Explore the vast range of titles available.

Background The association between urbanization and myocardial infarction in China was not fully understood. We aim to evaluate the association between urbanization level and the risk of myocardial infarction. Methods Participants’ data from China Health and Nutrition Survey were analyzed. The survey is an ongoing open cohort with a multistage, random cluster process to draw a sample of over 30,000 individuals in 15 provinces and municipal cities. A multi-component score, urbanization index, was used to measure urbanization level. Urbanization index was categorized by cut-off values of 40, 60, and 80. Baseline characteristics were presented as percentage for categorical variables, and mean or median for continuous variables depending on normality. The association of urbanization index and myocardial infarction risk was evaluated longitudinally by a multilevel mixed-effects parametric survival model with urbanization index as a time-dependent variable using Stata software. Results A total of 16,565 participants without previous myocardial infarction were involved in the cohort. At baseline, urbanization index was positively correlated with myocardial infarction risk factors, including blood pressure, body mass index, diabetes, and stroke. In the univariate model, urbanization index was associated with risk of myocardial infarction in a “U-shape” manner, with the lowest myocardial infarction risk in urbanization index ranging from 40 to 60 (Hazard Ratio 0.55 and p =  0.013). However, in the multivariate model adjusting for myocardial infarction risk factors and other confounders, urbanization index ≥ 40 was consistently associated with lower MI risks (Hazard Ratio 0.46 and p =  0.002 for urbanization index 40–60; Hazard Ratio 0.56 and p =  0.021 for urbanization index 60–80; and Hazard Ratio 0.44 and p =  0.002 for urbanization index ≥ 80). Conclusions Our study indicated that urbanization of living communities was associated with a reduced risk of myocardial infarction.

•Analysis of longitudinal trajectories of brain metabolite levels offer increased power to detect group differences and confirmed elevated GPC+PCh levels in PHIV children from 5-11 years in gray and white matter.•Age-related increases demonstrated in GPC+PCh in gray matter-containing regions.•Despite finding elevated GPC+PCh across regions and regionally elevated mI in children with PHIV, the trajectories of most metabolites were not different from those of HIV negative controls.•Age-related increases in BG NAA levels were lower in PHIV children than uninfected controls.•The CRM latent factor output shows that concentrations of metabolites within a region are associated more than across regions, apart from the concentration of GPC+PCh which is strongly associated across the three brain regions that were investigated. Treatment guidelines recommend that children with perinatal HIV infection (PHIV) initiate antiretroviral therapy (ART) early in life and remain on it lifelong. As part of a longitudinal study examining the long-term consequences of PHIV and early ART on the developing brain, 89 PHIV children and a control group of 85 HIV uninfected children (HIV-) received neuroimaging at ages 5, 7, 9 and 11 years, including single voxel magnetic resonance spectroscopy (MRS) in three brain regions, namely the basal ganglia (BG), midfrontal gray matter (MFGM) and peritrigonal white matter (PWM). We analysed age-related changes in absolute metabolite concentrations using a multivariate approach traditionally applied to ecological data, the Correlated Response Model (CRM) and compared these to results obtained from a multilevel mixed effect modelling (MMEM) approach. Both approaches produce similar outcomes in relation to HIV status and age effects on longitudinal trajectories. Both methods found similar age-related increases in both PHIV and HIV- children in almost all metabolites across regions. We found significantly elevated GPC+PCh across regions (95% CI=[0.033; 0.105] in BG; 95% CI=[0.021; 0.099] in PWM; 95% CI=[0.059; 0.137] in MFGM) and elevated mI in MFGM (95% CI=[0.131; 0.407]) among children living with PHIV compared to HIV- children; additionally the CRM model also indicated elevated mI in BG (95% CI=[0.008; 0.248]). These findings suggest persistent inflammation across the brain in young children living with HIV despite early ART initiation.

Background: Child mortality is a major public health issue. The studies on under-five mortality that ignore the hierarchical facts mislead the interpretation of the results due to observations in the same cluster sharing common cluster-level random effects. Objectives: The present study uses a multilevel model to analyze under-five mortality and identify the significant factors for under-five mortality in Manipur. Methods: National Family Health Survey-5 (2019-21) data are used in the present study. A multilevel mixed-effect Weibull parameter survival model was fitted to determine the factors affecting under-five mortality. We construct three-level data, individual levels are nested within primary sampling units (PSUs), and PSUs are nested within districts. Results: Out of the 3225 under-five children, 85 (2.64%) died. The three-level mixed-effects Weibull parametric survival model with PSUs nested within the districts, the likelihood-ratio test with Chi-square value = 10.98 and P = 0.004 < 0.05 indicated that the model with random-intercept effects model with PSUs nested within the districts fits the data better than the fixed effect model. The four covariates, namely the number of birth in the last 5 years, age of mother at first birth, use of contraceptive, and size of child at birth, were found as the risk factor for under-five mortality at a 5% level of significance. Conclusions: In the random-intercept effect model, the two estimated variances of the random-intercept effects for district and PSU levels are 0.27 and 0.31, respectively. The values indicate variations (unobserved heterogeneities) in the risk of death of the under-five children between districts and PSUs levels.

Growth functions have been used to predict market weight of pigs and maximize return over feed costs. This study was undertaken to compare 4 growth functions and methods of analyzing data, particularly one that considers nonlinear repeated measures. Data were collected from an experiment with 40 pigs maintained from birth to maturity and their BW measured weekly or every 2 wk up to 1,007 d. Gompertz, logistic, Bridges, and Lopez functions were fitted to the data and compared using information criteria. For each function, a multilevel nonlinear mixed effects model was employed because it allowed for estimation of all growth profiles simultaneously, and different sources of variation (i.e., sex, pig, and litter effects) were incorporated directly into the parameters. Furthermore, variance in-homogeneity and within-pig correlation were introduced to the functions. Inclusion of a variance of power function and a continuous autoregressive process of first order rendered a substantially improved fit to data for all 4 growth functions. The Lopez function provided the best fit to the data set and was used for characterizing mean growth curves for the 3 sexes (barrows, boars, and gilts). It was estimated that the maximum growth rate occurs at 117, 134, and 96 kg of BW for barrows, boars, and gilts, respectively. Hence, the gilts reached their maximum growth rate at an earlier stage in life compared with boars. Mature size of pigs varied systematically with sex and was estimated to be 466, 537, and 382 kg of BW for the barrows, boars, and gilts, respectively. These estimates are significantly affected by the duration of the experimental period, and it is recommended that future studies looking at estimating the mature size in animals are conducted long enough so that the BW visually stabilizes. Furthermore, studies should consider adding continuous autoregressive process when analyzing nonlinear mixed models with repeated measures.

The aim of this study was to analyze and measure idiosyncratic differences in CO2 emission trends over time and between the different geographical contexts of the G20 signatory countries and to assess whether these countries are fulfilling their carbon emission reduction commitments, as stipulated in the G20 sustainable development agendas. To this end, a multilevel mixed-effects model was used, considering CO2 emissions data from 1950 to 2021 sourced from the World Bank. The research model captured approximately 93.05% of the joint variance in the data and showed (i) a positive relationship between the increase in CO2 emissions and the creation of the G20 [CI90: +0.0080; + 0.1317]; (ii) that every year, CO2 emissions into the atmosphere are increased by an average of 0.0165 [CI95: +0.0009; +0.0321] billion tons by the G20 countries; (iii) that only Germany, France, and the United Kingdom have demonstrated a commitment to CO2 emissions reduction, showing a decreasing rate of CO2 emissions into the atmosphere; and (iv) that there seems to be a mismatch between the speed at which the G20 proposes climate policies and the speed at which these countries emit CO2.

Recent substantive research on biometric analyses of twin and family data has used both a biometric path analysis model (PAM) and a biometric variance components model (VCM). Methodological research on these same topics have suggested benefits of using linear structural equation model algorithms (SEMA) as well as mixed effect multilevel algorithms (MEMA). To better understand the potential similarities and differences among these approaches we first highlight the algebraic equivalence between the standard biometric PAM and the corresponding biometric VCM models for family data. Second, we demonstrate how several SEMA programs based on either the PAM or VCM approach produce equivalent estimates for all phenotypic and biometric parameters. Third, we show how the biometric VCM approach (but not the PAM approach) can be easily programmed using current MEMA programs (e.g., SAS PROC MIXED). We then expand the scope of these different approaches to include measured covariates, observed variable interactions and multiple relatives within each family. MEMA software is compared to SEMA software for programming complex models, including the flexibility of data input, treatment of missing data, inclusion of covariates, and ease of accommodating varying numbers of observations (per family or individual).

Agricultural water scarcity poses a significant threat to food security and sustainable development. Farmers’ adoption of agricultural water-saving irrigation technologies (WSIT) is essential for addressing this challenge. However, decisions to adopt such technologies are shaped by both individual and structural factors across multiple levels. This study investigated the multilevel determinants of WSIT adoption in the North China Plain (NCP) through a field survey conducted from late 2012 to early 2013. We collected retrospective data from 818 households spanning 2010 to 2012. Initially, we employed a binomial logistic regression model to identify significant household- and village-level factors influencing WSIT adoption. Recognizing the nested nature of adoption decisions within individual and village contexts, and the importance of multilevel interactions, we further utilized a multilevel mixed-effects logistic model. Our findings indicated that household-level factors, such as farm size, access to irrigation resources, education, input costs, and village leadership, were the primary drivers of WSIT adoption. Approximately 22.7% of the variation in adoption was attributable to differences between villages. At the village level, larger village size decreased the likelihood of adoption, while the presence of Water User Associations (WUAs) had a positive influence. Mechanism analysis suggested that household-level characteristics affected farmers’ adoption behavior through possible channels such as technical training, farmers’ perception of climate change, and the availability of alternative technologies. These findings highlight the importance of considering multiple contextual factors in studying farmers’ adoption behavior and provide important policy implications for decision-makers. JEL classification: D1; Q16; Q25 Plain language summary A multilevel analysis of the drivers of farmers’ adoption of water-saving irrigation technology The adoption of agricultural water-saving irrigation technologies (WSIT) for farmers is essential for mitigating water scarcity. This study investigated multi-level determinants of WSIT adoption in the North China Plain (NCP) through a field survey. We utilized a multilevel mixed-effects logistic model and found that WSIT adoption was mainly influenced by household-level and village-level factors. Approximately 22.7% of the variation in adoption was attributable to differences between villages. Mechanism analysis suggested that household-level characteristics affected WSIT adoption behavior through possible channels like technical training, farmers’ perception of climate change, and the availability of alternative technologies. Our study provides important policy implications for decision-makers.

PurposeThe purpose of this paper is to investigate the relationship between tax avoidance, firm value and managerial ability in Tehran Stock Exchange and Over the Counter (OTC), according to the related theoretical foundations.Design/methodology/approachTo calculate the managerial ability in this study, DEA is used based on the accounting data, company profile and industry and the hypotheses are estimated in a period of 12 years during 2004 to 2015 in TSE and OTC. Within the previous studies, to test the hypotheses, only the classical regression method is usually used and most of the times the effect of macroeconomic variables is not considered. In this study, in a new act for testing the hypotheses, three statistical methods are used, that is, classical regression models, mixed effects multilevel models and Bayesian multilevel models. Also in this study, the test of structural change is used to control the effects of macroeconomic variables, like inflation and other economic and political influence, on the results.FindingsThe results of these three methods show that the effect of income smoothing and earnings quality on the relationship between tax avoidance and firm value are significant.Originality/valueAlthough several studies are conducted so far on the subject of the study, the current study is the first project which combined Bayesian econometrics. Therefore, the results are quite noble.

Forest carbon sinks are vital in mitigating climate change, making it crucial to have highly accurate estimates of forest carbon stocks. A method that accounts for the spatial characteristics of inventory samples is necessary for the long-term estimation of above-ground forest carbon stocks due to the spatial heterogeneity of bottom-up methods. In this study, we developed a method for analyzing space-sensing data that estimates and predicts long time series of forest carbon stock changes in an alpine region by considering the sample’s spatial characteristics. We employed a nonlinear mixed-effects model and improved the model’s accuracy by considering both static and dynamic aspects. We utilized ground sample point data from the National Forest Inventory (NFI) taken every five years, including tree and soil information. Additionally, we extracted spectral and texture information from Landsat and combined it with DEM data to obtain topographic information for the sample plots. Using static data and change data at various annual intervals, we built estimation models. We tested three non-parametric models (Random Forest, Gradient-Boosted Regression Tree, and K-Nearest Neighbor) and two parametric models (linear mixed-effects and non-linear mixed-effects) and selected the most accurate model to estimate Pinus densata’s above-ground carbon stock. The results showed the following: (1) The texture information had a significant correlation with static and dynamic above-ground carbon stock changes. The highest correlation was for large-window mean, entropy, and variance. (2) The dynamic above-ground carbon stock model outperformed the static model. Additionally, the dynamic non-parametric models and parametric models experienced improvements in prediction accuracy. (3) In the multilevel nonlinear mixed-effects models, the highest accuracy was achieved with fixed effects for aspect and two-level nested random effects for the soil and elevation categories. (4) This study found that Pinus densata’s above-ground carbon stock in Shangri-La followed a decreasing, and then, increasing trend from 1987 to 2017. The mean carbon density increased overall, from 19.575 t·hm−2 to 25.313 t·hm−2. We concluded that a dynamic model based on variability accurately reflects Pinus densata’s above-ground carbon stock changes over time. Our approach can enhance time-series estimates of above-ground carbon stocks, particularly in complex topographies, by incorporating topographic factors and soil thickness into mixed-effects models.