Associations Between Built Environment, Neighborhood Socioeconomic Status, and SARS-CoV-2 Infection Among Pregnant Women in New York City

Emeruwa, Ukachi N.; Ona, Samsiya; Shaman, Jeffrey; Turitz, Amy L.; Wright, Jason D.; Gyamfi-Bannerman, Cynthia; Melamed, Alexander

doi:10.1001/jama.2020.11370

Cited by 161 publications

(164 citation statements)

References 3 publications

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“…Further, we were able to refine previous associations between infection and hospitalization rates with area-level household crowding. In fact, this association is consistent with smaller studies 33,34 and may partially explain racial disparities seen in aggregated county-level data. 19 For example, approximately one-fifth of US residential units are unsuitable for home quarantine based on the number of rooms available per individual, with racial and ethnic minorities overrepresented among these households.…”

Section: Baseline Characteristicssupporting

confidence: 88%

Geospatial Analysis of Individual and Community-Level Socioeconomic Factors Impacting SARS-CoV-2 Prevalence and Outcomes

Cromer

Lakhani

Wexler

et al. 2020

Preprint

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Background: The SARS-CoV-2 pandemic has disproportionately affected racial and ethnic minority communities across the United States. We sought to disentangle individual and census tract-level sociodemographic and economic factors associated with these disparities. Methods and Findings: All adults tested for SARS-CoV-2 between February 1 and June 21, 2020 were geocoded to a census tract based on their address; hospital employees and individuals with invalid addresses were excluded. Individual (age, sex, race/ethnicity, preferred language, insurance) and census tract-level (demographics, insurance, income, education, employment, occupation, household crowding and occupancy, built home environment, and transportation) variables were analyzed using linear mixed models predicting infection, hospitalization, and death from SARS-CoV-2. Among 57,865 individuals, per capita testing rates, individual (older age, male sex, non-White race, non-English preferred language, and non-private insurance), and census tract-level (increased population density, higher household occupancy, and lower education) measures were associated with likelihood of infection. Among those infected, individual age, sex, race, language, and insurance, and census tract-level measures of lower education, more multi-family homes, and extreme household crowding were associated with increased likelihood of hospitalization, while higher per capita testing rates were associated with decreased likelihood. Only individual-level variables (older age, male sex, Medicare insurance) were associated with increased mortality among those hospitalized. Conclusions: This study of the first wave of the SARS-CoV-2 pandemic in a major U.S. city presents the cascade of outcomes following SARS-CoV-2 infection within a large, multi-ethnic cohort. SARS-CoV-2 infection and hospitalization rates, but not death rates among those hospitalized, are related to census tract-level socioeconomic characteristics including lower educational attainment and higher household crowding and occupancy, but not neighborhood measures of race, independent of individual factors.

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Section: Baseline Characteristicssupporting

confidence: 88%

Geospatial Analysis of Individual and Community-Level Socioeconomic Factors Impacting SARS-CoV-2 Prevalence and Outcomes

Cromer

Lakhani

Wexler

et al. 2020

Preprint

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“…While there are many unanswered questions regarding the clinical course of COVID-19, we know that older adults, African-Americans, and those with comorbidities such as hypertension, chronic lung disease, and diabetes are disproportionately affected. 2,3 Though the mechanisms for these differences have not been fully elucidated, there is evidence that neighborhood level structural factors such as crowding may contribute, 4 and concern that already existing inequities in healthcare access and outcomes may be worsened by the pandemic. 5 From an epidemiological perspective, whether or not pregnant women experience more severe disease is challenging to answer.…”

Section: Introductionmentioning

confidence: 99%

Severity of COVID‐19 in pregnancy: A review of current evidence

Kucirka

Norton

Sheffield

2020

American J Rep Immunol

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Coronavirus disease 19 (COVID-19) has recently emerged as a major threat to human health. Infections range from asymptomatic to severe (increased respiratory rate, hypoxia, significant lung involvement on imaging) or critical (multi-organ failure or dysfunction or respiratory failure requiring mechanical ventilation or high-flow nasal cannula). Current evidence suggests that pregnancy women are at increased risk of severe disease, specifically the need for hospitalization, ICU admission, and mechanical ventilation, and the already complex management of infection with an emerging pathogen may be further complicated by pregnancy. The goal of this review is to provide an overview of what is known about the clinical course of COVID-19 in pregnancy, drawing on (a) experience with other coronaviruses such as SARS and MERS, (b) knowledge of immunologic and physiologic changes in pregnancy and how these might impact infection with SARS-CoV-2, and (c) the current literature reporting outcomes in pregnant women with SARS-CoV-2. We also briefly summarize considerations in management of severe COVID-19 in pregnancy.

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“…The Effect of interaction within a population community on the growth of virus infected number is demonstrated in Fig 7. The output from the model suggest that minimum the interaction, earlier the curve attens. The results from Shamam model [17] based on metapopulational SIER model indicates the same trend. The authors concluded that individual living in highly populated neighborhood was infected more.…”

Section: Effect Of Interactionmentioning

confidence: 54%

Predictive model for COVID 19 curve - An evolutionary approach

Rangarajan¹,

Poranki²,

Sammakia³

2020

Preprint

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In this manuscript we propose a novel method that models the evolution, spread and transmission of COVID 19 pandemic. The proposed model is inspired partly from the evolutionary based state of the art genetic algorithm. The rate of virus evolution, spread and transmission of the COVID 19 and its associated recovery and death rate are modeled using the principle inspired from evolutionary algorithm. Furthermore, the interaction within a community and interaction outside the community is modeled. Using this model, the maximum healthcare threshold is fixed as a constraint. Our evolutionary based model distinguishes between individuals in the population depending on the severity of their symptoms/infection based on the fitness value of the individuals. There is a need to differentiate between virus infected diagnosed (Self isolated) and virus infected non-diagnosed (Highly interacting) sub populations/group. In this study the model results does not compare the number outcomes with any actual real time data based curves. However, the results from the model demonstrates that a strict lockdown, social-distancing measures in conjunction with more number of testing and contact tracing is required to flatten the ongoing COVID-19 pandemic curve. A reproductive number of 2.4 during the initial spread of virus is predicted from the model for the randomly considered population. The proposed model has the potential to be further fine-tuned and matched accurately against real time data.

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Associations Between Built Environment, Neighborhood Socioeconomic Status, and SARS-CoV-2 Infection Among Pregnant Women in New York City

Cited by 161 publications

References 3 publications

Geospatial Analysis of Individual and Community-Level Socioeconomic Factors Impacting SARS-CoV-2 Prevalence and Outcomes

Geospatial Analysis of Individual and Community-Level Socioeconomic Factors Impacting SARS-CoV-2 Prevalence and Outcomes

Severity of COVID‐19 in pregnancy: A review of current evidence

Predictive model for COVID 19 curve - An evolutionary approach

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