The development of a road safety policy index and its application in evaluating the effects of road safety policy.

The paper presents an exploratory study of a road safety policy index developed for Norway. The index consists of ten road safety measures for which data on their use from 1980 to 2021 are available. The ten measures were combined into an index which had an initial value of 50 in 1980 and increased to a value of 185 in 2021. To assess the application of the index in evaluating the effects of road safety policy, negative binomial regression models and multivariate time series models were developed for traffic fatalities, fatalities and serious injuries, and all injuries. The coefficient for the policy index was negative, indicating the road safety policy has contributed to reducing the number of fatalities and injuries. The size of this contribution can be estimated by means of at least three estimators that do not always produce identical values. There is little doubt about the sign of the relationship: a stronger road safety policy (as indicated by index values) is associated with a larger decline in fatalities and injuries. A precise quantification is, however, not possible. Different estimators of effect, all of which can be regarded as plausible, yield different results.

Health-related quality of life and its predictors among hypertensive patients on follow up at public hospitals in Addis Ababa, Ethiopia: application of Tobit regression model.

BACKGROUND: Health-related quality of life and its associated factors among hypertensive patients living in Ethiopia are not well studied. Therefore, this study aims to assess the level of health-related quality of life and its associated factors in hypertensive patients on follow-up in Public Hospitals in Addis Ababa, Ethiopia. METHODS: A facility-based cross-sectional study was conducted among 339 hypertensive patients on follow-up at Yekatit 12 &Zewditu Hospitals. Data were collected through face-to-face interviews using Euro Quality of Life Groups 5 Dimensions 5 Levels (EQ-5D-5L) in combination with Euro Quality of Life Groups Visual Analog Scale (EQ-VAS). A multivariable Tobit regression model was employed to assess the association between EQ-5D-5L index, EQ-VAS, and potential predicting factors. RESULTS: The median index value and EQ-VAS Scales score was 0.86 (IQR = 0.74, 0.94) and 69 (IQR = 55, 80) respectively. The proportion of participants reporting anxiety/depression and pain/discomfort problems was highest, while the fewest patients reported problems in the self-care dimension. Older, rural residents, low income, higher stages of hypertension, increased use of antihypertensive medications, and patients with an increased hospitalization rate scored lower on health-related quality of life than others. CONCLUSION: Health-related quality of life among hypertensive patients attending public health hospitals in Addis Ababa is unacceptably poor. Emphasis should be given to patients with higher stages of hypertension, increased use of antihypertensive medications, and an increased hospitalization rate giving due focus to older, rural residents, and low-income patients to promote their health-related quality of life.

A new and unified method for regression analysis of interval-censored failure time data under semiparametric transformation models with missing covariates.

This paper discusses regression analysis of interval-censored failure time data arising from semiparametric transformation models in the presence of missing covariates. Although some methods have been developed for the problem, they either apply only to limited situations or may have some computational issues. Corresponding to these, we propose a new and unified two-step inference procedure that can be easily implemented using the existing or standard software. The proposed method makes use of a set of working models to extract partial information from incomplete observations and yields a consistent estimator of regression parameters assuming missing at random. An extensive simulation study is conducted and indicates that it performs well in practical situations. Finally, we apply the proposed approach to an Alzheimer's Disease study that motivated this study.

Factors associated with shame and stigma among head and neck cancer patients: a cross-sectional study.

PURPOSE: Head and neck cancer (HNC) patients often suffer from shame and stigma due to treatment limitations or due to societal factors. The purpose of this study was to assess perceived body image, depression, physical and psychosocial function, and self-stigma, as well as to identify factors that predicted shame and stigma in patients with HNC. METHODS: This cross-sectional study recruited 178 HNC patients from the outpatient radiation department of a medical center in Northern Taiwan. Patients were assessed for patient reported outcomes using the Body Image Scale (BIS), the Hospital Anxiety and Depression Scale-Depression Subscale (HADS-Depression Subscale), the University of Washington Quality of Life Scale (UW-QOL) version 4.0, and the Shame and Stigma Scale (SSS). Data were analyzed by descriptive analysis, Pearson's product-moment correlation, and multiple regression. RESULTS: The two top-ranked subscales of shame and stigma were: "speech and social concerns" and "regret". Shame and stigma were positively correlated with a longer time since completion of treatment, more body image concerns, and higher levels of depression. They were negatively correlated with being male and having lower physical function. Multiple regression analysis showed that female gender, a longer time since completing treatment, higher levels of body image concern, greater depression, and less physical function predicted greater shame and stigma. These factors explained 74.7% of the variance in shame and stigma. CONCLUSION: Patients' body image concerns, depression, time since completing treatment, and physical function are associated with shame and stigma. Oncology nurses should assess and record psychological status, provide available resources, and refer appropriate HNC patients to counselling.

Estimation of instantaneous peak flows in Canadian rivers: an evaluation of conventional, nonlinear regression, and machine learning methods.

Instantaneous peak flows (IPFs) are often required to derive design values for sizing various hydraulic structures, such as culverts, bridges, and small dams/levees, in addition to informing several water resources management-related activities. Compared to mean daily flows (MDFs), which represent averaged flows over a period of 24 h, information on IPFs is often missing or unavailable in instrumental records. In this study, conventional methods for estimating IPFs from MDFs are evaluated and new methods based on the nonlinear regression framework and machine learning architectures are proposed and evaluated using streamflow records from all Canadian hydrometric stations with natural and regulated flow regimes. Based on a robust model selection criterion, it was found that multiple methods are suitable for estimating IPFs from MDFs, which precludes the idea of a single universal method. The performance of machine learning-based methods was also found reasonable compared to conventional and regression-based methods. To build on the strengths of individual methods, the fusion modeling concept from the machine learning area was invoked to synthesize outputs of multiple methods. The study findings are expected to be useful to the climate change adaptation community, which currently heavily relies on MDFs simulated by hydrologic models.

Regression discontinuity design for the study of health effects of exposures acting early in life.

Regression discontinuity design (RDD) is a quasi-experimental approach to study the causal effect of an exposure on later outcomes by exploiting the discontinuity in the exposure probability at an assignment variable cut-off. With the intent of facilitating the use of RDD in the Developmental Origins of Health and Disease (DOHaD) research, we describe the main aspects of the study design and review the studies, assignment variables and exposures that have been investigated to identify short- and long-term health effects of early life exposures. We also provide a brief overview of some of the methodological considerations for the RDD identification using an example of a DOHaD study. An increasing number of studies investigating the effects of early life environmental stressors on health outcomes use RDD, mostly in the context of education, social and welfare policies, healthcare organization and insurance, and clinical management. Age and calendar time are the mostly used assignment variables to study the effects of various early life policies and programs, shock events and guidelines. Maternal and newborn characteristics, such as age, birth weight and gestational age are frequently used assignment variables to study the effects of the type of neonatal care, health insurance, and newborn benefits, while socioeconomic measures have been used to study the effects of social and welfare programs. RDD has advantages, including intuitive interpretation, and transparent and simple graphical representation. It provides valid causal estimates if the assumptions, relatively weak compared to other non-experimental study designs, are met. Its use to study health effects of exposures acting early in life has been limited to studies based on registries and administrative databases, while birth cohort data has not been exploited so far using this design. Local causal effect around the cut-off, difficulty in reaching high statistical power compared to other study designs, and the rarity of settings outside of policy and program evaluations hamper the widespread use of RDD in the DOHaD research. Still, the assignment variables' cut-offs for exposures applied in previous studies can be used, if appropriate, in other settings and with additional outcomes to address different research questions.

ERegPose: An explicit regression based 6D pose estimation for snake-like wrist-type surgical instruments.

BACKGROUND: Accurately estimating the 6D pose of snake-like wrist-type surgical instruments is challenging due to their complex kinematics and flexible design. METHODS: We propose ERegPose, a comprehensive strategy for precise 6D pose estimation. The strategy consists of two components: ERegPoseNet, an original deep neural network model designed for explicit regression of the instrument's 6D pose, and an annotated in-house dataset of simulated surgical operations. To capture rotational features, we employ an Single Shot multibox Detector (SSD)-like detector to generate bounding boxes of the instrument tip. RESULTS: ERegPoseNet achieves an error of 1.056 mm in 3D translation, 0.073 rad in 3D rotation, and an average distance (ADD) metric of 3.974 mm, indicating an overall spatial transformation error. The necessity of the SSD-like detector and L1 loss is validated through experiments. CONCLUSIONS: ERegPose outperforms existing approaches, providing accurate 6D pose estimation for snake-like wrist-type surgical instruments. Its practical applications in various surgical tasks hold great promise.

Genito-urinary infectious adverse events related to sodium glucose cotransporter-2 inhibitors: a network meta-analysis and meta-regression.

INTRODUCTION: Sodium glucose cotransporter-2 inhibitors (SGLT2is) are an emerging class of drugs with wide indications. Controversial evidence exists regarding the risk of urinary tract infection (UTI) and genital infections (GI) with SGLT2is paving way for undertaking this network meta-analysis and meta-regression study. METHODS: Data from randomized trials evaluating SGLT2is reporting the number of patients with UTI and GI were included. Odds ratios (OR) with 95% confidence intervals (95% CI) were the effect estimates. Meta-regression analysis identified risk factors. Number needed to harm (NNH) was estimated. RESULTS: Two hundred and sixty-four articles were included [UTI (213 studies; 150,140 participants) and GI (188 studies; 121,275 participants)]. An increased risk of UTI (OR: 1.11; 95% CI: 1.06, 1.16) and GI (OR: 3.5, 95% CI: 3.1, 3.9) was observed. Men showed a lower risk of UTI (OR: 0.2; 95% CI: 0.2, 0.3) and GI (OR: 0.4; 95% CI: 0.4, 0.5). Meta-regression analyses revealed BMI ≥ 30 kg/m2 and duration of SGLT2i treatment for ≥6 months as risk factors. NNH was 16 for UTI and 25 for GI. CONCLUSION: SGLT2is increase the risk of UTI and GI that needs to be incorporated in the treatment guidelines with precautions in high-risk patients. PROSPECTIVE PROTOCOL REGISTRATION: https://osf.io/5fwyk.

Analysis of multi-level barriers to physical activity among nursing students using regularized regression.

Physical inactivity is a growing societal concern with significant impact on public health. Identifying barriers to engaging in physical activity (PA) is a critical step to recognize populations who disproportionately experience these barriers. Understanding barriers to PA holds significant importance within patient-facing healthcare professions like nursing. While determinants of PA have been widely studied, connecting individual and social factors to barriers to PA remains an understudied area among nurses. The objectives of this study are to categorize and model factors related to barriers to PA using the National Institute on Minority Health and Health Disparities (NIMHD) Research Framework. The study population includes nursing students at the study institution (N = 163). Methods include a scoring system to quantify the barriers to PA, and regularized regression models that predict this score. Key findings identify intrinsic motivation, social and emotional support, education, and the use of health technologies for tracking and decision-making purposes as significant predictors. Results can help identify future nursing workforce populations at risk of experiencing barriers to PA. Encouraging the development and employment of health-informatics solutions for monitoring, data sharing, and communication is critical to prevent barriers to PA before they become a powerful hindrance to engaging in PA.

Study on the influence of rock bridge angle on the size effect of rock uniaxial compression.

As a basic parameter of rock, the rock bridge angle plays an important role in maintaining the stability of rock masses. To study the size effect of rock bridge angle on the uniaxial compressive strength of rocks, this paper adopts the principle of regression analysis and combines numerical simulation to carry out relevant research. The research results indicate that: (1) the uniaxial compressive strength decreases with the increase of the rock bridge angle, showing a power function relationship; (2) The uniaxial compressive strength decreases with the increase of rock size and tends to stabilize when the rock size is greater than 350 mm, showing a significant size effect. (3) The fluctuation coefficient of compressive strength increases with the increase of rock bridge angle and decreases with the increase of rock size; When the rock size is 350 mm, the fluctuation coefficient is less than 5%; (4) The characteristic compressive strength and characteristic size both increase with the increase of the rock bridge angle.

Joinpoint regression analysis of recent trends in desmoplastic malignant melanoma incidence and mortality: 15-year multicentre retrospective study.

BACKGROUND: Recent data reveal a marked rise in the detection and mortality rates of Desmoplastic Malignant Melanoma (DMM). This trend underscores the imperative for an in-depth analysis of DMM's epidemiology, which is crucial for the formulation of precise medical and public health strategies. This investigation seeks to elucidate the variations in the incidence and mortality of DMM over a 15-year period (2005-2019). METHODS: Data on DMM patients was sourced from the Surveillance, Epidemiology, and End Results (SEER) database. Both incidence and incidence-based mortality rates (IBM) were directly extracted from the SEER database. Joinpoint regression was used to analyze and calculate the average annual percent change (AAPC) and its 95% confidence interval (CI). RESULTS: Between 2005 and 2019, 3,384 DMM cases were identified, boasting an age-adjusted incidence rate of 36.3 cases per 1000,000 person-years (95% CI 3.51-3.76) and an IBM of 1.65cases per 1000,000 person-years (95% CI 1.57-1.74). Of these, 2,353 were males (69.53%) and 1,031 were females (30.47%). There were 1894 patients (55.97%) who were over 70 years old. Predominantly, DMM lesions manifested in exposed areas: Limbs (955, 28.22%), Face (906, 26.77%), and Scalp and Neck (865, 25.56%). The incidence of DMM increased significantly at a rate of APC = 0.9% during 2005-2019, while the incidence-based mortality showed a significant upward trend (APC = 7%) during 2005-2012, and slowly increasing trend (APC = 0.6%) during 2012-2019. In contrast to the modest upward trajectory in female incidence and mortality, male incidence initially surged, later declining, while male mortality peaked and stabilized post-2012. The primary sites for incidence and mortality were chronically sun-exposed areas: Face, Scalp and Neck, and Limbs. CONCLUSIONS: In recent years, the incidence and incidence-based mortality of DMM have significantly increased. Each subgroup analysis has different trends, and these trends can provide better support for our exploration of DMM.

Integrating classification and regression learners with bioimpedance methods for estimating weight status in infants and juveniles from the southern Cuba region.

OBJECTIVE: The search for other indicators to assess the weight and nutritional status of individuals is important as it may provide more accurate information and assist in personalized medicine. This work is aimed to develop a machine learning predictions of weigh status derived from bioimpedance measurements and other physical parameters of healthy younger volunteers from Southern Cuba Region. METHODS: A pilot random study at the Pediatrics Hospital was conducted. The volunteers were selected between 2002 and 2008, ranging in age between 2 and 18 years old. In total, 776 female and male volunteers are studied. Along the age and sex in the cohort, volunteers with class I obesity, overweight, underweight and with normal weight are considered. The bioimpedance parameters are obtained by measuring standard tetrapolar whole-body configuration. The bioimpedance analyser is used, collecting fundamental bioelectrical and other parameters of interest. A classification model are performed, followed by a prediction of the body mass index. RESULTS: The results derived from the classification leaner reveal that the size, body density, phase angle, body mass index, fat-free mass, total body water volume according to Kotler, body surface area, extracellular water according to Kotler and sex largely govern the weight status of this population. In particular, the regression model shows that other bioparameters derived from impedance measurements can be associated with weight status estimation with high accuracy. CONCLUSION: The classification and regression predictive models developed in this work are of the great importance to assist the diagnosis of weigh status with high accuracy. These models can be used for prompt weight status evaluation of younger individuals at the Pediatrics Hospital in Santiago de Cuba, Cuba.

Determination of Mouth Width for Facial Reconstruction Based on Statistical Regression Model.

INTRODUCTION: It is important to generate predictable statistical models by increasing the number of variables on the human skeletal and soft tissue structures on the face to increase the accuracy of human facial reconstructions. The purpose of this study was to determine mouth width 3-dimensionally based on statistical regression model. MATERIAL AND METHODS: Cone-beam computed tomography scan data from 130 individuals were used to measure the horizontal and vertical dimensions of orbital and nasal structures and intercanine width. The correlation between these hard tissue variables and the mouth width was evaluated using the statistical regression model. RESULTS: Orbital width, nasal width, and intercanine width were found to be strong predictors of the mouth width determination and were used to generate the regression formulae to find the most approximate position of the mouth. CONCLUSION: These specific variables may contribute to improving the accuracy of mouth width determination for oral and maxillofacial reconstructions.

A systematic analysis of regression models for protein engineering.

To optimize proteins for particular traits holds great promise for industrial and pharmaceutical purposes. Machine Learning is increasingly applied in this field to predict properties of proteins, thereby guiding the experimental optimization process. A natural question is: How much progress are we making with such predictions, and how important is the choice of regressor and representation? In this paper, we demonstrate that different assessment criteria for regressor performance can lead to dramatically different conclusions, depending on the choice of metric, and how one defines generalization. We highlight the fundamental issues of sample bias in typical regression scenarios and how this can lead to misleading conclusions about regressor performance. Finally, we make the case for the importance of calibrated uncertainty in this domain.

No Change in Complications Following Thyroidectomy Despite Increase in Thyroid Cancer Surgeries: A Meta-Regression Analysis.

PURPOSE: The increase in thyroid cancer incidence has inevitably led to an increase in thyroid cancer surgeries. This meta-regression analysis aimed to determine if the rate of post-thyroidectomy complications changes by year. MATERIALS AND METHODS: PubMed and Embase databases were used to perform a systematic literature search of studies published from January 1, 2005, using the keywords "thyroidectomy" and "complication." A meta-regression was performed for post-thyroidectomy hypocalcemia and bleeding. RESULTS: This meta-analysis included 25 studies involving 927751 individuals. Through the years of publications in this study, there was no significant difference in the proportion of post-thyroidectomy hypocalcemia and bleeding (p=0.9978, 0.6393). CONCLUSION: Although the number of thyroid surgeries has recently increased, the incidence of post-thyroidectomy hypocalcemia and bleeding did not significantly increase.

Regression models from portable NIR spectra for predicting the carcass traits and meat quality of beef cattle.

The aims of this study were to predict carcass and meat traits, as well as the chemical composition of the 9th to 11th rib sections of beef cattle from portable NIR spectra. The 9th to 11th rib section was obtained from 60 Nellore bulls and cull cows. NIR spectra were acquired at: P1 -center of Longissimus muscle; and P2 -subcutaneous fat cap. The models accurately estimated (P ≥ 0.083) all carcass and meat quality traits, except those for predicting red (a*) and yellow (b*) intensity from P1, and 12th-rib fat from P2. However, precision was highly variable among the models; those for the prediction of carcass pHu, 12th rib fat, toughness from P1, and those for 12th rib fat, a* and b* from P2 presented high precision (R2 ≥ 0.65 or CCC ≥ 0.63), whereas all other models evaluated presented moderate to low precision (R2 ≤ 0.39). Models built from P1 and P2 accurately estimated (P ≥ 0.066) the chemical composition of the meat plus fat, bones and, meat plus fat plus bones, except those for predicting the ether extract (EE) and crude protein (CP) of bones and the EE of Meat plus bones fraction from P2. However, precision was highly variable among the models (-0.08 ≤ R2 ≤ 0.86) of the 9th and 11th rib section. Those models for the prediction of dry matter (DM) and EE of the bones from P1; of EE from P1; and of EE, mineral matter (MM), CP from P2 of meat plus fat plus bones presented high precision (R2 ≥ 0.76 or CCC ≥ 0.62), whereas all other models evaluated presented moderate to low precision (R2 ≤ 0.45). Thus, models built from portable NIR spectra acquired at different points of the 9th to 11th rib section were recommended for predicting carcass and muscle quality traits as well as for predicting the chemical composition of this section of beef cattle. However, it is noteworthy, that the small sample size was one of the limitations of this study.

Mindfulness decreases driving anger expression: The mediating effect of driving anger and anger rumination.

Mindfulness is a state of being fully attentive to the current moment and is an experiential way of living in daily life. As a personal trait, mindfulness has been proven to enhance various negative emotions and behaviors. However, in the field of driving, there is still a lack of research on the mechanisms of mindfulness on anger expression behavior, specifically aggressive driving. Therefore, the purpose of this study is to reveal the impact of mindfulness on drivers' aggressive driving behaviors and the mediating effect of driving anger and anger rumination. A total of 350 (208 males and 142 females) participants in China voluntarily completed a series of questionnaires, including the Mindful Attention and Awareness Scale (MAAS), the Driving Anger Scale (DAS), the Anger Rumination Scale (ARS) and the Driving Anger Expression Inventory (DAX). The hierarchical multiple regression analysis and pathway analysis results showed that mindfulness negatively predicted driving anger, anger rumination and driving anger expression. Moreover, driving anger and anger rumination mediated the relationship between mindfulness and driving anger expression, accounting for 9.51% and 18.74% of the total effect, respectively. The chain-mediated effect of driving anger and anger rumination accounted for 8.00% of the total effect. This study has revealed some of the internal mechanisms through which mindfulness reduces aggressive driving. It fills a part of the gap in understanding the protective role of mindfulness in the driving domain. Furthermore, it suggests mindfulness interventions for drivers, which may have the potential to enhance overall road safety.

The outcomes of mental health care for depression over time: A meta-regression analysis of response rates in usual care.

BACKGROUND: Over the past decades dozens of randomized trials have shown that psychological treatments are more effective than care-as-usual (CAU). It could be expected that these treatments are implemented in routine care and that the response rates in usual care improve over time. The aim of the current meta-analysis is to examine if response and remission rates in usual care have improved over time. METHODS: We used an existing meta-analytic database of randomized controlled trials examining the effects of psychological treatments of depression and selected CAU control groups from these trials. We only included CAU conditions in primary care, specialized mental health care, perinatal care and general medical care. The response rate (50 % symptom reduction) was the primary outcome. RESULTS: We included 125 CAU control groups (8542 participants). The response rate for all CAU control groups was 0.22 (95 % CI: 0.19; 0.24) with high heterogeneity (I2 = 83; 95 % CI: 80; 85), with somewhat higher rates in primary care (0.27; 95 % CI: 0.23; 0.31). We found hardly any indications that the outcomes have improved over the years. The meta-regression analysis with publication year as predictor in the full dataset resulted in a coefficient of 0.1 (SE = 0.01; p = 0.0.35). A series of sensitivity analyses supported the main findings. Remission rates and pre-post effect sizes also did not significantly improve over time. CONCLUSIONS: Response and remission rates in usual care are low, with the large majority of patients not responding or remitting, and the outcomes have probably not improved over time.

A regressive analysis of the main environmental risk factors of human echinococcosis in 370 counties in China.

BACKGROUND: Echinococcosis is a natural focal, highly prevalent disease in China. Factors influencing the spread of echinococcosis are not only related to personal exposure but also closely related to the environment itself. The purpose of this study was to explore the influence of environmental factors on the prevalence of human echinococcosis and to provide a reference for prevention and control of echinococcosis in the future. METHODS: Data were collected from 370 endemic counties in China in 2018. By downloading Modis, DEM and other remote-sensing images in 2018. Data on environmental factors, i.e., elevation, land surface temperature (LST) and normalized difference vegetation index (NDVI) were collected. Rank correlation analysis was conducted between each environmental factor and the prevalence of echinococcosis at the county level. Negative binomial regression was used to analyze the impact of environmental factors on the prevalence of human echinococcosis at the county level. RESULTS: According to rank correlation analysis, the prevalence of human echinococcosis in each county was positively correlated with elevation, negatively correlated with LST, and negatively correlated with NDVI in May, June and July. Negative binomial regression showed that the prevalence of human echinococcosis was negatively correlated with annual LST and summer NDVI, and positively correlated with average elevation and dog infection rate. The prevalence of human cystic echinococcosis was inversely correlated with the annual average LST, and positively correlated with both the average elevation and the prevalence rate of domestic animals. The prevalence of human alveolar echinococcosis was positively correlated with both NDVI in autumn and average elevation, and negatively correlated with NDVI in winter. CONCLUSION: The prevalence of echinococcosis in the population is affected by environmental factors. Environmental risk assessment and prediction can be conducted in order to rationally allocate health resources and improve both prevention and control efficiency of echinococcosis.