US Space Medical Insurance Portfolio Project

Projects Portfolio Project: U.S. Medical Insurance Costs
1

Dear fellows,

I’ve reached the first Portfolio Project in Data Science and ask you for your feedback. I’ve added some story of lunar settlers insured by space insurance company just for fun.
The hardest was to scope the project, so if you would advise any interesting views on the data to consider, I would gladly update the project.
The second technical issue was to populate print() commands with names of the values, e.g.:

def bmi_costs(group):
    average_costs_bmi = round(sum(group) / len(group), 2)
    return average_costs_bmi 
    
print("Average insurance expences for settlers from Normal BMI group are " + str(bmi_costs(normal)) + "$.")
print("Average insurance expences for settlers from Overweight BMI group are " + str(bmi_costs(overweight)) + "$.")
print("Average insurance expences for settlers from Obese BMI group are " + str(bmi_costs(obese)) + "$.")
print("Average insurance expences for settlers from Extremely Obese BMI group are " + str(bmi_costs(extremely_obese)) + "$.")

would you advise more elegant solution to this?

You can have a look at the full project here: my-first-projects/us-medical-insurance-costs.ipynb at c3e6358c4db1d88b7c316dced935c00f3ce711f5 · dm-lav/my-first-projects · GitHub

2

Very interesting twist.

Some suggestions:

  • avoid using loaded words like “normal”. Objectivity is key here.

  • I realize that you didn’t choose the data set. But…do some research on BMI and how it’s a discriminatory marker & how it ignores other factors about one’s health. It’s not an accurate measure. It contributes to weight discrimination and stigma in healthcare, not to mention eating disorders in the general population. BMI doesn’t take into consideration an individual’s age, gender, race, muscle & bone mass, body composition, genetics, or medical history. US health insurance companies use this inaccurate number to charge people more in insurance premiums.

One thing that this data set does tell is the exorbitant the costs of US health insurance. Health insurance is also tied to employment here. A good introductory read:
https://www.census.gov/newsroom/press-releases/2022/income-poverty-health-insurance-coverage.html
Another interesting analysis on insurance costs & how many households don’t have enough money to pay premiums. The number one kind of debt in the U.S. is medical debt. It’s a uniquely American problem…