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.

In 2023, there were *** male deaths from all causes per 100,000 inhabitants in the United States. This statistics shows the death rate for all causes in the U.S. from 1950 to 2023, sorted by gender.

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

Cancer survival statistics are typically expressed as the proportion of patients alive at some point subsequent to the diagnosis of their cancer. Statistics compare the survival of patients diagnosed with cancer with the survival of people in the general population who are the same age, race, and sex and who have not been diagnosed with cancer.

Extracted in machine readable form from the AIHW Mortality Over Regions and Time (MORT) books.

MORT books are Excel workbooks that contain recent deaths data for specific geographical areas, sourced from the AIHW National Mortality Database. They present summary deaths statistics by sex for each geographic area, including counts, rates, median age at death, premature deaths, potential years of life lost and potentially avoidable deaths. The workbooks also present leading causes of death by sex for each geographic area.

The MORT books present data for 2015–2019. Due to changes in geographic classifications over time, long-term trends are not available.

For more information, please see Deaths data at AIHW or contact us at deaths@aihw.gov.au..

Also available on data.gov.au are the AIHW General Record of Incidence of Mortality (GRIM) data.

Rates were age standardized to the 2000 U.S. standard population.*Annual percent change based on rates that were age-adjusted to the 2000 U.S. standard population using regression analysis.†Annual percent change is statistically significantly different from zero (two-sided P

Introduction

This report presents projections of population from 2015 to 2025 by age and sex for Illinois, Chicago and Illinois counties produced for the Certificate of Need (CON) Program. As actual future population trends are unknown, the projected numbers should not be considered a precise prediction of the future population; rather, these projections, calculated under a specific set of assumptions, indicate the levels of population that would result if our assumptions about each population component (births, deaths and net migration) hold true. The assumptions used in this report, and the details presented below, generally assume a continuation of current trends.

Methodology These projections were produced using a demographic cohort-component projection model. In this model, each component of population change – birth, death and net migration – is projected separately for each five-year birth cohort and sex. The cohort – component method employs the following basic demographic balancing equation: P1 = P0 + B – D + NM Where: P1 = Population at the end of the period; P0 = Population at the beginning of the period; B = Resident births during the period; D = Resident deaths during the period; and NM = Net migration (Inmigration – Outmigration) during the period. The model roughly works as follows: for every five-year projection period, the base population, disaggregated by five-year age groups and sex, is “survived” to the next five-year period by applying the appropriate survival rates for each age and sex group; next, net migrants by age and sex are added to the survived population. The population under 5 years of age is generated by applying age specific birth rates to the survived females in childbearing age (15 to 49 years).

Base Population These projections began with the July 1, 2010 population estimates by age and sex produced by the U.S. Census Bureau. The most recent census population of April 1, 2010 was the base for July 1, 2010 population estimates.

Special Populations In 19 counties, the college dormitory population or adult inmates in correctional facilities accounted for 5 percent or more of the total population of the county; these counties were considered as special counties. There were six college dorm counties (Champaign, Coles, DeKalb, Jackson, McDonough and McLean) and 13 correctional facilities counties (Bond, Brown, Crawford, Fayette, Fulton, Jefferson, Johnson, Lawrence, Lee, Logan, Montgomery, Perry and Randolph) that qualified as special counties. When projecting the population, these special populations were first subtracted from the base populations for each special county; then they were added back to the projected population to produce the total population projections by age and sex. The base special population by age and sex from the 2010 population census was used for this purpose with the assumption that this population will remain the same throughout each projection period.

Mortality Future deaths were projected by applying age and sex specific survival rates to each age and sex specific base population. The assumptions on survival rates were developed on the basis of trends of mortality rates in the individual life tables constructed for each level of geography for 1989-1991, 1999-2001 and 2009-2011. The application of five-year survival rates provides a projection of the number of persons from the initial population expected to be alive in five years. Resident deaths data by age and sex from 1989 to 2011 were provided by the Illinois Center for Health Statistics (ICHS), Illinois Department of Public Health.

Fertility Total fertility rates (TFRs) were first computed for each county. For most counties, the projected 2015 TFRs were computed as the average of the 2000 and 2010 TFRs. 2010 or 2015 rates were retained for 2020 projections, depending on the birth trend of each county. The age-specific birth rates (ASBR) were next computed for each county by multiplying the 2010 ASBR by each projected TFR. Total births were then projected for each county by applying age-specific birth rates to the projected female population of reproductive ages (15 to 49 years). The total births were broken down by sex, using an assumed sex-ratio at birth. These births were survived five years applying assumed survival ratios to get the projected population for the age group 0-4. For the special counties, special populations by age and sex were taken out before computing age-specific birth rates. The resident birth data used to compute age-specific birth rates for 1989-1991, 1999-2001 and 2009-2011 came from ICHS. Births to females younger than 15 years of age were added to those of the 15-19 age group and births to women older than 49 years of age were added to the 45-49 age group.

Net Migration Migration is the major component of population change in Illinois, Chicago and Illinois counties. The state is experiencing a significant loss of population through internal (domestic migration within the U.S.) net migration. Unlike data on births and deaths, migration data based on administrative records are not available on a regular basis. Most data on migration are collected through surveys or indirectly from administrative records (IRS individual tax returns). For this report, net migration trends have been reviewed using data from different sources and methods (such as residual method) from the University of Wisconsin, Madison, Illinois Department of Public Health, individual exemptions data from the Internal Revenue Service, and survey data from the U.S. Census Bureau. On the basis of knowledge gained through this review and of levels of net migration from different sources, assumptions have been made that Illinois will have annual net migrants of -40, 000, -35,000 and -30,000 during 2010-2015, 2015-2020 and 2020-2025, respectively. These figures have been distributed among the counties, using age and sex distribution of net migrants during 1995-2000. The 2000 population census was the last decennial census, which included the question “Where did you live five years ago?” The age and sex distribution of the net migrants was derived, using answers to this question. The net migration for Chicago has been derived independently, using census survival method for 1990-2000 and 2000-2010 under the assumption that the annual net migration for Chicago will be -40,000, -30,000 and -25,000 for 2010-2015, 2015-2020 and 2020-2025, respectively. The age and sex distribution from the 2000-2010 net migration was used to distribute the net migrants for the projection periods.

Conclusion These projections were prepared for use by the Certificate of Need (CON) Program; they are produced using evidence-based techniques, reasonable assumptions and the best available input data. However, as assumptions of future demographic trends may contain errors, the resulting projections are unlikely to be free of errors. In general, projections of small areas are less reliable than those for larger areas, and the farther in the future projections are made, the less reliable they may become. When possible, these projections should be regularly reviewed and updated, using more recent birth, death and migration data.

Extracted in machine readable form from the AIHW Mortality Over Regions and Time (MORT) books. MORT books are Excel workbooks that contain recent deaths data for specific geographical areas, sourced …Show full descriptionExtracted in machine readable form from the AIHW Mortality Over Regions and Time (MORT) books. MORT books are Excel workbooks that contain recent deaths data for specific geographical areas, sourced from the AIHW National Mortality Database. They present summary deaths statistics by sex for each geographic area, including counts, rates, median age at death, premature deaths, potential years of life lost and potentially avoidable deaths. The workbooks also present leading causes of death by sex for each geographic area. The MORT books present data for 2015–2019. Due to changes in geographic classifications over time, long-term trends are not available. For more information, please see Deaths data at AIHW or contact us at deaths@aihw.gov.au.. Also available on data.gov.au are the AIHW General Record of Incidence of Mortality (GRIM) data.

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

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.

In 2022, there were around **** deaths from suicide per 100,000 population among males in the U.S. aged ** years and *****. Males aged 75 years and older were more likely to die from suicide than any other age group for both males and females. The suicide death rate for males in general is constantly greater than that for females. Suicide method by gender Not only do suicide rates differ by gender, but the method of suicide varies as well. Suicide by firearm accounts for ** percent of suicides among males, but only ** percent of those among females. However, suicide by poisoning accounts for a much larger share of suicides among females than males. In 2019, there were a total of ****** firearm suicides and ***** poisoning suicides. Substance abuse, mental health, and suicide Those who suffer from substance abuse and certain mental health disorders are at a much greater risk of falling victim to suicide. It’s been found that around ** percent of those with drug or alcohol dependence or abuse had serious thoughts of suicide in the past year, compared to just ***** percent of those with no such substance dependence of abuse. Similarly, around *** percent of those with a major depressive episode in the past year had attempted suicide, while only *** percent of those without a major depressive episode had done so.

BackgroundCombination antiretroviral therapy (ART) has significantly increased survival among HIV-positive adults in the United States (U.S.) and Canada, but gains in life expectancy for this region have not been well characterized. We aim to estimate temporal changes in life expectancy among HIV-positive adults on ART from 2000–2007 in the U.S. and Canada.MethodsParticipants were from the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD), aged ≥20 years and on ART. Mortality rates were calculated using participants' person-time from January 1, 2000 or ART initiation until death, loss to follow-up, or administrative censoring December 31, 2007. Life expectancy at age 20, defined as the average number of additional years that a person of a specific age will live, provided the current age-specific mortality rates remain constant, was estimated using abridged life tables.ResultsThe crude mortality rate was 19.8/1,000 person-years, among 22,937 individuals contributing 82,022 person-years and 1,622 deaths. Life expectancy increased from 36.1 [standard error (SE) 0.5] to 51.4 [SE 0.5] years from 2000–2002 to 2006–2007. Men and women had comparable life expectancies in all periods except the last (2006–2007). Life expectancy was lower for individuals with a history of injection drug use, non-whites, and in patients with baseline CD4 counts <350 cells/mm3.ConclusionsA 20-year-old HIV-positive adult on ART in the U.S. or Canada is expected to live into their early 70 s, a life expectancy approaching that of the general population. Differences by sex, race, HIV transmission risk group, and CD4 count remain.

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 3;Income adequacy quintile 2 ...), Age (14 items: At 25 years; At 30 years; At 35 years; At 40 years ...), Sex (3 items: Both sexes; Females; Males ...), Characteristics (3 items: Probability of survival; Low 95% confidence interval; life expectancy; High 95% confidence interval; life expectancy ...).

The county-level geographic mortality differentials have persisted in the past four decades in the United States (US). Though several socioeconomic factors (e.g., inequality) partially explain this phenomenon, the role of race/ethnic segregation, in general, and the different dimensions of segregation, more specifically, has been underexplored. Focusing on all-cause age-sex standardized US county-level mortality (2004–2008), this study has two substantive goals: (1) to understand whether segregation is a determinant of mortality and if yes, how the relationship between segregation and mortality varies by racial/ethnic dyads (e.g., white/black), and (2) to explore whether different dimensions of segregation (i.e., evenness, exposure, concentration, centralization, and clustering) are associated with mortality. A third goal is methodological: to assess whether spatial autocorrelation influences our understanding of the associations between the dimensions of segregation and mortality. Race/ethnic segregation was found to contribute to the geographic mortality disparities. Moreover, the relationship with mortality differed by both race/ethnic group and the dimension of segregation. Specifically, white/black segregation is positively related to mortality, whereas the segregation between whites and non-black minorities is negatively associated with mortality. Among the five dimensions of segregation, evenness and exposure are more strongly related to mortality than other dimensions. Spatial filtering approaches also identified six unique spatial patterns that significantly affect the spatial distribution of mortality. These patterns offer possible insights that help identify omitted variables related to the persistent patterning of mortality in the US.

In 2022, around 80 percent of transgender people in the United States had considered suicide, while around 40 percent had attempted suicide. There has been an upward trend in both the considered and attempted suicide rate since 2000, when 61 percent of transgender people considered committing suicide and 28 percent had attempted it.

South Korea currently has the highest overall suicide rate among OECD countries worldwide. The suicide rate among women in South Korea is significantly higher than that of women in any other country. Nevertheless, suicide is commonly more prevalent among men than women. Suicide in the U.S. The suicide rate in the United States has risen since the year 2000. As of 2022, there were around **** deaths from suicide per 100,000 population. The suicide rate among men in the U.S. is over ***** times what it is for females, a considerable and troubling difference. The suicide rate among men increases with age, with the highest rates found among men aged 75 years and older. Adolescent suicide Adolescent suicide is always a serious and difficult topic. A recent survey found that around ** percent of female high school students in the United States had seriously considered attempting suicide in the past year, compared to ** percent of male students. On average, there are around ** suicide deaths among adolescents per 100,000 population in the United States. The states with the highest rates of adolescent suicide include New Mexico, Idaho, and Oklahoma.