Random Slope Research Articles

Decomposing Impact on Longitudinal Outcome of Time-Varying Covariate into Baseline Effect and Temporal Effect

Longitudinal processes are often associated with each other over time; therefore, it is important to investigate the associations among developmental processes and understand their joint development. The traditional latent growth curve model (LGCM) with a time-varying covariate (TVC) provides a method to estimate the TVC effect on a longitudinal outcome while modeling the outcome’s change. However, it does not allow the TVC to predict variations in the random growth coefficients. We propose decomposing the TVC into initial trait and temporal states using three methods to address this limitation. In each method, the baseline of the TVC is viewed as an initial trait, and the corresponding effects are obtained by regressing random intercepts and slopes on the baseline value. Temporal states are characterized as (a) interval-specific slopes, (b) interval-specific changes, or (c) changes from the baseline at each measurement occasion, depending on the method. We demonstrate our methods through simulations and real-world data analyses, assuming a linear–linear functional form for the longitudinal outcome. The results demonstrate that LGCMs with a decomposed TVC can provide unbiased and precise estimates with target confidence intervals. We also provide OpenMx and Mplus 8 code for these methods with commonly used linear and nonlinear functions.

Regional Variability in MRI Scans with Different Magnetic Field Strengths in Japan: Implications for Healthcare Preparedness for Alzheimer's Disease Treatment.

(1) Background: The 2023 approval of lecanemab for early-stage Alzheimer's disease (AD) highlighted the need for routine 1.5T or 3.0T MRI scans to monitor amyloid-related imaging abnormalities (ARIAs). Regional disparities in MRI scan frequency, MRI scanner availability, and scanner magnetic field strengths could affect readiness for anti-amyloid therapy and lead to inconsistencies in ARIA detection nationwide. (2) Methods: We assessed regional variance in MRI scan frequency and field strength across Japan using the National Database (NDB) Open Data website, which summarizes Japanese public health insurance claims from the fiscal years (FYs) 2015 to 2021. We employed a mixed-effects model with prefecture-level random intercepts and slopes over time, subsequently categorizing prefectures into clusters based on MRI usage. (3) Results: 1.5T MRI was the most common magnetic field strength, remaining stable from FY2015 to FY2021. 3.0T MRI usage slightly increased, although the COVID-19 pandemic in FY2020 led to a maximum reduction of 5%. Prefecture-level variance was higher for 3.0T MRIs, with more frequent usage in western Japan. (4) Conclusions: This study highlights prefecture-level variance in MRI usage across Japan. The insights gained could be instrumental in improving healthcare preparedness for anti-amyloid treatment and patient management.

A novel block-coordinate gradient descent algorithm for simultaneous grouped selection of fixed and random effects in joint modeling.

Joint models for longitudinal and time-to-event data are receiving increasing attention owing to its capability of capturing the possible association between these two types of data. Typically, a joint model consists of a longitudinal submodel for longitudinal processes and a survival submodel for the time-to-event response, and links two submodels by common covariates that may carry both fixed and random effects. However, research gaps still remain on how to simultaneously select fixed and random effects from the two submodels under the joint modeling framework efficiently and effectively. In this article, we propose a novel block-coordinate gradient descent (BCGD) algorithm to simultaneously select multiple longitudinal covariates that may carry fixed and random effects in the joint model. Specifically, for the multiple longitudinal processes, a linear mixed effect model is adopted where random intercepts and slopes serve as essential covariates of the trajectories, and for the survival submodel, the popular proportional hazard model is employed. A penalized likelihood estimation is used to control the dimensionality of covariates in the joint model and estimate the unknown parameters, especially when estimating the covariance matrix of random effects. The proposed BCGD method can successfully capture the useful covariates of both fixed and random effects with excellent selection power, and efficiently provide a relatively accurate estimate of fixed and random effects empirically. The simulation results show excellent performance of the proposed method and support its effectiveness. The proposed BCGD method is further applied on two real data sets, and we examine the risk factors for the effects of different heart valves, differing on type of tissue, implanted in the aortic position and the risk factors for the diagnosis of primary biliary cholangitis.

The effectiveness of coronary computed tomography angiography and functional testing for the diagnosis of obstructive coronary artery disease: results from the individual patient data Collaborative Meta-Analysis of Cardiac CT (COME-CCT)

AimTo determine the effectiveness of functional stress testing and computed tomography angiography (CTA) for diagnosis of obstructive coronary artery disease (CAD).Methods and resultsTwo-thousand nine-hundred twenty symptomatic stable chest pain patients were included in the international Collaborative Meta-Analysis of Cardiac CT consortium to compare CTA with exercise electrocardiography (exercise-ECG) and single-photon emission computed tomography (SPECT) for diagnosis of CAD defined as ≥ 50% diameter stenosis by invasive coronary angiography (ICA) as reference standard. Generalised linear mixed models were used for calculating the diagnostic accuracy of each diagnostic test including non-diagnostic results as dependent variables in a logistic regression model with random intercepts and slopes. Covariates were the reference standard ICA, the type of diagnostic method, and their interactions. CTA showed significantly better diagnostic performance (p < 0.0001) with a sensitivity of 94.6% (95% CI 92.7–96) and a specificity of 76.3% (72.2–80) compared to exercise-ECG with 54.9% (47.9–61.7) and 60.9% (53.4–66.3), SPECT with 72.9% (65–79.6) and 44.9% (36.8–53.4), respectively. The positive predictive value of CTA was ≥ 50% in patients with a clinical pretest probability of 10% or more while this was the case for ECG and SPECT at pretest probabilities of ≥ 40 and 28%. CTA reliably excluded obstructive CAD with a post-test probability of below 15% in patients with a pretest probability of up to 74%.ConclusionIn patients with stable chest pain, CTA is more effective than functional testing for the diagnosis as well as for reliable exclusion of obstructive CAD. CTA should become widely adopted in patients with intermediate pretest probability.Systematic review registrationPROSPERO Database for Systematic Reviews—CRD42012002780.Critical relevance statementIn symptomatic stable chest pain patients, coronary CTA is more effective than functional testing for diagnosis and reliable exclusion of obstructive CAD in intermediate pretest probability of CAD.Key PointsCoronary computed tomography angiography showed significantly better diagnostic performance (p < 0.0001) for diagnosis of coronary artery disease compared to exercise-ECG and SPECT.The positive predictive value of coronary computed tomography angiography was ≥ 50% in patients with a clinical pretest probability of at least 10%, for ECG ≥ 40%, and for SPECT 28%.Coronary computed tomography angiography reliably excluded obstructive coronary artery disease with a post-test probability of below 15% in patients with a pretest probability of up to 74%.Graphical

Associations of Amyloid Burden, White Matter Hyperintensities, and Hippocampal Volume With Cognitive Trajectories in the 90+ Study.

Amyloid pathology, vascular disease pathology, and pathologies affecting the medial temporal lobe are associated with cognitive trajectories in older adults. However, only limited evidence exists on how these pathologies influence cognition in the oldest old. We evaluated whether amyloid burden, white matter hyperintensity (WMH) volume, and hippocampal volume (HV) are associated with cognitive level and decline in the oldest old. This was a longitudinal, observational community-based cohort study. We included participants with 18F-florbetapir PET and MRI data from the 90+ Study. Amyloid load was measured using the standardized uptake value ratio in the precuneus/posterior cingulate with eroded white matter mask as reference. WMH volume was log-transformed. All imaging measures were standardized using sample means and SDs. HV and log-WMH volume were normalized by total intracranial volume using the residual approach. Global cognitive performance was measured by the Mini-Mental State Examination (MMSE) and modified MMSE (3MS) tests, repeated every 6 months. We used linear mixed-effects models with random intercepts; random slopes; and interaction between time, time squared, and imaging variables to estimate the associations of imaging variables with cognitive level and cognitive decline. Models were adjusted for demographics, APOE genotype, and health behaviors. The sample included 192 participants. The mean age was 92.9 years, 125 (65.1%) were female, 71 (37.0%) achieved a degree beyond college, and the median follow-up time was 3.0 years. A higher amyloid load was associated with a lower cognitive level (βMMSE = -0.82, 95% CI -1.17 to -0.46; β3MS = -2.77, 95% CI -3.69 to -1.84). A 1-SD decrease in HV was associated with a 0.70-point decrease in the MMSE score (95% CI -1.14 to -0.27) and a 2.27-point decrease in the 3MS score (95% CI -3.40 to -1.14). Clear nonlinear cognitive trajectories were detected. A higher amyloid burden and smaller HV were associated with faster cognitive decline. WMH volume was not significantly associated with cognitive level or decline. Amyloid burden and hippocampal atrophy are associated with both cognitive level and cognitive decline in the oldest old. Our findings shed light on how different pathologies contributed to driving cognitive function in the oldest old.

Post-COVID-19 recovery and geriatric rehabilitation care: a European inter-country comparative study.

There is variation in organization of geriatric rehabilitation across Europe. The purpose of this study was to describe the selection criteria for referral to geriatric rehabilitation, care provided, and recovery trajectories of post-COVID-19 patients referred to geriatric rehabilitation in Europe. This observational cohort study included 723 patients in 59 care facilities for geriatric rehabilitation across 10 countries. Patient data were collected from medical records on admission to geriatric rehabilitation (between September 2020 and October 2021), discharge, 6weeks and 6months follow-up. The primary and secondary outcomes were recovery in daily functioning (Barthel Index) and Quality of Life (EQ-5D-5L) from admission to discharge. These were examined using linear mixed models with two levels (measurements nested in patients) and country as an independent variable. Random intercept and random linear slope parameters were added when they improved model fit. A survey about organization of geriatric rehabilitation for post-COVID-19 patients was filled out by country coordinators and data were analyzed using descriptive statistics and inductive coding of answers to open questions. Patients had a mean age of 75.7years old and 52.4% were male. Many countries used various combinations of the selection criteria, such as functional status, age, frailty, Comprehensive Geriatric Assessment, comorbidities, and cognitive impairments. Most patients received physiotherapy (88.8%) and occupational therapy (69.7%), but there was substantial variance between countries in the percentages of patients that received protein or calorie enriched diets, oxygen therapy, and other treatment components. In all countries, patients showed recovery in daily functioning and quality of life, although there was variation in between countries in rate of recovery. Daily functioning seemed to increase most rapidly in the Czech Republic, Germany, and Russia. The steepest increases in quality of life were seen in the Czech Republic, Germany, and Spain. Post-COVID-19 patients showed recovery during geriatric rehabilitation, albeit at variable rates. The observed variation may be explained by the heterogeneity in selection criteria and care provided. This study highlights the need for harmonization of measurements in geriatric rehabilitation order to perform explanatory research and optimize geriatric rehabilitation throughout Europe to ensure optimal patient recovery.

Individual capacity growth over time in leadership courses: an intra-individual multilevel model approach

PurposeOur study was designed to investigate the longitudinal trajectories of student leader development capacities in a sample of students enrolled in multiple leadership-focused courses across several semesters. Our goal was to assess the degree to which course enrollment was associated with growth over the time that students engage as undergraduates in academic leadership programs, and if so, to assess the shape and speed of capacity change.Design/methodology/approachWe utilized a multilevel intra-individual modeling approach assessing students’ motivation to lead, leader self-efficacy, and leadership skills across multiple data collection points for students in a campus major or minor focused on leadership studies. We compared an unconditional model, a fixed effect model, a random intercept model, a random slope model, and a random slope and intercept model to determine the shape of score trajectories. Our approach was not to collect traditional pre-test and post-test data – choosing to collect data only at the beginning of each semester – to reduce time cues typically inherent within pre-test and post-test collections.FindingsOur results strongly suggested that individual students differ greatly in the degree to which they report the capacity to lead when initially enrolling in their first class. Surprisingly, the various models were unable to predict a pattern of longitudinal leader development through repeated course enrollment in our sample.Originality/valueOur investigation employed statistical methods that are not often utilized in leadership education quantitative research, and also included a data collection effort designed to avoid a linear pre-test/post-test score comparison.

Estimating nonlinear effects of random slopes: A comparison of multilevel structural equation modeling with a two-step, a single-indicator, and a plausible values approach

Multilevel structural equation modeling (MSEM) is a statistical framework of major relevance for research concerned with people’s intrapersonal dynamics. An application domain that is rapidly gaining relevance is the study of individual differences in the within-person association (WPA) of variables that fluctuate over time. For instance, an individual’s social reactivity – their emotional response to social situations – can be represented as the association between repeated measurements of the individual’s social interaction quantity and momentary well-being. MSEM allows researchers to investigate the associations between WPAs and person-level outcome variables (e.g., life satisfaction) by specifying the WPAs as random slopes in the structural equation on level 1 and using the latent representations of the slopes to predict outcomes on level 2. Here, we are concerned with the case in which a researcher is interested in nonlinear effects of WPAs on person-level outcomes – a U-shaped effect of a WPA, a moderation effect of two WPAs, or an effect of congruence between two WPAs – such that the corresponding MSEM includes latent interactions between random slopes. We evaluate the nonlinear MSEM approach for the three classes of nonlinear effects (U-shaped, moderation, congruence) and compare it with three simpler approaches: a simple two-step approach, a single-indicator approach, and a plausible values approach. We use a simulation study to compare the approaches on accuracy of parameter estimates and inference. We derive recommendations for practice and provide code templates and an illustrative example to help researchers implement the approaches.

Characterising the killing of girls and women in urban settings in Latin America, 2000–2019: an analysis of variability and time trends using mortality data from vital registration systems

IntroductionLatin America is burdened by high levels of violence. Although boys and men often experience more violence and fatalities, girls and women face a greater risk of being killed by...

A novel approach to stability analysis of random soil-rock mixture slopes using finite element method in ABAQUS

A novel approach to stability analysis of random soil-rock mixture slopes using finite element method in ABAQUS

Optimizing short-term in-class process evaluation: analyzing the effectiveness of teaching interventions in pharmaceutical education using repeated measures analysis

BackgroundThe assessment of the effectiveness of teaching interventions in enhancing students’ understanding of the Pharmaceutical Care Network Europe (PCNE) Classification System is crucial in pharmaceutical education. This is especially true in regions like China, where the integration of the PCNE system into undergraduate teaching is limited, despite its recognized benefits in addressing drug-related problems in clinical pharmacy practice. Therefore, this study aimed to evaluate the effectiveness of teaching interventions in improving students’ understanding of the PCNE Classification System in pharmaceutical education.MethodsUndergraduate pharmacy students participated in a series of sessions focused on the PCNE system, including lectures (t1), case analyses (t2), and practical implementation (t3). The levels of understanding were evaluated using time-course questionnaires. Initially, paired samples t-Tests were used to compare understanding levels between different time points. Subsequently, Repeated Measures Analysis (RMA) was employed. Pearson correlation analysis was conducted to examine the relationship between understanding levels and the usability and likelihood of using the PCNE system, as reported in the questionnaires.ResultsThe paired samples t-Tests indicated insignificant differences between t2 and t3, suggesting limited improvement following the practical implementation of the PCNE system. However, RMA revealed significant time effects on understanding levels in effective respondents and the focused subgroup without prior experience (random intercept models: all p < 0.001; random slope models: all p < 0.001). These results confirmed the effectiveness of all three teaching interventions. Pearson correlation analysis demonstrated significant positive correlations between understanding levels and the usability and likelihood of using the PCNE system at all examined time points. This finding highlighted the reliability of the understanding levels reported in the questionnaires. The homework scores were used as external calibration standards, providing robust external validation of the questionnaire’s validity.ConclusionThe implementation of RMA provided robust evidence of the positive impact of time on understanding levels. This affirmed the effectiveness of all teaching interventions in enhancing students’ comprehension of the PCNE Classification System. By utilizing RMA, potential errors inherent in common statistical methods, such as t-Tests, were mitigated. This ensured a more comprehensive and accurate assessment of the effectiveness of the teaching interventions.

Clarifying the relationship between trait aggression and self-control using random item slope regression.

Myriad psychological research evinces a negative association between self-control and aggression with some arguing for self-control failure as a cause of aggression. Recent literature suggests that the relationship between aggression and self-control is likely more complex and even positive in some cases. One source of such conflict in the literature could be the presence of unaccounted for random item slopes in commonly used measures of self-control which may inflate the likelihood of Type I errors. This study (N = 1386) tested the hypothesis that self-control would share random item slopes with the facets of trait aggression using random item slope regression. We measured trait aggression and self-control via two common self-reports: the Buss-Perry Questionnaire and the Brief Self-Control Scale. Our analyses revealed that the facets of trait aggression shared significant random item slopes with self-control and that many of these slopes were positive, rather than negative. We also found that Type I error inflation was evident in models that did not account for these random slopes. These findings may in part explain some of the conflicting results in the literature and that researchers interested in studying self-control and aggression should test for random item slopes.

Beyond Personal Factor: A Multilevel Analysis for Predicting the Effect of Moral Self-regulation and School Climate on Muslim Adolescent Aggressivity

Aggressive behaviors among adolescents have been increasingly observed in Indonesian Islamic boarding schools (pesantren), garnering significant attention due to their incongruity with the schools' ethos. Addressing these behaviors in pesantren requires a different approach from mainstream schools due to their unique characteristics. This study investigates the impact of moral self-regulation and school climate on aggressiveness among Muslim adolescents, using a multilevel analysis that considers both individual and institutional factors within the educational setting as units of analysis. This quantitative study involved 428 participants (mean=16.45, SD=1.01) both males and females from ten pesantren in three provinces in Indonesia selected using convenience sampling. Data were collected using the modified version of the aggressivity scale, moral self-regulation scale, and school climate scale. They were analyzed using multilevel regression analysis techniques with the aid of the R lmerTest program. An initial evaluation of the need for multilevel analysis was done by checking the significance of the Likelihood Ratio Test (LRT) results for the Intercept models and Intraclass correlation (ICC) above 5%. LRT and Akaike Information Criterion (AIC) were used as the selection criteria for the best hierarchical models involving random intercepts and random slopes. The study found that better moral self-regulation in students significantly reduces adolescent aggressivity. Additionally, a positive school climate helps prevent aggressive behavior among students. Addressing aggression in Muslim adolescents in Indonesia requires focusing on both individual and school factors that promote positive behavior, emphasizing the importance of moral self-regulation and a supportive school environment.

Implementing linear mixed effects models to enhance estimation of the dimensional stability of wood of Laurus nobilis L.

Aim of study: The properties of wood of laurel (Laurus nobilis L.) have not yet been adequately described. For example, information on variables related to dimensional stability during drying (shrinkage) is lacking, even though this is a key factor determining the suitability of the material for industrial uses with high added value. The aim of this study was to construct models for estimating shrinkage variables by using wood density as the predictor variable. Area of study: Seventeen laurel trees were felled in an inland area of Galicia (north-western Spain) in order to obtain the material for testing and modelling. Material and methods: The experimental tests were performed on 958 small standardised, defect-free wood specimens. Main results: The wood under study was moderately heavy and volumetrically unstable. Density varied only slightly, but volumetric shrinkage varied statistically significantly within and between trees. A linear mixed effects model was constructed to predict the variation in volumetric shrinkage from the oven-dry density, including the factors tree and height in the stem, with random slopes and intercepts. Research highlights: The model proved valid for all sampled individuals up to a height of two metres in the stem, thus enabling estimation of the volumetric shrinkage in commercial basal logs.

Predicting individual thermal preferences in an office: Assessing the performance of mixed-effects models

Multiple studies suggested that existing thermal comfort models inadequately predict occupants' individual thermal preferences. Personalised comfort models offer an alternative to conventional comfort models aiming to forecast individual's thermal preference. Implementation of these personalised models in occupant-centric control of heating, ventilation, and air-conditioning (HVAC) systems can enhance their performance.A promising technique for personalised comfort modelling is mixed-effects (ME) modelling. A ME model accounts for fixed effects, representing the trends in the general sample, and for random effects, representing variations of specific clusters in the data. In contrast to fixed-effects (FE) models, ME models can capture individual differences. However, its effectiveness in predicting occupants' thermal preferences based on field measurement data, as well as the influence of variations in ME models on prediction accuracy, remains to be thoroughly investigated.This study aims to assess the prediction accuracy of ME models in contrast to FE models using field measurement data, including thermal preference votes from 30 unique occupants. The prediction performance was evaluated across three testing scenarios, each representing a different application of the models. Furthermore, two random effect structures were tested for the ME model: an intercept-only model and an intercept and slope model. The results show that ME models, in comparison to FE models, achieve an improved prediction performance of 8.0 % on average and up to 28.4 % for individual occupants. Moreover, the addition of a random slope to the ME resulted in deteriorated predictions. Finally, occupants’ individual variations were determined with an uncertainty of 6 % after 20 observations.

A comparison of word humor ratings across speakers of North American, British, and Singapore English.

Large-scale collection of lexical-semantic norms for words in a given language has been instrumental in the progress of psycholinguistic research. However, such norms tend to be collected from speakers of the dominant variant or dialect. This research aims to determine if there may be differences across speakers of various dialects of English in the humor of individual words. Engelthaler and Hills (2018, Behavior Research Methods, 50[3], 1116-1124) observed that their humor ratings were most strongly correlated with inverse word frequency: Less frequent words tended to be rated as more humorous. We hypothesized that words that are less frequently occurring in a given English dialect should be perceived as more humorous by speakers of the same dialect. We selected words of relatively higher and lower frequencies across various corpora of North American, British, or Singapore English, and presented these words to participants who were native English speakers of North American, British, or Singapore English. Study 1 compared humor ratings of North Americans and Singaporeans; Study 2 compared humor ratings of North Americans and the British. Analyses of participants' random slope coefficients of frequency extracted from cumulative link mixed-effects models indicated that humor ratings were more strongly (and inversely) associated with the word's frequency in the corpora that aligned with the rater's English dialect. These results provide evidence that people are sensitive to the statistics of their specific language environment, and importantly suggest that creators of lexical-semantic norm databases should consider how the cultural, historical, or sociopolitical context of raters might influence the nature of their ratings.

Depression-Anxiety Coupling Strength as a predictor of relapse in major depressive disorder: A CAN-BIND wellness monitoring study report

BackgroundA critical challenge in the study and management of major depressive disorder (MDD) is predicting relapse. We examined the temporal correlation/coupling between depression and anxiety (called Depression-Anxiety Coupling Strength, DACS) as a predictor of relapse in patients with MDD. MethodsWe followed 97 patients with remitted MDD for an average of 394 days. Patients completed weekly self-ratings of depression and anxiety symptoms using the Quick Inventory of Depressive Symptoms (QIDS-SR) and the Generalized Anxiety Disorder 7-item scale (GAD-7). Using these longitudinal ratings we computed DACS as random slopes in a linear mixed effects model reflecting individual-specific degree of correlation between depression and anxiety across time points. We then tested DACS as an independent variable in a Cox proportional hazards model to predict relapse. ResultsA total of 28 patients (29 %) relapsed during the follow-up period. DACS significantly predicted confirmed relapse (hazard ratio [HR] 1.5, 95 % CI [1.01, 2.22], p = 0.043; Concordance 0.79 [SE 0.04]). This effect was independent of baseline depressive or anxiety symptoms or their average levels over the follow-up period, and was identifiable more than one month before relapse onset. LimitationsSmall sample size, in a single study. Narrow phenotype and comorbidity profiles. ConclusionsDACS may offer opportunities for developing novel strategies for personalized monitoring, early detection, and intervention. Future studies should replicate our findings in larger, diverse patient populations, develop individual patient prediction models, and explore the underlying mechanisms that govern the relationship of DACS and relapse.

Does involving another person in day-to-day activity-travel participation affect social and mental health?

IntroductionThis study investigates the effects of different types of social networks within different activities on an individual’s social and mental health in developing countries since the effects of involving other people or social networks on health have been studied particularly in developing countries. MethodsThe data was collected through a survey conducted from August to October 2019. The survey involved 420 individuals from 92 households, covering inputs on their socio-demographics, travel characteristics, time use, and activity diaries and the involvement of various social network types during the activities, travels performed, and Health Quality of Life. Multilevel modelling with a random slope is applied. For time-use and activity-travel participation information, respondents were asked to record their activities and travel within 15-min intervals for 5 consecutive weekdays with 25 activity classifications grouped as mandatory, leisure, and maintenance activities including online activities that could be conducted in or outside the home. It is also recorded whether other people are involved in each time slice of each activity. Companionship within different activities is then disaggregated into different types of social networks, namely other household members, relatives, close friends, and online friends. ResultEngaging with other people is not necessary to gain positive results on social and mental health. Within different types of activities and different contexts, such as in developing countries that have higher percentages of low-income households, much time spent in engagements with other household members can worsen social and mental health. ConclusionIn developing country contexts, better social health might be indicated by having more engagements with other people beyond household members and spending more time on travelling or out-of-home activities. However, more disposable income, more time for socialising and leisure activities, and more engagements with close and online friends all seem to be able to gain positive mental health conditions.

Joint clinical determinants for bivariate hematological parameter among TB/HIV co-infected adults under TB/HIV treatment in university of Gondar comprehensive specialized hospital: Retrospective panel data study

BackgroundWorldwide ranking above HIV/AIDS, tuberculosis is continues to have a significant effect on public health and the leading cause of death due to high progression of HIV. The objective of current study was identify joint clinical determinants that affecting bivariate hematological parameter among TB/HIV co-infected adults under TB/HIV treatment in university of Gondar comprehensive specialized hospital.MethodThe result of these study was conducted at university of Gondar comprehensive specialized hospital, Gondar, Ethiopia by using a retrospective cohort follow up study from September 2015-march 2022 G.C. The source of data in this study was secondary data obtained from patients chart. Bayesian approach of longitudinal linear mixed effect sub model was used in panel data set to get wide range of information about TB/HIV co-infected patients.ResultOut of 148 co-infected participants more than half of the patients (56.1%) and (52.7%) accounted for CPT and INH non users, of which 10.8% and 10.3% had the outcome of mortality respectively. The random intercept and slope model were selected for repeated measure hemoglobin level and hematocrit based on deviance information criteria (DIC), and probability of direction (Pd) under the full model.ConclusionCurrent study revealed that clinical predictors red blood cell count, platelet cell count, fair and good treatment adherence, other ART regiment, IPT drug users, and viral load count < 10,000 copies/mL, were associated with high hemoglobin level concentration while, lymphocyte count, WHO clinical stage-IV,1e ART regiment, and patients with OIs results for low hemoglobin level concentration. Likewise, red blood cell count, platelet cell count, fair and good treatment adherence, IPT drug users, and viral load count < 10,000 copies/mL co-infected patients had high hematocrit, while lymphocyte count, WHO clinical stage-III,1c ART regiment, and patients with OIs significantly leads to low hematocrit. Health professionals give more attention to these important predictors to reduce progression of disease when the co-infected patients come back again in the hospital. In addition, health staff should conduct health related education for individuals to examine continuous check-up of co-infected patients.

The gender pay gap at the top floor: A multilevel analysis of Spanish listed companies

The gender pay gap, including at the top management level, is a socially relevant issue; however, it remains controversial. This study explores the distribution of pay by gender for executive directors of Spanish listed companies over the period 2012 to 2021. The analysis is based on multilevel regression. Three-level models with a fixed slope for gender show that female directors face an average 45% pay penalty relative to their male peers after controlling for time-varying individual and board/firm variables, time-invariant firm characteristics, and year of observation. A three-level model including all control variables and a random slope for gender shows that the gender pay penalty varies significantly across boards/firms (from 18% to 78%), with an average value of 46%. This finding suggests that unobservable heterogeneity among firms is a primary source of the observed gender pay gap.