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

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 here that gives us the distribution of ages of people who died from COVID-19 in France, 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 9 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. Using the most recent SSA data, we find that number to be also 9 years. Assuming people live till 90, the average number of years lost is 7. If we use data from WHO, the average number of years lost (if we take the middle of the age range), is 11.

import pandas as pd

from lost_years import lost_years_hld, lost_years_ssa, lost_years_who

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/covid-cedc-quot.csv",
    usecols=["cl_age90", "Dc_Elec_Covid_cum"],
    delimiter=";",
)
df.columns = ["age", "n_deaths"]
df.drop(df.loc[df.age == 0].index, inplace=True)
df

	age	n_deaths
73	9	0
74	9	0
75	9	0
76	9	0
77	9	0
...	...	...
16055	90	0
16056	90	0
16057	90	0
16058	90	0
16059	90	0

14600 rows × 2 columns

gdf = df.groupby("age").agg({"n_deaths": sum})
df = gdf.reset_index()
df2 = pd.DataFrame(
    {
        "lowest_age": [0, 10, 20, 30, 40, 50, 60, 70, 80, 90],
        "middle_age": [5, 15, 25, 35, 45, 55, 65, 75, 85, 99],
        "highest_age": [9, 19, 29, 39, 49, 59, 69, 79, 89, 99],
    }
)
df = df.join(df2)
df["year"] = 2020
df["country"] = "FRA"
df["sex"] = "M"
df

	age	n_deaths	lowest_age	middle_age	highest_age	year	country	sex
0	9	11	0	5	9	2020	FRA	M
1	19	68	10	15	19	2020	FRA	M
2	29	437	20	25	29	2020	FRA	M
3	39	1561	30	35	39	2020	FRA	M
4	49	3628	40	45	49	2020	FRA	M
5	59	14106	50	55	59	2020	FRA	M
6	69	36555	60	65	69	2020	FRA	M
7	79	76238	70	75	79	2020	FRA	M
8	89	145018	80	85	89	2020	FRA	M
9	90	92290	90	99	99	2020	FRA	M

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()

	age	n_deaths	lowest_age	middle_age	highest_age	year	country	sex	hld_country	hld_region	...	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	9	11	0	5	9	2020	FRA	M	FRA	0	...	1	3201.06	2015	2017	4	1	0	1	79.23	79.26
1	19	68	10	15	19	2020	FRA	M	FRA	0	...	1	3201.06	2015	2017	4	1	10	5	69.63	69.66
2	29	437	20	25	29	2020	FRA	M	FRA	0	...	1	3201.06	2015	2017	4	1	20	5	59.77	59.79
3	39	1561	30	35	39	2020	FRA	M	FRA	0	...	1	3201.06	2015	2017	4	1	30	5	50.12	50.15
4	49	3628	40	45	49	2020	FRA	M	FRA	0	...	1	3201.06	2015	2017	4	1	40	5	40.57	40.6

5 rows × 23 columns

Note that the year we are matching to is 2015.

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

	age	n_deaths	lowest_age	middle_age	highest_age	year	country	sex	hld_country	hld_region	...	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	9	11	0	5	9	2020	FRA	M	FRA	0	...	3201.06	2015	2017	4	1	0	1	79.23	79.26	0.002356
1	19	68	10	15	19	2020	FRA	M	FRA	0	...	3201.06	2015	2017	4	1	10	5	69.63	69.66	0.012800
2	29	437	20	25	29	2020	FRA	M	FRA	0	...	3201.06	2015	2017	4	1	20	5	59.77	59.79	0.070610
3	39	1561	30	35	39	2020	FRA	M	FRA	0	...	3201.06	2015	2017	4	1	30	5	50.12	50.15	0.211503
4	49	3628	40	45	49	2020	FRA	M	FRA	0	...	3201.06	2015	2017	4	1	40	5	40.57	40.6	0.397900
5	59	14106	50	55	59	2020	FRA	M	FRA	0	...	3201.06	2015	2017	4	1	50	5	31.41	31.43	1.197770
6	69	36555	60	65	69	2020	FRA	M	FRA	0	...	3201.06	2015	2017	4	1	60	5	23.05	23.07	2.277819
7	79	76238	70	75	79	2020	FRA	M	FRA	0	...	3201.06	2015	2017	4	1	70	5	15.60	15.6	3.215124
8	89	145018	80	85	89	2020	FRA	M	FRA	0	...	3201.06	2015	2017	4	1	80	5	8.94	8.9	3.504782
9	90	92290	90	99	99	2020	FRA	M	FRA	0	...	3201.06	2015	2017	4	1	90	5	4.36	4.15	1.087784

10 rows × 24 columns

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

12.0

lowest_ldf = lost_years_hld(
    df, {"age": "highest_age", "country": "country", "sex": "sex", "year": "year"}
)
lowest_ldf.head()

	age	n_deaths	lowest_age	middle_age	highest_age	year	country	sex	hld_country	hld_region	...	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	9	11	0	5	9	2020	FRA	M	FRA	0	...	1	3201.02	2013	2015	1	1	9	1	70.42	70.42
1	19	68	10	15	19	2020	FRA	M	FRA	0	...	1	3201.02	2013	2015	1	1	19	1	60.53	60.53
2	29	437	20	25	29	2020	FRA	M	FRA	0	...	1	3201.02	2013	2015	1	1	29	1	50.88	50.89
3	39	1561	30	35	39	2020	FRA	M	FRA	0	...	1	3201.02	2013	2015	1	1	39	1	41.33	41.33
4	49	3628	40	45	49	2020	FRA	M	FRA	0	...	1	3201.02	2013	2015	1	1	49	1	32.13	32.14

5 rows × 23 columns

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

lowest_ldf["years_lost"] = (
    lowest_ldf["hld_life_expectancy"]
    * lowest_ldf["n_deaths"]
    / lowest_ldf["n_deaths"].sum()
)
lowest_ldf

	age	n_deaths	lowest_age	middle_age	highest_age	year	country	sex	hld_country	hld_region	...	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	9	11	0	5	9	2020	FRA	M	FRA	0	...	3201.02	2013	2015	1	1	9	1	70.42	70.42	0.002094
1	19	68	10	15	19	2020	FRA	M	FRA	0	...	3201.02	2013	2015	1	1	19	1	60.53	60.53	0.011127
2	29	437	20	25	29	2020	FRA	M	FRA	0	...	3201.02	2013	2015	1	1	29	1	50.88	50.89	0.060108
3	39	1561	30	35	39	2020	FRA	M	FRA	0	...	3201.02	2013	2015	1	1	39	1	41.33	41.33	0.174409
4	49	3628	40	45	49	2020	FRA	M	FRA	0	...	3201.02	2013	2015	1	1	49	1	32.13	32.14	0.315123
5	59	14106	50	55	59	2020	FRA	M	FRA	0	...	3201.02	2013	2015	1	1	59	1	23.74	23.74	0.905287
6	69	36555	60	65	69	2020	FRA	M	FRA	0	...	3201.02	2013	2015	1	1	69	1	16.21	16.21	1.601885
7	79	76238	70	75	79	2020	FRA	M	FRA	0	...	3201.02	2013	2015	1	1	79	1	9.42	9.42	1.941440
8	89	145018	80	85	89	2020	FRA	M	FRA	0	...	3201.02	2013	2015	1	1	89	1	4.51	4.52	1.768072
9	90	92290	90	99	99	2020	FRA	M	FRA	0	...	3201.05	2014	2016	4	1	99	1	2.19	2.41	0.546387

10 rows × 24 columns

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

7.0

middle_ldf = lost_years_hld(
    df, {"age": "middle_age", "country": "country", "sex": "sex", "year": "year"}
)
middle_ldf.head()

	age	n_deaths	lowest_age	middle_age	highest_age	year	country	sex	hld_country	hld_region	...	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	9	11	0	5	9	2020	FRA	M	FRA	0	...	1	3201.06	2015	2017	4	1	5	5	74.60	74.63
1	19	68	10	15	19	2020	FRA	M	FRA	0	...	1	3201.06	2015	2017	4	1	15	5	64.66	64.69
2	29	437	20	25	29	2020	FRA	M	FRA	0	...	1	3201.06	2015	2017	4	1	25	5	54.93	54.96
3	39	1561	30	35	39	2020	FRA	M	FRA	0	...	1	3201.06	2015	2017	4	1	35	5	45.32	45.35
4	49	3628	40	45	49	2020	FRA	M	FRA	0	...	1	3201.06	2015	2017	4	1	45	5	35.92	35.94

5 rows × 23 columns

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

middle_ldf["years_lost"] = (
    middle_ldf["hld_life_expectancy"]
    * middle_ldf["n_deaths"]
    / middle_ldf["n_deaths"].sum()
)
middle_ldf

	age	n_deaths	lowest_age	middle_age	highest_age	year	country	sex	hld_country	hld_region	...	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	9	11	0	5	9	2020	FRA	M	FRA	0	...	3201.06	2015	2017	4	1	5	5	74.60	74.63	0.002218
1	19	68	10	15	19	2020	FRA	M	FRA	0	...	3201.06	2015	2017	4	1	15	5	64.66	64.69	0.011886
2	29	437	20	25	29	2020	FRA	M	FRA	0	...	3201.06	2015	2017	4	1	25	5	54.93	54.96	0.064892
3	39	1561	30	35	39	2020	FRA	M	FRA	0	...	3201.06	2015	2017	4	1	35	5	45.32	45.35	0.191247
4	49	3628	40	45	49	2020	FRA	M	FRA	0	...	3201.06	2015	2017	4	1	45	5	35.92	35.94	0.352294
5	59	14106	50	55	59	2020	FRA	M	FRA	0	...	3201.06	2015	2017	4	1	55	5	27.09	27.11	1.033034
6	69	36555	60	65	69	2020	FRA	M	FRA	0	...	3201.06	2015	2017	4	1	65	5	19.26	19.27	1.903289
7	79	76238	70	75	79	2020	FRA	M	FRA	0	...	3201.06	2015	2017	4	1	75	5	12.12	12.11	2.497904
8	89	145018	80	85	89	2020	FRA	M	FRA	0	...	3201.06	2015	2017	4	1	85	5	6.28	6.18	2.461972
9	90	92290	90	99	99	2020	FRA	M	FRA	0	...	3201.05	2014	2016	4	1	99	1	2.19	2.41	0.546387

10 rows × 24 columns

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

9.0

Assume the Longevity is the Same as People in the US

ssa_middle_ldf = lost_years_ssa(df, {"age": "middle_age", "sex": "sex", "year": "year"})
ssa_middle_ldf.head()

	age	n_deaths	lowest_age	middle_age	highest_age	year	country	sex	ssa_age	ssa_year	ssa_life_expectancy
0	9	11	0	5	9	2020	FRA	M	5	2016	71.60
1	19	68	10	15	19	2020	FRA	M	15	2016	61.70
2	29	437	20	25	29	2020	FRA	M	25	2016	52.30
3	39	1561	30	35	39	2020	FRA	M	35	2016	43.15
4	49	3628	40	45	49	2020	FRA	M	45	2016	34.08

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

	age	n_deaths	lowest_age	middle_age	highest_age	year	country	sex	ssa_age	ssa_year	ssa_life_expectancy	years_lost
0	9	11	0	5	9	2020	FRA	M	5	2016	71.60	0.002129
1	19	68	10	15	19	2020	FRA	M	15	2016	61.70	0.011342
2	29	437	20	25	29	2020	FRA	M	25	2016	52.30	0.061785
3	39	1561	30	35	39	2020	FRA	M	35	2016	43.15	0.182090
4	49	3628	40	45	49	2020	FRA	M	45	2016	34.08	0.334248
5	59	14106	50	55	59	2020	FRA	M	55	2016	25.52	0.973164
6	69	36555	60	65	69	2020	FRA	M	65	2016	17.92	1.770869
7	79	76238	70	75	79	2020	FRA	M	75	2016	11.18	2.304172
8	89	145018	80	85	89	2020	FRA	M	85	2016	5.94	2.328681
9	90	92290	90	99	99	2020	FRA	M	99	2016	2.25	0.561356

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

9.0

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()

	age	n_deaths	lowest_age	middle_age	highest_age	year	country	sex	y90_life_expectancy
0	9	11	0	5	9	2020	FRA	M	85
1	19	68	10	15	19	2020	FRA	M	75
2	29	437	20	25	29	2020	FRA	M	65
3	39	1561	30	35	39	2020	FRA	M	55
4	49	3628	40	45	49	2020	FRA	M	45

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

	age	n_deaths	lowest_age	middle_age	highest_age	year	country	sex	y90_life_expectancy	years_lost
0	9	11	0	5	9	2020	FRA	M	85	0.002528
1	19	68	10	15	19	2020	FRA	M	75	0.013787
2	29	437	20	25	29	2020	FRA	M	65	0.076789
3	39	1561	30	35	39	2020	FRA	M	55	0.232096
4	49	3628	40	45	49	2020	FRA	M	45	0.441348
5	59	14106	50	55	59	2020	FRA	M	35	1.334669
6	69	36555	60	65	69	2020	FRA	M	25	2.470520
7	79	76238	70	75	79	2020	FRA	M	15	3.091465
8	89	145018	80	85	89	2020	FRA	M	5	1.960169
9	90	92290	90	99	99	2020	FRA	M	-9	-2.245426

y90_middle_ldf["years_lost"].sum().round()

7.0

Get Human Life Table data columns from WHO dataset

who_highest_ldf = lost_years_who(
    df, {"age": "lowest_age", "country": "country", "sex": "sex", "year": "year"}
)
who_highest_ldf.head()

	age	n_deaths	lowest_age	middle_age	highest_age	year	country	sex	who_age	who_country	who_life_expectancy	who_sex	who_year
0	9	11	0	5	9	2020	FRA	M	1	FRA	80.1	MLE	2016
0	9	11	0	5	9	2020	FRA	M	1	FRA	79.4	MLE	2016
1	19	68	10	15	19	2020	FRA	M	10	FRA	70.5	MLE	2016
2	29	437	20	25	29	2020	FRA	M	20	FRA	60.6	MLE	2016
3	39	1561	30	35	39	2020	FRA	M	30	FRA	50.9	MLE	2016

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

who_highest_ldf["years_lost"] = (
    who_highest_ldf["who_life_expectancy"]
    * who_highest_ldf["n_deaths"]
    / who_highest_ldf["n_deaths"].sum()
)
who_highest_ldf

	age	n_deaths	lowest_age	middle_age	highest_age	year	country	sex	who_age	who_country	who_life_expectancy	who_sex	who_year	years_lost
0	9	11	0	5	9	2020	FRA	M	1	FRA	80.1	MLE	2016	0.002382
0	9	11	0	5	9	2020	FRA	M	1	FRA	79.4	MLE	2016	0.002361
1	19	68	10	15	19	2020	FRA	M	10	FRA	70.5	MLE	2016	0.012959
2	29	437	20	25	29	2020	FRA	M	20	FRA	60.6	MLE	2016	0.071588
3	39	1561	30	35	39	2020	FRA	M	30	FRA	50.9	MLE	2016	0.214788
4	49	3628	40	45	49	2020	FRA	M	40	FRA	41.3	MLE	2016	0.405048
5	59	14106	50	55	59	2020	FRA	M	50	FRA	32.1	MLE	2016	1.224046
6	69	36555	60	65	69	2020	FRA	M	60	FRA	23.8	MLE	2016	2.351865
7	79	76238	70	75	79	2020	FRA	M	70	FRA	16.2	MLE	2016	3.338683
8	89	145018	80	85	89	2020	FRA	M	80	FRA	9.4	MLE	2016	3.685008
9	90	92290	90	99	99	2020	FRA	M	85	FRA	6.7	MLE	2016	1.671545

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

13.0

Get Human Life Table data columns from WHO dataset

who_lowest_ldf = lost_years_who(
    df, {"age": "highest_age", "country": "country", "sex": "sex", "year": "year"}
)
who_lowest_ldf.head()

	age	n_deaths	lowest_age	middle_age	highest_age	year	country	sex	who_age	who_country	who_life_expectancy	who_sex	who_year
0	9	11	0	5	9	2020	FRA	M	10	FRA	70.5	MLE	2016
1	19	68	10	15	19	2020	FRA	M	20	FRA	60.6	MLE	2016
2	29	437	20	25	29	2020	FRA	M	30	FRA	50.9	MLE	2016
3	39	1561	30	35	39	2020	FRA	M	40	FRA	41.3	MLE	2016
4	49	3628	40	45	49	2020	FRA	M	50	FRA	32.1	MLE	2016

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

who_lowest_ldf["years_lost"] = (
    who_lowest_ldf["who_life_expectancy"]
    * who_lowest_ldf["n_deaths"]
    / who_lowest_ldf["n_deaths"].sum()
)
who_lowest_ldf

	age	n_deaths	lowest_age	middle_age	highest_age	year	country	sex	who_age	who_country	who_life_expectancy	who_sex	who_year	years_lost
0	9	11	0	5	9	2020	FRA	M	10	FRA	70.5	MLE	2016	0.002096
1	19	68	10	15	19	2020	FRA	M	20	FRA	60.6	MLE	2016	0.011140
2	29	437	20	25	29	2020	FRA	M	30	FRA	50.9	MLE	2016	0.060131
3	39	1561	30	35	39	2020	FRA	M	40	FRA	41.3	MLE	2016	0.174283
4	49	3628	40	45	49	2020	FRA	M	50	FRA	32.1	MLE	2016	0.314828
5	59	14106	50	55	59	2020	FRA	M	60	FRA	23.8	MLE	2016	0.907575
6	69	36555	60	65	69	2020	FRA	M	70	FRA	16.2	MLE	2016	1.600897
7	79	76238	70	75	79	2020	FRA	M	80	FRA	9.4	MLE	2016	1.937318
8	89	145018	80	85	89	2020	FRA	M	85	FRA	6.7	MLE	2016	2.626626
9	90	92290	90	99	99	2020	FRA	M	85	FRA	6.7	MLE	2016	1.671595

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

9.0

Get Human Life Table data columns from WHO dataset

who_middle_ldf = lost_years_who(
    df, {"age": "middle_age", "country": "country", "sex": "sex", "year": "year"}
)
who_middle_ldf.head()

	age	n_deaths	lowest_age	middle_age	highest_age	year	country	sex	who_age	who_country	who_life_expectancy	who_sex	who_year
0	9	11	0	5	9	2020	FRA	M	5	FRA	75.4	MLE	2016
1	19	68	10	15	19	2020	FRA	M	15	FRA	65.5	MLE	2016
2	29	437	20	25	29	2020	FRA	M	25	FRA	55.8	MLE	2016
3	39	1561	30	35	39	2020	FRA	M	35	FRA	46.1	MLE	2016
4	49	3628	40	45	49	2020	FRA	M	45	FRA	36.6	MLE	2016

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

who_middle_ldf["years_lost"] = (
    who_middle_ldf["who_life_expectancy"]
    * who_middle_ldf["n_deaths"]
    / who_middle_ldf["n_deaths"].sum()
)
who_middle_ldf

	age	n_deaths	lowest_age	middle_age	highest_age	year	country	sex	who_age	who_country	who_life_expectancy	who_sex	who_year	years_lost
0	9	11	0	5	9	2020	FRA	M	5	FRA	75.4	MLE	2016	0.002242
1	19	68	10	15	19	2020	FRA	M	15	FRA	65.5	MLE	2016	0.012041
2	29	437	20	25	29	2020	FRA	M	25	FRA	55.8	MLE	2016	0.065920
3	39	1561	30	35	39	2020	FRA	M	35	FRA	46.1	MLE	2016	0.194538
4	49	3628	40	45	49	2020	FRA	M	45	FRA	36.6	MLE	2016	0.358963
5	59	14106	50	55	59	2020	FRA	M	55	FRA	27.8	MLE	2016	1.060108
6	69	36555	60	65	69	2020	FRA	M	65	FRA	20.0	MLE	2016	1.976416
7	79	76238	70	75	79	2020	FRA	M	75	FRA	12.7	MLE	2016	2.617440
8	89	145018	80	85	89	2020	FRA	M	85	FRA	6.7	MLE	2016	2.626626
9	90	92290	90	99	99	2020	FRA	M	85	FRA	6.7	MLE	2016	1.671595

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

11.0