Healthcare Spending and Life Expectancy at Birth

Healthcare Spending and Life Expectancy at Birth

Healthcare Spending and Life Expectancy at Birth

Conventional logic holds that an increase in the input of an ingredient or factor leads to an increase in the output of a given product or result. This paper attempts to explore the logic and attempt to discover whether it holds true in the healthcare field.

Life Expectancy at Birth

Life expectancy at birth can be defined as that age to which a person is reasonably expected to live, barring any unusual incidents such as war or Act of God. This is calculated based on statistical data pertaining to the country of birth, sex, race and such. This differs from country to country and within the same country based on sex and within the same sex based on race. This is considered as an indicator of health conditions and by extension an indicator of the prosperity or well-being of a nation's population.

There are numerous factors which go into extending a person's lifespan or age. These range from sanitation facilities such as clean drinking water, atmospheric pollutants (such as proximity to toxic emissions, industrial areas and such) , access to healthcare, preventive treatment and education. While conceding the plethora of factors impacting lifespan, this paper will seek to focus on a single factor, healthcare spending, to evaluate whether an increase in spending leads to increased lifespan or vice versa.

Data has been collected from different countries and presented as easy to understand graphs and pictorial representations. This data and supporting graphs have been collected from University of California, Santa Cruz website (1). The chart produced below shows that Cuba has a life expectancy which is very close to that of United States, while spending almost 1/10 of what the United States spends on healthcare.

Thus, it can be seen that beyond a certain point, increased expenditure on healthcare does not seem to produce any significant increase in the resulting life expectancy.

The chart used in this study is a scatter graph. Scatter plots are similar to line graphs in that they use horizontal and vertical axes to plot data points. However, they have a very specific purpose. Scatter plots show how much one variable is affected by another. The relationship between two variables is called their