Composite Indices of Social Determinants of Health: Overview, Measurement Gaps, and Research Priorities for Health Equity.

The goal of health equity is for all people to have opportunities and resources for optimal health outcomes regardless of their social identities, residence in marginalized communities, and/or experience with oppressive systems. Social determinants of health (SDOH)-the conditions in which we are born, grow, live, work, and age-are inextricably tied to health equity. Advancing health equity thus requires reliable measures of SDOH. In the United States, comprehensive individual-level data on SDOH are difficult to collect, may be inaccurate, and do not capture all dimensions of inequitable outcomes. Individual area-based indicators are widely available, but difficult to use in practice. Numerous area-level composite indices are available to describe SDOH, but there is no consensus on which indices are most appropriate to use. This article presents an analytic taxonomy of currently available SDOH composite indices and compares their components and predictive ability, providing insights into gaps and areas for further research.

Automated electrohysterographic detection of uterine contractions for monitoring of pregnancy: feasibility and prospects.

BACKGROUND: Preterm birth is a major public health problem in developed countries. In this context, we have conducted research into outpatient monitoring of uterine electrical activity in women at risk of preterm delivery. The objective of this preliminary study was to perform automated detection of uterine contractions (without human intervention or tocographic signal, TOCO) by processing the EHG recorded on the abdomen of pregnant women. The feasibility and accuracy of uterine contraction detection based on EHG processing were tested and compared to expert decision using external tocodynamometry (TOCO) . METHODS: The study protocol was approved by local Ethics Committees under numbers ID-RCB 2016-A00663-48 for France and VSN 02-0006-V2 for Iceland. Two populations of women were included (threatened preterm birth and labour) in order to test our system of recognition of the various types of uterine contractions. EHG signal acquisition was performed according to a standardized protocol to ensure optimal reproducibility of EHG recordings. A system of 18 Ag/AgCl surface electrodes was used by placing 16 recording electrodes between the woman's pubis and umbilicus according to a 4 × 4 matrix. TOCO was recorded simultaneously with EHG recording. EHG signals were analysed in real-time by calculation of the nonlinear correlation coefficient H2. A curve representing the number of correlated pairs of signals according to the value of H2 calculated between bipolar signals was then plotted. High values of H2 indicated the presence of an event that may correspond to a contraction. Two tests were performed after detection of an event (fusion and elimination of certain events) in order to increase the contraction detection rate. RESULTS: The EHG database contained 51 recordings from pregnant women, with a total of 501 contractions previously labelled by analysis of the corresponding tocographic recording. The percentage recognitions obtained by application of the method based on coefficient H2 was 100% with 782% of false alarms. Addition of fusion and elimination tests to the previously obtained detections allowed the false alarm rate to be divided by 8.5, while maintaining an excellent detection rate (96%). CONCLUSION: These preliminary results appear to be encouraging for monitoring of uterine contractions by algorithm-based automated detection to process the electrohysterographic signal (EHG). This compact recording system, based on the use of surface electrodes attached to the skin, appears to be particularly suitable for outpatient monitoring of uterine contractions, possibly at home, allowing telemonitoring of pregnancies. One of the advantages of EHG processing is that useful information concerning contraction efficiency can be extracted from this signal, which is not possible with the TOCO signal.

A node-wise analysis of the uterine muscle networks for pregnancy monitoring.

The recent past years have seen a noticeable increase of interest in the correlation analysis of electrohysterographic (EHG) signals in the perspective of improving the pregnancy monitoring. Here we propose a new approach based on the functional connectivity between multichannel (4×4 matrix) EHG signals recorded from the women's abdomen. The proposed pipeline includes i) the computation of the statistical couplings between the multichannel EHG signals, ii) the characterization of the connectivity matrices, computed by using the imaginary part of the coherence, based on the graph-theory analysis and iii) the use of these measures for pregnancy monitoring. The method was evaluated on a dataset of EHGs, in order to track the correlation between EHGs collected by each electrode of the matrix (called `node-wise' analysis) and follow their evolution along weeks before labor. Results showed that the strength of each node significantly increases from pregnancy to labor. Electrodes located on the median vertical axis of the uterus seemed to be the more discriminant. We speculate that the network-based analysis can be a very promising tool to improve pregnancy monitoring.

Genome-wide analysis of the AP2/ERF family in Eucalyptus grandis: an intriguing over-representation of stress-responsive DREB1/CBF genes.

BACKGROUND: The AP2/ERF family includes a large number of developmentally and physiologically important transcription factors sharing an AP2 DNA-binding domain. Among them DREB1/CBF and DREB2 factors are known as master regulators respectively of cold and heat/osmotic stress responses. EXPERIMENTAL APPROACHES: The manual annotation of AP2/ERF family from Eucalyptus grandis, Malus, Populus and Vitis genomes allowed a complete phylogenetic study for comparing the structure of this family in woody species and the model Arabidopsis thaliana. Expression profiles of the whole groups of EgrDREB1 and EgrDREB2 were investigated through RNAseq database survey and RT-qPCR analyses. RESULTS: The structure and the size of the AP2/ERF family show a global conservation for the plant species under comparison. In addition to an expansion of the ERF subfamily, the tree genomes mainly differ with respect to the group representation within the subfamilies. With regard to the E. grandis DREB subfamily, an obvious feature is the presence of 17 DREB1/CBF genes, the maximum reported to date for dicotyledons. In contrast, only six DREB2 have been identified, which is similar to the other plants species under study, except for Malus. All the DREB1/CBF and DREB2 genes from E. grandis are expressed in at least one condition and all are heat-responsive. Regulation by cold and drought depends on the genes but is not specific of one group; DREB1/CBF group is more cold-inducible than DREB2 which is mainly drought responsive. CONCLUSION: These features suggest that the dramatic expansion of the DREB1/CBF group might be related to the adaptation of this evergreen tree to climate changes when it expanded in Australia.

Detecting labor using graph theory on connectivity matrices of uterine EMG.

Premature labor is one of the most serious health problems in the developed world. One of the main reasons for this is that no good way exists to distinguish true labor from normal pregnancy contractions. The aim of this paper is to investigate if the application of graph theory techniques to multi-electrode uterine EMG signals can improve the discrimination between pregnancy contractions and labor. To test our methods we first applied them to synthetic graphs where we detected some differences in the parameters results and changes in the graph model from pregnancy-like graphs to labor-like graphs. Then, we applied the same methods to real signals. We obtained the best differentiation between pregnancy and labor through the same parameters. Major improvements in differentiating between pregnancy and labor were obtained using a low pass windowing preprocessing step. Results show that real graphs generally became more organized when moving from pregnancy, where the graph showed random characteristics, to labor where the graph became a more small-world like graph.

Classification of pregnancy and labor contractions using a graph theory based analysis.

In this paper, we propose a new framework to characterize the electrohysterographic (EHG) signals recorded during pregnancy and labor. The approach is based on the analysis of the propagation of the uterine electrical activity. The processing pipeline includes i) the estimation of the statistical dependencies between the different recorded EHG signals, ii) the characterization of the obtained connectivity matrices using network measures and iii) the use of these measures in clinical application: the classification between pregnancy and labor. Due to its robustness to volume conductor, we used the imaginary part of coherence in order to produce the connectivity matrix which is then transformed into a graph. We evaluate the performance of several graph measures. We also compare the results with the parameter mostly used in the literature: the peak frequency combined with the propagation velocity (PV +PF). Our results show that the use of the network measures is a promising tool to classify labor and pregnancy contractions with a small superiority of the graph strength over PV+PF.

Evaluation of muscle force classification using shape analysis of the sEMG probability density function: a simulation study.

In this work, we propose to classify, by simulation, the shape variability (or non-Gaussianity) of the surface electromyogram (sEMG) amplitude probability density function (PDF), according to contraction level, using high-order statistics (HOS) and a recent functional formalism, the core shape modeling (CSM). According to recent studies, based on simulated and/or experimental conditions, the sEMG PDF shape seems to be modified by many factors as: contraction level, fatigue state, muscle anatomy, used instrumentation, and also motor control parameters. For sensitivity evaluation against these several sources (physiological, instrumental, and neural control) of variability, a large-scale simulation (25 muscle anatomies, ten parameter configurations, three electrode arrangements) is performed, by using a recent sEMG-force model and parallel computing, to classify sEMG data from three contraction levels (20, 50, and 80% MVC). A shape clustering algorithm is then launched using five combinations of HOS parameters, the CSM method and compared to amplitude clustering with classical indicators [average rectified value (ARV) and root mean square (RMS)]. From the results screening, it appears that the CSM method obtains, using Laplacian electrode arrangement, the highest classification scores, after ARV and RMS approaches, and followed by one HOS combination. However, when some critical confounding parameters are changed, these scores decrease. These simulation results demonstrate that the shape screening of the sEMG amplitude PDF is a complex task which needs both efficient shape analysis methods and specific signal recording protocol to be properly used for tracking neural drive and muscle activation strategies with varying force contraction in complement to classical amplitude estimators.

Nonlinear estimation of coupling and directionality between signals: application to uterine EMG propagation.

Understanding the direction and quantity of information flowing in a complex system is a fundamental task in signal processing. Several measures have been proposed to detect the quantity of synchronization and the directionality between time series and in physiological data. In this paper we use two methods that are widely used in synchronization and directionality analysis: Nonlinear correlation coefficient (h(2)) and the general synchronization (H). The performances of both methods were tested on four dimensional coupled synthetic nonlinear Rössler models. They were then applied to a single real labor contraction uterine EMG burst with the aim of using them to detect synchronization and to plot the map of direction of information flow between the whole signal channels. The results on synthetic signal show a slight superiority of H over h(2). The results obtained on a single contraction are encouraging for the future use of these tools for resolving the open question of the directionality of uterine contractions and may provide a way of finding their source loci.

Comparison of different EHG feature selection methods for the detection of preterm labor.

Numerous types of linear and nonlinear features have been extracted from the electrohysterogram (EHG) in order to classify labor and pregnancy contractions. As a result, the number of available features is now very large. The goal of this study is to reduce the number of features by selecting only the relevant ones which are useful for solving the classification problem. This paper presents three methods for feature subset selection that can be applied to choose the best subsets for classifying labor and pregnancy contractions: an algorithm using the Jeffrey divergence (JD) distance, a sequential forward selection (SFS) algorithm, and a binary particle swarm optimization (BPSO) algorithm. The two last methods are based on a classifier and were tested with three types of classifiers. These methods have allowed us to identify common features which are relevant for contraction classification.

[Evolution of electrohysterogram signals synchronization according to term of pregnancy: interest for preterm labor diagnosis]. / Évolution de la synchronisation des signaux de l'électromyogramme utérin en fonction du terme de grossesse, et intérêt pour la détection précoce de la menace d'accouchement prématuré

OBJECTIVES: Our study focuses on the analysis of the uterine electromyogram recorded on women during pregnancy. We were interested in evaluating the synchronization of this electrical signal at various terms in order to follow evolution of synchronization as labor approaches. This study attempts to deepen our understanding of the myometrial maturation close to labor and to provide reliable parameters for improving preterm labor diagnosis. PATIENTS AND METHODS: We performed a prospective study by recording the electrical signals of physiological uterine contractions (causing no delivery) on 16 pregnant women. We then calculated the non-linear correlation coefficient h(2) to estimate synchronization between EMG signals collected for each contraction. We expressed the results by grouping synchronization values by class of term in order to study the evolution of this coefficient along gestation. This study has been approved by the ethical committee of our hospital. RESULTS: Our results show a non-significant increase of the h(2) value along term. There is however a trend towards an increase of the synchronization of EMG signals as labor approaches but not enough to conclude definitively. DISCUSSION AND CONCLUSION: With a confirmation of the increase of h(2) along term, the study of the synchronization of uterine electrical activity could be an important clue to support the notion of myometrial maturation close to labor. Synchronization analysis could also be a promising parameter for reliable diagnosis of preterm labor.

Quantitative performance analysis of four methods of evaluating signal nonlinearity: application to uterine EMG signals.

Recently, much attention has been paid to the use of nonlinear analysis techniques for the characterization of biological signals. Several measures have been proposed to detect nonlinear characteristics in time series. The sensitivity of several nonlinear methods to the actual nonlinearity level and their sensitivity to noise have never been evaluated. In this paper we perform this analysis for four methods that are widely used in nonlinearity detection: Time reversibility, Sample Entropy, Lyapunov Exponents and Delay Vector Variance. The evolution of methods with complexity degree (CD) and with different Signal to Noise Ratio was computed for the four methods on nonlinear synthetic signals. The methods were then applied to real uterine EMG signals with the aim of using them to distinguish between pregnancy and labor signals. The results show a clear superiority of the Time reversibility method, in classification of pregnancy and labor signals.

Cross-correlation analysis of multichannel uterine EMG signals.

The prevention of preterm labor remains one of the primary goals of obstetric research. One way to achieve this goal effectively is to understand the mechanisms regulating the uterine contractility. Herein, we evaluate the correlation between uterine electrical activities recorded from spatially-distributed regions by calculating the nonlinear regression coefficient. Results have shown that, during pregnancy, the degree of interdependence between signals is very high whereas, at labor, the correlation between the signals decreases remarkably. We conclude that pregnancy is characterized by the presence of few local potential sources dominating the other sources while at the onset of labor, the number of these sources increases remarkably which affects therefore the correlation between the signals.

Detecting missing signals in multichannel recordings by using higher order statistics.

In real world applications, a multichannel acquisition system is susceptible of having one or many of its sensors displaced or detached, leading therefore to the loss or corruption of the recorded signals. In this paper, we present a technique for detecting missing or corrupted signals in multichannel recordings. Our approach is based on Higher Order Statistics (HOS) analysis. Our approach is tested on real uterine electromyogram (EMG) signals recorded by 4×4 electrode grid. Results have shown that HOS descriptors can discriminate between the two classes of signals (missing vs. non-missing). These results are supported by statistical analysis using the t-test which indicated good statistical significance of 95% confidence level.

Classification of multichannel uterine EMG signals.

Classification of multichannel uterine electromyogram (EMG) signals is addressed. Signals were recorded by a matrix of 16 electrodes. First, signals corresponding to each channel were individually classified using an artificial neural network (ANN) based on radial basis functions (RBF). The results have shown that the classification performance varies from one channel to another. Then, a decision fusion method based on these classification performances was tested. After fusion, the network yielded better classification accuracy than any individual channel could provide. The high percentage of correctly classified labor/non-labor events proves the efficiency of multichannel recordings in detecting labor. These findings can be very useful for the aim of classifying antepartum versus labor patients.

Classification performance of the frequency-related parameters derived from uterine EMG signals.

Frequency-related parameters derived from the uterine electromyogram (EMG) signals are widely used in many pregnancy monitoring and preterm delivery prediction studies. Although they are classical parameters, they are well suited for quantifying uterine EMG signals and have many advantages over amplitude-related parameters. The present work aims to compare various frequency-related parameters according to their classification performances (pregnancy vs. labor) using the receiver operating characteristic (ROC) curve analysis. The comparison between the parameters indicates that median frequency is the best frequency-related parameter that can be used for distinguishing between pregnancy and labor contractions. We conclude that median frequency can be the representative frequency-related parameter for classification problems of uterine EMG.

Complexity analysis of the uterine electromyography.

In respect to the main goal of our ongoing work for predicting preterm birth, we analyze in this paper the complexity of the uterine electromyography (EMG) by using the sample entropy (SampEn) algorithm. By considering recent methodological developments, we measure the SampEn over multiple scales using the wavelet packet decomposition method. The results obtained from the analyzed data indicate that SampEn decreases along pregnancy. Furthermore, we demonstrate that the computed SampEn parameter may discriminate between the two classes (pregnancy/labor). The results are supported by statistical analysis using t-test indicating good statistical significance with a confidence level of 95%. A surrogate data test is also performed to investigate the nature of the underlying dynamics of our experimental data. The results are very promising for monitoring pregnancy and detecting labor to help identify preterm labor.

Evolution of phase synchronization of the two frequency components of the electrohysterogram (EHG): application to the detection of human labor.

The analysis of the electrical activity of the uterus recorded externally, the electrohysterogram (EHG), may find an application in the prediction of labor. In the literature parameters that are supposed to be related to the excitability of the uterine cells have almost exclusively been used for this purpose. In the present paper we evaluate the possible use of synchronization parameters for EHG measured in several places on the uterus for this prediction. The EHG is mainly composed of two frequency components called FWL and FWH. These components may be related to the synchronization and the excitability phenomenon respectively. In order to study independently these frequency components, we extracted the corresponding ridges of the wavelet transform of the EHG using the GVF-snake method. For each frequency component we computed parameters that are related to the frequency content of the signals and parameters that consider the synchronization relationship between signals. The synchronization parameters used were the mean phase coherence and the phase entropy. The values of the different parameters were compared during pregnancy and labor by statistical analysis. The detection quality of each parameter was evaluated using ROC curves. Our results suggest that synchronization parameters could be used for the detection of labor in addition to the classical previously published parameters. Another important result of our study is that both FWH and FWL seem related to excitability but only FWL seems to be related to the synchronization of the uterus at term.