Chronic Disease Risks by Country and Year
Source:R/chronic_disease_risks.R
chronic_disease_risks.RdReturns daily micromorts from chronic 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_chronic_deaths.csv) covers seven
chronic cause categories across ~20 countries. To refresh the snapshot from
the live OWID catalog, run data-raw/owid_chronic_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_risks() for microlife-based chronic lifestyle factors.
Examples
# Default: UK, latest year
chronic_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 Cardio… United… GBR 2019 120. 3.30 1205
#> 2 Chroni… United… GBR 2019 8.4 0.230 84
#> 3 Chroni… United… GBR 2019 12.1 0.332 121
#> 4 Chroni… United… GBR 2019 35.8 0.981 358
#> 5 Diabet… United… GBR 2019 10.2 0.280 102
#> 6 Digest… United… GBR 2019 15.6 0.427 156
#> 7 Neopla… United… GBR 2019 136. 3.73 1362
# Specific country
chronic_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 Cardio… United… USA 2019 151. 4.13 1508
#> 2 Chroni… United… USA 2019 14.2 0.389 142
#> 3 Chroni… United… USA 2019 15.8 0.433 158
#> 4 Chroni… United… USA 2019 42.3 1.16 423
#> 5 Diabet… United… USA 2019 18.6 0.510 186
#> 6 Digest… United… USA 2019 18.4 0.504 184
#> 7 Neopla… United… USA 2019 120. 3.30 1205
# Multiple countries
chronic_disease_risks(c("GBR", "USA", "JPN"))
#> # A tibble: 21 × 7
#> cause country iso3 year deaths_per_100k daily_micromorts annual_micromorts
#> <chr> <chr> <chr> <int> <dbl> <dbl> <dbl>
#> 1 Cardi… Japan JPN 2019 75.4 2.07 754
#> 2 Chron… Japan JPN 2019 4.2 0.115 42
#> 3 Chron… Japan JPN 2019 9.8 0.268 98
#> 4 Chron… Japan JPN 2019 18.2 0.499 182
#> 5 Diabe… Japan JPN 2019 6.8 0.186 68
#> 6 Diges… Japan JPN 2019 11.4 0.312 114
#> 7 Neopl… Japan JPN 2019 118. 3.24 1182
#> 8 Cardi… United… GBR 2019 120. 3.30 1205
#> 9 Chron… United… GBR 2019 8.4 0.230 84
#> 10 Chron… United… GBR 2019 12.1 0.332 121
#> # ℹ 11 more rows
# All countries in bundled dataset
chronic_disease_risks("all")
#> # A tibble: 182 × 7
#> cause country iso3 year deaths_per_100k daily_micromorts annual_micromorts
#> <chr> <chr> <chr> <int> <dbl> <dbl> <dbl>
#> 1 Cardi… Argent… ARG 2019 179. 4.89 1786
#> 2 Chron… Argent… ARG 2019 12.8 0.351 128
#> 3 Chron… Argent… ARG 2019 14.2 0.389 142
#> 4 Chron… Argent… ARG 2019 38.6 1.06 386
#> 5 Diabe… Argent… ARG 2019 18.4 0.504 184
#> 6 Diges… Argent… ARG 2019 16.8 0.460 168
#> 7 Neopl… Argent… ARG 2019 124. 3.40 1242
#> 8 Cardi… Austra… AUS 2019 98.6 2.70 986
#> 9 Chron… Austra… AUS 2019 9.2 0.252 92
#> 10 Chron… Austra… AUS 2019 10.4 0.285 104
#> # ℹ 172 more rows
# Filter by cause after calling
chronic_disease_risks("GBR") |>
dplyr::filter(cause == "Cardiovascular diseases")
#> # A tibble: 1 × 7
#> cause country iso3 year deaths_per_100k daily_micromorts annual_micromorts
#> <chr> <chr> <chr> <int> <dbl> <dbl> <dbl>
#> 1 Cardio… United… GBR 2019 120. 3.30 1205