Nigel Bruce, PhD is Emeritus Professor of Public Health at the Department of Public Health and Policy, University of Liverpool, UK.

Daniel Pope, PhD is Senior Lecturer in Epidemiology and Public Health at the Department of Public Health and Policy, University of Liverpool, UK.

Debbi Stanistreet, PhD is Senior Lecturer and Faculty Director of Widening Participation at the Department of Public Health and Policy, University of Liverpool, UK.

Preface xv

About the Companion Website xxi

1 Philosophy of Science and Introduction to Epidemiology 1

Introduction and Learning Objectives 1

1.1 Approaches to Scientific Research 2

1.1.1 History and Nature of Scientific Research 2

1.1.2 What is Epidemiology? 6

1.1.3 What are Statistics? 7

1.1.4 Approach to Learning 8

1.2 Formulating a Research Question 8

1.2.1 Importance of a Well-Defined Research Question 8

1.2.2 Development of Research Ideas 10

1.3 Rates: Incidence and Prevalence 11

1.3.1 Why Do We Need Rates? 11

1.3.2 Measures of Disease Frequency 12

1.3.3 Prevalence Rate 12

1.3.4 Incidence Rate 12

1.3.5 Relationship Between Incidence, Duration, and Prevalence 15

1.4 Concepts of Prevention 16

1.4.1 Introduction 16

1.4.2 Primary, Secondary, and Tertiary Prevention 17

1.5 Answers to Self-Assessment Exercises 18

2 Routine Data Sources and Descriptive Epidemiology 25

Introduction and Learning Objectives 25

2.1 Routine Collection of Health Information 26

2.1.1 Deaths (Mortality) 26

2.1.2 Compiling Mortality Statistics: The Example of England and Wales 28

2.1.3 Suicide Among Men 29

2.1.4 Suicide Among Young Women 31

2.1.5 Variations in Deaths of Very Young Children 31

2.2 Descriptive Epidemiology 33

2.2.1 What is Descriptive Epidemiology? 33

2.2.2 International Variations in Rates of Lung Cancer 33

2.2.3 Illness (Morbidity) 34

2.2.4 Sources of Information on Morbidity 35

2.2.5 Notification of Infectious Disease 35

2.2.6 Illness Seen in General Practice 38

2.3 Information on the Environment 39

2.3.1 Air Pollution and Health 39

2.3.2 Routinely Available Data on Air Pollution 39

2.4 Displaying, Describing, and Presenting Data 41

2.4.1 Displaying the Data 41

2.4.2 Calculating the Frequency Distribution 42

2.4.3 Describing the Frequency Distribution 44

2.4.4 The Relative Frequency Distribution 57

2.4.5 Scatterplots, Linear Relationships and Correlation 60

2.5 Routinely Available Health Data 69

2.5.1 Introduction 69

2.5.2 Classification of Routine Health Information Sources 69

2.5.3 Demographic Data 71

2.5.4 Health Event Data 73

2.5.5 Population-Based Health Information 78

2.5.6 Deprivation Indices 79

2.5.7 Routine Data Sources for Countries Other Than the UK 80

2.6 Descriptive Epidemiology in Action 80

2.6.1 The London Smogs of the 1950s 80

2.6.2 Ecological Studies 82

2.7 Overview of Epidemiological Study Designs 84

2.8 Answers to Self-Assessment Exercises 86

3 Standardisation 101

Introduction and Learning Objectives 101

3.1 Health Inequalities in Merseyside 101

3.1.1 Socio-Economic Conditions and Health 101

3.1.2 Comparison of Crude Death Rates 102

3.1.3 Usefulness of a Summary Measure 104

3.2 Indirect Standardisation: Calculation of the Standardised Mortality Ratio (SMR) 105

3.2.1 Mortality in Liverpool 105

3.2.2 Interpretation of the SMR 107

3.2.3 Dealing With Random Variation: The 95 per cent Confidence Interval 107

3.2.4 Increasing Precision of the SMR Estimate 108

3.2.5 Mortality in Sefton 108

3.2.6 Comparison of SMRs 110

3.2.7 Indirectly Standardised Mortality Rates 110

3.3 Direct Standardisation 110

3.3.1 Introduction 110

3.3.2 An Example: Changes in Deaths From Stroke Over Time 111

3.3.3 Using the European Standard Population 112

3.3.4 Direct or Indirect: Which Method is Best? 113

3.4 Standardisation for Factors Other Than Age 114

3.5 Answers to Self-Assessment Exercises 115

4 Surveys 123

Introduction and Learning Objectives 123

Resource Papers 124

4.1 Purpose and Context 124

4.1.1 Defining the Research Question 124

4.1.2 Political Context of Research 126

4.2 Sampling Methods 127

4.2.1 Introduction 127

4.2.2 Sampling 127

4.2.3 Probability 129

4.2.4 Simple Random Sampling 130

4.2.5 Stratified Sampling 131

4.2.6 Cluster Random Sampling 132

4.2.7 Multistage Random Sampling 133

4.2.8 Systematic Sampling 133

4.2.9 Convenience Sampling 133

4.2.10 Sampling People Who are Difficult to Contact 133

4.2.11 Quota Sampling 134

4.2.12 Sampling in Natsal- 3 135

4.3 The Sampling Frame 137

4.3.1 Why Do We Need a Sampling Frame? 137

4.3.2 Losses in Sampling 137

4.4 Sampling Error, Confidence Intervals, and Sample Size 139

4.4.1 Sampling Distributions and the Standard Error 139

4.4.2 The Standard Error 140

4.4.3 Key Properties of the Normal Distribution 145

4.4.4 Confidence Interval (CI) for the Sample Mean 146

4.4.5 Estimating Sample Size 149

4.4.6 Sample Size for Estimating a Population Mean 149

4.4.7 StandardErrorand95percentCIforaPopulationProportion 150

4.4.8 Sample Size to Estimate a Population Proportion 151

4.5 Response 153

4.5.1 Determining the Response Rate 153

4.5.2 Assessing Whether the Sample is Representative 154

4.5.3 Maximising the Response Rate 154

4.6 Measurement 157

4.6.1 Introduction: The Importance of Good Measurement 157

4.6.2 Interview or Self-Completed Questionnaire? 157

4.6.3 Principles of Good Questionnaire Design 158

4.6.4 Development of a Questionnaire 161

4.6.5 Checking How Well the Interviews and Questionnaires Have Worked 161

4.6.6 Assessing Measurement Quality 165

4.6.7 Overview of Sources of Error 169

4.7 Data Types and Presentation 171

4.7.1 Introduction 171

4.7.2 Types of Data 172

4.7.3 Displaying and Summarising the Data 173

4.8 Answers to Self-Assessment Exercises 176

5 Cohort Studies 185

Introduction and Learning Objectives 185

Resource Papers 186

5.1 Why Do a Cohort Study? 186

5.1.1 Objectives of the Study 186

5.1.2 Study Structure 188

5.2 Obtaining the Sample 188

5.2.1 Introduction 188

5.2.2 Sample Size 190

5.3 Measurement 190

5.3.1 Importance of Good Measurement 190

5.3.2 Identifying and Avoiding Measurement Error 190

5.3.3 The Measurement of Blood Pressure 191

5.3.4 Case Definition 192

5.4 Follow-Up 193

5.4.1 Nature of the Task 193

5.4.2 Deaths (Mortality) 193

5.4.3 Non-Fatal Cases (Morbidity) 194

5.4.4 Challenges Faced with Follow-Up of a Cohort in a Different Setting 194

5.4.5 Assessment of Changes During Follow-Up Period 196

5.5 Basic Presentation and Analysis of Results 198

5.5.1 Initial Presentation of Findings 198

5.5.2 Relative Risk 199

5.5.3 Hypothesis Test for Categorical Data: The Chi-Squared Test 201

5.5.4 Hypothesis Tests for Continuous Data: The z-Test and the t-Test 209

5.6 How Large Should a Cohort Study Be? 214

5.6.1 Perils of Inadequate Sample Size 214

5.6.2 Sample Size for a Cohort Study 215

5.6.3 Example of Output from Sample Size Calculation 216

5.7 Assessing Whether an Association is Causal 218

5.7.1 The Hill Viewpoints 218

5.7.2 Confounding: What Is It and How Can It Be Addressed? 220

5.7.3 Does Smoking Cause Heart Disease? 222

5.7.4 Confounding in the Physical Activity and Cancer Study 222

5.7.5 Methods for Dealing with Confounding 224

5.8 Simple Linear Regression 224

5.8.1 Approaches to Describing Associations 224

5.8.2 Finding the Best Fit for a Straight Line 226

5.8.3 Interpreting the Regression Line 227

5.8.4 Using the Regression Line 228

5.8.5 Hypothesis Test of the Association Between the Explanatory and Outcome Variables 228

5.8.6 How Good is the Regression Model? 229

5.8.7 Interpreting SPSS Output for Simple Linear Regression Analysis 231

5.8.8 First Table: Variables Entered/Removed 232

5.9 Introduction to Multiple Linear Regression 235

5.9.1 Principles of Multiple Regression 235

5.9.2 Using Multivariable Linear Regression to Study Independent Associations 235

5.9.3 Investigation of the Effect of Work Stress on Bodyweight 235

5.9.4 Multiple Regression in the Cancer Study 239

5.9.5 Overview of Regression Methods for Different Types of Outcome 240

5.10 Answers to Self-Assessment Exercises 242

6 Case-Control Studies 251

Introduction and Learning Objectives 251

Resource Papers 252

6.1 Why do a Case-Control Study? 253

6.1.1 Study Objectives 253

6.1.2 Study Structure 254

6.1.3 Approach to Analysis 255

6.1.4 Retrospective Data Collection 257

6.1.5 Applications of the Case-Control Design 258

6.2 Key Elements of Study Design 259

6.2.1 Selecting the Cases 259

6.2.2 The Controls 260

6.2.3 Exposure Assessment 262

6.2.4 Bias in Exposure Assessment 263

6.3 Basic Unmatched and Matched Analysis 265

6.3.1 The Odds Ratio (OR) 265

6.3.2 Calculation of the OR-Simple Matched Analysis 269

6.3.3 Hypothesis Tests for Case-Control Studies 271

6.4 Sample Size for a Case-Control Study 273

6.4.1 Introduction 273

6.4.2 What Information is Required? 273

6.4.3 An Example of Sample Size Calculation Using OpenEpi 274

6.5 Confounding and Logistic Regression 276

6.5.1 Introduction 276

6.5.2 Stratification 277

6.5.3 Logistic Regression 278

6.5.4 Example: Multivariable Logistic Regression 281

6.5.5 Matched Studies - Conditional...