DOCSLIB.ORG

Systematic Predictive Analysis of Personalized Life Expectancy Using Smart Devices

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Systematic Predictive Analysis of Personalized Life Expectancy Using Smart Devices technologies Review Systematic Predictive Analysis of Personalized Life Expectancy Using Smart Devices James Jin Kang * ID and Sasan Adibi ID School of Information Technology, Deakin University, Burwood, VIC 3125, Australia; [email protected] * Correspondence: [email protected]; Tel.: +61-439-192855 Received: 19 July 2018; Accepted: 9 August 2018; Published: 10 August 2018 Abstract: With the emergence of technologies such as electronic health and mobile health (eHealth/mHealth), cloud computing, big data, and the Internet of Things (IoT), health related data are increasing and many applications such as smartphone apps and wearable devices that provide wellness and fitness tracking are entering the market. Some apps provide health related data such as sleep monitoring, heart rate measuring, and calorie expenditure collected and processed by the devices and servers in the cloud. These requirements can be extended to provide a personalized life expectancy (PLE) for the purpose of wellbeing and encouraging lifestyle improvement. No existing works provide this PLE information that is developed and customized for the individual. This article is based on the concurrent models and methodologies to calculate and predict life expectancy (LE) and proposes an idea of using multi-phased approaches to the solution as the project requires an immense and broad range of work to accomplish. As a result, the current prediction of LE, which was found to be up to a maximum of five years could potentially be extended to a lifetime prediction by utilizing generic health data. In this article, the novel idea of the solution proposing a PLE on an individual basis, which can be extended to lifetime is presented in addition to the existing works. Keywords: Life Expectancy (LE); Personalized Life Expectation (PLE); Predicted Life Expectancy (PrLE); Mobile Health (mHealth) 1. Introduction The problem of processing datasets such as electronic medical records (EMR), and their integration with genomics, environmental factors, socioeconomic factors and patient behavior variations have posed a problem for researchers in the health industry. Due to the evolution of data science technologies such as big data virtualization and analytics, data wrangling and with the cloud, health workers now have an improved way of processing and developing meaningful information from huge datasets that have been accumulated over many years. For example, a case study [1] shows that big data and machine learning techniques can benefit public health researchers with analyzing thousands of variables to obtain data regarding life expectancy and anxiety disorders. They used the demographics of selected regional areas and multiple behavioral health disorders across regions to find correlations between individual behavior indicators and behavioral health outcomes. Smart environment and wireless network technologies [2–5] have also been used to improve the monitoring of chronic diseases with the evolutions in the Internet of Things (IoT) and cloud computing by building smart cities and homes, which allowed the rapidly growing elderly population to access healthcare resources in a cost-effective way. Banaee et al. [6] proposes the idea of a potential application model that can be elaborated on as an application of using personal health status. As described in Figure1, it may be possible to create a prediction of personal life expectancy, which can be further used to calculate health indexes on a generic level for which the individualized expectancies may be compared against. Technologies 2018, 6, 74; doi:10.3390/technologies6030074 www.mdpi.com/journal/technologies Technologies 2018, 6, x FOR PEER REVIEW 2 of 17 Technologiesa prediction2018 of, 6 ,personal 74 life expectancy, which can be further used to calculate health indexes2 on of 18a generic level for which the individualized expectancies may be compared against. Health status Personal life expectancy F E Health status C prediction A D Generic life expectancy B birth death Age Figure 1. Health Status and personalized Li Lifefe Expectancy (Adapted from [[7]).7]). This model can also depict the life expectancy predicted by an inference system, which transmits healthThis data model over can wireless also depict sensor the networks life expectancy [8]. Generic predicted life by expectancies an inference may system, be calculated which transmits from multiplehealth data sources over obtained wireless and sensor analyzed networks by big [8 data.]. Generic These lifevalues expectancies can be used may to create be calculated a personalized from graphmultiple that sources most resembles obtained and the analyzedindividual by in big question, data. These with values consideration can be used for personal to create acharacteristics personalized suchgraph as that their most age, resembles gender, ethnicity, the individual living inenviro question,nment with and consideration current comorbidities for personal or lifestyle characteristics habits. Theresuch as may their be age, an gender,enormous ethnicity, number living of variables environment to consider, and current of which comorbidities increasing orthe lifestyle number habits. may Thereobviously may increase be an enormous the accuracy number of a LE of variablesprediction to. This consider, personalized of which graph increasing can then the be number compared may withobviously other increase individuals the accuracywho may of be a LEliving prediction. similar lifestyles This personalized and share graph similar can traits then to be provide compared an withidea ofother the individuals generic life who expectancy. may be living This similarconcept lifestyles is described and share in Figure similar 1 traitswith tothe provide red and an black idea oflines the superimposedgeneric life expectancy. on each Thisother. concept During is point described A, the in graph Figure 1shows with thethat red the and user black was lines ill and superimposed there was a decreaseon each other.in the overall During health point A,status the until graph point shows C when that thethe user recovered, was ill and followed there was by a another decrease case in ofthe illness overall at healthpoint C status until untilthey pointrecovered C when finally the at user point recovered, E. Point followedF describes by the another present case moment of illness in time,at point at Cwhich until theypoint recovered any values finally following at point this E. Pointpoint F would describes be thean presentinferred moment prediction intime, of the at whichindividual’s point anyhealth values status following for the future. this point This would prediction be an would inferred be prediction an inference of themade individual’s of physiological health datastatus analyzed for the future. by the Thiscloud prediction computation. would Whilst be an the inference red line made shows of a physiological trend line of the data history analyzed of the by user’sthe cloud health computation. status and Whilst a rough the estimate red line of shows his or a trendher future line of health, the history the graph of the may user’s also health show status fine trendsand a rough as depicted estimate by of the his green or her line. future This health, would the be graph used mayto inform also show short fine term trends information as depicted about by whetherthe green the line. user’s This health would is be improving used to inform or declining short term on informationa more detailed about level. whether the user’s health is improving or declining on a more detailed level. ([]) F(x) = , where x = age, and g[PQ] = health index function from point X1 to X2, () (1) d(g[PQ]) F(xThe) = Health index, wherefunction x =g[PQ]age, is and based g[PQ on] the = differentialhealth index of functionhealth st fromatus between point X1 age to X2, X1 and (1) X2. For example,d(x) when F(x) < 0, the health index function would suggest a decline in health whereas F(x) > 0 suggests that the health status is improving (recovery) during the period of A (X1) and B The Health index function g[PQ] is based on the differential of health status between age X1 and X2. (X2). Periods of illness may be reflected in the health index by a sum illness cycle, which would For example, when F(x) < 0, the health index function would suggest a decline in health whereas F(x) describe the number and the total durations of illness days. Inference should take into account the > 0 suggests that the health status is improving (recovery) during the period of A (X1) and B (X2). information held within its own database along with that received from external agents, such as from Periods of illness may be reflected in the health index by a sum illness cycle, which would describe the monitoring centers (MC) or caregiver terminals (CT) in mHealth networks. number and the total durations of illness days. Inference should take into account the information As presented in [8], the health status of each user would vary from the average values and held within its own database along with that received from external agents, such as from monitoring thresholds of other users, and fine calibration or optimization may be required and could be centers (MC) or caregiver terminals (CT) in mHealth networks. undertaken by a trained professional, for example a physician. A combination of variables may alter As presented in [8], the health status of each user would vary from the average values and the inference result, as some sensed data may be interdependent and affect one another; e.g., insulin thresholds of other users, and fine calibration or optimization may be required and could be undertaken levels and blood pressure. Thus, in inferring whether a change in the user’s health status should by a trained professional, for example a physician. A combination of variables may alter the inference trigger a health alarm warning of a potential health event, sensed data should be interpreted together result, as some sensed data may be interdependent and affect one another; e.g., insulin levels and with related variables that are known to affect each other.
Load more

Recommended publications
  • How to Measure the Burden of Mortality? L Bonneux
    J Epidemiol Community Health: first published as 10.1136/jech.56.2.128 on 1 February 2002. Downloaded from 128 THEORY AND METHODS How to measure the burden of mortality? L Bonneux ............................................................................................................................. J Epidemiol Community Health 2002;56:128–131 Objectives: To explore various methods to quantify the burden of mortality, with a special interest for the more recent method at the core of calculations of disability adjusted life years (DALY). Design: Various methods calculating the age schedule at death are applied to two historical life table populations. One method calculates the “years of life lost”, by multiplying the numbers of deaths at age ....................... L Bonneux, Department of x by the residual life expectancy. This residual life expectancy may be discounted and age weighted. Public Health, Erasmus The other method calculates the “potential years of life lost” by multiplying the numbers of deaths at age University Rotterdam, the x by the years missing to reach a defined threshold (65 years or 75 years). Netherlands Methods: The period life tables describing the mortality of Dutch male populations from 1900–10 Correspondence to: (high mortality) and from 1990–1994 (low mortality). Dr L Bonneux, Department Results: A standard life table with idealised long life expectancy increases the burden of death more of Public Health, Erasmus if mortality is lower. People at old age, more prevalent if mortality is low, lose more life years in an ide- University Rotterdam, PO alised life table. The discounted life table decreases the burden of death strongly if mortality is high: Box 1738, 3000 DR Rotterdam, the the life lost by a person dying at a young age is discounted.
    [Show full text]
  • Incidence, Mortality, Disability-Adjusted Life Years And
    Original article The global burden of injury: incidence, mortality, disability-adjusted life years and time trends from the Global Burden of Disease study 2013 Juanita A Haagsma,1,60 Nicholas Graetz,1 Ian Bolliger,1 Mohsen Naghavi,1 Hideki Higashi,1 Erin C Mullany,1 Semaw Ferede Abera,2,3 Jerry Puthenpurakal Abraham,4,5 Koranteng Adofo,6 Ubai Alsharif,7 Emmanuel A Ameh,8 9 10 11 12 Open Access Walid Ammar, Carl Abelardo T Antonio, Lope H Barrero, Tolesa Bekele, Scan to access more free content Dipan Bose,13 Alexandra Brazinova,14 Ferrán Catalá-López,15 Lalit Dandona,1,16 Rakhi Dandona,16 Paul I Dargan,17 Diego De Leo,18 Louisa Degenhardt,19 Sarah Derrett,20,21 Samath D Dharmaratne,22 Tim R Driscoll,23 Leilei Duan,24 Sergey Petrovich Ermakov,25,26 Farshad Farzadfar,27 Valery L Feigin,28 Richard C Franklin,29 Belinda Gabbe,30 Richard A Gosselin,31 Nima Hafezi-Nejad,32 Randah Ribhi Hamadeh,33 Martha Hijar,34 Guoqing Hu,35 Sudha P Jayaraman,36 Guohong Jiang,37 Yousef Saleh Khader,38 Ejaz Ahmad Khan,39,40 Sanjay Krishnaswami,41 Chanda Kulkarni,42 Fiona E Lecky,43 Ricky Leung,44 Raimundas Lunevicius,45,46 Ronan Anthony Lyons,47 Marek Majdan,48 Amanda J Mason-Jones,49 Richard Matzopoulos,50,51 Peter A Meaney,52,53 Wubegzier Mekonnen,54 Ted R Miller,55,56 Charles N Mock,57 Rosana E Norman,58 Ricardo Orozco,59 Suzanne Polinder,60 Farshad Pourmalek,61 Vafa Rahimi-Movaghar,62 Amany Refaat,63 David Rojas-Rueda,64 Nobhojit Roy,65,66 David C Schwebel,67 Amira Shaheen,68 Saeid Shahraz,69 Vegard Skirbekk,70 Kjetil Søreide,71 Sergey Soshnikov,72 Dan J Stein,73,74 Bryan L Sykes,75 Karen M Tabb,76 Awoke Misganaw Temesgen,77 Eric Yeboah Tenkorang,78 Alice M Theadom,79 Bach Xuan Tran,80,81 Tommi J Vasankari,82 Monica S Vavilala,57 83 84 85 ▸ Vasiliy Victorovich Vlassov, Solomon Meseret Woldeyohannes, Paul Yip, Additional material is 86 87 88,89 1 published online only.
    [Show full text]
  • A Review of Life Table Construction
    Biometrics & Biostatistics International Journal Research Article Open Access A review of life table construction Abstract Volume 5 Issue 3 - 2017 Life table is the one of the oldest statistical techniques and is extensively used by Ilker Etikan, Sulaiman Abubakar, Rukayya medical statisticians and actuaries. It is also the scheme for expressing the form of mortality in terms of probabilities. The life table is constructed from census data Alkassim Near East University Faculty of Medicine Department of and death registration data, it can also be constructed from vital registration or Biostatistics, Nicosia-TRNC, Cyprus retrospective surveys. Generally, they are constructed for age, gender, ethnic groups and occupational groups and so on. Some classification of life table such as cohort Correspondence: Ilker Etikan, Near East University Faculty of or current life table which are classified according to the reference year, unabridged Medicine Department of Biostatistics, Nicosia-TRNC, Cyprus, (complete) or abridge life table classified according to length of age interval and single Email or multiple decrement life table classified according to the number of characteristics considered were discussed in the article. Received: November 24, 2016 | Published: March 02, 2017 Keywords: life tables, survival analysis, kaplan meirer Introduction In 1693, the first mortality table is usually credited to Edmund halley who constructed a life table base on the data from Breslau city in A life table is a terse way of showing the probabilities of a member Poland. The table thought to be the first and as well as best of its kind. 1 of a particular population living to or dying at a precise age.
    [Show full text]
  • Life Tables for 191 Countries: Data, Methods and Results
    LIFE TABLES FOR 191 COUNTRIES: DATA, METHODS AND RESULTS Alan D Lopez Joshua Salomon Omar Ahmad Christopher JL Murray Doris Mafat GPE Discussion Paper Series: No.9 EIP/GPE/EBD World Health Organization 1 I Introduction The life table is a key summary tool for assessing and comparing mortality conditions prevailing in populations. From the time that the first modern life tables were constructed by Graunt and Halley during the latter part of the 17th century, life tables have served as a valuable analytical tool for demographers, epidemiologists, actuaries and other scientists. The basic summary measure of mortality from the life table, the expectation of life at birth, is widely understood by the general public and trends in life expectancy are closely monitored as the principal measure of changes in a population's health status. The construction of a life table requires reliable data on a population's mortality rates, by age and sex. The most reliable source of such data is a functioning vital registration system where all deaths are registered. Deaths at each age are related to the size of the population in that age group, usually estimated from population censuses, or continuous registration of all births, deaths and migrations. The resulting age-sex-specific death rates are then used to calculate a life table. While the legal requirement for the registration of deaths is virtually universal, the cost of establishing and maintaining a system to record births and deaths implies that reliable data from routine registration is generally only available in the more economically advanced countries. Reasonably complete national data to calculate life tables in the late 1990s was only available for about 65 countries, covering about one-third of the deaths estimated to have occurred in 1999.
    [Show full text]
  • Disability Adjusted Life Years (Dalys)
    Article Disability Adjusted Life Years (DALYs) in Terms of Years of Life Lost (YLL) Due to Premature Adult Mortalities and Postneonatal Infant Mortalities Attributed to PM2.5 and PM10 Exposures in Kuwait Ali Al-Hemoud 1,*, Janvier Gasana 2, Abdullah N. Al-Dabbous 1, Ahmad Al-Shatti 3 and Ahmad Al-Khayat 4 1 Crisis Decision Support Program, Environment and Life Sciences Research Center, Kuwait Institute for Scientific Research, P.O. Box 24885, 13109 Safat, Kuwait; [email protected] 2 Faculty of Public Health, Health Sciences Center, Kuwait University, P.O. Box 24923, 13110 Hawalli, Kuwait; [email protected] 3 Occupational Health Department, Kuwait Ministry of Health, P.O. Box 51360, 53454 Riqqa, Kuwait; [email protected] 4 Techno-Economics Division, Kuwait Institute for Scientific Research, P.O. Box 24885, 13109 Safat, Kuwait; [email protected] * Correspondence: [email protected]; Tel.: +09-652-498-9464 Received: 9 October 2018; Accepted: 17 November 2018; Published: 21 November 2018 Abstract: Ambient air pollution in terms of fine and coarse particulate matter (PM2.5 and PM10) has been shown to increase adult and infant mortalities. Most studies have estimated the risk of mortalities through attributable proportions and number of excess cases with no reference to the time lost due to premature mortalities. Disability adjusted life years (DALYs) are necessary to measure the health impact of PM over time. In this study, we used life-tables for three years (2014–2016) to estimate the years of life lost (YLL), a main component of DALYs, for adult mortalities (age 30+ years) and postneonatal infant mortalities (age 28+ days–1 year) associated with PM2.5 exposure and PM10 exposure, respectively.
    [Show full text]
  • A New Look at Halley's Life Table
    J. R. Statist. Soc. A (2011) 174, Part 3, pp. 823–832 A new look at Halley’s life table David R. Bellhouse University of Western Ontario, London, Canada [Received March 2010. Revised November 2010] Summary. Edmond Halley published his Breslau life table in 1693, which was arguably the first in the world based on population data. By putting Halley’s work into the scientific context of his day and through simple plots and calculations, new insights into Halley’s work are made. In particular, Halley tended to round his numbers and to massage his data for easier presentation and calculation. Rather than highlighting outliers as would be done in a modern analysis, Halley instead smoothed them out. Halley’s method of life table construction for early ages is exam- ined. His lifetime distribution at higher ages, which is missing in his paper, is reconstructed and a reason is suggested for why Halley neglected to include these ages in his table. Keywords: Data analysis; Demographic statistics; Life tables; 17th century; Smoothing 1. Introduction In 1693 Edmond Halley constructed a life table or more correctly, as noted by Greenwood (1941, 1943), a population table. It was based on data collected for the years 1687–1691 from the city of Breslau, which is now called Wrocław, by the Protestant pastor of the town, Caspar Neumann. The data that Halley used were the numbers of births and deaths recorded in the parish registers of the town, which was then under the control of the Habsburg monarchy of Austria. It was a town in a Polish area comprised predominantly of German speakers adhering to Lutheranism within an officially Roman Catholic empire (Davies and Moorhouse (2002), pages 159–160, 180).
    [Show full text]
  • National Vital Statistics Reports Volume 69, Number 12 November 17, 2020
    National Vital Statistics Reports Volume 69, Number 12 November 17, 2020 United States Life Tables, 2018 by Elizabeth Arias, Ph.D., and Jiaquan Xu, M.D., Division of Vital Statistics Abstract Introduction Objectives—This report presents complete period life tables There are two types of life tables: the cohort (or generation) for the United States by Hispanic origin, race, and sex, based on life table and the period (or current) life table. The cohort life age-specific death rates in 2018. table presents the mortality experience of a particular birth Methods—Data used to prepare the 2018 life tables are cohort—all persons born in the year 1900, for example—from 2018 final mortality statistics; July 1, 2018 population estimates the moment of birth through consecutive ages in successive based on the 2010 decennial census; and 2018 Medicare data calendar years. Based on age-specific death rates observed for persons aged 66–99. The methodology used to estimate through consecutive calendar years, the cohort life table reflects the life tables for the Hispanic population remains unchanged the mortality experience of an actual cohort from birth until no from that developed for the publication of life tables by Hispanic lives remain in the group. To prepare just a single complete origin for data year 2006. The methodology used to estimate cohort life table requires data over many years. It is usually the 2018 life tables for all other groups was first implemented not feasible to construct cohort life tables entirely on the basis with data year 2008. In 2018, all 50 states and the District of of observed data for real cohorts due to data unavailability or Columbia reported deaths by race based on the 1997 Office of incompleteness (1).
    [Show full text]
  • Vital and Health Statistic, Series 2, No
    Series 2 No. 129 Vital and Health Statistics From the CENTERS FOR DISEASE CONTROL AND PREVENTION / National Center for Health Statistics Method for Constructing Complete Annual U.S. Life Tables December 1999 U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES Centers for Disease Control and Prevention National Center for Health Statistics Copyright Information All material appearing in this report is in the public domain and may be reproduced or copied without permission; citation as to source, however, is appreciated. Suggested citation Anderson RN. Method for constructing complete annual U.S. life tables. National Center for Health Statistics. Vital Health Stat 2(129). 1999. Library of Congress Cataloging-in-Publication Data Anderson, Robert N. Method for constructing complete annual U.S. life tables / [by Robert N. Anderson]. p. cm. — (Vital and health statistics. Series 2, Data evaluation and methods research ; no. 129) (DHHS publication ; no. (PHS) 99-1329) ‘‘This report describes a method for constructing complete annual U.S. life tables and for extending ...’’ ‘‘November, 1999.’’ Includes bibliographical references. ISBN 0-8406-0560-9 1. United States—Statistics, Vital—Methodology. 2. Insurance, Life—United States—Statistical methods. 3. Insurance, Health—United States—Statistical methods. 4. Mortality—United States—Statistical methods. 5. Public health—United States—Statistical methods. I. National Center for Health Statistics (U.S.) II. Title. III. Series. IV. Series: DHHS publication ; no. (PHS) 99-1329 RA409.U45 no. 129 [HB1335.A3] 362.1'0723 s—dc21 [614.4'273'0727] 99-058350 For sale by the U.S. Government Printing Office Superintendent of Documents Mail Stop: SSOP Washington, DC 20402-9328 Printed on acid-free paper.
    [Show full text]
  • Age-Specific Prevalence and Years of Healthy Life in a System with Three
    STATISTICS IN MEDICINE Statist. Med. (2007) Published online in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/sim.3056 Age-specific prevalence and years of healthy life in a system with three health states Paula Diehr1,2,∗,†,DavidYanez1, Ann Derleth3 and Anne B. Newman4,5 1Department of Biostatistics, University of Washington, Box 357232, Seattle, WA 98195, U.S.A. 2Department of Health Services, University of Washington, Seattle, WA, U.S.A. 3Veterans Administration Center for Excellence in Research on the Older Adult, Puget Sound Health Care System, Seattle, WA, U.S.A. 4Department of Medicine, University of Pittsburgh School of Medicine, U.S.A. 5Department of Epidemiology, Graduate School of Public Health, Pittsburgh, PA, U.S.A. SUMMARY Consider a 3-state system with one absorbing state, such as Healthy, Sick, and Dead. Over time, the prevalence of the Healthy state will approach an ‘equilibrium’ value that is independent of the initial conditions. We derived this equilibrium prevalence (Prev:Equil) as a function of the local transition probabilities. We then used Prev:Equil to estimate the expected number of years spent in the healthy state over time. This estimate is similar to the one calculated by multi-state life table methods, and has the advantage of having an associated standard error. In longitudinal data for older adults, the standard error was accurate when a valid survival table was known from other sources, or when the available data set was sufficient to estimate survival accurately. Performance was better with fewer waves of data. If validated in other situations, these estimates of prevalence and years of healthy life (YHL) and their standard errors may be useful when the goal is to compare YHL for different populations.
    [Show full text]
  • Demographic Models of Health and Mortality at Both Extremes of the Lifespan
    University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2017 Demographic Models Of Health And Mortality At Both Extremes Of The Lifespan Julio Enrique Romero Prieto University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Demography, Population, and Ecology Commons, and the Statistics and Probability Commons Recommended Citation Romero Prieto, Julio Enrique, "Demographic Models Of Health And Mortality At Both Extremes Of The Lifespan" (2017). Publicly Accessible Penn Dissertations. 2555. https://repository.upenn.edu/edissertations/2555 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/2555 For more information, please contact [email protected]. Demographic Models Of Health And Mortality At Both Extremes Of The Lifespan Abstract This dissertation consists of three essays. The first chapter proposes a model life table to investigate the human mortality at early ages. The model was estimated from the vital records, observing the experiences of 24 countries, which in some cases are at end of the nineteenth century and much of the twentieth century. Using few input values, the model predicts a mortality schedule for the first days, weeks, months, and years of life. Furthermore, the model is flexible ot represent age patterns in conditions of either low or high mortality. Thus, the main application is as a method for indirect estimation, in contexts where vital records are incomplete, imperfect, or non-existent. In this direction, the second chapter takes advantage of the model to investigate the mortality patterns and the quality issues of the mortality estimates from self-reported data.
    [Show full text]
  • National Vital Statistics Reports Volume 68, Number 7 June 24, 2019
    National Vital Statistics Reports Volume 68, Number 7 June 24, 2019 United States Life Tables, 2017 by Elizabeth Arias, Ph.D., and Jiaquan Xu, M.D., Division of Vital Statistics Abstract cohort life table requires data over many years. It is usually not feasible to construct cohort life tables entirely on the basis Objectives—This report presents complete period life tables of observed data for real cohorts due to data unavailability or for the United States by race, Hispanic origin, and sex, based on incompleteness (1). For example, a life table representation of age-specific death rates in 2017. the mortality experience of a cohort of persons born in 1970 Methods—Data used to prepare the 2017 life tables are would require the use of data projection techniques to estimate 2017 final mortality statistics; July 1, 2017 population estimates deaths into the future (2,3). based on the 2010 decennial census; and 2017 Medicare data Unlike the cohort life table, the period life table does not for persons aged 66–99. The methodology used to estimate the represent the mortality experience of an actual birth cohort. life tables for the Hispanic population remains unchanged from Rather, the period life table presents what would happen to a that developed for the publication of life tables by Hispanic origin hypothetical cohort if it experienced throughout its entire life the for data year 2006. The methodology used to estimate the 2017 mortality conditions of a particular period in time. For example, life tables for all other groups was first implemented with data a period life table for 2017 assumes a hypothetical cohort that year 2008.
    [Show full text]
  • Life Table Suit
    for each x, and the rest of the computations follow Life Table suit. From any given schedule of death probabili- ties q0,q1,q2,..., the lx table is easily computed A life table is a tabular representation of central sequentially by the relation lx 1 lx(1 qx ) for x features of the distribution of a positive random 0, 1, 2,.... Much of the effort+ in= life-table− construc-= variable, say T , with an absolutely continuous tion therefore is concentrated on providing such a distribution. It may represent the lifetime of an schedule qx . individual, the failure time of a physical component, More conventional{ } methods of life-table construc- the remission time of an illness, or some other tion use grouped survival times. Suppose for sim- duration variable. In general, T is the time of plicity that the range of the lifetime T is subdivided occurrence of some event that ends individual into intervals of unit length and that the number of survival in a given status. Let its cumulative failures observed during interval x is Dx. Let the distribution function (cdf) be F (t) Pr(T t) corresponding total person-time recorded under risk = ≤ and let the corresponding survival function be of failure in the same interval be Rx . Then, if µ(t) S(t) 1 F (t), where F(0) 0. If F( ) has the is constant over interval x (the assumption of piece- = − = · probability density function (pdf) f(), then the risk wise constancy), then the death rate µ D /R is · x x x of event occurrence is measured by the hazard the maximum likelihood estimator ofˆ this= constant.
    [Show full text]