gives the values of all properties for the specified demographic.
gives the value of the specified property for the specified demographic.
Details and Options
- The demographic spec is an association of the form Association["Age"->age,"Gender"->gender, "Country"->country, "Year"year].
- Age can be given as a positive Quantity of time or as a birth date using DateObject. It can also be specified as a list of such objects.
- Data is available for ages between and including 0 and 110 years.
- Age will be rounded to the nearest whole year of age for determining property values.
- Gender can be given as "Male" or "Female". It can also be the appropriate gender Entity.
- If gender is not specified, then an Association with results for both "Male" and "Female" is returned.
- Country should be an Entity or the standard name of the country.
- MortalityData["Countries"] gives a list of available countries.
- Country is assumed to be "UnitedStates" if unspecified.
- Including "Year" allows historical data to be requested, where year refers to the date of the source mortality information. year should be an integer or DateObject.
- By default, the most recent year of available data is used.
- property should be a canonical name or EntityProperty.
- MortalityData["Properties"] gives a list of available properties.
- Available properties include:
"CDF" cumulative distribution function for ages at time of death "CDFDimensionless" as "CDF" without Quantity formatting "DeathProbabilityBefore" q[x], probability of dying between ages x and x+1 years "DeathRate" m[x], death rate between ages x and x+1 years "Distribution" distribution of deaths for a cohort of 100000 "DistributionDimensionless" as "Distribution" without Quantity formatting "InverseCDF" inverse CDF for ages at time of death "InverseCDFDimensionless" as "InverseCDF" without Quantity formatting "InverseSurvivalFunction" inverse survival function for ages at time of death "InverseSurvivalFunctionDimensionless" as "InverseSurvivalFunction" without Quantity formatting "LifeExpectancy" total life expectancy in years "MortalityForce" μ[x], the force of mortality function "MortalityForceDimensionless" as "MortalityForce" without Quantity formatting "NumberDying" l[x], people dying during year x from a cohort of 100000 "NumberSurviving" d[x], survivors out of 100000 born alive at year x of age "PDF" probability density function for ages at time of death "PDFDimensionless" as "PDF" without Quantity formatting "PersonYearsLived" L[x], person-years lived between age x and x+1 years "PersonYearsRemaining" T[x], total number of person-years lived past year x of age "Quantile" quantile function for ages at time of death "QuantileDimensionless" as "Quantile" without Quantity formatting "RemainingLifeExpectancy" e[x], years of expected life remaining "SurvivalFunction" survival function for distribution of ages at time of death "SurvivalFunctionDimensionless" as "SurvivalFunction" without Quantity formatting "SurvivalProbabilityPast" p[x], probability at x years old of surviving to age x+1 years
- "CDF", "InverseCDFQuantity", "InverseSurvivalFunction", "MortalityForce", "PDF", and "SurvivalFunction" are all returned as functions with Quantity input and output.
- "DeathProbabilityBefore" and "SurvivalProbabilityPast" can be provided with a qualifier for a final age. For "DeathProbabilityBefore", this denotes the probability of a person of the specified demographic dying before that age, while "SurvivalProbabilityPast" returns the probability of that person surviving past that age.
- MortalityData is based on a wide range of sources, with enhancement at Wolfram Research by both human and algorithmic processing. The principal source is: Human Mortality Database at www.mortality.org.
Examplesopen allclose all
Basic Examples (1)
Obtain all properties for a specified demographic:
Learn the death rate for a given age:
Learn how many people out of a cohort of 100000 die for each year of life:
Learn the remaining life expectancy at a given age:
Discover the probability of a 33-year-old woman dying before 40:
Compare death rates in different counties by including the "Country" key:
Obtain a list of supported nationalities:
Examine historical life expectancy by specifying the "Year" key:
Possible Issues (3)
Neat Examples (4)
Compare life expectancy for countries that were once part of the USSR:
Examine the probability of death on a yearly basis based on historical data through a hypothetical lifetime:
Compare it with data from the beginning and end of the range:
Compare different mortality models:
Examine the age distribution of the United States and, using death probabilities, estimate how many people of each age from the span 5 to 85 die each year:
Estimate the total number of people dying between the ages of 5 and 85:
Wolfram Research (2015), MortalityData, Wolfram Language function, https://reference.wolfram.com/language/ref/MortalityData.html (updated 2016).
Wolfram Language. 2015. "MortalityData." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2016. https://reference.wolfram.com/language/ref/MortalityData.html.
Wolfram Language. (2015). MortalityData. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/MortalityData.html