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Health · reviewed 2026-05-28

If a parent had alcohol use disorder, what are the odds you'll develop alcohol use disorder yourself?

Evidence quality 4.38/5

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

D1 Source grounding
5/5
D2 Source authority
5/5
D3 Arithmetic
4/5
D4 Uncertainty
4/5
D5 Scope
4/5
D6 Prose
4/5
D7 Perception honesty
4/5
D8 Caveat completeness
5/5
Average 4.38/5
Direct evidence

Lifetime probability · lifetime, subgroup

1 in 3.5

29% lifetime chance

range 1 in 4.5 to 1 in 2.0

lifetime, subgroup each band = 10× rarer → zoomed to your factors See full scale →
certain 1 in 1K 1 in 1M 1 in 1B
1 in 2.2 1 in 6.9

● your factors — click this risk ▾ to reveal

≈ As likely as

A single wooden chair next to an empty kitchen table at dusk, soft window light, flat vector illustration in muted tones.

Perceived

There is no rigorous public-perception survey of how adult children of alcoholics estimate their own AUD risk, but the cultural script around inherited alcoholism is strong: family memoirs, recovery literature, and pop-genetics commentary often imply something close to a coin flip. The intuitive frame compresses two distinct claims — that AUD is heritable (true: roughly half the variance is genetic) and that an individual child of an AUD parent will likely develop AUD themselves (substantially overstated). The best direct measurement (COGA) puts the elevation at roughly 2x baseline, not 5x or 10x, and the majority of adult children of alcoholics never meet criteria for the disorder. The gap between inheritance-as-mechanism and inheritance-as-destiny is where the perception calibration sits.

Rough estimate: many adult children of alcoholics believe their lifetime risk is 50% or higher

Source: editorial intuition, not polled

Actual

28.8% of first-degree relatives of an alcohol-dependent proband meet lifetime DSM-IV criteria for alcohol dependence, vs 14.4% of controls (COGA, Nurnberger et al. 2004)

First-degree relatives (parents, siblings, offspring) of alcohol-dependent probands ascertained through the Collaborative Studies on the Genetics of Alcoholism (COGA); N=8,296 relatives vs N=1,654 community controls

Show derivation

The headline figure is the directly measured lifetime DSM-IV alcohol-dependence rate in COGA's first-degree-relative pool (Nurnberger et al. 2004): 28.8% of relatives vs 14.4% of community controls assessed with the same Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) instrument — a ~2.0x relative risk. This is preferred over inferring a number by multiplying NESARC-III's 29.1% DSM-5 lifetime AUD baseline by the 2x family-history effect for three reasons. (1) Apples-to-apples instrumentation: relatives and controls in COGA were diagnosed identically. (2) DSM-IV alcohol dependence is more restrictive than DSM-5 AUD (which collapsed abuse + dependence into a single disorder with a 2-criterion floor), so the COGA figure is conservative relative to a hypothetical DSM-5 re-analysis. (3) No nationally representative study has measured DSM-5 AUD prevalence specifically in adult children of AUD parents; the COGA first-degree-relative pool is the largest direct measurement available. Important framing limitation: COGA's "first-degree relatives" pools parents, siblings, and offspring. The paper does not break results down by relationship type, so the 28.8% figure is the best available proxy for offspring-specific risk, not a direct measurement of it. Adult children of AUD parents are likely near this figure given (a) shared genetic loading equivalent to siblings and (b) shared early-life environmental exposure; the COGA estimate is anchored to a population that includes them but is not exclusively them. Heritability estimates from the Verhulst, Neale & Kendler (2015) meta-analysis of 12 twin and 5 adoption studies converge on h² = 0.49 (95% CI 0.43-0.53), supporting the magnitude of the elevation as part-genetic. The uncertainty band reflects cohort-definition heterogeneity (one parent vs both, biological vs adopted-out studies), DSM edition (DSM-IV ~28% in COGA vs likely higher under DSM-5 criteria), and the offspring-specific vs first-degree-relative-pooled distinction.

Caveats: Three framing limitations matter for reading this number correctly. (1) Populati…

Three framing limitations matter for reading this number correctly. (1) Population mismatch: COGA's "first-degree relatives" pools parents, siblings, and offspring of alcohol-dependent probands. The 28.8% figure is the best direct measurement available for offspring-specific risk because the genetic loading and early-environment exposure are comparable across these relationships, but it is not a study of adult children of AUD parents exclusively. (2) Diagnostic edition: COGA used DSM-IV alcohol dependence, which is more restrictive than DSM-5 AUD (DSM-5 collapsed the earlier "abuse" and "dependence" categories into a single disorder with a 2-criterion floor). A hypothetical DSM-5 re-analysis of COGA would likely yield higher absolute prevalences in both relatives and controls, but the 2x ratio is expected to hold. (3) Ascertainment: COGA probands were recruited through treatment programs, which selects for severe and clinically identified cases; the relative risk for offspring of a parent with mild, never-treated AUD is likely smaller. The 28.8% figure should not be read as a forecast for any individual — most adult children of alcoholic parents (about 71%) do not meet lifetime criteria for alcohol dependence. Heritability is not destiny: the Verhulst meta-analysis h² of 0.49 means roughly half the variance in liability is genetic, leaving substantial room for environmental, behavioral, and policy factors to shift individual outcomes. Adoption studies have separately established that the elevation persists in offspring of AUD biological parents even when raised by non-alcohol-using adoptive families, confirming that the genetic component is real and not purely a learned behavior, but those same studies also show that the elevation is substantially attenuated in protective environments.

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Compare to:

The clearest direct measurement comes from the Collaborative Studies on the Genetics of Alcoholism (COGA), reported by Nurnberger and colleagues in 2004. COGA assessed 8,296 first-degree relatives of alcohol-dependent probands and 1,654 community controls with the same Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) interview. Lifetime DSM-IV alcohol dependence appeared in 28.8% of relatives and 14.4% of controls — a roughly 2-fold elevation. That is the cleanest apples-to-apples comparison in the literature: same diagnostic instrument, same threshold, same era, families recruited from the same multi-site framework. The convergence with NESARC-III’s 29.1% general-population lifetime DSM-5 AUD figure is a coincidence of definitions, not a finding — DSM-5 AUD is a substantially broader category than DSM-IV alcohol dependence, so the absolute numbers happen to land near each other despite measuring different things in different populations. The signal to anchor on is the 2x ratio within COGA, not the absolute 28.8% read against the unrelated NESARC-III baseline.

That 2x elevation is part-genetic and part-environmental, and twin and adoption studies can separate the two. The Verhulst, Neale and Kendler 2015 meta-analysis of 12 twin studies and 5 adoption studies put the heritability of AUD at h² = 0.49 (95% CI 0.43-0.53), with no evidence of heterogeneity by sex. Roughly half the variance in liability is genetic. Adoption studies — which follow biological children of alcohol-dependent parents raised by non-related adoptive families — have separately shown that the elevation persists even when the early-life drinking environment is removed, confirming that the genetic component is real rather than purely a modeled behavior. The remaining ~50% of variance is environmental, which is why protective adoptive environments and policy-level interventions (drinking-onset delay, monitored adolescent alcohol access, screening in primary care) can meaningfully shift outcomes even for high-genetic-liability individuals.

The framing this number resists is “inheritance as destiny.” About 71% of COGA first-degree relatives of alcohol-dependent probands did not meet lifetime criteria for alcohol dependence. That is the modal outcome, not the exception. The cultural script around “alcoholism runs in families” often implies something closer to 50% or higher lifetime risk in adult children of alcoholics; the direct measurement is closer to 29%. The elevation is real and substantial — a 2x relative risk for a disorder with a 14% baseline is one of the larger first-degree-relative effects in psychiatric epidemiology — but it operates within a probabilistic, not deterministic, frame. Most adult children of alcoholic parents reach midlife without developing AUD themselves.

Three caveats worth foregrounding. COGA’s first-degree-relative pool combines parents, siblings, and offspring of probands; the paper does not break results out by relationship type, so the 28.8% figure is the best available proxy for offspring-specific risk rather than a direct measurement of it. DSM-IV alcohol dependence is a higher bar than DSM-5 AUD, so a re-analysis under current diagnostic criteria would likely yield higher absolute figures in both groups while preserving the 2x ratio. And COGA recruited probands through treatment programs, which selects for clinically severe cases — the elevation in the children of an undiagnosed or mild AUD parent is likely smaller than the COGA figure suggests.

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] Nurnberger JI Jr, Wiegand R, Bucholz K, et al. — Archives of General Psychiatry, 2004 — A family study of alcohol dependence: coaggregation of multiple disorders in relatives of alcohol-dependent probands
    A family study of alcohol dependence: coaggregation of multiple disorders in relatives of alcohol-dependent probands
    Statistic
    Lifetime risk rates of 28.8% and 14.4% for DSM-IV alcohol dependence in first-degree relatives of alcohol-dependent probands and controls, respectively; relative risk approximately 2-fold
    Excerpt
    “"Lifetime risk rates of 28.8% and 14.4% for DSM-IV alcohol dependence in relatives of probands and controls, respectively... The risk of alcohol dependence in relatives of probands compared with controls is increased about 2-fold." ”
    Source data from
    2004-12-01
    Accessed
    2026-05-28 · archived copy
    Calculation
    Native numerator (28.8 per 100) is taken directly from the Nurnberger 2004 finding for first-degree relatives of alcohol-dependent probands (parents, siblings, offspring). The 14.4% control rate is the matched same-instrument baseline, yielding a ~2.0x relative risk. Sample sizes: N=8,296 relatives of probands and N=1,654 controls assessed via the SSAGA structured diagnostic interview. DSM-IV alcohol-dependence criteria were applied; the broader DSM-5 AUD criteria would likely yield somewhat higher absolute prevalences in both groups while preserving the ~2x ratio. The 28.8% figure is used as the lifetime_us_adult for this subgroup; this is the best direct measurement available because no nationally representative study has measured DSM-5 AUD specifically in offspring of AUD parents. The first-degree-relative pool includes adult children of probands but is not exclusively offspring; this is acknowledged in `assumptions` and `caveats`.
    Independence
    The Collaborative Studies on the Genetics of Alcoholism (COGA) is a NIAAA-funded multi-site project (Indiana University, Washington University in St. Louis, SUNY Downstate, University of Connecticut, University of California San Diego, University of Iowa) that ascertained alcohol-dependent probands through inpatient and outpatient treatment programs and recruited their relatives plus a matched community-control sample. Probands and controls were diagnosed with the same SSAGA instrument, making the relative-vs-control comparison methodologically clean.
  2. [2] Verhulst B, Neale MC, Kendler KS — Psychological Medicine, 2015 — The heritability of alcohol use disorders: a meta-analysis of twin and adoption studies
    The heritability of alcohol use disorders: a meta-analysis of twin and adoption studies
    Statistic
    Best-fit estimate of the heritability of AUD: h² = 0.49 (95% CI 0.43-0.53); meta-analysis of 12 twin studies and 5 adoption studies; no evidence for heterogeneity by sex
    Excerpt
    “"The best-fit estimate of the heritability of AUD was 0.49 [95% confidence interval (CI) 0.43-0.53]... There was no evidence for heterogeneity by study design, sex or assessment method." ”
    Source data from
    2015-03-01
    Accessed
    2026-05-28 · archived copy
    Calculation
    Establishes that roughly half of the variance in AUD risk is heritable, consistent with the magnitude of the COGA family-aggregation finding being part-genetic rather than purely shared-environmental. Used as supporting evidence for the mechanism behind the 2x elevation; not used to compute the headline rate. Twin and adoption studies separately estimate the genetic component because identical twins reared apart and adoptees raised by non-biological parents disentangle shared genes from shared environment; the meta-analytic h² = 0.49 means that even substantial early-life environmental exposure to a parent's drinking does not, on its own, drive the elevated risk seen in offspring.
    Independence
    This is a meta-analysis of independent twin and adoption studies conducted across multiple countries and decades, including the Virginia Twin Registry, Swedish adoption studies, and Australian twin samples. The aggregate h² estimate is methodologically independent of the COGA family-aggregation data.
  3. [3] Grant BF, Chou SP, Saha TD, et al. — JAMA Psychiatry, 2015 — Epidemiology of DSM-5 Alcohol Use Disorder: Results From the National Epidemiologic Survey on Alcohol and Related Conditions III
    Epidemiology of DSM-5 Alcohol Use Disorder: Results From the National Epidemiologic Survey on Alcohol and Related Conditions III

    See all 2 Likelier entries citing this source →

    Statistic
    Lifetime prevalence of DSM-5 alcohol use disorder among US adults: 29.1% overall; 12-month prevalence 13.9% (NESARC-III, N=36,309)
    Excerpt
    “"In 2012-2013, US prevalences of DSM-5 12-month and lifetime AUD among adults 18 years and older were 13.9% and 29.1%, respectively. Prevalence was generally highest for men (17.6% and 36.0%, respectively), White and Native American respondents, and younger and never-married adults." ”
    Source data from
    2015-08-01
    Accessed
    2026-05-28 · archived copy
    Calculation
    Used as the US adult general-population reference for context. The coincidence between NESARC-III lifetime DSM-5 AUD (29.1%) and the COGA first-degree-relative DSM-IV alcohol-dependence figure (28.8%) is a methodological artifact, not a finding: DSM-5 AUD has a much broader definition than DSM-IV alcohol dependence, so the absolute numbers happen to land near each other despite measuring different things in different populations. The clean apples-to-apples comparison is COGA relatives 28.8% vs COGA controls 14.4%, both DSM-IV alcohol dependence, both SSAGA instrument — that is where the 2x elevation comes from.
    Independence
    NESARC-III was conducted by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) using probability sampling of the US non-institutionalized civilian population and the AUDADIS-5 structured diagnostic interview. Methodologically independent from COGA (which is a treatment-ascertained family study) and from the heritability meta-analysis.
  4. [4] Grant BF — Alcohol Health and Research World, 1998 — The impact of a family history of alcoholism on the relationship between age at onset of alcohol use and DSM-IV alcohol dependence: results from the National Longitudinal Alcohol Epidemiologic Survey
    The impact of a family history of alcoholism on the relationship between age at onset of alcohol use and DSM-IV alcohol dependence: results from the National Longitudinal Alcohol Epidemiologic Survey
    Statistic
    Family history of alcoholism is associated with higher lifetime DSM-IV alcohol-dependence prevalence; earlier age at first drink is independently associated with higher lifetime risk (NLAES, nationally representative)
    Excerpt
    “"People with a family history of alcoholism had a higher prevalence of lifetime alcohol dependence than did people without such a history. Respondents with an earlier age of drinking onset were more likely to become alcohol dependent compared with respondents with a later age of drinking onset." ”
    Source data from
    1998-01-01
    Accessed
    2026-05-28 · archived copy
    Calculation
    Provides nationally representative cross-validation that family history of alcoholism elevates lifetime alcohol-dependence prevalence in the respondent, consistent with the direction of the COGA finding. Also establishes that early age at first drink interacts with family history to compound risk — supporting the framing that the elevation is probabilistic and modifiable by behavior, not deterministic. Not used to derive the headline rate; the NLAES instrument and sampling differ from COGA/SSAGA, so the figures are not directly substitutable.
    Independence
    The National Longitudinal Alcohol Epidemiologic Survey (NLAES, 1991-1992) was conducted by NIAAA using the AUDADIS instrument, independent of COGA's SSAGA-based family study.

412 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 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 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 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 Visa overstay deportation — 1 in 5.6 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 Divorce — 1 in 2.4 Burst pipe damage — 1 in 2.2 Workplace bullying — 1 in 2.1 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 Cat litter toxoplasmosis — 1 in 48 Mental health LTD claim — 1 in 45 Drug overdose — 1 in 42 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 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 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 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 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 Infant pool submersion — 1 in 800 MS — 1 in 769 Workplace fatality — 1 in 690 Typhoid fever — 1 in 654 Unsafe imported products — 1 in 565 Brain aneurysm — 1 in 400 COVID-19 — 1 in 400 Fireworks injury — 1 in 385 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 Skipping daily showers — 1 in 200 Not scrubbing feet — 1 in 200 Marrow donation risk — 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 108 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 694 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 Police duty death — 1 in 313 Homicide — 1 in 287 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 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 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 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 Fatal bee/wasp sting — 1 in 76,923 Anesthesia death — 1 in 50,000 Dog hot car death — 1 in 41,667 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 Railroad crossing death — 1 in 20,704 Child bike trailer — 1 in 14,286 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 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 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 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