key: cord-0721778-leei8b4v authors: Mutti, Antonio title: Challenges in assessing the impact of environmental health hazards on populations date: 2022-04-26 journal: Med Lav DOI: 10.23749/mdl.v113i2.13065 sha: ead889a60bbebad0443684889e8835f7547e74b4 doc_id: 721778 cord_uid: leei8b4v nan Whereas the default paradigm for risk assessment used for decades still holds for deterministic effects of xenobiotics, it gives rise to concern and controversy when applied to multifactorial outcomes. In the latter case, uncertainties associated with inference largely exceed available evidence. A recent article presenting an original viewpoint on health impact assessment (HIA) is stimulating a discussion originating from the failure to acknowledge critical assumptions inherent in different models, a weakness that gets somehow lost among the mathematical formulae proposed by epidemiologists. The point on which the HIA gives rise to controversial positions is the baseline rate to compute the cases attributable to a project under evaluation. According to Zocchetti's proposal, it should multiply the fraction of the baseline rate attributable only to the exposure under assessment [5] . According to other authors, it should apply to the overall baseline rate of the population [6] . On the one hand, the etiological fraction attributable to environmental exposures for most diseases is limited compared to other factors, such as genetic traits, diet, and lifestyle. Therefore, the incremental effect of such exposures is expected to increase only the marginal fraction of attributable cases. On the other hand, a new source of pollution will act on the whole population, and therefore it is expected to cause new additional total cases. The outcome will depend on the interaction between exposures resulting from the novel setting and pre-existing risk factors. However, in deriving the standard HIA interactions have to be assumed when intervention is still at the planning stage, i.e., when it is still unknown whether risk factors will act in an additive, multiplicative or competitive mode. Considering that we cannot anticipate the interaction among multiple exposures before its occurrence, any a priori "assessment" is challenging. Whereas in risk assessment, the probability is calculated for an adverse effect under specific exposure conditions, i.e. based on empirical data possibly adjusted for uncertainties of extrapolations, the challenge of HIA is to calculate the occurrence of measurable outcomes (impact) of something elusive in nature. Indeed, the health impact to be assessed will result from multiple factors behaving differently at subsequent stages, depending on unknown interactions among multiple factors. In standard HIA procedures, the relative effect of exposure is assumed to be constant across strata of confounders (and therefore applies equally to all individuals independently of their characteristics). Such an assumption does not seem to rely on the necessary pathophysiological ground. That authoritative institutions adopt a particular methodology does not necessarily mean that such a methodology is error-free. For example, voting to classify a given substance or process as a human carcinogen based on epidemiological evidence alone may be misleading. The strength of the association between exposure and excess mortality is just one of the nine criteria or "viewpoints" listed by Austin Bradford Hill, "none of which can bring indisputable evidence for or against the cause-and-effect hypothesis and none can be required as a sine qua non" [7] . Defining a range of possible scenarios would be more appropriate than a numerical outcome calculated with a dogmatic formula that does not include the potential role of confounders and effect modifiers, requiring unsupported assumptions. Instead of being "assessed", the health impact should be "evaluated" by either political or technical decision-makers, considering such additional criteria as costs, social acceptability, and risk-benefit balance. Epidemiology faces challenges in identifying causal links between several factors contributing to any given outcome in its post hoc assessments, such as estimating the excess mortality ascribed to the COVID-19 pandemic. Attempts to predict the future relying on assumptions and calling them "assessments" does not per se confer validity to a forecast, which should not become divination. Several known unknowns and many unknown unknowns contribute to uncertainties that should always be considered. We should never forget that "Risk assessment also includes characterization of the uncertainties inherent in the process of inferring risk" [4] . Algorithms for artificial intelligence may show surprising performance associated with big data correlation analysis. They could make the traditional scientific method of using hypotheses, causal models, and tests ob-solete. However, causality is an essential part of human thinking, and it is particularly relevant to prevention science. We simply need to maintain the necessary rigour, critical attitude, prudence and wisdom, resisting the temptation to delegate our judgment on complex issues to simple formulae or sophisticated algorithms. COVID-19 Excess Mortality Collaborators. Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality Excess total mortality during the Covid-19 pandemic in Italy: updated estimates indicate persistent excess in recent months COVID mortality in India: National survey data and health facility deaths US) Committee on the Institutional Means for Assessment of Risks to Public Health. Risk Assessment in the Federal Government: Managing the Process The Nature of Risk Assessment Epidemiologic Health Impact Assessment: Estimation of Attributable Cases and Application to Decision Making Health impact assessment should be based on correct methods The environment and disease: association or causation?