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A causal claim about the relation between governments policies and the livelihood of people
It is not a secret that appropriate healthcare offered in time is one of the most credible chances to extend a human life. This is why the governments try to develop the most important implications on the citizens livelihood. The leaders truly believe that their policies may considerably improve the lives of people they represent and rule. Still, it is very important to understand that lengthy and sometimes conflicting negations considering the interests of different politically powerful medical subcultures should take place in order to introduce the required portion of healthcare (Arbuckle 2012).
The role of government in the life of each citizen is crucial indeed, and the way of how healthcare is presented to people may influence the quality of life. However, it is hard to define the nature of a causal relationship between healthcare spending and life expectancy in different countries due to the presence of such factors like social inequality (Navarro, Muntaner, Borrell, Benach, & Quiroga 2006), pharmaceutical issues (Shaw, Horrace, & Vogel 2005), or the necessity to meet the other demands of society. Each country has its own peculiarities, social norms, and citizens expectations.
On the Figure 1, the data about some countries and their life expectancy and healthcare spending indicators from 1968 to 2013 is given. At a glimpse, it is evident that the Americans are the nation that is in need of certain healthcare reforms. Nevertheless, the other representatives help to create a more or less certain causal claim that, as a rule, higher level of OECD governments healthcare spending (x-axis) causes a higher level life expectancy (y-axis), still, some expectations should be taken into consideration. Of course, there are a number of hurdles that define the quality of such claim, and their evaluation is a good chance to comprehend the essence of the relations better (Kellstedt & Whitten 2008).
Hurdles in establishing a relation between healthcare spending and life expectancy
According to Kellstedt and Whitten (2008), there are four main hurdles that should be taken into consideration while evaluating the chosen causal claim. Our claim is that higher level of OECD governments healthcare spending leads to a higher level life expectancy. So, it is high time to answer the following questions: is it possible to prove life expectancys dependency on healthcare spending? Yes, it is.
There are many developed countries that find it necessary to spend much money on health care in order to improve population health (Funtleyder 2008). However, each year, financial needs necessary for health care increase considerably, and the governments want to know whether it is still beneficial to support the chosen spending.
Figure 1, as well as the investigations introduced by Anderson and Fronger (2008), Goldsmith (2010), or Williams (2010), proves that the United States spent about $6000 per capita on health care in 2005 and demonstrated one of the lowest results in life expectancy. The other countries demonstrated the opposite results and proved that healthcare spending influence life expectancy; still, it is difficult to demonstrate a casual influence of healthcare spending on life expectancy.
The reverse causal mechanism shows that it is possible to reject an idea that life expectancy defines healthcare spending because health care spending is defined by the period after birth and the time to death, and life expectancy cannot directly increase healthcare demands as the majority of such demands are usually postponed in such situations. In other words, it is wrong to believe that life expectancy can directly influence healthcare spending.
At the same time, the rejection of correlation between the two variables under consideration is absurd. The connection between healthcare spending and life expectancy is evident and cannot be neglected as in case people get an appropriate financial support and spend it to improve their health; they have a chance to live a longer life.
However, life expectancy may be determined by many other factors (confounders) like the style of life, personal habits (smoking, alcohol, etc.), environment (air or water pollution), or living conditions. The level of GDP is another important confounder that should be mentioned (World Health Organisation 2010). At the same time, Japan with the indicator of GDP lower than the one of the USA (Japan 7.9% and USA 15%) demonstrates a higher life expectancy level due to the above-mentioned factors (Wise & Yashiro 2007).
Other forms of evidence to test a claim
The connection between different determinants of healthcare spending and life expectancy has been proved above. Still, it is necessary to admit the existence of some other forms of evidence that could be used to test the chosen causal claim. For example, health care reforms may have an impact on life expectancy in its own particular way. Health systems differ between countries as they usually combine various forms of provisions and financing. This is why the way of how the reforms are introduced and implemented in society indirectly still effectively influence the quality of life.
Reference List
Anderson, GF & Frogner, BK 2008, Health spending in OECD countries: Obtaining value per dollar, Health Affairs, vol. 27, no. 6, pp. 1718-1727.
Arbuckle, G 2012, Humanising healthcare reforms, Jessica Kingsley Publishers, London.
Freedman, DA 2009, Statistical models and causal inference: A dialogue with the social sciences, Cambridge University Press, Cambridge.
Funtleyder, L 2008, Healthcare investing, chapter 4 anatomy of the health-care system, McGraw Hill Professional, New York.
Goldsmith, S 2010, Principles of health care management: Foundations for changing health care system, Jones & Bartlett Learning, Sudbury.
Kellstedt, PM & Whitten, GD 2008, The fundamentals of political science research, Cambridge University Press, New York.
King, G, Keohane, R & Verba, S 1994, Designing social inquiry: Scientific inference in qualitative research, Princeton University Press, Princeton.
Laan, MJ & Rose, S 2011, Targeted learning: Causal inference for observational and experimental data, Springer Science & Business Media, New York.
Morgan, SL & Winship, C 2007, Counterfactuals and causal inference, Cambridge University Press, Cambridge.
Navarro, V, Muntaner, C, Borrell, C, Benach, J, & Quiroga, A 2006, Politics and health outcomes, Lancet, vol. 368, pp. 1033-1037.
Pearl, J 2009, Causal inference in statistics: An overview, Statistics Surveys, vol. 3, pp. 96-146.
Rothman, KJ & Greenland, S 2005, Causation and causal inference in epidemiology, American Journal of Public Health, vol. 95, no. S1, pp. 144-150.
Shaw, JW, Horrace, WC, & Vogel, RJ 2005, The determinants of life expectancy: An analysis of the OECD health data, Southern Economic Journal, vol. 71, no. 4, pp. 768-783.
Williams, NP 2010, Fixing everything: Government spending, taxes, entitlements, healthcare, pensions, immigration, tort reform, crime& AuthorHouse, Bloomington.
Wise, DA & Yashiro, N 2007, Health care issues in the United States and Japan, University of Chicago Press, Chicago.
World Health Organisation 2010, World Health Statistics 2010, World Health Organisation, Geneva.
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