Essential Epidemiology Brochure EMEA

CONTENTS

Chapter 8 Muddied waters: The challenge of confounding An example of confounding: Is alcohol a risk factor for lung cancer?

Characteristics of a confounder The effects of confounding Control of confounding through study design Control of confounding in data analysis Confounding: The bottom line

Chapter 1: Epidemiology is …

at the University of Queensland and an Adjunct Professor in the School of Public Health and

Chapter 12 Surveillance: Collecting health-related data for Social Work at the Queensland University of Technology. epidemiological intelligence and public health action The scope of surveillance Chris Bain has taught epidemiology to public health and medical students for over Why conduct surveillance? three decades, and has co-authored a book on systematic reviews, as well as many Types of surveillance Source: Cartoon by Conan de Vries, licensed by CartoonStock.com.

Source: Cartoon by Conan de Vries, licensed by CartoonStock.com.

Surveillance essentials research papers.

Descriptive studies: person, place and time

ESSENTIAL

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Epidemiology An Introduction for Students and Health Professionals FOURTH EDITION

Andrew Page is Professor of Epidemiology in the School of MedicineByat‘person’ Western In some countries there is concern over health differences between indigenous peoples and Sydney University.

Descriptive studies: person, place and time

Request an inspection copy

the rest of the population. Figure 1.5 shows Australian mortality data comparing Indigenous with non-Indigenous people. The bars show how many times higher mortality from lung

By ‘person’

Standardised mortality ratio

Chapter 13 Outbreaks, epidemics and clusters Outbreaks, epidemics and clusters Epidemiology of infectious diseases Outbreak management and investigation Evidence for causation Non-infectious clusters and outbreaks

7

men

women you a relevant are considering using one of our textbooksindigenous as a set text InGoing someIftocountries there concern over health differences between peoples and Cover image: L.S. Lowry theteach Match, 1928.iscourse Oil on and canvas, Chapter 14 Prevention: Better than cure?

5 then you can request a free inspection copy (*subject to course and title requirements).

Disease prevention in public health 4 71 x 91.5 cm. © The Estate L.S. All rights reserved. the of rest ofLowry. the population.

Figure 1.5 shows Australian mortality data comparing Indigenous 3 The scope for preventive medicine DACS/Copyright Agency, 2020.Order online or contact us to make a request: with non-Indigenous people. The bars show how2 many times higher mortality from lung Strategies for prevention 1 The population attributable fraction as a guide to prevention Image supplied by The Medici Society Limited.

0 Prevention in practice All causes Lung cancer Diabetes Respiratory disease Online Evaluation of preventive interventions in practice Cause of death 7 A final (cautionary) word men www.cambridge.org/9781108766807 Figure 1.5 Age-standardised mortality ratios for selected diseases in the Indigenous compared to the non-Indigenous population in Australia, 2016. The bars indicate how many times higher 6 Chapter 15 Early detection: What benefits at what cost? mortality was among Indigenous men and women compared to non-Indigenous people women Email Why screen?

(SMR = 1).

5 Source: ABS, 2017.

The requirements of a screening program Purchase this book to gaininspectioncopy@cambridge.org access to your complementary VitalSource Evaluation of a screening program eBook. Fully integrated 4 with the print book, this enhanced version of

Chapter 16 Epidemiology and the public’s health Translating epidemiological research into practice Challenges Limiting error Improving measurement Synthesis and integration Epidemiology and complex systems A final word

Standardised mortality ratio

Chapter 7 All that glitters is not gold: The problem of error Sources of error in epidemiological studies Selection bias Measurement or information error

Penelope Webb, Chris Bain and Andrew Page

6

FOURTH EDITION

Chapter 6 Heads or tails? The role of chance Random sampling error Statistical significance: Could an apparent association have arisen by chance?

Confidence intervals Power: Could we have missed a true association?

Interpreting p-values and confidence intervals Statistical versus clinical significance

important goal is to bring rigour to the collection, analysis and interpretation of health data,

Chapter 11 Assembling the building blocks: Reviews and their to improve health at a global scale. Essential Epidemiology is highly regarded as the resource uses What is a systematic review? that provides readers with the tools to achieve that goal.

Specifying the research question Identifying the literature Appraising the literature Drawing conclusions Assessing the quality of a systematic review Penelope Webb is a Principal Research Fellow and Group Leader at the QIMR Berghofer Making judgements in practice Medical Research Institute in Brisbane, an Honorary Professor in the School of Public Health The end result

ESSENTIAL Epidemiology

Chapter 5 Why? Linking exposure and disease Looking for associations Ratio measures (relative risk) Risk ratios Difference measures (attributable risk) Relative risk versus attributable risk: An example Looking for associations when the measures are continuous

Webb, Bain & Page

Chapter 4 Healthy research: Study designs for public health The ideal study Intervention studies or trials Observational studies A word about ethics

Essential Epidemiology

Chapter 3 Who, what, where and when? Descriptive epidemiology Case reports and case series Vital statistics and mortality data Morbidity data Creative use of existing data Confidentiality

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understandable level. Complexities are presented as optional material for extension.

Chapter 10 Who sank the boat? Association and causation What do we mean by a cause?

With more than 90 questions and answers to work through in the print book, and hundreds Association versus causation more in the enhanced eBook, this is an essential resource for students, practitioners and Evaluating causation anyone else who needs to interpret health data in their studies or work. Epidemiology’s most An example: Does H. pylori cause stomach cancer?

Snow, J. (1855). On the Mode of Communication of Cholera, 2nd edn. London Churchill.

Thomas, S., Acton, C., Nixon, J. et al. (1994). Effectiveness of bicycle helmets in preventing head injury in children: case–control study. British Medical J 308: 173–6.

Vessey, M. P., Villard-Mackintosh, L., McPherson, K. and Yeates, D. (1989). Morta among oral contraceptive users: 20 year follow up of women in a coho British Medical Journal, 299: 1487–91.

Chapter 2 How long is a piece of string? Measuring disease frequency What are we measuring?

The concepts: Prevalence and incidence Measuring disease occurrence in practice: Epidemiological studies Measuring disease occurrence in practice: Using routine data Other measures commonly used in public health Global health indicators

Chapter 1: Epidemiology is …

Now in its fourth edition, Essential Epidemiology is an engaging and accessible introduction

Chapter 9 Reading between the lines: Reading and writing to the foundations of epidemiology. This text introduces the core concepts and shows epidemiological papers The research question and study design the essential role of epidemiology in public health and medicine across a broad range of Internal validity health monitoring and research activities. It draws on cases from chronic and infectious So what? Are the results important? diseases, with vibrant contemporary, historical and hypothetical examples that enable Generalisability (external validity) Descriptive studies students to engage with the content, while maintaining the mathematics requirement at an Writing papers 144

Chapter 1 Epidemiology is …

A case of food poisoning Subdisciplines of epidemiology On epidemics An historical epidemic The beginnings What does epidemiology offer?

What do epidemiologists do?

A natural experiment

Essential Epidemiology contains useful self-assessment tools, including 3 interactive questions, links to web resources and supplementary information.

9781108766807c01_p01-26.indd This extra content can be downloaded to your device in one convenient package.

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1 available to adopters of the A variety of instructor resources are book at www.cambridge.edu.au/academic/XXX. 0

All causes Lung cancer

Diabetes

Cause of death

Respiratory disease

Includes bonus enhanced eBook

Medicine at Western

ESSENTIAL

Description Chapter 2: Measuring health

Epidemiology FOURTH EDITION

FOURTH EDITION

on. ackage.

Includes bonus enhanced eBook

AVAILABLE: February 2020 ISBN: 9781108766807 RRP: £57.00 FORMAT: Paperback with enhanced eBook

Alternative explanations Chapters 6–8

Integration & interpretation Chapters 9–11

28 30 32 33 34 34 35 38 39 39 39 41 41 42 43 45 46 47 47 48 50 50 51 51 52 55 56 57 57 58 59 61 63 63

A clear navigational structure at the start of each chapter breaks the text into logical chunks, allowing students to easily follow along and maintain a “big picture” perspective throughout the text.

Chapter 3: Who, what, where and when? Descriptive epidemiology

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70

Men – 65 60

Women – birth

50

Women – 50 Women – 65

40

0 1750

FULL COLOUR Now in full-colour, the text’s graphs, tables, and images are clearer than ever.

1800

1850

1900

1950

2000

Year Time trends in life expectancy at birth, age 50 and age 65 years, by sex, in Sweden 27

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Describe the changes in life expectancy by age and sex over time shown in Figure 3.1 and comment on the patterns.

In the late eighteenth century, average life expectancy in Sweden was only about 35 years for men and women, although then, as now, women could expect to live slightly longer than men. This young average age was a consequence of the very high mortality rates in babies and children at the time, and improvements in this area led to much of the large gain in average life expectancy, particularly between 1850 and 1950. However, even in 1800, if an individual survived their childhood years and made it to their 65th birthday they could then expect to live to about 75 years. Today, most deaths in a country such as Sweden occur over the age of 65; thus, someone who reaches that age can still only expect to live until about 85 years, a much more modest improvement.

Asthma

No asthma

Total

Non-smokers

40

360

400

Smokers

30

170

200 Recognising the enormous value of the information, particularly the mortality data col-

Total

70

530

600

No asthma 360 ÷ 400 = 0.9 = 90%

400 = 100%

VARYING EXAMPLES

Total

Smokers

30 ÷ 200 = 0.15 = 15%

170 ÷ 200 = 0.85 = 85%

200 = 100%

This tells us that 10% of non-smokers and 15% of smokers have asthma.

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Alternatively, we could use the same data to calculate the percentages of people with and without asthma who smoke – these are column percentages because now we use the total of each column, the number of people with or without asthma, as the denominator: Asthma

170 ÷ 530 = 32%

Total

70 = 100%

530 = 100%

This tells us that 43% of people with asthma and 32% of people without asthma are smokers.

It is very important to decide first which percentages are most relevant for a particular situation and then to calculate and interpret the percentages correctly.

Saying that 43% of people with asthma are smokers (correct) is not the same as saying that 43% of smokers have asthma (incorrect; 15% of smokers have asthma).

Percentages can be confusing because there is often more than one way in which they can be calculated, and this can lead to problems with interpretation – see Box 2.2 for some additional guidance.

Real-life cases from around the world illustrate key points with relevant current and historical data.

No asthma 360 ÷ 530 = 68%

BOX 15.4

Prevalence

Chapter 15: Early detection: What benefits at what cost?

339

C

ROC curves and likelihood ratio tests

One way to visualise the relation between sensitivity and specificity is with a ‘receiver

characteristic’ or ROC curve. These were developed by the British during The prevalence of a disease tells us what proportion of a population operating actually has the World II to disease at a specific point in time: an estimated 0.3% or 300 of every 100 000 War people in measure how well their radar receivers (hence the name) could discriminate between incoming German planes and flocks of birds. To generate a Western/Central Europe and North America were living with HIV in 2016.

In practice ROC curve we you may see references to point prevalence, which is a snapshot of the situation at plot a the sensitivity of a test against 1 (or 100%) minus the specificity of the test.ofFigure single point in time, and period prevalence, which measures the proportion the 15.4 shows the ROC curve for the M test (red line). The closer the curve goes to the population that had the disease at any time during a specified period. Period prevalence is top left-hand corner of the graph where the sensitivity = 100%

100%

AUTHORS

IN-TEXT ICONS Icons highlight interactive content, advanced material, information on clinical epidemiology, and extra material.

ROC curve for M test inferior test

90% 80% 70% 60% 50%

Chris Bain has taught epidemiology to public health and medical students for over three decades, and has coauthored a book on systematic reviews, as well as many research papers.

20% Chapter 2: How long is a piece of string? Measuring disease frequency 10%

INSTRUCTOR RESOURCES The comprehensive companion website contains a trove of online instructor resources. Content includes multiple-choice questions, short answer questions (with answers), video links with descriptions, further reading links, and PowerPoint slides.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

QUESTIONS

Questions

1-Specificity

2. Two thousand women aged 55 years were given a health check, and 100 were found(1 – sensitivity) ÷ specificity or 8.7 ÷ 89.9 = 0.1. to have high blood pressure. Ten years later, all 2000 women attended a second The large LR positive and small LR negative indicate that someone who is positive on this test is much more likely to actually have the disease than not, while someone check, and another 300 women had developed high blood pressure. a. What was the prevalence of high blood pressure in the women (i) at age 55, andwho tests negative is very unlikely to have it. Their new ‘post-test’ probability of having/not having the disease depends on their ‘pre-test’ probability (before the (ii) at age 65? b. How many women were ‘at risk’ of developing high blood pressure at the start oftest) and the LR and can be estimated using Bayes’ nomogram (Attia, 2003).

4. Look back to Table 2.10. What does this tell us about the relative importance of chronic obstructive pulmonary disease and lower respiratory infections as causes of mortality and ill health? Explain the patterns you see.

REFERENCES Agarwal, M. M. and Punnose, J. (2002). Gestational diabetes: implications of variation in diagnostic criteria. International Journal of Gynecology and Obstetrics, 78: 45–6. https://www.ncbi.nlm.nih.gov/pubmed/12113970 Ahmad, O. B., Boschi-Pinto, C., Lopez, A. D., Murray, C. J. L., Lozano, R. and Inoue, M. (2001). Age-standardization of rates: a new WHO standard. GPE Discussion Paper Series: No.31. World Health Organization.

Anderson, R. and Rosenberg, H. (1998). Age standardization of death rates: implementation of the Year 2000 standard. National Vital Statistics Reports, vol.

47, no. 3. Hyattsville, MD: National Center for Health Statistics.

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S ECO N D E D I T I O N Bayes nomogram

END-OF-CHAPTER QUESTIONS End-of-chapter questions allow students to self-assess and consolidate their learning. Suggested responses are included in the VitalSource eBook and at the back of the book.

Public Health

9781108766807pre_i-xxii.indd 20 9781108766807pre_i-xxii.indd

local & global perspectives

E D I T E D BY

pranee liamputtong

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Links Available now

the 10-year period? c. What was the incidence of high blood pressure in these women? Is this an incidence proportion or an incidence rate? d. Calculate the total number of person-years at risk (of developing high blood pressure) during the 10 years. e. What was the incidence rate of high blood pressure in these women?

3. Community A and community B both have crude mortality rates for ischaemic heart disease of 4 per 1000 population per year. The age-adjusted mortality rate for ischaemic heart disease in community A is 5 per 1000 population and the ageadjusted rate for ischaemic heart disease in community B is 3 per 1000 population.

Which of the following is correct?

A – Community A has a younger population than community B B – Community A has an older population than community B C – Diagnosis is more accurate in community A D – Diagnosis is more accurate in community B

Respond toinformation the questions at the end of each Additional

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15.4 ROC curve for use of M levels to diagnose disease compared to an inferior test 1. For each of the following scenarios, calculate a measure of the incidence of diseaseFigure Questions and identify the type of measure you have calculated: a. One thousand healthy women were followed for 8 years, and 15 developed high You will also hear clinicians talking about the likelihood ratio (LR) for a test. blood pressure.

This is the probability of getting a particular test result in someone who truly has b. A large group of elderly men was followed for a total of 5000 person-years, andthe condition of interest divided by the probability of getting the same result in 75 of the men had a stroke during the duration of the study. someone who does not have the condition. For a positive result this is equal to the c. In a community with a population of 50 000 people, 27 developed diabetessensitivity ÷ (1 – specificity). So for the M test with a cut-off of ≥ 20 mg/L, the LR for during a 1-year period. a positive test is 91.3 ÷ 10.1 = 9.0. For a negative test, the likelihood ratio is equal to

9781108766807c02_p27-64.indd 63

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9781108766807c15_p324-352.indd 339

Andrew Page is Professor of Epidemiology in the School of Medicine at Western Sydney University.

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Penelope Webb is a Principal Research Fellow and Group Leader at the QIMR Berghofer Medical Research Institute in Brisbane, an Honorary Professor in the School of Public Health at the University of Queensland and an Adjunct Professor in the School of Public Health and Social Work at the Queensland University of Technology.

Questions

ments available to download from your eBook to your device. Click the icon to download the

and specificity = 100% (i.e. 100% – specificity = 0%) the better the test, so the blue curve shows a test that performs less well than the M test. One way to assess how well a test discriminates between people with and without disease is by calculating the area under the curve (often referred to as the AUC or C-statistic) where a value of 1.0 indicates a perfect test and a value of 0.5 indicates the test is performing no 22/07/19 7:57 PM better than chance (shown by the dashed line in the figure).

9781108766807c02_p27-64.indd 32

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9781108766807pre_i-xxii.indd 19

other health data sets; we will discuss this further in Chapter 4.

Asthma 40 ÷ 400 = 0.1 = 10%

30÷70 = 43%

xx

the solution pop-ups can be moved about the

lected by their registries of births, marriages and deaths, many countries that have a comprehensive civil registration system now also operate a national death register or index

Non-smokers

40÷70 = 57%

All purchasers of the print textbook have access to the fully integrated enhanced VitalSource eBook. This eBook Once you have registered your VitalSource access code (see the inside front cover for instructions), you will have access to the enhanced eBook via your VitalSource Bookshelf. The navigation instructions houses useful self-assessment tools, including short-answer questions, pop-up key definitions andfeatures other online belowterm provide a general overview of the primary used within this enhanced eBook. resources, such as additional content that can be downloaded to your device.

Icon

xix

National death registers

There are two ways that we can look at these data. One way would be to research. These electronic registers hold information about the name, to facilitate health calculate the percentages of (a) non-smokers and (b) smokers whodate have ofasthma – birth and sex of every individual who has died since the register began, as well these are row percentages because we use the total of each row,asthe number of and cause of death, and they allow bona-fide researchers conducting their date, place non-smokers or smokers, as the denominator (note: the denominator is the bottom scientifically and ethically approved studies to obtain death information for individuals half of a fraction and the numerator the top half): in their studies. In some countries it is also possible to get approval to ‘link’ these data to

Smokers

The icon descriptor indicates the topic available.

xx

10

Life expectancy at birth increased by about 45 years compared to increases of only 10–15 years for life expectancy at ages 50 and 65. Why is this?

Non-smokers

Further detail on some topics is found

30

?

Now in its fourth edition, Essential Epidemiology is an engaging and accessible introduction to the foundations of epidemiology. It addresses the study of infectious and chronic diseases, public health and clinical epidemiology, and the role of epidemiology in a range of health monitoring and research activities. Contemporary, historical and hypothetical examples enable students to engage with content, while mathematics is kept understandable with complex mathematics housed in optional material so the book remains accessible.

exclusively within the VitalSource eBook.

VITALSOURCE INTERACTIVE EBOOK

20

Source: Statistics Sweden http://www.scb.se/en/, accessed 3 June 2018.

Imagine a study that gave the following results:

How to use your VitalSource eBook

Additional information

How to use your VitalSource eBook

Men – 50

9781108766807c02_p27-64.indd 27

Essential Epidemiology

Using your VitalSource eBook

Additional information ADDITIONAL CONTENT Respond to the questions at the end of each Further detail on some topics is found Navigation and search chapter Move andbetween use guided solutions to Further detail on topics can be found exclusivelyexclusively in thewithin the VitalSource eBook. pages the and sections in multiple ways, including via the linked table of contents and the search tool.

The icon descriptor indicates the topic assess your response.

Note that the solution VitalSource eBook. The icon descriptor indicatesavailable. the topic pop-ups can be moved about the page. available.

Men – birth

80

(1751–2017)

32

xx

This icon is used throughout the textbook to indicate the presence of an interactive component in the eBook. A descriptor below indicates the type of content available.

Figure 3.1

BOX 2.2 A note about percentages

With over 90 questions and answers to work through in the print book, and hundreds more in the interactive eBook, this book is an essential resource for students, practitioners and anyone else who needs to interpret health data in their studies or work. Epidemiology’s most important goal is to bring rigour to the collection, analysis and interpretation of health data to improve health on a global scale; Essential Epidemiology provides readers the tools to achieve that goal.

CLEAR NAVIGATION

Practical applications Chapters 12–15

What are we measuring?

The concepts: Prevalence and incidence Prevalence Incidence Population at risk The relationship between incidence and prevalence Measuring disease occurrence in practice: Epidemiological studies Incidence rates versus incidence proportion Example Prevalence Incidence proportion (cumulative incidence) Incidence rate Measuring disease occurrence in practice: Using routine data Crude incidence and mortality rates Age-specific incidence and mortality rates Standardised incidence and mortality rates A note about standard populations Measuring risk using routine statistics Other measures commonly used in public health Standardised incidence and mortality ratios The proportional (or proportionate) mortality ratio The case fatality ratio Survival rate and relative survival rate Global health indicators Mortality indicators Life expectancy Disability-free life expectancy Potential years of life lost Quality-adjusted life years Health-adjusted life expectancy Disability-adjusted life years Summary Questions References

An Introduction for Students and Health Professionals

rce of

Association Chapters 4–5

Life expectancy in years

dical students for over eviews, as well as many

2

How long is a piece of string?

Measuring disease frequency

Sensitivity

ader at the QIMR Berghofer r in the School of Public Health the School of Public Health and

ESSENTIAL Epidemiology

n the print book, and hundreds r students, practitioners and es or work. Epidemiology’s most nd interpretation of health data, highly regarded as the resource

Penelope Webb, Chris Bain and Andrew Page

Webb, Bain & Page

ing and accessible introduction core concepts and shows cine across a broad range of rom chronic and infectious cal examples that enable mathematics requirement at an material for extension.

308: 173–6.

Vessey, M. P., Villard-Mackintosh, L., McPherson, K. and Yeates, D. (1989). Mortality among oral contraceptive users: 20 year follow up of women in a cohort study.

British Medical Journal, 299: 1487–91.

19

9781108456456 Paperback £55.00 255 mm x 190 mm / 448 pp

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Pranee Liamputtong’s Public Health: Local and Global Perspectives, Second Edition provides students with a comprehensive overview of Australian and international public health issues and contexts. It introduces the discipline of public health and aims to deepen students’ understanding of the determinants of health, historical and theoretical perspectives of public health, current health research and evidence-based practice.

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