scholarly journals Examining the Relationship between Household Vehicle Ownership and Ridesharing Behaviors in the United States

2018 ◽  
Vol 10 (8) ◽  
pp. 2720 ◽  
Author(s):  
Yuanyuan Zhang ◽  
Yuming Zhang
Keyword(s):  
United States ◽  
Population Density ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Cost Effective ◽  
The United States ◽  
Potential Effect ◽  
Transportation Systems ◽  
Transport Systems ◽  
Vehicle Ownership

To improve the sustainability and efficiency of transport systems, communities and government agencies throughout the United States (US) are looking for ways to reduce vehicle ownership and single-occupant trips by encouraging people to shift from driving to using more sustainable transport modes (such as ridesharing). Ridesharing is a cost-effective, sustainable and effective alternative transportation mode that is beneficial to the environment, the economy and society. Despite the potential effect of vehicle ownership on the adoption of ridesharing services, individuals’ ridesharing behaviors and the interdependencies between vehicle ownership and ridesharing usage are not well understood. This study aims to fill the gap by examining the associations between household vehicle ownership and the frequency and probability of ridesharing usage, and to estimate the effects of household vehicle ownership on individuals’ ridesharing usage in the US. We conducted zero-inflated negative binomial regression models using data from the 2017 National Household Travel Survey. The results show that, in general, one-vehicle reduction in households was significantly associated with a 7.9% increase in the frequency of ridesharing usage and a 23.0% increase in the probability of ridesharing usage. The effects of household vehicle ownership on the frequency of ridesharing usage are greater for those who live in areas with a higher population density than those living in areas with a lower population density. Young people, men, those who are unable to drive, individuals with high household income levels, and those who live in areas with rail service or a higher population density, tend to use ridesharing more frequently and are more likely to use it. These findings can be used as guides for planners or practitioners to better understand individuals’ ridesharing behaviors, and to identify policies and interventions to increase the potential of ridesharing usage, and to decrease household vehicle ownership, depending on different contextual features and demographic variables. Comprehensive strategies that limit vehicle ownership and address the increasing demand for ridesharing have the potential to improve the sustainability of transportation systems.

scholarly journals Exploring the Relationship between Ridesharing and Public Transit Use in the United States

2018 ◽  
Vol 15 (8) ◽  
pp. 1763 ◽  
Author(s):  
Yuanyuan Zhang ◽  
Yuming Zhang
Keyword(s):  
United States ◽  
Negative Binomial ◽  
Public Transit ◽  
Negative Binomial Regression ◽  
Cost Effective ◽  
The United States ◽  
Sustainable Mobility ◽  
Environmentally Sustainable ◽  
Individual Level ◽  
Transit Use

Car travel accounts for the largest share of transportation-related greenhouse gas emissions in the United States (U.S.), leading to serious air pollution and negative health effects; approximately 76.3% of car trips are single-occupant. To reduce the negative externalities of cars, ridesharing and public transit are advocated as cost-effective and more environmentally sustainable alternatives. A better understanding of individuals’ uses of these two transport modes and their relationship is important for transport operators and policymakers; however, it is not well understood how ridesharing use is associated with public transit use. The objective of this study is to examine the relationships between the frequency and probability of ridesharing use and the frequency of public transit use in the U.S. Zero-inflated negative binomial regression models were employed to investigate the associations between these two modes, utilizing individual-level travel frequency data from the 2017 National Household Travel Survey. The survey data report the number of times the respondent had used ridesharing and public transit in the past 30 days. The results show that, generally, a one-unit increase in public transit use is significantly positively related to a 1.2% increase in the monthly frequency of ridesharing use and a 5.7% increase in the probability of ridesharing use. Additionally, the positive relationship between ridesharing and public transit use was more pronounced for people who live in areas with a high population density or in households with fewer vehicles. These findings highlight the potential for integrating public transit and ridesharing systems to provide easier multimodal transportation, promote the use of both modes, and enhance sustainable mobility, which are beneficial for the environment and public health.

scholarly journals Effects of Government Mandated Social Distancing Measures on Cumulative Incidence of COVID-19 in the United States and its Most Populated Cities

2020 ◽  
Author(s):  
Bhuma Krishnamachari ◽  
Alexander Morris ◽  
Diane Zastrow ◽  
Andrew Dsida ◽  
Brian Harper ◽  
...  
Keyword(s):  
United States ◽  
Population Density ◽  
Cumulative Incidence ◽  
Negative Binomial ◽  
State Level ◽  
The United States ◽  
Reverse Causality ◽  
Social Distancing ◽  
School Closing ◽  
Adjusted Rate

AbstractCOVID-19, caused by the SARS-CoV-2 virus, has quickly spread throughout the world, necessitating assessment of the most effective containment methods. Very little research exists on the effects of social distancing measures on this pandemic. The purpose of this study was to examine the effects of government implemented social distancing measures on the cumulative incidence rates of COVID-19 in the United States on a state level, and in the 25 most populated cities, while adjusting for socio-demographic risk factors. The social distancing variables assessed in this study were: days to closing of non-essential business; days to stay home orders; days to restrictions on gathering, days to restaurant closings and days to school closing. Using negative binomial regression, adjusted rate ratios and 95% confidence intervals were calculated comparing two levels of a binary variable: “above median value,” and “median value and below” for days to implementing a social distancing measure. For city level data, the effects of these social distancing variables were also assessed in high (above median value) vs low (median value and below) population density cities. For the state level analysis, days to school closing was associated with cumulative incidence, with an adjusted rate ratio of 1.59 (95% CI:1.03,2.44), p=0.04 at 35 days. Some results were counterintuitive, including inverse associations between cumulative incidence and days to closure of non-essential business and restrictions on gatherings. This finding is likely due to reverse causality, where locations with slower growth rates initially chose not to implement measures, and later implemented measures when they absolutely needed to respond to increasing rates of infection. Effects of social distancing measures seemed to vary by population density in cities. Our results suggest that the effect of social distancing measures may differ between states and cities and between locations with different population densities. States and cities need individual approaches to containment of an epidemic, with an awareness of their own structure in terms of crowding and socio-economic variables. In an effort to reduce infection rates, cities may want to implement social distancing in advance of state mandates.

scholarly journals Statewide Interventions and Covid-19 Mortality in the United States: An Observational Study

2020 ◽  
Author(s):  
Nadir Yehya ◽  
Atheendar Venkataramani ◽  
Michael O Harhay
Keyword(s):  
United States ◽  
Negative Binomial ◽  
State Level ◽  
Negative Binomial Regression ◽  
The United States ◽  
School Closure ◽  
Sensitivity Analyses ◽  
Test Model ◽  
School Closures ◽  
Social Distancing

ABSTRACT Background Social distancing is encouraged to mitigate viral spreading during outbreaks. However, the association between distancing and patient-centered outcomes in Covid-19 has not been demonstrated. In the United States social distancing orders are implemented at the state level with variable timing of onset. Emergency declarations and school closures were two early statewide interventions. Methods To determine whether later distancing interventions were associated with higher mortality, we performed a state-level analysis in 55,146 Covid-19 non-survivors. We tested the association between timing of emergency declarations and school closures with 28-day mortality using multivariable negative binomial regression. Day 1 for each state was set to when they recorded ≥ 10 deaths. We performed sensitivity analyses to test model assumptions. Results At time of analysis, 37 of 50 states had ≥ 10 deaths and 28 follow-up days. Both later emergency declaration (adjusted mortality rate ratio [aMRR] 1.05 per day delay, 95% CI 1.00 to 1.09, p=0.040) and later school closure (aMRR 1.05, 95% CI 1.01 to 1.09, p=0.008) were associated with more deaths. When assessing all 50 states and setting day 1 to the day a state recorded its first death, delays in declaring an emergency (aMRR 1.05, 95% CI 1.01 to 1.09, p=0.020) or closing schools (aMRR 1.06, 95% CI 1.03 to 1.09, p<0.001) were associated with more deaths. Results were unchanged when excluding New York and New Jersey. Conclusions Later statewide emergency declarations and school closure were associated with higher Covid-19 mortality. Each day of delay increased mortality risk 5 to 6%.

Evolving epidemiology of reported cryptosporidiosis cases in the United States, 1995–2012

2015 ◽  
Vol 144 (8) ◽  
pp. 1792-1802 ◽  
Author(s):  
J. E. PAINTER ◽  
J. W. GARGANO ◽  
J. S. YODER ◽  
S. A. COLLIER ◽  
M. C. HLAVSA
Keyword(s):  
United States ◽  
Risk Factors ◽  
Race And Ethnicity ◽  
Negative Binomial ◽  
Disease Outbreaks ◽  
Negative Binomial Regression ◽  
The United States ◽  
Case Status ◽  
Estimate Rate ◽  
Rate Ratios

SUMMARYCryptosporidiumis the leading aetiology of waterborne disease outbreaks in the United States. This report briefly describes the temporal and geographical distribution of US cryptosporidiosis cases and presents analyses of cryptosporidiosis case data reported in the United States for 1995–2012. The Cochran–Armitage test was used to assess changes in the proportions of cases by case status (confirmedvs.non-confirmed), sex, race, and ethnicity over the study period. Negative binomial regression models were used to estimate rate ratios (RR) and 95% confidence intervals (CI) for comparing rates across three time periods (1995–2004, 2005–2008, 2009–2012). The proportion of confirmed cases significantly decreased (P< 0·0001), and a crossover from male to female predominance in case-patients occurred (P< 0·0001). Overall, compared to 1995–2004, rates were higher in 2005–2008 (RR 2·92, 95% CI 2·08–4·09) and 2009–2012 (RR 2·66, 95% CI 1·90–3·73). However, rate changes from 2005–2008 to 2009–2012 varied by age group (Pinteraction< 0·0001): 0–14 years (RR 0·55, 95% CI 0·42–0·71), 15–44 years (RR 0·99, 95% CI 0·82–1·19), 45–64 years (RR 1·47, 95% CI 1·21–1·79) and ⩾65 years (RR 2·18, 95% CI 1·46–3·25). The evolving epidemiology of cryptosporidiosis necessitates further identification of risk factors in population subgroups. Adding systematic molecular typing ofCryptosporidiumspecimens to US national cryptosporidiosis surveillance would help further identify risk factors and markedly expand understanding of cryptosporidiosis epidemiology in the United States.

Risk Analysis of Freight Train Collisions in the United States, 2000 to 2014

2016 ◽  
Author(s):  
Yanlei Wang ◽  
Shuang Xu ◽  
Xiang Liu
Keyword(s):  
United States ◽  
Risk Analysis ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
The United States ◽  
Rolling Stock ◽  
Environmental Damage ◽  
Collision Risk ◽  
Freight Train ◽  
Damage Costs

Train accidents damage infrastructure and rolling stock, disrupt operations, and may result in casualties and environmental damage. While the majority of previous studies focused on the safety risks associated with train derailments or highway-rail grade crossing collisions, much less work has been undertaken to evaluate train collision risk. This paper develops a statistical risk analysis methodology for freight-train collisions in the United States between 2000 and 2014. Negative binomial regression models are developed to estimate the frequency of freight-train collisions as a function of year and traffic volume by accident cause. Train collision severity, measured by the average number of railcars derailed, varied with accident cause. Train collision risk, defined as the product of collision frequency and severity, is predicted for 2015 to 2017, based on the 2000 to 2014 safety trend. The statistical procedures developed in this paper can be adapted to various other types of consequences, such as damage costs or casualties. Ultimately, this paper and its sequent studies aim to provide the railroad industry with data analytic tools to discover useful information from historical accidents so as to make risk-informed safety decisions.

Psychological distress among Bhutanese refugees living in the northeast region of the United States

2021 ◽  
Author(s):  
Ramraj Gautam ◽  
Jason Rydberg ◽  
Ivy Ho ◽  
Bhola Siwakoti ◽  
William Chadbourne ◽  
...  
Keyword(s):  
Mental Health ◽  
United States ◽  
Psychological Distress ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
The United States ◽  
Future Research ◽  
Related Factors ◽  
Bhutanese Refugees ◽  
The Impact

Abstract The purpose of this study was to examine predictors of psychological distress among adult Bhutanese refugees living in the United States. We recruited 376 adult Bhutanese refugees living in the northeast US region, the majority of whom were employed, married men in their 40 s who were US citizens. Using Bayesian Negative Binomial Regression modelling, we examined the impact of sociodemographic measures, health status and refugee integration measures on psychological distress outcomes. The most common predictors of depression, stress and anxiety were social connection deficits and self-reported health conditions. Other associated factors included: health access deficits, number of years living in the US, paid employment, citizenship and living in more than one US city. The findings of our study revealed that mental health outcomes in this subset of the population of Bhutanese refugees are impacted by a variety of social and health related factors. Public policy makers and practitioners need to recognize the complex issues affecting mental health of Bhutanese refugees. Advocacy for expanded culturally congruent policies and evidence based mental health services are recommended. Future research needs to examine culturally relevant concepts and measures related to mental health and integration in this population.

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 Predictors of User Engagement With Facebook Posts Generated by a National Sample of Lesbian, Gay, Bisexual, Transgender, and Queer Community Centers in the United States: Content Analysis

10.2196/16382 ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. e16382
Author(s):  
William C Goedel ◽  
Harry Jin ◽  
Cassandra Sutten Coats ◽  
Adedotun Ogunbajo ◽  
Arjee J Restar
Keyword(s):  
United States ◽  
Social Services ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Application Programming Interface ◽  
The United States ◽  
National Sample ◽  
User Engagement ◽  
Community Centers ◽  
Factors Associated

Background Lesbian, gay, bisexual, transgender, and queer (LGBTQ) community centers remain important venues for reaching and providing crucial health and social services to LGBTQ individuals in the United States. These organizations commonly use Facebook to reach their target audiences, but little is known about factors associated with user engagement with their social media presence. Objective This study aimed to identify factors associated with engagement with Facebook content generated by LGBTQ community centers in the United States. Methods Content generated by LGBTQ community centers in 2017 was downloaded using Facebook’s application programming interface. Posts were classified by their content and sentiment. Correlates of user engagement were identified using negative binomial regression. Results A total of 32,014 posts from 175 community centers were collected. Posts with photos (incidence rate ratio, [IRR] 1.07; 95% CI 1.06-1.09) and videos (IRR 1.54; 95% CI 1.52-1.56) that contained a direct invitation for engagement (IRR 1.03; 95% CI 1.02-1.04), that expressed a positive sentiment (IRR 1.11; 95% CI 1.10-1.12), and that contained content related to stigma (IRR 1.16; 95% CI 1.14-1.17), mental health (IRR 1.33; 95% CI 1.31-1.35), and politics (IRR 1.28; 95% CI 1.27-1.29) received higher levels of engagement. Conclusions The results of this study provide support for the use of Facebook to extend the reach of LGBTQ community centers and highlight multiple factors that can be leveraged to optimize engagement.

scholarly journals County-level exposures to greenness and associations with COVID-19 incidence and mortality in the United States

2020 ◽  
Author(s):  
Jochem O Klompmaker ◽  
Jaime E Hart ◽  
Isabel Holland ◽  
M Benjamin Sabath ◽  
Xiao Wu ◽  
...  
Keyword(s):  
United States ◽  
Population Density ◽  
Vegetation Index ◽  
Negative Binomial ◽  
The United States ◽  
Systems Science ◽  
County Level ◽  
Incidence And Mortality ◽  
The Us ◽  
At Home

AbstractBackgroundCOVID-19 is an infectious disease that has killed more than 246,000 people in the US. During a time of social distancing measures and increasing social isolation, green spaces may be a crucial factor to maintain a physically and socially active lifestyle while not increasing risk of infection.ObjectivesWe evaluated whether greenness is related to COVID-19 incidence and mortality in the United States.MethodsWe downloaded data on COVID-19 cases and deaths for each US county up through June 7, 2020, from Johns Hopkins University, Center for Systems Science and Engineering Coronavirus Resource Center. We used April-May 2020 Normalized Difference Vegetation Index (NDVI) data, to represent the greenness exposure during the initial COVID-19 outbreak in the US. We fitted negative binomial mixed models to evaluate associations of NDVI with COVID-19 incidence and mortality, adjusting for potential confounders such as county-level demographics, epidemic stage, and other environmental factors. We evaluated whether the associations were modified by population density, proportion of Black residents, median home value, and issuance of stay-at-home order.ResultsAn increase of 0.1 in NDVI was associated with a 6% (95% Confidence Interval: 3%, 10%) decrease in COVID-19 incidence rate after adjustment for potential confounders. Associations with COVID-19 incidence were stronger in counties with high population density and in counties with stay-at-home orders. Greenness was not associated with COVID-19 mortality in all counties; however, it was protective in counties with higher population density.DiscussionExposures to NDVI had beneficial impacts on county-level incidence of COVID-19 in the US and may have reduced county-level COVID-19 mortality rates, especially in densely populated counties.

scholarly journals The populist hotbed: How political attitudes, resentment, and justice beliefs predict both exposure to and avoidance of specific populist news features in the United States

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258220
Author(s):  
Sebastian Scherr ◽  
Dominik Leiner
Keyword(s):  
United States ◽  
Negative Binomial ◽  
Political Attitudes ◽  
Negative Binomial Regression ◽  
The United States ◽  
Selective Exposure ◽  
Self Report ◽  
Political Polarization ◽  
Political Environment ◽  
Self Report Data

A politics of resentment has shaped a low-dialogue political environment in the United States, feeding into populism, and characterized by perceived distributive injustice, detachment between politicians and “the people”, and political polarization. In this political environment, independent of editorial lines, news can spread based on populist content features and drive the political divide even further. However, we still do not understand well, how the forces of political disconnect as well as potentially unifying elements such as political knowledge and the willingness to connect with the other (political) side predict audience interest in populist news featuring people-centrism, anti-elitism, restoring popular sovereignty, and the exclusion of others. To better understand what drives (dis-)interest in populist news features, we combined self-report data from a non-student US sample (N = 440) on political attitudes with unobtrusively measured data on their selective exposure to populist news. We analyzed the data using zero-inflated negative binomial regression models, in which we simultaneously modeled selective exposure to and avoidance of populist news. The findings indicate that especially the will to connect with others explained exposure to news about anti-elitism, especially among Democrats, while Republicans’ news avoidance seems to be specifically geared toward people-centrism. Populist communication features promoting “us” vs. “them” dichotomies seem to not automatically resonate with the views of resentful voters and their motivated reasoning.

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