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Correlation, Causation and County Maps
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jmbmilholland wrote: »Ah, yes @senecarr I can get behind the possibility of different treatment, coming from a visible minority, different treatment, and the stresses around that. In Canada we have a poor record on how we treated the native populations.
I've been thinking through your race vs. ethnic origin question, and I have come to the conclusion that my mind is not smart enough to handle it, because they seem to blur into each other and both change with changing cultural constructions and attitudes, whether scientifically defined as with race or community defined as with ethnic origin. Ugh! It's especially challenging when you look at the so-called "Hispanic" demographic; for example, to take two examples where the Hispanic demographic is by far the most dominant, the Texas county Hidalgo is on the heavy end of the spectrum (34% obese), while Miami-Dade is on the light end of the spectrum (21% obese). Miami Dade has a large Cuban population and probably many other Hispanic groups; I am guessing Hidalgo is primarily Mexican. Hispanics from all countries can range from Caucasian to African to Native American (which might be lumped in with Asians...?). How does a demographer even tease through that? Racially, a Hispanic could be the same as a "white" or a "black" but ethnic origin, which encompasses culture, there are huge differences.
At any rate, looking at Indiana, the two heaviest counties are very rural, somewhat hilly, in the southern part of the state--with a culture/ethnic origin that is comparable to Appalachian areas. And of course a very poor, slandered ("hillbilly"), mistreated demographic. Fun fact: John Mellancamp's "Small Town" is about Seymour, Indiana, which is in the heaviest Indiana county. Of course, then you also have one of the two thinnest Indiana counties located in the hilly south, Monroe, which encompasses Bloomington and Indiana University.
Oh, and as a corollary to that, while most cities in Texas have a high Mexican Hispanic population as opposed to Hispanics originally from other countries, there's also a big divide as to which part of Mexico they come from - and the different areas of Mexico have food cultures as diverse as the different regions of Italy.
Dallas, for example, has very few people that originated from the Oaxaca region and it's been almost impossible to find that region's food here for that reason. In fact, a restaurant just opened last month with some traditional Oaxacan dishes and I can't wait to try it out. Houston, on the other hand has more of a blend. Not sure about Austin or San Antonio.
That is fascinating. Now I want to eat ALL the Mexican food just thinking about it!
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lemurcat12 wrote: »Here's an income one for you (I'm guessing you will like it): http://www.census.gov/censusexplorer/censusexplorer.html
The IL counties that do poorly on obesity also are on the low income side, but the problem is that's distorted by the fact they are quite rural.
The most interesting information is based on census tract, and we don't have the obesity information for that (I am positive that in Chicago it would track the low income areas, but that's hard to separate out from the minority stats, at least with certain areas).
The income inequality it shows in places like Chicago isn't surprising if you know anything about the area, but extremely dramatic.
Before I went to grad school in Chicago and learned the south side better, my then-boyfriend and I somehow got off the Dan Ryan and ended up traveling down Michigan Avenue from Garfield Park to the Loop. It was a fascinating experience and we enjoyed being trailed by several Chicago cops till we reached Chinatown. A couple of idiots with Indiana plates.0 -
@senecarr suggested that a sedentary lifestyle and population density are related to obesity. So I pulled a population density map.
It appears the rural southwest is in much deeper trouble, and population density is not their problem.
I also suggest that not all dense urban areas are un-walkable. Manhattan is highly walkable.
Whether walkable or not, if the area is dense, people will need to make use of parking structure and walk to and from that to their location instead of parking in the lot at their location.
My comment was more aimed at California and the East Coast locations.
This is the relation to which I gravitate from personal experience. I've lived in at least two cities that are highly walkable to the point of having a car being somewhat of an annoyance if not a disadvantage, one where a car is quite helpful but not strictly necessary, and at least five where a car is a necessity. The amount of walking I did in the highly walkable cities was the equivalent to several miles a day and that's before any intentional exercise. On the other hand, where I currently live people will drive their cars to simply go 1 mile or less. When exercise is just part of life I have found it easier to keep my weight down, primarily becuase it's equivalent to "bonus" exercise and I did it without thinking about it.
Put another way, one doesn't see a lot of fat people in places like New York, London, Hong Kong, Tokyo, or Paris. The residents may also have a higher educational level which tends to correlate with health markers, but it's hard for me not to focus on that extra exercise.0 -
sunnybeaches105 wrote: »@senecarr suggested that a sedentary lifestyle and population density are related to obesity. So I pulled a population density map.
It appears the rural southwest is in much deeper trouble, and population density is not their problem.
I also suggest that not all dense urban areas are un-walkable. Manhattan is highly walkable.
Whether walkable or not, if the area is dense, people will need to make use of parking structure and walk to and from that to their location instead of parking in the lot at their location.
My comment was more aimed at California and the East Coast locations.
This is the relation to which I gravitate from personal experience. I've lived in at least two cities that are highly walkable to the point of having a car being somewhat of an annoyance if not a disadvantage, one where a car is quite helpful but not strictly necessary, and at least five where a car is a necessity. The amount of walking I did in the highly walkable cities was the equivalent to several miles a day and that's before any intentional exercise. On the other hand, where I currently live people will drive their cars to simply go 1 mile or less. When exercise is just part of life I have found it easier to keep my weight down, primarily becuase it's equivalent to "bonus" exercise and I did it without thinking about it.
Put another way, one doesn't see a lot of fat people in places like New York, London, Hong Kong, Tokyo, or Paris. The residents may also have a higher educational level which tends to correlate with health markers, but it's hard for me not to focus on that extra exercise.
For New York, I have a co-worker who used to work at our main office in New York. He said that last week he worked there he got his Fitbit and was easily doing 10K to 15K steps just going into work. I also note that in Super Size me, something glossed over is that the guy mentions he uses a pedometer in the movie to intentionally limit his steps because of the difference between a New Yorker and most of America.0 -
That was something I noticed when I started out. I was losing more than predicted and got a Fitbit and was surprised at how much I walked just by deciding I'd take the L instead of the bus (one block walk vs. an extra mile roundtrip, and I often add extra), and otherwise do all errands/everything I could through walking (which is everything, unless I have something really huge to carry or have to go to the 'burbs). Even at my worst, when I'd drive more, I walked quite a bit (and live in a 4th floor walk-up), so couldn't be as inactive as many. The great thing now is I can get in lots of exercise by biking or running (or occasionally walking partway) to/from work, which wouldn't be an option for many people, I know.
I have a couple of accidently in risky Detroit and Chicago neighborhoods stories, but what strikes me here is one about a trip out to the 'burbs I had for work, before I had a car. I felt weird about not having a car and didn't want to rent one, so I took the train and cabbed from the station and intended to walk back. It wasn't a terribly long walk -- a mile or 2, which I cover routinely in Chicago -- but there turned out to be no sidewalks and no one else walking and I guess I looked strange walking along in my suit and heels and a police car pulled over, asked me what I was doing, and then offered me a ride to the train station. Who knew walking in some suburbs was so remarkable.
I know there are many exceptions, but it's something I always think of in considering daily activity one element of the obesity crisis.0 -
I notice looking at these county maps that urban centers, generally, are healthier. It could be walkability. I suspect also easy access to health services, health education.0
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I'm skeptical about health services/education being much of it, or really understandable from a map, but I'm open to being convinced.
The urban centers might look better (25% vs. 35% obese), but I think they are likely really uneven, and track the poverty stats. Chicago/Cook County looks pretty good comparatively, but childhood obesity and obesity-related illnesses are a huge crisis in many neighborhoods, probably worse than the stats in the counties that look worse.
There is a lot of focus on health/nutrition education in the public schools, but given the overwhelming issues in some public schools, don't know if it's particularly effective.0 -
Well, I see a pattern over several states. The health/obesity rates are generally a lighter colour (better outcomes) in the counties with large populations, and the darkest counties tend to be rural.
So one has to ask; what does a city have that the country doesn't?
Unless it's a matter of granularity. Problems tend to disappear on average when surrounded by a whole bunch of non-problems.
For instance, it used to be believed that Apple Pie was America's pie. But when pies were reduced to individual sizes, different preferences emerged. It turns out that Apple Pie is the consensus choice (most people's second favourite).0 -
Here are the counties in the four states profiled so far with the largest populations, where their health rankings are at least fair, with three counties that don't conform to the pattern I'm seeing.
Texas: HAS, WIS, DEN, PAR, TAR, and BEX
California: OR, SE, AL
Illinois: LA
Florida: DA, BW, PB, NB
The counties that don't conform to my observation are California (SA), Illinois (CX) and (WB). Cook County (CX), which @lemurcat12 most rightly has been profiling, does stand out. The population is VERY dense, and VERY black.
http://www.countyhealthrankings.org/app/illinois/2015/rankings/cook/county/outcomes/overall/snapshot
Is 23% uninsured adults high?0 -
Just looked at Sacramento (SA), California, another densely populated area with not so hot health outcomes. The community is about 50% white. They've got danger signals all over the place; obesity, STD's, unemployment, children in poverty. What they have in common with Cook county is a nearly identical food insecurity and uninsured adults.
Would it be fair to characterize Cook County and Sacramento as being communities in crisis? They've got so many things going on it's hard to separate the chickens from the eggs. It's hard for policy makers then to decide on a course of action. Should they be running school lunch programs or paving walking paths? It can feel like scooping the ocean out with a bucket.
I heard this guy on TED who suggested that violence, when treated as a disease vector, can be targetted and "cured". I wonder if chaotic communities, if they received targeted intervention, might turn around?
http://cureviolence.org/post/staff/gary-slutkin/0 -
Here are the counties in the four states profiled so far with the largest populations, where their health rankings are at least fair, with three counties that don't conform to the pattern I'm seeing.
Texas: HAS, WIS, DEN, PAR, TAR, and BEX
California: OR, SE, AL
Illinois: LA
Florida: DA, BW, PB, NB
The counties that don't conform to my observation are California (SA), Illinois (CX) and (WB). Cook County (CX), which @lemurcat12 most rightly has been profiling, does stand out. The population is VERY dense, and VERY black.
http://www.countyhealthrankings.org/app/illinois/2015/rankings/cook/county/outcomes/overall/snapshot
Is 23% uninsured adults high?
Cook County is 43% non Hispanic white, 25% Hispanic, and 24.4% African American (or black). Chicago alone is close to a third, a third, a third. The Chicago metro area (which includes multiple counties and some other towns, like Naperville, that rank as among the biggest in IL) is somewhat whiter. I wouldn't call Chicago very black (as for dense, it's a big city, but there are other big cities that will be as dense). What is striking about Chicago and some other cities, especially in the industrial midwest, probably, is that the population is still quite segregated, with a few exceptions.
It's challenging to compare a city to a more rural area, as there are lots of differences, and the differences within a city are so great.0 -
Here are the counties in the four states profiled so far with the largest populations, where their health rankings are at least fair, with three counties that don't conform to the pattern I'm seeing.
Texas: HAS, WIS, DEN, PAR, TAR, and BEX
California: OR, SE, AL
Illinois: LA
Florida: DA, BW, PB, NB
The counties that don't conform to my observation are California (SA), Illinois (CX) and (WB). Cook County (CX), which @lemurcat12 most rightly has been profiling, does stand out. The population is VERY dense, and VERY black.
http://www.countyhealthrankings.org/app/illinois/2015/rankings/cook/county/outcomes/overall/snapshot
Is 23% uninsured adults high?
So for Texas that's
HAS - Haskell? There is no matching abbreviation for a TX county. Haskell's a tiny county, ~3000 people; Harris would be the other choice - that'd be Houston
WIS - Wise (Decatur)
DEN - Denton
PAR - Parker (Weatherford), west of and neighbor to Fort Worth
TAR - Tarrant (Fort Worth and Arlington),
BEX - Bexar (San Antonio)0 -
Here's a map for uninsured rates: https://www.enrollamerica.org/research-maps/maps/changes-in-uninsured-rates-by-county/0
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@stealthq I used the population density map above and found the corresponding counties here:
http://www.countyhealthrankings.org/app/texas/2015/overview
HAS - Harris
Wise, Denton, Parker, Terrant
BEX - Bexar0 -
This is always controversial, but here's a food desert map: http://www.ers.usda.gov/data/fooddesert/0
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Thanks for the uninsured maps, @lemurcat12 . I notice the areas of Texas and Florida with poor health outcomes, less populated, and rural, also have low insurance rates. That would be the southwest border of Texas and the northern border of Florida.
On the county maps I was looking at, Cook county was listed as adult uninsured 23% but on this latest map it shows an uninsured rate of 9%.
Oh, and thanks for correcting me on the proportion of race. I used the dot map for my first observation for Cook, but the statistics page on the county maps for the California example.0 -
This is information on what states have adopted Medicaid expansion (which influences uninsured rate): http://kff.org/health-reform/state-indicator/state-activity-around-expanding-medicaid-under-the-affordable-care-act/0
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I had a thought about this over the weekend and keep forgetting to post it here.
One reason I am skeptical about the focus on individual differences between or within counties being the big explanation--or maybe not the key focus--is that they overstate the difference. While CO looks good compared to MS, it looks horrible compared to CO (and some other states) 20 years ago. The colors on the map create the appearance of a big difference between a 24% and 28% rate, when the dramatic story is the change over time.
http://stateofobesity.org/adult-obesity/ -- current state map with trends
http://obesity.procon.org/view.resource.php?resourceID=006026 -- map with illustration of change over time0 -
Thank you for that. I'm still mulling over these maps and what they might mean. Here's a trend that's been growing since the 1980's. Income inequality and the likely causes.
http://www.theatlantic.com/business/archive/2015/11/cities-economic-fates-diverge/417372/0 -
lemurcat12 wrote: »I had a thought about this over the weekend and keep forgetting to post it here.
One reason I am skeptical about the focus on individual differences between or within counties being the big explanation--or maybe not the key focus--is that they overstate the difference. While CO looks good compared to MS, it looks horrible compared to CO (and some other states) 20 years ago. The colors on the map create the appearance of a big difference between a 24% and 28% rate, when the dramatic story is the change over time.
http://stateofobesity.org/adult-obesity/ -- current state map with trends
http://obesity.procon.org/view.resource.php?resourceID=006026 -- map with illustration of change over time
Data visualization is an interest of mine (and a facet of my job). Colors are one of the worst ways to depict changes in magnitude. Humans aren't able to make the fine distinctions of intensity or color gradations needed.
One thing I'm learning from looking at these maps is that whomever put them together is not educated in proper data visualization techniques. Data comparisons between the maps, even judging the data on a single map is much too difficult for many of them.0 -
Here's how anyone can make such a county map using a mac.
http://flowingdata.com/2009/11/12/how-to-make-a-us-county-thematic-map-using-free-tools/0 -
lemurcat12 wrote: »I had a thought about this over the weekend and keep forgetting to post it here.
One reason I am skeptical about the focus on individual differences between or within counties being the big explanation--or maybe not the key focus--is that they overstate the difference. While CO looks good compared to MS, it looks horrible compared to CO (and some other states) 20 years ago. The colors on the map create the appearance of a big difference between a 24% and 28% rate, when the dramatic story is the change over time.
http://stateofobesity.org/adult-obesity/ -- current state map with trends
http://obesity.procon.org/view.resource.php?resourceID=006026 -- map with illustration of change over time
Data visualization is an interest of mine (and a facet of my job). Colors are one of the worst ways to depict changes in magnitude. Humans aren't able to make the fine distinctions of intensity or color gradations needed.
One thing I'm learning from looking at these maps is that whomever put them together is not educated in proper data visualization techniques. Data comparisons between the maps, even judging the data on a single map is much too difficult for many of them.
That's very interesting -- I'd love to read more about data visualization and how it's done well. Do you have any thoughts about better ways to convey these?0 -
Kimberly_Harper wrote: »Holy crap that's a big change in obesity percentage in a matter of 10 years!
The CDC also redefined obesity in that period (1998 to be precise). I don't think they updated their old map or statistics to reflect the new definition, however.
For what it's worth, the American Association of Clinical Endocrinologists (AACE) and the American College of Endocrinology (ACE) is suggesting yet another change to the definition obesity. This time it is a functional change, but it would make a BMI > 25 "obese", if you have any health issues correlated to obesity.Obesity-related complications include the following: metabolic syndrome; prediabetes; type 2 diabetes; dyslipidemia; hypertension; nonalcoholic fatty liver disease; polycystic ovary syndrome; sleep apnea; osteoarthritis; gastroesophageal reflux disease; and disability/immobility.
Look for millions more Americans to be labeled obese in the near future if these recommendations are taken up by the CDC.0 -
Kimberly_Harper wrote: »Holy crap that's a big change in obesity percentage in a matter of 10 years!
The CDC also redefined obesity in that period (1998 to be precise). I don't think they updated their old map or statistics to reflect the new definition, however.
I think the maps are comparing like to like -- the second set specifically say they are using BMI>30 for the obesity definition, even for the earlier ones. (The change was 28/27 to 25 for overweight.)0 -
lemurcat12 wrote: »lemurcat12 wrote: »I had a thought about this over the weekend and keep forgetting to post it here.
One reason I am skeptical about the focus on individual differences between or within counties being the big explanation--or maybe not the key focus--is that they overstate the difference. While CO looks good compared to MS, it looks horrible compared to CO (and some other states) 20 years ago. The colors on the map create the appearance of a big difference between a 24% and 28% rate, when the dramatic story is the change over time.
http://stateofobesity.org/adult-obesity/ -- current state map with trends
http://obesity.procon.org/view.resource.php?resourceID=006026 -- map with illustration of change over time
Data visualization is an interest of mine (and a facet of my job). Colors are one of the worst ways to depict changes in magnitude. Humans aren't able to make the fine distinctions of intensity or color gradations needed.
One thing I'm learning from looking at these maps is that whomever put them together is not educated in proper data visualization techniques. Data comparisons between the maps, even judging the data on a single map is much too difficult for many of them.
That's very interesting -- I'd love to read more about data visualization and how it's done well. Do you have any thoughts about better ways to convey these?
There are basic rules for what types of data to depict using different devices. I have a chart of those from an presentation that used info from the book Fundamentals of Computer Graphics by Munzner. I think I've managed to add a screenshot of the chart from the presentation (originally from the book).
Based on human perception, quantitative values like magnitude are best visually represented by changes in position. Next best is changes in length, then changes in angle, slope, and on down the list. You can see lightness, saturation and hue are all in the latter half of choices.
The problem is that people rendering the data are more interested in an eye-catching visual for presentations than in something useful for quick and accurate visual analysis. Ah, well.
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Kimberly_Harper wrote: »Holy crap that's a big change in obesity percentage in a matter of 10 years!
The CDC also redefined obesity in that period (1998 to be precise). I don't think they updated their old map or statistics to reflect the new definition, however.
For what it's worth, the American Association of Clinical Endocrinologists (AACE) and the American College of Endocrinology (ACE) is suggesting yet another change to the definition obesity. This time it is a functional change, but it would make a BMI > 25 "obese", if you have any health issues correlated to obesity.Obesity-related complications include the following: metabolic syndrome; prediabetes; type 2 diabetes; dyslipidemia; hypertension; nonalcoholic fatty liver disease; polycystic ovary syndrome; sleep apnea; osteoarthritis; gastroesophageal reflux disease; and disability/immobility.
Look for millions more Americans to be labeled obese in the near future if these recommendations are taken up by the CDC.
Hmmm. This piqued my interest. I used to teach research methodology for majors in the sciences, social sciences and humanities, and one of the fundamental things I taught was the shenanigans that can take place with definitions. The CDC is at the same time a political and scientific entity (although all science has a political aspect to it), so I wonder if the change in definition has anything to do with public policy shaping: http://content.healthaffairs.org/content/21/6/142.full
It's an old article, but very interesting as it contextualizes obesity with drinking, drugs, sexuality and tobacco use. Here is a passage specifically on poor/minorities:
"Demonizing users—especially poor people and minority groups who drink, take drugs, or harbor sexually transmitted diseases—has been one of the most powerful spurs to government action in U.S. history. There is nothing quite like the fear of sinister others to overcome the stalemate of American policy making.
Although overweight Americans have faced popular prejudice for more than a century, critiques of gluttony have not translated into demonization. Antiobesity activists do not portray overweight people as dangerous to society—like drug addicts or smokers polluting the air with secondhand toxins. In part this may be because more than half of U.S. adults are overweight, and nearly one in five is obese.15 Still, each of the other cases challenged a commonplace activity or condition. In 1965, for example, an estimated 43 percent of American adults were habitual smokers—a figure that has plummeted with changes in social mores, regulatory efforts, and disapproval bordering on the demonization of smokers.16
One common thread in past demonization episodes is at least latent in the obesity case. Poor people and members of minority groups tend to be more obese than other Americans are.17 Given the historical patterns of other ostensibly private consumption practices, the opportunity for demonization may well be present. But, to date, this has not been taken up by those calling for action against obesity.
Demon industry.
In all four of our comparative cases, activists attack the producers or suppliers. They charge corporate villains with seeking profits by peddling poison. Worse, the greedy industry lures children into destructive habits."
And one of the conclusions is as follows:
"Although social scientists often depict the U.S. government as relatively weak, it has been far more ready than most Western regimes have been to regulate (or prohibit) private behavior. The politics of social control generally feature the seven triggers discussed here. Of course, political history does not permit causal claims, but we believe that these descriptive analogies across time and issue areas offer a useful policy guide. Context also matters: In every example of state intervention, political action becomes possible when a “window of opportunity” opens. Even when all seven triggers are in place, policy efforts may fail—without propitious circumstances, luck, timing, or a political plan primed to go when opportunity strikes."
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lemurcat12 wrote: »I think the maps are comparing like to like -- the second set specifically say they are using BMI>30 for the obesity definition, even for the earlier ones. (The change was 28/27 to 25 for overweight.)
I actually don't think they actually do, though, because the graphs changed dramatically overnight as well. I remember discussing this when it happened in my biomedical ethics class at UT-Houston MD Anderson Graduate School of Biomedical Sciences.0 -
lemurcat12 wrote: »I think the maps are comparing like to like -- the second set specifically say they are using BMI>30 for the obesity definition, even for the earlier ones. (The change was 28/27 to 25 for overweight.)
I actually don't think they actually do, though, because the graphs changed dramatically overnight as well. I remember discussing this when it happened in my biomedical ethics class at UT-Houston MD Anderson Graduate School of Biomedical Sciences.
I'm talking about this specific set of maps (the ones I linked above), which are labeled "over 30 BMI" back to 1990. I'd be curious if you can find something to suggest they are labeled wrong. The change from 1995 to 2000 seems similar to the change from 1990 to 1995, and I thought the obesity rate remained at 30, but the overweight cutoff changed.0 -
The obesity definition also dropped, from a BMI of 35 to 30.
Kuczmarski, Robert J., and Katherine M. Flegal. "Criteria for definition of overweight in transition: background and recommendations for the United States." The American journal of clinical nutrition 72.5 (2000): 1074-1081.
It would be difficult to assess how they constructed the maps but it seems entirely possible to me they'd stored obesity rates by county, not actual raw weight data by county. Data was not nearly so cheap to store or parse historically.0
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