Likelihood Ratio Test Research Articles

Clinical utility of an artificial intelligence radiomics-based tool for risk stratification of pulmonary nodules.

Clinical utility data on pulmonary nodule (PN) risk stratification biomarkers are lacking. We aimed to determine the incremental predictive value and clinical utility of using an artificial intelligence (AI) radiomics-based computer-aided diagnosis (CAD) tool in addition to routine clinical information to risk stratify PNs among real-world patients. We performed a retrospective cohort study of patients with PNs who underwent lung biopsy. We collected clinical data and used a commercially available AI radiomics-based CAD tool to calculate a Lung Cancer Prediction (LCP) score. We developed logistic regression models to evaluate a well-validated clinical risk prediction model (the Mayo Clinic model) with and without the LCP score (Mayo vs Mayo + LCP) using area under the curve (AUC), risk stratification table, and standardized net benefit analyses. Among the 134 patients undergoing PN biopsy, cancer prevalence was 61%. Addition of the radiomics-based LCP score to the Mayo model was associated with increased predictive accuracy (likelihood ratio test, P = .012). The AUCs for the Mayo and Mayo + LCP models were 0.58 (95% CI = 0.48 to 0.69) and 0.65 (95% CI = 0.56 to 0.75), respectively. At the 65% risk threshold, the Mayo + LCP model was associated with increased sensitivity (56% vs 38%; P = .019), similar false positive rate (33% vs 35%; P = .8), and increased standardized net benefit (18% vs -3.3%) compared with the Mayo model. Use of a commercially available AI radiomics-based CAD tool as a supplement to clinical information improved PN cancer risk prediction and may result in clinically meaningful changes in risk stratification.

An Item Response Theory Analysis of the Psychosis Screening Questionnaire in Ethiopia.

Few psychosis screening instruments have been tested for use in Africa, yet appropriate tools can increase the detection of self-reported psychotic symptoms, improve the detection of psychosis and impact its prognosis. The construct validity and factor structure of Psychosis Screening Questionnaire (PSQ) were tested with confirmatory factor analysis (CFA) and item response theory (IRT) in a sample of 1928 Ethiopian adults without any history of psychosis. We tested a unidimensional model with and without an item on mania. For IRT, unidimensional latent structure one-parameter logistic (1PL) and two-parameter (2PL) logistic models were tested and compared for relative fit using a likelihood-ratio test. The prevalence of lifetime positive screens was 2.8% in an Ethiopian sample of adults from a general medical setting. A unidimensional model demonstrated good fit for the PSQ, (CFI = 0.993, TLI = 0.986 and RMSEA = 0.025). For IRT, a 2PL model was the best fitting one. IRT tests of item difficulty and discrimination parameters showed that paranoia had the highest discrimination and lowest difficulty ( ), likely to be endorsed at low levels of psychotic features. Thought insertion had the highest item difficulty ( ). Overall, the measure captures the psychosis construct at higher levels of the latent trait and may be suited for detecting moderate to severe levels of psychosis. The PSQ is found to have good construct validity in screening for psychosis among Ethiopian adults. Future studies may focus on the diagnostic validity of the PSQ comparing it with a structured clinical interview.

Internet use, spatial variation and its determinants among reproductive age women in Ethiopia

BackgroundThe Internet is the preferred source of health information for retrieving relevant information. In Ethiopia, the Internet penetration rate is improving year to year, but it is still at a low level compared to the rest of the world and neighboring African countries. Due to a lack of adequate information, it is important to assess Internet use, spatial variation, and determinants of Internet use among reproductive-age group women in Ethiopia.MethodSecondary data from EDHS 2016 were used to analyze 15,683 women aged 15–49 years. Spatial analysis was performed using ArcGIS 10.7. The Bernoulli model was used by applying Kuldorff’s methods using SaTScan 10.1.2 software to analyze the purely spatial clusters of Internet use. A multilevel mixed-effect logistic regression was applied to estimate community variance to identify individual- and community-level factors associated with Internet use. All models were fitted in STATA version 17.0, and finally, the adjusted odds ratio (AOR) with a corresponding 95% confidence interval (CI) was reported.ResultThe magnitude of Internet use was 4.97% ± 95% CI (4.63–5.32). The overall average age of women was 24.21 ± 8.06 years, with the age range 15–24 years constituting the larger group (39.2%). Women with secondary and above education [AOR = 6.47; 95% CI (5.04, 8.31)], unmarried [AOR = 2.60; 95% CI (1.89, 3.56)], rich [AOR = 1.95; 95% CI (1.00, 3.80)], own a mobile phone [AOR = 3.74; 95% CI (2.75, 5.09)], media exposure [AOR = 2.63; 95% CI (2.03, 3.42)], and urban [AOR = 1.80; 95% CI (1.08, 3.01)] had higher odds of Internet use. The spatial variation in Internet use was found to be nonrandom (global Moran’s I = 0.58, p value < 0.001). Fifty-seven primary clusters were identified that were located in Addis Ababa city with a relative likelihood of 10.24 and a log-likelihood ratio of 425.16.ConclusionsInternet use among reproductive-age women in Ethiopia is 4.97 and has significant spatial variation across the country. Both community- and individual-level factors affect Internet use in Ethiopia. Therefore, educating women, improving access to media, encouraging women to use family planning, and supporting household wealth could improve women’s Internet use.

Ophthalmological Examination and Imaging Features that Better Predict Risk of Angle Closure in Siblings

Characteristics of individuals with angle closure may be useful in targeted screening of family members. Here, we assess if findings gathered during examination and imaging of patients with a known angle closure diagnosis (probands) could better determine the risk of angle closure in the patients' siblings. Cross-sectional study of patients with known angle closure and their siblings. Participants, and Controls: South Indian patients (probands) 30 years and older with open angles, suspect primary angle closure (PACS), or primary angle closure/primary angle closure glaucoma (PAC/PACG), and a biological sibling age 30 years or older (n=292 proband/sibling pairs). Demographic data, relevant ocular history, and a comprehensive ophthalmic examination with Anterior Segment Optical Coherence Tomography (ASOCT) were obtained. Three clinically relevant models were created to analyze the contribution of specific proband factors in predicting sibling angle closure diagnosis, using demographic (age, gender), ocular exam (gonioscopy, optic nerve exam, visual acuity, intraocular pressure [IOP]), and ASOCT features to improve prediction beyond proband diagnosis alone evaluated by log likelihood ratio testing and statistical comparison of receiver operating characteristics (ROC). Sibling angle closure diagnostic accuracy. Demographic and ocular exam metrics did not improve the prediction of sibling angle closure for all three outcomes (sibling diagnosis: (1) PACS/PAC/PACG vs OA, (2) PAC/PACG vs PACS/OA, and (3) PAC/PACG vs PACS), adding no model improvement when compared to diagnosis alone. Models adding ASOCT metrics to the prior model including proband diagnosis, demographics and ocular exam measures led to significantly improved prediction of 2 of the 3 angle closure outcomes. Specifically, improvement was noted via likelihood ratio testing for prediction of PAC/PACG vs PACS/OA (p=0.01), or PAC/PACG vs PACS (p=0.001). For all 3 angle closure outcomes, ROC comparisons demonstrated significant improvement in AUC between the three models predicting sibling outcomes, demonstrating an increase in AUC with each successive nested model across all 3 sibling angle closure outcomes. Structural features of eyes with angle closure may assist in stratifying the risk of angle closure in patients' siblings. Further studies should consider evaluating this approach to achieve more targeted screenings.

Incremental value of tricuspid annular enlargement to progressive tricuspid regurgitation in patients with longstanding persistent atrial fibrillation.

Tricuspid annular enlargement in patients with atrial fibrillation (AF) can induce tricuspid regurgitation (TR). However, risk factors associated with TR progression in patients with AF have not been defined. This study aimed to clarify an association between tricuspid annular diameter (TAD) and TR progression in patients with longstanding persistent AF. We retrospectively analyzed data from 228 patients who had longstanding persistent AF for > 1year and mild or less TR on baseline echocardiograms. We defined significant TR as moderate or greater TR, graded according to the jet area and vena contracta. The optimal cut-off value of the TAD index (TADI), based on body surface area for predicting progression to significant TR, was estimated using receiver operating characteristic (ROC) curves. The independence and incremental value of the TADI were evaluated using multivariate Cox proportional hazard regression analysis and likelihood ratio tests. Over a median follow-up of 3.7years, 55 (24.1%) patients developed significant TR. The optimal cut-off value of 21.1mm/m2 for the TADI at baseline and ROC curves predicted TR progression with 70.4% sensitivity and 86% specificity. Furthermore, TADI was an independent predictor of TR progression (hazard ratio, 1.32; 95% confidence interval, 1.17-1.49, P < 0.001) and had a significant incremental value that exceeded that of models constructed using clinical parameters. In conclusion, TADI was significantly associated with TR progression and was an independent predictor of TR progression in longstanding persistent AF.

Prediction of incident atrial fibrillation using deep learning, clinical models and polygenic scores.

Deep learning applied to electrocardiograms (ECG-AI) is an emerging approach for predicting atrial fibrillation or flutter (AF). This study introduces an ECG-AI model developed and tested at a tertiary cardiac centre, comparing its performance with clinical and AF polygenic scores (PGS). ECG in sinus rhythm from the Montreal Heart Institute were analysed, excluding those from patients with preexisting AF. The primary outcome was incident AF at 5 years. An ECG-AI model was developed by splitting patients into non-overlapping datasets: 70% for training, 10% for validation, and 20% for testing. Performance of ECG-AI, clinical models and PGS was assessed in the test dataset. The ECG-AI model was externally validated in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) hospital dataset. A total of 669,782 ECGs from 145,323 patients were included. Mean age was 61±15 years, and 58% were male. The primary outcome was observed in 15% of patients and the ECG-AI model showed an area under the receiver operating characteristic curve (AUC) of 0.78. In time-to-event analysis including the first ECG, ECG-AI inference of high risk identified 26% of the population with a 4.3-fold increased risk of incident AF (95% confidence interval 4.02-4.57). In a subgroup analysis of 2,301 patients, ECG-AI outperformed CHARGE-AF (AUC=0.62) and PGS (AUC=0.59). Adding PGS and CHARGE-AF to ECG-AI improved goodness-of-fit (likelihood ratio test p<0.001), with minimal changes to the AUC (0.76-0.77). In the external validation cohort (mean age 59±18 years, 47% male, median follow-up 1.1 year) ECG-AI model performance= remained consistent (AUC=0.77). ECG-AI provides an accurate tool to predict new-onset AF in a tertiary cardiac centre, surpassing clinical and polygenic scores.

Investigating the impact of temporal instability in smart roadway retrofitting on terrain-related crash injury severity

The advancement of intelligent road systems in developing countries poses unique challenges in identifying risk factors and implementing safety strategies. The variability of factors affecting crash injury severity leads to different risks across levels of roadway smartness, especially in hazardous terrains, complicating the adaptation of smart technologies. Therefore, this study investigates the temporal instability of factors affecting injury severities in crashes across various terrains, with a focus on the evolution of road smartness. Crash data from selected complex terrain regions in Shaanxi Province during smart road adaptation were used, and categorized into periods before, during, and after smart road implementations. A series of mixed logit models were employed to account for unobserved heterogeneity in mean and variance, and likelihood ratio tests were conducted to assess the spatio-temporal instability of model parameters across different topographic settings and smart processes. Moreover, a comparison between partially constrained and unconstrained temporal modeling approaches was made. The findings reveal significant differences in injury severity determinants across terrain conditions as roadway intelligence progressed. On the other hand, certain factors like pavement damage, truck and pedestrian involvement were identified that had relatively stable effects on crash injury severities. Out-of-sample predictions further emphasize the need for modeling across terrain and roadway development stages. These insights are crucial for developing tailored safety measures for smart road retrofitting in different terrain conditions, thereby supporting the transition towards smarter road systems in developing regions.

Homogeneity Test of Ratios of Two Proportions in Stratified Bilateral and Unilateral Data

In paired-organ studies such as ophthalmology, otolaryngology, and rheumatology, etc., various approaches take highly correlated bilateral data into account for homogeneity tests but are less likely to focus on combined bilateral and unilateral data structures. Also, it is necessary and important to adjust the effect of confounders on stratified combined bilateral and unilateral data since, in these data structures, ignoring intra-class correlation and confounding effects can cause biased statistical inference. This article derived three homogeneity tests (the likelihood ratio test, the Wald test, and the score test) concerning these cooperative structure data to detect if ratios of proportions retain consistency across strata. Simulation shows that the score test provides a robust Type I error rate and satisfactory power performance. Finally, a real example is applied to demonstrate the application of these three proposed tests.

Association of COVID-19 Vaccination With Risk of Medically Attended Postacute Sequelae of COVID-19 During the Ancestral, Alpha, Delta, and Omicron Variant Eras.

Uncertainty exists regarding the effectiveness of COVID-19 vaccine to prevent postacute sequelae of COVID-19 (PASC) following a breakthrough infection. While most studies based on symptom surveys found an association between preinfection vaccination status and PASC symptoms, studies of medically attended PASC are less common and have reported conflicting findings. In this retrospective cohort of patients with an initial SARS-CoV-2 infection who were continually empaneled for primary care in a large US health system, the electronic health record was queried for preinfection vaccination status, demographics, comorbidity index, and diagnosed conditions. Multivariable logistic regression was used to model the outcome of a medically attended PASC diagnosis within 6 months of SARS-CoV-2 infection. Likelihood ratio tests were used to assess the interaction between vaccination status and prevalent variant at the time of infection and between vaccination status and hospitalization for SARS-CoV-2 infection. During the observation period, 6.9% of patients experienced medically attended and diagnosed PASC. A diagnosis of PASC was associated with older age, female sex, hospitalization for the initial infection, and an increased severity-weighted comorbidity index and was inversely associated with infection during the Omicron period. No difference in the development of diagnosed PASC was observed between unvaccinated patients and those vaccinated with either 2 doses of an mRNA vaccine or >2 doses. We found no association between vaccination status at the time of infection and development of medically diagnosed PASC. Vaccine remains an important measure to prevent SARS-CoV-2 infection and severity. Further research is needed to identify effective measures to prevent and treat PASC.

A Penalized Empirical Likelihood Approach for Estimating Population Sizes under the Negative Binomial Regression Model

In capture–recapture experiments, the presence of overdispersion and heterogeneity necessitates the use of the negative binomial regression model for inferring population sizes. However, within this model, existing methods based on likelihood and ratio regression for estimating the dispersion parameter often face boundary and nonidentifiability issues. These problems can result in nonsensically large point estimates and unbounded upper limits of confidence intervals for the population size. We present a penalized empirical likelihood technique for solving these two problems by imposing a half-normal prior on the population size. Based on the proposed approach, a maximum penalized empirical likelihood estimator with asymptotic normality and a penalized empirical likelihood ratio statistic with asymptotic chi-square distribution are derived. To improve numerical performance, we present an effective expectation-maximization (EM) algorithm. In the M-step, optimization for the model parameters could be achieved by fitting a standard negative binomial regression model via the R basic function glm.nb(). This approach ensures the convergence and reliability of the numerical algorithm. Using simulations, we analyze several synthetic datasets to illustrate three advantages of our methods in finite-sample cases: complete mitigation of the boundary problem, more efficient maximum penalized empirical likelihood estimates, and more precise penalized empirical likelihood ratio interval estimates compared to the estimates obtained without penalty. These advantages are further demonstrated in a case study estimating the abundance of black bears (Ursus americanus) at the U.S. Army’s Fort Drum Military Installation in northern New York.

Modelling climate change-induced nonstationarity in rainfall extremes: A comprehensive approach for hydrological analysis

Climate change and global warming have induced a dynamic shift in extreme rainfall patterns, leading to nonstationary behaviour. This alteration in behaviour challenges conventional hydrologic design, which assumes stationarity and can yield misleading outcomes. This study aims to address nonstationarity by modelling distribution parameters with covariates. Utilizing a 70-year (1951–2020) high-resolution India Meteorological Department (IMD) gridded dataset, extreme annual rainfall across diverse Indian cities was extracted and modelled. Previous research and goodness-of-fit tests favour the Generalized Extreme Value (GEV) distribution for modelling extremes. This study incorporates indices like Nino3.4, dipole mode index, global and local temperature, CO2, and time to characterize nonstationarity in extreme annual rainfall, leveraging climate cycles and global warming. Performance assessment employs the Akaike information criterion, Bayesian information criterion and Likelihood ratio test, while quantile reliability is evaluated through confidence intervals (CIs). Findings reveal widespread nonstationary trends in most grid points, translating to wider CIs in estimated quantiles for chosen non-exceedance probability and covariates in fitted models. Generally, nonstationary conditions are associated with broader confidence bands in return levels, highlighting nonstationary model weaknesses compared to stationary models. However, the results showed that the rainfall extremes follow a nonstationary pattern. Hence, there is a strong need to develop nonstationary models of low uncertainty.

Persymmetric direction detector for range‐spread target in subspace interference plus homogeneous and partially homogeneous clutter

AbstractThe problem of detecting the direction of range‐spread targets in the presence of subspace interference, along with homogeneous and partially homogeneous Gaussian clutter backgrounds characterised by an unknown persymmetric covariance matrix, is being investigated. It is assumed that the interference subspace is linearly independent of the signal subspace, and the coordinates of both subspaces are unknown. Leveraging the property of persymmetry, the two‐step Generalised Likelihood Ratio Test and the two‐step Wald test are proposed to formulate three detectors in the presence of subspace interference, homogeneous, and partially homogeneous Gaussian clutter. Theoretical derivations and simulations demonstrate that three proposed detectors maintain a constant false alarm rate with respect to the unknown clutter covariance matrix. In comparison to existing detectors in similar backgrounds, particularly in scenarios with limited training data, these three detectors exhibit superior detection performance.

Characterisation of view relative to solar-control systems

Solar shading can be an effective way of avoiding overheating by reducing solar gains in buildings. However, solar shading systems can block or obscure the view-out with the consequence that occupants may refrain from using the shading system. As such, there is a need to quantify the effects of shading on occupants’ perception of the view.In this study, we developed a method to identify the photometric parameters and compositions that are effective in characterising the view-out in relation to different solar-control systems. We hypothesized that the photometric composition, e.g., contrast, in the visual environment as a result of using solar-control systems impacts the subjective assessment of the view. We conducted objective photometric measurements using calibrated luminance cameras and subjective responses from 64 participants in a semi-controlled work environment to test the hypotheses. The participants were randomly allocated to a combination of five view-outs and six solar shading systems in a work environment where they answered questions related to the indoor environment and view quality. The relation between view and solar-control systems and their impact on the subjective view assessment was tested using linear mixed-effects models. The models were developed using forward and backward selection based on AIC and likelihood ratio tests (LRT) to test the effects of adding or removing variables. The achromatic contrast calculated based on the measured luminance data both locally and globally was significantly associated with view assessment and satisfaction when using different shading types.

Sex Differences in the Associations of mental Health, Suicidality, Screentime, and Sleep: A Mediation Effect Analysis of Sleep Using Youth Risk Behavioral Surveillance Survey 2021.

Objective: This study was conducted to explore the sex differences in the direct and indirect associations among mental health/suicidality, sleep, and screen time. Methods: Using the 2021 Youth Risk Behavior Survey (YRBS) data, 9408 participants were included in the analyses. The associations of endogenous variables (mental health and suicidality), exogenous variable (screen time), mediator (sleep), and covariates (demographic features and risky behaviors) were analyzed using the Structural Equation Model and "medsem" package, as well as logistic regression and bootstrapping methods. To explore the sex differences in the mediation effect, the Likelihood Ratio Test was used for the multiple-group analysis to compare the unconstrained model with the mediation path-constrained model. Results: Screen time had a significant negative association with sleep duration (female: β = -.09, p < .001; male: β = -.04, p < .001), positive relations to mental health problems (female: β = .10, p < .001; male: β = .12; p < .001), and suicidality (female: OR: 1.07, 95% CI: 1.02-1.13; male: OR: 1.06, 95% CI: 1.01-1.23). Sleep duration is negatively associated with mental health problems (female: β = -.16, p < .001; male: β = -.14; p < .001) and negatively associated with the likelihood of suicidality (female: .89, 95% CI: .85-.94; male: OR: .84, 95% CI: .79-.90). Furthermore, female adolescents had higher frequencies in mental health problems, higher odds ratio in suicidality, shorter sleep duration, and bigger mediation effects of sleep, compared to their male counterparts. Conclusion: Mental health and suicidality were affected differently by screen time and sleep between female and male adolescents. Future research may continue to explore sex differences and their underlying reasons.

Addition of an audience response system in a biomedical science course improves learning environment and student performance.

The effectiveness of incorporating an audience response system (ARS) in improving the learning environment and student performance was assessed in a didactic biomedical science course at a large US dental school. Instructors and students were surveyed for their experiences of using Top Hat-based ARS in a multi-disciplinary biomedical science course. Average exam scores and grade distribution for challenging sections on immunology and developmental biology were compared before and after incorporating ARS. Pearson's chi-squared test, likelihood ratio test, Student's t-test and Fisher's exact test using beta regression model were used to assess statistical significance (p < 0.05). Student survey results with 78% (82/105) response rate, indicated that incorporating ARS improved student engagement, reinforced lecture material, and prepared them better for exams (p < 0.0001). Sixty seven percent of student respondents recommended continued integration of ARS questions in lectures (p < 0.0001). Although faculty survey data (n = 5, 100% response rate) were not statistically significant, the majority of faculty agreed that real-time feedback through ARS allowed them to appropriately pace lecture delivery and restructure forthcoming material. The average exam scores and grade distribution for challenging sections showed modest improvement after incorporation of ARS (p < 0.05). There was no significant correlation between final course grades and ARS grades for participation or correctness. Incorporating the ARS in a multi-disciplinary biomedical science course is perceived by faculty and students as an effective instructional approach that improves the learning environment, teaching strategies, and student performance.

Testing the ratio of two Poisson means based on an inferential model

The ratio of two Poisson means is commonly used in biological, epidemiological, and medical. In this article, we consider the problem of testing the ratio of two Poisson means and propose a valid and efficient test based on the inference model (IM). The simulation studies show that the type I error and power of the IM-based test are competitive or even better than the six recommended tests: The likelihood ratio, mid-p, logarithmic transformation, and three tests based on the method of variance estimates recovery (MOVER). For R < 1 , the Rao-MOVER and fiducial-MOVER tests are slightly liberal, the mid-p test is somewhat conservative. For R > 1 , the Rao-MOVER and fiducial-MOVER tests are slightly conservative. The likelihood ratio and logarithmic transformation tests cannot control the type I error well. Therefore, the IM test could be recommended for practical applications. A real numerical example is presented to illustrate the flexibility of the proposed test.

Urinary Free Glycosaminoglycans Identify Adults at High Risk of Developing Early-stage High-grade Bladder Cancer

Background and objectiveScreening for bladder cancer (BCa) could reduce mortality via early detection of early-stage high-grade (Ta/T1 N0 M0 grade 2–3) disease. Noninvasive biomarkers could aid in screening, but current markers lack the specificity required. The urinary free glycosaminoglycan profile (GAGome) is a promising biomarker for early detection of BCa metabolism. MethodsIn a prospective case-control development study, we included patients with BCa or no evidence of disease (NED) and measured the urinary GAGome. We then developed a score to predict the probability of BCa using GAGome features that correlated with BCa versus NED according to Bayesian regression. Next, in a retrospective, population-based, case-control study, we included adults from the Lifelines Cohort Study who were presumed healthy at baseline. All cases with BCa confirmed in the cancer registry by the 2-yr or 6-yr study visit were matched to randomly selected control subjects. We developed a reference logistic regression model using age and sex to predict BCa at 7 yr after baseline. We then added the GAGome score to the model and assessed model improvement using the likelihood ratio test. We dichotomized outputs for the reference model and saturated model (reference + GAGome score) into high-risk versus low-risk categories using a 99% specificity cutoff and estimated the sensitivity for association with BCa at 7 yr. Key findings and limitationsWe prospectively included 51 individuals with BCa and 38 with NED and observed alterations in three GAGome features compatible with BCa. We developed a score that discriminated BCa with an area under the receiver operating characteristic curve of 0.77 (95% confidence interval [CI] 0.67–0.87). We retrospectively selected a cohort of 1088 presumed healthy adults (median age 48 yr, 56% females), of whom 48 had developed BCa by 7 yr after baseline (median time to diagnosis 1.4 yr). The GAGome score was an independent predictor of BCa at 7 yr when added to the reference model (p < 0.001). The sensitivity for BCa at 7 yr for high-risk subjects was 31% (95% CI 20–43%) using the saturated model and 17% (95% CI 4.7–29%) using the reference model at 99% specificity (95% CI 98–99%). Conclusions and clinical implicationsThe urinary free GAGome is specifically altered in BCa and can be used for noninvasive identification of adults at high risk of developing BCa, independent of age and sex. This information could be useful for the design of risk-stratified targeted screening programs for BCa. Patient summaryWe tested whether measurement of a class of sugars called glycosaminoglycans (GAGs) in urine could be used for early detection of bladder cancer. Our results show that GAG levels in urine can distinguish people at high risk of developing bladder cancer within 7 years, even if they are healthy at the time of the urine sampling.

Hematological parameters of COVID-19 patients at Jakarta Pondok Kopi Islamic Hospital

Background: The 2019 Corona Virus Disease (Covid-19) has become a pandemic. As of 15 January 2021, 223 countries have been infected, with a total number of 92,262,621 confirmed cases, and 1,995,037 deaths. In Indonesia, as of the same date, there were 896,642 confirmed cases and 25,767 deaths.1 Diagnosis of COVID-19 is based on real-time reverse transcriptase-polymerase chain reaction (RT-PCR) results on oropharyngeal/nasopharyngeal swab samples.2 A basic hematological examination is also conducted on patients with suspected COVID-19. The results of baseline hematological examinations are thought to be a predictor of the patient being infected with COVID-19 before the RT-PCR results are released. The purpose of this study is to identify the hematological parameters of COVID-19 patients at Pondok Kopi Islamic Hospital in Jakarta (RSIJ PK) in order to provide an overview of the hematological parameters that can be used to predict the possibility of a patient being infected with COVID-19. Methods: The study adopted a cross-sectional approach, using the database on COVID-19 patients at Pondok Kopi Islamic Hospital. Data variables were categorized and described by frequency and percentage. Statistical analysis was conducted using the likelihood ratio test and SPSS (statistical package for the social sciences) version 26. Results: The number of research respondents was 250, with 142 males (56.8%). The majority of participants were between the ages of 40 and 59, at 127 (50.8%). Most patients exhibited a moderate level of clinical severity, at 215 (86%). The three most common comorbidities were hypertension in 82 patients (32.8%), diabetes in 68 patients (27.2%), and chronic kidney disease in 27 patients (10.8%). The dominant hematological parameters were normal blood leukocytes in 157 patients (62.8%), increased Neutrophil/Lymphocyte Ratio (NLR) (&gt;3.13) in 148 patients (59.2%), low Absolute Lymphocyte Count (ALC) (&lt;1500/µL) in 133 patients (53.2%), increased C-Reactive Proteins (CRP) (&gt;10 mg/L) in 171 patients (68.4%), and increased d-dimer (&gt;0.5 µg/mL) in 148 patients (59.2%). There was a significant correlation between age (p 0.028), comorbid hypertension (p 0.002), comorbid diabetes (p 0.011), leukocyte levels (p 0.045), and ALC levels (p 0.025), and the severity of the COVID-19 disease. Conclusions: The majority of COVID-19 patients had normal blood leukocyte levels, increased NLR, low ALC, and increased CRP and d-dimer.

A Generalized Linear Model of HIV/AIDS Patients in Kenya: A Case Study of Nyeri County Referral Hospital

With millions of new cases and deaths reported every year, HIV/AIDS is a significant worldwide health concern. Creating successful public health policies and interventions requires an understanding of the dynamics of HIV transmission and progression. WHO predicted that by the end of 2022 roughly 39 million individuals worldwide would be living with HIV, out of which 37.5 million are adults, whereas 1.5 million are children. Despite outstanding global gains in HIV/AIDS prevention, treatment and care, Kenya continues to struggle to effectively handle the HIV epidemic, particularly in areas like Nyeri County. Nyeri County Referral Hospital is a critical healthcare institution for HIV/AIDS patients in the region. However, there is still a lack of understanding about the epidemiological characteristics of HIV/AIDS in this particular population. This study’s aim was to use a GLM on HIV/AIDS data in Nyeri County Referral Hospital in Kenya. To determine the significance of model parameters, Likelihood Ratio Test was used whereas significance of regression coefficients was determined using Wald Chi- Square Test. Deviance was utilized to test for the goodness of fit. R software version 4.4.1 was utilized. This project may help health policymakers in developing or refining HIV/AIDS care programs. Findings from the study can help healthcare planners and policymakers allocate resources more efficiently to meet the requirements of HIV/AIDS patients. The fitted model showed that, only ART use was significant (p-value = 2.684562 × 10&amp;lt;sup&amp;gt;−13&amp;lt;/sup&amp;gt;). Because some covariates were not significant, each of them was analyzed separately. Age was a significant predictor (p-value = 0.0001536103). The other variables were not significant. This finding is consistent with previous evidence, which stresses the relevance of ART in lowering viral load, enhancing immunological function, and extending the lives of people living with HIV. To build upon the current findings, future research should explore additional variables that may influence HIV status, for example cultural beliefs, and access to healthcare services. Again, future studies may involve the use of survival analysis through GLM in analyzing similar data.

The nonlinear relationship between triglyceride glucose-waist circumference and stroke risk in middle-aged and elderly people: a nationwide prospective cohort study of the CHARLS

Background and aimsNumerous research have focused on the relationship of metabolic markers and stroke risk, yet limited research has focused on the triglyceride glucose-waist circumference (TyG-WC) index. This study explored the possible association of TyG-WC and stroke among moderately aged and old Chinese adults over 45 years of age.MethodsThis observational cohort analysis involved 9054 participants from the Chinese Longitudinal Study of Health and Retirement and employed a standardized questionnaire administered via in-person interviews. Cox proportional hazard model, smoothed curve fitting, and threshold effect analysis were conducted for examining the potential nonlinear relationships among TyG-WC and stroke risk.ResultsWithin an average follow-up period of six years, 463 new strokes occurred, representing 5.11% of the total number of patients. After adjusting for possible confounding factors, a nonlinear association between TyG-WC and stroke risk was identified, with a significant dose–response relationship (P = 0.023 for the log-likelihood ratio test). A turning point was identified at the TyG-WC level of 554.48, beyond that the likelihood of stroke increased markedly (HR = 1.323, 95% CI = 1.098–1.594, P = 0.003).ConclusionThis study revealed a specific curvilinear association with the TyG-WC score and stroke risk, identifying a key threshold value. This study focused on Chinese middle-aged and senior adults over the age of 45, emphasizing that increased stroke risk is linked to higher TyG-WC levels.