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The Relationship between Obesity and Life Expectancy

On 13th December 2012, CNN reported that obesity is a bigger health crisis globally than hunger, and the leading cause of disabilities around the world according to a new report published in the British medical journal The Lancet.

The report revealed that every country, with the exception of those in sub-Saharan Africa, faces alarming obesity rates an increase of 82% globally in the past two decades. Middle Eastern countries are more obese than ever, seeing a 100% increase since 1990.

(http://edition.cnn.com/2012/12/13/health/global-burden-report/index.html?iref=allsearch)

According to a research of the Ministry of Health, Labor and Welfare, obesity is one of grave concerns in Japan as well. Then, to what extent obesity correlates to life expectancy? In this blog, the relationship between obesity and life duration will be examined.

The ministry shows the data of obesity rate through The National Nutrition Survey carried out in 2010 (http://www.mhlw.go.jp/english/, accessed 23rd Dec 2012). From this survey, the obesity rates by prefecture can be seen. The boundary data are obtained from the ESRI Japan web site (http://www.esrij.com/, accessed 23rd Dec 2012). Below is a distribution map of obesity rate by prefecture.

As shown by above figures, there seems to
be regional differences and the rates range widely from under 25 to 45. In
order to study the association with life duration, the data of mean life
expectancy are got through electronic open database of the Ministry of Health,
Labor and Welfare in Japan. The prefectural data are retrieved from the Population
Survey Report released in 2010.

As is the case with the obesity rate,
differences among regions are likely to be seen on the map of mean life
expectancy. In an effort to clarify the spatial relationship between these two
variables, a regression analysis is calculated.

__A Regression Analysis__

For the purpose of testing to see whether
there is a statistical relationship between obesity and life expectancy, a
following regression analysis is examined in a condition that ‘mean life
expectancy’ is set as the dependent variable and ‘obesity rate’ is used as the
explanatory variable. Below is the result of the regression analysis.

R-squared value, which indicates how
effectively the model fits, is calculated through the regression analysis. The
value ranges from 0 to 1. From the above result, the value is 0.2285. It means
that it would be interpreted that life expectancy is probable to have a relation
with obesity.

__The Degree of Spatial Autocorrelation__

Two methods of measurement are used on this
study so as to know the degree of spatial autocorrelation of life duration. One
is Moran’s I, and another is Geary’s C.

Firstly, the degree of spatial
autocorrelation can be calculated by Moran’s I. Moran (1950) was the first
person to develop the measure of spatial autocorrelation with an aim to clarify
stochastic phenomena which are distributed in space. Like a correlation
coefficient, the values of Moran's Index range from -1 to +1. While +1 means
strong positive spatial autocorrelation, -1 means strong negative spatial
autocorrelation and 0 indicates a random pattern. It shows the degree of
interdependency between the variables. In this test, the result of Moran’s I is
0.568. Therefore, it can be said that there is spatial clustering of the values
because it is relatively near to +1.

Secondly, Geary's C is tested, which is
similar to Moran's I but it is non-identical. Geary's C focuses more on local
spatial autocorrelation while Moran's I is a way of measuring global spatial
autocorrelation. Geary’s C value ranges from 0 to 2. In this analysis, 1
indicates no spatial autocorrelation and values ranging from 0 to 1 means
increasing positive spatial autocorrelation, whilst values higher than 1
demonstrate increasing negative spatial autocorrelation. In this case, the result
of Geary’s C test is 0.572. Hence, as with Moran’s I, it also means positive
spatial autocorrelation on the grounds that the value is between 0 to 1.

__Conclusion__

Although there seems to be a certain relationship
between obesity and life expectancy, the values of spatial analyses are not
necessarily enough to conclude. Therefore, further researches are required to
elucidate the role of obesity as a factor relating to length of life.

__References__

Cliff A.D.and Ord J.K. (1981) ‘Spatial
processes’, Pion, p. 21.

ESRI Japan, http://www.esrij.com/, accessed
23rd Dec 2012.

Gehlke C.E, Biehl K (1934) ‘Certain effects
of grouping upon the size of the correlation coefficient in census tract
material’, Journal of the American Statistical Association, 29, pp. 169–170.

Goodchild M.F. (1987) ‘Spatial
Autocorrelation’. CATMOG, 47, GEO BOOKS.

Moran, P.A.P. (1950) ‘Notes on continuous
stochastic phenomena’. Biometrika, 37, 17.

The Ministry of Health, Labor and Welfare,
http://www.mhlw.go.jp/english/, accessed 23rd Dec 2012.