US Children Under 18 Don’t Have Health Care?

Healthcare is often a topic of political discussion in the US. Many countries provide free healthcare/ health insurance to their citizens for free. The US unfortunately has not followed which to many is a huge topic of debate. Personally, I think that the US should do the same as having access to health care seems like a simple right that everyone should be afforded no matter what. Even with some plans put into place to try and make it easier for US citizens to get health care, there are still many people nationwide who can not afford health insurance.

All of that said, I took a look at a data set that covered United States counties from each state. This data set included the following columns/categories: trump_2016 trump_2020 16_20_shift state county covid_cases, covid_deaths, Pct_Single_Female, Pct_HS_Grad, Pct_College_Grad Pct_Veteran Pct_Disability, Pct_Live_Same_House_1YA, Pct_Born_Same_State, Pct_Foreign_After_2010, Pct_Foreign_Born_LatAm, Pct_English_Only, Pct_Broadband, Pct_Armed_Forces, Pct_Unemployed, Median_Income, Pct_Assistance, Pct_Health_Insurance, Pct_Family_Poverty, Population, Pct_Under_18, Pct_Over_65, Pct_Female, Pct_White, Pct_Hispanic, Pct_Over_18_Citizen, covid_cases_percapita, and covid_deaths_percapita.

My goal was to find an “unusual” relationship between two of the variables. In my notebook, my original plan was to essentially create a scatter plot for different pairs of variables until I ended up with a scatter plot that I thought showed a somewhat strong relationship. I realized that with the amount of columns in the dataset, this was a highly inefficient way to try and find a relationship. I then wrote a block to calculate the correlation coefficient for each of the columns against the percent health insurance column because I did want to look at health insurance.

What I found interesting and somewhat unusual was the relationship between the percentage of the population with health insurance and the percentage of the population that are under 18. The correlation coefficient for the relationship between these two variables is -0.34. Meaning as the percentage of the population that are under 18 increases the percentage of the population that has health insurance decreases. I found this interesting because I expected that kids would be automatically included on a family or parent’s healthcare plan and would have expected to see the opposite relationship. I thought about this relationship and considered the fact that maybe they do not count children who are included on a family’s insurance plan as someone with health insurance. But I would not expect this to be the case as they do have health insurance. Below is the scatterplot showing the relationship.

Although this relationship is not very strong I think this is definitely something of interest considering that children tend to be more vulnerable to illness and injuries. I think this certainly strengthens the argument that the United States should follow other countries in providing free health care to their citizens. Especially when there is some evidence that children are being negatively impacted.

Again I would like to mention that there is a chance that maybe the data is not considering people under 18 on a family insurance plan a part of the population as a part of the insured population. I think that is important to consider while reading and reflecting on this post. But even if that is the case I think there are still negative impacts. For example, if the population of children under 18 is rising and the number of people with health insurance is decreasing that still means that there is a high likelihood that many children are not being insured even through their parents.

This has been a debate for a long time in the United States and I have always been on the side that we should provide free health care to our citizens. Seeing negative impacts like what I have investigated in this post has made me feel more strongly about the debate. It has also made me feel as though legislators should dig more deeply into who it is really impacting and how that could impact the future population. Considering we just lived through a global pandemic and the cost of living has risen dramatically there are far more impacts that may compound to cause much larger issues in the United States if there is not a change made soon. I think it would be interesting to dig deeper into this issue as well as see how it is impacting the older population throughout the United States since they also tend to be a more vulnerable population group.