This dataset contains polygons that represent the boundaries of statistical neighborhoods as defined by the DC Department of Health (DC Health). DC Health delineates statistical neighborhoods to facilitate small-area analyses and visualization of health, economic, social, and other indicators to display and uncover disparate outcomes among populations across the city. The neighborhoods are also used to determine eligibility for some health services programs and support research by various entities within and outside of government. DC Health Planning Neighborhood boundaries follow census tract 2010 lines defined by the US Census Bureau. Each neighborhood is a group of between one and seven different, contiguous census tracts. This allows for easier comparison to Census data and calculation of rates per population (including estimates from the American Community Survey and Annual Population Estimates). These do not reflect precise neighborhood locations and do not necessarily include all commonly-used neighborhood designations. There is no formal set of standards that describes which neighborhoods are included in this dataset. Note that the District of Columbia does not have official neighborhood boundaries. Origin of boundaries: each neighborhood is a group of between one and seven different, contiguous census tracts. They were originally determined in 2015 as part of an analytical research project with technical assistance from the Centers for Disease Control and Prevention (CDC) and the Council for State and Territorial Epidemiologists (CSTE) to define small area estimates of life expectancy. Census tracts were grouped roughly following the Office of Planning Neighborhood Cluster boundaries, where possible, and were made just large enough to achieve standard errors of less than 2 for each neighborhood's calculation of life expectancy. The resulting neighborhoods were used in the DC Health Equity Report (2018) with updated names. HPNs were modified slightly in 2019, incorporating one census tract that was consistently suppressed due to low numbers into a neighboring HPN (Lincoln Park incorporated into Capitol Hill). Demographic information were analyzed to identify the bordering group with the most similarities to the single census tract. A second change split a neighborhood (GWU/National Mall) into two to facilitate separate analysis.

This dataset contains polygons that represent the boundaries of statistical neighborhoods as defined by the DC Department of Health (DC Health). DC Health delineates statistical neighborhoods to facilitate small-area analyses and visualization of health, economic, social, and other indicators to display and uncover disparate outcomes among populations across the city. The neighborhoods are also used to determine eligibility for some health services programs and support research by various entities within and outside of government. DC Health Planning Neighborhood boundaries follow census tract 2010 lines defined by the US Census Bureau. Each neighborhood is a group of between one and seven different, contiguous census tracts. This allows for easier comparison to Census data and calculation of rates per population (including estimates from the American Community Survey and Annual Population Estimates). These do not reflect precise neighborhood locations and do not necessarily include all commonly-used neighborhood designations. There is no formal set of standards that describes which neighborhoods are included in this dataset. Note that the District of Columbia does not have official neighborhood boundaries. Origin of boundaries: each neighborhood is a group of between one and seven different, contiguous census tracts. They were originally determined in 2015 as part of an analytical research project with technical assistance from the Centers for Disease Control and Prevention (CDC) and the Council for State and Territorial Epidemiologists (CSTE) to define small area estimates of life expectancy. Census tracts were grouped roughly following the Office of Planning Neighborhood Cluster boundaries, where possible, and were made just large enough to achieve standard errors of less than 2 for each neighborhood's calculation of life expectancy. The resulting neighborhoods were used in the DC Health Equity Report (2018) with updated names. HPNs were modified slightly in 2019, incorporating one census tract that was consistently suppressed due to low numbers into a neighboring HPN (Lincoln Park incorporated into Capitol Hill). Demographic information were analyzed to identify the bordering group with the most similarities to the single census tract. A second change split a neighborhood (GWU/National Mall) into two to facilitate separate analysis.

Asian and Pacific Islander populations are only available in 5-year estimates due to low numbers.

Data Source: DC Vital Records, CDC WONDER single-race single-year population estimates and American Community Survey (ACS) 1-year estimates.

Why This Matters

Life expectancy reflects a community’s mortality levels and overall health. In the U.S. life expectancy has been stagnant since 2010 and declined during the COVID-19 Pandemic, primarily due to heart disease, cancer, COVID-19, and fatal drug overdoses.

Changes and disparities in life expectancy at birth reflect trends and inequities in living standards, access to quality health care, and other social and economic factors.

Nationally, life expectancy at birth is lower among Black and Native Americans compared to other racial and ethnic groups. These racial disparities are rooted in a long history of racial segregation, economic and employment discrimination, and environmental racism, among other racist practices, as noted by the National Health Atlas.

The District Response

Ensuring District residents access to various healthcare programs, such as Medicaid, DC Healthcare Alliance Program, and DC Healthy Families. For more information on these programs, click here.

Initiatives and programs to reduce disparities in housing, employment, and food insecurity through programs and services, such as Supplemental Nutrition Assistance Program (SNAP), DC Child Care Subsidy Program, and DC Infrastructure Academy.

Promoting health through free DPR fitness centers, wellness classes, the MoveDC Plan for active transportation, and health and PE classes in public schools to encourage lifelong exercise habits.

The dataset presents life expectancy at birth estimates based on annual complete period life tables for each of the 50 states and the District of Columbia (D.C.) in 2020 for the total, male and female populations.

The dataset presents life expectancy at birth estimates based on annual complete period life tables for each of the 50 states and the District of Columbia (D.C.) in 2021 for the total, male and female populations.

The dataset presents life expectancy at birth estimates based on annual complete period life tables for each of the 50 states and the District of Columbia (D.C.) in 2018 for the total, male and female populations.

The dataset presents life expectancy at birth estimates based on annual complete period life tables for each of the 50 states and the District of Columbia (D.C.) in 2019 for the total, male and female populations.

Analysis of ‘U.S. State Life Expectancy by Sex, 2018’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/1cf440cb-b00a-4e10-9e7c-3fc4e8a52fbb on 26 January 2022.

--- Dataset description provided by original source is as follows ---

The dataset presents life expectancy at birth estimates based on annual complete period life tables for each of the 50 states and the District of Columbia (D.C.) in 2018 for the total, male and female populations.

--- Original source retains full ownership of the source dataset ---

Individuals differ in many ways. Most produce few offspring; a handful produce many. Some die early; others live to old age. It is tempting to attribute these differences in outcomes to differences in individual traits, and thus in the demographic rates experienced. However, there is more to individual variation than meets the eye of the biologist. Even among individuals sharing identical traits, life history outcomes (life expectancy and lifetime reproduction) will vary due to individual stochasticity, i.e., to chance. Quantifying the contributions of heterogeneity and chance is essential to understanding natural variability. Inter-individual differences vary across environmental conditions, hence heterogeneity and stochasticity depend on environmental conditions. We show that favorable conditions increase the contributions of individual stochasticity, and reduce the contributions of heterogeneity, to variance in demographic outcomes in a seabird population. The opposite is true under poor conditions. This result has important consequence for understanding the ecology and evolution of life history strategies.

Specifically, three life-history complexes exist in a population of southern fulmar (defined as sets of life-history characteristics that occur together through the lifetime of an individual). They are reminiscent of the gradient of life- history strategy observed among species:

1. Group 1 (14% of offspring at fledging) is a slow-paced life history where individuals tend to delay recruitment, recruit successfully, and extend their reproductive lifespan.
2. Group 2 (67% of offspring at fledging) consists of individuals that are less likely to recruit, have high adult survival, and skip breeding often.
3. Group 3 (19% of offspring at fledging) is a fast-paced life history where individuals recruit early and attempt to breed often but have a short lifespan.

Individuals in groups 1 and 3 are considered “high-quality” individuals because they produce, on average, more offspring over their lives than do individuals in group 2. But group 2 is made-up of individuals that experience the highest levels of adult survival.

Differences between these groups, i.e. individual heterogeneity, only explains a small fraction of variance in life expectancy (5.9%) and lifetime reproduction (22%) when environmental conditions are ordinary. We expect that the environmental context experienced, especially when environmental conditions get extreme, is key to characterizing individual heterogeneity and its contribution to life history outcomes. Here, we build on previous studies to quantify the impact of extreme environmental conditions on the relative contributions of individual heterogeneity and stochasticity to variance in life history outcomes. We found that the differences in vital rates and demographic outcomes among complexes depend on the sea ice conditions individuals experience. Importantly, differences across life history complexes are amplified when sea ice concentration get extremely low. Sea ice conditions did not only affect patterns of life history traits, but also the variance of life history outcomes and the relative proportion of individual unobserved heterogeneity to the total variance. These new results advance the current debate on the relative importance heterogeneity (i.e. potentially adaptive) and stochasticity (i.e. enhances genetic drift) in shaping potentially neutral vs. adaptive changes in life histories.

1. Differences among individuals within a population are ubiquitous. Those differences are known to affect the entire life cycle with important consequences for all demographic rates and outcomes. One source of among-individual phenotypic variation that has received little attention from a demographic perspective is animal personality, which is defined as consistent and heritable behavioral differences between individuals. While many studies have shown that individual variation in individual personality can generate individual differences in survival and reproductive rates, the impact of personality on all demographic rates and outcomes remains to be assessed empirically.

   1. Here, we used a unique, long-term, dataset coupling demography and personality of wandering albatross (Diomedea exulans) in the Crozet Archipelago and a comprehensive analysis based on a suite of approaches (capture-mark-recapture statistical models, Markov chains models and structured matrix population models). We assessed the effect of boldness on annual demographic rates (survival, breeding probability, breeding success), life-history out-comes (life expectancy, lifetime reproductive outcome, occupancy times), and an integrative demographic outcome (population growth rate).

   2. We found that boldness had little impact on female demographic rates, but was very likely associated with lower breeding probabilities in males. By integrating the effects of boldness over the entire life cycle, we found that bolder males had slightly lower lifetime reproductive success compared to shyer males. Indeed, bolder males spent a greater proportion of their lifetime as non-breeders, which suggests longer inter-breeding intervals due to higher reproductive allocation.

   3. Our results reveal that the link between boldness and demography is more complex than anticipated by the pace-of-life literature and highlight the importance of considering the entire life cycle with a comprehensive approach when assessing the role of personality on individual performance and demography.

Of the 44 men who have served as President of the United States, eight died while in office, while 31 passed away after their term had ended. Five presidents, including incumbent President Donald Trump, are still alive today. The most common state in which U.S. presidents have died was New York, which has seen the deaths of nine U.S. presidents. A total of fourteen presidents have passed away in the same state in which they were born, which includes all four who passed away in Virginia, but none of those who did so in Washington D.C. Although seven presidents were born in Ohio, Rutherford B. Hayes is the only to have passed away in this state. The most recent presidential death occurred in November 2018, when George H. W. Bush passed away in his family home in Houston, Texas. At 94 years old, Bush Sr. had been the oldest living president at the time of his death; however that title has since passed to Jimmy Carter, who will turn 96 years old in October 2020. John F. Kennedy was the president who died at the youngest age, when he was assassinated at 46 years old; while James K. Polk was the youngest president to die of natural causes, at 53 years of age.