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Health · reviewed 2026-05-16

What are the odds of developing opioid addiction after a standard surgical prescription?

Evidence quality 4.5/5

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
5/5
D6 Prose
5/5
D7 Perception honesty
4/5
D8 Caveat completeness
5/5
Average 4.5/5
Direct evidence

Lifetime probability · lifetime, US adult

1 in 114

0.9% lifetime chance

Most people underestimate this.

range 1 in 333 to 1 in 40

lifetime, US adult each band = 10× rarer → See full scale →
certain 1 in 1K 1 in 1M 1 in 1B

≈ As likely as

A single prescription pill bottle on a plain surface beside a hospital wristband, flat vector illustration in muted tones.

Perceived

Most surgical patients receive no explicit warning about addiction risk from opioid prescriptions before they leave the recovery room. The dominant cultural model of opioid addiction involves illicit drug use or long-term chronic pain management, not a standard post-operative pain prescription. When the topic does surface, patients typically anchor on the rare, dramatic case — the athlete who developed dependence after a sports injury — rather than on any population-level probability. Survey data on pre-surgical opioid addiction risk perception do not exist in any rigorous form, which is itself informative: it is a risk most patients have never been asked to estimate, and most clinicians have not routinely quantified.

Source: editorial intuition, not polled

Actual

~1.2% of opioid-naive surgical patients develop prolonged opioid use (3+ months post-surgery); 6.7% across all patients including those with prior opioid use

US adults, opioid-naive prior to surgery (JAMA Network Open 2020 meta-analysis of 33 studies, 1.9M patients)

Show derivation

The JAMA Network Open 2020 meta-analysis (Huang et al.) of 33 studies covering more than 1.9 million patients found a pooled incidence of prolonged opioid use after surgery of 6.7% (95% CI 4.5%-9.8%) across ALL patients. Critically, when restricted to opioid-naive patients specifically, the pooled rate dropped to 1.2% (95% CI 0.4%-3.9%). The much-cited Brummett et al. (JAMA Surgery, 2017) figure of 5.9%-6.5% defined "opioid-naive" as no opioid fills 12 months to 1 month before surgery — a looser criterion that may include patients with earlier opioid exposure. The 2020 meta-analysis provides the better estimate for truly opioid-naive patients, which is the relevant population for the question "what happens after a standard surgical prescription." Central estimate: 1.2% per surgical opioid exposure for opioid-naive patients. For lifetime normalization: a US adult has roughly 2-3 surgical procedures over their lifetime requiring opioid prescription (conservative estimate). Using 3 lifetime exposures and assuming independence: cumulative risk of at least one prolonged use episode = 1 - (1 - 0.012)^3 = 0.0356. Adjusting for the OUD conversion rate (~20-30% of persistent users develop OUD): 0.0356 x 0.25 = 0.0089. We use 0.0088 as the central estimate (~1 in 114). The SAMHSA 4.8M OUD prevalence / 260M US adults = 1.85% annual prevalence serves as a plausibility anchor (captures all pathways, not just surgical). Note: for patients with prior opioid exposure, the 6.7% pooled rate applies, with preoperative opioid use conferring 5.3-fold excess risk.

Caveats: The 1.2% persistent opioid use figure for opioid-naive patients (JAMA Network Op…

The 1.2% persistent opioid use figure for opioid-naive patients (JAMA Network Open 2020 meta-analysis) measures continued prescription filling beyond 3 months, which is a proxy for problematic use — not a direct diagnosis of opioid use disorder (OUD). Not all persistent users develop OUD; estimates of the OUD conversion rate range from 8-26% depending on the study and definition used. The widely cited 6% figure (Brummett et al., 2017; 6.7% in the meta-analysis) applies to all surgical patients including those with prior opioid exposure — preoperative opioid use confers a 5.3-fold excess risk. The risk is also dramatically elevated for patients with prior substance use disorders, mood disorders, anxiety disorders, or pre-existing chronic pain conditions. Risk is higher for longer initial prescriptions (>7 days) and higher morphine milligram equivalents. The 1.2% headline figure applies to truly opioid-naive adults receiving standard post-surgical prescriptions; for patients with any prior opioid history, the 6.7% all-patient rate is more applicable.

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Compare to:

The 2020 JAMA Network Open meta-analysis of 33 studies covering more than 1.9 million patients found a pooled incidence of 6.7% for prolonged opioid use beyond three months across all surgical patients — but for opioid-naive patients specifically, the rate dropped to 1.2% (95% CI 0.4%-3.9%). That distinction matters enormously: the much-cited Brummett et al. (2017) figure of 5.9%-6.5% used a looser definition of “opioid-naive” (no fills in the prior 12 months to 1 month before surgery), while the meta-analysis restricted analysis showed that truly naive patients face a much lower per-exposure risk. Preoperative opioid use was the strongest risk factor, conferring a 5.3-fold excess risk. The non-operative control rate was only 0.4%, confirming that the surgical prescription itself is the mechanism. Translated to population scale: SAMHSA’s 2024 National Survey on Drug Use and Health found 4.8 million Americans had opioid use disorder in the past year — a figure that includes all pathways, but the prescription surgery route is one of the most systematically documented entry points.

The gap between perceived and actual risk here runs in an unusual direction: it is the underestimation that creates harm. Most patients going into elective procedures receive a post-operative opioid prescription the same way they receive discharge instructions for wound care — as a routine part of the package, without a quantified risk conversation. The clinical model that dominated prescribing for decades held that pain patients rarely become addicted; the empirical literature from the 2010s and 2020s has comprehensively revised that view. What makes the prescription pathway particularly insidious is that the exposure is clinically authorized, the initial use is legitimate, and the transition to problematic use happens on a timeline — weeks to months — that can feel indistinguishable from normal recovery variation until it does not.

Risk is not evenly distributed across the surgical population. Patients with prior substance use disorders face five to ten times higher rates of persistent opioid use post-surgery, and those with mood or anxiety disorders, chronic pain conditions, or tobacco use are also substantially elevated. Longer initial prescriptions — more than seven days — and higher daily doses independently predict persistent use regardless of surgical type. The 1.2% figure applies to opioid-naive patients; for those with any prior opioid history, the 6.7% all-patient rate is more applicable. For a 55-year-old with no prior substance history receiving a three-day supply after an uncomplicated knee arthroscopy, the risk is at the low end. For a 38-year-old with anxiety, a prior alcohol problem, and chronic back pain receiving a two-week supply after lumbar fusion, it is materially higher — potentially approaching or exceeding the 6.7% average.

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] Brummett CM et al. — JAMA Surgery, 2017 — New Persistent Opioid Use After Minor and Major Surgical Procedures in US Adults
    New Persistent Opioid Use After Minor and Major Surgical Procedures in US Adults
    Statistic
    5.9%-6.5% of opioid-naive surgical patients develop new persistent opioid use after surgery; rate is similar across minor and major procedures; 14x higher than the 0.4% rate in non-surgical controls.
    Excerpt
    “"The rates of new persistent opioid use were similar between the minor and major surgical groups (5.9%-6.5%). By comparison, the incidence in the nonoperative control cohort was only 0.4%." ”
    Source data from
    2017-06-21
    Accessed
    2026-04-24
    Calculation
    Brummett et al. found 5.9%-6.5% persistent opioid use among patients they classified as "opioid-naive" (no opioid fills 12 months to 1 month before surgery). However, the later JAMA Network Open 2020 meta-analysis found that when restricted to strictly opioid-naive patients, the rate drops to 1.2% (95% CI 0.4%-3.9%), suggesting that Brummett's looser "naive" definition included patients with earlier opioid exposure. The non-operative control rate of 0.4% confirms the surgical prescription as the mechanism. The Brummett 5.9%-6.5% figure applies to all surgical patients (including those with some prior opioid history) and should not be cited as the opioid-naive rate.
  2. [2] JAMA Network Open, 2020 — Rate and Risk Factors Associated With Prolonged Opioid Use After Surgery: A Systematic Review and Meta-analysis
    Rate and Risk Factors Associated With Prolonged Opioid Use After Surgery: A Systematic Review and Meta-analysis
    Statistic
    Pooled incidence of prolonged opioid use after surgery was 6.7% (95% CI 4.5%-9.8%) across all patients; 1.2% (95% CI 0.4%-3.9%) when restricted to opioid-naive patients; preoperative opioid use confers 5.3-fold excess risk.
    Excerpt
    “"In this systematic review and meta-analysis of 33 observational studies including more than 1.9 million patients, 7% of patients continued to fill opioid prescriptions more than 3 months after surgery. … Preoperative use of opioids, illicit cocaine use, and pain conditions before surgery had the strongest associations with prolonged opioid use after surgery." ”
    Source data from
    2020-06-19
    Accessed
    2026-04-24 · archived copy
    Calculation
    The pooled incidence of 6.7% (95% CI 4.5%-9.8%) from this meta-analysis of 33 studies involving more than 1.9 million patients covers all surgical patients regardless of prior opioid history. The critical finding for this entry is the restricted analysis: for opioid-naive patients specifically, the pooled rate is 1.2% (95% CI 0.4%-3.9%) — roughly one-fifth of the all-patient rate. This confirms that preoperative opioid use (OR 5.32) is the dominant driver of post-surgical persistent use. The 1.2% figure is used as the native rate because it best answers the question for a general adult facing a standard surgical prescription without prior opioid history.
  3. [3] Substance Abuse and Mental Health Services Administration (SAMHSA) — Key Substance Use and Mental Health Indicators in the United States: Results from the 2024 National Survey on Drug Use and Health
    Key Substance Use and Mental Health Indicators in the United States: Results from the 2024 National Survey on Drug Use and Health

    See all 3 Likelier entries citing this source →

    Statistic
    4.8 million Americans aged 12 or older had opioid use disorder in the past year in 2024 (1.7% of that population).
    Excerpt
    “"In 2024, 4.8 million people aged 12 or older had a past year opioid use disorder, representing 1.7 percent of the population aged 12 or older." ”
    Source data from
    2025-07-14
    Accessed
    2026-04-24 · archived copy
    Calculation
    The SAMHSA 4.8M annual OUD prevalence provides a population-level cross-check. 4.8M / 260M US adults ≈ 1.85% current-year OUD prevalence. This figure captures all opioid sources (prescription and illicit), not just surgical pathways. It is used as a plausibility anchor for the normalized lifetime estimate rather than the primary calculation input.

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