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6 results found

ICCSCM 2018 Copenhagen

Jun 11-12, 2018
International Conference on Computational Statistics and Computational Methods
Submissions due:
2018-05-11 00:00:00
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ICCSCMA 2018 Tokyo

May 28-29, 2018
International Conference on Computational Statistics, Computational Methods and Applications
Submissions due:
2018-04-27 00:00:00
Conference Details

ICCSOM 2018 Vienna

Jun 14-15, 2018
International Conference on Computational Statistics and Optimization Methods
Submissions due:
2018-05-14 00:00:00
Conference Details

ICCSOMA 2018 Berlin

May 21-22, 2018
International Conference on Computational Statistics, Optimization Methods and Applications
Submissions due:
2018-04-20 00:00:00
Conference Details

ICASRM 2018 Prague

Sep 03-04, 2018
International Conference on Applied Statistics and Research Methods
Submissions due:
2018-08-03 00:00:00
Conference Details

Computational Methods in Official Statistics with an Example on Calculating and Predicting Diabetes Mellitus [DM] Prevalence in Different Age Groups within Australia in Future Years, in Light of the Aging Population

An analysis of the Australian Diabetes Screening
Study estimated undiagnosed diabetes mellitus [DM] prevalence in a
high risk general practice based cohort. DM prevalence varied from
9.4% to 18.1% depending upon the diagnostic criteria utilised with
age being a highly significant risk factor. Utilising the gold standard
oral glucose tolerance test, the prevalence of DM was 22-23% in
those aged >= 70 years and <15% in those aged 40-59 years.
Opportunistic screening in Australian general practice potentially can
identify many persons with undiagnosed type 2 DM. An Australian
Bureau of Statistics document published three years ago, reported the
highest rate of DM in men aged 65-74 years [19%] whereas the rate
for women was highest in those over 75 years [13%]. If you consider
that the Australian Bureau of Statistics report in 2007 found that 13%
of the population was over 65 years of age and that this will increase
to 23-25% by 2056 with a further projected increase to 25-28% by
2101, obviously this information has to be factored into the equation
when age related diabetes prevalence predictions are calculated. This
10-15% proportional increase of elderly persons within the
population demographics has dramatic implications for the estimated
number of elderly persons with DM in these age groupings.
Computational methodology showing the age related demographic
changes reported in these official statistical documents will be done
showing estimates for 2056 and 2101 for different age groups. This
has relevance for future diabetes prevalence rates and shows that
along with many countries worldwide Australia is facing an
increasing pandemic. In contrast Japan is expected to have a decrease
in the next twenty years in the number of persons with diabetes.

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