The methodology of quantitative risk assessment studies.

Once an external factor has been deemed likely to influence human health and a dose response function is available, an assessment of its health impact or that of policies aimed at influencing this and possibly other factors in a specific population can be obtained through a quantitative risk assessment, or health impact assessment (HIA) study. The health impact is usually expressed as a number of disease cases or disability-adjusted life-years (DALYs) attributable to or expected from the exposure or policy. We review the methodology of quantitative risk assessment studies based on human data. The main steps of such studies include definition of counterfactual scenarios related to the exposure or policy, exposure(s) assessment, quantification of risks (usually relying on literature-based dose response functions), possibly economic assessment, followed by uncertainty analyses. We discuss issues and make recommendations relative to the accuracy and geographic scale at which factors are assessed, which can strongly influence the study results. If several factors are considered simultaneously, then correlation, mutual influences and possibly synergy between them should be taken into account. Gaps or issues in the methodology of quantitative risk assessment studies include 1) proposing a formal approach to the quantitative handling of the level of evidence regarding each exposure-health pair (essential to consider emerging factors); 2) contrasting risk assessment based on human dose-response functions with that relying on toxicological data; 3) clarification of terminology of health impact assessment and human-based risk assessment studies, which are actually very similar, and 4) other technical issues related to the simultaneous consideration of several factors, in particular when they are causally linked.

InterMob: a 24-month randomised controlled trial comparing the effectiveness of an intervention including behavioural change techniques and free transport versus an intervention including air pollution awareness-raising on car use reduction among regular car users living in Grenoble, France.

BACKGROUND: Frequent car use contributes to health and environmental issues such as air pollution, climate change and obesity. Active and sustainable mobility (bike, walk, public transport, car sharing) may address these issues. Different strategies have been implemented in past research, involving hard levers, aimed at modifying the economical or geographical context (e.g., free public transport), and soft levers, aimed at modifying psychological processes (e.g., personalised transport advice). However, few studies have combined both hard and soft levers. In addition, few have used robust methodologies (e.g., randomised controlled trials), followed behavioural changes in the long-term, and been anchored in behaviour change theories. InterMob aims to address these limits by implementing a 24-month randomised controlled trial including hard and soft levers. The objectives of InterMob are to a) evaluate the effectiveness of an experimental arm versus an active controlled arm, and b) identify the processes of mobility change. METHODS: Regular car users living in Grenoble (N = 300) will be recruited and randomised to one of the two arms. The experimental arm consists in a six-month intervention combining hard levers (free access to transport/bikes), and soft levers (e.g., personalised transport advice). The control arm consists in a six-month intervention aimed at raising awareness on air pollution and its health effects. Both arms will include eight evaluation weeks (spread out over 24 months) based on a GPS, an accelerometer, and a pollution sensor. Moreover, participants will complete mobility logbooks and surveys measuring psychological constructs, socio-economical, and socio-spatial characteristics. DISCUSSION: InterMob will assess the effectiveness of two interventions aimed at reducing car use within regular car users in the short-, mid- and long-term. Moreover, InterMob will allow to better understand the psychological processes of behaviour change, and the socio-economical and geographical conditions under which the intervention is efficient in reducing car use. Finally, the benefits of mobility change in terms of physical activity, quality of life, and exposure to pollution will be quantified. TRIAL REGISTRATION: ClinicalTrials.gov : NCT05096000 on 27/10/2021 (retrospectively registered).

Designing local air pollution policies focusing on mobility and heating to avoid a targeted number of pollution-related deaths: Forward and backward approaches combining air pollution modeling, health impact assessment and cost-benefit analysis.

CONTEXT: Policies aiming at decreasing air pollutants (e.g., fine particulate matter, PM2.5) are often designed without targeting an explicit health benefit nor carrying out cost-benefit analyses. METHODS: We developed a transdisciplinary backward and forward approach at the conurbation level: from health objectives set by local decision-makers, we estimated which reductions in PM2.5 exposures and emissions would allow to reach them, and identified urban policies leading to these reductions (backward approach). We finally conducted health impact and cost-benefit analyses of these policies (forward approach). The policies were related to the most emitting sectors in the considered area (Grenoble, France), wood heating and transport sectors. The forward approach also considered the health impact and co-benefits of these policies related to changes in physical activity and CO2 emissions. FINDINGS: Decision-makers set three health targets, corresponding to decreases by 33% to 67% in PM2.5-attributable mortality in 2030, compared to 2016. A decrease by 42% in PM2.5 exposure (from 13.9 µg/m3) was required to reach the decrease by 67% in PM2.5-attributable mortality. For each Euro invested, the total benefit was about 30€ for policies focusing on wood heating, and 1 to 68€ for traffic policies. Acting on a single sector was not enough to attain a 67% decrease in PM2.5-attributable mortality. This target could be achieved by replacing all inefficient wood heating equipment by low-emission pellet stoves and reducing by 36% the traffic of private motorized vehicles. This would require to increase the share of active modes (walking, biking…), inducing increases in physical activity and additional health benefits beyond the initial target. Annual net benefits were between €484 and €629 per capita for policies with report on active modes, compared to between €162 and €270 without. CONCLUSIONS: Urban policies strongly reducing air pollution-attributable mortality can be identified by our approach. Such policies can be cost-efficient.

Which decreases in air pollution should be targeted to bring health and economic benefits and improve environmental justice?

BACKGROUND: Fine particulate matter (PM2.5) exposure entails large health effects in many urban areas. Public measures aiming at decreasing air pollution are often designed without targeting an explicit health benefit. Our objective was to investigate the health and economic benefits and the social inequalities in exposure resulting from several scenarios of reduction of PM2.5 exposure, in order to support decisions about urban policies. MATERIAL AND METHODS: In the French conurbations of Grenoble and Lyon (0.4 and 1.4 million inhabitants, respectively), PM2.5 yearly average exposure was estimated on a 10-m grid by coupling a PM2.5 dispersion model to population density. Changes in death cases, life expectancy, lung cancer and term low birth weight incident cases as well as associated health economic costs were estimated for ten PM2.5 reduction scenarios differing in terms of amplitude of reduction and spatial extent. Changes in social differences in PM2.5 exposure were also assessed. RESULTS: During the 2015-2017 period, PM2.5 average exposure was 13.9 µg/m3 in Grenoble and 15.3 µg/m3 in Lyon conurbations. Exposure to PM2.5 led to an estimated 145 (95% Confidence Interval, CI, 90-199) and 531 (95% CI, 330-729) premature deaths, 16 (95% CI, 8-24) and 65 (95% CI, 30-96) incident lung cancers, and 49 (95% CI, 19-76) and 193 (95% CI, 76-295) term low birth weight cases each year in Grenoble and Lyon conurbations, respectively, compared to a situation without PM2.5 anthropogenic sources, i.e. a PM2.5 concentration of 4.9 µg/m3. The associated costs amounted to 495 (Grenoble) and 1767 (Lyon) M€/year for the intangible costs related to all-cause non-accidental mortality and 27 and 105 M€ for the tangible and intangible costs induced by lung cancer. A PM2.5 exposure reduction down to the WHO air quality guideline (10 µg/m3) would reduce anthropogenic PM2.5-attributable mortality by half while decreases by 2.9 µg/m3 (Grenoble) and 3.3 µg/m3 (Lyon) were required to reduce it by a third. Scenarios focusing only on the most exposed areas had little overall impact. Scenarios seeking to reach a homogeneous exposure in the whole study area were the most efficient in alleviating social inequalities in exposure. CONCLUSIONS: Reduction scenarios targeting only air pollution hotspots had little expected impact on population health. We provided estimates of the PM2.5 change required to reduce PM2.5-attributable mortality by one third or more. Our approach can help targeting air pollution reduction scenarios expected to entail significant benefits, and it could easily be transposed to other urban areas.