Association between household cleaning product profiles evaluated by the Ménag'Score® index and asthma symptoms among women from the SEPAGES cohort.

OBJECTIVE: Considering household disinfectants and cleaning products (HDCP) as mixture of ingredients, rather than each ingredient individually, might help in characterizing their role in asthma. We investigated the association between HDCP and asthma, using the recently developed Ménag'Score®, a health risk assessment score based on exhaustive ingredient lists of HDCP. METHODS: The study is based on 103 female volunteers of the SEPAGES cohort (2014-2019), with repeated data (up to 3 collection times, 200 observations). HDCP use was assessed from a barcode-based smartphone application linked with an ingredient database. The Ménag'score® risks for health and environment were computed for each weekly used HDCP from their exhaustive ingredient data (from A: no known risk to E: highest risk). The association between the use of HDCP with a poor Ménag'score® (D or E; overall, health, environment scores) and asthma symptoms, was estimated by generalized estimating equations models adjusted for age, BMI and smoking status. RESULTS: Participants were on average 33 years old, 11% smoked and 20% had at least one asthma symptom. The Ménag'score® was computed for 540 HDCP scanned by participants. Weekly use of HDCP with a poor Ménag'score®-health (around 60% of the participants) was associated with a higher risk of asthma symptoms (OR 3.13, 95% CI [1.32-7.43]). No association was observed for the Ménag'score®-environment. CONCLUSION: The use of HDCP with a poor Ménag'score®-health was associated with asthma symptoms. The results support the use of the Ménag'score®-health to further evaluate the health risks of HDCP in observational studies and as a potential public health tool.

A Bi-Invariant Statistical Model Parametrized by Mean and Covariance on Rigid Motions.

This paper aims to describe a statistical model of wrapped densities for bi-invariant statistics on the group of rigid motions of a Euclidean space. Probability distributions on the group are constructed from distributions on tangent spaces and pushed to the group by the exponential map. We provide an expression of the Jacobian determinant of the exponential map of S E ( n ) which enables the obtaining of explicit expressions of the densities on the group. Besides having explicit expressions, the strengths of this statistical model are that densities are parametrized by their moments and are easy to sample from. Unfortunately, we are not able to provide convergence rates for density estimation. We provide instead a numerical comparison between the moment-matching estimators on S E ( 2 ) and R 3 , which shows similar behaviors.

Compressed Raman classification method with upper-bounded error probability.

Classification of different species with Raman measurements is analyzed when a total of exactly $ N $N photons are detected with binary filtered Raman spectra instead of fixing the measuring time. The optimal classification method for this problem leads to classification error probabilities upper-bounded by the Bhattacharyya bound and that are invariant to the multiplication of the spectrum intensities by an unknown factor. Furthermore, it is shown that this approach can be implemented with a number of binary filters smaller than the number of species to discriminate.