Ordinary Least-squares Linear Regression Research Articles

Economic Abuse and Predatory Financial Service use Among Intimate Partner Violence Survivors

Abstract Purpose Predatory financial services allow individuals to access financial resources quickly, but at high costs. Given the economic impacts that intimate partner violence (IPV), particularly economic abuse, often has, it is possible that survivors use predatory financial services to meet their basic financial needs. However, limited research has been done examining predatory financial service use among IPV survivors. The purpose of this exploratory study was to examine whether experiencing IPV, particularly economic abuse, is a predictor of predatory financial service use among survivors. Methods Data came from a non-probabilistic cross-sectional online survey administered in July 2022. Bivariate and multivariate ordinary least-squares linear multiple regression analyses were used to answer the research questions. Results Just over half of the sample reported using at least one predatory financial service in the past 30 days, with the most frequently used service being “using a reloadable debit card to make payments.” Factors positively associated with predatory financial service use included economic abuse, race/ethnicity (identifying as Black or African American, non-Hispanic compared to white, non-Hispanic), and material hardship, and factors negatively associated included education level (some college education compared to a high school degree or less) and having a personal bank account. Conclusions Findings suggest that survivors would benefit from policy-related advocacy that combats exploitative practices used by fringe financial service providers. Survivors may also benefit from programs that increase their awareness around other financial service options outside of “mainstream” services (e.g., credit unions), which often prioritize meeting the economic needs of underserved communities.

Hemophilia is associated with accelerated biological aging

Hemophilia is a rare X-linked bleeding disorder caused by mutations in the F8 or F9 gene (hemophilia A or B), leading to deficient factor VIII or IX proteins, respectively. Hemophilia-related complications caused by bleeding into the joints (the hallmark of hemophilia) and age-related comorbidities occur frequently and impact the functionality and quality of life of persons with hemophilia (PwH). Given the chronic nature of hemophilia, we hypothesized that hemophilia has an association with accelerated biological aging. Therefore, we investigated biological aging biomarkers, i.e., telomere length and mitochondrial DNA (mtDNA) copy number with a quantitative polymerase chain reaction-based assay in PwH (N=99) and age- and sex-matched healthy controls (N=61). The association of telomere length and mtDNA copy number with hemophilia severity was investigated using ordinary least-squares linear regression models allowing for interactions with chronological age. Telomere length (6.09 [interquartile range (IQR), 4.79-7.68] kb vs. 10.07 [IQR, 7.93-12.66] kb; P<0.001) and mtDNA copy number (243.17 [IQR, 206.54-293.79] vs. 495.52 [IQR, 399.60-615.06]; P<0.001) were significantly lower in PwH compared to healthy controls. Persons with severe hemophilia had longer telomere length than those with non-severe hemophilia (6.29 [IQR, 5.36-8.05] kb vs. 5.12 [IQR, 3.97-6.58] kb), while no difference in mtDNA copy number was observed. We observed accelerated biological aging, indicated by shorter telomere length and lower mtDNA copy number, in PwH compared to healthy controls. Interestingly, telomere length shortening was more pronounced in non-severe than in severe hemophilia, which needs further investigations. Further research is needed to understand the underlying mechanisms of biological aging in hemophilia.

Exploring the relationship between personal and cohabiting family members’ COVID-19 infection experiences and fear of COVID-19: a longitudinal study based on the Japan COVID-19 and Society Internet Survey (JACSIS)

ObjectivesThis study aims to explore the relationship between the combined experiences of COVID-19 infection in individuals and their family members and the resulting fear of COVID-19, with a focus on...

Decomposing the variance: The unique and shared associations of fine and ultrafine particulate matter exposed during pregnancy with child autism spectrum disorder

Decomposing the variance: The unique and shared associations of fine and ultrafine particulate matter exposed during pregnancy with child autism spectrum disorder

Prediction of Snatch and Clean and Jerk Performance From Physical Performance Measures in Elite Male Weightlifters.

Sandau, I and Kipp, K. Prediction of snatch and clean and jerk performance from physical performance measures in elite male weightlifters. J Strength Cond Res 39(1): 33-40, 2025-This study aimed to build a valid model to predict maximal weightlifting competition performance using ordinary least squares linear regression (OLR) and penalized (Ridge) linear regression (penLR) in 29 elite male weightlifters. One repetition maximum (1RM) or 3RM test results of assistant exercises were used as predictors. Maximal performance data of competition and assistant exercises were collected during a macrocycle in preparation for a competition. One repetition maximum snatch pull, 3RM back squat, 1RM overhead press, and body mass were used to predict the 1RM snatch; and 1RM clean pull, 3RM front squat, 1RM overhead press, and body mass were used to predict the 1RM clean and jerk. Model validation was performed using cross-validation (CV) and external validation (EV; random unknown dataset) for the coefficient of determination and root mean square error (RMSE). Results revealed that penLR models present more plausible output in the relative importance of highly correlated predictors. Of note, the 1RM snatch pull is the most relevant predictor for the 1RM snatch, whereas the 1RM clean pull and 3RM front squat are the most relevant predictors for the 1RM clean and jerk. Validation-based absolute predictive error (RMSE) ranged between ≈ 3-9 kg for the 1RM snatch and ≈ 3-7 kg for the 1RM clean and jerk, depending on the model (OLR vs. penLR) and validation procedure (CV vs. EV). In conclusion, penLR models should be used over OLR models to analyze highly correlated predictors because of more plausible model coefficients and smaller predictive errors.

Association of Fructosamine Levels With Glycemic Management in Children With Type 1 Diabetes as Determined by Continuous Glucose Monitoring: Results From the CGM TIME Trial

Association of Fructosamine Levels With Glycemic Management in Children With Type 1 Diabetes as Determined by Continuous Glucose Monitoring: Results From the CGM TIME Trial

Comparison of Machine Learning Models in Simulating Glacier Mass Balance: Insights from Maritime and Continental Glaciers in High Mountain Asia

Accurately simulating glacier mass balance (GMB) data is crucial for assessing the impacts of climate change on glacier dynamics. Since physical models often face challenges in comprehensively accounting for factors influencing glacial melt and uncertainties in inputs, machine learning (ML) offers a viable alternative due to its robust flexibility and nonlinear fitting capability. However, the effectiveness of ML in modeling GMB data across diverse glacier types within High Mountain Asia has not yet been thoroughly explored. This study addresses this research gap by evaluating ML models used for the simulation of annual glacier-wide GMB data, with a specific focus on comparing maritime glaciers in the Niyang River basin and continental glaciers in the Manas River basin. For this purpose, meteorological predictive factors derived from monthly ERA5-Land datasets, and topographical predictive factors obtained from the Randolph Glacier Inventory, along with target GMB data rooted in geodetic mass balance observations, were employed to drive four selective ML models: the random forest model, the gradient boosting decision tree (GBDT) model, the deep neural network model, and the ordinary least-square linear regression model. The results highlighted that ML models generally exhibit superior performance in the simulation of GMB data for continental glaciers compared to maritime ones. Moreover, among the four ML models, the GBDT model was found to consistently exhibit superior performance with coefficient of determination (R2) values of 0.72 and 0.67 and root mean squared error (RMSE) values of 0.21 m w.e. and 0.30 m w.e. for glaciers within Manas and Niyang river basins, respectively. Furthermore, this study reveals that topographical and climatic factors differentially influence GMB simulations in maritime and continental glaciers, providing key insights into glacier dynamics in response to climate change. In summary, ML, particularly the GBDT model, demonstrates significant potential in GMB simulation. Moreover, the application of ML can enhance the accuracy of GMB modeling, providing a promising approach to assess the impacts of climate change on glacier dynamics.

Self-identified barriers to health services among migrants 50 years of age or older: population-based survey study of Russian speakers in Finland

BackgroundThe compounded effect of a migratory background and ageing increases the risk of unequal medical treatment opportunities. The aim of this article is to investigate the social determinants of barriers to health services.MethodsThe study uses population-based survey data of Russian-speaking migrants (50 + years) residing in Finland (n = 1082, 57% of men, mean age 63 years). Multiple correspondence analysis was performed as a dimension reduction procedure on six barriers to health services. Multiple ordinary least-squares linear regression was used for the predicted score of the barriers as an outcome variable.ResultsMost of the sociodemographic characteristics were not associated with barriers to health services, except gender, as women tended to face more disadvantages. Migration-related factors, such as the need for interpreters for health services and experienced discrimination, were associated with an increased likelihood of reporting barriers to health services. Using the internet as a primary source of health information was associated with more access barriers to health services.ConclusionsMigrants 50 years of age or older face multiple barriers to health services. Given that the healthcare needs increase with age, addressing this issue becomes crucial, necessitating improved access to health services for older migrants.

Lack of structural brain alterations associated with insomnia: findings from the ENIGMA-Sleep Working Group.

Existing neuroimaging studies have reported divergent structural alterations in insomnia disorder (ID). In the present study, we performed a large-scale coordinated meta-analysis by pooling structural brain measures from 1085 subjects (mean [SD] age 50.5[13.9]years, 50.2% female, 17.4% with insomnia) across three international Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA)-Sleep cohorts. Two sites recruited patients with ID/controls: Freiburg (University of Freiburg Medical Center, Freiburg, Germany) 42/43 and KUMS (Kermanshah University of Medical Sciences, Kermanshah, Iran) 42/49, while the Study of Health in Pomerania (SHIP-Trend,University MedicineGreifswald,Greifswald,Germany) recruited population-based individuals with/without insomnia symptoms 75/662. The influence of insomnia on magnetic resonance imaging-based brain morphometry using an insomnia brain score was then assessed. Within each cohort, we used an ordinary least-squares linear regression to investigate the link between the individual regional cortical and subcortical volumes and the presence of insomnia symptoms. Then, we performed a fixed-effects meta-analysis across cohorts based on the first-level results. For the insomnia brain score, weighted logistic ridge regression was performed on one sample (Freiburg), which separated patients with ID from controls to train a model based on the segmentation measurements. Afterward, the insomnia brain scores were validated using the other two samples. The model was used to predict the log-odds of the subjects with insomnia given individual insomnia-related brain atrophy. After adjusting for multiple comparisons, we did not detect any significant associations between insomnia symptoms and cortical or subcortical volumes, nor could we identify a global insomnia-related brain atrophy pattern. Thus, we observed inconsistent brain morphology differences between individuals with and without insomnia across three independent cohorts. Further large-scale cross-sectional/longitudinal studies using both structural and functional neuroimaging are warranted to decipher the neurobiology of insomnia.

Machine Learning-based Inverse Model for Few-Mode Fiber Designs

The medium for next-generation communication is considered as fiber for fast, secure communication and switching capability. Mode division and space division multiplexing provide an excellent switching capability with high data transmission rate. In this work, the authors have approached an inverse modeling technique using regression-based machine learning to design a weakly coupled few-mode fiber for facilitating mode division multiplexing. The technique is adapted to predict the accurate profile parameters for the proposed few-mode fiber to obtain the maximum number of modes. It is for a three-ring-core few-mode fiber for guiding five, ten, fifteen, and twenty modes. Three types of regression models namely ordinary least-square linear multi-output regression, k-nearest neighbors of multi-output regression, and ID3 algorithm-based decision trees for multi-output regression are used for predicting the multiple profile parameters. It is observed that the ID3-based decision tree for multioutput regression is the robust, highly-accurate machine learning model for fast modeling of FMFs. The proposed fiber claims to be an efficient candidate for the next-generation 5G and 6G backhaul networks using mode division multiplexing.

The number of examinations required for the accurate prediction of the progression of the central 10-degree visual field test in glaucoma

The purpose of the study was to investigate the number of examinations required to precisely predict the future central 10-degree visual field (VF) test and to evaluate the effect of fitting non-linear models, including quadratic regression, exponential regression, logistic regression, and M-estimator robust regression model, for eyes with glaucoma. 180 eyes from 133 open angle glaucoma patients with a minimum of 13 Humphrey Field Analyzer 10-2 SITA standard VF tests were analyzed in this study. Using trend analysis with ordinary least squares linear regression (OLSLR), the first, second, and third future VFs were predicted in a point-wise (PW) manner using a varied number of prior VF sequences, and mean absolute errors (MAE) were calculated. The number of VFs needed to reach the minimum 95% confidence interval (CI) of the MAE of the OLSLR was investigated. We also examined the effect of applying other non-linear models. When predicting the first, second, and third future VFs using OLSLR, the minimum MAE was obtained using VF1–12 (2.15 ± 0.98 dB), VF1–11 (2.33 ± 1.10 dB), and VF1–10 (2.63 ± 1.36 dB), respectively. To reach the 95% CI of these MAEs, 10, 10, and 8 VFs were needed for the first, second and third future VF predictions, respectively. No improvement was observed by applying non-linear regression models. As a conclusion, approximately 8–10 VFs were needed to achieve an accurate prediction of PW VF sensitivity of the 10-degree central VF.

Data-driven numerical models for the prediction of the thermal resistance value of the Enclosed Airspaces (EAs) in building envelopes

ABSTRACT Enclosed airspaces (EAs) are a common component of different energy efficient technologies in building envelopes which have intrinsic adaptive behavior under various climatic conditions. The need for comprehensive accurate numerical models without restrictions for different applications still exists. Thus, for the first time, machine learning and regression-based techniques (Ordinary least squares Linear Regression (OLR), Support Vector Regression (SVR), Kernel Ridge Regression (KRR), k-Nearest Neighbors (kNN), r-Nearest Neighbors (rNN), and Artificial Neural Network (ANN)) were applied to develop precise models derived from the most credible experimental data with stochastic logic of testing and training in several runs. It was found that application of KRR, SVR, and ANN leads to the most desirable outcomes with highest R2 (0.97–0.99), and lowest errors, however OLR does not provide satisfactory results (R2 <0.6). Moreover, major deviations are observed for calculations by OLR, kNN, and rNN in horizontal EAs (thicknesses = 40mm) with downward heat flow direction.

MO323: Effects of Patient Frailty and Comorbidity on Duration of Hospitalisation as an Outcome Among Adults With Acute Kidney Injury

Abstract BACKGROUND AND AIMS Acute kidney injury (AKI) is commonly associated with an adverse outcome in hospitalised patients. Frailty and comorbidity are risk factors for acute kidney injury. The aim of this study was to assess the strength of association between frailty and comorbidity as modifiers for duration of hospitalisation (LOS) as an outcome among inpatients with acute kidney injury in our institution. METHOD A retrospective observational study as part of a service assessment of adult inpatients, in a 1-month period, during the pre-pandemic phase was conducted to evaluate for changes needed to existing care pathways in our institution. AKI was identified with the help of the national algorithm endorsed by NHS England, which is incorporated in hospital reporting systems. Clinical frailty and comorbidity were estimated using the Rockwood Clinical Frailty Scale (CFS) and the Charlson comorbidity index (CCI). Pairwise correlations between LOS, CFS and CCI were estimated and tested for statistical significance using Bonferroni-adjusted significance levels. Ordinary least-squares linear regression was used to assess the prediction of LOS using CFS and CCI in separate models. RESULTS In 148 patients in our cohort, the mean age was 76.4 [95% confidence interval (95% CI) 74.1–78.8] years. Of these, 54% were male, 24.3% were diabetic, 29.7% had at least one criterion for prior vascular disease and 16.2% had a history of cancer. A total of 22.3% had a prior history of chronic kidney disease. In terms of severity at presentation, 67.6% had stage 1 AKI, 18.9% had stage 2, and 13.5% had stage 3 AKI. The mean CCI was 5.4 (95% CI 5.06–5.81) and the mean RFI was 4.6 (95% CI 4.3–5.0). 50% were both at least moderately frail (CFS ≥ 5) and had a CCI of &amp;gt;5. The mean duration of hospitalisation was 8.7 days. Spearman's correlation coefficient constant for CFS with LOS was 0.23 and was 0.11 with CCI, with only the correlation between LOS and CFS being statistically significant at the 5% level after Bonferroni correction. As expected, there was a statistically significant correlation between CCI and CFS (0.49). Beta coefficients for ordinary least-squares linear regression individual models with LOS as the outcome variable were 0.69 (95% CI 0.18–1.19; P &amp;lt; .007) for CFS and 0.27 (95% CI –0.16 to 0.70; P = .22) for CCI. CONCLUSION Compared with patient comorbidity, clinical frailty has a stronger and statistically significant association with the duration of hospitalisation among hospitalised adults with AKI. This study quantifies the magnitude of factors over and above those resulting from patient comorbidity that are contributory to frailty in adult inpatients with AKI. Further studies are required to improve understanding of how clinical frailty affects duration of hospitalisation in patients with AKI, to help inform future clinical care pathways and improve duration of hospitalisation as a clinical outcome.

The Impact of COVID-19 on Mortality in Italy: Retrospective Analysis of Epidemiological Trends.

BackgroundDespite the available evidence on its severity, COVID-19 has often been compared with seasonal flu by some conspirators and even scientists. Various public discussions arose about the noncausal correlation between COVID-19 and the observed deaths during the pandemic period in Italy.ObjectiveThis paper aimed to search for endogenous reasons for the mortality increase recorded in Italy during 2020 to test this controversial hypothesis. Furthermore, we provide a framework for epidemiological analyses of time series.MethodsWe analyzed deaths by age, sex, region, and cause of death in Italy from 2011 to 2019. Ordinary least squares (OLS) linear regression analyses and autoregressive integrated moving average (ARIMA) were used to predict the best value for 2020. A Grubbs 1-sided test was used to assess the significance of the difference between predicted and observed 2020 deaths/mortality. Finally, a 1-sample t test was used to compare the population of regional excess deaths to a null mean. The relationship between mortality and predictive variables was assessed using OLS multiple regression models. Since there is no uniform opinion on multicomparison adjustment and false negatives imply great epidemiological risk, the less-conservative Siegel approach and more-conservative Holm-Bonferroni approach were employed. By doing so, we provided the reader with the means to carry out an independent analysis.ResultsBoth ARIMA and OLS linear regression models predicted the number of deaths in Italy during 2020 to be between 640,000 and 660,000 (range of 95% CIs: 620,000-695,000) against the observed value of above 750,000. We found strong evidence supporting that the death increase in all regions (average excess=12.2%) was not due to chance (t21=7.2; adjusted P<.001). Male and female national mortality excesses were 18.4% (P<.001; adjusted P=.006) and 14.1% (P=.005; adjusted P=.12), respectively. However, we found limited significance when comparing male and female mortality residuals’ using the Mann-Whitney U test (P=.27; adjusted P=.99). Finally, mortality was strongly and positively correlated with latitude (R=0.82; adjusted P<.001). In this regard, the significance of the mortality increases during 2020 varied greatly from region to region. Lombardy recorded the highest mortality increase (38% for men, adjusted P<.001; 31% for women, P<.001; adjusted P=.006).ConclusionsOur findings support the absence of historical endogenous reasons capable of justifying the mortality increase observed in Italy during 2020. Together with the current knowledge on SARS-CoV-2, these results provide decisive evidence on the devastating impact of COVID-19. We suggest that this research be leveraged by government, health, and information authorities to furnish proof against conspiracy hypotheses that minimize COVID-19–related risks. Finally, given the marked concordance between ARIMA and OLS regression, we suggest that these models be exploited for public health surveillance. Specifically, meaningful information can be deduced by comparing predicted and observed epidemiological trends.

Why do patients with chronic hepatitis C drink alcohol? An examination of pain, depression and drinking motives.

Alcohol consumption in the setting of chronic HCV is associated with accelerated progression towards cirrhosis, increased risk of hepatocellular carcinoma and higher mortality. This analysis contextualizes how sociodemographic factors, chronic pain and depression relate to the motivations of individuals with chronic HCV to consume alcohol. We conducted a secondary analysis of baseline data from the Hep ART trial of behavioural interventions on alcohol use among patients with HCV. Alcohol consumption was measured using the Drinking Motives Questionnaire and a novel 6-item measure of pain-related drinking motives. Statistical analyses performed included ANOVA for bivariate analyses and multivariable ordinary least-squares linear regression. At study baseline, 181 participants had an average age of 55years; the majority (66.7%) reported beyond-minor pain; and a third (37%) met criteria for depression; drinking motives were higher for individuals with beyond-minor pain (means 9.9 vs. 4.6, p<.001) and who met criteria for depression (means 10.9 vs. 6.4, p<.001) when using the pain-related drinking motives items. Average pain(coef=1.0410067141<.001) was significantly associated with increased motives to drink to relieve pain in the full baseline model specification controlling for all covariates using ordinary at least squares; depression (coef=7.06; 95% CI 1.32, 12.81; p=.016) was significantly associated with increased non-pain-related motives to drink. From baseline to 3-month follow-up, compared to participants who had mean average pain scores among the sample, motives to drink to relieve pain decreased in participants who had higher average pain scores (coef=-0.30; 95% CI -0.59, -0.01; p=.40). Physical pain and depression are associated with increased motives to consume alcohol. Patients with chronic liver disease should be screened for chronic pain and depression and, if present, referred to pain specialists or co-managed in partnership with pain specialists in hepatology clinics.

Modelling heating and cooling energy demand for building stock using a hybrid approach

Modelling heating and cooling energy demand for building stock using a hybrid approach

Evaluation of the external validity of a joint structure\u2013function model for monitoring glaucoma progression

The dynamic structure–function (DSF) model was previously shown to have better prediction accuracy than ordinary least square linear regression (OLSLR) for short series of visits. The current study assessed the external validity of the DSF model by testing its performance in an independent dataset (Ocular Hypertension Treatment Study–Confocal Scanning Laser Ophthalmoscopy [OHTS–CSLO] ancillary study; N = 178 eyes), and also on different test parameters in a sample selected from the Diagnostic Innovations in Glaucoma Study or the African Descent and Glaucoma Evaluation Study (DIGS/ADAGES). Each model was used to predict structure–function paired data at visits 4–7. The resulting prediction errors for both models were compared using the Wilcoxon signed-rank test. In the independent dataset, the DSF model predicted rim area and mean sensitivity paired measurements more accurately than OLSLR by 1.8–5.5% (p ≤ 0.004) from visits 4–6. Using the DIGS/ADAGES dataset, the DSF model predicted retinal nerve fiber layer thickness and mean deviation paired measurements more accurately than OLSLR by 1.2–2.5% (p ≤ 0. 007). These results demonstrate the external validity of the DSF model and provide a strong basis to develop it into a useful clinical tool.

Comparing the Rule of 5 to Trend-based Analysis for Detecting Glaucoma Progression on OCT

Comparing the Rule of 5 to Trend-based Analysis for Detecting Glaucoma Progression on OCT

Comparison of matrix-effect corrections for ordinary and uncertainty weighted linear regressions and determination of major element mean concentrations and total uncertainties of sixty-two international geochemical reference materials from wavelength-dispersive X-ray fluorescence spectrometry

Comparison of matrix-effect corrections for ordinary and uncertainty weighted linear regressions and determination of major element mean concentrations and total uncertainties of sixty-two international geochemical reference materials from wavelength-dispersive X-ray fluorescence spectrometry

Corneal Biomechanics and Visual Field Progression in Eyes with Seemingly Well-Controlled Intraocular Pressure

Corneal Biomechanics and Visual Field Progression in Eyes with Seemingly Well-Controlled Intraocular Pressure