SADC Course in Statistics Basic Life Table Computations - II (Session 13)

Slides:



Advertisements
Similar presentations
Welfare Rights Training 2009 Employment & Support Allowance & Incapacity Benefit (For Youth) Taxable: Yes (Short Term Rate No) Means Tested: No (But.
Advertisements

Describing Data: Measures of Central Tendency
Sampling Distributions and Estimators
1 Adding a statistics package Module 2 Session 7.
SADC Course in Statistics Analysis of Variance for comparing means (Session 11)
SADC Course in Statistics Basic summaries for epidemiological studies (Session 04)
SADC Course in Statistics Common Non- Parametric Methods for Comparing Two Samples (Session 20)
SADC Course in Statistics Multiple Linear Regresion: Further issues and anova results (Session 07)
SADC Course in Statistics Basic summaries for demographic studies (Session 03)
SADC Course in Statistics Estimating population characteristics with simple random sampling (Session 06)
SADC Course in Statistics Simple Linear Regression (Session 02)
The Poisson distribution
SADC Course in Statistics Comparing several proportions (Session 15)
Overview of Sampling Methods II
SADC Course in Statistics Further ideas concerning confidence intervals (Session 06)
SADC Course in Statistics Trends in time series (Session 02)
SADC Course in Statistics Tests for Variances (Session 11)
Assumptions underlying regression analysis
SADC Course in Statistics Basic principles of hypothesis tests (Session 08)
SADC Course in Statistics Meaning and use of confidence intervals (Session 05)
Basic Life Table Computations - I
SADC Course in Statistics The binomial distribution (Session 06)
SADC Course in Statistics Inferences about the regression line (Session 03)
SADC Course in Statistics Using Probability Ideas in Life Tables (Session 11)
SADC Course in Statistics Importance of the normal distribution (Session 09)
Correlation & the Coefficient of Determination
SADC Course in Statistics Confidence intervals using CAST (Session 07)
SADC Course in Statistics Sample size determinations (Session 11)
SADC Course in Statistics Multi-stage sampling (Sessions 13&14)
SADC Course in Statistics Session 4 & 5 Producing Good Tables.
SADC Course in Statistics Exploratory Data Analysis (EDA) in the data analysis process Module B2 Session 13.
SADC Course in Statistics Graphical summaries for quantitative data Module I3: Sessions 2 and 3.
SADC Course in Statistics Comparing two proportions (Session 14)
SADC Course in Statistics Linking tests to confidence intervals (and other issues) (Session 10)
SADC Course in Statistics Putting the Life Table in Context (Session 15)
SADC Course in Statistics Review and further practice (Session 10)
SADC Course in Statistics Revision using CAST (Session 04)
SADC Course in Statistics Conditional Probabilities and Independence (Session 03)
SADC Course in Statistics Fertility Ideas (Session 18)
Preparing & presenting demographic information: 1
SADC Course in Statistics Population Projections - II (Session 20)
SADC Course in Statistics To the Woods discussion (Sessions 10)
SADC Course in Statistics Review of ideas of general regression models (Session 15)
SADC Course in Statistics Setting the scene (Session 01)
SADC Course in Statistics A model for comparing means (Session 12)
SADC Course in Statistics Comparing Means from Paired Samples (Session 13)
SADC Course in Statistics Revision on tests for means using CAST (Session 17)
SADC Course in Statistics Revision on tests for proportions using CAST (Session 18)
Probability Distributions
SADC Course in Statistics Joint distributions (Session 05)
SADC Course in Statistics Laws of Probability (Session 02)
SADC Course in Statistics A Life Table Discussion Topic (Session 14)
SURVIVAL AND LIFE TABLES
Measures of disease frequency (II). Calculation of incidence Strategy #2 ANALYSIS BASED ON PERSON-TIME CALCULATION OF PERSON-TIME AND INCIDENCE RATES.
Auto-Moto Financial Services- The Old Process
Reliability McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.
Scoring Terminology Used in Assessment in Special Education
CHAPTER 16 Life Tables.
1 Confidence Interval for Population Mean The case when the population standard deviation is unknown (the more common case).
Ethan Cooper (Lead Tutor)
2011 WINNISQUAM COMMUNITY SURVEY YOUTH RISK BEHAVIOR GRADES 9-12 STUDENTS=1021.
Before Between After.
2011 FRANKLIN COMMUNITY SURVEY YOUTH RISK BEHAVIOR GRADES 9-12 STUDENTS=332.
Test B, 100 Subtraction Facts
SADC Course in Statistics Introduction and Study Objectives (Session 01)
SADC Course in Statistics Applications of Stationary Population Ideas (Session 16)
SADC Course in Statistics Paddy results: a discussion (Session 17)
SADC Course in Statistics The normal distribution (Session 08)
Chapter 7 LIFE TABLES AND POPULATION PROBLEMS
Life expectancy Stuart Harris Public Health Intelligence Analyst Course – Day 3.
Presentation transcript:

SADC Course in Statistics Basic Life Table Computations - II (Session 13)

To put your footer here go to View > Header and Footer 2 Learning Objectives At the end of this session, you will be able to construct a Life Table or abridged Life Table from a given set of mortality data express in words and in symbolic form the connections between the standard columns of the LT interpret the LT entries and begin to utilise LT thinking in more complex demographic calculations

To put your footer here go to View > Header and Footer 3 Why compute n L x ? The concept behind n L x is of some interest in its own right, but the main reason for its calculation as part of the Life Table is to contribute to the two remaining key columns found in most LT calculations, which look at the accumulation of years lived. Note that to explain these we start at the end of the South African Male LT and work backwards from the highest age!

To put your footer here go to View > Header and Footer 4 Two more Life Table columns: 1 Ages lxlx nLxnLx TxTx exex

To put your footer here go to View > Header and Footer 5 Computing T x A relatively complicated calculation, which we shall see makes very little difference in the end, judges that the 25 (0.025%) who survive to 100 will thereafter live a total of 43 person-years. Accept this for now. A standard calculation of n L x i.e. 5 L 95 - as explained above - says we can expect the 191 males who survive to age 95 will live for a total of 375 years between them between ages 95 and 99 inclusive.

To put your footer here go to View > Header and Footer 6 Computing T x Thus the Total amount of living done by the LT population from age 95 onwards is ( ) = 418 person-years. This is T 95. In the same way, 5 L 90 = 2686 person-years are expected to be lived between ages 90 and 94 inclusive, and the total from age 90 onwards is ( ) = 3103 = T 90. We continue totalling backwards in this way …

To put your footer here go to View > Header and Footer 7 Two more Life Table columns: 2 Ages lxlx nLxnLx TxTx exex <

To put your footer here go to View > Header and Footer 8 Calculating e 0 When we finally get back to age 0, we find that T 0 = 4,988,823. This is the total number of years that we expect the LT population of 100,000 baby boys to live. Averaged out, that is about 49.9 years each. The South African Male life expectancy at birth is about 49.9 years, on these figures. You can see the approximation in years lived after age 100 makes no difference to this answer!

To put your footer here go to View > Header and Footer 9 Observing e x The age 1 figure e 1 is This is described as the residual expectation of life, the further years expected to be lived by a survivor who reaches exact age 1. At first sight it seems odd that after having lived a year, he can now expect to live longer than he could at birth. Further values in the e x column go steadily downwards, but observe that after each [4 or] 5 year period the e x figure reduces by less than [4 or] 5. Ageexex <

To put your footer here go to View > Header and Footer 10 Explaining e x : 1 The life expectancy at birth, e 0, is in fact a weighted average over those babies who die before age 1, and all the others who survive beyond exact age 1. e 0 = 5465 x 0.3* x 52.8** = * ~ as on slide 12, this counts 0.3 of a year for each child that dies aged ** ~ 51.8 years future expected life, plus 1 year already lived. Weights are no. dying and no. surviving.

To put your footer here go to View > Header and Footer 11 Explaining e x : 2 Same effect applies to all changes e x e x+n The life expectancy at birth, e 0, is 49.9 as above ~ an overall average BUT note that on reaching age 50, a member of this population still has future life expectancy of 18.6 years. Can you now explain this, verbally and arithmetically in the same way as on the previous slide?

To put your footer here go to View > Header and Footer 12

To put your footer here go to View > Header and Footer 13 Note that if future life expectancy fell by 1 year for every year lived, the curve would be replaced by the diagonal line shown here!

To put your footer here go to View > Header and Footer 14 Practical work follows to ensure learning objectives are achieved…