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Background: Air pollution is one of the biggest problems in societies today. The intensity of indoor and outdoor air pollutants and the urbanization rate can cause or trigger many different diseases, especially lung cancer. In this context, this study's aim is to reveal the effects of the indoor and outdoor air pollutants, and urbanization rate on the lung cancer cases. Methods: Panel data analysis method is applied in this study. The research includes the period between 1990 and 2019 as a time series and the data type of the variables is annual. The dependent variable in the research model is lung cancer cases per 100,000 people. The independent variables are the level of outdoor air pollution, air pollution level indoor environment and urbanization rate of countries. Results: In the modeling developed for the developed country group, it is seen that the variable with the highest level of effect on lung cancer is the outdoor air pollution level. Conclusions: In parallel with the development of countries, it has been determined that the increase in industrial production wastes, in other words, worsening the air quality, may potentially cause an increase in lung cancer cases. Indoor air quality is also essential for human health; negative changes in this variable may negatively impact individuals' health, especially lung cancer.

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Accurate item parameters and standard errors (SEs) are crucial for many multidimensional item response theory (MIRT) applications. A recent study proposed the Gaussian Variational Expectation Maximization (GVEM) algorithm to improve computational efficiency and estimation accuracy (Cho et al., 2021). However, the SE estimation procedure has yet to be fully addressed. To tackle this issue, the present study proposed an updated supplemented expectation maximization (USEM) method and a bootstrap method for SE estimation. These two methods were compared in terms of SE recovery accuracy. The simulation results demonstrated that the GVEM algorithm with bootstrap and item priors (GVEM-BSP) outperformed the other methods, exhibiting less bias and relative bias for SE estimates under most conditions. Although the GVEM with USEM (GVEM-USEM) was the most computationally efficient method, it yielded an upward bias for SE estimates.

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OpenCap, a smartphone- and web-based markerless system, has shown acceptable accuracy compared to marker-based systems, but lacks information on repeatability. This study fills this gap by evaluating the intersession repeatability of OpenCap and investigating the effects of clothing on gait kinematics. Twenty healthy volunteers participated in a test-retest study, performing walking and sit-to-stand tasks with minimal clothing and regular street wear. Segment lengths and lower-limb kinematics were compared between both sessions and for both clothing conditions using the root-mean-square-deviation (RMSD) for entire waveforms and the standard error of measurement (SEM) and minimal detectable change (MDC) for discrete kinematic parameters. In general, the RMSD test-retest values were 2.8 degrees (SD: 1.0) for walking and 3.3 degrees (SD: 1.2) for sit-to-stand. The highest intersession variability was observed in the trunk, pelvis, and hip kinematics of the sagittal plane. SEM and MDC values were on average 2.2 and 6.0 degrees, respectively, for walking, and 2.4 and 6.5 degrees for sit-to-stand. Clothing had minimal effects on kinematics by adding on average less than one degree to the RMSD values for most variables. The segment lengths showed moderate to excellent agreement between both sessions and poor to moderate agreement between clothing conditions. The study highlights the reliability of OpenCap for markerless motion capture, emphasizing its potential for large-scale field studies. However, some variables showed high MDC values above 5 degrees and thus warrant further enhancement of the technology. Although clothing had minimal effects, it is still recommended to maintain consistent clothing to minimize overall variability.

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OBJECTIVES: Among the provisions within the Affordable Care Act (ACA), expanding Medicaid was arguably the greatest contributor to increasing access to care. For over a decade, researchers have investigated how Medicaid expansion impacted cancer outcomes. Over this same decade, statistical theory illuminated how state-based policy research could be compromised by invalid inference. After reviewing the literature to identify the inference strategies of state-based cancer registry Medicaid expansion research, this study aimed to assess how inference decisions could change the interpretation of Medicaid expansion's impact on staging, treatment, and mortality in cancer patients. DATA SOURCES: Cancer case data (2000-2019) was obtained from the Surveillance, Epidemiology, End Results (SEER) programme. Cases included all cancer sites combined, top 10 cancer sites combined, and three screening amenable cancers (colorectal, female breast, female cervical). STUDY DESIGN: A Difference-in-Differences design estimated the association between Medicaid expansion and four binary outcomes: distant stage, initiating treatment >1 month after diagnosis, no surgery recommendation, and death. Three inference techniques were compared: (1) traditional, (2) cluster, and (3) Wild Cluster Bootstrap. DATA COLLECTION: Data was accessed via SEER*Stat. PRINCIPAL FINDINGS: Estimating standard errors via traditional inference would suggest that Medicaid expansion was associated with delayed treatment initiation and surgery recommendations. Traditional and clustered inference also suggested that Medicaid expansion reduced mortality. Inference using Wild Cluster Bootstrap techniques never rejected the null hypotheses. CONCLUSIONS: This study reiterates the importance of explicit inference. Future state-based, cancer policy research can be improved by incorporating emerging techniques. These findings warrant caution when interpreting prior SEER research reporting significant effects of Medicaid expansion on cancer outcomes, especially studies that did not explicitly define their inference strategy.

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OBJECTIVES: To use individual participant data meta-analysis (IPDMA) to estimate the minimal detectable change (MDC) of the Geriatric Depression Scale-15 (GDS-15) and to examine whether MDC may differ based on participant characteristics and study-level variables. STUDY DESIGN AND SETTING: This was a secondary analysis of data from an IPDMA on the depression screening accuracy of the GDS. Datasets from studies published in any language were eligible for the present study if they included GDS-15 scores for participants aged 60 or older. MDC of the GDS-15 was estimated via random-effects meta-analysis using 2.77 (MDC95) and 1.41 (MDC67) standard errors of measurement. Subgroup analyses were used to evaluate differences in MDC by participant age and sex. Meta-regression was conducted to assess for differences based on study-level variables, including mean age, proportion male, proportion with major depression, and recruitment setting. RESULTS: 5876 participants (mean age 76 years, 40% male, 11% with major depression) from 21 studies were included. The MDC95 was 3.81 points (95% confidence interval [CI] 3.59, 4.04), and MDC67 was 1.95 (95% CI 1.83, 2.03). The difference in MDC95 was 0.26 points (95% CI 0.04, 0.48) between ≥80-year-olds and <80-year-olds; MDC95 was similar for females and males (0.05, 95% CI -0.12, 0.22). The MDC95 increased by 0.29 points (95% CI 0.17, 0.41) per 10% increase in proportion of participants with major depression; mean age had a small association (0.04 points, 95% CI 0.00 to 0.09) with MDC95, but sex and recruitment setting were not significantly associated. CONCLUSION: The MDC95 was 3.81 points and MDC67 was 1.95 points. MDC95 increased with the proportion of participants with major depression. Results can be used to evaluate individual changes in depression symptoms and as a threshold for assessing minimal clinical important difference estimates.

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BACKGROUND AND OBJECTIVE: Mediation analysis is used to gain insight into the mechanisms of exposure-outcome effects by dividing this effect into a direct and an indirect effect. One of the problems of mediation analysis is that in many situations, the standard error of the indirect effect is much lower than the standard errors of the total and direct effect. Because this problem is ignored in the epidemiological literature, the purpose of this paper was to illustrate this problem and to provide an advice regarding the statistical testing of indirect effects in mediation analysis. METHODS: To illustrate the problem of the estimation of the standard error of the indirect effect two real life datasets and several simulations are used. RESULTS: The paper shows that the problem of estimating the standard error of the indirect effect was most pronounced when the relationship between exposure and mediator and the relationship between mediator and outcome were equally strong. Furthermore, the magnitude of the estimation problem is different for different strengths of the mediation effect. CONCLUSION: The indirect effect in mediation analysis should not be tested for statistical significance but the importance of mediation should be evaluated by its clinical relevance.

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PURPOSE: The Quality of Life-Aged Care Consumers (QOL-ACC), a valid preference-based instrument, has been rolled out in Australia as part of the National Quality Indicator (QI) program since April 2023 to monitor and benchmark the quality of life of aged care recipients. As the QOL-ACC is being used to collect quality of life data longitudinally as one of the key aged care QI indicators, it is imperative to establish the reliability of the QOL-ACC in aged care settings. Therefore, we aimed to assess the reliability of the QOL-ACC and compare its performance with the EQ-5D-5L. METHODS: Home care recipients completed a survey including the QOL-ACC, EQ-5D-5L and two global items for health and quality of life at baseline (T1) and 2 weeks later (T2). Using T1 and T2 data, the Gwet's AC2 and intra-class correlation coefficient (ICC) were estimated for the dimension levels and overall scores agreements respectively. The standard error of measurement (SEM) and the smallest detectable change (SDC) were also calculated. Sensitivity analyses were conducted for respondents who did not change their response to global item of quality of life and health between T1 and T2. RESULTS: Of the 83 respondents who completed T1 and T2 surveys, 78 respondents (mean ± SD age, 73.6 ± 5.3 years; 56.4% females) reported either no or one level change in their health and/or quality of life between T1 and T2. Gwet's AC2 ranged from 0.46 to 0.63 for the QOL-ACC dimensions which were comparable to the EQ-5D-5L dimensions (Gwet's AC2 ranged from 0.52 to 0.77). The ICC for the QOL-ACC (0.85; 95% CI, 0.77-0.90) was comparable to the EQ-5D-5L (0.83; 95% CI, 0.74-0.88). The SEM for the QOL-ACC (0.08) was slightly smaller than for the EQ-5D-5L (0.11). The SDC for the QOL-ACC and the EQ-5D-5L for individual subjects were 0.22 and 0.30 respectively. Sensitivity analyses stratified by quality of life and health status confirmed the base case results. CONCLUSIONS: The QOL-ACC demonstrated a good test-retest reliability similar to the EQ-5D-5L, supporting its repeated use in aged care settings. Further studies will provide evidence of responsiveness of the QOL-ACC to aged care-specific interventions in aged care settings.

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In Australia, trifluralin is one of the commonly used herbicides to manage annual grasses and some broadleaf weeds. However, it may have some ecosystem impacts such as high toxicity to terrestrial and aquatic life, so it is vital to monitor the degradation of trifluralin for a considerable period for environmental safety. For risk assessment purposes, it is necessary to estimate the half-life of trifluralin, which is often evaluated using derived mathematical dissipation models. In the literature, bi-exponential (BEXP) and gamma models were suggested for modelling the dissipation of trifluralin in soil. Both models provide the half-life estimate without discussing the uncertainty of the estimate, which is a shortcoming in the literature. In this paper, we used simulation to illustrate the importance of estimate's uncertainty (standard error) and demonstrated a method to compute the standard error for the half-life estimate mathematically for kinetic dissipation models. Later, we evaluated the performance of the two suggested models using statistical indices. The computation of the half-life and the standard error of the half-life estimate were discussed. This allows us to describe the inference of the half-life parameter and determine whether the half-life estimates are significantly different against the co-variate (moisture) levels. We demonstrated the method to calculate the standard error of the half-life of trifluralin, which allows us to determine the statistical difference between the estimates. In this study, we found that the half-life of trifluralin in soil tends to increase with increasing moisture levels, and the half-life of trifluralin in soil with 100% moisture level is significantly greater than 40% and 70% moisture levels. Our findings suggest that soil moisture levels should be carefully considered before trifluralin application to minimize the non-target environmental damage.

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BACKGROUND: Tracking gait and balance impairment in time is paramount in the care of older neurological patients. The Minimal Detectable Change (MDC), built upon the Standard Error of the Measurement (SEM), is the smallest modification of a measure exceeding the measurement error. Here, a novel method based on linear mixed-effects models (LMMs) is applied to estimate the standard error of the measurement from data collected before and after rehabilitation and calculate the MDC of gait and balance measures. METHODS: One hundred nine older adults with a gait impairment due to neurological disease (66 stroke patients) completed two assessment sessions before and after inpatient rehabilitation. In each session, two trials of the 10-meter walking test and the Timed Up and Go (TUG) test, instrumented with inertial sensors, have been collected. The 95% MDC was calculated for the gait speed, TUG test duration (TTD) and other measures from the TUG test, including the angular velocity peak (ωpeak) in the TUG test's turning phase. Random intercepts and slopes LMMs with sessions as fixed effects were used to estimate SEM. LMMs assumptions (residuals normality and homoscedasticity) were checked, and the predictor variable ln-transformed if needed. RESULTS: The MDC of gait speed was 0.13 m/s. The TTD MDC, ln-transformed and then expressed as a percentage of the baseline value to meet LMMs' assumptions, was 15%, i.e. TTD should be < 85% of the baseline value to conclude the patient's improvement. ωpeak MDC, also ln-transformed and expressed as the baseline percentage change, was 25%. CONCLUSIONS: LMMs allowed calculating the MDC of gait and balance measures even if the test-retest steady-state assumption did not hold. The MDC of gait speed, TTD and ωpeak from the TUG test with an inertial sensor have been provided. These indices allow monitoring of the gait and balance impairment, which is central for patients with an increased falling risk, such as neurological old persons. TRIAL REGISTRATION: NA.

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The colour of seafood flesh is often not homogenous, hence measurement of colour requires repeat measurements to obtain a representative average. The aim of this study was to determine the optimal number of repeat colour measurements required for three different devices [machine vision (digital image using camera, and computer processing); Nix Pro; Minolta CR400 colorimeter] when measuring three species of seafood (Atlantic salmon, Salmo salar, n = 8; rockling, Genypterus tigerinus, n = 8; banana prawns, Penaeus merguiensis, n = 105) for raw and cooked samples. Two methods of analysis for number of repeat measurements required were compared. Method 1 was based on minimising the standard error of the mean and Method 2 was based on minimising the difference in colour over repeat measurements. Across species, using Method 1, machine vision required an average of four repeat measurements, whereas Nix Pro and Minolta required 13 and 12, respectively. For Method 2, machine vision required an average of one repeat measurement compared to nine for Nix Pro and Minolta. Machine vision required fewer repeat measurements due to its lower residual variance: 0.51 compared to 3.2 and 2.5 for Nix Pro and Minolta, respectively. In conclusion, machine vision requires fewer repeat measurements than colorimeters to precisely measure the colour of salmon, prawns, and rockling.

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Prenatal drug exposure is a public health problem, which results in profound behavioral problems during childhood and adolescence, mainly represented by an increase in the risk of cocaine abuse at an early age. In rodents, prenatal and postnatal cocaine exposure enhanced locomotor activity and cocaine- or nicotine-induced locomotor sensitization. Various authors consider that the adverse emotional states (anxiety and depression) that occur during cocaine withdrawal are the main factors that precipitate, relapse, and increase chronic cocaine abuse, which could increase the risk of relapse of cocaine abuse. Therefore, the objective of this study was to characterize anxiety- and depression-like behaviors at different times (30, 60, 90, and 120 days) of cocaine withdrawal in rats born to females exposed prenatally and postnatally to cocaine. A group of pregnant female Wistar rats were administered daily from day GD0 to GD21 with cocaine (cocaine preexposure group), and another group of pregnant female rats was administered daily with saline (saline preexposure group). Of the litters resulting from the cocaine-pre-exposed and saline-pre-exposed pregnant female groups, only the male rats were used for the recording of the anxiety- and depression-like behaviors at different times (30, 60, 90, and 120 days) of cocaine withdrawal The study found that prenatal and postnatal cocaine exposure dose-dependent enhanced anxiety- and depression-like behaviors. This suggests that prenatal and postnatal cocaine exposure can result in enhanced vulnerability to cocaine abuse in young and adult humans.

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Artificial intelligence (AI) has revolutionized structural biology by predicting protein 3D structures with near-experimental accuracy. Here, short backbone N-O distances in high-resolution crystal structures were compared to those in three-dimensional models based on AI AlphaFold/ColabFold, specifically considering their estimated standard errors. Experimental and computationally modeled distances very often differ significantly, showing that these models' precision is inadequate to reproduce experimental results at high resolution. T-tests and normal probability plots showed that these computational methods predict atomic position standard errors 3.5-6 times bigger than experimental errors. SYNOPSIS: Positional standard errors in AI-based protein 3D models are 3.5-6 times larger than in atomic resolution crystal structures.

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This study starts with a simple model by which Hardy-Weinberg proportions are attained in a single generation while maintaining gene frequencies. The question of differentiating between random and non-random mating is explored by simulation. Sample mating proportions are generated using the model as base. The difficulty of differentiating between random and non-random mating is illustrated.

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Conventional random-effects models in meta-analysis rely on large sample approximations instead of exact small sample results. While random-effects methods produce efficient estimates and confidence intervals for the summary effect have correct coverage when the number of studies is sufficiently large, we demonstrate that conventional methods result in confidence intervals that are not wide enough when the number of studies is small, depending on the configuration of sample sizes across studies, the degree of true heterogeneity and number of studies. We introduce two alternative variance estimators with better small sample properties, investigate degrees of freedom adjustments for computing confidence intervals, and study their effectiveness via simulation studies.

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At the start of a moderate-intensity square-wave exercise, after a short delay, breath-by-breath O2 uptake at the mouth is approximated to a mono-exponential function, whose time constant is considered matched to that of the O2 uptake of the working muscles. We compared the kinetic parameters obtained from the breath-by-breath gas exchange data yielded by the 'Independent-breath' algorithm (IND), which accounts for the changes in lung gas stores, with those obtained with the classical 'Expiration-only' algorithm (EXP). The two algorithms were applied on the same flow and gas fraction traces acquired on 10 healthy volunteers, performing 10 times the same moderate-intensity exercise transition. Repeated O2 uptake responses were stacked together and the kinetic parameters of a mono-exponential function were estimated by non-linear regression, removing the data pertaining to 1-s progressively longer initial periods (ΔTr ). Independently of ΔTr , the mean response time (time constant + time delay) obtained for the IND data was faster compared to the EXP data (∼43 s vs. ∼47 s, P < 0.001), essentially because of shorter time delays. Between ΔTr  = 16 s and ΔTr  = 29s, the time constants of the IND data decreased (30.7 s vs. 28.0 s, P < 0.05; drop = 10%), but less than those of the EXP data (32.2 s vs. 26.2 s, P < 0.001; drop = 23%); with the same ΔTr , the time constants of the two algorithms' data were not different (P > 0.07). The different decrease in the time constant, together with the different mean response time, suggests that the data yielded by the two algorithms provide a different picture of the phenomena occurring at the beginning of the exercise.

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Although elevated blood levels of trimethylamine N-oxide (TMAO) have been associated with atherosclerosis development in humans, the role of its gut microbiota-derived precursor, TMA, in this process has not been yet deciphered. Taking this into account, and the fact that increased intestinal fatty acid absorption contributes to atherosclerosis onset and progression, this study aimed to evaluate the effect of TMA on fatty acid absorption in a cell line that mimics human enterocytes. Caco-2 cells were treated with TMA 250 µM for 24 h. Fatty acid absorption was assessed by measuring the apical-to-basolateral transport and the intracellular levels of BODIPY-C12, a fluorescently labelled fatty acid analogue. Gene expression of the main intestinal fatty acid transporters was evaluated by real-time quantitative reverse transcription PCR. Compared to control conditions, TMA increased, in a time-dependent manner and by 20-50 %, the apical-to-basolateral transport and intracellular levels of BODIPY-C12 fatty acid in Caco-2 cells. Fatty acid transport protein 4 (FATP4) and fatty acid translocase (FAT)/CD36 gene expression were not stimulated by TMA, suggesting that TMA-induced increase in fatty acid transport may be mediated by an increase in FAT/CD36 and/or FATP4 activity and/or fatty acid passive transport. This study demonstrated that TMA increases the intestinal absorption of fatty acids. Future studies are necessary to confirm if this may constitute a novel mechanism that partially explains the existing positive association between the consumption of a diet rich in TMA sources (e.g. red meat) and the increased risk of atherosclerotic diseases.

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Item response theory (IRT) analyses are often used to evaluate measurement error in educational and psychological test instruments. In such contexts, the latent traits/proficiencies are typically assumed normally distributed and a cumulative normal/logistic measurement link function is applied. Such choices are consistent with constructs that are viewed as bipolar in nature and play a critical role in defining the latent proficiency metric against which the measurement error in the test is evaluated. Recently, alternative models that portray the construct as unipolar have been highlighted as being more appropriate for certain psychopathology and personality constructs. In this paper we extend consideration of unipolar IRT models for a recognition task measure, using several example datasets from various versions of the Author Recognition Test (ART), a measure of print exposure. We show how the decision between unipolar versus bipolar IRT modeling has substantial implications for the quantification and interpretation of measurement error in the ART. In sharp contrast to prior bipolar IRT analyses of the ART, under unipolar IRT measurement error in the ART is minimized at low levels of latent print exposure, and increases as latent print exposure increases. Implications for consideration of unipolar IRT with other constructs and measures (e.g., vocabulary, specialized forms of knowledge) that reflect a similar type of response process are considered in the discussion.

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This study introduces two new statistics for measuring the score comparability of computerized adaptive tests (CATs) based on comparing conditional standard errors of measurement (CSEMs) for examinees that achieved the same scale scores. One statistic is designed to evaluate score comparability of alternate CAT forms for individual scale scores, while the other statistic is designed to evaluate the overall score comparability of alternate CAT forms. The effectiveness of the new statistics is illustrated using data from grade 3 through 8 reading and math CATs. Results suggest that both CATs demonstrated reasonably high levels of score comparability, that score comparability was less at very high or low scores where few students score, and that using random samples with fewer students per grade did not have a big impact on score comparability. Results also suggested that score comparability was sometimes higher when the bottom 20% of scorers were used to calculate overall score comparability compared to all students. Additional discussion related to applying the statistics in different contexts is provided.