I am looking at data about health and wellness, etc.
The data sets I am most interested in are:
- Demographics (Age / Race / Household / Etc)
- Childhood Obesity rates
- CHIP rates
- Medicaid rates
- SNAP / WIC Enrollment
Local Sources: (Where (neighborhood vs highway and how many)
- Local Grocery Stores
- Local Fast Food
- Local Parks
- Public School Enrollment
- High School Graduation Rates
- Number of Adults with College Degrees / High School Degrees / No Degree / ETc
What I am interested in finding in some correlations between these factors and childhood obesity rates.
I am thinking of doing a comparison between Pennsylvania and two other states: Minnesota — which has a much lower childhood obesity rate and Kentucky — which has a slightly higher childhood obesity rate. I will also use Allegheny county and a comparison between two other counties in either state which are roughly equal to the state average (9.4 and 20.8).
Is there a correlation between childhood obesity rates and fast food availability, particularly fast food availability in neighborhoods vs by highways and grocery sources.
[Data points ]
I decided to focus on three states and three counties where I could find information:
Allegheny County, Pennsylvania
Hennepin County, Minnesota
Hinds County, Mississipii
I wanted to choose a county/state with higher childhood obesity (MI) and lower childhood obesity (MN)
The data points collected:
WIC Rate FY 2016 ( https://fns-prod.azureedge.net/sites/default/files/resource-files/26wifypart-11.pdf)
Number of fast-food restaurants in 2007(city-data)
Fast-food expenditures per capita — 2007(city-data)
Share (%) of total population that has low retail access (https://www.wilder.org/sites/default/files/imports/Healthy%20Food%20Access%20Study_Final%20Report_April%202016.pdf)
Poverty Rate (2017) (census)
Median household income (2017)(census)
Total accommodation and food services sales, 2012 (census)
total population estimates (2018) (census)
Total health care and social assistance receipts/revenue, 2012 (census)
I’m having trouble determining fast food restaurant sets — I may try to compile from yelp.
[basic thoughts about visualizations]
- I think it may be interesting to do a kind of board game — sort of like candyland but there is a wolf chasing children. Everyone would start the game at the same point with the wolf further down the game board. The wolf would move at a pace and based on factors the children would move more or less slowly.
- Example a child from Mississippi would move more slowly than a kid from Minnesota. If the child is on WIC it would move more slowly, etc until the child is eaten.
- Updated visualization: It would make more sense if every child token started evenly. The wolf would move at the same pace, but the players would draw attributes which make it more or less likely they will become obese. This would determine how many places they moved
ie: “enrolled in WIC program — move three spaces”, “live further than a two mile driving distance from a grocery store — go back one” etc
Story script update:
It’s dinnertime in the magical fairyland forest
and the wolves
and the witches
and the bears
and probably a shark
are hangry for their favorite meal
FAT AMERICAN CHILDREN
[[map of US colored states based on scale]]
…but it’s getting late, tummys are grumbling, and it’s a big country so there’s no time to waste looking in every neighborhood and underneath every hamburger wrapper.
What are the correlating factors that might predict where we could find a fat, tender, juicy, American kid?
“I think we should look for a place with a lot of fast food restaurants. Fat kids love french fries so that’s a likely hangout.”
[[french fry slide -out with stats]
Vermont (15.1, 1.6296)
Pennsylvania (17.4, 2.3658)
West Virginia (20.9, 3.3998)
Kansas (12.2, 4.0019)
Nebraska (12, 3.763)
Wyoming (11.8, 3.9815)
Not necessarily. Every state has fast food (range 1.9–4.1 per capita) and some states with a lot of fast food have relatively low childhood obesity rates.
and some states with high childhood obesity rates have far fewer fast food restaurants per capita.
“Find a place where the grocery stores are located far away. If families can’t travel to a grocery store they’ll likely make up the difference with convenience stores and fast food”
Alaska (9.9, 34)
Utah (8.7, 27)
Kentucky (20.8, 18)
West Virginia (20.9, 22)
Michigan (18.9, 23)
[radar map with states plotted on one page — turn page and map has obesity states next to markers]
Maybe, but also not a guarantee.
Several states with a high proportion of low retail access areas have low childhood obesity rates.
A few states with high childhood obesity rates have less than a quarter of their residents defined as having low retail access.
So what are the factors which correlate to childhood obesity?
States with the lowest rates of childhood obesity have two main things in common:
[Children representing states]
high median income:
Washington, $66, 174
[bar chart of stacked candies]
and low poverty rate:
[sprinkle line added to bar chart]
While states with high childhood obesity rates
[children representing states]
have low median income
[added to graph]
Mississippi, $42, 009
West Virginia, $44, 061
Louisiana, $46, 710
and high poverty rates
West Virginia, 19.1
They also have a high percentage of children who are food insecure
West Virginia, 20.2
and high rates of SNAP usage:
West Virginia, 19
So…. look for a poor kid in a poor neighborhood without consistent access to healthy food.
[wolf spys kid]
[kid sees wolf]
[kid says “hey”]
[sits on wolf]
There are numerous policies states have enacted to help kids health.
The obesity rate of children enrolled in WIC declined from 15.9% in 2010 to 13.9% in 2015 [shrinking shape]
Enrolling in head start can also help: Children who received 60 minutes of outdoor play time during Head Start are 42% less likely to be obese at the end of the Head Start year than children who played outside less often.
Children are not obese, they have obesity because like any other ailment it is not a permanent condition.
So eventually wolves and witches and bears and sharks will have to find something else to eat.
[wolves, bears, etc look at a cat]