Factors associated with health-related quality of life among people living with HIV in South Korea: Tobit regression analysis.

This study investigated health-related quality of life and identified factors affecting it among people with the HIV in South Korea. A total of 243 people living with HIV participated in this cross-sectional survey. Data were collected from five hospitals between November 2021 and August 2022 using structured online questionnaires. Data were analyzed using descriptive statistics, Mann-Whitney U test, Kruskal-Wallis test, Spearman's rho analysis, and Tobit regression analysis because a significant ceiling effect was observed for the dependent variable. The mean score for the health-related quality of life was 75.74 ± 16.48. The significant factors that positively influence the health-related quality of life were "employment" (B = 4.57, p = .035), "not participating in the self-help group" (B = 6.10, p = .004), "higher self-efficacy for managing symptoms" (B = 1.32, p = .036), "higher self-efficacy for getting support/help" (B = 0.95, p = .035), and "higher self-efficacy for managing fatigue" (B = 2.80, p < .001) in the Tobit regression analysis. The results suggest that interventions to increase self-efficacy should involve developing programs and policies for people living with HIV. There is a need for efforts to provide healthcare services linked to employment support, as well as to establish a social environment in which they can work without stigma. Further, self-help groups could be utilized as intervention channels.

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.

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.

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.

Regression models for average hazard.

Limitations of using the traditional Cox's hazard ratio for summarizing the magnitude of the treatment effect on time-to-event outcomes have been widely discussed, and alternative measures that do not have such limitations are gaining attention. One of the alternative methods recently proposed, in a simple 2-sample comparison setting, uses the average hazard with survival weight (AH), which can be interpreted as the general censoring-free person-time incidence rate on a given time window. In this paper, we propose a new regression analysis approach for the AH with a truncation time τ. We investigate 3 versions of AH regression analysis, assuming (1) independent censoring, (2) group-specific censoring, and (3) covariate-dependent censoring. The proposed AH regression methods are closely related to robust Poisson regression. While the new approach needs to require a truncation time τ explicitly, it can be more robust than Poisson regression in the presence of censoring. With the AH regression approach, one can summarize the between-group treatment difference in both absolute difference and relative terms, adjusting for covariates that are associated with the outcome. This property will increase the likelihood that the treatment effect magnitude is correctly interpreted. The AH regression approach can be a useful alternative to the traditional Cox's hazard ratio approach for estimating and reporting the magnitude of the treatment effect on time-to-event outcomes.

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.

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.

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.

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.

Can predictive factors determine the time to treatment initiation for oral and oropharyngeal cancer? A classification and regression tree analysis.

This ecological study aimed to identify the factors with the greatest power to discriminate the proportion of oral and oropharyngeal cancer (OOC) records with time to treatment initiation (TTI) within 30 days of diagnosis in Brazilian municipalities. A descriptive analysis was performed on the variables grouped into five dimensions related to patient characteristics, access to health services, support for cancer diagnosis, human resources, and socioeconomic characteristics of 3,218 Brazilian municipalities that registered at least one case of OOC in 2019. The Classification and Regression Trees (CART) technique was adopted to identify the explanatory variables with greater discriminatory power for the TTI response variable. There was a higher median percentage of records in the age group of 60 years or older. The median percentage of records with stage III and IV of the disease was 46.97%, and of records with chemotherapy, radiation, or both as the first treatment was 50%. The median percentage of people with private dental and health insurance was low. Up to 75% had no cancer diagnostic support services, and up to 50% of the municipalities had no specialist dentists. Most municipalities (49.4%) started treatment after more than 30 days. In the CART analysis, treatment with chemotherapy, radiotherapy, or both explained the highest TTI in all municipalities, and it was the most relevant for predicting TTI. The final model also included anatomical sites in the oral cavity and oropharynx and the number of computed tomography services per 100,000. There is a need to expand the availability of oncology services and human resources specialized in diagnosing and treating OOC in Brazilian municipalities for a timely TTI of OOC.

Improving the robustness of the Sequentially Optimized Reconstruction Strategy (SORS) for visual field testing.

Perimetry, or visual field test, estimates differential light sensitivity thresholds across many locations in the visual field (e.g., 54 locations in the 24-2 grid). Recent developments have shown that an entire visual field may be relatively accurately reconstructed from measurements of a subset of these locations using a linear regression model. Here, we show that incorporating a dimensionality reduction layer can improve the robustness of this reconstruction. Specifically, we propose to use principal component analysis to transform the training dataset to a lower dimensional representation and then use this representation to reconstruct the visual field. We named our new reconstruction method the transformed-target principal component regression (TTPCR). When trained on a large dataset, our new method yielded results comparable with the original linear regression method, demonstrating that there is no underfitting associated with parameter reduction. However, when trained on a small dataset, our new method used on average 22% fewer trials to reach the same error. Our results suggest that dimensionality reduction techniques can improve the robustness of visual field testing reconstruction algorithms.

Predictors of maternal health services uptake in West African region: a multilevel multinomial regression analysis of demographic health survey reports.

BACKGROUND: Pursuant to studies, receiving the three key maternal health services (Antenatal Care, Skilled Delivery Service, and Postnatal Care) in a continuum could prevent 71% of global maternal deaths. Despite the Western African region being known for its high maternal death and poor access to maternal health services, there is a dearth of studies that delve into the spectrum of maternal health services uptake. Hence, this study aimed to assess the level and predictors of partial and adequate utilization of health services in a single analytical model using the most recent Demographic and Health Survey (DHS) data (2013-2021). METHODS: This study was based on the appended women's (IR) file of twelve West African countries. STATA software version 16 was used to analyze a weighted sample of 89,504 women aged 15-49 years. A composite index of maternal health service utilization has been created by combining three key health services and categorizing them into 'no', 'partial', or 'adequate' use. A multilevel multivariable multinomial logistic regression analysis was carried out to examine the effects of each predictor on the level of service utilization. The degree of association was reported using the adjusted relative risk ratio (aRRR) with a corresponding 95% confidence interval, and statistical significance was declared at p < 0.05. RESULTS: 66.4% (95% CI: 64.9, 67.7) and 23.8% (95% CI: 23.3, 24.2) of women used maternal health services partially and adequately, respectively. Togo has the highest proportion of women getting adequate health care in the region, at 56.7%, while Nigeria has the lowest proportion, at 11%. Maternal education, residence, wealth index, parity, media exposure (to radio and television), enrolment in health insurance schemes, attitude towards wife beating, and autonomy in decision-making were identified as significant predictors of partial and adequate maternal health service uptake. CONCLUSION: The uptake of adequate maternal health services in the region was found to be low. Stakeholders should plan for and implement interventions that increase women's autonomy. Program planners and healthcare providers should give due emphasis to those women with no formal education and from low-income families. The government and the private sectors need to collaborate to improve media access and increase public enrolment in health insurance schemes.

Development and validation of the relational behavior interactions scale for couples.

In this research, we developed and validated a measure of couple-based reported behavior interactions (RBI). Specifically, Study 1 was designed to describe the development of the scale and to examine its reliability; Study 2 (N = 222), was designed to examine factors that could differentiate men and women. Additionally, we tested if women's behaviors could predict their partner's behavior. Results from the exploratory factor analysis (EFA) revealed a three-factor structure for couples' RBI which were labelled: Social Companionship and Affective Behavior Interactions (SAI) (Factor 1), Fulfilling Obligations and Duties of the Partner (FOD) (Factor 2) and Openness in the Relationship (OR) (Factor 3). In linear regression analyses, there was a significant difference between men and women in the second factor, which represents behaviors associated with fulfilling the responsibilities of a partner. On the other hand, neither the SAI factor nor the OR factor showed any distinct gender differences. The SPSS PROCESS analysis revealed that women's Social Companionship and Affective Behavior Interactions (Factor 1), and Openness in the Relationship (Factor 3) significantly predicted their male partner's behaviors. The relationship duration significantly moderated the association between women's and men's behaviors for both factors. Results are discussed in light of the need for a broader understanding of romantic behavioral interactions.

Out-of-Pocket Costs and Payments in Autologous and Implant-Based Breast Reconstruction: A Nationwide Analysis.

BACKGROUND: Many factors influence a patient's decision to undergo autologous versus implant-based breast reconstruction, including medical, social, and financial considerations. This study aims to investigate differences in out-of-pocket and total spending for patients undergoing autologous and implant-based breast reconstruction. METHODS: The IBM MarketScan Commercial Databases were queried to extract all patients who underwent inpatient autologous or implant-based breast reconstruction from 2017 to 2021. Financial variables included gross payments to the provider (facility and/or physician) and out-of-pocket costs (total of coinsurance, deductible, and copayments). Univariate regressions assessed differences between autologous and implant-based reconstruction procedures. Mixed-effects linear regression was used to analyze parametric contributions to total gross and out-of-pocket costs. RESULTS: The sample identified 2079 autologous breast reconstruction and 1475 implant-based breast reconstruction episodes. Median out-of-pocket costs were significantly higher for autologous reconstruction than implant-based reconstruction ($597 vs $250, P < 0.001) as were total payments ($63,667 vs $31,472, P < 0.001). Type of insurance plan and region contributed to variable out-of-pocket costs (P < 0.001). Regression analysis revealed that autologous reconstruction contributes significantly to increasing out-of-pocket costs (B = $597, P = 0.025) and increasing total costs (B = $74,507, P = 0.006). CONCLUSION: The US national data demonstrate that autologous breast reconstruction has higher out-of-pocket costs and higher gross payments than implant-based reconstruction. More study is needed to determine the extent to which these financial differences affect patient decision-making.

Stem moisture content prediction model for Larix olgensis based on beta regression.

To investigate the longitudinal variation patterns of sapwood, heartwood, bark and stem moisture content along the trunk of artificial Larix olgensis, we constructed mixed effect models of moisture content based on beta regression by combining the effects of sampling plot and sample trees. We used two sampling schemes to calibrate the model, without limiting the relative height (Scheme Ⅰ) and with a limiting height of less than 2 m (Scheme II). The results showed that sapwood and stem moisture content increased gradually along the trunk, heartwood moisture content decreased slightly and then increased along the trunk, and bark moisture content increased along the trunk and then levelled off before increasing. Relative height, height to crown base, stand area at breast height per hectare, age, and stand dominant height were main factors driving moisture content of L. olgensis. Scheme Ⅰ showed the stable prediction accuracy when randomly sampling moisture content measurements from 2-3 discs to calibrate the model, with the mean absolute percentage error (MAPE) of up to 7.2% for stem moisture content (randomly selected 2 discs), and the MAPE of up to 7.4%, 10.5% and 10.5% for sapwood, heartwood and bark moisture content (randomly selected 3 discs), respectively. Scheme Ⅱ was appropriate when sampling moisture content measurements from discs of 1.3 and 2 m height and the MAPE of sapwood, heartwood, bark and stem moisture content reached 7.8%, 11.0%, 10.4% and 7.1%, respectively. The prediction accuracies of all mixed effect beta regression models were better than the base model. The two-level mixed effect beta regression models, considering both plot effect and tree effect, would be suitable for predicting moisture content of each part of L. olgensis well.

Assessing the predictability of surrogate safety measures as crash precursors based on vehicle trajectory data prior to crashes.

This study aims to investigate the predictability of surrogate safety measures (SSMs) for real-time crash risk prediction. We conducted a year-long drone video collection on a busy freeway in Nanjing, China, and collected 20 rear-end crashes. The predictability of SSMs was defined as the probability of crash occurrence when using SSMs as precursors to crashes. Ridge regression models were established to explore contributing factors to the predictability of SSMs. Four commonly used SSMs were tested in this study. It was found that modified time-to-collision (MTTC) outperformed other SSMs when the early warning capability was set at a minimum of 1 s. We further investigated the cost and benefit of SSMs in safety interventions by evaluating the number of necessary predictions for successful crash prediction and the proportion of crashes that can be predicted accurately. The result demonstrated these SSMs were most efficient in proactive safety management systems with an early warning capability of 1 s. In this case, 308, 131, 281, and 327,661 predictions needed to be made before a crash could be successfully predicted by TTC, MTTC, DRAC, and PICUD, respectively, achieving 75 %, 85 %, 35 %, and 100 % successful crash identifications. The ridge regression results indicated that the predefined threshold had the greatest impact on the predictability of all tested SSMs.

HE4-based nomogram for predicting overall survival in patients with idiopathic pulmonary fibrosis: construction and validation.

Idiopathic pulmonary fibrosis (IPF) is a life-threatening interstitial lung disease. Identifying biomarkers for early diagnosis is of great clinical importance. The epididymis protein 4 (HE4) is important in the process of inflammation and fibrosis in the epididymis. Its prognostic value in IPF, however, has not been studied. The mRNA and protein levels of HE4 were used to determine the prognostic value in different patient cohorts. In this study, prognostic nomograms were generated based on the results of the cox regression analysis. We identified the HE4 protein level increased in IPF patients, but not the HE4 gene expression. The increased expression of HE4 correlated positively with a poor prognosis for patients with IPF. The HR and 95% CI were 2.62 (1.61-4.24) (p < 0.001) in the training set. We constructed a model based on the risk-score = 0.16222182 * HE4 + 0/0.37580659/1.05003609 (for GAP index 0-3/4-5/6-8) + (- 1.1183375). In both training and validation sets, high-risk patients had poor prognoses (HR: 3.49, 95%CI 2.10-5.80, p = 0.001) and higher likelihood of dying (HR: 6.00, 95%CI 2.04-17.67, p = 0.001). Analyses of calibration curves and decision curves suggest that the method is effective in predicting outcomes. Furthermore, a similar formulation was used in a protein-based model based on HE4 that also showed prognostic value when applied to IPF patients. Accordingly, HE4 is an independent poor prognosis factor, and it has the potential to predict IPF patient survival.

The effect of retirement on physical and mental health in China: a nonparametric fuzzy regression discontinuity study.

BACKGROUND: With the rapid aging of the domestic population, China has a strong incentive to increase the statutory retirement age. How retirement affects the health of the elderly is crucial to this policymaking. The health consequences of retirement have been debated greatly. This study aims to investigate the effects of retirement on physical and mental health among Chinese elderly people. METHODS: The data we use in this study comes from four waves (2011, 2013, 2015, and 2018) of the Harmonized China Health and Retirement Longitudinal Study (Harmonized CHARLS), a prospective cohort. We use the nonparametric fuzzy regression discontinuity design to estimate the effects of retirement on physical and mental health. We test the robustness of our results with respect to different bandwidths, kernel functions, and polynomial orders. We also explore the heterogeneity across gender and education. RESULTS: Results show that retirement has an insignificant effect on a series of physical and mental health outcomes, with and without adjusting several sociodemographic variables. Heterogeneity exists regarding gender and education. Although stratified analyses indicate that the transition from working to retirement leaves minimal effects on males and females, the effects go in the opposite direction. This finding holds for low-educated and high-educated groups for health outcomes including depression and cognitive function. Most of the results are stable with respect to different bandwidths, kernel functions, and polynomial orders. CONCLUSIONS: Our results suggest that it is possible to delay the statutory retirement age in China as retirement has insignificant effects on physical and mental health. However, further research is needed to assess the long-term effect of retirement on health.

Regression modelling strategies for projected and sustainable kraft pulping of wheat straw.

The demand for paper and paper-based packaging has seen a massive increase in past years, resulting in accelerated deforestation to meet the rising demand, negatively impacting the environment, and there is a need to look towards different non-woody raw materials. Kraft pulping (KP) is widely used in paper making, for which the chemical dose, temperature, time, and energy required must be optimized, for which many insignificant experimental trials are performed. An effort is made to solve this problem by developing the regression equations with the help of Excel using One Factor at a Time Analysis (OFAT), followed by carrying out design of experiments (DoE) using orthogonal approach and regression analysis in Minitab software. Life cycle Assessment (LCA) using the Open-LCA software estimates the effect of chemicals and energy required during pulping on human health, ecosystem quality, and resource depletion. Using regression analysis, the equations for predicting kappa number, yield (%), total energy consumed, and mechanical properties of the paper sheet showed a good fit with an R2 value in the range of 0.90-0.99. Apart from that, the mechanical properties, namely tensile index (41.43 Nm/g), tear index (6.96 mN m2/g), bending stiffness (0.5 mN m), and burst index (3.92 kPa m2/g) of the unbeaten sheet, were determined experimentally at optimized conditions. Based on the Open-LCA result, the optimized pulping conditions had less impact on human health, ecosystem quality, and resource depletion. Industries can use the model to predict the values of kappa number, yield, mechanical properties, and energy consumption without performing optimization experiments that may impact the industry's economy to a greater extent.