Eliminating smoking and setting high values of total cholesterol and SBP to their recommended target levels increased average potential years of life per person by between 6.0 and 7.3 years for men and 6.1 and 7.1 years for women. The increase was greatest in Outer Regional/Remote areas for all risk factors except for SBP in women, where the Major City increase was equal to the Outer Regional/Remote area increase.
Mortality differences between regions affect men at all risk levels but mainly apply to higher risk women. Eliminating smoking would reduce mortality differences between Major Cities and each other region for both men and women. In addition, reducing high cholesterol would reduce mortality differences between Major Cities and each other region for men. Reducing high cholesterol in women or high SBP in men would reduce the mortality difference between Major Cities and Outer Regional/Remote areas but not Inner Regional areas. The combined effect of reducing high cholesterol and high SBP and eliminating smoking would reduce the APYL difference between Major Cities and Outer Regional/Remote areas by 45.4% for men and 35.6% for women.
Previous research has found a complex relationship between geographic variation in risk factors and mortality. Papastergiou and colleagues found that low economic activity and access to health services were significant drivers of regional variation in all-cause mortality in Greece [4
]. Romeri and colleagues studied regional variation in mortality in England and Wales and found that while mortality increased with deprivation, the relationship was strongest for smoking-related causes--suggesting an unmeasured role for smoking [5
]. Men et al. analysed regional trends in Russian mortality and concluded that fluctuations in mortality correlated strongly with underlying economic and societal factors but that risk factors (particularly alcohol) played a part at an individual level [6
]. Bassuk and colleagues studied mortality in the elderly in four US communities and concluded that while individual characteristics such as health risk factors played a role, it was also important to consider community attributes that mediate or modify the pathways through which socioeconomic conditions may influence health [7
]. Our study provides quantification in the Australian population of the effect of individual risk factors. However, as noted by the researchers quoted here, the individual risk factors must be viewed in the context of broader societal and economic influences. Hence, while our study did not address health services, societal and economic factors, these broader factors may be the principal drivers of regional mortality differentials while the health risk factors may act as mediators.
The WHO Comparative Risk Assessment project developed methods for comparing the disease burden attributable to different health risk factors in a standardised way. These methods are based on the use of population attributable fractions (PAF) with a consistent theoretical framework that uses the 'hypothetical minimum' as the counterfactual against which burden due to a risk is calculated. They also include continuous risk variables accounting for the full range of risk from elevated blood pressure and serum cholesterol, rather than defining thresholds for hypertension and hypercholesterolaemia [25
]. These methods have been used to develop estimates of the Australian disease burden for the major health risk factors [17
]. In principle, these methods could be used to model the difference in burden between regions by examining differences in risk factor prevalence and inferring differences in disease burden. However, our methods have the strength of directly modelling the absolute effects of specific risk factors on the inter-regional difference in all-cause mortality. PAF's are typically derived for specific causes of death and so would require separate modelling for each cause related to each risk factor and then aggregating across different causes. Our methods do, however, have the limitation of using risk factor threshold values rather than using the whole risk factor distribution. This may lead to an underestimate of the health impact of the risk factor and hence a conservative estimate of its impact on inter-regional mortality differences.
The major strength of our modelling approach is the ability to apportion mortality to percentiles of mortality risk based on measured rather than self-reported cholesterol and blood pressure values and total population and mortality counts. This allows us to model the effect of changing risk factor profiles on the distribution of risk within the population.
One limitation of our study is the use of the SCORE equation as a proxy for all-causes mortality risk. Our study requires an all-causes mortality risk prediction equation based on the modifiable risk factors measured in our population based sample. Our literature search found two such equations in the literature which could potentially be suitable for application to our population survey data--one based on the Multiple Risk Factor Intervention Trial (MRFIT) study developed by Kannel et al. [26
] and one based on the Aerobics Center Longitudinal Study at the Cooper Clinic in Dallas, Texas (the Cooper Clinic Mortality Risk Index) developed by Janssen et al. [27
]. However, both of these equations were developed for men only and we need to be able to apply our risk equation to both men and women. Further, the MRFIT equation used diastolic blood pressure as its blood pressure measure rather than the more commonly used systolic blood pressure and the Cooper Clinic index incorporated an exercise stress test of cardiorespiratory fitness which was not available in our population based sample. So neither equation was suitable for our study.
Cardiovascular disease is the leading cause of death in Australia--comprising 34% of all deaths registered in 2008 [28
]. Further, tobacco smoking as well as being a leading risk factor for cardiovascular disease (CVD) is also a leading risk factor for death from a wide range of other causes [7
]. This suggests that a cardiovascular mortality risk prediction equation incorporating tobacco use among its predictor variables such as the SCORE index may be a suitable proxy for an all-causes mortality risk prediction equation. Aktas et al. examined the SCORE equation as a predictor of all cause mortality risk in a sample of 3,554 asymptomatic adults (2871 men and 683 women) aged 50 - 75 years at the Cleveland Clinic Foundation in Cleveland, Ohio [3
]. They found that the SCORE index was strongly predictive of all-cause mortality in their sample. Further, they found that SCORE was a considerably better predictor of all-cause mortality than the Framingham risk score, which is the other most commonly used CVD risk score. We used a similar approach to Aktas et al. in using a Cox proportional hazards regression model to predict all-cause mortality within our population based sample with the SCORE as the predictor variable. Our Cox regression analysis showed that the SCORE index was a good predictor of all-causes mortality for both men and women at ages 40 and over and at ages 65 and over. The original SCORE index was derived for men and women aged 45 - 64. Our regression for people aged 40 and over supports our use of the SCORE index as a proxy for all-cause mortality risk in our risk percentiles model. Our regression for people aged 65 and over demonstrates that the SCORE index can be used as a proxy for all-causes mortality risk at older ages than the original group for which it was derived.
Another limitation is that the AusDiab survey participants are known to have lower mortality risk than the general Australian population, despite being drawn from a population-based random sample. However, our study relies on the ordering of risk within the population rather than the absolute level of risk and so should be relatively robust to this limitation.
A further limitation is that our study excludes Very Remote areas and does not provide separate results for Remote areas. This is a limitation of a population based modelling approach as less than three per cent of the Australian population live in Remote and Very Remote areas. Hence the numbers of people and of deaths are too small in these areas to support separate estimates using our modelling approach.
The study is limited to those risk factors incorporated in the SCORE risk prediction equation--smoking, blood pressure and cholesterol. Tobacco and blood pressure are the two leading risk factors associated with disease burden in Australia and cholesterol is the fifth (after obesity and physical inactivity), so these risk factors are appropriate for this study [17
]. The exclusion of other risk factors renders our study results conservative in estimating the total contribution of modifiable risk factors to life expectancy differentials. Further work in this area would benefit from developing an all-causes mortality risk prediction equation incorporating further risk factors.
Our study is an 'ecological' study in that it only examines risk factor prevalence at the regional level and hence neglects the distribution of risk factors and life expectancy within these regions. This may also lead to underestimation of the contribution of the risk factors to the mortality differential.
Our modelling is aimed at quantifying the contribution of modifiable health risk factors to regional mortality differentials. Hence our estimates represent an upper bound to the gains that could be made from interventions targeting these risk factors rather than a projection of the actual gains from any specific health promotion activity. However, they do demonstrate that there are potentially substantial gains in health equity which could arise from addressing modifiable health risk factors in outer regional and remote areas.