Gordis Epidemiology

Citation

Celentano, D. D., Szklo, M., & Farag, Y. (2025). Gordis Epidemiology (7th ed.). Elsevier.

Chapter Summary

The seventh edition continues the textbook first written by Leon Gordis, now by three epidemiologists at Johns Hopkins. Its twenty chapters (about 420 pages of text) are arranged in three sections (Section I, The Epidemiologic Approach to Disease and Intervention, Chapters 1–6; Section II, Using Epidemiology to Identify the Cause of Disease, Chapters 7–16; Section III, Applying Epidemiology to Evaluation and Policy, Chapters 17–20); the publisher describes the chapters as progressing from observation and the development of hypotheses to data collection and analysis, and the last section turns to evaluation and policy. Chapters end with review questions, answers are collected at the back, and the publisher offers more than 200 further multiple-choice questions online. The new edition adds examples drawn from the COVID-19 pandemic; the authors' preface remarks that it took a pandemic for the world to recognize the role of epidemiology.

Chapter 1: Introduction

An overview of what epidemiology is for. In one passage a graph of the decline in rheumatic fever deaths in the United States from 1910 to 1977, most of which occurred before penicillin, is used to warn against attributing falling mortality to medical treatment without rigorous study, and the chapter then argues that prevention and treatment should be integrated rather than seen as alternatives.

Chapter 2: The Dynamics of Disease Transmission

How infectious diseases spread through populations, the starting point for the book's treatment of disease occurrence.

Chapters 3–4: The Occurrence of Disease: I. Disease Surveillance and Measures of Morbidity; II. Mortality and Other Measures of Disease Impact

A two-part treatment of measuring disease: surveillance and measures of morbidity first, then mortality and other measures of the impact of disease.

Chapter 5: Assessing the Validity and Reliability of Diagnostic and Screening Tests

How well tests classify people with and without disease, and how consistent their results are.

Chapter 6: The Natural History of Disease: Ways of Expressing Prognosis

Ways of describing the course of disease and expressing prognosis.

Chapters 7–9: Observational Studies; Cohort Studies; Comparing Cohort and Case-Control Studies

The observational designs, with a full chapter on cohort studies and a chapter comparing the cohort and case-control approaches.

Chapters 10–11: Assessing Preventive and Therapeutic Interventions: Randomized Trials; Randomized Trials: Some Further Issues

Two chapters on randomized trials as the means of assessing prevention and treatment.

Chapters 12–13: Estimating Risk: Is There an Association?; More on Risk: Estimating the Potential for Prevention

Measures of association, then measures that show how much disease could be prevented.

Chapters 14–15: From Association to Causation: Deriving Inferences From Epidemiologic Studies; More on Causal Inference: Bias, Confounding, and Interaction

The move from an observed association to a causal conclusion, followed by the main threats to that inference.

Chapter 16: Identifying the Roles of Genetic and Environmental Factors in Disease Causation

Genetic and environmental contributions to disease; the publisher notes that the book includes a glossary of genetic terminology.

Chapters 17–18: Using Epidemiology to Evaluate Health Services; Epidemiologic Approach to Evaluating Screening Programs

Epidemiology applied to judging whether health services and screening programmes work. Chapter 1 already points ahead to Chapter 17 for study designs used to evaluate medical care.

Chapters 19–20: Epidemiology and Public Policy; Ethical and Professional Issues in Epidemiology

The book closes with the role of epidemiology in policy and with ethical and professional questions in the field.

Key Concepts

Section I: Measuring Disease

• Disease transmission: the dynamics by which infections spread, introduced before the measures of occurrence.
• Surveillance and morbidity measures: systematic monitoring of disease and the measures used to describe how much illness exists.
• Mortality and disease impact: measures that summarize deaths and the wider burden of disease.
• Validity and reliability of tests: whether a diagnostic or screening test classifies people correctly, and whether it gives consistent results.

Section II: Finding Causes

• Cohort and case-control studies: the two main observational designs, compared directly in Chapter 9.
• Randomized trials: experimental assessment of preventive and therapeutic interventions.
• Association and potential for prevention: estimating risk and how much disease an intervention could avert.
• Bias, confounding and interaction: the threats and complications that must be considered before an association is judged causal.

Section III: Evaluation and Policy

• Evaluation of health services and screening programmes: epidemiologic designs used to judge whether care and screening achieve their aims.
• Ethics in epidemiology: the professional issues raised by epidemiologic research and practice.

Critical Analysis

Strengths

• Accessible presentation. Both the publisher and Doody's reviewers stress clear writing and practical examples. The 2025 Doody's review of this edition (Tristan Milarch) singled out its emphasis on real-world applications and judged it especially useful for readers new to epidemiology; the review of the sixth edition (Michael A. O'Rorke) called it ideal for students who need grounding in the principles. Chapter 1, for example, makes its point about medical care and mortality with a historical graph and a cartoon.
• Built-in self-testing. Chapters close with review questions and the answers are printed at the back, with over 200 further multiple-choice questions online.
• Broad applied coverage. Beyond study design and causation, the book gives whole chapters to screening tests and screening programmes, prognosis, randomized trials, genetic and environmental causes, health-services evaluation, policy and ethics, so it serves medical and public health students alike.
• A long-tested structure. The copyright page lists earlier editions from 1996 onward; the three-section plan is now in its seventh revision, and this edition updates its examples with lessons from COVID-19.

Limitations

• Statistical analysis is not its focus. The contents contain no chapters on data analysis methods such as stratified analysis or regression; the treatment of confounding and interaction is concentrated in a single chapter on causal inference. Students who must analyse data will need a methods text.
• Many chapters to choose from. Twenty chapters, with randomized trials, risk and causal inference each split over two chapters, is more than a short introductory module can cover; instructors and self-learners have to select.

Where It Fits in the Course

Of the two introductions in this course written for newcomers, Gordis Epidemiology is the fuller. Rothman, Huybrechts and Murray's Epidemiology: An Introduction is shorter and more conceptual: it opens with confounding and causation, keeps formulas to a minimum, and adds introductory chapters on analysis and regression, but prints no answers to its questions. Gordis offers more on measuring disease, screening, trials and the use of epidemiology in services and policy, with self-test questions throughout. Reading Gordis for coverage and practice and Rothman for the reasoning behind the methods is an effective combination.

Essential Epidemiology by Webb, Bain and Page covers similar ground for public health and the health professions, with weight on prevention, outbreaks and policy. For those continuing in the subject, Mark Woodward's Epidemiology: Study Design and Data Analysis supplies the statistical design and analysis that Gordis largely leaves aside, and Modern Epidemiology is the comprehensive graduate reference on causal inference, bias and analysis.

How to Use This Book

The publisher addresses the book to students of public health at undergraduate and master's level, medical students, and any programme with an introductory epidemiology course. Basic arithmetic is enough to begin.

A practical reading plan:

• Core: Chapters 1–5 (what epidemiology does, transmission, measures of morbidity and mortality, test validity), then Chapters 7–15, the heart of the book on study designs, trials, measures of risk and causal inference.
• By programme: medical students should not skip Chapters 5 and 6 on tests and prognosis; public health students should add Chapters 17–19 on services, screening programmes and policy. Chapter 16 suits those with an interest in genetics; Chapter 20 on ethics belongs in any full course.
• Practice: answer the review questions at the end of each chapter before checking the answers at the back, then use the online multiple-choice questions for revision.

Work actively with the figures and tables: recompute a rate or a measure of association from a table in the text, and say in a sentence what it means. Keep a list of the biases and design features introduced in Chapters 7–15 and apply it to one published study each week. If you want a deeper account of why the methods work, read the corresponding chapters of Rothman's Epidemiology: An Introduction alongside.