Countdown to Census Day, April 1, 2020:

Demographics of New Orleans and early COVID-19 Hot Spots in the U.S

Published: Mar 25, 2020

This webpage highlights key demographic data relevant to mitigating the spread of COVID-19 and related economic impacts in New Orleans. Indicators include age distribution, poverty levels, access to vehicles and the internet, along with comparisons to Seattle, Westchester County, NY, and New York City. This webpage is intended to inform decisionmakers in policymaking, program design, and resource allocation.

Income and Poverty

Income and poverty measures can indicate the extent to which a community may be able to successfully adhere to COVID-19 mitigation measures (such as “stay at home” and “quarantine family members who are sick”). Lower-income individuals are more likely to be living in crowded households where there are more people than rooms, making quarantining sick family members more difficult.i Low-income individuals are more likely to be working in service positions on the front line of COVID-19 including as at-home health aides for seniors, grocery store clerks, and nannies, and may not be able to “stay at home.”ii Lower-income individuals are more likely to experience health conditions such as high blood pressureiii and diabetesiv that may increase the likelihood of hospitalization and death among COVID patients.v There are a myriad of reasons why lower-income and poorer communities will be more vulnerable to the COVID-19 virus as explored in additional indicators on this webpage.

Source: The Data Center analysis of data from the 2018 American Community Survey

Source: The Data Center analysis of data from the 2018 American Community Survey

Households in Liquid Asset Poverty and Zero Net Worth

Savings or wealth is very important for supporting a family’s ability to be resilient in the face of health and economic shocks. Liquid asset poverty rates indicate the percent of households that do not have enough liquid net worth (savings) to survive at the poverty level for three months without income. When a large share of a community does not have sufficient savings, this can have severe implications for residents who have been laid off due to COVID-19-related business closures and residents who bear unexpected health costs due to contracting COVID-19.vi

Source: The Data Center analysis of data from the CFED/Prosperity Now Racial Wealth Divide in New Orleans report

Source: The Data Center analysis of data from the CFED/Prosperity Now Racial Wealth Divide in New Orleans report

Access to Vehicles

The share of households without access to a vehicle can help cities determine the best approach for making available widespread COVID-19 testing. Drive-up testing will be less effective in cities and counties where larger shares of the population do not have access to vehicles. Additional means of testing should be considered for most cities and counties.

Source: The Data Center analysis of data from the 2018 American Community Survey

Internet Access

Internet access is an important indicator of access to information, ability to work from home, and ability to participate in online learning. An increasingly common way to access the Internet is through a smartphone or some other cellular device. But having access only through a smartphone restricts ability to fully leverage the Internet to complete at-home work tasks such as writing documents, assisting children with homework, creating content for an internet business, or analyzing data about your neighborhood. Access only at locations inaccessible during lockdown refers to households who only have access through group locations such as school, work, a library, or coffee shop—all of which are not available given “stay at home” orders.

Source: The Data Center analysis of data from the 2018 American Community Survey

Housing Costs Relative to Incomes

This indicator measures the share of renters paying more than half of their household income on rent. When the majority of a paycheck goes toward rent, families often have to make dificult choices between food, medicines, and utilities. When incomes are lost, families that are severely “housing cost burdened” may suddenly face homelessness.vi The COVID-19 crisis and related layoffs of low-wage service workers could significantly increase housing instability in cities where many renters are already severely housing cost burdened (paying more than 50 percent of their pre-tax household income on rent and utilities).

Source: The Data Center analysis of data from the 2018 American Community Survey

Underlying Health Conditions (Co-Morbidities) related to COVID-19

Emerging studies have identified high blood pressure, diabetes (or “sugar diabetes” as it is called by many New Orleanians), coronary heart disease, chronic obstructive pulmonary disease (COPD-often associated with smoking), chronic kidney disease, and cancer as preexisting health conditions that may increase the likelihood of severe outcomes for people who get infected with COVID-19.vii Cities with higher rates of these health conditions may see higher hospitalization rates and more deaths from COVID-19 infections.

Source: 500 Cities Project Data (2018) Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health

Age Distribution

Early studies on the COVID-19 virus indicate that the virus more seriously affects older adults as well as those with preexisting health problems. Death rates have been highest among older adults.viii Nonetheless, these studies also reveal that more adults age 20 to 64 have been hospitalized with COVID-19 disease than adults age 65+ in the United States.ix The age distribution of a community can help inform likely COVID-19 hospitalization and death rates.

Source: The Data Center analysis of data from Population Estimates 2018.

Sources

i Lopoo, L. and London, A. (2018). How Does Household Crowding Affect Education Outcomes? Retrieved from https://housingmatters.urban.org/research-summary/how-does-household-crowding-affect-education-outcomes

iiRoss, M. (2020) A Closer Look at Low-wage Workers Across the Country. Brookings Institution. Retrieved from https://www.brookings.edu/interactives/low-wage-workforce/

iii Carroll, W. (2011). Hypertension in America: Estimates for the U.S. Civilian Noninstitutionalized Population, Age 18 and Older, 2008. Medical Expenditure Panel Survey. Retrieved from https://meps.ahrq.gov/data_files/publications/st315/stat315.pdf

iv Gaskin, D. et al. (2014) Disparities in Diabetes: The Nexus of Race, Poverty, and Place. American Journal of Public Health. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4021012/

vi Guan, W. J., Liang, W. H., Zhao, Y., Liang, H. R., Chen, Z. S., Li, Y. M., … and Ou, C. Q. (2020). Comorbidity and its impact on 1,590 patients with COVID-19 in China: A Nationwide Analysis. medR

vii Yang, J., Zheng, Y., Gou, X., Pu, K., Chen, Z., Guo, Q., … & Zhou, Y. (2020). Prevalence of comorbidities in the novel Wuhan coronavirus (COVID-19) infection: a systematic review and meta-analysis. International Journal of Infectious Diseases.

viii Severe housing cost burdened: Percentage of households that spend 50% or more of their household income on housing.(2020). County Health Rankings and Roadmaps, a Robert Wood Johnson Foundation program. Retrieved from: https://www.countyhealthrankings.org/explore-health-rankings/measures-data-sources/county-health-rankings-model/health-factors/physical-environment/housing-transit/severe-housing-cost-burden

ix Guan, W. et al. (2020). Comorbidity and its impact on 1,590 patients with COVID-19 in China: A Nationwide Analysis. Retrieved from: https://www.medrxiv.org/content/10.1101/2020.02.25.20027664v1. And Yang, J., et al. (2020). Prevalence of comorbidities in the novel Wuhan coronavirus (COVID-19) infection: a systematic review and meta-analysis. (2020) International Journal of Infectious Diseases. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/32173574

x The Novel Coronavirus Pneumonia Emergency Response Epidemiology Team. (2020) The Epidemiological Characteristics of an Outbreak of 2019 Novel Coronavirus Diseases (COVID-19) – China, 2020. Retrieved from https://github.com/cmrivers/ncov/blob/master/COVID-19.pdf

x CDC COVID-19 Response Team. (2020) Severe Outcomes Among Patients with Coronavirus Disease 2019 (COVID-19) — United States, February 12–March 16, 2020. Retrieved from https://www.cdc.gov/mmwr/volumes/69/wr/mm6912e2.htm?s_cid=mm6912e2_w#F2_down

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