The life expectancy for men aged 65 years in the U.S. has gradually increased since the 1960s. Now men in the United States aged 65 can expect to live 17 more years on average. Women aged 65 years can expect to live around 19.7 more years on average.

Life expectancy in the U.S.

As of 2021, the average life expectancy at birth in the United States was 76.33 years. Life expectancy in the U.S. had steadily increased for many years but has recently dropped slightly. Women consistently have a higher life expectancy than men but have also seen a slight decrease. As of 2019, a woman in the U.S. could be expected to live up to 79.3 years.

Leading causes of death

The leading causes of death in the United States include heart disease, cancer, unintentional injuries, chronic lower respiratory diseases and cerebrovascular diseases. However, heart disease and cancer account for around 38 percent of all deaths. Although heart disease and cancer are the leading causes of death for both men and women, there are slight variations in the leading causes of death. For example, unintentional injury and suicide account for a larger portion of deaths among men than they do among women.

In the United States in 2021, the death rate was highest among those aged 85 and over, with about 17,190.5 men and 14,914.5 women per 100,000 of the population passing away. For all ages, the death rate was at 1,118.2 per 100,000 of the population for males, and 970.8 per 100,000 of the population for women. The death rate Death rates generally are counted as the number of deaths per 1,000 or 100,000 of the population and include both deaths of natural and unnatural causes. The death rate in the United States had pretty much held steady since 1990 until it started to increase over the last decade, with the highest death rates recorded in recent years. While the birth rate in the United States has been decreasing, it is still currently higher than the death rate. Causes of death There are a myriad number of causes of death in the United States, but the most recent data shows the top three leading causes of death to be heart disease, cancers, and accidents. Heart disease was also the leading cause of death worldwide.

The life expectancy of men at birth in the United States saw no significant changes in 2023 in comparison to the previous year 2022 and remained at around 75.8 years. However, 2023 marked the second consecutive increase of the life expectancy. Life expectancy at birth refers to the number of years the average newborn is expected to live, providing that mortality patterns at the time of birth do not change thereafter.Find more statistics on other topics about the United States with key insights such as total fertility rate, infant mortality rate, and total life expectancy at birth.

Number of deaths and mortality rates, by age group, sex, and place of residence, 1991 to most recent year.

VITAL SIGNS INDICATOR Life Expectancy (EQ6)

FULL MEASURE NAME Life Expectancy

LAST UPDATED April 2017

DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time.

DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link

California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

U.S. Census Bureau: Decennial Census ZCTA Population (2000-2010) http://factfinder.census.gov

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2013) http://factfinder.census.gov

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population that can be compared across time and populations. More information about the determinants of life expectancy that may lead to differences in life expectancy between neighborhoods can be found in the Bay Area Regional Health Inequities Initiative (BARHII) Health Inequities in the Bay Area report at http://www.barhii.org/wp-content/uploads/2015/09/barhii_hiba.pdf. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and ZIP Codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population.

Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential ZIP Code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality.

For the ZIP Code-level life expectancy calculation, it is assumed that postal ZIP Codes share the same boundaries as ZIP Code Census Tabulation Areas (ZCTAs). More information on the relationship between ZIP Codes and ZCTAs can be found at http://www.census.gov/geo/reference/zctas.html. ZIP Code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 ZIP Code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for ZIP Codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest ZIP Code with population. ZIP Code population for 2000 estimates comes from the Decennial Census. ZIP Code population for 2013 estimates are from the American Community Survey (5-Year Average). ACS estimates are adjusted using Decennial Census data for more accurate population estimates. An adjustment factor was calculated using the ratio between the 2010 Decennial Census population estimates and the 2012 ACS 5-Year (with middle year 2010) population estimates. This adjustment factor is particularly important for ZCTAs with high homeless population (not living in group quarters) where the ACS may underestimate the ZCTA population and therefore underestimate the life expectancy. The ACS provides ZIP Code population by age in five-year age intervals. Single-year age population estimates were calculated by distributing population within an age interval to single-year ages using the county distribution. Counties were assigned to ZIP Codes based on majority land-area.

ZIP Codes in the Bay Area vary in population from over 10,000 residents to less than 20 residents. Traditional life expectancy estimation (like the one used for the regional- and county-level Vital Signs estimates) cannot be used because they are highly inaccurate for small populations and may result in over/underestimation of life expectancy. To avoid inaccurate estimates, ZIP Codes with populations of less than 5,000 were aggregated with neighboring ZIP Codes until the merged areas had a population of more than 5,000. ZIP Code 94103, representing Treasure Island, was dropped from the dataset due to its small population and having no bordering ZIP Codes. In this way, the original 305 Bay Area ZIP Codes were reduced to 217 ZIP Code areas for 2013 estimates. Next, a form of Bayesian random-effects analysis was used which established a prior distribution of the probability of death at each age using the regional distribution. This prior is used to shore up the life expectancy calculations where data were sparse.

This statistic shows the average life expectancy in North America for those born in 2022, by gender and region. In Canada, the average life expectancy was 80 years for males and 84 years for females.

Life expectancy in North America

Of those considered in this statistic, the life expectancy of female Canadian infants born in 2021 was the longest, at 84 years. Female infants born in America that year had a similarly high life expectancy of 81 years. Male infants, meanwhile, had lower life expectancies of 80 years (Canada) and 76 years (USA).

Compare this to the worldwide life expectancy for babies born in 2021: 75 years for women and 71 years for men. Of continents worldwide, North America ranks equal first in terms of life expectancy of (77 years for men and 81 years for women). Life expectancy is lowest in Africa at just 63 years and 66 years for males and females respectively. Japan is the country with the highest life expectancy worldwide for babies born in 2020.

Life expectancy is calculated according to current mortality rates of the population in question. Global variations in life expectancy are caused by differences in medical care, public health and diet, and reflect global inequalities in economic circumstances. Africa’s low life expectancy, for example, can be attributed in part to the AIDS epidemic. In 2019, around 72,000 people died of AIDS in South Africa, the largest amount worldwide. Nigeria, Tanzania and India were also high on the list of countries ranked by AIDS deaths that year. Likewise, Africa has by far the highest rate of mortality by communicable disease (i.e. AIDS, neglected tropics diseases, malaria and tuberculosis).

This table contains 2394 series, with data for years 1991 - 1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Population group (19 items: Entire cohort; Income adequacy quintile 1 (lowest);Income adequacy quintile 2;Income adequacy quintile 3 ...), Age (14 items: At 25 years; At 30 years; At 40 years; At 35 years ...), Sex (3 items: Both sexes; Females; Males ...), Characteristics (3 items: Life expectancy; High 95% confidence interval; life expectancy; Low 95% confidence interval; life expectancy ...).

Over the past 160 years, life expectancy (from birth) in the United States has risen from 39.4 years in 1860, to 78.9 years in 2020. One of the major reasons for the overall increase of life expectancy in the last two centuries is the fact that the infant and child mortality rates have decreased by so much during this time. Medical advancements, fewer wars and improved living standards also mean that people are living longer than they did in previous centuries.

Despite this overall increase, the life expectancy dropped three times since 1860; from 1865 to 1870 during the American Civil War, from 1915 to 1920 during the First World War and following Spanish Flu epidemic, and it has dropped again between 2015 and now. The reason for the most recent drop in life expectancy is not a result of any specific event, but has been attributed to negative societal trends, such as unbalanced diets and sedentary lifestyles, high medical costs, and increasing rates of suicide and drug use.

BackgroundLife expectancy at birth in the United States will likely surpass 80 years in the coming decade. Yet recent studies suggest that longevity gains are unevenly shared across age and socioeconomic groups. First, mortality in midlife has risen among non-Hispanic whites. Second, low-educated whites have suffered stalls (men) or declines (women) in adult life expectancy, which is significantly lower than among their college-educated counterparts. Estimating the number of life years lost or gained by age and cause of death, broken down by educational attainment, is crucial in identifying vulnerable populations.Methods and FindingsUsing U.S. vital statistics data from 1990 to 2010, this study decomposes the change in life expectancy at age 25 by age and cause of death across educational attainment groups, broken down by race and gender. The findings reveal that mortality in midlife increased for white women (and to a lesser extent men) with 12 or fewer years of schooling, accounting for most of the stalls or declines in adult life expectancy observed in those groups. Among blacks, mortality declined in nearly all age and educational attainment groups. Although an educational gradient was found across multiple causes of death, between 60 and 80 percent of the gap in adult life expectancy was explained by cardiovascular diseases, smoking-related diseases, and external causes of death. Furthermore, the number of life years lost to smoking-related, external, and other causes of death increased among low- and high school-educated whites, explaining recent stalls or declines in longevity.ConclusionsLarge segments of the American population—particularly low- and high school-educated whites under age 55—are diverging from their college-educated counterparts and losing additional years of life to smoking-related diseases and external causes of death. If this trend continues, old-age mortality may also increase for these birth cohorts in the coming decades.

BackgroundSocioeconomic inequalities in death rates from all causes combined widened from 1960 until 1990 in the U.S., largely because cardiovascular death rates decreased more slowly in lower than in higher socioeconomic groups. However, no studies have examined trends in inequalities using recent US national data.Methodology/Principal FindingsWe calculated annual age-standardized death rates from 1993–2001 for 25–64 year old non-Hispanic whites and blacks by level of education for all causes and for the seven most common causes of death using death certificate information from 43 states and Washington, D.C. Regression analysis was used to estimate annual percent change. The inequalities in all cause death rates between Americans with less than high school education and college graduates increased rapidly from 1993 to 2001 due to both significant decreases in mortality from all causes, heart disease, cancer, stroke, and other conditions in the most educated and lack of change or increases among the least educated. For white women, the all cause death rate increased significantly by 3.2 percent per year in the least educated and by 0.7 percent per year in high school graduates. The rate ratio (RR) comparing the least versus most educated increased from 2.9 (95% CI, 2.8–3.1) in 1993 to 4.4 (4.1–4.6) in 2001 among white men, from 2.1 (1.8–2.5) to 3.4 (2.9–3–9) in black men, and from 2.6 (2.4–2.7) to 3.8 (3.6–4.0) in white women.ConclusionSocioeconomic inequalities in mortality are increasing rapidly due to continued progress by educated white and black men and white women, and stable or worsening trends among the least educated.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

Data Source: DC Office of the Chief Medical Examiner (OCME) and American Community Survey (ACS) 1-Year Estimates

Why This Matters

Motor vehicle accidents are one of the leading causes of death in the US, with over 40,000 annual deaths and millions of additional injuries. Transportation is critical to accessing work, food, medical care, social events, and more. Yet, many people are injured or die in motor vehicle accidents while accessing these services.

When people live in areas where cars drive faster, they are at greater risk of dying while accessing these services. Most people (90%) hit by cars driving 23 miles per hour (mph) survive. Yet, most people (75%) hit by cars driving 50 mph die.

Modern transportation and road infrastructure has been shaped by policies of racial segregation and inequitable federal investments. Nationally, individuals who are Black, Indigenous, or people of color are more likely to live near dangerous roadways with fast-moving traffic. They are also more likely to live in communities with less pedestrian infrastructure.

The District Response

Vision Zero was launched in 2014 to improve road safety and set a goal of zero traffic fatalities or serious injuries.

The District Department of Transportation’s Bicycle Lane Program has built over 100 miles of bikes lanes since 2001, including 24 miles of protected bike lanes.Automatic Traffic Enforcement reduces dangerous traffic violations and enhances safety for people travelling by all modes.

In 2023, about **** million deaths were reported in the United States. This figure is an increase from **** million deaths reported in 1990, and from **** in 2019. This sudden increase can be attributed to the COVID-19 pandemic.

A global phenomenon, known as the demographic transition, has seen life expectancy from birth increase rapidly over the past two centuries. In pre-industrial societies, the average life expectancy was around 24 years, and it is believed that this was the case throughout most of history, and in all regions. The demographic transition then began in the industrial societies of Europe, North America, and the West Pacific around the turn of the 19th century, and life expectancy rose accordingly. Latin America was the next region to follow, before Africa and most Asian populations saw their life expectancy rise throughout the 20th century.

BackgroundGeographic inequality in US mortality has increased rapidly over the last 25 years, particularly between metropolitan and nonmetropolitan areas. These gaps are sizeable and rival life expectancy differences between the US and other high-income countries. This study determines the contribution of smoking, a key contributor to premature mortality in the US, to geographic inequality in mortality over the past quarter century.MethodsWe used death certificate and census data covering the entire US population aged 50+ between Jan 1, 1990 and Dec 31, 2019. We categorized counties into 40 geographic areas cross-classified by region and metropolitan category. We estimated life expectancy at age 50 and the index of dissimilarity for mortality, a measure of inequality in mortality, with and without smoking for these areas in 1990–1992 and 2017–2019. We estimated the changes in life expectancy levels and percent change in inequality in mortality due to smoking between these periods.ResultsWe find that the gap in life expectany between metros and nonmetros increased by 2.17 years for men and 2.77 years for women. Changes in smoking-related deaths are responsible for 19% and 22% of those increases, respectively. Among the 40 geographic areas, increases in life expectancy driven by changes in smoking ranged from 0.91 to 2.34 years for men while, for women, smoking-related changes ranged from a 0.61-year decline to a 0.45-year improvement. The most favorable trends in years of life lost to smoking tended to be concentrated in large central metros in the South and Midwest, while the least favorable trends occurred in nonmetros in these same regions. Smoking contributed to increases in mortality inequality for men aged 70+, with the contribution ranging from 8 to 24%, and for women aged 50–84, ranging from 14 to 44%.ConclusionsMortality attributable to smoking is declining fastest in large cities and coastal areas and more slowly in nonmetropolitan areas of the US. Increasing geographic inequalities in mortality are partly due to these geographic divergences in smoking patterns over the past several decades. Policies addressing smoking in non-metropolitan areas may reduce geographic inequality in mortality and contribute to future gains in life expectancy.

This layer shows health insurance coverage sex and race by age group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Sums may add to more than the total, as people can be in multiple race groups (for example, Hispanic and Black)This layer is symbolized to show the percent of population with no health insurance coverage. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B27010, C27001B, C27001C, C27001D, C27001E, C27001F, C27001G, C27001H, C27001I (Not all lines of these tables are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

The Charleston Heart Study (CHS) is a prospective cohort study of 2,283 subjects (1,394 whites, 889 blacks) in which risk factors of coronary disease have been examined for the past 43 years. The CHS began enrolling a random selection of community residents who in 1960 were 35 years of age and older ����?? including men and women, black and white. A unique feature of this cohort is the fact that 102 high socio-economic status (SES) black men were purposefully included. The primary hypothesis of the original study was to investigate racial differences in the manifestation and risk factors for coronary disease. Over the ensuing 40+ years, a variety of outcome measurements were incorporated into the re-examination of the participants, including psychosocial, behavioral, aging and functional measures. Subjects were initially interviewed and examined in 1960 and 1963. Subsequent interviews and examinations took place during the following time periods: 1974-1975, 1984-1985, 1987-1989, and 1990-1991. During the most recent questionnaire (1990-1991), the following topics were examined: general health, smoking, functional disability, physical disability, cardiovascular health, sexual dysfunction, cognitive disability, depression, coffee consumption, medication history, medical history, nutrition, and body image. In addition, serum samples and blood pressure measurements were taken, and a physical exam was performed by a physician. A search of the National Death Index was completed through the year 2000, matching individuals with date and cause of death. Vital status of the CHS study participants through 12-31-2000 is presented below. Dead * White Men 539 (82.5%) * White Women 500 (67.5%) * Black Men 281 (84.4%) * High SES Black Men 59 (57.8%) * Black Women 343 (75.6%) Data Availability: Datasets are stored in the National Archive of Computerized Data on Aging (NACDA) in the ICPSR as Study No. 4050. Data are also available from the Medical University of South Carolina Library; contact a PI, Paul J. Nietert, nieterpj (at) musc.edu for further information. * Dates of Study: 1960-2000 * Study Features: Longitudinal, Minority Oversamples, Anthropometric Measures * Sample Size: 1960: 2,283 (baseline) Link ICPSR, http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/04050

Graph and download economic data for Labor Force Participation Rate - Men (LNS11300001) from Jan 1948 to Jun 2025 about males, participation, 16 years +, labor force, labor, household survey, rate, and USA.

Since the 1950s, the suicide rate in the United States has been significantly higher among men than women. In 2022, the suicide rate among men was almost four times higher than that of women. However, the rate of suicide for both men and women has increased gradually over the past couple of decades. Facts on suicide in the United States In 2022, the rate of suicide death in the United States was around 14 per 100,000 population. The suicide rate in the U.S. has generally increased since the year 2000, with the highest rates ever recorded in the years 2018 and 2022. In the United States, death rates from suicide are highest among those aged 45 to 64 years and lowest among younger adults aged 15 to 24. The states with the highest rates of suicide are Montana, Alaska, and Wyoming, while New Jersey and Massachusetts have the lowest rates. Suicide among men In 2023, around 4.5 percent of men in the United States reported having serious thoughts of suicide in the past year. Although this rate is lower than that of women, men still have a higher rate of suicide death than women. One reason for this may have to do with the method of suicide. Although firearms account for the largest share of suicide deaths among both men and women, firearms account for almost 60 percent of all suicides among men and just 35 percent among women. Suffocation and poisoning are the other most common methods of suicide among women, with the chances of surviving a suicide attempt from these methods being much higher than surviving an attempt by firearm. The age group with the highest rate of suicide death among men is by far those aged 75 years and over.

Since 1789, the United States has had 45 different men serve as president, of which five are still alive today. At 78 years and two months, Joe Biden became the oldest man to ascend to the presidency for the first time in 2021, however Donald Trump was older when he re-entered the White House, at 78 years and seven months. Eight presidents have died while in office, including four who were assassinated by gunshot, and four who died of natural causes. The president who died at the youngest age was John F. Kennedy, who was assassinated at 46 years old in Texas in 1963; Kennedy was also the youngest man ever elected to the office of president. The longest living president in history is Jimmy Carter, who celebrated his 100th birthday in just before his death in 2024. The youngest currently-living president is Barack Obama, who turned 63 in August 2024. Coincidentally, presidents Clinton, Bush Jr., and Trump were all born within 66 days of one another, between June and August 1946. George Washington The U.S.' first president, George Washington, died after developing a severe inflammation of the throat, which modern scholars suspect to have been epiglottitis. However, many suspect that it was the treatments used to treat this illness that ultimately led to his death. After spending a prolonged period in cold and wet weather, Washington fell ill and ordered his doctor to let one pint of blood from his body. As his condition deteriorated, his doctors removed a further four pints in an attempt to cure him (the average human has between eight and twelve pints of blood in their body). Washington passed away within two days of his first symptoms showing, leading many to believe that this was due to medical malpractice and not due to the inflammation in his throat. Bloodletting was one of the most common and accepted medical practices from ancient Egyptian and Greek times until the nineteenth century, when doctors began to realize how ineffective it was; today, it is only used to treat extremely rare conditions, and its general practice is heavily discouraged. Zachary Taylor Another rare and disputed cause of death for a U.S. president was that of Zachary Taylor, who died sixteen months into his first term in office. Taylor had been celebrating the Fourth of July in the nation's capital in 1850, where he began to experience stomach cramps after eating copious amounts of cherries, other fruits, and iced milk. As his condition worsened, he drank a large amount of water in an attempt to alleviate his symptoms, but to no avail. Taylor died of gastroenteritis five days later, after being treated with a heavy dose of drugs and bloodletting. The most commonly accepted theories for his illness are that the ice used in the milk and the water consumed afterwards were contaminated with cholera, and that this was further exacerbated by the large amounts of acid in his system from eating so much fruit. There are some suggestions that recovery was feasible, but the actions of his doctors had made this impossible. Additionally, there have been conspiracy theories suggesting that Taylor was poisoned by pro-slavery secessionists from the Southern States, although there appears to be no evidence to back this up.