Death Clock
This death clock turns your country’s life table into an estimated month and year, with a likely range, your odds of reaching 80, 90 and 100 and the changes worth the most years for you. Every number comes from a named study, and the model is checked against them below. It is an educational estimate, not a prediction. Your answers stay in your browser.
Loading your country’s life table…
What moves your estimate
Worth the most years for you
Educational estimate from population life tables and published studies, not a prediction. How it works · How we check it · See your life in weeks
If you are struggling or having thoughts of ending your life, you are not alone. In the US, call or text 988 (Suicide & Crisis Lifeline). In the UK and Ireland, call Samaritans on 116 123. Elsewhere, find a free local helpline at findahelpline.com.
How the death clock works
- Your country’s death rates. We start from the chance of dying at each age for people of your sex in your country: the UN World Population Prospects 2024 life tables, single years of age, for all 237 countries and areas.
- Future years, not just today’s. Death rates keep falling. For each future age we use the UN’s projected rate for the calendar year in which you will reach it (between 2026 and 2100). That is why a 30-year-old’s figure is higher than a table based only on today’s death rates.
- Your answers as hazard ratios. Each answer multiplies your yearly risk by a factor from a large published study (table below). Because the life table already describes the average person, each factor is divided by the average factor in the population, so “not sure” or an average answer leaves the table unchanged.
- Overlap and age. Habits overlap (people who smoke also tend to move less), so the combined effect of your habits is softly limited, and published effects shrink after 70, as they do in the studies.
- A date, a range and odds. From your survival curve we read the age by which half of people like you would have died (the date shown), the average age at death, the middle 50% and 80% of outcomes, and your chance of reaching any age or year.
| Answer | Effect on the death rate | Source |
|---|---|---|
| Baseline | Your country’s life table by single year of age and sex; future ages use the UN projection for the year you reach them | UN World Population Prospects 2024 |
| Smoking | Current smoker 2.8× (men) / 3.0× (women) the death rate of never smokers; under 10 a day 1.87×. Former smokers keep 3% of the extra risk if they quit before 35, 12% at 35–44, 25% at 45–54, 45% at 55–64, 65% after | Jha 2013; Inoue-Choi 2017; quit shares set to match Jha’s years gained |
| Weight (BMI) | 1.51× under 18.5; 1.13× at 18.5–20; 1.00 at 20–25; 1.07× at 25–27.5; 1.20× at 27.5–30; 1.45× at 30–35; 1.94× at 35–40; 2.76× at 40+ | Global BMI Mortality Collaboration, Lancet 2016 |
| Daily steps | vs under 4,000: 0.60× at 4,000–7,000; 0.55× at 7,000–10,000; 0.47× at 10,000+ (0.55× from age 60, where the benefit levels off) | Paluch, Lancet Public Health 2022 |
| Alcohol (drinks a week) | vs 1–7: 1.06× at 8–14; 1.18× at 15–24; 1.70× at 25+ (set to match Wood’s years lost at age 40). None is treated like 1–7 | Wood, Lancet 2018 |
| Diet | vs mostly processed: 0.90× average; 0.85× good; 0.80× excellent | Shan, JAMA Intern Med 2023 |
| Sleep | 1.12× under 6 hours; 1.30× 9 hours or more | Cappuccio, Sleep 2010 |
| Social contact | 1.29× if mostly isolated | Holt-Lunstad 2015 |
| Self-rated health | vs excellent or very good: 1.23× good; 1.44× fair; 1.92× poor | DeSalvo, J Gen Intern Med 2006 |
| Long-term conditions | 1.9× diabetes; 2.0× past heart attack or coronary heart disease; 2.1× past stroke (multiplied together) | Emerging Risk Factors Collaboration, JAMA 2015 |
Limits. The “average person” mix of answers we divide by (for example 10.8% current smokers and 40.3% with obesity) comes from US surveys, so in countries with fewer smokers or less obesity the effect of healthy answers may be slightly overstated. Some shares (alcohol, sleep and social contact) are our assumptions. Long-term conditions are not rescaled, so “none” gives the plain table figure, which is slightly cautious. Studies show association, not proof that a change will add exactly those years. The model doesn’t know your medical history, income, genes or what will happen next.
How we check it: the model against the published figures
Every time this page is generated, the model is run on the same comparisons as the studies it uses. If any result falls outside the tolerance, the page is not generated. (US life tables, today’s death rates, as the studies used.)
| Comparison | Published | Our model |
|---|---|---|
| US life expectancy at 65, men (no answers) | NCHS US Life Tables 2024: 18.4 years | 18.9 |
| US life expectancy at 65, women (no answers) | NCHS US Life Tables 2024: 20.8 years | 21.4 |
| Current smoker vs never smoker, man aged 40 | Jha 2013: “more than 10 years” shorter | -10.1 |
| Quitting at 30 vs continuing to smoke | Jha 2013: about 10 years gained (quit at 25–34) | +10.5 |
| Quitting at 40 vs continuing to smoke | Jha 2013: about 9 years gained (quit at 35–44) | +8.3 |
| Quitting at 50 vs continuing to smoke | Jha 2013: about 6 years gained (quit at 45–54) | +6.0 |
| 8–14 drinks a week vs up to 7, age 40 | Wood 2018: about 0.5 year shorter (100–200 g) | -0.5 |
| 15–24 drinks a week vs up to 7, age 40 | Wood 2018: 1–2 years shorter (200–350 g) | -1.5 |
| 25+ drinks a week vs up to 7, age 40 | Wood 2018: 4–5 years shorter (over 350 g) | -5.0 |
| Diabetes and a past heart attack vs neither, age 60 | ERFC 2015: about 12 years shorter (any two) | -10.0 |
| Diabetes, heart attack and stroke vs none, age 60 | ERFC 2015: about 15 years shorter | -14.0 |
| 5 healthy habits vs none, man aged 50 | Li 2018: 12.1 years longer | +13.1 |
| 5 healthy habits vs none, woman aged 50 | Li 2018: 14.1 years longer | +12.3 |
The model is a little cautious for women’s healthy habits (12.3 years vs 14.1 in the study), because it uses the same effect sizes for both sexes.
What the date does and doesn’t mean
The date is the median outcome for people of your age, sex, country and answers: half live longer, half shorter. For most adults the spread is wide. For a 65-year-old American man, the middle half of outcomes covers more than ten years. That spread is why the range and the odds are more useful than the date, and why no honest tool can give you a day.
For what artificial intelligence can and can’t add, read Can AI predict when you’ll die?. To see how US states compare, see life expectancy by state, and for any country, life expectancy by country.
Frequently asked questions
Is the death clock real?
It is real maths, not a real prediction. No calculator, app or AI can tell you the date you will die. This death clock takes the death rates of people of your age, sex and country, adjusts them for your answers using published studies, and shows the point by which half of people like you would have died. Real lifespans spread over decades around that point, which is why the range and the odds matter more than the date.
How accurate is a death clock?
For a group of people it can be well calibrated: our model reproduces the published figures it is built on (see the validation table on this page). For one person it is a rough guide. Even with perfect answers, most of the uncertainty is about chance, so the likely range stays wide whatever you enter.
Death Clock app vs this calculator: what is the difference?
Apps such as Death Clock AI give an exact day and say their model was trained on many studies, but they do not publish the model or show how well it works. This page is free, needs no sign-up, keeps your answers in your browser, shows a month rather than a day, and lists every number it uses, with its source and a check against the published result.
Why does the result show a month and not a day?
Because a single day would suggest a precision that no method has. Even the month is only a way of picturing the middle of a wide range. Half of people with your answers live past it, and half don’t.
Why is my date later than the life expectancy I read about?
Three reasons. Life expectancy at birth includes people who die young, and you have already survived those years. Death rates are expected to keep falling, so we use the UN’s projected rates for the years in which you will reach each age. And the date is the median (the 50:50 point), which for most adults is a few years later than the average (mean) age at death, also shown in the result.
How can I push my date back?
The result lists the changes worth the most years for you. In general the biggest are not smoking (quitting before 40 avoids about 90% of the extra risk), walking more (risk keeps falling up to about 8,000 to 10,000 steps a day), keeping alcohol under about 7 drinks a week, a healthy weight and a good diet. Change one answer at a time to see each effect, or try our Add Years simulator.
Sources
- United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects 2024, complete life tables (medium variant). population.un.org/wpp
- Arias E, Xu JQ, Tejada-Vera B. United States Life Tables, 2024. National Vital Statistics Reports 75(5), NCHS, 2026 (validation). cdc.gov
- Jha P, Ramasundarahettige C, Landsman V, et al. 21st-century hazards of smoking and benefits of cessation in the United States. N Engl J Med 2013;368:341–50. PubMed
- Inoue-Choi M, Liao LM, Reyes-Guzman C, et al. Association of long-term, low-intensity smoking with all-cause and cause-specific mortality in the NIH-AARP Diet and Health Study. JAMA Intern Med 2017;177(1):87–95.
- Global BMI Mortality Collaboration. Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents. Lancet 2016;388:776–86. PubMed
- Paluch AE, Bajpai S, Bassett DR, et al. Daily steps and all-cause mortality: a meta-analysis of 15 international cohorts. Lancet Public Health 2022;7:e219–28. PubMed
- Wood AM, Kaptoge S, Butterworth AS, et al. Risk thresholds for alcohol consumption: combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies. Lancet 2018;391:1513–23. PubMed
- Shan Z, Wang F, Li Y, et al. Healthy eating patterns and risk of total and cause-specific mortality. JAMA Intern Med 2023;183(2):142–53.
- Cappuccio FP, D’Elia L, Strazzullo P, Miller MA. Sleep duration and all-cause mortality: a systematic review and meta-analysis of prospective studies. Sleep 2010;33(5):585–92. PubMed
- Holt-Lunstad J, Smith TB, Baker M, Harris T, Stephenson D. Loneliness and social isolation as risk factors for mortality: a meta-analytic review. Perspect Psychol Sci 2015;10(2):227–37. PubMed
- DeSalvo KB, Bloser N, Reynolds K, He J, Muntner P. Mortality prediction with a single general self-rated health question: a meta-analysis. J Gen Intern Med 2006;21(3):267–75. doi
- Emerging Risk Factors Collaboration. Association of cardiometabolic multimorbidity with mortality. JAMA 2015;314(1):52–60. PubMed
- Li Y, Pan A, Wang DD, et al. Impact of healthy lifestyle factors on life expectancies in the US population. Circulation 2018;138:345–55 (validation). PubMed
- NCHS. Obesity and severe obesity prevalence in adults: United States, August 2021–August 2023 (Data Brief 508) and Health E-Stat 2026; CDC NHIS 2023 cigarette smoking (reference mix).
Educational estimate, not a prediction or medical advice. The date is the middle outcome for people with your answers, shown as a month. It cannot tell you when you will die. Talk to a doctor about your own health. See our medical disclaimer.