The Chicago Actuarial Association is having a Zoom presentation for members on 7/21 2:30 PM (Central, I assume).
A Consistent Approach to Projecting Mortality Improvement, led by Larry Stern and Patrick Wiese
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Thanks – I’m going to see if I can attend
I’m dropping this here, but I think the bulk of the increase in heart disease deaths was really COVID:
and this is the video from that:
On age-adjusted death rates
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Cancer deaths in 2020
Did a blog post w/ the video:
Basically, cancer deaths were flat. No effect from pandemic.
This one will be fun going through:
I wish I could convince them to do a tile grid map
That said, I think I see the fried food belt… Also, the highest category for the 1959 life expectancies are lower than the lowest category for the 2018 maps
Thanks for pointing that out. I hadn’t zoomed in yet and was thinking dang, some back-slid. I would have seen it eventually, but I read the rest of your post first.
Yes, I am going to bitch at them for the misleading graphs. 
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yes, I know the largest effects are from the pandemic, but thought I’d put it here for archivery.
Came across this lovely little report with a great cover
Just did a quick estimate of deaths in each year (not really exact… it’s using weekly reporting, so some overlap years, but it’s close enough):
2018 54905
2019 54392
2020 81663
Just to make it simple, I assume a population of 8.3 million each year (based on this estimate of 2019 population - https://www.census.gov/quickfacts/newyorkcitynewyork), we get a rate:
2018 6.6 per 1000
2019 6.6 per 1000
2020 9.8 per 1000
These are just crude death rates
Although there is a large gap between Black and White American life expectancies, the gap fell 48.9% between 1990-2018, mainly due to mortality declines among Black Americans. We examine age-specific mortality trends and racial gaps in life expectancy in rich and poor U.S. areas and with reference to six European countries.
Inequalities in life expectancy are starker in the U.S. than in Europe. In 1990 White Americans and Europeans in rich areas had similar overall life expectancy, while life expectancy for White Americans in poor areas was lower. But since then even rich White Americans have lost ground relative to Europeans. Meanwhile, the gap in life expectancy between Black Americans and Europeans decreased by 8.3%.
Black life expectancy increased more than White life expectancy in all U.S. areas, but improvements in poorer areas had the greatest impact on the racial life expectancy gap. The causes that contributed the most to Black mortality reductions included: Cancer, homicide, HIV, and causes originating in the fetal or infant period.
Life expectancy for both Black and White Americans plateaued or slightly declined after 2012, but this stalling was most evident among Black Americans even prior to the COVID-19 pandemic. If improvements had continued at the 1990-2012 rate, the racial gap in life expectancy would have closed by 2036. European life expectancy also stalled after 2014. Still, the comparison with Europe suggests that mortality rates of both Black and White Americans could fall much further across all ages and in both rich and poor areas.
We began by requesting the California Department of Public Health’s public-use death index files for the years 2010-2020. The records, which include deaths by any cause, contain columns for date of birth, date of death and county of death. Any records with missing or invalid dates were excluded. The data released by the state is not comprehensive of all deaths and contains some duplicate records. We removed duplicate rows in the data containing matching first and last names, sex, date of birth, date of death and county of death. We excluded 2020 from the analysis to avoid confounding results due to the COVID-19 pandemic.
We compared the result to a Centers for Disease Control database containing a monthly breakdown of deaths for all U.S. counties. The state’s daily records vary slightly from the federal dataset, more noticeably so in smaller counties. However, the trends in deaths over time shown in both sources match closely, and the total deaths in the de-duplicated CDPH data are 1.1% higher than those of the CDC.
Because these data contained no description of cause of death, we were unable to exclude accidental or traumatic deaths, which is a common practice in the scientific literature. However, there is evidence to suggest a link between high temperatures and accidental death, said Kate Weinberger, an environmental epidemiologist at the University of British Columbia School of Population and Public Health, who conducted a study of excess deaths in 297 U.S. counties.
Next, we transformed the individual deaths-by-day data to find the total number of deaths per day in each of California’s 58 counties. Annual population estimates were downloaded from the California Department of Finance.
We defined a heat event as any day between the months of May and October on which the max temperature exceeded the 95th percentile of a given county’s “normal” temperature distribution. To establish a baseline of normal temperatures, we calculated a 30-year average max temperature for each day and month of the year in each county using data downloaded from Oregon State University’s PRISM Climate Group. This average is based on data starting in 1981, the earliest year available, and 2010.
Using a normal temperature distribution allows us to define a heat event differently across California’s diverse climate and account for adaptation and acclimatization in various regions. Using this method, we can compare any temperature for a given day and county to what’s normal or expected. In theory, a county could end up with no heat events for the summer or see multiple week-long heat waves.
The PRISM website allows users to download gridded temperature data for multiple locations. We obtained max temperature data at the max resolution available, 4 km, and centered the points according to the U.S. Census’s 2010 Centers of Population report. This allowed us to estimate the max temperature felt by the most people in each area.
Working with Logan Arnold, a health data analyst, we created a negative binomial regression model to predict the number of deaths that would be expected to occur in the absence of a heat event in a given county. The model controls for weekends, month and population changes over time.
We then calculated excess deaths by simply subtracting the number of observed deaths on an extreme heat day by the number of predicted deaths, as well as a margin of error, for each county included in the analysis and a state total. These calculations are based on the methods outlined in a 2010 paper by Hoshiko et al, which estimated excess deaths related to the deadly 2006 California heatwave.
Historical, England:
I want to highlight – these are period curves, not cohort [of course, for the 1951+ cohort, most of the curve would be projection]
@meep Not sure if I should post this here or in the COVID mortality thread.
They seem to put a lot of the blame for younger deaths on COVID. I thought you showed that was not the case.
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Maybe work on more readable labels when using the dark theme?