Essential Epidemiology: An Introduction for Students and Health Professionals

Citation

Webb, P., Bain, C., & Page, A. (2024). Essential Epidemiology: An Introduction for Students and Health Professionals (5th ed.). Cambridge University Press.

Chapter Summary

The book has 16 chapters over about 380 pages, followed by eight short appendices, a glossary, answers to the end-of-chapter questions and an index; the full volume is 458 pages. It is written by Australian academics. The first half builds the toolkit (measuring disease, descriptive and analytic studies, chance, error and confounding, reading papers, causation and reviews), and the second half applies it to surveillance, outbreaks, prevention, screening and public health practice. Chapter 12 (surveillance) and Chapter 13 (outbreaks and pandemics) were revised most heavily in this edition to add material on COVID-19.

Chapters 1–3: Epidemiology is …; How long is a piece of string? Measuring disease frequency; Who, what, where and when? Descriptive epidemiology

Chapter 1 (pp. 1–26) defines epidemiology as measuring health in populations, identifying the causes of ill-health and intervening to improve health, and discusses what is meant by "health". Chapter 2 (pp. 27–64) introduces the basic measures: the incidence rate, incidence proportion (risk, or cumulative incidence) and prevalence, and how they are calculated and used. Chapter 3 (pp. 65–92) treats descriptive epidemiology, answering "who, what, where and when" through case reports, surveys, national data systems and time trends.

Chapters 4–5: Healthy research: Study designs for public health; Why? Linking exposure and disease

Chapter 4 (pp. 93–120) covers analytic studies, which make planned comparisons: the intervention, cohort and case–control designs. Chapter 5 (pp. 121–145) explains the measures of association used to describe the link between an exposure and a health outcome.

Chapters 6–8: Heads or tails? The role of chance; All that glitters is not gold: The problem of error; Muddied waters: The challenge of confounding

Three chapters on the alternative explanations for an association: chance (Chapter 6, pp. 146–160), bias and error (Chapter 7, pp. 161–186) and confounding (Chapter 8, pp. 187–211). Together they teach the reader to ask whether a finding can be believed before asking what it means.

Chapters 9–11: Reading between the lines: Reading and writing epidemiological papers; Who sank the boat? Association and causation; Assembling the building blocks: Reviews and their uses

The appraisal chapters. Chapter 9 (pp. 212–232) asks whether the results of a single study are valid. Chapter 10 (pp. 233–249) asks whether an association is causal. Chapter 11 (pp. 250–270) covers reviews of the literature, from traditional narrative reviews to more systematic scoping reviews, as ways of integrating evidence.

Chapters 12–13: Public health surveillance: Collecting data for public health action; Clusters, outbreaks, epidemics and pandemics

Chapter 12 (pp. 271–291) looks at the design and use of surveillance systems that detect new risks, track known diseases and supply data for planning. Chapter 13 (pp. 292–313) focuses on outbreak investigation, mainly of infectious disease but also of toxins and chemicals, with COVID-19 as its opening example.

Chapters 14–15: Prevention: Better than cure?; Early detection: What benefits at what cost?

Chapter 14 (pp. 314–336) distinguishes primary, secondary and tertiary prevention. Chapter 15 (pp. 337–365) examines screening: what a successful program requires and the difficulties in showing that it does more good than harm.

Chapter 16: Epidemiology and the public's health

The closing chapter (pp. 366–380) takes a broader view of how epidemiological evidence is translated into practice and policy.

Appendices

Eight appendices cover direct and indirect standardisation, standard populations, calculating risk and lifetime risk from routine data, calculating life expectancy from a life table, the odds ratio and relative risk, formulae for confidence intervals, and the Mantel–Haenszel method for pooled odds ratios.

Key Concepts

Chapters 1–5: Measures and designs

• Incidence rate, incidence proportion and prevalence: the three fundamental measures of disease frequency (Chapter 2).
• Descriptive epidemiology: describing disease by person, place and time (Chapter 3).
• Analytic studies: intervention, cohort and case–control studies (Chapter 4).
• Measures of association: ways of quantifying the link between exposure and outcome (Chapter 5).

Chapters 6–11: Judging evidence

• Chance, bias and confounding: the three alternative explanations that must be considered for any association (Chapters 6–8).
• Causation: deciding whether an association is causal (Chapter 10).
• Literature reviews: narrative and scoping reviews, among other types, as ways of integrating studies (Chapter 11).

Chapters 12–16: Epidemiology in practice

• Public health surveillance: ongoing collection of data for public health action (Chapter 12).
• Outbreak investigation: tracing the source and spread of clusters and epidemics (Chapter 13).
• Primary, secondary and tertiary prevention: the three levels of prevention (Chapter 14).
• Screening: early detection and the conditions for a worthwhile program (Chapter 15).

Critical Analysis

Strengths

• A consistent teaching format. The publisher describes a "trademark clear and consistent pedagogical structure": information boxes, margin notes with supplementary facts and question prompts in every chapter, and end-of-chapter questions with guided solutions.
• Mathematics kept optional. Statistical analysis is explained simply, with the harder concepts placed in optional advanced boxes, and the formulae for standardisation, confidence intervals and the Mantel–Haenszel method are collected in appendices, so the main text is readable without them.
• Logical build-up. The sequence from measurement to design to chance, bias and confounding to causation and reviews matches the way a student learns to appraise a study, and the later chapters show the same ideas at work in surveillance, outbreaks and screening.
• Local and international examples. The publisher reports examples from Australia and elsewhere, including COVID-19, and the book covers both public health and clinical epidemiology.
• Current edition. Chapters 12 and 13 were revised substantially to include COVID-19.

Limitations

• Introductory depth. The text is an introduction, and the advanced mathematics is placed in optional boxes and appendices; students heading for epidemiological research will need a more advanced methods text.
• Australian emphasis. The authors are Australian academics and the publisher highlights local examples alongside international ones, so some data sources and policy settings will be unfamiliar to readers elsewhere.
• Length. At 16 chapters and 458 pages including appendices, it is a full course text rather than a compact introduction.
• Organised by method, not by disease. The contents list follows methods and public health functions; it has no chapters devoted to particular disease groups, such as cancer or chronic disease epidemiology.

Where It Fits in the Course

Within the public health and epidemiology course, this is a full course textbook with a methods-first structure and an applied second half. Gordis Epidemiology is another introductory textbook, moving from the natural history and transmission of disease through measures of frequency, screening and study design to causal inference, and is a natural alternative or companion. Epidemiology: An Introduction by Rothman and colleagues is a compact, concept-first book that explains why the methods take their form; it works well alongside the early chapters here.

For students who want statistics, Epidemiology: Study Design and Data Analysis is a textbook on the statistical design and analysis of studies, for statistical depth beyond the optional boxes here. Modern Epidemiology is the comprehensive graduate text and reference for readers who go further.

How to Use This Book

The book is written for students of public health and the health professions, and for practising health professionals. Because the book starts from a definition of epidemiology in Chapter 1, no prior epidemiology is needed; basic numeracy and some familiarity with health and disease are enough, though an introductory statistics course helps.

A practical plan:

• Core: Chapters 1 to 8 in order. They give the measures, the designs and the three threats to validity that the rest of the book uses.
• Appraisal: Chapters 9 to 11 for reading papers, judging causation and using reviews. Read them alongside papers from your own course.
• Applications: Chapters 12 to 16 can be taken in any order after the core, according to what the course covers: surveillance and outbreaks, prevention and screening, or epidemiology in policy.
• Optional mathematics: read the advanced boxes and appendices only after the main text is comfortable.
• Active use: answer the end-of-chapter questions and check the guided solutions, and use the question prompts in the margins as a self-test.