The estimation of potential health impacts can for example be acquired via an evaluation of the literature and interviews with stakeholders. Such qualitative methods are valuable and provide good insights into the potential effects of the action. They can provide information about the expected direction of the impact and even about the order of magnitude of the effects. However, quantifying the expected impacts may be a useful addition to these qualitative methods. Quantitative estimates of impact are important in policy decisions since they relate to measurable goals and can be linked to economic measures. For example, a reduction in smoking may be linked to quantifiable increases in population health and therefore increased labour participation and labour productivity. It could also lead to a reduction in costs for health care and social benefits. Moreover, quantitative estimates of the health impact can be used in a cost effectiveness or cost benefit analysis.
When quantifying impacts, it is important to know how an intervention influences health outcomes. In general, an intervention or policy does not directly impact health but impacts the determinants of health or risk factors. This is described in Figure 34. Information about a direct relation is often unavailable (estimate ‘a’ in the figure). In order to estimate the impact of the policy or intervention on health and health inequalities, it is therefore necessary to obtain information about the effects of the intervention or policy on these determinants (estimate ‘b’ in the figure) and on the effects the determinant has on health (estimate ‘c’ in the figure). These estimates on ‘b’ and ‘c’ are normally more easily obtained from literature or previous experience. The information about ‘b’ and ‘c’ can then be used to obtain an estimate of ‘a’ via quantitative modelling (estimate ‘a*’ in the figure).
For example, a question that may need to be answered is whether any lives will be saved if a ban on alcohol advertising is introduced. A literature review reveals that there is no information available on the relation between alcohol advertising and mortality (estimate ‘a’. However, because of previous evaluations of interventions, there is evidence in the literature that provides an estimate on how much alcohol consumption will decrease when alcohol advertising is banned (estimate ‘b’). In addition, there is information from cohort studies available on how alcohol consumption is linked to mortality (a so-called ‘relative risk’ of dying due to alcohol consumption, estimate ‘c’). This information can then be used to estimate the impact of alcohol advertising on mortality via quantitative modelling (estimate ‘a*’).
Quantitative models calculate how changes in the prevalence of determinants (risk factors), caused by exposure to an intervention, will impact the health of the population or population groups. In most quantitative modelling tools, the user can change the prevalence (such as the decrease in alcohol consumption that is expected to occur when alcohol advertising is banned) and the model will than calculate the change in mortality or another outcome. These tools can normally be tailored to include local or regional data when such data are sufficiently available.
There are several quantitative modelling tools available. For example, the DYNAMO-HIA is a dynamic European web-based tool that includes multiple health outcomes and risk factors. The Chronic Disease Model, by the Dutch National Institute for Public Health and the Environment, is also a dynamic modelling tool in which different risk factors can be modelled together. These dynamic quantitative models are rather comprehensive and may be useful in providing relatively realistic dynamic estimations of health impacts. However, these models are also complex to use, have large data needs and are not geared to modelling health inequalities.
Therefore, a simple, user-friendly quantitative modelling tool was developed in the Health Equity 2020 project, which is specifically designed to estimate the impact of policies and interventions in inequalities in health. In this tool, a shift in risk factor distributions (for example a 15% decrease in smoking prevalence), can be modelled in order to obtain estimates of these shifts on mortality and socioeconomic inequalities in mortality. More information on the Health Equity 2020 quantitative tool can be found in the user’s guide of the tool.
Quantitative models and the estimates they produce, should always be interpreted in light of some limitations. All quantitative models simplify reality and are based on assumptions (e.g. the assumption that smoking affects health of all people to the same degree) Furthermore, the quality of the estimations will depend heavily on the availability and quality of the data put into the model.
In order to be able to formulate evidence-based recommendations in the next step, the estimates of a health impact assessment should not be interpreted in isolation. They should be part of the larger health impact assessment that considers the current situation and issues in the area affected and possible alternative interventions.