Specifying both the percentage of people who benefit from a health intervention (HBp) and, among these, the average years of life gained without the disorder in question (myocardial infarction and stroke in our examples) is a simple, informative way of expressing benefits in preventive medicine. Our analysis shows the importance of determining whether a holistic or reductionist model is used to calculate these estimates.
The holistic model is appropriate when the preventive measures exhibit a continuous biological action, such as blood pressure reduction, in which everyone experiences a reduction and the health benefits are expected to accrue to everyone who would have had an event in the absence of preventive intervention by delaying the event as well as possibly avoiding it. Had the reductionist model been used, only an estimated 19 % (33 % minus 15 % from Table 1) of people aged 50 or over would benefit, but they would gain, on average, more years of event-free life—14 years instead of 8.0.
With a 6 g/day reduction in salt intake, using the holistic model estimates showed that 33 % of people benefit and gain an average of 2.8 years of life without a myocardial infarction or stroke. The corresponding figures using the reductionist model are 6 % and 16 years respectively.
Risk of a myocardial infarction or stroke is currently often estimated in terms of the probability that a person will develop a clinical event over the next 10 years, and a risk “threshold” (say a 20 % 10-year risk) is used to identify people for preventive treatment . Giving a risk estimate for “the next 10 years” for a preventive treatment that is intended to be taken indefinitely will underestimate both the risk and the potential benefit, as most of the preventable events will arise after 10 years. We therefore used lifetime benefit, in which the relative risk reduction decreases with age and the absolute risk reduction increases. For example, the relative risk reduction from age 50–59 is 81 % (see Table 6 in the Appendix), and the absolute annual risk reduction is 0.21 % over this ten year period. At age 80–89 the relative risk reduction is 55 % and the corresponding absolute annual risk reduction is 1.0 %. Another problem with using risk to prompt intervention is that it is the size of the health benefit of the proposed treatment that is relevant, rather than the risk itself. It is the translation of the reduction in incidence rates into extended years of life that is important. Identifying a high risk group without an effective treatment is pointless. It is the final benefit that needs to be the basis for decision making, and the estimate of health benefit should be life-long, not time limited.
Regardless of whether, in a particular context, the reductionist or holistic model is appropriate, the two specified measures of health benefit overcome limitations associated with the use of relative and absolute risk reduction, but the latter are still needed to calculate the two specified measures of health benefit. Our estimate of the benefit is robust for two reasons. First the estimates of relative risk reduction come from the results of large cohort studies and many randomized trials that show considerable consistency between studies. Second, sensitivity analyses showed that estimation of the specified health benefits were robust to small changes in the estimates of relative risk reduction, with an approximate proportional relationship between relative risk reduction and years of life gained without a myocardial infarction or stroke. So, for example, a 5 % change in the relative risk reduction would result in about a 5 % change in years of life gained. Sensitivity analyses also showed that changes in the incidences of the disorders in question affect the percentage of people who benefit from preventive interventions to an approximately proportionate extent, so that, for example, doubling the incidence in our examples increases the proportion of people who benefit from 33 to 50 %, or from 1:2 to 2:2. However, among those who benefit, the gain in life without the specified disorders remains similar.
Sometimes the benefit from a health intervention is expressed as the number needed to treat (NNT), which is the inverse of the absolute risk reduction. The NNT defined in this way is valid under the reductionist model, but not under the holistic model. The benefit from a health intervention is also sometimes expressed as the years of life gained divided by the number of people who adopt the preventive intervention. This is misleading, because some people who adopt the intervention cannot possibly benefit, for example, a person who takes a statin and dies in a road traffic accident a month later or someone who simply stops treatment. Instead of estimating the benefit to everyone adopting the intervention, it is more informative to separately estimate the proportion of people who will benefit, and among them estimate the average years of life gained without a clinical event the treatment prevents.
If the age-specific incidence rate of serious adverse effects were known, these could be included in the life table analysis together with the incidence of myocardial infarction or stroke. The benefit is then the avoidance of all these outcomes rather than preventing a myocardial infarction or stroke only. In our examples, the issue is minor, because there is strong evidence that salt reduction and the components of the polypill are almost free from serious adverse effects, with the exception of the rare occurrence of statin induced rhabdomyolysis. If this were included as a hazard, neither the percentage of people who benefit nor the years of life gained would differ at the level of precision used here, because of the rarity of the adverse reaction. Current estimates suggest that statin therapy may increase the risk of clinical diabetes by about 9 % . Our method allows for any increase in the risk of myocardial infarctions and stroke arising in this way, but not for other complications of diabetes.
In this paper we consider years of life gained without an incident myocardial infarction or stroke. The same method of analysis as that described here could be applied to the prevention of death from these disorders, in which case the proportion of people benefitting would be less as not everyone who has a myocardial infarction or stroke will die from these disorders, but the years of life gained would be greater due to the inclusion of years of life after a first clinical event, as well as years gained before such an event. We selected myocardial infarction and stroke since they are “hard” end points for which estimates of incidence are available. Had, for example, angina been included, the benefits would have been greater.
The approach we propose, which is based on using standard life-table methods could, to advantage, be readily adopted, relying on estimates of relative and absolute risk reductions and data on cause-specific mortality from national vital statistics. The calculations are straightforward. Life-table methods are often used in economic cost-benefit analyses, but less so in papers that assess only health benefits.
In summary, the health benefits of preventive interventions are usefully presented in terms of the proportion of people receiving an intervention who benefit from it and their average years of life gained. These two measures overcome the apparent contradictory impressions arising from reporting estimates of the absolute and relative risk reduction. In the prevention of chronic disease, where the biological actions of an intervention exhibit continuous effects, the two measures of health benefit, calculated using the holistic model, provide a simple and accurate summary of the impact of the intervention for individuals and for populations as a whole.