Risk of congenital anomalies around a municipal solid waste incinerator: a GIS-based case-control study

Malagoli, Carlotta; Fabbi, Sara; Teggi, Sergio; Rodolfi, Rossella; Garavelli, Livia; Astolfi, Gianni; Rivieri, Francesca

doi:10.1186/1476-072x-8-8

Cited by 36 publications

(26 citation statements)

References 31 publications

(28 reference statements)

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“…To date a number of epidemiological studies have investigated the relationship between incineration and health [4–12], with most focused on its association with risk of cancer and more recently, the risk of adverse birth outcomes [8, 12–24]. The UK Committee on Carcinogenicity of Chemicals in Food, Consumer Products and the Environment released a statement about MSWIs and cancer in 2000 (updated in 2009), stating that, “… any potential risk of cancer due to residency near to municipal solid waste incinerators was exceedingly low and probably not measureable by the most modern epidemiological techniques ” [6, 7].…”

Section: Introductionmentioning

confidence: 99%

“…Some studies have included additional information alongside proximity to strengthen this method, including wind patterns, soil concentrations [18], local topography, and complaints of nuisance caused by the plumes [24]. Only a limited number of more recent studies have used dispersion models [8, 12, 13, 17, 23] to assess exposures. As far as the authors are aware, no existing studies on incinerators have compared these two exposure assessment methods and quantified the extent of exposure misclassification between the two.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Assessment of Particulate Air Pollution Exposure from Municipal Solid Waste Incinerator Emissions

Ashworth

Fuller

Toledano

et al. 2013

Journal of Environmental and Public Health

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Background. Research to date on health effects associated with incineration has found limited evidence of health risks, but many previous studies have been constrained by poor exposure assessment. This paper provides a comparative assessment of atmospheric dispersion modelling and distance from source (a commonly used proxy for exposure) as exposure assessment methods for pollutants released from incinerators. Methods. Distance from source and the atmospheric dispersion model ADMS-Urban were used to characterise ambient exposures to particulates from two municipal solid waste incinerators (MSWIs) in the UK. Additionally an exploration of the sensitivity of the dispersion model simulations to input parameters was performed. Results. The model output indicated extremely low ground level concentrations of PM10, with maximum concentrations of <0.01 μg/m3. Proximity and modelled PM10 concentrations for both MSWIs at postcode level were highly correlated when using continuous measures (Spearman correlation coefficients ~ 0.7) but showed poor agreement for categorical measures (deciles or quintiles, Cohen's kappa coefficients ≤ 0.5). Conclusion. To provide the most appropriate estimate of ambient exposure from MSWIs, it is essential that incinerator characteristics, magnitude of emissions, and surrounding meteorological and topographical conditions are considered. Reducing exposure misclassification is particularly important in environmental epidemiology to aid detection of low-level risks.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative Assessment of Particulate Air Pollution Exposure from Municipal Solid Waste Incinerator Emissions

Ashworth

Fuller

Toledano

et al. 2013

Journal of Environmental and Public Health

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show abstract

“…Aggregate data can often be obtained from disease registries or from the U.S. Census Bureau (12). These data provide greater privacy protection for subjects (13) and are less costly to obtain than point data (12). For residential location, an individual's residence may be expressed with precision using an exact street address (point data) or may be estimated using the zipcode centroid (aggregate data).…”

Section: Introductionmentioning

confidence: 99%

Estimating Proximity to Care

Bliss¹,

Katz

Wright³

et al. 2012

Medical Care

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Background Spatial accessibility of health care may be measured by proximity of patient residence to health services, typically in driving distance or driving time. Precise driving distances and times are rarely available. While straight line distances between zipcode centroids and between precise address locations are used as proxy measures for distance to care, the accuracy of these measures has received little study. Methods Among a cohort of Medicare beneficiaries, actual driving distances and times between patient residence and clinic were obtained from commercial software (MapQuest). We used a split-sample design to build and validate linear regression models that predict actual driving distances and times from estimated distances between zipcode centroids and between precise residential and hospital locations, adjusting for urban/suburban/rural residential status. Results On average, predicted driving distances and times were larger than actual values. Zipcode centroid distances alone predicted longer driving distances than observed values: rural +19% (3.2miles), suburban +23% (3.7miles), and urban +27% (2.0miles). Predicted time was 36% (9.4minutes) longer in rural, 32% (6.8minutes) longer in suburban, and 38% (4.7minutes) longer in urban areas than observed values. Including urban/suburban/rural categorization of residence improved the accuracy of predicted driving distance and time for suburban and urban areas but diminished accuracy for rural areas. Similar trends were observed for distance estimates from precise locations. Conclusions Distances between zipcode centroids and precise residential/hospital locations provide reasonable estimates of driving distance and time for epidemiologic research. Estimates are improved for suburban and urban residences when data are augmented by urban categorization.

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“…One alternative approach has been presented by Jacquez and Rommel (2009). In this approach, the sensitivity of analysis results to perturbations (resulting from positional error) is calculated as a local statistic.…”

Section: Framework For Error Propagation Modelingmentioning

confidence: 99%

Error propagation models to examine the effects of geocoding quality on spatial analysis of individual-level datasets

Zandbergen

Hart

Lenzer

et al. 2012

Spatial and Spatio-temporal Epidemiology

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The quality of geocoding has received substantial attention in recent years. A synthesis of published studies shows that the positional errors of street geocoding are somewhat unique relative to those of other types of spatial data: 1) the magnitude of error varies strongly across urban-rural gradients; 2) the direction of error is not uniform, but strongly associated with the properties of local street segments; 3) the distribution of errors does not follow a normal distribution, but is highly skewed and characterized by a substantial number of very large error values; and 4) the magnitude of error is spatially autocorrelated and is related to properties of the reference data. This makes it difficult to employ analytic approaches or Monte Carlo simulations for error propagation modeling because these rely on generalized statistical characteristics. The current paper describes an alternative empirical approach to error propagation modeling for geocoded data and illustrates its implementation using three different case-studies of geocoded individual-level datasets. The first case-study consists of determining the land cover categories associated with geocoded addresses using a point-in-raster overlay. The second case-study consists of a local hotspot characterization using kernel density analysis of geocoded addresses. The third case-study consists of a spatial data aggregation using enumeration areas of varying spatial resolution. For each case-study a high quality reference scenario based on address points forms the basis for the analysis, which is then compared to the result of various street geocoding techniques. Results show that the unique nature of the positional error of street geocoding introduces substantial noise in the result of spatial analysis, including a substantial amount of bias for some analysis scenarios. This confirms findings from earlier studies, but expands these to a wider range of analytical techniques.

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Risk of congenital anomalies around a municipal solid waste incinerator: a GIS-based case-control study

Cited by 36 publications

References 31 publications

Comparative Assessment of Particulate Air Pollution Exposure from Municipal Solid Waste Incinerator Emissions

Comparative Assessment of Particulate Air Pollution Exposure from Municipal Solid Waste Incinerator Emissions

Estimating Proximity to Care

Error propagation models to examine the effects of geocoding quality on spatial analysis of individual-level datasets

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