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dc.contributor.authorShanmuganathan, S
dc.contributor.authorClaster, W
dc.date.accessioned2012-04-22T21:42:10Z
dc.date.available2012-04-22T21:42:10Z
dc.date.copyright2009
dc.date.issued2012-04-23
dc.identifier.citation18th World IMACS / MODSIM Congress, 2009-07-13 - 2009-07-17, pages 171 - 177
dc.identifier.urihttp://hdl.handle.net/10292/3936
dc.description.abstractWhile many international and national institutions world over, such as, World Health Organisation (WHO), the World Bank and national/ state health institutions, strive to promote health, prevent and control disease, formulate policies, programmes and evaluate interventions, and mobilise resources, all of these with an ultimate aim to prolong life of the world citizens as well as increase access to health care, health inequalities between advantaged and disadvantaged populations continue to increase at unprecedented rates and with greater complexity. In this digital age there is no dearth for data on public health, health disparities and related factors, such as socioeconomic, income and GDP. However, lately, transforming this digital data into useful knowledge to learn more about human wellbeing and public health issues linked with appropriate factors is becoming a huge challenge even though it is seen as an inevitable requisite for the international and national health institutions to overcome the health disparities that are reaching alarming proportions. Public as well as private health care research institutions, such as, WHO and Pan-American Health Organisation, academic studies use different sets of core data and approaches for generating composite indices that best present the situation, progress and areas of urgent need in spending. A literature review on the subject shows that our ability to understand the processes as becoming increasingly complicated. Empirical studies into theorising health inequalities, linking relevant factors with inequities and understanding the underlying cause, reveal that we are now investigating into gaining more insights in the mechanisms that link poorer health to poorer circumstance. This is being carried out after having established that all health inequities are inherently inequitable thereby needing political intervention which is understandably seen as a troubling presumption. In view of these factors, the paper presents an approach to group countries based on public health and related factors selected from an initial study by WHO using the World Bank and UN’s Millennium Ecosystem Assessment (MA) goals as well as tables with advanced (but simpler) statistical data analysis methodologies. The results portray the public health issues that dominate the national agenda, political intervention, evaluation measures and budget of these different country groups, especially the difference within countries are seen obvious and cannot be ignored even though they cannot be validated with standard statistical methods such as significance tests. For example, in developed countries the issues are focused on how to manage the escalating obesity and diabetes (type 2) by introducing more tax on fast food whereas, in African countries the focus is on reducing poverty to meet the basic needs of malnourished children, the younger generation, who are growing without their parents; victims of AIDS epidemic, starvation and natural disasters.
dc.publisherThe Modelling and Simulation Society of Australia and New Zealand Inc. and the International Association for Mathematics and Computers in Simulation.
dc.relation.isreplacedby10292/4195
dc.relation.isreplacedbyhttp://hdl.handle.net/10292/4195
dc.relation.urihttp://www.mssanz.org.au/modsim09/A2/shanmuganathan_A2.pdf
dc.rightsCopyright © 2009 The Modelling and Simulation Society of Australia and New Zealand Inc. and the International Association for Mathematics and Computers in Simulation. All rights reserved.
dc.titleStatistical methods in analysing health inequalities among the world citizens
dc.typeConference Contribution
dc.rights.accessrightsOpenAccess
dc.identifier.roid9475en_NZ


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