Notably, one study [ 57 ] considered also the location of workplace of studied subjects. Residence location can be determined with various degrees of precision. In this way the same exposure level is assigned to large groups of population, but this assumption was rarely discussed and no measures of exposure variability inside groups were reported.
Thus, it was impossible to evaluate the degree of ecological bias [ 58 ] that is, how well the variation in risk between groups with different average exposure applies to the variation in risk between individuals. Some studies used census block or full postcode for determining residence position. The dimension of these blocks may vary greatly depending on the location: normally these blocks are smaller in populated areas but may become very large in other rural zones.
In our case study census blocks had an average area of 0.
This was true especially for more exposed areas, since the incinerator is located in a less densely populated area with large census blocks. This aspect could lead to different degree of errors in exposure assignment, that increase with the level of pollutant or proximity to the incinerator. The most precise way to locate residences is to address geocoding: this procedure assigns a couple of geographic coordinates to each address. Errors in address positioning depend on the quality of the database used but is generally in the order of tens to hundreds meters [ 61 , 62 ], thus small in comparison with the use of census blocks or full postcode.
In future studies maximum disaggregation of data, to maximise information and minimize potentially differential ecological biases [ 63 ], is thus recommended. Nevertheless, home location may not well represent total exposure because people may experience shorter but more intense exposures outside home, and residence is a proxy only for inhalation exposure and does not account for indirect pathways [ 66 ] Figure 1.
Although this technique has well-known limitations, it is often the only method available, particularly for large populations or for reconstructing historical exposures.
Temporal variability in exposure is an issue rarely explored in the reviewed studies. Temporal variability may result both from changes in source emissions over time or from residential mobility of the population and may be a cause of incorrect exposure assignment [ 67 , 68 ]. Only one published study [ 47 ] explicitly accounts for historical exposure variability by reconstructing residential histories and evolution of dioxin emissions from the sources considered.
However the exposure indicator chosen i. A better indicator could have been cumulative exposure, that is, the sum of the annual exposure concentration over the exposure duration. One study [ 29 ] considered the modification of incinerator emissions over time indirectly, without considering changes in the final statistical model, but evaluating how the morphology of fallout maps was similar in time.
Almost all papers used categorical definitions of exposure i. One issue rarely discussed is the rationale behind the choice of cut-off values used to classify continuous variables. In the absence of toxicological reference values for this type of exposure, in our case study we used a criterion expected to make the results of the statistical analysis more stable and reliable, that is, having roughly the same number of exposed individuals in each class. In reviewed studies a priori cutoffs of exposure were generally chosen without an explicit justification [ 33 — 35 , 51 ].
When categorical exposure variables are measured with error, they are said to be misclassified. Misclassification can be differential or nondifferential with respect to disease status of an individual person [ 26 ], the latter being more probable in reviewed studies and generally leading to risk estimations biased toward the null. Nevertheless, in presence of more than two exposure categories, non-differential misclassification can move estimates of risk away from null and disrupt exposure-response trends [ 69 ].
Our case study showed that i for exposure measures based on distance a relevant part of the population may be classified in the wrong exposure category assuming that dispersion model better represents real exposure , with relevant percentages of subjects moving by more than one category; ii the use of census blocks to identify the residence may introduce a certain degree of differential misclassification since the error is higher in more exposed areas and lower for less exposed. Both these factors may bias risk estimates away from the null or modify exposure-response trends.
In practice, since no such gold standard is generally available, we recommend researchers to conduct sensitivity analyses on exposure assessment [ 71 ] and discuss the magnitude of error that may be present in their data. Another issue that is only partially dealt with in reviewed literature is confounding. Confounding occurs when a risk factor different from the exposure variable under study causes bias in the estimation of association between exposure and disease, due to its differential distribution in exposed and non exposed groups [ 72 ]. Many reviewed studies did not account for any confounder in the epidemiological model [ 33 , 47 , 59 , 73 — 77 ].
Some studies collected information about personal lifestyles or socio-economic status directly through questionnaires [ 38 — 40 , 51 , 78 , 79 ]. Unfortunately the use of questionnaires and surveys is unfeasible for large populations; thus a large part of the studies did not consider personal lifestyles but included socio-economic indicators e. These indexes are generally constructed based on census statistics. Of particular concern is the general lack of information about environmental confounding. Many of the pathologies under study have been associated with various atmospheric pollutants e.
Often, waste incinerators are located inside industrial areas or near other major sources of pollution. As a result, most exposed subjects, as identified by the dispersion model, were also more exposed to other sources of pollution. It will be difficult to correctly identify the possible health effect of this incinerator, unless we have some information about the difference in population exposure to other sources between the exposed and nonexposed groups.
Only few studies included information about environmental confounders. Biggeri et al. Notably, one recent study [ 29 ] used atmospheric dispersion models to estimate pollution concentrations at the address of residence from other local sources of atmospheric pollution road traffic, industrial plants, and heating. This represents a notable improvement since the confounding factor was evaluated with the same spatial resolution as exposure to the incinerator.
As the quantitative contribution of well-managed modern incinerators to total pollution levels in a study area and to baseline health risks is expected to be low, we suggest to draw a careful attention to other local sources of pollution and to implement multisite studies on large populations where feasible. We reviewed 41 articles from the literature with the main aim of retrieving information for the definition of an exposure assessment protocol to be used in a large study on health effects of pollution due to incinerators MONITER project.
Overall, our analysis showed a trend of improvement in exposure assessment quality over time, with a massive use of dispersion models in exposure assessment after year Nevertheless, the lack of a common framework for exposure assessment is demonstrated by the use of a variety of methods, also in recent papers, with different quality of epidemiological findings and difficulties in comparisons of results. In most of the selected studies the characterization of exposure can be significantly improved by using more detailed data for population residency and better simulation models.
Recent development of informative systems and high availability of environmental and demographic data suggest the use of dispersion models of pollutants emitted from a source, combined with precise methods of geographic localizations of people under study, as the state of the art method to assess exposure of population in epidemiological studies. Considerations about residential mobility, temporal variations in pollution emissions, latency period of investigated diseases, and treatment of environmental and sociodemographic confounders can improve exposure assessment accuracy.
All these aspects of exposure assessment are particularly relevant as most of environmental conflicts usually arise from the evaluation of the contribution of the various pollution sources to the overall contamination. The present work has been carried out within the activities of the Project Line no.
The authors thank Dr. Paola Angelini and all project participants. Journal of Environmental and Public Health. Indexed in Web of Science. Journal Menu. Special Issues Menu. Subscribe to Table of Contents Alerts. Table of Contents Alerts. Abstract Incineration is a common technology for waste disposal, and there is public concern for the health impact deriving from incinerators. Introduction Incineration is one of the most common technologies for waste disposal [ 1 ]. Material and Methods 2. Literature Review We analyzed papers referenced in previously published reviews on incinerator health effects [ 9 — 13 , 20 ] and, additionally, searched for further references on MEDLINE, PubMed, Scopus, and Google Scholar by using a number of keywords combinations e.
Figure 1: Conceptual model representing the principal impact pathways that determine exposure to atmospheric emissions from an incinerator. Contamination of drinking water is not represented. Figure 2: Temporal evolution of exposure assessment methods. Methods are classified according to Table 1 and sorted in the -axis from the less precise to the best one. Figure 3: Representation of the area considered in the case study of Parma.
Figure 4: Results of exposure assessment by using different methodologies. Boxes represent the interquartile range IQR , the horizontal line inside the box is the median value, and the whiskers extend to 1. The line represents the linear regression model. Table 2: Evaluation of the agreement between concentration maps and other exposure assessment methods.
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