What do various countries’ healthcare capacities look like?

Healthcare data and metrics visualised and compared by country in light of the coronavirus (COVID-19).

A comparison of growth rates vs capacities (dashboard link)

For many of us, our lives are on a temporary standstill while the world deals with the COVID-19 outbreak.

March was something of an inflection point for many countries with a huge increase in COVID-19 suppression measures being implemented. The reason for it is a combination of seeing what has happened in some countries, and dire warnings from modelling.

With no control measures, the New York Times writes, there would be 500,000 new cases per day in the United States by late May.

500,000 new cases. IN ONE DAY. JUST IN THE UNITED STATES.

Even with ‘some’ control measures, there might be 300,000 new cases a day by late June. One silver lining is that this future is not written in stone. This paper for instance outlines that social distancing and other “suppression” measures can make a huge difference. So I’ve been doing that – staying indoors. A lot. Like, house arrest a lot.

Photo by Per Lööv on Unsplash

All this time spent at home has also given me a bit of time to think and read, and given the times being what they are, much of the recent reading material has been pandemic-relevant. (Who knew viruses were so interesting!)

I also began to wonder what various countries’ capabilities to deal with this might look like.

You see, like many people these days, I have a global network of family and friends. My particular one extends across Korea, Australia, the U.S., the U.K. and New Zealand.

So I decided to research healthcare statistics around the world, and one thing led to another. You know how it goes. Before I knew it, I was compiling figures, and putting them into an online dashboard.

It turned out to be an interesting exercise. I learned a lot about disparities and relationships in healthcare capabilities, the sizes of older, more vulnerable populations, and economic capabilities. Also, I put together a crude simulation on how long it might take for systems to be overwhelmed by demand, especially with geometric growth.

To be very clear, I am not a health or policy expert nor anywhere near knowing much about either. This post is simply me putting data together out of curiosity, between watching Netflix and Disney+ and wondering what normalcy might have become when we get to it.

Data source & analysis

I have obtained the data from the World Bank Open Data project, who state their mission being to provide free and open access to global development data.

Not all data is available for all years – but I have collected here the latest available data, and only from within the last 10 years. After eliminating countries for which I could not find much data, I ended up with compiling information from 155 countries — from as large as China to as small as Antigua and Barbuda.

Data

Healthcare expenditure

How much does each system spend on healthcare per person? I would think that these figures indicate the ability to purchase medication and equipment (although it would not then take into account varying costs in each country).

155 countries is probably too many to show on one graph, so I plot this data for the 20 countries with the highest values in the dataset.

Healthcare expenditure per capita (highest values) — Data source: worldbank.org

And to contrast, these are the 20 countries with the highest populations.

Healthcare expenditure per capita (largest populations) — Data source: worldbank.org

The inequality in resources is quite stark.

Given this kind of disparities in resources, and the fact that COVID-19 is a respiratory disease is worrying. For example, it’s widely reported in the media that many patients require support from mechanical ventilators – how will the less affluent countries afford them?

Let’s take a look at some other measures.

% of population 65 or older

Like many other illnesses, age appears to be a factor in how vulnerable you are to this disease. Here is a look at percentages of population who are 65 or older as one indicator of age distributions.

% of population who are 65+ of age (largest populations) — Data source: worldbank.org

Interestingly, Japan has a very high percentage of aged population, as does France and Germany. We can plot the top 20 countries based on this metric.

% of population who are 65+ of age (largest populations) — Data source: worldbank.org

After Japan, this list appears to be dominated by European countries, quite interestingly. If this is an indicator of the size of the most vulnerable population, would healthcare system capacities be related to this measure?

Hospital beds per person

One of the key arguments for flattening the curve is to prevent overtaxing healthcare systems. One proxy for healthcare system capacity is the number of hospital beds available per capita. Let’s take a look at those figures for the countries with the highest populations once again.

Hospital beds per 1000 people — Data source: worldbank.org

Interestingly Japan is also the leader in this category, as they were with the size of 65+ population size. The top countries are similar to what we saw in the graph immediately above.

Does that mean that these two metrics are well-correlated?

Correlations against no. of hospital beds

Yes, and no. Take a look at the graph below.

Correlation — population size 65 or above vs number of hospital beds

For the large countries, there appears to be good correlation, but look at the cluster of dots with low 65+ population on the bottom right. There exists quite a large spread of hospital bed numbers within that cluster, indicating low correlations.

Generally, a better correlation can be found below — with healthcare expenditures per capita.

Correlation — Healthcare expenditures vs number of hospital beds

Generally, healthcare expenditure per capita appears to be a better predictor for numbers of hospital beds per capita. That is, except for those cases with about 10% (or higher) of 65+ population.

Number of physicians

That’s hospital infrastructure capacity. But what about the personnel who might need to attend to the patients? These are the countries with the highest physician per 1000 people values:

Physicians per 1000 people (highest values) The World Bank

Cuba leads this list, by a mile! That’s really interesting. As I said, I don’t know much about the area, but I wonder what some reasons for this statistic might be. For example, I wonder if certain healthcare systems and policies choose to focus more than others on resources (equipment, medication, R&D) vs training physicians.

Still, very surprised to see Cuba at the top of this list. You learn something new every day.

Correlation — Aged population vs physicians per 1000

Plotting the data as correlations, there is generally a great correlation again between population size 65 and above and physicians per 1000.

Clearly, though, there is a huge range of differences in healthcare capacities across the world. And yet, none of them might be enough as we are seeing. Here’s some crude maths as to why:

Why we should all #StayAtHome

Basically, all the data that we are seeing on this virus says that it is far more dangerous than the flu — somewhere between 10 to 20 times more dangerous, according to Dr. Fauci of the United States.

And how quickly has it been growing? Very fast.

Growth rates of deaths due to COVID-19 (from John Burn-Murdoch on Twitter)

This is an exponential curve — and this is just showing the number of deaths. Assuming that hospitalisations increase similarly (i.e. exponentially), this is how quickly the number would grow from the first hospitalisation.

Growth rates vs available hospital beds

Even if 10% of all hospital beds were available, the U.S capacity would be met in under two months based on three days’ doubling rate of hospitalisations. Just over three days’ doubling rate is about what the U.S. data is currently on, just by the way — as of March 24. Some areas like NYC is showing two days’ doubling rates.

As you can see from the graph — a tenfold increase in capacity would only buy a matter of days (notice the graph being in log scale to display exponential increases).

Meanwhile, a change in doubling rates has a much larger impact on the outcome, and getting to capacity numbers. This is why the current focus is on flattening the curve.

So there you go — stay safe, stay at home. Save not just one life, but potentially a lot more than that. Until effective and widespread testing is available along with tracing, everybody needs to be vigilant — because currently, it’s impossible to identify the disease quickly enough, and to trace where they’ve been to let those exposed know that they might be at risk to themselves and others.

I have uploaded the interactive version of this analysis online here. Please feel free to take a look, and let me know if you have any comments.