Purpose: This document collects authoritative sources for each of the 50 calculator questions. Tone: Prudent, evidence-based, non-alarmist. Target audience: End users interested in estimating remaining life expectancy and improving lifestyle habits. Disclaimer: This is not clinical medical advice. Associations described are observational unless stated otherwise.
1. Current age
Calculator variable: age โ The strongest baseline driver for remaining life expectancy.
Why it matters: Age is the primary determinant of remaining life expectancy. Life tables from national statistical agencies show that remaining years decrease predictably as age increases, though the rate varies by sex, country, and calendar year. Baseline estimates are derived from period life tables, which assume current mortality rates continue into the future.
Recommended sources
Title: WHO Global Health Estimates โ Life expectancy and healthy life expectancy
Publisher: World Health Organization
Year/date: Updated annually
Why useful: Provides standardized life expectancy estimates and life tables for all WHO member states, used as baseline reference for age-specific remaining life expectancy.
Evidence strength: High
Title: Human Mortality Database โ Life tables for developed countries
URL: https://www.mortality.org/
Publisher: University of California, Berkeley (USA) & Max Planck Institute for Demographic Research (Germany)
Year/date: Continuously updated
Why useful: Gold-standard harmonized life tables for 40+ countries; allows comparison of age-specific mortality patterns.
Evidence strength: High
Title: OECD Data โ Life expectancy at birth
URL: https://www.oecd.org/en/data/indicators/life-expectancy-at-birth.html
Publisher: Organisation for Economic Co-operation and Development
Year/date: Updated annually
Why useful: Direct OECD indicator page for comparable life expectancy at birth data across OECD countries, with definitions and downloadable data.
Evidence strength: High
Suggested reference text for website
Your current age is the strongest single predictor of remaining life expectancy. We use national life tables from the WHO and other statistical agencies to establish a baseline: older age means fewer years remain on average, but the gap between your current age and life expectancy shrinks more slowly than you might expect. This is known as "mortality deceleration."
Notes / caveats
- Period life tables assume current mortality rates remain constant, which may underestimate future gains.
- Cohort effects (improvements in healthcare over a lifetime) are not captured in period tables.
- Baseline estimates vary substantially by country and sex.
2. Sex at birth
Calculator variable: sex โ Used only for population-level actuarial baseline differences.
Why it matters: Females consistently live longer than males in nearly all populations, with a global gap of approximately 4โ7 years. The difference stems from a combination of biological factors (hormonal, genetic, immune system differences) and behavioral/social factors (risk-taking, healthcare-seeking, occupational hazards). The calculator applies a modest adjustment to reflect these actuarial differences.
Recommended sources
Title: WHO Global Health Observatory โ Life expectancy and healthy life expectancy by sex
URL: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/life-expectancy-at-birth-(years)
Publisher: World Health Organization
Year/date: Updated annually
Why useful: Provides sex-disaggregated life expectancy data globally; shows the consistent female advantage across regions.
Evidence strength: High
Title: From sex differences to sex inequalities in life expectancy: A cross-country observational benchmarking analysis
URL: https://doi.org/10.1371/journal.pmed.1004828
Publisher: PLOS Medicine
Year/date: 2025
Why useful: Cross-country analysis of sex differences and inequalities in life expectancy across 237 countries and multiple ages.
Evidence strength: High
Title: Our World in Data โ Life expectancy by sex
URL: https://ourworldindata.org/life-expectancy
Publisher: Our World in Data / University of Oxford
Year/date: Updated annually
Why useful: Accessible, well-sourced visualizations showing how the sex gap varies by country and over time.
Evidence strength: High
Suggested reference text for website
On average, women live longer than men in nearly every country โ a gap of about 4โ7 years globally. This reflects a mix of biology (hormonal, genetic, and immune differences) and behavior (risk-taking, healthcare use, occupational hazards). The calculator applies a small actuarial adjustment based on sex, but individual habits and health matter far more.
Notes / caveats
- The gap narrows when adjusting for behavioral and occupational factors.
- Intersex and non-binary individuals are not captured in population-level life tables.
- The gap varies widely by country (from ~2 years in some regions to >10 years in others).
3. Country and city where you live
Calculator variable: country โ Country and city improve the estimate with healthcare access, air quality, safety, walkability, income, pollution, and local mortality patterns.
Why it matters: Life expectancy differs by 10โ20+ years between countries and even between cities within the same country. Key drivers include healthcare system quality and access, air pollution levels, violent crime rates, road safety, walkability, income levels, and infectious disease burden. The calculator uses country-specific adjustments based on published life tables, with additional city-level adjustments that draw on available air quality, safety, and health data. These city adjustments should be considered approximate proxies rather than precise actuarial tables for each city.
Recommended sources
Life expectancy
Title: WHO Global Health Observatory โ Life expectancy at birth, by country
URL: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/life-expectancy-at-birth-(years)
Publisher: World Health Organization
Year/date: Updated annually
Why useful: The standard reference for country-level life expectancy, enabling direct comparison.
Evidence strength: High
Title: Human Mortality Database โ Life tables for developed countries
URL: https://www.mortality.org/
Publisher: University of California, Berkeley & Max Planck Institute for Demographic Research
Year/date: Continuously updated
Why useful: Gold-standard harmonized life tables allowing comparison of age-specific mortality patterns across 40+ countries.
Evidence strength: High
Air pollution
Title: WHO Ambient Air Quality Database, update Jan 2024, Version 6.1
URL: https://www.who.int/data/gho/data/themes/air-pollution/who-air-quality-database
Publisher: World Health Organization
Year/date: 2024
Why useful: Specific WHO city and country air-quality database covering PMโ.โ and other pollutant levels, used as a directional input for pollution-related mortality context.
Evidence strength: High
Safety and road risk
Title: WHO Global Status Report on Road Safety 2023
URL: https://www.who.int/publications/i/item/9789240086517
Publisher: World Health Organization
Year/date: 2023
Why useful: Country-level road traffic death rates, a major component of external-cause mortality variation between locations.
Evidence strength: High
Subnational variation
Title: GBD Results Tool โ life expectancy and mortality by location
URL: https://vizhub.healthdata.org/gbd-results/
Publisher: Institute for Health Metrics and Evaluation (IHME)
Year/date: Updated regularly
Why useful: Interactive GBD tool for filtering mortality, rates, causes, locations, years, sex, and age groups; useful for checking location-level mortality context.
Evidence strength: High
Title: Our World in Data โ Life expectancy by country
URL: https://ourworldindata.org/grapher/life-expectancy-at-birth
Publisher: Our World in Data / University of Oxford
Year/date: Updated annually
Why useful: Clean, accessible visualizations of global and subnational life expectancy data with sources linked.
Evidence strength: High
Suggested reference text for website
Where you live has a major impact on how long you can expect to live. National life expectancy varies by more than 20 years between countries, driven by differences in healthcare, pollution, safety, income, and lifestyle patterns. Even within the same country, city-level differences can be several years. Our city adjustments draw on published life expectancy data, air quality indices, and safety statistics, but should be considered approximate.
Notes / caveats
- Country-level averages mask large within-country variation (urban vs. rural, rich vs. poor regions).
- The calculator uses a curated set of cities; other locations within the same country may differ.
- Data quality and reporting standards vary between countries.
4. Smoking status
Calculator variable: smoking โ Tobacco exposure is one of the largest modifiable longevity factors.
Why it matters: Cigarette smoking is the leading preventable cause of premature death globally. It is causally linked to lung cancer, cardiovascular disease, COPD, and many other conditions. Former smokers gradually reduce excess risk, though full risk reversal takes years and depends on cumulative exposure. The calculator differentiates between never, former, occasional, and daily smokers.
Recommended sources
Title: The Health Consequences of Smoking โ 50 Years of Progress: A Report of the Surgeon General
URL: https://www.ncbi.nlm.nih.gov/books/NBK179276/
Publisher: U.S. National Library of Medicine / CDC (NCBI Bookshelf)
Year/date: 2014
Why useful: Comprehensive synthesis of decades of evidence on smoking harms; gold-standard reference for mortality impact estimates.
Evidence strength: High
Title: WHO global report on trends in prevalence of tobacco use 2000โ2024 and projections 2025โ2030, sixth edition
URL: https://www.who.int/publications/i/item/9789240116276
Publisher: World Health Organization
Year/date: 2025
Why useful: Latest WHO global report on tobacco-use trends and projections, replacing the older 2000โ2025 edition.
Evidence strength: High
Title: Smoking and mortality โ beyond established causes
URL: https://www.nejm.org/doi/full/10.1056/NEJMsa1407211
Publisher: New England Journal of Medicine
Year/date: 2015
Why useful: Quantifies excess mortality from smoking beyond the well-known causes, using large pooled cohorts.
Evidence strength: High
Suggested reference text for website
Smoking is the single largest modifiable risk factor for premature death. Studies estimate that daily smokers lose approximately 6โ10 years of life compared to never-smokers, with variation by pack-years and age of initiation. The good news: quitting at any age reduces excess risk, and former smokers' life expectancy partially recovers over time.
Notes / caveats
- Estimates vary by pack-years, age of initiation, and duration of cessation.
- Former smokers' residual risk depends on cumulative exposure and time since quitting.
- Causal association is well established (not merely observational).
5. Body mass index (BMI)
Calculator variable: bmi โ Body composition can refine estimates.
Why it matters: Both low BMI (underweight) and high BMI (obesity) are associated with increased mortality risk in large epidemiological studies. The relationship is J-shaped, with lowest mortality typically in the 22โ25 kg/mยฒ range for most populations. Abdominal fat distribution may be more predictive, but BMI remains the most widely studied and available metric.
Recommended sources
Title: Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies (The Global BMI Mortality Collaboration)
URL: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(16)30175-1/fulltext
Publisher: The Lancet
Year/date: 2016
Why useful: Large-scale meta-analysis (10.6 million participants) quantifying BMI-mortality associations across four continents.
Evidence strength: High
Title: WHO Fact Sheet โ Obesity and overweight
URL: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight
Publisher: World Health Organization
Year/date: Updated 2024
Why useful: Official WHO reference on BMI categories and associated health risks.
Evidence strength: High
Title: Global burden of 87 risk factors in 204 countries and territories, 1990โ2019: a systematic analysis for the Global Burden of Disease Study 2019
URL: https://doi.org/10.1016/S0140-6736(20)30752-2
Publisher: The Lancet / Global Burden of Disease
Year/date: 2020
Why useful: Comprehensive GBD 2019 risk-factor analysis covering high body-mass index across 204 countries and territories.
Evidence strength: High
Suggested reference text for website
Your body mass index is a widely studied predictor of overall mortality risk. Both very low and very high BMI are associated with shorter life expectancy, with the lowest risk typically in the 22โ25 range. These estimates come from large international meta-analyses involving millions of participants.
Notes / caveats
- BMI does not distinguish muscle from fat; athletes may be misclassified.
- The optimal BMI may shift upward with older age ("obesity paradox" is debated).
- Waist circumference may be a stronger risk predictor (see Waist-to-height question).
6. Weekly physical activity
Calculator variable: activity โ Count brisk walking, cycling, sport, gym, active commuting, or comparable effort.
Why it matters: Physical activity is one of the most powerful modifiable factors for longevity. Meeting WHO guidelines (150โ300 minutes of moderate activity per week) is associated with a 20โ30% reduction in all-cause mortality. Higher volumes provide additional benefits, though evidence suggests benefits may plateau at very high volumes rather than reverse.
Recommended sources
Title: WHO Guidelines on Physical Activity and Sedentary Behaviour
URL: https://www.who.int/publications/i/item/9789240015128
Publisher: World Health Organization
Year/date: 2020
Why useful: Official global recommendations; summarizes evidence on dose-response between physical activity and mortality.
Evidence strength: High
Title: Dose-response associations between accelerometry measured physical activity and sedentary time and all cause mortality
URL: https://doi.org/10.1136/bmj.l4570
Publisher: The BMJ
Year/date: 2019
Why useful: Device-measured activity study supporting dose-response associations between physical activity, sedentary time, and all-cause mortality without relying only on self-report.
Evidence strength: High
Title: Physical Activity Guidelines for Americans (2nd edition)
URL: https://health.gov/sites/default/files/2019-09/Physical_Activity_Guidelines_2nd_edition.pdf
Publisher: U.S. Department of Health and Human Services
Year/date: 2018
Why useful: Comprehensive review of evidence linking physical activity to reduced all-cause mortality, CVD, diabetes, and cancer.
Evidence strength: High
Suggested reference text for website
Regular physical activity is strongly linked to longer life. Adults who meet the WHO guideline of 150โ300 minutes of moderate activity per week have a 20โ30% lower risk of early death compared to inactive individuals. More activity generally brings more benefit, with evidence suggesting benefits may plateau at very high volumes rather than reverse.
Notes / caveats
- Self-reported activity tends to overestimate actual levels.
- Benefits are consistent across age, sex, and ethnic groups.
- Even small increases in activity among the most sedentary produce meaningful risk reduction.
7. Overall diet pattern
Calculator variable: diet โ Mediterranean-style, minimally processed, high-fiber diets generally score higher.
Why it matters: Dietary patterns rich in whole plant foods, healthy fats, and low in ultra-processed foods are consistently associated with lower all-cause mortality, cardiovascular disease, and cancer risk. The Mediterranean diet is the most extensively studied pattern for longevity outcomes.
Recommended sources
Title: Association of changes in diet quality with total and cause-specific mortality (NHS / HPFS)
URL: https://www.nejm.org/doi/full/10.1056/NEJMoa1613502
Publisher: New England Journal of Medicine
Year/date: 2017
Why useful: Demonstrated that improving diet quality over 12 years is associated with reduced subsequent mortality.
Evidence strength: High
Title: Global Burden of Disease โ Health effects of dietary risks in 195 countries (GBD 2017 Diet Collaborators)
URL: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(19)30041-8/fulltext
Publisher: The Lancet
Year/date: 2019
Why useful: Comprehensive global estimates of diet-attributable mortality; highlights low whole grains, high sodium, and low fruit as top dietary risks.
Evidence strength: High
Title: The Mediterranean diet and health: a comprehensive overview
URL: https://pubmed.ncbi.nlm.nih.gov/34423871/
Publisher: Journal of Internal Medicine
Year/date: 2021
Why useful: Broad peer-reviewed overview of Mediterranean diet evidence across cardiovascular, cancer, metabolic, and mortality-related outcomes.
Evidence strength: High
Suggested reference text for website
What you eat day to day is one of the most important longevity levers. Diets rich in whole plant foods, healthy fats, and fiber โ such as the Mediterranean diet โ are consistently associated with lower mortality risk. Conversely, high consumption of ultra-processed foods is linked to higher risk.
Notes / caveats
- Dietary assessment relies on self-report, which has measurement error.
- Whole dietary patterns are easier to study than isolated nutrients, but residual confounding is possible.
- Benefits likely reflect cumulative long-term exposure.
8. Average sleep duration
Calculator variable: sleep โ Long-term sleep regularity and duration influence metabolic and cardiovascular risk.
Why it matters: Both short (<6 hours) and long (>9 hours) habitual sleep duration are associated with increased all-cause mortality in meta-analyses of prospective cohorts. The lowest risk is consistently found around 7โ8 hours per night. Causal mechanisms include metabolic dysregulation, inflammation, and cardiovascular strain.
Recommended sources
Title: Sleep duration and all-cause mortality: a systematic review and meta-analysis of prospective studies
URL: https://academic.oup.com/sleep/article/33/5/585/2453896
Publisher: Sleep (Oxford Academic) / NIH
Year/date: 2010
Why useful: Meta-analysis of 16 prospective studies showing U-shaped association; both short and long sleep increase mortality risk.
Evidence strength: High
Title: CDC Sleep and Sleep Disorders โ How Much Sleep Do You Need?
URL: https://www.cdc.gov/sleep/about/index.html
Publisher: U.S. Centers for Disease Control and Prevention
Year/date: Updated 2024
Why useful: Official public health recommendations with supporting evidence.
Evidence strength: High
Title: Recommended Amount of Sleep for a Healthy Adult: A Joint Consensus Statement of the American Academy of Sleep Medicine and Sleep Research Society
URL: https://doi.org/10.5665/sleep.4716
Publisher: Sleep
Year/date: 2015
Why useful: Consensus recommendation that adults should sleep at least 7 hours per night, based on systematic evidence review.
Evidence strength: High
Suggested reference text for website
Sleep duration follows a U-shaped relationship with mortality: both too little (under 6 hours) and too much (over 9 hours) are associated with higher risk. Seven to eight hours appears optimal for most adults. Quality and consistency matter as much as duration.
Notes / caveats
- Long sleep may be a marker of underlying illness (reverse causation) rather than a direct cause.
- Sleep quality is an important confounder not captured by duration alone.
- Individual variability exists; some people function well on less sleep without apparent harm.
9. Blood pressure status
Calculator variable: blood_pressure โ Use treated or untreated status if you know it.
Why it matters: Elevated blood pressure is the leading modifiable risk factor for cardiovascular disease and premature death globally. Each 20 mmHg increase in systolic BP doubles cardiovascular mortality risk. Treatment effectively reduces risk, though treated individuals still have somewhat elevated risk compared to those with naturally normal BP.
Recommended sources
Title: Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990โ2021
URL: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(24)00933-4/fulltext
Publisher: The Lancet / Global Burden of Disease Study
Year/date: 2024
Why useful: GBD risk-factor analysis quantifying high systolic blood pressure as one of the leading contributors to global disease burden and mortality.
Evidence strength: High
Title: WHO Fact Sheet โ Hypertension
URL: https://www.who.int/news-room/fact-sheets/detail/hypertension
Publisher: World Health Organization
Year/date: Updated 2023
Why useful: Authoritative overview of hypertension prevalence, risks, and treatment benefits worldwide.
Evidence strength: High
Title: Age-specific relevance of usual blood pressure to vascular mortality (Prospective Studies Collaboration)
URL: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(02)11911-8/fulltext
Publisher: The Lancet
Year/date: 2002
Why useful: Landmark meta-analysis of 1 million adults showing log-linear relationship between BP and mortality down to 115/75 mmHg.
Evidence strength: High
Suggested reference text for website
High blood pressure is the single largest contributor to premature death worldwide. The relationship between blood pressure and mortality is continuous: even moderately elevated levels increase cardiovascular risk. Treatment reduces but does not fully eliminate this excess risk.
Notes / caveats
- A single reading is not diagnostic; the calculator asks about general status.
- Treated hypertension still carries residual risk compared to normotensive individuals.
- BP targets vary by age and comorbidity; recent guidelines differ between organizations.
10. Major chronic disease
Calculator variable: diagnoses โ Includes diagnosed heart disease, cancer, diabetes, COPD, kidney disease, or stroke.
Why it matters: A diagnosis of major chronic disease significantly reduces life expectancy, though the magnitude varies by disease, severity, and management quality. Well-managed single conditions have a much smaller impact than multiple or poorly controlled conditions. These are among the strongest predictors used by the calculator.
Recommended sources
Title: Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990โ2021
URL: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(24)00367-2/fulltext
Publisher: The Lancet / Global Burden of Disease Study
Year/date: 2024
Why useful: Cause-specific mortality and life expectancy decomposition reference for major diseases by age, sex, location, and year.
Evidence strength: High
Title: WHO Fact Sheet โ Cardiovascular diseases (CVDs)
URL: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)
Publisher: World Health Organization
Year/date: Updated regularly
Why useful: Direct WHO fact sheet for the largest chronic-disease mortality category; used as an institutional context source alongside GBD and multimorbidity evidence.
Evidence strength: High
Title: Relationship between multimorbidity, demographic factors and mortality: findings from the UK Biobank cohort
URL: https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-019-1305-x
Publisher: BMC Medicine
Year/date: 2019
Why useful: Large UK Biobank cohort study quantifying how multimorbidity and demographic factors relate to mortality risk.
Evidence strength: High
Suggested reference text for website
A diagnosis of a major chronic disease โ such as heart disease, cancer, diabetes, COPD, kidney disease, or stroke โ significantly affects life expectancy. The impact depends on the specific condition, how well it is managed, and whether multiple conditions are present. Well-controlled single conditions have a much smaller effect than multiple or poorly controlled ones.
Notes / caveats
- Self-reported diagnosis may be inaccurate; the calculator asks to only report formally diagnosed conditions.
- "Well-managed" is self-assessed and may not correspond to clinical metrics.
- Prefer not to say option allows skipping without penalty.
11. Alcohol intake
Calculator variable: alcohol โ Average weekly pattern.
Why it matters: Alcohol consumption is causally linked to multiple cancers, liver disease, cardiovascular conditions, and accidents. Recent large-scale studies have challenged the notion of protective effects from moderate drinking, suggesting that any alcohol intake may increase overall mortality risk, although the risk gradient is steepest at high consumption levels.
Recommended sources
Title: Global Burden of Disease โ Alcohol use and burden for 195 countries (GBD 2016 Alcohol Collaborators)
URL: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(18)31310-2/fulltext
Publisher: The Lancet
Year/date: 2018
Why useful: Large-scale analysis concluding that no level of alcohol consumption improves health; the safest level is zero.
Evidence strength: High
Title: WHO Fact Sheet โ Alcohol
URL: https://www.who.int/news-room/fact-sheets/detail/alcohol
Publisher: World Health Organization
Year/date: Updated 2024
Why useful: Official WHO position on alcohol as a carcinogen and contributor to mortality and disease burden.
Evidence strength: High
Title: Association Between Daily Alcohol Intake and Risk of All-Cause Mortality
URL: https://doi.org/10.1001/jamanetworkopen.2023.6185
Publisher: JAMA Network Open
Year/date: 2023
Why useful: Updated systematic review and meta-analysis showing no significant protective association for low-volume drinking and increased mortality risk at higher intake levels.
Evidence strength: High
Suggested reference text for website
Alcohol consumption is associated with increased risk of cancer, liver disease, and cardiovascular conditions. The evidence increasingly suggests that no level of drinking is completely risk-free. Heavy and binge drinking patterns carry particularly high mortality risks.
Notes / caveats
- The "J-shaped curve" (suggesting benefit at low doses) is disputed and may reflect confounding by socioeconomic status and health status.
- Patterns of drinking (binge vs. regular moderate) matter as much as total volume.
- The calculator asks about average weekly pattern rather than precise units.
12. Hours sitting per day
Calculator variable: sitting โ Work, commuting, and leisure combined.
Why it matters: Prolonged sedentary time is associated with increased all-cause mortality, cardiovascular disease, and diabetes risk, independent of physical activity levels. The risk is most pronounced at >8 hours/day and is partially mitigated by high levels of physical activity.
Recommended sources
Title: Sedentary Time and Its Association With Risk for Disease Incidence, Mortality, and Hospitalization in Adults: A Systematic Review and Meta-analysis
URL: https://www.acpjournals.org/doi/10.7326/M14-1651
Publisher: Annals of Internal Medicine
Year/date: 2015
Why useful: Meta-analysis of 47 studies showing higher disease incidence, mortality, and hospitalization risk with prolonged sedentary time, with risk attenuated but not eliminated by physical activity.
Evidence strength: High
Title: WHO Guidelines on Physical Activity and Sedentary Behaviour (Chapter on sedentary behaviour)
URL: https://www.who.int/publications/i/item/9789240015128
Publisher: World Health Organization
Year/date: 2020
Why useful: Official guidelines recommending limiting sedentary time and replacing it with activity of any intensity.
Evidence strength: High
Suggested reference text for website
Prolonged sitting is linked to higher mortality risk, even for people who exercise regularly. The risk increases with total daily sitting time, especially above 8 hours. Breaking up sitting time with light movement throughout the day may help reduce this risk.
Notes / caveats
- Sitting estimates are self-reported and often undercounted.
- The interaction with physical activity is complex: high activity buffers but does not eliminate sitting risk.
- The type of sitting (e.g., active vs. passive screen time) may matter.
13. Average daily steps
Calculator variable: steps โ Phone/watch estimate is enough.
Why it matters: Daily step count is an intuitive, device-measurable proxy for total daily movement. Large studies using accelerometer data show a non-linear dose-response: mortality risk decreases progressively up to about 8,000โ12,000 steps/day, with diminishing returns beyond that.
Recommended sources
Title: Association of Daily Step Count and Step Intensity With Mortality Among US Adults
URL: https://jamanetwork.com/journals/jama/fullarticle/2763292
Publisher: JAMA
Year/date: 2020
Why useful: Large NHANES accelerometer study showing higher daily step counts are associated with lower all-cause mortality.
Evidence strength: High
Title: Daily step count and all-cause mortality: a dose-response meta-analysis of 15 international cohorts
URL: https://www.thelancet.com/journals/lanpub/article/PIIS2468-2667(21)00302-9/fulltext
Publisher: The Lancet Public Health
Year/date: 2022
Why useful: Meta-analysis of 15 cohorts (~50,000 participants) confirming inverse association between steps and mortality.
Evidence strength: High
Suggested reference text for website
Daily step count is a simple way to estimate your total daily movement. Studies using step counters show that mortality risk continues to decrease up to about 10,000 steps per day. Even modest increases from a low baseline can yield meaningful benefits.
Notes / caveats
- Step counts from phones and watches are reasonably accurate but not clinical-grade.
- Step intensity (cadence) may confer additional benefit beyond total count.
- Benefits plateau at high step volumes.
14. Strength training
Calculator variable: strength โ Resistance training, calisthenics, loaded work, or equivalent.
Why it matters: Muscle strength and mass are inversely associated with all-cause mortality. Resistance training independently reduces mortality risk beyond the benefits of aerobic activity. It also helps maintain metabolic health, bone density, and functional independence in older age.
Recommended sources
Title: Muscle-strengthening activities are associated with lower risk and mortality in major non-communicable diseases: a systematic review and meta-analysis of cohort studies
URL: https://doi.org/10.1136/bjsports-2021-105061
Publisher: British Journal of Sports Medicine (BMJ)
Year/date: 2022
Why useful: Large meta-analysis showing muscle-strengthening activities associated with 10โ20% lower all-cause mortality, independent of aerobic activity.
Evidence strength: High
Title: Muscular strength in male adolescents and premature death: cohort study of 1 million participants
URL: https://www.bmj.com/content/345/bmj.e7279
Publisher: The BMJ
Year/date: 2012
Why useful: Large cohort showing inverse association between adolescent muscle strength and later mortality.
Evidence strength: High
Suggested reference text for website
Strength training โ whether with weights, bodyweight exercises, or resistance bands โ is associated with lower mortality risk, independently of aerobic exercise. Two sessions per week appear sufficient to see a benefit. Maintaining muscle strength also supports mobility and health in later years.
Notes / caveats
- Different studies use different definitions of "resistance training," making dose-response estimates approximate.
- Benefits of strength training are partly mediated through lean mass maintenance.
- Combined aerobic + resistance training may be optimal.
15. Cardiorespiratory fitness
Calculator variable: vo2 โ Relative to people your age.
Why it matters: Cardiorespiratory fitness (VOโmax) is a strong predictor of mortality in several cohort studies. A 1-MET increase in exercise capacity is associated with approximately 10โ15% reduction in mortality risk.
Recommended sources
Title: Exercise capacity and mortality among men referred for exercise testing (The Cleveland Clinic study)
URL: https://www.nejm.org/doi/full/10.1056/NEJMoa011858
Publisher: New England Journal of Medicine
Year/date: 2002
Why useful: Landmark study showing exercise capacity is a stronger predictor of mortality than established risk factors.
Evidence strength: High
Title: Cardiorespiratory fitness and mortality from all causes, cardiovascular disease and cancer: doseโresponse meta-analysis of cohort studies
URL: https://doi.org/10.1136/bjsports-2021-104876
Publisher: British Journal of Sports Medicine (BMJ)
Year/date: 2022
Why useful: Dose-response meta-analysis confirming strong inverse association between cardiorespiratory fitness and all-cause, CVD, and cancer mortality.
Evidence strength: High
Suggested reference text for website
How fit your heart and lungs are โ your cardiorespiratory fitness โ is one of the strongest predictors of longevity, possibly even stronger than standard risk factors like smoking or high blood pressure. Even small improvements in fitness are associated with meaningful reductions in mortality risk.
Notes / caveats
- VOโmax is rarely measured directly; self-assessment relative to peers is approximate.
- Fitness is partly genetic but highly modifiable through training.
- The question is meant as a subjective self-assessment for users without measured values.
16. Waist-to-height risk
Calculator variable: waist โ If waist is more than half height, risk is generally higher.
Why it matters: Waist-to-height ratio (WHtR) captures abdominal adiposity better than BMI in many studies. A waist circumference more than half your height is considered a useful screening threshold for increased cardiometabolic risk. WHtR may be a stronger predictor of mortality than BMI, especially in older adults.
Recommended sources
Title: Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis
URL: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1467-789X.2011.00952.x
Publisher: Obesity Reviews (Wiley)
Year/date: 2012
Why useful: Systematic review and meta-analysis supporting waist-to-height ratio as a screening tool and the practical โwaist less than half heightโ rule.
Evidence strength: High
Title: Central fatness and risk of all cause mortality: systematic review and dose-response meta-analysis of 72 prospective cohort studies
URL: https://doi.org/10.1136/bmj.m3324
Publisher: The BMJ
Year/date: 2020
Why useful: Large systematic review and dose-response meta-analysis covering central adiposity measures, including waist-to-height ratio, and all-cause mortality.
Evidence strength: High
Suggested reference text for website
Where you carry your body fat matters. A waist circumference greater than half your height is associated with higher cardiometabolic risk, even if your BMI is normal. This simple ratio is a practical screening tool supported by extensive research.
Notes / caveats
- Waist measurement is user-reported and may be imprecise.
- WHtR is a screening tool, not a diagnostic measure.
- The threshold of 0.5 is a population-level guideline, not an absolute cutoff for individuals.
17. Family longevity
Calculator variable: family โ Parents or grandparents living into late 80s/90s without major disability.
Why it matters: Parental longevity is one of the strongest predictors of individual longevity, partly reflecting shared genetics and partly shared environment and health behaviors. Offspring of parents who lived into their 90s have significantly lower mortality risk across all major causes of death.
Recommended sources
Title: Health and function of participants in the Long Life Family Study: A comparison with other cohorts
URL: https://pubmed.ncbi.nlm.nih.gov/21258136/
Publisher: Aging
Year/date: 2011
Why useful: Long Life Family Study comparison describing health and function patterns among members of long-lived families.
Evidence strength: High
Title: Parental longevity and mortality risk in offspring: the Framingham Heart Study
URL: https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.111.089060
Publisher: Circulation (American Heart Association)
Year/date: 2012
Why useful: Prospective study showing parental survival to age 85+ associated with significantly lower offspring mortality.
Evidence strength: High
Suggested reference text for website
Having parents or grandparents who lived into their late 80s or 90s without major disability is associated with a higher probability of living longer yourself. This reflects a combination of shared genetics, family health habits, and socioeconomic environment.
Notes / caveats
- Longevity is moderately heritable (estimates range 15โ30%).
- Parental longevity can be confounded by shared environment, which is hard to separate from genetics.
- Absence of family longevity does not preclude individual longevity through healthy behaviors.
18. Social connection
Calculator variable: social โ Regular supportive contact with friends, family, partner, or community.
Why it matters: Social isolation and loneliness are associated with increased all-cause mortality โ with effect sizes comparable to smoking and obesity in some meta-analyses. Mechanisms include direct physiological effects (inflammation, stress response) and behavioral pathways (lower health maintenance, delayed care-seeking).
Recommended sources
Title: Social relationships and mortality risk: a meta-analytic review
URL: https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1000316
Publisher: PLOS Medicine
Year/date: 2010
Why useful: Meta-analysis of 148 studies showing a 50% increased odds of survival for those with stronger social relationships.
Evidence strength: High
Title: Social isolation and loneliness among older people: advocacy brief
URL: https://www.who.int/publications/i/item/9789240030749
Publisher: World Health Organization
Year/date: 2021
Why useful: Official WHO advocacy brief recognizing social isolation and loneliness as important health risks among older adults.
Evidence strength: High
Suggested reference text for website
Strong social connections โ with friends, family, or community โ are consistently associated with longer life. The mortality risk associated with social isolation is comparable to that of smoking or obesity in some studies. Social connection appears to benefit both mental and physical health through multiple pathways.
Notes / caveats
- Social connection is self-reported and multidimensional (structure, function, quality).
- Causal direction is debated: healthier people may be more socially active.
- Loneliness (subjective) and isolation (objective) are distinct but related concepts.
19. Chronic stress
Calculator variable: stress โ How often stress feels persistent or hard to recover from.
Why it matters: Chronic psychological stress activates the hypothalamic-pituitary-adrenal (HPA) axis and sympathetic nervous system, contributing to accelerated biological aging, cardiovascular disease, metabolic dysfunction, and immune suppression. Perceived stress scales are well-validated predictors of health outcomes.
Recommended sources
Title: Allostatic Load and Mortality: A Systematic Review and Meta-Analysis
URL: https://doi.org/10.1016/j.amepre.2022.02.003
Publisher: American Journal of Preventive Medicine
Year/date: 2022
Why useful: Systematic review and meta-analysis linking high allostatic load, a cumulative stress-burden measure, with all-cause mortality.
Evidence strength: High
Title: Psychological stress and cardiovascular disease (INTERHEART study)
URL: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(04)17019-0/fulltext
Publisher: The Lancet
Year/date: 2004
Why useful: Large case-control study across 52 countries showing psychosocial stress is a significant risk factor for myocardial infarction.
Evidence strength: High
Suggested reference text for website
Long-term stress that feels hard to recover from is associated with increased health risks, including cardiovascular disease and accelerated biological aging. The body's stress response system, when constantly activated, can affect nearly every organ system.
Notes / caveats
- Stress perception is highly subjective; the same objective load affects individuals differently.
- The relationship is bidirectional: poor health increases stress, and stress worsens health.
- Coping resources and social support moderate the impact of stress.
20. Mental health stability
Calculator variable: mental_health โ Mood, anxiety, purpose, and ability to function day to day.
Why it matters: Mental health conditions โ particularly depression and anxiety disorders โ are associated with increased all-cause mortality, partly through direct physiological effects, partly through behavioral pathways (lower physical activity, poor diet, smoking, reduced treatment adherence), and partly through increased suicide risk.
Recommended sources
Title: Comprehensive Meta-Analysis of Excess Mortality in Depression in the General Community Versus Patients With Specific Illnesses
URL: https://psychiatryonline.org/doi/10.1176/appi.ajp.2013.13030325
Publisher: American Journal of Psychiatry
Year/date: 2014
Why useful: Meta-analysis quantifying excess mortality associated with depression across community and illness-specific populations.
Evidence strength: High
Title: WHO Mental Health and Substance Use
URL: https://www.who.int/health-topics/mental-health
Publisher: World Health Organization
Year/date: Updated regularly
Why useful: Official WHO reference on the global burden of mental health conditions.
Evidence strength: High
Suggested reference text for website
Mental health is closely linked to physical health and longevity. Depression and chronic anxiety are associated with higher all-cause mortality, though the relationship is complex and involves both biological and behavioral pathways. Getting support for mental health can benefit both quality of life and physical health outcomes.
Notes / caveats
- Mental health is self-assessed; the question is not a diagnostic tool.
- Treatment substantially improves outcomes; managed conditions carry much lower risk.
- Suicide is a direct cause of mortality in severe untreated depression.
21. Preventive care
Calculator variable: preventive โ Checkups, screenings, vaccines, dental care, and early treatment.
Why it matters: Regular preventive care is associated with earlier detection of treatable conditions, better management of chronic diseases, and higher vaccination rates โ all of which reduce mortality. However, the evidence for general annual checkups in healthy adults is mixed; targeted screenings based on age, sex, and risk profile are more strongly supported.
Recommended sources
Title: General health checks in adults for reducing morbidity and mortality from disease
URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC6353639/
Publisher: Cochrane Database of Systematic Reviews
Year/date: 2019
Why useful: Cochrane review showing that general health checks have limited or no mortality reduction in broad adult populations, supporting a cautious interpretation of preventive-care benefits.
Evidence strength: Medium
Title: WHO โ Primary health care
URL: https://www.who.int/health-topics/primary-health-care
Publisher: World Health Organization
Year/date: Updated regularly
Why useful: Official WHO framework on preventive health services, screenings, and primary care.
Evidence strength: High
Title: U.S. Preventive Services Task Force (USPSTF) Recommendations
URL: https://www.uspreventiveservicestaskforce.org/uspstf/
Publisher: U.S. Preventive Services Task Force
Year/date: Updated regularly
Why useful: Evidence-based recommendations for clinical preventive services; gold standard for screening guidelines.
Evidence strength: High
Suggested reference text for website
Regular preventive care โ including age-appropriate screenings, vaccinations, and dental visits โ helps detect health issues early and manage existing conditions better. While the evidence for annual checkups in healthy adults is mixed, targeted preventive care based on your age and risk factors is well-supported.
Notes / caveats
- Access to preventive care varies by country and insurance status.
- Over-screening can lead to harm (false positives, overdiagnosis) โ recommendations balance benefits and harms.
- The question captures general tendency rather than specific guideline adherence.
22. Cholesterol risk
Calculator variable: cholesterol โ LDL/non-HDL status or known lipid risk.
Why it matters: Elevated LDL cholesterol is a well-established causal risk factor for atherosclerotic cardiovascular disease, which is the leading cause of death globally. Lipid-lowering treatment (statins, lifestyle) significantly reduces cardiovascular events and mortality in both primary and secondary prevention.
Recommended sources
Title: 2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk
URL: https://doi.org/10.1093/eurheartj/ehz455
Publisher: European Heart Journal
Year/date: 2019
Why useful: Major clinical guideline summarizing evidence on LDL-C and lipid modification to reduce cardiovascular risk.
Evidence strength: High
Title: WHO Fact Sheet โ Cardiovascular diseases (CVDs)
URL: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)
Publisher: World Health Organization
Year/date: Updated 2024
Why useful: Overview of CVD as the leading cause of death globally, with cholesterol as a key risk factor.
Evidence strength: High
Suggested reference text for website
Elevated LDL cholesterol is a well-established risk factor for cardiovascular disease, the leading cause of death worldwide. Lowering LDL through diet, exercise, or medication (when indicated) reduces cardiovascular risk. The relationship between cholesterol and mortality is continuous, not threshold-based.
Notes / caveats
- Total cholesterol alone does not capture the LDL/HDL/triglyceride profile.
- The question asks for self-reported understanding of lipid status, which may be imprecise.
- Cholesterol's role differs at older ages; the association weakens in the very elderly.
23. Glucose / diabetes status
Calculator variable: glucose โ Use A1C, fasting glucose, or diagnosis if known.
Why it matters: Diabetes mellitus is a major cause of blindness, kidney failure, heart attacks, stroke, and lower limb amputation. Poorly controlled diabetes substantially reduces life expectancy, while well-managed diabetes has a much more modest impact. Prediabetes also carries elevated cardiovascular risk.
Recommended sources
Title: WHO Fact Sheet โ Diabetes
URL: https://www.who.int/news-room/fact-sheets/detail/diabetes
Publisher: World Health Organization
Year/date: Updated 2024
Why useful: Official overview of diabetes prevalence, complications, and mortality burden globally.
Evidence strength: High
Title: Effect of intensive glucose lowering treatment on all cause mortality, cardiovascular death, and microvascular events in type 2 diabetes: meta-analysis of randomised controlled trials
URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC3144314/
Publisher: The BMJ
Year/date: 2011
Why useful: Meta-analysis of randomized trials showing the complex risk-benefit trade-offs of intensive glucose lowering in type 2 diabetes.
Evidence strength: High
Suggested reference text for website
Diabetes, particularly when poorly controlled, is associated with substantially reduced life expectancy. The risk comes from cardiovascular complications, kidney disease, and other long-term effects. However, well-managed diabetes carries a much smaller impact, and prevention of progression from prediabetes is possible.
Notes / caveats
- Type 1 and type 2 diabetes have different trajectories; the question does not distinguish.
- A1C is the gold standard for control assessment but may not be known to the user.
- "Prediabetes" is a risk category, not a disease; many do not progress to diabetes.
24. Air quality exposure
Calculator variable: pollution โ Long-term exposure around home/work.
Why it matters: Ambient air pollution โ especially fine particulate matter (PMโ.โ ) โ is the leading environmental risk factor for premature death globally, contributing to cardiovascular disease, lung cancer, and respiratory infections. Even moderate urban pollution levels measurably increase mortality risk.
Recommended sources
Title: WHO Ambient Air Quality Database, update Jan 2024, Version 6.1
URL: https://www.who.int/data/gho/data/themes/air-pollution/who-air-quality-database
Publisher: World Health Organization
Year/date: 2024
Why useful: Specific WHO city and country air-quality database covering PMโ.โ and other pollutant levels, useful for estimating pollution exposure context.
Evidence strength: High
Title: Global Burden of Disease โ Air pollution as a risk factor
URL: https://www.healthdata.org/research-analysis/health-risks-issues/air-pollution
Publisher: Institute for Health Metrics and Evaluation (IHME)
Year/date: Updated 2024
Why useful: Quantifies deaths and DALYs attributable to air pollution by region and pollution source.
Evidence strength: High
Suggested reference text for website
Air quality is a significant environmental factor in life expectancy. Long-term exposure to fine particulate matter (PMโ.โ ) is associated with increased risk of cardiovascular and respiratory disease, even at levels below current regulatory standards in many countries.
Notes / caveats
- Individual exposure depends on specific location, time spent outdoors, and housing quality.
- Air pollution effects accumulate over decades; short-term spikes have different health impacts.
- Indoor air quality (not captured here) also matters.
25. Daily safety risk
Calculator variable: safety โ Traffic, workplace hazards, violence, or extreme-risk activities.
Why it matters: External causes of death โ accidents, violence, and occupational hazards โ vary significantly by location, occupation, and lifestyle. In many settings, injury mortality is a major contributor to reduced life expectancy, especially at younger ages.
Recommended sources
Title: WHO Global Status Report on Road Safety 2023
URL: https://www.who.int/publications/i/item/9789240086517
Publisher: World Health Organization
Year/date: 2023
Why useful: Comprehensive global data on road traffic deaths, a leading cause of injury mortality.
Evidence strength: High
Title: GBD Results Tool โ injury and violence mortality estimates
URL: https://vizhub.healthdata.org/gbd-results/
Publisher: Institute for Health Metrics and Evaluation (IHME)
Year/date: Updated regularly
Why useful: Interactive GBD tool for filtering injuries, deaths, rates, location, year, sex, and age; useful for comparing external-cause mortality context.
Evidence strength: High
Suggested reference text for website
Injuries from traffic, violence, or workplace hazards are a leading cause of premature death worldwide, particularly in younger age groups. The risk varies substantially by location, occupation, and individual behaviors. Our adjustment reflects published data on injury mortality rates.
Notes / caveats
- Safety is self-assessed; objective risk may differ from perceived risk.
- The question is not meant to cause alarm; most users will fall into low-risk categories.
- Different risk types (traffic vs. violence vs. occupational) are combined into one question.
26. Driving safety
Calculator variable: driving โ Seatbelts, speed, distraction, intoxication, and helmet use.
Why it matters: Road traffic injuries are a leading cause of death globally for people aged 5โ29. Individual driving behaviors (seatbelt use, speed, distraction, alcohol) profoundly influence personal injury risk. Seatbelt use alone reduces the risk of fatal injury by about 50%.
Recommended sources
Title: WHO Global Status Report on Road Safety 2023
URL: https://www.who.int/publications/i/item/9789240086517
Publisher: World Health Organization
Year/date: 2023
Why useful: Detailed data on road traffic deaths, seatbelt use, speed laws, and distracted driving globally.
Evidence strength: High
Title: Seatbelt use and risk of major injuries sustained by vehicle occupants during motor-vehicle crashes: a systematic review and meta-analysis of cohort studies
URL: https://doi.org/10.1186/s12889-018-6280-1
Publisher: BMC Public Health (Springer)
Year/date: 2018
Why useful: Meta-analysis quantifying that seatbelt use is associated with substantially reduced risk of fatal and major injuries in motor-vehicle crashes.
Evidence strength: High
Suggested reference text for website
Driving behaviors โ including seatbelt use, speed, distraction, and intoxication โ strongly influence the risk of fatal injury. Seatbelt use alone reduces the risk of death in a crash by about 50%. Road traffic crashes are a leading cause of premature death, especially in younger adults.
Notes / caveats
- Driving risk varies by location (urban vs. rural, country-specific road safety).
- The question combines multiple behaviors into a single self-assessment.
- Non-drivers (pedestrians, cyclists) also face road traffic injury risk.
27. Work pattern
Calculator variable: work โ Physical strain, shifts, autonomy, and recovery.
Why it matters: Occupational factors โ shift work, physical demands, job strain, and recovery time โ are associated with cardiovascular and metabolic disease risk. Night shift work is classified as a probable carcinogen by IARC. High-strain jobs (high demand, low control) show the strongest associations with adverse health outcomes.
Recommended sources
Title: Job strain as a risk factor for coronary heart disease: a collaborative meta-analysis of individual participant data
URL: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(12)60994-5/fulltext
Publisher: The Lancet
Year/date: 2012
Why useful: Large pooled individual-participant meta-analysis showing job strain (high demand and low control) associated with increased coronary heart disease risk.
Evidence strength: High
Title: WHO/IARC โ Night shift work as a probable carcinogen
Publisher: International Agency for Research on Cancer (IARC / WHO)
Year/date: 2020
Why useful: Official classification of night shift work as Group 2A (probable) carcinogen; review of evidence linking shift work to cancer and other diseases.
Evidence strength: High
Suggested reference text for website
Your work pattern โ including physical demands, shift timing, and recovery โ can influence long-term health. High-strain jobs and night shift work are associated with increased cardiovascular risk. Adequate recovery and autonomy may help buffer these effects.
Notes / caveats
- The question combines multiple dimensions (physical, temporal, psychosocial) into one self-assessment.
- Work effects interact with other lifestyle factors (sleep, activity, diet).
- The relationship is bidirectional: health affects work capacity, and work affects health.
28. Financial security
Calculator variable: income_security โ Ability to afford housing, food, healthcare, and emergencies.
Why it matters: Income and financial security are among the most powerful social determinants of health. The relationship between income and life expectancy is graded โ each step up the income ladder is associated with longer life. Financial insecurity creates stress, limits access to healthcare, healthy food, and safe housing, and is associated with higher mortality.
Recommended sources
Title: The association between income and life expectancy in the United States (Chetty et al.)
URL: https://jamanetwork.com/journals/jama/fullarticle/2513561
Publisher: JAMA
Year/date: 2016
Why useful: Landmark study showing a 10โ15 year life expectancy gap between highest and lowest income Americans.
Evidence strength: High
Title: WHO โ Social determinants of health
URL: https://www.who.int/health-topics/social-determinants-of-health
Publisher: World Health Organization
Year/date: Updated regularly
Why useful: Framework source for social determinants of health; useful as context, not as a direct income-life expectancy estimate.
Evidence strength: Medium
Suggested reference text for website
Financial security is strongly associated with life expectancy. People with higher income and financial stability tend to live longer, reflecting better access to healthcare, nutrition, safe housing, and lower stress. This relationship exists across the entire income spectrum, not just at the bottom.
Notes / caveats
- The income-mortality gradient is well established but causal pathways are complex.
- Relative income within a society may matter as much as absolute income.
- The question asks about subjective financial security, which is not the same as objective income.
29. Education / health literacy
Calculator variable: education โ Comfort navigating health information and services.
Why it matters: Education level is one of the strongest social determinants of health. Higher educational attainment is associated with lower mortality, better health behaviors, higher health literacy, and more effective navigation of healthcare systems. Each additional year of education is associated with a measurable reduction in mortality risk.
Recommended sources
Title: The causal effects of education on adult health, mortality and income: evidence from Mendelian randomization and the raising of school leaving age
URL: https://pubmed.ncbi.nlm.nih.gov/37463867/
Publisher: International Journal of Epidemiology
Year/date: 2023
Why useful: Mendelian randomization and policy-change evidence supporting a causal relationship between education and adult health, mortality, and income outcomes.
Evidence strength: High
Title: Effects of education on adult mortality: a global systematic review and meta-analysis
URL: https://www.thelancet.com/journals/lanpub/article/PIIS2468-2667(23)00306-7/fulltext
Publisher: The Lancet Public Health
Year/date: 2024
Why useful: Global systematic review and meta-analysis estimating the association between years of education and adult all-cause mortality risk.
Evidence strength: High
Suggested reference text for website
Education and health literacy โ the ability to find, understand, and use health information โ are consistently associated with longer life. Higher education levels are linked to better health behaviors, more effective healthcare use, and lower mortality risk across all major causes of death.
Notes / caveats
- The association is partly causal and partly reflects confounding (socioeconomic background, intelligence, etc.).
- Health literacy can be high even without formal education.
- The relationship holds across countries with different educational systems and healthcare models.
30. Sense of purpose
Calculator variable: purpose โ Feeling that daily life has direction and meaningful commitments.
Why it matters: A sense of purpose or meaning in life is associated with lower all-cause mortality, cardiovascular events, and cognitive decline in prospective cohort studies. The association persists after adjusting for depression, physical health, and socioeconomic status.
Recommended sources
Title: Purpose in Life as a Predictor of Mortality Across Adulthood
URL: https://journals.sagepub.com/doi/10.1177/0956797614531799
Publisher: Psychological Science (SAGE)
Year/date: 2014
Why useful: Longitudinal MIDUS sample showing that purpose in life predicted lower mortality across 14 years.
Evidence strength: Medium
Title: Purpose in Life and Its Relationship to All-Cause Mortality and Cardiovascular Events: A Meta-Analysis
URL: https://pubmed.ncbi.nlm.nih.gov/26630073/
Publisher: Psychosomatic Medicine
Year/date: 2016
Why useful: Meta-analysis of prospective evidence linking purpose in life with all-cause mortality and cardiovascular events.
Evidence strength: Medium
Suggested reference text for website
Having a sense of purpose and direction in life is associated with better health and longer life. People who report strong life purpose tend to have lower mortality risk, possibly through better stress regulation, healthier behaviors, and greater social engagement.
Notes / caveats
- Causal direction is debated: healthier people may report higher purpose.
- Purpose is culturally shaped and highly individual.
- The association is moderate in size but consistent across cohorts.
31. Oral health
Calculator variable: oral_health โ Gum disease and dental care can track broader inflammation and access.
Why it matters: Poor oral health โ particularly periodontitis โ is associated with higher risk of cardiovascular disease, diabetes complications, respiratory infections, and all-cause mortality. Periodontal disease is a marker of chronic inflammation and may contribute directly through systemic inflammatory pathways.
Recommended sources
Title: Oral health and all-cause, cardiovascular disease, and respiratory mortality in older people in the UK and USA
URL: https://pubmed.ncbi.nlm.nih.gov/34385519/
Publisher: Scientific Reports
Year/date: 2021
Why useful: Large cohort analysis linking oral health indicators with all-cause, cardiovascular, and respiratory mortality in older adults.
Evidence strength: Medium
Title: WHO Global Oral Health Status Report 2022
URL: https://www.who.int/publications/i/item/9789240061484
Publisher: World Health Organization
Year/date: 2022
Why useful: Comprehensive global report on oral disease burden and its links to systemic health.
Evidence strength: High
Suggested reference text for website
Oral health is linked to overall health. Gum disease (periodontitis) is associated with increased risk of cardiovascular disease and other systemic conditions, likely through shared inflammatory pathways. Regular dental care and good oral hygiene are associated with better long-term health outcomes.
Notes / caveats
- The relationship between oral health and systemic disease is partly confounded by smoking, socioeconomic status, and healthcare access.
- The question does not differentiate between gingivitis, periodontitis, and edentulism.
- Regular dental visits are a proxy for both oral health and general healthcare engagement.
32. Sun protection
Calculator variable: sun โ Especially relevant with high UV exposure or fair skin.
Why it matters: Sun exposure has a dual relationship with health: moderate exposure is essential for vitamin D synthesis, but excessive exposure is the primary cause of skin cancer (including melanoma). UV radiation is classified as a Group 1 carcinogen by IARC. Sun protection behaviors (sunscreen, clothing, shade-seeking) significantly reduce skin cancer risk.
Recommended sources
Title: IARC Monographs โ Radiation, Volume 100D: Solar and ultraviolet radiation
Publisher: International Agency for Research on Cancer (IARC / WHO)
Year/date: 2012
Why useful: Official IARC monograph volume that includes solar and ultraviolet radiation classification and evidence review.
Evidence strength: High
Title: WHO โ Ultraviolet radiation and skin cancer
URL: https://www.who.int/news-room/fact-sheets/detail/ultraviolet-radiation
Publisher: World Health Organization
Year/date: Updated 2024
Why useful: Official WHO fact sheet on UV-related skin cancer risks and prevention recommendations.
Evidence strength: High
Suggested reference text for website
Protecting your skin from excessive sun exposure reduces the risk of skin cancer, including melanoma. The relationship between UV exposure and skin cancer is well-established. Sun protection is especially important for people with fair skin, those in high-UV regions, and those with significant cumulative outdoor exposure.
Notes / caveats
- Some sun exposure is beneficial for vitamin D synthesis; the question is about excessive unprotected exposure.
- Skin cancer mortality is relatively low compared to other cancers, but preventable.
- Risk varies greatly by skin type, latitude, and sun behavior.
33. Non-prescribed drug risk
Calculator variable: substances โ Includes opioid, stimulant, sedative, or other high-risk use.
Why it matters: Non-prescribed drug use โ particularly opioids, stimulants, and sedatives โ is associated with substantially increased mortality risk through overdose, infectious diseases (HIV, hepatitis), cardiovascular events, and accidents. The opioid crisis has significantly reduced life expectancy in affected countries.
Recommended sources
Title: WHO โ Opioid overdose
URL: https://www.who.int/news-room/fact-sheets/detail/opioid-overdose
Publisher: World Health Organization
Year/date: Updated 2024
Why useful: Official data on opioid overdose mortality globally; risk factors and prevention strategies.
Evidence strength: High
Title: CDC NCHS โ Provisional Drug Overdose Death Counts
URL: https://www.cdc.gov/nchs/nvss/vsrr/drug-overdose-data.htm
Publisher: U.S. Centers for Disease Control and Prevention, National Center for Health Statistics
Year/date: Updated regularly
Why useful: Official overdose death surveillance data; useful for tracking overdose mortality trends, though it is not itself a life expectancy decomposition.
Evidence strength: High
Title: Impact of opioid overdoses on US life expectancy and years of life lost, 2019โ2022
URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC11228948/
Publisher: PubMed Central
Year/date: 2024
Why useful: Specific analysis connecting opioid overdose deaths with life expectancy and years of life lost in the United States.
Evidence strength: High
Suggested reference text for website
Non-prescribed use of drugs โ including opioids, stimulants, and sedatives โ carries significant health risks, including overdose, infectious disease, and cardiovascular complications. The opioid crisis has measurably reduced life expectancy in several countries. Past use, when no longer current, carries much lower risk.
Notes / caveats
- The question distinguishes current from past use, as risk profiles differ dramatically.
- Overdose risk depends on dose, purity, route of administration, and polydrug use.
- Stigma may lead to underreporting; the "Prefer not to say" option is available.
34. Sleep quality
Calculator variable: sleep_quality โ Restorative sleep, insomnia, snoring, or suspected apnea.
Why it matters: Sleep quality โ not just duration โ is independently associated with health outcomes. Poor sleep quality, insomnia symptoms, and sleep-disordered breathing (e.g., obstructive sleep apnea) are linked to cardiovascular disease, cognitive decline, metabolic dysfunction, and increased mortality.
Recommended sources
Title: Sudden death in individuals with obstructive sleep apnoea: a systematic review and meta-analysis
URL: https://doi.org/10.1136/bmjresp-2020-000656
Publisher: BMJ Open Respiratory Research
Year/date: 2021
Why useful: Systematic review and meta-analysis linking obstructive sleep apnea with sudden death risk; useful for the sleep-disordered breathing component of this question.
Evidence strength: Medium
Title: Insomnia and mortality: A meta-analysis
URL: https://pubmed.ncbi.nlm.nih.gov/30529432/
Publisher: Sleep Medicine Reviews
Year/date: 2019
Why useful: Meta-analysis of insomnia and mortality evidence; more directly matches the insomnia component of the sleep-quality question.
Evidence strength: Medium
Suggested reference text for website
Sleep quality matters as much as sleep duration. Chronic insomnia, unrefreshing sleep, and suspected sleep apnea are associated with worse health outcomes and, in some studies, higher mortality risk. Treating sleep disorders can improve quality of life and may reduce downstream cardiovascular and metabolic risk.
Notes / caveats
- Sleep quality is self-reported and subjective.
- Sleep apnea is underdiagnosed; snoring is a screening signal but not diagnostic.
- The question combines multiple dimensions (restfulness, insomnia, apnea indicators) into one assessment.
35. Sugary drinks
Calculator variable: hydration โ Daily sugar-sweetened beverages.
Why it matters: Sugar-sweetened beverage (SSB) consumption is consistently associated with increased risk of obesity, type 2 diabetes, cardiovascular disease, and all-cause mortality. SSBs provide calories without satiety and cause rapid glycemic spikes. Reducing SSB intake is one of the most actionable dietary changes for health improvement.
Recommended sources
Title: Sugar intake and all-cause mortalityโdifferences between sugar-sweetened beverages, artificially sweetened beverages, and pure fruit juices
URL: https://doi.org/10.1186/s12916-020-01579-w
Publisher: BMC Medicine
Year/date: 2020
Why useful: Prospective analysis comparing sugar-sweetened beverages, artificially sweetened beverages, and fruit juices in relation to all-cause mortality.
Evidence strength: High
Title: WHO Guideline โ Sugar intake for adults and children
URL: https://www.who.int/publications/i/item/9789241549028
Publisher: World Health Organization
Year/date: 2015
Why useful: Official WHO recommendation to limit free sugars (including SSBs) to <10% of total energy.
Evidence strength: High
Suggested reference text for website
Sugar-sweetened beverages are consistently linked to higher risk of obesity, diabetes, cardiovascular disease, and premature death. They provide calories without nutrients and spike blood sugar rapidly. Reducing or eliminating sugary drinks is one of the most effective dietary changes for health.
Notes / caveats
- The question specifically targets SSBs, not all beverages.
- Artificially sweetened beverages have a different (still debated) risk profile.
- Consumption patterns (e.g., with meals vs. alone) may influence metabolic impact.
36. Fiber intake
Calculator variable: fiber โ Legumes, whole grains, vegetables, fruits, nuts, and seeds.
Why it matters: Dietary fiber intake is consistently associated with reduced all-cause and cardiovascular mortality. High-fiber diets lower cholesterol, improve glycemic control, support gut microbiome health, and promote satiety. Most populations consume far less fiber than the recommended 25โ30 g/day.
Recommended sources
Title: Carbohydrate quality and human health: a series of systematic reviews and meta-analyses
URL: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(18)31809-9/fulltext
Publisher: The Lancet
Year/date: 2019
Why useful: Large systematic review series including evidence on dietary fibre, whole grains, all-cause mortality, cardiovascular mortality, and non-communicable disease outcomes.
Evidence strength: High
Title: WHO โ Healthy diet fact sheet
URL: https://www.who.int/news-room/fact-sheets/detail/healthy-diet
Publisher: World Health Organization
Year/date: Updated 2024
Why useful: Official WHO recommendations on fiber intake as part of a healthy diet.
Evidence strength: High
Suggested reference text for website
Fiber โ from whole grains, legumes, vegetables, fruits, nuts, and seeds โ is strongly associated with lower mortality risk. Most people eat far less than the recommended 25โ30 grams per day. Increasing fiber intake is one of the most evidence-backed dietary changes for longevity.
Notes / caveats
- Fiber intake is self-reported and hard to estimate accurately.
- Different fiber types (soluble vs. insoluble) have different health effects.
- High fiber intake may cause digestive discomfort if introduced too quickly.
37. Protein adequacy
Calculator variable: protein โ Especially important with aging and strength maintenance.
Why it matters: Adequate protein intake is essential for muscle maintenance, immune function, and metabolic health. Older adults need higher protein intake (1.2โ1.5 g/kg/day) than younger adults to counteract sarcopenia (age-related muscle loss). Low protein intake is associated with frailty and increased mortality in older populations.
Recommended sources
Title: Dietary intake of total, animal, and plant proteins and risk of all cause, cardiovascular, and cancer mortality: systematic review and dose-response meta-analysis of prospective cohort studies
URL: https://www.bmj.com/content/370/bmj.m2412
Publisher: The BMJ
Year/date: 2020
Why useful: Systematic review and dose-response meta-analysis examining total, animal, and plant protein intake in relation to all-cause and cause-specific mortality.
Evidence strength: High
Title: Evidence-Based Recommendations for Optimal Dietary Protein Intake in Older People: A Position Paper From the PROT-AGE Study Group
URL: https://doi.org/10.1016/j.jamda.2013.05.021
Publisher: Journal of the American Medical Directors Association (JAMDA)
Year/date: 2013
Why useful: Consensus position paper providing evidence-based protein intake recommendations for older adults to maintain muscle function and prevent sarcopenia.
Evidence strength: High
Suggested reference text for website
Getting enough protein supports muscle maintenance, immune function, and overall health โ especially as you age. Older adults may need more protein than younger adults to maintain strength and function. Most people in high-income countries get adequate protein, but distribution across meals matters.
Notes / caveats
- Protein adequacy is self-assessed and may not reflect actual intake.
- The quality and source of protein (animal vs. plant) may matter beyond total amount.
- Excessive protein intake (especially from animal sources) may carry other health risks.
38. Medication adherence
Calculator variable: med_adherence โ If prescribed medication, how reliably do you take it?
Why it matters: Medication non-adherence is associated with increased hospitalizations, disease progression, and mortality โ particularly for cardiovascular disease, diabetes, and hypertension. Approximately 50% of patients with chronic conditions do not take medications as prescribed.
Recommended sources
Title: Medication adherence: Importance, issues and policy: A policy statement from the American Heart Association
URL: https://pubmed.ncbi.nlm.nih.gov/32800791/
Publisher: Progress in Cardiovascular Diseases
Year/date: 2021
Why useful: Authoritative policy statement summarizing why medication adherence matters for chronic cardiovascular risk management and outcomes.
Evidence strength: Medium
Title: WHO โ Adherence to long-term therapies: evidence for action
URL: https://apps.who.int/iris/handle/10665/42682
Publisher: World Health Organization (IRIS)
Year/date: 2003
Why useful: Classic WHO report on the scope and impact of medication non-adherence globally.
Evidence strength: High
Suggested reference text for website
Taking prescribed medications as directed is associated with significantly better health outcomes. Poor adherence to medications for chronic conditions โ such as blood pressure, cholesterol, or diabetes drugs โ is linked to higher rates of hospitalization and mortality.
Notes / caveats
- Adherence is self-reported and likely overestimated.
- Reasons for non-adherence vary (cost, side effects, forgetfulness, beliefs) and have different implications.
- The question applies only to users prescribed medication; "no medication" is a valid option.
39. Vaccination status
Calculator variable: vaccines โ Routine adult vaccinations appropriate for your age and location.
Why it matters: Adult vaccination prevents vaccine-preventable diseases that cause significant morbidity and mortality, particularly in older adults and people with chronic conditions. Evidence for influenza vaccination is strongest in high-risk groups and varies by season, age, and baseline health status. COVID-19 vaccines have prevented millions of deaths globally.
Recommended sources
Title: WHO โ Vaccination and immunization
URL: https://www.who.int/health-topics/vaccines-and-immunization
Publisher: World Health Organization
Year/date: Updated regularly
Why useful: Official WHO information on vaccine-preventable diseases and adult immunization recommendations.
Evidence strength: High
Title: Impact of Influenza Vaccination on All-Cause Mortality and Hospitalization for Pneumonia in Adults and the Elderly with Diabetes: A Meta-Analysis of Observational Studies
URL: https://www.mdpi.com/2076-393X/8/2/263
Publisher: Vaccines
Year/date: 2020
Why useful: Meta-analysis focused on adults and older adults with diabetes; useful as a high-risk-population example, not as a general elderly-population estimate.
Evidence strength: Medium
Suggested reference text for website
Staying up to date with recommended adult vaccinations โ including flu, COVID-19, pneumococcal, and others based on your age and health status โ reduces the risk of serious infectious diseases. The mortality benefit is most evident in older adults and higher-risk groups, and exact effect sizes vary by vaccine, season, and population.
Notes / caveats
- Vaccine effectiveness varies by season (for influenza) and by individual immune response.
- Recommendations differ by country, age, and underlying health conditions.
- The question is about general vaccination status, not specific vaccines.
40. Late-night screen habit
Calculator variable: screen_time โ Screens close to bed can reduce sleep quality.
Why it matters: Late-night screen exposure suppresses melatonin production through blue light emission, potentially delaying sleep onset, reducing sleep quality, and disrupting circadian rhythms. Chronic circadian disruption is associated with metabolic disease, cardiovascular risk, and possibly cancer.
Recommended sources
Title: Electronic Media Use and Sleep Quality: Updated Systematic Review and Meta-Analysis
URL: https://doi.org/10.2196/48356
Publisher: Journal of Medical Internet Research
Year/date: 2024
Why useful: Updated systematic review and meta-analysis linking electronic media use with sleep quality outcomes.
Evidence strength: Medium
Title: Blue light has a dark side
URL: https://www.health.harvard.edu/staying-healthy/blue-light-has-a-dark-side
Publisher: Harvard Health Publishing
Year/date: July 24, 2024
Why useful: Accessible clinical overview of blue light effects on sleep and circadian rhythms; useful as context, not as a primary mortality source.
Evidence strength: Medium
Suggested reference text for website
Using screens late at night โ especially phones, tablets, and computers โ can interfere with sleep by suppressing melatonin and disrupting your body's internal clock. While the direct effect on mortality is small, the downstream effects on sleep quality and circadian health are well documented.
Notes / caveats
- The health impact of screen time is mediated primarily through sleep disruption.
- Blue light blocking features and night modes reduce but do not eliminate effects.
- Content type (e.g., stressful vs. relaxing) may matter independent of light exposure.
41. Time outdoors / nature
Calculator variable: nature โ Movement, daylight, recovery, and mental health proxy.
Why it matters: Time spent outdoors is associated with higher physical activity levels, better vitamin D status, improved mental health, and reduced mortality. Access to green space has been linked to lower cardiovascular mortality, better immune function, and improved well-being.
Recommended sources
Title: Green spaces and mortality: a systematic review and meta-analysis of cohort studies
URL: https://doi.org/10.1016/S2542-5196(19)30215-3
Publisher: The Lancet Planetary Health
Year/date: 2019
Why useful: Meta-analysis of cohort studies showing that green space exposure is associated with reduced all-cause and cardiovascular mortality.
Evidence strength: Medium
Title: Associations between Nature Exposure and Health: A Review of the Evidence
URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8125471/
Publisher: International Journal of Environmental Research and Public Health (via PubMed Central)
Year/date: 2021
Why useful: Broad review of health benefits associated with nature exposure, including mental health, activity, and other intermediate health outcomes.
Evidence strength: Medium
Suggested reference text for website
Time spent outdoors in nature is associated with better physical and mental health. Regular exposure to green spaces may reduce cardiovascular mortality risk through increased physical activity, stress reduction, and improved air quality.
Notes / caveats
- The question captures a broad behavior; specific mechanisms are hard to isolate.
- Confounding by socioeconomic status (access to green space correlates with income).
- The association is modest in magnitude but consistent across studies.
42. Heat/cold resilience
Calculator variable: heat โ Housing and work protection from extreme temperatures.
Why it matters: Extreme temperatures โ both heat and cold โ are associated with increased mortality, particularly in vulnerable populations (older adults, those with chronic disease). Climate change is increasing heat-related mortality globally. Housing quality, air conditioning access, and occupational exposure modify individual risk.
Recommended sources
Title: Heat and health (WHO)
URL: https://www.who.int/news-room/fact-sheets/detail/climate-change-heat-and-health
Publisher: World Health Organization
Year/date: Updated 2024
Why useful: Official WHO information on heat-related mortality and risk factors; includes adaptation strategies.
Evidence strength: High
Title: The 2024 report of the Lancet Countdown on health and climate change: facing record-breaking threats from delayed action
URL: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(24)01822-1/fulltext
Publisher: The Lancet
Year/date: 2024
Why useful: Annual tracking report covering climate-related health risks, including heat exposure and heat-related mortality trends.
Evidence strength: High
Suggested reference text for website
Exposure to extreme temperatures โ both heat and cold โ increases mortality risk, especially for older adults and those with chronic conditions. Housing quality, access to air conditioning, and protection at work can reduce this risk. Climate change is increasing the frequency of dangerous heat events.
Notes / caveats
- Cold-related mortality currently exceeds heat-related mortality in most regions.
- Individual vulnerability varies by age, health status, and socioeconomic resources.
- The question captures subjective protection status rather than objective temperature exposure.
43. Fall risk
Calculator variable: falls โ Balance, frailty, hazards, or previous falls.
Why it matters: Falls are the leading cause of injury-related death in adults aged 65+, and a major cause of disability. Fall risk increases with age, frailty, balance impairment, medication use, and environmental hazards. A history of previous falls is the strongest predictor of future falls.
Recommended sources
Title: WHO Fact Sheet โ Falls
URL: https://www.who.int/news-room/fact-sheets/detail/falls
Publisher: World Health Organization
Year/date: Updated 2024
Why useful: Global data on fall incidence, mortality, and risk factors; prevention strategies.
Evidence strength: High
Title: CDC โ STEADI (Stopping Elderly Accidents, Deaths & Injuries)
URL: https://www.cdc.gov/steadi/index.html
Publisher: U.S. Centers for Disease Control and Prevention
Year/date: Updated 2024
Why useful: Official CDC fall prevention program with evidence-based risk assessment tools.
Evidence strength: High
Suggested reference text for website
Falls are a leading cause of injury and death among older adults. Fall risk increases with age, balance problems, muscle weakness, and certain medications. Previous falls are the strongest predictor of future falls, but many falls are preventable through exercise, home modifications, and vision correction.
Notes / caveats
- Fall risk is most relevant for older adults (65+), but younger adults may also be at risk.
- Self-reported fall risk may not correlate well with objective measures.
- The question captures general awareness rather than clinical risk assessment.
44. Hearing/vision correction
Calculator variable: hearing โ Untreated sensory loss affects accidents, cognition, and social connection.
Why it matters: Untreated hearing loss is associated with increased cognitive decline, social isolation, depression, and all-cause mortality. Vision impairment increases fall risk, accidents, and reduces quality of life. Correction (hearing aids, glasses, cataract surgery) significantly mitigates these risks.
Recommended sources
Title: Association between hearing aid use and mortality in adults with hearing loss in the USA: a mortality follow-up study of a cross-sectional cohort
URL: https://pubmed.ncbi.nlm.nih.gov/38183998/
Publisher: The Lancet Healthy Longevity
Year/date: 2024
Why useful: Mortality follow-up study examining hearing aid use and mortality among adults with hearing loss.
Evidence strength: Medium
Title: WHO โ Deafness and hearing loss
URL: https://www.who.int/health-topics/hearing-loss
Publisher: World Health Organization
Year/date: Updated regularly
Why useful: Official WHO overview of hearing loss prevalence, consequences, and public-health responses.
Evidence strength: High
Title: WHO Fact Sheet โ Blindness and vision impairment
URL: https://www.who.int/news-room/fact-sheets/detail/blindness-and-visual-impairment
Publisher: World Health Organization
Year/date: Updated regularly
Why useful: Official WHO fact sheet on vision impairment, including functional consequences and prevention or correction pathways.
Evidence strength: High
Suggested reference text for website
Untreated hearing and vision loss are associated with higher mortality risk, likely through pathways including social isolation, cognitive decline, and accidents. Correcting these impairments with hearing aids, glasses, or surgery is associated with better health outcomes.
Notes / caveats
- The association with mortality is partly mediated by increased fall risk and cognitive decline.
- Hearing aid adoption rates remain low despite clear benefits.
- The question combines hearing and vision into one assessment.
45. Cognitive engagement
Calculator variable: cognition โ Learning, reading, complex work, languages, music, games, or creative projects.
Why it matters: Cognitive engagement and lifelong learning are associated with reduced risk of dementia and cognitive decline. The concept of "cognitive reserve" suggests that mentally stimulating activities build brain resilience. However, direct evidence linking cognitive engagement to all-cause mortality is modest.
Recommended sources
Title: Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission
URL: https://doi.org/10.1016/S0140-6736(24)01296-0
Publisher: The Lancet
Year/date: 2024
Why useful: Authoritative commission report on modifiable dementia risk factors, including education, cognitive reserve, social connection, and other prevention levers relevant to cognitive engagement.
Evidence strength: High
Title: WHO โ Risk reduction of cognitive decline and dementia
URL: https://www.who.int/publications/i/item/9789241550543
Publisher: World Health Organization
Year/date: 2019
Why useful: WHO guidelines identifying cognitive engagement as a recommended intervention for cognitive decline risk reduction.
Evidence strength: High
Suggested reference text for website
Staying mentally active through reading, learning, creative work, or complex hobbies is associated with better cognitive health in later life. While the direct effect on life expectancy is modest, maintaining cognitive function is important for quality of life and independent living.
Notes / caveats
- Cognitive engagement is difficult to measure and quantify.
- The association with dementia risk is confounded by education, socioeconomic status, and baseline cognitive ability.
- The primary benefit is on healthspan (quality of life) rather than lifespan extension per se.
46. Community participation
Calculator variable: community โ Clubs, volunteering, faith groups, local projects, or mutual aid.
Why it matters: Community participation and volunteering are associated with lower all-cause mortality and better mental health. Social integration provides emotional support, sense of purpose, and opportunities for physical activity. The effect is independent of other forms of social connection.
Recommended sources
Title: Volunteering by older adults and risk of mortality: A meta-analysis
URL: https://doi.org/10.1037/a0031519
Publisher: Psychology and Aging
Year/date: 2013
Why useful: Meta-analysis of organizational volunteering and mortality risk among late-middle-aged and older adults.
Evidence strength: Medium
Title: Association of Religious Service Attendance With Mortality Among Women
URL: https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2521827
Publisher: JAMA Internal Medicine
Year/date: 2016
Why useful: Prospective cohort study showing religious service attendance was associated with lower mortality among women, likely reflecting community participation, social support, and related behaviours.
Evidence strength: Medium
Suggested reference text for website
Regular participation in community activities โ clubs, volunteering, faith groups, or local projects โ is associated with lower mortality risk. This may reflect the benefits of social connection, sense of purpose, and the healthy behaviors that often accompany community involvement.
Notes / caveats
- Causal direction is hard to establish: healthier people may participate more.
- Different types of community participation may have different effects.
- The benefit appears to come from the quality of engagement, not just membership.
47. Care responsibilities
Calculator variable: pets_care โ Pets, family, children, or others who add movement and social rhythm.
Why it matters: Caregiving responsibilities have a dual impact on health. Moderate caregiving (e.g., pet ownership, childcare) is associated with better health outcomes. High-burden, unpaid caregiving without recovery time is associated with increased stress, depression, and mortality risk.
Recommended sources
Title: Dog, cat, bird, fish, and other pet ownership and mortality: Evidence from the HILDA cohort
URL: https://doi.org/10.1371/journal.pone.0305546
Publisher: PLOS ONE
Year/date: 2024
Why useful: Large cohort study examining different types of pet ownership and mortality; useful as a proxy for companionship and care responsibilities.
Evidence strength: Medium
Title: Family caregiving and all-cause mortality: a meta-analysis
URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC6227628/
Publisher: American Journal of Preventive Medicine (via PubMed Central)
Year/date: 2018
Why useful: Meta-analysis examining the mixed relationship between family caregiving and all-cause mortality.
Evidence strength: Medium
Suggested reference text for website
Care responsibilities โ for pets, children, or family members โ have mixed effects on health. Low-to-moderate caregiving can provide structure, movement, and companionship. High-burden caregiving without adequate recovery time is associated with increased stress and health risks.
Notes / caveats
- The question distinguishes supportive from burdensome caregiving.
- Pet ownership effects are modest and partly explained by increased physical activity.
- Caregiver burden depends on the care recipient's condition, available support, and caregiver's own health.
48. Outlook
Calculator variable: optimism โ General expectation that problems can be worked through.
Why it matters: Optimism โ a general expectation that good things will happen โ is associated with reduced all-cause mortality, cardiovascular disease, and better health behaviors. The association persists after adjusting for depression, health status, and socioeconomic factors.
Recommended sources
Title: Association of Optimism With Cardiovascular Events and All-Cause Mortality: A Systematic Review and Meta-analysis
URL: https://pubmed.ncbi.nlm.nih.gov/31560385/
Publisher: JAMA Network Open
Year/date: 2019
Why useful: Systematic review and meta-analysis linking optimism with cardiovascular events and all-cause mortality.
Evidence strength: Medium
Title: Optimism and Cardiovascular Health: Longitudinal Findings From the Coronary Artery Risk Development in Young Adults Study
URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC9901360/
Publisher: Psychosomatic Medicine
Year/date: 2020
Why useful: Longitudinal cohort evidence linking optimism with cardiovascular health trajectories.
Evidence strength: Medium
Suggested reference text for website
A generally optimistic outlook โ the sense that problems can be worked through โ is associated with better health and longer life. Optimistic people tend to have lower cardiovascular risk, better health behaviors, and more effective stress management.
Notes / caveats
- Optimism is partly heritable and partly shaped by life circumstances.
- The association is moderate in size and may be bidirectional.
- Dispositional optimism differs from unrealistic optimism (which may lead to risk-taking).
49. Food environment
Calculator variable: environment_food โ How easy it is to access healthy food near home/work.
Why it matters: The local food environment โ proximity to supermarkets, farmers' markets, fast food outlets, and food prices โ shapes dietary patterns and health outcomes. "Food deserts" (areas with limited access to healthy, affordable food) are associated with poorer diet quality, higher obesity rates, and increased cardiovascular mortality.
Recommended sources
Title: Time-varying exposure to food retailers and cardiovascular disease hospitalization and mortality in the Netherlands: a nationwide prospective cohort study
URL: https://doi.org/10.1186/s12916-024-03648-w
Publisher: BMC Medicine
Year/date: 2024
Why useful: Nationwide cohort study linking healthier food retail exposure with cardiovascular hospitalization and mortality outcomes.
Evidence strength: Medium
Title: WHO Fact Sheet โ Healthy diet
URL: https://www.who.int/news-room/fact-sheets/detail/healthy-diet
Publisher: World Health Organization
Year/date: Updated regularly
Why useful: Official WHO dietary guidance used as general nutrition context; the food-environment mortality link is supported more directly by the cohort source above.
Evidence strength: Medium
Suggested reference text for website
How easy it is to find healthy, affordable food in your neighborhood can influence your diet and health. Areas with limited access to fresh produce and whole foods โ sometimes called "food deserts" โ are associated with poorer health outcomes, including higher cardiovascular mortality.
Notes / caveats
- The question captures subjective perception of food access, which may differ from objective measures.
- Food environment effects are confounded by income, education, and neighborhood safety.
- Individual choices can partly overcome unfavorable food environments.
50. Readiness to improve
Calculator variable: readiness โ Useful for deciding what CTA should eventually offer.
Why it matters: Readiness to change is not directly a mortality predictor, but it is a well-established determinant of successful behavior change. The Stages of Change (Transtheoretical Model) shows that people who are "ready" are more likely to adopt and maintain health-improving behaviors. This question helps the calculator offer appropriate next steps.
Recommended sources
Title: The Transtheoretical Model of Health Behavior Change (Prochaska & Velicer)
URL: https://doi.org/10.4278/0890-1171-12.1.38
Publisher: American Journal of Health Promotion (SAGE)
Year/date: 1997
Why useful: Foundational paper on the Stages of Change model; readiness to change is associated with successful health behavior adoption.
Evidence strength: Medium
Title: The transtheoretical model and stages of change
URL: https://pubmed.ncbi.nlm.nih.gov/14760267/
Publisher: Health Education Research
Year/date: 2004
Why useful: Review of the stages-of-change model and readiness concepts used for behaviour-change tailoring.
Evidence strength: Medium
Suggested reference text for website
Your readiness to make changes is a strong predictor of whether health improvements will stick. People who feel ready to act are more likely to successfully adopt healthier habits. This question helps us tailor recommendations to where you are right now.
Notes / caveats
- Readiness is a psychological construct, not a direct health predictor.
- Motivation fluctuates over time; today's readiness may not predict long-term behavior.
- The question is included primarily to personalize calculator recommendations.
Public calculator weights
The table below shows the directional year adjustments currently used by the calculator. These are transparent heuristic weights, not clinically fitted coefficients.
Weights are applied to an age-based population baseline and capped inside the estimate. They are educational approximations from population-level evidence, not a validated medical, actuarial, or individual risk model.
| # | Question | Signal type | Adjustment used | How it is applied | Evidence |
|---|---|---|---|---|---|
| 1 | Current ageageRequired | Baseline | 18-29: baseline 82.5y; 30-39: baseline 83.2y; 40-49: baseline 84.0y; 50-59: baseline 85.0y; 60-69: baseline 86.8y; 70-79: baseline 89.2y; 80+: baseline 93.5y | Sets the population baseline life expectancy before answer-level adjustments. | Evidence |
| 2 | Sex at birthsexRequired | Population baseline | Female: +3.8y; Male: 0.0y; Intersex / prefer not to say: +1.6y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 3 | Country and city where you livecountryRequired | Location baseline | 204 city options: -2.3y to +3.4y; full city table below | Applies a fixed city-level heuristic using healthcare, air quality, safety, walkability, and mortality context. | Evidence |
| 4 | Smoking statussmokingRequired | Risk/protection | Never smoked: +2.2y; Former smoker: -0.8y; Occasional smoker: -3.0y; Daily smoker: -7.5y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 5 | Body mass indexbmiRequired | Risk/protection | <18.5: -1.5y; 18.5-24.9: +1.4y; 25-29.9: -0.5y; 30-34.9: -2.0y; 35+: -4.5y | Applies a rule-based adjustment from the entered value. | Evidence |
| 6 | Weekly physical activityactivityRequired | Protective behavior | 300+ minutes: +3.2y; 150-299 minutes: +2.1y; 60-149 minutes: +0.4y; Under 60 minutes: -2.5y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 7 | Overall diet patterndietRequired | Protective behavior | Mediterranean / whole-food focused: +2.4y; Mostly balanced: +1.0y; Mixed, many processed foods: -0.8y; Fast food / low produce most days: -2.8y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 8 | Average sleep durationsleepRequired | Risk/protection | 7-8 hours: +1.7y; 6 or 9 hours: +0.2y; Under 6 hours: -1.8y; Over 9 hours: -1.0y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 9 | Blood pressure statusblood_pressureRequired | Clinical risk | Normal: +1.7y; Elevated but monitored: -0.4y; High, treated: -1.0y; High, untreated / unknown: -2.8y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 10 | Major chronic diseasediagnosesRequired | Clinical risk | None known: +1.5y; One, well managed: -2.2y; Multiple or poorly controlled: -6.0y; Prefer not to say: -1.0y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 11 | Alcohol intakealcohol | Behavioral risk | None or rare: +0.6y; Moderate: +0.1y; Heavy weekly use: -2.5y; Binge drinking: -3.0y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 12 | Hours sitting per daysitting | Behavioral risk | 0-5 hours: +0.8y; 6-8 hours: -0.2y; >8 hours: -1.5y | Applies a rule-based adjustment from the entered value. | Evidence |
| 13 | Average daily stepssteps | Protective behavior | 10,000+: +1.4y; 7,000-9,999: +0.9y; 4,000-6,999: 0.0y; Under 4,000: -1.4y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 14 | Strength trainingstrength | Protective behavior | 2+ times weekly: +1.1y; Sometimes: +0.2y; Rarely: -0.5y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 15 | Cardiorespiratory fitnessvo2 | Fitness proxy | High: +2.0y; Average: +0.4y; Low: -2.0y; Unknown: 0.0y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 16 | Waist-to-height riskwaist | Clinical proxy | Waist is under half my height: +0.8y; Waist is over half my height: -1.4y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 17 | Family longevityfamily | Non-modifiable proxy | Strong longevity: +1.8y; Average: 0.0y; Early cardiovascular/cancer history: -1.6y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 18 | Social connectionsocial | Social risk/protection | Strong: +1.1y; Moderate: +0.2y; Isolated: -1.8y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 19 | Chronic stressstress | Psychosocial proxy | 1: +1.0y; 2: +0.5y; 3: 0.0y; 4: -0.8y; 5: -1.8y | Applies a rule-based adjustment from the entered value. | Evidence |
| 20 | Mental health stabilitymental_health | Psychosocial proxy | Stable and supported: +0.9y; Some challenges, managed: -0.2y; Frequent unmanaged difficulty: -1.7y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 21 | Preventive carepreventive | Care access/proxy | Consistent: +1.3y; Occasional: 0.0y; Rare: -1.4y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 22 | Cholesterol riskcholesterol | Clinical risk | Optimal or treated well: +0.9y; Borderline: -0.3y; High / untreated / unknown: -1.5y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 23 | Glucose / diabetes statusglucose | Clinical risk | Normal: +0.9y; Prediabetes: -1.1y; Diabetes, managed: -2.0y; Diabetes, poorly controlled: -4.0y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 24 | Air quality exposurepollution | Environmental risk | Clean air most days: +0.6y; Moderate urban exposure: -0.3y; High pollution / smoke exposure: -1.4y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 25 | Daily safety risksafety | Injury risk | Low: +0.5y; Moderate: -0.4y; High: -2.0y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 26 | Driving safetydriving | Injury risk | Very safe: +0.5y; Average: 0.0y; Frequent risk: -1.2y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 27 | Work patternwork | Occupational proxy | Balanced / autonomous: +0.7y; Sedentary but manageable: -0.2y; Night shifts / high strain: -1.4y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 28 | Financial securityincome_security | Socioeconomic proxy | Secure: +1.0y; Some pressure: -0.4y; Insecure: -1.7y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 29 | Education / health literacyeducation | Socioeconomic proxy | High: +0.8y; Medium: +0.2y; Low: -0.8y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 30 | Sense of purposepurpose | Psychosocial proxy | 1: -1.0y; 2: -0.4y; 3: +0.2y; 4: +0.8y; 5: +1.2y | Applies a rule-based adjustment from the entered value. | Evidence |
| 31 | Oral healthoral_health | Health proxy | Good, regular dental care: +0.4y; Some issues: -0.3y; Poor / untreated: -0.9y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 32 | Sun protectionsun | Cancer-risk proxy | Consistent: +0.3y; Sometimes: 0.0y; Rarely: -0.5y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 33 | Non-prescribed drug risksubstances | High-risk behavior | None: +0.5y; Past, not current: -0.3y; Current risk: -4.0y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 34 | Sleep qualitysleep_quality | Risk/protection | Restorative: +0.8y; Mixed: -0.2y; Poor / possible apnea: -1.5y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 35 | Sugary drinkshydration | Dietary proxy | Rare: +0.4y; Several weekly: -0.2y; Daily: -0.9y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 36 | Fiber intakefiber | Dietary proxy | High most days: +0.9y; Moderate: +0.2y; Low: -0.8y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 37 | Protein adequacyprotein | Nutrition proxy | Adequate: +0.4y; Unsure: 0.0y; Often low: -0.5y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 38 | Medication adherencemed_adherence | Care behavior | No medication / fully adherent: +0.6y; Occasional misses: -0.4y; Often not taken: -1.4y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 39 | Vaccination statusvaccines | Preventive care | Up to date: +0.6y; Partly: 0.0y; Avoid/unknown: -0.8y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 40 | Late-night screen habitscreen_time | Sleep proxy | Rare: +0.2y; Sometimes: 0.0y; Most nights: -0.4y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 41 | Time outdoors / naturenature | Lifestyle/environment proxy | Most days: +0.5y; Weekly: +0.1y; Rarely: -0.4y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 42 | Heat/cold resilienceheat | Environmental risk | Well protected: +0.3y; Some exposure: -0.3y; Frequent exposure: -1.0y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 43 | Fall riskfalls | Injury risk | Low: +0.4y; Some concern: -0.5y; High: -1.5y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 44 | Hearing/vision correctionhearing | Sensory-function proxy | Good or corrected: +0.4y; Some untreated issues: -0.4y; Significant untreated issues: -1.0y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 45 | Cognitive engagementcognition | Cognitive-health proxy | Daily: +0.6y; Weekly: +0.2y; Rare: -0.4y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 46 | Community participationcommunity | Social proxy | Regular: +0.7y; Occasional: +0.1y; None: -0.3y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 47 | Care responsibilitiespets_care | Caregiving proxy | Supportive rhythm: +0.3y; Neutral: 0.0y; High burden/no recovery: -0.7y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 48 | Outlookoptimism | Psychosocial proxy | 1: -0.8y; 2: -0.2y; 3: +0.2y; 4: +0.6y; 5: +0.9y | Applies a rule-based adjustment from the entered value. | Evidence |
| 49 | Food environmentenvironment_food | Food-access proxy | Easy: +0.5y; Mixed: 0.0y; Difficult: -0.7y | Applies the fixed adjustment attached to the selected answer. | Evidence |
| 50 | Readiness to improvereadiness | Behavior-change proxy | Ready now: +0.6y; Interested but unsure: +0.1y; Not a priority: -0.4y | Applies the fixed adjustment attached to the selected answer. | Evidence |
Country and city weights
The selected city applies one fixed location adjustment. City values are approximate proxies for healthcare access, air quality, safety, walkability, and local mortality context.
| Country | City | Adjustment used | Signals |
|---|---|---|---|
| Argentina | Buenos Aires | +0.1y | Healthcare moderate; air moderate; safety mixed; walkability high; mortality context mixed |
| Argentina | Cordoba | 0.0y | Healthcare moderate; air moderate; safety mixed; walkability moderate; mortality context mixed |
| Argentina | Mendoza | +0.2y | Healthcare moderate; air moderate; safety moderate; walkability moderate; mortality context mixed |
| Australia | Adelaide | +2.0y | Healthcare high; air high; safety high; walkability moderate; mortality context high |
| Australia | Brisbane | +2.0y | Healthcare high; air high; safety high; walkability moderate; mortality context high |
| Australia | Canberra | +2.2y | Healthcare high; air high; safety very high; walkability moderate; mortality context high |
| Australia | Melbourne | +2.2y | Healthcare high; air high; safety high; walkability high; mortality context high |
| Australia | Perth | +2.0y | Healthcare high; air high; safety high; walkability moderate; mortality context high |
| Australia | Sydney | +2.1y | Healthcare high; air high; safety high; walkability high; mortality context high |
| Austria | Graz | +2.2y | Healthcare high; air high; safety high; walkability high; mortality context high |
| Austria | Vienna | +2.3y | Healthcare high; air high; safety high; walkability very high; mortality context high |
| Belgium | Antwerp | +1.9y | Healthcare high; air moderate; safety high; walkability high; mortality context high |
| Belgium | Brussels | +1.9y | Healthcare high; air moderate; safety high; walkability high; mortality context high |
| Belgium | Ghent | +2.0y | Healthcare high; air moderate; safety high; walkability high; mortality context high |
| Brazil | Belo Horizonte | -0.6y | Healthcare moderate; air moderate; safety mixed; walkability moderate; mortality context mixed |
| Brazil | Brasilia | -0.6y | Healthcare moderate; air moderate; safety mixed; walkability low; mortality context mixed |
| Brazil | Curitiba | -0.4y | Healthcare moderate; air moderate; safety mixed; walkability moderate; mortality context mixed |
| Brazil | Florianopolis | -0.2y | Healthcare moderate; air high; safety moderate; walkability moderate; mortality context mixed |
| Brazil | Porto Alegre | -0.6y | Healthcare moderate; air moderate; safety mixed; walkability moderate; mortality context mixed |
| Brazil | Recife | -0.9y | Healthcare moderate; air moderate; safety mixed; walkability moderate; mortality context mixed |
| Brazil | Rio de Janeiro | -0.9y | Healthcare moderate; air moderate; safety mixed; walkability high; mortality context mixed |
| Brazil | Salvador | -1.0y | Healthcare moderate; air moderate; safety mixed; walkability moderate; mortality context mixed |
| Brazil | Sao Paulo | -0.7y | Healthcare moderate; air mixed; safety mixed; walkability high; mortality context mixed |
| Canada | Calgary | +2.1y | Healthcare high; air high; safety high; walkability moderate; mortality context high |
| Canada | Edmonton | +2.0y | Healthcare high; air high; safety high; walkability moderate; mortality context high |
| Canada | Halifax | +2.1y | Healthcare high; air high; safety high; walkability moderate; mortality context high |
| Canada | Montreal | +2.1y | Healthcare high; air high; safety high; walkability high; mortality context high |
| Canada | Ottawa | +2.2y | Healthcare high; air high; safety very high; walkability moderate; mortality context high |
| Canada | Quebec City | +2.2y | Healthcare high; air high; safety very high; walkability high; mortality context high |
| Canada | Toronto | +2.2y | Healthcare high; air high; safety high; walkability high; mortality context high |
| Canada | Vancouver | +2.3y | Healthcare high; air high; safety high; walkability high; mortality context high |
| Chile | Santiago | +0.7y | Healthcare moderate; air mixed; safety moderate; walkability moderate; mortality context moderate |
| Chile | Valparaiso | +0.5y | Healthcare moderate; air moderate; safety mixed; walkability moderate; mortality context moderate |
| China | Beijing | +0.6y | Healthcare high; air mixed; safety high; walkability moderate; mortality context moderate |
| China | Chengdu | +0.5y | Healthcare moderate; air mixed; safety high; walkability moderate; mortality context moderate |
| China | Chongqing | +0.4y | Healthcare moderate; air mixed; safety high; walkability moderate; mortality context moderate |
| China | Guangzhou | +0.6y | Healthcare high; air mixed; safety high; walkability moderate; mortality context moderate |
| China | Hangzhou | +0.8y | Healthcare high; air moderate; safety high; walkability moderate; mortality context moderate |
| China | Nanjing | +0.7y | Healthcare high; air mixed; safety high; walkability moderate; mortality context moderate |
| China | Shanghai | +0.9y | Healthcare high; air moderate; safety high; walkability high; mortality context moderate |
| China | Shenzhen | +0.8y | Healthcare high; air moderate; safety high; walkability moderate; mortality context moderate |
| China | Suzhou | +0.7y | Healthcare high; air moderate; safety high; walkability moderate; mortality context moderate |
| China | Wuhan | +0.5y | Healthcare high; air mixed; safety high; walkability moderate; mortality context moderate |
| China | Xian | +0.4y | Healthcare moderate; air mixed; safety high; walkability moderate; mortality context moderate |
| Czechia | Brno | +0.8y | Healthcare high; air moderate; safety high; walkability high; mortality context moderate |
| Czechia | Prague | +0.9y | Healthcare high; air moderate; safety high; walkability very high; mortality context moderate |
| Denmark | Aarhus | +2.3y | Healthcare high; air high; safety very high; walkability high; mortality context high |
| Denmark | Copenhagen | +2.3y | Healthcare high; air high; safety very high; walkability very high; mortality context high |
| Egypt | Alexandria | -1.8y | Healthcare mixed; air mixed; safety mixed; walkability moderate; mortality context low |
| Egypt | Cairo | -2.1y | Healthcare mixed; air low; safety mixed; walkability moderate; mortality context low |
| Finland | Helsinki | +2.3y | Healthcare high; air high; safety very high; walkability high; mortality context high |
| Finland | Tampere | +2.2y | Healthcare high; air high; safety very high; walkability moderate; mortality context high |
| France | Bordeaux | +2.0y | Healthcare high; air high; safety high; walkability high; mortality context high |
| France | Lille | +1.9y | Healthcare high; air moderate; safety high; walkability high; mortality context high |
| France | Lyon | +2.1y | Healthcare high; air moderate; safety high; walkability high; mortality context high |
| France | Marseille | +1.9y | Healthcare high; air moderate; safety mixed; walkability moderate; mortality context high |
| France | Montpellier | +2.0y | Healthcare high; air high; safety high; walkability high; mortality context high |
| France | Nantes | +2.0y | Healthcare high; air high; safety high; walkability high; mortality context high |
| France | Nice | +2.0y | Healthcare high; air high; safety high; walkability high; mortality context high |
| France | Paris | +2.1y | Healthcare high; air moderate; safety high; walkability very high; mortality context high |
| France | Strasbourg | +2.0y | Healthcare high; air high; safety high; walkability very high; mortality context high |
| France | Toulouse | +2.0y | Healthcare high; air high; safety high; walkability high; mortality context high |
| Germany | Berlin | +2.1y | Healthcare high; air high; safety high; walkability very high; mortality context high |
| Germany | Cologne | +2.0y | Healthcare high; air moderate; safety high; walkability high; mortality context high |
| Germany | Dresden | +2.0y | Healthcare high; air high; safety high; walkability high; mortality context high |
| Germany | Dusseldorf | +2.0y | Healthcare high; air moderate; safety high; walkability high; mortality context high |
| Germany | Frankfurt | +2.0y | Healthcare high; air moderate; safety high; walkability high; mortality context high |
| Germany | Hamburg | +2.0y | Healthcare high; air high; safety high; walkability high; mortality context high |
| Germany | Hannover | +2.0y | Healthcare high; air high; safety high; walkability moderate; mortality context high |
| Germany | Leipzig | +1.9y | Healthcare high; air high; safety high; walkability high; mortality context high |
| Germany | Munich | +2.2y | Healthcare high; air high; safety very high; walkability high; mortality context high |
| Germany | Stuttgart | +2.0y | Healthcare high; air moderate; safety high; walkability moderate; mortality context high |
| Greece | Athens | +1.2y | Healthcare high; air moderate; safety moderate; walkability high; mortality context moderate |
| Greece | Thessaloniki | +1.0y | Healthcare high; air moderate; safety moderate; walkability high; mortality context moderate |
| India | Ahmedabad | -1.8y | Healthcare mixed; air low; safety mixed; walkability moderate; mortality context low |
| India | Bangalore | -1.4y | Healthcare moderate; air mixed; safety mixed; walkability moderate; mortality context low |
| India | Chennai | -1.7y | Healthcare moderate; air mixed; safety mixed; walkability moderate; mortality context low |
| India | Delhi | -2.2y | Healthcare moderate; air low; safety mixed; walkability moderate; mortality context low |
| India | Hyderabad | -1.6y | Healthcare moderate; air mixed; safety mixed; walkability moderate; mortality context low |
| India | Kolkata | -1.9y | Healthcare mixed; air low; safety mixed; walkability moderate; mortality context low |
| India | Mumbai | -1.7y | Healthcare moderate; air mixed; safety mixed; walkability high; mortality context low |
| India | Pune | -1.5y | Healthcare moderate; air mixed; safety mixed; walkability moderate; mortality context low |
| Ireland | Cork | +1.9y | Healthcare high; air high; safety high; walkability moderate; mortality context high |
| Ireland | Dublin | +1.9y | Healthcare high; air high; safety high; walkability high; mortality context high |
| Israel | Haifa | +2.0y | Healthcare high; air moderate; safety moderate; walkability moderate; mortality context high |
| Israel | Jerusalem | +2.0y | Healthcare high; air moderate; safety moderate; walkability high; mortality context high |
| Israel | Tel Aviv | +2.1y | Healthcare high; air moderate; safety moderate; walkability very high; mortality context high |
| Italy | Bari | +1.9y | Healthcare high; air high; safety moderate; walkability high; mortality context high |
| Italy | Bologna | +2.2y | Healthcare high; air moderate; safety high; walkability very high; mortality context high |
| Italy | Catania | +1.6y | Healthcare high; air moderate; safety mixed; walkability moderate; mortality context high |
| Italy | Florence | +2.2y | Healthcare high; air high; safety high; walkability very high; mortality context high |
| Italy | Genoa | +2.0y | Healthcare high; air high; safety high; walkability moderate; mortality context high |
| Italy | Milan | +2.3y | Healthcare high; air mixed; safety high; walkability very high; mortality context high |
| Italy | Naples | +1.7y | Healthcare high; air moderate; safety mixed; walkability high; mortality context high |
| Italy | Padua | +2.2y | Healthcare high; air moderate; safety high; walkability high; mortality context high |
| Italy | Palermo | +1.7y | Healthcare high; air moderate; safety mixed; walkability moderate; mortality context high |
| Italy | Rome | +2.1y | Healthcare high; air moderate; safety high; walkability high; mortality context high |
| Italy | Turin | +2.0y | Healthcare high; air mixed; safety high; walkability high; mortality context high |
| Italy | Venice | +2.1y | Healthcare high; air moderate; safety high; walkability very high; mortality context high |
| Italy | Verona | +2.1y | Healthcare high; air moderate; safety high; walkability high; mortality context high |
| Japan | Fukuoka | +3.1y | Healthcare very high; air high; safety very high; walkability high; mortality context very high |
| Japan | Kyoto | +3.2y | Healthcare very high; air high; safety very high; walkability very high; mortality context very high |
| Japan | Nagoya | +3.1y | Healthcare very high; air high; safety very high; walkability high; mortality context very high |
| Japan | Osaka | +3.1y | Healthcare very high; air high; safety very high; walkability very high; mortality context very high |
| Japan | Sapporo | +3.1y | Healthcare very high; air high; safety very high; walkability high; mortality context very high |
| Japan | Tokyo | +3.3y | Healthcare very high; air high; safety very high; walkability very high; mortality context very high |
| Japan | Yokohama | +3.1y | Healthcare very high; air high; safety very high; walkability high; mortality context very high |
| Malaysia | George Town | +0.3y | Healthcare moderate; air moderate; safety moderate; walkability high; mortality context moderate |
| Malaysia | Kuala Lumpur | +0.2y | Healthcare moderate; air mixed; safety moderate; walkability moderate; mortality context moderate |
| Mexico | Guadalajara | -0.7y | Healthcare moderate; air moderate; safety mixed; walkability moderate; mortality context mixed |
| Mexico | Merida | -0.4y | Healthcare moderate; air high; safety moderate; walkability moderate; mortality context mixed |
| Mexico | Mexico City | -0.8y | Healthcare moderate; air mixed; safety mixed; walkability high; mortality context mixed |
| Mexico | Monterrey | -0.6y | Healthcare moderate; air mixed; safety mixed; walkability moderate; mortality context mixed |
| Mexico | Puebla | -0.7y | Healthcare moderate; air moderate; safety mixed; walkability moderate; mortality context mixed |
| Netherlands | Amsterdam | +2.1y | Healthcare high; air high; safety high; walkability very high; mortality context high |
| Netherlands | Eindhoven | +2.0y | Healthcare high; air high; safety high; walkability high; mortality context high |
| Netherlands | Rotterdam | +2.0y | Healthcare high; air moderate; safety high; walkability high; mortality context high |
| Netherlands | The Hague | +2.1y | Healthcare high; air high; safety high; walkability very high; mortality context high |
| New Zealand | Auckland | +2.2y | Healthcare high; air high; safety high; walkability moderate; mortality context high |
| New Zealand | Christchurch | +2.2y | Healthcare high; air high; safety high; walkability moderate; mortality context high |
| New Zealand | Wellington | +2.2y | Healthcare high; air high; safety high; walkability high; mortality context high |
| Norway | Bergen | +2.4y | Healthcare high; air high; safety very high; walkability moderate; mortality context high |
| Norway | Oslo | +2.5y | Healthcare high; air high; safety very high; walkability high; mortality context high |
| Norway | Trondheim | +2.4y | Healthcare high; air high; safety very high; walkability high; mortality context high |
| Poland | Gdansk | +0.6y | Healthcare high; air moderate; safety high; walkability high; mortality context moderate |
| Poland | Krakow | +0.5y | Healthcare high; air mixed; safety high; walkability high; mortality context moderate |
| Poland | Warsaw | +0.4y | Healthcare high; air moderate; safety high; walkability high; mortality context moderate |
| Poland | Wroclaw | +0.5y | Healthcare high; air moderate; safety high; walkability high; mortality context moderate |
| Portugal | Braga | +2.0y | Healthcare high; air high; safety high; walkability moderate; mortality context high |
| Portugal | Coimbra | +2.0y | Healthcare high; air high; safety high; walkability high; mortality context high |
| Portugal | Lisbon | +2.0y | Healthcare high; air high; safety high; walkability very high; mortality context high |
| Portugal | Porto | +2.0y | Healthcare high; air high; safety high; walkability high; mortality context high |
| Singapore | Singapore | +3.4y | Healthcare very high; air moderate; safety very high; walkability high; mortality context very high |
| South Africa | Cape Town | -2.0y | Healthcare mixed; air moderate; safety mixed; walkability moderate; mortality context low |
| South Africa | Durban | -2.2y | Healthcare mixed; air moderate; safety mixed; walkability moderate; mortality context low |
| South Africa | Johannesburg | -2.3y | Healthcare mixed; air moderate; safety mixed; walkability low; mortality context low |
| South Africa | Pretoria | -2.1y | Healthcare mixed; air moderate; safety mixed; walkability low; mortality context low |
| South Korea | Busan | +2.7y | Healthcare very high; air high; safety very high; walkability high; mortality context high |
| South Korea | Daegu | +2.6y | Healthcare very high; air moderate; safety very high; walkability high; mortality context high |
| South Korea | Daejeon | +2.7y | Healthcare very high; air high; safety very high; walkability moderate; mortality context high |
| South Korea | Incheon | +2.6y | Healthcare very high; air moderate; safety very high; walkability moderate; mortality context high |
| South Korea | Seoul | +2.8y | Healthcare very high; air moderate; safety very high; walkability very high; mortality context high |
| Spain | Barcelona | +2.1y | Healthcare high; air moderate; safety high; walkability very high; mortality context high |
| Spain | Bilbao | +2.1y | Healthcare high; air high; safety high; walkability high; mortality context high |
| Spain | Madrid | +2.2y | Healthcare high; air moderate; safety high; walkability high; mortality context high |
| Spain | Malaga | +2.0y | Healthcare high; air high; safety high; walkability high; mortality context high |
| Spain | Seville | +2.0y | Healthcare high; air moderate; safety high; walkability high; mortality context high |
| Spain | Valencia | +2.1y | Healthcare high; air high; safety high; walkability very high; mortality context high |
| Sweden | Gothenburg | +2.3y | Healthcare high; air high; safety very high; walkability high; mortality context high |
| Sweden | Malmo | +2.2y | Healthcare high; air high; safety high; walkability very high; mortality context high |
| Sweden | Stockholm | +2.4y | Healthcare high; air high; safety very high; walkability very high; mortality context high |
| Switzerland | Basel | +3.0y | Healthcare very high; air high; safety very high; walkability high; mortality context very high |
| Switzerland | Bern | +3.1y | Healthcare very high; air high; safety very high; walkability high; mortality context very high |
| Switzerland | Geneva | +3.1y | Healthcare very high; air high; safety very high; walkability high; mortality context very high |
| Switzerland | Lausanne | +3.1y | Healthcare very high; air high; safety very high; walkability high; mortality context very high |
| Switzerland | Zurich | +3.2y | Healthcare very high; air high; safety very high; walkability very high; mortality context very high |
| Taiwan | Kaohsiung | +2.2y | Healthcare high; air moderate; safety high; walkability moderate; mortality context high |
| Taiwan | Taichung | +2.3y | Healthcare high; air moderate; safety high; walkability moderate; mortality context high |
| Taiwan | Taipei | +2.4y | Healthcare high; air moderate; safety high; walkability very high; mortality context high |
| Thailand | Bangkok | -0.3y | Healthcare moderate; air mixed; safety moderate; walkability moderate; mortality context mixed |
| Thailand | Chiang Mai | -0.4y | Healthcare moderate; air mixed; safety moderate; walkability moderate; mortality context mixed |
| Turkey | Ankara | -0.2y | Healthcare moderate; air moderate; safety moderate; walkability moderate; mortality context mixed |
| Turkey | Istanbul | -0.4y | Healthcare moderate; air mixed; safety moderate; walkability high; mortality context mixed |
| United Arab Emirates | Abu Dhabi | +0.9y | Healthcare high; air mixed; safety very high; walkability moderate; mortality context moderate |
| United Arab Emirates | Dubai | +0.8y | Healthcare high; air mixed; safety high; walkability moderate; mortality context moderate |
| United Kingdom | Belfast | +1.4y | Healthcare high; air high; safety moderate; walkability moderate; mortality context moderate |
| United Kingdom | Birmingham | +1.5y | Healthcare high; air moderate; safety moderate; walkability moderate; mortality context moderate |
| United Kingdom | Bristol | +1.7y | Healthcare high; air high; safety high; walkability high; mortality context moderate |
| United Kingdom | Cardiff | +1.6y | Healthcare high; air high; safety high; walkability high; mortality context moderate |
| United Kingdom | Edinburgh | +1.7y | Healthcare high; air high; safety high; walkability very high; mortality context moderate |
| United Kingdom | Glasgow | +1.3y | Healthcare high; air high; safety moderate; walkability high; mortality context moderate |
| United Kingdom | Leeds | +1.5y | Healthcare high; air moderate; safety moderate; walkability moderate; mortality context moderate |
| United Kingdom | Liverpool | +1.4y | Healthcare high; air moderate; safety moderate; walkability high; mortality context moderate |
| United Kingdom | London | +1.7y | Healthcare high; air moderate; safety high; walkability very high; mortality context moderate |
| United Kingdom | Manchester | +1.5y | Healthcare high; air moderate; safety moderate; walkability high; mortality context moderate |
| United Kingdom | Newcastle | +1.4y | Healthcare high; air high; safety moderate; walkability high; mortality context moderate |
| United States | Atlanta | +0.4y | Healthcare high; air moderate; safety mixed; walkability moderate; mortality context mixed |
| United States | Austin | +0.6y | Healthcare high; air moderate; safety moderate; walkability moderate; mortality context mixed |
| United States | Baltimore | +0.2y | Healthcare high; air moderate; safety mixed; walkability moderate; mortality context mixed |
| United States | Boston | +0.9y | Healthcare high; air high; safety high; walkability very high; mortality context mixed |
| United States | Charlotte | +0.4y | Healthcare high; air moderate; safety moderate; walkability low; mortality context mixed |
| United States | Chicago | +0.5y | Healthcare high; air moderate; safety mixed; walkability high; mortality context mixed |
| United States | Dallas | +0.4y | Healthcare high; air moderate; safety moderate; walkability low; mortality context mixed |
| United States | Denver | +0.7y | Healthcare high; air moderate; safety moderate; walkability moderate; mortality context mixed |
| United States | Houston | +0.3y | Healthcare high; air mixed; safety moderate; walkability low; mortality context mixed |
| United States | Las Vegas | +0.2y | Healthcare high; air moderate; safety moderate; walkability low; mortality context mixed |
| United States | Los Angeles | +0.5y | Healthcare high; air mixed; safety moderate; walkability moderate; mortality context mixed |
| United States | Miami | +0.5y | Healthcare high; air high; safety moderate; walkability moderate; mortality context mixed |
| United States | Minneapolis | +0.7y | Healthcare high; air high; safety high; walkability high; mortality context mixed |
| United States | Nashville | +0.3y | Healthcare high; air moderate; safety moderate; walkability low; mortality context mixed |
| United States | New Orleans | +0.1y | Healthcare high; air moderate; safety mixed; walkability moderate; mortality context mixed |
| United States | New York | +0.8y | Healthcare high; air moderate; safety moderate; walkability very high; mortality context mixed |
| United States | Orlando | +0.4y | Healthcare high; air moderate; safety moderate; walkability low; mortality context mixed |
| United States | Philadelphia | +0.5y | Healthcare high; air moderate; safety mixed; walkability high; mortality context mixed |
| United States | Phoenix | +0.2y | Healthcare high; air moderate; safety moderate; walkability low; mortality context mixed |
| United States | Portland | +0.7y | Healthcare high; air high; safety moderate; walkability high; mortality context mixed |
| United States | Sacramento | +0.5y | Healthcare high; air moderate; safety moderate; walkability moderate; mortality context mixed |
| United States | San Antonio | +0.3y | Healthcare high; air moderate; safety moderate; walkability low; mortality context mixed |
| United States | San Diego | +0.8y | Healthcare high; air high; safety high; walkability moderate; mortality context mixed |
| United States | San Francisco | +0.8y | Healthcare high; air high; safety moderate; walkability very high; mortality context mixed |
| United States | San Jose | +0.8y | Healthcare high; air high; safety high; walkability moderate; mortality context mixed |
| United States | Seattle | +0.9y | Healthcare high; air high; safety high; walkability high; mortality context mixed |
| United States | Tampa | +0.4y | Healthcare high; air high; safety moderate; walkability low; mortality context mixed |
| United States | Washington | +0.7y | Healthcare high; air moderate; safety mixed; walkability very high; mortality context mixed |