Systemic Determinants in Surgery: Nonclinical Factors Affecting Time to Operation for Incarcerated Ventral Hernias

2021 ◽  
pp. 000313482110111
Author(s):  
David E. Wang ◽  
Paul J. Chung ◽  
Rafael Barrera ◽  
Gene F. Coppa ◽  
Antonio E. Alfonso ◽  
...  
Keyword(s):  
Operating Room ◽  
Negative Binomial ◽  
Private Insurance ◽  
Negative Binomial Regression ◽  
Outcome Variable ◽  
Hospital Size ◽  
Factors Affecting ◽  
Binomial Regression ◽  
Comorbidity Status ◽  
Ventral Hernias

Introduction We explore nonclinical factors affecting the amount of time from admission to the operating room for patients requiring nonelective repair of ventral hernias. Methods Using the 2005-2012 Nationwide Inpatient Sample, we identified adult patients with a primary diagnosis of ventral hernia without obstruction/gangrene, who underwent nonelective repair. The outcome variable of interest was time from admission to surgery. We performed univariate and multivariable analyses using negative binomial regression, adjusting for age, sex, race, income, insurance, admission day, comorbidity status (van Walraven score), diagnosis, procedure, hospital size, location/teaching status, and region. Results 7,253 patients met criteria, of which majority were women (n = 4,615) and white (n = 5,394). The majority of patients had private insurance (n = 3,015) followed by Medicare (n = 2,737). Median time to operation was 0 days. Univariate analysis comparing operation <1 day to ≥1 day identified significant differences in race, day of admission, insurance, length of stay, comorbidity status, hospital location, type, and size. Negative binomial regression showed that weekday admission (IRR 4.42, P < .0001), private insurance (IRR 1.53-2.66, P < .0001), rural location (IRR 1.39-1.76, P < .01), small hospital size (IRR 1.26-1.36, P < .05), white race (IRR 1.30-1.34, P < .01), healthier patients (van Walraven score IRR 1.05, P < .0001), and use of mesh (IRR 0.39-0.56, P < .02) were associated with shorter time until procedure. Conclusion Shorter time from admission to the operating room was associated with several nonclinical factors, which suggest disparities may exist. Further prospective studies are warranted to elucidate these disparities affecting patient care.

scholarly journals Factors Affecting Crash Frequencies: A Negative Binomial Regression Based Analysis of Indus Highway, Pakistan

2019 ◽  
Vol 296 ◽  
pp. 01005
Author(s):  
Rafi Ullah Khan ◽  
Jingbo Yin ◽  
Faluk Shair Mustafa
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Crash Frequency ◽  
Risk Recognition ◽  
Factors Affecting ◽  
Trade Routes ◽  
Vital Importance ◽  
Behavior Understanding ◽  
Binomial Regression ◽  
Spss Software

The increase in vehicular traffic have also increased the highway crash frequency with the passage of time. Improvements in highway safety is of vital importance as it could save vast life and monetary losses. The highway crash frequency analysis of major Pakistani highways is a subject less discovered and many important strategic and trade routes are not studied in this regard. This study is aimed to analyze the crash frequency and the prominent factors that cause these crashes on a 302 km section of Indus highway; one of the most important trade routes of the country. Eight years’ data from 2011 till 2018 was arranged into 19 variables where the crash frequency is set as dependent variable, while the eighteen prominent causation factors as independent variables. The tool used for analysis was negative binomial regression being run in the SPSS software. The results indicate that the driver’s behavior, understanding & risk recognition, negligence and law adherence have a significant effect on the crash frequency. Furthermore, highway crash frequency significantly increases with increase in highway segment lengths, number of lanes and lane widths. Similarly, the highway crash frequency significantly enhances when the light, pavement surface and climate condition gets deteriorated. The results of this study are of vital importance to government, transportation companies and general public in order to recognize the most important accident causing factors and devise the transport policies, rules and behaviors accordingly.

What do we know about career and development? Insights from Career Development International at age 25

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Arup Varma ◽  
Satish Kumar ◽  
Riya Sureka ◽  
Weng Marc Lim
Keyword(s):  
Career Development ◽  
Negative Binomial ◽  
Citation Impact ◽  
Negative Binomial Regression ◽  
Key Factors ◽  
Thematic Structure ◽  
Content Type ◽  
Factors Affecting ◽  
Binomial Regression ◽  
Bibliographic Data

PurposeCareer Development International (CDI) is an established source of scientific research on careers and development. The journal reached its 25-years milestone in 2021. To commemorate the occasion, the article aims to provide a retrospective of the major trends, research constituents, thematic structure and key factors explaining the citation impact of CDI articles between 1996 and 2020.Design/methodology/approachThe article extracts bibliographic data of CDI from Scopus and uses that data in (1) a series of bibliometric analyses to explain the major trends, research constituents and thematic structure and (2) a negative binomial regression analysis to explain the key factors affecting the citation impact of CDI.FindingsThe article finds that CDI has progressed and contributed substantially to the scientific community since its inception 25 years ago. The contributions in CDI are mainly from America and Europe and can be organized around five major clusters, namely career development, work engagement, entrepreneurship career, career outcomes and career mentoring.Research limitations/implicationsThe article provides a rich overview of CDI, but the findings are limited to the accuracy and availability of bibliographic data of CDI from Scopus.Originality/valueThe article extends Akkermans and Kubasch's (2017) 5-years retrospection of major journals on career and development through a 25-years retrospection of CDI, and in doing so, the article provides a longer and more accurate representation of CDI's contributions to the extant literature on career and development.

scholarly journals Application of Discrete Regression Models for Analyzing K-8 Students Nonchronic Absenteeism in the United States

2021 ◽  
Vol 10 (4) ◽  
pp. 127
Author(s):  
Khairul Islam ◽  
Tanweer J. Shapla
Keyword(s):  
United States ◽  
Regression Models ◽  
Negative Binomial ◽  
Descriptive Analysis ◽  
Negative Binomial Regression ◽  
The United States ◽  
Chronic Absenteeism ◽  
Factors Affecting ◽  
National Crisis ◽  
Binomial Regression

Absenteeism is a national crisis in the United States, and must be addressed adequately at the early stages or at its onset, to prevent consequential disaster and burden due to absenteeism. A pervasive and persuasive nonchronic absenteeism results in chronic absenteeism, and causes severe damage to students&rsquo; life, schools and societies. While a good number of articles address various issues relating to chronic absenteeism, no evidence of research exists investigating nonchronic absenteeism. The aim of this article is to investigate factors affecting nonchronic absenteeism in K-8 students in the United States by applying discrete regression models. Initially, we investigate K-8 students nonchronic absenteeism discrepancies due to socio-demographic and parental involvement factors via descriptive analysis and then employ Poisson and negative binomial regression models for exploring significant factors of K-8 nonchronic absenteeism. The findings of this study will be of great use to stakeholders in developing appropriate incentive measures for reducing nonchronic absenteeism early and thereby reducing chronic absenteeism.

scholarly journals Comparison between Efficiency of Poisson Regression Model and Negative Binomial Regression in the Analysis of Factors Affecting Mortality from Cardiovascular Diseases in Yazd Province in 2017

2021 ◽  
Author(s):  
Mohammad Mirjani Arjenan ◽  
Mohsen Askarshahi ◽  
Mahmud Vakili
Keyword(s):  
Cardiovascular Diseases ◽  
Regression Model ◽  
Poisson Regression ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Poisson Regression Model ◽  
Registration System ◽  
Factors Affecting ◽  
Binomial Regression

Introduction: Despite the advances in cardiovascular diseases, death caused by these diseases is still considered as the leading cause of mortality. In this study, some of the effective factors on the deaths caused by cardiovascular diseases were investigated Methods: This cross-sectional analytical study investigated the efficacy of Poisson regression models and negative binomial regression models on factors affecting mortality from cardiovascular diseases. The death data were extracted from the death registration system for Yazd province in 2017.Gender, age, education, occupation, location, and city of death were also extracted for each deceased. The two regression models were then fitted to the data Results:  A total of 5,015 deaths were recorded, of which 1,642 were due to cardiovascular diseases. Cardiovascular disease mortality rates were significant using negative binomial regression in terms of the educational variables, place of residence, type of residence, and age. Death rates caused by cardiovascular diseases were not significant for age and occupational, educational, and residential variables. Conclusion: If the time of death is considered as an offset variable, the regression model of two negative sentences is more effective in showing the factors affecting death due to cardiovascular diseases according to AIC and BIC criteria. In the case that the total number of deaths is considered as the offset variable, the Poisson regression model is more efficient.

scholarly journals Geographically Weighted Negative Binomial Regression (GWNBR) Modeling to Analyze Factors Affecting the Number of New HIV/AIDS Cases on The Island of Java in 2019

2021 ◽  
Vol 2021 (1) ◽  
pp. 858-867
Author(s):  
Rifal Miju ◽  
Achmad Prasetyo
Keyword(s):  
Total Population ◽  
Negative Binomial ◽  
Health Sector ◽  
Negative Binomial Regression ◽  
General Description ◽  
Factors Affecting ◽  
Aids Epidemic ◽  
Binomial Regression ◽  
The Republic ◽  
Hiv Aids

One of the problems in the health sector is the HIV/AIDS epidemic. The country of Indonesia itself is in the 5th most at risk of HIV/AIDS in the Asian continent (Ministry of Health of the Republic of Indonesia, 2013). The island of Java as the area with the highest number of cases with a population of about 56 percent of the total population of Indonesia. The purposes of this study are to find out the general description of the distribution of HIV/AIDS in Java and to identify the influencing factors by considering the effect of regional proximity. The results of the analysis show that the distribution of the number of HIV/AIDS cases is relatively clustered between regions that have high and low number of cases, this indicates a spatial effect between regions. Then the GWNBR shows that there are differences in the significance of the variables in each region and forms 9 groups of regency/municipality based on the significant variables.

Heterogeneity and Number of Export Destinations of Italian Firms: A Hurdle Negative Binomial Regression Approach

2013 ◽  
Vol 13 (03n04) ◽  
pp. 391-416 ◽  
Author(s):  
Maria Rosaria Ferrante ◽  
Marco Novelli
Keyword(s):  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Outcome Variable ◽  
Export Activity ◽  
Multiple Markets ◽  
Binomial Regression ◽  
Regression Approach ◽  
Excess Of Zeros ◽  
Hurdle Regression

This article addresses on an aspect of firms internationalization so far little explored, the choice of the number of export destinations and a proxy of the complexity of the export activity. As the outcome variable is a count with an excess of zeros, we use a hurdle regression model for count data that also allow disentangling the aspect of heterogeneity related to the decision to export from those measuring the number of markets served. Some differences arise by the comparison between the estimates regarding the propensity to export model and those of the model describing the number of export destinations. Regarding the propensity to export, the estimated models support the familiar evidences already presented in literature: exporters are larger, more productive, more innovative and invest more. With reference to the number of export destinations, it seems that not only the larger the number of markets served the more productive, large and willing to invest is the firm but also firms engaged in multiple markets seem to be older, financially stable, and willing to support organizational and managerial innovations.

scholarly journals Socioeconomic and comorbid factors affecting mortality and length of stay in COVID-19

2021 ◽  
Author(s):  
Adam Delora ◽  
Ashlynn Mills ◽  
David Jacobson ◽  
Brendon Cornett ◽  
W. Frank Peacock ◽  
...  
Keyword(s):  
Length Of Stay ◽  
Hospital Mortality ◽  
Negative Binomial ◽  
Private Insurance ◽  
Negative Binomial Regression ◽  
Mortality Data ◽  
Factors Affecting ◽  
Black Patients ◽  
Testing Patient ◽  
Insurance Rates

Background The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic exposed and exacerbated health disparities between socioeconomic groups. Our purpose was to determine which disparities are most prevalent and their impact on length of stay (LoS) and in hospital mortality in patients diagnosed with Covid-19. Methods De-Identified data for patients who tested positive for COVID-19 was abstracted from the HCA enterprise database. Data was binned into summary tables. A negative binomial regression with LoS as the dependent variable and a logistic regression of in-hospital mortality data, using age, insurance status, sex, comorbidities as the dependent variables, were performed. Results From March 1, 2020 to August 23, 2020, of 111,849 covid testing patient records, excluding those with missing data (n=7), without confirmed COVID-19 (n=27,225), and those from a carceral environment (n=1,861), left 84,624 eligible patients. Compared to the US population, the covid cohort had more black patients (23.17% vs 13.4%). Compared to the white cohort, the black cohort had higher private insurance rates (28.52% vs. 23.68%), shorter LoS (IRR=0.97 CI=0.95-0.99, P<0.01) and lower adjusted mortality (OR 0.81, 95% CI 0.75-0.97). Increasing age was associated with increased mortality and LoS. Patients with Medicare or Medicaid had longer LoS (IRR=1.07, 95% CI=1.04-1.09) and higher adjusted mortality rates (OR=1.11, 95% CI=1-1.23) than those with private insurance Conclusion Conclusions We found that when blacks have higher rates of private insurance, they have shorter hospitalizations and lower mortality than whites, when diagnosed with Covid-19. Some other psychiatric and medical conditions also significantly impacted outcomes in patients with Covid-19.

scholarly journals Exploring the Built Environment Factors in the Metro That Influence the Ridership and the Market Share of the Elderly and Students

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jiang Ning ◽  
Tao Lyu ◽  
Yuanqing Wang
Keyword(s):  
Built Environment ◽  
Market Share ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
The Elderly ◽  
Negative Binomial Regression Model ◽  
Factors Affecting ◽  
Smart Card Data ◽  
Binomial Regression ◽  
Fractional Response

The metro has developed rapidly in the past two decades and has become one of the crucial patterns of transportation for urban residents in China. Many studies have explored the factors affecting metro ridership, but few have focused on the metro usage of specific groups, such as the elderly and students. This paper uses the negative binomial regression model to explore the relationship between the built environment and the metro ridership of three types of people (adults, the elderly, and students) by using the metro smart card data of Qingdao. We also used the fractional response model to discuss the factors that influence the ridership share for the elderly and students. The results show that most variables promote the metro usage of the three groups of people but have a significantly different effect on the market share of those groups. Specifically, the number of schools, hospitals, supermarkets, squares, parks, and scenic spots near metro stations significantly increases the proportion of the elderly metro usage. The number of bus stops and schools substantially increases the share of metro ridership by students. The research results can provide valuable insights for promoting the metro’s overall ridership and minimizing the gap in allocating public transport resources among different groups.

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