Background: This study evaluated early childhood comorbidities of cerebral palsy (CP) in low birth weight (LBW) children and assessed the impact of maternal bio-psychosocial factors on CP risk in preterm infants of varying birth weights (BWs). Methods: Data from 15,181 preterm infants (2009–2013) and 151,810 controls were analyzed using Taiwan’s National Health Insurance Research Database. CP prevalence and LBW-associated comorbidities were examined, and odds ratios (ORs) were calculated. Results: This study confirmed increasing prematurity and LBW rates in Taiwan, with LBW infants showing higher CP prevalence. Significant maternal risk factors included age extremes (40 years). LBW infants exhibited higher risks for respiratory, circulatory, nervous system, and psycho-developmental comorbidities compared with controls, with the lowest BW having even higher ORs. Maternal factors such as family income, the number of hospital admissions, and length of hospital stay were remarkably correlated with BW and subsequent complications. Each additional gestational week crucially reduced the risk of complications in premature infants. Conclusions: LBW infants are at a higher risk for CP and various comorbidities, with maternal bio-psychosocial factors playing a critical role. Addressing these factors in prenatal care and interventions is essential to improve outcomes for premature infants.

Crude oil price volatility impacts the global economy in general, as well as the economies of Europe and the United States in particular; it is supremely difficult to describe its tendency precisely, hence it leads to a forecasting methodology. This study aims to use the autoregressive integrated moving average (ARIMA), and seasonal autoregressive integrated moving average (SARIMA) approaches to cope with this problem in the United States and Europe. The data was gathered from the U.S. Energy Information Administration and federal research economic data (FRED) from January 2017 to September 2021. Simultaneously, values from January 2017 to March 2021, with 51 observations accounting for 90% of the total samples, were employed for the training phase, and the rest were used for the testing phase. The forecast result also indicated that the root mean square error (RMSE) and mean absolute percentage error (MAPE) values, applied by ARIMA models in Europe and the United States, have higher accurate indicators than SARIMA models. As a result, the ARIMA model achieved the best accuracy in both Europe and the USA, with ${\mathrm{MAPE}}_{\mathrm{Europe}-\mathrm{ARIMA}}$ = 0.05, and ${\mathrm{MAPE}}_{\mathrm{USA}-\mathrm{ARIMA}}=0.05$. Based on these accuracy parameters, the forecasting models appear incredibly reliable; similarly, the study results might assist governing bodies in making significant decisions, thereby accelerating socio-economic development in the world’s two largest economies.

This paper aims to examine the symmetric and asymmetric effects of third country exchange rate volatility on the trade flow between the US and EU from January 2003 through March 2021. The monthly disaggregated data of the top twelve export and import industries are the sample frame. We find that separating increased volatility from declines and introducing a nonlinear adjustment to the volatility shows a more significant outcome than symmetric analysis. Different industries carry distinctive behaviors regarding exchange rate risk, and the third country effect plays a vital role in trade. Moreover, increased CNY/USD real exchange rate volatility increases bilateral trade between the US and EU.

Disease severity in plant pathology is often measured by the amount of a plant or plant part that exhibits disease symptoms. This is typically assessed using a numerical scale, which allows a standardized, convenient, and quick method of rating. These scales, known as quantitative ordinal scales (QOS), divide the percentage scale into a predetermined number of intervals. There are various ways to analyze these ordinal data, with traditional methods involving the use of midpoint conversion to represent the interval. However, this may not be precise enough, as it is only an estimate of the true value. In this case, the data may be considered interval-censored, meaning that we have some knowledge of the value but not an exact measurement. This type of uncertainty is known as censoring, and techniques that address censoring, such as survival analysis (SA), use all available information and account for this uncertainty. To investigate the pros and cons of using SA with QOS measurements, we conducted a simulation based on three pathosystems. The results showed that SA almost always outperformed midpoint conversion with data analyzed using a t test, particularly when data were not normally distributed. Midpoint conversion is currently a standard procedure. In certain cases, the midpoint approach required a 400% increase in sample size to achieve the same power as the SA method. However, as the mean severity increases, fewer additional samples are needed (approximately an additional 100%), regardless of the assessment method used. Based on these findings, we conclude that SA is a valuable method for enhancing the power of hypothesis testing when analyzing QOS severity data. Future research should investigate the wider use of survival analysis techniques in plant pathology and their potential applications in the discipline.

Meta-analysis is a useful research methodology which combines results of multiple studies to estimate means of outcomes of interest. To effectively conduct a meta-analysis, it is critical to assess whether study homogeneity is a reasonable assumption. In literature, many approaches have been proposed to estimate the between-study variance and test the assumption of study homogeneity. One approach is the parametric bootstrap method, which has the disadvantage of underestimating type I error rates at small sample size. In this article, we propose a novel bootstrap method to improve the shortcomings of the parametric bootstrap. We focus on testing homogeneity of the odds-ratio in different studies. In our approach, we first estimate response probabilities in the 2 $\times$ 2 tables under the assumption of homogeneity and then generate bootstrap samples based on the estimated probabilities. The performance of the novel bootstrap is evaluated through simulation studies conducted based on Cochran Q-statistics and several commonly used heterogeneity estimators. Simulation results show that Cochran Q-statistics based on both bootstrap methods properly control type I error rate except in the situation where both event probability and sample size are small. In general, the novel bootstrap tends to overestimate type I error while the parametric bootstrap tends to underestimate type I error. However, overestimation does not occur when using the novel bootstrap combined with tests based on the ML and REML. In this case, the novel bootstrap method controls type I error rates better than the parametric bootstrap and is as powerful as the parametric bootstrap. We illustrate the proposed method using two real data sets.