nytexcess: NYT Excess Mortality Estimates, current as of Sunday, January...
In kjhealy/covdata: COVID-19 Data

nytexcess

R Documentation

NYT Excess Mortality Estimates, current as of Sunday, January 22, 2023

Description

All-cause mortality is widely used by demographers and other researchers to understand the full impact of deadly events, including epidemics, wars and natural disasters. The totals in this data include deaths from Covid-19 as well as those from other causes, likely including people who could not be treated or did not seek treatment for other conditions.

Usage

nytexcess

Format

A tibble with 7,258 rows and 12 columns

country: character Country Name
placename: character Place Name
frequency: character Reporting period. Weekly or monthly, depending on how the data is recorded.
start_date: date The first date included in the period.
end_date: date The last date included in the period,
year: character Year of data. Note that this variable is of type character and not integer because several observations are notes to the effect that the year is an average of two years.
month: integer Numerical month.
week: integer Numerical week.
deaths: integer The total number of confirmed deaths recorded from any cause.
expected_deaths: integer The baseline number of expected deaths, calculated from a historical average. See details below.
excess_deaths: integer The number of deaths minus the expected deaths.
baseline: character The years used to calculate expected_deaths.

Details

Table: Data summary


Name	nytexcess
Number of rows	7258
Number of columns	12
_______________________
Column type frequency:
Date	2
character	5
numeric	5
________________________
Group variables	None

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
start_date	768	0.89	2010-01-09	2020-12-23	2018-02-05	1267
end_date	768	0.89	2010-01-15	2020-12-29	2018-02-11	1267

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
country	0	1.00	4	14	0	35	0
placename	6883	0.05	6	8	0	4	0
frequency	0	1.00	6	7	0	2	0
year	0	1.00	4	17	0	15	0
baseline	5990	0.17	20	25	0	7	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
month	0	1.00	6.60	3.36	1	4.00	7.0	9.0	12	▇▆▆▆▇
week	666	0.91	26.77	14.58	2	14.00	27.0	39.0	52	▇▇▇▇▇
deaths	0	1.00	7968.24	14334.14	455	1460.00	2395.5	10486.0	141292	▇▁▁▁▁
expected_deaths	5990	0.17	9237.09	15850.00	548	1443.00	2423.0	10771.5	139343	▇▁▁▁▁
excess_deaths	5990	0.17	1195.43	3242.72	-6721	-42.25	76.5	926.0	30400	▇▂▁▁▁

Expected deaths for each area based on historical data for the same time of year. These expected deaths are the basis for our excess death calculations, which estimate how many more people have died this year than in an average year.

The number of years used in the historical averages changes depending on what data is available, whether it is reliable and underlying demographic changes. See Data Sources for the years used to calculate the baselines. The baselines do not adjust for changes in age or other demographics, and they do not account for changes in total population.

The number of expected deaths are not adjusted for how non-Covid-19 deaths may change during the outbreak, which will take some time to figure out. As countries impose control measures, deaths from causes like road accidents and homicides may decline. And people who die from Covid-19 cannot die later from other causes, which may reduce other causes of death. Both of these factors, if they play a role, would lead these baselines to understate, rather than overstate, the number of excess deaths.