Dot density to show Covid-19 deaths over time

The United States passed 425,000 coronavirus deaths this week. For The New York Times, Lazaro Gamio and Lauren Leatherby used dot density over time to show how we got to this point.

Each dark pixel represents a death, and each tick mark represents a day. So the strip starts light with sparsely placed dots, and then it gets darker and darker. Get to present day, and there’s hardly any white space.

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Lifelines

Using estimates from a report by the Well Being Trust and the Robert Graham Center, Periscopic shows projected deaths of despair in Lifelines.

Lights, each representing a life, float above the water, and as you adjust levels of mental health care, employment, and social connection, the lives either sink to the bottom or stay above the water. How do we keep as many as we can above water?

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Covid-19, the third leading cause of death

For Scientific American, Youyou Zhou made a line chart that shows cause of death in the United States, from 2015 up to present. Covid-19 was the leading cause of death in April and is now sitting at number 3. The rise in unclassified deaths also stands out.

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Gaps between mortality rates for black and white Americans

For NYT Opinion, Gus Wezerek charted the gaps between white and black mortality rate:

If Black people had died at the same age-adjusted rate as white people in 2018, they would have avoided 65,000 premature, excess deaths — the equivalent of three coach buses filled with Black people crashing and killing them all every day of the year.

…oof.

The variable width bar chart above is one of several graphics in the piece. Height represents rate. Width represents the gap. Direction represents which group has the higher rate.

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Comparing the coronavirus to past deadly events

One way to estimate the impact of the coronavirus is to compare it against expected mortality. People are still dying of other causes. The virus has increased the total counts around the world. The New York Times compared these increases against other deadly events:

Only the worst disasters completely upend normal patterns of death, overshadowing, if only briefly, everyday causes like cancer, heart disease and car accidents. Here’s how the devastation brought by the pandemic in 25 cities and regions compares with historical events.

The result is a vertical scroll that starts at a normal mortality rate and takes you through increasingly deadlier events like the HIV/AIDS crisis, Hurricane Katrina, and eventually up to the Spanish Flu outbreak. You see how the coronavirus increased deaths in major cities along the way.

When you couple these events with memories of how we reacted, the current state of affairs is tough to comprehend.

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Possible coronavirus deaths compared against other causes

Based on estimates from public health researcher James Lawler, The Upshot shows the range of coronavirus deaths, given variable infection and fatality rate. Adjust with the sliders and see how the death count (over a year) compares against other major causes of death:

Dr. Lawler’s estimate, 480,000 deaths, is higher than the number who die in a year from dementia, emphysema, stroke or diabetes. There are only two causes of death that kill more Americans: cancer, which kills just under 600,000 in a year, and heart disease, which kills around 650,000.

A coronavirus death toll near the top of the C.D.C. range (1.7 million) would mean more deaths from the disease than the number of Americans typically killed by cancer and heart disease put together.

Can we all agree now that brushing off coronavirus by floating annual flu numbers is a bad comparison? The most worrisome part of the data we have is the uncertainty and then the range of possibilities that come out of that uncertainty.

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With terminal cancer, a patient tracks drug dose in a dashboard over her final days

Kelly Martin died of cancer on September 30. She was able to enjoy her final days at home, and as she knew the end was near, she kept track of her drug doses in a dashboard:

Brain tumors are unpredictable. I don’t want my last days with a personality that isn’t mine. I wanted to laugh, to enjoy the days, and fart around in the garden as much as possible. We added in a variety of medications to use as needed to manage symptoms and tracked what worked and what didn’t in a Tableau dashboard. It was the only way to see the patterns and to get more good days.

From Bridget Cogley, Martin’s friend who took over the writing as Martin grew too ill:

63% of Canadians with a terminal illness want to die at home. Only about 15% do. Kelly Martin died on September 30, 2019 in her home with her son and me (Bridget) at her side and her mother on the phone. A true honor she gifted us knowingly. We used this dashboard to provide care and communicate with providers. It was crafted in a couple of hours, edited with Kelly’s feedback, and used to provide a better death. Seeing the data can truly be life-changing.

I… just. Wow.

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Heatmap shows deaths by age in different countries

This interactive heatmap by Jonas Schöley shows mortality rates by age. Just use the dropdown menu to see the data for various countries. You can also compare male and female populations and countries.

As you might expect, you can see mortality rates decrease steadily, especially in the younger ages. Spikes or abrupt color changes might indicate war or disease. [via @maartenzam]

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Years of life lost due to breathing bad air

Researchers at the University of Chicago’s Energy Policy Institute estimated the number of years lost and the number of people affected due to particulate matter in the air. They estimated per country. The Washington Post used a mosaic plot, aka a Marimekko chart, to show the differences.

The width of each column represents total population for a country. The sections in each columns represent the number of people who will lose a certain number of years. Color represents average years of life lost.

These charts are often a bit confusing at first glance, but the scrolling format used here provides some guidance.

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Weighing the risk of moderate alcohol consumption

A research study on mortality and alcohol consumption is making the rounds. Its main conclusion is that all alcohol consumption is bad for you, because of increased risk. David Spiegelhalter, the chair of the Winton Centre for Risk and Evidence Communication, offers a different interpretation of the data:

Let’s consider one drink a day (10g, 1.25 UK units) compared to none, for which the authors estimated an extra 4 (918–914) in 100,000 people would experience a (serious) alcohol-related condition.

That means, to experience one extra problem, 25,000 people need to drink 10g alcohol a day for a year, that’s 3,650g a year each.

To put this in perspective, a standard 70cl bottle of gin contains 224 g of alcohol, so 3,650g a year is equivalent to around 16 bottles of gin per person. That’s a total of 400,000 bottles of gin among 25,000 people, being associated with one extra health problem. Which indicates a rather low level of harm in these occasional drinkers.

Therefore:

The paper argues that their conclusions should lead public health bodies “to consider recommendations for abstention”.

But claiming there is no ‘safe’ level does not seem an argument for abstention. There is no safe level of driving, but government do not recommend that people avoid driving.

Come to think of it, there is no safe level of living, but nobody would recommend abstention.

Burn.

See also Spiegelhalter talk about weighing risk against benefits in a video from 2010.

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