about the Affordable Care Act, well, you could be listening to the desire to defund Planned Parenthood. Because, while that organisation cannot use any federal funding for abortions, it is the nation’s largest provider of that service. So if you follow that logic, you must strip all federal funds from the organisation.
Yeah, it makes no sense. But whatever, those are part of the Republican plans. But, if you look at the data, abortion rates are now at the lowest level since Roe v. Wade in the 1970s.
It just won’t die. Grandma, that is, in front of the death panels of Obamacare. Remember those? Well, even if you don’t, the Affordable Care Act (the actual name for Obamacare) is still around despite repeated attempts to repeal it. So in this piece from Bloomberg, Obamacare is examined from the perspective of leaving 27 million people uninsured. In 2010, there were 47 million Americans without insurance and so the programme worked for 20 million people. But what about those remaining 27?
I am not usually a fan of tree maps, because it is difficult to compare areas. However, in this piece the designers chose to animate each section of the tree as they move along their story. And because the data set remains consistent, e.g. the element of the 20 million who gained insurance, the graphic becomes a familiar part of the article and serves as a branching off point—see what I did there?—to explore different slices of the data.
So in the end, this becomes one of those cases where I actually think the tree map worked to great effect. Now there is a cartogram in the article, that I am less sure about. It uses squares within squares to represent the number of uninsured and ineligible for assistance as a share of the total uninsured.
Some of the visible patterns come from states that refused to expand Medicaid. It was supposed to cover the poorest, but the Supreme Court ruled it was optional not mandatory and 19 states refused to expand the coverage. But surely that could have been done in a clearer fashion than the map?
Credit for the piece goes to Jeremy Scott Diamond, Zachary Tracer, and Chloe Whiteaker.