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Nostrand 1
Helen Nostrand
Professor Ian Hales
Capstone Projects
13 March 2015
Disease and Development
Improving health around the world is something that would be
beneficial to all countries. According to Gallup and Sachs in The Economic Burden of
Malaria, wiping out diseases like Malaria in sub-Saharan Africa could increase the
continent’s per capita growth by 2.6% a year, and stated in a recent report by the
World Health Organization:
“In today’s world, poor health has particularly pernicious effects on economic
development in sub-Saharan Africa, South Asia, and pockets of high disease and
intense poverty elsewhere” (WHO, 24)
And ultimately “extending the coverage of crucial health services to the
worlds poor could save millions of lives each year, reduce poverty, spur economic
development and promote global security” (WHO, i) .
As of right now the evidence supporting this unfortunately has not yet been
proven. However, it is evident that there is a link between a countries level of
human development and the prevalence of communicable disease in that country. A
comparison of data from the World Health Organization and the United Nations
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Development Report describing the Human Development Index (HDI) shows that
there is a strong correlation between a countries rating on the HDI scale and the
number of deaths by infectious disease per capita in that country. It has long been
recognized that these infections may depend upon metapopulation dynamics, and
that there are many factors associated with the state of the country and its
correlation to disease impact. By utilizing this data, the results will show and reveal
in a clear and concise way what countries are desperately in need of help. This
paper will discuss the two data sets used, the methods used to compare them, a
discussion of the results of the comparison, and an overview of real world
applications and consequences of findings.
Human Development Index Analysis
To frame the empirical evidence, this paper will first clarify what exactly an
HDI or Human Development Index is. The HDI is a comparison of several data sets
that produces a number with a value from 0-1. The scale identifies factors in health,
financial well being, and education including life expectancy, average years of
schooling, expected years of schooling, and gross national income per capita (GNI).
The HDI was developed as a tool to judge the human development of a country
through means other than simple economic development. Including the happiness
of the population as a variable in the scale allows it to challenge popular notions of
cultural development. Some countries may have a high GNI per capita but a low
average level of education. Others may exhibit a high level of education in the
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population but a relatively low GNI per capita. Countries are ranked in a system of
very high development, high development, medium development, and low human
development. High development countries range from .8 to .9, and include countries
like United States and Norway. The high human development countries are ranked
at .7, medium; .5-.6, and low; anything below a .5. On the HDI, Norway is at the top of
the scale while Niger is at the bottom (UNDP, Table 1: Human Development Index).
The UNDP notes that the HDI is not a perfect scale in that it does not directly reflect
things like gender and race inequalities, poverty, and empowerment of women. The
UNDP considers the above things integral to determining human development of a
country; however, and as such they note that they believe the indices that the HDI
references provide ample evidence of those factors even if they are not directly
Communicable Disease Analysis
The next set of data utilized was the number of deaths due to communicable
diseases. Highly contagious pathogens so easily invade numerous host populations,
in many of our world’s countries. Without proper vaccination or immunization,
diseases wreck havoc on countries heath and ecosystems. Communicable diseases
include all diseases that are infectious; they spread from another human or from
animal to a person. The data set utilized to analyze the rate of deaths per country
were those from the 2012 records from the World Health Organization. According
to the The New England journal of medicine notorious modern communicable
diseases that are serious in our world right now are Hepatitis, Influenza, Ebola,
HIV/AIDS, Malaria, and Tuberculosis. These are just a few of the infectious diseases
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that plague our world, and cause an outstanding number of deaths each year
(Communicable Diseases). These records list the number of deaths per 100,000
people of many countries worldwide separated by the cause of death.
In comparing the two data sets, there were many factors to consider. First
and foremost, there was the matter of an appropriate comparison. I chose to
compare the data sets for the HDI value of each country with those countries deaths
per 100,000 people by communicable disease. I also used Pearson’s correlation
coefficient to compare the two data sets. Pearson’s correlation coefficient provides a
measure of the “strength of linear association between two variables” (Sedgwick,
BMJ 345). It tells you how to sets are closely associate. The two variables X or Y in
this case X (HDI) or Y (Deaths per 100,000 people due to communicable diseases),
giving a value between +1 and -1, where 1 is total positive correlation, 0 is no
correlation, and -1 is total negative correlation. When looking at Figure 1.1, we can
see that our Pearson’s correlation coefficient equals -0.839768194. A negative
coefficient correlation indicates that the two data sets are closer to being inversely
related. This means that there is a strong negative correlation between the position
of each country in the HDI ratings and their deaths per 100,000 people from
infectious disease. As we can see on Figure 1.1, this means that, as the HDI gets
closer to 1, the fewer amounts of deaths there are due to communicable diseases.
Figure 1.1
Nostrand 5
HDI vs. Deaths by Communicable
Deaths per 100,000 people
due to communicable
Human Development Index (HDI)
As we can see in Figure 1.1, the data set that emerged when the average
deaths per capita from communicable disease was divided by the countries HDI
rating was a set of numbers ranging from 10-3600. As we can see that the top 71.6%
are countries that have a high HDI and also have a lower number of deaths by
communicable diseases. The statistical outliers were areas with a higher HDI and a
relatively high amount of deaths. What was interesting was that these were often
sub-Saharan regions with similar HDI rankings as their North African counterparts.
This analysis found that often there is a strong relationship between people
living in the lowest capita incomes and the burden of disease. This means that in
under developed countries they are more commonly affected by disease. Since they
do not have the money to create and establish health center or cures these countries
often fall victim to the diseases control. In a study done by MIT researcher
Acemoglu, students explore the major health improvements from the early 1940’s to
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estimate the overall beneficence of disease control on economic performance and
life expectancy in various countries.
“in the 1930s life expectancy was low in many poor and middle-income
countries, this transition brought their levels of life expectancy close to those
prevailing in richer parts of the less-developed world today, though still in dire need
of improvement, are significantly better than the corresponding health conditions
were in the west at the same time of development” (Acemoglu, 935).
This shows that we, as a global community, have always faced an
abundance of various diseases. The study also shows that the disease-specific
mortality declined in these nations once global interventions became prevalent.
When global interventions became necessary so did the invention of the mass
production of antibiotics such as penicillin. This often, helped undeveloped
countries become disease free and often boosted their economy. This boosted their
overall productivity and increased their economic output and overall wellbeing.
Leading them to become powerful nations.
Often detrimental effects left by disease impacts are often what root the large
gap in between low income, underdeveloped countries, to that of developed
countries. By focusing our resources by extending health services to countries in
need we will be able to help reduce this correlation between low human index
development rankings and that of disease burden, and help to create stronger and
more resilient global economies.
Through the data presented above, we can start to understand the variables
at play. But this is just a start, as it provides evidence that a countries development
Nostrand 7
is not established purely on economic reasons, but that there are a quandary of
variables that go into affecting a nation. Such factors as population genetics,
environmental resources, and so on play a huge role in inhibiting the growth and
prosperities of some countries. What is important in these circumstances is for a
country to be able to take control of these factors. A good instance of this would be
diseases caused by Mosquitos. For example, if you have access tax income you have
the ability to spray for Mosquitos, which inhibit or decrease chances of getting
deadly diseases such as Malaria or West Nile. When eliminating these factors,
countries have a better ability to raise their human development ranking.
A consensus in academic and political studies has deemed that
differences in disease environments and health conditions are often the stem for
gaps in between the developments of countries. With this, we can conclude that
improving the health within one country will be beneficial to our world. From these
findings, I hope to make this data more accessible. It is important to understand the
strong association between human development and that of disease victimization.
Through this data we are able to assess the importance of the correlation. Through
this we can infer which countries are in need of immediate help, and utilizing the
various variables associated with human development index we can pinpoint in
what ways we can support and service.
Nostrand 8
Works Cited
Acemoglu, Daron. "Disease and Development: The Effect of Life Expectancy on
Economic Growth." (n.d.): n. pag. Nber. National Bureau of Economic
Research, May 2006. Web. 4 Mar. 2015.
Gallup, John, and Jeffery Sachs. THE ECONOMIC BURDEN OF MALARIA (n.d.): n. pag.
The American Society of Tropical Medicine. Web.
" Your Health, Our World." Communicable Diseases. N.p., n.d. Web.
11 Mar. 2015.
"Human Development Reports." UNDP. Human Development Index (HDI). N.p., n.d.
Web. 23 Feb. 2015.
"Human Development Reports." UNDP. Table 1: Human Development Index and Its
Components. N.p., n.d. Web. 11 Feb. 2015.
Langmuir, Alexander D. "The surveillance of communicable diseases of national
importance." The New England journal of medicine 268 (1963): 182.
Sedgwick, Philip. "Pearson’s correlation coefficient." BMJ 345 (2012).
"WHO | World Health Organization." WHO | World Health Organization. N.p., n.d.
Web. 11 Feb. 2015.