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BMC Public Health. 2012; 12: 79.
Published online Jan 25, 2012. doi:  10.1186/1471-2458-12-79
PMCID: PMC3349565
The effect of modifiable risk factors on geographic mortality differentials: a modelling study
Christopher E Stevenson,corresponding author1 Haider Mannan,1 Anna Peeters,1 Helen Walls,1 Dianna J Magliano,2 Jonathan E Shaw,2 and John J McNeil1
1School of Public Health and Preventive Medicine, Monash University, The Alfred Centre, 99 Commercial Road, Melbourne, Victoria, Australia
2Baker IDI Heart and Diabetes Institute, The Alfred Centre, 99 Commercial Road, Melbourne, Victoria, Australia
corresponding authorCorresponding author.
Christopher E Stevenson: christopher.stevenson/at/deakin.edu.au; Haider Mannan: haider.mannan/at/monash.edu; Anna Peeters: anna.peeters/at/monash.edu; Helen Walls: helen.walls/at/anu.edu.au; Dianna J Magliano: dianna.magliano/at/bakeridi.edu.au; Jonathan E Shaw: jonathan.shaw/at/bakeridi.edu.au; John J McNeil: john.mcneil/at/monash.edu
Received February 16, 2011; Accepted January 25, 2012.
Abstract
Background
Australian mortality rates are higher in regional and remote areas than in major cities. The degree to which this is driven by variation in modifiable risk factors is unknown.
Methods
We applied a risk prediction equation incorporating smoking, cholesterol and blood pressure to a national, population based survey to project all-causes mortality risk by geographic region. We then modelled life expectancies at different levels of mortality risk by geographic region using a risk percentiles model. Finally we set high values of each risk factor to a target level and modelled the subsequent shift in the population to lower levels of mortality risk and longer life expectancy.
Results
Survival is poorer in both Inner Regional and Outer Regional/Remote areas compared to Major Cities for men and women at both high and low levels of predicted mortality risk. For men smoking, high cholesterol and high systolic blood pressure were each associated with the mortality difference between Major Cities and Outer Regional/Remote areas--accounting for 21.4%, 20.3% and 7.7% of the difference respectively. For women smoking and high cholesterol accounted for 29.4% and 24.0% of the difference respectively but high blood pressure did not contribute to the observed mortality differences. The three risk factors taken together accounted for 45.4% (men) and 35.6% (women) of the mortality difference. The contribution of risk factors to the corresponding differences for inner regional areas was smaller, with only high cholesterol and smoking contributing to the difference in men-- accounting for 8.8% and 6.3% respectively-- and only smoking contributing to the difference in women--accounting for 12.3%.
Conclusions
These results suggest that health intervention programs aimed at smoking, blood pressure and total cholesterol could have a substantial impact on mortality inequities for Outer Regional/Remote areas.
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