scholarly journals Subway Ridership, Crowding, or Population Density: Determinants of COVID-19 Infection Rates in New York City

2021 ◽  
Author(s):  
Shima Hamidi ◽  
Iman Hamidi
Keyword(s):  
New York ◽  
New York City ◽  
Population Density ◽  
York City ◽  
Infection Rates

scholarly journals Urban Park Usage During the COVID-19 Pandemic

2021 ◽  
pp. 2150008
Author(s):  
Bita Alizadehtazi ◽  
Korin Tangtrakul ◽  
Sloane Woerdeman ◽  
Anna Gussenhoven ◽  
Nariman Mostafavi ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
Population Density ◽  
Stress Reduction ◽  
York City ◽  
Social Vulnerability ◽  
Urban Residents ◽  
Wide Range ◽  
Significant Difference ◽  
Early Phases

Urban parks and green spaces provide a wide range of ecosystem services, including social interaction and stress reduction. When COVID-19 closed schools and businesses and restricted social gatherings, parks became one of the few places that urban residents were permitted to visit outside their homes. With a focus on Philadelphia, PA and New York City, NY, this paper presents a snapshot of the park usage during the early phases of the pandemic. Forty-three Civic Scientists were employed by the research team to observe usage in 22 different parks selected to represent low and high social vulnerability, and low, medium, and high population density. Despite speculation that parks could contribute to the spread of COVID-19, no strong correlation was found between the number of confirmed COVID-19 cases in adjacent zip codes and the number of park users. High social vulnerability neighborhoods were associated with a significantly higher number of COVID-19 cases ([Formula: see text]). In addition, no significant difference in the number of park users was detected between parks in high and low vulnerability neighborhoods. The number of park users did significantly increase with population density in both cities ([Formula: see text]), though usage varied greatly by park. Males were more frequently observed than females in parks in both high vulnerability and high-density neighborhoods. Although high vulnerability neighborhoods reported higher COVID-19 cases, residents of Philadelphia and New York City appear to have been undeterred from visiting parks during this phase of the pandemic. This snapshot study provides no evidence to support closing parks during the pandemic. To the contrary, people continued to visit parks throughout the study, underscoring their evident value as respite for urban residents during the early phases of the pandemic.

scholarly journals Job Type, Neighborhood Prevalence, and Risk of COVID-19 Among Healthcare Workers in New York City

2021 ◽  
pp. 1-12
Author(s):  
Fran A. Ganz-Lord ◽  
Kathryn R. Segal
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Healthcare Workers ◽  
Environmental Services ◽  
Infection Rates ◽  
Job Type ◽  
Increased Risk

NARRATIVE ABSTRACT This study compared the risk of COVID-19 between clinical and non-clinical HCWs while adjusting for home zip codes. Clinical HCWs did not have higher risk of COVID-19, but living in higher-risk zip codes was associated with increased infection rates. However, environmental services workers showed increased risk of COVID-19.

scholarly journals Estimating the infection fatality risk of COVID-19 in New York City, March 1–May 16, 2020

2020 ◽  
Author(s):  
Wan Yang ◽  
Sasikiran Kandula ◽  
Mary Huynh ◽  
Sharon K. Greene ◽  
Gretchen Van Wye ◽  
...  
Keyword(s):  
Public Health ◽  
New York ◽  
New York City ◽  
York City ◽  
Age Groups ◽  
Credible Interval ◽  
Infection Rates ◽  
Inference System ◽  
Fatality Risk ◽  
Pandemic Response

AbstractDuring March 1-May 16, 2020, 191,392 laboratory-confirmed COVID-19 cases were diagnosed and reported and 20,141 confirmed and probable COVID-19 deaths occurred among New York City (NYC) residents. We applied a network model-inference system developed to support the City’s pandemic response to estimate underlying SARS-CoV-2 infection rates. Based on these estimates, we further estimated the infection fatality risk (IFR) for 5 age groups (i.e. <25, 25-44, 45-64, 65-74, and 75+ years) and all ages overall, during March 1–May 16, 2020. We estimated an overall IFR of 1.45% (95% Credible Interval: 1.09-1.87%) in NYC. In particular, weekly IFR was estimated as high as 6.1% for 65-74 year-olds and 17.0% for 75+ year-olds. These results are based on more complete ascertainment of COVID-19-related deaths in NYC and thus likely more accurately reflect the true, higher burden of death due to COVID-19 than previously reported elsewhere. It is thus crucial that officials account for and closely monitor the infection rate and population health outcomes and enact prompt public health responses accordingly as the pandemic unfolds.

scholarly journals Variation in Covid-19 Cases Across New York City

2020 ◽  
Author(s):  
Awi Federgruen ◽  
Sherin Naha
Keyword(s):  
New York ◽  
New York City ◽  
Population Density ◽  
Incidence Rate ◽  
York City ◽  
Incidence Rates ◽  
Household Size ◽  
Model Specification ◽  
Basic Reproductive Number ◽  
Zip Code

AbstractThe number of confirmed COVID-19 cases, relative to population size, has varied greatly throughout the United States and even within the same city. In different zip codes in New York City, the epicentre of the epidemic, the number of cases per 100,000 residents has ranged from 437 to 4227, a 1:10 ratio. To guide policy decisions regarding containment and reopening of the economy, schools and other institutions, it is vital to identify the factors that drive this large variation.This paper reports on a statistical study of incidence variation by zip code across New York City. Among many socio-economic and demographic measures considered, the average household size emerges as the single most important explanatory variable: an increase in average household size by one member increases the zip code incidence rate, in our final model specification, by at least 876 cases, 23% of the range of incidence rates, at a 95% confidence level.The percentage of the population above the age of 65, the percentage below the poverty line, and their interaction term are also strongly positively associated with zip code incidence rates, In terms of ethnic/racial characteristics, the percentages of African Americans, Hispanics and Asians within the population, are significantly associated, but the magnitude of the impact is considerably smaller. (The proportion of Asians within a zip code has a negative association.)These significant associations may be explained by comorbidities, known to be more (less) prevalent among the black and Hispanic (Asian) population segments. In turn, the increased prevalence of these comorbidities among the black and Hispanic population, is, in large part, the result of poorer dietary habits and more limited access to healthcare, themselves driven by lower incomesContrary to popular belief, population density, per se, does not have a significantly positive impact. Indeed, population density and zip code incidence rate are negatively correlated, with a -33% correlation coefficient.Our model specification is based on a well-established epidemiologic model that explains the effects of household sizes on R0, the basic reproductive number of an epidemic.Our findings support implemented and proposed policies to quarantine pre-acute and post-acute patients, as well as nursing home admission policies

“HIV Is Still Real”: Perceptions of HIV Testing and HIV Prevention Among Black Men Who Have Sex With Men in New York City

2008 ◽  
Vol 3 (2) ◽  
pp. 150-164 ◽  
Author(s):  
José Nanín ◽  
Tokes Osubu ◽  
Ja'Nina Walker ◽  
Borris Powell ◽  
Donald Powell ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
Hiv Testing ◽  
Black Men ◽  
York City ◽  
The United States ◽  
Infection Rates ◽  
Factors Affecting ◽  
Race And Class ◽  
Sex With Men

Rising HIV infection rates have been recently occurring among Black men who have sex with men (BMSM) in the United States. As a result, promoting HIV testing among members of this population is now considered a priority among local and federal health officials. A study was conducted to explore concerns about HIV testing among BMSM in New York City. In early 2006, data were gathered from focus groups with 29 BMSM. Discussions revealed factors affecting HIV testing, including stigma, sexuality, religion, race, and class, emphasizing responsibility, testing concerns, and media influences, among others. Recommendations were submitted to New York City health officials to inform HIV testing and prevention efforts.

scholarly journals The Influence of Tobacco Retailer Density and Poverty on Tobacco Use in a Densely Populated Urban Environment

2019 ◽  
Vol 134 (2) ◽  
pp. 164-171 ◽  
Author(s):  
Shannon M. Farley ◽  
Andrew R. Maroko ◽  
Shakira F. Suglia ◽  
Lorna E. Thorpe
Keyword(s):  
New York ◽  
New York City ◽  
Population Density ◽  
Urban Environment ◽  
York City ◽  
Smoking Prevalence ◽  
Sociodemographic Characteristics ◽  
Housing Type ◽  
Neighborhood Poverty ◽  
Neighborhood Level

Objectives: Researchers have identified associations between neighborhood-level factors (eg, income level, tobacco retailer density) and smoking behavior, but few studies have assessed these factors in urban environments. We explored the effect of tobacco retailer density, neighborhood poverty, and housing type (multiunit and public) on smoking in a large urban environment (New York City). Methods: We used data on smoking prevalence and individual sociodemographic characteristics from the 2011-2013 New York City Community Health Survey, data on tobacco retailers from the 2012 New York City Department of Consumer Affairs, data on neighborhood sociodemographic characteristics and population density from the 2009-2013 American Community Survey, and data on multiunit and public housing from the 2012 New York City Primary Land Use Tax Lot Output data set. We used aggregate neighborhood-level variables and ordinary least squares regression, geographic weighted regression, and multilevel models to assess the effects of tobacco retailer density and neighborhood poverty on smoking prevalence, adjusting for sociodemographic characteristics (age, sex, race/ethnicity, and education) and neighborhood population density. We also assessed interactions between tobacco retailer density and poverty and each housing type on smoking. Results: Neighborhood poverty positively and significantly modified the association between tobacco retailer density and prevalence of neighborhood smoking ( β = 0.003, P = .01) when we controlled for population density, sociodemographic characteristics, and types of housing. Neighborhood poverty was positively associated with the prevalence of individual smoking ( β = 0.0099, P < .001) when we adjusted for population density, sociodemographic characteristics, and type of housing. Conclusion: More research is needed to determine all the environmental factors associated with smoking prevalence in a densely populated urban environment.

scholarly journals Modeling COVID-19 epidemics in an Excel spreadsheet: Democratizing the access to first-hand accurate predictions of epidemic outbreaks

2020 ◽  
Author(s):  
Mario Moisés Alvarez ◽  
Everardo González-González ◽  
Grissel Trujillo-de Santiago
Keyword(s):  
New York ◽  
New York City ◽  
Population Density ◽  
York City ◽  
Large City ◽  
Epidemiological Model ◽  
Viral Diseases ◽  
Excel Spreadsheet ◽  
Two Parameters ◽  
Governmental Response

AbstractCOVID-19, the first pandemic of this decade and the second in less than 15 years, has harshly taught us that viral diseases do not recognize boundaries; however, they truly do discriminate between aggressive and mediocre containment responses.We present a simple epidemiological model that is amenable to implementation in Excel spreadsheets and sufficiently accurate to reproduce observed data on the evolution of the COVID-19 pandemics in different regions (i.e., Italy, Spain, and New York City (NYC)). We also show that the model can be adapted to closely follow the evolution of COVID-19 in any large city by simply adjusting two parameters related to (a) population density and (b) aggressiveness of the response from a society/government to epidemics. Moreover, we show that this simple epidemiological simulator can be used to assess the efficacy of the response of a government/society to an outbreak.The simplicity and accuracy of this model will greatly contribute to democratizing the availability of knowledge in societies regarding the extent of an epidemic event and the efficacy of a governmental response.

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