In 2024, the average life expectancy in the world was 71 years for men and 76 years for women. The lowest life expectancies were found in Africa, while Oceania and Europe had the highest. What is life expectancy?Life expectancy is defined as a statistical measure of how long a person may live, based on demographic factors such as gender, current age, and most importantly the year of their birth. The most commonly used measure of life expectancy is life expectancy at birth or at age zero. The calculation is based on the assumption that mortality rates at each age were to remain constant in the future. Life expectancy has changed drastically over time, especially during the past 200 years. In the early 20th century, the average life expectancy at birth in the developed world stood at 31 years. It has grown to an average of 70 and 75 years for males and females respectively, and is expected to keep on growing with advances in medical treatment and living standards continuing. Highest and lowest life expectancy worldwide Life expectancy still varies greatly between different regions and countries of the world. The biggest impact on life expectancy is the quality of public health, medical care, and diet. As of 2022, the countries with the highest life expectancy were Japan, Liechtenstein, Switzerland, and Australia, all at 84–83 years. Most of the countries with the lowest life expectancy are mostly African countries. The ranking was led by the Chad, Nigeria, and Lesotho with 53–54 years.

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 18.2 more years on average. Women aged 65 years can expect to live around 20.7 more years on average. Life expectancy in the U.S. As of 2023, the average life expectancy at birth in the United States was 78.39 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 2023, a woman in the U.S. could be expected to live up to 81.1 years. Leading causes of death The leading causes of death in the United States include heart disease, cancer, unintentional injuries, and cerebrovascular diseases. However, heart disease and cancer account for around 42 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.

For those born in 2024, the average life expectancy at birth across Africa was 62 years for men and 66 years for women. The average life expectancy globally was 71 years for men and 76 years for women in mid-2024. Additional information on life expectancy in Africa With the exception of North Africa where life expectancy is around the worldwide average for men and women, life expectancy across all African regions paints a negative picture. Comparison of life expectancy by continent shows the gap in average life expectancy between Africa and other continents. Africa trails Asia, the continent with the second lowest average life expectancy, by 10 years for men and 11 years for women. Life expectancy in Africa is the lowest globally Moreover, countries from across the African regions dominate the list of countries with the lowest life expectancy worldwide. Nigeria and Chad had the lowest life expectancy for those born in 2024 for women and men, respectively. However, there is reason for hope despite the low life expectancy rates in many African countries. The Human Development index rating in Sub-Saharan Africa has increased significantly from nearly 0.44 to 0.57 between 2000 and 2023, demonstrating an improvement in quality of life and, as a result, greater access to vital services that allow people to live longer lives. One such improvement has been successful efforts to reduce the rate of aids infection and research into combating its effects. The number of new HIV infections across sub-Saharan Africa has decreased from over 1.3 million in 2015 to close to 650,000 in 2024. However, the sub-region still accounts for 50 percent of the total new HIV infections.

In 2022, a newborn Hispanic child in the United States had a projected life expectancy of 80 years. In comparison, the life expectancy at birth for a Asian, non-Hispanic child in 2022 was 84.4 years, the highest life expectancy among the ethnic groups studied.

It is only in the past two centuries where demographics and the development of human populations has emerged as a subject in its own right, as industrialization and improvements in medicine gave way to exponential growth of the world's population. There are very few known demographic studies conducted before the 1800s, which means that modern scholars have had to use a variety of documents from centuries gone by, along with archeological and anthropological studies, to try and gain a better understanding of the world's demographic development. Genealogical records One such method is the study of genealogical records from the past; luckily, there are many genealogies relating to European families that date back as far as medieval times. Unfortunately, however, all of these studies relate to families in the upper and elite classes; this is not entirely representative of the overall population as these families had a much higher standard of living and were less susceptible to famine or malnutrition than the average person (although elites were more likely to die during times of war). Nonetheless, there is much to be learned from this data. Impact of the Black Death In the centuries between 1200 and 1745, English male aristocrats who made it to their 21st birthday were generally expected to live to an age between 62 and 72 years old. The only century where life expectancy among this group was much lower was in the 1300s, where the Black Death caused life expectancy among adult English noblemen to drop to just 45 years. Experts assume that the pre-plague population of England was somewhere between four and seven million people in the thirteenth century, and just two million in the fourteenth century, meaning that Britain lost at least half of its population due to the plague. Although the plague only peaked in England for approximately eighteen months, between 1348 and 1350, it devastated the entire population, and further outbreaks in the following decades caused life expectancy in the decade to drop further. The bubonic plague did return to England sporadically until the mid-seventeenth century, although life expectancy among English male aristocrats rose again in the centuries following the worst outbreak, and even peaked at more than 71 years in the first half of the sixteenth century.

Absolute changes in life expectancy at age 20 among people in prisons, by race & sex across periods, 2000–2014.

This dataset documents rates and trends in local hypertension-related cardiovascular disease (CVD) death rates. Specifically, this report presents county (or county equivalent) estimates of hypertension-related CVD death rates in 2000-2019 and trends during two intervals (2000-2010, 2010-2019) by age group (ages 35–64 years, ages 65 years and older), race/ethnicity (non-Hispanic American Indian/Alaska Native, non-Hispanic Asian/Pacific Islander, non-Hispanic Black, Hispanic, non-Hispanic White), and sex (female, male). The rates and trends were estimated using a Bayesian spatiotemporal model and a smoothed over space, time, and demographic group. Rates are age-standardized in 10-year age groups using the 2010 US population. Data source: National Vital Statistics System.

Tunisia had the highest projected life expectancy at birth in Africa as of 2025. A newborn infant was expected to live about 77 years in the country. Algeria, Cabo Verde, Morocco, and Mauritius followed, with a life expectancy between 77 and 75 years. On the other hand, Nigeria registered the lowest average, at 54.8 years. Overall, the life expectancy in Africa was just over 64 years in the same year.

Create maps of U.S. heart disease death rates by county. Data can be stratified by age, race/ethnicity, and sex. Visit the CDC/DHDSP Atlas of Heart Disease and Stroke for additional data and maps. Atlas of Heart Disease and StrokeData SourceMortality data were obtained from the National Vital Statistics System. Bridged-Race Postcensal Population Estimates were obtained from the National Center for Health Statistics. International Classification of Diseases, 10th Revision (ICD-10) codes: I00-I09, I11, I13, I20-I51; underlying cause of death.Data DictionaryData for counties with small populations are not displayed when a reliable rate could not be generated. These counties are represented in the data with values of '-1.' CDC/DHDSP excludes these values when classifying the data on a map, indicating those counties as 'Insufficient Data.' Data field names and descriptionsstcty_fips: state FIPS code + county FIPS codeOther fields use the following format: RRR_S_aaaa (e.g., API_M_35UP)   RRR: 3 digits represent race/ethnicity     All - Overall     AIA - American Indian and Alaska Native, non-Hispanic     API - Asian and Pacific Islander, non-Hispanic     BLK - Black, non-Hispanic     HIS - Hispanic     WHT - White, non-Hispanic   S: 1 digit represents sex     A - All    F - Female     M - Male aaaa: 4 digits represent age. The first 2 digits are the lower bound for age and the last 2 digits are the upper bound for age. 'UP' indicates the data includes the maximum age available and 'LT' indicates ages less than the upper bound.  Example: The column 'BLK_M_65UP' displays rates per 100,000 black men aged 65 years and older.MethodologyRates are calculated using a 3-year average and are age-standardized in 10-year age groups using the 2000 U.S. Standard Population. Rates are calculated and displayed per 100,000 population. Rates were spatially smoothed using a Local Empirical Bayes algorithm to stabilize risk by borrowing information from neighboring geographic areas, making estimates more statistically robust and stable for counties with small populations. Data for counties with small populations are coded as '-1' when a reliable rate could not be generated. County-level rates were generated when the following criteria were met over a 3-year time period within each of the filters (e.g., age, race, and sex).At least one of the following 3 criteria: At least 20 events occurred within the county and its adjacent neighbors.ORAt least 16 events occurred within the county.ORAt least 5,000 population years within the county.AND all 3 of the following criteria:At least 6 population years for each age group used for age adjustment if that age group had 1 or more event.The number of population years in an age group was greater than the number of events.At least 100 population years within the county.More Questions?Interactive Atlas of Heart Disease and StrokeData SourcesStatistical Methods

The American black bear (Ursus americanus) has one of the broadest geographic distributions of any mammalian carnivore in North America. Populations occur from high to low elevations and from mesic to arid environments, and their demographic traits have been documented in a wide variety of environments. However, the demography of American black bears in semiarid environments, which comprise a significant portion of the geographic range, is poorly documented. To fill this gap in understanding, we used data from a long-term mark-recapture study of black bears in the semiarid environment of eastern Utah, USA. Cub and yearling survival were low and adult survival was high relative to other populations. Adult life stages had the highest reproductive value, comprised the largest proportion of the population, and exhibited the highest elasticity contribution to the population growth rate (i.e., λ). Vital rates of black bears in this semiarid environment are skewed toward higher survival of adults, and lower survival of cubs compared to other populations. Methods Mark-Recapture study We estimated survival rates from long-term mark-recapture data gathered as part of a 27-year study on American black bears of the East Tavaputs Plateau. During the first 12 years of the study (June to August 1991-2003) female bears were captured and radio-collared, and all bears were tagged in the ear, except for cubs and yearlings. For the entire study (1992 – 2019), collared females were visited in their dens annually during their winter hibernation to count newborn cubs and surviving yearlings. Age of individual bears was determined by 2 methods: (1) direct observation of cubs or yearlings (i.e., year of birth was known) or (2) cementum annuli analysis of a cross-section of the root of an extracted premolar (Palochak, 2004; Willey, 1974). The data we used to derive survival and fecundity rates consisted of the ID_number, cohort (cub, yearling, subadult, prime-aged adult, and old adult), age in years, sex (female, male, unknown), number of cubs, number of yearlings, first observation of individual, last observation of individual, days from last observation, and survival status. We did not include subadult and adult male bears in the analysis. Survival rates To determine the average survival rates for each life stage, we used a Cox proportional hazards model in program R (Team, 2022). This model accommodates staggered entries, where individuals enter the study at different times, and censoring, where the event of interest (e.g., mortality) is not observed for all individuals due to the inability to follow-up or the study ending before the event occurs. These features allow for a more accurate representation of survival over time, even with incomplete data (Cox, 1972). The Cox model is a semi-parametric approach that examines how covariates, such as age and environmental factors, influence the risk of death at any given point in time. Unlike fully parametric models, which require defining the baseline hazard function (the risk of death when all covariates are at baseline levels), the Cox model does not require this step, making it highly flexible and suitable for diverse data and applications (Zhang, 2016). The hazard function in this context refers to the rate or likelihood of an event (e.g., death) occurring at a specific moment, given that the individual has survived up to that time. The Cox model is expressed as follows: h(t|X) = h0(t) exp(β1X1 + β2X2 +...+ βpXp) where h(t|X) is the hazard function at time t given covariates X, h0(t) is the baseline hazard function β1, β2, …, βp are the coefficients for the predictor variables X1, X2, …, Xp. The model assumes proportional hazards, meaning the relative risk of death (the hazard ratio) between two groups remains constant over time (Zhang, 2016). The advantage of the Cox model is its ability to handle censored data, common in survival analysis. Censoring occurs when some individuals have not experienced mortality by the end of the study, so we only know that they survived up to that point. Moreover, the Cox model can incorporate time-dependent covariates, enabling a dynamic analysis of how risk factors influence survival over time (Therneau & Grambsch, 2000). For our analysis, we formulated four Cox proportional hazards models as follows: 1) constant survival, 2) a model with the effect of maternal age, 3) a model with the effect of cohort, and 4) a model with the combined effect of age and cohort. We compared these models using Akaike’s Information Criterion (AIC) to identify the best fit and then evaluate the effect sizes of covariates based on the β coefficients from the top-performing model (Burnham et al., 2011; Symonds & Moussalli, 2011). When there was uncertainty in model selection, we used model averaging to estimate effect sizes and β coefficients. Each model was also checked for uninformative parameters (Arnold, 2010). We reviewed the model summaries to assess the estimated effects of covariates (constant survival, maternal age, cohort, and the combination of age and cohort) on survival outcomes. Fecundity rates To determine fecundity rates, we used females monitored through the use of radio-collars. All females that were ≥ four years old were counted in the breeding pool. We removed any female ≥ 25 years of age from the breeding pool (Noyce, 2010). We classified old adults as ≥ 15 years old and prime-aged adults as 4-14 years of age. We visited dens of females to observe whether they were alone or accompanied by cubs or yearlings as well as the sexes of their offspring. At the height of the study, we had 15 prime-aged adult females, along with a few old-adult females. There was variation in the number of adult females and old-adult females throughout the study period and we had at least two old-adult females in each year for 12 years during the study. Matrix Transition Model and Analysis We developed a transition matrix model based on adult females and their offspring to estimate population growth and additional demographic parameters. In the model, we assumed every cub was born on January 1st and survived through the full year if they were alive through the 15th of October. We assumed density of males does not affect breeding success (Lewis et al., 2014). We divided the population into five age-based stages: cub (0–1 year-old); yearling (1–2 years old), subadult (2–4 years old), prime-aged adult (4–14 years old), and old adult (15+). We used the term sm to indicate the probability of surviving and transitioning to a new stage (matrix sub diagonal), and the term ss indicated the probability of surviving and staying in the same stage (matrix diagonal). We used f to indicate fecundity or reproduction (matrix upper right corner; Fig. 1A, 1B). We used the software Unified Life Models (ULM; (Legendre & Clobert, 1995) to evaluate the matrix model and to calculate population growth rate, stable age distribution, reproductive value, and sensitivity and elasticity matrices. We summed elasticity values across all stages for the three demographic processes: fecundity (f), growth (sm, transition from one age stage to another), and stasis (ss, survival without transitioning). Our matrix transition model differed from the matrix transition model generated by Beston (2011), which used nine life stages. To ensure an accurate comparison between the two models, we combined the nine life stages from the matrix transition model in the meta-analysis (Beston, 2011) into five broader stages: cub, yearling, subadult, adult, and old adult. We selected five life stages due to the assumption that age might influence reproductive output, a pattern supported by research on other mammals (Hilderbrand et al., 2019; Nussey et al., 2008; Promislow & Harvey, 1990).

2018 2020, county-level U.S. heart disease death rates. Dataset developed by the Centers for Disease Control and Prevention, Division for Heart Disease and Stroke Prevention.Create maps of U.S. heart disease death rates by county. Data can be stratified by age, race/ethnicity, and sex.Visit the CDC/DHDSP Atlas of Heart Disease and Stroke for additional data and maps. Atlas of Heart Disease and StrokeData SourceMortality data were obtained from the National Vital Statistics System. Bridged-Race Postcensal Population Estimates were obtained from the National Center for Health Statistics. International Classification of Diseases, 10th Revision (ICD-10) codes: I00-I09, I11, I13, I20-I51; underlying cause of death.Data DictionaryData for counties with small populations are not displayed when a reliable rate could not be generated. These counties are represented in the data with values of '-1.' CDC/DHDSP excludes these values when classifying the data on a map, indicating those counties as 'Insufficient Data.'Data field names and descriptionsstcty_fips: state FIPS code + county FIPS codeOther fields use the following format: RRR_S_aaaa (e.g., API_M_35UP)  RRR: 3 digits represent race/ethnicity    All - Overall    AIA - American Indian and Alaska Native, non-Hispanic    API - Asian and Pacific Islander, non-Hispanic    BLK - Black, non-Hispanic    HIS - Hispanic    WHT - White, non-Hispanic  S: 1 digit represents sex    A - All    F - Female    M - Male  aaaa: 4 digits represent age. The first 2 digits are the lower bound for age and the last 2 digits are the upper bound for age. 'UP' indicates the data includes the maximum age available and 'LT' indicates ages less than the upper bound. Example: The column 'BLK_M_65UP' displays rates per 100,000 black men aged 65 years and older.MethodologyRates are calculated using a 3-year average and are age-standardized in 10-year age groups using the 2000 U.S. Standard Population. Rates are calculated and displayed per 100,000 population. Rates were spatially smoothed using a Local Empirical Bayes algorithm to stabilize risk by borrowing information from neighboring geographic areas, making estimates more statistically robust and stable for counties with small populations. Data for counties with small populations are coded as '-1' when a reliable rate could not be generated. County-level rates were generated when the following criteria were met over a 3-year time period within each of the filters (e.g., age, race, and sex).At least one of the following 3 criteria:At least 20 events occurred within the county and its adjacent neighbors.ORAt least 16 events occurred within the county.ORAt least 5,000 population years within the county.AND all 3 of the following criteria:At least 6 population years for each age group used for age adjustment if that age group had 1 or more event.The number of population years in an age group was greater than the number of events.At least 100 population years within the county.More Questions?Interactive Atlas of Heart Disease and StrokeData SourcesStatistical Methods

Create maps of U.S. stroke death rates by county. Data can be stratified by age, race/ethnicity, and sex. Visit the CDC/DHDSP Atlas of Heart Disease and Stroke for additional data and maps. Atlas of Heart Disease and StrokeData SourceMortality data were obtained from the National Vital Statistics System. Bridged-Race Postcensal Population Estimates were obtained from the National Center for Health Statistics. International Classification of Diseases, 10th Revision (ICD-10) codes: I60-I69; underlying cause of death.Data DictionaryData for counties with small populations are not displayed when a reliable rate could not be generated. These counties are represented in the data with values of '-1.' CDC/DHDSP excludes these values when classifying the data on a map, indicating those counties as 'Insufficient Data.' Data field names and descriptionsstcty_fips: state FIPS code + county FIPS codeOther fields use the following format: RRR_S_aaaa (e.g., API_M_35UP)   RRR: 3 digits represent race/ethnicity     All - Overall     AIA - American Indian and Alaska Native, non-Hispanic     API - Asian and Pacific Islander, non-Hispanic     BLK - Black, non-Hispanic     HIS - Hispanic     WHT - White, non-Hispanic   S: 1 digit represents sex     A - All    F - Female     M - Male aaaa: 4 digits represent age. The first 2 digits are the lower bound for age and the last 2 digits are the upper bound for age. 'UP' indicates the data includes the maximum age available and 'LT' indicates ages less than the upper bound.  Example: The column 'BLK_M_65UP' displays rates per 100,000 black men aged 65 years and older.MethodologyRates are calculated using a 3-year average and are age-standardized in 10-year age groups using the 2000 U.S. Standard Population. Rates are calculated and displayed per 100,000 population. Rates were spatially smoothed using a Local Empirical Bayes algorithm to stabilize risk by borrowing information from neighboring geographic areas, making estimates more statistically robust and stable for counties with small populations. Data for counties with small populations are coded as '-1' when a reliable rate could not be generated. County-level rates were generated when the following criteria were met over a 3-year time period within each of the filters (e.g., age, race, and sex).At least one of the following 3 criteria: At least 20 events occurred within the county and its adjacent neighbors.ORAt least 16 events occurred within the county.ORAt least 5,000 population years within the county.AND all 3 of the following criteria:At least 6 population years for each age group used for age adjustment if that age group had 1 or more event.The number of population years in an age group was greater than the number of events.At least 100 population years within the county.More Questions?Interactive Atlas of Heart Disease and StrokeData SourcesStatistical Methods

Background: Researchers have reported gender differences in the association between perceived racial discrimination (PRD) and substance use including marijuana use (MU). A limited number of longitudinal studies, however, have documented the long-term effect of PRD during adolescence on subsequent MU in young adulthood.Objective: In the current longitudinal study, we tested gender differences in the association between baseline PRD during adolescence and subsequent MU during young adulthood within Black population.Methods: A cohort of 595 Black (278 male and 317 female) ninth grade students were followed for 13 years from 1999 (mean age 20) to 2012 (mean age 33). Participants were selected from an economically disadvantaged urban area in the Midwest, United States. The independent variable was PRD measured in 1999. The outcome was average MU between 2000 and 2012 (based on eight measurements). Covariates included age, socio-demographics (family structure, and parental employment), and substance use by friends and parents. Gender was the focal moderator. Linear regression was used for statistical analysis.Results: In the pooled sample, PRD in 1999 was not associated with average MU between 2000 and 2012. We did, however, find an interaction effect between baseline PRD and gender on average MU, suggesting stronger association for males than females. In gender-specific models, baseline PRD predicted average MU between 2000 and 2012 for males, but not for females.Conclusion: Exposure to PRD during late adolescence may have a larger role on MU of male than female Black young adults. Although we found that males are more vulnerable to the effects of PRD on MU, PRD should be prevented regardless of race, gender, and other social identities. While PRD is pervasive among Black Americans, exposure to PRD increase the risk of MU for Black males. Hence, substance use prevention efforts for Black males, in particular, should emphasize coping with PRD.

BackgroundAccording to one USA Renal Data System report, 57% of end-stage renal disease (ESRD) cases are attributed to hypertensive and diabetic nephropathy. Yet, trends in hypertension related ESRD mortality rates in adults ≥ 35 years of age have not been studied.ObjectivesThe aim of this retrospective study was to analyze the different trends hypertension related ESRD death rates among adults in the United States.MethodsDeath records from the CDC (Centers for Disease Control and Prevention Wide-Ranging OnLine Data for Epidemiologic Research) database were analyzed from 1999 to 2020 for hypertension related ESRD mortality in adults ≥ 35 years of age. Age-Adjusted mortality rates (AAMRs) per 100,000 persons and annual percent change (APC) were calculated and stratified by year, sex, race/ethnicity, place of death, and geographic location.ResultsHypertension-related ESRD caused a total of 721,511 deaths among adults (aged ≥ 35 years) between 1999 and 2020. The overall AAMR for hypertension related ESRD deaths in adults was 9.70 in 1999 and increased all the way up to 43.7 in 2020 (APC: 9.02; 95% CI: 8.19-11.04). Men had consistently higher AAMRs than woman during the analyzed years from 1999 (AAMR men: 10.8 vs women: 9) to 2020 (AAMR men: 52.2 vs women: 37.2). Overall AAMRs were highest in Non-Hispanic (NH) Black or African American patients (45.7), followed by NH American Indian or Alaska Natives (24.7), Hispanic or Latinos (23.4), NH Asian or Pacific Islanders (19.3), and NH White patients (15.4). Region-wise analysis also showed significant variations in AAMRs (overall AAMR: West 21.2; South: 21; Midwest: 18.3; Northeast: 14.2). Metropolitan areas had slightly higher AAMRs (19.1) than nonmetropolitan areas (19). States with AAMRs in 90th percentile: District of Columbia, Oklahoma, Mississippi, Tennessee, Texas, and South Carolina, had roughly double rates compared to states in 10th percentile.ConclusionsOverall hypertension related ESRD AAMRs among adults were seen to increase in almost all stratified data. The groups associated with the highest death rates were NH Black or African Americans, men, and populations in the West and metropolitan areas of the United States. Strategies and policies targeting these at-risk groups are required to control the rising hypertension related ESRD mortality.

2018 - 2020, county-level U.S. stroke death rates. Dataset developed by the Centers for Disease Control and Prevention, Division for Heart Disease and Stroke Prevention.Create maps of U.S. stroke death rates by county. Data can be stratified by age, race/ethnicity, and sex.Visit the CDC/DHDSP Atlas of Heart Disease and Stroke for additional data and maps. Atlas of Heart Disease and StrokeData SourceMortality data were obtained from the National Vital Statistics System. Bridged-Race Postcensal Population Estimates were obtained from the National Center for Health Statistics. International Classification of Diseases, 10th Revision (ICD-10) codes: I60-I69; underlying cause of death.Data DictionaryData for counties with small populations are not displayed when a reliable rate could not be generated. These counties are represented in the data with values of '-1.' CDC/DHDSP excludes these values when classifying the data on a map, indicating those counties as 'Insufficient Data.'Data field names and descriptionsstcty_fips: state FIPS code + county FIPS codeOther fields use the following format: RRR_S_aaaa (e.g., API_M_35UP)  RRR: 3 digits represent race/ethnicity    All - Overall    AIA - American Indian and Alaska Native, non-Hispanic    API - Asian and Pacific Islander, non-Hispanic    BLK - Black, non-Hispanic    HIS - Hispanic    WHT - White, non-Hispanic  S: 1 digit represents sex    A - All    F - Female    M - Male  aaaa: 4 digits represent age. The first 2 digits are the lower bound for age and the last 2 digits are the upper bound for age. 'UP' indicates the data includes the maximum age available and 'LT' indicates ages less than the upper bound. Example: The column 'BLK_M_65UP' displays rates per 100,000 black men aged 65 years and older.MethodologyRates are calculated using a 3-year average and are age-standardized in 10-year age groups using the 2000 U.S. Standard Population. Rates are calculated and displayed per 100,000 population. Rates were spatially smoothed using a Local Empirical Bayes algorithm to stabilize risk by borrowing information from neighboring geographic areas, making estimates more statistically robust and stable for counties with small populations. Data for counties with small populations are coded as '-1' when a reliable rate could not be generated. County-level rates were generated when the following criteria were met over a 3-year time period within each of the filters (e.g., age, race, and sex).At least one of the following 3 criteria:At least 20 events occurred within the county and its adjacent neighbors.ORAt least 16 events occurred within the county.ORAt least 5,000 population years within the county.AND all 3 of the following criteria:At least 6 population years for each age group used for age adjustment if that age group had 1 or more event.The number of population years in an age group was greater than the number of events.At least 100 population years within the county.More Questions?Interactive Atlas of Heart Disease and StrokeData SourcesStatistical Methods

2019 - 2021, county-level U.S. heart disease death rates. Dataset developed by the Centers for Disease Control and Prevention, Division for Heart Disease and Stroke Prevention.Data SourceMortality data were obtained from the National Vital Statistics System. Bridged-Race Postcensal Population Estimates were obtained from the National Center for Health Statistics. International Classification of Diseases, 10th Revision (ICD-10) codes: I00-I09, I11, I13, I20-I51; underlying cause of death.Data DictionaryData for counties with small populations are not displayed when a reliable rate could not be generated. These counties are represented in the data with values of '-1.' CDC excludes these values when classifying the data on a map, indicating those counties as 'Insufficient Data.'Data field names and descriptionsstcty_fips: state FIPS code + county FIPS codeOther fields use the following format: RRR_S_aaaa (e.g., API_M_35UP)  RRR: 3 digits represent race/ethnicity    All - Overall    AIA - American Indian and Alaska Native, non-Hispanic    ASN - Asian, non-Hispanic    BLK - Black, non-Hispanic    HIS - Hispanic NHP – Native Hawaiian or Other Pacific Islander, non-Hispanic MOR – More than one race, non-Hispanic    WHT - White, non-Hispanic  S: 1 digit represents sex    A - All    F - Female    M - Male  aaaa: 4 digits represent age. The first 2 digits are the lower bound for age and the last 2 digits are the upper bound for age. 'UP' indicates the data includes the maximum age available and 'LT' indicates ages less than the upper bound. Example: The column 'BLK_M_65UP' displays rates per 100,000 black men aged 65 years and older.MethodologyRates are calculated using a 3-year average and are age-standardized in 10-year age groups using the 2000 U.S. Standard Population. Rates are calculated and displayed per 100,000 population. Rates were spatially smoothed using a Local Empirical Bayes algorithm to stabilize risk by borrowing information from neighboring geographic areas, making estimates more statistically robust and stable for counties with small populations. Data for counties with small populations are coded as '-1' when a reliable rate could not be generated. County-level rates were generated when the following criteria were met over a 3-year time period within each of the filters (e.g., age, race, and sex).At least one of the following 3 criteria:At least 20 events occurred within the county and its adjacent neighbors.ORAt least 16 events occurred within the county.ORAt least 5,000 population years within the county.AND all 3 of the following criteria:At least 6 population years for each age group used for age adjustment if that age group had 1 or more event.The number of population years in an age group was greater than the number of events.At least 100 population years within the county.More Questions?Interactive Atlas of Heart Disease and StrokeData SourcesStatistical Methods

Information about the rates of cancer deaths in each state is reported. The data shows the total rate as well as rates based on sex, age, and race. Rates are also shown for three specific kinds of cancer: breast cancer, colorectal cancer, and lung cancer.

Original Data

Column Description

Key	List of...	Comment	Example Value
State	String	The name of a U.S. State (e.g., Virginia)	"Alabama"
Total.Rate	Float	Total Cancer Deaths (Rate per 100,000 Population, 2007-2013) 214.2	214.2
Total.Number	Float	Total Cancer Deaths (2007-2013)	71529.0
Total.Population	Float	Cumulative Population (Denominator Total_Cancer deaths total_) 2007-2013	33387205.0
Rates.Age.< 18	Float	Total Cancer Deaths (Under 18 Years, Rate per 100,000 Population, 2007-2013)	2.0
Rates.Age.18-45	Float	Total Cancer Deaths (18 to 44 Years, Rate per 100,000 Population, 2007-2013)	18.5
Rates.Age.45-64	Float	Total Cancer Deaths (45 to 64 Years, Rate per 100,000 Population, 2007-2013)	244.7
Rates.Age.> 64	Float	Total Cancer Deaths (65 Years and Over, Rate per 100,000 Population, 2007-2013)	1017.8
Rates.Age and Sex.Female.< 18	Float	Female under 18	2.0
Rates.Age and Sex.Male.< 18	Float	Male under 18	2.1
Rates.Age and Sex.Female.18 - 45	Float	Female 18 - 45	20.1
Rates.Age and Sex.Male.18 - 45	Float	Male 18 - 45	16.8
Rates.Age and Sex.Female.45 - 64	Float	Female 45 to 64 Years	201.0
Rates.Age and Sex.Male.45 - 64	Float	Male 45 to 64 Years	291.5
Rates.Age and Sex.Female.> 64	Float	Female 65 Years and Over	803.6
Rates.Age and Sex.Male.> 64	Float	Male 65 Years and Over	1308.6
Rates.Race.White	Float	Total Cancer Deaths (White, Rate per 100,000 Population, 2007-2013)	186.1
Rates.Race.White non-Hispanic	Float	Total Cancer Deaths (White non-Hispanic, Rate per 100,000 Population, 2007-2013)	187.5
Rates.Race.Black	Float	Total Cancer Deaths (Black or African American, Rate per 100,000 Population, 2007-2013)	216.1
Rates.Race.Asian	Float	Total Cancer Deaths (Asian or Pacific Islander, Rate per 100,000 Population, 2007-2013)	81.3
Rates.Race.Indigenous	Float	Total Cancer Deaths (American Indian or Alaska Native, Rate per 100,000 Population, 2007-2013)	69.9
Rates.Race and Sex.Female.White	Float	Female: White	149.2
Rates.Race and Sex.Female.White non-Hispanic	Float	Female: White non-Hispanic	150.2
Rates.Race and Sex.Female.Black	Float	Female: Black or African American	167.2
Rates.Race and Sex.Female.Black non-Hispanic	Float	Female: Black or African American non-Hispanic	167.9
Rates.Race and Sex.Female.Asian	Float	Female: Asian or Pacific Islander	84.9
Rates.Race and Sex.Female.Indigenous	Float	Female: American Indian or Alaska Native	53.8
...

This dataset provides information on the unemployment rates for different demographic groups in the United States.

The data is sourced from the Economic Policy Institute’s State of Working America Data Library and economic research conducted by the Federal Reserve Bank of St. Louis.

The dataset contains unemployment rates for various age groups, education levels, genders, races, and more.

Interesting Task Ideas:

1. See how unemployment rates have changed for different groups of people over time.
2. Look into how education levels can affect unemployment rates.
3. Compare unemployment rates between different races / genders.
4. Check out how unemployment rates can vary across different age groups and genders.
5. Find out if there's a connection between education levels and unemployment rates within specific racial or gender groups.
6. Explore how economic downturns can impact unemployment rates for specific groups of people.
7. Use the data to create visuals that show how unemployment rates differ across all sorts of factors.

Don't forget to upvote this dataset if you find it useful! 😊💝

Checkout my other datasets

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Column Descriptions

Columns	Description
date	Date of the data collection. (type: str, format: YYYY-MM-DD)
all	Unemployment rate for all demographics, ages 16 and older. (type: float)
16-24	Unemployment rate for the age group 16-24. (type: float)
25-54	Unemployment rate for the age group 25-54. (type: float)
55-64	Unemployment rate for the age group 55-64. (type: float)
65+	Unemployment rate for the age group 65 and older. (type: float)
less_than_hs	Unemployment rate for individuals with less than a high school education. (type: float)
high_school	Unemployment rate for individuals with a high school education. (type: float)
some_college	Unemployment rate for individuals with some college education. (type: float)
bachelor's_degree	Unemployment rate for individuals with a bachelor's degree. (type: float)
advanced_degree	Unemployment rate for individuals with an advanced degree. (type: float)
women	Unemployment rate for women of all demographics. (type: float)
women_16-24	Unemployment rate for women in the age group 16-24. (type: float)
women_25-54	Unemployment rate for women in the age group 25-54. (type: float)
women_55-64	Unemployment rate for women in the age group 55-64. (type: float)
women_65+	Unemployment rate for women in the age group 65 and older. (type: float)
women_less_than_hs	Unemployment rate for women with less than a high school education. (type: float)
women_high_school	Unemployment rate for women with a high school education. (type: float)
women_some_college	Unemployment rate for women with some college education. (type: float)
women_bachelor's_degree	Unemployment rate for women with a bachelor's degree. (type: float)
women_advanced_degree	Unemployment rate for women with an advanced degree. (type: float)
men	Unemployment rate for men of all demographics. (type: float)
men_16-24	Unemployment rate for men in the age group 16-24. (type: float)
men_25-54	Unemployment rate for men in the age group 25-54. (type: float)
men_55-64	Unemployment rate for men in the age group 55-64. (type: float)
men_65+	Unemployment rate for men in the age group 65 and older. (type: float)
men_less_than_hs	Unemployment rate for men with less than a high school education. (type: float)
men_high_school	Unemployment rate for men with a high school education. (type: float)
men_some_college	Unemployment rate for men with some college education. (type: float)
men_bachelor's_degree	Unemployment rate for men with a bachelor's degree. (type: float)
men_advanced_degree	Unemployment rate for men with an advanced degree. (type: float)
black	Unemployment rate for the Black/African American demographic. (type: float)
black_16-24	Unemployment rate for Black/African American individuals in the age group 16-24. (type: float)
black_25-54	Unemployment rate for Black/African American individuals in the age group 25-54. (type: float)
black_55-64	Unemployment...

Survival from age 35 to 75 and socioeconomic characteristics of White and Black men and women within 102 US CBSAs.

BackgroundCOVID-19 has had a disproportionate impact on racial and ethnic minorities compared to White people. Studies have not sufficiently examined how sex and age interact with race/ethnicity, and potentially shape COVID-19 outcomes. We sought to examine disparities in COVID-19 outcomes by race, sex and age over time, leveraging data from Michigan, the only state whose Department of Health and Human Services (DHSS) publishes cross-sectional race, sex and age data on COVID-19.MethodsThis is an observational study using publicly available COVID-19 data (weekly cases, deaths, and vaccinations) from August 31 2020 to June 9 2021. Outcomes for descriptive analysis were age-standardized COVID-19 incidence and mortality rates, case-fatality rates by race, sex, and age, and within-gender and within-race incidence rate ratios and mortality rate ratios. We used descriptive statistics and linear regressions with age, race, and sex as independent variables.ResultsThe within-sex Black-White racial gap in COVID-19 incidence and mortality decreased at a similar rate among men and women but the remained wider among men. As of June 2021, compared to White people, incidence was lower among Asian American and Pacific Islander people by 2644 cases per 100,000 people and higher among Black people by 1464 cases per 100,000 people. Mortality was higher among those aged 60 or greater by 743.6 deaths per 100,000 people vs those 0–39. The interaction between race and age was significant between Black race and age 60 or greater, with an additional 708.5 deaths per 100,000 people vs White people aged 60 or greater. Black people had a higher case fatality rate than White people.ConclusionCOVID-19 incidence, mortality and vaccination patterns varied over time by race, age and sex. Black-White disparities decreased over time, with a larger effect on Black men, and Older Black people were particularly more vulnerable to COVID-19 in terms of mortality. Considering different individual characteristics such as age may further help elucidate the mechanisms behind racial and gender health disparities.