Sensitivity scenarios

Sensitivity of results pertaining to: (i) dynamics in health risk reduction following pollution reduction and (ii) the discount rate will be analysed.

Walton (2010)’s thorough review of empirical evidence from cohort, inter- vention and smoking cessation studies stressed on the level of uncertainty about patterns of health risk reduction following exposure decrement. The US EPA considered R¨o¨osli et al. (2005)’s exponential decay model as an alternative to the 20-year distributed lag described in section 4.5.1. However, results were found to be very sensitive to the choice of time constant for the model and it

is not clear which source of evidence should inform this parameter (U.S. EPA - Science Advisory Board and Health Effects Subcommittee, 2009). Exponen- tial decay will therefore not be considered in the present sensitivity analysis of time lag. Instead, the two following scenarios, summarised in Table 4.4, will be evaluated. Scenario “No CL” assumes the absence of a cessation lag, and thus represents an upper bound to the possible health benefits associated with the intervention. (ii) As the smoking cessation literature suggests that lung cancer risk may decrease more slowly than cardiovascular death risk (Walton, 2010; Rabl, 2003), scenario “Mixed CL” assumes that the decrease in lung cancer risk is progressive over 40 years, while the change in risks of other health effects is assumed to follow the US EPA’s 20 year distributed time lag.

For analysis of investments with pay-offs accruing over time-horizons above 50 years, the UK treasury suggests to used staged discount rates (Lowe, 2008). The two staged discounting structures proposed by the UK treasury, which are described in Table 4.4, will be used in sensitivity analysis.

Scenario Cessation lag (CL) Discounting Base case 20-year distributed lag(a) 3.5% p.a.

Mixed CL

Lung cancer: progressive reduc-

tion over 40 years(b) 3.5% p.a.

Other health impacts: 20-year distributed lag(a)

No CL Immediate effect 3.5% p.a.

Staged discounting 1 20-year distributed lag(a) Year 1 to 30: 3.5% p.a.

Year 31 to 60: 3% p.a. Staged discounting 2(c) 20-year distributed lag(a) Year 1 to 30: 3% p.a.

Year 31 to 60: 2.57% p.a. Table 4.4: Scenarios of sensitivity analysis against base case.

(a)30% of risk reduction in year 1, an additional 12.5% every year between year 2 to year 5 and the final

20% being phased in gradually over year 6 to year 20.

(b)i.e. a cumulative decrease in risk at a rate of 2.5% every year. (c)Excludes the element of pure social time preference.

4.7

Conclusion

Whilst the understanding of air pollution impacts on length and quality of life is expected to be of particular interest to policy-makers, so far all past attempts at measuring the QALY gain from air pollution reduction have been simplistic and inaccurate. In this chapter, it was argued that the Markov- based simultaneous approach presented in Chapter 3 would enable, for the first time, to fully capture air pollution’s joint impact on quality and length of life by encompassing air pollution’s influence on population individuals’ baseline

quality of life, life expectancy and level of susceptibility to adverse effects. A Markov model structured around three disease pathways, for which there is robust epidemiological evidence of association with long-term exposure to fine particulate pollution, namely chronic obstructive pulmonary disease, coronary heart disease and lung cancer, was therefore developed.

This chapter focused on the core steps to model construction and param- eterisation and underlined two distinct data gaps for parameterisation, which will be addressed in the following two chapters. Chapter 5 develops a frame- work to estimate the annual probability of being diagnosed at a given stage of COPD and applies it to the general population of England. Chapter 6 per- forms a systematic review and two meta-analyses of the association between long-term exposure to particulate air pollution and respectively all-cause mor- tality and lung cancer. Modelling results will be presented in Chapter 7. The limitations of the presently developed model will be discussed in Chapter 8.

Chapter 5

Estimation of COPD incidence

in England by severity stages

5.1

Introduction

In order to estimate the QALY gain and health care resource impact of air pollution control in the UK, Chapter 4 developed a Markov model structured around three disease pathways: CHD, lung cancer and COPD. The latter is a slowly progressive disease of airflow obstruction.

The transition probabilities that parameterise the model are informed by mortality and disease incidence (i.e. diagnostic) statistics for England and Wales. In the case of COPD however, available incidence data suffers from bias as the disease is largely underdiagnosed, especially in its milder stages. In England, up to 80% of adults above 30 affected by spirometry-defined COPD were found to report no respiratory diagnosis (Shahab et al., 2006). Con- sequently, using primary care data on COPD incidence to parameterise the model developed in Chapter 4, would seriously underestimate the total pool of individuals who would benefit from a reduction in the risk of developing COPD following air quality improvement.

An estimation of the underlying, i.e. “true”, prevalence of COPD by sever- ity stage in England was, however, carried out by the UK Department of Health

(2010). The present objective is therefore to develop a coherent probabilistic framework to estimate the probability of COPD diagnosis in the English popu- lation at different severity stages of the disease, by exploiting existing estimates of underlying prevalence and linkages between disease prevalence, incidence, survival and progression.

The chapter is structured alongside the following sections. In section 2, I expand upon the key characteristics of COPD and its implications for cost- effectiveness analysis of preventive interventions such as air pollution control. In section 3, I outline the linkages between prevalence and incidence, I describe the approach used by the UK Department of Health to estimate the underlying prevalence of COPD in England and I further justify the structure of the COPD disease pathway of the model built in Chapter 4. In section 4, I first describe an incidence estimation model proposed by Podgor & Leske (1986) for a single- stage chronic disease and, in a second step, I extend this model to a multi-stage setting by allowing for disease severity progression and survival stratified by severity level. In section 6, I apply the developed framework to estimate the probabilities of COPD diagnosis in England by severity stage. In sections 6 and 7, I present and discuss the results.