COVID-19 Health Inequities in Cities Project

Roughly 80% of the US population lives in urban areas and cities are often not only the most dense parts of the US but also contain the most diverse populations. We believe that understanding and fighting the COVID-19 pandemic requires a framework which focuses on cities but also on what drives inequities in cities.

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The COVID-19 pandemic has exposed deeply entrenched inequities in cities across the United States. These inequities are the result of long-standing racial, economic, and social inequalities that existed prior to the pandemic. Over the course of the pandemic these inequities have manifested themselves as inequities in access to testing, in incidence, in hospitalization and in mortality related to COVID-19. In this dashboard we provide descriptive data of how COVID-19 related outcomes, and especially inequities in these outcomes, are evolving in the cities that are part of the Big Cities Health Coalition. We use available data to characterize, compare, and track inequities along three dimensions (1) across people within cities; (2) across neighborhoods within cities, and (3) across cities. You can find more details on our conceptual framework here, the indicators we use to measure inequities here, and the metrics we use here. We hope that these data may be useful to the cities as they continue to develop strategies to protect population health and promote health equity.

Dashboard Features

Contents of the dashboard are summarized below. Please refer to the UHC Narrative Brief on the COVID-19 Health Inequities in Cities Project for further details.

Feature #1: City Reports

The City Reports section focuses on displaying data for each city separately. Depending on the city and data available, the dashboard provides visualizations of 1) trends in cases, deaths, and hospitalizations over time 2) trends in testing and positivity rates over time 3) inequities by race/ethnicity and 4) inequities across neighborhoods.

This figure tracks trends in COVID-19 outcomes and presents various outcomes as percent of peak (% of the daily value relative to highest daily value for each outcome). Here we see that daily trends in deaths lag behind daily new cases by about two weeks.

Test positivity ratio (percentage of total COVID-19 tests that are positive) is a key COVID-19 indicator which captures whether levels of testing are keeping up with levels of transmission. Here we can see that for New York City test positivity ratio (blue line) was highest between March and May but dropped significantly after May.

This panel shows cumulative rates of COVID-19 outcomes by race and ethnicity for the selected city. Here we see that in New York City, Hispanics and non-Hispanic Blacks have higher age-adjusted cumulative incidence rates than non-Hispanic whites.

These maps display the spatial heterogeneity in neighborhood level COVID-19 outcomes (left) and neighborhood characteristics (right). Here we can see that in New York City, mortality and social vulnerability are spatially co-located: more vulnerable neighborhoods tend to have higher COVID-19 mortality.

Feature #2: Trends Across Cities

This section focuses on comparing COVID-19 trends across cities and across groups of cities that share certain characteristics (e.g., by region of the country or city size). For example, the user can compare daily new cases across different cities or examine how the total number of new cases is distributed across different sized cities over time.

Users can highlight cities of interest to compare trends in COVID-19 outcomes across cities. In this figure we can see that New York reached its peak daily incidence rate in late March but had a new increase in cases in November. In contrast, San Francisco had a smaller increase in March, a second wave in August, and its most severe peak as of late December.

This figure displays trends in COVID-19 outcomes by city characteristics. Here we aggregate cities based on region and show that earlier in the pandemic (March and June) the Northeast had much higher incidence relative to other regions. Shortly after, cases started rising in the South, especially during the summer and early fall.

Feature #3: Individual-Level Inequities

Key individual-level indicators used to describe health inequities include race and ethnicity and measures of socioeconomic position including education, occupation, income, and wealth. Based on data availability, the dashboard focuses on race and ethnicity. For example, the user can observe how incidence rates in Black populations differ from city to city, and also compare the size of the Black-White inequity across cities.

This panel shows cumulative rates of COVID-19 outcomes by race and ethnicity across cities. Here we see that in almost all cities displayed, non-Hispanic Blacks have a higher cumulative incidence rate than non-Hispanic whites.

We can also display differences in COVID-19 outcomes between race and ethnic groups for each city. By hovering over the dots in each city, users can view a more detailed interpretation of inequities. For example, in Boston the incidence rate for non-Hispanic Black residents is 101% higher than the rate in non-Hispanic white residents.

Feature #4: Neighborhood-Level Inequities

This section of the dashboard describes inequities across neighborhoods using neighborhood-level data. Due to data availability, zip codes are used to approximate neighborhoods. Key neighborhood indicators often used to describe neighborhood health inequities include measures of neighborhood income, education, occupation, and segregation. Several of these neighborhood features (and others such as overcrowding, English proficiency, and access to public transportation) may affect COVID-19 outcomes.

For each city, the user can contrast COVID-19 outcome rates in the most advantaged compared to the least advantaged neighborhoods. The magnitude of these differences can be compared across cities.

This figure compares COVID-19 incidence rates in the most socially vulnerable neighborhoods vs. the least socially vulnerable neighborhoods, across several cities. By hovering over the dots in each city, users can obtain a more detailed interpretation of neighborhood level inequities. For example, in San Diego the incidence rate is 251% higher in the most socially vulnerable neighborhoods compared to the least socially vulnerable neighborhoods. Details of the selected neighborhood characteristic can be found by hovering over the black information bubble (“i”).

Feature #5: City-Level Inequities

Cities are heterogeneous in terms of residents and neighborhoods. They also have unique city-level characteristics that may affect health. City-level characteristics that may be related to differences in health (and COVID-19 outcomes) across cities include: city poverty rates, income inequality, population density, race and ethnic composition and city-level segregation. This section of the dashboard displays inequities in COVID-19 outcomes between cities categorized by top and bottom quartiles of selected city-level characteristics.

This example compares COVID-19 incidence rates in cities located in the top quartile vs. cities located in the bottom quartile of selected city- level characteristics. By hovering over the dots in each city, the user can obtain a more detailed interpretation of city- level inequities. For example, the rate is 15% higher in cities in the highest quartile of poverty compared to cities with the lowest quartile of poverty.

About this Project

The Big Cities Health Coalition (BCHC) is a forum for the leaders of America’s largest metropolitan health departments to exchange strategies and jointly address issues to promote and protect the health and safety of the nearly 62 million people they serve. The COVID-19 Health Inequities in Cities Project is led by the Urban Health Collaborative at the Drexel Dornsife School of Public Health and supported by the Robert Wood Johnson Foundation and the de Beaumont Foundation.