scholarly journals Study on the Factors Influencing Total Health Cost Based on Multiple Regression Model

2021 ◽  
Vol 2 (6) ◽  
Author(s):  
Xiaoduo Zu ◽  
Jun Fang
Keyword(s):  
Regression Model ◽  
Structural Adjustment ◽  
Social Economy ◽  
Health Financing ◽  
Multiple Regression Model ◽  
Institutional Transformation ◽  
Medical Expenditure ◽  
Coordinated Development ◽  
Financing Policy ◽  
Using Data

In recent years, China's social economy and income level of residents have increased rapidly, the total cost of health has increased rapidly, and the level of medical expenditure of residents has been increasing. This paper establishes a multivariate linear regression model using data from 1996 to 2020, and analyzes several important influencing factors that affect overall health expenditure. The aim is to formulate a health financing policy suitable for the coordinated development of China's social economy, and to provide a basis for adapting to the needs of economic development, structural adjustment and institutional transformation.

scholarly journals Equity in Health Care Expenditure in Nigeria

2013 ◽  
Vol 2 (3) ◽  
pp. 76-88
Author(s):  
Frances Susan Obafemi ◽  
Olanrewaju Olaniyan ◽  
Frances Ngozi Obafemi
Keyword(s):  
Health Care ◽  
Health Care Expenditure ◽  
Health Financing ◽  
Living Standard ◽  
National Bureau ◽  
Financing Policy ◽  
Risk Pooling ◽  
Poor Households ◽  
Out Of Pocket Payments ◽  
Using Data

Equity is one of the basic principles of health systems and features explicitly in the Nigerian health financing policy. Despiteacclaimed commitment to the implementation of this policy through various pro-poor health programmes and interventions,the level of inequity in health status and access to basic health care interventions remain high. This paper examines theequity of health care expenditure by individuals in Nigeria. The paper evaluated equity in out-of-pocket spending (OOP) forthe country and separately for the six geopolitical zones of the country. The methodological framework rests on KakwaniProgressivity Indices (KPIs), Reynold-Smolensky indices and concentration indices (CIs) using data from the 2004 Nigerian National Living Standard Survey (NLSS) collected by the National Bureau of Statistic. The results reveal that health financing is regressive with the incidence disproportionately resting on poor households with about 70% of the total expenditure on health being financed through out-of-pocket payments by households. Poor households are prone to bear most of the expenses in the event of any health shock. The catastrophic consequences thus push some into poverty, and aggravate the poverty of others. The paper therefore suggests that the country’s health financing systems must be such that allows people to access services when they are needed, but must also protect household, from financial catastrophe, by reducing OOP spending through risk pooling and prepayment schemes within the health system.

Analysis of Water Quality of Lake Hachiroko in Japan Using a Fuzzy Multiple Regression Model with ALOS AVNIR-2 Data

2016 ◽  
Vol 20 (6) ◽  
pp. 992-1003 ◽  
Author(s):  
Dejian Wang ◽  
◽  
Yoichi Kageyama ◽  
Makoto Nishida ◽  
Hikaru Shirai ◽  
...  
Keyword(s):  
Water Quality ◽  
Regression Model ◽  
Multiple Regression ◽  
Near Infrared ◽  
Multiple Regression Model ◽  
Fuzzy Regression ◽  
Quality Data ◽  
Water Quality Data ◽  
Using Data

The distribution of water pollution is often assessed by remote sensing. In this study, we develop a fuzzy multiple regression model and analyze water quality using data collected by the Advanced Visible and Near Infrared Radiometer type-2 (AVNIR-2) of the Advanced Land Observing Satellite at different time points. We conduct a fuzzy multiple regression analysis of the AVNIR-2 data and direct measurements of the local water quality of Lake Hachiroko in Japan. The relationship between the AVNIR-2 and water quality data are analyzed by solving both min and max problems. We compare the estimated water quality maps with the actual distributions in the study area, and determine that the proposed method enables us to derive water quality conditions effectively from the AVNIR-2 data. Furthermore, by comparing maps created using AVNIR-2 data collected at different times, we obtain results revealing temporal changes in water quality. In addition, we compare maps created using the fuzzy multiple regression and fuzzy regression models. We demonstrate that the former offers a greater number of solutions and provides more details about water quality.

scholarly journals A Skellam regression model for quantifying positional value in soccer

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Konstantinos Pelechrinis ◽  
Wayne Winston
Keyword(s):  
Regression Model ◽  
Video Game ◽  
Soccer Player ◽  
Performance Metrics ◽  
Optical Tracking ◽  
General Managers ◽  
Annual Salary ◽  
Using Data ◽  
English Premier League ◽  
Positional Value

Abstract Soccer is undeniably the most popular sport world-wide and everyone from general managers and coaching staff to fans and media are interested in evaluating players’ performance. Metrics applied successfully in other sports, such as the (adjusted) +/− that allows for division of credit among a basketball team’s players, exhibit several challenges when applied to soccer due to severe co-linearities. Recently, a number of player evaluation metrics have been developed utilizing optical tracking data, but they are based on proprietary data. In this work, our objective is to develop an open framework that can estimate the expected contribution of a soccer player to his team’s winning chances using publicly available data. In particular, using data from (i) approximately 20,000 games from 11 European leagues over eight seasons, and, (ii) player ratings from the FIFA video game, we estimate through a Skellam regression model the importance of every line (attackers, midfielders, defenders and goalkeeping) in winning a soccer game. We consequently translate the model to expected league points added above a replacement player (eLPAR). This model can further be used as a guide for allocating a team’s salary budget to players based on their expected contributions on the pitch. We showcase similar applications using annual salary data from the English Premier League and identify evidence that in our dataset the market appears to under-value defensive line players relative to goalkeepers.

scholarly journals The Impact of Work Characteristics on Social Distancing: Implications at the Time of COVID-19

2021 ◽  
Vol 18 (10) ◽  
pp. 5074
Author(s):  
Keisuke Kokubun ◽  
Yoshinori Yamakawa
Keyword(s):  
Information Processing ◽  
Multiple Regression ◽  
Explanatory Power ◽  
Work Conditions ◽  
Multiple Regression Model ◽  
Social Characteristics ◽  
Work Characteristics ◽  
Social Distancing ◽  
Using Data ◽  
The Impact

The coronavirus disease (COVID-19) continues to spread globally. While social distancing has attracted attention as a measure to prevent the spread of infection, some occupations find it difficult to implement. Therefore, this study aims to investigate the relationship between work characteristics and social distancing using data available on O*NET, an occupational information site. A total of eight factors were extracted by performing an exploratory factor analysis: work conditions, supervisory work, information processing, response to aggression, specialization, autonomy, interaction outside the organization, and interdependence. A multiple regression analysis showed that interdependence, response to aggression, and interaction outside the organization, which are categorized as ”social characteristics,” and information processing and specialization, which are categorized as “knowledge characteristics,” were associated with physical proximity. Furthermore, we added customer, which represents contact with the customer, and remote working, which represents a small amount of outdoor activity, to our multiple regression model, and confirmed that they increased the explanatory power of the model. This suggests that those who work under interdependence, face aggression, and engage in outside activities, and/or have frequent contact with customers, little interaction outside the organization, and little information processing will have the most difficulty in maintaining social distancing.

The Shape of Demand: What Does It Tell Us about Direct-to-Consumer Marketing of Antidepressants?

2008 ◽  
Vol 8 (2) ◽  
Author(s):  
Chad D. Meyerhoefer ◽  
Samuel H Zuvekas
Keyword(s):  
Medical Expenditure Panel Survey ◽  
Demand Curve ◽  
Medical Expenditure ◽  
Direct To Consumer ◽  
Medication Costs ◽  
True Match ◽  
Promotional Campaign ◽  
Using Data ◽  
Direct To Consumer Advertising ◽  
Consumer Marketing

Abstract Much of the debate surrounding Direct-to-Consumer Advertising (DTCA) of pharmaceuticals centers on whether DTCA conveys useful information to consumers or indiscriminately increases requests for the advertised medication. By identifying how DTCA changes the shape of the demand curve for antidepressants, we seek to infer the promotional objectives of manufacturers. Using data from the 1996-2003 Medical Expenditure Panel Survey (MEPS), we find that advertising shifts the demand curve for antidepressants outward and rotates it counter-clockwise. DTCA increases the probability that an individual will initiate use of antidepressants, particularly when out-of-pocket medication costs are low, but does not necessarily increase utilization levels among those already taking antidepressants. This is consistent with a promotional campaign that seeks to alert consumers to the product's existence, but conveys no real information that would allow them to learn their true match with the product.

Abstract 9: Peak Width of Skeletonized Mean Diffusivity and Cognition in Cerebral Amyloid Angiopathy

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Mitchell J Horn ◽  
Elif Gokcal ◽  
J. A Becker ◽  
Alvin S Das ◽  
Kristin Schwab ◽  
...  
Keyword(s):  
White Matter ◽  
Regression Model ◽  
Cerebral Amyloid Angiopathy ◽  
Mean Diffusivity ◽  
Multiple Regression Model ◽  
Amyloid Angiopathy ◽  
Peak Width ◽  
Symbol Substitution ◽  
Relevant Variables ◽  
Cerebral Amyloid

Background: We hypothesized that Peak Width of Skeletonized Mean Diffusivity (PSMD), an automated marker of cerebral microangiopathy representing microstructural disruption of white matter (WM), would be increased in patients with cerebral amyloid angiopathy (CAA) compared to healthy controls (HCs) and increased PSMD would be associated with lower processing speed scores (PSSs) in patients with CAA. Methods: Seventy-two nondemented probable CAA patients and 23 HCs prospectively underwent high-resolution brain MRIs and cognitive tests. PSMD scores were quantified from a probabilistic skeleton of the WM tracts as previously validated (http://www.psmd-marker.com). In subjects with intracerebral hemorrhage (ICH, n=27), ICH regions were masked and removed from the PSMD pipeline. The analyses were repeated in the non-ICH hemisphere. Raw scores of Trail Making Test-B and Symbol Substitution Test were transformed into standardized z -scores and averaged to obtain PSSs. Results: The mean age (p=0.366) and sex (p=0.811) were similar between CAA patients and HCs. PSMD was higher in the CAA group [(3.95±0.9) х 10 –4 mm 2 /s] compared to HCs [(3.32±0.6) х 10 –4 mm 2 /s] (p=0.003). This association remained significant in a linear regression model corrected for age and sex (β=0.700, 95%CI 0.3-1, p=0.001). Within the CAA cohort, higher PSMD was associated with higher WM hyperintensity volume in a multiple regression model adjusted for all relevant variables (β=0.890, 95%CI 0.7-1, p<0.001). In a regression model corrected for age, sex, years of education and presence of ICH, a lower PSS was independently associated with increased PSMD (β=-0.405, 95%CI {-0.6}-{-0.2}, p<0.001). These results did not change when the non-ICH hemisphere was used for PSMD processing. Conclusion: PSMD is increased in CAA and is associated with worse PSSs supporting the view that disruption of white matter has a significant role in cognitive impairment in CAA.

Factors Affecting NPAs in Indian Banking Sector

Paradigm ◽  
2021 ◽  
Vol 25 (2) ◽  
pp. 181-193
Author(s):  
Nitya Garg
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Banking Sector ◽  
Relative Weight ◽  
Multiple Regression Model ◽  
Factors Affecting ◽  
Weight Analysis ◽  
Specific Factors ◽  
Relative Weight Analysis ◽  
Bank Specific Factors

Banking sector is the backbone of any economy, so it is necessary to focus on its performance which is largely affected by its non-performing assets (NPAs). In the year 2018–2019, NPA of scheduled banks was Rs 355,076 Crore which is 3.7% of net advances. The purpose of this study is to identify the determinants based on analysis from previous literatures, and majorly macroeconomic and bank specific factors which are affecting NPAs using the relative weight analysis and to frame a model to predict future NPAs using multiple regression model using SPSS. The study also attempts to focus on actions and remedies that banks should make to control future NPAs. Findings of the study will act as a scaffolding for financial analysts and policymakers to prevent the conversion of its performing assets into NPAs and also help in proper management of banks and also in the recovery of economy.

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