An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York City

Whittle, Richard; Diaz‐Artiles, Ana

doi:10.1101/2020.04.17.20069823

Cited by 14 publications

(17 citation statements)

References 51 publications

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“…There is a notable difference between respiratory cases and deaths, which may suggest that the profile of individuals who have the economic or social capital to seek care early for respiratory symptoms in Tijuana differs from those who do not interact with the medical system until after their death. This finding adds to a growing body of literature and social commentary suggesting that social inequalities may be translating into inequalities in the risk of infection or death from COVID-19 in numerous contexts [37][38][39][40][41][42][43] Conclusions EMS data provide a valuable tool to rapidly track the health of populations at risk of COVID-19 in LMICs, where other forms of real-time data may not be available. EMS information can be used to track excess out-of-hospital mortality and respiratory burden, as well as changing clinical or demographic features.…”

Section: Discussionmentioning

confidence: 78%

Excess Out-Of-Hospital Mortality and Declining Oxygen Saturation: The Sentinel Role of EMS Data in the COVID-19 Crisis in Tijuana, Mexico

Friedman

Calderón-Villarreal

Bojorquez

et al. 2020

Preprint

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Objective: Emergency medical services (EMS) may serve as a key source of real-time data about the evolving health of COVID-19 affected populations, especially in low-and-middle-income countries (LMICs) with less rapid and reliable vital statistic registration systems. Although official COVID-19 statistics in Mexico report almost exclusively in-hospital mortality events, excess out-of-hospital mortality has been identified in other settings, including one EMS study in Italy that showed a 58% increase. EMS and hospital reports from several countries have suggested that silent hypoxemia-low oxygen saturation (SpO2) in the absence of dyspnea-is associated with COVID-19 outbreaks. It is unclear, however, how these phenomena can be generalized to LMICs. We assess how EMS data can be used in a sentinel capacity in Tijuana, a city on the Mexico-United States border with earlier exposure to COVID-19 than many LMIC settings. Methods:We calculated numbers of weekly out-of-hospital deaths and respiratory cases seen by EMS in Tijuana, and estimate the difference between peak-epidemic rates (during April 14 th -May 11 th ) and forecasted 2014-2019 trends. Results were compared with official COVID-19 statistics, stratified by neighborhood socioeconomic status (SES), and examined for changing demographic or clinical features, including mean (SpO2).Results: An estimated 194.7 (95%CI: 135.5-253.9) excess out-of-hospital deaths events occurred, representing an increase of 145% (70%-338%) compared to forecasted trends. During the same window, only 8 COVID-19-positive, out-of-hospital deaths were reported in official statistics. This corresponded with a rise in respiratory cases of 274% (119%-1142%), and a drop in mean SpO2 to 77.7%, from 90.2% at baseline. The highest out-of-hospital death rates were observed in low-SES areas, although respiratory cases were more concentrated in high-SES areas.Conclusions: EMS systems may play an important sentinel role in monitoring excess out-of-hospital mortality and other trends during the COVID-19 crisis in LMICs. Using EMS data, we observed increases in out-of-hospital deaths in Tijuana that were nearly threefold greater magnitude than increases reported using EMS data in Italy. Increased testing in out-of-hospital settings may be required to determine if excess mortality is being driven by COVID-19 infection, health system saturation, or patient avoidance of healthcare. We also found evidence of worsening rates of hypoxemia among respiratory patients seen by EMS, suggesting a rise in silent hypoxemia, which should be met with increased detection and clinical management efforts. Finally, we observed that social disparities in out-of-hospital death that warrant monitoring and amelioration.

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Section: Discussionmentioning

confidence: 78%

Excess Out-Of-Hospital Mortality and Declining Oxygen Saturation: The Sentinel Role of EMS Data in the COVID-19 Crisis in Tijuana, Mexico

Friedman

Calderón-Villarreal

Bojorquez

et al. 2020

Preprint

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“…US data indicates that, for example, in Chicago approximately 70% of the deaths were from ethnic minorities [20]. Detailed data from New York showed that the number of COVID-19 cases associated with the percentage of dependents in the local population, the male:female ratio, and low-income neighborhoods [21]. United States-wide data gave a similar result, with proportion of residents >65 years old, ethnic minorities, male:females ratio, and the overall population density associating with increased frequency of COVID-19 [22].…”

Section: The Role Of Current Ses In Covid-19 Morbidity and Mortalitymentioning

confidence: 99%

The COVID-19 Pandemic: Does Our Early Life Environment, Life Trajectory and Socioeconomic Status Determine Disease Susceptibility and Severity?

Holuka

Merz

Fernandes

et al. 2020

IJMS

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A poor socioeconomic environment and social adversity are fundamental determinants of human life span, well-being and health. Previous influenza pandemics showed that socioeconomic factors may determine both disease detection rates and overall outcomes, and preliminary data from the ongoing coronavirus disease (COVID-19) pandemic suggests that this is still true. Over the past years it has become clear that early-life adversity (ELA) plays a critical role biasing the immune system towards a pro-inflammatory and senescent phenotype many years later. Cytotoxic T-lymphocytes (CTL) appear to be particularly sensitive to the early life social environment. As we understand more about the immune response to SARS-CoV-2 it appears that a functional CTL (CD8+) response is required to clear the infection and COVID-19 severity is increased as the CD8+ response becomes somehow diminished or exhausted. This raises the hypothesis that the ELA-induced pro-inflammatory and senescent phenotype may play a role in determining the clinical course of COVID-19, and the convergence of ELA-induced senescence and COVID-19 induced exhaustion represents the worst-case scenario with the least effective T-cell response. If the correct data is collected, it may be possible to separate the early life elements that have made people particularly vulnerable to COVID-19 many years later. This will, naturally, then help us identify those that are most at risk from developing the severest forms of COVID-19. In order to do this, we need to recognize socioeconomic and early-life factors as genuine medically and clinically relevant data that urgently need to be collected. Finally, many biological samples have been collected in the ongoing studies. The mechanisms linking the early life environment with a defined later-life phenotype are starting to be elucidated, and perhaps hold the key to understanding inequalities and differences in the severity of COVID-19.

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“…Despite the recent onset of the current COVID-19 pandemic, there is already growing evidence about both individual risk factors and population-level drivers of disease and mortality. This study adds to the number of very recent similar spatial analyses of ZCTA-level testing data released by the New York City Department of Health and Mental Hygeiene, [22][23][24] and illustrates the importance of sharing these kinds of data, as well as the informative nature of spatial epidemiology as the pandemic evolves across the nation and the world. Consistent with prior reports, we find that the clustering of positive COVID-19 testing results in NYC are unlikely to be due to chance, 9,23 and is driven in large measure by socioeconomics, age distribution, 24 and race.…”

Section: Discussionmentioning

confidence: 70%

“…This study adds to the number of very recent similar spatial analyses of ZCTA-level testing data released by the New York City Department of Health and Mental Hygeiene, [22][23][24] and illustrates the importance of sharing these kinds of data, as well as the informative nature of spatial epidemiology as the pandemic evolves across the nation and the world. Consistent with prior reports, we find that the clustering of positive COVID-19 testing results in NYC are unlikely to be due to chance, 9,23 and is driven in large measure by socioeconomics, age distribution, 24 and race. 9,23 Our study adds to this by demonstrating that the proportion of residents self-identifying as Black/African American is among the single strongest unadjusted bivariate predictors of the proportion of positive tests in a community.…”

Section: Discussionmentioning

confidence: 70%

Black/African American Communities are at Highest Risk of COVID-19: Spatial Modeling of New York City ZIP Code-Level Testing Results

DiMaggio

Klein

Berry

et al. 2020

Preprint

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Introduction. The population and spatial characteristics of COVID-19 infections are poorly understood, but there is increasing evidence that in addition to individual clinical factors, demographic, socioeconomic and racial characteristics play an important role. Methods. We analyzed positive COVID-19 testing results counts within New York City ZIP Code Tabulation Areas (ZCTA) with Bayesian hierarchical Poisson spatial models using integrated nested Laplace approximations. Results. Spatial clustering accounted for approximately 32% of the variation in the data, with hot spots in all five boroughs. Spatial risk did not correspond precisely to population-based rates of positive tests. The strongest univariate association with positive testing rates was the proportion of residents in a ZIP Code Tabulation Area with Chronic Obstructive Pulmonary Disease (COPD). For every one unit increase in a scaled standardized measure of COPD in a community, there was an approximate 8-fold increase in the risk of a positive COVID-19 test in a ZCTA (Incidence Density Ratio = 8.2, 95% Credible Interval 3.7, 18.3). The next strongest association was with the proportion of Black and African American residents, for which there was a nearly five-fold increase in the risk of a positive COVID-19 test. (IDR = 4.8, 95% Cr I 2.4, 9.7). Increases in the proportion of residents older than 65, housing density and the proportion of residents with heart disease were each associated with an approximate doubling of risk. In a multivariable model including estimates for age, COPD, heart disease, housing density and Black/African American race, the only variables that remained associated with positive COVID-19 testing with a probability greater than chance were the proportion of Black/African American residents and proportion of older persons. Conclusions. The population and spatial patterns of COVID-19 infections differ by race, age, physical environment and health status. Areas with large proportions of Black/African American residents are at markedly higher risk that is not fully explained by characteristics of the environment and pre-existing conditions in the population.

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An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York City

Cited by 14 publications

References 51 publications

Excess Out-Of-Hospital Mortality and Declining Oxygen Saturation: The Sentinel Role of EMS Data in the COVID-19 Crisis in Tijuana, Mexico

Excess Out-Of-Hospital Mortality and Declining Oxygen Saturation: The Sentinel Role of EMS Data in the COVID-19 Crisis in Tijuana, Mexico

The COVID-19 Pandemic: Does Our Early Life Environment, Life Trajectory and Socioeconomic Status Determine Disease Susceptibility and Severity?

Black/African American Communities are at Highest Risk of COVID-19: Spatial Modeling of New York City ZIP Code-Level Testing Results

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