Categorical Indicators Research Articles

Short-term rainfall forecasting using cumulative precipitation fields from station data: a probabilistic machine learning approach

Rainfall nowcasting supports emergency decision-making in hydrological, agricultural, and economical sectors. However, short-term prediction is challenging because meteorological variables are strongly interconnected and rapidly change during an event. Since machine learning do not require any previous physical assumption, this study investigates their ability to provide reliable and quick forecasts. This paper proposes a machine learning model for probabilistic rainfall nowcasting at 10 min intervals for short lead times - from 30 min up to 6 h. The model employs cumulative rainfall fields from station data as inputs for a feed forward neural network to predict rainfall interval and the corresponding probability of occurrence. Cumulative rainfall depths from station data were used to overcome the lack of temporal memory of the feed forward neural networks. In this way, using only the current rain field as input, the model exploited pattern recognition techniques combining both temporal - cumulative rainfall depth - and spatial - cumulative rainfall field – information. Based on 359 rain events observed in an area of 1619 km2 in Southern Italy, 95 machine learning models were independently trained for 19 recording stations and for each target lead-time (30, 60, 120, 180 and 360 min). Comprehensive nowcasts verification was performed to analyse the reliability of probabilistic nowcasts using both continuous (RMSE and RAE) and categorical (POD, FAR and CSI) indicators. The performance of the models was also compared with the results of Eulerian Persistence. All the models produced consistent nowcasts and learnt the complex relationship describing space–time rainfall evolution. As expected, predictive accuracy gradually decreased as the lead-time increase, according to physically based models. Results showed that the use of both temporal and spatial information enables the model to predict short-term rainfall using only the current measurements as input, resulting in a rapid, easily replicable and convenient nowcasting approach. The procedure is an effective way to predict multi-step rainfall fields and is suitable for operational early warning system.

Clinical phenotypes of patients with ischemic heart disease and borderline coronary artery stenoses

Background. Ischemic heart disease, despite ongoing therapeutic and preventive measures, remains the leading cause of disability and mortality in people of working age.
 Aim. Study of cardiovascular risk factors and assessment of the clinical and angiographic picture in patients with stable coronary heart disease and borderline coronary artery stenoses of various clinical phenotypes.
 Material and methods. 236 patients with functional class IIII stable angina pectoris aged 4959 years with borderline (4070%) coronary artery stenoses were examined. All patients underwent a general clinical examination, cardiac ultrasound and coronary angiography. The nonparametric MannWhitney U-test was used to compare indicators between groups. To compare binary and categorical indicators, Fisher's exact two-tailed test was used. Differences in the compared parameters were considered statistically significant at p 0.05.
 Results. The predominance of the following clinical phenotypes, which determined the further distribution into groups, was revealed: coronary heart disease without myocardial infarction the first group; coronary heart disease with myocardial infarction more than 6 months old the second group; coronary heart disease with a metabolically unhealthy phenotype of obesity without diabetes mellitus the third group, coronary heart disease with diabetes mellitus the fourth group. In the third and fourth groups, there was no significant difference in the frequency of myocardial infarction in the past, and patients in the third group with a history of myocardial infarction suffered it at the earliest age (44.3 [41; 50] years, p 0.01). Despite the presence of arterial hypertension in 97.5% of groups of patients with different phenotypes, the fourth group showed the highest values of the left ventricular myocardium mass index (240.6 [146.3; 286.3], p 0.01). In the same group, multivessel coronary artery lesions were more frequently recorded during angiographic examination in 25 patients (47.2 [33.4; 52.3]%, p 0.01).
 Conclusion. Despite the absence of obstructive coronary artery disease, patients with stable coronary heart disease and borderline coronary artery stenoses are at risk of cardiovascular complications due to the presence of arterial hypertension in the vast majority, a significant incidence of obesity, dyslipidemia, and carbohydrate metabolism disorders.

Correcting for Unreliability and Partial Invariance: A Two-Stage Path Analysis Approach

In path analysis, using composite scores without adjustment for measurement unreliability and violations of factorial invariance across groups lead to biased estimates of path coefficients. Although joint modeling of measurement and structural models can theoretically yield consistent structural association estimates, estimating a model with many variables is often impractical in small samples. A viable alternative is two-stage path analysis (2S-PA), where researchers first obtain factor scores and the corresponding individual-specific reliability coefficients, and then use those factor scores to analyze structural associations while accounting for their unreliability. The current paper extends 2S-PA to also account for partial invariance. Two simulation studies show that 2S-PA outperforms joint modeling in terms of model convergence, the efficiency of structural parameter estimation, and confidence interval coverage, especially in small samples and with categorical indicators. We illustrate 2S-PA by reanalyzing data from a multiethnic study that predicts drinking problems using college-related alcohol beliefs.

Appraisal of Satellite Rainfall Products for Malwathu, Deduru, and Kalu River Basins, Sri Lanka

Satellite Rainfall Products (SRPs) are now in widespread use around the world as a better alternative for scarce observed rain gauge data. Upon proper analysis of the SRPs and observed rainfall data, SRP data can be used in many hydrological applications. This evaluation is very much necessary since, it had been found that their performances vary with different areas of interest. This research looks at the three prominent river basins; Malwathu, Deduru, and Kalu of Sri Lanka and evaluates six selected SRPs, namely, IMERG, TRMM 3B42, TRMM 3B42-RT, PERSIANN, PERSIANN-CCS, PERSIANN-CDR against 15+ years of observed rainfall data with the use of several indices. Four Continuous Evaluation Indices (CEI) such as Root Mean Square Error (RMSE), Percentage Bias (PBIAS), Pearson’s Correlation Coefficient (r), and Nash Sutcliffe Efficiency (NSE) were used to evaluate the accuracy of SRPs and four Categorical Indices (CI) namely, Probability of Detection (POD), Critical Success Index (CSI), False Alarm Ratio (FAR) and Proportion Correct (PC) was used to evaluate the detection and prediction accuracy of the SRPs. Then, the Mann–Kendall Test (MK test) was used to identify trends in the datasets and Theil’s and Sens Slope Estimator to quantify the trends observed. The study of categorical indicators yielded varying findings, with TRMM-3B42 performing well in the dry zone and IMERG doing well in the wet zone and intermediate zone of Sri Lanka. Regarding the CIs in the three basins, overall, IMERG was the most reliable. In general, all three basins had similar POD and PC findings. The SRPs, however, underperformed in the dry zone in terms of CSI and FAR. Similar findings were found in the CEI analysis, as IMERG gave top performance across the board for all four CEIs in the three basins. The three basins’ overall weakest performer was PERSIANN-CCS. The trend analysis revealed that there were very few significant trends in the observed data. Even when significant trends were apparent, the SRP projections seldom captured them. TRMM-3B42 RT had the best trend prediction performance. However, Sen’s slope analysis revealed that while the sense of the trend was properly anticipated, the amplitude of the prediction significantly differed from that of the observed data.

Two-stage path analysis with definition variables: An alternative framework to account for measurement error.

When estimating path coefficients among psychological constructs measured with error, structural equation modeling (SEM), which simultaneously estimates the measurement and structural parameters, is generally regarded as the gold standard. In practice, however, researchers usually first compute composite scores or factor scores, and use those as observed variables in a path analysis, for purposes of simplifying the model or avoiding model convergence issues. Whereas recent approaches, such as reliability adjustment methods and factor score regression, has been proposed to mitigate the bias induced by ignoring measurement error in composite/factor scores with continuous indicators, those approaches are not yet applicable to models with categorical indicators. In this article, we introduce the two-stage path analysis (2S-PA) with definition variables as a general framework for path modeling to handle categorical indicators, in which estimation of factor scores and path coefficients are separated. It thus allows for different estimation methods in the measurement and the structural path models and easier diagnoses of violations of model assumptions. We conducted three simulation studies, ranging from latent regression to mediation analysis with categorical indicators, and showed that 2S-PA generally produced similar estimates to those using SEM in large samples, but gave better convergence rates, less standard error bias, and better control of Type I error rates in small samples. We illustrate 2S-PA using data from a national data set, and show how researchers can implement it in Mplus and OpenMx. Possible extensions and future directions of 2S-PA are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Error Characteristic Analysis of Satellite-Based Precipitation Products over Mainland China

Satellite-based precipitation products (SPPs) provide valuable precipitation information for various applications. Their performance, however, varies significantly from region to region due to various data sources and production processes. This paper aims to evaluate four selected SPPs (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Gauge-adjusted Global Satellite Mapping of Precipitation (GSMAP-gauge), and Global Precipitation Measurement (GPM)) over mainland China from 2016 to 2019. Both conventional statistical indicators (e.g., correlation coefficients (CC), root mean square error (RMSE), mean absolute error (MAE), relative bias (RB), and Nash–Sutcliffe efficiency (NSE)) and categorical indicators (probability of detection (POD), probability of true detection (POTD), false-alarm rate (FAR), and critical success index (CSI)) are used for quantitative analysis. The results show that: (1) GSMAP-gauge and GPM perform best in reproducing the spatial distribution pattern of precipitation over mainland China, whereas SPPs generally underestimate summer precipitation with a high frequency of no-rain cases. (2) MSWEP has the best capability for recording precipitation events, although some parts of northern China exhibit abnormal overestimations for winter precipitation. (3) All SPPs, especially the PERSIANN-CDR, significantly underestimate the precipitation in the mountainous areas of southwestern China. (4) The GSMAP-gauge and GPM outperformed the other two of the four SPPs, in terms of the probability density function of daily precipitation for cases (PDFc) and the probability density function of daily precipitation for volume (PDFv). Generally, PERSIANN-CDR shows the poorest performance when compared to the other three products. The product’s algorithm for estimating heavy precipitation and mountainous precipitation needs further improvement.

Effects of a WeChat Mini-Program on Human Milk Feeding Rates in a Neonatal Intensive Care Unit During the COVID-19 Pandemic.

ObjectiveWe investigated changes in maternal daily milk pumping frequency and milk volume per expression and their derived lactation indicators, as well as human milk (HM) feeding status with a focus on amount and rates in preterm infants admitted to the neonatal intensive care unit (NICU) after using a WeChat mini-program during the 2019 coronavirus (COVID-19) pandemic.MethodsThe study was conducted with 482 mothers and their 544 babies. We prospectively enrolled mothers and infants with birth weight <1,500 g or gestational age <32 weeks born in 2020, and retrospectively included the same population in 2019. All study subjects were classified into three subgroups: pre-pandemic (PP, 2019), early pandemic (EP, January to April 2020), and late pandemic (LP, May to December 2020). From 1 January 2020, mothers recorded in an online pumping diary using the WeChat mini-program. We obtained the infants’ feeding information from an online database for analysis.ResultsMaternal lactation indicators did not change significantly. However, 56.7% (139/245) of mothers achieved milk volume ≥500 ml/day (CTV) in PP, 58.9% (33/156) in EP, and a slight increase to 60.7% (91/150) in LP. Maternal pumping frequency remained about eight times/day. In LP, daily milk volume was higher than the other two periods from day 4, and mothers achieved CTV by day 12, which was achieved in the other two groups by 13–14 days. There were several statistical differences in the amount and rates of feeding between the groups, particularly about HM and donor milk feeding, with the vast majority being decreased during EP, while during LP they returned to PP levels. Pleasingly, the median average daily dose of HM at 1–28 days was highest in LP (LP, 87.8 vs. PP, 75.5 or EP, 52.6 ml/kg/day, Pcorrected < 0.001). In addition, most categorical feeding indicators decreased in EP and recovered in LP.ConclusionAn education model based on the WeChat program could aid lactation education and management in mothers of preterm infants to maintain healthy lactation. The model, together with optimized management strategies, can ensure that the HM feeding rate is not compromised in vulnerable high-risk infants during NICU hospitalization in a public health emergency, like the COVID-19 pandemic.

A Data Mining Study on House Price in Central Regions of Taiwan Using Education Categorical Data, Environmental Indicators, and House Features Data

This study takes the city of Taichung, Taiwan, as the research area, combines the survey results about the demand for residential houses for the next year, and uses relevant parameters and data of real price registration as the prediction results. In this study, eight types of school district features (such as teachers and students of secondary and elementary schools) and five types of air pollution features are selected and processed with a data mining method to discover the total transactions of real estate properties in various districts of Taichung. The results of K-means clustering and decision tree classification reveal that the four districts of the old Taichung City, namely, Beitun District, North District, Xitun District, and Nantun District, have houses meeting the conditions of egg yolk districts; houses in the old Taichung County have attributes of egg white districts. The results of decision tree classification show that the total price is the most important attribute influencing egg yolk and egg white districts.

Causal effect analysis in nonrandomized data with latent variables and categorical indicators: The implementation and benefits of EffectLiteR.

Instead of using manifest proxies for a latent outcome or latent covariates in a causal effect analysis, the R package EffectLiteR facilitates a direct integration of latent variables based on structural equation models (SEM). The corresponding framework considers latent interactions and provides various effect estimates for evaluating the differential effectiveness of treatments. In addition, a user-friendly graphical interface customizes the implementation of the complex models. We aim to enable applications of EffectLiteR in more contexts, and therefore generalize the framework for incorporating latent variables measured with categorical indicators. This refers, for instance, to achievement tests in educational large-scale assessments (LSAs), which are typically constructed in the tradition of item response theory (IRT). We review different modeling strategies for incorporating latent variables from IRT models in an effect analysis (i.e., individual score estimates, plausible values, SEM for categorical indicators). The strategies differ in the handling of measurement error and, thus, have different implications for the accuracy and efficiency of causal effect estimates. We describe our extensions of EffectLiteR based on SEM for categorical indicators and illustrate the model specification step-by-step. In addition, we present a hands-on example, where we apply EffectLiteR in LSA data. The practical benefit of using latent variables in comparison to proficiency scores is of special interest in the application and discussion. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Elevated blood urea nitrogen-to-creatinine ratio increased the risk of Coronary Artery Disease in patients living with type 2 diabetes mellitus

BackgroundHigh Blood Urea Nitrogen (BUN) and high Serum Creatinine (SCr) levels are risk factors for Coronary Artery Disease (CAD). However, the relationship between the Blood Urea Nitrogen to Creatinine (BUN/SCr) ratio (UCR) and the risk of CAD in patients living with new-onset diabetes is unclear. This study aimed to examine the relationship between blood UCR and the risk of CAD in patients living with new-onset type 2 diabetes mellitus (T2DM).MethodsWe analyzed the data from the cohort of 12,299 patients living with type 2 diabetes mellitus. Primary endpoints were the events of CAD. The ANOVA test (continuous indicators) and χ2 test (categorical indicators) were used to assess the differences of baseline characteristics across the groups of UCR. In order to understand the correlation between variables, we performed correlation analysis on variables that have significant differences between CAD group and non-CAD group. Multivariate-adjusted Cox proportional hazard regression models were applied to estimate the association of the blood UCR with the risk of CAD in patients living with T2DM. The Kaplan–Meier survival function plotting and the log-rank test were used to evaluate the event-free survival according to the groups of UCR. The restricted cubic spline model was used to show the adjusted association between blood UCR and risk of CAD in patients living with T2DM.ResultsDuring a median follow-up of 2.66 years, 1173 CAD were recorded with an event rate of 28.49 events per 1000 person-years. In multivariate-adjusted Cox regression models, elevated blood urea nitrogen to creatinine ratio (UCR) was associated with higher risk of CAD in patients living with T2DM [hazard ratio (HR), 1.782; 95% confidence interval (CI), 1.237–2.567]. The Kaplan–Meier survival curves indicated that the high group of UCR tended to have a lower event-free survival than the low group and medium group. There was a nonlinear trend toward increasing risk of CAD across the groups of UCR. And cubic spline function graph suggested that the influence of UCR level on HR for CAD increased significantly at UCR levels above 6.67.ConclusionsAn elevated UCR was significantly associated with an increased risk for CAD in patients living with T2DM.

Collective Leadership: Developing a Tool to Assess Educator Readiness and Efficacy

Collective leadership is increasingly important because collective teacher efficacy, a subset of this type of leadership, is a significant factor in student learning. The purpose of this study is to provide evidence for the structural validity of a collective leadership instrument across public and private school settings using the multigroup factor analysis invariance framework for categorical indicators. The results supported invariance in factor structure, loadings, intercepts, and residual (co)variances and latent variances and covariances equality. Collective leadership development is theorized to play a central role in the effectiveness of both public and private schools alike so any instrument used to make decisions about collective leadership should be reflective of the pertinent constructs in general rather than the data being an artifact of the particular setting.

Brazilian version of Positive Oral Health and Well-Being: cross-cultural adaptation and psychometric analysis.

Specific measures to evaluate positive oral health have been in a nascent stage in Dentistry, especially in developing countries. The present study aimed to translate, cross-culturally adapt and evaluate the psychometric properties of the Brazilian version of Positive Oral Health and Well-Being (B-POHW). After forward-backwards translation to Brazilian Portuguese language, the cross-cultural adaptation of B-POHW was pretested, followed by the main study to perform psychometric analysis. We tested the model fit by Confirmatory Factor Analysis with categorical factor indicators in bifactor and simple structure models on a sample of 209 participants (mean age: 39.36 ± 12.26. Questionnaires about sociodemographic status, self-reported oral health-related outcomes, and general well-being were administered and used as external validation measures. Moreover, dental caries experience was clinically diagnosed. For test-retest reliability, 53 participants completed the B-POHW a fortnight later. The following results were found: a) the bifactor model presented the best model fit; b) the B-POHW demonstrated satisfactory internal consistency (Cronbach's α and McDonald's ω > 0.8); c) the intraclass correlation coefficient suggested good reliability for the Global Factor of B-POHW in the test-retest (ICC = 0.84); d) evidence based on other variables and construct representation was in line with the positive oral health framework. The B-POHW is psychometrically sound to be used in a Brazilian context, and evidence of its internal structure confirmed its theoretical framework for measuring positive oral health. These findings advance in holistic approaches, enabling to assess positive oral health in Dental practice in Brazil.

Multinational prediction of household and personal exposure to fine particulate matter (PM2.5) in the PURE cohort study.

Use of polluting cooking fuels generates household air pollution (HAP) containing health-damaging levels of fine particulate matter (PM2.5). Many global epidemiological studies rely on categorical HAP exposure indicators, which are poor surrogates of measured PM2.5 levels. To quantitatively characterize HAP levels on a large scale, a multinational measurement campaign was leveraged to develop household and personal PM2.5 exposure models. The Prospective Urban and Rural Epidemiology (PURE)-AIR study included 48-hour monitoring of PM2.5 kitchen concentrations (n=2,365) and male and/or female PM2.5 exposure monitoring (n=910) in a subset of households in Bangladesh, Chile, China, Colombia, India, Pakistan, Tanzania and Zimbabwe. PURE-AIR measurements were combined with survey data on cooking environment characteristics in hierarchical Bayesian log-linear regression models. Model performance was evaluated using leave-one-out cross validation. Predictive models were applied to survey data from the larger PURE cohort (22,480 households; 33,554 individuals) to quantitatively estimate PM2.5 exposures. The final models explained half (R2=54%) of the variation in kitchen PM2.5 measurements (root mean square error (RMSE) (log scale):2.22) and personal measurements (R2=48%; RMSE (log scale):2.08). Primary cooking fuel type, heating fuel type, country and season were highly predictive of PM2.5 kitchen concentrations. Average national PM2.5 kitchen concentrations varied nearly 3-fold among households primarily cooking with gas (20μg/m3 (Chile); 55μg/m3 (China)) and 12-fold among households primarily cooking with wood (36μg/m3 (Chile)); 427μg/m3 (Pakistan)). Average PM2.5 kitchen concentration, heating fuel type, season and secondhand smoke exposure were significant predictors of personal exposures. Modeled average PM2.5 female exposures were lower than male exposures in upper-middle/high-income countries (India, China, Colombia, Chile). Using survey data to estimate PM2.5 exposures on a multinational scale can cost-effectively scale up quantitative HAP measurements for disease burden assessments. The modeled PM2.5 exposures can be used in future epidemiological studies and inform policies targeting HAP reduction.

Testing Measurement Invariance of the Dark Triad Dirty Dozen in a Belgian Adult Sample.

The Dirty Dozen (Jonason & Webster, 2010) is a frequently used concise version of the Dark Triad to measure three socially aversive personality traits: Machiavellianism, psychopathy and, narcissism. The present study has examined measurement invariance in a sample of Belgian adults. The present study aims to assess measurement invariance of the Dutch version of the Dirty Dozen measure across gender in a large city-based representative adult sample in Belgium (N = 1587). Multi-group first-order confirmatory factor analysis for categorical indicators was utilized. In addition, unique associations between Dirty Dozen traits, trait self-control and, acceptance of illegitimate norms were examined in a series of structural equation models. Results indicated that the internal consistency of the Dirty Dozen subscales was good for Machiavellianism (α = 0.80) and narcissism (α = 0.80), but modest for psychopathy (α = 0.64). The hypothesized three correlated factors model with separate factors for Machiavellianism, psychopathy and, narcissism provided a poor fit for men and women. Invariance testing across gender showed evidence for weak invariance only, indicating that the underlying latent factors are measured the same way with the same metric in the two populations. However, we were not able to establish strong measurement invariance. Observed group differences should be interpreted with caution. Furthermore, Machiavellianism and psychopathy were strongly associated with trait self-control in both men and women. Strong correlations were found between acceptance of illegitimate norms and Dirty Dozen traits, Machiavellianism and, psychopathy, but not with narcissism.

The Specification and Impact of Prior Distributions for Categorical Latent Variable Models

ABSTRACT Latent class models can exhibit poor parameter recovery and low convergence rates under the traditional frequentist estimation approach. Bayesian estimation may be a viable alternative for estimating latent class models–especially when categorical items are present and priors can be placed directly on the categorical item-thresholds. We present a simulation study involving Bayesian latent class analysis (LCA) with categorical items. We demonstrate that the frequentist framework and the Bayesian framework with diffuse (non-informative) priors are unable to properly recover parameters (e.g., latent class item-thresholds); a substantive interpretation of the obtained results would lead to improper conclusions under these estimation conditions. However, specifying (weakly) informative priors within the Bayesian framework generally produced accurate parameter recovery, indicating that this may be a more viable estimation approach for LCA models with categorical indicators. The paper concludes with a general discussion surrounding the advantages of Bayesian estimation for LCA models.

Association between anxiety and mortality in patients with congestive heart failure after implantation of cardiac electronic devices

ObjectiveSeveral studies suggest that psychological factors including anxiety are associated with negative outcomes and in particular higher mortality rates among heart failure (HF) patients. However, the impact of anxiety on mortality in patients with implanted cardiac devices has not been fully appreciated. The aim of this study was to assess the association between state (SA) and trait (TA) anxiety and all-cause mortality in patients with HF after cardiac electronic devices implantation. MethodsThe monocentric prospective study enrolled 265 patients (215 men and 50 women) aged 23 to 84 years (mean age 57.1 ± 10.0), who received cardiac resynchronization therapy or cardioverter-defibrillator implantation. Mean duration of prospective follow-up was 62.3 ± 36.6 months. State-Trait Anxiety Inventory (STAI) was used to measure anxiety symptoms. Cox proportional hazards multivariate regression model was used to calculate hazard ratio (HR) of all-cause mortality with 95% confidence interval (95% CI). ResultsDuring the prospective follow-up period, 45 (17.0%) patients died due to all causes. According to quantitative analysis, HR for death used for SA scale was 1.04 (95% CI 1.00–1.07, p = 0.07) and for the TA scale 1.02 (95% CI 0.99–1.05, p = 0.21). Analysis of categorical indicators found statistically significant higher HR of mortality in patients with severe SA (2.35, 95% CI 1.17–4.71, p = 0.02), and TA (2.02, 95% CI 1.04–3.94, p = 0.04). ConclusionHigh levels of SA and TA was significantly and independently associated with a high risk of all-cause mortality in patients, who underwent implantation of cardiac electronic devices.

Application of Random Forest Algorithm for Merging Multiple Satellite Precipitation Products across South Korea

Precipitation is a crucial component of the water cycle and plays a key role in hydrological processes. Recently, satellite-based precipitation products (SPPs) have provided grid-based precipitation with spatiotemporal variability. However, SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution of these products is still relatively coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation based on a combination of rainfall observation data with multiple SPPs for the period of 2003–2017 across South Korea. A Random Forest (RF) machine-learning algorithm model was applied for producing a new merged precipitation product. In addition, several statistical linear merging methods have been adopted to compare with the results achieved from the RF model. To investigate the efficiency of RF, rainfall data from 64 observed Automated Synoptic Observation System (ASOS) installations were collected to analyze the accuracy of products through several continuous as well as categorical indicators. The new precipitation values produced by the merging procedure generally not only report higher accuracy than a single satellite rainfall product but also indicate that RF is more effective than the statistical merging method. Thus, the achievements from this study point out that the RF model might be applied for merging multiple satellite precipitation products, especially in sparse region areas.

COVID-19 vaccines and mental distress.

The COVID-19 pandemic brought about large increases in mental distress. The uptake of COVID-19 vaccines is expected to significantly reduce health risks, improve economic and social outcomes, with potential benefits to mental health. To examine short-term changes in mental distress following the receipt of the first dose of the COVID-19 vaccine. Participants included 8,003 adults from the address-based sampled, nationally representative Understanding America Study (UAS), surveyed at regular intervals between March 10, 2020, and March 31, 2021 who completed at least two waves of the survey. Respondents answered questions about COVID-19 vaccine status and self-reported mental distress as measured with the four-item Patient Health Questionnaire (PHQ-4). Fixed-effects regression models were used to identify the change in PHQ-4 scores and categorical indicators of mental distress resulting from the application of the first dose of the COVID-19 vaccine. People who were vaccinated between December 2020 and March 2021 reported decreased mental distress levels in the surveys conducted after receiving the first dose. The fixed-effects estimates show an average effect of receiving the vaccine equivalent to 4% of the standard deviation of PHQ-4 scores (p-value<0.01), a reduction in 1 percentage point (4% reduction from the baseline level) in the probability of being at least mildly depressed, and of 0.7 percentage points (15% reduction from the baseline level) in the probability of being severely depressed (p-value = 0.06). Getting the first dose of COVID-19 resulted in significant improvements in mental health, beyond improvements already achieved since mental distress peaked in the spring of 2020.

The Role of Blood Pressure in Carotid Plaque Incidence: Interactions With Body Mass Index, Age, and Sex-Based on a 7-Years Cohort Study.

Background: Although high blood pressure (BP) is a risk factor for carotid plaque, its long-term prognostic value might be underestimated due to its confounding interactions with BMI, age, and gender. Therefore, we conducted a 7-year prospective cohort study to evaluate the prognostic value of BP for the incidence of carotid plaque.Methods: The subjects enrolled in 2011 were free of carotid plaque at baseline and were followed up in 2018. Multivariate Cox proportional-hazards models were used to evaluate the association between BP and carotid plaque incidence.Results: During the follow-up study, the incidence of carotid plaque was 36.5%. The significant positive linear trend showed that subjects with higher BP levels at baseline were more likely to develop carotid plaques at the end. Especially in the female subpopulation, after confounders being adjusted, the carotid plaque was associated with higher BP (adjusted HR 1.52, 95% CI 1.02–2.26), pulse pressure (PP) (adjusted HR 1.15, 95% CI 0.76–1.75), and mean arterial pressure (MAP) (adjusted HR 1.44, 95% CI 1.00–2.08). The adjusted HRs of hypertension, PP, and MAP (HR 27.71, 95% CI 2.27–338.64; HR 14.47, 95% CI 1.53–137.18; HR 9.97, 95% CI 1.29–77.28) were significantly higher after the potential antagonistic interactions between BP categorical indicators and age being adjusted, respectively.Conclusion: High BP indicators might be associated with higher HRs of carotid plaque after adjusting interactions between BP indicators and BMI, age, and gender, which suggests that the incidence of carotid plaque in female adults with high BP indicators might increase significantly with the increase of age.

Temporal Patterning Integrating Diet and Physical Activity Shows Stronger Links to Health Indicators Compared to Patterning of Either Diet or Physical Activity Alone

Abstract Objectives Daily temporal patterns of energy intake (temporal dietary patterns, TDPs) and physical activity (temporal PA patterns, TPAPs) have been independently and jointly (joint temporal dietary and PA patterns, TDPAPs) associated with health indicators. The strength of the association between clusters of each pattern and health indicators including body mass index (BMI), waist circumference (WC), fasting plasma glucose (FPG), hemoglobin A1c (A1c), triglyceride (TAG), high-density lipoprotein cholesterol (HDL-C), total cholesterol (Total-C), blood pressure, type 2 diabetes (T2D), metabolic syndrome (MetS), and obesity, were compared. Methods The reported energy throughout a day from one reliable 24-hour weekday dietary recall and activity counts from a random weekday of PA accelerometer data of 1,836 U.S. adults from the National Health and Nutrition Examination Survey (2003–2006) were used to create TDP and TPAP respectively, and jointly for TDPAP. Constrained dynamic time warping distances computed over the time series were partitioned to four clusters using kernel-k means clustering algorithm. Measured BMI, WC, FPG, A1c, TAG, HDL-C, Total-C, and classified T2DM, MetS, and obesity were outcomes in multivariate regression models to determine associations with the clusters representing each pattern, controlling for potential confounders and adjusting for multiple comparisons (P &amp;lt; 0.05/6). Adjusted R2 and Akaike information criterion (AIC) compared the strength of the associations between clusters and continuous or categorical health indicators. Results All temporal patterns were significantly associated with BMI, WC, and obesity. Adjusted R2 of BMI and WC models for significant predictors’ effects were higher for TDPAPs (0.129 and 0.194) than TDPs (0.117 and 0.186) or TPAPs (0.077 and 0.143), and AIC of obesity for the TDPAPs (234,752,082) was smaller than for TDPs (236,650,170) or TPAPs (239,810,423). Conclusions TDPAPs incorporating time of day with energy intake and PA had the strongest associations with BMI, WC, and obesity compared with either independent temporal dietary or PA patterns. Patterns representing the integration of multiple daily behavioral habits hold promise for early detection of obesity. Funding Sources NIH (R21CA224764) and Purdue University.