Quality Prediction Research Articles

Sensory processing sensitivity as a predictor of health-related quality of life outcomes via stress and sleep quality

Sensory processing sensitivity (SPS), linked to processing external and internal stimuli, has drawn attention to its associations with clinical factors, particularly with health-related quality of life (HRQOL) variables. This study examined the relationships among SPS, stress, sleep quality, and HRQOL, establishing an explanation model. Eight hundred adults (M = 26.66 years, SD = 12.24; range age: 18–85 years) completed self-administered questionnaires on SPS, stress, sleep quality, and HRQOL. Correlation analysis and structural equation modeling (SEM) were used to analyze HRQOL pathways. Stress positively correlated with sleep quality disturbances (r = 0.442, p < 0.001), and SPS (r = 0.344, p < 0.001). Sleep quality disturbances were weakly positively associated with SPS (r = 0.242, p < 0.001). Weak negative correlations emerged between stress and physical (r = -0.283, p < 0.001) and mental (r = − 0.271, p < 0.001) health, HRQOL main dimensions. SEM results showed SPS positively influenced sleep quality disturbances (β = 0.242, p < 0.05) stress (β = 0.413, p < 0.001) while negatively affecting physical health (β = − 0.126, p < 0.001). Sleep quality disturbances negatively affected physical (β = − 0.168, p < 0.001), and mental (β = − 0.189 , p < 0.001) health, and stress on mental health (β = − 0.492, p < 0.01). Indirect effects between SPS and physical (β = -0.036, p < 0.001) and mental (β = − 0.091, p < 0.001) health through sleep were observed, as well as a mediation of stress between SPS and mental health (β = − 0.196, p < 0.001). SPS, sleep quality disturbances, and stress emerged as significant predictors of self-rated physical and mental health in adults.

Aqueous-organic and aqueous-vapor interfacial phenomena for three phase systems containing CO2, CH4, n-butanol, n-dodecane and H2O at saturation conditions

A fundamental understanding of the interfacial properties at elevated pressure is essential for processes in the context of the energy transition, such as the storage of CO2, H2 or CH4. Systems in such processes have traces of impurities. This work aims to systematically investigate these multi-component systems through simplified vapor-liquid-liquid systems comprising H2O, (n-butanol or n-dodecane), and (CO2 or CH4). The model systems are theoretically investigated using the density gradient theory and the PCP-SAFT. The interfacial tension and saturated phase density of the model systems are experimentally measured by the pendant drop and the oscillating tube method, respectively. Good agreement between the theoretical and experimental results is found. It was found that the pure and binary systems of these mixtures can be described well by the introduced model, delivering high quality predictions.

Spatio-temporal Prediction of Air Quality Using Spatio-temporal Clustering and Hierarchical Bayesian Model

Air pollution can cause many negative impacts, so it is meaningful to establish an air quality prediction model with high accuracy for air pollution prevention and control. In previous studies, most models did not consider the spatio-temporal distribution characteristics of air quality. So this study proposes a novel spatio-temporal prediction approach STC-HBM, which combines spatio-temporal clustering (STC) and hierarchical Bayesian model (HBM) to build a spatio-temporal prediction model. In this study, we first perform spatio-temporal clustering on the Beijing-Tianjin-Hebei region in China and then apply hierarchical Bayesian models to different clusters separately. We consider three hierarchical Bayesian models: the separable spatio-temporal (SST) model, the Gaussian process (GP) model, and the autoregressive (AR) model. Since the prior distribution affects the prediction accuracy of the HBM, the resultant output of the Bayesian linear regression (BLR) model is used as the prior input of the HBM, which improves the flexibility of the model. The experimental results show that 13 cities in the BTH region are clustered into two clusters according to their spatio-temporal characteristics. Based on MAE, RMSE, MAPE, and coverage (CVG), cluster 1, and cluster 2 are better in both temporal and spatial prediction compared to the overall prediction model. In addition, for cluster 1, the models with the best prediction in time and space are AR and GP, respectively; for cluster 2, the models with the best prediction in time and space are SST and GP, respectively.

PENERAPAN REGRESI LINEAR UNTUK PREDIKSI SUHU BADAN SAPI MENGGUNAKAN DATA SMART TERNAK DARI PT TELKOM INDONESIA: STUDI KASUS DI DESA PENGENGAT

Abstract West Nusa Tenggara (NTB) is one of the regions in Indonesia with significant potential in the field of livestock farming. In the context of the agricultural industry, livestock is considered a valuable asset, and monitoring their health is crucial for improving livestock productivity. This research aims to determine the relationship between the body temperature of cattle (Y) and environmental factors, namely air humidity (), environmental temperature (), and barometric pressure (), using smart livestock data provided by PT Telkom. The study was conducted in Pengengat Village, NTB, involving correlation and regression analysis. The analysis results indicate a significant positive correlation between the body temperature of cattle and environmental temperature () and a negative correlation with humidity (). Additionally, there is a strong positive correlation between the body temperature of cattle and barometric pressure (). The multiple linear regression equation obtained is as follows: = –26.816842418191094 + -0,0014707 + 0.39772325 + 0.04927779. Model testing shows excellent prediction quality with low Mean Squared Error (MSE) values (0.3849 for training and 0.4623 for testing). Furthermore, the high R-squared (R^2) values (0.7283 for training and 0.7110 for testing) indicate that the linear regression model can predict the body temperature of cattle based on the analyzed environmental factors. Keywords: Cattle Body Temperature, Smart Livestock Data, Prediction, Machine Learning, Linear Regression

Analysis and Prediction of Atmospheric Environmental Quality Based on the Autoregressive Integrated Moving Average Model (ARIMA Model) in Hunan Province, China

Based on the panel data of atmospheric environmental pollution in Hunan Province from 2016 to 2023, the autoregressive integrated moving average model (ARIMA) is introduced to evaluate and predict the current status of atmospheric environmental quality in Hunan Province of China, and the constructed ARIMA model has an excellent prediction effect on the atmospheric environmental quality in Hunan Province. The following conclusions are obtained through the prediction and analysis based on the ARIMA model: (1) the atmospheric environmental quality in Hunan Province shows a year-on-year improvement trend; (2) the ARIMA model prediction method is reliable and effective and can accurately analyze and predict the concentrations of air pollutants (PM2.5, PM10, SO2, and CO) and atmospheric environmental quality, and the prediction results show that the outdoor air quality of Hunan Province will improve gradually each year from 2024 to 2028; (3) this study contributes a better understanding of the ambient air quality in Hunan Province during 2016–2023 and provides good forecasting results for air pollutants during the period of 2024–2028.

Predictors of Poor Quality of Life in Patients with Gastroesophageal Reflux Disease Undergoing Sleeve Gastrectomy.

Background-Gastroesophageal reflux disease (GERD) is commonly diagnosed in patients with severe obesity. The outcomes of patients with preoperative GERD after sleeve gastrectomy (SG) are unclear, and some surgeons consider GERD a contraindication for SG. Methods-A retrospective analysis of a tertiary university hospital database was conducted. All patients with preoperative GERD undergoing SG between January 2012 and January 2020 and having at least two years of follow-up were included in the analysis. A validated GERD-associated quality of life questionnaire (GERD-HRQL) was completed by all patients. Results-During the study period, 116/1985 patients (5.8%) were diagnosed with GERD before SG. In total, 55 patients were available for a two-year follow-up and were included in the analysis. Median follow-up was 40 months (range 24-156 months). Mean total weight loss (TWL) was 24.0% ± 12.0%. On follow-up, 43 patients (78.1%) reported having GERD symptoms. In patients who underwent postoperative endoscopy, less than a third had esophagitis. The mean GERD-HRQL score was 25.2 ± 10.9. On univariable analysis, patients with poor GERD-HRQL had lower BMI at baseline (41.5 ± 12.4 vs. 44.9 ± 10.0 kg/m2, p = 0.03), were less commonly smokers at baseline (8.1% vs. 33.3%, p = 0.02), and had lower TWL at the end of the follow-up (22.2% ± 10.4% vs. 28.9% ± 13.7%, p = 0.05). On multivariable analysis, smoking status at baseline and TWL at last follow-up were independent predictors of better GERD-HRQL. Conclusions-In conclusion, most GERD patients after SG have a relatively high GERD-HRQL score, most patients still have GERD symptoms during the follow-up, and approximately a third of patients have endoscopic signs of esophagitis. There was an association between patients with higher TWL and smoking at baseline and better GERD-HRQL outcomes. The latter is potentially due to smoking cessation.

Modeling of PM10 Particulate Matter in Ahvaz City Using Remote Sensing and Meteorological Parameters

Introduction: In recent years, remote sensing (RS) products have emerged as effective tools for monitoring air pollution. This study aims to predict the concentrations of particulate matter with a diameter smaller than 10μm (PM10) using a multivariate linear regression (MLR) model, incorporating both Aerosol Optical Depth (AOD) products and meteorological parameters. Material and Methods: In this study, data on PM10 concentrations, Aerosol Optical Depth (AOD), and meteorological parameters (wind speed, temperature, humidity, and horizontal visibility) were used. The study focused on the time 15:00 each day, as this time was identified as having significant data relevance. The methodology section also consisted of three steps: 1) pairwise correlation analysis: The relationship between meteorological parameters, AOD, and PM10 was assessed using the pairwise correlation method. 2) Model development: A MLR model was developed to predict PM10 concentrations. 3) Validation: The model was validated using a separate dataset, ensuring that 70% of the data was used for training, and 30% for testing and validation. Results: The pairwise correlation analysis revealed a strong correlation (0.86) between AOD remote sensing index and PM10. The highest correlation (0.9) was observed during the spring season. The five developed equations to estimate the PM10 index yielded correlation coefficients ranging from 0.86 to 0.90. Notably, the highest correlation was achieved when AOD data and all the meteorological parameters were u tilized simultaneously. These results highlighted the utility of remote sensing products and meteorological data in air quality monitoring and prediction. Conclusion: This study demonstrates that a MLR model incorporating AOD and meteorological parameters can effectively predict PM10 concentrations in Ahvaz City, particularly during dust storms in hot seasons. These findings can aid policymakers and public health officials in developing strategies to mitigate the adverse effects of dust storms on air quality and public health.

IIS-FVIQA: Finger Vein Image Quality Assessment with intra-class and inter-class similarity

In recent years, Finger Vein Image Quality Assessment (FVIQA) has been recognized as an effective solution to the problem of erroneous recognition resulting from low image quality due to false and missing information in finger vein images, and has become an important part of finger vein recognition systems. Compared to traditional FVIQA methods that rely on domain knowledge, newer methods that reject low-quality images have been favored for their independence from human interference. However, these methods only consider intra-class similarity information and ignore valuable information from inter-class distribution, which is also an important factor in evaluating the performance of recognition systems. In this work, we propose a novel FVIQA approach, named IIS-FVIQA, which concurrently takes into account the intra-class similarity density and inter-class similarity distribution distance within recognition systems. Specifically, our method generates quality scores for finger vein images by combining the information entropy of intra-class similarity distribution and Wasserstein distance of inter-class distribution. Then, we train a regression network for quality prediction using training images and corresponding quality scores. When a new image enters the recognition system, the trained regression network directly predicts the quality score of the image, making it easier for the system to select the corresponding operation based on the quality score of the image. Extensive experiments conducted on benchmark datasets demonstrate that the IIS-FVIQA method proposed in this paper consistently achieves top performance across multiple public datasets. After filtering out 10% of low-quality images predicted by the quality regression network, the recognition system’s performance improves by 43.96% (SDUMLA), 32.23% (MMCBNU_6000), and 21.20% (FV-USM), respectively. Furthermore, the method exhibits strong generalizability across different recognition algorithms (e.g., LBP, MC, and Inception V3) and datasets (e.g., SDUMLA, MMCBNU_6000, and FV-USM).

A Lot-Based VMD-CNN-BIGRU Indoor Mariculture Water Quality Prediction Method Including Attention Mechanism

A Lot-Based VMD-CNN-BIGRU Indoor Mariculture Water Quality Prediction Method Including Attention Mechanism

Improve Metode Lightgbm untuk Prediksi Harga Mobil Bekas Menggunakan Hyper-Parameter Tuning

This study aims to predict used car prices using the LightGBM method and hyperparameter tuning techniques in the context of data science. The analysis process includes collecting historical data on used cars, preprocessing the data to clean and encode variables, and splitting the data into training and testing sets. The LightGBM model was trained and optimized through hyperparameter tuning using GridSearchCV to improve model performance. The model was evaluated using metrics such as Mean Squared Error (MSE) and R-squared. The results indicate that the well-optimized LightGBM model can accurately predict used car prices with high accuracy. The low MSE value (35207938112.028404) and high R-squared value (0.9462871489515565) demonstrate the model's excellent predictive quality. This research provides deeper insights into the factors influencing used car prices and contributes to the development of effective and reliable predictive models.

Assessing quality and reliability of online videos on tachycardia: a YouTube video-based study

BackgroundConsidering the adverse clinical consequences of pathologic tachycardia and the potential anxiety caused by physiological tachycardia in some heathy individuals, it is imperative to disseminate health information related to tachycardia for promotion in early diagnosis and appropriate management. YouTube has been increasingly used to access health care information. The aim of this study is to assess the quality and reliability of English YouTube videos focusing on tachycardia and further delve into strategies to enhance the quality of online health resources.MethodsWe conducted a search using the specific key words “tachycardia” in YouTube online library on December 2, 2023. The first 150 videos, ranked by “relevance”, were initially recorded. After exclusions, a total of 113 videos were included. All videos were extracted for characteristics and categorized based on different topics, sources or contents. Two independent raters assessed the videos using Journal of American Medical Association (JAMA) benchmark criteria, Modified DISCERN (mDISCERN) tool, Global Quality Scale (GQS) and Tachycardia-Specific Scale (TSS), followed by statistical analyses. All continuous data in the study were presented as median (interquartile range).ResultsThe videos had a median JAMA score of 2.00 (1.00), mDISCERN of 3.00 (1.00), GQS of 2.00 (1.00), and TSS of 6.00 (4.50). There were significant differences in JAMA (P < 0.001), mDISCERN (P = 0.004), GQS (P = 0.001) and TSS (P < 0.001) scores among different sources. mDISCERN (P = 0.002), GQS (P < 0.001) and TSS (P = 0.030) scores significantly differed among various contents. No significant differences were observed in any of the scores among video topics. Spearman correlation analysis revealed that VPI exhibited significant correlations with quality and reliability. Multiple linear regression analysis suggested that longer video duration, sources of academics and healthcare professionals were independent predictors of higher reliability and quality, while content of ECG-specific information was an independent predictor of lower quality.ConclusionsThe reliability and educational quality of current tachycardia-related videos on YouTube are low. Longer video duration, sources of academics and healthcare professionals were closely associated with higher video reliability and quality. Improving the quality of internet medical information and optimizing online patient education necessitates collaborative efforts.

Hall thruster model improvement by multidisciplinary uncertainty quantification

We study the analysis and refinement of a predictive engineering model for enabling rapid prediction of Hall thruster system performance across a range of operating and environmental conditions and epistemic and aleatoric uncertainties. In particular, we describe an approach by which experimentally-observed facility effects are assimilated into the model, with a specific focus on facility background pressure. We propose a multifidelity, multidisciplinary approach for Bayesian calibration of an integrated system comprised of a set of component models. Furthermore, we perform uncertainty quantification over the calibrated model to assess the effects of epistemic and aleatoric uncertainty. This approach is realized on a coupled system of cathode, thruster, and plume models that predicts global quantities of interest (QoIs) such as thrust, efficiency, and discharge current as a function of operating conditions such as discharge voltage, mass flow rate, and background chamber pressure. As part of the calibration and prediction, we propose a number of metrics for assessing predictive model quality. Based on these metrics, we found that our proposed framework produces a calibrated model that is more accurate, sometimes by an order of magnitude, than engineering models using nominal parameters found in the literature. We also found for many QoIs that the remaining uncertainty was not sufficient to account for discrepancy with experimental data, and that existing models for facility effects do not sufficiently capture experimental trends. Finally, we confirmed through a global sensitivity analysis the prior intuition that anomalous transport dominates model uncertainty, and we conclude by suggesting several paths for future model improvement. We envision that the proposed metrics and procedures can guide the refinement of future model development activities.

Deep Learning for Water Quality Prediction—A Case Study of the Huangyang Reservoir

Water quality prediction is a fundamental prerequisite for effective water resource management and pollution prevention. Accurate predictions of water quality information can provide essential technical support and strategic planning for the protection of water resources. This study aims to enhance the accuracy of water quality prediction, considering the temporal characteristics, variability, and complex nature of water quality data. We utilized the LTSF-Linear model to predict water quality at the Huangyang Reservoir. Comparative analysis with three other models (ARIMA, LSTM, and Informer) revealed that the Linear model outperforms them, achieving reductions of 8.55% and 10.51% in mean square error (MSE) and mean absolute error (MAE), respectively. This research introduces a novel method and framework for predicting hydrological parameters relevant to water quality in the Huangyang Reservoir. These findings offer a valuable new approach and reference for enhancing the intelligent and sustainable management of the reservoir.

Vaccine hesitancy as indecision: Creation and evaluation of the Unidimensional Vaccine Hesitancy Scale.

Several authors have argued that vaccine hesitancy should be conceptualized as indecision in the vaccination decision-making process, but no established measure with support for its psychometric properties and validity has been created from this operational definition. To resolve this tension, this article undergoes a four-study scale development process to create the 4-item Unidimensional Vaccine Hesitancy Scale (UVHS). We conduct four survey studies utilizing a total sample size of 884. In Studies 1 (n = 297) and 2 (n = 298), we provide psychometric support for the measure via exploratory and confirmatory factor analysis. In Studies 3 (n = 193) and 4 (n = 106), we support the concurrent and discriminant validity of the measure by assessing its relations with relevant constructs, such as vaccination readiness and acceptance, and we also provide initial indicators of the scale's possible predictive qualities by testing its time-separated effects with vaccination willingness, receipt and word-of-mouth. We leverage these results to provide a number of theoretical insights and suggestions for future practice. Of note, we highlight that different conceptualizations and operationalizations for the same construct can produce notably differing empirical findings, and vaccine hesitancy is no different. Our cumulative efforts indicate that the UVHS is an appropriate measure to assess vaccine hesitancy as indecision.

Artificial neural network based on microenvironmental parameters for quality prediction of kiwifruit in storage and transportation

Artificial neural network based on microenvironmental parameters for quality prediction of kiwifruit in storage and transportation

Carbon dioxide evaporation heat transfer coefficient prediction in porous media using Machine learning algorithms based on experimental data

The prediction of the internal heat transfer coefficient during evaporation is vital for vapor-compression refrigeration and closed-loop power cycles. Accurate measurements and understanding of CO2 heat transfer in porous evaporators are essential for optimal system design across various operating conditions. This study utilizes a reference dataset derived from previous experiments that investigated the impact of porous evaporators on CO2′s internal heat transfer coefficient under sub-critical conditions, employing gravel sand as the porous medium. The dataset encompasses key factors: gravel sand porosities ranging from 39.8 % to 44.5 %, evaporator inlet pressures between 3700 and 4300 kPa, CO2 mass flow rates from 10.7x10-5–18x10-5kg.s−1, and porous tube effective diameters spanning 1.53 x10-3–3.4x10-3m. Employing three machine learning techniques (SVM, GPR, OBEM), the study predicts the internal heat transfer coefficient using regression models. The models’ predictions are analyzed and compared to expected values for validation, evaluating their performance using four statistical criteria. Results indicate SVM, GPR, and OBEM models achieved RMSEs of 1.5471, 1.8212, and 3.6978, respectively, while MAE errors were 1.1479, 1.2418, and 2.9787, respectively. Comparison with the dimensional analysis method reveals the effectiveness of the proposed models in accurately predicting internal heat transfer coefficients. The models exhibit low uncertainty and maintain prediction quality on an extended dataset without overfitting concerns. Overall, this research contributes valuable insights for designing heat exchangers and systems in vapor-compression refrigeration and closed-loop power cycles.

Protein biomarkers in assessing kidney quality before transplantation‑current status and future perspectives (Review).

To meet the demand for kidney transplants (KTx), organs are frequently retrieved not only from standard criteria donors (SCD; a donor who is aged <50 years and suffered brain death from any number of causes, such as traumatic injuries or a stroke) but also from expanded criteria donors (any donor aged >60 years or donors aged >50 years with two of the following: A history of high blood pressure, a creatinine serum level ≥1.5 mg/dl or death resulting from a stroke). This comes at the cost of a higher risk of primary non‑function (the permanent hyperkalemia, hyperuremia and fluid overload that result in the need for continuous dialysis after KTx), delayed graft function (the need for dialysis session at least once during the first week after KTx), earlier graft loss and urinary complications (vesico‑ureteral reflux, obstruction of the vesico‑ureteral anastomosis, urine leakage). At present, there are no commercially available diagnostic tools for assessing kidney quality prior to KTx. Currently available predictive models based on clinical data, such as the Kidney Donor Profile Index, are insufficient. One promising option is the application of perfusion solutions for protein biomarkers of kidney quality and predictors of short‑ and long‑term outcomes. However, to date, protein markers that can be detected with ELISA, western blotting and cytotoxic assays have not been identified to be a beneficial predictors of kidney quality. These include lactate dehydrogenases, glutathione S‑transferases, fatty acid binding proteins, extracellular histones, IL‑18, neutrophil gelatinase‑associated lipocalin, MMPs and kidney injury molecule‑1. However, novel methods, including liquid chromatography‑mass spectrometry (LC‑MS) and microarrays, allow the analysis of all renal proteins suspended/dissolved in the acellular preservation solution used for kidney storage before KTx (including hypothermic machine perfusion as one of kidney storage methods) e.g. Belzer University of Wisconsin. Recent proteomic studies utilizing LC‑MS have identified complement pathway elements (C3, C1QB, C4BPA, C1S, C1R and C1RL), desmoplakin, blood coagulation pathway elements and immunoglobulin heavy variable 2‑26 to be novel predictors of kidney quality before transplantation. This was because they were found to correlate with estimated glomerular filtration rate at 3 and 12 months after kidney transplantation. However, further proteomic studies focusing on distinct markers obtained from hypothermic and normothermic machine perfusion are needed to confirm their predictive value and to improve kidney storage methods. Therefore, the present literature review from PubMed, Scopus, Embase and Web of Science was performed with the aims of summarizing the current knowledge on the most frequently studied single protein biomarkers. In addition, novel analytical methods and insights into organ injury during preservation were documented, where future directions in assessing organ quality before kidney transplantation were also discussed.

Role of meteorology and air pollution on fog conditions over Delhi during the peak winter 2024

Severe air pollution and foggy conditions during winter are persistent challenges, pose significant health hazards, and disrupt daily routines worldwide. In this study, we have investigated the conditions favoring the prolonged fog events in Delhi during January 2024 using observations, back trajectories, and reanalysis datasets. Analysis of visibility observations reveals that foggy (54, 121, 139, and 372 half-hours of very dense, dense, moderate, and shallow fog, respectively) conditions persisted in Delhi for 46 % of the time during the study period. The existence of 3–4 days of cold wave to severe cold wave conditions and the lack of passage of strong western disturbances across north and northwest India have also favored the prolonged fog formation. In addition, high relative humidity (>80 %), shallow boundary layer (216 m), stable weather conditions such as the absence of significant surface winds, the existence of cold wave to severe cold wave, temperature inversion (up to 4 °C), poor ventilation, and presence of high particulate matter (PM10: 298 μg/m3 and PM2.5: 182 μg/m3) facilitated the fog formation. Further, analyses reveal a spurt in daily particulate matter (PM10: 603 μg/m3 and PM2.5: 420 μg/m3; 13.4 and 28 times, respectively, exceeded the WHO air quality guideline levels) along with 4.5 h of zero visibility on 14th January. The analysis of particulate matter reveals the dominance of fine particles from nearby regions, which could have originated from the large-scale anthropogenic open biomass burning used for heating activities. The results derived from this study indicate the need for an accurate representation of the local anthropogenic emissions in the atmospheric models to improve the predictability of air quality and fog and provide insights into the need for their control, particularly during such extreme events.

The Impact of the Approach to Accounting for Age and Sex in Economic Models on Predicted Quality-Adjusted Life-Years.

The method used to model general population mortality estimates in cohort models can make a meaningful difference in appraisals; particularly in scenarios involving potentially curative treatments where a prior National Institute for Health and Care Excellence (NICE) appraisal demonstrated that this assumption alone could make a difference of ~£10,000 to the incremental cost-effectiveness ratio. Our objective was to evaluate the impact of different methods for calculating general population mortality estimates on the predicted total quality-adjusted life expectancy (QALE) as well as absolute and proportional quality-adjusted life year (QALY) shortfall calculations. We employed three distinct methods for deriving general population mortality estimates: firstly, utilizing the population mean age at baseline; secondly, modelling the distribution of mean age at baseline by fitting a parametric distribution to patient-level data sourced from the Health Survey for England (HSE); and thirdly, modelling the empirical age distribution. Subsequently, we simulated patient age distributions to explore the effects of mean starting age and variance levels on the predicted QALE and applicable severity modifiers. Provided sample code in R and Visual Basic for Applications (VBA) facilitates the utilization of individual patient age and sex data to generate weighted average survival and health-related quality of life (utility) outputs. We observed differences of up to 10.4% (equivalent to a difference of 1.01 QALYs in quality-adjusted life-expectancy) between methods using the HSE dataset. In our simulation study, increasing variance in baseline age diminished the accuracy of predictions relying solely on mean age estimation. Differences of -0.30 to 2.24 QALYs were found at a standard deviation of 20%; commonly observed in trials. For potentially curative treatments this would represent a difference in economically justifiable price of -£4,500-+£33,600 at a cost-effectiveness threshold of £30,000 per QALY for a treatment with a 50% cure rate. For lower baseline ages, the population mean method tended to overestimate QALE, whereas for higher baseline ages, it tended to underestimate QALE compared with individual patient age-based approaches. The severity modifier assigned did not vary, however, apart from simulations with means at the extremes of the age distribution or with very high variance. Our analysis underscores the necessity of accounting for the distribution of mean age at baseline, as failure to do so can lead to inaccurate QALE estimates, thereby affecting calculations of incremental costs and QALYs in models, which base survival and quality of life predictions on general population expectations. We would recommend that patient age and sex distribution should be accounted for when incorporating general population mortality in economic models. Provided sufficient sample size, utilizing the observed empirical distribution for the expected population in clinical practice is likely to yield the most accurate results. However, in the absence of patient-level data, selecting a suitable parametric distribution is recommended.

The Association of Smartphone Usage with Sleep Disturbances among Medical Students

Abstract Background Smartphones have become an important and vital instrument that all medical students utilize, but the usage of such devices has been found to be connected to sleep disturbances. The aim of this study was to estimate the prevalence and the relationship between smartphone addiction and poor sleep quality among medical students. Methods A cross-sectional study was conducted among the fifth- and sixth-year medical students at King Saud bin Abdulaziz University for Health Sciences in Riyadh, Saudi Arabia. A self-administered questionnaire was distributed to investigate the relation between smartphone usage and sleep disturbances. The questionnaires included demographic details, Pittsburgh Sleep Quality Index (PSQI) and smartphone addiction scale short version (SAS-SV). Results All 251 respondents had smartphones that were utilized for social media, communication, studying, etc. Most of the students were identified as smartphone addicts (65%), and 75% of the students had poor sleep quality. Sleep quality was found to be poor in 145 (85%) students with smartphone addiction. Smartphone addiction was significantly associated with poor sleep quality (odds ratio [OR]: 4.271; 95% confidence interval [CI]: 2.300–7.933; p &lt; 0.001). Gender and academic year were not significant predictors of poor sleep quality with p-values of 0.668 and 0.361, respectively. Smartphone addiction was significantly more prevalent among female students (80%) compared to male students (60%; p = 0.004), with the mean addiction score of 43.5 ± 11.5 and 33.5 ± 9.1, respectively (p &lt; 0.001). Conclusion Our study shows a significant association between smartphone addiction and poor sleep quality. It is strongly recommended that counseling services be provided to medical students to assist those suffering from smartphone addiction and sleep difficulties.