Human Mortality Database (HMD) series of weekly death counts across countries.

stmf

Format

A tibble with 580,395 rows and 17 variables:

country_code

Mortality database country code

cname

character Country name

iso2

character ISO2 country code

iso3

character ISO3 country code

year

double Year

week

double Week number. Each year in the STMF refers to 52 weeks, each week has 7 days. In some cases, the first week of a year may include several days from the previous year or the last week of a year may include days (and, respectively, deaths) of the next year. In particular, it means that a statistical year in the STMF is equal to the statistical year in annual country-specific statistics.

sex

character Sex. m = Males. f = Females. b = Both combined.

split

double Indicates if data were split from aggregated age groups (0 if the original data has necessary detailed age scale). For example, if the original age scale was 0-4, 5-29, 30-65, 65+, then split will be equal to 1

split_sex

double Indicates if the original data are available by sex (0) or data are interpolated (1)

forecast

double Equals 1 for all years where forecasted population exposures were used to calculate weekly death rates.

approx_date

double Approximate date (derived from the year and week number).

age_group

character Age group for death counts and rates

death_count

double Weekly death count. This number need not be an integer, because the age categories may be aggregated or split across the source national data.

death_rate

double Weekly death rate.

deaths_total

double Count of deaths for all ages combined.

rate_total

double Crude death rate.

Source

Human Mortality Database, http://mortality.org

Details

For further details on the construction of this dataset see the codebook at https://www.mortality.org/Public/STMF_DOC/STMFNote.pdf. For the original input data files in standardized form, see https://www.mortality.org/Public/STMF/Inputs/STMFinput.zip.

Countries and years covered in the dataset:

cname199019911992199319941995199619971998199920002001200220032004200520062007200820092010201120122013201420152016201720182019202020212022
Australia-------------------------YYYYYYYY
Austria----------YYYYYYYYYYYYYYYYYYYYYYY
Belgium----------YYYYYYYYYYYYYYYYYYYYYYY
Bulgaria----------YYYYYYYYYYYYYYYYYYYYYYY
Canada--------------------YYYYYYYYYYYYY
Chile--------------------------YYYYYYY
Croatia-----------YYYYYYYYYYYYYYYYYYYYYY
Czech Republic---------------YYYYYYYYYYYYYYYYYY
Denmark-----------------YYYYYYYYYYYYYYYY
England and Wales--------------------YYYYYYYYYYYYY
Estonia----------YYYYYYYYYYYYYYYYYYYYYYY
FinlandYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYY
France----------YYYYYYYYYYYYYYYYYYYYYYY
Germany----------YYYYYYYYYYYYYYYYYYYYYYY
Greece-------------------------YYYYYYYY
Hungary----------YYYYYYYYYYYYYYYYYYYYYYY
Iceland----------YYYYYYYYYYYYYYYYYYYYYYY
Israel----------YYYYYYYYYYYYYYYYYYYYYYY
Italy---------------------YYYYYYYYYYYY
Korea, Republic of--------------------YYYYYYYYYYYYY
Latvia----------YYYYYYYYYYYYYYYYYYYYYYY
Lithuania----------YYYYYYYYYYYYYYYYYYYYYYY
Luxembourg----------YYYYYYYYYYYYYYYYYYYYYYY
Netherlands-----YYYYYYYYYYYYYYYYYYYYYYYYYYYY
New Zealand--------------------YYYYYYYYYYYYY
Northern Ireland-------------------------YYYYYYYY
Norway----------YYYYYYYYYYYYYYYYYYYYYYY
Poland----------YYYYYYYYYYYYYYYYYYYYYYY
Portugal----------YYYYYYYYYYYYYYYYYYYYYYY
Russian Federation----------YYYYYYYYYYYYYYYYYYYYY--
Scotland----------YYYYYYYYYYYYYYYYYYYYYYY
Slovakia----------YYYYYYYYYYYYYYYYYYYYYYY
Slovenia----------YYYYYYYYYYYYYYYYYYYYYYY
Spain----------YYYYYYYYYYYYYYYYYYYYYYY
Sweden----------YYYYYYYYYYYYYYYYYYYYYYY
Switzerland----------YYYYYYYYYYYYYYYYYYYYYYY
Taiwan, Province of China----------YYYYYYYYYYYYYYYYYYYYYY-
United States-------------------------YYYYYYYY

Variables Table: Data summary

Namestmf
Number of rows580395
Number of columns17
_______________________
Column type frequency:
Date1
character7
numeric9
________________________
Group variablesNone

Variable type: Date

skim_variablen_missingcomplete_rateminmaxmediann_unique
approx_date011990-01-072023-01-012012-10-071722

Variable type: character

skim_variablen_missingcomplete_rateminmaxemptyn_uniquewhitespace
country_code01.00370380
cname01.005250380
iso2343800.94220350
continent358500.94413050
iso3343800.94330350
sex01.0011030
age_group01.0035050

Variable type: numeric

skim_variablen_missingcomplete_ratemeansdp0p25p50p75p100hist
year012011.586.8819902006.002012.002017.002022.00▁▂▆▆▇
week0126.5015.03113.0026.0039.0053.00▇▇▇▇▇
split010.120.3200.000.000.001.00▇▁▁▁▁
split_sex010.000.0700.000.000.001.00▇▁▁▁▁
forecast010.100.3000.000.000.001.00▇▁▁▁▁
death_count01617.601585.49039.00162.00449.7526362.00▇▁▁▁▁
death_rate010.050.0700.000.020.070.57▇▂▁▁▁
deaths_total013088.006498.292472.00998.002543.0087413.00▇▁▁▁▁
rate_total010.010.0000.010.010.010.04▅▇▁▁▁

References

"Short-term Mortality Fluctuations Dataseries" n.d., https://www.mortality.org/Public/STMF_DOC/STMFNote.pdf

Author

Kieran Healy