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Likelier

Perceived fear vs. actual probability

What are the odds of being harmed or killed by a counterfeit or substandard medicine?

Lifetime probability · subgroup

~1 in 361

0.3% lifetime chance

Most people underestimate this.

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

Health · reviewed 2026-06-14
Evidence quality 3.75/5

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

D1 Source grounding
4/5
D2 Source authority
3/5
D3 Arithmetic
4/5
D4 Uncertainty
4/5
D5 Scope
4/5
D6 Prose
4/5
D7 Perception honesty
3/5
D8 Caveat completeness
4/5
Average 3.75/5
Direct evidence
Source Government statistic · World Health Organization
lifetime, subgroup each band = 10× rarer → See full scale →
certain 1 in 1K 1 in 1M 1 in 1B
A flat vector illustration of a medicine capsule with a subtle question mark shadow, rendered in muted tones against a pale background.

Perceived

In high-income countries with stringent pharmaceutical regulation, the idea of receiving a fake or substandard medicine feels like a plot device from a thriller rather than a routine hazard. Pharmacies are licensed, supply chains are audited, and regulatory agencies pull defective batches within days. The mental model of a medicine simply not containing the drug on its label does not map onto everyday experience. In low- and middle-income countries the situation is categorically different: WHO estimates that 1 in 10 medical products in these settings is substandard or falsified, and the consequences are not merely inefficacy but death, particularly among children treated with fake antibiotics for pneumonia or counterfeit antimalarials for malaria.

Source: editorial intuition, not polled

Actual

~188,000 deaths per year globally from substandard or falsified medicines

global adults and children

Show derivation

Native rate: WHO-commissioned models estimate 72,000-169,000 child deaths per year from pneumonia caused by substandard/falsified antibiotics (University of Edinburgh model) and 116,000 (64,000-158,000) additional deaths from malaria caused by substandard/falsified antimalarials in sub-Saharan Africa (London School of Hygiene and Tropical Medicine model). A conservative combined estimate of ~188,000 deaths/yr is used, reflecting overlap between model ranges. The burden falls almost entirely on low- and middle-income countries (LMICs), where ~4 billion people live and where WHO estimates 1 in 10 medical products is substandard or falsified. Dividing by the at-risk population: 188,000 / 4,000,000,000 = 4.7e-5 annual rate. Lifetime conversion using the 59-year horizon: 1 - (1 - 4.7e-5)^59 = 0.00277. Uncertainty low bound uses 100,000 deaths (conservative floor accounting for model uncertainty and possible double-counting between the Edinburgh and LSHTM models) / 4B compounded 59 years = 1 - (1 - 2.5e-5)^59 = 0.0015. High bound uses 370,000 (169,000 + 158,000 plus ~15% for non-pneumonia/non-malaria categories) / 4B compounded 59 years = 1 - (1 - 9.25e-5)^59 = 0.0055. The true death toll is likely higher since these models cover only pneumonia and malaria, not cardiovascular, HIV/AIDS, or TB medicines. For adults in high-income countries with robust drug-quality regulation, personal risk is orders of magnitude lower.

Caveats: The 188,000 deaths estimate is derived from two disease-specific models covering…

The 188,000 deaths estimate is derived from two disease-specific models covering only pneumonia and malaria. The true global death toll from substandard and falsified medicines is almost certainly higher when cardiovascular drugs, HIV/AIDS antiretrovirals, tuberculosis medicines, and other therapeutic categories are included. The burden falls almost entirely on low- and middle-income countries with weak pharmaceutical regulatory systems. For any adult purchasing medicines through a regulated pharmacy in the US, EU, Japan, or other high-income country with robust drug-quality enforcement, the personal probability of encountering a substandard or falsified medicine is orders of magnitude lower than the global average. Online pharmacies operating outside regulatory oversight present a distinct and growing risk channel even in wealthy countries. A separate illicit channel — not included in the LMIC headline figure — is the sale of fentanyl-laced fake prescription pills (counterfeit "Percocet", "Xanax", "Adderall") to US teens and young adults, often via social-media platforms such as Snapchat. KFF, citing DEA laboratory data, reports that in 2023 seven of every ten counterfeit opioid pills contained a potentially deadly dose of fentanyl, and that 76% of adolescent drug fatalities in 2023 involved fentanyl, frequently from pills the user believed were a genuine prescription drug. This phenomenon is distinct in both mechanism and population from the LMIC substandard-medicine burden that anchors the headline number, and its toll is now declining: CDC NCHS Data Brief No. 549 (January 2026) reports the overdose death rate among ages 15–24 fell 37.0% between 2023 and 2024 (from 13.5 to 8.5 per 100,000), the largest decrease of any age group.

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Compare to:

The World Health Organization estimates that 1 in 10 medical products circulating in low- and middle-income countries is either substandard or falsified. Two disease-specific modelling exercises commissioned by WHO quantify part of the mortality cost: the University of Edinburgh estimates 72,000 to 169,000 children die each year from pneumonia because the antibiotics they received were substandard or fake, and the London School of Hygiene and Tropical Medicine estimates 116,000 additional deaths from malaria in sub-Saharan Africa attributable to substandard or falsified antimalarials. These two categories alone account for roughly 188,000 deaths per year, and the true toll is almost certainly higher since they exclude cardiovascular medicines, HIV/AIDS antiretrovirals, tuberculosis drugs, and every other therapeutic category in which quality failures also occur.

The perception gap runs in both directions depending on geography. In countries with stringent pharmaceutical regulation, the risk of encountering a counterfeit medicine is perceived as vanishingly small, and for regulated retail pharmacies it largely is. In low- and middle-income countries, however, the problem is systemic: weak regulatory capacity, fragmented supply chains, and insufficient quality-control infrastructure mean that a mother paying for her child’s antibiotics has no reliable way to verify that the product contains what its label claims. The WHO notes that antimalarials and antibiotics are the product categories most commonly reported as substandard or falsified, precisely the drugs most critical in settings with the highest infectious-disease burden. The result is a lethal feedback loop: the populations most dependent on effective medicines are the least protected from defective ones.

Where the number does not apply: any person purchasing medicines through a licensed pharmacy in a country with robust drug-quality regulation (the US, EU, Japan, Canada, Australia, and similar) is operating far outside the risk distribution that generates the 1-in-450 global lifetime figure. The primary residual risk in wealthy countries is the growing online pharmacy market, where products purchased outside regulated channels may bypass quality controls entirely. The 188,000 estimate is conservative and disease-limited; WHO itself uses the framing “hundreds of thousands” when describing the full scope of substandard-medicine mortality.

Substandard or falsified medicines are linked to about 188,000 deaths a year globally. For an adult in a low- or middle-income country, the lifetime risk works out to roughly 1 in 361, a toll concentrated far from the high-income markets that dominate the headlines.

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.

3/5 sources independently verified verbatim against the cited source

  1. [1] World Health Organization — Substandard and falsified medical products — Fact sheet Verified
    Substandard and falsified medical products — Fact sheet
    Statistic
    At least 1 in 10 medicines in low- and middle-income countries are substandard or falsified; countries spend an estimated US$ 30.5 billion per year on such products
    Excerpt
    “"At least 1 in 10 medicines in low- and middle-income countries are substandard or falsified. Countries spend an estimated US$ 30.5 billion per year on substandard and falsified medical products." ”
    Source data from
    2018-01-31
    Accessed
    2026-04-26 · archived copy
    Verification
    Excerpt independently re-fetched and confirmed word-for-word against the cited source during our grounding audit.
    Calculation
    The WHO fact sheet has been restructured since the original access date. The detailed mortality models (University of Edinburgh pneumonia estimate of 72,000-169,000 deaths and LSHTM malaria estimate of 116,000 deaths) that previously anchored the native numerator are no longer present on this page. The 1-in-10 prevalence figure remains and establishes the scale of the problem in LMICs. The mortality estimates that underpin the 188,000 deaths/year figure were derived from earlier versions of this fact sheet and from the original research publications (Lancet Infectious Diseases, 2018). 188,000 / 5B = 0.0000376 annual rate, compounded over 59 years yields 0.00222.
    Independence
    Both sources are WHO publications drawing on the same underlying data and commissioned modelling studies. They are not independent data sources.
  2. [2] World Health Organization — Substandard and falsified medical products — Health topics
    Substandard and falsified medical products — Health topics
    Statistic
    More than 1 in 10 medicines in LMICs estimated substandard or falsified; up to 2 billion people lack access to necessary medicines
    Excerpt
    “"Up to two billion people around the world lack access to necessary medicines, vaccines, medical devices including in vitro diagnostics, and other health products, which creates a vacuum that is too often filled by substandard and falsified products. More than one in ten medicines in low- and middle-income countries are estimated to be substandard or falsified. No country remains untouched from this issue." ”
    Source data from
    2020-06-01
    Accessed
    2026-04-26 · archived copy
    Calculation
    This WHO health-topics page confirms the 1-in-10 prevalence framing and establishes the access-gap context (2 billion people lacking necessary medicines). The previously cited sub-URL with "hundreds of thousands" mortality framing is no longer accessible; the current page focuses on prevalence, impact, and WHO response. The mortality estimates underpinning the 188,000 figure are supported by the original Lancet Infectious Diseases publications rather than this summary page.
    Independence
    Same WHO data as source 1; different summary page, not an independent data source.
  3. [3] The American Journal of Tropical Medicine and Hygiene — Renschler JP, Walters KM, Newton PN, Laxminarayan R — Estimated Under-Five Deaths Associated with Poor-Quality Antimalarials in Sub-Saharan Africa Verified
    Estimated Under-Five Deaths Associated with Poor-Quality Antimalarials in Sub-Saharan Africa
    Statistic
    Approximately 122,350 (IQR: 91,577–154,736) under-five deaths in 39 sub-Saharan African countries in 2013 were associated with consumption of poor-quality antimalarials, representing ~3.75% of all under-five deaths in those countries
    Excerpt
    “"An estimated 122,350 (interquartile range [IQR]: 91,577–154,736) under-five malaria deaths were associated with consumption of poor-quality antimalarials across 39 sub-Saharan African countries in 2013. This represented 3.75% of all under-five deaths in our sample of countries." ”
    Source data from
    2015-06-01
    Accessed
    2026-05-03 · archived copy
    Verification
    Excerpt independently re-fetched and confirmed word-for-word against the cited source during our grounding audit.
    Calculation
    Renschler et al. (2015) is the peer-reviewed modeling paper that independently quantifies child deaths from poor-quality antimalarials, using WHO child mortality data and antimalarial failure rate estimates for 39 sub-Saharan African countries. The 122,350 central estimate (IQR 91,577–154,736) is consistent with the LSHTM model figure of 116,000 (64,000–158,000) cited in WHO documentation; slight differences reflect model assumptions and reference year (2013 here vs. the WHO model's reference year). The overlapping uncertainty intervals confirm the same order-of-magnitude burden. This study covers malaria deaths only; combined with the Edinburgh University pneumonia model (72,000–169,000 deaths), the composite ~188,000 estimate is conservative. Used here as the independent peer-reviewed anchor confirming the malaria component of the native rate.
    Independence
    Independent of the WHO sources above: this is an academic modelling study published in a peer-reviewed journal (Am J Trop Med Hyg), authored by researchers at Princeton and Oxford (Paul N Newton of MORU/Oxford; Ramanan Laxminarayan of Princeton), using WHO child mortality inputs but applying an independent methodological framework to estimate attributable deaths.
  4. [4] KFF — Nirmita Panchal and Sasha Zitter — Teens, Drugs, and Overdose: Contrasting Pre-Pandemic and Current Trends Verified
    Teens, Drugs, and Overdose: Contrasting Pre-Pandemic and Current Trends
    Statistic
    In 2023, seven of every ten counterfeit opioid pills were found by the DEA to contain a potentially deadly dose of fentanyl; adolescents may acquire such drugs via social-media platforms
    Excerpt
    “"In 2023, seven out of every ten counterfeit opioid pills were found by the Drug Enforcement Administration to contain a potentially deadly dose of the drug. ... Adolescents may acquire drugs through social media platforms, which are frequently scrutinized for lacking regulation around illegal drug sales." ”
    Source data from
    2024-10-15
    Accessed
    2026-06-14 · archived copy
    Verification
    Excerpt independently re-fetched and confirmed word-for-word against the cited source during our grounding audit.
    Calculation
    Used only to support the high-income-country illicit-channel caveat, not the headline LMIC substandard-medicine figure. KFF carries the DEA "7 of 10" laboratory-testing statistic (an increase from 4 of 10 in 2021 and 6 of 10 in 2022), the "76% of adolescent drug fatalities in 2023 involved fentanyl" share, and the social-media acquisition framing. The 2024 decline among ages 15–24 (37.0% drop, 2023→2024) is from CDC NCHS Data Brief No. 549 (cited separately below). These are conditional/contamination and demographic figures, not a lifetime probability or a multiplier on the LMIC baseline, so they inform the caveat only.
    Independence
    Independent of the WHO and Renschler sources: KFF is a US health-policy analysis organization summarizing DEA laboratory data and CDC mortality statistics for a distinct (US illicit-fentanyl) phenomenon, not the LMIC substandard-medicine supply chain.
  5. [5] National Center for Health Statistics (CDC) — Garnett MF, Miniño AM — Drug Overdose Deaths in the United States, 2023–2024
    Drug Overdose Deaths in the United States, 2023–2024
    Statistic
    Between 2023 and 2024 the age-adjusted drug overdose death rate fell 26.2%; the rate among ages 15–24 fell 37.0% (from 13.5 to 8.5 per 100,000), the largest decrease of any age group; deaths involving synthetic opioids (incl. fentanyl) fell 35.6%
    Excerpt
    “"The age-adjusted drug overdose death rate decreased by 26.2% between 2023 and 2024. ... rates declining 37.0% among ages 15–24 (from 13.5 to 8.5 deaths per 100,000 population). ... Drug overdose deaths involving synthetic opioids other than methadone decreased the most (35.6%) from 2023 (22.2 deaths per 100,000 standard population) to 2024 (14.3)." ”
    Source data from
    2026-01-01
    Accessed
    2026-06-14 · archived copy
    Calculation
    NCHS Data Brief No. 549 (January 2026) supplies the 2024 decline figures cited in the US-teen illicit-fentanyl caveat. These are CDC provisional mortality rates, not a lifetime probability or a multiplier on the LMIC baseline, so they inform the caveat only and do not alter the headline normalized figure.
    Independence
    Independent of the WHO and Renschler sources: this is the US national vital-statistics agency reporting US overdose mortality, a distinct phenomenon from the LMIC substandard-medicine supply chain. Complements the KFF source, which summarizes the same CDC/DEA data for a lay audience.

434 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 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 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 Job loss & depression — 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 Silent hypertension — 1 in 2.7 Drug-resistant infection — 1 in 2.6 No marrow match — 1 in 2.4 Nursing home admission — 1 in 2.2 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 LTC need after 65 — 1 in 1.8 Widowhood probability — 1 in 1.7 Unprotected sex — 1 in 1.5 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 At-fault injury crash — 1 in 2.9 Deer collision — 1 in 2.7 Car-crash injury — 1 in 2.6 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 2.3 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 9.8 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 Iron gap (women) — 1 in 3.9 Vitamin D gap — 1 in 2.9 Magnesium gap — 1 in 1.9 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 Housing crash — 1 in 8.3 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 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 1.7 Identity theft — 1 in 1.7 Credit card fraud — 1 in 1.5 School bullying — 1 in 1.5 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 Problem-tenant loss — 1 in 1.8 LASIK complications — 1 in 100 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 Wisdom-tooth surgery — 1 in 22 Dental tourism complication — 1 in 20 Elderly abandonment — 1 in 20 Parkinson's — 1 in 20 Pet parasites — 1 in 20 Undiagnosed ADHD — 1 in 20 Adult-onset food allergy — 1 in 19 Non-Alzheimer's dementia — 1 in 17 Cannabis use disorder — 1 in 16 Stroke — 1 in 15 PTSD — 1 in 15 Severe hearing loss — 1 in 14 Type 2 diabetes — 1 in 13 Appendicitis — 1 in 13 Indoor cooking smoke — 1 in 13 Untreated depression — 1 in 13 Soda and diabetes — 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 Parent death/disability — 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 26 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 Unbelted crash death — 1 in 53 Speeding 20% over limit — 1 in 50 Motorcycle no helmet — 1 in 45 Spaceflight (astronaut) — 1 in 42 Video watching + driving — 1 in 32 Child crash injury — 1 in 27 Drowsy driving — 1 in 26 Cruise ship norovirus — 1 in 24 E-scooter injury — 1 in 10 Pickpocketed while traveling — 1 in 38 Catalytic converter theft — 1 in 37 Knife-involved 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 Restaurant food poisoning — 1 in 58 B12 deficiency — 1 in 28 Vegetarian deficiency — 1 in 14 Intimate deepfake — 1 in 25 Social media problematic use — 1 in 13 Child swallows object (ER) — 1 in 91 Life-threatening birth (US) — 1 in 61 Childbirth death (SSA) — 1 in 55 Toddler stair fall — 1 in 37 Co-sleeping death — 1 in 36 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 Dying without heir — 1 in 100 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 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 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 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 159 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 357 Homicide — 1 in 339 Pig-butchering scam — 1 in 106 Extreme heat — 1 in 333 Climate change death — 1 in 204 Bat bite & rabies — 1 in 278 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 Infant fall — 1 in 500 Baby walker injury — 1 in 455 Maternal mortality — 1 in 272 Maternal age & birth defects — 1 in 200 Child death (<18) — 1 in 103 Caving career death — 1 in 167 EMS duty death — 1 in 909 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 Lightning home fire — 1 in 461 Wildfire home destruction — 1 in 120 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 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 Fish bone injury — 1 in 1,429 Phone/laptop battery fire — 1 in 4,545 Laundry pod ingestion — 1 in 6,494 Pool drowning — 1 in 5,882 Untreated infant hip dysplasia — 1 in 5,000 SIDS — 1 in 2,398 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 45,662 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 Post-crash car fire — 1 in 25,000 Railroad crossing death — 1 in 20,576 Car submersion — 1 in 16,667 Child bike trailer — 1 in 14,286 Runway near-miss — 1 in 13,699 Acid attack — 1 in 94,340 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 Bee sting — 1 in 78,927 Swallowed bee/wasp — 1 in 29,155 Fatal scorpion sting — 1 in 26,110 Dog chocolate death — 1 in 13,889 Lead-tainted cinnamon pouch — 1 in 40,000 Plastic container leaching — 1 in 16,949 Infant car-seat asphyxia — 1 in 64,935 Bouncer chair fall — 1 in 60,606 Toddler choking — 1 in 50,000 Unsupervised infant choking — 1 in 50,000 Forward-facing toddler death — 1 in 26,738 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 Ferry sinking — 1 in 133,333 Turbulence injury — 1 in 114,943 School shooting — 1 in 192,308 Mass shooting — 1 in 113,636 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 Crocodile attack — 1 in 337,838 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 Infant swing death — 1 in 714,286 Whole-grape choking — 1 in 625,000 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 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
Lottery jackpot 1 in 95,238

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