How does the difference in attributed credit come about?
Within IMPACT, reduced population risks of CHD death derive from two sources:
1 Better treatment: By definition this only applies to those patients who come to clinical attention, either because of the onset of clinical CHD or the detection of elevated risk factors. Treatment benefits derive either from reduced case fatality due to clinical management of an acute or chronic episode of CHD or from the therapeutic (pharmacological) reduction in elevated blood pressure or blood cholesterol levels (statin-induced LDL-C lowering for example). The gains from preventive treatments (such as statins) are shown distinct from risk factor improvement gains, (such as following reduction in population levels of cholesterol). To gain credit for a 'death prevented or postponed' an advance in treatment has to result in survival for at least 12 months longer than expected under baseline treatment. For the corresponding analyses using LYG, each death averted by advances in treatment is assigned an expected remaining life time (derivation discussed further below). The sum of these provides the LYG from treatment advances. When calculating LYG, the expected remaining lifetime is estimated using mortality rates observed in those surviving beyond the first thirty days after the onset of clinical CHD.
2 Risk factor changes: Shifts to more favourable risk factor levels are modelled to reduce progression through the natural history (Figure ). This increases the person time spent in states with lower risks of fatal CHD - which, in turn, reduces deaths (countable as DPP as outlined above). For each death averted, an expected remaining lifetime is assigned, based on the stage of the natural history to which the individual has progressed. The sum provides the LYG [12
IMPACT, schematic representation of transition probabilities reduced by favorable changes in risk factors and better treatments*.
* This is a simplification of the actual model, in which transitions between a larger range of states are modelled
Key for Figure
1- General US life expectancy
2- Mid-way between general US life expectancy and survival rate for post-AMI patient group
3- Equivalent to the survival rate for the post-AMI patient group
4- Patient group specific survival rates
The overall findings from these analyses are summarized in Figure and Table . The last column in Table shows that each death averted by better treatment is estimated to generate about half as many extra years lived as is each death averted by favorable shifts in risk factors.
Results of pairs of studies using the IMPACT model: for each of 4 countries one study was performed using Deaths Prevented or Postponed (DPPs) and one using Life Years Gained (LYG).
In Figure the DPP and LYG are plotted against the age of the averted death. Not surprisingly, when each death averted is weighted by its LYG, changes in population disease experience at earlier ages contribute more to the aggregated benefit. Deaths averted before age 65 by favourable shifts in risk factors contribute 15.9% of the total DPPs but 36.2% of total LYGs.
IMPACT results for US men in 2000 relative to 1980: Deaths prevented or postponed (DPP) and life years gained (LYG) attributed to better treatment and to risk factor changes by age (for DPP) and age of averted onset (LYG)*.
* All LYG are attributed to the age of averted death, not to the age at which the life years would have been lived
Where do the gained years come from?
The treatment arm of the US IMPACT model estimates patients' median survival using population-based data from unselected cohorts of MediCare patients following their hospital admission for acute myocardial infarction, heart failure, or revascularization. Additional age-specific median survival data for unstable angina patients were obtained from a large retrospective cohort study of unselected patients in the United Kingdom [16
], since MediCare only included US patients aged over 65.
The risk factor arm of the model estimates survival using the life expectancy of the general US population. Favourable shifts in risk factors both prevent and postpone (clinical) onset of CHD. It is expected, therefore, that some will live with asymptomatic CHD until their first CHD event - which results in death. For this group the post-AMI (excluding heart failure) rates are employed (the maximum estimate is set at halfway between the life expectancy of post-AMI patients and the general population and the minimum estimate is set at the life expectancy of post-AMI patients including those with heart failure). A portion of the CHD patients will also be expected to have their deaths postponed due to beneficial risk factor changes. This group is assumed to have approximately the same life expectancy as patients surviving after an uncomplicated AMI (Table ). The main survival functions employed are illustrated in Table .
Table 2 Life expectancies (ex) assigned to US men and women in 2000, comparing those who have not progressed to clinical CHD ('healthy') with those who have survived an acute myocardial infarction without the onset of heart failure and those who progress to heart (more ...)
How robust are the model extensions required for the LYG metric?
The US IMPACT model uses five main data sources to estimate survival and life expectancy (case-fatality rates for post-AMI, heart failure, revascularization and unstable angina patient groups and the general US life expectancy) which are subsequently used to apply weights to each CHD death prevented or postponed for both the treatment and the risk factor arms of the model. These weights are adjusted for different contexts and for the specific groups of people who benefit from clinical treatments or risk factor changes according to informed assumptions (listed in Table ).
Survival functions assigned to specified health states: sources, limitations and strengths
For all healthy people who never experience a CHD event, the model uses a universal weight, the general US life expectancy (weight 1 in Figure ). There are two potential problems with this. First, the general US population is made up of people who are both at risk of a CHD death and those that are not at risk. The impact of decreasing one's exposure to a risk factor or risk factors, however, might be expected to improve one's life expectancy above that of the general population. The assumption used here in the IMPACT model, therefore, is likely to lead to conservative estimates of the gains due to risk factor changes amongst the healthy population.
Second, a universal life expectancy applied to deaths averted amongst the 'healthy' populations due to risk factors changes means that the specific benefits gained from each risk factor change are lost. There are two reasons why this one weight is employed however. First the IMPACT model calculates the number of DPPs according to specific risk profiles and the prevalence of each risk factor. Any attempt to account for these differences again would potentially double count the impact.
A separate but related issue arises from risk factors being correlated [19
] so the impact of changes in individual risk factors is not additive. Thus, employing a common life expectancy for all those who avert a CHD death due to risk factor changes avoids the methodological problems that arise from non-additivity.
For the group of people with asymptomatic and/or undiagnosed CHD, the model uses a weight that is mid-way between the general US life expectancy and the survival rates of post-AMI patients (weight 2 in Figure ). Furthermore, people diagnosed with CHD may gain survival time either from better treatment or from favourable risk factor changes. This group are given a survival weight equivalent to that of the post-AMI patient group (weight 3 in Figure ).
These assumptions suggest that these groups of people have a small advantage in terms of survival and, again, probably provide a conservative assessment of the impact of risk factors on these groups of people.
Lastly, patient group specific survival rates are used to weight the death prevented or postponed from a treatment (weight 4 in Figure ). Weight 4 is based on case fatality data for most of the patient groups included in the model. Two main groups, however, community heart failure and hypertension, lack sufficient data to inform on survival. To estimate survival for these two groups the model uses the assumptions that those experiencing heart failure in the community have one third of the case fatality of those who suffer heart failure in the hospital and those with hypertension will have 20% less life expectancy than the average life expectancy. Little literature exists to inform these estimates and these assumptions may slightly overestimate the number of years lost due to these conditions [20
To answer the question posed above, therefore, all groups of people that may have avoided or postponed a CHD death have been accounted for in the US IMPACT model and the assumptions used to extend this analysis from the event-based metric DPP to the time-based metric LYG may at times seem speculative and even arbitrary, but the estimates are typically reasonable and often conservative. In other words, the model is likely to underestimate gains in life years from favourable changes in risk factors.