The average number of years a newborn can expect to live, assuming he or she experiences the currently prevailing rates of death through their lifespan. Source: Baltimore City Health Department Years Available: 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018

Zip Code, Life expectancy; Cancer deaths per 100,000 people; Heart disease deaths per 100,000 people; Alzheimer’s disease deaths per 100,000 people; Stroke deaths per 100,000 people; Chronic lower respiratory disease deaths per 100,000 people; Unintentional injury deaths per 100,000 people; Diabetes deaths per 100,000 people; Influenza and pneumonia deaths per 100,000 people; Hypertension deaths per 100,000 people. Percentages unless otherwise noted. Source information provided at: https://www.sccgov.org/sites/phd/hi/hd/Documents/City%20Profiles/Methodology/Neighborhood%20profile%20methodology_082914%20final%20for%20web.pdf

This dataset contains counts of deaths for California residents by ZIP Code based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths of California residents. The data tables include deaths of residents of California by ZIP Code of residence (by residence). The data are reported as totals, as well as stratified by age and gender. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

Projecting temperature-related mortality CONUS-wide Data Extract

This repository provides examples of mapped relative risks and mortality computation outputs used for projections in the manuscript Projecting temperature-related mortality CONUS-wide. Descriptions of each of the zipped datasets are below:

risk-map-ACCESS-CM-2-nationwide.zip - 2015-2100 extracts of mapped relative risks to LOCA2 temperatures for cool (November to March) and warm (May to September) season for ACCESS-CM-2 model for nationwide relative risks for . Outputs are 3-dimensional numpy data frames where the first two dimensions correspond to cell latitude and longitude (corresponding to LOCA2 resolution), and the third dimesion corresponds to to the day of year.

Output naming convention: relative_risks_{season}_hist_ACCESS-CM2_{scenario}_{time_period}_nationwide_extended_season.npy

season: either cool (djf) or warm (jja)

scenario: ssp245, ssp370, ssp585

time_period: 2015-2044, 2045-2074, 2075-2100

zip-mortality-ACCESS-CM2-nationwide.zip - Computed Daily zipcode-level mortality outputs for ACCESS-CM_2. Files are pickles that contain zipcode, daily mortality (daily column of day_0 - day_n), model name, season, and state. Highlighted variables represent the same value options as what is defined above.

Output naming convention: daily_at_deaths_{season}_ACCESS-CM2_{scenario}_{time_period}_nationwide_extended_season.pkl