scholarly journals Socioeconomic Disparities in SARS-CoV-2 Serological Testing and Positivity in New York City

Author(s):  
Wil Lieberman-Cribbin ◽  
Marta Galanti ◽  
Jeffrey Shaman

Abstract Background We characterized SARS-CoV-2 antibody test prevalence and positive test prevalence across New York City (NYC) in order to investigate disparities in testing outcomes by race and socioeconomic status (SES). Methods Serologic data were downloaded from the NYC Coronavirus data repository (August 2020–December 2020). Area-level characteristics for NYC neighborhoods were downloaded from U.S. census data and a socioeconomic vulnerability index was created. Spatial generalized linear mixed models were performed to examine the association between SES and antibody testing and positivity. Results The proportion of Hispanic population (Posterior Median: 0.001, 95% Credible Interval: 0.0003-0.002), healthcare workers (0.003, 0.0001-0.006), essential workers (0.003, 0.001-0.005), age ≥ 65 years (0.003, 0.00002-0.006), and high SES (SES quartile 3 vs 1: 0.034, 0.003-0.062) were positively associated with antibody tests per 100,000 residents. The White proportion (-0.002, -0.003- -0.001), SES index (quartile 3 vs 1: -0.068, -0.115- -0.017; quartile 4 vs 1: -0.077- -0.134, -0.018) and age ≥ 65 years (-0.005, -0.009- -0.002) were inversely associated with positivity, whereas the Hispanic (0.004, 0.002-0.006), and essential worker proportions (0.008, 0.003-0.012) had positive coefficients. Conclusions Disparities in serologic testing and seropositivity exist on socioeconomic status and race/ethnicity across NYC, indicative of excess COVID-19 burden in vulnerable and marginalized populations.

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wil Lieberman-Cribbin ◽  
Naomi Alpert ◽  
Raja Flores ◽  
Emanuela Taioli

Abstract Background New York City (NYC) was the epicenter of the COVID-19 pandemic, and is home to underserved populations with higher prevalence of chronic conditions that put them in danger of more serious infection. Little is known about how the presence of chronic risk factors correlates with mortality at the population level. Here we determine the relationship between these factors and COVD-19 mortality in NYC. Methods A cross-sectional study of mortality data obtained from the NYC Coronavirus data repository (03/02/2020–07/06/2020) and the prevalence of neighborhood-level risk factors for COVID-19 severity was performed. A risk index was created based on the CDC criteria for risk of severe illness and complications from COVID-19, and stepwise linear regression was implemented to predict the COVID-19 mortality rate across NYC zip code tabulation areas (ZCTAs) utilizing the risk index, median age, socioeconomic status index, and the racial and Hispanic composition at the ZCTA-level as predictors. Results The COVID-19 death rate per 100,000 persons significantly decreased with the increasing proportion of white residents (βadj = − 0.91, SE = 0.31, p = 0.0037), while the increasing proportion of Hispanic residents (βadj = 0.90, SE = 0.38, p = 0.0200), median age (βadj = 3.45, SE = 1.74, p = 0.0489), and COVID-19 severity risk index (βadj = 5.84, SE = 0.82, p <  0.001) were statistically significantly positively associated with death rates. Conclusions Disparities in COVID-19 mortality exist across NYC and these vulnerable areas require increased attention, including repeated and widespread testing, to minimize the threat of serious illness and mortality.


2017 ◽  
Vol 25 (2) ◽  
pp. 98-103 ◽  
Author(s):  
Boshen Jiao ◽  
Sooyoung Kim ◽  
Jonas Hagen ◽  
Peter Alexander Muennig

BackgroundNeighbourhood slow zones (NSZs) are areas that attempt to slow traffic via speed limits coupled with other measures (eg, speed humps). They appear to reduce traffic crashes and encourage active transportation. We evaluate the cost-effectiveness of NSZs in New York City (NYC), which implemented them in 2011.MethodsWe examined the effectiveness of NSZs in NYC using data from the city’s Department of Transportation in an interrupted time series analysis. We then conducted a cost-effectiveness analysis using a Markov model. One-way sensitivity analyses and Monte Carlo analyses were conducted to test error in the model.ResultsAfter 2011, road casualties in NYC fell by 8.74% (95% CI 1.02% to 16.47%) in the NSZs but increased by 0.31% (95% CI −3.64% to 4.27%) in the control neighbourhoods. Because injury costs outweigh intervention costs, NSZs resulted in a net savings of US$15 (95% credible interval: US$2 to US$43) and a gain of 0.002 of a quality-adjusted life year (QALY, 95% credible interval: 0.001 to 0.006) over the lifetime of the average NSZ resident relative to no intervention. Based on the results of Monte Carlo analyses, there was a 97.7% chance that the NSZs fall under US$50 000 per QALY gained.ConclusionWhile additional causal models are needed, NSZs appeared to be an effective and cost-effective means of reducing road casualties. Our models also suggest that NSZs may save more money than they cost.


1978 ◽  
Vol 7 (3) ◽  
pp. 247-250
Author(s):  
H Young

The fluorescent-antibody test, rapid carbohydrate utilization test (RCUT), and a test for penicillinase production were performed on the bacterial growth from primary cultures on modified New York City medium. Of 134 gonococcal infections in men, 88.8% were diagnosed by the fluorescent-antibody test and 70.9% by the RCUT after incubation for 24 h; the corresponding figures for 75 infections in women were 86.7 and 54.7%, respectively. After incubation for 48 h, 100% of infections were diagnosed by the fluorescent-antibody test, wherease 88.8% of infected males and 86.7% of females were also diagnosed by the RCUT. Primary isolation cultures on modified New York City medium proved suitable for determining the ability of strains to produce penicillinase by a modified RCUT procedure. The method of isolation and identification by using primary cultures from modified New York City medium is both rapid and economical. The rapidity of diagnosis provided by the RCUT makes this a very useful diagnostic method, particularly in laboratories lacking immunofluorescence equipment. Since the penicillinase production test forms part of a routine identification procedure, no extra culture media or subcultures are required. The rapidity of this test should make it of value in tracing contacts of patients infected with penicillinase-producing strains.


2020 ◽  
Vol 222 (Supplement_5) ◽  
pp. S322-S334
Author(s):  
Ashly E Jordan ◽  
Charles M Cleland ◽  
Katarzyna Wyka ◽  
Bruce R Schackman ◽  
David C Perlman ◽  
...  

Abstract Background Hepatitis C virus (HCV) incidence has increased in the worsening opioid epidemic. We examined the HCV preventive efficacy of medication-assisted treatment (MAT), and geographic variation in HCV community viral load (CVL) and its association with HCV incidence. Methods HCV incidence was directly measured in an open cohort of patients in a MAT program in New York City between 1 January 2013 and 31 December 2016. Area-level HCV CVL was calculated. Associations of individual-level factors, and of HCV CVL, with HCV incidence were examined in separate analyses. Results Among 8352 patients, HCV prevalence was 48.7%. Among 2535 patients seronegative at first antibody test, HCV incidence was 2.25/100 person-years of observation (PYO). Incidence was 6.70/100 PYO among those reporting main drug use by injection. Female gender, drug injection, and lower MAT retention were significantly associated with higher incidence rate ratios. Female gender, drug injection, and methadone doses &lt;60 mg were independently associated with shorter time to HCV seroconversion. HCV CVLs varied significantly by geographic area. Conclusions HCV incidence was higher among those with lower MAT retention and was lower among those receiving higher methadone doses, suggesting the need to ensure high MAT retention, adequate doses, and increased HCV prevention and treatment engagement. HCV CVLs vary geographically and merit further study as predictors of HCV incidence.


2019 ◽  
Vol 229 (4) ◽  
pp. S161
Author(s):  
Numa P. Perez ◽  
David C. Chang ◽  
Sahael M. Stapleton ◽  
Zhi Ven Fong ◽  
Robert N. Goldstone ◽  
...  

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