Utilizing CT imaging for evaluating late gastrointestinal tract side effects of radiotherapy in uterine cervical cancer: a risk regression analysis.

BACKGROUND: Radiotherapy (RT) is effective for cervical cancer but causes late side effects (SE) to nearby organs. These late SE occur more than 3 months after RT and are rated by clinical findings to determine their severity. While imaging studies describe late gastrointestinal (GI) SE, none demonstrate the correlation between the findings and the toxicity grading. In this study, we demonstrated the late GI toxicity prevalence, CT findings, and their correlation. METHODS: We retrospectively studied uterine cervical cancer patients treated with RT between 2015 and 2018. Patient characteristics and treatment(s) were obtained from the hospital's databases. Late RTOG/EORTC GI SE and CT images were obtained during the follow-up. Post-RT GI changes were reviewed from CT images using pre-defined criteria. Risk ratios (RR) were calculated for CT findings, and multivariable log binomial regression determined adjusted RRs. RESULTS: This study included 153 patients, with a median age of 57 years (IQR 49-65). The prevalence of ≥ grade 2 RTOG/EORTC late GI SE was 33 (27.5%). CT findings showed 91 patients (59.48%) with enhanced bowel wall (BW) thickening, 3 (1.96%) with bowel obstruction, 7 (4.58%) with bowel perforation, 6 (3.92%) with fistula, 0 (0%) with bowel ischemia, and 0 (0%) with GI bleeding. Adjusted RRs showed that enhanced BW thickening (RR 9.77, 95% CI 2.64-36.07, p = 0.001), bowel obstruction (RR 5.05, 95% CI 2.30-11.09, p < 0.001), and bowel perforation (RR 3.82, 95% CI 1.96-7.44, p < 0.001) associated with higher late GI toxicity grades. CONCLUSIONS: Our study shows CT findings correlate with grade 2-4 late GI toxicity. Future research should validate and refine these findings with different imaging and toxicity grading systems to assess their potential predictive value.

Different estimation methods of the modified Kies Topp-Leone model with applications and quantile regression.

This paper introduces the modified Kies Topp-Leone (MKTL) distribution for modeling data on the (0, 1) or [0, 1] interval. The shapes of the density and hazard rate functions manifest desirable shapes, making the MKTL distribution suitable for modeling data with different characteristics at the unit interval. Twelve different estimation methods are utilized to estimate the distribution parameters, and Monte Carlo simulation experiments are executed to assess the performance of the methods. The simulation results suggest that the maximum likelihood method is the superior method. The usefulness of the new distribution is illustrated by utilizing three data sets, and its performance is juxtaposed with that of other competing models. The findings affirm the superiority of the MKTL distribution over the other candidate models. Applying the developed quantile regression model using the new distribution disclosed that it offers a competitive fit over other existing regression models.

The Relationship Between Anxiety, Depression and Cognitive Functioning in Older Adults: An Exploratory Cross-Sectional Analysis of Wave 1 Lothian Birth Cohort 1936 Data.

OBJECTIVES: To explore the strength of the association between cognitive functioning and depression and anxiety in older people without dementia. METHODS: An exploratory, cross-sectional analysis of Wave 1 (2004-2007) data from the Lothian Birth Cohort 1936 dataset. Three subgroups were based on Hospital Anxiety and Depression Scale (HADS) subscales: no probable anxiety or depression (N = 592), probable anxiety no depression (N = 122), probable depression with/without anxiety (depression) (N = 30). Regression analyses determined relationships between subgroups and identified cognitive test variables. RESULTS: Participants were 744 individuals (male = 385 [51.5%]; mean [M] age = 69.5 years [Standard deviation = 0.83]); characteristics for subgroups were similar. Participants with probable depression had slower simple reaction time scores than those with no anxiety or depression (regression slope [ß] on the log10 scale = 0.05, 95% Confidence Interval [0.03, 0.08], p ≤ 0.001). Those with probable anxiety had significantly worse scores on other tests: Spatial span (ß = -0.80 [-1.36, -0.25], p ≤ 0.005), Symbol Search (ß = -1.67 [-2.90, -0.45], p ≤ 0.01), Matrix Reasoning (ß = -1.58 [-2.55, -0.60], p ≤ 0.005) and Block Design (ß = -3.33 [-5.29, -1.37], p ≤ 0.001), than those without probable anxiety or depression. CONCLUSION: Probable depression and anxiety were found to be associated with lower cognitive function in those without evidence of dementia. People with probable anxiety showed poorer performance in tests that concerned making decisions. People with probable depression showed slower processing speed.

Multivariate Scalar on Multidimensional Distribution Regression With Application to Modeling the Association Between Physical Activity and Cognitive Functions.

We develop a new method for multivariate scalar on multidimensional distribution regression. Traditional approaches typically analyze isolated univariate scalar outcomes or consider unidimensional distributional representations as predictors. However, these approaches are suboptimal because (i) they fail to utilize the dependence between the distributional predictors and (ii) neglect the correlation structure of the response. To overcome these limitations, we propose a multivariate distributional analysis framework that harnesses the power of multivariate density functions and multitask learning. We develop a computationally efficient semiparametric estimation method for modeling the effect of the latent joint density on the multivariate response of interest. Additionally, we introduce a new conformal prediction algorithm for quantifying the uncertainty of our multivariate predictions based on subject characteristics and individualized distributional predictors, providing valuable insights into the conditional distribution of the response. We validate the effectiveness of our proposed method through comprehensive numerical simulations, clearly demonstrating its superior performance compared to traditional methods. The application of the proposed method is demonstrated on triaxial accelerometer data from the National Health and Nutrition Examination Survey 2011-2014 for modeling the association between cognitive scores across various domains and distributional representation of physical activity among the older adult population. Our results highlight the advantages of the proposed approach, emphasizing the significance of incorporating multidimensional distributional information in the triaxial accelerometer data.

Regression: Understanding What Covariates and Confounds Do in Adjusted Analyses.

The use of regression analysis is common in research. This article presents an introductory section that explains basic terms and concepts such as independent and dependent variables (IVs and DVs), covariates and confounds, zero-order correlations and multiple correlations, variance explained by variables and shared variance, bivariate and multivariable linear regression, line of least squares and residuals, unadjusted and adjusted analyses, unstandardized (b) and standardized (ß) coefficients, adjusted R2, interaction terms, and others. Next, this article presents a more advanced section with the help of 3 examples; the raw data files for these examples are included in supplementary materials, and readers are encouraged to download the data files and run the regressions on their own in order to better follow what is explained in the text (this, however, is not mandatory, and readers who do not do so can also follow the discussions in the text). The 3 examples illustrate many points. When important covariates are not included in regressions, the included IVs explain a smaller proportion of the variance in the DV, and the relationships between the included IVs and the DV may not be correctly understood. Including interaction terms between IVs can improve the explanatory value of the model whether the IVs are intercorrelated or not. When IVs are intercorrelated (such as when one is a confound), although their net effect in multivariable regression may explain a greater proportion of the variance in the DV, their individual b and ß coefficients decrease in proportion to the shared variance that is removed. Thus, variables that were found statistically significant in unadjusted analyses may lose statistical significance in fully adjusted analyses. Readers may find it useful to keep these points in mind when running regressions on their data or when reading studies that present their results through regressions.

Impact of regression modeling on the assessment and harmonization of a point-of-care analyzer and commercial laboratory analyzer for feline plasma biochemistry testing.

BACKGROUND: Regression describes the relationship of results from two analyzers, and the generated equation can be used to harmonize results. Point-of-care (POC) analyzers cannot be calibrated by the end user, so regression offers an opportunity for calculated harmonization. Harmonization (uniformity) of laboratory results facilitates the use of common reference intervals and medical decision thresholds. OBJECTIVE: Our aims were to characterize the relationship of results for multiple biochemistry analytes on a POC and a commercial laboratory analyzer (CL) with three regression techniques and to use regression equations to harmonize the POC results with those of the CL. Harmonized results were assessed by recognized quality goals. We used harmonized results to assess the regression techniques. METHODS: After analyzer imprecision assessments, paired clinical samples were assessed with one dataset to calculate regression parameters that were applied to a second dataset. Three regression techniques were performed, and each was used to harmonize the POC results with those from the CL. POC results were assessed for bias and the number of results reaching quality goals before and after harmonization. RESULTS: All regression techniques could be used to harmonize most analytes so that 95% of results were within ASVCP TEa guidelines. Harmonization could be further improved with alternate regression techniques or exclusions. CONCLUSIONS: Regression offers a means to harmonize POC and CL analyzers. Further work is needed to assess how few samples can reliably be used and to assess likely species differences. No regression technique reliably describes the relationship between methods when correlation is poor.

Trends in under-five mortality rate in China, 1996-2020: a Joinpoint regression and correlation analysis.

OBJECTIVES: To analyse annual trends of the under-five mortality rate (U5MR) and main cause-specific U5MR in China from 1996 to 2020 and to assess the potential correlation of the healthcare system and health expenditure with the U5MR in China. DESIGN: A retrospective observational study using national data from 1996 to 2020. Joinpoint regression was employed to model U5MR trends and Pearson correlation analysis was conducted to examine the relationship between healthcare system factors, health expenditure and U5MR. SETTING: Nationwide study covering both rural and urban populations across China over a 25-year period. RESULTS: The U5MR in China experienced a three-stage decline from 1996 to 2020 with an average annual percentage rate change (AAPC) of -7.27 (p<0.001). The AAPC of the rural U5MR (-7.07, p<0.001) was higher than that in urban areas (-5.57, p<0.001). Among the five main causes, the decrease in pneumonia-caused U5MR was the fastest while the decreases in congenital heart disease and accidental asphyxia were relatively slow. The rates of hospital delivery (r=-0.981, p<0.001), neonatal visits (r=-0.848, p<0.001) and systematic health management (r=-0.893, p<0.001) correlated negatively with U5MR. The proportion of government health expenditure in the total health expenditure (THE) correlated negatively with the national U5MR (r=-0.892, p<0.001) while the proportion of out-of-pocket health expenditure in THE correlated positively (r=0.902, p<0.001). CONCLUSION: China made significant advances in reducing U5MR from 1996 to 2020. The rural-urban gap in U5MR has narrowed, though rural areas remain a key concern. To further reduce U5MR, China should focus on rural areas, pay more attention to congenital heart disease and accidental asphyxia, further improve its health policies, and continue to increase the government health expenditure.

Exploring the influence of rock bridge angle on the rock secant modulus.

Rock can undergo shape deformation and damage due to the influence of joint fissures, and the range of damage caused by joints at different rock bridge angles varies. To study the influence of rock bridge angle on the size effect of rock secant modulus, this paper adopts the principle of regression analysis and combines numerical simulation to carry out relevant research. The research results indicate that: (1) As the rock bridge angle increases, the secant modulus gradually decreases, following a power function relationship. (2) As the rock size increases, the secant modulus shows a trend of first decreasing and then stabilizing, following a power function relationship. (3) As the rock bridge angle increases, the characteristic tangent modulus and characteristic size gradually decrease, following a power function relationship. On this basis, we obtained their relationship formulas separately.

Progress and scenario-based projections of health service availability and coverage towards UHC in the post-conflict and post-pandemic Iraq: a Bayesian hierarchical regression approach.

BACKGROUND: Two decades have passed since the beginning of the Iraq War in 2003. Iraq has long suffered from conflicts and instability, where the people have limited access to healthcare. The coronavirus disease (COVID-19) pandemic brought additional disruption to health service provision. OBJECTIVES: At the midpoint towards universal health coverage (UHC) in 2030, this study aims to gain a better understanding of the trends of UHC progress in Iraq in the context of the conflicts and the COVID-19 pandemic and to indicate possible pragmatic options. DESIGN: This study employed Bayesian hierarchical regression models to estimate trends and projections of health service availability and coverage indicators up to 2030. Furthermore, for health service coverage, four scenarios were defined based on the availability of health services, and projections were made for each scenario up to 2030. SETTING: Our approach used the yearly data from the Ministry of Health and four nationally representative household surveys between 2000 and 2020. We evaluated the subnational-level progress in three health service availability indicators and 13 health service coverage indicators in 18 governorates in Iraq from 2000 to 2030. RESULTS: The findings from 2000 to 2020 revealed a lack of progress in the indicators of health facility and inpatient bed, and pronounced detrimental effects from major conflicts and the pandemic on all measured health service coverage indicators. Despite these setbacks, several health service coverage indicators demonstrated resilience and elasticity in their recovery. The projected trends for 2021 to 2030 indicated limited alternations in the health service availability. By 2030, five health service coverage indicators will achieve the designated 80% targets. A scenario-based analysis predicts improved coverage of antenatal care, and child immunisation and treatment if health service availability is bolstered to globally recommended standards. Under this scenario, several governorates-Anbar, Baghdad, Nainawa, Qadissiyah, Salahaddin, Thiqar and Wasit-presented improved health service coverage in more indicators. CONCLUSION: Strengthened health service availability has the potential to significantly improve fragile health service coverage indicators and in more vulnerable governorates.

Early warning and predicting of COVID-19 using zero-inflated negative binomial regression model and negative binomial regression model.

BACKGROUND: It is difficult to detect the outbreak of emergency infectious disease based on the exiting surveillance system. Here we investigate the utility of the Baidu Search Index, an indicator of how large of a keyword is in Baidu's search volume, in the early warning and predicting the epidemic trend of COVID-19. METHODS: The daily number of cases and the Baidu Search Index of 8 keywords (weighted by population) from December 1, 2019 to March 15, 2020 were collected and analyzed with times series and Spearman correlation with different time lag. To predict the daily number of COVID-19 cases using the Baidu Search Index, Zero-inflated negative binomial regression was used in phase 1 and negative binomial regression model was used in phase 2 and phase 3 based on the characteristic of independent variable. RESULTS: The Baidu Search Index of all keywords in Wuhan was significantly higher than Hubei (excluded Wuhan) and China (excluded Hubei). Before the causative pathogen was identified, the search volume of "Influenza" and "Pneumonia" in Wuhan increased with the number of new onset cases, their correlation coefficient was 0.69 and 0.59, respectively. After the pathogen was public but before COVID-19 was classified as a notifiable disease, the search volume of "SARS", "Pneumonia", "Coronavirus" in all study areas increased with the number of new onset cases with the correlation coefficient was 0.69 ~ 0.89, while "Influenza" changed to negative correlated (rs: -0.56 ~ -0.64). After COVID-19 was closely monitored, the Baidu Search Index of "COVID-19", "Pneumonia", "Coronavirus", "SARS" and "Mask" could predict the epidemic trend with 15 days, 5 days and 6 days lead time, respectively in Wuhan, Hubei (excluded Wuhan) and China (excluded Hubei). The predicted number of cases would increase 1.84 and 4.81 folds, respectively than the actual number of cases in Wuhan and Hubei (excluded Wuhan) from 21 January to 9 February. CONCLUSION: The Baidu Search Index could be used in the early warning and predicting the epidemic trend of COVID-19, but the search keywords changed in different period. Considering the time lag from onset to diagnosis, especially in the areas with medical resources shortage, internet search data can be a highly effective supplement of the existing surveillance system.

Biologicals for the treatment of lupus nephritis: a Bayesian network meta-regression analysis.

Background: Previous studies comparing the efficacy and safety of different treatment regimens for lupus nephritis are scarce. Moreover, confounding factors such as the duration of follow-up were hardly adjusted in those studies, potentially compromising the results and their extents to clinical settings. Objective: To rigorously investigate the efficacy and safety of biologics in patients with lupus nephritis using Bayesian network meta-regression analyses that adjust for the follow-up period, in order to provide more robust evidence for clinicians. Methods: Databases comprising PubMed, Embase, MedlinePlus, Cochrane Library, Google Scholars, and Scopus were retrieved for eligible articles from inception to February 29, 2024. The primary endpoint was the complete response rate, the secondary endpoint was the partial response rate, the tertiary endpoints were the adverse events, and infection-related adverse events. Napierian Logarithm of hazard ratio (lnHR) and the standard error of lnHR (selnHR) were generated for dichotomous variants by STATA 18.0 MP and then put into Rstudio 4.3.2 to conduct Bayesian network meta-analysis as well as network meta-regression analysis to yield hazard ratio (HR) as pairwise effect size. Results: Ten studies involving 2138 patients and 11 treatment regimens were ultimately included. In the original analysis, for the primary endpoint, compared to the control group, obinutuzumab (22.6 months), abatacept-30mg (20.5 months), abatacept-10mg (17.8 months), and belimumab (23.3 months) demonstrated significant superiority (HR ranged from 1.6 to 2.5), more ever, their significance regarding relative efficacy was correlated with follow up period, namely "time window" (shown in parentheses above). For the secondary endpoint, compared to the control group, obinutuzumab and abatacept-30mg showed conspicuous preponderance (HR ranged from 1.6 to 2.4), "time window" was also detected in abatacept-30mg (20.5 months), whereas obinutuzumab remained consistently obviously effective regardless of the follow-up period (shown in parentheses above). For the tertiary endpoint, there were no differences among active regimens and control. Conclusions: Considering the efficacy and safety and "time window" phenomenon, we recommend obinutuzumab as the preferred treatment for LN. Certainly, more rigorous head-to-head clinical trials are warranted to validate those findings.

The multinomial mixed-effect regression model for predicting PCOC phases in hospice patients.

PURPOSE: The Palliative Care Outcomes Collaboration (PCOC) aims to enhance patient outcomes systematically. However, identifying crucial items and accurately determining PCOC phases remain challenging. This study aims to identify essential PCOC data items and construct a prediction model to accurately classify PCOC phases in terminal patients. METHODS: A retrospective cohort study assessed PCOC data items across four PCOC phases: stable, unstable, deteriorating, and terminal. From July 2020 to March 2023, terminal patients were enrolled. A multinomial mixed-effect regression model was used for the analysis of multivariate PCOC repeated measurement data. RESULTS: The dataset comprised 1933 terminally ill patients from 4 different hospice service settings. A total of 13,219 phases of care were analyzed. There were significant differences in the symptom assessment scale, palliative care problem severity score, Australia-modified Karnofsky performance status, and resource utilization groups-activities of daily living among the four PCOC phases of care. Clinical needs, including pain and other symptoms, declined from unstable to terminal phases, while psychological/spiritual and functional status for bed mobility, eating, and transfers increased. A robust prediction model achieved areas under the curves (AUCs) of 0.94, 0.94, 0.920, and 0.96 for stable, unstable, deteriorating, and terminal phases, respectively. CONCLUSIONS: Critical PCOC items distinguishing between PCOC phases were identified, enabling the development of an accurate prediction model. This model enhances hospice care quality by facilitating timely interventions and adjustments based on patients' PCOC phases.

Predicting students' satisfaction with academic services at a multicultural engineering university in Bangladesh: A multiple regression analysis.

This empirical study sought to determine the levels of satisfaction among engineering students enrolled at a multicultural international institution in Bangladesh with a reputation for excellence. An assortment of first- and fourth-year undergraduate students participated in the study by completing an online survey. The study focused on selected determinants namely; support services (SS), campus life (CL), economic factors (EF) and University corporate image (CI). The researchers used a survey research design (SRD) to illuminate students' opinions and views. A multiple regression analysis (MRA) was used to regress opinions of 326 respondents who participated in the study. The disproportional stratified random sampling was used to determine the samples. The study was guided by two hypotheses. The study analyzed predictors of student satisfaction with academic services by employing standard multiple regression analysis. The findings showed that the four determinants SS, CL, EF and CI were statistically significant to predict students' satisfaction levels [F(4,321) = 143.786, p < .001]. It was empirically discovered that Support Services had the highest impact to the model [ß = .496, p < .05] followed by university Corporate Image [ß = .365, p < .05]. The findings showed that Campus Life and Economic Factors were not statistically significant (p>.05) in the model of predictors implying that they do not influence students' satisfaction levels on their academic career at the university. The researchers recommend that in order to maintain students' satisfaction levels on their academic experiences, universities should consolidate on support services provided to the students as well as improving their corporate image and world view.

What Determines the Use of Clinical Decision Support Systems in Nursing? Results of a Multiple Regression Analysis.

INTRODUCTION: In nursing, professionals are expected to base their practice on evidence-based knowledge, however the successful implementation of this knowledge into nursing practice is not always assured. Clinical Decision Support Systems (CDSS) are considered to bridge this evidence-practice gap. METHODS: This study examines the extent to which evidence-based nursing (EBN) practices influence the use of CDSS and identifies what additional factors from acceptance theories such as UTAUT play a role. RESULTS AND DISCUSSION: Our findings from three regression models revealed that nursing professionals and nursing students who employ evidence-based practices are not more likely to use an evidence-based CDSS. The relationship between an EBN composite score (model 1) or is individual dimensions (model 2) and CDSS use was not significant. However, a more comprehensive model (model 3), incorporating items from the UTAUT such as Social Influences, Facilitating Conditions, Performance Expectancy, and Effort Expectancy, supplemented by Satisfaction demonstrated a significant variance explained (R2 = 0.279). Performance Expectancy and Satisfaction were found to be significantly associated with CDSS utilization. CONCLUSION: This underscores the importance of user-friendliness and practical utility of a CDSS. Despite potential limitations in generalizability and a limited sample size, the results provide insights into that CDSS first and foremost underly the same mechanisms of use as other health IT systems.

Health state utility values of type 2 diabetes mellitus and related complications: a systematic review and meta-regression.

BACKGROUND: This study aimed to synthesize and quantitatively examine Health State Utility Values (HSUVs) for Type 2 Diabetes Mellitus (T2DM) and its complications, providing a robust meta-regression framework for selecting appropriate HSUV estimates. METHOD: We conducted a systematic review to extract HSUVs for T2DM and its complications, encompassing various influencing factors. Relevant literature was sourced from a review spanning 2000-2020, supplemented by literature from PubMed, Embase, and the Web of Science (up to March 2024). Multivariate meta-regression was performed to evaluate the impact of measurement tools, tariffs, health status, and clinical and demographic variables on HSUVs. RESULTS: Our search yielded 118 studies, contributing 1044 HSUVs. The HSUVs for T2DM with complications varied, from 0.65 for cerebrovascular disease to 0.77 for neuropathy. The EQ-5D-3L emerged as the most frequently employed valuation method. HSUV differences across instruments were observed; 15-D had the highest (0.89), while HUI-3 had the lowest (0.70) values. Regression analysis elucidated the significant effects of instrument and tariff choice on HSUVs. Complication-related utility decrement, especially in diabetic foot, was quantified. Age <70 was linked to increased HSUVs, while longer illness duration, hypertension, overweight and obesity correlated with reduced HSUVs. CONCLUSION: Accurate HSUVs are vital for the optimization of T2DM management strategies. This study provided a comprehensive data pool for HSUVs selection, and quantified the influence of various factors on HSUVs, informing analysts and policymakers in understanding the utility variations associated with T2DM and its complications.

unmconf : an R package for Bayesian regression with unmeasured confounders.

The inability to correctly account for unmeasured confounding can lead to bias in parameter estimates, invalid uncertainty assessments, and erroneous conclusions. Sensitivity analysis is an approach to investigate the impact of unmeasured confounding in observational studies. However, the adoption of this approach has been slow given the lack of accessible software. An extensive review of available R packages to account for unmeasured confounding list deterministic sensitivity analysis methods, but no R packages were listed for probabilistic sensitivity analysis. The R package unmconf implements the first available package for probabilistic sensitivity analysis through a Bayesian unmeasured confounding model. The package allows for normal, binary, Poisson, or gamma responses, accounting for one or two unmeasured confounders from the normal or binomial distribution. The goal of unmconf is to implement a user friendly package that performs Bayesian modeling in the presence of unmeasured confounders, with simple commands on the front end while performing more intensive computation on the back end. We investigate the applicability of this package through novel simulation studies. The results indicate that credible intervals will have near nominal coverage probability and smaller bias when modeling the unmeasured confounder(s) for varying levels of internal/external validation data across various combinations of response-unmeasured confounder distributional families.

Prediction of Fatty Acid Intake from Serum Fatty Acid Levels Using Machine Learning Technique in Women Living in Toyama Prefecture.

Preventing lifestyle-related diseases requires understanding and managing the intake of total fats and specific types of fatty acids, especially trans fatty acids. There are several methods for measuring fat intake, each with its own strengths and limitations. Guidelines for nutritional epidemiology studies recommend employing objective biomarkers. This study aimed to estimate fatty acid intake based on serum fatty acid levels using multiple regression analysis and a machine learning technique, and to compare their accuracy. The subjects were healthy women aged 18 to 64 living in Toyama, Japan. A dietary survey to determine fatty acid intake was conducted using a 3-day dietary record completed by the participant. Blood samples were collected after an overnight fast, and serum was obtained through centrifugation. A total of 300 women participated in the study. The fatty acid levels in serum were determined using gas chromatography with a capillary column. Using multiple regression analysis and neural networks, the intakes of saturated, monounsaturated, n-6 polyunsaturated, n-3 polyunsaturated, and trans fatty acids from serum fatty acid levels were predicted. Significant correlations were observed between the intakes of the five classified fatty acids and the predicted intakes obtained from the multiple regression analysis (r = 0.39 - 0.49, p < 0.01). Significant correlations were also observed between the five classified fatty acid intakes and the intakes predicted by the neural network (r = 0.52 - 0.79, p < 0.01), and the correlation coefficient showed a significantly higher value than that predicted by the multiple regression analysis. These results suggest that serum fatty acid levels may be used as biomarkers to estimate the intake of fatty acids, including that of trans fatty acids, and that machine learning may be able to predict fatty acid intake with higher accuracy than multiple regression analysis.

Analysis of hospitalization costs in adult inguinal hernia: based on quantile regression model.

BACKGROUND: Inguinal hernia repair is a common surgical procedure with significant variability in hospitalization costs. Traditional cost analysis methods often overlook the distribution of costs across patient demographics and clinical factors. This study employs a quantile regression model to explore the determinants of hospitalization costs for adult inguinal hernia surgery, providing a detailed understanding of cost variations across different quantiles. METHODS: We analyzed data from adult patients who underwent inguinal hernia surgery at Beijing Chaoyang Hospital from January 2015 to June 2023. The study included patient demographics, hernia-related information, surgery-related details, and cost-related data. A quantile regression model was used to assess the impact of various factors on hospitalization costs at different quantiles (10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%). Data were processed using StataSE 15.0 software. RESULTS: Our study included 16,602 patients, predominantly male (91.86%) and Han Chinese (96.48%), with the 51-64 years age group being the largest (26.80%). The quantile regression analysis revealed significant cost variations across different quantiles. Younger patients incurred higher costs, with age coefficients ranging from -40.541 at the 90th quantile to -3.082 at the 10th quantile. Uninsured patients faced higher costs, with coefficients from 214.747 at the 80th quantile to 501.78 at the 10th quantile. Longer hospital stays correlated with increased costs, with coefficients from 342.15 at the 80th quantile to 405.613 at the 90th quantile. Patients hospitalized multiple times (≥3) had lower costs, with coefficients from -767.353 at the 40th quantile to -311.575 at the 80th quantile. Comorbidities significantly raised costs, with coefficients for three or more comorbidities ranging from 806.122 at the 80th quantile to 1,456.02 at the 40th quantile. Laparoscopic surgery was more expensive than open surgery, with coefficients from 1,834.206 at the 80th quantile to 2,805.281 at the 10th quantile. Bilateral surgeries and the use of biological mesh also resulted in higher costs, with coefficients for bilateral surgeries ranging from 1,067.708 at the 10th quantile to 2,871.126 at the 90th quantile and for biological mesh from 3,221.216 at the 40th quantile to 6,117.598 at the 90th quantile. CONCLUSIONS: Hospitalization costs for inguinal hernia surgery are influenced by multiple factors, with significant variations across different patient groups. Strategies to control costs should be tailored to address the specific needs of patients, optimize surgical methods, and improve perioperative care. Future research should extend these findings across different healthcare settings and consider the latest advancements in medical technology and policy changes.

Unsupervised Liu-type shrinkage estimators for mixture of regression models.

The mixture of probabilistic regression models is one of the most common techniques to incorporate the information of covariates into learning of the population heterogeneity. Despite its flexibility, unreliable estimates can occur due to multicollinearity among covariates. In this paper, we develop Liu-type shrinkage methods through an unsupervised learning approach to estimate the model coefficients in the presence of multicollinearity. We evaluate the performance of our proposed methods via classification and stochastic versions of the expectation-maximization algorithm. We show using numerical simulations that the proposed methods outperform their Ridge and maximum likelihood counterparts. Finally, we apply our methods to analyze the bone mineral data of women aged 50 and older.