Identifying Communities at Risk for COVID-19–Related Burden Across 500 US Cities and Within New York City: Unsupervised Learning of the Coprevalence of Health Indicators (Preprint)

2020 ◽  
Author(s):  
Andrew Deonarine ◽  
Genevieve Lyons ◽  
Chirag Lakhani ◽  
Walter De Brouwer
Keyword(s):  
New York ◽  
New York City ◽  
Built Environment ◽  
Social Determinants Of Health ◽  
Satellite Imagery ◽  
Risk Score ◽  
Social Determinants ◽  
York City ◽  
Complex Disease ◽  
Determinants Of Health

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.

scholarly journals Identifying communities at risk for COVID-19-related burden across 500 U.S. Cities and within New York City

2020 ◽  
Author(s):  
Chirag J Patel ◽  
Andrew Deonarine ◽  
Genevieve Lyons ◽  
Chirag M Lakhani ◽  
Arjun K Manrai
Keyword(s):  
New York ◽  
New York City ◽  
Built Environment ◽  
Social Determinants Of Health ◽  
Satellite Imagery ◽  
Risk Score ◽  
Social Determinants ◽  
Census Tract ◽  
York City ◽  
Census Tracts

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.

scholarly journals Covid-19 and Inequity: A comparative spatial analysis of New York City and Chicago hot spots

2020 ◽  
Author(s):  
Andrew Maroko ◽  
Denis Nash ◽  
Brian Pavilonis
Keyword(s):  
New York ◽  
New York City ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
York City ◽  
Hot Spots ◽  
Determinants Of Health ◽  
Cold Spots ◽  
The Impact ◽  
Zip Code

AbstractThere have been numerous reports that the impact of the ongoing COVID-19 epidemic has disproportionately impacted traditionally vulnerable communities, including well-researched social determinants of health, such as racial and ethnic minorities, migrants, and the economically challenged. The goal of this ecological cross-sectional study is to examine the demographic and economic nature of spatial hot and cold spots of SARS-CoV-2 rates in New York City and Chicago as of April 13, 2020.In both cities, cold spots (clusters of low SARS-CoV-2 rate ZIP code tabulation areas) demonstrated typical protective factors associated with the social determinants of health and the ability to social distance. These neighborhoods tended to be wealthier, have higher educational attainment, higher proportions of non-Hispanic white residents, and more workers in managerial occupations. Hot spots (clusters of high SARS-CoV-2 rate ZIP code tabulation areas) also had similarities, such as lower rates of college graduates and higher proportions of people of color. It also appears to be larger households (more people per household), rather than overall population density, that may to be a more strongly associated with hot spots.Findings suggest important differences between the cities’ hot spots as well. They can be generalized by describing the NYC hot spots as working-class and middle-income communities, perhaps indicative of service workers and other occupations (including those classified as “essential services” during the pandemic) that may not require a college degree but pay wages above poverty levels. Chicago’s hot spot neighborhoods, on the other hand, are among the city’s most vulnerable, low-income neighborhoods with extremely high rates of poverty, unemployment, and non-Hispanic Black residents.

Integrating a Social Determinants of Health Screener at an Outpatient Pediatric Clinic in East Harlem, New York City

2021 ◽  
Vol 32 (4) ◽  
pp. 2267-2277
Author(s):  
Rebecca Rinehart ◽  
Lauren Zajac ◽  
Jennifer Acevedo ◽  
Rebecca Kann Victoria Mayer ◽  
Leora Mogilner
Keyword(s):  
New York ◽  
New York City ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
York City ◽  
Determinants Of Health ◽  
East Harlem ◽  
Pediatric Clinic

scholarly journals The Environmental and Social Determinants of Health Matter in a Pandemic: Predictors of COVID-19 Case and Death Rates in New York City

2021 ◽  
Vol 18 (16) ◽  
pp. 8416
Author(s):  
Maria De Jesus ◽  
Shalini S. Ramachandra ◽  
Zoe Jafflin ◽  
Imani Maliti ◽  
Aquilah Daughtery ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
Health Insurance ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
York City ◽  
Determinants Of Health ◽  
Grocery Stores ◽  
Ozone Level ◽  
Death Rates

Our research objective was to determine which environmental and social factors were predictive of coronavirus disease 2019 (COVID-19) case and death rates in New York City (NYC), the original epicenter of the pandemic in the US, and any differential impacts among the boroughs. Data from various sources on the demographic, health, and environmental characteristics for NYC zip codes, neighborhoods, and boroughs were analyzed along with NYC government’s reported case and death rates by zip code. At the time of analysis, the Bronx had the highest COVID-19 case and death rates, while Manhattan had the lowest rates. Significant predictors of a higher COVID-19 case rate were determined to be proportion of residents aged 65 years plus; proportion of residents under 65 years with a disability; proportion of White residents; proportion of residents without health insurance; number of grocery stores; and a higher ozone level. For COVID-19 death rates, predictors include proportion of residents aged 65 years plus; proportion of residents who are not US citizens; proportion on food stamps; proportion of White residents; proportion of residents under 65 years without health insurance; and a higher level of ozone. Results across boroughs were mixed, which highlights the unique demographic, socioeconomic, and community characteristics of each borough. To reduce COVID-19 inequities, it is vital that the NYC government center the environmental and social determinants of health in policies and community-engaged interventions adapted to each borough.

scholarly journals Primary and Secondary Health Impacts of COVID-19 among Minority Individuals in New York State

2021 ◽  
Vol 18 (2) ◽  
pp. 683
Author(s):  
Lauren A. Clay ◽  
Stephanie Rogus
Keyword(s):  
New York ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
New York State ◽  
Determinants Of Health ◽  
Health Impacts ◽  
Minority Populations ◽  
York State ◽  
The Social ◽  
Chi Square Analysis

In addition to the direct health impacts of COVID-19, the pandemic disrupted economic, educational, healthcare, and social systems in the US. This cross-sectional study examined the primary and secondary impacts of the COVID-19 pandemic among low-income and minority groups in New York State using the social determinants of health framework. New Yorkers were recruited to complete a web-based survey through Qualtrics. The survey took place in May and June 2020 and asked respondents about COVID-19 health impacts, risk factors, and concerns. Chi-square analysis examined the health effects experienced by race and ethnicity, and significant results were analyzed in a series of logistic regression models. Results showed disparities in the primary and secondary impacts of COVID-19. The majority of differences were reported between Hispanic and white respondents. The largest differences, in terms of magnitude, were reported between other or multiracial respondents and white respondents. Given the disproportionate burden of COVID-19 on minority populations, improved policies and programs to address impacts on lower-paying essential jobs and service positions could reduce exposure risks and improve safety for minority populations. Future research can identify the long-term health consequences of the pandemic on the social determinants of health among populations most at risk.

scholarly journals Microbial Community Patterns Associated with Automated Teller Machine Keypads in New York City

mSphere ◽  
2016 ◽  
Vol 1 (6) ◽  
Author(s):  
Holly M. Bik ◽  
Julia M. Maritz ◽  
Albert Luong ◽  
Hakdong Shin ◽  
Maria Gloria Dominguez-Bello ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
Built Environment ◽  
York City ◽  
Geographic Area ◽  
Unique Contribution ◽  
Public Health Perspective ◽  
Transit Systems ◽  
Population Demographics ◽  
Eukaryotic Microbes

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.

scholarly journals The urban built environment and overdose mortality in New York City neighborhoods

2005 ◽  
Vol 11 (2) ◽  
pp. 147-156 ◽  
Author(s):  
C. Hembree ◽  
S. Galea ◽  
J. Ahern ◽  
M. Tracy ◽  
T. Markham Piper ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
Built Environment ◽  
York City ◽  
Overdose Mortality

Making the Scene

2017 ◽  
Author(s):  
Thomas H. Greenland
Keyword(s):  
New York ◽  
New York City ◽  
Social Determinants ◽  
York City ◽  
Extended Family ◽  
Occupational Hazards ◽  
The People ◽  
Musical Taste ◽  
Group Identities ◽  
Improvised Music

This chapter examines how intimate social correspondence between active participants in New York City's avant-jazz scene engenders individual and group identities—a sense of who we are, where we go, what we love, and how we live. It first considers how fellowship, and particularly camaraderie, develops among fans during and after jazz performances. It then looks at how jazz fans interface with “club/houses” and the people that run them and goes on to discuss social determinants of musical taste. It also explores one of the occupational hazards associated with jazz fandom in New York City, what Steve Dalachinsky called “divided nights.” The chapter shows that active concertgoers, particularly avant-jazz fans, collectively identify and express themselves through improvised music, and describes gregarious yet self-contained, intimate jazz communities as an example of both an extended family and “a group of separates.”

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