A few years ago I tried my hand at writing a few NIH grant proposals. I struck out. I had a couple applications scored but none were funded and they didn’t really come very close. In any case, while I was researching my topic I came across the map below on the National Cancer Institute’s website:
Melanoma is more common in people who live in areas that get large amounts of UV radiation from the sun. For example, melanoma is more common in California than in Minnesota, where the sun is not as strong.
“Gee, don’t forget your sunscreen if you cross the border from Utah into Nevada.”
Are you kidding me? Is this really the state of the art at the National Cancer Institute? Melanoma is serious business. According to NCI it killed about 8,700 people last year. Couldn’t we take the time to make a better map?
Now, let’s take a look at a map of annual solar radiation from the National Renewable Energy Lab (NREL):
See any differences between the two maps? Clearly, a neat and tidy south to north UV gradient does not exist, at least not in the US. And UV exposure doesn’t seem to pay much attention to state borders. Why didn’t the peer review process catch these Geography 101 errors? Perhaps a state-level map isn’t really appropriate for this type of screening? Probably there should be UV risk indices by nearest city or CBSA. Would it really be so much trouble to ask patients to select their city or enter a ZIP code rather than just current state of residence? And, while noting general medical history, couldn’t they determine the approximate number of years spent in residence per location to estimate cumulative lifetime UV exposure?
In the age of Google Maps on every laptop and smart phone, everyone thinks that they’re a cartographer. “Oh, making maps isn’t so hard. I painted the northern, central and southern states different colors. It was easy.”
Not really. There’s more to it than that.
My hope is by the time my 3-year old daughter decides on a career there will be as many people studying well-constructed maps and looking for spatial patterns in geographic data as we currently have staring into microscopes and looking for patterns of disease in skin tissue samples. John Snow should have convinced us long ago that both scales of analysis are critical for tackling public health problems.