A comprehensive dataset of activities and their associated acute mortality risk in micromorts, with calculated microlives and source references.
Usage
common_risks(profile = list(), duration_hours = NULL)Arguments
- profile
A named list of condition variables for filtering conditional risks, e.g.
list(health_profile = "dvt_risk_factors")orlist(country = "US")for country-specific road traffic and homicide risks. Defaultlist()returns unconditional/healthy defaults.- duration_hours
Optional numeric. For duration-dependent activities, selects the nearest pre-computed duration bucket within each activity. All flying variants (2h, 5h, 8h, 12h) are retained.
NULL(default) returns all duration buckets. Userisk_for_duration()to select a single activity family result.
Value
A tibble with columns:
- activity
Activity name
- micromorts
Risk in micromorts (1 = 1-in-a-million death probability)
- microlives
Equivalent microlives (micromorts x 0.7)
- category
Activity category
- period
Human-readable period description
- period_type
Normalized period type: "event", "day", "hour", "year", "period"
- period_days
Typical duration in days (for cross-activity comparison)
- micromorts_per_day
Micromorts normalized per day
- source_url
Data source URL
- n_components
Number of atomic components summed
- hedgeable_pct
Percent of total micromorts that are hedgeable
Details
Aggregates from atomic_risks(), summing component-level micromorts
per activity.
Micromort: one-in-a-million chance of death (acute risk). Microlife: 30 minutes of life expectancy lost.
Data sources: Wikipedia, micromorts.rip, CDC MMWR, academic literature.
References
Howard RA (1980). "On Making Life and Death Decisions." In Schwing & Albers (eds), Societal Risk Assessment: How Safe Is Safe Enough?
See also
atomic_risks() for the component-level data.
Examples
common_risks()
#> # A tibble: 104 × 11
#> activity micromorts microlives category period period_type period_days
#> <chr> <dbl> <dbl> <chr> <chr> <chr> <dbl>
#> 1 Mt. Everest as… 37932 26552. Mountai… per a… event 60
#> 2 Himalayan moun… 12000 8400 Mountai… per e… event 45
#> 3 COVID-19 infec… 10000 7000 COVID-19 per i… event 14
#> 4 Spanish flu in… 3000 2100 Disease per i… event 14
#> 5 Matterhorn asc… 2840 1988 Mountai… per a… event 60
#> 6 Living in US d… 500 350 COVID-19 per m… month 30
#> 7 Living (one da… 463 324. Daily L… per d… day 1
#> 8 Base jumping (… 430 301 Sport per j… event 0.003
#> 9 First day of l… 430 301 Daily L… per d… day 1
#> 10 COVID-19 unvac… 234 164. COVID-19 11 we… period 77
#> # ℹ 94 more rows
#> # ℹ 4 more variables: micromorts_per_day <dbl>, source_url <chr>,
#> # n_components <int>, hedgeable_pct <dbl>
common_risks() |> dplyr::filter(category == "COVID-19")
#> # A tibble: 19 × 11
#> activity micromorts microlives category period period_type period_days
#> <chr> <dbl> <dbl> <chr> <chr> <chr> <dbl>
#> 1 COVID-19 infec… 10000 7000 COVID-19 per i… event 14
#> 2 Living in US d… 500 350 COVID-19 per m… month 30
#> 3 COVID-19 unvac… 234 164. COVID-19 11 we… period 77
#> 4 COVID-19 unvac… 76 53.2 COVID-19 11 we… period 77
#> 5 COVID-19 monov… 55 38.5 COVID-19 11 we… period 77
#> 6 Living in NYC … 50 35 COVID-19 per 8… period 56
#> 7 COVID-19 bival… 23 16.1 COVID-19 11 we… period 77
#> 8 COVID-19 unvac… 20 14 COVID-19 11 we… period 77
#> 9 COVID-19 monov… 9 6.3 COVID-19 11 we… period 77
#> 10 COVID-19 unvac… 8 5.6 COVID-19 11 we… period 77
#> 11 Living in Mary… 7 4.9 COVID-19 per 8… period 56
#> 12 COVID-19 monov… 4 2.8 COVID-19 11 we… period 77
#> 13 COVID-19 bival… 3 2.1 COVID-19 11 we… period 77
#> 14 COVID-19 monov… 2 1.4 COVID-19 11 we… period 77
#> 15 COVID-19 unvac… 1 0.7 COVID-19 11 we… period 77
#> 16 COVID-19 bival… 1 0.7 COVID-19 11 we… period 77
#> 17 COVID-19 bival… 1 0.7 COVID-19 11 we… period 77
#> 18 COVID-19 monov… 0.2 0.1 COVID-19 11 we… period 77
#> 19 COVID-19 bival… 0.05 0 COVID-19 11 we… period 77
#> # ℹ 4 more variables: micromorts_per_day <dbl>, source_url <chr>,
#> # n_components <int>, hedgeable_pct <dbl>
common_risks() |> dplyr::filter(micromorts > 100)
#> # A tibble: 14 × 11
#> activity micromorts microlives category period period_type period_days
#> <chr> <dbl> <dbl> <chr> <chr> <chr> <dbl>
#> 1 Mt. Everest as… 37932 26552. Mountai… per a… event 60
#> 2 Himalayan moun… 12000 8400 Mountai… per e… event 45
#> 3 COVID-19 infec… 10000 7000 COVID-19 per i… event 14
#> 4 Spanish flu in… 3000 2100 Disease per i… event 14
#> 5 Matterhorn asc… 2840 1988 Mountai… per a… event 60
#> 6 Living in US d… 500 350 COVID-19 per m… month 30
#> 7 Living (one da… 463 324. Daily L… per d… day 1
#> 8 Base jumping (… 430 301 Sport per j… event 0.003
#> 9 First day of l… 430 301 Daily L… per d… day 1
#> 10 COVID-19 unvac… 234 164. COVID-19 11 we… period 77
#> 11 Caesarean birt… 170 119 Medical per e… event 1
#> 12 Scuba diving, … 164 115. Sport per y… year 365
#> 13 Vaginal birth … 120 84 Medical per e… event 1
#> 14 Living (one da… 105 73.5 Daily L… per d… day 1
#> # ℹ 4 more variables: micromorts_per_day <dbl>, source_url <chr>,
#> # n_components <int>, hedgeable_pct <dbl>