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Perceived fear vs. actual probability

How likely is a buy-now-pay-later user to miss a payment?

Lifetime probability · subgroup

About 41% (per-year, among BNPL users)

41% lifetime chance

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

Other · reviewed 2026-06-13

How likely is a buy-now-pay-later user to miss a payment?

Evidence quality 4.0/5

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
4/5
D4 Uncertainty
4/5
D5 Scope
3/5
D6 Prose
4/5
D7 Perception honesty
4/5
D8 Caveat completeness
4/5
Average 4.0/5
Direct evidence
Source Government statistic · Federal Reserve Bank of Richmond (Economic Brief No. 26-05, by Zhu Wang)
lifetime, subgroup each band = 10× rarer → See full scale →
certain 1 in 1K 1 in 1M 1 in 1B
An abstract editorial symbol evoking "BNPL missed payment", muted two-tone palette, flat vector.

Perceived

Buy-now-pay-later is marketed as interest-free and frictionless, and many users treat it as a budgeting convenience rather than debt. The popular fear, when it surfaces, is the opposite extreme: a 'debt spiral' of stacked, untrackable loans ('phantom debt') ending in collapse. No rigorous survey of how BNPL users rate their own risk of missing a payment was located, so the perception here is inferred from how the product is framed and used, not measured.

Rough estimate: Users tend to perceive a near-zero chance of trouble because the product carries no interest; the measured 12-month late-payment rate is about 4 in 10.

Source: editorial intuition, not polled

Actual

About 41 in 100 BNPL users missed a payment in the past year

US adults who currently use buy-now-pay-later

Show derivation

Subgroup = US adults who currently use buy-now-pay-later (roughly half of all US adults report having used it). Horizon = the past 12 months, NOT extrapolated to a 59-year remaining-life lifetime: annual repetition of a 0.41 rate would approach certainty, and BNPL pay-in-four is a roughly five-year-old mass-market product with no long-run individual-level history. The headline is therefore a one-year prevalence held as-is under the subgroup_lifetime label. Native: a LendingTree survey (April 2-3 2025, n=2,000 US adults 18-79; about half were BNPL users, so ~1,000 in the denominator) found 41% of BNPL users reported at least one late BNPL payment in the prior 12 months. Expressed per 1,000 BNPL users: 410/1000 = 0.41. Uncertainty band 0.34-0.47 spans the survey-wave trend (34% reported a year earlier; a later LendingTree tracker reported a higher figure near 47%), not a sampling confidence interval; the point estimate 0.41 sits inside it.

Caveats: This is a one-year prevalence among current BNPL users, not a lifetime probabili…

This is a one-year prevalence among current BNPL users, not a lifetime probability for a general US adult; the subgroup_lifetime scope holds the 12-month figure as-is rather than extrapolating it, because annual repetition would approach certainty and BNPL pay-in-four has no long individual-level track record. The headline outcome is a missed payment, which the survey's own analyst characterized as mostly late 'by no more than a week or so' — a minor, usually recoverable event, distinct from the rarer ~2% default and the much rarer bankruptcy that the 'debt spiral' fear evokes. The 41% figure comes from a single LendingTree/QuestionPro survey (n=2,000; about half BNPL users) reported via CNBC and confirmed verbatim by a Federal Reserve Bank of Richmond brief; the LendingTree page itself could not be opened (403 live, 503 on Wayback), so it is cited secondhand. A later LendingTree tracker reportedly put the figure near 47%, hence the upper bound. 'Phantom debt' is a real structural feature: most pay-in-four loans were historically not reported to credit bureaus, so missed payments and stacked balances are largely invisible in standard credit data — a measurement gap, not a clean probability. 8D self-score average 4.0 with all dimensions at 3 or above; the weakest dimension is scope (D5=3), because pinning a 12-month survey prevalence onto the lifetime axis is inherently strained and is handled by the no-extrapolation assumption rather than a conversion.

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Compare to:

About 41% of buy-now-pay-later users reported missing at least one payment in the prior 12 months, according to a 2025 LendingTree survey of roughly 2,000 US adults, about half of whom used the product. The same survey put the figure at 34% a year earlier, and a Federal Reserve Bank of Richmond brief cites both numbers and the upward trend. That is a per-year rate among users, not a lifetime odds for the general public, and it sits close to the annual frequency of losing money to an online scam (about 0.40) rather than anywhere near the rare end of the scale. The survey’s own analyst noted that most late payers were behind by no more than a week or so.

The interesting gap is between framing and outcome. Pay-in-four BNPL is sold as interest-free and frictionless, which encourages users to treat it as budgeting rather than borrowing, while the louder cultural fear runs to the opposite pole: a runaway ‘debt spiral’ of stacked, untrackable loans. The measured reality is neither. Missing a payment is common, and stacking is real — CFPB supervisory data showed 63% of borrowers carrying more than one BNPL loan at once and 33% borrowing across multiple companies — yet the realized default rate stayed near 2% and the industry charge-off rate actually fell to 1.83% in 2023. The frequent-but-mild event is the late payment; the catastrophic event the fear pictures is far rarer.

Where the number does not transfer: it describes current BNPL users, a group the CFPB found to be more indebted, more likely to revolve credit-card balances, and more likely to use payday and overdraft credit than non-users, so a missed BNPL payment tends to co-occur with broader strain rather than stand alone. It also rests on a single survey reported secondhand, and on a product only a few years into mass use. The deeper limit is visibility: because most pay-in-four loans were not reported to credit bureaus, the ‘phantom debt’ that worries regulators is by construction hard to count, which is why the honest figure here is a survey prevalence held at one year rather than a clean lifetime probability.

About 41% of buy-now-pay-later users reported missing at least one payment in the prior 12 months, per a 2025 LendingTree survey cited by the Federal Reserve Bank of Richmond. That is a per-year rate among users, and most late payers were behind by no more than a week or so.

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] Federal Reserve Bank of Richmond (Economic Brief No. 26-05, by Zhu Wang) — Buy Now, Pay Later: Recent Developments and Implications
    Buy Now, Pay Later: Recent Developments and Implications
    Statistic
    41% of BNPL users made at least one late payment in the past year (2025 survey), up from 34% a year earlier; BNPL loans are generally not reported to credit bureaus; 2023 charge-off rate 1.83%.
    Excerpt
    “There is indicative evidence from consumer surveys that late payment and delinquency behavior among BNPL users has increased since 2023. For example, LendingTree's 2025 survey reports that 41 percent of BNPL users made at least one late payment in the past year, up from 34 percent a year earlier.”
    Source data from
    2026-02-01
    Accessed
    2026-06-13 · archived copy
    Calculation
    Provides the authoritative, verbatim-confirmed late-payment figure (41%, up from 34%) used as the native numerator basis, and separately the 'lenders generally do not report loan performance to credit bureaus' statement supporting the phantom-debt caveat. Charge-off rate 1.83% (2023) used as the rare-severe-end contrast.
    Independence
    Federal Reserve analysis citing the LendingTree survey and CFPB data; independent of the BNPL industry.
  2. [2] CNBC — More Americans are financing groceries with buy now, pay later loans — and more are paying those bills late, survey says
    More Americans are financing groceries with buy now, pay later loans — and more are paying those bills late, survey says
    Statistic
    Of ~2,000 US adults 18-79 surveyed April 2-3 2025, about half used BNPL; among them 41% made a late payment in the past year (up from 34%), 25% used BNPL for groceries (up from 14% in 2024), and 60% had multiple loans at once with nearly a fourth holding three or more.
    Excerpt
    “In a survey conducted April 2-3 of 2,000 U.S. consumers ages 18 to 79, around half reported having used buy now, pay later services. Of those consumers, 25% of respondents said they were using BNPL loans to buy groceries, up from 14% in 2024 and 21% in 2023, the firm said. Meanwhile, 41% of respondents said they made a late payment on a BNPL loan in the past year, up from 34% in the year prior, the survey found.”
    Source data from
    2025-04-26
    Accessed
    2026-06-13 · archived copy
    Calculation
    Carries the verbatim survey detail (LendingTree, reported via CNBC) for denominator construction: 2,000 adults, ~half BNPL users (~1,000), 41% late = ~410/1000. Also the grocery-use and loan-stacking context (60% multiple loans, nearly a fourth with three or more) used in prose.
    Independence
    Reports the LendingTree/QuestionPro survey; LendingTree page itself returned 403 live and 503 on Wayback, so the survey is cited through CNBC rather than the primary page.
  3. [3] Payments Dive — Buy now, pay later users pile on debt, CFPB finds
    Buy now, pay later users pile on debt, CFPB finds
    Statistic
    Per the Jan 2025 CFPB report (145M applications 2017-2022): in 2022, 63% of borrowers took out more than one BNPL loan at a time and 33% borrowed from multiple companies; 61% of BNPL users had subprime/deep-subprime scores; ~2% default rate 2019-2022.
    Excerpt
    “The average number of transactions per borrower increased from 8.5 in 2021 to 9.5 in 2022. And in 2022, 63% of borrowers took out more than one BNPL loan at a time, and 33% of borrowers who use BNPL took out loans from multiple companies.”
    Source data from
    2025-01-14
    Accessed
    2026-06-13 · archived copy
    Calculation
    Verbatim loan-stacking figures (63% multiple simultaneous loans, 33% from multiple companies) supporting the 'untrackable stacked loans' prose; ~2% default rate corroborates the low realized-loss contrast. Reports the CFPB 'Consumer Use of Buy Now, Pay Later and Other Unsecured Debt' study.
    Independence
    News summary of the authoritative CFPB report; CFPB report PDF downloaded but not machine-readable in this environment, so cited through Payments Dive plus the CFPB landing pages below.
  4. [4] Consumer Financial Protection Bureau — Consumer Use of Buy Now, Pay Later: Insights from the CFPB Making Ends Meet Survey
    Consumer Use of Buy Now, Pay Later: Insights from the CFPB Making Ends Meet Survey

    See all 2 Likelier entries citing this source →

    Statistic
    BNPL borrowers are, on average, much more likely to be highly indebted, revolve credit-card balances, have delinquencies in traditional credit products, and use payday/pawn/overdraft than non-BNPL borrowers.
    Excerpt
    “BNPL borrowers were, on average, much more likely to be highly indebted, revolve on their credit cards, have delinquencies in traditional credit products, and use high-interest financial services such as payday, pawn, and overdraft compared to non-BNPL borrowers.”
    Source data from
    2023-03-02
    Accessed
    2026-06-13
    Calculation
    Authoritative basis for the caveat that the BNPL-user subgroup is already financially stretched, so a missed BNPL payment co-occurs with broader debt stress rather than being an isolated lapse. Supports the heterogeneity paragraph.
  5. [5] Consumer Financial Protection Bureau — The Buy Now, Pay Later Market
    The Buy Now, Pay Later Market
    Statistic
    BNPL market grew 2019-2023; CFPB obtained pay-in-four data from six large BNPL companies covering loan volume, users, frequency, average loan size, late fees, and charge-off rates.
    Excerpt
    “The report includes key market metrics including BNPL loan volume, number of users, frequency of use, average loan size, late fees, and charge-off rates.”
    Source data from
    2025-12-10
    Accessed
    2026-06-13
    Calculation
    Authoritative market-scale and late-fee/charge-off context (Dec 2025 report) framing the supply side; corroborates that the six-lender supervisory dataset underlies the Richmond Fed and Payments Dive figures.

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

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