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Review
. 2024 Jan 27;23(1):13.
doi: 10.1186/s12940-023-01039-x.

The methodology of quantitative risk assessment studies

Maxime Rigaud  1 , Jurgen Buekers  2 , Jos Bessems  2 , Xavier Basagaña  3   4   5 , Sandrine Mathy  6 , Mark Nieuwenhuijsen  3   4   5 , Rémy Slama  7
Affiliations
Review

The methodology of quantitative risk assessment studies

Maxime Rigaud et al. Environ Health. .

Abstract

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.

Keywords: Dose–response; Environment; Hazard; Health impact; Policy; Risk.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Position of quantitative risk assessment in the process of risk characterization and management. Risk assessment can be used to assess the impacts of the “factors” considered (leftmost box) and of policies aiming at managing and limiting their impacts. Adapted from [11]
Fig. 2
Fig. 2
Overview of the main steps of risk assessment studies. The starting point of the study (or the counterfactual scenarios) can be formulated in terms of policy, program, project (1), environmental emissions (2), environmental level (3A), behavior (3B), human exposure (4)
Fig. 3
Fig. 3
Cross-sectional variations of fine particulate matter (PM2.5) throughout the urban area of Lyon, as estimated from a fine-scale dispersion model, and typical locations of background permanent monitoring stations (black circles). Adapted from [40]
Fig. 4
Fig. 4
Spatial resolutions of various air pollution (nitrogen dioxide) exposure models developed in a middle size city. a Estimates based on permanent background monitoring stations; b geostatistical model relying on a fine-scale measurement campaign; c dispersion model taking into account emission and meteorological conditions; d Land-use regression model relying on the same measurement points as geostatistical model (b) [50]
Fig. 5
Fig. 5
Meta-analysis of the relative-risk (RR) of lung cancer associated with PM2.5 exposure, by region [53]
Fig. 6
Fig. 6
Illustration of non-linear exposure response functions: A) Fine particulate matter and mortality [85]; B) Temperature and mortality in Rome [86], C) Physical activity and cardiovascular events [87]. MET: Metabolic equivalents: RR: Relative risk
Fig. 7
Fig. 7
Causal diagram summarizing the causal relations between hypothetical risk factors (A, B and C) and a disease D. Here, A and B are assumed to independently affect the probability of disease, while a part of the effect of B on D is mediated by C
Fig. 8
Fig. 8
Illustration of possible cessation lags (in years) considered in the estimation of the impact of fine particulate matter exposure on mortality [89]. The first year of the intervention implementation is designated as year one
Fig. 9
Fig. 9
Illustration of the similitude of the principles of risk assessment of an exposure (A) and of a policy or program (B). When considering an exposure (A), the fraction of disease cases attributable to a specific exposure (compared to a lower and theoretically achievable level) is estimated for time t (typically assumed to correspond to the current time). When considering a policy (B), the expected health benefit of the project or policy (consisting in changing the level of one or more environmental factors) is estimated, considering the population at the current time or at a later time t, comparing it to the situation without change. Both approaches can be seen as aiming to estimate the impact of a theoretical policy or intervention lowering (or, more generally, changing) the level of one or several environmental factors, compared to a reference situation considered at the same time period
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