A new set of risk equations for predicting long term risk of all-cause mortality using cardiovascular risk factors.

OBJECTIVES: As population ages and treatment for cardiovascular disease improves the risk of all-cause mortality has become a more meaningful outcome. We develop all-cause mortality equations for predicting long term risk using cardiovascular risk factors. METHODS: The 24-year risk of all-cause mortality was evaluated using Cox model for participants aged 40-81 years at the 10th or 11th examination of the Framingham original cohort and the first examination of the offspring cohort-all of whom were free of major chronic diseases. RESULTS: The predictors of all-cause mortality were age, sex, systolic blood pressure, total cholesterol/HDL ratio and smoking status. Risk prediction improved significantly when intensity of smoking and time since quitting were included into smoking status. A reduced model based on non-laboratory risk factors also demonstrated good predictive performance. CONCLUSIONS: All-cause mortality risk equations incorporating cardiovascular risk factors provide an improved tool to quantify risk and guide prevention of mortality. There are great potentials for prevention of the CVD epidemic and for increased longevity with health, through improved life-styles and consequent lower levels of blood pressure, cholesterol and smoking.

Using a Markov simulation model to assess the impact of changing trends in coronary heart disease incidence on requirements for coronary artery revascularization procedures in Western Australia.

BACKGROUND: The population incidence of coronary heart disease (CHD) has been declining in Australia and many other countries. This decline has been due to reduced population levels of risk factors for CHD and improved medical care for those at higher risk of CHD. However, there are signs that there may be a slowing down or even reversal in the decline of CHD incidence due to the 'obesity epidemic' and other factors and this will have implications for the requirements for surgical treatments for those with CHD. METHODS: Using a validated Markov simulation model applied to the population of Western Australia, different CHD incidence trend scenarios were developed to explore the effect of changing CHD incidence on requirements for coronary artery bypass graft (CABG) and percutaneous coronary interventions (PCI), together known as coronary artery revascularization procedures (CARPs). RESULTS: The most dominant component of CHD incidence is the risk of CHD hospital admission for those with no history of CHD and if this risk leveled off and the trends in all other risks continued unchanged, then the projected numbers of CABGs and PCIs are only minimally changed. Further, the changes in the projected numbers remained small even when this risk was increased by 20 percent (although it is an unlikely scenario). However, when the other CHD incidence components that had also been declining, namely, the risk of CABG and that of CHD death for those with no history of CHD, were also projected to level off as these were declining in 1998-2000 and the risk of PCI for those with no history of CHD (which was already increasing) was projected to further increase by 5 percent, it had a substantial effect on the projected numbers of CARPs. CONCLUSION: There needs to be dramatic changes to several CHD incidence components before it has a substantial impact on the projected requirements for CARPs. Continued monitoring of CHD incidence and also the mix of initial presentation of CHD incidence is required in order to understand changes to future CARP requirements.

Adapting a Markov Monte Carlo simulation model for forecasting the number of coronary artery revascularisation procedures in an era of rapidly changing technology and policy.

BACKGROUND: Treatments for coronary heart disease (CHD) have evolved rapidly over the last 15 years with considerable change in the number and effectiveness of both medical and surgical treatments. This period has seen the rapid development and uptake of statin drugs and coronary artery revascularization procedures (CARPs) that include Coronary Artery Bypass Graft procedures (CABGs) and Percutaneous Coronary Interventions (PCIs). It is difficult in an era of such rapid change to accurately forecast requirements for treatment services such as CARPs. In a previous paper we have described and outlined the use of a Markov Monte Carlo simulation model for analyzing and predicting the requirements for CARPs for the population of Western Australia (Mannan et al, 2007). In this paper, we expand on the use of this model for forecasting CARPs in Western Australia with a focus on the lack of adequate performance of the (standard) model for forecasting CARPs in a period during the mid 1990s when there were considerable changes to CARP technology and implementation policy and an exploration and demonstration of how the standard model may be adapted to achieve better performance. METHODS: Selected key CARP event model probabilities are modified based on information relating to changes in the effectiveness of CARPs from clinical trial evidence and an awareness of trends in policy and practice of CARPs. These modified model probabilities and the ones obtained by standard methods are used as inputs in our Markov simulation model. RESULTS: The projected numbers of CARPs in the population of Western Australia over 1995-99 only improve marginally when modifications to model probabilities are made to incorporate an increase in effectiveness of PCI procedures. However, the projected numbers improve substantially when, in addition, further modifications are incorporated that relate to the increased probability of a PCI procedure and the reduced probability of a CABG procedure stemming from changed CARP preference following the introduction of PCI operations involving stents. CONCLUSION: There is often knowledge and sometimes quantitative evidence of the expected impacts of changes in surgical practice and procedure effectiveness and these may be used to improve forecasts of future requirements for CARPs in a population.

A Markov simulation model for analyzing and forecasting the number of coronary artery revascularization procedures in Western Australia.

PURPOSE: A Markov chain Monte Carlo simulation model was developed to analyze and forecast the numbers of coronary artery bypass graftings, percutaneous coronary interventions (PCIs) , incident coronary heart disease (CHD) events, and CHD deaths for different age and sex groups in the population of Western Australia (population approximately 1.7 million). METHODS: The Western Australian health information system contains linked records of all hospital admissions and deaths for individuals from 1980 to the present. This system allows the separation of the population into groups according to CHD/coronary artery revascularization procedure history and also allows the estimation of event probabilities directly from population-level data. RESULTS AND CONCLUSIONS: The results for the 1990 Western Australian population over the period 1990 to 1994 and the 1995 population over the period 1995 to 1999 indicated that the Markov model fits well and produces good forecasts under "stable" conditions. The model can also be useful in ascertaining the impact of system changes, such as the widespread introduction of stents in PCI operations in 1995.

Latent mixed Markov modelling of smoking transitions using Monte Carlo bootstrapping.

It has been established that measures and reports of smoking behaviours are subject to substantial measurement errors. Thus, the manifest Markov model which does not consider measurement error in observed responses may not be adequate to mathematically model changes in adolescent smoking behaviour over time. For this purpose we fit several Mixed Markov Latent Class (MMLC) models using data sets from two longitudinal panel studies--the third Waterloo Smoking Prevention study and the UWO smoking study, which have varying numbers of measurements on adolescent smoking behaviour. However, the conventional statistics used for testing goodness of fit of these models do not follow the theoretical chi-square distribution when there is data sparsity. The two data sets analysed had varying degrees of sparsity. This problem can be solved by estimating the proper distribution of fit measures using Monte Carlo bootstrap simulation. In this study, we showed that incorporating response uncertainty in smoking behaviour significantly improved the fit of a single Markov chain model. However, the single chain latent Markov model did not adequately fit the two data sets indicating that the smoking process was heterogeneous with regard to latent Markov chains. It was found that a higher percentage of students (except for never smokers) changed their smoking behaviours over time at the manifest level compared to the latent or true level. The smoking process generally accelerated with time. The students had a tendency to underreport their smoking behaviours while response uncertainty was estimated to be considerably less for the Waterloo smoking study which adopted the 'bogus pipeline' method for reducing measurement error while the UWO study did not. For the two-chain latent mixed Markov models, incorporating a 'stayer' chain to an unrestricted Markov chain led to a significant improvement in model fit for the UWO study only. For both data sets, the assumption for the existence of an independence chain did not lead to significant improvement in model fit. The unrestricted two-chain latent mixed Markov model led to a significant improvement of model fit compared to a simple latent Markov model, but this model was overparameterized when the latent transition probabilities and/or response probabilities were assumed nonstationary. For the other models, the manifest/latent transition probabilities and response probabilities (for the four-wave Waterloo study only) were tested to be nonstationary for both data sets.

Computer programs to estimate overoptimism in measures of discrimination for predicting the risk of cardiovascular diseases.

BACKGROUND: Development of chronic disease risk prediction models has become a growing area of research in recent years. The internal validity of such models is sometimes lower than estimated from the development sample. Overfitting or overoptimism of the developed model and/or differences between the samples are likely causes for this. For modelling of an uncommon outcome, bootstrapping for overoptimism is the preferred method for afterwards shrinking of regression coefficients and the model's discrimination and calibration for overoptimism. However, computer programs for different types of bootstrap validation are not readily available. We developed two SAS macro programs--one for the simple bootstrap that compares the discriminatory performance of the Cox proportional hazards model from the original sample in bootstrap samples; and another (which is more efficient), known as stepwise bootstrap validation, that makes the same comparison but from models developed by variable selection from bootstrap samples in the original sample. These are illustrated through an example from cardiovascular disease (CVD) risk prediction. METHODS: Two SAS macro programs for Cox proportional hazards model using Proc PHREG were developed for estimating overoptimism in Harrell's C and Somers' D statistics. The computer programs were applied to data on CVD incidence for a Framingham cohort that combined both the original and offspring exams. The risk factors considered were current smoking, diabetes, age, sex, systolic blood pressure, diastolic blood pressure, total cholesterol, high-density lipoprotein cholesterol, triglycerides and body mass index. RESULTS: The degree of overoptimism in both Harrell's C and Somers' D statistics were low. Both these statistics were corrected for overoptimism by subtracting overoptimism from their observed values. Between the two bootstrap validation algorithms, the degree of overoptimism was estimated to be higher for stepwise bootstrap validation. CONCLUSION: The programs are very useful for evaluating the 'overoptimism corrected' predictive performance of Cox proportional hazards model.

Projected socioeconomic disparities in the prevalence of obesity among Australian adults.

OBJECTIVE: To project prevalence of normal weight, overweight and obesity by educational attainment, assuming a continuation of the observed individual weight change in the 5-year follow-up of the national population survey, the Australian Diabetes, Obesity and Lifestyle study (AusDiab; 2000-2005). METHODS: Age-specific transition probabilities between BMI categories, estimated using logistic regression, were entered into education-level-specific, incidence-based, multi-state life tables. Assuming a continuation of the weight change observed in AusDiab, these life tables estimate the prevalence of normal weight, overweight and obesity for Australian adults with low (secondary), medium (diploma) and high (degree) levels of education between 2005 and 2025. RESULTS: The prevalence of obesity among individuals with secondary level educational attainment is estimated to increase from 23% in 2000 to 44% in 2025. Among individuals with a degree qualification or higher, it will increase from 14% to 30%. If all current educational inequalities in weight change could be eliminated, the projected difference in the prevalence of obesity by 2025 between the highest and lowest educated categories would only be reduced by half (to a 6 percentage point difference from 14 percentage points). CONCLUSION: We predict that almost half of Australian adults with low educational status will be obese by 2025. Current trends in obesity have the potential to drive an increase in the absolute difference in obesity prevalence between educational categories in future years. IMPLICATIONS: Unless obesity prevention and management strategies focus specifically on narrowing social inequalities in obesity, inequalities in health are likely to widen.

Projected progression of the prevalence of obesity in Australia.

Several country-specific and global projections of the future obesity prevalence have been conducted. However, these projections are obtained by extrapolating past prevalence of obesity or distributions of body weight. More accurate would be to base estimates on the most recent measures of weight change. Using measures of overweight and obesity incidence from a national, longitudinal study, we estimated the future obesity prevalence in Australian adults. Participants were adults aged ≥25 years in 2000 participating in the Australian Diabetes, Obesity, and Lifestyle (AusDiab) study (baseline 2000, follow-up 2005). In this population, approximately one-fifth of those with normal weight or overweight progressed to a higher weight category within 5 years. Between 2000 and 2025, the adult prevalence of normal weight was estimated to decrease from 40.6 to 28.1% and the prevalence of obesity to increase from 20.5 to 33.9%. By the time, those people aged 25-29 in 2000 reach 60-64 years, 22.1% will be normal weight, and 42.4% will be obese. On average, normal-weight females aged 25-29 years in 2000 will live another 56.2 years: 26.6 years with normal weight, 15.6 years with overweight, and 14.0 years with obesity. Normal-weight males aged 25-29 years in 2000 will live another 51.5 years: 21.6 years with normal weight, 21.1 years with overweight, and 8.8 years with obesity. If the rates of weight gain observed in the first 5 years of this decade are maintained, our findings suggest that normal-weight adults will constitute less than a third of the population by 2025, and the obesity prevalence will have increased by 65%.

Reductions in transport mortality in Australia: evidence of a public health success.

OBJECTIVE: To describe trends in transport mortality for a range of common transport types in Australia over a 30-year period (1975-1977 to 2005-2007). METHODS: Mortality data on all-cause and transport-related causes of death were supplied by the Australian Institute of Health and Welfare (AIHW). Mortality rates, expected number of deaths and probabilities of death were compared for three time periods: 1975-1977, 1990-1992 and 2005-2007. RESULTS: There were significant decreasing trends between 1975-1977 and 2005-2007 in all-cause and most other transport mortality types for both men and women. There were significant reductions in the contribution of transport-related mortality to all-cause mortality; however the difference in mortality between men and women (higher for men) changed little over the evaluated period. CONCLUSIONS: Between 1975-1977 and 2005-2007 there were marked reductions in key causes of transport-related mortality amongst Australian adults, and the reductions in transport-related mortality exceeded reductions in all-cause mortality. The reductions could be attributed to better preventive measures and improved medical treatment for people involved in transport crashes. Although there is scope for further improvement, the reductions are evidence of a success in the prevention of crashes and the medical treatment of crash victims.

Age at quitting smoking as a predictor of risk of cardiovascular disease incidence independent of smoking status, time since quitting and pack-years.

BACKGROUND: Risk prediction for CVD events has been shown to vary according to current smoking status, pack-years smoked over a lifetime, time since quitting and age at quitting. The latter two are closely and inversely related. It is not known whether the age at which one quits smoking is an additional important predictor of CVD events. The aim of this study was to determine whether the risk of CVD events varied according to age at quitting after taking into account current smoking status, lifetime pack-years smoked and time since quitting. FINDINGS: We used the Cox proportional hazards model to evaluate the risk of developing a first CVD event for a cohort of participants in the Framingham Offspring Heart Study who attended the fourth examination between ages 30 and 74 years and were free of CVD. Those who quit before the median age of 37 years had a risk of CVD incidence similar to those who were never smokers. The incorporation of age at quitting in the smoking variable resulted in better prediction than the model which had a simple current smoker/non-smoker measure and the one that incorporated both time since quitting and pack-years. These models demonstrated good discrimination, calibration and global fit. The risk among those quitting more than 5 years prior to the baseline exam and those whose age at quitting was prior to 44 years was similar to the risk among never smokers. However, the risk among those quitting less than 5 years prior to the baseline exam and those who continued to smoke until 44 years of age (or beyond) was two and a half times higher than that of never smokers. CONCLUSIONS: Age at quitting improves the prediction of risk of CVD incidence even after other smoking measures are taken into account. The clinical benefit of adding age at quitting to the model with other smoking measures may be greater than the associated costs. Thus, age at quitting should be considered in addition to smoking status, time since quitting and pack-years when counselling individuals about their cardiovascular risk.

Comparing trends in BMI and waist circumference.

The nature of excess body weight may be changing over time to one of greater central adiposity. The aim of this study is to determine whether BMI and waist circumference (WC) are increasing proportionately among population subgroups and the range of bodyweight, and to examine the public health implications of the findings. Our data are from two cross-sectional surveys (the US National Health and Nutrition Examination Studies (NHANES) in 1988-1994 (NHANES III) and 2005-2006), from which we have used samples of 15,349 and 4,176 participants aged ≥20 years. Between 1988-1994 and 2005-2006 BMI increased by an average of 1.8 kg/m² and WC by 4.7 cm (adjusted for sex, age, race-ethnicity, and education). The increase in WC was more than could be attributed simply to increases in BMI. This independent increase in WC (of on average, 0.9 cm) was consistent across the different BMI categories, sexes, education levels, and race-ethnicity groups. It occurred in younger but not older age groups. Overall in each BMI category, the prevalence of low-risk WC decreased and the prevalence of increased-risk or substantially increased-risk WC increased. These results suggest that the adverse health consequences associated with obesity may be increasingly underestimated by trends in BMI alone. Since WC is closely linked to adverse cardiovascular outcomes, it is important to know the prevailing trends in both of these parameters.