---
title: "Introduction to Micromorts and Risk Visualization"
format:
  html:
    code-fold: true
    code-summary: "Show code"
vignette: >
  %\VignetteIndexEntry{Introduction to Micromorts and Risk Visualization}
  %\VignetteEngine{quarto::html}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = ""
)

if (requireNamespace("micromort", quietly = TRUE)) {
  library(micromort)
} else {
  pkgload::load_all(quiet = TRUE)
}

library(targets)
library(DT)

# Shared safe_tar_read with RDS fallback (see inst/vignette_utils.R)
source(file.path(tryCatch(rprojroot::find_root(rprojroot::is_r_package),
  error = function(e) "."), "inst", "vignette_utils.R"))
```

This vignette introduces the **micromort** package, which provides tools for understanding and visualizing risks.

## 1. Micromorts (Acute Risk) {#micromorts}

A **micromort** is a unit of risk representing a one-in-a-million chance of death. More precisely, it's a **microprobability** of death — a one-in-a-million chance of a specific event (death) occurring.

### Definition

| Term | Definition | Example |
|------|------------|---------|
| **Microprobability** | 1-in-a-million chance of any event | 1 micromort = microprobability of death |
| **Micromort** | 1-in-a-million chance of death, per event | Skydiving: 8 micromorts per jump |

### Comparing Risks: Period Matters!

**CRITICAL:** When comparing micromort values, ensure the **period is the same**. For example:

| Activity | Micromorts | Period | Comparable? |
|----------|------------|--------|-------------|
| Scuba diving | 5 | per dive | Per-event |
| Scuba diving | 164 | per year | Per-year (assumes ~33 dives/year) |
| Skydiving | 8 | per jump | Per-event |

The "per year" figure (164) conflates frequency with risk-per-event. A diver doing 5 dives/year vs 50 dives/year faces very different annual risk.

### Conditional Risks {#selection-effects}

Many activities have **selection effects**. For example:

*   **Marathon running (7 micromorts):** Runners are self-selected for fitness. This low figure reflects the health of participants, not the risk to an average person attempting a marathon.
*   **Motorcycle riding (10 micromorts/60 miles):** Experienced riders face lower risk than novices, but the quoted figure is an average.

These figures answer: "Given that someone completed this activity, what was their death risk?" Not: "What would happen if a random person attempted this?"

### Converting Probabilities to Micromorts

```{r introduction-chunk-1}
#| echo: false
demos <- safe_tar_read("vig_intro_api_demos")
# 1 in 10,000 chance of death = 100 micromorts
demos$micromort_from_prob
```

### Common Risks Table

```{r introduction-chunk-2}
#| echo: false
DT::datatable(safe_tar_read("vig_intro_common_risks"),
  caption = "Common risks in micromorts — click headers to sort, search box to filter",
  filter = "top", rownames = FALSE,
  options = list(pageLength = 10, scrollX = TRUE))
```

### Visualizing Risks

Using [`plot_risks()`](../reference/plot_risks.html), we can see the relative magnitude of different activities on a logarithmic scale. The plot is split into COVID-19 and Other risks to make comparisons easier:

```{r introduction-chunk-3, fig.width=10, fig.height=14}
#| echo: false
#| warning: false
#| message: false
#| fig-cap: "Logarithmic risk ladder showing micromorts per activity, split by COVID-19 and other categories. Activities range from a chest X-ray (0.1 micromort) to BASE jumping (430 micromorts)."
#| fig-alt: "Horizontal bar chart on a log scale showing micromorts for ~40 activities. COVID-19 risks (vaccination, infection by age) are grouped separately from other risks (transport, medical, recreational). Bars are coloured by category."
show_target("vig_intro_risk_plot")
```

#### Interactive Version

For interactive exploration with hover details and category filtering, use [`plot_risks_interactive()`](../reference/plot_risks_interactive.html):

```{r introduction-chunk-4}
#| echo: false
#| warning: false
#| message: false
show_target("vig_intro_risk_plot_interactive")
```

## 2. Microlives (Chronic Risk) {#microlives}

While micromorts measure sudden death, **microlives** measure the impact of chronic habits on your life expectancy. A microlife represents a 30-minute change in life expectancy **per day of exposure**.

### Living at Different Speeds

A useful way to think about microlives: we all "use up" **48 microlives per day** just by living (24 hours = 48 × 30 minutes). Unhealthy habits accelerate this consumption, while healthy habits slow it down.

A smoker who smokes 20 cigarettes per day uses up an additional 10 microlives, which can be interpreted as **rushing towards death at 29 hours per day** instead of 24. Conversely, someone with excellent lifestyle habits might effectively live at only 22 hours per day.

**Healthcare bonus:** Modern healthcare and healthier lifestyles give us a "payback" of approximately **12 microlives per day** — our expected death is moving away from us even as we age.

### Common Chronic Risks

| Factor | Microlives/day | Interpretation |
|--------|----------------|----------------|
| Smoking 1 cigarette | -1 | Lose 30 min life expectancy |
| Being 5kg overweight | -1 | Lose 30 min/day |
| 20 min moderate exercise | +2 | Gain 60 min life expectancy |
| 2+ hours TV daily | -1 | Sedentary behavior |
| 5+ servings fruit/veg | +2 | Healthy diet |

### Converting Life Expectancy to Microlives

```{r introduction-chunk-5}
#| echo: false
# as_microlife() converts minutes of life expectancy change to microlives
# Unit: 1 microlife = 30 minutes of life expectancy change PER DAY
cat("Heavy smoker (20/day):", demos$microlife_smoker, "microlives/day (life lost)\n")
cat("Moderate exercise:", demos$microlife_exercise, "microlives/day (life gained)\n")
cat("5kg overweight:", demos$microlife_overweight, "microlife/day (life lost)\n")
```

## 3. Relationship Between Micromorts and Microlives {#relationship}

### Theoretical Conversion

Micromorts (acute, per-event risk) and microlives (chronic, per-day attrition) measure different phenomena, but can be approximately converted using expected value theory.

**Key relationship:** 1 micromort ≈ 0.7 microlives

**Assumptions for this conversion:**
1. Remaining life expectancy = 40 years (use `lle(prob, life_expectancy = ...)` to [adjust for any age](rest_api.html#age-based-hazard-rates); see also `daily_hazard_rate()`)
2. Death occurs immediately upon the event (worst case)
3. Linear approximation (valid for small probabilities)

**Mathematical derivation:**

- 1 micromort = 1/1,000,000 probability of death
- Expected life lost = probability × remaining life
- = 1e-6 × 40 years = 4e-5 years
- = 4e-5 × 525,960 minutes/year ≈ 21 minutes
- 1 microlife = 30 minutes, so 21 minutes ≈ 0.7 microlives

**Why `common_risks()` uses microlives = micromorts × 0.7:**

The conversion allows comparing a single risky event (like one skydive at 8 micromorts) to chronic daily habits (like smoking at -10 microlives/day). However, **the approximation breaks down when:**

1. **Remaining life expectancy differs** from 40 years (a 20-year-old loses more per micromort than an 80-year-old)
2. **The risk is not immediate death** (injuries, disabilities are not captured)
3. **Repeated exposures** compound non-linearly

### Unit Definitions (Summary)

| Metric | Unit Definition | Scope | Sign |
|--------|-----------------|-------|------|
| **Micromort** | 1-in-a-million probability of death | Per discrete event (1 surgery, 1 flight) | Always ≥ 0 |
| **Microlife** | 30 minutes of life expectancy change per day | Per day of exposure/habit | + = gain, − = loss |

### When to Use Each

*   **Use micromorts** for discrete, short-duration events with binary outcomes (death or survival): surgery, skydiving, a single car trip
*   **Use microlives** for chronic, daily habits that accumulate over a lifetime: smoking, exercise, diet
*   **Convert between them** for policy decisions, but state your assumptions (age, remaining life expectancy)

## 4. Value of Statistical Life (VSL) {#vsl}

The **Value of a Statistical Life (VSL)** is the monetary value used to justify safety spending. It is NOT the value of an individual life, but the aggregate willingness to pay for small risk reductions.

### US Valuation

Example: If a safety feature costs $50 and saves 1 life in 100,000 people (10 micromorts), is it worth it?
Cost per micromort saved = $50 / 10 = $5.
If VSL = $10M, then 1 micromort = $10. Since $5 < $10, it is cost-effective.

```{r introduction-chunk-6}
#| echo: false
# Standard US VSL of $10M implies $10 per micromort
cat("$", demos$vsl_us, "per micromort\n")
```

### UK Valuation: Micromorts ≈ Microlives

Interestingly, two UK government agencies arrive at similar valuations for micromorts and microlives:

| Agency | Metric | Value | Per Unit |
|--------|--------|-------|----------|
| **NICE** (NHS) | 1 QALY | ~£30,000 | **£1.70 per microlife** |
| **Dept of Transport** | Value of Statistical Life | £1,600,000 | **£1.60 per micromort** |

This near-equivalence (£1.60 ≈ £1.70) provides empirical support for the theoretical conversion: **1 micromort ≈ 1 microlife** in policy terms.

```{r introduction-chunk-7}
#| echo: false
# UK Department of Transport VSL: £1.6M → £1.60 per micromort
cat("\u00a3", demos$vsl_uk, "per micromort\n")
```

This consistency suggests that policy decisions affecting acute risks (transport safety) and chronic risks (healthcare interventions) can be compared on a common scale.

## 5. Loss of Life Expectancy (LLE) {#lle}

**Loss of Life Expectancy (LLE)** estimates the average time lost from a lifespan due to a specific risk. It converts abstract probabilities into a tangible duration — answering "how much life does this cost me on average?"

**Formula:** LLE = probability of death × remaining life expectancy

For a 1-in-a-million risk (1 micromort), assuming 40 years remaining life:

LLE = 1/1,000,000 × 40 years × 525,960 min/year ≈ **21 minutes**

```{r introduction-chunk-8}
#| echo: false
#| results: hide
# Loss of life expectancy from 1 micromort (assuming 40 years remaining)
cat(round(demos$lle_one_micromort, 1), "minutes\n")
```

### Worked Examples

| Activity | Micromorts | LLE per exposure | Equivalent time cost |
|----------|-----------|-----------------|---------------------|
| Chest X-ray | 0.1 | ~2 minutes | Making a cup of tea |
| Skydiving (1 jump) | 8 | ~3 hours | Watching a long film |
| General anaesthetic | 10 | ~3.5 hours | A half-day at work |
| Motorcycle ride (250 miles) | 40 | ~14 hours | A night's sleep + commute |
| BASE jumping (1 jump) | 430 | ~6 days | A short holiday |

### Age Sensitivity

LLE depends critically on remaining life expectancy. The same 10-micromort surgery "costs" different amounts depending on age:

| Age | Remaining LE | LLE from 10 micromorts |
|-----|-------------|----------------------|
| 20 | ~60 years | 5.3 hours |
| 40 | ~40 years | 3.5 hours |
| 60 | ~22 years | 1.9 hours |
| 80 | ~9 years | 0.8 hours |

Use [`lle(prob, life_expectancy = ...)`](../reference/lle.html) to calculate for any age. See also [`daily_hazard_rate()`](../reference/daily_hazard_rate.html) for converting age-specific mortality into micromorts per day.

## 6. Complementary Metrics: QALY, DALY, and Morbidity {#complementary-metrics}

Micromorts and microlives focus on mortality. But many conditions (like the common cold) cause significant quality of life loss without being fatal. Complementary metrics capture this morbidity burden.

### [QALY](https://www.nice.org.uk/process/pmg9/chapter/the-reference-case){target="_blank"} (Quality-Adjusted Life Year)

Measures years of life adjusted for quality. **1 QALY = 1 year of perfect health.** See the [NICE Methods Guide](https://www.nice.org.uk/process/pmg9/chapter/the-reference-case){target="_blank"} for how QALYs are used in health technology assessment.

*   Health states are weighted 0 (death) to 1 (perfect health)
*   A year with chronic pain at 0.7 quality = 0.7 QALYs
*   Used to assess cost-effectiveness of medical interventions (e.g., £20,000-30,000 per QALY threshold in UK)
*   Measured using instruments like the [EQ-5D](https://euroqol.org/eq-5d-instruments/){target="_blank"} (mobility, self-care, usual activities, pain, anxiety)

### [DALY](https://www.who.int/data/gho/indicator-metadata-registry/imr-details/158){target="_blank"} (Disability-Adjusted Life Years)

Measures disease burden as the sum of two components. Defined by the [WHO Global Burden of Disease](https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/global-health-estimates-leading-causes-of-dalys){target="_blank"} study.

*   **DALY = YLL + YLD**, where:
    *   **YLL (Years of Life Lost):** From premature mortality
    *   **YLD (Years Lived with Disability):** From morbidity, weighted by [disability weights](https://ghdx.healthdata.org/record/ihme-data/gbd-2019-disability-weights){target="_blank"}

For fatal diseases like COVID-19, YLL dominates. For non-fatal conditions like the common cold, YLD dominates.

### Comparing Metrics

| Metric | Unit Definition | Scope | Sign | Best For |
|--------|-----------------|-------|------|----------|
| **Micromort** | 1/1,000,000 death probability | Per discrete event (surgery, flight, climb) | ≥ 0 (probability) | Comparing single risky activities |
| **Microlife** | 30 min life expectancy change per day | Per day of chronic exposure | + gain / − loss | Daily lifestyle interventions |
| **QALY** | 1 year at perfect health (quality=1.0) | Per treatment/intervention | ≥ 0 | Cost-effectiveness in healthcare |
| **DALY** | 1 year lost to disease (YLL + YLD) | Per condition/population | ≥ 0 (burden) | Global health prioritization |
| **QALD** | 1 day at perfect health | Per illness episode | ≥ 0 | Short-term morbidity (colds, flu) |

### References

*   Spiegelhalter D (2012). "Using speed of ageing and 'microlives'." BMJ 2012;345:e8223. [doi:10.1136/bmj.e8223](https://doi.org/10.1136/bmj.e8223)
*   Spiegelhalter D (2012). "Understanding uncertainty: Microlives." Plus Magazine. [plus.maths.org](https://plus.maths.org/content/understanding-uncertainty-microlives)
*   Spiegelhalter D (2009). "Micromorts." Plus Magazine. [plus.maths.org](https://plus.maths.org/os/issue55/features/risk/index) — includes the additivity formula for combining independent risks.
*   WHO Global Burden of Disease: [ghdx.healthdata.org](https://ghdx.healthdata.org/)
*   NICE Methods Guide: [nice.org.uk](https://www.nice.org.uk/process/pmg9/chapter/the-reference-case)

## 7. Conditional Risks: Cancer, Vaccination, and Risk Hedging {#conditional-risks}

### Cancer Risks by Type and Sex

The [`cancer_risks()`](../reference/cancer_risks.html) function provides mortality data stratified by cancer type, sex, and age group:

```{r introduction-chunk-9}
#| echo: false
safe_tar_read("vig_intro_cancer_top3") |>
  DT::datatable(
    caption = "Top 3 cancers by mortality rate (click to sort)",
    options = list(pageLength = 6, dom = "t"),
    rownames = FALSE
  )
```

**Family history impact:** The `family_history_rr` column shows relative risk increase with a first-degree relative's diagnosis. For example, prostate cancer risk increases 2.5× with family history.

```{r introduction-chunk-10}
#| echo: false
safe_tar_read("vig_intro_cancer_family_history") |>
  DT::datatable(
    caption = "Cancer risk with family history (Male, 50-64 years)",
    options = list(pageLength = 5, dom = "t"),
    rownames = FALSE
  )
```

### Vaccination Risk Reduction

The [`vaccination_risks()`](../reference/vaccination_risks.html) function quantifies micromorts avoided through vaccination:

```{r introduction-chunk-11}
#| echo: false
safe_tar_read("vig_intro_vaccination_childhood") |>
  DT::datatable(
    caption = "Complete childhood vaccination schedule impact",
    options = list(pageLength = 5, dom = "t"),
    rownames = FALSE
  )
```

```{r introduction-chunk-12}
#| echo: false
safe_tar_read("vig_intro_vaccination_adult") |>
  DT::datatable(
    caption = "Adult vaccination benefits (US, age 65+)",
    options = list(pageLength = 5, dom = "t"),
    rownames = FALSE
  )
```

### Hedged vs Unhedged: Optimal Lifestyle Comparison

The [`conditional_risk()`](../reference/conditional_risk.html) function compares risk factors between optimal ("hedged") and suboptimal ("unhedged") states:

```{r introduction-chunk-13}
#| echo: false
safe_tar_read("vig_intro_cardiovascular_risk") |>
  DT::datatable(
    caption = "Cardiovascular hedging: microlives gained by optimal choices",
    options = list(pageLength = 10, scrollX = TRUE),
    rownames = FALSE
  )
```

### Total Portfolio Effect

The [`hedged_portfolio()`](../reference/hedged_portfolio.html) function calculates total life expectancy gain from adopting all optimal lifestyle choices:

```{r introduction-chunk-14}
#| echo: false
show_target("vig_intro_portfolio_by_category")
```

```{r introduction-chunk-15}
#| echo: false
show_target("vig_intro_portfolio_summary")
```

**Interpretation:** A fully "hedged" individual (non-smoker, regular exercise, healthy diet, vaccinated, etc.) can expect to gain significant additional life expectancy compared to an "unhedged" baseline.

### Conditional Acute Risks: Age Changes Everything

Conditional risk analysis isn't limited to chronic lifestyle factors. Some acute risks show extreme **age-conditioning** that makes population averages misleading.

**Example: Falling out of bed** kills ~450 Americans per year ([CPSC](https://www.cpsc.gov/Newsroom/News-Releases/2022/Older-Americans-Are-More-Likely-to-Suffer-Fatalities-from-Falls-and-Fire-CPSC-Report-Highlights-Hidden-Hazards-Around-the-Home)). The population average is 1.36 micromorts/year — but the [CDC age-stratified data](https://www.cdc.gov/nchs/products/databriefs/db532.htm) reveals a **2,500-fold** difference across age groups:

| Age group | Sex | Fall deaths per 100,000/year | Micromorts per night of sleep |
|-----------|-----|------------------------------|------------------------------|
| Under 65 | Both | ~0.4 | **0.004** |
| 65-74 | Male | 24.7 | **0.68** |
| 65-74 | Female | 14.2 | **0.39** |
| 85+ | Male | 373.3 | **10.2** |
| 85+ | Female | 319.7 | **8.8** |

An 85-year-old man going to bed faces ~10 micromorts per night — comparable to riding a motorcycle 60 miles (10 micromorts/trip) or a single dose of ecstasy (13 micromorts/dose). For someone under 65, the same activity carries 0.004 micromorts per night — essentially zero.

This pattern recurs across many "exotic" risks:

*   **Bee/wasp stings** (72 deaths/year, US): Nearly all fatalities are among the ~1% of the population with venom allergy. Conditional on allergy: high risk per sting. Conditional on no allergy: near-zero.
*   **Cow trampling** (22 deaths/year, US): Population rate is 0.07 micromorts/year. For cattle farmers handling animals daily: ~7.5 micromorts/year — a 100-fold increase.
*   **Lightning strike** (28 deaths/year, US): Population rate is 0.08 micromorts/year. For outdoor agricultural workers: ~1.2 micromorts/year — a 15-fold increase.

The lesson: **always ask "conditional on what?"** before comparing micromort values across populations or activities. For a deeper treatment of how confounding variables distort risk data, including Simpson's paradox and stratification strategies, see the [Confounding Variables](confounding.html) vignette.

## 8. Data Quality {#data-quality}

A micromort value is only meaningful when paired with a clear **exposure denominator** — *Deaths ÷ Exposures = Probability*. Many widely-cited risk figures fail this test: unknown denominators (how many bee sting *exposures* per year?), misleading population averages (bed falls: 0.004 mm/night under-65 vs 10.2 mm/night age 85+ male), or fabricated numerators (the "150 coconut deaths/year" myth).

For the full treatment — denominator failures, inclusion criteria, cross-validation methods, and worked examples — see the [Data Reliability](data_reliability.html) vignette. For how confounding variables (age, geography, health profile) reshape risk rankings, see [Confounding Variables](confounding.html).

## 10. The Perception Gap {#perception-gap}

Why do people fear flying (0.05 micromorts per flight) more than driving to the airport (1--3 micromorts)? David Ropeik's *Risk: A Practical Guide for Deciding What's Really Safe and What's Really Dangerous* (2002) identifies a set of psychological factors that systematically distort risk perception away from statistical reality.

**Ropeik's key factors:**

-   **Dread:** Risks that evoke vivid suffering (cancer, plane crashes) feel larger than equivalent-mortality risks that kill quietly (heart disease, falls)
-   **Control:** Voluntary risks (skiing, smoking) feel smaller than imposed risks (pollution, food additives) even at equal micromort levels
-   **Familiarity:** Novel risks (vaping, CRISPR) trigger more fear than longstanding ones (alcohol, driving) regardless of the evidence
-   **Catastrophic potential:** A single event killing 200 people generates more fear than 200 separate events each killing one person, though the mortality is identical

These factors explain a persistent pattern in this package's data: **the activities people worry about most are rarely the ones with the highest micromort counts**.

Consider the cancer example from `cancer_risks()`: cancer accounts for roughly 25% of UK deaths, yet surveys consistently rank it as the *most feared* cause of death --- ahead of cardiovascular disease, which kills more people ([Ropeik 2010](https://doi.org/10.1111/j.1539-6924.2010.01412.x)). The `chronic_disease_risks()` data confirms that cardiovascular daily micromorts exceed cancer daily micromorts in every country in the dataset.

## 11. Calibrating Intuition {#calibrating-intuition}

The practical goal of the `micromort` package is to help calibrate intuition against evidence. Ropeik's framework suggests three strategies:

1.  **Anchor on familiar risks.** When evaluating a new risk, compare it to something you already accept. The quiz vignettes do exactly this --- presenting pairs like "one skydive vs. 2.5 days of UK cardiovascular risk" to build intuitive anchors.

2.  **Separate frequency from dread.** A risk that feels terrifying (shark attack: 0.001 micromorts/beach visit) may be statistically negligible compared to a risk that feels mundane (driving: 0.3 micromorts per 100 km). The `atomic_risks()` function puts both on the same scale.

3.  **Account for chronic accumulation.** Ropeik emphasises that people consistently underestimate slow, cumulative risks relative to dramatic one-off events. The `combined_quiz_pairs()` function is designed to make this comparison explicit: one acute micromort event vs. the equivalent number of days of chronic exposure.

For further reading, see Slovic's *The Perception of Risk* ([2000](https://doi.org/10.4324/9781315661773)) and Fischhoff et al.'s foundational psychometric work ([1978](https://doi.org/10.1016/0032-5910(78)80030-X)), which provide the empirical basis for Ropeik's framework.

## 12. Conclusion {#conclusion}

The `micromort` package helps translate abstract probabilities into concrete units for better decision-making. By comparing acute risks (micromorts), chronic risks (microlives), and quality-of-life metrics (<a href="https://www.nice.org.uk/glossary?letter=q"><abbr title="Quality-adjusted life year: 1 QALY = 1 year in perfect health">QALYs</abbr></a>, <a href="https://www.who.int/data/gho/indicator-metadata-registry/imr-details/158"><abbr title="Disability-adjusted life year: 1 DALY = 1 year of healthy life lost">DALYs</abbr></a>), individuals and policymakers can make more informed choices about risk trade-offs.

The new conditional risk functions enable:

*   **Cancer risk assessment:** Compare baseline risk to family history scenarios
*   **Vaccination value:** Quantify micromorts avoided through vaccination schedules
*   **Lifestyle optimization:** Calculate total life expectancy gain from adopting optimal "hedged" behaviors

## Reproducibility {#reproducibility}

```{r introduction-session-info, eval=TRUE}
sessionInfo()
```

```{r build-info}
#| echo: false
#| results: asis
cat(safe_tar_read("vig_build_info") %||% "")
```