Vaccinate Me, Baby, One More Time

With the rollout of the first vaccination programme in the United Kingdom, the BBC had a helpful comparison table stating the differences between the four primary options. It’s a small piece, but as I often say, we don’t necessarily need large and complex graphics.

A nice little comparison table

Since there are only four vaccines to compare and only a handful of metrics, a table makes a lot of sense.

But I wanted to take it a step further and so I threw together a quick piece that showed some of the key differences. In particular I wanted to focus on the effectiveness, storage temperatures (key to distribution in the developing world), and cost.

My quick take

You can pretty quickly see why the United Kingdom’s vaccine developed by Oxford University and produced by AstraZeneca is so crucial to global efforts. The cost is a mere fraction of those of the other players and then for storage temperature, along with Russia’s Sputnik vaccine, it can be stored at common refrigerator temperatures. Both Pfizer’s and Moderna’s need to be kept chilled at temperatures beyond your common freezer.

And in terms of effectiveness, which is what we all really care about, they’re fairly similar, except for the Oxford University version. Oxford’s has an overall effectiveness of 70%. (In)famously, it exhibited a wide range of effectiveness during trials of between just over 60% and 90%.

The 60-odd% effectiveness was achieved when using the recommended dosage. However, in one small group of trial participants, they erroneously were given a half-dosage. And in that case, the dosage was found to be far more effective, approximately 90%. And this is why we would normally have longer, wider-ranging trials, to dial in things like doses. But, you know, pandemic and we’re trying to return to some sense of normalcy in a hurry.

All that said, Oxford’s will be crucial to the developing world, where incomes and government expenditures are lower and cold-storage infrastructure much less, well, developed. And we need to get this coronavirus under control globally, because if we don’t, the virus could persist in reservoirs, mutating for years until the right mutation comes along and the next pandemic sweeps across the globe.

I know we’re presently all fighting about wearing masks, but when we get to having vaccines available to the public, let’s really try to not make that a political issue.

Credit for the original piece goes to the BBC.

Credit for my piece goes to me.

The Ebola Outbreak in the Congo

Ebola, which killed 11,000 people in West Africa in 2014 (which I covered in a couple of different posts), is back and this time ravaging the Congo region, specifically the Democratic Republic of the Congo (DRC). The BBC published an article looking at the outbreak, which at 1,400 deaths is still far short of the West Africa outbreak, but is still very significant.

That's looking like a tenuous border right now…
That’s looking like a tenuous border right now…

The piece uses a small multiples of choropleths for western Congo. The map is effective, using white as the background for the no case districts. However, I wonder, would be more telling if it were cases per month? That would allow the user to see to where the outbreak is spreading as well as getting a sense of if the outbreak is accelerating or decelerating.

The rest of the article features four other graphics. One is a line chart that also looks at cumulative cases and deaths. And again, that makes it more difficult to see if the outbreak is slowing or speeding up. Another is how the virus works and then two are about dealing with the virus in terms of suits and the containment camps. But those are graphics the BBC has previously produced, one of which is in the above links.

Credit for the piece goes to the BBC graphics department.

The 2017–18 Flu Season

Last week I covered the Pennsylvania congressional district map changes quite a bit. Consequently I was not able to share a few good pieces of work. Let’s hope nothing goes terribly wrong this week and maybe we can catch up.

From last Friday we have this nice piece from FiveThirtyEight looking at the spread of influenza this season.

Red is definitely bad
Red is definitely bad

The duller blues and greens give way to a bright red from south to north. Very quickly you can see how from, basically, Christmas on, the flu has been storming across the United States. It looks as if your best bets are to head to either Maine or Montana. Maybe DC, it’s too small to tell, but I kind of doubt that.

As you all know, I am a fan of small multiples and so I love this kind of work. To play Devil’s advocate, however, I wonder if an interactive piece that featured one large map could have worked better? Could the ability to select the week and then the state yield information on how the flu has spread across each state? I am always curious what other other forms and options were under consideration before they chose this path.

Credit for the piece goes to the FiveThirtyEight graphics department.

The NHS Winter Crisis

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.

Some of the worst hospitals
Some of the worst hospitals

Credit for the piece goes to the Josh Holder.

Repealing the Individual Mandate

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.

Exiting the pool
Exiting the pool

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.

Credit for the piece goes to Haeyoun Park.

An Ailing Graphic on the Healthcare Labour Force

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.

We need more. Just more.
We need more. Just more.

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.

Bursting these bubbles…
Bursting these bubbles…

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.

The Cassidy-Graham Healthcare Bill

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.

Hyperlinked to the online version of the article
Hyperlinked to the online version of the article

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.

Credit for the piece goes to Haeyoun Park.

Comparing the US Healthcare System to the World

Spoiler, we don’t look so great.

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.

This is the only graphic I'm not sure of in the entire piece.
This is the only graphic I’m not sure of in the entire piece.

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.

Someone's getting cheated out of a lot of money. Oh wait, that's us…
Someone’s getting cheated out of a lot of money. Oh wait, that’s us…

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.

When all the options look bad, why not work with what you have?
When all the options look bad, why not work with what you have?

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.

Here We Go Again

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.

Round and round we go
Round and round we go

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.

It’s going to be a long day.

Credit for the piece goes to Harry Enten.

How Trumpcare Differs from Obamacare

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.

How the changes affect the young, middle-aged, and the elderly by income level
How the changes affect the young, middle-aged, and the elderly by income level

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.