Epidemiology Kept Simple: An Introduction to Traditional and Modern Epidemiology

2012·4.22 MB·English

by  B. Burt Gerstman

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