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 ...).

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.

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.

This table contains mortality indicators by sex for Canada and all provinces except Prince Edward Island. These indicators are derived from three-year complete life tables. Mortality indicators derived from single-year life tables are also available (table 13-10-0837). For Prince Edward Island, Yukon, the Northwest Territories and Nunavut, mortality indicators derived from three-year abridged life tables are available (table 13-10-0140).

Across the world, people are living longer.

In 1900, the average life expectancy of a newborn was 32 years. By 2021 this had more than doubled to 71 years.

But where, when, how, and why has this dramatic change occurred?

To understand it, we can look at data on life expectancy worldwide.

The large reduction in child mortality has played an important role in increasing life expectancy. But life expectancy has increased at all ages. Infants, children, adults, and the elderly are all less likely to die than in the past, and death is being delayed.

This remarkable shift results from advances in medicine, public health, and living standards. Along with it, many predictions of the ‘limit’ of life expectancy have been broken.

, you will find global data and research on life expectancy and related measures of longevity: the probability of death at a given age, the sex gap in life expectancy, lifespan inequality within countries, and more. Life expectancy has increased across the world In 2021, the global average life expectancy was just over 70 years. This is an astonishing fact – because just two hundred years ago, it was less than half.

This was the case for all world regions: in 1800, no region had a life expectancy higher than 40 years.

The average life expectancy has risen steadily and significantly across all regions.1

This extraordinary rise is the result of a wide range of advances in health – in nutrition, clean water, sanitation, neonatal healthcare, antibiotics, vaccines, and other technologies and public health efforts – and improvements in living standards, economic growth, and poverty reduction.

legacy-wordpress-upload Twice as long – life expectancy around the world Life expectancy has doubled over the last two centuries around the world. How has this happened?

📌### ******What you should know about this data****** Period life expectancy is a metric that summarizes death rates across all age groups in one particular year. For a given year, it represents the average lifespan for a hypothetical group of people, if they experienced the same age-specific death rates throughout their whole lives as the age-specific death rates seen in that particular year. This data is compiled from three sources: the United Nations’ World Population Prospects (UN WPP), Zijdeman et al. (2015)2, and Riley (2005)3. For data points before 1950, we use Human Mortality Database data4 combined with Zijdeman (2015). From 1950 onwards, we use UN WPP data. For pre-1950 data on world regions and the world as a whole, we use estimates from Riley (2005). Riley (2005)3 compiles life expectancy estimates from hundreds of historical sources and calculates the average of estimates that met an acceptable quality threshold, such as having estimates for entire nations or regions. Less historical data is available from the pre-health transition period in countries – this is especially the case for Africa, Asia, Oceania, and the former Soviet Union. Zijdeman et al. (2015)2 compiles data from various sources: the OECD.Stat database library, the United Nations World Population Prospects Database (UN WPP), the Human Mortality Database (HMD), the Montevideo-Oxford Latin American Economic History Database (MOxLAD), and Gapminder. In some cases, regional databases are used, such as Wrigley et al. (1997)5 for life expectancy in England in the 17th, 18th and early 19th centuries; the ONS for Australia; Kannisto et al. (1999)6 for Finland; and data from the Estonian Interuniversity Population Research Centre for Estonia. The UN WPP estimates life expectancy in various countries using data on mortality rates. In poorer countries, where death registration data is often lacking, the underlying data often comes from national household surveys, which are then used to estimate mortality rates and life expectancy.

📌## There are wide differences in life expectancy around the world In 2021, Nigeria's life expectancy was thirty years lower than Japan’s.

This striking fact reflects the wide differences in life expectancy between countries, which you can see on the map.

These wide differences are also reflected within countries. Countries with a lower average life expectancy also tend to have wider variations in lifespans.

📌**## Life expectancy has increased at all ages** It’s a common misconception that life expectancy has only increased because of declines in child mortality.

This is part of what happened. Child mortality used to be high and contributed significantly to short lifespans in the past, and it has declined greatly over time.

But, especially in recent decades, child mortality declines have contributed much less to increasing life expectancy8, and large declines in mortality are seen across all age groups.

You can see this in the chart. It shows the total life expectancy for people who have already survived to older ages.

F...

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

Introduction

Blue Zones refer to five specific geographic areas around the world where people live significantly longer, often reaching 100 years of age or more, and enjoy higher rates of well-being and lower incidences of chronic diseases. The term was popularised by Dan Buettner, a National Geographic journalist and author, who, along with a team of researchers and scientists, studied these regions to understand why their populations experience longer and healthier lives. link

The five recognised Blue Zones are:

1. Okinawa, Japan
2. Sardinia, Italy
3. Nicoya Peninsula, Costa Rica
4. Ikaria, Greece
5. Loma Linda, California, USA

Key Factors of Longevity in Blue Zones

Across these regions, Buettner and his team identified common lifestyle, dietary, and social factors that contribute to the long and healthy lives of the inhabitants. These include:

Plant-Based Diets: The diets of Blue Zone populations are largely plant-based, rich in whole grains, legumes, vegetables, fruits, and nuts, with limited amounts of meat and processed foods. While there is some variation, a diet high in plant-based nutrition seems to be a central factor.

Physical Activity: Regular, low-intensity physical activity is part of everyday life in these communities. People often walk long distances, farm, garden, or do manual labour as part of their daily routines, ensuring that they remain active throughout their lives.

Social Connections: Strong social ties, including family connections, close friendships, and a sense of belonging within a community, contribute significantly to mental and emotional well-being. Loneliness and social isolation, which are risk factors for mortality, are less common in Blue Zones.

Purpose (Ikigai): Many people in Blue Zones have a strong sense of purpose, often referred to as "ikigai" in Japan or "plan de vida" in Costa Rica. This purpose gives individuals a reason to get up every day, which is linked to longevity and life satisfaction.

Moderation and Fasting: Intermittent fasting and moderation in eating are practices commonly seen across Blue Zones. In Okinawa, for example, people follow the "hara hachi bu" principle, which means eating until one is 80% full. Limiting caloric intake without malnutrition is thought to promote longevity.

Stress Management: Stress is inevitable, but Blue Zone populations have developed effective ways to manage it. This includes practices like meditation, prayer, spending time in nature, and taking time to relax or nap in the middle of the day.

Now, let’s explore the characteristics of each Blue Zone in more detail.

1. Okinawa, Japan

Okinawa is home to one of the highest concentrations of centenarians (people aged 100 and older) in the world. Okinawans have traditionally followed a plant-heavy diet rich in vegetables like sweet potatoes, bitter melon, and tofu, along with small amounts of fish and occasionally pork. Their practice of "hara hachi bu," eating only until they are 80% full, helps them avoid overeating and maintain a healthy weight.

Okinawans also benefit from close-knit social networks known as "moai," which provide emotional support and reduce loneliness. They maintain a deep sense of purpose, or "ikigai," which has been shown to improve mental and physical health.

2. Sardinia, Italy

The Blue Zone of Sardinia is found in the mountainous region of the island, where men, in particular, have extremely long lifespans. Sardinians follow a Mediterranean-style diet rich in whole grains, vegetables, fruits, and beans, with a moderate amount of goat’s milk, cheese, and wine. Meat is consumed sparingly, mostly on special occasions.

Their longevity is also attributed to a lifestyle that involves a lot of physical activity, especially in farming and herding. Sardinians have strong family bonds and social connections, which contribute to their happiness and mental well-being.

3. Nicoya Peninsula, Costa Rica

The Nicoya Peninsula in Costa Rica is known for having a lower rate of middle-age mortality and a higher life expectancy than the rest of the country. Nicoyans follow a traditional Mesoamerican diet based on beans, corn, and squash, often referred to as the "three sisters" of agriculture. This diet is low in calories but rich in nutrients and antioxidants.

Nicoyans maintain a strong sense of purpose or "plan de vida," and their family-centred lifestyle fosters intergenerational support, which contributes to emotional well-being. Regular physical activity is part of daily life, with many Nicoyans walking, working outdoors, and engaging in manual labour even into old age.

4. Ikaria, Greece

Ikaria, a small island in the Aegean Sea, has one of the world's lowest rates of dementia and heart disease, along with an unus...

In 2023, about 17.7 percent of the American population was 65 years old or over; an increase from the last few years and a figure which is expected to reach 22.8 percent by 2050. This is a significant increase from 1950, when only eight percent of the population was 65 or over. A rapidly aging population In recent years, the aging population of the United States has come into focus as a cause for concern, as the nature of work and retirement is expected to change to keep up. If a population is expected to live longer than the generations before, the economy will have to change as well to fulfill the needs of the citizens. In addition, the birth rate in the U.S. has been falling over the last 20 years, meaning that there are not as many young people to replace the individuals leaving the workforce. The future population It’s not only the American population that is aging -- the global population is, too. By 2025, the median age of the global workforce is expected to be 39.6 years, up from 33.8 years in 1990. Additionally, it is projected that there will be over three million people worldwide aged 100 years and over by 2050.

this graph was created in OurDataWorld:

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But where, when, how, and why has this dramatic change occurred?

To understand it, we can look at data on life expectancy worldwide.

The large reduction in child mortality has played an important role in increasing life expectancy. But life expectancy has increased at all ages. Infants, children, adults, and the elderly are all less likely to die than in the past, and death is being delayed.

This remarkable shift results from advances in medicine, public health, and living standards. Along with it, many predictions of the ‘limit’ of life expectancy have been broken.

On this page, you will find global data and research on life expectancy and related measures of longevity: the probability of death at a given age, the sex gap in life expectancy, lifespan inequality within countries, and more.

In 2021, the global average life expectancy was just over 70 years. This is an astonishing fact – because just two hundred years ago, it was less than half.

This was the case for all world regions: in 1800, no region had a life expectancy higher than 40 years.

The average life expectancy has risen steadily and significantly across all regions.1

This extraordinary rise is the result of a wide range of advances in health – in nutrition, clean water, sanitation, neonatal healthcare, antibiotics, vaccines, and other technologies and public health efforts – and improvements in living standards, economic growth, and poverty reduction.

In this article, we cover this in more detail:

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

The infant mortality rate is defined as the number of deaths of children under one year of age, expressed per 1 000 live births. Some of the international variation in infant mortality rates is due to variations among countries in registering practices for premature infants. The United States and Canada are two countries which register a much higher proportion of babies weighing less than 500g, with low odds of survival, resulting in higher reported infant mortality. In Europe, several countries apply a minimum gestational age of 22 weeks (or a birth weight threshold of 500g) for babies to be registered as live births. This indicator is measured in terms of deaths per 1 000 live births.

This indicator is a summary measure of premature mortality, providing an explicit way of weighting deaths occurring at younger ages, which may be preventable. The calculation of Potential Years of Life Lost (PYLL) involves summing up deaths occurring at each age and multiplying this with the number of remaining years to live up to a selected age limit (age 75 is used in OECD Health Statistics). In order to assure cross-country and trend comparison, the PYLL are standardised, for each country and each year. The total OECD population in 2010 is taken as the reference population for age standardisation. This indicator is presented as a total and per gender. It is measured in years lost per 100 000 inhabitants (total), per 100 000 men and per 100 000 women, aged 0-69.

Life expectancy at birth is defined as how long, on average, a newborn can expect to live, if current death rates do not change. However, the actual age-specific death rate of any particular birth cohort cannot be known in advance. If rates are falling, actual life spans will be higher than life expectancy calculated using current death rates. Life expectancy at birth is one of the most frequently used health status indicators. Gains in life expectancy at birth can be attributed to a number of factors, including rising living standards, improved lifestyle and better education, as well as greater access to quality health services. This indicator is presented as a total and per gender and is measured in years.

BackgroundGlobally in 2016, 1.7 million people died of Tuberculosis (TB). This study aimed to estimate all-cause mortality rate, identify features associated with mortality and describe trend in mortality rate from treatment initiation.MethodA 5-year (2012–2016) retrospective analysis of electronic TB surveillance data from Kilifi County, Kenya. The outcome was all-cause mortality within 180 days after starting TB treatment. The risk factors examined were demographic and clinical features at the time of starting anti-TB treatment. We performed survival analysis with time at risk defined from day of starting TB treatment to time of death, lost-to-follow-up or completing treatment. To account for ‘lost-to-follow-up’ we used competing risk analysis method to examine risk factors for all-cause mortality.Results10,717 patients receiving TB treatment, median (IQR) age 33 (24–45) years were analyzed; 3,163 (30%) were HIV infected. Overall, 585 (5.5%) patients died; mortality rate of 12.2 (95% CI 11.3–13.3) deaths per 100 person-years (PY). Mortality rate increased from 7.8 (95% CI 6.4–9.5) in 2012 to 17.7 (95% CI 14.9–21.1) in 2016 per 100PY (Ptrend

BackgroundThe National Health Service (NHS) Health Check programme was introduced in 2009 in England to systematically assess all adults in midlife for cardiovascular disease risk factors. However, its current benefit and impact on health inequalities are unknown. It is also unclear whether feasible changes in how it is delivered could result in increased benefits. It is one of the first such programmes in the world. We sought to estimate the health benefits and effect on inequalities of the current NHS Health Check programme and the impact of making feasible changes to its implementation.Methods and findingsWe developed a microsimulation model to estimate the health benefits (incident ischaemic heart disease, stroke, dementia, and lung cancer) of the NHS Health Check programme in England. We simulated a population of adults in England aged 40–45 years and followed until age 100 years, using data from the Health Survey of England (2009–2012) and the English Longitudinal Study of Aging (1998–2012), to simulate changes in risk factors for simulated individuals over time. We used recent programme data to describe uptake of NHS Health Checks and of 4 associated interventions (statin medication, antihypertensive medication, smoking cessation, and weight management). Estimates of treatment efficacy and adherence were based on trial data. We estimated the benefits of the current NHS Health Check programme compared to a healthcare system without systematic health checks. This counterfactual scenario models the detection and treatment of risk factors that occur within ‘routine’ primary care. We also explored the impact of making feasible changes to implementation of the programme concerning eligibility, uptake of NHS Health Checks, and uptake of treatments offered through the programme. We estimate that the NHS Health Check programme prevents 390 (95% credible interval 290 to 500) premature deaths before 80 years of age and results in an additional 1,370 (95% credible interval 1,100 to 1,690) people being free of disease (ischaemic heart disease, stroke, dementia, and lung cancer) at age 80 years per million people aged 40–45 years at baseline. Over the life of the cohort (i.e., followed from 40–45 years to 100 years), the changes result in an additional 10,000 (95% credible interval 8,200 to 13,000) quality-adjusted life years (QALYs) and an additional 9,000 (6,900 to 11,300) years of life. This equates to approximately 300 fewer premature deaths and 1,000 more people living free of these diseases each year in England. We estimate that the current programme is increasing QALYs by 3.8 days (95% credible interval 3.0–4.7) per head of population and increasing survival by 3.3 days (2.5–4.1) per head of population over the 60 years of follow-up. The current programme has a greater absolute impact on health for those living in the most deprived areas compared to those living in the least deprived areas (4.4 [2.7–6.5] days of additional quality-adjusted life per head of population versus 2.8 [1.7–4.0] days; 5.1 [3.4–7.1] additional days lived per head of population versus 3.3 [2.1–4.5] days). Making feasible changes to the delivery of the existing programme could result in a sizable increase in the benefit. For example, a strategy that combines extending eligibility to those with preexisting hypertension, extending the upper age of eligibility to 79 years, increasing uptake of health checks by 30%, and increasing treatment rates 2.5-fold amongst eligible patients (i.e., ‘maximum potential’ scenario) results in at least a 3-fold increase in benefits compared to the current programme (1,360 premature deaths versus 390; 5,100 people free of 1 of the 4 diseases versus 1,370; 37,000 additional QALYs versus 10,000; 33,000 additional years of life versus 9,000). Ensuring those who are assessed and eligible for statins receive statins is a particularly important strategy to increase benefits. Estimates of overall benefit are based on current incidence and management, and future declines in disease incidence or improvements in treatment could alter the actual benefits observed in the long run. We have focused on the cardiovascular element of the NHS Health Check programme. Some important noncardiovascular health outcomes (e.g., chronic obstructive pulmonary disease [COPD] prevention from smoking cessation and cancer prevention from weight loss) and other parts of the programme (e.g., brief interventions to reduce harmful alcohol consumption) have not been modelled.ConclusionsOur model indicates that the current NHS Health Check programme is contributing to improvements in health and reducing health inequalities. Feasible changes in the organisation of the programme could result in more than a 3-fold increase in health benefits.

Purpose of App:The app has swiping capabilities to view areas with and without USACE project improvements. The scenario of the 100-year flood event is shown in this map in blue. Moving the swiper left to right shows increased flooding and indicates impacts of without area project improvements.Moving the swiper right to left shows less flooding and benefits of with project area improvements.The app also includes layer of Social Vulnerability (SVI 2018) from the CDC for comparison of socially vulnerable populations and their proximity to flooding affects in the 100 year flood range. Social Vulnerability refers to the extent that persons are likely to have long standing negative effects from natural disasters.These areas are less likely to bounce back after events like flooding. This is due to many factors such as: limited resources and assets, health disparities (such as disabilities), whether or not English is spoken and understood, and is correlated with higher likelihood of living in poverty. All of these factors contribute to a higher risk of losses and harm from natural disasters. The map shows areas of high vulnerability in red; these have the highest chances of loss, and greatest threat of harm from natural disasters. These areas are less likely to bounce back after events like flooding. This is due to many factors such as: limited resources and assets, health disparities (such as disabilities), and higher likelihood of living poverty. All of these factors contribute to a higher risk of losses and harm from natural disasters. There are three categories of vulnerability shown on this map. Areas in red (51% to 100%) refer to areas where people live that are the most at risk. The yellow areas range (26 to 50%), indicate mid-range vulnerability. The green areas of the map (0 to 25%) indicate the lowest vulnerability category.Purpose and Intent of Map:This product shows geography at the Census tract level for the scope of nine Texas coastal counties that occupy the USACE Galveston District and are affected by the 100 year flood levels. This map also incorporates Overall Social Vulnerability rankings by Census Tracts. These decimal rankings were converted to percentile format and symbolized in three different categories- (0 to 25 percent, 26 to 50 percent, and 51 to 100 percent). Higher percentile numbers indicate higher vulnerability and risks for that area due to increased percentage of social factors for that geographic area. This data was retrieved from the most up-to-date Social Vulnerability Index (SVI) 2018 released data, and sourced from the CDC and ATSDR.Website: https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.htmlWhat is CDC Social Vulnerability Index?ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) has created a tool to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event.The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every county and tract. CDC SVI ranks each county and tract on 15 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes:SocioeconomicHousing Composition and DisabilityMinority Status and LanguageHousing and Transportation VariablesFor a detailed description of variable uses, please refer to the full SVI 2018 documentation.Overall rankings were converted to percentile format and symbolized in 3 categories- (0 to 25, 26 to 50, and 51 to 100). Higher percentile indicates higher vulnerability and risks due to increased percentage of social factors associated with risk and vulnerability.

Slides for the 100 Year Starship Symposium 2012 presentation:

Vessel Archives - Existential Risk, Human Survival, and the Future of Life int he Universe

(No notes. See alternate for notes.)

BackgroundCancer is one of the main causes of death in the worldwide. Pancreatic Cancer (PC) is prevalent in developed and increasing in developing countries. PC is important because of its low survival rate, high fatality, and increasing incidence. Therefore, identifying risk factors to prevent its development is necessary. This study aimed to determine incidence of PC and its risk factors in the Golestan Cohort Study (GCS) in Iran.MethodThis study is a prospective population-based cohort study in the frame of GCS with 15 years of follow-up for PC. GCS was launched in the Golestan province of Iran with 50045 participants who were 40 to 75 years old. variables included: age, gender, education status, smoking, alcohol consumption, opium usage, type of blood group, dyslipidemia, body mass index (BMI), waist circumference (WC), family history (FH) of PC, ethnicity, and history of diabetes mellitus (DM).ResultAmong 50045 participants of GCS during 15 years of follow up, 100 people were diagnosed PC. PC incidence was 0.2%. Age-standardized incidence rate (ASR) of PC in the study population was 11.12 per 100,000 person-years. People with age ≥60 years were 46, in 50–59 years old group were 36, and 18 of them were

The Federal Poverty Level is a measure of poverty issued every year by the US Department of Health and Human Services. The 2022 FPL thresholds for a family of four correspond to annual incomes of $27,750 (100% FPL), $55,500 (200% FPL), and $83,250 (300% FPL).The Federal Poverty Level is used to determine eligibility for certain programs and benefits. Living in poverty has a profound impact on health and wellbeing. People living in poverty are at high risk for economic hardship, housing insecurity, food insecurity, chronic stress, and inadequate access to healthcare.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Demographic Trends and Health Outcomes in the U.S

Inequalities,Risk Factors and Access to Care

By Data Society [source]

About this dataset

This dataset contains key demographic, health status indicators and leading cause of death data to help us understand the current trends and health outcomes in communities across the United States. By looking at this data, it can be seen how different states, counties and populations have changed over time. With this data we can analyze levels of national health services use such as vaccination rates or mammography rates; review leading causes of death to create public policy initiatives; as well as identify risk factors for specific conditions that may be associated with certain populations or regions. The information from these files includes State FIPS Code, County FIPS Code, CHSI County Name, CHSI State Name, CHSI State Abbreviation, Influenza B (FluB) report count & expected cases rate per 100K population , Hepatitis A (HepA) Report Count & expected cases rate per 100K population , Hepatitis B (HepB) Report Count & expected cases rate per 100K population , Measles (Meas) Report Count & expected cases rate per 100K population , Pertussis(Pert) Report Count & expected case rate per 100K population , CRS report count & expected case rate per 100K population , Syphilis report count and expected case rate per 100k popuation. We also look at measures related to preventive care services such as Pap smear screen among women aged 18-64 years old check lower/upper confidence intervals seperately ; Mammogram checks among women aged 40-64 years old specified lower/upper conifence intervals separetly ; Colonosopy/ Proctoscpushy among men aged 50+ measured in lower/upper limits ; Pneumonia Vaccination amongst 65+ with loewr/upper confidence level detail Additionally we have some interesting trend indicating variables like measures of birth adn death which includes general fertility ratye ; Teen Birth Rate by Mother's age group etc Summary Measures covers mortality trend following life expectancy by sex&age categories Vressionable populations access info gives us insight into disablilty ratio + access to envtiromental issues due to poor quality housing facilities Finally Risk Factors cover speicfic hoslitic condtiions suchs asthma diagnosis prevelance cancer diabetes alcholic abuse smoking trends All these information give a good understanding on Healthy People 2020 target setings demograpihcally speaking hence will aid is generating more evience backed policies

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How to use the dataset

What the Dataset Contains

This dataset contains valuable information about public health relevant to each county in the United States, broken down into 9 indicator domains: Demographics, Leading Causes of Death, Summary Measures of Health, Measures of Birth and Death Rates, Relative Health Importance, Vulnerable Populations and Environmental Health Conditions, Preventive Services Use Data from BRFSS Survey System Data , Risk Factors and Access to Care/Health Insurance Coverage & State Developed Types of Measurements such as CRS with Multiple Categories Identified for Each Type . The data includes indicators such as percentages or rates for influenza (FLU), hepatitis (HepA/B), measles(MEAS) pertussis(PERT), syphilis(Syphilis) , cervical cancer (CI_Min_Pap_Smear - CI_Max\Pap \Smear), breast cancer (CI\Min Mammogram - CI \Max \Mammogram ) proctoscopy (CI Min Proctoscopy - CI Max Proctoscopy ), pneumococcal vaccinations (Ci min Pneumo Vax - Ci max Pneumo Vax )and flu vaccinations (Ci min Flu Vac - Ci Max Flu Vac). Additionally , it provides information on leading causes of death at both county levels & national level including age-adjusted mortality rates due to suicide among teens aged between 15-19 yrs per 100000 population etc.. Furthermore , summary measures such as age adjusted percentage who consider their physical health fair or poor are provided; vulnerable populations related indicators like relative importance score for disabled adults ; preventive service use related ones ranging from self reported vaccination coverage among men40-64 yrs old against hepatitis B virus etc...

Getting Started With The Dataset

To get started with exploring this dataset first your need to understand what each column in the table represents: State FIPS Code identifies a unique identifier used by various US government agencies which denote states . County FIPS code denotes counties wi...

BackgroundCancer is one of the main causes of death in the worldwide. Pancreatic Cancer (PC) is prevalent in developed and increasing in developing countries. PC is important because of its low survival rate, high fatality, and increasing incidence. Therefore, identifying risk factors to prevent its development is necessary. This study aimed to determine incidence of PC and its risk factors in the Golestan Cohort Study (GCS) in Iran.MethodThis study is a prospective population-based cohort study in the frame of GCS with 15 years of follow-up for PC. GCS was launched in the Golestan province of Iran with 50045 participants who were 40 to 75 years old. variables included: age, gender, education status, smoking, alcohol consumption, opium usage, type of blood group, dyslipidemia, body mass index (BMI), waist circumference (WC), family history (FH) of PC, ethnicity, and history of diabetes mellitus (DM).ResultAmong 50045 participants of GCS during 15 years of follow up, 100 people were diagnosed PC. PC incidence was 0.2%. Age-standardized incidence rate (ASR) of PC in the study population was 11.12 per 100,000 person-years. People with age ≥60 years were 46, in 50–59 years old group were 36, and 18 of them were

Life expectancy in Italy was just under thirty in the year 1870, and over the course of the next 150 years, it is expected to have increased to 83.3 by the year 2020. Although life expectancy has generally increased throughout Italy's history, there were several times where the rate deviated from its previous trajectory. The most noticeable changes were a result of the First World War and Spanish Flu epidemic, and also the Second World War and Italian Civil War.