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18 results found

ICCSCM 2019 Copenhagen

Jun 11-12, 2019
International Conference on Computational Statistics and Computational Methods
Submissions due:
2019-05-11 00:00:00
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ICCSCMA 2019 Rome

Jan 17-18, 2019
International Conference on Computational Statistics, Computational Methods and Applications
Submissions due:
2018-12-17 00:00:00
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ICCSDACM 2019 Barcelona

Jun 11-12, 2019
International Conference on Computational Statistics, Data Analysis and Computational Methods
Submissions due:
2019-05-10 00:00:00
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ICCSM 2019 Oslo

Jun 24-25, 2019
International Conference on Computational Statistics and Methods
Submissions due:
2019-05-24 00:00:00
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ICCSAM 2019 Tokyo

May 27-28, 2019
International Conference on Computational Statistics, Applications and Methods
Submissions due:
2019-04-26 00:00:00
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ICCSOM 2019 Vienna

Jun 20-21, 2019
International Conference on Computational Statistics and Optimization Methods
Submissions due:
2019-05-20 00:00:00
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ICCSOMA 2019 Berlin

May 21-22, 2019
International Conference on Computational Statistics, Optimization Methods and Applications
Submissions due:
2019-04-21 00:00:00
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ICCOMS 2019 Melbourne

Feb 01-02, 2019
International Conference on Computational Optimization Methods in Statistics
Submissions due:
2018-12-17 00:00:00
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ICSSRM 2019 Dubai

Mar 21-22, 2019
International Conference on Social Statistics and Research Methods
Submissions due:
2019-02-11 00:00:00
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ICOMS 2019 Rome

Feb 18-19, 2019
International Conference on Optimization Methods in Statistics
Submissions due:
2019-01-18 00:00:00
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ICMSM 2019 Melbourne

Feb 01-02, 2019
International Conference on Multivariate Statistics and Methods
Submissions due:
2018-12-17 00:00:00
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ICASRM 2019 Prague

Sep 05-06, 2019
International Conference on Applied Statistics and Research Methods
Submissions due:
2019-06-05 00:00:00
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ICOMAS 2019 Paris

Feb 21-22, 2019
International Conference on Optimization Methods and their Application in Statistics
Submissions due:
2019-01-21 00:00:00
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ICONMS 2019 Amsterdam

Feb 07-08, 2019
International Conference on Optimization and Numerical Methods in Statistics
Submissions due:
2019-01-07 00:00:00
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ICOMAS 2019 Istanbul

Jan 30-31, 2019
International Conference on Optimization Methods for Applications in Statistics
Submissions due:
2018-12-31 00:00:00
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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

ICMSSS 2019 Bangkok

Feb 04-05, 2019
International Conference on Methods and Statistics in Social Sciences
Submissions due:
2019-01-04 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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