Micromort - Curated Risk Datasets • micromort

A data package providing curated, cross-sectional snapshots of micromort (acute risk) and microlife (chronic risk) values from authoritative sources including Wikipedia, CDC MMWR, IHME GBD 2023 via OWID, and academic literature.

120 activity IDs across 193 atomic risk rows. 35+ chronic factors. 6-country disease mortality + risk factors from GBD 2023. 3 interactive quizzes. Closeread scrollytelling. REST API.

Explore

Load the Datasets

library(micromort)

# Load acute risks (micromorts per event)
acute <- load_acute_risks()
nrow(acute)
#> [1] 104

# Load chronic risks (microlives per day)
chronic <- load_chronic_risks()
nrow(chronic)
#> [1] 38

Acute Risks (Micromorts per Event)

# Top 10 riskiest activities
acute |>
  dplyr::select(activity, micromorts, category, period) |>
  head(10)
#> # A tibble: 10 × 4
#>    activity                                micromorts category       period     
#>    <chr>                                        <dbl> <chr>          <chr>      
#>  1 Mt. Everest ascent                           37932 Mountaineering per ascent 
#>  2 Himalayan mountaineering                     12000 Mountaineering per expedi…
#>  3 COVID-19 infection (unvaccinated)            10000 COVID-19       per infect…
#>  4 Spanish flu infection                         3000 Disease        per infect…
#>  5 Matterhorn ascent                             2840 Mountaineering per ascent 
#>  6 Living in US during COVID-19 (Jul 2020)        500 COVID-19       per month  
#>  7 Living (one day, age 90)                       463 Daily Life     per day    
#>  8 Base jumping (per jump)                        430 Sport          per jump   
#>  9 First day of life (newborn)                    430 Daily Life     per day    
#> 10 COVID-19 unvaccinated (age 80+)                234 COVID-19       11 weeks (…

Chronic Risks (Microlives per Day)

# Factors that reduce life expectancy
chronic |>
  dplyr::filter(direction == "loss") |>
  dplyr::select(factor, microlives_per_day, category) |>
  head(10)
#> # A tibble: 10 × 3
#>    factor                              microlives_per_day category      
#>    <chr>                                            <dbl> <chr>         
#>  1 Smoking 20 cigarettes                              -10 Smoking       
#>  2 Smoking 10 cigarettes                               -5 Smoking       
#>  3 Untreated hypertension                              -4 Cardiovascular
#>  4 Being male (vs female)                              -4 Demographics  
#>  5 Being 15 kg overweight                              -3 Weight        
#>  6 Type 2 diabetes (poorly controlled)                 -3 Cardiovascular
#>  7 Being 10 kg overweight                              -2 Weight        
#>  8 4th-5th alcoholic drink                             -2 Alcohol       
#>  9 Sitting 8+ hours/day                                -2 Sedentary     
#> 10 High LDL cholesterol (untreated)                    -2 Cardiovascular

Visualize

plot_risks(common_risks() |> dplyr::filter(micromorts >= 1))

Risk comparison in micromorts (log scale). Bars show death probability per event. COVID-19 and other risks shown in separate panels for clarity.

Analyse

Compare Lifestyle Interventions

compare_interventions(list(
  "Quit 10 cigarettes/day" = list(factor = "Smoking 10 cigarettes", change = -1),
  "Lose 5kg" = list(factor = "Being 5 kg overweight", change = -1)
))
#> # A tibble: 2 × 7
#>   intervention      factor original_ml_per_day change net_ml_per_day annual_days
#>   <chr>             <chr>                <dbl>  <dbl>          <dbl>       <dbl>
#> 1 Quit 10 cigarett… Smoki…                  -5     -1             -5       -38  
#> 2 Lose 5kg          Being…                  -1     -1             -1        -7.6
#> # ℹ 1 more variable: lifetime_years <dbl>

Calculate Baseline Risk by Age

daily_hazard_rate(35)
#> # A tibble: 1 × 9
#>     age sex   daily_prob micromorts micromorts_lower micromorts_upper
#>   <dbl> <chr>      <dbl>      <dbl>            <dbl>            <dbl>
#> 1    35 male  0.00000296          3              2.7              3.3
#> # ℹ 3 more variables: microlives_consumed <dbl>, precision_note <chr>,
#> #   interpretation <chr>

Lifestyle Tradeoffs

lifestyle_tradeoff("Smoking 2 cigarettes", "20 min moderate exercise")
#> # A tibble: 1 × 6
#>   bad_habit            bad_ml_per_day good_habit    good_ml_per_day units_needed
#>   <chr>                         <dbl> <chr>                   <dbl>        <dbl>
#> 1 Smoking 2 cigarettes             -1 20 min moder…               2          0.5
#> # ℹ 1 more variable: interpretation <chr>

Country-Level Disease Risk

# Compare disease burden: UK vs Nigeria
common_risks(profile = list(country = "NG")) |>
  dplyr::filter(grepl("mortality risk", activity)) |>
  dplyr::select(activity, micromorts)
#> # A tibble: 4 × 2
#>   activity                                 micromorts
#>   <chr>                                         <dbl>
#> 1 Daily CVD mortality risk (Nigeria)             7.33
#> 2 Daily cancer mortality risk (Nigeria)          2.79
#> 3 Daily LRI mortality risk (Nigeria)             1.77
#> 4 Daily diarrheal mortality risk (Nigeria)       0.88

Quizzes

Quiz	Instant (static JS)	Shinylive (30-60s load)	Local
Which Is Riskier?	Play	Play	`launch_quiz()`
Microlife Quiz	Play	Play	`launch_chronic_quiz()`
Rank the Risks	Play	Play	`ranking_quiz_questions()`

All quizzes have score submission + percentile ranking via Google Forms. Static JS versions load instantly; Shinylive versions use WebR (30-60s load).

# Generate geography-specific quiz pairs
geography_quiz_pairs(countries = c("UK", "NG"), seed = 42)

Install

R-Universe (Recommended)

install.packages("micromort", repos = "https://johngavin.r-universe.dev")

GitHub

devtools::install_github("JohnGavin/micromort")

Nix Users

./default.sh   # Reproducible R env with cachix cache
R

Using with rix

library(rix)
rix(
  r_pkgs = c("dplyr", "ggplot2"),
  git_pkgs = list(list(
    package_name = "micromort",
    repo_url = "https://github.com/JohnGavin/micromort",
    commit = "main"
  )),
  project_path = ".",
  overwrite = TRUE
)

API

Launch the REST API for programmatic access:

launch_api()
# Swagger docs at http://localhost:8080/__docs__/

Core endpoints (30 total — full reference):

Endpoint	Returns
`GET /v1/risks/acute`	Acute risks (micromorts)
`GET /v1/risks/chronic`	Chronic risks (microlives)
`GET /v1/risks/cancer`	Cancer mortality by type/sex/age
`GET /v1/analysis/equivalence`	Risk equivalence lookup
`GET /v1/convert/hazard-rate?age=35`	Daily hazard rate

Concepts

Micromort (Acute Risk)

A micromort = one-in-a-million probability of death per event. Skydiving: ~8 micromorts per jump.

Microlife (Chronic Risk)

A microlife = 30 minutes of life expectancy change per day. Smoking 2 cigarettes: -1 microlife/day.

Conversion

1 micromort ≈ 0.7 microlives (at 40 years remaining life expectancy).

Architecture

See the Architecture vignette for detailed diagrams.

Data Sources

Source	Type	Data
Wikipedia: Micromort	Encyclopedia	~50 acute risks
Wikipedia: Microlife	Encyclopedia	~20 chronic risks
micromorts.rip	Database	~45 acute risks
CDC MMWR	Government	COVID vaccine data
Spiegelhalter (2012) BMJ	Academic	Microlife framework
SEER Cancer Statistics	Government	Cancer mortality by type/sex
IHME GBD 2023 via OWID	Academic	Disease + risk-factor mortality by country

Glossary

Acronym	Definition
DALY	Disability-Adjusted Life Year (YLL + YLD)
LLE	Loss of Life Expectancy (minutes)
QALY	Quality-Adjusted Life Year
VSL	Value of Statistical Life (~$10M USD)

References

Howard RA (1980). “On Making Life and Death Decisions.” Societal Risk Assessment.
Spiegelhalter D (2012). “Using speed of ageing and ‘microlives’.” BMJ 345:e8223.
Blastland M, Spiegelhalter D (2013). The Norm Chronicles.

License

MIT. See LICENSE.