A hierarchical modelling approach to assess multi pollutant effects in time-series studies

Blangiardo, Marta; Pirani, Monica; Kanapka, Lauren; Hansell, Anna; Fuller, Gary W.

doi:10.1371/journal.pone.0212565

Cited by 13 publications

(12 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the single-pollutant approach has typically been used in previous studies due to issues related to correlations between pollutants and different levels of measurement error for different pollutants. 20 No associations were observed between occupational exposures to rubber dust, rubber fumes and N-Nitrosamines with premature mortality from asthma, urinary diseases, bronchitis, emphysema, liver disease, and diseases of the oesophagus, stomach and duodenum. Previous studies also found no excess risks of mortality from liver and urinary disease in the rubber industry.…”

Section: Discussionmentioning

confidence: 99%

“…In multipollutant models, excess risks of dying from cerebrovascular, circulatory, digestive, respiratory diseases and IHD were only found for NSS exposure, but linear exposure–response associations were not observed. However, the single-pollutant approach has typically been used in previous studies due to issues related to correlations between pollutants and different levels of measurement error for different pollutants 20. No associations were observed between occupational exposures to rubber dust, rubber fumes and N-nitrosamines with premature mortality from asthma, urinary diseases, bronchitis, emphysema, liver disease and diseases of the oesophagus, stomach and duodenum.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Lifetime cumulative exposure to rubber dust, fumes and N-nitrosamines and non-cancer mortality: a 49-year follow-up of UK rubber factory workers

Hidajat

McElvenny²,

Ritchie³

et al. 2020

Occup Environ Med

View full text Add to dashboard Cite

ObjectivesTo examine associations between occupational exposures to rubber dust, rubber fumes and N-nitrosamines and non-cancer mortality.MethodsA cohort of 36 441 males aged 35+ years employed in British rubber factories was followed-up to 2015 (94% deceased). Competing risk survival analysis was used to assess risks of dying from non-cancer diseases (respiratory, urinary, cerebrovascular, circulatory and digestive diseases). Occupational exposures to rubber dust, rubber fumes, N-nitrosamines were derived based on a population-specific quantitative job-exposure matrix which in-turn was based on measurements in the EU-EXASRUB database.ResultsExposure–response associations of increased risk with increasing exposure were found for N-nitrosomorpholine with mortality from circulatory diseases (subdistribution hazard ratio (SHR) 1.17; 95% CI 1.12 to 1.23), ischaemic heart disease (IHD) (SHR 1.19; 95% CI 1.13 to 1.26), cerebrovascular disease (SHR 1.19; 95% CI 1.07 to 1.32) and exposures to N-nitrosodimethylamine with respiratory disease mortality (SHR 1.41; 95% CI 1.30 to 1.53). Increased risks for mortality from circulatory disease, IHD and digestive diseases were found with higher levels of exposures to rubber dust, rubber fumes and N-nitrosamines sum, without an exposure-dependent manner. No associations were observed between rubber dust, rubber fumes and N-nitrosamines exposures with mortality from asthma, urinary disease, bronchitis, emphysema, liver disease and some digestive diseases.ConclusionsIn a cohort of rubber factory workers with 49 years of follow-up, increased risk for mortality from circulatory, cerebrovascular, respiratory and digestive diseases were found to be associated with cumulative occupational exposures to specific agents.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Lifetime cumulative exposure to rubber dust, fumes and N-nitrosamines and non-cancer mortality: a 49-year follow-up of UK rubber factory workers

Hidajat

McElvenny²,

Ritchie³

et al. 2020

Occup Environ Med

View full text Add to dashboard Cite

show abstract

“…Finally, the best pollutant is reported. In recent years, especially for air pollutants, hierarchical methods that model spatio-temporal processes and measurement noise were popular [ 34 , 35 ]. We plan to apply such models in the future to make our results more accurate.…”

Section: Discussionmentioning

confidence: 99%

Multisensor Data Fusion for Localization of Pollution Sources in Wastewater Networks

Chachuła

Słojewski

Nowak

2022

Sensors

View full text Add to dashboard Cite

Illegal discharges of pollutants into sewage networks are a growing problem in large European cities. Such events often require restarting wastewater treatment plants, which cost up to a hundred thousand Euros. A system for localization and quantification of pollutants in utility networks could discourage such behavior and indicate a culprit if it happens. We propose an enhanced algorithm for multisensor data fusion for the detection, localization, and quantification of pollutants in wastewater networks. The algorithm processes data from multiple heterogeneous sensors in real-time, producing current estimates of network state and alarms if one or many sensors detect pollutants. Our algorithm models the network as a directed acyclic graph, uses adaptive peak detection, estimates the amount of specific compounds, and tracks the pollutant using a Kalman filter. We performed numerical experiments for several real and artificial sewage networks, and measured the quality of discharge event reconstruction. We report the correctness and performance of our system. We also propose a method to assess the importance of specific sensor locations. The experiments show that the algorithm’s success rate is equal to sensor coverage of the network. Moreover, the median distance between nodes pointed out by the fusion algorithm and nodes where the discharge was introduced equals zero when more than half of the network nodes contain sensors. The system can process around 5000 measurements per second, using 1 MiB of memory per 4600 measurements plus a constant of 97 MiB, and it can process 20 tracks per second, using 1.3 MiB of memory per 100 tracks.

show abstract

“…Similarly, Dionisio et al (2016) fitting two-pollutant time-series models with additive and multiplicative error reported total effect attenuation up to 85% for NO 2 (close to our estimates for multiplicative error), indicating multi-pollutant model estimates are even more susceptible to ME [ 38 ]. Blangiardo et al (2019) also found, under a Bayesian framework, that NO 2 effects were considerably biased when error-prone concentrations were used [ 39 ]. However, they focused on collinearity in multi-pollutant models without assessing error structures/types.…”

Section: Discussionmentioning

confidence: 99%

Quantifying the short-term effects of air pollution on health in the presence of exposure measurement error: a simulation study of multi-pollutant model results

et al. 2021

View full text Add to dashboard Cite

Background Most epidemiological studies estimate associations without considering exposure measurement error. While some studies have estimated the impact of error in single-exposure models we aimed to quantify the effect of measurement error in multi-exposure models, specifically in time-series analysis of PM2.5, NO2, and mortality using simulations, under various plausible scenarios for exposure errors. Measurement error in multi-exposure models can lead to effect transfer where the effect estimate is overestimated for the pollutant estimated with more error to the one estimated with less error. This complicates interpretation of the independent effects of different pollutants and thus the relative importance of reducing their concentrations in air pollution policy. Methods Measurement error was defined as the difference between ambient concentrations and personal exposure from outdoor sources. Simulation inputs for error magnitude and variability were informed by the literature. Error-free exposures with their consequent health outcome and error-prone exposures of various error types (classical/Berkson) were generated. Bias was quantified as the relative difference in effect estimates of the error-free and error-prone exposures. Results Mortality effect estimates were generally underestimated with greater bias observed when low ratios of the true exposure variance over the error variance were assumed (27.4% underestimation for NO2). Higher ratios resulted in smaller, but still substantial bias (up to 19% for both pollutants). Effect transfer was observed indicating that less precise measurements for one pollutant (NO2) yield more bias, while the co-pollutant (PM2.5) associations were found closer to the true. Interestingly, the sum of single-pollutant model effect estimates was found closer to the summed true associations than those from multi-pollutant models, due to cancelling out of confounding and measurement error bias. Conclusions Our simulation study indicated an underestimation of true independent health effects of multiple exposures due to measurement error. Using error parameter information in future epidemiological studies should provide more accurate concentration-response functions.

show abstract

A hierarchical modelling approach to assess multi pollutant effects in time-series studies

Cited by 13 publications

References 31 publications

Lifetime cumulative exposure to rubber dust, fumes and N-nitrosamines and non-cancer mortality: a 49-year follow-up of UK rubber factory workers

Lifetime cumulative exposure to rubber dust, fumes and N-nitrosamines and non-cancer mortality: a 49-year follow-up of UK rubber factory workers

Multisensor Data Fusion for Localization of Pollution Sources in Wastewater Networks

Quantifying the short-term effects of air pollution on health in the presence of exposure measurement error: a simulation study of multi-pollutant model results

Contact Info

Product

Resources

About