Title Page Page: iii
Copyright Page: iv
Contents Page: vii
Preface to the Third Edition Page: xi
Preface to the First Edition Page: xiii
Acknowledgments Page: xv
Chapter 1 Epidemiology Past and Present Page: 1
1.1 Epidemiology and its uses Page: 2
What is epidemiology? Page: 2
What is public health? Page: 2
What is health? Page: 3
Additional useful terms Page: 3
Uses of epidemiology Page: 4
1.2 Evolving patterns of morbidity and mortality Page: 5
Twentieth century changes in demographics and disease patterns Page: 5
Mortality trends since 1950 Page: 7
Trends in life expectancy Page: 7
1.3 Selected historical figures and events Page: 8
Roots of epidemiology Page: 9
John Graunt Page: 11
Germ theory Page: 12
M´edecine d'observation and La M´ethode Numerique (Pinel and Louis) Page: 14
The London Epidemiological Society Page: 17
William Farr Page: 17
John Snow Page: 19
Twentieth-century epidemiology Page: 25
Emile Durkheim Page: 25
Joseph Goldberger Page: 26
The British Doctors Study Page: 28
1.4 Chapter summary Page: 30
Epidemiology and its uses Page: 30
Evolving patterns of morbidity and mortality Page: 30
Selected historical figures and events Page: 31
Review questions Page: 31
References Page: 32
Chapter 2 Causal Concepts Page: 36
2.1 Natural history of disease Page: 36
Stages of disease Page: 36
Stages of prevention Page: 40
2.2 Variability in the expression of disease Page: 40
Spectrum of disease Page: 40
The epidemiologic iceberg Page: 40
2.3 Causal models Page: 41
Definition of cause Page: 41
Component cause model (causal pies) Page: 42
Causal web Page: 44
Agent, host, and environment Page: 45
2.4 Causal inference Page: 48
Types of decisions Page: 48
Report of the Advisory Committee to the U.S. Surgeon General, 1964 Page: 49
Hill's framework for causal inference Page: 50
Philosophical considerations Page: 56
Exercises Page: 58
Review questions Page: 61
References Page: 63
Chapter 3 Epidemiologic Measures Page: 66
3.1 Measures of disease frequency Page: 67
Background Page: 67
Incidence proportion (risk) Page: 69
Incidence rate (incidence density) Page: 70
Prevalence Page: 72
3.2 Measures of association Page: 74
Background Page: 74
Absolute versus relative comparisons Page: 75
Absolute measures of effect Page: 75
Relative measures of effect Page: 76
Odds ratios Page: 77
Relation between the RR and RD Page: 78
3.3 Measures of potential impact Page: 79
Attributable fraction in the population Page: 79
Attributable fraction in exposed cases Page: 81
Preventable fraction Page: 82
3.4 Rate adjustment Page: 82
Background Page: 83
Direct adjustment Page: 84
Indirect adjustment Page: 85
Adjustment for multiple factors Page: 89
Section summary Page: 90
Notation used in Section 3.4 Page: 90
Exercises Page: 90
Review questions Page: 98
References Page: 99
Addendum: additional mathematical details Page: 101
Chapter 4 Descriptive Epidemiology Page: 104
4.1 Introduction Page: 104
What is descriptive epidemiology? Page: 104
Case series Page: 105
Surveillance systems Page: 105
National health surveys and vital record systems Page: 107
4.2 Epidemiologic variables Page: 108
Person Page: 109
Place Page: 111
Time Page: 111
4.3 Ecological correlations Page: 116
Aggregate-level data Page: 116
The ecological fallacy Page: 119
Other types of aggregate-level variables Page: 120
Exercises Page: 121
Review questions Page: 123
References Page: 124
Chapter 5 Introduction to Epidemiologic Study Design Page: 126
5.1 Etiologic research Page: 126
Hypothesis statement Page: 126
Variables Page: 128
Data Page: 128
5.2 Ethical conduct of studies involving human subjects Page: 129
5.3 Selected study design elements Page: 130
Necessity of a referent ("control") group Page: 130
Experimental versus observational study designs Page: 131
Unit of observation Page: 132
Longitudinal versus cross-sectional observations Page: 133
Cohort versus case-control samples Page: 135
5.4 Common types of epidemiologic studies Page: 137
Exercises Page: 138
Review questions Page: 140
References Page: 141
Chapter 6 Experimental Studies Page: 142
6.1 Introduction Page: 142
6.2 Historical perspective Page: 144
Comment regarding use of the term "natural experiment" Page: 145
6.3 General concepts Page: 146
The control group Page: 146
Randomization and comparability Page: 148
Checking group comparability Page: 149
Recruitment and eligibility criteria Page: 149
Follow-up and outcome ascertainment Page: 151
Intention-to-treat analysis versus per-protocol analysis Page: 151
6.4 Data analysis Page: 152
Measures of effect Page: 152
Statistical inference Page: 153
Sample size requirements Page: 155
Exercises Page: 156
Review questions Page: 157
References Page: 157
Chapter 7 Observational Cohort Studies Page: 159
7.1 Introduction Page: 159
7.2 Historical perspective Page: 161
7.3 Assembling and following a cohort Page: 163
7.4 Prospective, retrospective, and ambidirectional cohorts Page: 164
7.5 Addressing the potential for confounding Page: 165
7.6 Data analysis Page: 166
7.7 Historically important study: Wade Hampton Frost's birth cohorts Page: 170
Exercises Page: 174
Review questions Page: 177
References Page: 177
Chapter 8 Case-Control Studies Page: 180
8.1 Introduction Page: 180
8.2 Identifying cases and controls Page: 182
Ascertainment of cases Page: 182
Selection of controls Page: 183
Number of controls per case Page: 184
Sample size considerations Page: 184
8.3 Obtaining information on exposure Page: 185
8.4 Data analysis Page: 186
Dichotomous exposure Page: 186
Multiple levels of exposure Page: 186
Matched pairs Page: 189
Matched tuples Page: 191
8.5 Statistical justifications of case-control odds ratio as relative risks Page: 193
Incidence density sampling Page: 193
Cumulative incidence sampling Page: 194
Exercises Page: 194
Review questions Page: 198
References Page: 199
Chapter 9 Error in Epidemiologic Research Page: 201
9.1 Introduction Page: 201
Random error and systematic error Page: 201
Parameters and estimates Page: 202
9.2 Random error (imprecision) Page: 203
Probability Page: 203
Introduction to statistical inference Page: 205
Estimation (confidence intervals) Page: 206
Hypothesis testing (p-values) Page: 208
9.3 Systematic error (bias) Page: 209
Selection bias Page: 210
Information bias Page: 212
Confounding Page: 213
Exercises Page: 217
Review questions Page: 219
References Page: 220
Chapter 10 Screening for Disease Page: 222
10.1 Introduction Page: 223
10.2 Reliability (agreement) Page: 224
Essential background Page: 224
The kappa statistic Page: 225
The kappa paradox Page: 227
10.3 Validity Page: 228
Sensitivity and specificity Page: 229
Predictive value positive and predictive value negative Page: 230
True prevalence and apparent prevalence Page: 231
Relation between prevalence and the predictive value of a positive test Page: 232
Relation between prevalence and the predictive value of a negative test Page: 234
Selecting a cutoff point for positive and negative test results Page: 235
Key points Page: 238
Reliability notation Page: 238
Validity notation Page: 239
Summary Page: 238
Exercises Page: 239
Review questions Page: 243
References Page: 243
10.4 Chapter addendum (case study) Page: 244
Screening for antibodies to the human immunodeficiency virus Page: 244
Further reading-screening for HIV Page: 248
Further reading-general concepts of screening Page: 248
Answers to case study: screening for antibodies to the human immunodeficiency virus Page: 249
Chapter 11 The Infectious Disease Process Page: 255
11.1 The infectious disease process Page: 255
Agents Page: 256
Reservoirs Page: 257
Portals of entry and exit Page: 259
Transmission Page: 260
Host immunity Page: 261
11.2 Herd immunity Page: 265
What is herd immunity? Page: 265
Stemming an outbreak through herd immunity Page: 265
Epidemic modeling Page: 267
Exercises Page: 267
Review questions Page: 268
References Page: 270
Chapter 12 Outbreak Investigation Page: 271
12.1 Background Page: 272
Initial detection of outbreaks Page: 272
Goals and methods of outbreak investigations Page: 272
12.2 CDC prescribed investigatory steps Page: 273
Step 1: Prepare for field work Page: 273
Step 2: Establish the existence of an outbreak Page: 273
Steps 3 and 4: Verify diagnoses of cases and search for additional cases Page: 274
Step 5: Conduct descriptive epidemiologic studies Page: 275
Step 6: Develop hypotheses Page: 279
Steps 7 and 8: Evaluate hypotheses; as necessary, reconsider or refine hypotheses and conduct additi Page: 280
Step 9: Implement control and prevention measures Page: 281
Step 10: Communicate findings Page: 282
Review questions Page: 282
References Page: 283
Drug-disease outbreak Page: 283
Answers to case study: a drug-disease outbreak Page: 285
References-a drug-disease outbreak Page: 286
Food borne outbreal in Rhynedale, California Page: 286
Answers to case study: food-borne disease outbreak Page: 300
Chapter 13 Confidence Intervals and p-Values Page: 302
13.1 Introduction Page: 303
Parameters and estimates Page: 303
Population and sample Page: 303
Statistical inference Page: 303
13.2 Confidence intervals Page: 304
Estimation Page: 304
Confidence intervals for proportions (incidence proportion and prevalence) Page: 304
Confidence intervals for rates Page: 306
Confidence intervals for proportion ratios (risk ratios and prevalence ratios) Page: 306
Confidence intervals for rate ratios Page: 308
Confidence intervals for proportion differences (risk differences and prevalence differences) Page: 308
Confidence intervals for rate differences Page: 309
Confidence intervals for odds ratios, independent samples Page: 310
Confidence intervals for odds ratios, matched pairs Page: 310
13.3 p-Values Page: 312
Hypothesis tests of statistical significance Page: 312
Fallacies of p-values and statistical testing Page: 313
Testing a proportion Page: 314
Testing a rate Page: 315
Chi-square test of association Page: 315
Fisher's exact test Page: 317
Testing independent rates Page: 318
McNemar’s test for matched pairs Page: 319
13.4 Minimum Bayes factors Page: 319
Introduction Page: 319
Bayes factor Page: 320
Interpretation of the Bayes factor Page: 320
Prior odds Page: 320
Method to calculate a minimum Bayes factor Page: 320
References Page: 322
Chapter 14 Mantel-Haenszel Methods Page: 323
14.1 Ways to prevent confounding Page: 323
14.2 Simpson's paradox Page: 325
14.3 Mantel-Haenszel methods for risk ratios Page: 325
Mixing of effects Page: 325
Homogeneity assumption Page: 326
Mantel-Haenszel summary risk ratio Page: 327
Confidence interval for the Mantel-Haenszel risk ratio Page: 328
Mantel-Haenszel test statistic Page: 329
14.4 Mantel-Haenszel methods for other measures of association Page: 329
Differences between proportions (incidence proportion difference and prevalence difference) Page: 330
Odds ratios Page: 330
Rate ratios Page: 331
Rate differences Page: 333
Test statistic for stratified person-time data Page: 333
Exercise Page: 335
References Page: 335
Chapter 15 Statistical Interaction: Effect Measure Modification Page: 337
15.1 Two types of interaction Page: 337
Types of interaction Page: 337
Biological interaction Page: 337
Statistical interaction Page: 338
15.2 Chi-square test for statistical Page: 340
15.3 Strategy for stratified analysis Page: 342
Exercises Page: 344
References Page: 345
Chapter 16 Case Definitions and Disease Classification Page: 347
16.1 Case definitions Page: 347
Establishing a case definition Page: 347
Multiple-choice criteria Page: 348
Chronic fatigue syndrome, as an example Page: 348
Evolution of the AIDS case definition, as an example Page: 350
Classification of case status based on certainty Page: 350
16.2 International classification of disease Page: 351
16.3 Artifactual fluctuations in reported rates Page: 353
16.4 Summary Page: 354
References Page: 355
Chapter 17 Survival Analysis Page: 356
17.1 Introduction Page: 356
17.2 Stratifying rates by follow-up time Page: 359
17.3 Actuarial method of survival analysis Page: 360
17.4 Kaplan-Meier method of survival analysis Page: 362
17.5 Comparing the survival experience of two groups Page: 364
Risk differences and risk ratios at selected points in time Page: 365
Comparing survival functions as a whole Page: 366
Cochran-Mantel-Haenszel chi-square statistic Page: 368
Exercises Page: 369
References Page: 371
Chapter 18 Current Life Tables Page: 373
18.1 Introduction Page: 373
18.2 Complete life table Page: 374
Predicting probabilities from rates Page: 375
Special circumstances surrounding the first year of life Page: 376
General formula Page: 377
Constructing a complete life table Page: 377
18.3 Abridged life table Page: 380
Exercises Page: 383
References Page: 384
Chapter 19 Random Distribution of Cases in Time and Space Page: 385
19.1 Introduction Page: 385
19.2 The Poisson distribution Page: 386
Use of the Poisson formula Page: 387
Calculating the expected number of cases Page: 387
Post hoc identification of clusters Page: 389
19.3 Goodness of fit of the Poisson distribution Page: 390
Fitting the Poisson distribution Page: 390
19.4 Summary Page: 394
Exercises Page: 395
References Page: 396
Answers to Exercises and Review Questions Page: 398
Index Page: 455
Epidemiology Kept Simple introduces the epidemiological principles and methods that are increasingly important in the practice of medicine and public health. With minimum use of technical language it fully explains terminology, concepts, and techniques associated with traditional and modern epidemiology. Topics include disease causality, epidemiologic measures, descriptive epidemiology, study design, clinical and primary prevention trials, observational cohort studies, case-control studies, and the consideration of random and systematic error in studies of causal factors. Chapters on the infectious disease process, outbreak investigation, and screening for disease are also included. The latter chapters introduce more advanced biostatistical and epidemiologic techniques, such as survival analysis, Mantel-Haenszel techniques, and tests for interaction.
This third edition addresses all the requirements of the American Schools of Public Health (ASPH) Epidemiological Competencies, and provides enhanced clarity and
readability on this difficult subject. Updated with new practical exercises, case studies and real world examples, this title helps you develop the necessary tools to interpret epidemiological data and prepare for board exams, and now also includes review questions at the end of each chapter.
Epidemiology Kept Simple continues to provide an introductory guide to the use of epidemiological methods for graduate and undergraduate students studying public health, health education and nursing, and for all practicing health professionals seeking professional development.
