How Reliable Are These Numbers?

Risk numbers disagree. The WHO and the Institute for Health Metrics and Evaluation (IHME) report malaria deaths as 550,000 and 760,000 respectively — a 38% gap from the same underlying deaths. Our World in Data’s Deadliest Animals chart is visually compelling, but converting annual death counts to per-encounter micromorts is non-trivial. This vignette documents how we handle that uncertainty.

1. Why Risk Numbers Disagree

Three factors drive disagreement between sources:

1. Numerator uncertainty: Death attribution varies by coding system (ICD-10 codes, verbal autopsy, hospital records)
2. Denominator uncertainty: How many people were exposed? A “deaths per year” figure means nothing without knowing the exposure population
3. Temporal and geographic aggregation: A global annual average hides enormous regional and seasonal variation

Our inclusion criteria: traceable numerator + defined denominator + reproducible calculation. We reject risks where we cannot identify both the death count and the population at risk.

2. The Confidence System

Every entry in atomic_risks() carries a confidence tier:

Validation status (new)

Within each confidence tier, we now track how thoroughly the estimate has been cross-checked:

3. Geographic and Health Profile Conditioning

Geography is the biggest source of variation in risk data — the same snake bite ranges from 0.5 mm (US, with antivenom) to 18.5 mm (rural sub-Saharan Africa) �� a 37x difference. Health profile conditioning shows similar magnitude: a bee sting is 0.03 mm for the general population but 31 mm for someone with a known allergy (1,000x).

For the full analysis of how geography and demographics reshuffle risk rankings, including disease mortality by country (IHME GBD data) and age-conditioned confounders (bed falls, anaesthesia), see the Confounding Variables vignette.

The common_risks() function supports profile-based filtering:

# Default: returns high-income, all-ages estimates
common_risks()

# Geographic and health profile conditioning
common_risks(profile = list(country = "NG"))
common_risks(profile = list(health_profile = "allergic"))

4. Cross-Validation Methods

We use five methods to assess data reliability:

Source triangulation

Compare the same risk across independent sources. For wildlife risks, we cross-reference:

• OWID (Our World in Data) annual death counts (numerator)
• CDC (Centers for Disease Control and Prevention) injury surveillance (US denominator)
• WHO (World Health Organization) fact sheets (global denominator)
• ISAF (International Shark Attack File) species-specific data

Denominator audit

The most common failure mode. Does the source report both a numerator (deaths) and a denominator (exposures)?

Temporal stability

Has the number changed significantly across editions of the source? Stable estimates across 5+ years increase confidence.

Geographic consistency

Do US, UK, and global estimates agree within an order of magnitude? Large discrepancies suggest unmeasured confounders (see Confounding Variables).

Order-of-magnitude test

Is the number physically plausible? A micromort value that implies more deaths than the population can support is a red flag.

5. Worked Example: Animal Risks from OWID

Our World in Data reports annual deaths by animal. Converting to per-encounter micromorts requires:

$$\text{micromorts} = \frac{\text{deaths per year}}{\text{encounters per year}} \times 10^6$$

Mosquito, crocodile, and elephant fail our inclusion criteria: there is no defensible per-encounter denominator. Mosquito bites are ubiquitous in endemic regions, making a per-bite risk meaningless. We cite OWID for context but do not include these as micromort entries.

6. Estimate Ranges

For wildlife entries, we document plausible ranges reflecting source disagreement:

The range reflects uncertainty in both the numerator (death counts vary by year and reporting) and denominator (exposure estimates are often rough). The point estimate is our best central value; the range brackets the plausible minimum and maximum.

7. What You Can Contribute

If you find a better source for an existing entry, or want to propose a new risk: open an issue at github.com/johngavin/micromort with:

1. Numerator: Death count and source citation
2. Denominator: Exposure count and source citation
3. Geography/condition: Does the estimate apply globally, or to a specific population?
4. Time period: When was the data collected?

Entries start at validation_status = "single_source" and get upgraded as more sources confirm them.

References

• Spiegelhalter D (2009). “Micromorts.” Plus Magazine. plus.maths.org
• Spiegelhalter D (2012). “Microlives.” Plus Magazine. plus.maths.org
• micromorts.rip — curated micromort database
• Wikipedia: Micromort

Reproducibility

sessionInfo()
R version 4.6.1 (2026-06-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 26.04 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.32.so;  LAPACK version 3.12.0

locale:
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time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] dplyr_1.2.1     micromort_0.2.0

loaded via a namespace (and not attached):
 [1] base64url_1.4     jsonlite_2.0.0    compiler_4.6.1    tidyselect_1.2.1 
 [5] Rcpp_1.1.1-1.1    callr_3.8.0       yaml_2.3.12       fastmap_1.2.0    
 [9] R6_2.6.1          generics_0.1.4    igraph_2.3.3      knitr_1.51       
[13] backports_1.5.1   targets_1.12.0    tibble_3.3.1      units_1.0-1      
[17] maketools_1.3.2   rprojroot_2.1.1   pillar_1.11.1     rlang_1.2.0      
[21] xfun_0.59         sys_3.4.3         otel_0.2.0        cli_3.6.6        
[25] magrittr_2.0.5    ps_1.9.3          digest_0.6.39     processx_3.9.0   
[29] secretbase_1.3.0  lifecycle_1.0.5   prettyunits_1.2.0 vctrs_0.7.3      
[33] evaluate_1.0.5    glue_1.8.1        data.table_1.18.4 codetools_0.2-20 
[37] buildtools_1.0.0  rmarkdown_2.31    tools_4.6.1       pkgconfig_2.0.3  
[41] htmltools_0.5.9  

micromort 0.1.0 | Git 94d93d2 | R 4.5.2 | Built 2026-04-18 12:20:56