Diagnostic accuracy of endoscopic ultrasonographic shear wave elastography for assessing early chronic pancreatitis using the Japanese diagnostic criteria 2019.

Background and Aim: Endoscopic ultrasound shear wave elastography (EUS-SWE) can facilitate an objective evaluation of pancreatic fibrosis. Although it is primarily applied in evaluating chronic pancreatitis, its efficacy in assessing early chronic pancreatitis (ECP) remains underinvestigated. This study evaluated the diagnostic accuracy of EUS-SWE for assessing ECP diagnosed using the Japanese diagnostic criteria 2019. Methods: In total, 657 patients underwent EUS-SWE. Propensity score matching was used, and the participants were classified into the ECP and normal groups. ECP was diagnosed using the Japanese diagnostic criteria 2019. Pancreatic stiffness was assessed based on velocity (Vs) on EUS-SWE, and the optimal Vs cutoff value for ECP diagnosis was determined. A practical shear wave Vs value of ≥50% was considered significant. Results: Each group included 22 patients. The ECP group had higher pancreatic stiffness than the normal group (2.31 ± 0.67 m/s vs. 1.59 ± 0.40 m/s, p < 0.001). The Vs cutoff value for the diagnostic accuracy of ECP, as determined using the receiver operating characteristic curve, was 2.24m/s, with an area under the curve of 0.82 (95% confidence interval: 0.69-0.94). A high Vs was strongly correlated with the number of EUS findings (rs = 0.626, p < 0.001). Multiple regression analysis revealed that a history of acute pancreatitis and ≥2 EUS findings were independent predictors of a high Vs. Conclusions: There is a strong correlation between EUS-SWE findings and the Japanese diagnostic criteria 2019 for ECP. Hence, EUS-SWE can be an objective and invaluable diagnostic tool for ECP diagnosis.

Optimal variable identification for accurate detection of causal expression Quantitative Trait Loci with applications in heart-related diseases.

Gene expression plays a pivotal role in various diseases, contributing significantly to their mechanisms. Most GWAS risk loci are in non-coding regions, potentially affecting disease risk by altering gene expression in specific tissues. This expression is notably tissue-specific, with genetic variants substantially influencing it. However, accurately detecting the expression Quantitative Trait Loci (eQTL) is challenging due to limited heritability in gene expression, extensive linkage disequilibrium (LD), and multiple causal variants. The single variant association approach in eQTL analysis is limited by its susceptibility to capture the combined effects of multiple variants, and a bias towards common variants, underscoring the need for a more robust method to accurately identify causal eQTL variants. To address this, we developed an algorithm, CausalEQTL, which integrates L 0 +L 1 penalized regression with an ensemble approach to localize eQTL, thereby enhancing prediction performance precisely. Our results demonstrate that CausalEQTL outperforms traditional models, including LASSO, Elastic Net, Ridge, in terms of power and overall performance. Furthermore, analysis of heart tissue data from the GTEx project revealed that eQTL sites identified by our algorithm provide deeper insights into heart-related tissue eQTL detection. This advancement in eQTL mapping promises to improve our understanding of the genetic basis of tissue-specific gene expression and its implications in disease. The source code and identified causal eQTLs for CausalEQTL are available on GitHub: https://github.com/zhc-moushang/CausalEQTL.

Development and validation of a nomogram predictive model for cognitive impairment in cerebral small vessel disease: a comprehensive retrospective analysis.

Background: Cerebral small vessel disease (CSVD) is a common neurodegenerative condition in the elderly, closely associated with cognitive impairment. Early identification of individuals with CSVD who are at a higher risk of developing cognitive impairment is crucial for timely intervention and improving patient outcomes. Objective: The aim of this study is to construct a predictive model utilizing LASSO regression and binary logistic regression, with the objective of precisely forecasting the risk of cognitive impairment in patients with CSVD. Methods: The study utilized LASSO regression for feature selection and logistic regression for model construction in a cohort of CSVD patients. The model's validity was assessed through calibration curves and decision curve analysis (DCA). Results: A nomogram was developed to predict cognitive impairment, incorporating hypertension, CSVD burden, apolipoprotein A1 (ApoA1) levels, and age. The model exhibited high accuracy with AUC values of 0.866 and 0.852 for the training and validation sets, respectively. Calibration curves confirmed the model's reliability, and DCA highlighted its clinical utility. The model's sensitivity and specificity were 75.3 and 79.7% for the training set, and 76.9 and 74.0% for the validation set. Conclusion: This study successfully demonstrates the application of machine learning in developing a reliable predictive model for cognitive impairment in CSVD. The model's high accuracy and robust predictive capability provide a crucial tool for the early detection and intervention of cognitive impairment in patients with CSVD, potentially improving outcomes for this specific condition.

Prediction model for severe maternal morbidity in pregnant women with hypertensive disorders of pregnancy.

Objectives: To investigate risk factors for severe maternal morbidity (SMM) in pregnant women with hypertensive disorders of pregnancy (HDP) and to develop a risk prediction model. Methods: A prospective observational cohort study was conducted among pregnant women who were hospitalized for hypertensive disorders of pregnancy (HDP) between January 2016 and December 2020 in Fujian College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Province, China (a training set), and a risk predictive model was constructed. Pregnant women with HDP who were hospitalized between January 2021 and December 2021 were selected as a validation set. Concordance index (C-index) and calibration curves were used to test predictive model discrimination and calibration. Results: We included 970 pregnant women (790 in the training set and 180 in the validation set). Least absolute shrinkage and selection operator regression was used to screen for nine related variables such as intra-uterine growth retardation (IUGR), diastolic blood pressure (DBP) and systolic blood pressure (SBP) at suspected diagnosis, total bilirubin, albumin (ALB), uric acid, total cholesterol, serum magnesium, and suspected gestational age. SBP at suspected diagnosis (OR =1.22, 95%CI:1.08-1.42) and total cholesterol (OR = 1.78, 95%CI:1.17-2.80) were independent risk factors of severe maternal morbidity in pregnant women with HDP. A nomogram was constructed, and internal validation of the nomogram model was done using the bootstrap self-sampling method. C-index in the training and the validation set was 0.798 and 0.909, respectively. Conclusion: Our prediction model can be used to determine gestational hypertension severity in pregnant women.

Statistical Assessment of the Effectiveness of Multiple COVID-19 Vaccinations on Daily Confirmed Cases in Seoul City.

This study investigates the effectiveness of multiple COVID-19 vaccinations on daily confirmed cases in Seoul City. Utilizing comprehensive data on vaccinated individuals and confirmed cases sourced from the official website of the Korean Ministry of the Interior and Safety, we conducted detailed statistical analyses to assess the impact of each vaccination dose. The study covers data from April 21, 2021, to September 29, 2022. Statistical multiple linear regression was employed to analyze the relationship between daily confirmed cases (positive outcomes from PCR tests) and multiple vaccine doses, using p-values as the criteria for determining the effectiveness of each dose. The analysis included data from four vaccination doses. The analysis reveals that the first, second, and third doses of the COVID-19 vaccines have a statistically significant positive effect associated with the daily confirmed cases. However, the study finds that the fourth dose does not show a statistically significant impact on the reduction of daily confirmed cases. This suggests that while the initial three doses are crucial for establishing and maintaining high levels of immunity, the incremental benefit of subsequent doses may diminish.

Temporal evolution of suicide by levels of rurality and deprivation among Japanese adults aged 20 years or over between 2009 and 2022.

PURPOSE: Previous studies have reported that levels of rurality and deprivation are factors associated with suicide risk. Reports on the association between rurality, deprivation and suicide incidence during the COVID-19 pandemic are scarce. The study aims to investigate how suicide rates evolved in areas with different levels of rurality and deprivation among Japanese adults aged 20 years or older between 2009 and 2022. METHODS: This study used population density in 2020 as an indicator of rurality and per capita prefectural income in 2019 as a proxy for deprivation in Japan's 47 prefectures. Joinpoint regression analysis was performed to analyze secular trends in suicide rates by rurality and deprivation. RESULTS: Suicide rates for both men and women at different levels of rurality and deprivation remained roughly parallel during the research period. Suicide rates for men and women at all levels of rurality and deprivation were on a downward trend until around 2019, just before the onset of the pandemic. Following this, suicide rates in women showed a clear upward trend, while the trend in suicide rates for men also changed around 2019, with a slightly increasing or flat trend thereafter. Changes in suicide rates were greater among women and those aged 20-59 years. CONCLUSIONS: In Japan, time trends in suicide rates for both men and women have changed before and after the pandemic, but levels of rurality and deprivation across the 47 prefectures do not appear to have contributed much to these changes.

Remediation of phenanthrene contaminated soil by persulphate coupled with Pseudomonas aeruginosa GZ7 based on oxidation prediction model.

Chemical oxidation coupled with microbial remediation has attracted widespread attention for the removal of polycyclic aromatic hydrocarbons (PAHs). Among them, the precise evaluation of the feasible oxidant concentration of PAH-contaminated soil is the key to achieving the goal of soil functional ecological remediation. In this study, phenanthrene (PHE) was used as the target pollutant, and Fe2+-activated persulphate (PS) was used to remediate four types of soils. Linear regression analysis identified the following important factors influencing remediation: PS dosage and soil PHE content for PHE degradation, Fe2+ dosage, hydrolysable nitrogen (HN), and available phosphorus for PS decomposition. A comprehensive model of "soil characteristics-oxidation conditions-remediation effect" with a high predictive accuracy was constructed. Based on model identification, Pseudomonas aeruginosa GZ7, which had high PAHs degrading ability after domestication, was further applied to coupling repair remediation. The results showed that the optimal PS dose was 0.75% (w/w). The response relationship between soil physical, chemical, and biological indicators at the intermediate interface and oxidation conditions was analysed. Coupled remediation effects were clarified using microbial diversity sequencing. The introduction of Pseudomonas aeruginosa GZ7 stimulated the relative abundance of Cohnella, Enterobacter, Paenibacillus, and Bacillus, which can promote material metabolism and energy transformation during remediation.

A novel bounded loss framework for support vector machines.

This paper introduces a novel bounded loss framework for SVM and SVR. Specifically, using the Pinball loss as an illustration, we devise a novel bounded exponential quantile loss (Leq-loss) for both support vector machine classification and regression tasks. For Leq-loss, it not only enhances the robustness of SVM and SVR against outliers but also improves the robustness of SVM to resampling from a different perspective. Furthermore, EQSVM and EQSVR were constructed based on Leq-loss, and the influence functions and breakdown point lower bounds of their estimators are derived. It is proved that the influence functions are bounded, and the breakdown point lower bounds can reach the highest asymptotic breakdown point of 1/2. Additionally, we demonstrated the robustness of EQSVM to resampling and derived its generalization error bound based on Rademacher complexity. Due to the Leq-loss being non-convex, we can use the concave-convex procedure (CCCP) technique to transform the problem into a series of convex optimization problems and use the ClipDCD algorithm to solve these convex optimization problems. Numerous experiments have been conducted to confirm the effectiveness of the proposed EQSVM and EQSVR.

Remnant cholesterol is associated with the progression and regression of metabolic dysfunction-associated steatotic liver disease in Chinese adults.

BACKGROUND: The aim of this study was to explore the associations of serum remnant cholesterol (RC) levels with the progression and regression of metabolic dysfunction-associated steatotic liver disease (MASLD) in Chinese adults. METHODS: We conducted a cross-sectional study in 13,903 individuals who underwent transient elastography tests (cohort 1) and a longitudinal study in 17,752 individuals who underwent at least two health check-up exams with abdominal ultrasound (cohort 2). Anthropometric and biochemical parameters were collected. Serum RC levels were calculated. Noninvasive fibrosis indices such as FIB-4 were evaluated in cohort 2. RESULTS: In cohort 1, serum RC levels were positively and independently associated with the severity of hepatic steatosis and liver fibrosis according to logistic regression analysis. In cohort 2, baseline serum RC levels were increased in participants with the incidence of MASLD and decreased in participants with the regression of MASLD during the follow-up period. Baseline serum RC levels were independently associated with an increased risk of development and a decreased likelihood of regression of MASLD: the fully adjusted hazard ratios (HR) were 2.785 (95 % CI 2.332-3.236, P < 0.001) and 2.925 (95 % CI 2.361-3.623, P < 0.001), respectively. In addition, when we used FIB-4 to evaluate liver fibrosis, baseline serum RC levels were positively correlated with the incidence of high-intermediate probability of advanced fibrosis. However, we did not find an association between serum RC levels and the regression of liver fibrosis. CONCLUSION: Serum RC levels are independently correlated with the progression and regression of MASLD in Chinese adults, suggesting that RC may participate in the pathophysiological process of MASLD.

Hepatic arterial infusion chemotherapy plus camrelizumab and apatinib for advanced hepatocellular carcinoma.

BACKGROUND AND AIMS: There is limited information on combination of hepatic arterial infusion chemotherapy (HAIC) and systemic therapy for advanced hepatocellular carcinoma (Ad-HCC). We aim to compare the efficacy and safety of HAIC plus camrelizumab (a PD-1 inhibitor) and apatinib (an VEGFR-2 inhibitor) versus camrelizumab and apatinib for Ad-HCC. METHODS: From April 2019 to October 2022, 416 patients with Ad-HCC who received either HAIC plus camrelizumab and apatinib (TRIPLET protocol, n = 207) or camrelizumab and apatinib (C-A protocol, n = 209) were reviewed retrospectively. The propensity score matching (PSM) was used to reduce selective bias. Overall survival (OS) and progression-free survival (PFS) were compared using the Kaplan-Meier method with the log-rank test. Cox regression analyses of independent prognostic factors were evaluated. RESULTS: After PSM 1:1, 109 patients were assigned to two groups. The median OS of not reached in the TRIPLET group was significantly longer than that of 19.9 months in the C-A group (p < 0.001), while in the TRIPLET group, the median PFS of 11.5 months was significantly longer than that of 9.6 months in the C-A group (p < 0.001). Multivariate analyses showed that the factors significantly affected the OS were CTP grade, tumor number > 3, and TRIPLET treatment (p < 0.001). Grade 3/4 adverse events occurred at a rate of 82.1% vs. 71.3% in TRIPLET and C-A groups, respectively. CONCLUSION: The TRIPLET protocol has promising survival benefits in the management of patients with Ad-HCC, with acceptable safety. TRAIL REGISTRATION: The study has been retrospectively registered at Chinese Clinical Trial Registry ( https://www.chictr.org.cn/ , ChiCTR2300075828).

AI-based digital pathology provides newer insights into lifestyle intervention-induced fibrosis regression in MASLD: An exploratory study.

BACKGROUND AND AIMS: Lifestyle intervention is the mainstay of therapy for metabolic dysfunction-associated steatohepatitis (MASH), and liver fibrosis is a key consequence of MASH that predicts adverse clinical outcomes. The placebo response plays a pivotal role in the outcome of MASH clinical trials. Second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) microscopy with artificial intelligence analyses can provide an automated quantitative assessment of fibrosis features on a continuous scale called qFibrosis. In this exploratory study, we used this approach to gain insight into the effect of lifestyle intervention-induced fibrosis changes in MASH. METHODS: We examined unstained sections from paired liver biopsies (baseline and end-of-intervention) from MASH individuals who had received either routine lifestyle intervention (RLI) (n = 35) or strengthened lifestyle intervention (SLI) (n = 17). We quantified liver fibrosis with qFibrosis in the portal tract, periportal, transitional, pericentral, and central vein regions. RESULTS: About 20% (7/35) and 65% (11/17) of patients had fibrosis regression in the RLI and SLI groups, respectively. Liver fibrosis tended towards no change or regression after each lifestyle intervention, and this phenomenon was more prominent in the SLI group. SLI-induced liver fibrosis regression was concentrated in the periportal region. CONCLUSION: Using digital pathology, we could detect a more pronounced fibrosis regression with SLI, mainly in the periportal region. With changes in fibrosis area in the periportal region, we could differentiate RLI and SLI patients in the placebo group in the MASH clinical trial. Digital pathology provides new insight into lifestyle-induced fibrosis regression and placebo responses, which is not captured by conventional histological staging.

Normative data for executive function tests in an Ecuadorian Waranka minority population.

Objective: To generate normative data (ND) for executive functions tests in the Waranka minority population of Ecuador. Method: Four-hundred participants aged 6-17 completed the Symbol-Digit Modalities Test (SDMT), Trail-Making Test (TMT), Modified-Wisconsin Card Sorting Test (M-WCST), and Test of Colors-Words (STROOP). Scores were normed using multiple linear regressions, including age, age2, natural logarithm of mean parent education (MPE), sex, bilingualism, and two-way interactions as predictors. Results: Age by MPE and Age2 by MPE interactions arose for SDMT, so that children with illiterate parents scored lower than those with literate parents. Girls scored higher in SDMT. All TMT and M-WCST scores were influenced by age2. Age by MPE interaction was found for TMT-A, so that children with higher MPE went faster; and age by bilingualism interaction for TMT-B, so that more bilingual children needed less time. Stroop-Word and Color were influenced by age2 by MPE interaction, so that children, while older, scored higher, especially those with higher MPE. Also, age2 by sex interaction arose, so that girls increased scores curvilinearly while boys linearly. Word-Color was influenced by age, while Stroop-interference by age2. Age by MPE interaction was found for MCST-Categories and Perseveration, so that perseverations decreased to then increased, especially in those with illiterate parents. M-WCST-Category scores increased to then decrease later on age in children with illiterate parents. Z-scores calculated through indigenous ND were significantly lower than generated through non-indigenous norms. Conclusions: ND for minority populations are critical since Waranka sample performed worse when using non-indigenous norms for z-score calculation.

Effect of body mass index on progesterone level on trigger day in gonadotropin-releasing hormone antagonist cycles.

OBJECTIVE: To investigate the effect of body mass index (BMI) on progesterone (P) level on trigger day in gonadotropin-releasing hormone antagonist (GnRH-ant) cycles. METHODS: This study was a retrospective cohort study. From October 2017 to April 2022, 412 in-vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) patients who were treated with GnRH-ant protocol for controlled ovarian hyperstimulation (COH) in the reproductive center of our hospital were selected as the research objects. Patients were divided into three groups according to BMI level: normal weight group (n = 230):18.5 kg/m2≤BMI < 24 kg/m2; overweight group (n = 122): 24 kg/m2≤BMI < 28 kg/m2; Obesity group (n = 60): BMI ≥ 28 kg/m2. Variables with p < .10 in univariate analysis (BMI, basal FSH, basal P, FSH days, Gn starting dose and E2 level on trigger day) and variables that may affect P level on trigger day (infertility factors, basal LH, total FSH, HMG days and total HMG) were included in the multivariate logistic regression model to analyze the effect of BMI on P level on trigger day of GnRH-ant protocol. RESULTS: After adjustment for confounding factors, compared with that in normal weight patients, the risk of serum P elevation on trigger day was significantly lower in overweight and obese patients (OR = 0.434 and 0.199, respectively, p < .05). CONCLUSION: The risk of P elevation on trigger day in GnRH-ant cycles decreased with the increase of BMI, and BMI could be used as one of the predictors of P level on trigger day in GnRH-ant cycles.

Erratum: Effects of COVID-19-targeted non-pharmaceutical interventions on pediatric hospital admissions in North Italian hospitals, 2017 to 2022: a quasi-experimental study interrupted time-series analysis.

[This corrects the article DOI: 10.3389/fpubh.2024.1393677.].

Estimating the strength of soil stabilized with cement and lime at optimal compaction using ensemble-based multiple machine learning.

It has been imperative to study and stabilize cohesive soils for use in the construction of pavement subgrade and compacted landfill liners considering their unconfined compressive strength (UCS). As long as natural cohesive soil falls below 200 kN/m2 in strength, there is a structural necessity to improve its mechanical property to be suitable for the intended structural purposes. Subgrades and landfills are important environmental geotechnics structures needing the attention of engineering services due to their role in protecting the environment from associated hazards. In this research project, a comparative study and suitability assessment of the best analysis has been conducted on the behavior of the unconfined compressive strength (UCS) of cohesive soil reconstituted with cement and lime and mechanically stabilized at optimal compaction using multiple ensemble-based machine learning classification and symbolic regression techniques. The ensemble-based ML classification techniques are the gradient boosting (GB), CN2, naïve bayes (NB), support vector machine (SVM), stochastic gradient descent (SGD), k-nearest neighbor (K-NN), decision tree (Tree) and random forest (RF) and the artificial neural network (ANN) and response surface methodology (RSM) to estimate the (UCS, MPa) of cohesive soil stabilized with cement and lime. The considered inputs were cement (C), lime (Li), liquid limit (LL), plasticity index (PI), optimum moisture content (OMC), and maximum dry density (MDD). A total of 190 mix entries were collected from experimental exercises and partitioned into 74-26% train-test dataset. At the end of the model exercises, it was found that both GB and K-NN models showed the same excellent accuracy of 95%, while CN2, SVM, and Tree models shared the same level of accuracy of about 90%. RF and SGD models showed fair accuracy level of about 65-80% and finally (NB) badly producing an unacceptable low accuracy of 13%. The ANN and the RSM also showed closely matched accuracy to the SVM and the Tree. Both of correlation matrix and sensitivity analysis indicated that UCS is greatly affected by MDD, then the consistency limits and cement content, and lime content comes in the third place while the impact of (OMC) is almost neglected. This outcome can be applied in the field to obtain optimal compacted for a lime reconstituted soil considering the almost negligible impact of compactive moisture.

The predictive capability of several anthropometric indices for identifying the risk of metabolic syndrome and its components among industrial workers.

Metabolic syndrome (MetS) is closely associated with adverse cardiometabolic outcomes. The objective of this study was to identify practical methods that could enable the effective identification of MetS based on anthropometric indices. The basis of our study involved retrospective database obtained from routine medical prophylactic examinations. This was a cross-sectional study on the health status of male workers employed in hazardous working conditions at industrial enterprises in the Ural region conducted in 2019. A total of 347 male workers employed under hazardous working conditions were investigated. The presence of MetS was established by a healthcare professional in accordance with the guidelines of the International Diabetes Federation (IDF). Simple linear regression was used to evaluate the associations between anthropometric indices and MetS incidence. Logistic regression was used to determine the odds ratios of MetS in relation to increases in anthropometric indices. ROC curves were calculated to compare the ability of each anthropometric index to predict MetS and to determine the diagnostic thresholds of the indicators considered. According to the IDF criteria, 36.3% of the workers had MetS. A direct relationship was found between the individual components of MetS and the anthropometric indices studied. The highest OR was shown by the Body Roundness Index (BRI) of 2.235 (95% CI 1.796-2.781). For different age quartiles, the optimal cut-off values for predicting MetS were as follows: BRI, 4.1-4.4 r.u.; body shape index (ABSI), 0.080-0.083 m11/6 kg-2/3; and lipid accumulation product (LAP), 49.7-70.5 cm mmol/l. The most significant associations with MetS were observed where the values were greater than these cut-off points (Se = 97.4%). The results of this study demonstrated the rapid use of new anthropometric indicators, which have shown good predictive ability and are quite easy to use.

Assessing the relationship between malaria incidence levels and meteorological factors using cluster-integrated regression.

This paper introduces a novel approach to modeling malaria incidence in Nigeria by integrating clustering strategies with regression modeling and leveraging meteorological data. By decomposing the datasets into multiple subsets using clustering techniques, we increase the number of explanatory variables and elucidate the role of weather in predicting different ranges of incidence data. Our clustering-integrated regression models, accompanied by optimal barriers, provide insights into the complex relationship between malaria incidence and well-established influencing weather factors such as rainfall and temperature.We explore two models. The first model incorporates lagged incidence and individual-specific effects. The second model focuses solely on weather components. Selection of a model depends on decision-makers priorities. The model one is recommended for higher predictive accuracy. Moreover, our findings reveal significant variability in malaria incidence, specific to certain geographic clusters and beyond what can be explained by observed weather variables alone.Notably, rainfall and temperature exhibit varying marginal effects across incidence clusters, indicating their differential impact on malaria transmission. High rainfall correlates with lower incidence, possibly due to its role in flushing mosquito breeding sites. On the other hand, temperature could not predict high-incidence cases, suggesting that other factors other than temperature contribute to high cases.Our study addresses the demand for comprehensive modeling of malaria incidence, particularly in regions like Nigeria where the disease remains prevalent. By integrating clustering techniques with regression analysis, we offer a nuanced understanding of how predetermined weather factors influence malaria transmission. This approach aids public health authorities in implementing targeted interventions. Our research underscores the importance of considering local contextual factors in malaria control efforts and highlights the potential of weather-based forecasting for proactive disease management.

Efficiency evaluation and promoter identification of primary health care system in China: an enhanced DEA-Tobit approach.

BACKGROUND: With Primary Health Care (PHC) being a cornerstone of accessible, affordable, and effective healthcare worldwide, its efficiency, especially in developing countries like China, is crucial for achieving Universal Health Coverage (UHC). This study evaluates the efficiency of PHC systems in a southwest China municipality post-healthcare reform, identifying factors influencing efficiency and proposing strategies for improvement. METHODS: Utilising a 10-year provincial panel dataset, this study employs an enhanced Data Envelopment Analysis (DEA) model integrating Slack-Based Measure (SBM) and Directional Distance Function (DDF) with the Global Malmquist-Luenberger (GML) index for efficiency evaluation. Tobit regression analysis identifies efficiency determinants within the context of China's healthcare reforms, focusing on horizontal integration, fiscal spending, urbanisation rates, and workforce optimisation. RESULTS: The study reveals a slight decline in PHC system efficiency across the municipality from 2009 to 2018. However, the highest-performing county achieved a 2.36% increase in Total Factor Productivity (TFP), demonstrating the potential of horizontal integration reforms and strategic fiscal investments in enhancing PHC efficiency. However, an increase in nurse density per 1,000 population negatively correlated with efficiency, indicating the need for a balanced approach to workforce expansion. CONCLUSIONS: Horizontal integration reforms, along with targeted fiscal inputs and urbanisation, are key to improving PHC efficiency in underdeveloped regions. The study underscores the importance of optimising workforce allocation and skillsets over mere expansion, providing valuable insights for policymakers aiming to strengthen PHC systems toward achieving UHC in China and similar contexts.

The relationship between general self-efficacy and nursing practice competence for second-year nurses: Empirical quantitative research.

AIM: To examine the relationship between general self-efficacy and nursing practice competence for nurses in the second year of employment. DESIGN: A cross-sectional design was used. DATA SOURCES: The study included 596 nurses in their second year of employment at 75 medical facilities across Japan and used an online questionnaire survey for data collection. RESULTS: The covariance structure analysis showed the path from general self-efficacy (latent variable) to nursing practice competence. Positive correlations were found between all factors on both scales. Multiple regression analysis results showed that the general self-efficacy factors of 'positivity in behavior' and 'confidence in social competence' affect nursing practice competence. CONCLUSION: This study emphasizes the importance of enhancing the general self-efficacy of second-year nurses to improve their nursing practice competence. To achieve this, it suggests developing strategies from the perspective of the factors that comprise general self-efficacy. IMPLICATIONS FOR THE PROFESSION AND PATIENT CARE: The findings suggest that improving general self-efficacy can enhance nursing practice competence, which could inform the development of interventions to support nurses in improving their competence. The study provides basic data for improving nurses' practice competence. IMPACT: This study is the first to establish a relationship between general self-efficacy and nursing practice competence among second-year nurses. It demonstrates the significance of general self-efficacy in enhancing nursing practice competence, particularly for second-year nurses worldwide who may be struggling with their nursing practice competence and considering leaving the profession. The findings offer practical implications for stakeholders involved in nursing education and training programs, with potential applications in professional development. REPORTING METHOD: This manuscript adheres to the STROBE guidelines for the reporting of cross-sectional studies. PATIENT OR PUBLIC CONTRIBUTION: There was no patient or public contribution.

Analysis of Microbiome Data.

The microbiome represents a hidden world of tiny organisms populating not only our surroundings but also our own bodies. By enabling comprehensive profiling of these invisible creatures, modern genomic sequencing tools have given us an unprecedented ability to characterize these populations and uncover their outsize impact on our environment and health. Statistical analysis of microbiome data is critical to infer patterns from the observed abundances. The application and development of analytical methods in this area require careful consideration of the unique aspects of microbiome profiles. We begin this review with a brief overview of microbiome data collection and processing and describe the resulting data structure. We then provide an overview of statistical methods for key tasks in microbiome data analysis, including data visualization, comparison of microbial abundance across groups, regression modeling, and network inference. We conclude with a discussion and highlight interesting future directions.