scholarly journals Absenteeism and indirect costs during the year following the diagnosis of an operable breast cancer

2019 ◽  
Author(s):  
Clement Ferrier ◽  
Clémence Thebaut ◽  
Pierre Levy ◽  
Sandrine Baffert ◽  
Bernard Asselain ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Human Capital ◽  
Linear Regression ◽  
Sick Leave ◽  
Multiple Linear Regression ◽  
Early Stage ◽  
Total Duration ◽  
Multiple Linear Regression Model ◽  
Indirect Costs ◽  
Friction Cost

AbstractIntroductionThe consequences of disease on work for individual patients as well as the consequences of absenteeism from work are subjects of interest for decision-makers.MethodsWe analyzed duration of absenteeism and related indirect costs for patients with a paid job during the year following diagnosis of early-stage breast cancer in the prospective OPTISOINS01 cohort. A human capital and friction costs approaches were considered for evaluation of lost working days. For this analysis, the friction period was estimated from recent French data. Statistical analysis included simple and multiple linear regression to identify determinants of absenteeism and indirect costs.Results93% of patients had at least one period of sick leave, with an average of 2 periods of sick leave and a mean total duration of 186 days. 24% of patients returned to work part-time after an average sick leave of 114 days (i.e. 41 LWD). Estimated indirect costs were €22,722.00 and €7,724.00 per patient, for the human capital and friction cost approaches, respectively. In the multiple linear regression model, factors associated with absenteeism were: invasive tumor (p=.043), mastectomy (p=.038), redo surgery (p=.002), chemotherapy (p=.027), being a manager (p=.025) or a craftsman (p=.005).ConclusionBreast cancer is associated with long periods of absenteeism during the year following diagnosis, but almost all patients were able to return to work. Major differences in the results were observed between the friction cost and human capital approaches, highlighting the importance of considering both approaches in such studies.

scholarly journals A Hypercoagulable Hematological Metastasis Breast Cancer Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wen-Jing Yang ◽  
Gan-Lin Zhang ◽  
Ke-Xin Cao ◽  
Guo-Wang Yang
Keyword(s):  
Breast Cancer ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Lung Metastasis ◽  
Multiple Linear Regression Model ◽  
Tumor Burden ◽  
Hematogenous Metastasis ◽  
4T1 Cells

Background. The hypercoagulable status, which forms a vicious cycle with hematogenous metastasis, is a common systemic alteration in cancers. As modeling is a key approach in research, a model which is suitable for studying how the hypercoagulable status promotes hematogenous metastasis in breast cancer is urgently needed. Methods. Based on the tumor-bearing period (TBP) and postoperative incubation period (PIP), 4T1-breast cancer models were constructed to evaluate coagulation and tumor burden to generate multiple linear regression-based lung metastasis prediction formula. Platelets and 4T1 cells were cocultured for 30 min or 24 h in vitro to evaluate the early and late phases of their crosstalk, and then the physical characteristics (concentration and size) and procoagulant activity of the coculture supernatants were assayed. Results. The multiple linear regression model was constructed as log 10   photon   number = 0.147   TBP + 0.14   PIP + 3.303 ( TBP ≤ 25 and PIP ≤ 17 ) to predict lung metastasis. Coculture of platelets and 4T1 cells contributed to the release of extracellular vesicles (EVs) and the development of the hypercoagulable status. Conclusions. In vivo and in vitro hypercoagulable status models were developed to explore the mechanism of hypercoagulable status which is characterized by platelet activation and promotes hematogenous metastasis in breast cancer.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

Predictive Factors for Hamstring Autograft Diameter in Anterior Cruciate Ligament Reconstruction

2019 ◽  
Author(s):  
Willem M.P. Heijboer ◽  
Mathijs A.M. Suijkerbuijk ◽  
Belle L. van Meer ◽  
Eric W.P. Bakker ◽  
Duncan E. Meuffels
Keyword(s):  
Anterior Cruciate Ligament ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Acl Reconstruction ◽  
Cruciate Ligament ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Gracilis Tendon ◽  
Thigh Circumference ◽  
Anterior Cruciate

AbstractMultiple studies found hamstring tendon (HT) autograft diameter to be a risk factor for anterior cruciate ligament (ACL) reconstruction failure. This study aimed to determine which preoperative measurements are associated with HT autograft diameter in ACL reconstruction by directly comparing patient characteristics and cross-sectional area (CSA) measurement of the semitendinosus and gracilis tendon on magnetic resonance imaging (MRI). Fifty-three patients with a primary ACL reconstruction with a four-stranded HT autograft were included in this study. Preoperatively we recorded length, weight, thigh circumference, gender, age, preinjury Tegner activity score, and CSA of the semitendinosus and gracilis tendon on MRI. Total CSA on MRI, weight, height, gender, and thigh circumference were all significantly correlated with HT autograft diameter (p < 0.05). A multiple linear regression model with CSA measurement of the HTs on MRI, weight, and height showed the most explained variance of HT autograft diameter (adjusted R 2 = 44%). A regression equation was derived for an estimation of the expected intraoperative HT autograft diameter: 1.2508 + 0.0400 × total CSA (mm2) + 0.0100 × weight (kg) + 0.0296 × length (cm). The Bland and Altman analysis indicated a 95% limit of agreement of ± 1.14 mm and an error correlation of r = 0.47. Smaller CSA of the semitendinosus and gracilis tendon on MRI, shorter stature, lower weight, smaller thigh circumference, and female gender are associated with a smaller four-stranded HT autograft diameter in ACL reconstruction. Multiple linear regression analysis indicated that the combination of MRI CSA measurement, weight, and height is the strongest predictor.

scholarly journals Magnetization Transfer Ratio of Peripheral Nerve and Skeletal Muscle

2021 ◽  
Author(s):  
Olivia Fösleitner ◽  
Véronique Schwehr ◽  
Tim Godel ◽  
Fabian Preisner ◽  
Philipp Bäumer ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Body Weight ◽  
Sciatic Nerve ◽  
Linear Regression ◽  
Peripheral Nerve ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Magnetization Transfer ◽  
Multiple Linear Regression Model

Abstract Purpose To assess the correlation of peripheral nerve and skeletal muscle magnetization transfer ratio (MTR) with demographic variables. Methods In this study 59 healthy adults evenly distributed across 6 decades (mean age 50.5 years ±17.1, 29 women) underwent magnetization transfer imaging and high-resolution T2-weighted imaging of the sciatic nerve at 3 T. Mean sciatic nerve MTR as well as MTR of biceps femoris and vastus lateralis muscles were calculated based on manual segmentation on six representative slices. Correlations of MTR with age, body height, body weight, and body mass index (BMI) were expressed by Pearson coefficients. Best predictors for nerve and muscle MTR were determined using a multiple linear regression model with forward variable selection and fivefold cross-validation. Results Sciatic nerve MTR showed significant negative correlations with age (r = −0.47, p < 0.001), BMI (r = −0.44, p < 0.001), and body weight (r = −0.36, p = 0.006) but not with body height (p = 0.55). The multiple linear regression model determined age and BMI as best predictors for nerve MTR (R2 = 0.40). The MTR values were different between nerve and muscle tissue (p < 0.0001), but similar between muscles. Muscle MTR was associated with BMI (r = −0.46, p < 0.001 and r = −0.40, p = 0.002) and body weight (r = −0.36, p = 0.005 and r = −0.28, p = 0.035). The BMI was selected as best predictor for mean muscle MTR in the multiple linear regression model (R2 = 0.26). Conclusion Peripheral nerve MTR decreases with higher age and BMI. Studies that assess peripheral nerve MTR should consider age and BMI effects. Skeletal muscle MTR is primarily associated with BMI but overall less dependent on demographic variables.

Musculoskeletal symptoms, psychosocial factors and work ability in Brazilian labor justice workers

Work ◽  
2021 ◽  
pp. 1-10
Author(s):  
Fauzi El Kadri Filho ◽  
Tha&ıs Moreira São-João ◽  
Neusa Maria Costa Alexandre ◽  
Sergio Roberto de Lucca ◽  
Cristiane Helena Gallasch ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Psychosocial Factors ◽  
Work Ability ◽  
Multiple Linear Regression Model ◽  
Female Gender ◽  
Musculoskeletal Symptoms ◽  
Judicial Process ◽  
Cross Sectional ◽  
Important Relationship

BACKGROUND: The implementation of the electronic judicial process (PJe) in recent years is associated with an increase in workload and stricter control through productivity targets in the Brazilian labor judiciary. OBJECTIVES: To evaluate the relations between musculoskeletal symptoms, psychosocial factors and work ability in civil servants of a labor justice body in the context of the PJe. METHODS: A cross-sectional exploratory study with a quantitative approach involving 449 workers. Sociodemographic, occupational and related data were collected through questionnaires validated in the Brazilian context. Data analysis was conducted by descriptive and inferential statistics: Mann-Whitney test, Spearman’s correlation coefficient and multiple linear regression. RESULTS: There was a correlation between musculoskeletal symptoms and psychosocial factors (p <  0.05), as well as between both musculoskeletal symptoms and psychosocial factors with reduced work ability (p <  0.05). The multiple linear regression model pointed to the female gender and the dimensions “demands”, “control” and “peer support” as related to the musculoskeletal symptoms. CONCLUSION: We highlight the importance of a broader approach, involving psychosocial factors in preventive actions related to musculoskeletal disorders considering the important relationship with work ability.

Development of a multiple linear regression model for predicting the 28-day compressive strength of Portland pozzolan cement

1999 ◽  
Vol 32 (2) ◽  
pp. 98-102
Author(s):  
G. CH. Kostogloudis ◽  
J. Anagnostou ◽  
CH. Ftikos ◽  
J. Marinos
Keyword(s):  
Compressive Strength ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model
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