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Coronavirus Death rates by State and Factors that Affect Mortality Rates

Coronavirus Death rates by State and Factors that Affect Mortality Rates

Coronavirus mortaliity rates in the United States, by stateCorrelations between States with High Coronavirus Mortality Rates

The CDC maintains a map of the U.S. showing the mortality rates (how many people per 100,000 of population) died from Coronavirus. The map at right is a snapshot from August 26, 2020.

This allows us to compare the death rates from Coronavirus between states. Of course, as Dr. Birx stated months ago, coroners were instructed to attribute any death to Coronavirus as long as the virus was detect in the person or there was even a suspicion that the victim had Covid19. This means the total number of fatalities attributed to Coronavirus are clearly exaggerated (it's like saying if you walked across the street and were hit and killed by a bus, and they detected the virus in your corpse's blood, you died from Coronavirus. )

But while the total numbers are exaggerated, the comparison between states should remove this and allow us to consider correlations to conditions that are likely causes.

Patterns within the states with the highest mortality rates

Notice in the chart that the highest rates occur in states with:

  • High population densities, like large cities (NY, NJ, IL, IN, MI, MA, PA, CT, etc.)
  • Aging populations (Florida, Arizona) and
  • High rates of obesity (LA, SC, MS) and in general the Deep South and Midwest.
Surgical mask, also called a face mask

See below for references for these facts.

Now, we accept that this is a correlation. Causation is not proven by statistical correlations, a fact often misunderstood by non-scientists.

But the authorities who are studying Coronavirus have already established a causal effect between specific underlying conditions (called co-morbidities), specifically::

  • age over 65
  • obesity
  • diabetes
  • heart conditions
  • respiratory problems


Obesity rates by state from CDCObesity rates by state

Each year CDC releases the Adult Obesity Prevalence Maps for all 50 states, the District of Columbia, and US territories. The maps show self-reported adult obesity prevalence by race, ethnicity, and location. The data comes from the Behavioral Risk Factor Surveillance System, an on-going state-based, telephone interview survey conducted by CDC and state health departments.

The 2018 maps show that obesity impacts some groups more than others. There are notable differences by race and ethnicity, as shown by combined data from 2016-2018:

  • 2 states had an obesity prevalence of 35 percent or higher among non-Hispanic white adults.
  • 9 states had an obesity prevalence of 35 percent or higher among Hispanic adults.
  • 29 states and the District of Columbia had an obesity prevalence of 35 percent or higher among non-Hispanic black adults.

Obesity is also high among another group at risk, the elderlyCensus - map of elderly

  • Percent of noninstitutionalized persons with obesity (2013-2016)
    • Men aged 65-74: 40.2%
    • Men aged 75 and over: 28.0%
    • Women aged 65-74: 43.5%
    • Women aged 75 and over: 32.7%

Source: Health, United States, 2018, table 26 [PDF � 9.8 MB]

States with high elderly populationsCensus age map - elderly ststes

The U.S. Census Bureau shows us which areas have the highest populations of elderly people. Examining these maps at right, we see that again, it correlates to those states with the higher mortality rates from Coronavirus, with the exception of Montana, which has a very low population density being a very rural and sparsely populated state..

U.S. Population DensityU.S. Population Density

The Census Bureau also has population density maps, shown at right. Again, we see a high degree of correlation of the states with the highest mortality rates an d the states with the area of highest population density.

When you drill down to a county-by-county correlation between age and COVID mortality, the fit is very, very, close, implying that the virus spreads more when people are in close proximity, and this leads to higher death rates, as would be expected.