Disparities in COVID-19 Testing and Positivity in New York City

Abstract

Introduction: Existing socioeconomic and racial disparities in healthcare access in New York City have likely impacted the public health response to COVID-19. An ecological study was performed to determine the spatial distribution of COVID-19 testing by ZIP code Tabulation Area and investigate if testing was associated with race or SES. Methods: Data were obtained from the New York City coronavirus data repository and 2018 American Community Survey 5-year estimates. A combined index of SES was created using principal component analysis and incorporated household income, gross rent, poverty, education, working class status, unemployment, and occupants per room. Multivariable Poisson regressions were performed to predict the number of total tests and the ratio of positive tests to total tests performed, using the SES index, racial composition, and Hispanic composition as predictors. Results: The number of total tests significantly increased with the increasing proportion of white residents (b=0.004, SE=0.001, p=0.0032) but not with increasing Hispanic composition or SES index score. The ratio of positive tests to total tests significantly decreased with the increasing proportion of white residents in the ZIP code Tabulation Area (b= À0.003, SE=0.000 6, p<0.001) and with increasing SES index score (b= À0.001 6, SE=0.0007, p=0.0159). Conclusions: In New York City, COVID-19 testing has not been proportional to need; existing socioeconomic and racial disparities in healthcare access have likely impacted public health response. There is urgent need for widespread testing and public health outreach for the most vulnerable communities in New York City.

Severe Acute Respiratory Syndrome Coronavirus 2 Clinical Syndromes and Predictors of Disease Severity in Hospitalized Children and Youth

“…8,9 However, it highlights that the young population is not universally spared from morbidity, and that even previously healthy children and youth can develop severe disease requiring supportive therapy. 37,38 J o u r n a l P r e -p r o o f 14 We found a wide array of clinical manifestations in children and youth hospitalized with SARS-CoV-2. Although most of the clinical manifestations in children have also been reported in adults, the frequency of some differ considerably.…”

“…The baseline characteristics of the overall cohort are detailed in The distribution of baseline patient characteristics varied significantly between the different clinical syndromes ( Table 3). Patients with respiratory disease were older than those with MIS-C (median age: 14 [IQR: [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] vs. 7 [IQR:3-11] years, respectively, P<.001).…”

An Ecologic Study of Disparities in COVID-19 Incidence and Case Fatality in Oakland County, MI, USA, During a State-Mandated Shutdown

“…Neighborhood characteristics may also influence health behavior and access to healthcare [ 19 ]. Race composition has been known to be associated with neighborhood resources which again may affect a person’s chances of successful recovery and, on the public health level, testing availability in the community [ 20 , 21 ]. In this paper, we focused on the key domains of the SDoH: race, economic stability, neighborhood and build environment, health and healthcare, education, and social and community context.…”

Data-Driven Development of a Small-Area COVID-19 Vulnerability Index for the United States

“…In New York City, Black and Latinx residents accounted for a greater relative proportion of confirmed cases, yet were less likely to have access to rapid testing. 2,6,7 Notably, Black, Indigenous, and Latinx individuals are more likely to be essential workers, live in confined housing conditions, and lack adequate healthcare access, however the degree to which these and other factors potentiated by systemic racism may contribute to higher infection risk is not fully known. 5,8 Assessments of social and economic disparities in infection risk have been limited to state, county, and isolated ZIP code-level analyses.…”

“…adults without health insurance have reduced access to high quality care and experience poorer health outcomes.23 Our INLA models demonstrated that a higher percentage of uninsured residents in a zip code locality was positively associated with COVID-19 diagnosis. This may be due to delays in isolation or treatment measures due to a reduced quantity of available early testing sites or kits, which has been previously documented in areas of high transmission 6. Similarly, patients may decline testing due to fear of associate financial costs.…”