River-mediated dynamic environmental factors and perinatal data analysis
Creators: name: Rathjens, Jonathan
Publisher: name: TU Dortmund
Year: 2021
Subjects: subject: Distributional copula regression
subject: Drinking water contamination
subject: Perfluorooctanoic acid
subject: Perinatal registry data
subject: River modelling
subject: Two spatial level data
subject: 310
subject: Statistisches Modell
subject: Regressionsanalyse
subject: Perfluoroctansäure
subject: Toxizität
subject: Trinkwasser
subject: Geburtsgewicht

description: Perfluorooctanoic acid (PFOA) and related per- and polyfluoroalkyl substances, a group of man-made persistent organic chemicals employed for many products, are widely distributed in the environment. Adverse health effects may occur even at low exposure levels. A large-scale PFOA contamination of drinking water resources, especially of the river Ruhr, was detected in North Rhine-Westphalia, Germany, in summer 2006. Subsequent measurements are available from the water supply stations along the river and elsewhere. The first state-wide environmental-epidemiological study on the general population analyses these secondary data together with routinely collected perinatal registry data, to estimate possible developmental-toxic effects of PFOA exposure, especially regarding birth weight (BW). Drinking water data are temporally and spatially modelled to assign estimated exposure values to the residents. A generalised linear model with an inverse link deals with the steeply decreasing temporal data pattern at mainly affected stations. Confirmed by a river-wide joint model, the river's segments between the main junctions are the most important factor to explain the spatial structure, besides local effects. Deductions from stations to areal units are made possible via estimated supply proportions. Regression of perinatal data with BW as response usually includes the gestational age (GA) as an important covariate in polynomial form. However, bivariate modelling of BW and GA is recommended to distinguish effects on each, on both, and between them. Bayesian distributional copula regression is applied, where the marginals for BW and GA as well as the copula representing their dependence structure are fitted independently and all parameters are estimated conditional on covariates. While a Gaussian is suitable for BW, the skewed GA data are better modelled by the three-parametric Dagum distribution. The Clayton copula performs better than the Gumbel and the symmetric Gaussian copula, although the lower tail dependence is weak. A non-linear trend of BW on GA is detected by the standard polynomial model. Linear effects of biometric and obstetric covariates and also of maternal smoking on BW mean are similar in both models, while the distributional copula regression also reveals effects on all other parameters. The local PFOA exposure is spatio-temporally assigned to the perinatal data of the most affected town of Arns\-berg and so included in the regression models. No significant effect results and a relatively high amount of noise remains. Perspectively and for larger regions, this can be dealt with by exposure modelling on area level using dependence information, by allowing further asymmetry in the bivariate distribution of BW and GA, and by respecting geographical structures in birth data.