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3.3.1 Overestimation of the change in morbidity cases

There is a straightforward interaction between morbidity and mortality ef- fects: the longer people live, the more likely they are to experience additional morbidity events, and conversely if their life expectancy is shortened.

Understandably it is not common practice to exhaustively account for the potential morbidity impacts associated with a change in mortality risk in a given target population. However, if a risk factor is associated with adverse impacts on both mortality and a selection of morbid conditions, the described interactional effect implies that the true change in number of cases of morbid endpoints of interest, net of the mortality impact, will always be lower than the “crude” change computed separately from the mortality effect.

We shall examine this relationship using the example of outdoor air pol- lution that is positively associated with a greater risk of dying prematurely from all-causes and of developing chronic conditions such as chronic bronchi- tis (Anderson et al., 2012; US EPA, 2009). Under a decrement in exposure to air pollution, the crude decrease in number of cases of chronic bronchitis, resulting from a reduced risk to develop this disease, is expected to be partly compensated by additional cases of chronic bronchitis taking place during the extended length of life enjoyed by individuals of the target population. Con- versely, under an increment in air pollution exposure, the crude increase in cases of chronic bronchitis is expected to be partly compensated by the short- ening of an individual’s average lifespan, during which they can develop this disease.

The magnitude of the overestimation bias in the change in cases of morbid endpoint y under the separate approach will be determined by the interaction between three main factors: (i) the target population’s baseline probability of developing condition y, i.e. disease incidence; (ii) the relative magnitude of the risk factor’s impact on the risk of death and on the risk of developing condition y; (iii) the analysis time-horizon. Indeed the longer the time-horizon, the more mortality impacts will matter when assessing the morbidity effects associated with a given intervention. The overestimation bias is therefore difficult to estimate a priori. However, modelling results based on a case study of air pollution reduction in London over a time horizon of 60 years, presented in section 3.5, shows that it can be substantial.

3.3.2 Limited ability to characterise the distribution of life ex- pectancy impacts

By causal pathways

The computation of an intervention’s impact on life expectancy separately from morbidity effects has two main limitations. The first pertains to the limited ability to identify the contribution of each morbid endpoints, i.e. each causal pathway, to the overall life expectancy impact.

Whilst cause-specific mortality risk estimates can help address this problem, their use is typically not recommended for health economic evaluation as it may lead to an under-estimation of the total mortality burden (WHO, 2013). Indeed, the effect of a severe disease on lifespan usually goes beyond death from this particular disease and includes an overall weakening of the general health condition, e.g. co-morbidities, that may lead to premature death from other causes. These premature deaths may not be captured by the narrower focus of cause-specific risk estimates. Second, the validity of cause-specific mortality risk estimates may be adversely affected by misclassification of causes of death in mortality registration. Background national statistics on all-cause mortality are therefore expected to be of greater precision than cause-specific death rates (Mathers et al., 2005). Finally, at least in the case of air pollution, cause-specific risk estimates are deemed more appropriate for meta-analysis, which is key to incorporate all relevant evidence and to decrease parameter uncertainty (WHO, 2013).

Between health-stratified population subgroups

Second, the life expectancy impact attributable to interactions between the presence of pre-existing morbid conditions, i.e. health status, and hazard ex- posure cannot be identified. Morbidity indeed typically affects mortality in

two main ways. Firstly, it can increase the individual’s baseline probability of death. For instance, subjects with chronic obstructive respiratory disease, especially in the severe or very severe stages of the disease, have a higher prob- ability of death (Mannino et al., 2006). Secondly, it can enhance individuals’ predisposition to experience adverse effects associated with hazard exposure, hereafter referred to as greater susceptibility to exposure. For instance, sur- vivors of a myocardial infarction were found to have a higher excess risk of death associated with air pollution exposure than individuals of the general population (Zanobetti & Schwartz, 2007).

A potential approach to encompassing interactions between health status and mortality may be to: (i) split the target population into subgroups whose health state has a known influence on baseline mortality risk and/or suscep- tibility to risk factor’s adverse effects and (ii) apply the life-table method to each subgroup. However, in addition to being cumbersome, when assess- ing air pollution control interventions, such an approach would be incomplete and underestimate health benefits. Indeed, air pollution not only affects peo- ple differently according to their health status but, as it increases the risk of developing chronic conditions, also impacts upon the risk on entering each susceptibility-stratified subgroup. Consequently, simply applying the life-table method to each subgroup would fail to capture air pollution’s influence on in- dividuals’ health distribution over time, and its interaction with health-related differential susceptibility. As a result, such an approach would underestimate total health benefits.

Reasons why the distribution of impacts matters

The ability to characterise the distribution of life expectancy impacts is extremely pertinent to implementing the concept of Healthy Public Policy. The latter indeed embraces concerns for both health and equity and places a particular emphasis on distributional analysis (WHO, 1999, 2005, 2008).

Firstly, combining knowledge of the distribution of life expectancy impacts by causal pathway and between population subgroups stratified by health sta-

tus, with evidence on social gradients in health outcomes (O’Neill et al., 2003) is key to finely characterise the distribution of health effects across socio- economic subgroups. It is therefore a crucial component of health inequality analysis.

Secondly, knowledge of impact distribution is paramount to the construction of summary measures of population health (SMPH), which require to adjust life expectancy estimates with health- or disability-related quality of life weights (Gold et al., 2002). Although SMPH are not used widely in health impact assessments (Briggs, 2008), they were suggested as a complementary metric to support resource prioritisation (Veerman et al., 2005).

Thirdly, instead of simply adding numbers of cases of morbid events, un- derstanding the impact of morbid events on individuals’ baseline probability of death as well as on their susceptibility to suffer from further adverse effects, helps provide an much more accurate picture of the morbidity health burden attributable to a given environmental health hazard.

Finally, distributional information is also pertinent to economic analyses, which are often performed for regulatory assessments. For instance, extending the life of a person with a medical condition or extending the time period during which a person remains healthy is expected to have opposite impacts on health care budgets.