RESUMO
This study aims to investigate the relationship between environmental rationality and economic activities that accelerate environmental indicators in Asia. In the ecosphere, the environment is a predator of human consumption. The continent Asia holding more than 60% of the world's population is vulnerable to environmental deployment. The purpose of the study is to find the link between economic growth, globalization, biocapacity, energy intensity, and renewable energy to the ecological footprint (EFP) in Asian countries for the period of 1990-2017. For empirics, the econometric techniques applied are panel OLS, fixed effects, random effects, fully modified-OLS, DOLS, and generalized methods of moments techniques. The results prove that by increasing the share of renewable energy in total energy consumption the environmental quality is improved. The Pedroni cointegration test shows a long-term relationship exists between globalization, economic growth, and the environment. The results of all techniques show the similar effectiveness of all used economic indicators in the model that disturbs the environment with different intensities in each test. The results of FM-OLS show that with a 1% increase in economic growth, globalization, biocapacity, and population density the ecological footprint increases by 0.55%, 0.08%, 0.06%, and 0.03%, respectively. However, renewable energy improves the environment by 0.04%. The Granger causality analysis revealed a bidirectional causality between ecological footprint and globalization and between EFP and energy intensity. This study recommends that Asian countries should make sustainable environmental policies to protect the environment from further damage.
Assuntos
Dióxido de Carbono , Desenvolvimento Econômico , Humanos , Dióxido de Carbono/análise , Ásia , Internacionalidade , Energia RenovávelRESUMO
BACKGROUND AND PURPOSE: With the "weekend effect" being well described, the Brain Attack Coalition released a set of "best practice" guidelines in 2005, with the goal to uniformly provide standard of care to patients with stroke. We attempted to define a "weekend effect" in outcomes among patients with intracranial hemorrhage (ICH) over the last decade, utilizing the Nationwide Inpatient Sample (NIS) data. We also attempted to analyze the trend of such an effect. MATERIALS AND METHODS: We determined the association of ICH weekend admissions with hospital outcomes including mortality, adverse discharge, length of stay, and cost compared to weekday admissions using multivariable logistic regression. We extracted our study cohort from the NIS, the largest all-payer data set in the United States. RESULTS: Of 485 329 ICH admissions from 2002 to 2011, 27.5% were weekend admissions. Overall, weekend admissions were associated with 11% higher odds of in-hospital mortality. When analyzed in 3-year groups, excess mortality of weekend admissions showed temporal decline. There was higher mortality with weekend admissions in nonteaching hospitals persisted (odds ratios 1.16, 1.13, and 1.09, respectively, for 3-year subgroups). Patients admitted during weekends were also 9% more likely to have an adverse discharge (odds ratio 1.09; 95% confidence interval: 1.07-1.11; P < .001) with no variation by hospital status. There was no effect of a weekend admission on either length of stay or cost of care. CONCLUSION: Nontraumatic ICH admissions on weekends have higher in-hospital mortality and adverse discharge. This demonstrates need for in-depth review for elucidating this discrepancy and stricter adherence to standard-of-care guidelines to ensure uniform care.
RESUMO
BACKGROUND: Heart failure and atrial fibrillation (AFib) are the twin epidemics of modern cardiovascular disease. The incidence of new-onset AFib in acute decompensated heart failure (ADHF) patients is difficult to predict and the short- and long-term outcomes of AFib in a cohort of patients admitted with ADHF are unknown. METHODS AND RESULTS: A total of 904 patients admitted with ADHF were studied. Incidence of AFib on admission was recorded and a multivariate analysis was performed using echocardiographic parameters to specify the predictors of AFib incidence in this cohort. In 904 ADHF patients (57% male, mean age 69 ± 14 years), 81% had history of hypertension, 40% were diabetics, and 51% were smokers. A total of 63% of the patients had known heart failure (HF) with mean ejection fraction of 34% ± 21%, and 33% of the patients had ischemic cardiomyopathy as the etiology of HF. Echocardiographic parameters were: left atrial (LA) diameter 4.5 ± 0.8 cm, left ventricular end-systolic 4.1 ± 1.3 cm, left ventricular end-diastolic 5.3 ± 1.1 cm. Right ventricular dysfunction (RVD) was present in 34% of the patients. A total of 191 (21%) patients subsequently developed AFib with two thirds of the cases occurring in patients with RVD. Using a univariate analysis, older age (OR 1.02; P < .0001), history of HF (OR 2.93; P < .0001), LA dilation (OR 1.58; P < .0001), the presence of left ventricular hypertrophy (OR 3.01, P < .0001), and RVD (OR 4.93; P < .00001) were the strongest predictors for AFib. Controlling for LA size and left ventricular hypertrophy using a forward stepwise regression, RVD remained the strongest predictor (OR 4.45; P < .0001). Patients with RVD had more events (cardiac readmission and mortality) than those with normal RV (56% versus 38%; P < .00001), notably; all-cause mortality was 4.7%/year in the abnormal RV group versus 2.9%/year in the normal RV group; P < .05. RV function analyses by echocardiography further risk stratified these patients based on their rhythm categorizing those patients with abnormal RV and AFib as the ones with the worse prognosis. CONCLUSION: RV dysfunction is a strong predictor for developing AFib in acutely decompensated systolic failure patients. Patients with AFib and RVD have the worse outcome specially when is combined with LV dysfunction, therefore; evaluation of RV function may substantiate the difference in HF prognosis.
Assuntos
Fibrilação Atrial , Insuficiência Cardíaca , Avaliação de Processos e Resultados em Cuidados de Saúde , Disfunção Ventricular Direita , Doença Aguda , Idoso , Idoso de 80 Anos ou mais , Fibrilação Atrial/etiologia , Estudos de Coortes , Complicações do Diabetes/fisiopatologia , Feminino , Sistemas Pré-Pagos de Saúde , Insuficiência Cardíaca/complicações , Insuficiência Cardíaca/diagnóstico por imagem , Insuficiência Cardíaca/mortalidade , Insuficiência Cardíaca/fisiopatologia , Humanos , Hipertrofia Ventricular Esquerda/complicações , Hipertrofia Ventricular Esquerda/diagnóstico por imagem , Hipertrofia Ventricular Esquerda/fisiopatologia , Masculino , Pessoa de Meia-Idade , Admissão do Paciente , Fatores Desencadeantes , Prognóstico , Volume Sistólico , Ultrassonografia , Disfunção Ventricular Direita/complicações , Disfunção Ventricular Direita/diagnóstico por imagem , Disfunção Ventricular Direita/fisiopatologiaRESUMO
BACKGROUND: The degree to which interindividual variation in the mass of select high metabolic rate organs (HMROs) mediates variability in resting energy expenditure (REE) is unknown. OBJECTIVE: The objective was to investigate how much REE variability is explained by differences in HMRO mass in adults and whether age, sex, and race independently predict REE after adjustment for HMRO. DESIGN: A cross-sectional evaluation of 55 women [30 African Americans aged 48.7 +/- 22.2 y (mean +/- SD) and 25 whites aged 46.4 +/- 17.7 y] and 32 men (8 African Americans aged 34.3 +/- 18.2 y and 24 whites aged 51.3 +/- 20.6 y) was conducted. Liver, kidney, spleen, heart, and brain masses were measured by magnetic resonance imaging, and fat and fat-free mass (FFM) were measured by dual-energy X-ray absorptiometry. REE was measured by indirect calorimetry. RESULTS: REE estimated from age (P = 0.001), race (P = 0.006), sex (P = 0.31), fat (P = 0.001), and FFM (P < 0.001) accounted for 70% (adjusted (2)) of the variability in REE. The addition of trunk HMRO (P = 0.001) and brain (P = 0.006) to the model increased the explained variance to 75% and rendered the contributions of age, sex, and race statistically nonsignificant, whereas fat and FFM continued to make significant contributions (both P < 0.05). The addition of brain to the model rendered the intercept (69 kcal . kg(-1) . d(-1)) consistent with zero, which indicated zero REE for zero body mass. CONCLUSIONS: Relatively small interindividual variation in HMRO mass significantly affects REE and reduces the role of age, race, and sex in explaining REE. Decreases in REE with increasing age may be partly related to age-associated changes in the relative size of FFM components.