Average Number of Years Lost For People Who Died of Coronavirus in China

We illustrate the use of the package by estimating the average number of years by which people’s lives are shortened due to coronavirus. Using data from Table 1 of the paper that gives us the distribution of ages of people who died from COVID-19 in China, with conservative assumptions (assuming gender of the dead person to be male, taking the middle of age ranges) we find that people’s lives are shortened by about 11 years on average. These estimates are conservative for one additional reason: there is likely an inverse correlation between people who die and their expected longevity. And note that given a bulk of the deaths are among older people, when people are more infirm, the quality adjusted years lost is likely yet more modest. Given that the last life tables from China are from 1981, we estimate the average number of years lost if people had the same profile as Americans. Using the most recent SSA data, we find that number to be 16. Compare this to deaths from road accidents, the modal reason for death among 5-24 and 25-44 ages in the US. Male life expectancy in the US at 25 is another ~ 52 years.

table1.png

import pandas as pd

from lost_years import lost_years_hld

Prepare example input in DataFrame

Please look at country codes here:- https://www.lifetable.de/cgi-bin/country_codes.php

df = pd.read_csv("data/table_1.csv")
df["year"] = 2020
df
lowest_age highest_age middle_age n_deaths sex country year
0 10 19 15 1 M CHN 2020
1 20 29 25 7 M CHN 2020
2 30 39 25 18 M CHN 2020
3 40 49 45 38 M CHN 2020
4 50 59 55 130 M CHN 2020
5 60 69 65 309 M CHN 2020
6 70 79 75 312 M CHN 2020
7 80 89 85 208 M CHN 2020

Get Human Life Table data columns from HLD dataset

highest_ldf = lost_years_hld(
    df, {"age": "lowest_age", "country": "country", "sex": "sex", "year": "year"}
)
highest_ldf.head()
lowest_age highest_age middle_age n_deaths sex country year hld_country hld_region hld_residence ... hld_version hld_ref-id hld_year1 hld_year2 hld_typelt hld_sex hld_age hld_age_interval hld_life_expectancy hld_life_expectancy_orig
0 10 19 15 1 M CHN 2020 CHN 0 0 ... 1 223.40 1981 1981 1 1 10 1 60.38 60.37
0 10 19 15 1 M CHN 2020 CHN 10 0 ... 1 224.90 1981 1981 1 1 10 1 60.74 60.73
0 10 19 15 1 M CHN 2020 CHN 20 0 ... 1 224.19 1981 1981 1 1 10 1 62.22 62.2
0 10 19 15 1 M CHN 2020 CHN 30 0 ... 1 224.18 1981 1981 1 1 10 1 58.97 58.96
0 10 19 15 1 M CHN 2020 CHN 40 0 ... 1 224.10 1981 1981 1 1 10 1 59.25 59.24

5 rows × 22 columns

Note that the year we are matching to is 1981.

# From table 1, 95.7% deaths in Hubei region == 110 (https://www.lifetable.de/cgi-bin/regions.php)
# Note: Current function version doesn't return region data, using all available data for China
highest_ldf = highest_ldf.copy()
highest_ldf

Assuming all the people who died were at the bottom of the age ranges

highest_ldf["years_lost"] = (
    highest_ldf["hld_life_expectancy"]
    * highest_ldf["n_deaths"]
    / highest_ldf["n_deaths"].sum()
)
highest_ldf
lowest_age highest_age middle_age n_deaths sex country year hld_country hld_region hld_residence ... hld_ref-id hld_year1 hld_year2 hld_typelt hld_sex hld_age hld_age_interval hld_life_expectancy hld_life_expectancy_orig years_lost
0 10 19 15 1 M CHN 2020 CHN 110 0 ... 224.26 1981 1981 1 1 10 1 58.30 58.3 0.056989
1 20 29 25 7 M CHN 2020 CHN 110 0 ... 224.26 1981 1981 1 1 20 1 48.88 48.88 0.334467
2 30 39 25 18 M CHN 2020 CHN 110 0 ... 224.26 1981 1981 1 1 30 1 39.69 39.68 0.698358
3 40 49 45 38 M CHN 2020 CHN 110 0 ... 224.26 1981 1981 1 1 40 1 30.56 30.55 1.135171
4 50 59 55 130 M CHN 2020 CHN 110 0 ... 224.26 1981 1981 1 1 50 1 21.87 21.87 2.779179
5 60 69 65 309 M CHN 2020 CHN 110 0 ... 224.26 1981 1981 1 1 60 1 14.34 14.33 4.331437
6 70 79 75 312 M CHN 2020 CHN 110 0 ... 224.26 1981 1981 1 1 70 1 8.70 8.68 2.653372
7 80 89 85 208 M CHN 2020 CHN 110 0 ... 224.26 1981 1981 1 1 80 1 4.88 4.85 0.992219

8 rows × 23 columns

highest_ldf["years_lost"].sum().round()

13.0

lowest_ldf = lost_years_hld(
    df, {"age": "highest_age", "country": "country", "sex": "sex", "year": "year"}
)
lowest_ldf.head()
lowest_age highest_age middle_age n_deaths sex country year hld_country hld_region hld_residence ... hld_version hld_ref-id hld_year1 hld_year2 hld_typelt hld_sex hld_age hld_age_interval hld_life_expectancy hld_life_expectancy_orig
0 10 19 15 1 M CHN 2020 CHN 0 0 ... 1 223.40 1981 1981 1 1 19 1 51.83 51.82
0 10 19 15 1 M CHN 2020 CHN 10 0 ... 1 224.90 1981 1981 1 1 19 1 52.14 52.13
0 10 19 15 1 M CHN 2020 CHN 20 0 ... 1 224.19 1981 1981 1 1 19 1 53.53 53.51
0 10 19 15 1 M CHN 2020 CHN 30 0 ... 1 224.18 1981 1981 1 1 19 1 50.43 50.42
0 10 19 15 1 M CHN 2020 CHN 40 0 ... 1 224.10 1981 1981 1 1 19 1 50.72 50.7

5 rows × 22 columns

Assuming all the people who died were at the top of the age ranges

# From table 1, 95.7% deaths in Hubei region == 110 (https://www.lifetable.de/cgi-bin/regions.php)
# Note: Current function version doesn't return region data, using all available data for China
lowest_ldf = lowest_ldf.copy()
lowest_ldf

lowest_ldf["years_lost"] = (
    lowest_ldf["hld_life_expectancy"]
    * lowest_ldf["n_deaths"]
    / lowest_ldf["n_deaths"].sum()
)
lowest_ldf
lowest_age highest_age middle_age n_deaths sex country year hld_country hld_region hld_residence ... hld_ref-id hld_year1 hld_year2 hld_typelt hld_sex hld_age hld_age_interval hld_life_expectancy hld_life_expectancy_orig years_lost
0 10 19 15 1 M CHN 2020 CHN 110 0 ... 224.26 1981 1981 1 1 19 1 49.81 49.8 0.048690
1 20 29 25 7 M CHN 2020 CHN 110 0 ... 224.26 1981 1981 1 1 29 1 40.62 40.61 0.277947
2 30 39 25 18 M CHN 2020 CHN 110 0 ... 224.26 1981 1981 1 1 39 1 31.46 31.46 0.553548
3 40 49 45 38 M CHN 2020 CHN 110 0 ... 224.26 1981 1981 1 1 49 1 22.70 22.69 0.843206
4 50 59 55 130 M CHN 2020 CHN 110 0 ... 224.26 1981 1981 1 1 59 1 15.02 15.01 1.908700
5 60 69 65 309 M CHN 2020 CHN 110 0 ... 224.26 1981 1981 1 1 69 1 9.19 9.17 2.775865
6 70 79 75 312 M CHN 2020 CHN 110 0 ... 224.26 1981 1981 1 1 79 1 5.19 5.16 1.582874
7 80 89 85 208 M CHN 2020 CHN 110 0 ... 224.26 1981 1981 1 1 89 1 2.89 2.7 0.587605

8 rows × 23 columns

lowest_ldf["years_lost"].sum().round()

9.0

middle_ldf = lost_years_hld(
    df, {"age": "middle_age", "country": "country", "sex": "sex", "year": "year"}
)
middle_ldf.head()
lowest_age highest_age middle_age n_deaths sex country year hld_country hld_region hld_residence ... hld_version hld_ref-id hld_year1 hld_year2 hld_typelt hld_sex hld_age hld_age_interval hld_life_expectancy hld_life_expectancy_orig
0 10 19 15 1 M CHN 2020 CHN 0 0 ... 1 223.40 1981 1981 1 1 15 1 55.61 55.6
0 10 19 15 1 M CHN 2020 CHN 10 0 ... 1 224.90 1981 1981 1 1 15 1 55.94 55.93
0 10 19 15 1 M CHN 2020 CHN 20 0 ... 1 224.19 1981 1981 1 1 15 1 57.37 57.35
0 10 19 15 1 M CHN 2020 CHN 30 0 ... 1 224.18 1981 1981 1 1 15 1 54.20 54.19
0 10 19 15 1 M CHN 2020 CHN 40 0 ... 1 224.10 1981 1981 1 1 15 1 54.47 54.46

5 rows × 22 columns

Assuming all the people who died were at the middle of the age ranges

# From table 1, 95.7% deaths in Hubei region == 110 (https://www.lifetable.de/cgi-bin/regions.php)
# Note: Current function version doesn't return region data, using all available data for China
middle_ldf = middle_ldf.copy()
middle_ldf

middle_ldf["years_lost"] = (
    middle_ldf["hld_life_expectancy"]
    * middle_ldf["n_deaths"]
    / middle_ldf["n_deaths"].sum()
)
middle_ldf
lowest_age highest_age middle_age n_deaths sex country year hld_country hld_region hld_residence ... hld_ref-id hld_year1 hld_year2 hld_typelt hld_sex hld_age hld_age_interval hld_life_expectancy hld_life_expectancy_orig years_lost
0 10 19 15 1 M CHN 2020 CHN 110 0 ... 224.26 1981 1981 1 1 15 1 53.55 53.55 0.052346
1 20 29 25 7 M CHN 2020 CHN 110 0 ... 224.26 1981 1981 1 1 25 1 44.32 44.32 0.303265
2 30 39 25 18 M CHN 2020 CHN 110 0 ... 224.26 1981 1981 1 1 25 1 44.32 44.32 0.779824
3 40 49 45 38 M CHN 2020 CHN 110 0 ... 224.26 1981 1981 1 1 45 1 26.12 26.11 0.970244
4 50 59 55 130 M CHN 2020 CHN 110 0 ... 224.26 1981 1981 1 1 55 1 17.91 17.9 2.275953
5 60 69 65 309 M CHN 2020 CHN 110 0 ... 224.26 1981 1981 1 1 65 1 11.34 11.33 3.425279
6 70 79 75 312 M CHN 2020 CHN 110 0 ... 224.26 1981 1981 1 1 75 1 6.65 6.63 2.028152
7 80 89 85 208 M CHN 2020 CHN 110 0 ... 224.26 1981 1981 1 1 85 1 3.64 3.57 0.740098

8 rows × 23 columns

middle_ldf["years_lost"].sum().round()

11.0

Assume the Longevity is the Same as People in the US

from lost_years import lost_years_ssa

ssa_middle_ldf = lost_years_ssa(df, {"age": "middle_age", "sex": "sex", "year": "year"})
ssa_middle_ldf.head()
lowest_age highest_age middle_age n_deaths sex country year ssa_age ssa_year ssa_life_expectancy
0 10 19 15 1 M CHN 2020 15 2016 61.70
1 20 29 25 7 M CHN 2020 25 2016 52.30
2 30 39 25 18 M CHN 2020 25 2016 52.30
3 40 49 45 38 M CHN 2020 45 2016 34.08
4 50 59 55 130 M CHN 2020 55 2016 25.52 
ssa_middle_ldf["years_lost"] = (
    ssa_middle_ldf["ssa_life_expectancy"]
    * ssa_middle_ldf["n_deaths"]
    / ssa_middle_ldf["n_deaths"].sum()
)
ssa_middle_ldf
lowest_age highest_age middle_age n_deaths sex country year ssa_age ssa_year ssa_life_expectancy years_lost
0 10 19 15 1 M CHN 2020 15 2016 61.70 0.060313
1 20 29 25 7 M CHN 2020 25 2016 52.30 0.357869
2 30 39 25 18 M CHN 2020 25 2016 52.30 0.920235
3 40 49 45 38 M CHN 2020 45 2016 34.08 1.265924
4 50 59 55 130 M CHN 2020 55 2016 25.52 3.243011
5 60 69 65 309 M CHN 2020 65 2016 17.92 5.412786
6 70 79 75 312 M CHN 2020 75 2016 11.18 3.409736
7 80 89 85 208 M CHN 2020 85 2016 5.94 1.207742 
ssa_middle_ldf["years_lost"].sum().round()

16.0

Assume Everyone Lives Till 90

y90_middle_ldf = df.copy()
y90_middle_ldf["y90_life_expectancy"] = 90 - y90_middle_ldf["middle_age"]
y90_middle_ldf.head()
lowest_age highest_age middle_age n_deaths sex country year y90_life_expectancy
0 10 19 15 1 M CHN 2020 75
1 20 29 25 7 M CHN 2020 65
2 30 39 25 18 M CHN 2020 65
3 40 49 45 38 M CHN 2020 45
4 50 59 55 130 M CHN 2020 35 
y90_middle_ldf["years_lost"] = (
    y90_middle_ldf["y90_life_expectancy"]
    * y90_middle_ldf["n_deaths"]
    / y90_middle_ldf["n_deaths"].sum()
)
y90_middle_ldf
lowest_age highest_age middle_age n_deaths sex country year y90_life_expectancy years_lost
0 10 19 15 1 M CHN 2020 75 0.073314
1 20 29 25 7 M CHN 2020 65 0.444770
2 30 39 25 18 M CHN 2020 65 1.143695
3 40 49 45 38 M CHN 2020 45 1.671554
4 50 59 55 130 M CHN 2020 35 4.447703
5 60 69 65 309 M CHN 2020 25 7.551320
6 70 79 75 312 M CHN 2020 15 4.574780
7 80 89 85 208 M CHN 2020 5 1.016618 
y90_middle_ldf["years_lost"].sum().round()

21.0