COVID-19 Cases and the Built Environment: Initial Evidence from New York City

2021 ◽  
pp. 1-12
Author(s):  
Calvin P. Tribby ◽  
Chris Hartmann
mSphere ◽  
2016 ◽  
Vol 1 (6) ◽  
Author(s):  
Holly M. Bik ◽  
Julia M. Maritz ◽  
Albert Luong ◽  
Hakdong Shin ◽  
Maria Gloria Dominguez-Bello ◽  
...  

ABSTRACT Automated teller machine (ATM) keypads represent a specific and unexplored microhabitat for microbial communities. Although the number of built environment and urban microbial ecology studies has expanded greatly in recent years, the majority of research to date has focused on mass transit systems, city soils, and plumbing and ventilation systems in buildings. ATM surfaces, potentially retaining microbial signatures of human inhabitants, including both commensal taxa and pathogens, are interesting from both a biodiversity perspective and a public health perspective. By focusing on ATM keypads in different geographic areas of New York City with distinct population demographics, we aimed to characterize the diversity and distribution of both prokaryotic and eukaryotic microbes, thus making a unique contribution to the growing body of work focused on the “urban microbiome.” In New York City, the surface area of urban surfaces in Manhattan far exceeds the geographic area of the island itself. We have only just begun to describe the vast array of microbial taxa that are likely to be present across diverse types of urban habitats. In densely populated urban environments, the distribution of microbes and the drivers of microbial community assemblages are not well understood. In sprawling metropolitan habitats, the “urban microbiome” may represent a mix of human-associated and environmental taxa. Here we carried out a baseline study of automated teller machine (ATM) keypads in New York City (NYC). Our goal was to describe the biodiversity and biogeography of both prokaryotic and eukaryotic microbes in an urban setting while assessing the potential source of microbial assemblages on ATM keypads. Microbial swab samples were collected from three boroughs (Manhattan, Queens, and Brooklyn) during June and July 2014, followed by generation of Illumina MiSeq datasets for bacterial (16S rRNA) and eukaryotic (18S rRNA) marker genes. Downstream analysis was carried out in the QIIME pipeline, in conjunction with neighborhood metadata (ethnicity, population, age groups) from the NYC Open Data portal. Neither the 16S nor 18S rRNA datasets showed any clustering patterns related to geography or neighborhood demographics. Bacterial assemblages on ATM keypads were dominated by taxonomic groups known to be associated with human skin communities (Actinobacteria, Bacteroides, Firmicutes, and Proteobacteria), although SourceTracker analysis was unable to identify the source habitat for the majority of taxa. Eukaryotic assemblages were dominated by fungal taxa as well as by a low-diversity protist community containing both free-living and potentially pathogenic taxa (Toxoplasma, Trichomonas). Our results suggest that ATM keypads amalgamate microbial assemblages from different sources, including the human microbiome, eukaryotic food species, and potentially novel extremophilic taxa adapted to air or surfaces in the built environment. DNA obtained from ATM keypads may thus provide a record of both human behavior and environmental sources of microbes. IMPORTANCE Automated teller machine (ATM) keypads represent a specific and unexplored microhabitat for microbial communities. Although the number of built environment and urban microbial ecology studies has expanded greatly in recent years, the majority of research to date has focused on mass transit systems, city soils, and plumbing and ventilation systems in buildings. ATM surfaces, potentially retaining microbial signatures of human inhabitants, including both commensal taxa and pathogens, are interesting from both a biodiversity perspective and a public health perspective. By focusing on ATM keypads in different geographic areas of New York City with distinct population demographics, we aimed to characterize the diversity and distribution of both prokaryotic and eukaryotic microbes, thus making a unique contribution to the growing body of work focused on the “urban microbiome.” In New York City, the surface area of urban surfaces in Manhattan far exceeds the geographic area of the island itself. We have only just begun to describe the vast array of microbial taxa that are likely to be present across diverse types of urban habitats. Podcast: A podcast concerning this article is available.


2005 ◽  
Vol 11 (2) ◽  
pp. 147-156 ◽  
Author(s):  
C. Hembree ◽  
S. Galea ◽  
J. Ahern ◽  
M. Tracy ◽  
T. Markham Piper ◽  
...  

2016 ◽  
Vol 10 (2) ◽  
pp. 181-197
Author(s):  
MARK SLOBIN

AbstractThe article introduces the topic of film music's relationship to the built environment of cinema. The discussion springboards from James Sanders's analysis of New York City sets, based on the architecture of selected movies (mostly from the 1930s and 40s), as presented in his Celluloid Skyline. The focus is on four locations: the brownstone façade, the working-class street, the enclosed courtyard, and the construction site. The argument is that two key components of American cinema's structure—music and architecture—are sometimes in direct dialogue, as composers and filmmakers both render New York ethnographically accurately and offer sometimes a mythic imagining of a particular place.


2020 ◽  
Author(s):  
Andrew Deonarine ◽  
Genevieve Lyons ◽  
Chirag Lakhani ◽  
Walter De Brouwer

BACKGROUND Although it is well-known that older individuals with certain comorbidities are at the highest risk for complications related to COVID-19 including hospitalization and death, we lack tools to identify communities at the highest risk with fine-grained spatial resolution. Information collected at a county level obscures local risk and complex interactions between clinical comorbidities, the built environment, population factors, and other social determinants of health. OBJECTIVE This study aims to develop a COVID-19 community risk score that summarizes complex disease prevalence together with age and sex, and compares the score to different social determinants of health indicators and built environment measures derived from satellite images using deep learning. METHODS We developed a robust COVID-19 community risk score (COVID-19 risk score) that summarizes the complex disease co-occurrences (using data for 2019) for individual census tracts with unsupervised learning, selected on the basis of their association with risk for COVID-19 complications such as death. We mapped the COVID-19 risk score to corresponding zip codes in New York City and associated the score with COVID-19–related death. We further modeled the variance of the COVID-19 risk score using satellite imagery and social determinants of health. RESULTS Using 2019 chronic disease data, the COVID-19 risk score described 85% of the variation in the co-occurrence of 15 diseases and health behaviors that are risk factors for COVID-19 complications among ~28,000 census tract neighborhoods (median population size of tracts 4091). The COVID-19 risk score was associated with a 40% greater risk for COVID-19–related death across New York City (April and September 2020) for a 1 SD change in the score (risk ratio for 1 SD change in COVID-19 risk score 1.4; <i>P</i>&lt;.001) at the zip code level. Satellite imagery coupled with social determinants of health explain nearly 90% of the variance in the COVID-19 risk score in the United States in census tracts (<i>r</i><sup>2</sup>=0.87). CONCLUSIONS The COVID-19 risk score localizes risk at the census tract level and was able to predict COVID-19–related mortality in New York City. The built environment explained significant variations in the score, suggesting risk models could be enhanced with satellite imagery.


Urban Health ◽  
2019 ◽  
pp. 309-315
Author(s):  
Karen Lee

New York City has been a global leader in healthy urban design and in improving the built environment—the human-made environment consisting of our neighborhoods, streets, buildings, and their amenities—to assist in the prevention and control of the current epidemics of noncommunicable disease and their risk factors. This chapter shows how, through the translation of research-based health evidence into the development and implementation of user-friendly resources with and for non–health professionals involved in the planning, design, construction, maintenance, and renovation of the built environment, such as the Active Design Guidelines and its supplements, NYC pioneered formal efforts toward systematic evidence-based environmental design that can decrease physical inactivity and sedentariness, key risk factors for mortality and morbidity around the world today, while addressing other key public health issues like safety and equity.


JAMA ◽  
2020 ◽  
Vol 324 (4) ◽  
pp. 390 ◽  
Author(s):  
Ukachi N. Emeruwa ◽  
Samsiya Ona ◽  
Jeffrey L. Shaman ◽  
Amy Turitz ◽  
Jason D. Wright ◽  
...  

2020 ◽  
Author(s):  
Chirag J Patel ◽  
Andrew Deonarine ◽  
Genevieve Lyons ◽  
Chirag M Lakhani ◽  
Arjun K Manrai

AbstractBackgroundWhile it is well-known that older individuals with certain comorbidities are at highest risk for complications related to COVID-19 including hospitalization and death, we lack tools to identify communities at highest risk with fine-grained spatial and temporal resolution. Information collected at a county level obscures local risk and complex interactions between clinical comorbidities, the built environment, population factors, and other social determinants of health.MethodsWe develop a robust COVID-19 Community Risk Score (C-19 Risk Score) that summarizes the complex disease co-occurrences for individual census tracts with unsupervised learning, selected on their basis for association with risk for COVID complications, such as death. We mapped the C-19 Risk Score onto neighborhoods in New York City and associated the score with C-19 related death. We further predict the C-19 Risk Score using satellite imagery data to map the built environment in C-19 Risk.ResultsThe C-19 Risk Score describes 85% of variation in co-occurrence of 15 diseases that are risk factors for COVID complications among 26K census tract neighborhoods (median population size of tracts: 4,091). The C-19 Risk Score is associated with a 40% greater risk for COVID-19 related death across NYC (April and September 2020) for a 1SD change in the score (Risk Ratio for 1SD change in C19 Risk Score: 1.4, p < .001). Satellite imagery coupled with social determinants of health explain nearly 90% of the variance in the C-19 Risk Score in the United States in held-out census tracts (R2 of 0.87).ConclusionsThe C-19 Risk Score localizes COVID-19 risk at the census tract level and predicts COVID-19 related morbidity and mortality.


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