Sunday, March 29, 2020

Death and Treatment Need Statistics 2020 Pandemic



Statistical models are needed to guide government leaders and health service providers concerned with maximizing the number of infected people who survive and providing high quality care to those in need.

All models have multiple assumptions. In the midst of a pandemic such as Covid-19, new data are constantly being processed. Thus, parameters will need to be changed as new data change the models. Multiple outcomes must be considered without biased interpretations favoring either lower or higher estimates.

NYT 13 March 2020 reported by Sheri Fink

This report refers to four scenarios and is early than the ones further down the page.

"Between 160 million and 214 million people in the United States could be infected over the course of the epidemic, according to a projection that encompasses the range of the four scenarios. That could last months or even over a year, with infections concentrated in shorter periods, staggered across time in different communities, experts said. As many as 200,000 to 1.7 million people could die."

HealthData.org 25 March 2020 research paper submitted for peer review.

This document can be downloaded for careful study. The authors detail their assumptions and provide charts and tables indicating projected deaths and needs for various resources such as ICU beds and ventilators. The data are for the US as a whole as well as for each state. Figure 9 projects the cumulative deaths plotted by month and the range from low to high is wide. They estimate 81 thousand deaths over the next four months. Again, the estimate is subject to a wide margin of statistical error. For most states, peak hospital needs are projected to occur in April (See Table 1).

Dr. Fauci March 29 2020 also in Slate

According to Taylor Hatmaker writing for Techcrunch, Dr. Fauci estimated between 100,000 to 200,000 deaths from Covid-19.

Bases for Comparison
For the USA

  Number of deaths  = 2,913,503 (population in 2018 = 327.2 million)
  Life expectancy = 78.6 years (This will vary with various parameters)
  Influenza and Pneumonia deaths = 55,672 
         Note: 7 conditions cause more deaths according to the CDC 

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Wednesday, March 25, 2020

Watch those Covid 19 curves


Some highly intelligent scientists have used available data to plot trends related to the spread of Covid-19 such as number of identified infections, recoveries, and deaths. Others plotted ideas rather than data such as flattening the curve. Some rely on "common sense" and others rely on data.

Given my experience in teaching graduate research methods and statistics and talking with physicians and psychologists about statistics, I think it worth reminding any readers of this post that a lot of very intelligent people have difficulty with statistical models. There are many PhDs younger than I who know more methods of sophisticated data analyses than I learned during my PhD or since; however, not everyone stops to consider their assumptions when modeling data.

That's my point here--we need to review and challenge the assumptions about the data, the way the data are plotted, and how people interpret those data.

Missing data are very important to developing an accurate model. 
Unfortunately, in the early stages of a pandemic, there are a lot of missing data. Missing data can skew distributions. And it is very hard to identify outliers until sufficient data are available. People using different testing methods have different delay times in getting results. Some nations have more test kits in service than others.

Validity of data is crucial to developing a valid model. 
We have data from various nations, but we do not have evidence of verification of those data. Nor do we know if we are dealing with sufficient samples to make judgments about the population of an entire nation. Data from a small country can yield very different ratios than data from a large country. We need to know what percentage of a population are in the tested sample.

Generalization of data from one nation to another can be misleading.
In any research study, scientists usually caution readers about the limitations of their research. The current "real-time" reports of data translated into graphs showing different trends for different nations do not necessarily consider all the relevant variables that ensure trends from China may be applicable to future trends in the UK or the USA. So, in addition to the above problems, we cannot assume that the trend lines will be the same.

Different methods of plotting can lead to different conclusions.
I've seen plots of raw data and transformed data. Remember all that high school math about logarithms? Do you recall exponential growth curves? Plotting raw data is a good idea. Transforming those data requires that the assumptions for transformation make sense. It's one thing to present data in different ways as one seeks to understand the spread of a virus (or any other behavior), but it is quite another thing to make decisions for the public based on such assumptions.

Drawing conclusions from early data can lead to bad policy. 
It is generally wise to be conservative in drawing conclusions about data--especially when we do not know enough to truly plot future trends. I do not claim to know the best way to monitor the extent of the human tragedy due to Covid-19 virus effects. My inclination is to skeptically monitor the available data and various models, and follow conservative guidelines for the sake of my health and the health of others.

We might think about who has the most to gain from advice to do this or that in response to health and related economic policies during the time of the current pandemic and at other times as well. People usually promote ideas that are in their best interest (self-serving bias) and they view data and draw conclusions influenced by a confirmation bias (we ignore data that contradicts our assumptions).

In the case of Covid-19 policy, we might ask the following questions.

  1. Who will win or lose a future election based on the number of people who get infected or die?
  2. Who will gain or lose large sums of money based on the number of people who get infected or die?
  3. Who will win or lose a future election based on how well the economy is doing?
  4. Who will win or lose a future election based on the number of people who are unemployed?
  5. Who will gain or lose large sums of money based on the length of time people are under severe restrictions?
  6. Who will benefit from claiming shortages of various items considered "essential" or "nonessential"
  7. Who will benefit from having their employment labeled as "essential" or "nonessential?"
  8. How do decisions made during a crisis based on incomplete data reveal prejudice or even hate? For example, are the elderly worth less than the young? Are foreigners worth less than citizens? Are the lives of the wealthy worth more than the lives of the poor?
  9. What data tell us how policies will deferentially affect vulnerable populations? For example, I have hypothesized that requiring some people to remain at home might increase child abuse and domestic violence. Others have wondered about increased suicide in response to some healthcare policies focused heavily on Covid19.

You can probably generate more questions based on your profession and /or knowledge base.

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Monday, March 16, 2020

Life expectancy and lifespan assessment in psychology

Lifespan is not the same concept as life expectancy.

Lifespan is the maximum period of time a species lives. The human lifespan is measured in years. As of 2020, the documented human lifespan is 122 years (see also lifespan concept in psychological science).

Life expectancy is the average period of time a member of a population with certain characteristics lives. Human life expectancy, measured in years, varies by sex and environment. Human life expectancy varies by the age group. For example, life expectancy of people at birth will be different from a group of people who are alive at age 70.

United Nations data are reported by sex and country. Overall, there has been an increase in human life expectancy on a worldwide basis between 1950 (47.0 years) and 2020 (73.2 years; worldometers). I have rounded the numbers which were reported up to two decimal places.

Examples of recent life expectancy data for wealthy nations reveal marked differences compared to other nations.

Data from the United Nations -- see the full chart and details at  worldometers.



Life expectancy of laboratory animals varies with the species and strains of the species. The life expectancy of rats and mice is measured in days.

Life expectancy data are also reported in life tables. Period life tables are available from the US Social Security program (Social Security Administration; SSA). These tables organize data by age group and sex. SSA also provides downloadable reports. The tables also give the probability of death for men and women. The tables are available based on historical data as well as estimates of the future (for examples, see  ssa).

SSA has a life expectancy calculator based on gender and age (ssa calculator). The results provide an estimate, but they note that the estimate does not consider such relevant factors as health, lifestyle and family history.

Death rates are usually reported by sex, year or time period, and per 100,000 people in a population. According to US Social Security study 120 in the year 2000, people died at the rate of 867 per 100,000 but this varied by age group. Under age 65s died at 243 and over 65s at 5,261. The data were different for men and women. For those over age 65, 6458 men died compared to 4,530 women (https://www.ssa.gov/oact/NOTES/actstud.html). Note- I have rounded the numbers.

Cite This Blog Post

Sutton, G.W. (2020, March 16). Life expectancy and lifespan assessment in psychology Assessment, Statistics, & Research. https://statistics.suttong.com/ 2020/03/life-expectancy-and-lifespan-assessment.html


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