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.

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.

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.

Dynamic Temporal Relationship Between Autonomic Function and Cerebrovascular Reactivity in Moderate/Severe Traumatic Brain Injury

There has been little change in morbidity and mortality in traumatic brain injury (TBI) in the last 25 years. However, literature has emerged linking impaired cerebrovascular reactivity (a surrogate of cerebral autoregulation) with poor outcomes post-injury. Thus, cerebrovascular reactivity (derived through the pressure reactivity index; PRx) is emerging as an important continuous measure. Furthermore, recent literature indicates that autonomic dysfunction may drive impaired cerebrovascular reactivity in moderate/severe TBI. Thus, to improve our understanding of this association, we assessed the physiological relationship between PRx and the autonomic variables of heart rate variability (HRV), blood pressure variability (BPV), and baroreflex sensitivity (BRS) using time-series statistical methodologies. These methodologies include vector autoregressive integrative moving average (VARIMA) impulse response function analysis, Granger causality, and hierarchical clustering. Granger causality testing displayed inconclusive results, where PRx and the autonomic variables had varying bidirectional relationships. Evaluating the temporal profile of the impulse response function plots demonstrated that the autonomic variables of BRS, ratio of low/high frequency of HRV and very low frequency HRV all had a strong relation to PRx, indicating that the sympathetic autonomic response may be more closely linked to cerebrovascular reactivity, then other variables. Finally, BRS was consistently associated with PRx, possibly demonstrating a deeper relationship to PRx than other autonomic measures. Taken together, cerebrovascular reactivity and autonomic response are interlinked, with a bidirectional impact between cerebrovascular reactivity and circulatory autonomics. However, this work is exploratory and preliminary, with further study required to extract and confirm any underlying relationships.

Landslide Displacement Prediction Based on Time Series Analysis and Double-BiLSTM Model

In recent years, machine learning models facilitated notable performance improvement in landslide displacement prediction. However, most existing prediction models which ignore landslide data at each time can provide a different value and meaning. To analyze and predict landslide displacement better, we propose a dynamic landslide displacement prediction model based on time series analysis and a double-bidirectional long short term memory (Double-BiLSTM) model. First, the cumulative landslide displacement is decomposed into trend and periodic displacement components according to time series analysis via the exponentially weighted moving average (EWMA) method. We consider that trend displacement is mainly influenced by landslide factors, and we apply a BiLSTM model to predict landslide trend displacement. This paper analyzes the internal relationship between rainfall, reservoir level and landslide periodic displacement. We adopt the maximum information coefficient (MIC) method to calculate the correlation between influencing factors and periodic displacement. We employ the BiLSTM model for periodic displacement prediction. Finally, the model is validated against data pertaining to the Baishuihe landslide in the Three Gorges, China. The experimental results and evaluation indicators demonstrate that this method achieves a better prediction performance than the classical prediction methods, and landslide displacement can be effectively predicted.

Network Analysis of Time Series: Novel Approaches to Network Neuroscience

In the last two decades, there has been an explosion of interest in modeling the brain as a network, where nodes correspond variously to brain regions or neurons, and edges correspond to structural or statistical dependencies between them. This kind of network construction, which preserves spatial, or structural, information while collapsing across time, has become broadly known as "network neuroscience." In this work, we provide an alternative application of network science to neural data: network-based analysis of non-linear time series and review applications of these methods to neural data. Instead of preserving spatial information and collapsing across time, network analysis of time series does the reverse: it collapses spatial information, instead preserving temporally extended dynamics, typically corresponding to evolution through some kind of phase/state-space. This allows researchers to infer a, possibly low-dimensional, "intrinsic manifold" from empirical brain data. We will discuss three methods of constructing networks from nonlinear time series, and how to interpret them in the context of neural data: recurrence networks, visibility networks, and ordinal partition networks. By capturing typically continuous, non-linear dynamics in the form of discrete networks, we show how techniques from network science, non-linear dynamics, and information theory can extract meaningful information distinct from what is normally accessible in standard network neuroscience approaches.

Using Complementary Ensemble Empirical Mode Decomposition and Gated Recurrent Unit to Predict Landslide Displacements in Dam Reservoir

It is crucial to predict landslide displacement accurately for establishing a reliable early warning system. Such a requirement is more urgent for landslides in the reservoir area. The main reason is that an inaccurate prediction can lead to riverine disasters and secondary surge disasters. Machine learning (ML) methods have been developed and commonly applied in landslide displacement prediction because of their powerful nonlinear processing ability. Recently, deep ML methods have become popular, as they can deal with more complicated problems than conventional ML methods. However, it is usually not easy to obtain a well-trained deep ML model, as many hyperparameters need to be trained. In this paper, a deep ML method—the gated recurrent unit (GRU)—with the advantages of a powerful prediction ability and fewer hyperparameters, was applied to forecast landslide displacement in the dam reservoir. The accumulated displacement was firstly decomposed into a trend term, a periodic term, and a stochastic term by complementary ensemble empirical mode decomposition (CEEMD). A univariate GRU model and a multivariable GRU model were employed to forecast trend and stochastic displacements, respectively. A multivariable GRU model was applied to predict periodic displacement, and another two popular ML methods—long short-term memory neural networks (LSTM) and random forest (RF)—were used for comparison. Precipitation, reservoir level, and previous displacement were considered to be candidate-triggering factors for inputs of the models. The Baijiabao landslide, located in the Three Gorges Reservoir Area (TGRA), was taken as a case study to test the prediction ability of the model. The results demonstrated that the GRU algorithm provided the most encouraging results. Such a satisfactory prediction accuracy of the GRU algorithm depends on its ability to fully use the historical information while having fewer hyperparameters to train. It is concluded that the proposed model can be a valuable tool for predicting the displacements of landslides in the TGRA and other dam reservoirs.

Network inference with Granger causality ensembles on single-cell transcriptomics

Cellular gene expression changes throughout a dynamic biological process, such as differentiation. Pseudotimes estimate cells' progress along a dynamic process based on their individual gene expression states. Ordering the expression data by pseudotime provides information about the underlying regulator-gene interactions. Because the pseudotime distribution is not uniform, many standard mathematical methods are inapplicable for analyzing the ordered gene expression states. Here we present single-cell inference of networks using Granger ensembles (SINGE), an algorithm for gene regulatory network inference from ordered single-cell gene expression data. SINGE uses kernel-based Granger causality regression to smooth irregular pseudotimes and missing expression values. It aggregates predictions from an ensemble of regression analyses to compile a ranked list of candidate interactions between transcriptional regulators and target genes. In two mouse embryonic stem cell differentiation datasets, SINGE outperforms other contemporary algorithms. However, a more detailed examination reveals caveats about poor performance for individual regulators and uninformative pseudotimes.

Correlation-Based Discovery of Disease Patterns for Syndromic Surveillance

Early outbreak detection is a key aspect in the containment of infectious diseases, as it enables the identification and isolation of infected individuals before the disease can spread to a larger population. Instead of detecting unexpected increases of infections by monitoring confirmed cases, syndromic surveillance aims at the detection of cases with early symptoms, which allows a more timely disclosure of outbreaks. However, the definition of these disease patterns is often challenging, as early symptoms are usually shared among many diseases and a particular disease can have several clinical pictures in the early phase of an infection. As a first step toward the goal to support epidemiologists in the process of defining reliable disease patterns, we present a novel, data-driven approach to discover such patterns in historic data. The key idea is to take into account the correlation between indicators in a health-related data source and the reported number of infections in the respective geographic region. In an preliminary experimental study, we use data from several emergency departments to discover disease patterns for three infectious diseases. Our results show the potential of the proposed approach to find patterns that correlate with the reported infections and to identify indicators that are related to the respective diseases. It also motivates the need for additional measures to overcome practical limitations, such as the requirement to deal with noisy and unbalanced data, and demonstrates the importance of incorporating feedback of domain experts into the learning procedure.

Analysis of Movement Entropy during Community Dance Programs for People with Parkinson's Disease and Older Adults: A Cohort Study

Dance therapy can improve motor skills, balance, posture, and gait in people diagnosed with Parkinson’s disease (PD) and healthy older adults (OA). It is not clear how specific movement patterns during dance promote these benefits. The purpose of this cohort study was to identify differences and complexity in dance movement patterns among different dance styles for PD and OA participants in community dance programs using approximate entropy (ApEn) analysis. The hypothesis was that PD participants will show greater ApEn during dance than OA participants and that the unique dance style of tango with more pronounced foot technique and sharp direction changes will show greater ApEn than smoother dance types such as foxtrot and waltz characterized by gradual changes in direction and gliding movement with rise and fall. Individuals participated in one-hour community dance classes. Movement data were captured using porTable 3D motion capture sensors attached to the arms, torso and legs. Classes were also video recorded to assist in analyzing the dance steps. Movement patterns were captured and ApEn was calculated to quantify the complexity of movements. Participants with PD had greater ApEn in right knee flexion during dance movements than left knee flexion (p = 0.02), greater ApEn of right than left hip flexion (p = 0.05), and greater left hip rotation than right (p = 0.03). There was no significant difference in ApEn of body movements (p > 0.4) or mean body movements (p > 0.3) at any body-segment in OA. ApEn analysis is valuable for quantifying the degree of control and predictability of dance movements and could be used as another tool to assess the movement control of dancers and aid in the development of dance therapies.

Differential impact of the COVID-19 pandemic on primary care utilization related to common mental disorders in four European countries: A retrospective observational study

BACKGROUND

The COVID-19 pandemic is commonly believed to have increased common mental disorders (CMD, i.e., depression and anxiety), either directly due to COVID-19 contractions (death of near ones or residual conditions), or indirectly by increasing stress, economic uncertainty, and disruptions in daily life resulting from containment measure. Whereas studies reporting on initial changes in self-reported data frequently have reported increases in CMD, pandemic related changes in CMD related to primary care utilization are less well known. Analyzing time series of routinely and continuously sampled primary healthcare data from Sweden, Norway, Netherlands, and Latvia, we aimed to characterize the impact of the pandemic on CMD recorded prevalence in primary care. Furthermore, by relating these changes to country specific time-trajectories of two classes of containment measures, we evaluated the differential impact of containment strategies on CMD rates. Specifically, we wanted to test whether school restrictions would preferentially affect age groups corresponding to those of school children or their parents.

METHODS

For the four investigated countries, we collected time-series of monthly counts of unique CMD patients in primary healthcare from the year 2015 (or 2017) until 2021. Using pre-pandemic timepoints to train seasonal Auto Regressive Integrated Moving Average (ARIMA) models, we predicted healthcare utilization during the pandemic. Discrepancies between observed and expected time series were quantified to infer pandemic related changes. To evaluate the effects of COVID-19 measures on CMD related primary care utilization, the predicted time series were related to country specific time series of levels of social distancing and school restrictions.

RESULTS

In all countries except Latvia there was an initial (April 2020) decrease in CMD care prevalence, where largest drops were found in Sweden (Prevalence Ratio, PR = 0.85; 95% CI 0.81-0.90), followed by Netherlands (0.86; 95% CI 0.76-1.02) and Norway (0.90; 95% CI 0.83-0.98). Latvia on the other hand experienced increased rates (1.25; 95% CI 1.08-1.49). Whereas PRs in Norway and Netherlands normalized during the latter half of 2020, PRs stayed low in Sweden and elevated in Latvia. The overall changes in PR during the pandemic year 2020 was significantly changed only for Sweden (0.91; 95% CI 0.90-0.93) and Latvia (1.20; 95% CI 1.14-1.26). Overall, the relationship between containment measures and CMD care prevalence were weak and non-significant. In particular, we could not observe any relationship of school restriction to CMD care prevalence for the age groups best corresponding to school children or their parents.

CONCLUSION

Common mental disorders prevalence in primary care decreased during the initial phase of the COVID-19 pandemic in all countries except from Latvia, but normalized in Norway and Netherlands by the latter half of 2020. The onset of the pandemic and the containment strategies were highly correlated within each country, limiting strong conclusions on whether restriction policy had any effects on mental health. Specifically, we found no evidence of associations between school restrictions and CMD care prevalence. Overall, current results lend no support to the common belief that the pandemic severely impacted the mental health of the general population as indicated by healthcare utilization, apart from in Latvia. However, since healthcare utilization is affected by multiple factors in addition to actual need, future studies should combine complementary types of data to better understand the mental health impacts of the pandemic.

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.

Long- and short-term fluctuations compared for several organ systems across sleep stages

Some details of cardiovascular and cardio-respiratory regulation and their changes during different sleep stages remain still unknown. In this paper we compared the fluctuations of heart rate, pulse rate, respiration frequency, and pulse transit times as well as EEG alpha-band power on time scales from 6 to 200 s during different sleep stages in order to better understand regulatory pathways. The five considered time series were derived from ECG, photoplethysmogram, nasal air flow, and central electrode EEG measurements from full-night polysomnography recordings of 246 subjects with suspected sleep disorders. We applied detrended fluctuation analysis, distinguishing between short-term (6-16 s) and long-term (50-200 s) correlations, i.e., scaling behavior characterized by the fluctuation exponents α ₁ and α ₂ related with parasympathetic and sympathetic control, respectively. While heart rate (and pulse rate) are characterized by sex and age-dependent short-term correlations, their long-term correlations exhibit the well-known sleep stage dependence: weak long-term correlations during non-REM sleep and pronounced long-term correlations during REM sleep and wakefulness. In contrast, pulse transit times, which are believed to be mainly affected by blood pressure and arterial stiffness, do not show differences between short-term and long-term exponents. This is in constrast to previous results for blood pressure time series, where α ₁ was much larger than α ₂, and therefore questions a very close relation between pulse transit times and blood pressure values. Nevertheless, very similar sleep-stage dependent differences are observed for the long-term fluctuation exponent α ₂ in all considered signals including EEG alpha-band power. In conclusion, we found that the observed fluctuation exponents are very robust and hardly modified by body mass index, alcohol consumption, smoking, or sleep disorders. The long-term fluctuations of all observed systems seem to be modulated by patterns following sleep stages generated in the brain and thus regulated in a similar manner, while short-term regulations differ between the organ systems. Deviations from the reported dependence in any of the signals should be indicative of problems in the function of the particular organ system or its control mechanisms.

The risk of respiratory incidents in response to the increase of ozone concentration in the ambient air, in the Silesian Voivodeship, in 2016-2017

INTRODUCTION

Due to the high level of urbanization and industrialization, Silesian Voivodeship remains a region with the poorest quality ambient air, especially in the winter season in which alarm levels are constantly being exceeded. However, in the summertime, there are observed short-term episodes of high ozone concentrations for which their impact on the population health is poorly documented. The aim of the study was to assess the risk of daily respiratory health problems related to an increased pollutants concentration typical for photochemical smog.

MATERIAL AND METHODS

In the ecological type of study, secondary epidemiological data were used. They were obtained from the National Health Fund (NFZ) in Katowice and included the number of outpatient visits in primary health care and hospitalizations due to respiratory diseases (J00-J99) and selected acute respiratory incidents registered between 01/01/2016 and 31/08/2017 in the Silesian Voivodeship.

RESULTS

In the summertime of both years (2016 and 2017), there were observed short-term episodes of photochemical smog in the study region. Obtained results show a significant increase in the risk of outpatient visits due to total respiratory diseases, and also due to acute pharyngitis, acute laryngotracheitis, bronchitis, and asthma in response to the increase in ozone concentration. Similarly, a significant increase in the risk of hospitalization for all respiratory diseases was found, however, it appeared with a delay of at least two to three weeks. In the case of hospitalization due to bronchitis statistically significant risk was observed 2-4 days after the increase in exposure.

CONCLUSIONS

The occurrence of registered respiratory incidents was confirmed in response to the increase in ozone concentration, characteristic of the summertime in the Silesian Voivodeship.

A Square-Root Second-Order Extended Kalman Filtering Approach for Estimating Smoothly Time-Varying Parameters

Researchers collecting intensive longitudinal data (ILD) are increasingly looking to model psychological processes, such as emotional dynamics, that organize and adapt across time in complex and meaningful ways. This is also the case for researchers looking to characterize the impact of an intervention on individual behavior. To be useful, statistical models must be capable of characterizing these processes as complex, time-dependent phenomenon, otherwise only a fraction of the system dynamics will be recovered. In this paper we introduce a Square-Root Second-Order Extended Kalman Filtering approach for estimating smoothly time-varying parameters. This approach is capable of handling dynamic factor models where the relations between variables underlying the processes of interest change in a manner that may be difficult to specify in advance. We examine the performance of our approach in a Monte Carlo simulation and show the proposed algorithm accurately recovers the unobserved states in the case of a bivariate dynamic factor model with time-varying dynamics and treatment effects. Furthermore, we illustrate the utility of our approach in characterizing the time-varying effect of a meditation intervention on day-to-day emotional experiences.

Shift in Clinical Profile of Hospitalized Pneumonia in Children in the Non-pharmaceutical Interventions Period During the COVID-19 Pandemic: A Prospective Multicenter Study

Non-pharmaceutical interventions (NPIs) against coronavirus disease 2019 were implemented in March 2020. These measures were followed by a major impact on viral and non-viral diseases. We aimed to assess the impact of NPI implementation in France on hospitalized community-acquired pneumonia (hCAP) frequency and the clinical and biological characteristics of the remaining cases in children. We performed a quasi-experimental interrupted time-series analysis. Between June 2014 and December 2020, eight pediatric emergency departments throughout France reported prospectively all cases of hCAP in children from age 1 month to 15 years. We estimated the impact on the monthly number of hCAP using segmented linear regression with autoregressive error model. We included 2,972 hCAP cases; 115 occurred during the NPI implementation period. We observed a sharp decrease in the monthly number of hCAP after NPI implementation [-63.0% (95 confidence interval, -86.8 to -39.2%); p < 0.001]. Children with hCAP were significantly older during than before the NPI period (median age, 3.9 vs. 2.3 years; p < 0.0001), and we observed a higher proportion of low inflammatory marker status (43.5 vs. 33.1%; p = 0.02). Furthermore, we observed a trend with a decrease in the proportion of cases with pleural effusion (5.3% during the NPI period vs. 10.9% before the NPI; p = 0.06). NPI implementation during the COVID-19 (coronavirus disease 2019) pandemic led not only to a strong decrease in the number of hCAP cases but also a modification in the clinical profile of children affected, which may reflect a change in pathogens involved.

The Reconstruction of Causal Networks in Physiology

We systematically compare strengths and weaknesses of two methods that can be used to quantify causal links between time series: Granger-causality and Bivariate Phase Rectified Signal Averaging (BPRSA). While a statistical test method for Granger-causality has already been established, we show that BPRSA causality can also be probed with existing statistical tests. Our results indicate that more data or stronger interactions are required for the BPRSA method than for the Granger-causality method to detect an existing link. Furthermore, the Granger-causality method can distinguish direct causal links from indirect links as well as links that arise from a common source, while BPRSA cannot. However, in contrast to Granger-causality, BPRSA is suited for the analysis of non-stationary data. We demonstrate the practicability of the Granger-causality method by applying it to polysomnography data from sleep laboratories. An algorithm is presented, which addresses the stationarity condition of Granger-causality by splitting non-stationary data into shorter segments until they pass a stationarity test. We reconstruct causal networks of heart rate, breathing rate, and EEG amplitude from young healthy subjects, elderly healthy subjects, and subjects with obstructive sleep apnea, a condition that leads to disruption of normal respiration during sleep. These networks exhibit differences not only between different sleep stages, but also between young and elderly healthy subjects on the one hand and subjects with sleep apnea on the other hand. Among these differences are 1) weaker interactions in all groups between heart rate, breathing rate and EEG amplitude during deep sleep, compared to light and REM sleep, 2) a stronger causal link from heart rate to breathing rate but disturbances in respiratory sinus arrhythmia (breathing to heart rate coupling) in subjects with sleep apnea, 3) a stronger causal link from EEG amplitude to breathing rate during REM sleep in subjects with sleep apnea. The Granger-causality method, although initially developed for econometric purposes, can provide a quantitative, testable measure for causality in physiological networks.

Temporal Relationships Between Abdominal Pain, Psychological Distress and Coping in Patients With IBS - A Time Series Approach

OBJECTIVE

Irritable bowel syndrome (IBS) is a chronic disease leading to abdominal pain that is often related to psychological distress. The aim of the study was to investigate the temporal relationships between abdominal pain and psychological variables in patients with IBS.

METHODS

This longitudinal diary study included eight patients from a waiting group, recruited in the frame of a pilot intervention study. During their waiting time of 3 months the patients answered questions daily regarding somatic and psychological variables using an online diary. All patients were considered and analyzed as single cases. The temporal dynamics between the time series of psycho-somatic variables were analyzed using a vector autoregressive (VAR) modeling approach.

RESULTS

For all patients, positive same-day correlations between somatic and psychological time series were observed. The highest same-day correlations were found between somatic symptoms and pain-related discomfort (r = 0.40 to r = 0.94). Altogether, n = 26 significant lagged relationships were identified; n = 17 (65%) indicated that somatic values were predictive of psychological complaints on the following days. N = 9 (35%) lagged relationships indicated an opposite relationship in that psychological complaints were predictive of somatic symptoms. Three patients showed a significant positive same-day correlation between abdominal pain and use of a positive coping strategy. However, significant lagged relationships in two patients showed that for these patients the use of positive thinking as a coping strategy was unhelpful in reducing pain on the following days.

CONCLUSIONS

In patients with IBS abdominal symptoms appear to be closely related to psychological symptoms. For some patients, somatic complaints predict psychological complaints, in other patients the directionality is opposite. The impact of coping strategies on somatic symptoms varies among patients, therefore their role for a possible reduction of pain should be further explored. The results suggest the need of characterizing patientsindividually for effective health interventions. Individual time series analyses provide helpful tools for finding reasonable person-level moderators.

Application of the ARIMA Model to Predict Under-Reporting of New Cases of Hansen's Disease during the COVID-19 Pandemic in a Municipality of the Amazon Region

This work aimed to apply the ARIMA model to predict the under-reporting of new Hansen’s disease cases during the COVID-19 pandemic in Palmas, Tocantins, Brazil. This is an ecological time series study of Hansen’s disease indicators in the city of Palmas between 2001 and 2020 using the autoregressive integrated moving averages method. Data from the Notifiable Injuries Information System and population estimates from the Brazilian Institute of Geography and Statistics were collected. A total of 7035 new reported cases of Hansen’s disease were analyzed. The ARIMA model (4,0,3) presented the lowest values for the two tested information criteria and was the one that best fit the data, as AIC = 431.30 and BIC = 462.28, using a statistical significance level of 0.05 and showing the differences between the predicted values and those recorded in the notifications, indicating a large number of under-reporting of Hansen’s disease new cases during the period from April to December 2020. The ARIMA model reported that 177% of new cases of Hansen’s disease were not reported in Palmas during the period of the COVID-19 pandemic in 2020. This study shows the need for the municipal control program to undertake immediate actions in terms of actively searching for cases and reducing their hidden prevalence.

Selective dry cow therapy effect on milk yield and somatic cell count: A retrospective cohort study

Antibiotic dry cow therapy (aDCT) at the end of lactation is an effective mastitis control measure. Selective dry cow therapy means that only infected or presumed-infected cows are treated, instead of aDCT being used as a treatment for all cows. Because antibiotic resistance poses a global threat, livestock production is under increasing pressure to reduce antibiotic use. Changes in management should not, however, impair animal welfare or cause significant economic losses. Our objective was to compare milk yield and somatic cell count (SCC) between aDCT-treated and untreated cows in herds that used selective aDCT, taking into account risk factors for reduced yield and high SCC. The information source was 2015 to 2017 Dairy Herd Improvement data, with 4,720 multiparous cows from 172 Finnish dairy farms. The response variables were test-day milk yield (kg/d) and naturally log-transformed composite SCC (×1,000 cells/mL) during the first 154 d in milk (DIM). The statistical tool was a linear mixed-effects model with 2-level random intercepts, cows nested within herds, and a first-order autoregressive [AR(1)] correlation structure. The overall proportion of aDCT-treated cows was 25% (1,176/4,720). Due to the interaction effect, SCC on the last test day prior to dry-off affected postcalving milk yield differently in aDCT-treated cows than in untreated cows. A higher SCC prior to dry-off correlated with a greater daily yield difference after calving between cows treated and untreated. The majority of cows had SCC < 200,000 cells/mL before dry-off, and as SCC before dry-off decreased, difference in yield between aDCT-treated and untreated cows decreased. Postcalving SCC was lower for aDCT-treated cows compared with untreated cows. To illustrate, for cows with an SCC of 200,000 cells/mL before dry-off, compared with untreated cows, aDCT-treated cows produced 0.97 kg/d more milk and, at 45 DIM, had an SCC that was 20,000 cells/mL lower. Higher late-lactation SCC and lactational mastitis treatments were associated with higher postcalving SCC. A dry period lasting more than 30 d was associated with higher yields but not with SCC. Our findings indicate that a missed aDCT treatment for a high-SCC cow has a negative effect on subsequent lactation milk yield and SCC, which emphasizes the importance of accurate selection of cows to be treated.

Evolving Network Analysis of S&P500 Components: COVID-19 Influence of Cross-Correlation Network Structure

The economy is a system of complex interactions. The COVID-19 pandemic strongly influenced economies, particularly through introduced restrictions, which formed a completely new economic environment. The present work focuses on the changes induced by the COVID-19 epidemic on the correlation network structure. The analysis is performed on a representative set of USA companies-the S&P500 components. Four different network structures are constructed (strong, weak, typically, and significantly connected networks), and the rank entropy, cycle entropy, averaged clustering coefficient, and transitivity evolution are established and discussed. Based on the mentioned structural parameters, four different stages have been distinguished during the COVID-19-induced crisis. The proposed network properties and their applicability to a crisis-distinguishing problem are discussed. Moreover, the optimal time window problem is analysed.

Heart Rate Modeling and Prediction Using Autoregressive Models and Deep Learning

Physiological time series are affected by many factors, making them highly nonlinear and nonstationary. As a consequence, heart rate time series are often considered difficult to predict and handle. However, heart rate behavior can indicate underlying cardiovascular and respiratory diseases as well as mood disorders. Given the importance of accurate modeling and reliable predictions of heart rate fluctuations for the prevention and control of certain diseases, it is paramount to identify models with the best performance in such tasks. The objectives of this study were to compare the results of three different forecasting models (Autoregressive Model, Long Short-Term Memory Network, and Convolutional Long Short-Term Memory Network) trained and tested on heart rate beats per minute data obtained from twelve heterogeneous participants and to identify the architecture with the best performance in terms of modeling and forecasting heart rate behavior. Heart rate beats per minute data were collected using a wearable device over a period of 10 days from twelve different participants who were heterogeneous in age, sex, medical history, and lifestyle behaviors. The goodness of the results produced by the models was measured using both the mean absolute error and the root mean square error as error metrics. Despite the three models showing similar performance, the Autoregressive Model gave the best results in all settings examined. For example, considering one of the participants, the Autoregressive Model gave a mean absolute error of 2.069 (compared to 2.173 of the Long Short-Term Memory Network and 2.138 of the Convolutional Long Short-Term Memory Network), achieving an improvement of 5.027% and 3.335%, respectively. Similar results can be observed for the other participants. The findings of the study suggest that regardless of an individual's age, sex, and lifestyle behaviors, their heart rate largely depends on the pattern observed in the previous few minutes, suggesting that heart rate can be reasonably regarded as an autoregressive process. The findings also suggest that minute-by-minute heart rate prediction can be accurately performed using a linear model, at least in individuals without pathologies that cause heartbeat irregularities. The findings also suggest many possible applications for the Autoregressive Model, in principle in any context where minute-by-minute heart rate prediction is required (arrhythmia detection and analysis of the response to training, among others).