Time-varying Effects Research Articles

Age-varying association between discrimination, childhood family support, and substance use disorders among Latin American immigrants in the United States.

The cultural stress theory posits that immigrants experience a constellation of cultural stressors such as discrimination that could exacerbate alcohol- and other substance-related problems. Drawing on cultural stress theory, this study investigated the age-varying association between past-year discrimination and substance use disorders (SUDs) among Latin American immigrants aged 18-60 and whether childhood family support moderated the above association. We used data from the National Epidemiologic Survey on Alcohol and Related Conditions-III (NESARC-III) among adults aged 18-60 who identified as a Latin American immigrant (N = 3,049; 48% female). Time-varying effect models (TVEMs) revealed that experiencing past-year discrimination was associated with greater odds of having a SUD during young and middle adulthood for Latin American immigrants. Furthermore, for immigrants with lower childhood family support, discrimination was associated with SUD risk in young and middle adulthood. The present study documents that past-year discrimination was linked to greater SUD risk during young and middle adulthood. Childhood family support may serve as a protective factor in the association between discrimination and risk for SUD among Latin American immigrants. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The Dynamics of Fire Activity in the Brazilian Pantanal: A Log-Gaussian Cox Process-Based Structural Decomposition

We present a novel statistical methodology for analyzing shifts in spatio-temporal fire occurrence patterns within the Brazilian Pantanal, utilizing remote sensing data. Our approach employs a Log-Gaussian Cox Process to model the spatiotemporal dynamics of fire occurrence, deconstructing the intensity function into components of trend, seasonality, cycle, covariates, and time-varying spatial effects components. The results indicate a negative correlation between rainfall and fire intensity, with lower precipitation associated with heightened fire intensity. Forest formations exhibit a positive effect on fire intensity, whereas agricultural land use shows no significant impact. Savannas and grasslands, typical fire-dependent ecosystems, demonstrate a positive relationship with fire intensity. Human-induced fires, often used for agricultural purposes, contribute to an increase in both fire frequency and intensity, particularly in grassland areas. Trend analysis reveals fluctuating fire activity over time, with notable peaks in 2018–2021.

AN ADDITIVE SUBDISTRIBUTION HAZARDS MODEL FOR COMPETING RISKS DATA

Competing risk failure time data occur frequently in medical a number of methods have been proposed for the analysis of these data. The classic approach is to model all cause-specific hazards and then estimate the cumulative incidence curve based on these cause-specific hazards. Unfortunately, the cause-specific hazard function does not have a direct interpretation in terms of survival probabilities for the particular failure type. In this paper, we consider a more flexible model for the subdistribution. It is a combination of the additive model and the Cox model and allows one to perform a more detailed study of covariate effects. One advantage of this approach is that our regression modeling allows for non-proportional hazards. This leads to a new simple goodness-of-fit procedure for the proportional subdistribution hazards assumption that is very easy to use. We applied this method to melanoma data and estimated the cumulative death rate for those who died from melanoma after surgical removal of the tumor. It was found that two covariates had a time-varying effect and two other covariates had a constant effect in predicting the cumulative incidence curve in patients who died of melanoma following tumor removal surgery.

Time-varying impacts of green credit on carbon productivity in China: New evidence from a non-parametric panel data model

In the context of global climate change threatening human survival, and in a post-pandemic era that advocates for a global green and low-carbon economic recovery, conducting an in-depth analysis to assess whether green finance can effectively support low-carbon economic development from a dynamic perspective is crucial. Unlike existing research, which focuses solely on the average effects of green credit (GC) on carbon productivity (CP), we introduce a non-parametric panel data model to investigate GC's impact on CP across 30 provinces in China from 2003 to 2021, verifying a significant time-varying effect. Specifically, during the first phase (2003–2008), GC negatively impacted CP. In the second phase (2009–2014), this negative influence gradually diminished and transformed into a positive effect. In the third phase (2015–2021), GC continued to positively influence CP, although this effect became insignificant during the pandemic. Further subgroup analysis reveals that in the regions with low environmental regulations, GC did not significantly boost CP throughout the sample period. In contrast, in the regions with high environmental regulations, GC's positive effect persisted in the mid to late stages of the sample period. Additionally, compared to the regions with low levels of marketization, the impact of GC on CP was more pronounced in highly marketized regions. This indicates that the promoting effect of GC on CP depends on strong support from environmental regulations and well-functioning market mechanisms. By adopting a non-parametric approach, this study reveals variations in the impact of GC on CP across different stages and under the influence of the pandemic shock, offering new insights into the relationship between GC and China's CP.

Estimating causal effects from panel data with dynamic multivariate panel models

Panel data are ubiquitous in scientific fields such as social sciences. Various modeling approaches have been presented for observational causal inference based on such data. Existing approaches typically impose restrictive assumptions on the data-generating process such as Gaussian responses or time-invariant effects, or they can only consider short-term causal effects. To surmount these restrictions, we present the dynamic multivariate panel model (DMPM) that supports time-varying, time-invariant, and individual-specific effects, multiple responses across a wide variety of distributions, and arbitrary dependency structures of lagged responses of any order. We formally demonstrate how DMPM facilitates causal inference within the structural causal modeling framework and we take a Bayesian approach for the estimation of the posterior distributions of the model parameters and causal effects of interest. We demonstrate the use of DMPM by applying the approach to both real and synthetic data.

Abnormal dynamic functional connectivity in young nondisabling intracerebral hemorrhage patients.

Previous resting-state functional magnetic resonance imaging studies on intracerebral hemorrhage patients have focused more on the static characteristics of brain activity, while the time-varying effects during scanning have received less attention. Therefore, the current study aimed to explore the dynamic functional network connectivity changes of intracerebral hemorrhage patients. Using independent component analysis, the sliding window approach, and the k-means clustering analysis method, different dynamic functional network connectivity states were detected from resting-state functional magnetic resonance imaging data of 37 intracerebral hemorrhage patients and 44 healthy controls. The inter-group differences in dynamic functional network connectivity patterns and temporal properties were investigated, followed by correlation analyses between clinical scales and abnormal functional indexes. Ten resting-state networks were identified, and the dynamic functional network connectivity matrices were clustered into four different states. The transition numbers were decreased in the intracerebral hemorrhage patients compared with healthy controls, which was associated with trail making test scores in patients. The cerebellar network and executive control network connectivity in State 1 was reduced in patients, and this abnormal dynamic functional connectivity was positively correlated with the animal fluency test scores of patients. The current study demonstrated the characteristics of dynamic functional network connectivity in intracerebral hemorrhage patients and revealed that abnormal temporal properties and functional connectivity may be related to the performance of different cognitive domains after ictus. These results may provide new insights into exploring the neurocognitive mechanisms of intracerebral hemorrhage.

How do oil prices affect the GDP and its components? New evidence from a time-varying threshold model

Revealing the precise thresholds at which fluctuations in oil prices start to affect gross domestic product and its various components (consumption, investment, expenditure and exports) holds significant implications for policymakers in both oil-importing and oil-exporting countries. Existing studies assessing the effects of oil prices on economic activity typically assume constant or stable threshold values. However, recent evidence suggests that this restrictive assumption may not accurately capture the dynamic nature of these relationships. We address this issue by adopting a more realistic framework that allows for the possibility that oil prices will have a time-varying effect on economic activity. We also employ the innovative time-varying threshold regression kink model of Yang and Su (2018). Our analysis focuses on a sample of 20 top oil-importing and oil-exporting countries during the period 1995Q1 to 2023Q2. The findings of our investigation provide compelling evidence to support the existence of time-varying threshold levels in the relationship between oil prices and macroeconomic activity for most countries in our sample. Notably, our research unveils a substantial heterogeneity in the oil price thresholds across the investigated countries, thereby challenging the notion of a universal threshold applicable to all.

Analyzing Nexus between Crude Oil, Gold, Dollar and Equity Markets with Structural Break: ARDL Evidence from India

This paper explores the price relations between the Crude oil, Gold, US dollar, and equities during several economic episodes. The long- and short-term causal relationships between asset markets are examined in this study. The research uses a ‘Vector Error Correction Model (VECM)’ and an ‘Autoregressive Distributed Lag (ARDL)’ bounds test to examine monthly data from April 1999 to March 2022. The results demonstrate the weak connections between asset markets throughout the different sample periods. Furthermore, the integration of most markets is uneven and changes over time. The currency and equity markets are adjusting to the long-run equilibrium only at a slow pace. We suggest that systematic risk factors must be taken into account while jointly modeling market linkages. This study improves on previous research in the subject by demonstrating the time-varying effects of asset price links on portfolio optimizations in different economic episodes.

A Bayesian model calibration framework for stochastic compartmental models with both time-varying and time-invariant parameters

We consider state and parameter estimation for compartmental models having both time-varying and time-invariant parameters. In this manuscript, we first detail a general Bayesian computational framework as a continuation of our previous work. Subsequently, this framework is specifically tailored to the susceptible-infectious-removed (SIR) model which describes a basic mechanism for the spread of infectious diseases through a system of coupled nonlinear differential equations. The SIR model consists of three states, namely, the susceptible, infectious, and removed compartments. The coupling among these states is controlled by two parameters, the infection rate and the recovery rate. The simplicity of the SIR model and similar compartmental models make them applicable to many classes of infectious diseases. However, the combined assumption of a deterministic model and time-invariance among the model parameters are two significant impediments which critically limit their use for long-term predictions. The tendency of certain model parameters to vary in time due to seasonal trends, non-pharmaceutical interventions, and other random effects necessitates a model that structurally permits the incorporation of such time-varying effects. Complementary to this, is the need for a robust mechanism for the estimation of the parameters of the resulting model from data. To this end, we consider an augmented state vector, which appends the time-varying parameters to the original system states whereby the time evolution of the time-varying parameters are driven by an artificial noise process in a standard manner. Distinguishing between time-varying and time-invariant parameters in this fashion limits the introduction of artificial dynamics into the system, and provides a robust, fully Bayesian approach for estimating the time-invariant system parameters as well as the elements of the process noise covariance matrix. This computational framework is implemented by leveraging the robustness of the Markov chain Monte Carlo algorithm permits the estimation of time-invariant parameters while nested nonlinear filters concurrently perform the joint estimation of the system states and time-varying parameters. We demonstrate performance of the framework by first considering a series of examples using synthetic data, followed by an exposition on public health data collected in the province of Ontario.

Abstract PO1-02-02: Update of RSClin with extended TAILORx follow-up and development and validation of a new tool for risk of late distant recurrence

Abstract Background: RSClin® (Sparano et al J Clin Oncol 2020, PMID 33306425) is an educational tool that estimates the 10-year risk of distant recurrence (DR) and the absolute benefit of chemotherapy in HR-positive, HER2-negative, node-negative early breast cancer. The RSClin tool integrates the Recurrence Score® (RS) result with the clinical-pathological features tumor grade, tumor size and patient age using patient-specific meta-analysis (PSMA) applied to the NSABP B-14, TAILORx and NSABP B-20 studies. Approximately 3 years of additional follow-up data have accumulated in the TAILORx study since the RSClin tool was developed, including 50% more DR events in the RS 11-25 group (375 vs. 250), and 46% more DR events in the overall population (561 vs. 384). Methods: The RSClin tool estimates were updated to include the extended follow-up data from TAILORx. As before, the PSMA included 10,004 women with hormone-receptor positive, HER2-negative, node-negative breast cancer from the NSABP B-14 (n=577) trial receiving endocrine therapy (ET) alone and the TAILORx trial receiving ET alone (n=4854) or chemotherapy (CT) plus ET (n=4573). The baseline risk estimate used TAILORx event rates with ET alone. Since there was evidence of differing hazard ratios in years 0-5 versus 5+, models using time-varying effects were used. A patient-specific estimator of absolute CT benefit was computed by combining a PSMA estimate of the individualized relative CT effect from the TAILORx and NSABP B-20 (n=550 HER2-negative) trials with risk estimates for ET alone. Similar methods were used to develop and validate a new tool for assessing the risk of late DR (from year 5 to 10) in patients who survive DR-free for 5 years on ET alone after surgery. External validation of DR risk estimation was performed in an independent cohort of 1098 women enrolled in the Clalit Health Service registry. Results: With the updated data, the model integrating RS result with clinical-pathological features was more informative for DR risk than either RS result alone or the clinical-pathological features alone, both for 10-year DR risk and for late DR risk (likelihood ratio tests, each p≤.001). The updated 10-year RSClin risk estimates are similar to the existing RSClin tool risk estimates with slightly lower risk estimates for very high RS results and more precise estimates for mid-range RS results. The updated chemotherapy effect estimates are slightly lower than for the existing RSClin. The updated RSClin tool 10-year risk estimates were strongly associated with DR risk in the independent Clalit registry (Cox regression p< .001) and closely approximated the observed DR risk (Lin concordance 0.87). The new tool’s late DR risk estimates beyond 5 years were strongly associated with late DR risk in the Clalit registry (Cox regression p=.002) and closely approximated the observed late DR risk (Lin concordance 0.92). Conclusions: The RSClin tool 10-year DR risk estimates have been updated to incorporate extended follow-up data from the TAILORx study. A new tool for assessing late DR risk has been developed and validated that provides more prognostic information than either clinical-pathological features or genomic risk used individually. Citation Format: Joseph Sparano, Michael Crager, Robert Gray, Gong Tang, Jess Hoag, Frederick Baehner, Charles Geyer, Steven Shak, Norman Wolmark, Salomon Stemmer. Update of RSClin with extended TAILORx follow-up and development and validation of a new tool for risk of late distant recurrence [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-02-02.

Associations between prenatal metal and metalloid mixtures in teeth and reductions in childhood lung function

BackgroundMetal(oid)s have been cross-sectionally associated with lung function outcomes in childhood but there is limited data on their combined effects starting in utero. Child sex may further modify these effects. ObjectiveExamine associations between in utero and early life exposure to metals assessed via novel dentine biomarkers and childhood lung function and explore effect modification by child sex. MethodsAnalyses included 291 children enrolled in the Programming Research in Obesity, Growth, Environment and Social Stressors (PROGRESS) study, a longitudinal birth cohort study in Mexico City. Weekly dentine levels of arsenic (As), cadmium (Cd), cobalt (Co), copper (Cu), manganese (Mn), nickel (Ni), and lead (Pb) were measured from 15 weeks pre-birth to 15 weeks post birth in deciduous children's teeth. Lung function was tested at ages 8–14 years and then modeled as age, height and sex adjusted z-scores. Associations were modeled using lagged weighted quantile sum (LWQS) regression to evaluate the potential for a time-varying mixture effect adjusting for maternal age and education at enrollment and exposure to environmental tobacco smoke in pregnancy. Models were also stratified by sex. ResultsWe identified a window of susceptibility at 12–15 weeks pre-birth in which the metal mixture was associated with lower FVC z-scores in children aged 8–14 years. Cd and Mn were the largest contributors to the mixture effect (70 %). There was also some evidence of effect modification by sex, in which the mean weights and weighted correlations over the identified window was more evident in males when compared to females. In the male stratum, Cd, Mn and additionally Pb also dominated the mixture association. ConclusionsPrenatal metal(oid) exposure was associated with lower lung function in childhood. These findings underscore the need to consider both mixtures and windows of susceptibility to fully elucidate effects of prenatal metal(oid) exposure on childhood lung function.

Effects of Budget Deficit Financing on the Economy of Nigeria

The study investigated the effects of deficit financing on the Nigerian economy using data that covered 41 years (1981 to 2021). Real gross domestic product (RGDP) was the dependent variable, while government budget deficit financing disaggregated into different sources of budget deficit financing represented the explanatory variables. The ordinary least squares (OLS) regression method was used for the tests and analyses. Results established that both non-bank public sources of deficit financing and banking system sources of deficit financing had positive and significant effects on growth. However, non-bank public deficit financing positively led the Nigerian economy and was followed by banking system deficit financing. Both ways and means and external deficit financing sources were negative and insignificant in influencing the Nigerian economy. Ways and means were third while external deficit financing was fourth in descending order of influence on RGDP growth. The study further applied the Augmented Dickey-Fuller (ADF) approach to unit root tests and observed that the variables were integrated at both levels and first difference, leading to the application of the Autoregressive Distributed lag (ARDL) approach to data estimation. The ARDL bounds tests showed that the model specified for the study followed a long-run path and that a long-run relationship existed between the dependent variable and the explanatory variables. The estimation of the long-run and error correction estimation indicated that the independent variables had a time-varying effect on the real gross domestic product of Nigeria. That ARDL estimation of the error correction mechanism also showed that RGDP adjusted rapidly to short-run discrepancies in the long run. Finally, the error correction mechanism showed that external sources of budget deficit financing, non-banking system public deficit financing, ways and means source of deficit financing, gross capital formation, real interest rate and exchange rate, all had robust effects on growth, though with varying directions of influence. Based on the foregoing, the study has recommended deficit financing, more especially non-bank public deficit financing and banking system deficit financing as better options for attaining the much desired rapid and sustainable economic growth of Nigeria as they have been proven to be non-inflationary in practice compared to ways and means and external source of deficit financing over the years.

Differences in Fermentation Time Varying Cassava Tapai Substrate, Purple Sweet Potatoes and Banana Kepok Based on Physical Properties, Organoleptic Characteristics and pH

Tapai is a fermented product that is very popular with the public and has better nutritional value. Using various substrates in producing tapai can be a new alternative in diversifying food processing, one of which is purple sweet potato and Kepok banana, which are a problem in production and occupy the top ranking of agricultural products in Indonesia. Purple sweet potatoes and Kepok bananas have the same essential ingredients as cassava, namely starch. This research was conducted to determine the effect of fermentation time and substrate variations on the physical properties, organoleptic characteristics and acidity levels of cassava tapai, purple sweet potato and Kepok banana. This research was conducted using a completely randomized design experimental research method with a factorial pattern consisting of two factors. The first factor is the substrate factor, and the second is the fermentation time length. The analysis showed that the fermentation time and substrate variations influenced the physical properties, organoleptic characteristics and acidity level of the tapai. Overall, tapai-fermented food was preferred by panelists on the third day of fermentation.

Causal interpretation of the hazard ratio in randomized clinical trials.

Although the hazard ratio has no straightforward causal interpretation, clinical trialists commonly use it as a measure of treatment effect. We review the definition and examples of causal estimands. We discuss the causal interpretation of the hazard ratio from a two-arm randomized clinical trial, and the implications of proportional hazards assumptions in the context of potential outcomes. We illustrate the application of these concepts in a synthetic model and in a model of the time-varying effects of COVID-19 vaccination. We define causal estimands as having either an individual-level or population-level interpretation. Difference-in-expectation estimands are both individual-level and population-level estimands, whereas without strong untestable assumptions the causal rate ratio and hazard ratio have only population-level interpretations. We caution users against making an incorrect individual-level interpretation, emphasizing that in general a hazard ratio does not on average change each individual's hazard by a factor. We discuss a potentially valid interpretation of the constant hazard ratio as a population-level causal effect under the proportional hazards assumption. We conclude that the population-level hazard ratio remains a useful estimand, but one must interpret it with appropriate attention to the underlying causal model. This is especially important for interpreting hazard ratios over time.

Research on soft sensor modeling method for complex chemical processes based on local semi-supervised selective ensemble learning

To address the adverse effects of nonlinearity and dynamic time-varying in complex chemical processes on the accuracy of the soft sensor model, a local-semi-supervised ensemble learning for soft sensor modeling (local semi-supervised-selective ensemble learning-long short term memory, LS-SEL-LSTM) method is proposed in this article. Firstly, a hierarchical clustering method incorporating spatiotemporal criteria is proposed to reduce the influence of nonlinearity in global model prediction accuracy. The method considers the dynamic time-varying characteristics of temporal data and generates multiple local datasets. Then, to address the issue of multi-rate between auxiliary variables and dominant variables, a semi-supervised weight fusion mechanism based on temporal correlation is proposed, which effectively utilizes auxiliary variables to reconstruct local semi-supervised datasets and establishes local soft sensing models using LSTM. Concurrently, the parameters of the established model were optimized using the flower pollination algorithm. Subsequently, a selective ensemble learning method based on sub-model prediction accuracy and an adaptive combination weight calculation method for sub-models were proposed to improve the prediction accuracy. Finally, the effectiveness of the proposed method was verified through the actual dataset of the sulfur recovery process. The results indicate that LS-SEL-LSTM performs well in handling complex chemical processes with nonlinear and dynamic time-varying characteristics.

Exploring the asymmetric influence of economic policy uncertainty on the nonlinear relationship between exchange rate and carbon prices in China

Our objective is to investigate the impact of economic policy uncertainty (EPU) on the risk spillover effect between exchange rates and carbon prices. First, we employ the time-varying parametric vector autoregression (TVP-VAR) method to assess the dynamic nonlinear risk spillover effect between four exchange rates and China's carbon price for the period from September 2, 2013, to April 29, 2022. Our findings reveal a significant time-varying spillover effect between exchange rates and carbon prices. Among them, carbon prices and the EUR/CNY exchange rate predominantly act as risk contributors, and a pronounced correlation is observed among various exchange rates. Furthermore, the magnitude of risk spillover and interdependence intensifies significantly in response to major events. Second, we employ quantile-to-quantile regression (QQR) to examine the impact of EPU on risk spillover. Our results indicate that the influence of EPU on risk spillover exhibits is characterized by marked asymmetry and heterogeneity. At high levels of EPU, EPU has a substantial positive impact on risk spillover. However, when the risk spillover is already elevated, the effect of EPU tends to diminish. Based on these findings, we propose several policy recommendations, including prudent treatment of exchange rate adjustments, fostering stability in carbon prices, and the enactment of robust economic policies.

Long-term trends in beverage-specific drinking in the National Alcohol Surveys: Differences by sex, age, and race and ethnicity.

Despite substantial declines in underage drinking and binge drinking, alcohol consumption has increased in the past 30 years. This study examined how beverage-specific drinking patterns varied by sex, age, and race and ethnicity from 1979 to 2020. Secondary data analysis was conducted on pooled data from the National Alcohol Survey series from 1979 to 2020 of a sample of U.S. adults ages 18 years or older. Total and beverage-specific volume were calculated from graduated frequency questions on reported beverage type, which included beer, wine, and spirits. Sex-stratified analyses focused on descriptive trends of each alcohol measure over time and by age and race and ethnicity. Time-varying effect models were also conducted to identify subgroups at higher risk for increased consumption over time. Women's drinking increased, with alcohol volume rising substantially from 2000 to 2020; the largest increase was among women 30 and older. Men's alcohol volume also rose over this period but remained below the levels of 1979 and 1984, with older adults increasing their consumption those 18-29 decreased their drinking. Beverage-specific trends showed some similarities by gender: in 2020, wine volume was at its highest level for both women and men, while spirits volumealso was at its highest level for men and in women was tied with the 1979 peak in spirits consumption. Increases were also found among Black men and women and Latina women from 2000 to 2020. Given the increases in alcohol volume, particularly among women and older age groups, alcohol policy, interventions, and education should consider ways to reduce harms associated with alcohol use among these groups. Continued monitoring of beverage-specific drinking patterns is needed to track policy-relevant changes.

Ekstraksi Bunga Kecombrang dan Rosela dengan Metode MAE serta Aplikasinya pada Produk Permen Jelly

Research in recent years has shown the antioxidant activities of kecombrang and roselle flowers, as well as the antibacterial properties of kecombrang, so these materials have potentials to be developed as a functional food product. With an easy manufacturing process and basic ingredients that are easy to obtain, jelly candy is one of the preparations that can be applied to kecombrang and roselle flower extracts. This research aims 1) to determine the effect of variations in microwave-assisted extraction time and power on the physicochemical characteristics of the resulting extract and 2) to determine the effect of variations in the ratio of kecombrang and roselle flower extracts as ingredients for jelly candy products on the physical, chemicall, and sensory characteristics of the product. Research on the extraction of kecombrang and roselle flower powder used a randomized block design (RBD). Treatment consisted of two factors: 1) extraction power (150, 200, and 250 watts); 2) extraction time (3, 5, and 7 minutes). The data obtained were analyzed using the SPSS program. The analysis results showed that the best extraction of kecombrang and roselle flowers were at 250 watts of power and 5 minutes of extraction time. The best extraction results are then processed into jelly candy with the best sensory results in the 15%:25% sample. This jelly candy product contains reduced sugar levels of 12.41%, vitamin C levels of 23.58 mg/100g, total phenol levels of 47.58 mg/100g, total flavonoid levels of 18.80 mg/100g, and antioxidant activity amounting to 28.24 mg/100g. Keywords: extraction, jelly candy, kecombrang, MAE, rosella

Time-varying effects of crude oil price fluctuations on tuna fish prices

This research presents an investigation of the time-varying effects of crude oil on the price of three tuna species, namely skipjack, albacore, and yellow fin. The investigation analyses the impact coefficient of oil price fluctuation on tuna species over time with specific phases related to time points when crude oil prices fall, including December 2008 (due to the impact of the Financial Crisis), February 2016 (due to the impact of the US shale oil and gas revolution), and April 2020 (due to the impact of the global COVID-19). The analysis shows that the price of yellow fin and skipjack shows sensitivity to these phased oil price shocks but stays consistent after recovery. This research finds that the relationship between oil price and tuna price depends on specific phases of oil price fluctuations and that global crude oil price shocks could have immediate and short-term impacts on fish markets, especially during a period of financial crisis.

Crohn's disease after surgery: Changes in post-operative management strategies over time and their impact on long-term re-operation rate-A retrospective multicentre real-world study.

Few data are available addressing the impact of post-operative management of Crohn's disease (CD) on long-term clinical course. To assess the evolution of post-operative management strategies over the last 40 years and their impact on the re-operation rate of CD. We included 657 patients with CD who had undergone their first radical ileo-caecal resection between 1980 and 2020. Three cohorts were defined according to year of surgery: cohort 1 (1980-1998; n = 198), cohort 2 (1999-2009; n = 218) and cohort 3 (2010-2020; n = 241). We estimated exposure to immunomodulators and anti-TNFα agents after surgery and rates of re-operation using Kaplan-Meier survival analyses. We used Cox proportional hazards regression to assess the association of clinical variables with time to re-operation. Immunosuppressants, (IMMs) and anti-TNFα exposure within 5 years after surgery increased significantly from cohort 1 to cohort 2 and cohort 3 (IMMs: 1.6%, 38.2% and 28.0%, respectively, p < 0.001; anti-TNFα: 0.0%, 20.7% and 52.0%, respectively, p < 0.001). There was no significant difference across cohorts regarding the cumulative probability of re-operation within 5 and 10 years. Multivariate analysis identified IMMs/anti-TNFα exposure before the first surgery (HR 9.15; 95% CI 2.77-30.21) and post-operatively (HR: 0.24; 95% CI 0.07-0.74) as variables associated with the risk of re-operation. However, these associations had a time-varying effect and become non-significant after 5 and 2 years after surgery, respectively. Despite increased post-operative use of IMMs and anti-TNFα agents in the last two decades, the impact of these strategies on the risk of long-term re-operation rate has been modest.