Skip to content
Likelier

Perceived fear vs. actual probability

What are the odds of causing a fatal crash by driving within a few hours of using cannabis?

Lifetime probability · activity

~1 in 52

1.9% lifetime chance

Most people underestimate this.

Scopes vary — shown as typical adult lifetime odds. See methodology.

Transport · reviewed 2026-06-14

What are the odds of causing a fatal crash by driving within a few hours of using cannabis?

Evidence quality 4.63/5

Eight-dimension review score against the quality rubric . Each dimension scored 1–5.

D1 Source grounding
4/5
D2 Source authority
5/5
D3 Arithmetic
5/5
D4 Uncertainty
4/5
D5 Scope
5/5
D6 Prose
5/5
D7 Perception honesty
4/5
D8 Caveat completeness
5/5
Average 4.63/5
Direct evidence
Source Government statistic · Compton, R.P. & Berning, A. — National Highway Traffic Safety Administration
lifetime, activity-specific each band = 10× rarer → zoomed to your factors See full scale →
certain 1 in 1K 1 in 1M 1 in 1B
1 in 11 1 in 263

● your factors — click this risk ▾ to reveal

  1. Your factors
A muted flat vector illustration of a single car steering wheel with a small leaf shape outline beside it, on a pale background.

Perceived

Most regular cannabis users believe driving stoned is meaningfully safer than driving drunk, and a substantial minority believe it is safe or even improves their driving. Self-report surveys in legal-cannabis US states find that roughly 30-50% of past-month cannabis users have driven within two hours of use, and most of those describe themselves as cautious or unaffected. The subjective experience of acute cannabis impairment is qualitatively different from alcohol: drivers typically feel slower, more focused, and self-correctingly conservative — they drive more carefully on the straightaways and miss the high-attention edge cases (lane departures, sudden brake events, peripheral movement) where the per-trip crash risk actually lives.

Rough estimate: most regular cannabis users believe driving stoned is far safer than driving drunk

Source: editorial intuition, not polled

Actual

~4 per 100,000 trips result in a fatal crash for a driver within ~2 hours of cannabis use at ~5 ng/mL blood THC (≈2× the sober-driver rate)

US adult driver within ~2 hours of cannabis use at acute-effect concentrations (≥5 ng/mL whole-blood THC), per-trip crash involvement rate derived from Albrecht 2025 meta-regression applied to NHTSA per-trip baseline

Show derivation

The US population-average per-trip fatal-crash probability for a sober driver is approximately 1 in 50,000 (the baseline used in the driving-at-0.1pct-bac entry, derived from FARS and NHTSA per-trip estimates). Albrecht et al. 2025 (Drug Science, Policy and Law) pooled culpability studies in a dose-response meta-regression and found crash-culpability risk roughly doubles at ~5 ng/mL whole-blood THC and roughly quadruples at ~10 ng/mL; below ~1.5 ng/mL the increase is not distinguishable from baseline. Applying a 2× per-trip multiplier at acute-effect concentrations gives ~1 in 25,000 per cannabis-impaired trip. For a driver who operates within two hours of cannabis use roughly monthly (12 trips/year over 40 years ≈ 480 impaired trips), cumulative probability is 1 − (1 − 1/25000)^480 ≈ 0.019, or roughly 1 in 52. The uncertainty band brackets a spread of per-trip crash-responsibility multipliers: Rogeberg & Elvik 2016 pooled adjusted OR 1.36 and the French VOIESUR study (Martin 2017) adjusted OR 1.65 sit below the Albrecht acute-dose 2× doubling (so casual / any-positive use yields a lower estimate), while the European DRUID study found OR ≈ 6.6 at ≥5 ng/mL (closer to 0.15% BAC), which would push the high-end estimate to ~1 in 9. The 2024-2025 evidence (Leavitt 2024 FARS prevalence, the 2025 ACS conference abstract) is consistent with this framing but adds prevalence data, not a revised per-trip risk, so the headline point estimate is unchanged.

Caveats: The cannabis-driving evidence base has unusually wide spread because the publish…

The cannabis-driving evidence base has unusually wide spread because the published estimates depend heavily on how the impaired population is defined. "THC-positive" includes drivers who used cannabis days earlier and retain detectable but non-impairing residual concentrations; "acute use" means within roughly 1-3 hours of inhalation or 2-4 hours of edible peak. Pooled "any positive" ORs (Rogeberg 1.36, Compton 1.00 adjusted) are dominated by the residual group and underestimate acute-use risk. Dose-response meta-regressions (Albrecht 2025, the DRUID studies) isolate the acute-use signal and find substantially higher per-trip risk at concentrations consistent with recent inhalation. The headline 1 in 52 estimate uses the acute- use Albrecht multiplier at ~5 ng/mL, which corresponds to typical post-inhalation peak concentrations but is conservative for high-dose edibles or concentrate use. The combination with even modest alcohol use is super-additive and not captured in the headline figure. Compared with the 0.10% BAC entry, the per-trip risk multiplier is smaller (~2× vs ~5.5×) but the per-event frequency for regular cannabis consumers can be higher (weekly is more common than weekly drunk driving), so the lifetime totals end up closer than the per-trip comparison would suggest. The 2024-2025 evidence refreshes the exposure picture without moving the per-trip risk: a 2024 peer-reviewed FARS analysis (Leavitt, Chihuri & Li) found 33.4% of 14,079 fatally injured US drivers tested positive for cannabis, and a 2025 American College of Surgeons conference abstract reported 41.9% of 246 deceased drivers in one Ohio county positive for active THC at a mean 30.7 ng/mL. Both are prevalence figures from fatally-injured (selection-biased) samples and the ACS figure is not yet peer reviewed, so neither licenses raising the headline lifetime estimate. The French VOIESUR case-control study (Martin 2017) gives an adjusted crash-responsibility OR of 1.65 for cannabis alone and — importantly — found no significant alcohol x cannabis interaction, implying the combined risk is multiplicative (the product of the two ORs) rather than additive or super-additive, a more conservative reading of the combined-use factor than older culpability studies suggested.

Related risks

Other risks on similar themes — for exploring related fears.

Transport

Driving on sedating meds

What are the odds of causing a fatal crash by driving while on a sedating prescription medication?

Transport

Drowsy driving

What are the odds of causing a fatal crash by driving without enough sleep?

Transport

Car crash

What are the odds of dying in a car crash?

Transport

Hand-held phone call + driving

What are the odds of a crash from holding a phone to your ear while driving?

Transport

Driving at 0.10% BAC

What are the odds of causing a fatal crash when driving with a 0.10% blood alcohol level?

Transport

Eating while driving

What are the odds of a crash from eating or drinking while driving?

Transport

Petrol car fire

What are the odds of a petrol car catching fire spontaneously?

Transport

Self-driving car fatality

What are the odds of being killed by a self-driving car?

Compare to:

The cleanest recent estimate of acute cannabis-impaired driving risk comes from Albrecht et al.’s 2025 dose-response meta-regression in Drug Science, Policy and Law, which pooled culpability studies that reported blood THC concentrations rather than just THC-positive/negative status. They found a clear dose-response curve: below roughly 1.5 ng/mL whole-blood THC, crash- culpability risk is statistically indistinguishable from baseline; risk doubles around 5 ng/mL and quadruples around 10 ng/mL. The 5 ng/mL point corresponds approximately to the peak concentration produced by typical inhaled cannabis use within the first hour. Pooled “any THC positive” odds ratios — Rogeberg & Elvik’s 2016 adjusted 1.36, the 2015 NHTSA Virginia Beach study’s adjusted 1.00 — appear much lower because the positive group is dominated by drivers with residual THC from prior days who are not currently impaired.

The lifetime framing is similar in shape to the alcohol-impaired case but with a smaller per-trip multiplier and (for regular consumers) a higher per-event frequency. For a driver who operates within two hours of cannabis use roughly monthly — 480 acute-use trips over 40 years — applying the Albrecht 2× per-trip multiplier to the sober per-trip baseline of ~1 in 50,000 gives a cumulative lifetime probability of roughly 1 in 52 of being involved in a fatal crash specifically caused by their cannabis impairment. A weekly cannabis-driving pattern pushes the estimate toward 1 in 13; daily use with high-dose edibles or concentrates pushes it further still. The uncertainty band is wide because the European DRUID studies found OR ≈ 6.6 at ≥5 ng/mL (comparable to 0.15% BAC), which would land the headline closer to 1 in 9 lifetime — a meaningful contrast with the Albrecht meta-regression’s 2×.

Two structural features of cannabis impairment make the lifetime calculation harder than the alcohol case. First, the impairment is short-lived but the detection window is long: detectable THC can persist for days after impairment has resolved, which contaminates every observational study that uses positive/negative status as the exposure variable. Second, the subjective experience does not match the objective deficit. Drivers under acute cannabis influence typically drive more slowly and leave larger following distances, but their reaction times to unexpected events lengthen and lane-keeping degrades — the deficit shows up in the high-attention edge cases, not the routine straightaway. The combination with even modest alcohol is super-additive and not captured in the headline figure: a 0.04% BAC plus acute cannabis use roughly multiplies risks rather than adding them, which is the pattern most regular cannabis-using social drinkers encounter most often.

A driver who gets behind the wheel within two hours of cannabis use about monthly carries a ~1 in 52 lifetime chance of a fatal crash, roughly double the sober-driver rate. Many regular users assume stoned driving is far safer than drunk driving.

Claim ledger

Every number below is what each source reported, with the verbatim quote we relied on and how we arrived at our figure. Click any link to verify directly.

  1. [1] Albrecht, Hasan, Kekez, Zhou — Drug Science, Policy and Law — Dose-response relationship between blood concentrations of THC and crash culpability risk: An updated meta-regression of culpability studies
    Dose-response relationship between blood concentrations of THC and crash culpability risk: An updated meta-regression of culpability studies
    Statistic
    Crash culpability risk increases with rising whole-blood THC concentration, with an inflection around 1.5-3.0 ng/mL where risk begins to rise above baseline; risk approximately doubles at ~5 ng/mL and approximately quadruples at ~10 ng/mL. Below ~1.5 ng/mL culpability is statistically indistinguishable from baseline (<30% increase).
    Excerpt
    “"Crash culpability risk increases with increasing THC concentration, with an inflection around 1.5-3.0 ng/ml where risk begins to increase... a doubling of culpability risk around 5 ng/ml and a potential quadrupling of risk around 10 ng/ml." ”
    Source data from
    2025-03-01
    Accessed
    2026-05-25 · archived copy
    Calculation
    Albrecht 2025 is the most recent dose-response meta-regression and the cleanest source for translating a blood THC concentration into a per-trip crash-culpability multiplier. The doubling at ~5 ng/mL whole blood is used here as the headline per-trip risk multiplier for "driving within ~2 hours of typical inhaled cannabis use" (which produces peak THC concentrations in roughly that range). The quadrupling at ~10 ng/mL and Rogeberg 2016 pooled 1.36 OR bracket the uncertainty band.
  2. [2] Rogeberg, O. & Elvik, R. — Addiction 111(8):1348-1359 — The effects of cannabis intoxication on motor vehicle collision revisited and revised
    The effects of cannabis intoxication on motor vehicle collision revisited and revised
    Statistic
    Pooled odds ratio across 28 estimates from 21 observational studies for acute cannabis use and motor-vehicle collision involvement was 1.36 (95% CI 1.15-1.61); roughly half of earlier higher estimates from the Asbridge 2012 BMJ meta-analysis disappear after correcting for confounding and methodological inconsistencies.
    Excerpt
    “"Our updated meta-analysis suggests that the increase in crash risk caused by cannabis intoxication is moderate, around 20-30%, much smaller than that of drink-driving and similar in magnitude to that of driving with a blood alcohol concentration between 0.01% and 0.05%." ”
    Source data from
    2016-08-01
    Accessed
    2026-05-25 · archived copy
    Calculation
    Rogeberg & Elvik 2016 (DOI 10.1111/add.13347, PMID 26878835) is the most methodologically careful pooled estimate available. It used 28 estimates from 21 observational studies (case-control and culpability designs) and explicitly corrected for the confounding by age, sex, and time-of-day that the Asbridge 2012 BMJ meta-analysis partially missed. The 1.36 odds ratio is the low-end pooled multiplier used in the uncertainty band; it represents the "any cannabis-positive driver" average, dominated by drivers with residual THC from prior days rather than acute peak concentrations. Albrecht's 5 ng/mL doubling is the appropriate reference for acute post-use trips and anchors the headline.
  3. [3] Compton, R.P. & Berning, A. — National Highway Traffic Safety Administration — Drug and Alcohol Crash Risk (Research Note, DOT HS 812 117)
    Drug and Alcohol Crash Risk (Research Note, DOT HS 812 117)
    Statistic
    The unadjusted odds ratio for crash involvement among THC-positive drivers in the Virginia Beach case-control study was 1.25; after adjustment for age, sex, race/ethnicity, and alcohol concentration the odds ratio dropped to 1.00 (95% CI 0.77-1.31), indicating no statistically significant elevation. The same study found 0.08% BAC associated with adjusted OR of 3.93 and 0.15% BAC with adjusted OR of 12.04.
    Excerpt
    “"After adjustment for age, gender, race/ethnicity, and alcohol use, the odds of being a crash-involved driver was not statistically different from that of a control driver who tested positive for THC (OR=1.00, 95% CI 0.77-1.31)." ”
    Source data from
    2015-02-01
    Accessed
    2026-05-25 · archived copy
    Calculation
    The 2015 NHTSA Virginia Beach study is the largest US case-control study of drug-positive driving and is frequently cited as evidence that cannabis-impaired driving carries no elevated risk after controlling for confounders. Methodologists (Rogeberg, Albrecht, Compton) note that the null result reflects the dominance of residual-THC-positive drivers in the cannabis-positive group; separating acute-use from residual-positive cases (which the blood-concentration meta-regressions do) recovers a measurable dose-response signal. Used here as the lower bound on the "any THC positive" risk multiplier and as evidence that the headline number depends strongly on how the impaired population is defined.
  4. [4] Martin, J.-L. et al. — PLOS ONE 12(11):e0187320 (VOIESUR project) — Cannabis, alcohol and fatal road accidents
    Cannabis, alcohol and fatal road accidents
    Statistic
    In a French population-based case-control analysis of 4,059 drivers involved in 2,870 fatal accidents (VOIESUR, 2011), the adjusted odds ratio of being responsible for a fatal crash for a driver under the influence of cannabis alone was 1.65 (95% CI 1.16-2.34). No first-order interaction between alcohol and cannabis was significant (alcohol x cannabis p = 0.29), so the combined-substance odds ratio is estimated as the product of the individual ORs (multiplicative, not synergistic).
    Excerpt
    “"Drivers under the influence of cannabis multiply their risk of being responsible for causing a fatal accident by 1.65 (1.16-2.34)... All first-order interactions were tested. None were significant. In particular, we found no significant interaction of alcohol x cannabis (p = 0.29)... the odds ratio for a driver under the influence of both alcohol and cannabis can be estimated by the product of the OR relative to alcohol and the OR relative to cannabis." ”
    Source data from
    2017-11-08
    Accessed
    2026-06-14 · archived copy
    Calculation
    Martin et al. 2017 is a peer-reviewed national case-control study of crash responsibility, adjusted for age, gender, vehicle category, and time of accident. Its adjusted OR of 1.65 for cannabis alone sits between the Rogeberg & Elvik 2016 pooled 1.36 and the Albrecht 2025 acute-dose 2× doubling, and so anchors the lower-middle of the uncertainty band — it is a population-average per-trip crash-responsibility multiplier vs sober, not a sub-group factor. Its no-interaction finding refines the combined cannabis+alcohol framing: the joint risk is multiplicative (product of the two ORs) rather than additive or super-additive.
  5. [5] Leavitt, T.C., Chihuri, S., Li, G. — Injury Epidemiology 11:18 — State cannabis laws and cannabis positivity among fatally injured drivers
    State cannabis laws and cannabis positivity among fatally injured drivers
    Statistic
    Using 2019 and 2020 Fatality Analysis Reporting System (FARS) toxicology data for 14,079 fatally injured US drivers, 4,702 (33.4%) tested positive for cannabis. Cannabis positivity was a recurring feature of fatal-crash drivers regardless of state legalization status.
    Excerpt
    “"Of the 14,079 fatally injured drivers, 4702 (33.4%) tested positive for cannabis." ”
    Source data from
    2024-04-11
    Accessed
    2026-06-14 · archived copy
    Calculation
    Leavitt et al. 2024 is the largest recent US prevalence figure for cannabis among fatally injured drivers. It is a prevalence (exposure) statistic, not a per-trip risk multiplier: a fatally-injured sample is selection-biased toward crash outcomes, so it cannot be used to revise the headline lifetime probability. It is cited here to refresh the evidence base on how common cannabis-positive driving is among the population that ends up in fatal crashes.

443 risks with measured probability
1 in 10 1 in 100 1 in 1K 1 in 10K 1 in 100K 1 in 1M 1 in 10M 1 in 100M 1 in 1B certain rarer → Cosmetic surgery abroad risk — 1 in 10 Infant sugar/salt and adult disease — 1 in 10 Endometriosis — 1 in 10 Hair transplant Turkey risk — 1 in 10 Knee replacement — 1 in 10 Chronic painkillers — 1 in 10 Elderly abandonment — 1 in 9.1 Complete tooth loss — 1 in 9.1 Alzheimer's — 1 in 8.3 Sleep deprivation — 1 in 8.3 Smokeless tobacco — 1 in 8.3 Cycling w/o helmet — 1 in 8.0 Bruxism tooth damage — 1 in 7.7 Skipping care over ICE fear — 1 in 7.1 Vision loss — 1 in 6.7 Hernia from lifting — 1 in 6.7 Hip fracture risk — 1 in 6.7 Regular drinking — 1 in 6.7 First heart attack — 1 in 5.9 Infertility — 1 in 5.7 5+ years paid LTC — 1 in 5.6 CTE (football) — 1 in 5.0 Major depression — 1 in 4.9 Hiking injury — 1 in 4.8 Infection from sharing food with child — 1 in 4.2 Lyme disease — 1 in 4.0 Loneliness & health — 1 in 3.8 Job loss & depression — 1 in 3.7 Inheriting AUD risk — 1 in 3.5 Alcohol use disorder — 1 in 3.4 Anxiety disorder — 1 in 3.2 Menopause CV risk acceleration — 1 in 3.0 Silent diabetes — 1 in 3.0 Flying with cold — 1 in 2.9 Tick illness (forest) — 1 in 2.9 Silent high cholesterol — 1 in 2.9 Grandparent loss in childhood — 1 in 2.8 Pacifier floor drop — 1 in 2.8 Drug-resistant infection — 1 in 2.6 No marrow match — 1 in 2.4 Nursing home admission — 1 in 2.2 Skipping dental checkups — 1 in 2.1 False-positive mammogram — 1 in 2.0 Regular smoking — 1 in 2.0 Travelers' diarrhea — 1 in 2.0 Adventure sports — 1 in 1.8 Family caregiver probability — 1 in 1.8 LTC need after 65 — 1 in 1.8 Widowhood probability — 1 in 1.7 Unprotected sex — 1 in 1.5 Silent hypertension — 1 in 1.3 Chronic back pain — 1 in 1.3 Hand hygiene — 1 in 1.0 Cancer (any) — 1 in 7.1 E-scooter no helmet — 1 in 4.5 E-bike no helmet — 1 in 4.0 Mishandled luggage — 1 in 3.7 Deer collision — 1 in 2.7 At-fault injury crash — 1 in 2.5 Flight cancellation — 1 in 1.8 Trip disruption: war or disaster — 1 in 1.7 Home burglary (global) — 1 in 9.1 Hitchhiking assault — 1 in 8.8 Mail check fraud — 1 in 7.7 Child sexual abuse — 1 in 6.8 Stalking — 1 in 6.2 Student sexual assault — 1 in 5.7 Domestic violence — 1 in 3.7 Night walk assault — 1 in 3.6 Bicycle theft — 1 in 2.9 Sexual assault — 1 in 2.9 Home burglary — 1 in 2.6 Sexual harassment (lifetime) — 1 in 1.6 Water scarcity — 1 in 2.5 Carrington-class solar storm — 1 in 1.9 WAIS tipping point — 1 in 1.1 Indoor cat escape harm — 1 in 10 Off-leash dog bite — 1 in 8.9 Rabbit dies in 4 years — 1 in 3.3 Dog bite (non-fatal) — 1 in 1.8 Hamster dies before teenager — 1 in 1.0 Vitamin D gap — 1 in 2.9 Undercooked food — 1 in 1.6 Raw meat cross-contamination — 1 in 1.4 Food left out — 1 in 1.2 AI voice scam — 1 in 2.9 Online scam loss — 1 in 2.5 Teen cyberbullying — 1 in 2.0 Kids & explicit content — 1 in 1.9 Data breach — 1 in 1.1 Miscarriage — 1 in 6.7 Teen suicide attempt — 1 in 5.6 Postpartum depression — 1 in 4.8 Painkiller before infant vaccination — 1 in 3.8 Excessive pregnancy weight — 1 in 2.6 Unvaxxed child & measles — 1 in 2.0 Child head lice — 1 in 2.0 Elder fraud loss — 1 in 10 Pension fund collapse — 1 in 10 Personal bankruptcy — 1 in 10 Housing crash — 1 in 8.3 Crypto total loss — 1 in 6.7 IRS audit — 1 in 6.7 Currency collapse — 1 in 5.6 Visa overstay deportation — 1 in 5.6 Subprime auto-loan repossession — 1 in 5.0 Long term disability working age — 1 in 4.0 Student loan default — 1 in 3.8 Whistleblower retaliation — 1 in 3.2 Career obsolescence — 1 in 2.9 Forced job exit before retirement — 1 in 2.9 Retirement shortfall — 1 in 2.6 BNPL missed payment — 1 in 2.4 Divorce — 1 in 2.4 Burst pipe damage — 1 in 2.2 Workplace bullying — 1 in 2.1 Prolonged grid blackout — 1 in 2.0 Deportation (undocumented) — 1 in 1.8 Funeral cost shock — 1 in 1.8 Identity theft — 1 in 1.7 Credit card fraud — 1 in 1.5 School bullying — 1 in 1.5 Insurance claim denial — 1 in 1.4 Frontline soldier casualty — 1 in 1.3 Economic recession — 1 in 1.0 Stock market crash — 1 in 1.0 Hail roof damage — 1 in 3.0 Dry toilet paper harm — 1 in 100 Secondhand smoke — 1 in 91 Gaming disorder (adults) — 1 in 83 High-heel ER visit — 1 in 79 Child throwing object — 1 in 67 Medication reaction — 1 in 58 Drug overdose — 1 in 56 Gas-stove asthma in a child — 1 in 50 Cat litter toxoplasmosis — 1 in 48 Mental health LTD claim — 1 in 45 Benzo dependence — 1 in 40 Tap water lead — 1 in 40 Medication misuse — 1 in 35 Traumatic brain injury — 1 in 33 Hospital infection — 1 in 31 Air pollution — 1 in 29 End-stage kidney disease — 1 in 29 Traveler's diarrhea (water) — 1 in 26 Skiing injury — 1 in 26 Bipolar disorder — 1 in 23 Dental tourism complication — 1 in 20 Pet parasites — 1 in 20 Undiagnosed ADHD — 1 in 20 Adult-onset food allergy — 1 in 19 Indoor cooking smoke — 1 in 18 Non-Alzheimer's dementia — 1 in 17 Working-age disabling stroke — 1 in 17 Cannabis use disorder — 1 in 16 Stroke — 1 in 15 PTSD — 1 in 15 Parent death/disability — 1 in 14 Severe hearing loss — 1 in 14 Type 2 diabetes — 1 in 13 Appendicitis — 1 in 13 Untreated depression — 1 in 13 Untreated back pain disability — 1 in 13 Heart disease — 1 in 12 Medical error death — 1 in 12 Compulsive sexual behavior — 1 in 12 Eating disorder — 1 in 11 Hip replacement — 1 in 11 Kidney stones — 1 in 11 Sedentary lifestyle — 1 in 11 Salon infection — 1 in 11 Ovarian cancer — 1 in 91 Colorectal cancer — 1 in 77 Breast cancer — 1 in 59 Liver cancer — 1 in 59 Lung cancer — 1 in 56 Prostate cancer — 1 in 50 Melanoma (UV) — 1 in 29 Low-fiber CRC risk — 1 in 23 Red meat & CRC — 1 in 21 Charred meat & cancer — 1 in 20 Maintenance crash — 1 in 83 Driving on sedating meds — 1 in 77 Texting + driving — 1 in 56 Driving after cannabis — 1 in 53 Eating while driving — 1 in 53 Unbelted crash death — 1 in 53 Speeding 20% over limit — 1 in 48 Motorcycle no helmet — 1 in 45 Spaceflight (astronaut) — 1 in 42 Video watching + driving — 1 in 32 Drowsy driving — 1 in 26 E-scooter injury — 1 in 26 Cruise ship norovirus — 1 in 24 Driving at 0.10% BAC — 1 in 16 Catalytic converter theft — 1 in 83 Pickpocketed while traveling — 1 in 38 Stabbed in an assault — 1 in 37 Vehicle theft — 1 in 34 Street robbery / mugging — 1 in 26 Wrongful conviction — 1 in 24 Drink spiking — 1 in 17 Keyless relay car theft — 1 in 13 Protest under autocracy — 1 in 12 AMOC collapse — 1 in 20 Sting anaphylaxis — 1 in 50 Cat collar injury — 1 in 25 Fish bone injury — 1 in 68 Restaurant food poisoning — 1 in 58 Vegetarian deficiency — 1 in 25 Intimate deepfake — 1 in 25 Social media problematic use — 1 in 13 Infant fall — 1 in 100 Child swallows object (ER) — 1 in 91 Childbirth death (SSA) — 1 in 55 Co-sleeping death — 1 in 43 Toddler stair fall — 1 in 37 Play swing & slide injury — 1 in 33 Autism diagnosis — 1 in 31 C-section complications — 1 in 29 Toy injury requiring ER (child) — 1 in 21 Preeclampsia — 1 in 20 Severe birth tearing — 1 in 17 Gestational diabetes — 1 in 13 Child fall head injury — 1 in 12 Sports betting financial ruin — 1 in 100 Fighter pilot death — 1 in 48 Commercial fishing career death — 1 in 45 Logging career death — 1 in 34 Dying without heir — 1 in 33 Medical bankruptcy — 1 in 25 Compulsive buying disorder — 1 in 20 Rental listing scam loss — 1 in 20 Losing SNAP under 2025 work rules — 1 in 18 Mortgage foreclosure — 1 in 14 Musculoskeletal LTD claim — 1 in 14 Day-trading losses — 1 in 13 Extremist govt catastrophe — 1 in 13 Hurricane home destruction — 1 in 17 LASIK complications — 1 in 1,000 NAION (Ozempic) — 1 in 909 Infant pool submersion — 1 in 800 MS — 1 in 769 Workplace fatality — 1 in 690 Typhoid fever — 1 in 654 GLP-1 anesthesia aspiration — 1 in 613 Unsafe imported products — 1 in 565 Brain aneurysm — 1 in 400 COVID-19 — 1 in 400 Fireworks injury — 1 in 385 Too much caffeine — 1 in 366 Sickle cell disease — 1 in 365 Counterfeit medicine — 1 in 361 Spinal cord injury — 1 in 313 Childhood cancer diagnosis — 1 in 285 Next pandemic death — 1 in 208 Dengue (travel) — 1 in 200 Heat-triggered preterm birth — 1 in 200 Skipping daily showers — 1 in 200 Not scrubbing feet — 1 in 200 Marrow donation risk — 1 in 167 Tick-borne encephalitis — 1 in 167 Schizophrenia — 1 in 143 Accidental fall — 1 in 135 Parkinson's — 1 in 125 Sudden death during exercise — 1 in 123 Suicide (US) — 1 in 121 Opioid addiction — 1 in 114 Tuberculosis (global) — 1 in 109 HIV diagnosis — 1 in 105 Radon cancer — 1 in 435 Testicular cancer — 1 in 250 Cervical cancer — 1 in 167 Pancreatic cancer — 1 in 125 Pedestrian death — 1 in 806 Motorcycle crash — 1 in 709 Boating drowning — 1 in 685 Driver kills pedestrian — 1 in 552 Phone-distracted walking injury — 1 in 400 EV battery fire — 1 in 333 Cyclist killed by car — 1 in 196 Hand-held phone call + driving — 1 in 143 Petrol car fire — 1 in 125 Self-driving car fatality — 1 in 115 Car crash — 1 in 105 Firefighter duty death — 1 in 455 Homicide — 1 in 339 Police duty death — 1 in 313 Pig-butchering scam — 1 in 106 Extreme heat — 1 in 333 Climate change death — 1 in 204 Swallowed bee/wasp — 1 in 500 Bat bite & rabies — 1 in 238 Mosquito-borne disease — 1 in 190 Food poisoning (global) — 1 in 317 Solar panel fire — 1 in 667 Untreated childhood scoliosis — 1 in 1,000 Child window fall — 1 in 855 Walker stair fall — 1 in 625 Baby walker injury — 1 in 455 Maternal mortality — 1 in 272 Untreated childhood flat feet — 1 in 250 Maternal age & birth defects — 1 in 200 Child death (<18) — 1 in 143 Caving career death — 1 in 167 EMS duty death — 1 in 794 Civilian war casualty — 1 in 499 Soldier in combat — 1 in 270 Student visa revocation — 1 in 263 Mining career death — 1 in 214 Gambling financial ruin — 1 in 159 Wildfire home destruction — 1 in 120 Lightning home fire — 1 in 105 Malaria (travel) — 1 in 10,000 Infection from shared drink — 1 in 10,000 Chagas disease — 1 in 8,475 Wild berry fox tapeworm — 1 in 8,475 Child nicotine-pouch ingestion — 1 in 7,937 Schistosomiasis death — 1 in 6,667 Sudden death (young adult) — 1 in 3,922 Unsafe wiring — 1 in 3,390 Sepsis from wound — 1 in 2,857 Anesthesia awareness — 1 in 2,500 Heat stroke (outdoor) — 1 in 1,905 House fire — 1 in 1,818 Rabies from dogs — 1 in 1,449 Drowning — 1 in 1,379 Shallow-water diving SCI — 1 in 1,111 Choking — 1 in 1,099 EVALI vaping hospitalization — 1 in 1,064 Betel nut cancer — 1 in 1,290 Blood clot (flight) — 1 in 4,651 Killing a cyclist — 1 in 3,937 Teen road-crash death — 1 in 3,030 Child rear bike seat — 1 in 2,500 Child without restraint — 1 in 2,000 Fatal police encounter — 1 in 4,739 Honor killing — 1 in 2,381 Intimate-partner homicide — 1 in 1,767 Hurricane — 1 in 8,929 Drought famine death — 1 in 6,536 Blizzard death — 1 in 4,367 Earthquake — 1 in 3,802 Dog chocolate death — 1 in 2,000 Listeria from deli meat — 1 in 6,061 Serious E. coli from fresh produce — 1 in 4,831 Food poisoning (US) — 1 in 1,862 Fish mercury — 1 in 1,695 Phone/laptop battery fire — 1 in 1,136 SIDS — 1 in 7,143 Laundry pod ingestion — 1 in 6,494 Untreated infant hip dysplasia — 1 in 5,000 Pool drowning — 1 in 2,299 War (civilian) — 1 in 2,000 Flu brain swelling in a child (IAE/ANE) — 1 in 100,000 Fatal bee/wasp sting — 1 in 76,923 Locally-acquired dengue (continental US) — 1 in 66,667 Anesthesia death — 1 in 50,000 Dog hot car death — 1 in 41,667 Vibrio vulnificus wound infection — 1 in 32,051 Anaphylaxis — 1 in 27,548 Chiropractic neck manipulation — 1 in 16,667 CO poisoning — 1 in 14,006 Hepatitis A (travel) — 1 in 12,500 Skipping allergy immunotherapy — 1 in 11,111 Acrylamide & cancer — 1 in 16,667 Bus crash — 1 in 100,000 Plane crash — 1 in 58,824 Child pedestrian (residential) — 1 in 45,455 Post-crash car fire — 1 in 25,000 Railroad crossing death — 1 in 20,704 Car submersion — 1 in 16,667 Child bike trailer — 1 in 14,286 Runway near-miss — 1 in 13,699 Acid attack — 1 in 89,286 Terrorism — 1 in 77,519 Child stranger abduction — 1 in 38,760 Stranger kidnapping — 1 in 35,211 Dowry death — 1 in 13,158 Accidental gun death — 1 in 11,299 Wildfire — 1 in 100,000 Tornado — 1 in 80,645 Tsunami — 1 in 52,632 Ocean drowning — 1 in 29,155 Flood — 1 in 20,202 Post-hurricane heat death — 1 in 20,000 Landslide death — 1 in 18,416 Supervolcano eruption — 1 in 12,376 Crocodile attack — 1 in 84,746 Bee sting — 1 in 78,927 Fatal scorpion sting — 1 in 26,110 Lead-tainted cinnamon pouch — 1 in 40,000 Plastic container leaching — 1 in 16,949 Infant in car seat — 1 in 64,935 Bouncer chair fall — 1 in 60,606 Toddler choking — 1 in 50,000 Unsupervised infant choking — 1 in 50,000 Magnet ingestion — 1 in 12,048 Snorkeling death — 1 in 21,739 Pet in transport — 1 in 20,000 Death in ICE custody — 1 in 17,065 Landmine or UXO injury — 1 in 14,728 Vaccine reaction — 1 in 763,359 Aluminum & Alzheimer's — 1 in 169,492 Residential gas leak — 1 in 140,845 Child hot car death — 1 in 102,041 Glyphosate & cancer — 1 in 1,000,000 Teflon cookware cancer — 1 in 169,492 Roller coaster injury — 1 in 312,500 Cruise ship accident — 1 in 188,679 Ferry sinking — 1 in 133,333 Turbulence injury — 1 in 114,943 School shooting — 1 in 192,308 Mass shooting — 1 in 113,636 Nuclear accident — 1 in 833,333 Avalanche — 1 in 210,526 Lightning — 1 in 209,205 Snake bite — 1 in 884,956 Spider bite — 1 in 833,333 Hippo attack — 1 in 564,972 Dog bite — 1 in 142,045 Pesticide residue — 1 in 1,000,000 Dirty can illness — 1 in 200,000 PLA bioplastic harm — 1 in 169,492 Charger left plugged in — 1 in 200,000 Infant swing death — 1 in 714,286 Child blind cord strangulation — 1 in 416,667 Child plastic bag suffocation — 1 in 263,158 Button battery — 1 in 250,000 Inclined sleeper death — 1 in 238,095 Elevator/escalator death — 1 in 188,324 Japanese encephalitis (travel) — 1 in 2,000,000 Kid + front airbag — 1 in 10,000,000 Asteroid impact — 1 in 1,351,351 Banana spider eggs — 1 in 10,000,000 Shark attack — 1 in 5,681,818 Bear attack — 1 in 3,787,879 Wild berry poisoning — 1 in 2,222,222 Space debris hits property — 1 in 10,000,000 Piranha attack — 1 in 135,135,135 Phone at gas pump — 1 in 1,000,000,000 Phone on plane — 1 in 1,000,000,000 Alien contact — 1 in 169,491,525
Lottery jackpot 1 in 95,238

Recently viewed on this device