Last month the Washington Post gained access to ARCOS, the U.S. Drug Enforcement Agency’s database of controlled substance transactions, which tracks the path, from manufacturer to pharmacy, of every pain pill in the United States. The Post’s initial analysis found that some 76 billion oxycodone and hydrocodone pills were distributed in the U.S. between 2006 and 2012, that only a few companies manufactured and distributed the bulk of the pills, and some regions of the country were utterly saturated with the pills. That’s where the maps come in: the Post has county-level maps of all this data.
Comparing county-level maps of opioid overdose deaths and pill shipments reveal a virtual opioid belt of more than 90 counties stretching southwest from Webster County, W.Va., through southern Virginia and ending in Monroe County, Ky. This swath includes 18 of the top 20 counties ranked by per-capita prescription opioid deaths nationwide and 12 of the top 20 counties for opioid pills distributed per capita.
The New York Times maps confirmed measles cases in the United States as of April 29, 2019. “Measles was declared eliminated in the United States in 2000 but the highly contagious disease has returned in recent years in communities with low vaccination rates. The number of cases reported this year is already nearly double last year’s count and has surpassed the previous post-elimination high of 667 cases in 2014.”
On Thursday, the World Health Organization released a map showing parts of the Democratic Republic of the Congo that are currently being affected by Ebola. The map showed four cases in Wangata, one of three “health zones” in the large city of Mbandaka. Wangata, according to the map, lies north of the main city, in a forested area on the other side of a river.
That is not where Wangata is.
“It’s actually here, in the middle of Mbandaka city,” says Cyrus Sinai, indicating a region about 8 miles farther south, on a screen that he shares with me over Skype.
Almost all the maps of the outbreak zone that have thus far been released contain mistakes of this kind. Different health organizations all seem to use their own maps, most of which contain significant discrepancies. Things are roughly in the right place, but their exact positions can be off by miles, as can the boundaries between different regions. […]
To be clear, there’s no evidence that these problems are hampering the response to the current outbreak. It’s not like doctors are showing up in the middle of the forest, wondering why they’re in the wrong place. “Everyone on the ground knows where the health zones start and end,” says Sinai. “I don’t think this will make or break the response. But you surely want the most accurate data.”
Earlier this year, a study in the Swiss Medical Weekly explored the spatial patterns of Swiss mortality rates between 2008 and 2012. The study looked at the most common causes of death and produced a number of maps. The Tages Anzeiger’s story on the study (in German) focused on only two of them—diabetes and liver disease—that produced the most dramatic regional variations: basically, people in German-speaking regions are more likely to die of diabetes, and people in French-speaking regions are more likely to die of liver disease. The newspaper’s interactive maps are nicer, too:
Over all, the gains are substantial: a seven-percentage-point drop in the uninsured rate for adults. But there remain troublesome regional patterns. Many people in the South and the Southwest still don’t have a reliable way to pay for health care, according to the new, detailed numbers from a pair of groups closely tracking enrollment efforts. Those patterns aren’t an accident. As our maps show, many of the places with high uninsured rates had poor coverage before the Affordable Care Act passed. They tend to be states with widespread poverty and limited social safety nets. Look at Mississippi and Texas, for example.
A map on a display at the CDC’s in-house museum hides in plain sight what U.S. government authorities are reluctant to admit: the origin of the 2011 cholera epidemic in Haiti (a U.N. peacekeeping base housing a batallion from Nepal). All the more amazing by its juxtaposition with John Snow’s famous 1854 cholera map of London. It’s as if they wanted us to tell us something while being prevented from doing so.
Vox’s lead exposure risk map takes a nationwide look at a crisis some might have thought was limited to Flint, Michigan. “The areas where kids are at highest risk of lead exposure—an estimate calculated using government data about the surroundings—are scattered all across the country.” Lead exposure data is hard to come by, so exposure risk is calculated based on Washington State’s methodology, which uses age of housing and poverty as risk factors. [Mapbox]
Maps about the Zika virus have been cropping up lately. I’ve been reluctant to post them, initially because I didn’t want to play a role in whipping up unnecessary panic, but also because—the more I looked at them—many of the maps are problematic in and of themselves.
Some, like this CDC map of countries with active Zika virus transmission, lack useful detail. Or if they have detail, it’s not at all helpful: The Economist’s map shows the local risk of transmission and the number of travellers from Brazil; this map aggregates news stories about the virus and overlays the predicted distribution—predicted, mind—of two mosquito species. Neither map says anything about the spread of the virus itself; both could do a great job of scaring the crap out of anyone who gives either map a casual look. Finally, like these Scientific American maps, they can be extremely U.S.-centric, suggesting that the virus is only a problem insofar as it affects us. [via]
The New York Times maps the rise in deaths from drug overdoses. “Some of the largest concentrations of overdose deaths were in Appalachia and the Southwest, according to new county-level estimates released by the Centers for Disease Control and Prevention. […] The death rate from drug overdoses is climbing at a much faster pace than other causes of death, jumping to an average of 15 per 100,000 in 2014 from nine per 100,000 in 2003.” [via]
County-by-county life expectancy estimates released last month by the Institute for Health Metrics and Evaluation reveal a startling gap between the longest-lived and shortest-lived areas of the country: the difference can be as much as 15 years.
The range of life expectancies is so broad that in some counties, such as Stearns, Minnesota, lifespans rival some of the places where people live the longest—Japan, Hong Kong, and France—while in other counties, life expectancies are lower than places that spend far less on health care—Egypt, Indonesia, and Colombia. Even within states, there are large disparities. Women in Fairfax, Virginia, have among the best life expectancies in the world at 84.1 years, while in Sussex, Virginia, they have among the worst at 75.9 years.
And the situation isn’t improving either: “In 661 counties, life expectancy stopped dead or went backwards for women since 1999. By comparison, life expectancy for men stopped or reversed in 166 counties.” When people refer to the U.S. as a Third World country, this sort of thing—the disparity, the decline—is usually one of the reasons why. Via Tobias Buckell.