Translation-invariant functional clustering on COVID-19 deaths adjusted on population risk factors

This paper focuses on clustering the COVID-19 death rates reported in Europe and the United States. Several methods have been developed to cluster such functional data. However, these methods are not translation-invariant (TI) and thus cannot handle different times of arrivals of the disease, nor can they consider external covariates and so are unable to adjust for the population risk factors of each region. We propose a novel three steps clustering method to circumvent these issues. First, feature extraction is performed by TI wavelet decomposition, which permits to deal with the different onsets. Then, single-index regression is used to neutralize disparities caused by population risk factors. Finally, a nonparametric mixture is fitted on the regression residuals to achieve the region clustering.

Household Cleaning and Poor Asthma Control Among Elderly Women

BACKGROUND

Asthma control is suboptimal in nearly half of adults with asthma. Household exposure to disinfectants and cleaning products (DCP) has been associated with adverse respiratory effects, but data on their association with asthma control are scant.

OBJECTIVES

To investigate the association between household use of DCP and asthma control in a large cohort of French elderly women.

METHODS

We used data from a case-control study on asthma (2011-2013) nested in the E3N cohort. Among 3023 women with current asthma, asthma control was defined by the Asthma Control Test (ACT). We used a standardized questionnaire to assess the frequency of cleaning tasks and DCP use. We also identified household cleaning patterns using a clustering approach. Associations between DCP and ACT were adjusted for age, smoking status, body mass index, and education.

RESULTS

Data on ACT and DCP use were available for 2223 women (70 ± 6 years old). Asthma was controlled (ACT = 25), partly controlled (ACT = 20-24), and poorly controlled (ACT ≤ 19) in 29%, 46%, and 25% of the participants, respectively. Weekly use of sprays and chemicals was associated with poorly controlled asthma (odds ratio [95% confidence interval]: 1 spray: 1.31 [0.94-1.84], ≥2 sprays: 1.65 [1.07-2.53], P trend: .01; 1 chemical: 1.24 [0.94-1.64], ≥2 chemicals: 1.47 [1.03-2.09], P trend: .02). Risk for poor asthma control increased with the patterns "very frequent use of products" (1.74 [1.13-2.70]) and "infrequent cleaning tasks and intermediate use of products" (1.62 [1.05-2.51]).

CONCLUSION

Regular use of DCP may contribute to poor asthma control in elderly women. Limiting their use may help improve asthma management.

VarSelLCM: an R/C++ package for variable selection in model-based clustering of mixed-data with missing values

SUMMARY

VarSelLCM allows a full model selection (detection of the relevant features for clustering and selection of the number of clusters) in model-based clustering, according to classical information criteria. Data to be analyzed can be composed of continuous, integer and/or categorical features. Moreover, missing values are managed, without any pre-processing, by the model used to cluster with the assumption that values are missing completely at random. Thus, VarSelLCM also allows data imputation by using mixture models. A Shiny application is implemented to easily interpret the clustering results.

AVAILABILITY AND IMPLEMENTATION

VarSelLCM is available to download at https://CRAN.R-project.org/package=VarSelLCM/.

TUTORIAL

vignette is available online at http://varsellcm.r-forge.r-project.org/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Clusters of diet, physical activity, television exposure and sleep habits and their association with adiposity in preschool children: the EDEN mother-child cohort

Background

Despite the growing interest in the relation between adiposity in children and different lifestyle clusters, few studies used a longitudinal design to examine a large range of behaviors in various contexts, in particular eating- and sleep-related routines, and few studies have examined these factors in young children. The objectives of this study were to identify clusters of boys and girls based on diet, sleep and activity-related behaviors and their family environment at 2 and 5 years of age, and to assess whether the clusters identified varied across maternal education levels and were associated with body fat at age 5.

Methods

At 2 and 5 years, respectively, 1436 and 1195 parents from the EDEN mother-child cohort completed a questionnaire including behavioral data. A latent class analysis aimed to uncover gender-specific behavioral clusters. Body fat percentage was estimated by anthropometric and bioelectrical impedance measurements. Association between cluster membership and body fat was assessed with mutivariable linear regression models.

Results

At 2 years, two clusters emerged that were essentially characterized by opposite eating habits. At 5 years, TV exposure was the most distinguishing feature, but the numbers and types of clusters differed by gender. An association between cluster membership and body fat was found only in girls at 5 years of age, with girls in the cluster defined by very high TV exposure and unfavorable mealtime habits (despite high outdoor playing and walking time) having the highest body fat. Girls whose mother had low educational attainment were more likely to be in this high-risk cluster. Girls who were on a cluster evolution path corresponding to the highest TV viewing time and the least favorable mealtime habits from 2 to 5 years of age had higher body fat at 5 years.

Conclusions

Efforts to decrease TV time and improve mealtime routines may hold promise for preventing overweight in young children, especially girls growing up in disadvantaged families. These preventive efforts should start as early in life as possible, ideally before the age of two, and should be sustained over the preschool years.

Patterns of cleaning product exposures using a novel clustering approach for data with correlated variables

PURPOSE

Clustering methods may be useful in epidemiology to better characterize exposures and account for their multidimensional aspects. In this context, application of clustering models allowing for highly dependent variables is of particular interest. We aimed to characterize patterns of domestic exposure to cleaning products using a novel clustering model allowing for highly dependent variables.

METHODS

To identify domestic cleaning patterns in a large population of French women, we used a mixture model of dependency blocks. This novel approach specifically models within-class dependencies, and is an alternative to the latent class model, which assumes conditional independence. Analyses were conducted in 19,398 participants of the E3N study (women aged 61-88 years) who completed a questionnaire regarding household cleaning habits.

RESULTS

Seven classes were identified, which differed with the frequency of cleaning tasks (e.g., dusting/sweeping/hoovering) and use of specific products (e.g., bleach, sprays). The model also grouped the variables into conditionally independent blocks, providing a summary of the main dependencies among the variables.

CONCLUSIONS

The mixture model of dependency blocks, a useful alternative to the latent class model, may have broader application in epidemiology, in particular, in the context of exposome research and growing need for data-reduction methods.

Bayesian model selection in logistic regression for the detection of adverse drug reactions

Spontaneous adverse event reports have a high potential for detecting adverse drug reactions. However, due to their dimension, the analysis of such databases requires statistical methods. In this context, disproportionality measures can be used. Their main idea is to project the data onto contingency tables in order to measure the strength of associations between drugs and adverse events. However, due to the data projection, these methods are sensitive to the problem of coprescriptions and masking effects. Recently, logistic regressions have been used with a Lasso type penalty to perform the detection of associations between drugs and adverse events. On different examples, this approach limits the drawbacks of the disproportionality methods, but the choice of the penalty value is open to criticism while it strongly influences the results. In this paper, we propose to use a logistic regression whose sparsity is viewed as a model selection challenge. Since the model space is huge, a Metropolis-Hastings algorithm carries out the model selection by maximizing the BIC criterion. Thus, we avoid the calibration of penalty or threshold. During our application on the French pharmacovigilance database, the proposed method is compared to well-established approaches on a reference dataset, and obtains better rates of positive and negative controls. However, many signals (i.e., specific drug-event associations) are not detected by the proposed method. So, we conclude that this method should be used in parallel to existing measures in pharmacovigilance. Code implementing the proposed method is available at the following url: https://github.com/masedki/MHTrajectoryR.