Please Note

The Washington Post is providing this story for free so that all readers have access to this important information about the coronavirus. For more free stories, sign up for our daily Coronavirus Updates newsletter.

Covid-19 may pose the greatest risk to Americans in rural areas if deaths from the coronavirus pandemic are similar to those from the standard flu. Other concentrations of sick and old people may also be at risk.

With more than 100 Americans already dead, scientists project the future by measuring the people at greatest risk and deaths from similar threats.

Biostatistician and infectious disease specialist Nicholas Reich from the University of Massachusetts is participating in the White House Coronavirus Task Force modeling efforts. He said the death rates from flu for people over 50 could be a good indicator of vulnerability for covid-19. He said flu death rates are “probably not a perfect measure but a good place to start.”

Flu mortality, ages 50 and older

Deaths per 100,000 people

0–2.5

2.5–5

5–7.5

7.5–10

More than 10

Circles are scaled by county population

Note: All rates are for 2014-2018, the last five years of

data available.

Flu mortality, ages 50 and older

Deaths per 100,000 people

0–2.5

2.5–5

5–7.5

7.5–10

More than 10

Circles are scaled by county population

Note: All rates are for 2014-2018, the last five years of data available.

Flu mortality, ages 50 and older

Deaths per 100,000 people

0–2.5

2.5–5

5–7.5

7.5–10

More than 10

Circles are scaled by county population

Note: All rates are for 2014-2018, the last five years of data available.

Flu mortality, ages 50 and older

Deaths per 100,000 people

0–2.5

2.5–5

5–7.5

7.5–10

More than 10

Circles are scaled by county population

Note: All rates are for 2014-2018, the last five years of data available.

Flu mortality, ages 50 and older

Deaths per 100,000 people

0–2.5

2.5–5

5–7.5

7.5–10

More than 10

Circles are scaled by county population

Note: All rates are for 2014-2018, the last five years of data available.

Seattle, Boston, St. Louis and Portland, Ore., have had high rates of flu death, according to the past five years of data from the Centers for Disease Control and Prevention. Urban areas around Chicago, New York, Washington, Los Angeles, San Francisco, Dallas, Detroit and Atlanta have had very low rates.

Rural and small city areas in Iowa and Missouri, around the Missouri River and Mississippi River, have had high death rates. Kansas, Nebraska, the Dakotas, Indiana and New England show rates higher than any of the big cities.

The push for social distancing and isolation make dense crowds and public transportation in big cities seem like the deadliest environment.

The pattern of flu deaths over the past five years, however, shows that big metro areas are not hot spots for high flu death rates. Most of the deaths are among the large population in big cities, but the risk for any individual person goes up dramatically where homes are sparse.

Very rural areas have a 60 percent higher death rate from flu than the big metro areas, according to analysis of CDC death records.

Death rate

from flu

4.2 per

100,000

Large

urban

cities

Share of

U.S. population

31%

Large

city

suburbs

4.0

25%

Medium

cities

5.3

21%

Small

cities

5.4

9%

6.5

Towns

9%

6.9

Rural

7%

Note: For ages 50 and older. Population

percentages does not sum to 100 due to rounding.

Share of

U.S. population

31%

Death rate

from flu

4.2 per 100,000

Large urban

cities

Large city

suburbs

4.0

25%

Medium

cities

5.3

21%

Small

cities

5.4

9%

6.5

Towns

9%

6.9

7%

Rural

Note: For ages 50 and older. Population percentages does not

sum to 100 due to rounding.

6.9

6.5

Death rate

from flu

4.2 per 100,000

5.4

5.3

4.0

Share of

U.S. population

28%

25%

21%

9%

9%

7%

Large urban cities

Large city suburbs

Medium cities

Small

cities

Towns

Rural

Note: For ages 50 and older. Population percentages does not sum to 100 due to rounding.

6.9

6.5

5.4

5.3

Death rate

from flu

4.2 per 100,000

4.0

Share of U.S. population

28%

25%

21%

9%

9%

7%

Large urban cities

Large city suburbs

Medium cities

Small

cities

Towns

Rural

Note: For ages 50 and older. Population percentages does not sum to 100 due to rounding.

Collectively, the 68 most rural counties of Kansas, for instance, have nearly 14 deaths per 100,000 people age 50 or older, well over double the rate for the county around Topeka (6.6), the state capital. And the rate around New York City (3.4) is around half of that. All rates are for 2014 to 2018, the most recent five years of data available.

The higher rates in remote areas may be due to difficulty getting health care. Rural residents have greater travel distances for more limited resources. And that was before the pandemic raised the threat of overwhelming even the nation’s most advanced hospitals.

Flu mortality is very notable for low rates in the warmest states: California and the belt of states from Arizona to Florida. But as Reich said, the flu is not a perfect predictor for covid-19. It’s not known if warm weather will provide protection against the new coronavirus the way it does with the standard flu.

The impact of weather is not known, but the threat for older people is clear, with death rates seeming to double with each decade beyond age 50. Florida, Arizona and retirement havens along the coasts have concentrations of the oldest people with dark blue circles in the map below. And because the majority of Americans live in large metro areas, many seniors are congregated in the biggest cities, the dense groups of light blue circles. Appalachia, Western Pennsylvania, Eastern Michigan, and areas to the east and west of St. Louis have concentrated light blue patterns as well.

Percent population over the age of 50

up to 30%

30-40%

40-50%

>50%

one dot = 100,000 people

Source: Census data from IPUMS USA, 2018

Percent population over the age of 50

up to 30%

30-40%

40-50%

>50%

one dot = 100,000 people

Source: Census data from IPUMS USA, 2018

Percent population over the age of 50

up to 30%

30-40%

40-50%

greater than 50%

one dot = 100,000 people

Source: Census data from IPUMS USA, 2018

Percent population over the age of 50

up to 30%

30-40%

40-50%

greater than 50%

one dot = 100,000 people

Source: Census data from IPUMS USA, 2018

Percent population over the age of 50

up to 30%

30-40%

40-50%

greater than 50%

one dot = 100,000 people

Source: Census data from IPUMS USA, 2018

Even among the elderly, covid-19 is deadliest for people who are already ill. There’s no national measure of everyone’s health at any given moment, but the index mapped below creates a score for vulnerability. It starts with the percent of people over age 50, then gradually increases the score for people in their 60s, 70s, 80s and older. It then increases the score again for any older person living in poverty, a relatively small share because of Social Security. The score increases again for seniors in confined institutions, such as nursing homes, because there is a greater likelihood of existing illness. For seniors living in their own homes, the score is increased for people who have challenges getting effective health care because they have no car or no Internet connection or no one in the house who speaks English.

This enhanced measure of age-based vulnerability highlights potential risk in retirement areas such as Florida and Arizona, as well as Michigan and Appalachia. The urban areas do not reflect as much risk because the elderly group is diluted by the large population overall.

Higher age-based vulnerability

Low

High

Source: Census data from IPUMS USA, 2018

Higher age-based vulnerability

Low

High

Source: Census data from IPUMS USA, 2018

Higher age-based vulnerability

Low

High

Source: Census data from IPUMS USA, 2018

Higher age-based vulnerability

Low

High

Source: Census data from IPUMS USA, 2018

Covid-19’s impact may resemble the vulnerability projections or may be very different depending on how it moves compared with the flu and how people act. Epidemiologists are constantly updating models of the risk and learning more about the pandemic that can more accurately guide planning and response.