Book contents
- Frontmatter
- Contents
- Foreword
- Preface
- 1 Epidemiology is…
- 2 How long is a piece of string? Measuring disease frequency
- 3 Who, what, where and when? Descriptive epidemiology
- 4 Healthy research: study designs for public health
- 5 Why? Linking exposure and disease
- 6 Heads or tails: the role of chance
- 7 All that glitters is not gold: the problem of error
- 8 Muddied waters: the challenge of confounding
- 9 Reading between the lines: reading and writing epidemiological papers
- 10 Who sank the boat? Association and causation
- 11 Assembling the building blocks: reviews and their uses
- 12 Outbreaks, epidemics and clusters
- 13 Watching not waiting: surveillance and epidemiological intelligence
- 14 Prevention: better than cure?
- 15 Early detection: what benefits at what cost?
- 16 A final word…
- Answers to questions
- Appendix 1 Direct standardisation
- Appendix 2 Standard populations
- Appendix 3 Calculating cumulative incidence and lifetime risk from routine data
- Appendix 4 Indirect standardisation
- Appendix 5 Calculating life expectancy from a life table
- Appendix 6 The Mantel-Haenszel method for calculating pooled odds ratios
- Appendix 7 Formulae for calculating confidence intervals for common epidemiological measures
- Glossary
- Index
Appendix 5 - Calculating life expectancy from a life table
- Frontmatter
- Contents
- Foreword
- Preface
- 1 Epidemiology is…
- 2 How long is a piece of string? Measuring disease frequency
- 3 Who, what, where and when? Descriptive epidemiology
- 4 Healthy research: study designs for public health
- 5 Why? Linking exposure and disease
- 6 Heads or tails: the role of chance
- 7 All that glitters is not gold: the problem of error
- 8 Muddied waters: the challenge of confounding
- 9 Reading between the lines: reading and writing epidemiological papers
- 10 Who sank the boat? Association and causation
- 11 Assembling the building blocks: reviews and their uses
- 12 Outbreaks, epidemics and clusters
- 13 Watching not waiting: surveillance and epidemiological intelligence
- 14 Prevention: better than cure?
- 15 Early detection: what benefits at what cost?
- 16 A final word…
- Answers to questions
- Appendix 1 Direct standardisation
- Appendix 2 Standard populations
- Appendix 3 Calculating cumulative incidence and lifetime risk from routine data
- Appendix 4 Indirect standardisation
- Appendix 5 Calculating life expectancy from a life table
- Appendix 6 The Mantel-Haenszel method for calculating pooled odds ratios
- Appendix 7 Formulae for calculating confidence intervals for common epidemiological measures
- Glossary
- Index
Summary
Life expectancy is calculated based on what we expect to happen to a hypothetical cohort of 100,000 newborn infants if they experience the same mortality rates that currently operate within the population. (The cohort size is often denoted Ix where x is the age of interest, thus at the start age = 0 and I0 = 100,000.) The table shows the first and last few rows of a standard life table for Australian males based on mortality rates from 2005–7.
If the probability of a male dying before his first birthday (qo) is 0.00527 then we would expect 527 deaths in our cohort in the first year of life (d0 = Io × q0) leaving 99,473 survivors at age = 1 (i.e. I1 = 99,473). We can also estimate the numbers of years of life lived between the ages of 0 and 1. Because most infant deaths occur shortly after birth this is estimated as 99,535 years, but for older ages we assume that those who died did so, on average, halfway through the year and thus contribute 0.5 years of life. Thus, for example, at age = 3 the total years of life L3 = 99,409 – (19 ÷ 2) = 99,399. If we repeat these calculations for each year of age up to 100 we end up with 1,412 men from our original cohort of 100,000 who survive to age 100, 445 of whom will die before their 101st birthday.
- Type
- Chapter
- Information
- Essential EpidemiologyAn Introduction for Students and Health Professionals, pp. 411 - 412Publisher: Cambridge University PressPrint publication year: 2010