Molecular Choreography of Acute Exercise.

Acute physical activity leads to several changes in metabolic, cardiovascular, and immune pathways. Although studies have examined selected changes in these pathways, the system-wide molecular response to an acute bout of exercise has not been fully characterized. We performed longitudinal multi-omic profiling of plasma and peripheral blood mononuclear cells including metabolome, lipidome, immunome, proteome, and transcriptome from 36 well-characterized volunteers, before and after a controlled bout of symptom-limited exercise. Time-series analysis revealed thousands of molecular changes and an orchestrated choreography of biological processes involving energy metabolism, oxidative stress, inflammation, tissue repair, and growth factor response, as well as regulatory pathways. Most of these processes were dampened and some were reversed in insulin-resistant participants. Finally, we discovered biological pathways involved in cardiopulmonary exercise response and developed prediction models revealing potential resting blood-based biomarkers of peak oxygen consumption.

Robust inference of causality in high-dimensional dynamical processes from the Information Imbalance of distance ranks.

We introduce an approach which allows detecting causal relationships between variables for which the time evolution is available. Causality is assessed by a variational scheme based on the Information Imbalance of distance ranks, a statistical test capable of inferring the relative information content of different distance measures. We test whether the predictability of a putative driven system Y can be improved by incorporating information from a potential driver system X, without explicitly modeling the underlying dynamics and without the need to compute probability densities of the dynamic variables. This framework makes causality detection possible even between high-dimensional systems where only few of the variables are known or measured. Benchmark tests on coupled chaotic dynamical systems demonstrate that our approach outperforms other model-free causality detection methods, successfully handling both unidirectional and bidirectional couplings. We also show that the method can be used to robustly detect causality in human electroencephalography data.

Network inference from short, noisy, low time-resolution, partial measurements: Application to C. elegans neuronal calcium dynamics.

Network link inference from measured time series data of the behavior of dynamically interacting network nodes is an important problem with wide-ranging applications, e.g., estimating synaptic connectivity among neurons from measurements of their calcium fluorescence. Network inference methods typically begin by using the measured time series to assign to any given ordered pair of nodes a numerical score reflecting the likelihood of a directed link between those two nodes. In typical cases, the measured time series data may be subject to limitations, including limited duration, low sampling rate, observational noise, and partial nodal state measurement. However, it is unknown how the performance of link inference techniques on such datasets depends on these experimental limitations of data acquisition. Here, we utilize both synthetic data generated from coupled chaotic systems as well as experimental data obtained from Caenorhabditis elegans neural activity to systematically assess the influence of data limitations on the character of scores reflecting the likelihood of a directed link between a given node pair. We do this for three network inference techniques: Granger causality, transfer entropy, and, a machine learning-based method. Furthermore, we assess the ability of appropriate surrogate data to determine statistical confidence levels associated with the results of link-inference techniques.

Intermittent ERK oscillations downstream of FGF in mouse embryonic stem cells.

Signal transduction networks generate characteristic dynamic activities to process extracellular signals and guide cell fate decisions such as to divide or differentiate. The differentiation of pluripotent cells is controlled by FGF/ERK signaling. However, only a few studies have addressed the dynamic activity of the FGF/ERK signaling network in pluripotent cells at high time resolution. Here, we use live cell sensors in wild-type and Fgf4-mutant mouse embryonic stem cells to measure dynamic ERK activity in single cells, for defined ligand concentrations and differentiation states. These sensors reveal pulses of ERK activity. Pulsing patterns are heterogeneous between individual cells. Consecutive pulse sequences occur more frequently than expected from simple stochastic models. Sequences become more prevalent with higher ligand concentration, but are rarer in more differentiated cells. Our results suggest that FGF/ERK signaling operates in the vicinity of a transition point between oscillatory and non-oscillatory dynamics in embryonic stem cells. The resulting heterogeneous dynamic signaling activities add a new dimension to cellular heterogeneity that may be linked to divergent fate decisions in stem cell cultures.

Temporal classification of short time series data.

MOTIVATION: Within the frame of their genetic capacity, organisms are able to modify their molecular state to cope with changing environmental conditions or induced genetic disposition. As high throughput methods are becoming increasingly affordable, time series analysis techniques are applied frequently to study the complex dynamic interplay between genes, proteins, and metabolites at the physiological and molecular level. Common analysis approaches fail to simultaneously include (i) information about the replicate variance and (ii) the limited number of responses/shapes that a biological system is typically able to take. RESULTS: We present a novel approach to model and classify short time series signals, conceptually based on a classical time series analysis, where the dependency of the consecutive time points is exploited. Constrained spline regression with automated model selection separates between noise and signal under the assumption that highly frequent changes are less likely to occur, simultaneously preserving information about the detected variance. This enables a more precise representation of the measured information and improves temporal classification in order to identify biologically interpretable correlations among the data. AVAILABILITY AND IMPLEMENTATION: An open source F# implementation of the presented method and documentation of its usage is freely available in the TempClass repository, https://github.com/CSBiology/TempClass  [58].

Mitochondrial membrane potential instability on reperfusion after ischemia does not depend on mitochondrial Ca2+ uptake.

Physiologic Ca2+ entry via the Mitochondrial Calcium Uniporter (MCU) participates in energetic adaption to workload but may also contribute to cell death during ischemia/reperfusion (I/R) injury. The MCU has been identified as the primary mode of Ca2+ import into mitochondria. Several groups have tested the hypothesis that Ca2+ import via MCU is detrimental during I/R injury using genetically-engineered mouse models, yet the results from these studies are inconclusive. Furthermore, mitochondria exhibit unstable or oscillatory membrane potentials (ΔΨm) when subjected to stress, such as during I/R, but it is unclear if the primary trigger is an excess influx of mitochondrial Ca2+ (mCa2+), reactive oxygen species (ROS) accumulation, or other factors. Here, we critically examine whether MCU-mediated mitochondrial Ca2+ uptake during I/R is involved in ΔΨm instability, or sustained mitochondrial depolarization, during reperfusion by acutely knocking out MCU in neonatal mouse ventricular myocyte (NMVM) monolayers subjected to simulated I/R. Unexpectedly, we find that MCU knockout does not significantly alter mCa2+ import during I/R, nor does it affect ΔΨm recovery during reperfusion. In contrast, blocking the mitochondrial sodium-calcium exchanger (mNCE) suppressed the mCa2+ increase during Ischemia but did not affect ΔΨm recovery or the frequency of ΔΨm oscillations during reperfusion, indicating that mitochondrial ΔΨm instability on reperfusion is not triggered by mCa2+. Interestingly, inhibition of mitochondrial electron transport or supplementation with antioxidants stabilized I/R-induced ΔΨm oscillations. The findings are consistent with mCa2+ overload being mediated by reverse-mode mNCE activity and supporting ROS-induced ROS release as the primary trigger of ΔΨm instability during reperfusion injury.

Implementation impact assessment of Uber System on mortality from traffic injuries in Brazilian capitals.

The rapid expansion of Uber System and e-hailing apps has been transforming the logistics network and urban mobility around the world. The present work aims at evaluating the impact of Uber System on the traffic injury mortality (TI) in implementation in Brazilian capitals. A quasi-experimental design of interrupted time series was used. The monthly mortality rates for TI standardized by age were analised. The date of availability of Uber app, specific to each capital, was considered as the start date. Data from the Brazilian Mortality Information System and the Brazilian Institute of Geography and Statistics were used. For the data analysis from interrupted time series design, ARIMA with transfer function were fitted. In 95,6% (n=25) of Brazilian capitals, there was no impact of Uber System implementation, twelve months after the start of its activities, on mortality from traffic injuries. A reduction in mortality from this cause was observed after the System was implemented in Belo Horizonte e Rio de Janeiro. The impact was progressive and continuous on TI mortality in both. More studies are needed to establish the factors associated with the inequalities observed in the impact of the Uber System implementation between different locations and the heterogeneity of effects.

Suicide mortality following the implementation of tobacco packaging and pricing policies in Korea: an interrupted time-series analysis.

BACKGROUND: To prevent tobacco use in Korea, the national quitline number was added to tobacco packages in December 2012, tobacco prices were raised by 80% in January 2015, and graphic health warning labels were placed on tobacco packages in December 2016. This study evaluated the association of these tobacco packaging and pricing policies with suicide mortality in Korea. METHODS: Monthly mortality from suicide was obtained from Cause-of-Death Statistics in Korea from December 2007 to December 2019. Interrupted time-series analysis was performed using segmented Poisson regression models. Relative risks (RRs) and 95% confidence intervals (CIs) were calculated adjusted for suicide prevention strategies. RESULTS: Suicide mortality was 20 per 1,000,000 in December 2007 and showed a downward trend over the study period. After the implementation of tobacco packaging and pricing policies, suicide mortality immediately declined by - 0.09 percent points (95% CI = - 0.19 to 0.01; P > 0.05) for the national quitline number, - 0.22 percent points (95% CI = - 0.35 to - 0.09; P < 0.01) for tobacco prices, and - 0.30 percent points (95% CI = - 0.49 to - 0.11; P < 0.01) for graphic health warning labels. The corresponding RRs for these post-implementation changes compared with the pre-implementation level were 0.91 (95% CI = 0.83 to 1.00), 0.80 (95% CI = 0.70 to 0.91), and 0.74 (95% CI = 0.61 to 0.90), respectively. Significant associations between tobacco control policies and suicide mortality were observed even when stratified by sex and region. CONCLUSIONS: The findings of this study provide new evidence for an association between tobacco control policies and deaths by suicide. An array of effective tobacco control policies should be considered for prevention programs targeting suicide.

Unveiling the underlying drivers of Phanerozoic marine diversification.

In investigating global patterns of biodiversity through deep time, many large-scale drivers of diversification have been proposed, both biotic and abiotic. However, few robust conclusions about these hypothesized effectors or their roles have been drawn. Here, we use a linear stochastic differential equation (SDE) framework to test for the presence of underlying drivers of diversification patterns before examining specific hypothesized drivers. Using a global dataset of observations of skeletonized marine fossils, we infer origination, extinction and sampling rates (collectively called fossil time series) throughout the Phanerozoic using a capture-mark-recapture approach. Using linear SDEs, we then compare models including and excluding hidden (i.e. unmeasured) drivers of these fossil time series. We find evidence of large-scale underlying drivers of marine Phanerozoic diversification rates and present quantitative characterizations of these. We then test whether changing global temperature, sea-level, marine sediment area or continental fragmentation could act as drivers of the fossil time series. We show that it is unlikely any of these four abiotic factors are the hidden drivers we identified, though there is evidence for correlative links between sediment area and origination/extinction rates. Our characterization of the hidden drivers of Phanerozoic diversification and sampling will aid in the search for their ultimate identities.

Predictability of ecological and evolutionary dynamics in a changing world.

Ecological and evolutionary predictions are being increasingly employed to inform decision-makers confronted with intensifying pressures on biodiversity. For these efforts to effectively guide conservation actions, knowing the limit of predictability is pivotal. In this study, we provide realistic expectations for the enterprise of predicting changes in ecological and evolutionary observations through time. We begin with an intuitive explanation of predictability (the extent to which predictions are possible) employing an easy-to-use metric, predictive power PP(t). To illustrate the challenge of forecasting, we then show that among insects, birds, fishes and mammals, (i) 50% of the populations are predictable at most 1 year in advance and (ii) the median 1-year-ahead predictive power corresponds to a prediction R 2 of only 20%. Predictability is not an immutable property of ecological systems. For example, different harvesting strategies can impact the predictability of exploited populations to varying degrees. Moreover, incorporating explanatory variables, accounting for time trends and considering multivariate time series can enhance predictability. To effectively address the challenge of biodiversity loss, researchers and practitioners must be aware of the information within the available data that can be used for prediction and explore efficient ways to leverage this knowledge for environmental stewardship.

Temporal trends in hemodialysis access creation during the fistula first era.

OBJECTIVE: Although forearm arteriovenous fistulas (AVFs) are the preferred initial vascular access for hemodialysis based on national guidelines, there are no population-level studies evaluating trends in creation of forearm vs upper arm AVFs and arteriovenous grafts (AVGs). The purpose of this study was to report temporal trends in first-time permanent hemodialysis access type, and to assess the effect of national initiatives on rates of AVF placement. METHODS: Retrospective cross-sectional study (2012-2022) utilizing the Vascular Quality Initiative database. All patients older than 18 years with creation of first-time upper extremity surgical hemodialysis access were included. Anatomic location of the AVF or AVG (forearm vs upper arm) was defined based on inflow artery, outflow vein, and presumed cannulation zone. Primary analysis examined temporal trends in rates of forearm vs upper arm AVFs and AVGs using time series analyses (modified Mann-Kendall test). Subgroup analyses examined rates of access configuration stratified by age, sex, race, dialysis, and socioeconomic status. Interrupted time series analysis was performed to assess the effect of the 2015 Fistula First Catheter Last initiative on rates of AVFs. RESULTS: Of the 52,170 accesses, 57.9% were upper arm AVFs, 25.2% were forearm AVFs, 15.4% were upper arm AVGs, and 1.5% were forearm AVGs. From 2012 to 2022, there was no significant change in overall rates of forearm or upper arm AVFs. There was a numerical increase in upper arm AVGs (13.9 to 18.2 per 100; P = .09), whereas forearm AVGs significantly declined (1.8 to 0.7 per 100; P = .02). In subgroup analyses, we observed a decrease in forearm AVFs among men (33.1 to 28.7 per 100; P = .04) and disadvantaged (Area Deprivation Index percentile ≥50) patients (29.0 to 20.7 per 100; P = .04), whereas female (17.2 to 23.1 per 100; P = .03), Black (15.6 to 24.5 per 100; P < .01), elderly (age ≥80 years) (18.7 to 32.5 per 100; P < .01), and disadvantaged (13.6 to 20.5 per 100; P < .01) patients had a significant increase in upper arm AVGs. The Fistula First Catheter Last initiative had no effect on the rate of AVF placement (83.2 to 83.7 per 100; P=.37). CONCLUSIONS: Despite national initiatives to promote autogenous vascular access, the rates of first-time AVFs have remained relatively constant, with forearm AVFs only representing one-quarter of all permanent surgical accesses. Furthermore, elderly, Black, female, and disadvantaged patients saw an increase in upper arm AVGs. Further efforts to elucidate factors associated with forearm AVF placement, as well as potential physician, center, and regional variation is warranted.

Time-Series MR Images Identifying Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using a Deep Learning Approach.

BACKGROUND: Pathological complete response (pCR) is an essential criterion for adjusting follow-up treatment plans for patients with breast cancer (BC). The value of the visual geometry group and long short-term memory (VGG-LSTM) network using time-series dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for pCR identification in BC is unclear. PURPOSE: To identify pCR to neoadjuvant chemotherapy (NAC) using deep learning (DL) models based on the VGG-LSTM network. STUDY TYPE: Retrospective. POPULATION: Center A: 235 patients (47.7 ± 10.0 years) were divided 7:3 into training (n = 164) and validation set (n = 71). Center B: 150 patients (48.5 ± 10.4 years) were used as test set. FIELD STRENGTH/SEQUENCE: 3-T, T2-weighted spin-echo sequence imaging, and gradient echo DCE sequence imaging. ASSESSMENT: Patients underwent MRI examinations at three sequential time points: pretreatment, after three cycles of treatment, and prior to surgery, with tumor regions of interest manually delineated. Histopathology was the gold standard. We used VGG-LSTM network to establish seven DL models using time-series DCE-MR images: pre-NAC images (t0 model), early NAC images (t1 model), post-NAC images (t2 model), pre-NAC and early NAC images (t0 + t1 model), pre-NAC and post-NAC images (t0 + t2 model), pre-NAC, early NAC and post-NAC images (t0 + t1 + t2 model), and the optimal model combined with the clinical features and imaging features (combined model). The models were trained and optimized on the training and validation set, and tested on the test set. STATISTICAL TESTS: The DeLong, Student's t-test, Mann-Whitney U, Chi-squared, Fisher's exact, Hosmer-Lemeshow tests, decision curve analysis, and receiver operating characteristics analysis were performed. P < 0.05 was considered significant. RESULTS: Compared with the other six models, the combined model achieved the best performance in the test set yielding an AUC of 0.927. DATA CONCLUSION: The combined model that used time-series DCE-MR images, clinical features and imaging features shows promise for identifying pCR in BC. TECHNICAL EFFICACY: Stage 4.

Impact of narcotics information management system on inappropriate benzodiazepine receptor agonist prescriptions: A quasi-experimental analysis in South Korea.

AIMS: The South Korean government implemented the narcotics information management system (NIMS) on 18 May 2018 to manage benzodiazepine receptor agonists (BzRAs) and narcotics effectively and establish a reporting mechanism for these drugs. This study assessed the effects of NIMS on inappropriate use of BzRAs. METHODS: Using national patient sample data from 2016 to 2020, we analysed adult outpatients who were prescribed oral BzRAs. We conducted a time series and segmented regression analysis using selected indicators to analyse the monthly variations related to the inappropriate use of these medications. RESULTS: The study revealed no significant changes in the indicators of inappropriate BzRA use following the NIMS implementation. Contrary to expectations, there was a significant increase in the proportion of patients exceeding defined daily dose (DDD) and in those receiving concurrent prescriptions of multiple BzRAs, following the implementation of NIMS. The immediate impact of the COVID-19 pandemic was an increase in DDD exceedance; however, overall, this did not significantly affect BzRA use. CONCLUSIONS: The introduction of NIMS did not significantly enhance the management of BzRA misuse. Additional measures, including continuous monitoring, system improvements and comprehensive education for prescribers and patients, are recommended to ensure the appropriate use of psychotropic medications.

Impact of the pandemic on surgical oncology in Piedmont, Italy: A retrospective observational study.

BACKGROUND AND METHODS: This retrospective observational study analyzes how the COVID-19 pandemic affected surgical oncology healthcare in a large sample from Piedmont, Northern Italy. Patients admitted for regular hospitalization were included (n = 99 651). Data from 2020 were compared to the averages from 2016 to 2019, stratified by tumor site, year, month, and admission method, using interrupted time series analysis post-March 2020. RESULTS: In 2020, oncological surgeries decreased by 12.3% (n = 17 923) compared to the 2016-2019 average (n = 20 432), notably dropping post-March (incidence rate ratio = 0.858; p < 0.001). The greatest reduction was observed for breast (-19.2%), colon (-18.2%), bladder (-17.5%), kidney (-14.2%), and prostate (-14%) surgeries. There was a huge reduction in nonemergency admissions (-13.6%), especially for colon (-23.8%), breast (-19.4%), and bladder (-18.7%). The proportion of hospitalizations with emergency access increased (p < 0.001). CONCLUSIONS: The COVID-19 pandemic led to a significant decrease in cancer surgeries in Piedmont in 2020, with an increase in the proportion of admissions through emergency access. DISCUSSION: The research provides valuable insights for comparing data with other regions and evaluating the effectiveness of efforts to recover lost surgical procedures. These findings can be useful to policymakers in developing coordinated measures and more efficient access strategies to healthcare services in any future emergency situations.

The influence of the COVID-19 pandemic on physical activity in Stockholm County - Evidence from time series models of smartphone measured daily steps data spanning over 3 years.

BACKGROUND: It has been reported that physical activity levels decreased during the COVID-19 pandemic. Previous studies often relied on self-reported physical activity, which has low accuracy. Studies based on objectively measured physical activity have had short data collection periods, thereby not allowing the consideration of pre-pandemic levels of physical activity or the influence over the different waves of the pandemic. METHODS: In this study, we utilize smartphone-measured step data from a nonprobability sample in Stockholm County, Sweden, where measures to limit the spread of COVID-19 differed from those in many other countries. The results are based on 522 individuals and 532,739 person-days with step data spanning from 2019 to 2021. Generalized additive models were fitted for each individual, and meta-regression was used to combine the results from individual models. RESULTS: Daily steps decreased during the first wave but increased during the third wave compared to individual pre-pandemic levels. The decrease in daily steps occurred primarily in young individuals and those with occupations allowing remote work. Individuals of retirement age on the contrary increased their daily steps during the same period. CONCLUSIONS: This study reveal that the influence of the COVID-19 pandemic was temporary and that younger age and the possibility of working from home were associated with a decreasing trend in physical activity.

Drug utilisation in children and adolescents before and after the start of the COVID-19 pandemic: Interrupted time-series analyses in three European countries.

BACKGROUND: The COVID-19 pandemic has affected children and adolescents in several ways, including worsened mental health, improvement of asthma, and increases in diabetes ketoacidosis. Less is known about how medication use in children and adolescents has been affected by the pandemic. OBJECTIVES: To explore how the COVID-19 pandemic affected drug utilisation in children and adolescents in Norway, Sweden, and Italy, by child age. METHODS: We conducted a longitudinal drug utilisation study among all children and adolescents (<18 years old) in Norway and Sweden and a nationwide paediatric database covering 3% of the paediatric population in Italy. We conducted an interrupted time-series analysis from January 2018 to December 2021, with March 2020 as the interruption point. Dispensing or prescription rates of antidepressants, anxiolytics, sleep medications, attention-deficit/hyperactivity disorder (ADHD) medications, insulin, and asthma medications were examined. RESULTS: The study population in January 2018 consisted of 3,455,521 children and adolescents (136,188 from Italy, 1,160,431 from Norway, and 2,158,902 from Sweden). For sleep medications and insulin, there were only minor changes in level or trend in some age groups after March 2020. For asthma medications, the pandemic was associated with an immediate decrease in dispensing in Norway and Sweden (range of change in level: -19.2 to -3.7 dispensings per 1000 person-months), and an increasing trend in all countries afterward (range of change in trend: 0.3-6.4 dispensings per 1000 person-months), especially for the youngest age groups. Among adolescents, the pandemic was associated with an increased trend for ADHD medications, antidepressants, and anxiolytics in Norway and Sweden, but not in Italy. CONCLUSIONS: The increasing trend of psychotropic medication dispensing, especially among adolescents after the start of the pandemic, is concerning and should be investigated further. Aside from a temporary effect on asthma medication dispensing, the pandemic did not greatly affect the dispensing of the medications investigated.

An Intelligent Early Warning System for Harmful Algal Blooms: Harnessing the Power of Big Data and Deep Learning.

Harmful algal blooms (HABs) pose a significant ecological threat and economic detriment to freshwater environments. In order to develop an intelligent early warning system for HABs, big data and deep learning models were harnessed in this study. Data collection was achieved utilizing the vertical aquatic monitoring system (VAMS). Subsequently, the analysis and stratification of the vertical aquatic layer were conducted employing the "DeepDPM-Spectral Clustering" method. This approach drastically reduced the number of predictive models and enhanced the adaptability of the system. The Bloomformer-2 model was developed to conduct both single-step and multistep predictions of Chl-a, integrating the " Alert Level Framework" issued by the World Health Organization to accomplish early warning for HABs. The case study conducted in Taihu Lake revealed that during the winter of 2018, the water column could be partitioned into four clusters (Groups W1-W4), while in the summer of 2019, the water column could be partitioned into five clusters (Groups S1-S5). Moreover, in a subsequent predictive task, Bloomformer-2 exhibited superiority in performance across all clusters for both the winter of 2018 and the summer of 2019 (MAE: 0.175-0.394, MSE: 0.042-0.305, and MAPE: 0.228-2.279 for single-step prediction; MAE: 0.184-0.505, MSE: 0.101-0.378, and MAPE: 0.243-4.011 for multistep prediction). The prediction for the 3 days indicated that Group W1 was in a Level I alert state at all times. Conversely, Group S1 was mainly under an Level I alert, with seven specific time points escalating to a Level II alert. Furthermore, the end-to-end architecture of this system, coupled with the automation of its various processes, minimized human intervention, endowing it with intelligent characteristics. This research highlights the transformative potential of integrating big data and artificial intelligence in environmental management and emphasizes the importance of model interpretability in machine learning applications.

Evaluation of a regional tobacco control programme (Greater Manchester's Making Smoking History) on quitting and smoking in England 2014 to 2022: a time-series analysis.

INTRODUCTION: This study aimed to assess the impact of Greater Manchester's Making Smoking History programme - a region-wide smoking cessation programme launched in January 2018 - on key smoking and quitting outcomes. METHODS: Data were from a nationally-representative monthly survey, 2014-2022 (n=171,281). We used interrupted time-series analyses (Autoregressive Integrated Moving Average [ARIMA] and generalised additive models [GAM]) to examine regional differences between Greater Manchester and the rest of England, before and during the programme's first five years. Outcomes were rates of quit attempts and overall quits among smokers, quit success rates among smokers who tried to quit (pre-registered outcomes), and current smoking prevalence among adults (unregistered outcome). RESULTS: Results showed mixed effects of the programme on quitting. Primary ARIMA models showed comparative reductions in quit success rates (change in quarterly difference between regions = -11.03%; 95%CI -18.96;-3.11) and overall quit rates in Greater Manchester compared with the rest of England (-2.56%; 95%CI -4.95;-0.18), and no significant change in the difference in the quit attempt rate (+2.95%; 95%CI -11.64;17.54). These results were not consistently observed across sensitivity analyses or GAM analyses. Exploratory ARIMA models consistently showed smoking prevalence in Greater Manchester declined more quickly than in the rest of England following initiation of the programme (-2.14%; 95%CI -4.02;-0.27). CONCLUSIONS: The first five years of Greater Manchester's Making Smoking History programme did not appear to be associated with substantial increases in quitting activity. However, exploratory analyses showed a significant reduction in the regional smoking rate, over and above changes in the rest of England over the same period. IMPLICATIONS: Taken together, these results show a relative decline in smoking prevalence in Greater Manchester but equivocal data on quitting, introducing some uncertainty. It is possible the programme has reduced smoking prevalence in the absence of any substantial change in quitting activity by changing norms around smoking and reducing uptake, or by reducing the rate of late relapse. It is also possible that an undetected effect on quitting outcomes has still contributed to the programme's impact on reducing prevalence to some degree. It will be important to evaluate the overall impact of the programme over a longer timeframe.

Changes in surgery rates among hospitalized patients with inflammatory bowel disease in Japan from 2015 to 2019: A nationwide administrative database analysis.

BACKGROUND AND AIM: Treatment of inflammatory bowel disease (IBD), consisting of ulcerative colitis (UC) and Crohn's disease (CD), has advanced with the application of biologics or Janus kinase inhibitor (JAKi); however, some patients still need surgery. We assessed time trends of surgery and biologics or JAKi variety in Japan. METHODS: Patients hospitalized due to IBD were analyzed using Diagnosis Procedure Combination data between 2015 and 2019. Longitudinal trend analysis was performed for demographics, and interrupted time-series analysis was performed to examine the association between surgery rates and an increase in the types of biologics or JAKi. RESULTS: Totally, 37 867 cases with UC and 35 493 cases with CD were analyzed. Over 5 years, the surgery rate decreased in both UC and CD. The proportion of biologics and JAKi usage increased in UC and stabilized in CD. Between decreasing surgery and expanding treatment options of biologic or JAKi, interrupted time-series analysis results showed no significant correlation (level change in UC, -1.13, 95% confidence interval [CI]: -2.11 to -0.16, P = 0.0235; slope change in UC, -0.05, 95% CI: -0.26 to 0.16, P = 0.6372; level change in CD, -0.55, 95% CI: -1.82 to 0.71, P = 0.3815; slope change in CD, -0.22, 95% CI: -0.63 to 0.19, P = 0.2892). CONCLUSIONS: In Japan, from 2015 to 2019, the use of biologics or JAKi increased for UC and slightly decreased for CD, while the number of surgeries decreased in both. Our findings suggest that more widespread use of biologics or JAKi therapy could reduce surgeries in patients with IBD.

Potential impact of blood cholesterol guidelines on statin treatment in the U.S. population using interrupted time series analysis.

BACKGROUND: The 2013 ACC/AHA Guideline was a paradigm shift in lipid management and identified the four statin-benefit groups. Many have studied the guideline's potential impact, but few have investigated its potential long-term impact on MACE. Furthermore, most studies also ignored the confounding effect from the earlier release of generic atorvastatin in Dec 2011. METHODS: To evaluate the potential (long-term) impact of the 2013 ACC/AHA Guideline release in Nov 2013 in the U.S., we investigated the association of the 2013 ACC/AHA Guideline with the trend changes in 5-Year MACE survival and three other statin-related outcomes (statin use, optimal statin use, and statin adherence) while controlling for generic atorvastatin availability using interrupted time series analysis, called the Chow's test. Specifically, we conducted a retrospective study using U.S. nationwide de-identified claims and electronic health records from Optum Labs Database Warehouse (OLDW) to follow the trends of 5-Year MACE survival and statin-related outcomes among four statin-benefit groups that were identified in the 2013 ACC/AHA Guideline. Then, Chow's test was used to discern trend changes between generic atorvastatin availability and guideline potential impact. RESULTS: 197,021 patients were included (ASCVD: 19,060; High-LDL: 33,907; Diabetes: 138,159; High-ASCVD-Risk: 5,895). After the guideline release, the long-term trend (slope) of 5-Year MACE Survival for the Diabetes group improved significantly (P = 0.002). Optimal statin use for the ASCVD group also showed immediate improvement (intercept) and long-term positive changes (slope) after the release (P < 0.001). Statin uses did not have significant trend changes and statin adherence remained unchanged in all statin-benefit groups. Although no other statistically significant trend changes were found, overall positive trend change or no changes were observed after the 2013 ACC/AHA Guideline release. CONCLUSIONS: The 2013 ACA/AHA Guideline release is associated with trend improvements in the long-term MACE Survival for Diabetes group and optimal statin use for ASCVD group. These significant associations might indicate a potential positive long-term impact of the 2013 ACA/AHA Guideline on better health outcomes for primary prevention groups and an immediate potential impact on statin prescribing behaviors in higher-at-risk groups. However, further investigation is required to confirm the causal effect of the 2013 ACA/AHA Guideline.