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/

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. 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 https://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). 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. In this way, the original 305 Bay Area Zip codes were reduced to 218 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.

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.

Average number IDU living with AIDS, number of AIDS deaths, and AIDS mortality rates of IDUs living with AIDS per 10,000 adult population (age 15–64) in 86 large metropolitan statistical areas in the USA 1993–1995 (Pre-HAART) and 2004–2006 (HAART era), and the AIDS mortality rate ratio between late and early years.

Footnotes: 1 Sources: Statistics Canada, Canadian Vital Statistics, Birth, Death and Stillbirth Databases. The table 13-10-0110-01 is an update of table 13-10-0408-01. 2 Infant mortality corresponds to the death of a child under one year of age. Expressed as a rate per 1,000 live births. 3 Perinatal deaths include late fetal deaths (stillbirths with a gestational age of 28 weeks or more) and early neonatal deaths (deaths of infants aged less than one week). 4 Numbers and rates in this table may differ from those found in similar data published by the Vital Statistics program as the data here have been tabulated based on postal codes available for place of residence. 5 2017 data for Yukon are not available. 6 The number of births, stillbirths, and deaths in Ontario for 2016 and 2017 are considered preliminary. 7 Due to improvements in methodology and timeliness, the duration of data collection has been shortened compared to previous years. As a result, there may have been fewer births and stillbirths captured by the time of the release. The 2017 data are therefore considered preliminary. 8 A census metropolitan area (CMA) is an area consisting of one or more adjacent municipalities situated around a major urban core. To form a census metropolitan area, the urban core must have a population of at least 100,000. The CMAs are those defined for the 2016 Census. To form a census agglomeration, the urban core must have a population of at least 10,000. 9 The metropolitan influenced zone (MIZ) classification is an approach to better differentiate areas of Canada outside of census metropolitan areas and census agglomerations. Census subdivisions that lie outside these areas are classified into one of four zones of influence. They are assigned to categories based on the flow of residents travelling to work in an urban area with a population greater than 10,000. Municipalities where more that 30% of the residents commute to work in an urban core are assigned to the strong MIZ category. Municipalities where between 5% and 30% of the residents commute to work in an urban core are assigned to the moderate MIZ category. Municipalities where between 0% and 5% of the residents commute to work in an urban core are assigned to the weak MIZ category. Municipalities where fewer than 40 or none of the residents commute to work in an urban core are assigned to the zero MIZ category. 10 Geographical areas are modified every 5 years to reflect the most recent census definitions, therefore, data are not strictly comparable historically. 11 Counts and rates in this table are based on three consecutive years of data. 12 The 95% confidence interval (CI) illustrates the degree of variability associated with a rate. 13 Wide confidence intervals (CIs) indicate high variability, thus, these rates should be interpreted and compared with due caution. 14 The following standard symbols are used in this Statistics Canada table: (..) for figures not available for a specific reference period, (...) for figures not applicable and (x) for figures suppressed to meet the confidentiality requirements of the Statistics Act. 15 The figures shown in the tables have been subjected to a confidentiality procedure known as controlled rounding to prevent the possibility of associating statistical data with any identifiable individual. Under this method, all figures, including totals and margins, are rounded either up or down to a multiple of 5. Controlled rounding has the advantage over other types of rounding of producing additive tables as well as offering more protection.

The Sulu Sea is located in the 'warm pool' of the western Pacific Ocean, where mean annual temperatures are the highest of anywhere on Earth. Because this large heat source supplies the atmosphere with a significant portion of its water vapour and latent heat, understanding the climate history of the region is important for reconstructing global palaeoclimate and for predicting future climate change. Changes in the oxygen isotope composition of planktonic foraminifera from Sulu Sea sediments have previously been shown to reflect changes in the planetary ice volume at glacial–interglacial and millenial timeseales, and such records have been obtained for the late Pleistocene epoch and the last deglaciation (Linsley and Thunell, 1990, doi:10.1029/PA005i006p01025; Lindley and Dunbar, 1994, doi:10.1029/93PA03216; Kudrass et al., 1991, doi:10.1038/349406a0). Here I present results that extend the millenial time resolution record back to 150,000 years before present. On timescales of around 10,000 years, the Sulu Sea oxygen-isotope record matches changes in sea level deduced from coral terraces on the Huon peninsula (Chappell and Shackleton, doi:10.1038/324137a0). This is particularly the case during isotope stage 3 (an interglacial period 23,000 to 58,000 years ago) where the Sulu Sea oxygen-isotope record deviates from the SPECMAP deep-ocean oxygen-isotope record (Imbrie et al., 1984). Thus these results support the idea (Chappell and Shackleton, doi:10.1038/324137a0; Shackleton, 1987, doi:10.1016/0277-3791(87)90003-5) that there were higher sea levels and less continental ice during stage 3 than the SPECMAP record implies and that sea level during this interglacial was just 40–50 metres below present levels. The subsequent rate of increase in continental ice volume during the return to full glacial conditions was correspondingly faster than previously thought. Calendar year calculated following Stuiver and Braziunas (1993) for raw uncorrected ages 9,000 14C yr. Sedimentation rates for the radiocarbon-dated portion of Site 124-769 record average 11.2 cm/ka, whereas over the entire record sedimentation rates average 7.5 cm/ka.

Note: ARDA – Association of Religious Data Archives; BLS – Bureau of Labor Statistics; CDC – Centers for Disease Control and Prevention; FBI – Federal Bureau of Investigation; SAMSHA N-SSATS – Substance Abuse and Mental Health Services National Survey of Substance Abuse Treatment Services. We used intercensal estimates of population aged 15–64 [66], [67].*US AIDS Mortality Surveillance Data for 1991–2006 received by special data request (2009) from the US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for HIV and TB Prevention.**Estimates of IDUs per 10,000 adult population are estimates of the proportion of the adult population who injected drugs in the prior year.***Gini coefficients are measures of the extent to which distributions of resources within a population would need to change to create equality. Zero represents equality, 1 represents maximum inequality. The household Gini used here presents data on inequality in household incomes.

Health Poverty Index - Situation of Health: Premature mortality: Directly age and gender standardised rate of average annual years of life lost up to age 75 per 10,000 resident population

Source: Department of Health (DoH), ONS registered Deaths 1999-2001, ONS Mid Year Estimates 2001

Publisher: Health Poverty Index

Geographies: Local Authority District (LAD), National

Geographic coverage: England

Time coverage: (Data from different timepoints between 1999 and 2001)

Type of data: Administrative data (age standardised)

Notes: The indicator is a directly age and gender standardised measure of premature death (i.e. under the age of 75),

Population and Housing Censuses: Main dwellings according to tenancy regime by average age of the household. National.

In the Bible, the Patriarchs may broadly apply to the 20 men in the ancestral line between Adam and Abraham, or it may specifically refer to the Prophets Abraham, his son Isaac, and Isaac's son Jacob. All of these men's stories are found in the book of Genesis, and this ancestral line can even be traced through both the Old and New Testament to Jesus, through figures such as David, Solomon, and Zerubbabel, to both family lines of Mary and Joseph. Oldest person ever The first 10 patriarchs in Genesis are referred to as the antediluvian as their stories are largely told before the Great Flood, and most of these men are said to have lived for over 900 years, with Methusela often cited as the oldest man in history, at 969 years old. After the Flood, lifespans of the patriarchs tend to grow shorter, but they are still longer than any of those recorded in recent history. The lifespans of the patriarchs in the Bible is in stark contrast to estimates for average life expectancy before the industrial era, which was usually around 24 years, or around 50-60 years for those who survived into adulthood. Significance Most of the numbers given in this genealogy are stated in Chapters five and eleven of Genesis, where three numbers are generally attached to each patriarch - their age at the time of their son's birth, their lifespan thereafter, and their total lifespan. These figures can then show how much overlap there was between the lives of each patriarch, and depending on the bible's translation, this gives a timeframe of somewhere between 2,000 and 4,000 years. When combined with the subsequent events spread across the Bible, this is a large part of why many adherents believe that the earth is somewhere between 5,000 and 10,000 years old (5783 in the Hebrew calendar), in contrast to the figure of 4.54 billion years that is generally accepted among the scientific community.

Mortality rates (per 10,000 prisoners) and the relative percentage change in prisoner mortality for forty-four states reporting to the NCRP, 2000–2014.

Age-adjusted rate of Asthma Hospitalizations per 10,000 from 2010-2020. These data include inpatient hospitalizations of individuals who are hospitalized in acute care hospitals. These data are based only on primary discharge diagnosis codes. These data do not include individuals who were seen by an Emergency Department, but not admitted to the hospital. Veterans Affairs, Indian Health Services and institutionalized (prison) populations are excluded. No attempt has been made to remove records resulting from transfer among acute care hospitals, as a result there may be duplicate records for a single hospitalization event. Effective October 1, 2015, hospital record data transitioned to a new coding system called the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM). Differences between counts and rates in years prior to 2015 compared with 2015 and subsequent years could be a result of this coding change and not an actual difference in the number of events.

Several organisms, including humans, display a deceleration in mortality rates at advanced ages. This mortality deceleration is sufficiently rapid to allow late-life mortality to plateau in old age in several species, causing the apparent cessation of biological ageing. Here, it is shown that late-life mortality deceleration (LLMD) and late-life plateaus are caused by common demographic errors. Age estimation and cohort blending errors introduced at rates below 1 in 10,000 are sufficient to cause LLMD and plateaus. In humans, observed error rates of birth and death registration predict the magnitude of LLMD. Correction for these sources of demographic error using a mixed linear model eliminates LLMD and late-life mortality plateaus (LLMPs) without recourse to biological or evolutionary models. These results suggest models developed to explain LLMD have been fitted to an error distribution, that ageing does not slow or stop during old age in humans, and that there is a finite limit to human longevity.

"Population and housing census. Population structure" is intended to provide information on the number of residents in the C. A of the Basque Country, in addition to its spatial distribution by municipality, district, section, entity. The population by neighbourhood is made in municipalities with more than 10,000 inhabitants. The description of the population is made by means of demographic variables such as sex, age, place of birth and nationality. This operation provides information as of January 1 of each year since 2001, now providing information as of July 1 as of 2022. The main source is the administrative register of the Municipal Register, in addition to others such as the Birth Statistics and the Death Statistics.

Footnotes: 1 Sources: Statistics Canada, Canadian Vital Statistics, Birth, Death and Stillbirth Databases. The table 13-10-0110-01 is an update of table 13-10-0408-01. 2 Infant mortality corresponds to the death of a child under one year of age. Expressed as a rate per 1,000 live births. 3 Perinatal deaths include late fetal deaths (stillbirths with a gestational age of 28 weeks or more) and early neonatal deaths (deaths of infants aged less than one week). 4 Numbers and rates in this table may differ from those found in similar data published by the Vital Statistics program as the data here have been tabulated based on postal codes available for place of residence. 5 2017 data for Yukon are not available. 6 The number of births, stillbirths, and deaths in Ontario for 2016 and 2017 are considered preliminary. 7 Due to improvements in methodology and timeliness, the duration of data collection has been shortened compared to previous years. As a result, there may have been fewer births and stillbirths captured by the time of the release. The 2017 data are therefore considered preliminary. 8 A census metropolitan area (CMA) is an area consisting of one or more adjacent municipalities situated around a major urban core. To form a census metropolitan area, the urban core must have a population of at least 100,000. The CMAs are those defined for the 2016 Census. To form a census agglomeration, the urban core must have a population of at least 10,000. 9 The metropolitan influenced zone (MIZ) classification is an approach to better differentiate areas of Canada outside of census metropolitan areas and census agglomerations. Census subdivisions that lie outside these areas are classified into one of four zones of influence. They are assigned to categories based on the flow of residents travelling to work in an urban area with a population greater than 10,000. Municipalities where more that 30% of the residents commute to work in an urban core are assigned to the strong MIZ category. Municipalities where between 5% and 30% of the residents commute to work in an urban core are assigned to the moderate MIZ category. Municipalities where between 0% and 5% of the residents commute to work in an urban core are assigned to the weak MIZ category. Municipalities where fewer than 40 or none of the residents commute to work in an urban core are assigned to the zero MIZ category. 10 Geographical areas are modified every 5 years to reflect the most recent census definitions, therefore, data are not strictly comparable historically. 11 Counts and rates in this table are based on three consecutive years of data. 12 The 95% confidence interval (CI) illustrates the degree of variability associated with a rate. 13 Wide confidence intervals (CIs) indicate high variability, thus, these rates should be interpreted and compared with due caution. 14 The following standard symbols are used in this Statistics Canada table: (..) for figures not available for a specific reference period, (...) for figures not applicable and (x) for figures suppressed to meet the confidentiality requirements of the Statistics Act. 15 The figures shown in the tables have been subjected to a confidentiality procedure known as controlled rounding to prevent the possibility of associating statistical data with any identifiable individual. Under this method, all figures, including totals and margins, are rounded either up or down to a multiple of 5. Controlled rounding has the advantage over other types of rounding of producing additive tables as well as offering more protection.

There were 10,156 deaths registered in England and Wales for the week ending July 11, 2025, compared with 10,019 in the previous week. During this time period, the two weeks with the highest number of weekly deaths were in April 2020, with the week ending April 17, 2020, having 22,351 deaths, and the following week 21,997 deaths, a direct result of the COVID-19 pandemic in the UK. Death and life expectancy As of 2022, the life expectancy for women in the UK was just over 82.5 years, and almost 78.6 years for men. Compared with 1765, when average life expectancy was under 39 years, this is a huge improvement in historical terms. Even in the more recent past, life expectancy was less than 47 years at the start of the 20th Century, and was under 70 as recently as the 1950s. Despite these significant developments in the long-term, improvements in life expectancy stalled between 2009/11 and 2015/17, and have even gone into decline since 2020. Between 2020 and 2022, for example, life expectancy at birth fell by 23 weeks for females, and 37 weeks for males. COVID-19 in the UK The first cases of COVID-19 in the United Kingdom were recorded on January 31, 2020, but it was not until a month later that cases began to rise exponentially. By March 5 of this year there were more than 100 cases, rising to 1,000 days later and passing 10,000 cumulative cases by March 26. At the height of the pandemic in late April and early May, there were around six thousand new cases being recorded daily. As of January 2023, there were more than 24.2 million confirmed cumulative cases of COVID-19 recorded in the United Kingdom, resulting in 202,156 deaths.

Footnotes: 1 Sources: Statistics Canada, Canadian Vital Statistics, Birth, Death and Stillbirth Databases. The table 13-10-0110-01 is an update of table 13-10-0408-01. 2 Infant mortality corresponds to the death of a child under one year of age. Expressed as a rate per 1,000 live births. 3 Perinatal deaths include late fetal deaths (stillbirths with a gestational age of 28 weeks or more) and early neonatal deaths (deaths of infants aged less than one week). 4 Numbers and rates in this table may differ from those found in similar data published by the Vital Statistics program as the data here have been tabulated based on postal codes available for place of residence. 5 2017 data for Yukon are not available. 6 The number of births, stillbirths, and deaths in Ontario for 2016 and 2017 are considered preliminary. 7 Due to improvements in methodology and timeliness, the duration of data collection has been shortened compared to previous years. As a result, there may have been fewer births and stillbirths captured by the time of the release. The 2017 data are therefore considered preliminary. 8 A census metropolitan area (CMA) is an area consisting of one or more adjacent municipalities situated around a major urban core. To form a census metropolitan area, the urban core must have a population of at least 100,000. The CMAs are those defined for the 2016 Census. To form a census agglomeration, the urban core must have a population of at least 10,000. 9 The metropolitan influenced zone (MIZ) classification is an approach to better differentiate areas of Canada outside of census metropolitan areas and census agglomerations. Census subdivisions that lie outside these areas are classified into one of four zones of influence. They are assigned to categories based on the flow of residents travelling to work in an urban area with a population greater than 10,000. Municipalities where more that 30% of the residents commute to work in an urban core are assigned to the strong MIZ category. Municipalities where between 5% and 30% of the residents commute to work in an urban core are assigned to the moderate MIZ category. Municipalities where between 0% and 5% of the residents commute to work in an urban core are assigned to the weak MIZ category. Municipalities where fewer than 40 or none of the residents commute to work in an urban core are assigned to the zero MIZ category. 10 Geographical areas are modified every 5 years to reflect the most recent census definitions, therefore, data are not strictly comparable historically. 11 Counts and rates in this table are based on three consecutive years of data. 12 The 95% confidence interval (CI) illustrates the degree of variability associated with a rate. 13 Wide confidence intervals (CIs) indicate high variability, thus, these rates should be interpreted and compared with due caution. 14 The following standard symbols are used in this Statistics Canada table: (..) for figures not available for a specific reference period, (...) for figures not applicable and (x) for figures suppressed to meet the confidentiality requirements of the Statistics Act. 15 The figures shown in the tables have been subjected to a confidentiality procedure known as controlled rounding to prevent the possibility of associating statistical data with any identifiable individual. Under this method, all figures, including totals and margins, are rounded either up or down to a multiple of 5. Controlled rounding has the advantage over other types of rounding of producing additive tables as well as offering more protection.

Simulated age-standardized rates per 10000 person-years of incident coronary heart disease and CHD deaths in adults aged 35 to 64 years with low or high socioeconomic status, by gender.

As of 2018, the largest distribution of U.S. physicians was between the ages of 55 and 65 years old. At that time about ** percent of physicians fell within this age group. With just **** percent of all physicians, the smallest distribution of U.S. physicians was among those aged 35 years or younger. Data suggests that in the U.S. the average age of medical students is around 24 years old and the average age of matriculants is about **.

U.S. Physician demographics  

It is estimated that one of the best ways to combat aging population health needs is to increase the number of doctors practicing in the U.S. In general, the number of physicians in the U.S. has been on the rise. Every year about 20 thousand new physicians join the U.S. workforce. Despite an increase in the number of physicians the number of active physicians per 10,000 people has remained relatively stagnant in recent years. As of 2019, the specialty with the largest number of physicians was psychiatry, followed by surgery.

Physician compensation  

Physician compensation varies significantly between regions and genders. With graduates owing an average of ******* U.S. dollars in student loans upon graduation, equal compensation has become especially important. However, women in the medical industry make significantly less income than their male counterparts. As of 2019, female physicians earned between ** and ** thousand U.S. dollars less than male physicians. Regionally, there are also significant differences. As of 2018, physicians working in the North Central U.S. had higher annual compensation than those in other areas. Those working in the Northeast had the lowest annual compensation.

Footnotes: 1 Sources: Statistics Canada, Canadian Vital Statistics, Birth, Death and Stillbirth Databases. The table 13-10-0110-01 is an update of table 13-10-0408-01. 2 Infant mortality corresponds to the death of a child under one year of age. Expressed as a rate per 1,000 live births. 3 Perinatal deaths include late fetal deaths (stillbirths with a gestational age of 28 weeks or more) and early neonatal deaths (deaths of infants aged less than one week). 4 Numbers and rates in this table may differ from those found in similar data published by the Vital Statistics program as the data here have been tabulated based on postal codes available for place of residence. 5 2017 data for Yukon are not available. 6 The number of births, stillbirths, and deaths in Ontario for 2016 and 2017 are considered preliminary. 7 Due to improvements in methodology and timeliness, the duration of data collection has been shortened compared to previous years. As a result, there may have been fewer births and stillbirths captured by the time of the release. The 2017 data are therefore considered preliminary. 8 A census metropolitan area (CMA) is an area consisting of one or more adjacent municipalities situated around a major urban core. To form a census metropolitan area, the urban core must have a population of at least 100,000. The CMAs are those defined for the 2016 Census. To form a census agglomeration, the urban core must have a population of at least 10,000. 9 The metropolitan influenced zone (MIZ) classification is an approach to better differentiate areas of Canada outside of census metropolitan areas and census agglomerations. Census subdivisions that lie outside these areas are classified into one of four zones of influence. They are assigned to categories based on the flow of residents travelling to work in an urban area with a population greater than 10,000. Municipalities where more that 30% of the residents commute to work in an urban core are assigned to the strong MIZ category. Municipalities where between 5% and 30% of the residents commute to work in an urban core are assigned to the moderate MIZ category. Municipalities where between 0% and 5% of the residents commute to work in an urban core are assigned to the weak MIZ category. Municipalities where fewer than 40 or none of the residents commute to work in an urban core are assigned to the zero MIZ category. 10 Geographical areas are modified every 5 years to reflect the most recent census definitions, therefore, data are not strictly comparable historically. 11 Counts and rates in this table are based on three consecutive years of data. 12 The 95% confidence interval (CI) illustrates the degree of variability associated with a rate. 13 Wide confidence intervals (CIs) indicate high variability, thus, these rates should be interpreted and compared with due caution. 14 The following standard symbols are used in this Statistics Canada table: (..) for figures not available for a specific reference period, (...) for figures not applicable and (x) for figures suppressed to meet the confidentiality requirements of the Statistics Act. 15 The figures shown in the tables have been subjected to a confidentiality procedure known as controlled rounding to prevent the possibility of associating statistical data with any identifiable individual. Under this method, all figures, including totals and margins, are rounded either up or down to a multiple of 5. Controlled rounding has the advantage over other types of rounding of producing additive tables as well as offering more protection.

Unadjusted average monthly rate (per 10,000 population) of RV-AGE and overall AGE hospitalizations and ED visits before and after RV immunization program implementation, August 1, 2005 to March 31, 2013: Ontario, Canada.