Difference between mortality and case fatality rate

In light of loose use of the term "mortality" with respect to the COVID-19 epidemic, it is important to remember that there is a dramatic difference between mortality and case fatality rates. What I've noticed is that many are referring to a 2-3% "mortality" rate for COVID when what they mean is 2-3% case fatality rate.

Here's why the difference matters...

Mortality Rate = Deaths / Population at Risk Often the denominator for mortality rate is the entire population at the beginning of some time period (usually a year), but sometimes we mean a specific sub-population (for example, Infant Mortality Rate = Infant Deaths / Live Births).

Case Fatality Rate = Deaths / Population with the Condition The case fatality rate is a special subset of mortality rates in that the rate is calculated only based on those with the condition.

Because not everyone gets every condition, Case Fatality Rates are higher--sometimes dramatically so--compared with overall Mortality Rates.

Example:

The Case Fatality Rate for measles is approximately 15%. This means that of those people who get measles, approximately 15 percent die. But we have widespread vaccination, so most people do not get measles. From 2016-2019 there were 0 measles deaths among the United States population of nearly 330 million. Thus, while the Case Fatality Rate remains high, the observed measles Mortality Rate for 2016-2019 in the United States was 0.

In other words: the risk of death is still high for those who contract measles, but the number of cases has been low due to preventative measures (like vaccines).

For COVID-19:
(Based on March 1, 2020 data from WHO)

Mortality Rate = unknown
(was 3,000 deaths / 7,000,000,000 population as of March 1, 2020 but is still rising
Note: The year-end mortality rate will be much higher because the disease is still spreading, but the current number is well below 0.001%)

Case Fatality Rate = 3,000 / 87,137 = 3.4%
Note: This estimate may be too high because not every country has implemented widespread testing, so the number-of-cases denominator may be somewhat low. Best estimates to date of COVID-19 Case Fatality Rate are between 2%-3.4%.

So when you see someone refer to 2-3.4% for the COVID-19 "mortality rate," that does not mean the virus will exterminate 3 percent of the global population. It does mean, however, that we should take the risk seriously.

We can slow the spread through social distancing, good hygiene, and (eventual) vaccine development.

Young Women's Well-being Declined Gen X to Echo Boom

I haven't been writing for the blog lately, but I have been writing.

Mark Mather and I, along with the team at PRB, published a new report on the state of young women's well-being in the U.S. across four generations?

The news?
Not so great for Millennials.

There has been progress in some areas (rising educational attainment, falling rates of cigarette smoking and homicide) but dramatic reversals in others (rising rates of incarceration, poverty, suicide, and maternal mortality).

Read the full report:
Losing Ground Young Women’s Well-Being Across Generations in the United States

Or watch the video:

Casanova: Patient Zero

Sometimes historical demography requires overturning some unusual stones to get a sense of fertility patterns, family structure, public health, and other population dynamics in the past. My recent essay, published in Hektoen International Journal of Medical Humanities dives into a very unlikely source for demographic and public health information: Casanova's diary.

Giacomo Casanova, the infamous rake, is responsible for providing historians and anthropologists with a veritable treasure trove of historical health information. His life spanned from 1725 to 1798, and his memoir, Histoire de Ma Vie, recounts nearly every day of his life with meticulous detail, from the most basic breakfast (usually chocolate) to the most convoluted course of treatment for venereal disease (usually mercury). Far from being merely a smut-filled account of Casanova’s sexual conquests, the memoir provides modern readers with extraordinary insight into the world of public health, family planning, and the transmission and treatment of sexually transmitted disease in Europe in the eighteenth century... [continue reading]

Healthy living after age 65

A new report from CDC compares life expectancy and healthy life expectancy after age 65 across states in the U.S.

Life expectancy is the average remaining years of life a person can expect to live on the basis of the current mortality rates for the population.

Healthy life expectancy is a population health measure that estimates expected years of life in good health for people at a given age. The measure is useful for public health and public policy analysis. Healthy life expectancy, relative to total life expectancy, can be used to identify populations that might be enduring illness or disability for years. And differences within and among populations can be used to identify areas of greatest need for health interventions.

The most recent analysis shows that Southern states have lower life expectancy and fewer years of healthy life, regardless of race, after age 65 than other states. In the press release, CDC Director Tom Frieden, M.D., M.P.H. writes:

"Where you live in the United States shouldn't determine how long and how healthy you live - but it does...”

Other highlights include:

• Hawaiians have the longest life expectancy and healthy life expectancy after age 65.
• Mississippi residents have the shortest.
• For whites aged 65 years, healthy life expectancy varied from a low of 11.0 years in West Virginia to a high of 18.8 years in DC.
• Mississippi also has the lowest proportion of years of healthy life expectancy to overall life expectancy.
• Vermont has the highest ratio of healthy years to overall years of life expectancy.
• In each state women, on average, have higher life expectancy and healthy life expectancy than men.

For more information, see the full CDC report.

Has flu season peaked?

By the end of December nearly every state in the nation reported widespread cases of influenza.

Source: CDC

And by the first week of January, all states were reporting at least regional cases of the flu, with the vast majority reporting widespread outbreaks.

Source: CDC

However, flu season usually peaks in February or March, not in December, and while the spread of flu appears to have slowed, epidemiologists will not know for weeks whether or not the peak of flu season has passed. The slowdown may simply be part of the ebb and flow of this year's flu season.

Source: CDC

More about the data:

In addition, U.S. Centers for Disease Control and Prevention collect data from state health departments report. The first two maps below show data from the state-level reporting.

The maps are based on:

...the estimated level of geographic spread of influenza activity in their states each week through the State and Territorial Epidemiologists Reports. States report geographic spread of influenza activity as no activity, sporadic, local, regional, or widespread. These levels are defined as follows:
No Activity: No laboratory-confirmed cases of influenza and no reported increase in the number of cases of ILI.
Sporadic: Small numbers of laboratory-confirmed influenza cases or a single laboratory-confirmed influenza outbreak has been reported, but there is no increase in cases of ILI.
Local: Outbreaks of influenza or increases in ILI cases and recent laboratory-confirmed influenza in a single region of the state.
Regional: Outbreaks of influenza or increases in ILI and recent laboratory confirmed influenza in at least two but less than half the regions of the state with recent laboratory evidence of influenza in those regions.
Widespread: Outbreaks of influenza or increases in ILI cases and recent laboratory-confirmed influenza in at least half the regions of the state with recent laboratory evidence of influenza in the state.

CDC cautions, however, that the maps reflect the "geographic spread of influenza viruses, but does not measure the severity of influenza activity."

In addition, shown on map three, CDC monitors and reports on influenza through the Influenza-like Illness Surveillance Network (ILINet).

Activity levels are based on the percent of outpatient visits in a state due to ILI and are compared to the average percent of ILI visits that occur during spring and fall weeks with little or no influenza virus circulation... The map uses the proportion of outpatient visits to health care providers for influenza-like illness to measure the ILI activity level within a state. It does not, however, measure the extent of geographic spread of flu within a state. Therefore, outbreaks occurring in a single city could cause the state to display high activity levels. Data collected in ILINet may disproportionately represent certain populations within a state, and therefore, may not accurately depict the full picture of influenza activity for the whole state.  

For more information CDC also provides an interactive mapping tool, Flu View.

Influenza

In case you had not noticed the coughing and sneezing of your neighbors (or the complaints of fever and chills posted by friends on Facebook), CDC confirms that flu season is upon us...

The U.S. Centers for Disease Control and Prevention monitor and report on influenza through the Influenza-like Illness Surveillance Network (ILINet). In addition, CDC staff collect data from state health departments report. The maps below show data from the state-level reporting.

Influenza activity in early December...

Source: CDC

... and at the end of December.

Source: CDC

The maps are based on:

...the estimated level of geographic spread of influenza activity in their states each week through the State and Territorial Epidemiologists Reports. States report geographic spread of influenza activity as no activity, sporadic, local, regional, or widespread. These levels are defined as follows:
No Activity: No laboratory-confirmed cases of influenza and no reported increase in the number of cases of ILI.
Sporadic: Small numbers of laboratory-confirmed influenza cases or a single laboratory-confirmed influenza outbreak has been reported, but there is no increase in cases of ILI.
Local: Outbreaks of influenza or increases in ILI cases and recent laboratory-confirmed influenza in a single region of the state.
Regional: Outbreaks of influenza or increases in ILI and recent laboratory confirmed influenza in at least two but less than half the regions of the state with recent laboratory evidence of influenza in those regions.
Widespread: Outbreaks of influenza or increases in ILI cases and recent laboratory-confirmed influenza in at least half the regions of the state with recent laboratory evidence of influenza in the state.

According to the most recent information, flu virus is active in every one of the contiguous states and in Alaska. Hawaii is reporting only sporadic flu activity.

CDC cautions, however, that the maps reflect the "geographic spread of influenza viruses, but does not measure the severity of influenza activity."

For more information CDC also provides an interactive mapping tool, Flu View.

Counting empty calories from alcohol

While New York City enacts new regulations to curb consumption of empty calories in sugary soft drinks, a new report from the CDC suggests that alcoholic beverages may pose a similar empty-calories health problem.

From the report:

Although the risks of excessive alcohol consumption in terms of injury and chronic disease are well known, less is known about the calories consumed from alcoholic beverages. As with calorically sweetened beverages, alcoholic beverages are a top contributor to caloric intake but provide few nutrients.

Analysis is based on information collected in a 24-hour dietary recall interview conducted as part of the National Health and Nutrition Examination Survey (NHANES) between 2007 and 2010. NHANES sample size is approximately 10,000, and analysis of alcohol consumption focuses on respondents age 20 and older.

CDC finds that on any given day one third of men and nearly twenty percent of women consume alcoholic beverages. On average, adults in the U.S. consume an average of 100 calories* worth of alcohol per day. Men, on average, consume three times as many calories from alcoholic beverages as women do. Across both sexes, those who consume alcohol often do so in larger-than-recommended quantities:

...almost 20% of men and 6% of women consume more than 300 calories from alcoholic beverages, which is equivalent to 2 or more 12-ounce (oz) beers, more than 2½ glasses of wine (12.5 oz), or more than 4.5 oz of spirits.

The study authors note that "on a given day, consumers of alcoholic beverages obtain approximately 16% of their total caloric intake from alcoholic beverages" which is higher than the dietary recommendation that no more than 15 percent of calories come from discretionary solid fats and added sugars.

Avg. calories from alcoholic beverages per day among U.S. adults age 20+,
by sex, age, and type of alcohol (2007-10)
Source: CDC

With respect to age and sex differences, CDC finds that most alcoholic-beverage calories consumed by men are from beer. Among women, calorie consumption is evenly distributed across beer, wine, and liquor. Across both sexes, consumption is highest in the 20-39 age group and lowest among those age 60 and older.


*Note: According to CDC, one beer is approximately 150 calories, one glass of wine is approximately 120 calories, and 1.5 ounces of liquor is approximately 100 calories.

Hurricane seasons begins

It's June 1, so the 2011 hurricane season has begun...

Nearly 37 million people live in areas most at risk of hurricanes, an area covering 179,000 square miles along the coastal region stretching from Texas to North Carolina, according to the U.S. Census Bureau. Hurricanes occasionally strike farther north, but such events are rare.

Hurricane History
The Department of Housing and Urban Development estimates that in the summer of 2005 hurricanes Katrina, Rita, and Wilma damaged "more than one million housing units across five states." Of the damaged homes 515,000 were in Louisiana, 220,000 in Mississippi, and nearly 140,000 in Texas.

By 2010, according to the HUD study, three quarters of the 2005 hurricane-damaged properties on "significantly affected" blocks were in good condition (at least on the outside*), but nearly 15 percent of the properties still had substantial visible repair needs, and 11 percent no longer contained a permanent residential structure. Louisiana homes, of the state affected, are most likely to still have unrepaired damage. Mississippi homes were most likely to be either repaired or entirely demolished and left vacant.
*We should note that these estimates do not include homes with mold or other water damage issues that might render the structures uninhabitable.

From a business perspective, in the year following Katrina New Orleans had about 95,000 fewer jobs, with most losses in tourism and port operations. It took nearly two years after Katrina for the number of restaurants in New Orleans to rebound to it's pre-hurricane level (according to restaurant critic Tom Fitzmorris in his book Hungry Town).

Hurricane Demographics
In addition to their physical and economic damage, major hurricanes can cause huge demographic shifts. For example, during hurricane Katrina approximately 1.5 million people over the age of 16 left their homes in Louisiana, Mississippi, and Alabama. And while many have returned, not all have. For example between 2005 and 2010 New Orleans saw its population decline by 25 percent from an estimated 455,000 before the hurricane to 344,829 as of April 1, 2010.

And while New Orleans tends to dominate the news headlines because of the broken levees, Pass Christian, MS actually has sustained a greater proportionate loss in population. Though a small town before Katrina (just under 7,000 according to 2005 Census Bureau estimates) the population had shrunk in half in the year following the hurricane, and many residents did not return. The 2010 Census count shows a resident population of only 4,613 - a sustained decline of 34 percent since the hurricane. Gulfport, MS also declined by an estimated 5,500 residents (-8 percent) between 2005 and 2010.

Less notorious, but just as significant in terms of population shift, was Hurricane Ike in Galveston, TX. Nearly 10,000 residents remained displaced two years after the hurricane. The estimated population before Ike was 57,000 but was only 47,743 at the 2010 Census. The HUD report on housing shows that one quarter of homes in Texas hurricane-affected neighborhoods still showed significant damage in 2010, in part because of the 2005 series of hurricanes and in part because of Ike.

In natural disasters traditional sources of demographic data (building permits, school enrollment records, utility hookups, drivers licenses, etc...) are either no longer available, or provide misleading information about the displaced, remaining, and returning population. Some of the most clever demographic techniques I have seen to date were hurricane-related. At the 2010 Applied Demography conference Mark VanLandingham and Janna Knight presented their techniques for reverse-estimating the post-Katrina population of New Orleans. And Nazrul Hoque, Alelhie Valencia, and Karl Eschbach presented their techniques for filling in the data gaps for post-hurricane Galveston.

Hurricane Preparedness
And in case all this talk of hurricanes has you thinking it's time to update your emergency preparedness kit, the CDC has developed a useful but tongue-in-cheek checklist that will get you through any disaster - even a zombie apocalypse!
Click on image for more information from the CDC.

Image of Hurricane Katrina in the Gulf of Mexico courtesy of NASA-GSFC, data from NOAA GOES

The hefty toll of the obesity epidemic

Obesity rates are going up, up, up.

Today, Colorado has the nation’s lowest rate of adult obesity, at 18.6 percent, followed by Washington DC at 19.7 percent. California ranks 17, tied with Alaska, with an estimated 24.8 percent of adults in the obese category. The nation’s highest rates of obesity are in the southeast, with Louisiana (33%) and Mississippi (34%) topping the charts, according to data released last week by the Centers for Disease Control and Prevention (CDC).

Compare this with data from 1990, and the picture becomes quite alarming. In 1990 ten states had obesity rates below 10 percent. Today none do. In 1990 no states had obesity rates above 15 percent. Today none have rates below 15 percent.

According to the CDC, obesity is “defined as a Body Mass Index (BMI) of 30 or greater.BMI is calculated from a person's weight and height and provides a reasonable indicator of body fatness and weight categories that may lead to health problems. Obesity is a major risk factor for cardiovascular disease, certain types of cancer, and type 2 diabetes.”

These obesity-related diseases carry a hefty price tag. Estimates in 2000 put obesity-related health care costs in California at $7.7 billion dollars, and the national cost at $75 billion. According to more recent projections from United Health Foundation, the American Public Health Association and Partnership for Prevention, if current trends continue costs may reach as high $318 billion in the next eight years. Even if rates stay steady at their current high levels the report finds that “the U.S. could save an estimated $820 per adult in health care costs by 2018 ? a savings of almost $200 billion dollars.”

Perhaps not surprisingly, obesity is strongly correlated with physical activity. States where a high proportion of commuters walk, run, or bike to work have some of the nation’s lowest obesity rates. For example, nationwide about 3.4 percent of commuters walk or bike as their primary mode of commute to work. In Colorado, with the nation’s lowest obesity rate, the walk-or-bike rate is 4.2 percent - 25 percent higher than the national average. In Washington DC with the nation’s second lowest obesity rate, the walk-or-bike rate is 13.7 percent. Conversely, commuters in the three states with the highest levels of obesity (Mississippi, Louisiana, Tennessee) are only about half as likely to walk or bike to work as the national average.

For original publication, please see: http://www.examiner.com/x-43439-San-Diego-Economy-Examiner~y2010m8d10-The-hefty-toll-of-the-obesity-epidemic