کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8854992 | 1619010 | 2018 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A multicity study of air pollution and cardiorespiratory emergency department visits: Comparing approaches for combining estimates across cities
ترجمه فارسی عنوان
مطالعات متعدد درباره آلودگی هوا و بخش اورژانس قلب و عروق: مقایسه رویکردهای جمع آوری برآوردها در شهرها
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کلمات کلیدی
Cardiac dysrhythmiaDYSMCMURIChFIHDAir pollution - آلودگی هواemergency department - بخش اورژانسischemic heart disease - بیماری قلبی ایسکمیکCardiovascular diseases - بیماری قلبی-عروقی Chronic obstructive pulmonary disease - بیماری مزمن انسدادی ریهCOPD - بیماری مزمن انسدادی ریهRespiratory diseases - بیماری های تنفسیdegrees of freedom - درجه آزادیCVD - رسوب دهی شیمیایی بخار Upper respiratory infection - عفونت تنفسی فوقانیBayesian hierarchical models - مدل سلسله مراتبی بیزیTime-series models - مدل های سری زمانیcongestive heart failure - نارسایی احتقانی قلب
موضوعات مرتبط
علوم زیستی و بیوفناوری
علوم محیط زیست
شیمی زیست محیطی
چکیده انگلیسی
Determining how associations between ambient air pollution and health vary by specific outcome is important for developing public health interventions. We estimated associations between twelve ambient air pollutants of both primary (e.g. nitrogen oxides) and secondary (e.g. ozone and sulfate) origin and cardiorespiratory emergency department (ED) visits for 8 specific outcomes in five U.S. cities including Atlanta, GA; Birmingham, AL; Dallas, TX; Pittsburgh, PA; St. Louis, MO. For each city, we fitted overdispersed Poisson time-series models to estimate associations between each pollutant and specific outcome. To estimate multicity and posterior city-specific associations, we developed a Bayesian multicity multi-outcome (MCM) model that pools information across cities using data from all specific outcomes. We fitted single pollutant models as well as models with multipollutant components using a two-stage chemical mixtures approach. Posterior city-specific associations from the MCM models were somewhat attenuated, with smaller standard errors, compared to associations from time-series regression models. We found positive associations of both primary and secondary pollutants with respiratory disease ED visits. There was some indication that primary pollutants, particularly nitrogen oxides, were also associated with cardiovascular disease ED visits. Bayesian models can help to synthesize findings across multiple outcomes and cities by providing posterior city-specific associations building on variation and similarities across the multiple sources of available information.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Environment International - Volume 120, November 2018, Pages 312-320
Journal: Environment International - Volume 120, November 2018, Pages 312-320
نویسندگان
Jenna R. Krall, Howard H. Chang, Lance A. Waller, James A. Mulholland, Andrea Winquist, Evelyn O. Talbott, Judith R. Rager, Paige E. Tolbert, Stefanie Ebelt Sarnat,