Abstract
BackgroundWastewater-based epidemiology (WBE) is a novel approach in drug use epidemiology which aims to monitor the extent of use of various drugs in a community. In this study, we investigate functional principal component analysis (FPCA) as a tool for analysing WBE data and compare it to traditional principal component analysis (PCA) and to wavelet principal component analysis (WPCA) which is more flexible temporally.MethodsWe analysed temporal wastewater data from 42 European cities collected daily over one week in March 2013. The main temporal features of ecstasy (MDMA) were extracted using FPCA using both Fourier and B-spline basis functions with three different smoothing parameters, along with PCA and WPCA with different mother wavelets and shrinkage rules. The stability of FPCA was explored through bootstrapping and analysis of sensitivity to missing data.ResultsThe first three principal components (PCs), functional principal components (FPCs) and wavelet principal components (WPCs) explained 87.5-99.6 % of the temporal variation between cities, depending on the choice of basis and smoothing. The extracted temporal features from PCA, FPCA and WPCA were consistent. FPCA using Fourier basis and common-optimal smoothing was the most stable and least sensitive to missing data.ConclusionFPCA is a flexible and analytically tractable method for analysing temporal changes in wastewater data, and is robust to missing data. WPCA did not reveal any rapid temporal changes in the data not captured by FPCA. Overall the results suggest FPCA with Fourier basis functions and common-optimal smoothing parameter as the most accurate approach when analysing WBE data.Electronic supplementary materialThe online version of this article (doi:10.1186/s12874-016-0179-2) contains supplementary material, which is available to authorized users.
Highlights
Wastewater-based epidemiology (WBE) is a novel approach in drug use epidemiology which aims to monitor the extent of use of various drugs in a community
Results using Daubechies’ wavelets are similar to those found by traditional principal component analysis (PCA) on the raw data, except the first wavelet principal component (WPC) explained more of the temporal variation from the ecstasy (MDMA) curves
Even though the patterns extracted by PCA, functional principal component analysis (FPCA) and wavelet principal component analysis (WPCA) were qualitatively consistent, the interpretation of the principal components (PCs) and WPCs can be difficult to compare to the functional principal component (FPC)
Summary
Wastewater-based epidemiology (WBE) is a novel approach in drug use epidemiology which aims to monitor the extent of use of various drugs in a community. MDMA is not the most prevalently used illicit drug in Europe [6, 7], but its high weekend use compared to weekday use [8] has been a source of concern. Estimates of the consumption of illicit drugs have been derived from population-based surveys and administrative databases such as medical records, crime statistics, drug production and seizure data [6]. Population-based surveys are, often characterized by low response rates because of sensitive questions [9], while the use of administrative databases presents several methodological challenges since any analysis targets selected populations [10,11,12]. Data gathered from treatment facilities and drug-related programmes can underestimate prevalence as the number of places in treatment tend to be limited [10], while drug-related offences may overestimate prevalence [11, 12]
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