A nivel regional

Finally, it should be underlined that at the core of model development lies the intertwined concepts of choice as well as uncertainty. Indeed, in imple- menting a chosen model structure, assumptions are taken which require value judgements. In addition the evidence base may be uncertain. It is important to reflect any uncertainty in the evidence - referred to as parameter uncer- tainty - and assumptions - referred to as structural uncertainty - and explore how these may impact the decision.

Structural uncertainty steams from the fact that it is not possible to know for sure ex-ante whether the choices underpinning the structure of the hard implemented model (e.g. selection of relevant impacts, of appropriate simpli- fications) are right or wrong, i.e. whether they will substantially affect the model’s capacity to usefully inform the decision problem (Tappenden, 2012).

Parameter uncertainty, which was discussed in section 2.3.3, also depends on a string of modelling choices, including but not restricting to deciding which parameters are relevant and which source of evidence should characterise them (Tappenden, 2012). Such choices will be a particular focus on this thesis which, following an assessment of currently available evidence, will undertake the estimation of a subset of parameters required to parameterise the model of the health effects from air pollution exposure.

Importantly, the consequences of parameter and structural uncertainty are intrinsically linked since the structure of the model will determine the relation- ship of parameters between one another and their relative influence on final outcomes.

Chapter 3

Quantitative impact assessment:

why morbidity and mortality

impacts need to be

simultaneously considered

3.1

Introduction

This chapter challenges the current approach to quantification in health impact assessment of environmental health interventions, such as air pollution control.

Health impact assessments aim to predict the effects of projects, programmes or policies - hereafter also referred to as interventions - on population health and health inequalities. They are widely used to inform environmental policy and other public policies outside the health care sector and are championed by the World Health Organisation under the rubric of “Healthy Public Policy”, which calls for explicit consideration of health and equity matters in all policy areas (WHO, 1986; Kemm, 2001).

predictions that are valid (Veerman et al., 2007; Bathia & Seto, 2011) and that add value to the decision-making process pertaining to the design and/or implementation of interventions (Davenport et al., 2006; Kemm, 2001). This is often best done through quantitative risk assessment (QRA), which provides a more precise description of impacts and their magnitude and also supports economic evaluation, which is a key input to decision-making (Veerman et al., 2005; Fehr et al., 2012).

QRA has often been carried out to evaluate interventions that affect health by modifying exposure to environmental risk factors, air pollution in particular (Medina et al., 2013). As mentioned in Chapter 2, the quantification method consists in applying, for each health endpoint, a health impact function that links together: (i) the relevant epidemiological risk estimate, (ii) incidence data and (iii) the change in risk factor exposure and its distribution within the target population (O’Connell & Hurley, 2009; Bathia & Seto, 2011; Medina et al., 2013). This provides the change in number of cases of a selection of morbidity and mortality endpoints, attributable to the intervention under evaluation. For instance, in the assessments of large-scale programmes of air pollution control, such as Clear Air for Europe (Holland et al., 2005a), Revisions of the E.U. Gothenburg Protocol (Holland et al., 2011) and Revisions to the US National Ambient Air Quality Standards for particulate matter (USEPA 2006; USEPA 2009), commonly reported health impacts included numbers of premature deaths and/or of life years lost, counts of cardio-respiratory hospital admissions, numbers of cases of chronic bronchitis and so forth.

Morbidity and mortality are, however, known to interact in a number of ways. In particular, a chronically sick person is expected to have a shorter life expectancy than a healthy person. Additionally, morbidity can influence individuals’ susceptibility to the harmful effects of environmental hazards.

The objective of this chapter is twofold: it aims (i) to demonstrate the im- portance of encompassing interactions between morbidity and mortality im- pacts in QRA of environmental health interventions; and (ii) to show how to handle these interactions via simultaneous quantification of effects using Markov modelling, which is used extensively to support decision-making in the health care sector. This work is based on the example of outdoor air pol-

lution that is one of the environmental risk factors for which health effects have been most intensively quantified over the recent years (Medina et al., 2013). Study findings, however, are expected to apply to any environmental risk factor associated with both morbidity and mortality effects.

The work consists of four main components. First, I briefly recall the cur- rent approach to QRA, which has been described in greater detail in Chapter 2 and is hereafter referred to as the “separate” approach. Second, I identify lim- itations associated with the latter and advocate simultaneous quantification of morbidity and mortality impacts. Third, I outline two approaches to applying the Markov modelling technique to QRA. Fourth, I illustrate the advantages of the simultaneous approach to quantification, based on an illustrative inter- vention of air pollution reduction in London.