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Health · reviewed 2026-04-19

What are the odds of developing depression after losing your job?

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
3/5
D4 Uncertainty
4/5
D5 Scope
4/5
D6 Prose
5/5
D7 Perception honesty
4/5
D8 Caveat completeness
5/5
Average 4.38/5

Lifetime probability · lifetime, US adult

1 in 3.7

27% lifetime chance

Most people underestimate this.

range 1 in 6.7 to 1 in 2.5

lifetime, US adult each band = 10× rarer → zoomed to your factors See full scale →
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≈ As likely as

An empty office desk with a cardboard box and a wilting plant, flat vector illustration in muted tones.

Perceived

People fear job loss primarily for its financial consequences: missed mortgage payments, depleted savings, downward mobility. The mental health cost rarely features in the worry. When it does, most adults frame it as temporary stress rather than clinical illness, expecting the low mood to lift once re-employment arrives. Surveys on workplace anxiety focus almost entirely on the economic dimension; questions about depression as a downstream consequence of displacement are uncommon in public polling. The result is a risk that is structurally underweighted: the probability of developing clinical depression after involuntary job loss is roughly double the employed baseline, yet it seldom appears on anyone's list of things to fear about a layoff.

Rough estimate: Most people expect temporary stress, not clinical depression

Source: editorial intuition, not polled

Actual

~34% prevalence of clinical-level psychological problems among unemployed vs ~16% employed

unemployed adults (pooled across 237 cross-sectional studies, predominantly OECD countries)

Show derivation

The conditional probability of depression given unemployment is approximately 34% (Paul & Moser 2009 meta-analysis, 237 cross-sectional studies). This is total prevalence among unemployed, not the incremental risk attributable to job loss alone. To isolate the job-loss-attributable depression, we subtract the employed-baseline prevalence (~16%) to get an excess prevalence of ~18 percentage points, then add back the background lifetime depression rate (~20.6% per NIMH) that would have occurred regardless. The lifetime probability that a US adult will experience at least one episode of involuntary job loss is very high: BLS JOLTS data show a monthly layoff/discharge rate of ~1.0-1.1% of total nonfarm employment, and the BLS Displaced Workers Survey recorded 6.3 million displaced workers in the 2021-2023 period alone. Over a 40-year career, the probability of experiencing at least one involuntary separation approaches 0.80 or higher. Central estimate: P(depression | job loss) × P(job loss in career) ≈ 0.34 × 0.80 ≈ 0.27. This is conservative because it uses the cross-sectional prevalence (point-in-time) rather than incidence (new cases), and because many workers experience multiple displacement episodes. The uncertainty range reflects variation in both the conditional depression rate (which rises with unemployment duration) and the lifetime displacement probability.

Caveats: The 34% prevalence figure from Paul & Moser is a pooled estimate across 237 stud…

The 34% prevalence figure from Paul & Moser is a pooled estimate across 237 studies spanning several decades and many countries. It includes both pre-existing depression and new-onset cases triggered by unemployment, so the causal attributable fraction is smaller than 34%. The normalized lifetime estimate depends heavily on the assumed probability of involuntary job loss over a career, which varies enormously by occupation, industry, and economic conditions. The depression risk is strongly moderated by unemployment duration: brief episodes (under 3 months) carry much lower risk than prolonged unemployment. Country-level social protection also matters; the Paul & Moser meta-analysis found smaller effects in countries with generous unemployment benefits. The 27% central estimate is for clinical-level depression, not temporary sadness; many more people experience subclinical distress after job loss that does not meet diagnostic thresholds. Gender differences are notable: men show larger mental health effects from unemployment than women in most studies, possibly due to stronger identity investment in employment.

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Compare to:

The mental health cost of losing a job is one of the more systematically underestimated risks in the popular fear landscape. Paul and Moser’s 2009 meta-analysis, covering 237 cross-sectional and 87 longitudinal studies, found that 34% of unemployed people met the threshold for clinical-level psychological problems, compared to 16% of employed people. That is roughly a doubling of prevalence, and the longitudinal evidence confirms the causal direction: job loss causes the depression, not the other way around. The distress is not evenly distributed over time; it peaks sharply around the ninth month of unemployment (effect size d = 0.73, versus d = 0.51 overall) before stabilizing at moderately elevated levels through the second and third years.

What makes this fear interesting is the gap between what people worry about and what actually hurts them. Financial anxiety dominates the conversation around layoffs, but the mental health consequences are often more durable and harder to reverse than the economic ones. A Swedish longitudinal study found that displaced workers with no prior depression had a threefold risk of developing major depression; for men specifically, the risk was nearly fivefold. At the extreme end, Milner et al. (2013) found that unemployment raised suicide risk by a factor of 2.5 in the first five years. The financial shock of job loss is real, but it is typically time-limited by re-employment. Depression, once triggered, can persist well beyond the return to work and, in a vicious cycle, reduces the probability of re-employment itself.

The number varies enormously by individual circumstance. Unemployment lasting less than three months carries substantially lower depression risk than episodes stretching past six months. Country-level social protection matters: Paul and Moser found smaller mental health effects in nations with generous unemployment benefits, suggesting that the financial stress and the identity loss are separable channels. Gender differences are consistent across studies, with men showing larger effects, likely reflecting stronger identity investment in employment. Prior depression history roughly doubles the risk of recurrence after job loss, while strong social support networks and financial cushions cut it by about half. The 27% lifetime estimate assumes most American workers will face at least one significant involuntary separation over a career, which the BLS displacement data support.

About 27% of people who lose their job develop clinical depression within a year. Lifetime depression prevalence is ~21%. A single job loss can exceed the background lifetime rate in a matter of months.

Read more → ⇄ compare

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] Journal of Vocational Behavior (Paul & Moser) — Unemployment impairs mental health: Meta-analyses
    Unemployment impairs mental health: Meta-analyses
    Statistic
    34% prevalence of clinical-level psychological problems among unemployed vs 16% among employed; mean effect size d = 0.51
    Excerpt
    “"The average overall effect size was d = 0.51 with unemployed persons showing more distress than employed persons. A significant difference was found for several indicator variables of mental health (mixed symptoms of distress, depression, anxiety, psychosomatic symptoms, subjective well-being, and self-esteem). The average number of persons with psychological problems among the unemployed was 34%, compared to 16% among employed individuals." ”
    Source data from
    2009-06-01
    Accessed
    2026-04-19 · archived copy
    Calculation
    Paul & Moser (2009) conducted the largest meta-analysis to date on unemployment and mental health, covering 237 cross-sectional and 87 longitudinal studies. The 34% vs 16% prevalence figures are the central estimates for the native rate. The effect size d = 0.51 represents a medium effect. Moderator analyses showed that distress peaks around month 9 of unemployment (d = 0.73), with stabilization at medium levels during the second year. Men and blue-collar workers showed larger effects than women and white-collar workers. The 87 longitudinal studies confirmed the causal direction: unemployment causes mental health deterioration, not merely the reverse.
    Independence
    This is the foundational meta-analysis in the field. Independent from SAMHSA administrative data and from the Milner et al. suicide meta-analyses, which use different outcome measures and study pools.
  2. [2] PLOS ONE (Milner, Page & LaMontagne) — Long-Term Unemployment and Suicide: A Systematic Review and Meta-Analysis
    Long-Term Unemployment and Suicide: A Systematic Review and Meta-Analysis
    Statistic
    Pooled RR of suicide after unemployment = 1.70 (95% CI 1.22-2.18); within 5 years RR = 2.50 (95% CI 1.83-3.17)
    Excerpt
    “"A random effects meta-analysis on a subsample of six cohort studies indicated that the pooled relative risk of suicide in relation to average follow-up time after unemployment was 1.70 (95% CI 1.22 to 2.18). The greatest risk of suicide occurred within five years of unemployment compared to the employed population (RR = 2.50, 95% CI 1.83 to 3.17)." ”
    Source data from
    2013-01-09
    Accessed
    2026-04-19 · archived copy
    Calculation
    Milner et al. (2013) established the suicide risk gradient by unemployment duration. The RR of 2.50 in the first five years is consistent with the Paul & Moser finding that depression peaks during the first year and remains elevated. This source is used here not for the normalized probability (which is about depression, not suicide) but to corroborate the severity of mental health consequences and to anchor the outcome_severity classification. Suicide is the extreme end of the depression spectrum that job loss can trigger.
    Independence
    Uses different outcome (suicide mortality) and different study pool from Paul & Moser. Methodologically independent.
  3. [3] U.S. Bureau of Labor Statistics — Displaced Workers Summary, January 2024
    Displaced Workers Summary, January 2024
    Statistic
    6.3 million workers displaced in the 2021-2023 period; layoff/discharge rate ~1.0-1.1% of nonfarm employment per month
    Excerpt
    “"From January 2021 to December 2023, 6.3 million workers were displaced from jobs they had held for at least 3 years or from jobs held for less than 3 years. In January 2024, 65.7 percent of the 2.6 million long-tenured displaced workers were reemployed." ”
    Source data from
    2024-08-29
    Accessed
    2026-04-19 · archived copy
    Calculation
    The BLS Displaced Workers Survey provides the denominator for estimating lifetime job-loss probability. With approximately 6.3 million displaced workers over a 3-year period in a labor force of ~160 million, the annual displacement rate is roughly 1.3%. JOLTS data show a monthly layoff/discharge rate of ~1.0-1.1% (including short-tenure workers), or roughly 12-13% per year including all separations classified as involuntary. Over a 40-year career, using the conservative displaced-worker definition (1.3%/year), the probability of at least one displacement is 1 - (1 - 0.013)^40 ≈ 0.41. Using the broader JOLTS layoff/discharge rate (~12%/year), the figure approaches certainty, but many of those separations are brief and may not trigger the sustained unemployment that drives depression. We use ~0.80 as a central estimate for at least one significant involuntary job loss over a career, reflecting the reality that most American workers will experience this at least once.
    Independence
    BLS administrative survey data, independent from the clinical studies on depression prevalence. Different data collection pipeline (employer establishment survey and household survey) from the mental health literature.
  4. [4] BMC Public Health (Magnusson Hanson et al.) — Depressive symptoms as a cause and effect of job loss in men and women: evidence in the context of organisational downsizing
    Depressive symptoms as a cause and effect of job loss in men and women: evidence in the context of organisational downsizing
    Statistic
    Displaced workers had a threefold risk of incident major depression; men showed nearly fivefold risk after layoff
    Excerpt
    “"In the total sample including men and women, displaced workers experienced a more than threefold risk of incident major depression and twofold risk of less severe symptoms among those with no depression at baseline. A nearly fivefold risk of incident major depression was observed in unemployed men with no depression at baseline." ”
    Source data from
    2015-10-06
    Accessed
    2026-04-19 · archived copy
    Calculation
    Magnusson Hanson et al. (2015) used the Swedish Longitudinal Occupational Survey of Health (SLOSH) to examine depression as both cause and effect of job loss during organisational downsizing. The threefold risk of incident major depression among displaced workers (and fivefold among men) is higher than the ~2x implied by the Paul & Moser prevalence ratio (34%/16%), likely because the SLOSH study isolated incident cases (new-onset depression in previously non-depressed workers) rather than point prevalence. This suggests the 34% prevalence figure includes both pre-existing and new-onset depression, and the true causal effect of job loss may be larger than the cross-sectional data indicate.
    Independence
    Swedish longitudinal cohort study using different population, design (prospective longitudinal vs cross-sectional meta-analysis), and outcome measure (incident major depression vs prevalence) from Paul & Moser. Methodologically independent.

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 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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