Daily micromorts from infectious diseases by country
Source:R/infectious_disease_risks.R
infectious_disease_risks.RdReturns daily micromorts from infectious diseases for one or more countries, using a bundled snapshot of IHME Global Burden of Disease data sourced via Our World in Data.
Arguments
- country
Character vector of ISO-3 country codes (e.g.
"GBR",c("GBR", "USA")). Default is"GBR". Use"all"to return every country in the bundled dataset.- year
Integer or
NULL. IfNULL(default), the most recent available year is returned. If a specific year is supplied, only rows matching that year are returned; an error is raised if the year is absent.
Value
A tibble::tibble() with columns:
- cause
Disease cause label (character)
- country
Country name (character)
- iso3
ISO-3 country code (character)
- year
Data year (integer)
- deaths_per_100k
Age-standardised deaths per 100,000 per year (double)
- daily_micromorts
Daily micromort risk (double)
- annual_micromorts
Annual micromort risk (double)
Details
The bundled CSV (inst/extdata/owid_infectious_deaths.csv) covers seven
infectious cause categories across 26 countries. To refresh the snapshot,
run data-raw/owid_infectious_deaths.R.
Conversion formula
daily_micromorts = (deaths_per_100k / 365) * 10
Because: annual deaths per 100,000 → divide by 100,000 for annual
probability → divide by 365 for daily probability → multiply by 1,000,000
for micromorts. The 100,000 and 1,000,000 cancel to a factor of 10:
rate / 100,000 / 365 × 1,000,000 = rate / 365 × 10.
References
Institute for Health Metrics and Evaluation (IHME). Global Burden of Disease Study 2019. Seattle, WA: IHME, 2020. https://www.healthdata.org/research-analysis/gbd
Our World in Data. Cause of Death. https://ourworldindata.org/causes-of-death
See also
chronic_disease_risks() for chronic disease micromort risks by
country.
Examples
# Default: UK, latest year
infectious_disease_risks()
#> # A tibble: 7 × 7
#> cause country iso3 year deaths_per_100k daily_micromorts annual_micromorts
#> <chr> <chr> <chr> <int> <dbl> <dbl> <dbl>
#> 1 Diarrh… United… GBR 2019 0.5 0.0137 5
#> 2 HIV/AI… United… GBR 2019 0.8 0.0219 8
#> 3 Hepati… United… GBR 2019 0.4 0.011 4
#> 4 Lower … United… GBR 2019 20 0.548 200
#> 5 Malaria United… GBR 2019 0 0 0
#> 6 Mening… United… GBR 2019 0.3 0.0082 3
#> 7 Tuberc… United… GBR 2019 0.5 0.0137 5
# Specific country
infectious_disease_risks("USA")
#> # A tibble: 7 × 7
#> cause country iso3 year deaths_per_100k daily_micromorts annual_micromorts
#> <chr> <chr> <chr> <int> <dbl> <dbl> <dbl>
#> 1 Diarrh… United… USA 2019 1.2 0.0329 12
#> 2 HIV/AI… United… USA 2019 1.5 0.0411 15
#> 3 Hepati… United… USA 2019 0.5 0.0137 5
#> 4 Lower … United… USA 2019 15 0.411 150
#> 5 Malaria United… USA 2019 0 0 0
#> 6 Mening… United… USA 2019 0.2 0.0055 2
#> 7 Tuberc… United… USA 2019 0.1 0.0027 1
# Multiple countries
infectious_disease_risks(c("GBR", "USA", "IND"))
#> # A tibble: 21 × 7
#> cause country iso3 year deaths_per_100k daily_micromorts annual_micromorts
#> <chr> <chr> <chr> <int> <dbl> <dbl> <dbl>
#> 1 Diarr… India IND 2019 30 0.822 300
#> 2 HIV/A… India IND 2019 1.5 0.0411 15
#> 3 Hepat… India IND 2019 8 0.219 80
#> 4 Lower… India IND 2019 65 1.78 650
#> 5 Malar… India IND 2019 3 0.0822 30
#> 6 Menin… India IND 2019 5 0.137 50
#> 7 Tuber… India IND 2019 30 0.822 300
#> 8 Diarr… United… GBR 2019 0.5 0.0137 5
#> 9 HIV/A… United… GBR 2019 0.8 0.0219 8
#> 10 Hepat… United… GBR 2019 0.4 0.011 4
#> # ℹ 11 more rows
# All countries in bundled dataset
infectious_disease_risks("all")
#> # A tibble: 189 × 7
#> cause country iso3 year deaths_per_100k daily_micromorts annual_micromorts
#> <chr> <chr> <chr> <int> <dbl> <dbl> <dbl>
#> 1 Diarr… Argent… ARG 2019 3.2 0.0877 32
#> 2 HIV/A… Argent… ARG 2019 4.1 0.112 41
#> 3 Hepat… Argent… ARG 2019 1.2 0.0329 12
#> 4 Lower… Argent… ARG 2019 18.5 0.507 185
#> 5 Malar… Argent… ARG 2019 0 0 0
#> 6 Menin… Argent… ARG 2019 0.8 0.0219 8
#> 7 Tuber… Argent… ARG 2019 2.8 0.0767 28
#> 8 Diarr… Austra… AUS 2019 0.6 0.0164 6
#> 9 HIV/A… Austra… AUS 2019 0.4 0.011 4
#> 10 Hepat… Austra… AUS 2019 0.5 0.0137 5
#> # ℹ 179 more rows
# Filter by cause after calling
infectious_disease_risks("GBR") |>
dplyr::filter(cause == "Tuberculosis")
#> # A tibble: 1 × 7
#> cause country iso3 year deaths_per_100k daily_micromorts annual_micromorts
#> <chr> <chr> <chr> <int> <dbl> <dbl> <dbl>
#> 1 Tuberc… United… GBR 2019 0.5 0.0137 5