Demos
The United States and the United Kingdom are two of the most powerful conglomerate of states across the globe, so if you are in the public or private sector, it would benefit you to have a bird’s eye view of each of their demographics. Whether you are in business, corporate world, public office or just a simple family-oriented person, you need this data.
John Snow Labs includes in its healthcare databases catalog a set of data packages that extensively talk about the different populations and the diverging variables affecting life expectancy singularly or collectively. However, and before going through with the dataset overview, let’s decode a few terminologies that are often used interchangeably, but actually have different meanings; these are the terms “life expectancy” and “lifespan”.
According to Population Education (PopEd), an online organization focused on human population issues, “Lifespan is the number of years that one person lives. We can calculate an average lifespan of people in a group if we have birth and death dates for its members”. Life expectancy, according to the same group, “is also based on averages, but it’s the number of years that someone is expected to live from a specific starting point. Your own life expectancy changes as you grow older, and as you face different risks”.
Lifespan is very simple; it means how long a person lives given the birth and death rates of a certain population. Life expectancy, on the other hand, is trickier as it involves actuarial tools to predict the number of years a person is expected to live based on his age and health risks.
To explain this better, let’s take the illustration from PopEd: “Consider this: Many infants and young children in America used to die due to infections. High infant and child mortality kept life expectancy (which is usually expressed as life expectancy from birth) low until well into the 20th century, but once people passed those vulnerable early years, the life expectancy was much higher. For instance, the life expectancy for a female born in 1900 was about 48 years old, but if she reached age 20, her life expectancy was over 60, and at age 40, her life expectancy was nearly 70”.
To keep it simpler, the World Health Organization (WHO) defines life expectancy as “Average number of years that a newborn is expected to live if current mortality rates continue to apply”.
UK Life Expectancy Datasets
UK Deaths by Cause talks about deaths in England in 1990 and 2013 for all ages and age‐standardized rates (per 100,000) by sex.UK Disability Adjusted Life Year by Risk Factor provides Disability Adjusted Life Year (DALYs) in thousands of attributable-risk factors or risk factor clusters and median percent change of age-standardized DALYs PAFs in England for both sexes, males, and females.
UK Disability Adjusted Life Year presents Disability Adjusted Life Year (DALYs) in England from 1990 to 2013 for all ages and age‐standardized rates (per 100,000) by sex.
UK Global Burden of Disease by Cause offers results for the burden of disease and injury in the United Kingdom through the following metrics: years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life years (DALYs).
UK Global Burden of Disease by Risk Factor supplies results for the risk factors for burden of disease and injury in the United Kingdom through the following metrics: years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life years (DALYs).
UK Life Expectancy At the Age of 75 contains indicator values for NHS (National Health Service) Outcomes Framework indicator – the average number of additional years a man or woman aged 75 can be expected to live if they continue to live in the same place and the death rates in their area remain the same for the rest of their life.
UK Years Lived with Disability by Cause supplies years lived with disability (YLDs) in England in 1990 and 2013 for all ages and age‐standardized rates (per 100,000) by sex.
UK Years of Life Lost by Cause presents years of life lost (YLLs) in England in 1990 and 2013 for all ages and age‐standardized rates (per 100,000) by sex.
US Life Expectancy Datasets
US Global Burden of Disease By Cause provides estimates of the burden of diseases and injuries in the United States through the following metrics: years of life lost (YLLs), years lived with disability (YLDs) and disability-adjusted life years (DALYs).US Global Burden of Disease by Risk Factor delivers estimates of the burden of diseases, injuries and risk factors in the United States through the following metrics: years of life lost (YLLs), years lived with disability (YLDs) and disability-adjusted life years (DALYs).
US Life Expectancy 1980 to 2014 offers estimates for life expectancy at birth at the county level for each state, the District of Columbia, and the United States as a whole for 1980-2014, as well as the changes in life expectancy and mortality risk for each location during this period.
US Life Expectancy 1985 to 2010 yields estimates for life expectancy by county and sex from January 1, 1985, through December 31, 2010, in the United States.
US Life Expectancy 1987 to 2007 presents estimates for life expectancy from January 1, 1987, through December 31, 2007, in the United States.
US Life Expectancy by Age and Sex contains the life expectancy of US population across all ages from 2000 to 2015. Data is based on official estimates of life expectancy. The age pattern of mortality is based on life tables from the Human Mortality Database.
These datasets aggregated and carefully curated by domain experts from John Snow Labs will help you forecast the increase or decrease in life expectancy as an important key measure and indicator of population health. Students, researchers, policymakers will have an amplified view of the data to complement research initiatives, economic measures and health policies.
Exploring life expectancy data across the US and UK offers insights into healthcare trends and outcomes. For further advancements, technologies like Generative AI in Healthcare and Healthcare Chatbot tools can provide data-driven support, enhancing patient care and predictive analytics.
