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Test Bank for Epidemiology 5th Edition by Leon Gordis

By: Leon Gordis
ISBN-10: 145573733X
/ ISBN-13: 9781455737338

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Edition: 5th Edition
Format: Downloadable ZIP File
Authors: Leon Gordis
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Front Matter

    • Dedication
    • Preface
    • Acknowledgments

Section 1 The Epidemiologic Approach to Disease and Intervention

    • The Epidemiologic Approach to Disease and Intervention
    • Chapter 1 Introduction
    • What is Epidemiology?
    • The Objectives of Epidemiology
    • Changing Patterns of Community Health Problems
    • Figure 1-1 Sign in cemetery in Dudley, England, in 1839.
    • Figure 1-2 Ten leading causes of death in the United States, 1900 and 2009. Although the definitions of the diseases in this figure are not exactly comparable in 1900 and 2009, the bars in the graphs are color coded to show chronic diseases (pink), infectious diseases (purple), injuries (aqua), and diseases of aging (white).
    • TABLE 1-1 Fifteen Leading Causes of Death, and Their Percents of All Deaths, United States, 2009
    • Figure 1-3 Life expectancy at birth and at 65 years of age, by race and sex, United States, 1900, 1950, and 2007.
    • Epidemiology and Prevention
    • Primary, Secondary, and Tertiary Prevention
    • TABLE 1-2 Three Types of Prevention
    • Two Approaches to Prevention: A Different View
    • Epidemiology and Clinical Practice
    • Figure 1-4 “You’ve got whatever it is that’s going around.”
    • The Epidemiologic Approach
    • Figure 1-5 Frequency of agents by age of children with pharyngitis, 1964–1965.
    • Figure 1-6 Gonorrhea: reported cases per 100,000 population, United States and territories, 2010.
    • From Observations to Preventive Actions
    • 1. Ignáz Semmelweis and Childbed Fever
    • Figure 1-7 Relationship between rate of dental caries in children’s permanent teeth and fluoride content of public water supply.
    • Figure 1-8 DMF indices after 10 years of fluoridation, 1954–1955. DMF, decayed, missing, and filled teeth.
    • Figure 1-9 Effect of discontinuing fluoridation in Antigo, Wisconsin, November 1960. DMF, decayed, missing, and filled teeth; FL+, during fluoridation; FL−, after fluoridation was discontinued.
    • Figure 1-10 Portrait of Ignáz Philipp Semmelweis.
    • Figure 1-11 Maternal mortality due to childbed fever, First and Second Clinics, General Hospital, Vienna, Austria, 1842.
    • Figure 1-12 Maternal mortality due to childbed fever, by type of care provider, General Hospital, Vienna, Austria, 1841–1850.
    • TABLE 1-3 Compliance with Hand Hygiene among Physicians, by Specialty, at University of Geneva Hospitals
    • 2. Edward Jenner and Smallpox
    • Figure 1-13 Portrait of Edward Jenner.
    • Figure 1-14 Une des premières vaccinations d’Edward Jenner [One of the first vaccinations by Edward Jenner], by Gaston Melingue.
    • Figure 1-15 Photograph of Dr. D. A. Henderson, who directed the World Health Organization Smallpox Eradication Program.
    • 3. John Snow and Cholera
    • Figure 1-16 Portrait of John Snow.
    • TABLE 1-4 Deaths from Cholera in 10,000 Inhabitants by Elevation of Residence above Sea Level, London, 1848–1849
    • Figure 1-17 A drop of Thames water, as depicted by Punch in 1850.
    • TABLE 1-5 Deaths from Cholera per 10,000 Houses, by Source of Water Supply, London, 1854
    • Figure 1-18 Breast versus lung cancer mortality: White females versus black females, United States, 1975–2009, age-adjusted to 2000 standard.
    • When the Frequency of a Disease Declines, WHO Deserves the Credit?
    • Figure 1-19 Decline in death rates in England and Wales for (A) whooping cough, (B) diphtheria, (C) scarlet fever (children younger than 15 years of age), and (D) respiratory tuberculosis.
    • Figure 1-20 Decline in crude death rates from rheumatic fever, United States, 1910–1977.
    • Integrating Prevention and Treatment
    • Figure 1-21 Prevention and therapy viewed as mutually exclusive activities.
    • Conclusion
    • References
    • Chapter 2 The Dynamics of Disease Transmission
    • Learning Objectives
    • Modes of Transmission
    • Figure 2-1 The epidemiologic triad of a disease.
    • TABLE 2-1 Factors That May Be Associated with Increased Risk of Human Disease
    • TABLE 2-2 Modes of Disease Transmission
    • Figure 2-2 Droplet dispersal following a violent sneeze.
    • Figure 2-3 Body surfaces as sites of microbial infection and shedding.
    • Clinical and Subclinical Disease
    • Figure 2-4 The “iceberg” concept of infectious diseases at the level of the cell and of the host.
    • Figure 2-5 Distribution of clinical severity for three classes of infections (not drawn to scale).
    • Clinical Disease
    • Nonclinical (Inapparent) Disease
    • Carrier Status
    • Endemic, Epidemic, and Pandemic
    • Figure 2-6 Endemic versus epidemic disease.
    • Figure 2-7 Daytime (10:30 am) photographs of the Great Smog’s toxic pollution. A, Due to reduced visibility, a bus is guided by an official (lower left, in silhouette) with a flashlight. B, The dim orange-gray ball in the sky is the Sun.
    • Figure 2-8 Approximate weekly mortality and sulfur dioxide (SO2) concentrations for Greater London, 1952–1953.
    • Figure 2-9 Colorized transmission electron micrograph of Avian influenza A H5N1 viruses (seen in gold) grown in MDCK cells (seen in green).
    • Disease Outbreaks
    • Figure 2-10 Number of passengers and crew members reporting to the ship’s infirmary with symptoms of acute gastroenteritis during a 14-day cruise by date of illness onset, Spain to Florida, October 25–November 8, 2002.
    • Immunity and Susceptibility
    • Herd Immunity
    • Incubation Period
    • Figure 2-11 Effect of herd immunity, United States, 1958–1961: A, Expected number of paralytic poliomyelitis cases if the vaccine’s effect was limited to vaccinated people. B, Number of cases observed as a result of herd immunity.
    • TABLE 2-3 Probable Cases of Severe Acute Respiratory Syndrome (SARS), SARS-Related Deaths, and SARS Case-Fatality, by Country, November 1, 2002–July 31, 2003
    • Figure 2-12 Incubation periods of viral diseases.
    • Figure 2-13 Incubation periods for 191 delegates affected by a Salmonella typhimurium outbreak at a medical conference in Wales, 1986.
    • Figure 2-14 Number of cases plotted against time and against the logarithm of time.
    • Attack Rate
    • Exploring Occurrence of Disease
    • Who
    • Gonorrhea
    • Pertussis
    • Figure 2-15 Gonorrhea—rates by sex, United States, 1990–2010.
    • Figure 2-16 Pertussis (whooping cough) incidence per 100,000 population by year, United States, 1979–2009.
    • Figure 2-17 Pertussis (whooping cough), reported numbers of cases by age group, United States, 2009.
    • When
    • Where
    • Figure 2-18 Aseptic meningitis, reported cases per 100,000 population by month, United States, 1986–1993.
    • Figure 2-19 Lyme disease, reported cases by county, United States, 2009.
    • Figure 2-20 West Nile virus activity by state, United States, 1999–2002. NHC, no human cases.
    • Figure 2-21 Number of human West Nile meningoencephalitis cases, by location and week and month of illness onset, United States, June–November 2002.
    • Outbreak Investigation
    • TABLE 2-4 Steps in Investigating an Acute Outbreak
    • Cross-Tabulation
    • TABLE 2-5 Food-Specific Attack Rates for Items Consumed August 16, 1974, Dade County Jail, Miami
    • TABLE 2-6 Cross-Table Analysis for Egg Salad and Beverage Consumed August 16, 1974, Dade County Jail, Miami
    • Conclusion
    • References
    • Review Questions for Chapter 2
    • Total Number of Persons Who Ate Each Specified Combination of Food Items
    • Total Number of Persons Who Ate Each Specified Combination of Food Items and Who Later Became Sick (with Acute Sore Throats)
    • Chapter 3 The Occurrence of Disease: I. Disease Surveillance and Measures of Morbidity
    • Learning Objectives
    • Surveillance
    • Passive and Active Surveillance
    • Stages of Disease in an Individual and in a Population
    • Figure 3-1 A, The natural history of disease. B, The natural history of disease and some sources of data relating to each interval.
    • Figure 3-2 A–C, The population: progression from health to varying degrees of disease severity. D, The population: the occurrence of deaths in each group.
    • Measures of Morbidity
    • Incidence Rate
    • Figure 3-3 Trends of incidence of childhood thyroid cancer in Belarus, Ukraine, and Russia, 1986–1994.
    • People at Risk Who Are Observed throughout a Defined Time Period
    • When All People Are Not Observed for the Full Time Period, Person-Time, or Units of Time When Each Person Is Observed
    • Figure 3-4 When all the people in the population being studied are observed for the entire period: Person-years (py) of observation.
    • Identifying New Cases in Order to Calculate Incidence
    • Figure 3-5 A–L, But what if the people at risk in the population are observed for different lengths of time? Calculation of person-time as person-years (py) observed. (See p. 42 for explanation in text.)
    • Attack Rate
    • Figure 3-6 Identifying newly detected cases of a disease. Step 1: Screening for prevalent cases at baseline. See page 43 for explanation in text.
    • Figure 3-7 Identifying newly detected cases of a disease. Step 2: Follow-up and rescreening at 1 year to identify cases that developed during the year.
    • Prevalence
    • Figure 3-8 Expected and observed number of tuberculosis cases, United States, 1980–1992.
    • Figure 3-9 Annual age-adjusted cancer incidence rates among males and females for selected cancers, United States, 1975–2007 (age-adjusted to the 2000 U.S. standard population).
    • TABLE 3-1 Examples of Point and Period Prevalence and Cumulative Incidence in Interview Studies of Asthma
    • Figure 3-10 Example of incidence and prevalence: I.
    • Figure 3-11 Example of incidence and prevalence: II.
    • Figure 3-12 Relationship between incidence and prevalence. A, Level of prevalence in the population. B, Increased prevalence resulting from incidence. C, Decreased prevalence resulting from deaths and/or cures. D, Overall impact on prevalence of incidence, deaths, and/or cures.
    • Figure 3-13 Current asthma prevalence in children ages 0 to 17 years of age, by state, annual average for 2001–2005.
    • Figure 3-14 Trends in prevalence of obesity, by state, United States, 1990, 1995, 2000, 2005, and 2010, based on self-reported height and weight. Obesity was defined by BMI (body mass index) ≥30, or ~30 lbs overweight for a 5′4″ person.
    • Problems with Incidence and Prevalence Measurements
    • Problems with Numerators
    • TABLE 3-2 Some Sources of Morbidity Statistics
    • Figure 3-15 Percent of population with a diagnosis of rheumatoid arthritis: New York criteria versus American Rheumatism Association (ARA) criteria, Sudbury, Massachusetts, 1964.
    • TABLE 3-3 Criteria for Rheumatoid Arthritis*
    • Figure 3-16 Number of people with and prevalence (%) of dementia in the Canadian Study of Health and Aging cohort (n = 1,879) as diagnosed by different classification systems. The various abbreviations refer to commonly used diagnostic manuals for medical conditions.
    • TABLE 3-4 Some Possible Sources of Error in Interview Surveys
    • Figure 3-17 Age-adjusted uterine cancer incidence rates, corrected and uncorrected by hysterectomy status, Alameda County, California.
    • TABLE 3-5 Some Limitations of Hospital Data
    • TABLE 3-6 Some Notes Dictated by Physicians for Inclusion in Patients’ Medical Records
    • Problems with Denominators
    • TABLE 3-7 Hypothetical Example of Chest X-Ray Screening: I. Populations Screened and Numbers with Positive X-Rays
    • TABLE 3-8 Hypothetical Example of Chest X-Ray Screening: II. Point Prevalence
    • Problems with Hospital Data
    • Relationship between Incidence and Prevalence
    • TABLE 3-9 Hypothetical Example of Chest X-Ray Screening: III. Prevalence, Incidence, and Duration
    • Figure 3-18 Percentage of births that were extramarital in New Zealand, 1962–1979, based on data from the Department of Statistics.
    • Figure 3-19 Births to married and unmarried women in New Zealand, 1965–1978, based on data from the Department of Statistics.
    • Figure 3-20 Breast cancer incidence rates in white women and distribution of cases by age.
    • Spot Maps
    • Figure 3-21 Spot map of residence distribution of patients with rheumatic fever, ages 5 to 19 years, hospitalized for first attacks, Baltimore, 1960–1964.
    • Conclusion
    • Figure 3-22 Spot map for patients with rheumatic fever, ages 5 to 19 years, hospitalized for first attacks in Baltimore, 1977–1981.
    • References
    • Review Questions for Chapter 3
    • Chapter 4 The Occurrence of Disease: II. Mortality and Other Measures of Disease Impact
    • Learning Objectives
    • Measures of Mortality
    • Figure 4-1 Trend in numbers of cancer deaths observed in the United States in the early and mid 20th century and forecast to the year 2011.
    • Figure 4-2 Cancer death rates for males, United States, 1930–2007 (age-adjusted to the 2000 U.S. standard population).
    • Figure 4-3 Cancer death rates for females, United States, 1930–2007 (age-adjusted to the 2000 U.S. standard population). *Uterine cancer rates are for uterine cervix and corpus combined.
    • Figure 4-4 Death rates from cancer and heart disease for ages younger than 85 and 85 or older (age-adjusted to the 2000 U.S. standard population).
    • Figure 4-5 Major causes of death in children younger than age 5 years and in neonates (aged 0–27 days) in 2008.
    • Mortality Rates
    • Case-Fatality
    • TABLE 4-1 Comparison of Mortality Rate with Case-Fatality in the Same Year
    • Proportionate Mortality
    • Figure 4-6 Deaths from heart disease as a percentage of deaths from all causes, by age group, United States, 2008.
    • TABLE 4-2 Comparison of Mortality Rate and Proportionate Mortality: I. Deaths from Heart Disease in Two Communities
    • TABLE 4-3 Hypothetical Example of Mortality Rates and Proportionate Mortality in Two Periods
    • Figure 4-7 Hypothetical example of proportionate mortality: Changes in proportionate mortality from heart disease, cancer, and other causes from the early period to the late period.
    • Figure 4-8 Understanding proportionate mortality.
    • TABLE 4-4 Comparison of Mortality Rate and Proportionate Mortality: II. Deaths from Heart Disease in Two Communities
    • Years of Potential Life Lost
    • Figure 4-9 Years of potential life lost (YPLL) before age 75, all races, both sexes, all deaths, United States, 2008.
    • Figure 4-10 Years of potential life lost (YPLL) before age 65 years among children younger than 20 years from injuries and other diseases, United States, 1986.
    • TABLE 4-5 Estimated Years of Potential Life Lost (YPLL) Before Age 65 Years and Mortality Rates per 100,000 Persons, by Cause of Death, United States, 1989 and 1990
    • Figure 4-11 Annual death rates (per 100,000 population) for the leading causes of death among men 25 to 44 years old, by year, 1987–2008. (For 1982 to 1986, estimates were made because an International Classification of Diseases [ICD]-9 code for HIV did not yet exist. For 1999–2000, deaths were classified according to ICD-10; for 1987–1998, ICD-10 rules were retroactively applied to deaths that were previously coded according to ICD-9 rules.)
    • Why Look at Mortality?
    • Figure 4-12 Annual death rates (per 100,000 population) for leading causes of death among women 25 to 44 years old, by year, 1987–2008. (See also Fig. 4-11.)
    • Figure 4-13 Ectopic pregnancy rates (per 1,000 reported pregnancies), by year, United States, 1970–1987.
    • Figure 4-14 Ectopic pregnancy death rates (per 10,000 ectopic pregnancies), by year, United States, 1970–1987.
    • Figure 4-15 Breast cancer incidence and mor­tality: white women versus black women.
    • Figure 4-16 Thyroid cancer incidence and mortality, 1973–2002.
    • Figure 4-17 Histologic types of thyroid cancer and their prognoses.
    • Problems with Mortality Data
    • Figure 4-18 Trends in incidence of thyroid cancer (1973–2002) in the United States.
    • Figure 4-19 Trends in incidence of papillary tumors of the thyroid, by size, United States, 1988–2002.
    • Figure 4-20 Death certificate for the state of Maryland.
    • Figure 4-21 Example of a completed cause-of-death section on a death certificate, including immediate and underlying causes and other significant conditions.
    • Figure 4-22 Drop in death rates for diabetes among 55- to 64-year-old men and women, United States, 1930–1960, due to changes in ICD coding.
    • Comparing Mortality in Different Populations
    • Figure 4-23 AIDS cases by quarter year of report, United States, 1984–2000.
    • Figure 4-24 Age-adjusted uterine cancer mortality rates, corrected and uncorrected by hysterectomy status, Alameda County, California.
    • TABLE 4-6 Some Causes of Death That Were Reported on Death Certificates in the Early 1900s
    • TABLE 4-7 Crude Mortality Rates by Race, Baltimore City, 1965
    • TABLE 4-8 Death Rates by Age and Race, Baltimore City, 1965
    • Direct Age Adjustment
    • TABLE 4-9 A Hypothetical Example of Direct Age Adjustment: I. Comparison of Total Death Rates in a Population at Two Different Times
    • TABLE 4-10 A Hypothetical Example of Direct Age Adjustment: II. Comparison of Age-Specific Death Rates in Two Different Time Periods
    • TABLE 4-11 A Hypothetical Example of Direct Age Adjustment: III. Carrying Out an Age Adjustment Using the Total of the Two Populations as the Standard
    • TABLE 4-12 An Example of Direct Age Adjustment: Comparison of Age-adjusted Mortality Rates in Mexico and in the United States, 1995–1997
    • Indirect Age Adjustment (Standardized Mortality Ratios)
    • TABLE 4-13 Computation of a Standardized Mortality Ratio (SMR) for Tuberculosis, All Forms (TBC), for White Miners Ages 20 to 59 Years, United States, 1950
    • The Cohort Effect
    • Interpreting Observed Changes in Mortality
    • Other Measures of the Impact of Disease
    • Quality of Life
    • TABLE 4-14 Age-specific Death Rates per 100,000 from Tuberculosis (All Forms), Males, Massachusetts, 1880–1930
    • Figure 4-25 Leading causes of disease burden for women aged 15–44 years, high-income countries, and low- and middle-income countries, 2004.
    • Projecting the Future Burden of Disease
    • TABLE 4-15 Age-specific Death Rates per 100,000 from Tuberculosis (All Forms), Males, Massachusetts, 1880–1930
    • TABLE 4-16 Possible Explanations of Trends or Differences in Mortality: I. Artifactual
    • TABLE 4-17 Possible Explanations of Trends or Differences in Mortality: II. Real
    • TABLE 4-18 Leading Causes of Burden of Disease (DALYs), Countries Grouped by Income, 2004
    • Figure 4-26 The “epidemiologic transition”: Distribution of deaths from communicable and noncommunicable causes in developing countries, 1990 and projected into 2020.
    • Conclusion
    • Figure 4-27 “I’ll pause for a moment so you can let this information sink in.”
    • References
    • Review Questions for Chapter 4
    • Annual Cancer Deaths in White Male Workers in Two Industries
    • Numbers of People and Deaths from Disease Z by Age Group in Communities X and Y
    • Chapter 5 Assessing the Validity and Reliability of Diagnostic and Screening Tests
    • Learning Objectives
    • Biologic Variation of Human Populations
    • Figure 5-1 Distribution of tuberculin reactions.
    • Figure 5-2 Distribution of systolic blood pressure for men screened for the Multiple Risk Factor Intervention Trial.
    • Validity of Screening Tests
    • TABLE 5-1 Calculation of the Sensitivity and Specificity of Screening Examinations
    • Tests with Dichotomous Results (Positive or Negative)
    • TABLE 5-2 Comparison of the Results of a Dichotomous Test with Disease Status
    • Figure 5-3 A–G, The effects of choosing different cutoff levels to define a positive test result when screening for diabetes using a continuous marker, blood sugar, in a hypothetical population. (See discussion in the text under the subheading “Tests of Continuous Variables” below.)
    • Tests of Continuous Variables
    • Figure 5-4 A, Distribution of blood sugar levels in hospital patients with diabetes and without diabetes. (The number of people with diabetes is shown for each specific blood sugar level in the [upper] distribution for persons without diabetes. Because of limited space, the number of people for each specific level of blood sugar is not shown in the [lower] distribution for persons with diabetes.)B and C show two different blood sugar cutpoints that were used in the study to define diabetes. Data from the graphs are presented to the right of each graph in a 2 × 2 table. B, When a blood sugar cutpoint of ≥80 mg/dL is used to define diabetes in this population, sensitivity of the screening test is 100%, but specificity is low. C, When a blood sugar cutpoint of ≥200 mg/dL is used to define diabetes in this population, sensitivity of the screening test is low, but specificity is 100%. (See explanation in the text under the subheading “Tests of Continuous Variables” on p. 92.)
    • Figure 5-5 Diagram showing four possible groups resulting from screening with a dichotomous test.
    • Figure 5-6 Diagram showing the two groups of people resulting from screening with a dichotomous screening test: all people with positive test results and all people with negative test results.
    • Figure 5-7 A–B, Hypothetical example of a two-stage screening program. A, Findings using Test 1 in a population of 10,000 people. B, Findings using Test 2 in participants who tested positive using Test 1. (See explanation in the text under the subheading “Sequential (Two-stage) Testing” below.)
    • Use of Multiple Tests
    • Sequential (Two-stage) Testing
    • Simultaneous Testing
    • TABLE 5-3 Results of Screening with Test A
    • Net Sensitivity Using Two Simultaneous Tests
    • TABLE 5-4 Results of Screening with Test B
    • TABLE 5-5 Results of Screening with Test A
    • Net Specificity Using Two Simultaneous Tests
    • TABLE 5-6 Results of Screening with Test B
    • Figure 5-8 A–F, Net sensitivity: Hypothetical example of simultaneous testing. (See explanation in the text under the subheading “Net Sensitivity Using Two Simultaneous Tests” on p. 96.)
    • Comparison of Simultaneous and Sequential Testing
    • Figure 5-9 A–F, Net specificity: Hypothetical example of simultaneous testing. (See explanation in the text under the subheading “Net Specificity Using Two Simultaneous Tests” on p. 97.)
    • Predictive Value of a Test
    • Figure 5-10 “Whoa—way too much information.” A physician comments on excessive information.
    • TABLE 5-7 Predictive Value of a Test
    • Relationship between Positive Predictive Value and Disease Prevalence
    • TABLE 5-8 Relationship of Disease Prevalence to Positive Predictive Value
    • Figure 5-11 Relationship between disease prevalence and predictive value in a test with 95% sensitivity and 95% specificity.
    • Figure 5-12 Amniotic fluid α-fetoprotein (AFP) levels in normal subjects and subjects with spina bifida.
    • TABLE 5-9 Calculations of Predictive Values for Neural Tube Defects (NTD)* for α-Fetoprotein (AFP) Test in High- and Low-Risk Women
    • Relationship between Positive Predictive Value and Specificity of the Test
    • Figure 5-13 A–D, Relationship of specificity to positive predictive value (PPV). (See explanation in the text under the subheading “Relationship between Positive Predictive Value and Specificity of the Test” above.)
    • Reliability (Repeatability) of Tests
    • Intrasubject Variation
    • Intraobserver Variation
    • TABLE 5-10 Relationship of Specificity to Positive Predictive Value
    • TABLE 5-11 Examples Showing Variation in Blood Pressure Readings during a 24-Hour Period
    • Figure 5-14 “This is a second opinion. At first, I thought you had something else.” One view of a second opinion.
    • Interobserver Variation
    • Percent Agreement
    • TABLE 5-12 Observer or Instrument Variation: Percent Agreement
    • Kappa Statistic
    • Figure 5-15 A–D, Calculating the percent agreement between two observers. A, Percent agreement when examining paired observations between observer 1 and observer 2. B, Percent agreement when examining paired observations between observer 1 and observer 2, considering that cell d (agreement on the negatives) is very high. C, Percent agreement when examining paired observations between observer 1 and observer 2, ignoring cell d. D, Percent agreement when examining paired observations between observer 1 and observer 2, using only cells a, b, and c for the calculation.
    • Rationale of the Kappa Statistic.
    • Calculation of the Kappa Statistic—An Example.
    • Figure 5-16 A, Histologic classification by subtype of 75 slides of non–small cell carcinoma, by two pathologists (A and B). B, Percent agreement by pathologist A and pathologist B. C, Percent agreement by pathologist A and pathologist B expected by chance alone.
    • Relationship between Validity and Reliability
    • Conclusion
    • Figure 5-17 Graph of hypothetical test results that are reliable, but not valid.
    • Figure 5-18 Graph of hypothetical test results that are valid, but not reliable.
    • Figure 5-19 Graph of hypothetical test results that are both valid and reliable.
    • References
    • Appendices to Chapter 5
    • Appendix 1 to Chapter 5: Measures of Test Validity and Their Interpretation
    • Appendix 2 to Chapter 5: The Three Steps Required for Calculating Kappa Statistic (κ)
    • Review Questions for Chapter 5
    • Classification of Chest X-Rays by Physician 1 Compared with Physician 2
    • Chapter 6 The Natural History of Disease: Ways of Expressing Prognosis
    • Learning Objectives
    • Figure 6-1 “How much time do I have, Doc?” Concern about prognosis.
    • Case-Fatality
    • Figure 6-2 The natural history of disease in a patient.
    • Person-Years
    • Figure 6-3 Two examples of 10 person-years: two people, each observed for 5 years, and five people, each observed for 2 years.
    • Figure 6-4 Timing of period of greatest risk is from shortly after diagnosis until about 20 months after diagnosis.
    • Figure 6-5 Two people, each observed for 5 years, and the relation to the period of greatest risk.
    • Figure 6-6 Five people, each observed for 2 years, and the relation to the period of greatest risk.
    • Figure 6-7 Two examples of 10 person-years in which the period of greatest risk is from shortly after diagnosis until about 20 months after diagnosis.
    • Five-Year Survival
    • Figure 6-8 The problem of 5-year survival in a screened population: I. Situation without screening.
    • Figure 6-9 The problem of 5-year survival in a screened population: II. Earlier disease detection by screening.
    • Figure 6-10 Five-year survival curves in two hypothetical populations.
    • Observed Survival
    • Rationale for the Life Table
    • TABLE 6-1 Hypothetical Study of Treatment Results in Patients Treated from 2000 to 2004 and Followed to 2005 (None Lost to Follow-up)
    • TABLE 6-2 Rearrangement of Data in Table 6-1, Showing Survival Tabulated by Years since Enrollment in Treatment (None Lost to Follow-up)
    • TABLE 6-3 Analysis of Survival in Patients Treated from 2000 to 2004 and Followed to 2005 (None Lost to Follow-up): I
    • TABLE 6-4 Analysis of Survival in Patients Treated from 2000 to 2004 and Followed to 2005 (None Lost to Follow-up): II
    • TABLE 6-5 Analysis of Survival in Patients Treated from 2000 to 2004 and Followed to 2005 (None Lost to Follow-up): III
    • TABLE 6-6 Analysis of Survival in Patients Treated from 2000 to 2004 and Followed to 2005 (None Lost to Follow-up): IV
    • TABLE 6-7 Analysis of Survival in Patients Treated from 2000 to 2004 and Followed to 2005 (None Lost to Follow-up): V
    • TABLE 6-8 Probability of Survival for Each Year of the Study
    • TABLE 6-9 Cumulative Probabilities of Surviving Different Lengths of Time
    • Figure 6-11 Survival curve for a hypothetical example of patients treated from 2000 to 2004 and followed until 2005.
    • Calculating a Life Table
    • TABLE 6-10 Rearrangement of Data in Standard Format for Life Table Calculations
    • TABLE 6-11 Calculating a Life Table
    • The Kaplan-Meier Method
    • Figure 6-12 Hypothetical example of a study of six patients analyzed by the Kaplan-Meier method.
    • TABLE 6-12 Calculating Survival Using the Kaplan-Meier Method*
    • Figure 6-13 Kaplan-Meier plot of the hypothetical survival study of six patients shown in Figure 6-12. Percentages in red show cumulative proportions surviving after each of the deaths shown in Figure 6-12 and are taken from column 6 in Table 6-12.
    • Assumptions Made in Using Life Tables
    • Figure 6-14 A, Kaplan-Meier analysis of overall survival among 126 patients with asymptomatic, but severe, aortic stenosis, compared with age- and sex-matched persons in the general population. This analysis included perioperative and postoperative deaths among patients who required valve replacement during follow-up. B, Kaplan-Meier analysis of event-free survival among 25 patients with no or mild aortic valve calcification, compared with 101 patients with moderate or severe calcification. The vertical bars indicate standard errors.
    • Example of Use of a Life Table
    • Figure 6-15 Survival of children aged 0 to 19 years with acute lymphocytic leukemia by race, metropolitan Baltimore, 1960–1975.
    • Figure 6-16 Temporal changes in survival of white children aged 0 to 19 years with acute lymphocytic leukemia, metropolitan Baltimore, 1960–1975.
    • Figure 6-17 Temporal changes in survival of black children aged 0 to 19 years with acute lymphocytic leukemia, metropolitan Baltimore, 1960–1975.
    • Apparent Effects on Prognosis of Improvements in Diagnosis
    • Figure 6-18 A– C, Stage migration. A, Classification of cases by presence or absence of detectable metastases in 1980. B, Presence of undetectable micro-metastases in 1980. C, Impact of improved diagnosis of micro-metastases in 2000 on classification of cases by presence or absence of detectable metastases.
    • Figure 6-19 A–C, Hypothetical example of stage migration. A, Assumed case-fatality by stage. B, Impact of improved diagnosis of micro-metastases on stage-specific case-fatality (CF). C, Apparent improvements in stage-specific survival as a result of stage migration even without any improvement in effectiveness of treatment.
    • Median Survival Time
    • Relative Survival
    • TABLE 6-13 Five-Year Observed and Relative Survival (%) by Age for Colon and Rectum Cancer: SEER Program (Surveillance, Epidemiology, and End Results Study), 1990–1998
    • Figure 6-20 A–D, Relative survival. A, 100% survival over 10 years. B, Observed survival. C, Observed and expected survival. D, Observed, expected, and relative survival.
    • Generalizability of Survival Data
    • Figure 6-21 Percentage of children who experienced nonfebrile seizures after one or more febrile seizures, by study design.
    • Conclusion
    • TABLE 6-14 Five Approaches to Expressing Prognosis
    • References
    • Review Questions for Chapter 6
    • Survival of Patients with AIDS after Diagnosis
    • Chapter 7 Assessing Preventive and Therapeutic Measures: Randomized Trials
    • Learning Objectives
    • Figure 7-1 Design of a randomized trial.
    • Selection of Subjects
    • Allocating Subjects to Treatment Groups Without Randomization
    • Studies without Comparison
    • Studies with Comparison
    • Historical Controls
    • Simultaneous Nonrandomized Controls
    • TABLE 7-1 Results of a Trial of Bacillus Calmette-Guérin (BCG) Vaccination: I
    • TABLE 7-2 Results of a Trial of Bacillus Calmette-Guérin (BCG) Vaccination: II
    • Allocating Subjects Using Randomization
    • Figure 7-2 How to predict the next patient’s treatment assignment in a randomized study.
    • TABLE 7-3 A Table of Random Numbers
    • TABLE 7-4 Examples of Using a Random Numbers Table for Allocating Patients to Treatment Groups in a Randomized Trial
    • What Is the Main Purpose of Randomization?
    • Figure 7-3 Nonrandomized versus randomized studies. I, If the study is not randomized, the proportions of patients with arrhythmia in the two intervention groups may differ. In this example, individuals with arrhythmia are less likely to receive the intervention than individuals without arrhythmia. II, If the study is randomized, the proportions of patients with arrhythmia in the two intervention groups are more likely to be similar.
    • Stratified Randomization
    • Figure 7-4 Example of stratified randomization. See discussion in text on pp. 145–146.
    • Data Collection on Subjects
    • Treatment (Assigned and Received)
    • Outcome
    • Prognostic Profile at Entry
    • Masking (Blinding)
    • TABLE 7-5 A Randomized Trial of Vitamin C and Placebo for the Common Cold: Results of a Questionnaire Study to Determine Whether Subjects Suspected Which Agent They Had Been Given
    • TABLE 7-6 Physicians’ Health Study: Side Effects According to Treatment Group
    • Crossover
    • Figure 7-5 A–F, Design of a planned crossover trial. See discussion in text on p. 148.
    • Figure 7-6 A-E, Unplanned crossover in a study of cardiac bypass surgery and the use of intention to treat analysis. A, Original study design. B-D, Unplanned crossovers. E, Use of intention to treat analysis.
    • Factorial Design
    • Figure 7-7 Factorial design for studying the effects of two treatments.
    • Figure 7-8 A-B, Factorial design. A, The effects of treatment A (orange cells) versus no treatment A. B, The effects of treatment B (purple cells) versus no treatment B.
    • Figure 7-9 Factorial design used in a study of aspirin and beta-carotene.
    • Noncompliance
    • Figure 7-10 Factorial design of the study of aspirin and beta-carotene in 2 × 2 table format.
    • Figure 7-11 A-B, Factorial design. A, The effects of aspirin (orange cells) versus no aspirin. B, The effects of beta-carotene (purple cells) versus no beta-carotene.
    • TABLE 7-7 Coronary Drug Project: Five-Year Mortality in Patients Given Clofibrate or Placebo
    • TABLE 7-8 Coronary Drug Project: Five-Year Mortality in Patients Given Clofibrate or Placebo According to Level of Compliance
    • TABLE 7-9 Coronary Drug Project: Five-Year Mortality in Patients Given Clofibrate or Placebo According to Level of Compliance
    • Conclusion
    • References
    • Chapter 8 Randomized Trials: Some Further Issues
    • Learning Objectives
    • Sample Size
    • Figure 8-1 Two opaque jars, each holding 100 beads, some blue and some white.
    • Figure 8-2 Samples of 10 beads from jar A and 10 beads from jar B.
    • Figure 8-3 Samples of 10 beads from jar A and 10 beads from jar B.
    • TABLE 8-1 Four Possible Conclusions When Testing Whether or Not the Treatments Differ
    • Figure 8-4 Possible outcomes of a randomized trial: type I and type II errors.
    • Figure 8-5 Possible outcomes of a randomized trial: α and β.
    • Figure 8-6 Possible outcomes of a randomized trial when the treatments differ.
    • Figure 8-7 Possible outcomes of a randomized trial: summary.
    • TABLE 8-2 Summary of Terms
    • TABLE 8-3 What Must Be Specified to Estimate the Sample Size Needed in a Randomized Trial?
    • TABLE 8-4 Number of Patients Needed in Each Group to Detect Various Differences in Cure Rates; α = 0.05; Power (1 − β) = 0.80 (Two-sided Test)
    • TABLE 8-5 Number of Patients Needed in Each Group to Detect Various Differences in Cure Rates; α = 0.05; Power (1 − β) = 0.80 (One-sided Test)
    • Recruitment and Retention of Study Participants
    • Ways of Expressing the Results of Randomized Trials
    • Interpreting the Results of Randomized Trials
    • Generalizability of Results beyond the Study Population
    • Figure 8-8 A, External validity (generalizability) in a randomized trial. Findings of the study are generalizable from the study population to the defined population, and presumably, to the total population. B, Internal validity in a randomized trial. The study was done properly and the findings of the study are therefore valid in the study population.
    • What Can the Results of a Randomized Trial Tell a Treating Physician about an Individual Patient?
    • Figure 8-9 A, Results of a hypothetical randomized trial comparing Therapy A with Therapy B. Blue areas indicate numbers of patients who benefited from each therapy, and white areas indicate those who did not respond to each therapy. B, Physician’s first question. C, Physician’s second question. D, Physician’s third question. (See text on p. 163.)
    • Comparative Effectiveness Research (CER)
    • Four Phases in Testing New Drugs in the United States
    • Three Major Randomized Trials in the United States
    • The Hypertension Detection and Follow-up Program
    • Figure 8-10 Design of the Hypertension Detection and Follow-up Program (HDFP). DBP, diastolic blood pressure.
    • Figure 8-11 Cumulative all-cause mortality by blood pressure status and type of care received in the HDFP.
    • The Multiple Risk Factor Intervention Trial
    • TABLE 8-6 Mortality from All Causes during the Hypertension Detection and Follow-up Program
    • Figure 8-12 Mean risk factor levels by year of follow-up for Multiple Risk Factor Intervention Trial Research Group participants. BP, blood pressure; S1, first screening visit; SI, special intervention; UC, usual care.
    • Figure 8-13 Cumulative coronary heart disease (CHD) and total mortality rates for Multiple Risk Factor Intervention Trial Research Group participants. The heavy line indicates men receiving usual care; the thin line indicates men receiving special intervention.
    • Study of Breast Cancer Prevention Using Tamoxifen
    • Figure 8-14 Cumulative rates of invasive and noninvasive breast cancer occurring in participants receiving placebo or tamoxifen.
    • Figure 8-15 Cumulative rates of invasive endometrial cancer occurring in participants receiving placebo or tamoxifen.
    • Randomized Trials for Evaluating Widely Accepted Interventions
    • A Trial of Arthroscopic Knee Surgery for Osteoarthritis
    • Figure 8-16 Design of a controlled trial of arthroscopic surgery for osteoarthritis of the knee.
    • Figure 8-17 Mean values (and 95% confidence intervals) on the Knee-Specific Pain Scale. Assessments were made before the procedure and 2 weeks, 6 weeks, 3 months, 6 months, 12 months, 18 months, and 24 months after the procedure. Higher scores indicate more severe pain.
    • Figure 8-18 Mean values (and 95% confidence intervals) on the Walking-Bending Subscale of the Arthritis Impact Measurement Scales (AIMS2). Assessments were made before the procedure and 2 weeks, 6 weeks, 3 months, 6 months, 12 months, 18 months, and 24 months after the procedure. Higher scores indicate poorer functioning.
    • Effect of Group Psychosocial Support on Survival of Patients with Metastatic Breast Cancer
    • Figure 8-19 Design of a randomized, controlled trial of group psychosocial support on survival in patients with metastatic breast cancer.
    • Figure 8-20 Kaplan-Meier survival curves for women assigned to the intervention group and the control group. There was no significant difference in survival between the two groups.
    • Registration of Clinical Trials
    • Ethical Considerations
    • Conclusion
    • Epilogue
    • References
    • Review Questions for Chapters 7 and 8
    • Number of Patients Needed in an Experimental and a Control Group for a Given Probability of Obtaining a Significant Result (Two-Sided Test)

Section 2 Using Epidemiology to Identify the Causes of Disease

    • Using Epidemiology to Identify the Causes of Disease
    • Chapter 9 Cohort Studies
    • Learning Objectives
    • Figure 9-1 If we observe an association between an exposure and a disease or another outcome (1.), the question is: Is the association causal (2.)?
    • Design of a Cohort Study
    • Figure 9-2 Design of a cohort study.
    • TABLE 9-1 Design of a Cohort Study
    • Comparing Cohort Studies with Randomized Trials
    • TABLE 9-2 Results of a Hypothetical Cohort Study of Smoking and Coronary Disease (CHD)
    • Figure 9-3 Selection of study groups in experimental and observational epidemiologic studies.
    • Selection of Study Populations
    • Figure 9-4 Design of a cohort study beginning with exposed and nonexposed groups.
    • Figure 9-5 Design of a cohort study beginning with a defined population.
    • Figure 9-6 Time frame for a hypothetical prospective cohort study begun in 2012.
    • Types of Cohort Studies
    • Figure 9-7 Time frame for a hypothetical retrospective cohort study begun in 2012.
    • Figure 9-8 Time frames for a hypothetical prospective cohort study and a hypothetical retrospective cohort study begun in 2012.
    • Examples of Cohort Studies
    • Example 1: The Framingham Study
    • TABLE 9-3 Derivation of the Framingham Study Population
    • Example 2: Incidence of Breast Cancer and Progesterone Deficiency
    • Figure 9-9 Design of Cowan’s retrospective cohort study of breast cancer.
    • Cohort Studies for Investigating Childhood Health and Disease
    • Figure 9-10 Design of a cohort study to investigate the effects of exposures during pregnancy on disease throughout life: Study beginning at birth.
    • Potential Biases in Cohort Studies
    • Figure 9-11 Design of a cohort study to investigate the effects of exposures during pregnancy on disease throughout life: Study beginning at about the time of conception.
    • Selection Biases
    • Information Biases
    • When is a Cohort Study Warranted?
    • Figure 9-12 Design of a cohort study. A, Starting with exposed and nonexposed groups. B, Measuring the development of disease in both groups. C, Expected findings if the exposure is associated with disease.
    • Conclusion
    • References
    • Review Questions for Chapter 9
    • Chapter 10 Case-Control and Other Study Designs
    • Learning Objectives
    • Figure 10-1 Design of a case-control study.
    • Design of a Case-Control Study
    • TABLE 10-1 Design of Case-Control Studies
    • TABLE 10-2 A Hypothetical Example of a Case-Control Study of Coronary Heart Disease and Cigarette Smoking
    • TABLE 10-3 History of Use of Artificial Sweeteners in Bladder Cancer Cases and Controls
    • TABLE 10-4 Distribution of 1,357 Male Lung Cancer Patients and a Male Control Group According to Average Number of Cigarettes Smoked Daily Over the 10 Years Preceding Onset of the Current Illness
    • Potential Biases in Case-Control Studies
    • Selection Bias
    • Sources of Cases
    • Using Incident or Prevalent Cases.
    • Selection of Controls
    • TABLE 10-5 Summary of Data from Pearl’s Study of Cancer and Tuberculosis
    • Sources of Controls.
    • Use of Nonhospitalized People as Controls.
    • Use of Hospitalized Patients as Controls.
    • Figure 10-2 Since both the cases and the hospital controls are selected from the defined population, any factors that affected admission of cases to a certain hospital would also affect the admission of hospital controls.
    • Problems in Control Selection.
    • TABLE 10-6 Distribution of Cases and Controls by Coffee-Drinking Habits and Estimates of Risk Ratios
    • Figure 10-3 Hypothetical example of a case-control study of coffee drinking and pancreatic cancer: Cases have a higher level of coffee drinking than controls.
    • TABLE 10-7 Estimates of Relative Risk* of Cancer of the Pancreas Associated with Use of Coffee and Cigarettes
    • Information Bias
    • Problems of Recall
    • Limitations in Recall.
    • Figure 10-4 Interpreting the results of a case-control study of coffee drinking and pancreatic cancer. A, Is the lower level of coffee drinking in the controls the expected level of coffee drinking in the general population? OR B, Is the higher level of coffee drinking in the cases the expected level of coffee drinking in the general population?
    • TABLE 10-8 Comparison of Patients’ Statements with Examination Findings Concerning Circumcision Status, Roswell Park Memorial Institute, Buffalo, New York
    • Recall Bias.
    • TABLE 10-9 Comparison of Patients’ Statements with Physicians’ Examination Findings Concerning Circumcision Status in the Study of Circumcision, Penile HPV, and Cervical Cancer
    • TABLE 10-10 Example of an Artificial Association Resulting from Recall Bias: A Hypothetical Study of Maternal Infections during Pregnancy and Congenital Malformations
    • Other Issues in Case-Control Studies
    • Matching
    • Group Matching
    • Individual Matching
    • Use of Multiple Controls
    • Figure 10-5 Study groups in Gold’s study of brain tumors in children.
    • Controls of the Same Type
    • Multiple Controls of Different Types
    • Figure 10-6 Rationale for using two control groups: A, Radiation exposure is the same in both Brain Tumor Cases and in Other Cancer Controls, but is higher in both groups than in Normal Controls: Could this be due to recall bias? B, Radiation exposure in Other Cancer Controls is the same as in Normal Controls, but is lower than in Brain Tumor Cases: Recall bias is unlikely.
    • When is a Case-Control Study Warranted?
    • Figure 10-7 Design of a case-control study. A, Start with the cases and the controls. B, Measure past exposure in both groups. C, Expected findings if the exposure is associated with the disease.
    • Case-Control Studies Based in a Defined Cohort
    • Figure 10-8 Design of a case-control study initiated within a cohort.
    • Nested Case-Control Studies
    • Figure 10-9 A–I, Design of a hypothetical nested case-control study: Steps in selecting cases and controls. Continued on next page. (See discussion in text on pp. 203 and 205.)
    • Case-Cohort Studies
    • Figure 10-10 Design of a hypothetical case-cohort study: Steps in selecting cases and controls.
    • Advantages of Embedding a Case-Control Study in a Defined Cohort
    • Other Study Designs
    • Case-Crossover Design
    • Figure 10-11 Design and findings of a hypothetical 4-month case-crossover study of air pollution and myocardial infarction (MI) (see discussion in text on p. 208). A, Times of development of MI cases. B, Periods of high air pollution (shown by the colored bands). C, Defining at-risk periods (red brackets). D, Defining control periods (blue brackets). E, Comparisons made of air pollution levels in at-risk and in control periods for each MI case in the study (yellow arrows).
    • Ecologic Studies
    • Figure 10-12 Correlation between dietary fat intake and breast cancer by country.
    • TABLE 10-11 Average Annual Crude Incidence Rates and Relative Risks of Acute Lymphocytic Leukemia by Cohort and Trimester of Flu Exposure for Children Younger Than 5 Years, San Francisco/Oakland (1969–1973)
    • Cross-Sectional Studies
    • Figure 10-13 Design of a hypothetical cross-sectional study: I. Identification of four subgroups based on presence or absence of exposure and presence or absence of disease.
    • Figure 10-14 Design of a hypothetical cross-sectional study—II: (top) A 2 × 2 table of the findings from the study; (bottom) two possible approaches to the analysis of results: (A) Calculate the prevalence of disease in exposed persons compared to the prevalence of disease in nonexposed persons, or (B) Calculate the prevalence of exposure in persons with disease compared to the prevalence of exposure in persons without disease.
    • Conclusion
    • TABLE 10-12 Finding Your Way in the Terminology Jungle
    • References
    • Review Questions for Chapter 10
    • Chapter 11 Estimating Risk: Is There an Association?
    • Learning Objectives
    • Figure 11-1 Design of a randomized clinical trial.
    • Absolute Risk
    • Figure 11-2 Design of a cohort study.
    • Figure 11-3 Design of a case-control study.
    • How Do We Determine Whether a Certain Disease is Associated with a Certain Exposure?
    • TABLE 11-1 A Foodborne Disease Outbreak: I. Percent of People Sick among Those Who Ate and Those Who Did Not Eat Specific Foods
    • TABLE 11-2 Foodborne Disease Outbreak: II. Ways of Calculating Excess Risk
    • Relative Risk
    • The Concept of Relative Risk
    • TABLE 11-3 An Example Comparing Two Ways of Calculating Excess Risk
    • Interpreting the Relative Risk
    • TABLE 11-4 Interpreting Relative Risk (RR) of a Disease
    • TABLE 11-5 Risk Calculations in a Cohort Study
    • Calculating the Relative Risk in Cohort Studies
    • TABLE 11-6 Smoking and Coronary Heart Disease (CHD): A Hypothetical Cohort Study of 3,000 Cigarette Smokers and 5,000 Nonsmokers
    • TABLE 11-7 Relationship between Serum Cholesterol Levels and Risk of Coronary Heart Disease by Age and Sex: Framingham Study during First 12 Years
    • Figure 11-4 Relative risk for myocardial infarction and death from coronary heart disease in men aged 30 to 62 years by serum cholesterol (left) and blood pressure levels (right) in relation to cigarette smoking. High cholesterol levels are defined as 220 mg/dL or greater.
    • The Odds Ratio (Relative Odds)
    • Defining the Odds Ratio in Cohort and in Case-Control Studies
    • TABLE 11-8 Calculation of Proportions Exposed in a Case-Control Study
    • The Odds Ratio in Cohort Studies
    • Figure 11-5 A, Odds ratio (OR) in a cohort study. B, Odds ratio (OR) in a case-control study. C, Cross-products ratio in both a cohort and a case-control study.
    • The Odds Ratio in a Case-Control Study
    • Interpreting the Odds Ratio
    • When Is the Odds Ratio a Good Estimate of the Relative Risk?
    • Figure 11-6 Example: The odds ratio is a good estimate of the relative risk when a disease is infrequent.
    • Figure 11-7 Example: The odds ratio is not a good estimate of the relative risk when a disease is not infrequent.
    • Examples of Calculating Odds Ratios in Case-Control Studies
    • Calculating the Odds Ratio in an Unmatched Case-Control Study
    • Figure 11-8 A case-control study of 10 cases and 10 unmatched controls.
    • Calculating the Odds Ratio in a Matched-Pairs Case-Control Study
    • TABLE 11-9 Example of Calculating an Odds Ratio from a Case-Control Study
    • Figure 11-9 A case-control study of 10 cases and 10 matched controls.
    • Figure 11-10 Birth weight of index child: Matched-pairs comparison of cases and normal controls (≥8 lbs vs.
    • Figure 11-11 Exposure of index child to sick pets: Matched-pairs comparison of cases and normal controls.
    • Conclusion
    • Reference
    • Review Questions for Chapter 11
    • Smoking History for Cases of Atherosclerotic Heart Disease (ASHD) Sudden Death and Controls (Current Smoker, 1+ Pack/Day) [Matched Pairs], Allegheny County, 1980
    • Rates of ASHD per 10,000 Population, by Age and Sex, Framingham, Massachusetts
    • Appendix to Chapter 11
    • (1) Formula 1:
    • (2) Formula 2:
    • (3) Formula 3:
    • Chapter 12 More on Risk: Estimating the Potential for Prevention
    • Learning Objectives
    • Attributable Risk
    • Attributable Risk for the Exposed Group
    • Figure 12-1 A, Total risks in exposed and nonexposed groups. B, Background risk. C, Incidence attributable to exposure and incidence not attributable to exposure.
    • Formula 12-1
    • Formula 12-2
    • Figure 12-2 The concept of attributable risk.
    • Attributable Risk for the Total Population—Population Attributable Risk (PAR)
    • TABLE 12-1 Smoking and Coronary Heart Disease (CHD): A Hypothetical Cohort Study of 3,000 Cigarette Smokers and 5,000 Nonsmokers
    • Formula 12-3
    • Formula 12-4
    • An Example of an Attributable Risk Calculation for the Exposed Group
    • Formula 12-1
    • Formula 12-2
    • An Example of an Attributable Risk Calculation in the Total Population (Population Attributable Risk—PAR)
    • Formula 12-3
    • Formula 12-3
    • Figure 12-3 Numbers of deaths attributed to major causes, United States, 2000.
    • Formula 12-4
    • Comparison of Relative Risk and Attributable Risk
    • TABLE 12-2 Lung Cancer and CHD Mortality in Male British Physicians: Smokers vs. Nonsmokers
    • TABLE 12-3 Summary of Attributable Risk Calculations
    • Conclusion
    • References
    • Review Questions for Chapter 12
    • Appendix to Chapter 12: Levin’s Formula for the Attributable Risk for the Total Population
    • Chapter 13 A Pause for Review: Comparing Cohort and Case-Control Studies
    • Figure 13-1 Design of cohort and case-control studies. A, Cohort study. B, Case-control study.
    • Figure 13-2 Comparison of cohort and case-control study designs. A, Groups compared. B, Outcome measurements.
    • Figure 13-3 In a cohort study that starts with an exposed group and a nonexposed group, we can study multiple outcomes but only one exposure.
    • TABLE 13-1 Comparisons of Cohort and Case-Control Studies
    • Figure 13-4 In a cohort study that starts with a defined population, we can study both multiple exposures and multiple outcomes.
    • Figure 13-5 In a case-control study which starts by identifying cases and controls, we can study multiple exposures but only one outcome.
    • Chapter 14 From Association to Causation: Deriving Inferences from Epidemiologic Studies
    • Learning Objectives
    • Figure 14-1 A, Do we observe an association between exposure and disease? B, Is the observed association between exposure and disease causal?
    • Approaches for Studying Disease Etiology
    • Approaches to Etiology in Human Populations
    • Figure 14-2 A frequent sequence of studies in human populations.
    • Figure 14-3 Another example of association or causation.
    • Types of Associations
    • Real or Spurious Associations
    • Interpreting Real Associations
    • Figure 14-4 Types of associations.
    • Figure 14-5 Interpreting an observed association between increased coffee drinking and increased risk of pancreatic cancer.
    • Figure 14-6 Interpreting an observed association between increased cholesterol level and increased risk of coronary heart disease (CHD).
    • Figure 14-7 Percentage distribution by birth weight of infants of mothers who did not smoke during pregnancy and of those mothers who smoked 1 pack of cigarettes or more per day.
    • Figure 14-8 Mean birth weight for week of gestation according to maternal smoking habit.
    • Figure 14-9 Percentage of pregnancies (n = 50,267) with infant weighing less than 2,500 g, by maternal cigarette smoking category.
    • Figure 14-10 Percentage of low-birth-weight infants by smoking status of their mothers (*P < .01; **P < .02).
    • Types of Causal Relationships
    • Figure 14-11 Direct versus indirect causes of disease.
    • Figure 14-12 Types of causal relationships: I. Factor A is both necessary and sufficient.
    • Figure 14-13 Types of causal relationships: II. Each factor is necessary, but not sufficient.
    • Necessary and Sufficient
    • Necessary, But Not Sufficient
    • Figure 14-14 Types of causal relationships: III. Each factor is sufficient, but not necessary.
    • Figure 14-15 Types of causal relationships: IV. Each factor is neither sufficient nor necessary.
    • Sufficient, But Not Necessary
    • Neither Sufficient Nor Necessary
    • Evidence for a Causal Relationship
    • Figure 14-16 The mean concentration of airborne particles (µg/m3) from the four inner monitoring stations in London and the count of daily deaths in the London Administrative County during the beginning of December 1952.
    • TABLE 14-1 Guidelines for Judging Whether an Observed Association Is Causal
    • Guidelines for Judging Whether an Observed Association is Causal
    • Figure 14-17 Age-standardized death rates due to well-established cases of bronchogenic carcinoma (exclusive of adenocarcinoma) by current amount of smoking.
    • Figure 14-18 Effects of terminating exposure: lung cancer death rates, standardized for age and amount smoked, among men continuing to smoke cigarettes and men who gave up smoking for different periods. The corresponding rate for nonsmokers was 0.07 per 1,000.
    • Figure 14-19 Reported dates of illness onset by month and year for cases of eosinophilia-myalgia syndrome, as reported to the Centers for Disease Control and Prevention, Atlanta, as of July 10, 1990.
    • Figure 14-20 Parallel trends between cigarette consumption and lung cancer in men (two curves on left) and in women (two curves on right), in England and Wales.
    • Deriving Causal Inferences: Two Examples
    • Peptic Ulcers and Gastric Cancer in Relation to Infection with Helicobacter pylori
    • Figure 14-21 Helicobacter pylori [Photograph].
    • Age of Onset of Alcohol Use and Lifetime Alcohol Abuse
    • TABLE 14-2 Assessment of the Evidence Suggesting Helicobacter pylori as a Causative Agent of Duodenal Ulcers
    • Figure 14-22 Kaplan-Meier analysis of the proportion of Helicobacter pylori–positive and Helicobacter pylori–negative patients who remained free of gastric cancer. During follow-up, gastric cancer developed in 36 of the 1,246 H. pylori–infected patients (2.9%), but in none of the 280 uninfected patients (P < .001).
    • Figure 14-23 Relation of age of onset of alcohol use to prevalence of lifetime alcohol abuse.
    • Modifications of the Guidelines for Causal Inferences
    • TABLE 14-3 The Process for Using the Evidence in Developing Recommendations on the Effectiveness of Prenatal Interventions
    • TABLE 14-4 U.S. Preventive Services Task Force Levels of Certainty* Regarding Net Benefit
    • Figure 14-24 Generic analytic framework for screening topics used by the U.S. Preventive Services Task Force. Numbers refer to key questions in the figure. (1) Does screening for X reduce morbidity and/or mortality? (2) Can a group at high risk for X be identified on clinical grounds? (3) Are there accurate (i.e., sensitive and specific) screening tests available? (4) Are treatments available that make a difference in intermediate outcomes when the disease is caught early, or detected by screening? (5) Are treatments available that make a difference in morbidity or mortality when the disease is caught early, or detected by screening? (6) How strong is the association between the intermediate outcomes and patient outcomes? (7) What are the harms of the screening test? (8) What are the harms of the treatment?
    • Figure 14-25 Grid used by the U.S Preventive Services Task Force for assessing the certainty of benefit and the magnitude of net benefit in determining the grade of its recommendations.
    • TABLE 14-5 What the USPSTF Grades Mean and Suggestions for Practice
    • Conclusion
    • References
    • Review Questions for Chapter 14
    • Chapter 15 More on Causal Inferences: Bias, Confounding, and Interaction
    • Learning Objectives
    • Bias
    • Selection Bias
    • TABLE 15-1 Results of a Matched-Pairs Analysis of a Case-Control Study of Reserpine Use and Breast Cancer
    • Information Bias
    • TABLE 15-2 Some Types and Sources of Information Bias
    • Confounding
    • TABLE 15-3 Relative Risks* (RR) and 95% Confidence Intervals (CI) of the Development of Breast Cancer at Ages 20 to 45 Years in Relation to Previous Induced Abortions Reported by Parous Women in All Regions and in Western and Southeastern Regions of The Netherlands
    • Figure 15-1 The association between increased coffee drinking and increased risk of pancreatic cancer.
    • TABLE 15-4 Hypothetical Example of Confounding in an Unmatched Case-Control Study: I. Numbers of Exposed and Nonexposed Cases and Controls
    • TABLE 15-5 Hypothetical Example of Confounding in an Unmatched Case-Control Study: II. Distribution of Cases and Controls by Age
    • TABLE 15-6 Hypothetical Example of Confounding in an Unmatched Case-Control Study: III. Relationship of Exposure to Age
    • Figure 15-2 Schematic representation of the issue of potential confounding.
    • TABLE 15-7 Hypothetical Example of Confounding in an Unmatched Case-Control Study: IV. Calculations of Odds Ratios after Stratifying by Age
    • TABLE 15-8 Approaches to Handling Confounding
    • TABLE 15-9 An Example of Stratification: Lung Cancer Rates by Smoking Status and Degree of Urbanization
    • Figure 15-3 Age-adjusted lung cancer death rates per 100,000 man-years by urban-rural classification and by smoking category.
    • TABLE 15-10 An Example of Further Stratification: Lung Cancer Rates by Smoking Level and Degree of Urbanization
    • Figure 15-4 Relationship of degree of urbanization to lung cancer death rates in nonsmokers. The sloping line connects the age-adjusted lung cancer death rates per 100,000 man-years by urban-rural classification in nonsmokers.
    • Interaction
    • Figure 15-5 Relative risk of developing cancer of the esophagus in relation to smoking and drinking habits.
    • Figure 15-6 Questions to ask regarding the nature of the relationship between exposure and outcome.
    • TABLE 15-11 Incidence Rates for Groups Exposed to Neither Risk Factor or to One or Two Risk Factors (Hypothetical Data)
    • TABLE 15-12 Incidence Rates and Attributable Risks for Groups Exposed to Neither Risk Factor or to One or Two Risk Factors (Hypothetical Data in an Additive Model: I)
    • TABLE 15-13 Incidence Rates and Attributable Risks for Groups Exposed to Neither Risk Factor or to One or Two Risk Factors (Hypothetical Data in an Additive Model: II)
    • TABLE 15-14 Incidence Rates for Groups Exposed to Neither Risk Factor or to One or Two Risk Factors (Hypothetical Data)
    • TABLE 15-15 Incidence Rates and Relative Risks for Groups Exposed to Neither Risk Factor or to One or Two Risk Factors (Hypothetical Data in a Multiplicative Model: I)
    • TABLE 15-16 Incidence Rates and Relative Risks for Groups Exposed to Neither Risk Factor or to One or Two Risk Factors (Hypothetical Data in a Multiplicative Model: II)
    • TABLE 15-17 Deaths from Lung Cancer (per 100,000) among Individuals with and without Exposure to Cigarette Smoking and Asbestos
    • TABLE 15-18 Relative Risks* of Oral Cancer According to Presence or Absence of Two Exposures: Smoking and Alcohol Consumption
    • TABLE 15-19 Risk Ratios* for Oral Cancer According to Level of Exposure to Alcohol and Smoking—I
    • TABLE 15-20 Risk Ratios* for Oral Cancer According to Level of Exposure to Alcohol and Smoking—II
    • TABLE 15-21 Relative Risks of Lung Cancer According to Smoking and Radiation Exposure in Two Populations
    • TABLE 15-22 Risks* of Liver Cancer for Persons Exposed to Aflatoxin or Chronic Hepatitis B Infection: An Example of Interaction
    • Conclusion
    • References
    • Review Questions for Chapter 15
    • Chapter 16 Identifying the Roles of Genetic and Environmental Factors in Disease Causation
    • Learning Objectives
    • Association with Known Genetic Diseases
    • TABLE 16-1 Examples of Conditions Associated with Diseases of Known Genetic Origin
    • Genetic Advances and Their Relationship to Epidemiologic Approaches
    • The Human Genome Project
    • Use of Genetic Markers
    • Figure 16-1 Drawing of the DNA double helix. Genetic information is encoded in the sequence of the four base pairs: Adenine (A), Thymine (T), Guanine (G), and Cytosine (C).
    • Gene Expression
    • Genome-Wide Association Studies (GWAS)
    • The Promise of the Human Genome Project
    • The Importance of Epidemiologic Approaches in Applying Genetic Methods to Human Disease
    • TABLE 16-2 HLA Disease Associations
    • Age at Onset
    • Figure 16-2 Retinoblastoma: age at onset of symptoms.
    • Figure 16-3 Two-hit model for the development of retinoblastoma.
    • Figure 16-4 Cumulative age distribution of patients with skin cancer.
    • Figure 16-5 Kaplan-Meier curves of age at onset of Alzheimer’s disease for subjects with 0, 1, and 2 APO-ε4 alleles. Each curve is labeled with the number 0, 1, or 2 to indicate the number of alleles.
    • Family Studies
    • Risk of the Disease in First-Degree Relatives
    • Figure 16-6 Pedigree of family reported with retinoblastoma occurring in four successive generations. Squares, men; circles, women.
    • Applying Molecular Biological Methods to Family Studies
    • Figure 16-7 Approaches used for assessing each step from genotype to phenotype.
    • Figure 16-8 DNA analysis of autosomal dominant disorders. Example: Polycystic kidney disorder.
    • Twin Studies
    • Figure 16-9 DNA analysis of autosomal recessive disorders. Example: Cystic fibrosis.
    • Figure 16-10 Concordance in twins for a dichotomous variable, such as leukemia.
    • TABLE 16-3 Age Distribution in Published Clinical Reports of Childhood Leukemia in Twins, 1928–1974
    • TABLE 16-4 Concordance for Alcoholism in Monozygotic (MZ) and Dizygotic (DZ) Twin Pairs Identified through an Alcoholic Member
    • TABLE 16-5 Concordance Rates of Anencephaly and Spina Bifida (ASB) in New York State, 1955–1974
    • Figure 16-11 Concordance in twins for a continuous variable, such as systolic blood pressure.
    • Figure 16-12 Use of concordance rates for continuous variables, such as blood pressure (BP), to explore the etiologic role of genetic factors.
    • TABLE 16-6 Correlation among Relatives for Systolic Blood Pressure
    • Figure 16-13 Incidence of Hodgkin’s disease in the white population of Brooklyn, 1943–1957.
    • TABLE 16-7 Concordance Rates for Hodgkin’s Disease in Twin Pairs with an Affected Member
    • TABLE 16-8 Concordance Rates for Parkinson’s Disease (PD) in Twin Pairs with at Least One Affected Member
    • Adoption Studies
    • TABLE 16-9 Types of Subjects Compared in Studies of Schizophrenia in Adopted Offspring
    • TABLE 16-10 Schizophrenia in Biologic and Adoptive Relatives of Adoptees Who Became Schizophrenic (National Study of Adoptees in Denmark)
    • TABLE 16-11 Correlation Coefficients for Parent-Child Aggregation of Blood Pressure
    • Time Trends in Disease Incidence
    • Figure 16-14 Cardiovascular disease mortality trends for men and women, United States: 1979–2000.
    • International Studies
    • Figure 16-15 Age-adjusted death rates per 100,000 for stomach cancer in 20 countries, men, 1976–1977.
    • Migrant Studies
    • Figure 16-16 Age-adjusted death rates per 100,000 for breast cancer in 20 countries, women, 1976–1977.
    • TABLE 16-12 Standardized Mortality Ratios for Cancer of the Stomach in Japanese Men, Issei, Nisei, and U.S. White Men
    • TABLE 16-13 Incidence of Multiple Sclerosis (MS) per 100,000 among European, African, and Asian Immigrants to Israel by Age at Immigration
    • TABLE 16-14 Issues in Interpreting the Results of Adoption and Migrant Studies
    • Interaction of Genetic and Environmental Factors
    • Figure 16-17 Prevalence of lifetime alcohol dependence by age at drinking onset.
    • Figure 16-18 Prevalence of lifetime alcohol dependence by age at drinking onset and family history of alcoholism. FHN, Family history negative; FHP, family history positive.
    • TABLE 16-15 Estimated Population Incidence per 10,000 Person-Years of First Venous Thrombosis in Women Aged 15 to 49 Years According to Presence of Factor V Leiden Mutation and Use of Oral Contraceptives
    • Figure 16-19 Association of p53 gene mutations with cigarette smoking and alcohol consumption in 129 patients with squamous cell carcinoma of the head and neck.
    • Prospects for the Future
    • Figure 16-20 Personalized cancer care as a continuous cycle. The cycle starts with the discovery of specific molecular alterations in tumors that are then linked to specific patient outcomes in clinical trials. The ability to capture molecular profiles and clinical information at the level of individual patients allows translation of the information into more personalized cancer care. Available relational databases and health information systems ensure more informed delivery of cancer therapies to future patients and can also guide the discovery of new therapies.
    • Conclusion
    • References
    • Review Questions for Chapter 16

Section 3 Applying Epidemiology to Evaluation and Policy

    • Applying Epidemiology to Evaluation and Policy
    • Chapter 17 Using Epidemiology to Evaluate Health Services
    • Learning Objectives
    • Figure 17-1 The earliest known evaluation (Genesis 1 : 1–4).
    • Studies of Process and Outcome
    • Studies of Process
    • Studies of Outcome
    • Efficacy, Effectiveness, and Efficiency
    • Efficacy
    • Effectiveness
    • Efficiency
    • TABLE 17-1 Some Possible Endpoints for Measuring Success of a Vaccine Program
    • Measures of Outcome
    • TABLE 17-2 Some Possible Endpoints for Measuring Success of a Throat Culture Program
    • Comparing Epidemiologic Studies of Disease Etiology and Epidemiologic Research Evaluating Effectiveness of Health Services
    • Figure 17-2 A, Classic epidemiologic research into etiology, taking into account the possible influence of other factors, including health care. B, Classic health services research into effectiveness, taking into account the possible influence of environmental and other factors.
    • Figure 17-3 National Hospital Discharge Survey (NHDS) and National Health Interview Survey (NHIS) short-stay hospital discharge rates, United States, 1980–1986.
    • Evaluation Using Group Data
    • Outcomes Research
    • Figure 17-4 Mortality rates according to race, sex, and income among Medicare beneficiaries 65 years or older, 1993. Rates are adjusted for age to the total Medicare population.
    • Figure 17-5 Rates of mammography according to race and income among female Medicare beneficiaries 65 years or older, 1993. Rates are adjusted for age to the total female Medicare population.
    • Potential Biases in Evaluating Health Services Using Group Data
    • Figure 17-6 Rates of amputation of all or part of the lower limb, according to race and income, among Medicare beneficiaries 65 years or older, 1993. Amputation rates are adjusted for age and sex to the total Medicare population.
    • Two Indices Used in Ecologic Studies of Health Services
    • Evaluation Using Individual Data
    • Randomized Designs
    • Figure 17-7 Design of a randomized study comparing care A and care B.
    • Figure 17-8 Profile of a randomized trial of strategies for stroke care. *Fifty-one patients in this group were admitted to the hospital within 2 weeks of randomization, but are included in the intention-to-treat analysis.
    • Nonrandomized Designs
    • Figure 17-9 Kaplan-Meier survival curves for different strategies of care after acute stroke.
    • Before–After Design (Historical Controls)
    • Simultaneous Nonrandomized Design (Program–No Program)
    • TABLE 17-3 In-Hospital Mortality and Rates for Bypass Surgery during Index Hospitalization According to Hospital Volume of Angioplasty Procedures Each Year
    • Comparison of Utilizers and Non-utilizers
    • Comparison of Eligible and Non-eligible Populations
    • Combination Designs
    • Figure 17-10 Adjusted operative mortality among Medicare patients in 1998 and 1999 according to level of surgeon volume for 4 cardiovascular procedures (panel A) and 4 cancer resection procedures (panel B). Operative mortality was defined as the rate of death before hospital discharge or within 30 days after the index procedure. Surgeon volume was based on the total number of procedures performed.
    • Figure 17-11 Design of a nonrandomized cohort study comparing utilizers with non-utilizers of a program.
    • TABLE 17-4 Relationship of Neonatal Mortality to History of Prenatal Care, Baltimore Residents, Younger Than 17 Years, 1960–1961
    • Figure 17-12 Design of a nonrandomized cohort study comparing people eligible with people not eligible for a program.
    • Figure 17-13 Two possible explanations that would result in an observed difference in morbidity between Group X and Group Y after Group Y (shown in black) has received a health care service. See discussion in text on pp. 319 and 321–322.
    • Case-Control Studies
    • Figure 17-14 Comprehensive care and rheumatic fever incidence per 100,000, 1968–1970; Baltimore, black population, aged 5 to 14 years.
    • Figure 17-15 Comprehensive care and changes in rheumatic fever incidence per 100,000, 1960–1964 and 1968–1970; Baltimore, black population, aged 5 to 14 years.
    • Conclusion
    • Figure 17-16 Changes in the annual incidence of first attacks of rheumatic fever (RF) in relation to presence or absence preceding clinically symptomatic sore throat. As seen in the figure, the entire decline in first attacks of RF was due to a decline in first attacks of RF that were preceded by clinically symptomatic sore throats.
    • References
    • Review Questions for Chapter 17
    • In-Hospital Case-Fatality (CF) for 100 Men Not Treated in a Coronary Care Unit (CCU) and for 100 Men Treated in a CCU, According to Three Clinical Grades of Severity of Myocardial Infarction
    • Chapter 18 The Epidemiologic Approach to Evaluating Screening Programs
    • Learning Objectives
    • TABLE 18-1 Assessing the Effectiveness of Screening Programs Using Operational Measures
    • TABLE 18-2 Assessing the Effectiveness of Screening Programs Using Outcome Measures
    • The Natural History of Disease
    • The Pattern of Disease Progression
    • Figure 18-1 A, Natural history of a disease. B, Natural history of a disease with preclinical and clinical phases. C, Natural history of a disease with points for primary, secondary, and tertiary prevention. D, Natural history of a disease with specific primary, secondary, and tertiary prevention measures.
    • Figure 18-2 Preclinical phase of the disease. A, Natural history with point at which disease is detectable by screening. B, Natural history with detectable preclinical phase. C, Natural history with lead time.
    • Figure 18-3 A, A single critical point in the natural history of a disease. B, Multiple critical points in the natural history of a disease. See text on p. 328.
    • Figure 18-4 A, Natural history of cervical cancer: I. Progression from normal cervix to invasive cancer. B, Natural history of cervical cancer: II. Extremely rapid progression and spontaneous regression.
    • Methodologic Issues
    • Selection Biases
    • Referral Bias (Volunteer Bias)
    • Figure 18-5 Design of a randomized trial of the benefits of screening.
    • Length-Biased Sampling (Prognostic Selection)
    • Figure 18-6 Short and long natural histories of disease: relationship of length of clinical phase to length of preclinical phase.
    • Figure 18-7 Hypothetical population of individuals with long and short natural histories.
    • Lead Time Bias
    • Figure 18-8 A, Outcome of diagnosis at the usual time, without screening. B-D, Three possible outcomes of an earlier diagnosis as a result of a screening program.
    • Figure 18-9 A, Natural history of a patient with colon cancer without screening. Disease diagnosed and treated in 2008. B, Disease detected by screening 3 years earlier in 2005 (lead time). C, Lead time bias resulting from screening 3 years earlier.
    • Lead Time and Five-Year Survival
    • Figure 18-10 A, Lead time bias-I: 5-year survival when diagnosis is made without screening. B, Lead time bias-II: Shift of 5-year period by screening and early detection (lead time). C, Lead time bias-III: Bias in survival calculation resulting from early detection.
    • Figure 18-11 The impact of overdiagnosis resulting from screening on estimation of survival. (See discussion in the text under the subheading “Overdiagnosis Bias” on p. 337.) A, Scenario 1—survival with no screening. B, Scenario 2—when screening results in overdiagnosis: Survival after 10 years. C, Comparison of 10-year survival in Scenario 1 and Scenario 2.
    • Overdiagnosis Bias
    • Study Designs for Evaluating Screening: Nonrandomized and Randomized Studies
    • Nonrandomized Studies
    • Figure 18-12 Design of a nonrandomized cohort study of the benefits of screening.
    • Randomized Studies
    • Figure 18-13 Design of a case-control study of the benefits of screening.
    • Figure 18-14 Design of the Health Insurance Plan (HIP) randomized controlled trial begun in 1963 to study the efficacy of mammography screening.
    • Figure 18-15 Numbers of deaths due to breast cancer and mortality rates from breast cancer in control and study groups; 5 years of follow-up after entry into study. Data for study group include deaths among women screened and those who refused screening.
    • Figure 18-16 Five-year case-fatality among patients with breast cancer. Case-fatality for those in whom detection was due to screening allow for a 1-year lead time.
    • Figure 18-17 Mortality from all causes excluding breast cancer per 10,000 person-years, Health Insurance Plan (HIP).
    • Figure 18-18 Five-year relative survival rates, by race, among women with breast cancer diagnosed 1964–1973 (SEER program).
    • Figure 18-19 A, Cumulative case-survival rates, first 10 years after diagnosis by race, Health Insurance Plan (HIP) control groups. B, Cumulative case-survival rates, first 10 years after diagnosis by race, Health Insurance Plan (HIP) study and control groups.
    • Further Examples of Studies Evaluating Screening
    • Mammography for Women 40 to 49 Years of Age
    • Figure 18-20 Cumulative breast cancer mortality rates in screened and unscreened women (A) ages 50 to 69 years and (B) ages 40 to 49 years. • = screened; ◯ = unscreened.
    • Screening for Cervical Cancer
    • Screening for Neuroblastoma
    • Figure 18-21 Percentage of neuroblastoma cases under 1 year of age in Sapporo and Hokkaido, Japan, before and after screening.
    • TABLE 18-3 Rate of Death from Neuroblastoma by 8 Years of Age in the Screened Quebec Cohort, as Compared with the Rates in Four Unscreened Cohorts*
    • Problems in Assessing the Sensitivity and Specificity of Screening Tests
    • TABLE 18-4 Rate of Death from Neuroblastoma by 8 Years of Age in the Screened Quebec Cohort, as Compared with the Rates in Unscreened Canadian Cohorts*
    • Figure 18-22 A, Problem of establishing sensitivity and specificity because of limited follow-up of those with negative test results. B, Problem of establishing sensitivity and specificity because of limited follow-up of those with negative test results for HIV using the enzyme-linked immunosorbent assay (ELISA) test. C, Problem of establishing sensitivity and specificity because of limited follow-up of those with negative test results using the prostate-specific antigen (PSA) test for prostatic cancer. TRUS, transrectal ultrasound.
    • Interpreting Study Results That Show No Benefit of Screening
    • Cost-Benefit Analysis of Screening
    • TABLE 18-5 Criteria Used by the American Cancer Society for Recommendations on Cancer-Related Check-ups
    • Conclusion
    • References
    • Review Questions for Chapter 18
    • Chapter 19 Epidemiology and Public Policy
    • Learning Objectives
    • Epidemiology and Prevention
    • Figure 19-1 Diagram of classic risk factor epidemiology.
    • Figure 19-2 Diagram of expanded risk factor epidemiology model to include determinants of exposure as well as social, psychological, family, economic, and community effects of the disease.
    • Figure 19-3 Diagram of expanded risk factor epidemiology model to include inter-relationships of factors that determine susceptibility or vulnerability.
    • Figure 19-4 Risk of what? How the endpoint may affect an individual’s perception of risk and willingness to act.
    • Population approaches Versus High-Risk Approaches to Prevention
    • Figure 19-5 A, Percent distribution by baseline systolic blood pressure of men screened for MRFIT. B, Relative risk of coronary heart disease (CHD) mortality in relation to level of systolic blood pressure in men screened for MRFIT. C, Percent distribution of excess CHD deaths by level of systolic blood pressure for men screened for MRFIT.
    • Figure 19-6 Representation of the effects of a population-based intervention strategy on the distri­bution of blood pressure.
    • Epidemiology and Clinical Medicine: Hormone Replacement Therapy in Postmenopausal Women
    • Figure 19-7 Kaplan-Meier estimates of the cumulative incidence of coronary heart disease events (death and nonfatal myocardial infarctions).
    • Figure 19-8 Disease rates for women assigned to estrogen plus progestin or to placebo in the Women’s Health Initiative (WHI) study.
    • Risk Assessment
    • Figure 19-9 Relationships among the four steps of risk assessment and between risk assessment and risk management.
    • TABLE 19-1 Sources of Exposure Data
    • Assessment of Exposure
    • Figure 19-10 What exposures are we trying to measure?
    • Meta-Analysis
    • Figure 19-11 Meta-analysis: odds ratios and 95% confidence intervals for nine U.S. epidemiologic studies of the hypothesized association between exposure to environmental tobacco smoke and lung cancer.
    • Publication Bias
    • Epidemiology in the Courts
    • Sources and Impact of Uncertainty
    • Figure 19-12 One jury’s approach to uncertainty.
    • TABLE 19-2 Examples of Possible Sources of Uncertainty in Epidemiology
    • Figure 19-13 Schematic presentation of some of the factors involved in the impact of uncertainty on the decision-making process for health policy.
    • Policy Issues Regarding Risk: What Should the Objectives Be?
    • Conclusion
    • References
    • Chapter 20 Ethical and Professional Issues in Epidemiology
    • Learning Objectives
    • Figure 20-1 “No man is an island”—a different view.
    • Ethical Issues in Epidemiology
    • Investigators’ Obligations to Study Subjects
    • Protecting Privacy and Confidentiality
    • Figure 20-2 Use of record linkage in occupational studies.
    • Access to Data
    • Race and Ethnicity in Epidemiologic Studies
    • Conflict of Interest
    • Figure 20-3 One view of the seemingly endless stream of reported risks confronting the public.
    • Figure 20-4 Dealing with scientific uncertainty.
    • Interpreting Findings
    • TABLE 20-1 Involuntary Risks
    • TABLE 20-2 Voluntary Risks
    • Conclusion
    • References

Answers to Review Questions

    • Answers to Review Questions
    • Chapter 1
    • Chapter 2
    • Chapter 3
    • Chapter 4
    • Chapter 5
    • Chapter 6
    • Chapters 7 and 8
    • Chapter 9
    • Chapter 10
    • Survival of Patients with AIDS Following Diagnosis
    • Chapter 11
    • Chapter 12
    • Chapter 13
    • Chapter 14
    • Chapter 15
    • Chapter 16
    • Chapter 17
    • Chapter 18
    • Chapters 19 and 20

Index

  • Index
  • A
  • B
  • C
  • D
  • E
  • F
  • G
  • H
  • I
  • J
  • K
  • L
  • M
  • N
  • O
  • P
  • Q
  • R
  • S
  • T
  • U
  • V
  • W
  • Y

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