Air Pollution in Kosovo: Short Term Effects on Hospital Visits of Children Due to Respiratory Health Diagnoses

The Republic of Kosovo is a small country in the Balkans. The capital city of Pristina hosts most of its population and is situated in a mountain basin with poor air exchange, especially during winter. Domestic heating, road transport, industry and coal-fired power plants contribute to high levels of air pollution. We performed a time-series analysis on effects of particulate air pollution (PM2.5) on respiratory health of children and adolescents, using hospital admission and ambulatory visit numbers from the pediatric university clinic. From 2018 until 2020, daily mean concentrations of PM2.5 ranged between 2.41 and 161.03 µg/m³. On average, there were 6.7 ambulatory visits per day with lower numbers on weekends and during the first COVID-19 wave in 2020. An increase in PM2.5 led to an immediate increase in visit numbers that lasted over several days. Averaged over a full week, this amounted to about a 1% increase per 10 µg/m³. There were, on average, 1.7 hospital admissions per day. Two and three days after a rise in air pollution, there was also a rise in admission numbers, followed by a decline during the consecutive days. This might indicate that the wards were overstressed because of high admission numbers and restricted additional admissions.

AI-Based Unmixing of Medium and Source Signatures From Seismograms: Ground Freezing Patterns

Seismograms always result from mixing many sources and medium changes that are complex to disentangle, witnessing many physical phenomena within the Earth. With artificial intelligence (AI), we isolate the signature of surface freezing and thawing in continuous seismograms recorded in a noisy urban environment. We perform a hierarchical clustering of the seismograms and identify a pattern that correlates with ground frost periods. We further investigate the fingerprint of this pattern and use it to track the continuous medium change with high accuracy and resolution in time. Our method isolates the effect of the ground frost and describes how it affects the horizontal wavefield. Our findings show how AI-based strategies can help to identify and understand hidden patterns within seismic data caused either by medium or source changes.

MaD GUI: An Open-Source Python Package for Annotation and Analysis of Time-Series Data

Developing machine learning algorithms for time-series data often requires manual annotation of the data. To do so, graphical user interfaces (GUIs) are an important component. Existing Python packages for annotation and analysis of time-series data have been developed without addressing adaptability, usability, and user experience. Therefore, we developed a generic open-source Python package focusing on adaptability, usability, and user experience. The developed package, Machine Learning and Data Analytics (MaD) GUI, enables developers to rapidly create a GUI for their specific use case. Furthermore, MaD GUI enables domain experts without programming knowledge to annotate time-series data and apply algorithms to it. We conducted a small-scale study with participants from three international universities to test the adaptability of MaD GUI by developers and to test the user interface by clinicians as representatives of domain experts. MaD GUI saves up to 75% of time in contrast to using a state-of-the-art package. In line with this, subjective ratings regarding usability and user experience show that MaD GUI is preferred over a state-of-the-art package by developers and clinicians. MaD GUI reduces the effort of developers in creating GUIs for time-series analysis and offers similar usability and user experience for clinicians as a state-of-the-art package.

Collinearity Issues in Autoregressive Models with Time-Varying Serially Dependent Covariates

First-order autoregressive models are popular to assess the temporal dynamics of a univariate process. Researchers often extend these models to include time-varying covariates, such as contextual factors, to investigate how they moderate processes' dynamics. We demonstrate that doing so has implications for how well one can estimate the autoregressive and covariate effects, as serial dependence in the variables can imply predictor collinearity. This is a noteworthy contribution, since in current practice serial dependence in a time-varying covariate is rarely considered important. We first recapitulate the role of predictor collinearity for estimation precision in an ordinary least squares context, by discussing how it affects estimator variances, covariances and correlations. We then derive a general formula detailing how predictor collinearity in first-order autoregressive models is impacted by serial dependence in the covariate. We provide a simulation study to illustrate the implications of the formula for different types of covariates. The simulation results highlight when the collinearity issue becomes severe enough to hamper interpretation of the effects. We also show that the effect estimates can be biased in small samples (i.e., 50 time points). Implications for study design, the use of time as a predictor, and related model variants are discussed.

High-Resolution Digital Phenotypes From Consumer Wearables and Their Applications in Machine Learning of Cardiometabolic Risk Markers: Cohort Study

BACKGROUND

Consumer-grade wearable devices enable detailed recordings of heart rate and step counts in free-living conditions. Recent studies have shown that summary statistics from these wearable recordings have potential uses for longitudinal monitoring of health and disease states. However, the relationship between higher resolution physiological dynamics from wearables and known markers of health and disease remains largely uncharacterized.

OBJECTIVE

We aimed to derive high-resolution digital phenotypes from observational wearable recordings and to examine their associations with modifiable and inherent markers of cardiometabolic disease risk.

METHODS

We introduced a principled framework to extract interpretable high-resolution phenotypes from wearable data recorded in free-living conditions. The proposed framework standardizes the handling of data irregularities; encodes contextual information regarding the underlying physiological state at any given time; and generates a set of 66 minimally redundant features across active, sedentary, and sleep states. We applied our approach to a multimodal data set, from the SingHEART study (NCT02791152), which comprises heart rate and step count time series from wearables, clinical screening profiles, and whole genome sequences from 692 healthy volunteers. We used machine learning to model nonlinear relationships between the high-resolution phenotypes on the one hand and clinical or genomic risk markers for blood pressure, lipid, weight and sugar abnormalities on the other. For each risk type, we performed model comparisons based on Brier scores to assess the predictive value of high-resolution features over and beyond typical baselines. We also qualitatively characterized the wearable phenotypes for participants who had actualized clinical events.

RESULTS

We found that the high-resolution features have higher predictive value than typical baselines for clinical markers of cardiometabolic disease risk: the best models based on high-resolution features had 17.9% and 7.36% improvement in Brier score over baselines based on age and gender and resting heart rate, respectively (P<.001 in each case). Furthermore, heart rate dynamics from different activity states contain distinct information (maximum absolute correlation coefficient of 0.15). Heart rate dynamics in sedentary states are most predictive of lipid abnormalities and obesity, whereas patterns in active states are most predictive of blood pressure abnormalities (P<.001). Moreover, in comparison with standard measures, higher resolution patterns in wearable heart rate recordings are better able to represent subtle physiological dynamics related to genomic risk for cardiometabolic disease (improvement of 11.9%-22.0% in Brier scores; P<.001). Finally, illustrative case studies reveal connections between these high-resolution phenotypes and actualized clinical events, even for borderline profiles lacking apparent cardiometabolic risk markers.

CONCLUSIONS

High-resolution digital phenotypes recorded by consumer wearables in free-living states have the potential to enhance the prediction of cardiometabolic disease risk and could enable more proactive and personalized health management.

A Time Series Analysis Evaluating Antibiotic Prescription Rates in Long-Term Care during the COVID-19 Pandemic in Alberta and Ontario, Canada

The COVID-19 pandemic affected access to care, and the associated public health measures influenced the transmission of other infectious diseases. The pandemic has dramatically changed antibiotic prescribing in the community. We aimed to determine the impact of the COVID-19 pandemic and the resulting control measures on oral antibiotic prescribing in long-term care facilities (LTCFs) in Alberta and Ontario, Canada using linked administrative data. Antibiotic prescription data were collected for LTCF residents 65 years and older in Alberta and Ontario from 1 January 2017 until 31 December 2020. Weekly prescription rates per 1000 residents, stratified by age, sex, antibiotic class, and selected individual agents, were calculated. Interrupted time series analyses using SARIMA models were performed to test for changes in antibiotic prescription rates after the start of the pandemic (1 March 2020). The average annual cohort size was 18,489 for Alberta and 96,614 for Ontario. A significant decrease in overall weekly prescription rates after the start of the pandemic compared to pre-pandemic was found in Alberta, but not in Ontario. Furthermore, a significant decrease in prescription rates was observed for antibiotics mainly used to treat respiratory tract infections: amoxicillin in both provinces (Alberta: −0.6 per 1000 LTCF residents decrease in weekly prescription rate, p = 0.006; Ontario: −0.8, p < 0.001); and doxycycline (−0.2, p = 0.005) and penicillin (−0.04, p = 0.014) in Ontario. In Ontario, azithromycin was prescribed at a significantly higher rate after the start of the pandemic (0.7 per 1000 LTCF residents increase in weekly prescription rate, p = 0.011). A decrease in prescription rates for antibiotics that are largely used to treat respiratory tract infections is in keeping with the lower observed rates for respiratory infections resulting from pandemic control measures. The results should be considered in the contexts of different LTCF systems and provincial public health responses to the pandemic.

Transcriptional progressive patterns from mild to severe renal ischemia/reperfusion-induced kidney injury in mice

The renal ischemia/reperfusion (I/R)-induced acute kidney injury incidence after nephron-sparing surgery for localized renal tumors is 20%, but the biological determinant process of postoperative acute kidney injury remains unclear. Using Gene Expression Omnibus database (GSE192883) and several bioinformatics analyses (discrete time points analysis, gene set enrichment analysis, dynamic network biomarker analysis, etc), combined with the establishment of the I/R model for verification, we identified three progressive patterns involving five core pathways confirmed using gene set enrichment analysis and six key genes (S100a10, Pcna, Abat, Kmo, Acadm, and Adhfe1) verified using quantitative polymerase chain reaction The dynamic network biomarker (DNB) subnetwork composite index value is the highest in the 22-min ischemia group, suggesting the transcriptome expression level fluctuated sharply in this group, which means 22-min ischemia is an critical warning point. This study illustrates the core molecular progressive patterns from mild to severe I/R kidney injury, laying the foundation for precautionary biomarkers and molecular intervention targets for exploration. In addition, the safe renal artery blocking time of nephron-sparing surgery that we currently accept may not be safe anymore.

Modelled estimates of hospitalisations attributable to respiratory syncytial virus and influenza in Australia, 2009-2017

Background

Respiratory syncytial virus (RSV) and influenza are important causes of disease in children and adults. In Australia, information on the burden of RSV in adults is particularly limited.

Methods

We used time series analysis to estimate respiratory, acute respiratory infection, pneumonia and influenza, and bronchiolitis hospitalisations attributable to RSV and influenza in Australia during 2009 through 2017. RSV and influenza‐coded hospitalisations in <5‐year‐olds were used as proxies for relative weekly viral activity.

Results

From 2009 to 2017, the estimated all‐age average annual rates of respiratory hospitalisations attributable to RSV and seasonal influenza (excluding 2009) were 54.8 (95% confidence interval [CI]: 20.1, 88.8) and 87.8 (95% CI: 74.5, 97.7) per 100,000, respectively. The highest estimated average annual RSV‐attributable respiratory hospitalisation rate per 100,000 was 464.2 (95% CI: 285.9, 641.2) in <5‐year‐olds. For seasonal influenza, it was 521.6 (95% CI: 420.9, 600.0) in persons aged ≥75 years. In ≥75‐year‐olds, modelled estimates were approximately eight and two times the coded estimates for RSV and seasonal influenza, respectively.

Conclusions

RSV and influenza are major causes of hospitalisation in young children and older adults in Australia, with morbidity underestimated by hospital diagnosis codes.

Differentiated Evolutionary Strategies of Genetic Diversification in Atlantic and Pacific Thaumarchaeal Populations

Some marine microbes are seemingly “ubiquitous,” thriving across a wide range of environmental conditions. While the increased depth in metagenomic sequencing has led to a growing body of research on within-population heterogeneity in environmental microbial populations, there have been fewer systematic comparisons and characterizations of population-level genetic diversity over broader expanses of time and space. Here, we investigated the factors that govern the diversification of ubiquitous microbial taxa found within and between ocean basins. Specifically, we use mapped metagenomic paired reads to examine the genetic diversity of ammonia-oxidizing archaeal (“Candidatus Nitrosopelagicus brevis”) populations in the Pacific (Hawaii Ocean Time-series [HOT]) and Atlantic (Bermuda Atlantic Time Series [BATS]) Oceans sampled over 2 years. We observed higher nucleotide diversity in “Ca. N. brevis” at HOT, driven by a higher rate of homologous recombination. In contrast, “Ca. N. brevis” at BATS featured a more open pangenome with a larger set of genes that were specific to BATS, suggesting a history of dynamic gene gain and loss events. Furthermore, we identified highly differentiated genes that were regulatory in function, some of which exhibited evidence of recent selective sweeps. These findings indicate that different modes of genetic diversification likely incur specific adaptive advantages depending on the selective pressures that they are under. Within-population diversity generated by the environment-specific strategies of genetic diversification is likely key to the ecological success of “Ca. N. brevis.”

IMPORTANCE Ammonia-oxidizing archaea (AOA) are one of the most abundant chemolithoautotrophic microbes in the marine water column and are major contributors to global carbon and nitrogen cycling. Despite their ecological importance and geographical pervasiveness, there have been limited systematic comparisons and characterizations of their population-level genetic diversity over time and space. Here, we use metagenomic time series from two ocean observatories to address the fundamental questions of how abiotic and biotic factors shape the population-level genetic diversity and how natural microbial populations adapt across diverse habitats. We show that the marine AOA “Candidatus Nitrosopelagicus brevis” in different ocean basins exhibits distinct modes of genetic diversification in response to their selective regimes shaped by nutrient availability and patterns of environmental fluctuations. Our findings specific to “Ca. N. brevis” have broader implications, particularly in understanding the population-level responses to the changing climate and predicting its impact on biogeochemical cycles.

Analysis of time-series gene expression data to explore mechanisms of isoniazid-induced liver toxicity

Isoniazid (INH) is a first-line anti-tuberculosis drug which can cause idiosyncratic liver injury, while the underlying mechanisms need to be further elucidated. In this study, we explored the time series gene expression profiling of a hepatocyte cell line under isoniazid treatment. Through cluster analysis and enrichment analysis of differentially expressed genes, we revealed a total of 6 gene clusters and a series of pathways related to hepatotoxicity, and 13 key candidate genes were identified according to the protein-protein interaction (PPI) network analysis and maSigPro analysis. These findings lay a foundation for understanding the mechanisms of isoniazid -induced liver toxicity and provide new target genes for the monitoring and treatment of INH-induced hepatotoxicity in the future.

Multi-Domain Variational Autoencoders for Combined Modeling of MRI-Based Biventricular Anatomy and ECG-Based Cardiac Electrophysiology

Human cardiac function is characterized by a complex interplay of mechanical deformation and electrophysiological conduction. Similar to the underlying cardiac anatomy, these interconnected physiological patterns vary considerably across the human population with important implications for the effectiveness of clinical decision-making and the accuracy of computerized heart models. While many previous works have investigated this variability separately for either cardiac anatomy or physiology, this work aims to combine both aspects in a single data-driven approach and capture their intricate interdependencies in a multi-domain setting. To this end, we propose a novel multi-domain Variational Autoencoder (VAE) network to capture combined Electrocardiogram (ECG) and Magnetic Resonance Imaging (MRI)-based 3D anatomy information in a single model. Each VAE branch is specifically designed to address the particular challenges of the respective input domain, enabling efficient encoding, reconstruction, and synthesis of multi-domain cardiac signals. Our method achieves high reconstruction accuracy on a United Kingdom Biobank dataset, with Chamfer Distances between reconstructed and input anatomies below the underlying image resolution and ECG reconstructions outperforming multiple single-domain benchmarks by a considerable margin. The proposed VAE is capable of generating realistic virtual populations of arbitrary size with good alignment in clinical metrics between the synthesized and gold standard anatomies and Maximum Mean Discrepancy (MMD) scores of generated ECGs below those of comparable single-domain approaches. Furthermore, we observe the latent space of our VAE to be highly interpretable with separate components encoding different aspects of anatomical and ECG variability. Finally, we demonstrate that the combined anatomy and ECG representation improves the performance in a cardiac disease classification task by 3.9% in terms of Area Under the Receiver Operating Characteristic (AUROC) curve over the best corresponding single-domain modeling approach.

The Effectiveness of Inquiry and Practice During Project Design Courses at a Technology University

Among the many teaching methods, inquiry-based teaching is considered to be an effective way for students to learn and solve problems on their own. However, most of the research related to inquiry-based teaching and learning has concentrated mainly on K-12 education, while few to no studies have focused on the application of inquiry-based teaching and learning in project design courses at university level. Therefore, in order to expand the understanding of the application effect of inquiry-based teaching at university level, this study adopted the quasi-experimental design method, and through the purposive sampling method, 20 students from the Department of Fashion Design at a University of Science and Technology were invited to participate in this study. During the 9-month period, teaching experiments were carried out using two inquiry models, QC/ADEAC and QD/ODEAC. First, when participants were thinking of a creative topic, they followed the process: Question (Q), Collection/Analysis (C/A), Discussion (D), Explanation (E), Amendment (A), and Confirmation (C) in the course. During the production process, the participants were allowed to improve on their work through the process of Question (Q), Doing/Observation (D/O), Discussion (D), Explanation (E), Amendment (A), and Confirmation (C). The teacher became a true guide, so that the participants could explore and work out how to improve their designs through independent inquiry and practice. In this study, questionnaires were administered to participants at five important stages of the design project: “theme development,” “color development,” “first Work,” “second Work,” and “third Work.” The results of the five surveys showed that the participants’ curriculum interest, curriculum value perception, and curriculum confidence in the inquiry program all increased.

How to overcome the stagnation of the first dose vaccine coverage curve against coronavirus disease 2019 in Brazil?

BACKGROUND

A large percentage of the population has not yet started vaccination, for which the increase in coverage is almost null.

METHODS

We used segmented regression analysis to estimate trends in the first dose coverage curve.

RESULTS

There has been a slowdown in the application of the first doses in Brazil since epidemiological week 36 (average percent change [APC] 0.83%, 95% confidence interval [CI] 0.75-0.91%), with a trend close to stagnation.

CONCLUSIONS

It is important to develop strategies to increase access to vaccination posts. Furthermore, it is recommended to expand vaccination to children, thereby increasing the eligible population.

Evaluation of 1D and 2D Deep Convolutional Neural Networks for Driving Event Recognition

Driving event detection and driver behavior recognition have been widely explored for many purposes, including detecting distractions, classifying driver actions, detecting kidnappings, pricing vehicle insurance, evaluating eco-driving, and managing shared and leased vehicles. Some systems can recognize the main driving events (e.g., accelerating, braking, and turning) by using in-vehicle devices, such as inertial measurement unit (IMU) sensors. In general, feature extraction is a commonly used technique to obtain robust and meaningful information from the sensor signals to guarantee the effectiveness of the subsequent classification algorithm. However, a general assessment of deep neural networks merits further investigation, particularly regarding end-to-end models based on Convolutional Neural Networks (CNNs), which combine two components, namely feature extraction and the classification parts. This paper primarily explores supervised deep-learning models based on 1D and 2D CNNs to classify driving events from the signals of linear acceleration and angular velocity obtained with the IMU sensors of a smartphone placed in the instrument panel of the vehicle. Aggressive and non-aggressive behaviors can be recognized by monitoring driving events, such as accelerating, braking, lane changing, and turning. The experimental results obtained are promising since the best classification model achieved accuracy values of up to 82.40%, and macro- and micro-average F1 scores, respectively, equal to 75.36% and 82.40%, thus, demonstrating high performance in the classification of driving events.

Methodological considerations for studying neural oscillations

Neural oscillations are ubiquitous across recording methodologies and species, broadly associated with cognitive tasks, and amenable to computational modelling that investigates neural circuit generating mechanisms and neural population dynamics. Because of this, neural oscillations offer an exciting potential opportunity for linking theory, physiology and mechanisms of cognition. However, despite their prevalence, there are many concerns-new and old-about how our analysis assumptions are violated by known properties of field potential data. For investigations of neural oscillations to be properly interpreted, and ultimately developed into mechanistic theories, it is necessary to carefully consider the underlying assumptions of the methods we employ. Here, we discuss seven methodological considerations for analysing neural oscillations. The considerations are to (1) verify the presence of oscillations, as they may be absent; (2) validate oscillation band definitions, to address variable peak frequencies; (3) account for concurrent non-oscillatory aperiodic activity, which might otherwise confound measures; measure and account for (4) temporal variability and (5) waveform shape of neural oscillations, which are often bursty and/or nonsinusoidal, potentially leading to spurious results; (6) separate spatially overlapping rhythms, which may interfere with each other; and (7) consider the required signal-to-noise ratio for obtaining reliable estimates. For each topic, we provide relevant examples, demonstrate potential errors of interpretation, and offer suggestions to address these issues. We primarily focus on univariate measures, such as power and phase estimates, though we discuss how these issues can propagate to multivariate measures. These considerations and recommendations offer a helpful guide for measuring and interpreting neural oscillations.

Do ridesharing transportation services alleviate traffic crashes? A time series analysis

OBJECTIVES

On-demand ridesharing services are suggested to provide several benefits, such as improving accessibility and mobility, reducing drive-alone trips and greenhouse gas emissions. However, the impacts of these services on traffic crashes are not completely clear. This paper investigates the availability of Via- an on-demand ridesharing service in Arlington, TX, to identify the effects of this service on traffic crashes. We hypothesize that the launch of Via would result in more shared rides, fewer drive-alone trips and fewer traffic crashes.

METHODS

We implement an Interrupted Time Series Analysis (ITSA) approach to study the impact of Via service availability on traffic crashes using weekly counts of all traffic crashes, the number of injuries, and serious injuries that occurred in Arlington from 2014 to 2021.

RESULTS

The results show a statistically significant reduction in the weekly number of total crashes and total injuries but do not show any significant impact on the number of serious injuries. Shared Autonomous Vehicles have the potential to reduce traffic crashes caused by driver's fault.

CONCLUSIONS

This study reveals the potential impacts ridesharing services can have on traffic crashes and injuries in a mid-sized city. The results of this study can help decision and policymakers to understand the full potential of ridesharing services that can contribute to making relevant decisions toward creating sustainable and safer transportation systems in cities.

Comprehensive Analysis of the COVID-19: Based on the Social-Related Indexes From NUMBEO

Background

The COVID-19 has been spreading globally since 2019 and causes serious damage to the whole society. A macro perspective study to explore the changes of some social-related indexes of different countries is meaningful.

Methods

We collected nine social-related indexes and the score of COVID-safety-assessment. Data analysis is carried out using three time series models. In particular, a prediction-correction procedure was employed to explore the impact of the pandemic on the indexes of developed and developing countries.

Results

It shows that COVID-19 epidemic has an impact on the life of residents in various aspects, specifically in quality of life, purchasing power, and safety. Cluster analysis and bivariate statistical analysis further indicate that indexes affected by the pandemic in developed and developing countries are different.

Conclusion

This pandemic has altered the lives of residents in many ways. Our further research shows that the impacts of social-related indexes in developed and developing countries are different, which is bounded up with their epidemic severity and control measures. On the other hand, the climate is crucial for the control of COVID-19. Consequently, exploring the changes of social-related indexes is significative, and it is conducive to provide targeted governance strategies for various countries. Our article will contribute to countries with different levels of development pay more attention to social changes and take timely and effective measures to adjust social changes while trying to control this pandemic.

Emergency Medical Services Calls Analysis for Trend Prediction during Epidemic Outbreaks: Interrupted Time Series Analysis on 2020-2021 COVID-19 Epidemic in Lazio, Italy

(1) Background: During the COVID-19 outbreak in the Lazio region, a surge in emergency medical service (EMS) calls has been observed. The objective of present study is to investigate if there is any correlation between the variation in numbers of daily EMS calls, and the short-term evolution of the epidemic wave. (2) Methods: Data from the COVID-19 outbreak has been retrieved in order to draw the epidemic curve in the Lazio region. Data from EMS calls has been used in order to determine Excess of Calls (ExCa) in the 2020−2021 years, compared to the year 2019 (baseline). Multiple linear regression models have been run between ExCa and the first-order derivative (D’) of the epidemic wave in time, each regression model anticipating the epidemic progression (up to 14 days), in order to probe a correlation between the variables. (3) Results: EMS calls variation from baseline is correlated with the slope of the curve of ICU admissions, with the most fitting value found at 7 days (R2 0.33, p < 0.001). (4) Conclusions: EMS calls deviation from baseline allows public health services to predict short-term epidemic trends in COVID-19 outbreaks, and can be used as validation of current data, or as an independent estimator of future trends.

Pesticide poisoning risk attributable to ambient temperature: a time series analysis in Qingdao China during 2007-2018

Pesticide poisoning prevention has become a public health issue of great concern. We estimated the association between temperature and attributable risk of pesticide poisoning using 3,545 pesticide poisoning cases in Qingdao China from June 2007 to July 2018. A distributed lag non-linear model was applied to estimate the temperature-pesticide poisoning associated with the assessment of attributable number and fraction. The hot temperature is responsible for the pesticide poisoning incidence, with backward and forward attributable fractions, respectively, 7.79% and 7.61%. Most of the pesticide poisoning burden (backward attributable fraction 5.30% and forward attributable fraction 5.06%) was caused by mild hot (22°C-26°C), whereas the burden due to extreme hot (27°C-31°C) was small (backward attributable fraction 2.94% and forward attributable fraction 2.69%).

More surgery in December among US patients with commercial insurance is offset by unrelated but lesser surgery among patients with Medicare insurance

Study Objective

Evaluate whether there is more surgery (in the US State of Florida) at the end of the year, specifically among patients with commercial insurance.

Design

Observational cohort study.

Setting

The 712 facilities in Florida that performed inpatient or outpatient elective surgery from January 2010 through December 2019.

Results

Among patients with commercial insurance, December had more cases than November (1.108 [1.092–1.125]) or January (1.257 [1.229–1.286]). In contrast, among patients with Medicare insurance (traditional or managed care), December had fewer cases than November (ratio 0.917 [99% confidence interval 0.904–0.930]) or January (0.823 [0.807–0.839]) of the same year. Summing among all cases, December did not have more cases than November (ratio 1.003 [0.992–1.014]) or January (0.998 [0.984–1.013]). Comparing December versus November (January) ratios for cases among patients with commercial insurance to the corresponding ratios for cases among patients with Medicare, years with more commercial insurance cases had more Medicare cases (Spearman rank correlation +0.36 [+0.25], both p < 0.0001).

Conclusions

In the US State of Florida, although some surgeons' procedural workloads may have seasonal variation if they care mostly for patients with one category of insurance, surgical facilities with patients undergoing many procedures will have less variability. Importantly, more commercial insurance cases were not causing Medicare cases to be postponed or vice‐versa, providing mechanistic explanation for why forecasts of surgical demand can reasonably be treated as the sum of the independent workloads among many surgeons.

In US State of Florida, patients with commercial insurance had more surgery in December

Patients with US Medicare insurance had less surgery in December than other months

Years with more commercial insurance cases in December had more US Medicare cases too

Implication for surgical suites: busier months for some patient groups balanced by less busy for others

Impulse Response Functions for Nonlinear, Nonstationary, and Heterogeneous Systems, Estimated by Deconvolution and Demixing of Noisy Time Series

Impulse response functions (IRFs) are useful for characterizing systems’ dynamic behavior and gaining insight into their underlying processes, based on sensor data streams of their inputs and outputs. However, current IRF estimation methods typically require restrictive assumptions that are rarely met in practice, including that the underlying system is homogeneous, linear, and stationary, and that any noise is well behaved. Here, I present data-driven, model-independent, nonparametric IRF estimation methods that relax these assumptions, and thus expand the applicability of IRFs in real-world systems. These methods can accurately and efficiently deconvolve IRFs from signals that are substantially contaminated by autoregressive moving average (ARMA) noise or nonstationary ARIMA noise. They can also simultaneously deconvolve and demix the impulse responses of individual components of heterogeneous systems, based on their combined output (without needing to know the outputs of the individual components). This deconvolution–demixing approach can be extended to characterize nonstationary coupling between inputs and outputs, even if the system’s impulse response changes so rapidly that different impulse responses overlap one another. These techniques can also be extended to estimate IRFs for nonlinear systems in which different input intensities yield impulse responses with different shapes and amplitudes, which are then overprinted on one another in the output. I further show how one can efficiently quantify multiscale impulse responses using piecewise linear IRFs defined at unevenly spaced lags. All of these methods are implemented in an R script that can efficiently estimate IRFs over hundreds of lags, from noisy time series of thousands or even millions of time steps.

Multi-Season Phenology Mapping of Nile Delta Croplands Using Time Series of Sentinel-2 and Landsat 8 Green LAI

Space-based cropland phenology monitoring substantially assists agricultural managing practices and plays an important role in crop yield predictions. Multitemporal satellite observations allow analyzing vegetation seasonal dynamics over large areas by using vegetation indices or by deriving biophysical variables. The Nile Delta represents about half of all agricultural lands of Egypt. In this region, intensifying farming systems are predominant and multi-cropping rotations schemes are increasing, requiring a high temporal and spatial resolution monitoring for capturing successive crop growth cycles. This study presents a workflow for cropland phenology characterization and mapping based on time series of green Leaf Area Index (LAI) generated from NASA's Harmonized Landsat 8 (L8) and Sentinel-2 (S2) surface reflectance dataset from 2016 to 2019. LAI time series were processed for each satellite dataset, which were used separately and combined to identify seasonal dynamics for a selection of crop types (wheat, clover, maize and rice). For the combination of L8 with S2 LAI products, we proposed two time series smoothing and fitting methods: (1) the Savitzky-Golay (SG) filter and (2) the Gaussian Processes Regression (GPR) fitting function. Single-sensor and L8-S2 combined LAI time series were used for the calculation of key crop Land Surface Phenology (LSP) metrics (start of season, end of season, length of season), whereby the detection of cropland growing seasons was based on two established threshold methods, i.e., a seasonal or a relative amplitude value. Overall, the developed phenology extraction scheme enabled identifying up to two successive crop cycles within a year, with a superior performance observed for the seasonal than for the relative threshold method, in terms of consistency and cropland season detection capability. Differences between the time series collections were analyzed by comparing the phenology metrics per crop type and year. Results suggest that L8-S2 combined LAI data streams with GPR led to a more precise detection of the start and end of growing seasons for most crop types, reaching an overall detection of 74% over the total planted crops versus 69% with S2 and 63% with L8 alone. Finally, the phenology mapping allowed us to evaluate the spatial and temporal evolution of the croplands over the agroecosystem in the Nile Delta.

The impact of the Antenatal Late Preterm Steroids trial on the administration of antenatal corticosteroids

BACKGROUND

In 2016 the Antenatal Late Preterm Steroids study was published, demonstrating that antenatal corticosteroid therapy given to women at risk of late preterm delivery reduces respiratory morbidity in infants. However, the administration of antenatal corticosteroid therapy in late-preterm infants remains controversial. Late-preterm infants do not suffer from the same rates of morbidity as early-preterm infants, and the short-term benefits of antenatal corticosteroid therapy are less pronounced; consequently, the risk of possible harm is more difficult to balance.

OBJECTIVE

This study aimed to evaluate the association between the publication of the Antenatal Late Preterm Steroids study or the subsequent changes in guidelines and the rates of antenatal corticosteroid therapy administration in late-preterm infants in the United States.

STUDY DESIGN

Data analyzed were publicly available US birth certificate data from January 1, 2016 to December 31, 2018. An interrupted time series design was used to analyze the association between publication of the Antenatal Late Preterm Steroids study and changes in monthly rates of antenatal corticosteroid administration in late preterm gestation (34+0 to 36+6 weeks). Births at 28+0 to 31+6 weeks' gestation were used as a control. Antenatal corticosteroid therapy administration in women with births at 32+0 to 34+6 weeks was explored to analyze whether the intervention influenced antenatal corticosteroid therapy administration in women in the subgroup approaching 34 weeks' gestation. Antenatal corticosteroid therapy administration in women with term births (>37 weeks' gestation) was analyzed to explore if the intervention influenced the number of term babies exposed to antenatal corticosteroid therapy. Our regression model allowed analysis of both step and slope changes. February 2016 was chosen as the intervention period.

RESULTS

Our sample size was 18,031,950 total births. Of these, 1,056,047 were births at 34+0 to 36+6 weeks' gestation, 123,788 at 28+0 to 31+6 weeks, 153,708 at 32 to 33 weeks, and 16,602,699 were term births. There were 95,708 births at <28 weeks' gestation. There was a statistically significant increase in antenatal corticosteroid therapy administration rates in late preterm births following the online publication of the Antenatal Late Preterm Steroids study (adjusted incidence rate ratio, 1.48; 95% confidence interval, 1.36-1.61; P=.00). A significant increase in antenatal corticosteroid therapy administration rates was also seen in full-term births following the online publication of the Antenatal Late Preterm Steroids study. No significant changes were seen in antenatal corticosteroid administration rates in gestational age groups of 32+0 to 33+6 weeks or 28+0 to 31+6 weeks.

CONCLUSION

Online publication of the Antenatal Late Preterm Steroids study was associated with an immediate and sustained increase in the rates of antenatal corticosteroid therapy administration in late preterm births across the United States, demonstrating a swift and successful implementation of the Antenatal Late Preterm Steroids study guidance into clinical practice. However, there is an unnecessary increase in full-term infants receiving antenatal corticosteroid therapy. Given that the long-term consequences of antenatal corticosteroid therapy are yet to be elucidated, efforts should be made to minimize the number of infants unnecessarily exposed to antenatal corticosteroid therapy.

Towards a Data-Driven Estimation of Resilience in Networked Dynamical Systems: Designing a Versatile Testbed

Estimating resilience of adaptive, networked dynamical systems remains a challenge. Resilience refers to a system's capacity "to absorb exogenous and/or endogenous disturbances and to reorganize while undergoing change so as to still retain essentially the same functioning, structure, and feedbacks." The majority of approaches to estimate resilience requires exact knowledge of the underlying equations of motion; the few data-driven approaches so far either lack appropriate strategies to verify their suitability or remain subject of considerable debate. We develop a testbed that allows one to modify resilience of a multistable networked dynamical system in a controlled manner. The testbed also enables generation of multivariate time series of system observables to evaluate the suitability of data-driven estimators of resilience. We report first findings for such an estimator.

Evaluating the Increased Burden of Cardiorespiratory Illness Visits to Adult Emergency Departments During Flu and Bronchiolitis Outbreaks in the Pediatric Population: Retrospective Multicentric Time Series Analysis

BACKGROUND

Cardiorespiratory decompensation (CRD) visits have a profound effect on adult emergency departments (EDs). Respiratory pathogens like respiratory syncytial virus (RSV) and influenza virus are common reasons for increased activity in pediatric EDs and are associated with CRD in the adult population. Given the seasonal aspects of such challenging pathology, it would be advantageous to predict their variations.

OBJECTIVE

The goal of this study was to evaluate the increased burden of CRD in adult EDs during flu and bronchiolitis outbreaks in the pediatric population.

METHODS

An ecological study was conducted, based on admissions to the adult ED of the Centre Hospitalier Universitaire (CHU) of Grenoble and Saint Etienne from June 29, 2015 to March 22, 2020. The outbreak periods for bronchiolitis and flu in the pediatric population were defined with a decision-making support tool, PREDAFLU, used in the pediatric ED. A Kruskal-Wallis variance analysis and a Spearman monotone dependency were performed in order to study the relationship between the number of adult ED admissions for the International Classification of Diseases (ICD)-10 codes related to cardiorespiratory diagnoses and the presence of an epidemic outbreak as defined with PREDAFLU.

RESULTS

The increase in visits to the adult ED for CRD and the bronchiolitis and flu outbreaks had a similar distribution pattern (CHU Saint Etienne: χ²₃=102.7, P<.001; CHU Grenoble: χ²₃=126.67, P<.001) and were quite dependent in both hospital settings (CHU Saint Etienne: Spearman ρ=0.64; CHU Grenoble: Spearman ρ=0.71). The increase in ED occupancy for these pathologies was also significantly related to the pediatric respiratory infection outbreaks. These 2 criteria gave an idea of the increased workload in the ED due to CRD during the bronchiolitis and flu outbreaks in the pediatric population.

CONCLUSIONS

This study established that CRD visits and bed occupancy for adult EDs were significantly increased during bronchiolitis and pediatric influenza outbreaks. Therefore, a prediction tool for these outbreaks such as PREDAFLU can be used to provide early warnings of increased activity in adult EDs for CRD visits.