Carbon Emissions From Patient Travel for Health Care

JAMA Network Open, Journal Year: 2025, Volume and Issue: 8(3), P. e252513 - e252513

Published: March 31, 2025

Importance The US health care sector accounts for about 8.5% of national greenhouse gas (GHG) emissions. Reliable estimates emissions associated with care–related travel are essential informing policy changes. Objective To generate a comprehensive estimate carbon due to patient in the US. Design, Setting, and Participants This cross-sectional study used data from 2022 National Household Travel Survey (NHTS), conducted January 2023. were selected using an address-based sample Postal Service Delivery Sequence File. Participating households reported all trips taken within 24 hours by household members aged 5 years or older. Approximate per mile obtained typical vehicle provided government institutions. Data analyzed between March 11 May 29, 2024. Main Outcomes Measures Estimated annual CO 2 equivalent (CO e) year, patient, trip, mile. A survey-weighted λ regression analysis was identify factors higher e trip. An alternative scenario estimated reductions if 30% 50% private users switched electric vehicles. Results included 16 997 participants weighted total 3 506 325 536 trips. Of these trips, 52.0% female travelers, 80.1% made urban areas, 19.9% rural areas. These accounted 84 057 963 340 miles, resulting 35.7 megatons (Mt) (95% CI, 27.5-43.9 Mt) e. Each traveled generated 424 g 418-428 g) Emissions trip (exponentiated coefficient [exp(β)], 2.19; 95% 1.51-2.86; P < .001) patients compared patients. However, 69.3% attributable 30.7% Patients median incomes $50 000 $99 999 (exp[β], 1.92; 1.09-2.76; = .003) those $25 less. shift vehicles reduce 27.6 Mt 20.7-34.6 e, lower 22.3 16.0-28.6 Conclusions Relevance that which small but important proportion findings decisions suggest strategies such as telehealth adoption may contribute significant reduction GHG

Language: Английский

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The Impact of the Digital Economy on Carbon Emission Levels and Its Coupling Relationships: Empirical Evidence from China

Sustainability, Journal Year: 2024, Volume and Issue: 16(13), P. 5612 - 5612

Published: June 30, 2024

The development of the digital economy has injected new vitality into global economy, but environmental issues it raises cannot be ignored. This paper analyzes impact on carbon emission levels and their coupling relationships using panel data from 30 provinces, cities, autonomous regions in mainland China 2013 to 2021. By employing coordination degree model PVAR model, study finds that shown an upward trend, while have exhibited a downward trend. between is relatively good, though still its early stages, indicating significant room for development. achieved positive cumulative effect can promote itself, negative levels.

Language: Английский

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The global effects of digestive system cancers worldwide on the COVID-19 pandemic by negative binomial (mixed) regression models

Journal of Radiation Research and Applied Sciences, Journal Year: 2024, Volume and Issue: 17(3), P. 100944 - 100944

Published: May 8, 2024

In this study, the main goal is to determine statistically significant relationships between biggest epidemic problem of last years as COVID-19 pandemic and digestive system cancers belonging 168 countries worldwide using generalized linear mixed model (GLMM) its special case (GLM) approaches, obtain global inferences that will shed light on pandemic. For goal, response variable "total cases per 100,000 people" until January 14, 2022. The explanatory variables are total number people suffering from colon rectum, stomach, lip oral cavity, esophageal, nasopharynx 2019, respectively. negative binomial (NB) regression in GLM iteratively reweighted least squares (IRLS) algorithm NB GLMM with "countries" taken "random effects" Adaptive Gauss-Hermite Quadrature (AGHQ) approximation method at 1, 2, 10, 20 quadrature points used for modelling systems cancer data. by information criteria, under log-link function random effects AGHQ point detected most appropriate

Language: Английский

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Modeling COVID-19 Binary Data in the Aspect of Neoplasms as a Potential Indicator of Cancer by Logit and Probit Regression Models

International Journal of Advanced Natural Sciences and Engineering Researches, Journal Year: 2023, Volume and Issue: 7(4), P. 400 - 407

Published: May 26, 2023

In this study, the effects of disability-adjusted life years (DALYs) from neoplasms and concomitant non-communicable diseases (NCDs) on total deaths COVID-19 pandemic until 21 July 2021 are examined globally for 179 countries. For purpose, explanatory variables taken as DALYs a measure burden in lost lived with disability neoplasm NCDs. number caused by has been made categorical help indicator variable then response variable. Thus, NCDs investigated using binary logit probit regression models family generalized linear (GLMs) statistical methods. Specific to superiority model which is based assumption that errors have normal distribution sense over logistic emphasized. As principle results major conclusion neoplasms, cirrhosis other chronic liver diseases, cardiovascular skin subcutaneous found statistically significant due pandemic.

Language: Английский

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A novel statistical modeling of air pollution and the COVID-19 pandemic mortality data by Poisson, geometric, and negative binomial regression models with fixed and random effects

Open Chemistry, Journal Year: 2023, Volume and Issue: 21(1)

Published: Jan. 1, 2023

Abstract The coronavirus disease 2019 (COVID-19) pandemic was defined by the World Health Organization (WHO) as a global epidemic on March 11, 2020, infectious that threatens public health fatally. In this study, main aim is to model impact of various air pollution causes mortality data due COVID-19 Generalized Linear Mixed Model (GLMM) approach make statistical inferences about 174 WHO member countries subjects in six regions. “Total number deaths these pandemic” until July 27, 2022, taken response variable. explanatory variables are regions, from per 100.000 population “household solid fuels,” “ambient particulate matter pollution,” and ozone pollution.” Poisson, geometric, negative binomial (NB) regression models with “country” fixed random effects, special cases GLMM, fitted variable aspect above-mentioned variables. NB models, Iteratively Reweighted Least Squares parameter estimation method Fisher-Scoring iterative algorithm under log-link function canonical link used. GLMM approach, Laplace approximation also used prediction effects. different effects established for all over world investigating relationships between “total deaths” “air causes.” As result “NB mixed-effects model” most appropriate pandemic.

Language: Английский

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A new trigonometric-inspired probability distribution: A simulation study and applications in reliability and hydrology

Alexandria Engineering Journal, Journal Year: 2024, Volume and Issue: 113, P. 181 - 194

Published: Nov. 19, 2024

Language: Английский

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