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.

scholarly journals Association between fatality rate of COVID-19 and selenium deficiency in China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hai-Yang Zhang ◽  
An-Ran Zhang ◽  
Qing-Bin Lu ◽  
Xiao-Ai Zhang ◽  
Zhi-Jie Zhang ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Viral Infections ◽  
Case Fatality ◽  
Selenium Deficiency ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Fatality Rate ◽  
Selenium Content ◽  
Individual Level ◽  
Binomial Regression

Abstract Background COVID-19 has impacted populations around the world, with the fatality rate varying dramatically across countries. Selenium, as one of the important micronutrients implicated in viral infections, was suggested to play roles. Methods An ecological study was performed to assess the association between the COVID-19 related fatality and the selenium content both from crops and topsoil, in China. Results Totally, 14,045 COVID-19 cases were reported from 147 cities during 8 December 2019–13 December 2020 were included. Based on selenium content in crops, the case fatality rates (CFRs) gradually increased from 1.17% in non-selenium-deficient areas, to 1.28% in moderate-selenium-deficient areas, and further to 3.16% in severe-selenium-deficient areas (P = 0.002). Based on selenium content in topsoil, the CFRs gradually increased from 0.76% in non-selenium-deficient areas, to 1.70% in moderate-selenium-deficient areas, and further to 1.85% in severe-selenium-deficient areas (P < 0.001). The zero-inflated negative binomial regression model showed a significantly higher fatality risk in cities with severe-selenium-deficient selenium content in crops than non-selenium-deficient cities, with incidence rate ratio (IRR) of 3.88 (95% CIs: 1.21–12.52), which was further confirmed by regression fitting the association between CFR of COVID-19 and selenium content in topsoil, with the IRR of 2.38 (95% CIs: 1.14–4.98) for moderate-selenium-deficient cities and 3.06 (1.49–6.27) for severe-selenium-deficient cities. Conclusions Regional selenium deficiency might be related to an increased CFR of COVID-19. Future studies are needed to explore the associations between selenium status and disease outcome at individual-level.

scholarly journals Statistical models for analyzing count data: predictors of length of stay among HIV patients in Portugal using a multilevel model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ahmed Nabil Shaaban ◽  
Bárbara Peleteiro ◽  
Maria Rosario O. Martins
Keyword(s):  
Length Of Stay ◽  
Regression Model ◽  
Random Effects ◽  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Comprehensive Approach ◽  
Negative Binomial Regression Model ◽  
Hiv Patients ◽  
Binomial Regression

Abstract Background This study offers a comprehensive approach to precisely analyze the complexly distributed length of stay among HIV admissions in Portugal. Objective To provide an illustration of statistical techniques for analysing count data using longitudinal predictors of length of stay among HIV hospitalizations in Portugal. Method Registered discharges in the Portuguese National Health Service (NHS) facilities Between January 2009 and December 2017, a total of 26,505 classified under Major Diagnostic Category (MDC) created for patients with HIV infection, with HIV/AIDS as a main or secondary cause of admission, were used to predict length of stay among HIV hospitalizations in Portugal. Several strategies were applied to select the best count fit model that includes the Poisson regression model, zero-inflated Poisson, the negative binomial regression model, and zero-inflated negative binomial regression model. A random hospital effects term has been incorporated into the negative binomial model to examine the dependence between observations within the same hospital. A multivariable analysis has been performed to assess the effect of covariates on length of stay. Results The median length of stay in our study was 11 days (interquartile range: 6–22). Statistical comparisons among the count models revealed that the random-effects negative binomial models provided the best fit with observed data. Admissions among males or admissions associated with TB infection, pneumocystis, cytomegalovirus, candidiasis, toxoplasmosis, or mycobacterium disease exhibit a highly significant increase in length of stay. Perfect trends were observed in which a higher number of diagnoses or procedures lead to significantly higher length of stay. The random-effects term included in our model and refers to unexplained factors specific to each hospital revealed obvious differences in quality among the hospitals included in our study. Conclusions This study provides a comprehensive approach to address unique problems associated with the prediction of length of stay among HIV patients in Portugal.

Evaluation of Strategies to Mitigate Culvert-Involved Crashes

2021 ◽  
pp. 036119812199207
Author(s):  
Hitesh Chawla ◽  
Megat-Usamah Megat-Johari ◽  
Peter T. Savolainen ◽  
Christopher M. Day
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Cost Effective ◽  
Negative Binomial Regression Model ◽  
Promising Alternative ◽  
Roadside Safety ◽  
Adverse Conditions ◽  
Traffic Characteristics ◽  
Annual Average Daily Traffic ◽  
Binomial Regression

The objectives of this study were to assess the in-service safety performance of roadside culverts and evaluate the potential impacts of installing various safety treatments to mitigate the severity of culvert-involved crashes. Such crashes were identified using standard fields on police crash report forms, as well as through a review of pertinent keywords from the narrative section of these forms. These crashes were then linked to the nearest cross-drainage culvert, which was associated with the nearest road segment. A negative binomial regression model was then estimated to discern how the risk of culvert-involved crashes varied as a function of annual average daily traffic, speed limit, number of travel lanes, and culvert size and offset. The second stage of the analysis involved the use of the Roadside Safety Analysis Program to estimate the expected crash costs associated with various design contexts. A series of scenarios were evaluated, culminating in guidance as to the most cost-effective treatments for different combinations of roadway geometric and traffic characteristics. The results of this study provide an empirical model that can be used to predict the risk of culvert-involved crashes under various scenarios. The findings also suggest that the installation of safety grates on culvert openings provides a promising alternative for most of the cases where the culvert is located within the clear zone. In general, a guardrail is recommended when adverse conditions are present or when other treatments are not feasible at a specific location.

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 Approaching precision public health by automated syndromic surveillance in communities

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0254479
Author(s):  
Ta-Chien Chan ◽  
Jia-Hong Tang ◽  
Cheng-Yu Hsieh ◽  
Kevin J. Chen ◽  
Tsan-Hua Yu ◽  
...  
Keyword(s):  
Regression Model ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Health Department ◽  
Negative Binomial Regression Model ◽  
Negatively Associated ◽  
Pearson Residuals ◽  
Binomial Regression ◽  
Taipei City ◽  
Icd 10

Background Sentinel physician surveillance in communities has played an important role in detecting early signs of epidemics. The traditional approach is to let the primary care physician voluntarily and actively report diseases to the health department on a weekly basis. However, this is labor-intensive work, and the spatio-temporal resolution of the surveillance data is not precise at all. In this study, we built up a clinic-based enhanced sentinel surveillance system named “Sentinel plus” which was designed for sentinel clinics and community hospitals to monitor 23 kinds of syndromic groups in Taipei City, Taiwan. The definitions of those syndromic groups were based on ICD-10 diagnoses from physicians. Methods Daily ICD-10 counts of two syndromic groups including ILI and EV-like syndromes in Taipei City were extracted from Sentinel plus. A negative binomial regression model was used to couple with lag structure functions to examine the short-term association between ICD counts and meteorological variables. After fitting the negative binomial regression model, residuals were further rescaled to Pearson residuals. We then monitored these daily standardized Pearson residuals for any aberrations from July 2018 to October 2019. Results The results showed that daily average temperature was significantly negatively associated with numbers of ILI syndromes. The ozone and PM2.5 concentrations were significantly positively associated with ILI syndromes. In addition, daily minimum temperature, and the ozone and PM2.5 concentrations were significantly negatively associated with the EV-like syndromes. The aberrational signals detected from clinics for ILI and EV-like syndromes were earlier than the epidemic period based on outpatient surveillance defined by the Taiwan CDC. Conclusions This system not only provides warning signals to the local health department for managing the risks but also reminds medical practitioners to be vigilant toward susceptible patients. The near real-time surveillance can help decision makers evaluate their policy on a timely basis.

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