US Life Expectancy Data

**Overview**
This data package contains datasets on causes, risk factor, deaths, death rate, years of life lost (YLL), years lived with disability (YLD), disability-adjusted life years (DALY), life expectancy and health-adjusted life expectancy (HALE) from the global burden of disease in the United States.

**Description**
This data package contains datasets on global burdens of disease by cause and risk factor in the United States. The datasets provide metrics to measure the overall disease burden expressed as Disability-adjusted life year (DALY), the sum of years of life lost (YLLs) and years lived with disability (YLDs), life expectancy and health-adjusted life expectancy (HALE). The datasets provide global, regional, and GBD location-specific and gender-specific data. The datasets included in this data package resulted from the Global Burden of Disease Study 2016.

**Benefits**
- This data is useful for researchers and policymakers to compare very different populations and health conditions across time. this can be used for further research
- public health officials and program managers for potential interventions, setting priority areas for action and evaluating progress
- policymakers for policy use and development
- public health practitioner and clinicians' aid in designing and implementing targeted interventions and monitoring the progress of public health strategies.

**License Information**
The use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes.

**Included Datasets**
- [US Global Burden of Disease By Cause](https://www.johnsnowlabs.com/marketplace/us-global-burden-of-disease-by-cause)
- This dataset 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](https://www.johnsnowlabs.com/marketplace/us-global-burden-of-disease-by-risk-factor)
- This dataset provides 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](https://www.johnsnowlabs.com/marketplace/us-life-expectancy-1980-to-2014)
- This dataset provides 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](https://www.johnsnowlabs.com/marketplace/us-life-expectancy-1985-to-2010)
- This dataset provides 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](https://www.johnsnowlabs.com/marketplace/us-life-expectancy-1987-to-2007)
- This dataset provides estimates for life expectancy from January 1, 1987, through December 31, 2007, in the United States.
- [US Life Expectancy by Age and Sex](https://www.johnsnowlabs.com/marketplace/us-life-expectancy-by-age-and-sex)
- The dataset 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.

**Data Engineering Overview**
**We deliver high-quality data**
- Each dataset goes through 3 levels of quality review
- 2 Manual reviews are done by domain experts
- Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints
- Data is normalized into one unified type system
- All dates, unites, codes, currencies look the same
- All null values are normalized to the same value
- All dataset and field names are SQL and Hive compliant
- Data and Metadata
- Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters
- Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated
- Data Updates
- Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted

**Our data is curated and enriched by domain experts**

Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:
- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning
- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset
- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations
- The data is always kept up to date – even when the source requires manual effort to get updates
- Support for data subscribers is provided directly by the domain experts who curated the data sets
- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.

**Need Help?**
If you have questions about our products, contact us at [[e-mail]](mailto:[e-mail]).