Additional file 1: of Exploring functional data analysis and wavelet principal component analysis on ecstasy (MDMA) wastewater data

Figure S1. Functional principal components (FPCs) resulting from functional principal component analysis (FPCA). Shows the mean of the fitted curves (solid line) and how the shape of an individual curve differs from the mean curve if a multiple of the principal component curve is added to (+ +) or subtracted from (- -) the mean curve. Panel A –First three FPCs resulting from a FPCA using Fourier basis functions and no smoothing parameter; Panel B – First three FPCs resulting from a FPCA using Fourier basis functions and common-optimal smoothing parameter; Panel C – First three FPCs resulting from a FPCA using Fourier basis functions and individual-optimal smoothing parameter; Panel D – First three FPCs resulting from a FPCA using B-splines basis functions and no smoothing parameter; Panel E – First three FPCs resulting from a FPCA using B-splines basis functions and common-optimal smoothing parameter; Panel F – First three FPCs resulting from a FPCA using B-splines basis functions and individual-optimal smoothing parameter. (PDF 27 kb

Complex patterns of concomitant medication use: A study among Norwegian women using paracetamol during pregnancy

<div><p>Background</p><p>Studies on medication safety in pregnancy often rely on an oversimplification of medication use into exposed or non-exposed, without considering intensity and timing of use in pregnancy, or concomitant medication use. This study uses paracetamol in pregnancy as the motivating example to introduce a method of clustering medication exposures longitudinally throughout pregnancy. The aim of this study was to use hierarchical cluster analysis (HCA) to better identify clusters of medication exposure throughout pregnancy.</p><p>Methods</p><p>Data from the Norwegian Mother and Child Cohort Study was used to identify subclasses of women using paracetamol during pregnancy. HCA with customized distance measure was used to identify clusters of medication exposures in pregnancy among children at 18 months.</p><p>Results</p><p>The pregnancies in the study (N = 9 778) were grouped in 5 different clusters depending on their medication exposure profile throughout pregnancy.</p><p>Conclusion</p><p>Using HCA, we identified and described profiles of women exposed to different medications in combination with paracetamol during pregnancy. Identifying these clusters allows researchers to define exposure in ways that better reflects real-world medication usage patterns. This method could be extended to other medications and used as pre-analysis for identifying risks associated with different profiles of exposure.</p></div

Flowchart of inclusion for the final study population.

<p>Flowchart of inclusion for the final study population.</p

Maternal and child characteristics of exposure to different medication groups by clusters.

<p>Maternal and child characteristics of exposure to different medication groups by clusters.</p

Maternal characteristics for the total cohort of 9 778 women by clusters.

<p>Maternal characteristics for the total cohort of 9 778 women by clusters.</p

Child characteristics for the total cohort of 9 778 women by clusters.

<p>Child characteristics for the total cohort of 9 778 women by clusters.</p

Dendrogram and heatmap of clustering results.

<p>The x-axis of the dendrogram indicates mothers exposed to paracetamol in pregnancy, while the y-axis of the dendrogram indicates at which height mothers are clustered together. The colors of the heatmap indicate the values of pairwise distance between mothers exposed to paracetamol in pregnancy. The distance values go from 0 (indicating no distance in blue) to 60 (indicating maximum distance in yellow color).</p

Wastewater drug loads for 42 European cities throughout the week.

<p>*Statistically significant difference between weekday (Mon-Fri) and weekend (Sat-Sun) loads using the Wilcox test (p-value < 0.001)</p><p>**No statistically significant difference between weekday (Mon-Fri) and weekend (Sat-Sun) loads using the Wilcox test</p><p>(p-value = 0.369)</p><p>The data sets supporting the table are freely available [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138669#pone.0138669.ref017" target="_blank">17</a>].</p><p>Wastewater drug loads for 42 European cities throughout the week.</p

Multiple regression analyses with functional principal component scores for ecstasy (MDMA) as dependent variable and longitude, latitude, gross domestic product, population density and relative size of the city as explanatory variables.

<p>* Akaike's information criterion.</p><p>a Number taken from <a href="http://en.wikipedia.org/wiki/List_of_countries_by_GDP_%28nominal%29_per_capita" target="_blank">http://en.wikipedia.org/wiki/List_of_countries_by_GDP_%28nominal%29_per_capita</a>.</p><p>b Number of inhabitants in city divided by the urban area in square kilometres.</p><p>c Number of inhabitants in city divided by the number of inhabitants in the country.</p><p>Multiple regression analyses with functional principal component scores for ecstasy (MDMA) as dependent variable and longitude, latitude, gross domestic product, population density and relative size of the city as explanatory variables.</p

FANOVA F-permutation test plot separately for each drug and for each explanatory variable.

<p>2.1–2.2 show how the p-value of the permutation F-test changes, as different values of longitude are chosen as grouping; 2.3–2.4 show how the p-value of the permutation F-test changes, as different values of latitude are chosen as grouping; 2.5–2.6 show how the p-value of the permutation F-test changes, as different values of density are chosen as grouping; 2.7–2.8 show how the p-value of the permutation F-test changes, as different values of relative size are chosen as grouping; 2.9–2.10 show how the p-value of the permutation F-test changes, as different values of gross domestic product (GDP) are chosen as grouping.</p