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Managerial Epidemiology

4 assignments one is a PowerPoint, the others are papers that need to be written. The subject matter is epidemiology. I have attached a breakdown of the chapters that it goes over in PowerPoints.

HSA-6520 Managerial Epidemiology: Week 1

Critical Reflection Paper: Chapters 1 to 3

Objective: To critically reflect your understanding of the readings and your ability to apply them to your Health care Setting.

ASSIGNMENT GUIDELINES (10%):

Students will disapprovingly evaluate the readings from Chapter 1 to 3 in your textbook. This assignment is designed to help you assessment, inquiry, and apply the readings to your Health Care setting as well as become the foundation for all of your remaining assignments.

You need to read the chapter assigned for week 1 and develop a 2-3-page paper reflecting your understanding and ability to apply the readings to your Health Care Setting. Each paper must be typewritten with 12-point font and double-spaced with standard margins. Follow APA style 7th edition format when referring to the selected articles and include a reference page.

EACH PAPER SHOULD INCLUDE THE FOLLOWING:

1.
Introduction (25%)
Provide a short-lived outline of the denotation (not a description) of each Chapter and articles you read, in your own words.

2.
Your Critique (50%)

What is your reaction to the content of the Chapters?

What did you absorb about the History and Scope of Epidemiology?

What did you acquire about the Practical applications of Epidemiology?

Did these Chapters change your thoughts about the Measurement of mortality and Morbidity? If so, how? If not, what remained the same?

3.
Conclusion (15%)

Fleetingly summarize your thoughts & deduction to your critique of the Chapters you read. How did these articles and Chapters impact your thoughts on Patient Protection and Affordable Care Act?

Evaluation will be based on how clearly you respond to the above, in particular:

a) The clarity with which you critique the Chapters.

b) The depth, scope, and organization of your paper; and,

c) Your conclusions, including a description of the impact of these Chapters on any Health Care Setting.

HSA-6520 Managerial Epidemiology: Week 2

Critical Reflection Paper: Chapters 4 &5

Objective: To censoriously reveal your understanding of the readings and your ability to apply them to your Health care Setting.

ASSIGNMENT GUIDELINES (10%):

Students will frowningly analyze the readings from Chapter 4 and 5 in your textbook. This assignment is premeditated to help you valuation, analysis, and apply the readings to your Health Care setting as well as become the groundwork for all of your outstanding assignments.

You need to read the chapters assigned for week 1 and develop a 2-3-page paper reflecting your understanding and ability to apply the readings to your Health Care Setting. Each paper must be typewritten with 12-point font and double-spaced with standard margins. Follow APA style 7th edition format when referring to the selected articles and include a reference page.

EACH PAPER SHOULD INCLUDE THE FOLLOWING:

1.
Introduction (25%)
Provide an ephemeral summary of the denotation (not a description) of each Chapters you read, in your own words.

2.
Your Critique (50%)

What is your reaction to the content of the Chapters?

What did you absorb about Descriptive and analytic epidemiology?

What did you acquire about the quality and utility of epidemiologic data?

Did these Chapter change your thoughts epidemiologic data sources and its weaknesses? If so, how? If not, what remained the same?

3.
Conclusion (15%)

Transiently recapitulate your thoughts & assumption to your critique of the Chapters you read. How did these Chapters impact your thoughts on the difference between secular trends and cohort effects?

Evaluation will be based on how clearly you respond to the above, in particular:

a) The clarity with which you critique the chapters.

b) The depth, scope, and organization of your paper; and,

c) Your conclusions, including a description of the impact of these Chapters on any Health Care Setting.

HSA-65

0Managerial Epidemiology: Assignment Week

Student PowerPoint Presentation: Chapter 6 and 7

Objectives: The presentation assignment has several goals. It requires students to apply concepts of study designs, ecology, cross-sectional and case control. Apply and differentiate cohort studies from other epidemiologic study designs able to be used in any Health Care Facility.

Format and Guidelines: The student will create a Power Point Presentation from Chapter 6 and 7 of the Textbook. The Presentation should have a minimum of

2 slides, including Title Page, Introduction, Conclusion, and References.

The student must use other textbooks, research papers, and articles as references (minimum 3).

Due date: Sunday, March 21, 2021 at 11:30PM.

EACH PAPER SHOULD INCLUDE THE FOLLOWING:

1. Title Page: Topic Name, Student Name

2. Introduction: Provide a brief synopsis of the meaning (not a description) of the topic you choose, in your own words

3. Content Body: Progress your theme, provide Material, illustrations and Diagram to explain, describe and clarify the Topic you choose.

4. Conclusion: Briefly summarize your thoughts & conclusion to your critique of the Chapters you read.

5. References: The student must use other textbooks, research papers, and articles as references (minimum 3).

HSA-6520 Managerial Epidemiology: Assignment Week 3

Grading Sheet

Student Name __________________________________ Date_____________________

Category	Possible Points	Actual Points
Presentation style and content.	3
Distributed bibliography w/ 3 additional readings			2
Inclusion of diversity content Pictures, Graphic, etc.
Length: Minimum 12 slides	1
Required Format
TOTAL	10

HSA-6520 Managerial Epidemiology: Assignment Week 4

Case Study: Chapters 8, 9 and 10

Objective: The students will complete a Case study assignments that give the opportunity to synthesize and apply the thoughts learned in this and previous coursework to examine a real-world scenario. This scenario will illustrate through example the practical importance and implications of various roles and functions of a Health Care Administrator. The investigative trainings will advance students’ understanding and ability to contemplate critically about Experimental Study Designs, Measures of Effect, Data interpretation issues, and their problem-solving skills. As a result of this assignment, students will be better able to comprehend, scrutinize and assess respectable superiority and performance by all institutional employees.

ASSIGNMENT GUIDELINES (10%):

Students will critically measure the readings from Chapters 8 to 10 in your textbook. This assignment is planned to help your examination, evaluation, and apply the readings and strategies to your Health Care organization and Managerial Epidemiology.
You need to read the Chapters assigned for week 4 and develop a 3–4-page paper reproducing your understanding and capability to apply the readings to your Health Care organization and Epidemiology. Each paper must be typewritten with 12-point font and double-spaced with standard margins. Follow APA style 7th edition format when referring to the selected articles and include a reference page.

EACH PAPER SHOULD INCLUDE THE FOLLOWING:

1.
Introduction (25%)
Provide a short-lived outline of the significance (not a description) of each Chapter you read, in your own words that will apply to the case study presented.

2.
Your Critique (50%):
Case Study

“Gastroenteritis following a retirement party at the State Capitol” 1 Outbreak Summary Approximately 300 persons attended a retirement party at the Nebraska State Capitol held on May 27, 2018. Most of the attendees worked in the Capitol. A private caterer (Caterer A) prepared and served food for the reception. Based on initial telephone interviews of persons reporting illness, the predominant symptoms were nausea and diarrhea, and the incubation period was approximately 24-30 hours. The following foods were served at the retirement reception: Swedish meatballs, taco dip, crab dip, a vegetable tray and herbed ranch dip, cake, nuts and mints. The vegetable tray consisted of cucumbers, broccoli, cauliflower, carrots, celery, green peppers, and radishes. All foods were prepared onsite on the day of the reception with the exception of the nuts, which were purchased by a coworker, and the mints, which were made by a coworker. The Swedish meatballs consisted of ground beef, ground pork, sour cream and flour. The meatballs were cooked twice. The taco dip contained layers of cream cheese mixed with salsa, ground beef, tomatoes, lettuce, onion, cheese and salsa. The taco dip was prepared manually by Caterer A in the kitchen at the State Capitol, and was not cooked after assembly. The crab dip contained canned real crab, cream cheese and ketchup. The investigators received completed surveys from 227 attendees. Of those 227 attendees, 128 (56%) persons reported a gastrointestinal illness within 72 hours of the reception. The average interval between time of food consumption and onset of illness was 32.3 hours (range 6 to 67 hours). Table 1 shows the symptoms reported by ill attendees. The duration of symptoms generally lasted 24 to 36 hours. One person reported being symptomatic for five days. Eight persons sought medical treatment, mostly for re-hydration therapy. Persons working in the security office at the State Capitol ate samples of the items served at the reception except the taco dip. None of the people from this office reported illness.

Table 1: Distribution of Symptoms Reported in Persons Meeting the Case-definition.

Symptom Number (%)

Nausea 117 (92.9%)

Diarrhea 111 (88.1%)

Abdominal Cramps 92 (74.2%)

Vomiting 90 (72.0%)

Headache 87 (70.7%)

Chills 73 (59.8%)

Muscle aches 72 (59.5%)

Sweats 67 (55.4%)

Bloody diarrhea 0 (0%)

CASE STUDY CHALLENGE

1. What were the main finding in the case study?

2. What are the Conclusions from these findings?

3. What are the increased risk for people to get gastroenteritis?

3.
Conclusion (15%)

Fleetingly recapitulate your thoughts & assumption to your critique of the case study and provide a possible outcome for the Managerial epidemiology and this case study. How did these Chapters influence your opinions about study designs?

Evaluation will be based on how clearly you respond to the above, in particular:

a) The clarity with which you critique the case study;

b) The depth, scope, and organization of your paper; and,

c) Your conclusions, including a description of the impact of these Case study on any Health Care Setting.

Chapter 9

Measures of Effect

Learning Objectives
Explain the meeting of absolute and relative effects
Calculate and interpret the following measures: risk difference, population risk difference, etiologic fraction, and population etiologic fraction
Discuss the role of statistical tests in epidemiologic research
Apply Hill’s criteria for evaluation of epidemiologic associations

Effect Measure
A quantity that measures the effect of a factor on the frequency or risk of a health outcome

Three Effect Measures
Attributable Fractions
Measure the fraction of cases due to a factor.
Risk and Rate Differences
Measure the amount a factor adds to the risk or rate of a disease.
Risk and Rate Ratio
Measure the amount by which a factor multiplies the risk or rate of disease.

Absolute vs. Relative Effects
Absolute
Attributable risk is also known as a rate difference or risk difference.
Population risk difference
Relative
Relative risk
Etiologic fraction
Population etiologic fraction

Risk Difference (Attributable Risk)
Risk difference–the difference between the incidence rate of disease in the exposed group (Ie) and the incidence rate of disease in the nonexposed group (Ine).
Risk difference = Ie – Ine

Calculation of Risk Difference
For women younger than age 75, the incidence (Ie) of hip fractures per 100,000 person-days was highest in the winter (0.41), and the incidence (Ine) was lowest in the summer (0.29). The risk difference between the two seasons (Ie – Ine) was 0.41 – 0.29, or 0.12 per 100,000 person-days.

Population Risk Difference
Measures the benefit to the population derived by modifying a risk factor.

Etiologic Fraction
Defined as the proportion of the rate in the exposed group that is due to the exposure.
Also termed attributable proportion or attributable fraction.

Population Etiologic Fraction
Provides an indication of the effect of removing a particular exposure on the burden of disease in the population.
Also termed attributable fraction in the population.

Statistical Measures of Effect
Significance tests
The P value
Confidence interval

Null Hypothesis
Underlying all statistical tests is a null hypothesis, which states that there is no difference among the groups being compared.
The parameters may consist of the prevalence or incidence of disease in the population.

Significance Tests
Used to decide whether to reject or fail to reject a null hypothesis.
Involves computation of a test statistic, which is compared with a critical value obtained from statistical tables.
The critical value is set by the significance level of the test.
The significance level is the chance of rejecting the null hypothesis when, in fact, it is true.

The P Value
Indicates the probability that the findings observed could have occurred by chance alone.
However, a nonsignificant difference is not necessarily attributable to chance alone.

The P Value (cont’d)
Possible meaning of nonsignificant differences: For studies with a small sample size the sampling error may be large, which can lead to a nonsignificant test even if the observed difference is caused by a real effect.

Confidence Interval (CI)
A computed interval of values that, with a given probability, contains the true value of the population parameter.
The degree of confidence is usually stated as a percentage; commonly the 95% CI is used.
Influenced by variability of the data and sample size.

Clinical vs. Statistical Significance
While small differences in disease frequency or low magnitudes of relative risk (RR) may be significant, they may have no clinical significance.
Conversely, with small sample sizes, large differences or measures of effect may be clinically important and worthy of additional study.

Statistical Power
The ability of a study to demonstrate an association if one exists.
Determined by:
Frequency of the condition under study.
Magnitude of the effect.
Study design.
Sample size.

Evaluating Epidemiologic Associations
Five key questions to be asked:
Could the association have been observed by chance?
Determined through the use of statistical tests.
Could the association be due to bias?
Bias refers to systematic errors, i.e., how samples were selected or how data was analyzed.

Evaluating Epidemiologic Associations (cont’d)
Could other confounding variables have accounted for the observed relationship?
To whom does this association apply?
Representativeness of sample
Participation rates
Does the association represent a cause-and-effect relationship?
Considers criteria of causality.

Types of Associations between Factors and Outcomes
Not statistically associated (independent)
Statistically associated

Statistical Association
When a factor and outcome are statistically associated, the relationship can be:
Non-causal
Causal
Indirect
Direct

Multiple Causality
Also referred to as multifactorial etiology.
“…requirement that more than one factor be present for disease to develop…”

Models of Multiple Causality
Epidemiologic triangle
Web of causation, e.g., in avian influenza
Wheel model, e.g., childhood lead poisoning
Pie model, e.g., lung cancer

Chapter 5

Sources of Data for Use in Epidemiology

Learning Objectives
Discuss criteria for assessing the quality and utility of epidemiologic data
Indicate privacy and confidentiality issues that pertain to epidemiologic data
Discuss the uses, strengths, and weaknesses of various epidemiologic data sources

Criteria for the Quality and Utility of Epidemiologic Data
Nature of the data
Availability of the data
Completeness of population coverage
Representativeness
Generalizability (external validity)
Thoroughness
Strengths versus limitations

Nature of the Data
Refers to the source of data, e.g., vital statistics, case registries, physicians’ records, surveys of the general population, or hospital and clinic cases.
Will affect the types of statistical analyses and inferences that are possible.

Availability of the Data
Refers to investigator’s access to data.
For example, medical records and other data with personal identifiers may not be used without patients’ consent.

Completeness of Population Coverage
Representativeness—the degree to which a sample resembles a parent population.
Generalizability (external validity)— ability to apply findings to a population that did not participate in the study.
Thoroughness—the care taken to identify all cases of a given disease.

Strengths versus Limitations
The utility of the data for various types of epidemiologic research.
Factors inherent in the data may limit their usefulness.
Incomplete diagnostic information.
Case duplication.

Online Sources of Epidemiologic Data
Online bibliographic databases include MEDLINE, TOXLINE, and commercial databases.
National Library of Medicine’s PubMed®
MEDLINE is the main part of PubMed®
Premier source of health-related literature
TOXLINE—keyed to toxicology and includes information on drugs and chemicals

Selected Internet Addresses
American Public Health Association—http://www.apha.org
Centers for Disease Control and Prevention—http://www.cdc.gov
PubMed®—http://www.ncbi.nlm.nih.gov/sites/entrez

Confidentiality
Privacy Act of 1974
Prohibits the release of confidential data without the consent of the individual
Freedom of Information Act
Mandates the release of government information to the public, except for personal and medical files
The Public Health Service Act
Protects confidentiality of information collected by some federal agencies, e.g., NCHS

The HIPAA Privacy Rule
Refers to the Health Insurance Portability and Accountability Act of 1996
Sections of HIPAA “…require the Secretary of HHS to publicize standards for the electronic exchange, privacy and security of health information…”
Categories of protected health information pertain to individually identifiable data re:
The individual’s physical and mental health
Provision of health care to the individual
Payment for provision of health care

Data Sharing
Refers to the voluntary release of information by one investigator or institution to another for the purpose of scientific research.
Can enhance data quality and increase knowledge from research.
Key issue is the primary investigator’s potential loss of control over information.

Record Linkage
Joining data from two or more sources, e.g., employment records and mortality data.
Applications include genetic research, planning of health services, and chronic disease tracking.

Statistics Derived from the Vital Registration System
Mortality statistics
Birth statistics: certificates of birth and fetal death.

Mortality Statistics
Mortality data are nearly complete, as most deaths in the U.S. and other developed countries are unlikely to be unreported.
Death certificates include demographic information about the deceased and cause of death (immediate cause and contributing factors).

Limitations of Mortality Data
Certification of cause of death.
For example, in an elderly person with chronic illness, exact cause of death may be unclear.
Lack of standardization of diagnostic criteria.
Stigma associated with certain diseases, e.g., AIDS, may lead to inaccurate reporting.

Limitations of Mortality Data (cont’d)
Errors in coding by nosologist
Changes in coding
Revisions in the (ICD) International Classification of Disease.
Sudden increases or decreases in a particular cause of death may be due to changes in coding.

Birth Statistics: Certificates of Birth and of Fetal Death
Birth certificate includes information that may affect the neonate, such as congenital malformations, birth weight, and length of gestation.
Sources of unreliability:
Mothers’ recall of events during pregnancy may be inaccurate.
Conditions that affect neonate may not be present at birth.

Birth Statistics (cont’d)
Varying state requirements for fetal death certificates.
Both types of certificates have been used in studies of environmental influences upon congenital malformations.
Both provide nearly complete data.

Reportable Disease Statistics
Federal and state statutes require health care providers to report those cases of diseases classified as reportable and notifiable.
Include infectious and communicable diseases that endanger a population, e.g., STDs, measles, foodborne illness.

Limitations of Reportable Disease Statistics
Possible incompleteness of population coverage.
For example, asymptomatic persons would not seek treatment.
Failure of physician to fill out required forms.
Unwillingness to report cases that carry a social stigma.

Screening Surveys
Conducted on an ad hoc basis to identify individuals who may have infectious or chronic diseases. Examples: breast cancer screenings, health fairs.
Clientele are highly selected.
Individuals who participate are concerned about the particular health issue.

Multiphasic Screening
Administration of 2 or more screening tests during a single screening program
Ongoing screening programs often are carried out at worksites.
Potential biases from worker attrition
Data can be useful for research on occupational health problems.
Data may not contain etiologic information.

Disease Registries
Registry–a centralized database for collection of data about a disease
Coding algorithms are used to maintain patient confidentiality.
Applications of registries:
Patient tracking
Identification of trends in rates of disease
Case-control studies
Example: SEER program

Surveillance, Epidemiology, and End Results (SEER) Program
Conducted by the National Cancer Institute (NCI)
Collects cancer data from different cancer registries across the U.S.
Provides information about trends in cancer incidence, mortality, and survival

Morbidity Surveys of the General Population
Morbidity surveys collect data on the health status of a population group.
Obtain more comprehensive information than would be available from routinely collected data
Example: National Health Interview Survey

National Health Survey
Authorized under the National Health Survey Act of 1956 to obtain information about the health of the U.S. population.
Refers generically to a group of surveys and not a single survey.
In response to the Act, the National Center for Health Statistics (NCHS) conducts three separate and distinct programs.

NCHS Survey Programs
National Health Interview Survey (NHIS)
Health Examination Survey (HES)
Various surveys of health resources
National Hospital Discharge Survey
National Ambulatory Medical Care Survey

National Health Interview Survey (NHIS)
General household health survey of the U.S. civilian noninstitutionalized population
Studies a comprehensive range of conditions such as diseases, injuries, disabilities, and impairments

Health Examination Survey (HES)
Provides direct information about morbidity through examinations, measurements, and clinical tests
Identifies conditions previously unreported or undiagnosed
Provides information not previously available for a defined population
Now known as the Health and Nutrition Examination Survey (HANES)

Behavioral Risk Factor Surveillance System (BRFSS)
Collects data on behaviorally related phenomena
Behavioral risks for chronic diseases
Preventive activities
Healthcare utilization
The largest telephone survey in the world

California Health Interview Survey (CHIS)
Provides information on the health and demographic characteristics of California residents
Uses telephone survey methods
Topics include
Physical and mental health
Health behaviors
Health insurance coverage and utilization
Conducted on a continuing basis

Insurance Data
Sources include:
Social Security–provides data on disability benefits and Medicare.
Health insurance–provides data on those who receive care through a prepaid medical program.
Life insurance–provides information on causes of mortality; also provides results of physical examinations.

Limitations of Insurance Data
Data may not be representative of entire population, as the uninsured are excluded.

Clinical Data Sources
Hospital data
Diseases treated in special clinics and hospitals
Data from physicians’ practices

Hospital Data
Consists of both inpatient and outpatient data
Deficiencies of data:
Not representative of any specific population
Different information collected on each patient
Settings may differ according to social class of patients; e.g., specialized clinics, emergency rooms

Diseases Treated in Special Clinics and Hospitals
Data cannot be generalized because patients are a highly selected group.
Case-control studies can be done with unusual and rare diseases.
However, it is not possible to determine incidence and prevalence rates without knowing the size of the denominator.

Data from Physicians’ Practices
Limited application due to:
Confidentiality of patient data
Highly selected group of patients
Lack of standardization of information collected
Useful for the purposes of:
Verification of self-reports
Source of exposure data

Absenteeism Data
Records of absenteeism from work or school
Possible deficiencies:
Data omit people who neither work nor attend school.
Not all people who are ill take time off.
Those absent are not necessarily ill.
Useful for the study of rapidly spreading conditions

School Health Programs
Provide information about immunizations, physical exams, and self-reports of illness
Have been used in studies of intelligence, mental retardation, and disease etiology
Paffenbarger, et al. used information from health records of college students to track causes of chronic diseases.

Morbidity Data from the Armed Forces
Reports from physicals, hospitalizations, and selective service examinations
Data have been used for:
Studies of disease etiology.
Study of twins serving in Korean War or WWII to determine influence of “nature and nurture” on cause of disease.
Studies investigating genetic factors in obesity

Other Data Sources Relevant to Epidemiologic Studies
U.S. Bureau of the Census publications:
Statistical Abstract of the United States
County and City Data Book
Decennial Censuses of Population and Housing
Historical Statistics of the United States, Colonial Time to 1970

U.S. Bureau of the Census
Provides information on the general, social, and economic characteristics of the U.S. population
U.S. Census is administered every 10 years.
Attempts to account for every person and his or her residence
Characterizes population according to sex, age, family relationships, and other demographic variables

Chapter 8

Experimental Study Designs

Learning Objectives (abridged)
State how study designs compare with respect to validity of causal inference
Distinguish between a controlled experiment and a quasi-experiment
Describe the scope of intervention studies
Define the term controlled clinical trials and give examples
Explain the phases in testing a new drug or vaccine

Learning Objectives (abridged)
Discuss blinding and crossover in clinical trials.
Define what is meant by community trials.
Discuss ethical aspects of experimentation with human subjects.

True Experimental Studies
Most convincing for conferring evidence of associations between risk factors and outcomes
Manipulation of study factor and randomization of subjects
An example is a randomized clinical trial.

Women’s Health Initiative
Hormone Replacement Therapy (HRT)
Epidemiologic studies had shown that HRT use had significant benefits against coronary heart disease.
Clinical trials had failed to demonstrate any benefit.
Large body of epidemiologic research had observed that women who took HRT had elevated risks of breast cancer.

Women’s Health Initiative
Hormone Replacement Therapy (HRT)
To resolve the question of risks versus benefits of HRT, a clinical trial was conducted.
Demonstrated that:
the epidemiologic findings on cancer were generally accurate
the benefits on cardiovascular disease had been overestimated
Results
Use of HRT decreased 40%-80% after the trial was stopped

Quasi-Experiment/Community Trial
Ranked immediately below controlled experiments in rigor
Investigator is unable to randomly allocate subjects to the conditions.
There may be contamination across the conditions of the study.

Intervention Studies
An investigation involving intentional change in some aspect of the status of subjects
Used to test efficacy of preventive or therapeutic measures
Manipulation of the study factor and randomization of study subjects

Intervention Studies
Two categories:
Clinical trials (focus on the individual)
Community trial or community intervention (focus on the group or community.
NOTE: Controlled clinical trials may be conducted both at the individual and community levels.

Clinical Trials: Definition
A research activity that involves the administration of a test regimen to humans to evaluate its efficacy and safety
Wide variation in usage:
The first use of the term was for studies in humans without any control treatment
Now denotes a rigorously designed and executed experiment involving RANDOM ALLOCATION of test and control treatments

Characteristics of Clinical Trials
Carefully designed and rigidly enforced protocol
Tightly controlled in terms of eligibility, delivery of the intervention, and monitoring out outcomes
Duration ranges from days to years
Participation is generally restricted to a highly selected group of individuals.

Characteristics of Clinical Trials
Once subjects agree to participate, they are randomly assigned to one of the study groups, e.g., intervention or control (placebo)

History of Clinical Trials
In 1537, Ambroise Paré applied experimental treatment for battlefield wounds.
East India Shipping Company (1600) found that lemon juice protected against scurvy.
James Lind (1747) used the concurrently treated control group method.

History of Clinical Trials
Edward Jenner’s efforts to develop a smallpox vaccine in the late 18th century
Most recent historical developments include the use of multicenter trials.
Instrumental in the development of treatments for infectious diseases and recently in chronic diseases that are of noninfectious origin

Prophylactic and Therapeutic Trials
A prophylactic trial evaluates the effectiveness of a substance that is used to prevent disease; it can also involve a prevention program.
A therapeutic trial involves the study of curative drugs or a new surgical procedure to improve the patient’s health.

Outcomes of Clinical Trials
Referred to as clinical end points
May include rates of disease, death, or recovery
The outcome of interest is measured in the intervention and control arms of the trial to evaluate efficacy–these must be measured in a comparable manner.

Examples of Clinical Trials
Medical Research Council Vitamin Study—studied role of folic acid in preventing neural tube defects.
South Bronx, NY, STD Program—evaluated effectiveness of education efforts to prevent spread of sexually transmitted diseases (STDs).

Blinding (Masking)
To maintain the integrity of a study and reduce the potential for bias, the investigator may utilize one of two popular approaches:
Single-blind design: subject unaware of group assignment
Double-blind design: Neither subject nor experimenter is aware of group assignment

Phases of Clinical Trials
Before a vaccine, drug, or treatment can be licensed for general use, it must go through several stages of development.
This lengthy process requires balance to:
protect the public from a potentially deleterious vaccine
satisfy the urgent needs for new vaccines

Stages in the Development of A Vaccination Program
Pre-licensing evaluation of vaccine
Phase I trials: Safety of adult volunteers
Phase II trials: Immunogenicity and reactogenicity in the target population.
Phase III trials: protective efficacy
Post-licensing evaluation
Safety and efficacy of vaccine
Disease surveillance
Serologic surveillance
Measurement of vaccine coverage

Phase IV Trials
There can be more than three phases in a clinical trial.
Phase IV trials involve post-marketing research to gather more information about risks and benefits of a drug.

Randomization
Method of choice for assigning subjects to the treatment or control conditions of a clinical trial.
Non-random assignment may cause mixing of the effects of the intervention with differences (e.g., demographic) among the participants of the trial.

Crossover Designs
Any change of treatment for a patient in a clinical trial involving a switch of study treatments
In planned crossovers a protocol is developed in advance, and the patient may serve as his or her own control.
Unplanned crossovers exist for various reasons, such as patient’s request to change treatment.

Ethical Aspects of Human Experimentation
Benefits must outweigh risks.
Ethical issues:
Informed consent
Withholding treatment known to be effective
Protective the interests of the individual patient
Monitoring for side effects
Deciding when to withdraw a patient

Reporting the Results of Clinical Trials
The CONSORT Statement is a protocol that guides the reporting of randomized trials by providing a 22-item checklist and a flowchart.

Summary of Clinical Trials
Strengths:
Provide the greatest control over:
the amount of exposure
the timing and frequency of exposure
the period of observation
Ability to randomize reduces the likelihood that groups will differ significantly.

Summary of Clinical Trials (cont’d)
Limitations:
Artificial setting
Limited scope of potential impact
Adherence to protocol is difficult to enforce
Ethical dilemmas

Community Trials
Community intervention trials determine the potential benefit of new policies and programs
Intervention: Any program or other planned effort designed to produce changes in a target population
Community refers to a defined unit, e.g., a county, state, or school district

Community Trials (cont’d)
Start by determining eligible communities and their willingness to participate
Collect baseline measures of the problem to be addressed in the intervention and control communities
Use a variety of measures, e.g., disease rates, knowledge, attitudes, and practices

Community Trials (cont’d)
Communities are randomized and followed over time
Outcomes of interest are measured

Examples of Community Trials
North Karelia Project
Minnesota Heart Health Program
Stanford Five-City Project
Pawtucket Heart Health Program
Community Intervention Trial for Smoking Cessation (COMMIT)
Project Respect

Summary of Community Trials: Advantages
They represent the only way to estimate directly the impact of change in behavior or modifiable exposure on the incidence of disease.

Summary of Community Trials: Disadvantages
They are inferior to clinical trials with respect to ability to control entrance into study, delivery of the intervention, and monitoring of outcomes.
Fewer study units are capable of being randomized, which affects comparability.
They are affected by population dynamics, secular trends, and nonintervention influences.

Four Stages of Evaluation
Formative: Will all plans and procedures work as conceived?
Process: Is the program serving the target group as planned?
Impact: Has the program produced any changes among the target group?
Outcome: Did the program accomplish its ultimate goal?

Overview of Quasi-Experimental Study Designs
Type of Study Design Group(s) Pretest Intervention Posttest
Posttest only Intervention O X X
(has only one group)
Pretest/Posttest Intervention X X X
(has only one group)
Pretest/Posttest/Control Intervention X X X
(has two groups) Control X O X
Solomon Four-Group Intervention 1 X X X
(has four groups) Intervention 2 O X X
Control 1 X O X
Control 2 O O X

Note. O = not used; X = used.

Quasi-Experimental Designs
Posttest only–observations are made only after the program has been delivered.
Pretest/Posttest–baseline and follow-up observations are made.
Pretest/Postest/Control–observations are made in both intervention and control groups before and after the program.

Quasi-Experimental Designs (cont’d)
Solomon Four-Group assignment:
Used to overcome the Hawthorne Effect.
Uses four equivalent groups, two intervention and two control:
Two are observed before and after intervention.
Two are observed only after intervention.

Chapter 2

Practical Applications of Epidemiology

Learning Objectives
Discuss uses and applications of epidemiology
Define the influence of population dynamics on community health
State how epidemiology may be used for operations research
Discuss the clinical applications of epidemiology
Cite causal mechanisms from the epidemiologic perspective

Seven Uses for Epidemiology
Health Status and Health Services
Study history of the health of populations
Diagnose the health of the community
Examine the working of health services
Disease Etiology
Estimate the individual risks and chances
Identify syndromes
Complete the clinical picture
Search for causes

Health Status and Health Services
Describing the occurrence of disease in the community
Planning for allocation of resources
Public health practitioners
Administrators
Evaluating programs, e.g., public health service programs

Disease Etiology
Epidemiologists continue to search for clues as to the nature of disease.
Knowledge that is acquired may be helpful in efforts to prevent the occurrence.

Historical Use of Epidemiology
Refers to the study of past and future trends in health and illness
For example: Secular trends–changes in disease frequency over time

Examples of Trends
Chronic diseases have replaced acute infectious diseases as the major causes of morbidity and mortality.
In 2009, the leading causes of U.S. deaths were heart disease, cancer, and chronic lower respiratory disease.
Increases were reported for Alzheimer’s disease, kidney disease, and hypertension.

Factors Affecting Reliability of Observed Changes
Lack of comparability over time due to altered diagnostic criteria
Aging of the general population
Changes in the fatal course of the condition

Four Trends in Disorders
Disappearing
Residual
Persisting
New epidemic

Disappearing Disorders
This category refers to conditions that were once common but are no longer present in epidemic form.
Examples include smallpox, poliomyelitis, and measles.
Brought under control by immunizations, improvement in sanitary conditions, and the use of antibiotics and other medications led to eradication of these diseases

Residual Disorders
Conditions for which the key contributing factors are largely known
Methods of control not implemented effectively
Examples:
STDs
Perinatal and infant mortality among low SES persons
Problems associated with alcohol and tobacco use

Persisting Disorders
Diseases for which there is no effective method of prevention or no known cure
Examples: certain types of cancer and mental disorders

New Epidemic Disorders
Diseases that are increasing in frequency
Examples: Lung cancer, AIDS, Obesity, Type 2 diabetes
The emergence of new epidemics of diseases may be a result of increased life expectancy of the population, new environmental exposures, or changes in lifestyle, diet, and other practices.

Predictions About the Future
A population pyramid represents the age and sex composition of the population of an area or country at a point in time.
By examining the distribution of a population by age and sex, one may view the impact of mortality from acute and chronic conditions.

Trends in the Age and Sex Distributions
Developing countries
In 1950 and 1990, countries had a triangular population distribution, which is associated with high death rates from infections, high birth rates, and other conditions.
By 2030, improvements in health are likely to result in greater survival of younger persons, causing a projected change in the shape of the population distribution.

Trends in the Age and Sex Distributions
Developed countries
Manifest a rectangular population distribution
Infections take a smaller toll and cause a greater proportion of children to survive into old age
Residents enjoy greater life expectancy
The population of developed countries will grow increasingly older due to continuing advances in medical care

Population Dynamics
Denotes changes in the demographic structure of populations associated with such factors as births and deaths and immigration and emigration
Two types of populations
Fixed populations
Dynamic populations

Population Terms
Fixed population
Adds no new members and, as a result, decreases in size due to deaths only
Examples: survivors of the 9-11 terrorist attack in New York, residents of New Orleans during Hurricane Katrina, and persons who had a medical procedure such as hip replacement

Population Terms
Dynamic population
Adds new members through immigration and births or loses members through emigration and deaths
Example: the population of a country, city, or state in the United States

Influences on Population Size
Three major factors affect the sizes of population births, deaths, and migration.
Population in equilibrium or a steady state
The three factors do not contribute to net increases or decreases in the number of persons.

Influences on Population Size
Population increasing in size
The number of persons immigrating plus the number of births exceeds the number of persons emigrating plus the number of deaths.
Population decreasing in size
The number of persons emigrating plus the number of deaths exceeds the number of persons immigrating plus the number of births.

Demographic Transition
Shift from high birth and death rates found in agrarian societies to lower birth and death rates found in developed countries.

Epidemiologic Transition
Shift in the pattern of morbidity and mortality from infectious and communicable diseases to chronic, degenerative diseases.

Epidemiology and the Health of the Community
Provides a key to the types of problems requiring attention
Determines the need for specific health services

Demographic and Social Variables
Age and sex distribution
Socioeconomic status
Family structure
Racial, ethnic, and religious composition

Variables Related to Community Infrastructure
Availability of social and health services
Quality of housing stock
Social stability (residential mobility)
Community policing
Employment opportunities

Health-Related Outcome Variables
Homicide and suicide rates
Infant mortality rate
Chronic and infectious diseases
Drug and alcohol abuse rates
Teen pregnancy rates
Sexually transmitted diseases
Birth rate

Environmental variables
Air pollution from stationary and mobile sources
Access to parks/recreational facilities
Availability of clean water
Availability of markers that supply healthful groceries
Number of liquor stores and fast-food outlets
Nutritional quality of foods and beverages vended to school-children

Health Disparities
Healthy People 2010, Goal 2
“ . . . To eliminate health disparities among segments of the population, including differences that occur by gender, race, or ethnicity, . . .”
Healthy People 2020
“. . .To achieve health equity, eliminate disparities, and improve the health of all groups. . .”

Health Disparities
Infant mortality in the U.S.
Income inequality (Gini index)
Ranges for 0 to 1
The closer the index is to one, the greater is the level of inequality.
States with the highest Gini Scores: Tennessee, Kentucky, and West Virginia

Epidemiology and Policy Evaluation
Using epidemiologic methodologies to evaluate public health policies
Examples: tobacco control, needle distribution programs, ban on plastic bags, printing of nutritional content on restaurant menus, removal of high fat and high sugar content foods from vending machines in schools, and prohibition of drivers’ use of cell phones

Working of Health Services
Operations research (OR)
Program evaluation

Operations Research (OR)
The study of the placement and optimum utilization of health services in a community
Contribution of epidemiology to OR is the development of research designs, analytic techniques, and measurement procedures

Examples of OR
Coordination of programs for the developmentally disabled
Studies of health care utilization
Residential care facilities

Program Evaluation
Uses epidemiologic tools to determine how well a health program meets certain stated goals

Epidemiology and Program Evaluation
Methods for selecting target populations
Design of instruments for data collection
Delimitation of types of health-related data
Methods for assessment of healthcare needs

Epidemiology and Disease Etiology
Applications include:
Search for causes
Individual risks
Specific clinical concerns

Causality in Epidemiologic Research
Epidemiologic research is the subject of criticism.
Many conflicting studies
Henle-Koch postulates are not relevant to many contemporary diseases.
Multivariate causality

Risk Factors Defined
Due to the uncertainty of “causal” factors the term risk factor is used.
Definition: exposure that is associated with a disease
Example of a risk factor: smoking.

Risk Factors Defined (cont’d)
Three Criteria for Risk Factors
The frequency of the disease varies by category or value of the factor, e.g., light smokers vs. heavy smokers.
The risk factor precedes onset of the disease.
The observation must not be due to error.

Modern Concepts of Causality: 1964 Surgeon General’s Report
Five criteria for causality
Strength of association
Time sequence
Consistency upon repetition
Specificity
Coherence of explanation

Modern Concepts of Causality: Sir Austin Bradford Hill
Hill expanded the list of criteria to include:
Biologic gradient
Plausibility
Experiment
Analogy

Study of Risks to Individuals
Etiologic study designs used
Case-control
Cohort

Case-Control Design
A type of design that compares persons who have a disease (cases) with those who are free from the disease (controls).
This design explores whether differences between cases and controls result from exposures to risk factors.

Cohort Design
A group of people free from a disease is assembled according to a variety of exposures.
The group (cohort) is followed over a period of time for development of disease.

How Results Impact Clinical Decisions
The following considerations determine a study’s influence:
Criteria of causality
Relevance to each patient
Size of the risk
Public health implications
Individual vs. population

Enlargement of the Clinical Picture of Disease
Cases of a new disease often the most dramatic cases
Need to survey a complete population
Example of a “new” disease—Legionnaires’ disease

Prevention of Disease
Research is applied to identify where in a disease’s natural history effective intervention might be implemented.
The natural history of disease refers to the course of disease from its beginning to its final clinical end points.

Natural History of Disease
Prepathogenesis–before agent reacts with host
Pathogenesis–after agent reacts with host
Later stages include development of active signs and symptoms.
Clinical end points are: recovery, disability, or death.

Primary Prevention as a General Concept
Occurs during prepathogenesis phase
Includes health promotion and specific protection against diseases

Primordial Prevention
Concerned with minimizing health hazards in general
Examples include improvement of:
Economic conditions
Social conditions
Behavioral conditions
Cultural patterns of living

Primary Prevention as a Specific Concept
Involves specific protection against disease-causing hazards
Examples:
Utilization of specific dietary supplements
Immunizations
Educational campaigns against unintentional injuries

Primary Prevention: Active and Passive
Active
Necessitates behavior change on the part of the subject
Examples: Vaccinations and wearing protective devices
Passive
Does not require any behavior change
Examples: Fluoridation of public water and vitamin fortifications of milk and bread products

Secondary Prevention
Occurs during pathogenesis phase
Designed to reduce the progress of disease
Examples are screening programs for cancer and diabetes.

Tertiary Prevention
Takes place during late pathogenesis
Designed to limit disability from disease
Also directed at restoring optimal functioning (rehabilitation)
Examples include: physical therapy for stroke patients, halfway houses for alcohol abuse recovery, and fitness programs for heart attack patients.

Chapter 6

Study Designs: Ecologic, Cross-Sectional, Case-Control

Learning Objectives
Define the basic differences between observational and experimental epidemiology
Identify an epidemiologic study design by its description
List the main characteristics, advantages, and disadvantages of ecologic, cross-sectional, and case-control studies
Describe sample designs used in epidemiologic research
Calculate and interpret an odds ratio

How Study Designs Differ
Number of observations made
Directionality of exposure
Data collection methods
Timing of data collection
Unit of observation
Availability of subjects

Observational vs. Experimental Approaches
Manipulation of study factor
Was exposure of interest controlled by investigator?
Randomization of study subjects
Was there use of a random process to determine exposure of study subjects?

Typology of Epidemiologic Research

Overview of Study Designs
Experimental studies
Quasi-experimental studies
Observational studies
Descriptive studies: cross-sectional surveys
Analytic studies: many ecologic studies, case-control studies, cohort studies

The 2 by 2 Table Represents the Association Between Exposure and Disease Status

Ecologic Studies
The unit of analysis is the group, not the individual.
They can be used for generating hypotheses.
The level of exposure for each individual in the unit being studied is unknown.
Generally makes use of secondary data.
Advantageous with cost and duration.

Types of Ecologic Studies
Ecologic comparison study—involves an assessment of the correlation between exposure rates and disease rates among different groups over the same time period.
Ecologic trend study—involves correlation of changes in exposure with changes in disease within the same community, country, or other aggregate unit.

Example of an Ecologic Correlation
The association between breast cancer and dietary fat for 39 countries.
High intakes of dietary fats associated with high rates of breast cancer mortality.

Examples of Questions Investigated by Ecologic Studies
Is the ranking of cities by air pollution levels associated with the ranking of cities by mortality from cardiovascular disease, adjusting for differences in average age, percent of the population below poverty level, and occupational structure?
What are long-term trends (1950-1995) for mortality from the major cancers in the US, Canada, and Mexico?

Applications of Ecologic Approach
The effect of fluoridation of the water supply on hip fractures
The association of naturally occurring fluoride levels and cancer incidence rates
The relationship between neighborhood or local area social characteristics and health outcomes

The Ecologic Fallacy: Definition
Observations made at the group level may not represent the exposure-disease relationship at the individual level.
The ecologic fallacy occurs when incorrect inferences about the individual are made from group level data.

Implications of the Ecologic Fallacy
The conclusions obtained from an ecologic study may be the reverse of those from a study that collects data on individual subjects.

The Ecologic Study: Example
An ecologic study examines 10 individuals who go into the sun.
The study finds that 7 persons (70%) have sunburned foreheads although 6 persons (60%) wore hats.
The expected number of sunburned foreheads is 4 (the number who did not wear hats).
The media report that wearing hats will not protect you from sunburn.

What the Individual Data Show

Individual Data (cont’d)
From the individual data, one observes that 100% of persons (4) who did not wear hats were sunburned.
Among persons who wore hats (6), only 50% were sunburned.
This conclusion reverses the conclusion from the ecologic data, i.e., that wearing hats affords little protection from sunburn.

Ecologic Studies: Advantages and Disadvantages
Advantages
Quick, simple, inexpensive
Good approach for generating hypotheses when a disease is of unknown etiology
Disadvantages
Ecological fallacy
Imprecise measurement of exposure and disease

Cross-Sectional Study
Also termed prevalence study
Exposure and disease measures obtained at the individual level.
Single period of observation
Exposure and disease histories are collected simultaneously.
Both probability and non-probability sampling is used.

Cross-Sectional Study: Examples
Surveys of smokeless tobacco use among high school students
Prevalence surveys of the number of vasectomies performed
Prevalence surveys of cigarette smoking among Cambodian Americans in Long Beach, California

Uses of Cross-Sectional Studies
Hypothesis generation
Intervention planning
Planning health services and administering medical care facilities
Estimation of the magnitude and distribution of a health problem
Examine trends in disease or risk factors that can vary over time

Limitations of Cross-Sectional Studies
Limited usefulness for inferring disease etiology
Do not provide incidence data
Cannot study low prevalence diseases
Cannot determine temporality of exposure and disease

Overview of Case-Control Studies
In a case-control study with two groups, one group has the disease of interest (cases) and a comparable group is free from the disease (controls).
The case-control study identifies possible causes of disease by finding out how the two groups differ with respect to exposure to some factor.

Characteristics of the Case-Control Study
A single point of observation
Unit of observation and the unit of analysis are the individual
Exposure is determined retrospectively
Does not directly provide incidence data
Data collection typically involves a combination of both primary and secondary sources.

Selection of Cases
Two tasks are involved in case selection:
Defining a case conceptually
Identifying a case operationally

Sources of Cases
Need to define a case conceptually
Ideally, identify and enroll all incident cases in a defined population in a specified time period
A tumor registry or vital statistics bureau may provide a complete listing of all cases
Medical facilities also may be a source of cases, but not always incident cases

Selection of Controls
The ideal controls should have the same characteristics as the cases (except for the exposure of interest).
If the controls were equal to the cases in all respects other than disease and the hypothesized risk factor, one would be in a stronger position to ascribe differences in disease status to the exposure of interest.

Sources of Controls
Population-based controls–Obtain a list that contains names and addresses of most residents in the same geographic area as the cases.
A driver’s license list would include most people between the ages of 16 and 65.
Tax lists, voting lists, and telephone directories
Patients from the same hospital as the cases
Relatives of cases

Measures of Association Used in Case-Control Studies

Case-Control Studies
Sample Calculation
On the association between chili pepper consumption and gastric cancer risk: a population-based case-control study conducted in Mexico City
Source: Lopez-Carillo, et al. Am J Epidemiol. 1994;139:263-71.

Sample Calculation (cont’d)
Chili Pepper Consumption Cases of Gastric Cancer Controls
Yes A = 204 B = 552
No C = 9 D = 145

The OR (unadjusted for age and sex) is:
AD = (204)(145) = 5.95
BC (552)(9)

Interpretation of an Odds Ratio (OR)
OR = 1 implies no association.
Assuming statistical significance:
OR = 2 suggests cases were twice as likely as controls to be exposed.
OR<1 suggests a protective factor. Odds Ratio (cont’d) An OR provides a good approximation of risk when: Controls are representative of a target population. Cases are representative of all cases. The frequency of disease in the population is small. Examples of Case-Control Studies Young women’s cancers resulting from utero exposure to diethylstilbestrol Green tea consumption and lung cancer Maternal anesthesia and development of fetal birth defects Passive smoking at home and risk of acute myocardial infarction Household antibiotic use and antibiotic resistant pneumococcal infection Advantages of Case-Control Studies Tend to use smaller sample sizes than surveys or prospective studies Quick and easy to complete Cost effective Useful for studies of rare diseases Limitations of Case-Control Studies Unclear temporal relationships between exposures and diseases Use of indirect estimate of risk Representativeness of cases and controls often unknown Key Points to Remember Descriptive studies: cross-sectional surveys (hypothesis generation) Analytic studies: ecologic, case-control, and cohort (hypothesis testing) Conclusion Study designs differ in a number of key respects, including the unit of observation; the unit of analysis; the timing of exposure data in relation to occurrence of disease endpoint; complexity; rigor; and amount of resources required. Disease Status Yes (Cases) No (Controls) Yes A B Exposure Status No C D A+C B+D Odds A/C B/D Odds Ratio AD/BC Disease Status Yes (Cases) No (Controls) Exposure Status Yes A B No C D A+C B+D Odds A/C B/D Odds Ratio AD/BC

Chapter 10

Data Interpretation Issues

Learning Objectives
Distinguish between random and systematic errors
State and describe sources of bias
Identify techniques to reduce bias at the design and analysis phases of a study
Define what is meant by the term confounding and provide three examples
Describe methods to control confounding

Validity of Study Designs
The degree to which the inference drawn from a study, is warranted when account it taken of the study, methods, the representativeness of the study sample, and the nature of the population from which it is drawn.

Validity of Study Designs
Two components of validity:
Internal validity
External validity

Internal Validity
A study is said to have internal validity when there have been proper selection of study groups and a lack of error in measurement.
Concerned with the appropriate measurement of exposure, outcome, and association between exposure and disease.

External Validity
External validity implies the ability to generalize beyond a set of observations to some universal statement.
A study is externally valid, or generalizable, if it allows unbiased inferences regarding some other target population beyond the subjects in the study.

Sources of Error in Epidemiologic Research
Random errors
Systematic errors (bias)

Random Errors
Reflect fluctuations around a true value of a parameter because of sampling variability.

Factors That Contribute to Random Error
Poor precision
Sampling error
Variability in measurement

Poor Precision
Occurs when the factor being measured is not measured sharply.
Analogous to aiming a rifle at a target that is not in focus.
Precision can be increased by increasing sample size or the number of measurements.
Example: Bogalusa Heart Study

Sampling Error
Arises when obtained sample values (statistics) differ from the values (parameters) of the parent population.
Although there is no way to prevent a non-representative sample from occurring, increasing the sample size can reduce the likelihood of its happening.

Variability in Measurement
The lack of agreement in results from time to time reflects random error inherent in the type of measurement procedure employed.

Bias (Systematic Errors)
“Deviation of results or inferences from the truth, or processes leading to such deviation. Any trend in the collection, analysis, interpretation, publication, or review of data that can lead to conclusions that are systematically different from the truth.”

Factors That Contribute to Systematic Errors
Selection bias
Information bias
Confounding

Selection Bias
Refers to distortions that result from procedures used to select subjects and from factors that influence participation in the study.
Arises when the relation between exposure and disease is different for those who participate and those who theoretically would be eligible for study but do not participate.
Example: Respondents to the Iowa Women’s Health Study were younger, weighed less, and were more likely to live in rural, less affluent counties than nonrespondents.

Information Bias
Can be introduced as a result of measurement error in assessment of both exposure and disease.
Types of information bias:
Recall bias: better recall among cases than among controls.
Example: Family recall bias

Information Bias (cont’d)
Interviewer/abstractor bias–occurs when interviewers probe more thoroughly for an exposure in a case than in a control.
Prevarication (lying) bias–occurs when participants have ulterior motives for answering a question and thus may underestimate or exaggerate an exposure.

Confounding
The distortion of the estimate of the effect of an exposure of interest because it is mixed with the effect of an extraneous factor.
Occurs when the crude and adjusted measures of effect are not equal (difference of at least 10%).
Can be controlled for in the data analysis.

Criteria of Confounders
To be a confounder, an extraneous factor must satisfy the following criteria:
Be a risk factor for the disease.
Be associated with the exposure.
Not be an intermediate step in the causal path between exposure and disease.

Simpson’s Paradox as an Example of Confounding
Simpson’s paradox means that an association in observed subgroups of a population may be reversed in the entire population.
Illustrated by examining the data (% of black and gray hats) first according to two individual tables and then by combining all the hats on a single table.

Simpson’s Paradox (cont’d)
When the hats are on separate tables, a greater proportion of black hats than gray hats on each table fit.
On table 1:
90% of black hats fit
85% of gray hats fit
On table 2:
15% of black hats fit
10% of gray hats fit

Simpson’s Paradox (cont’d)

Simpson’s Paradox (cont’d)
When the man returns the next day and all of the hats are on one table:
60% of gray hats fit (18 of 30)
40% of black hats fit (12 of 30)
Note that combining all of the hats on one table is analogous to confounding.

Examples of Confounding
Air pollution and bronchitis are positively associated. Both are influenced by crowding, a confounding variable.
The association between high altitude and lower heart disease mortality also may be linked to the ethnic composition of the people in these regions.

Techniques to Reduce Selection Bias
Develop an explicit (objective) case definition.
Enroll all cases in a defined time and region.
Strive for high participation rates.
Take precautions to ensure representativeness.

Reducing Selection Bias Among Cases
Ensure that all medical facilities are thoroughly canvassed.
Develop an effective system for case ascertainment.
Consider whether all cases require medical attention; consider possible strategies to identify where else the cases might be ascertained.

Reducing Selection Bias Among Controls
Compare the prevalence of the exposure with other sources to evaluate credibility.
Attempt to draw controls from a variety of sources.

Techniques to Reduce Information Bias
Use memory aids; validate exposures.
Blind interviewers as to subjects’ study status.
Provide standardized training sessions and protocols.
Use standardized data collection forms.
Blind participants as to study goals and classification status.
Try to ensure that questions are clearly understood through careful wording and pretesting.

Methods to Control Confounding
Prevention strategies–attempt to control confounding through the study design itself.
Three types of prevention strategies:
Randomization
Restriction
Matching
Two types of analysis strategies:
Stratification
Multivariate techniques

Randomization
Attempts to ensure equal distributions of the confounding variable in each exposure category.
Advantages:
Convenient, inexpensive; permits straightforward data analysis.
Disadvantages:
Need control over the exposure and the ability to assign subjects to study groups.
Need large sample sizes.

Restriction
May prohibit variation of the confounder in the study groups.
For example, restricting participants to a narrow age category can eliminate age as a confounder.
Provides complete control of known confounders.
Unlike randomization, cannot control for unknown confounders.

Matching
Matches subjects in the study groups according to the value of the suspected or known confounding variable to ensure equal distributions.
Frequency matching–the number of cases with particular match characteristics is tabulated.
Individual matching–the pairing of one or more controls to each case based on similarity in sex, race, or other variables.

Matching (cont’d)
Advantages:
Fewer subjects are required than in unmatched studies of the same hypothesis.
May enhance the validity of a follow-up study.
Disadvantages:
Costly because extensive searching and recordkeeping are required to find matches.

Two Analysis Strategies to Control Confounding
Stratification–analyses performed to evaluate the effect of an exposure within strata (levels) of the confounder.
Multivariate techniques–use computers to construct mathematical models that describe simultaneously the influence of exposure and other factors that may be confounding the effect.

Advantages of Stratification
Performing analyses within strata is a direct and logical strategy.
Minimum assumptions must be satisfied for the analysis to be appropriate.
The computational procedure is straightforward.

Disadvantages of Stratification
Small numbers of observations in some strata.
A variety of ways to form strata with continuous variables.
Difficulty in interpretation when several confounding factors must be evaluated.
Categorization results in loss of information.

Multivariate Techniques
Advantages:
Continuous variables do not need to be converted to categorical variables.
Allow for simultaneous control of several exposure variables in a single analysis.
Disadvantages:
Potential for misuse.

Publication Bias
Occurs because of the influence of study results on the chance of publication.
Studies with positive results are more likely to be published than studies with negative results.

Publication Bias (cont’d)
May result in a preponderance of false-positive results in the literature.
Bias is compounded when published studies are subjected to meta-analysis.

Study Designs: Cohort Studies

Chapter 7

Learning Objectives
Differentiate cohort studies from other study designs
List main characteristics, advantages, and disadvantages of cohort studies
Describe three research questions that lend themselves to cohort studies
Calculate and interpret a relative risk
Give three examples of published studies discussed in this chapter

Temporality
Temporality refers to the timing of information about cause and effect.
Did the information about cause and effect refer to the same point in time?
Or, was the information about the cause garnered before or after the information about the effect?

Limitations of Other Study Designs
Demonstrating temporality is a difficulty of most observational studies.

Limitations of Other Study Designs (cont’d)
Cross-sectional and case-control study designs are based on exposure and disease information that is collected at the same time.
Advantage: Efficient for generating and testing hypotheses.
Disadvantage: Leads to challenges regarding interpretation of results.

Limitations of Other Study Designs (cont’d)
Cross-sectional studies:
Present difficulties in distinguishing the exposures from the outcomes of the disease, especially if the outcome marker is a biological or physiological parameter.

Limitations of Other Study Designs (cont’d)
Case-control studies:
Raise concerns that recall of past exposures differs between cases and controls.

Limitations of Other Study Designs (cont’d)
There has been no actual lapse of time between measurement of exposure and disease.
None of the previous study designs is well suited for uncommon exposures.

What is a cohort?
A cohort is defined as a population group, or subset thereof, that is followed over a period of time.
The term cohort is said to originate from the Latin cohors, which referred to one of ten divisions of an ancient Roman legion.

What is a cohort? (cont’d)
Cohort group members experience a common exposure associated with a specific setting (e.g., an occupational cohort or a school cohort) or they share a non-specific exposure associated with a general classification (e.g., a birth cohort—being born in the same year or era).

Cohort Effect
The influence of membership in a particular cohort.
Example: Tobacco use in the U.S.
Fewer than 5% of population smoked around the early 1900s.
Free cigarettes for WWI troops increased prevalence of smoking in the population.
During WWI, age of onset varied greatly; then people began smoking earlier in life.
One net effect was a shift in the distribution of the age of onset of lung cancer.

Cohort Analysis
The tabulation and analysis of morbidity or mortality rates in relationship to the ages of a specific group of people (cohort) identified at a particular period of time and followed as they pass through different ages during part or all of their life span.

Wade Hampton Frost
Popularized cohort analysis method.
Arranged tuberculosis mortality rates in a table with age on one axis and year of death on the other.
One can quickly see the age-specific mortality for each of the available years on one axis and the time trend for each age group on the other.

Wade Hampton Frost

Life Table Methods
Give estimates for survival during time intervals and present the cumulative survival probability at the end of the interval.
Example: Life tables can be constructed to portray the survival times of patients in clinical trials.

Life Table Methods (cont’d)
There are two life table methods:
Cohort Life Table
Period (Current) Life Table

Life Table Methods (cont’d)
Cohort life table:
Shows the mortality experience of all persons born during a particular year, such as 1900.
Period life table:
Enables us to project the future life expectancy of persons born during the year as well as the remaining life expectancy of persons who have attained a certain age.

Describing the Mortality Experience of the Population
Years of Potential Life Lost (YPLL)
Disability-adjusted life years (DALYs)

YPLL
Years of potential life lost (YPLL)
Computed for each individual in a population by subtracting that person’s life span from the average life expectancy of the population

DALYs
Disability-adjusted life years (DALYs)
Adds the time a person has a disability to the time lost to early death

Survival Curves
A method for portraying survival times
In order to construct a survival curve, the following information is required:
Time of entry into the study
Time of death or other outcome
Status of patient at time of outcome, e.g., dead or censored (patient is lost to follow-up)

Cohort Studies
Start with a group of subjects who lack a positive history of the outcome of interest and are at risk for the outcome
Include at least two observation points: one to determine exposure status and eligibility and a second (or more) to determine the number of incident cases

Cohort Studies (cont’d)
Permit the calculation of incidence rates
Can be thought of as going from cause to effect
The individual forms the unit of observation and the unit of analysis.
Involve the collection of primary data, although secondary data sources are used sometimes for both exposure and disease assessment

Cohort Studies
Timing of Data Collection

Sampling and Cohort Formation Options
Cohort studies differ according to sampling strategy used.
The two strategies are population-based samples and exposure-based samples.

Population-Based Cohort Studies
The cohort includes either an entire population or a representative sample of the population.
Population-based cohorts have been used in studies of coronary heart disease.

Framingham Study
Conducted in Framingham, Massachusetts
Ongoing study of CHD initiated in 1948
Used a random sample of 6,500 from targeted age range of 30 to 59 years

Tecumseh Study
Conducted in Tecumseh, Michigan
A total community cohort study
Examined the contribution of environmental and constitutional factors to the maintenance of health and origins of illness
Started in 1959-1960 and enrolled 8,641 (88% of the community)

Population-Based Cohort Studies (cont’d)
Exposures unknown until the first period of observation when exposure information is collected
Examples: After administration of questionnaires, collection of biologic samples, and clinical examinations, there can be two or more levels of exposure.

Exposure-Based Cohort Studies
These studies overcome limitations of population-based cohort studies, which are not efficient for rare exposures.
Certain groups, such as occupational groups, may have higher exposures than the general population to specific hazards.

Definition of Exposure-Based Cohort
An exposure-based cohort is made up of subjects with a common exposure.
Examples:
Workers exposed to lead during battery production
Childhood cancer survivors
Veterans
College Graduates

Comparison (Non-Exposed Group)
Cohort studies involve the comparison of disease rates between exposed and non-exposed groups.
The comparison group is similar in demographics and geography to the exposed group, but lacks the exposure.
In an occupational setting, several categories of exposure may exist.

Outcome Measures
Discrete Events
Single events and multiple occurrences
Levels of Disease Markers
Changes in Disease Markers
Rate of change, change in level within time

Temporal Differences in Cohort Designs
There are several variations in cohort designs that depend on the timing of data collection.
These variations are:
prospective cohort studies
retrospective cohort studies

Prospective Cohort Study
Purely prospective in nature; characterized by determination of exposure levels at baseline (the present), and follow-up for occurrence of disease at some time in the future

Advantages of Prospective Cohort Studies
Enable the investigator to collect data on exposures; the most direct and specific test of the study hypothesis
The size of the cohort is under greater control by the investigators

Advantages of Prospective Cohort Studies (cont’d)
Biological and physiological assays can be performed with decreased concern that the outcome will be affected by the underlying disease process.
Direct measures of the environment (e.g., indoor radon levels, electromagnetic field radiation, cigarette smoke concentration) can be made.

Retrospective Cohort Study
Despite substantial benefits of prospective cohort studies, investigators have to wait for cases to accrue.
Retrospective cohort studies make use of historical data to determine exposure level at some baseline in the past.

Advantages of Retrospective Cohort Studies
A significant amount of follow-up may be accrued in a relatively short period of time.
The amount of exposure data collected can be quite extensive and available to the investigator at minimal cost.

Historical Prospective Cohort Study
A design that makes use of both retrospective features (to determine baseline exposure) and prospective features (to determine disease incidence in the future)
Also known as an ambispective cohort study

Practical Considerations Regarding Cohort Studies
Availability of exposure data
Size and cost of the cohort used
Data collection and data management
Follow-up issues
Sufficiency of scientific justification

Availability of Exposure Data
High quality historical exposure data are absolutely essential for retrospective cohort studies.
Need to trade off between a retrospective study design (with the benefits of more immediate follow-up time) and collection of primary exposure data in a prospective cohort design.

Size and Cost of the Cohort
The larger the size of the cohort, the greater the opportunity to obtain findings in a timely manner.
Resource constraints typically influence design decisions.

Data Collection and Data Management
Larger studies are more demanding than smaller ones; challenges due to data collection and data management.
Explicit protocols for quality control (e.g., double entry of data and scannable forms) should be considered in the design and implementation stage.

Data Collection and Data Management (cont’d)
Organizational and administrative burdens are increased when there are multiple levels of data collection (such as phone interviews, mailed questionnaires, consent forms to access medical records).

Follow-up Issues
There are two types of follow-up:
Active follow-up
Passive follow-up

Active Follow-up
The investigator, through direct contact with the cohort, must obtain data on subsequent incidence of the outcome (disease, change in risk factor, change in biological marker).
Accomplished through follow-up mailings, phone calls, or written invitations to return to study sites/centers.

Active Follow-up (cont’d)
Example: Minnesota Breast Cancer Family Study
Mailed survey
A reminder postcard 30 days later
A second survey
A telephone call to non-responders

Passive Follow-up
Does not require direct contact with cohort members.
Possible when databases containing the outcomes of interest are collected and maintained by organizations outside the investigative team.
Example: Used in the Iowa Women’s Health Study.

Sufficiency of Scientific Justification
There should be considerable scientific rationale for a cohort study.
Additional justification for cohort studies may come from laboratory experiments or animal studies.
Cohort studies are the only observational study design that permits examination of multiple outcomes.

Cohort Studies:
Measures of Effect
Relative risk is the ratio of the risk of disease or death among the exposed to the risk among the unexposed.
Recall that risk is estimated in epidemiologic studies only by the cumulative incidence.
When the relative risk is calculated with incidence rates or incidence density, then the term rate ratio is more precise.

Relative Risk
Relative risk =
Incidence rate in the exposed
Incidence rate in the non-exposed

Relative Risk
Using the notation from the 2 by 2 table, the relative risk can be expressed as
[A/(A+B)] / [C/(C+D)]

Measures of Association (cont’d)
Disease Status
Incidence
Exposure Yes No Totals Total
Status
Yes A B A+B A/(A+B)
No C D C+D C/(C+D)
A + C B + D N
Relative Risk [A/A+B]/[C/C+D]

Cohort Studies:
Sample Calculation
Is there an association between child abuse and suicide attempts among chemically dependent adolescents?
Source: Deykin EY, Buka SL. Am J Public Health. 1994;84:634-639.

Sample Calculation (cont’d)

Examples of Major Cohort Studies
The Alameda County Study
Studied factors associated with health and mortality
Involved residents of Alameda County, CA, ages 16-94 years
Data collected through mailed questionnaires; telephone interviews or home interviews of non-respondents
Follow-up with same procedures at years 9, 18, and 29

Examples of Major Cohort Studies (cont’d)
Honolulu Heart Program
Studied coronary heart disease and stroke in men of Japanese ancestry
Involved men of Japanese ancestry living on Oahu, HI, ages 45-65 years
Data were collected through mailed questionnaires, interviews, and clinic examinations.

Examples of Major Cohort Studies (cont’d)
Nurses’ Health Study
Originally studied oral contraceptive use; expanded to women’s health
Married female R.N.s ages 30-55 years
Data collected through mailed questionnaires
Follow-up every 2 years; toenail sample at year 6 and blood sample at year 13

Nested Case-Control Studies
A nested case-control study is defined as a type of case-control study “. . . in which cases and controls are drawn from the population in a cohort study.”
Example: nested case-control breast cancer study
Controls are a subset of the source population for the cohort study of breast cancer.
Cases of breast cancer identified from the cohort study would comprise the cases.

Advantages of Nested Case- Control Studies
Provide a degree of control over confounding factors.
Reduce cost because exposure information is collected from a subset of the cohort only.
An example is an investigation of suicide among electric utility workers.

Strengths of Cohort Studies
Permit direct determination of risk.
Time sequencing of exposure and outcome.
Can study multiple outcomes.
Can study rare exposures.

Limitations of Cohort Studies
Take a long time
Costly
Subjects lost to follow-up

Table 7-6
Table 7-6 summarizes various study designs by comparing their characteristics, advantages, and disadvantages.

Chapter 1

The History and Scope of Epidemiology

Learning Objectives
Define the term epidemiology
Define the components of epidemiology (determinants, distribution, morbidity, and mortality)
Name and describe characteristics of the epidemiologic approach
Discuss the importance of Hippocrates’ hypothesis and how it differed from the common beliefs of the time
Discuss Graunt’s contributions to biostatistics and how they affected modern epidemiology
Explain what is meant by the term natural experiments, and give at least one example

2009 H1N1 Influenza
During April 2009, 2 cases of 2009 H1N1 came to the attention of CDC.
The initial cases occurred in the U.S. and then expanded rapidly worldwide.
By summer 2010, the epidemic subsided and an estimated 60 million cases had occurred in the U.S.
Heavily affected people were from 18 to 64 years old. See Exhibit 1-1.

2006 Outbreak of Escherichia coli
Outbreak during late summer and fall of 2006
Affected 199 persons and caused 3 deaths
Caused 102 (51%) of ill persons to be hospitalized
A total of 31 patients (16%) were afflicted with hemolytic-uremic syndrome (HUS).
Spread across 26 states
Fresh spinach linked to the outbreak

Epidemiology Defined
Epidemiology derives from “epidemic,” a term which provides an immediate clue to its subject matter. Epidemiology originates from the Greek words, epi (upon) + demos (people) + logy (study of).

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Definition of Epidemiology
Epidemiology is concerned with the distribution and determinants of health and diseases, morbidity, injuries, disability, and mortality in populations.
Epidemiologic studies are applied to the control of health problems in populations.

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Key Aspects of This Definition
Determinants
Distribution
Population
Health phenomena
Morbidity and mortality

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Determinants
Factors or events that are capable of bringing about a change in health.

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Examples of Determinants
Biologic agents–bacteria
Chemical agents–carcinogens
Less specific factors–stress, drinking, sedentary lifestyle, or high-fat diet

The Search for Determinants
Outbreak of Fear–Ebola virus in Kikwit, Zaire
Fear on Seventh Ave.–Legionnaires’ disease in New York City
Red Spots on Airline Flight Attendants–dye from life vests
Bioterrorism-Associated Anthrax Cases

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Bioterrorism-Associated Anthrax Cases
Index case reported in Florida
Additional cases, including fatal cases, reported in New York, New Jersey, Connecticut
Contaminated mail linked to some of the cases

Distribution
Frequency of disease occurrence may vary from one population group to another.

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Disease Distribution Examples
In 2006, death rates from CHD and stroke were higher among African-Americans than among American Indians/Alaskan natives, Asian/Pacific Islanders, or whites.
Coronary heart disease occurrence differs between Hispanics and non-Hispanics.

Population
Epidemiology examines disease occurrence among population groups, not individuals.
Epidemiology is often referred to as population medicine.
The epidemiologic description indicates variation by age groups, time, geographic location, and other variables.

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Health Phenomena
Epidemiology investigates many different kinds of health outcomes:
Infectious diseases
Chronic diseases
Disability, injury, limitation of activity
Mortality
Active life expectancy
Mental illness, suicide, drug addiction

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Morbidity and Mortality
Morbidity–designates illness.
Mortality–refers to deaths that occur in a population or other group.
Note that most measures of morbidity and mortality are defined for specific types of morbidity or causes of death.

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Aims and Levels
To describe the health status of populations
To explain the etiology of disease
To predict the occurrence of disease
To control the occurrence of disease

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Foundations of Epidemiology
Interdisciplinary
Methods and procedures—quantification
Use of special vocabulary
Epidemic frequency of disease

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Epidemiology Is Interdisciplinary
Epidemiology is an interdisciplinary field that draws from biostatistics and the social and behavioral sciences, as well as from the medically related fields of toxicology, pathology, virology, genetics, microbiology, and clinical medicine.

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Quantification
Quantification is a central activity of epidemiology.
Epidemiologic measures often require counting the number of cases of disease.
Disease distributions are examined according to demographic variables such as age, sex, race, and other variables, such as exposure category and clinical features.

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Epidemic
“The occurrence in a community or region of cases of an illness (or an outbreak) clearly in excess of expectancy…”
Relative to usual frequency of the disease

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Key Terms in “Epidemic”
Communicable disease
An illness caused by an infectious agent that can be transmitted from one person to another.
Infectious disease
A synonym for a communicable disease
Outbreak
A localized disease epidemic, e.g., in a town or health care facility

Concept of Epidemic and Non-Infectious Diseases
Some examples that use the concept of an epidemic are:
Love Canal
Red spots among airline flight attendants
Toxic Shock Syndrome
Brown lung disease
Asbestosis among shipyard workers
Diseases associated with lifestyle

Pandemic
“ . . . an epidemic on a worldwide scale; during a pandemic, large numbers of persons may be affected and a disease may cross international borders.” An example is a flu pandemic.

Ascertainment of Epidemics
Surveillance
Epidemic threshold

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Surveillance
The systematic collection of data pertaining to the occurrence of specific diseases.
Analysis and interpretation of these data.
Dissemination of disease-related information
Common activities include monitoring food born disease outbreaks and tracking influenza.

Epidemic Threshold
The minimum number of cases (or deaths) that would support the conclusion than an epidemic was underway.
This is based on statistical projections.
Figure1-6 demonstrates that the combined pneumonia and influenza deaths peaked substantially above the epidemic threshold during early 2008, late 2009, and early 2011.

Historical Antecedents
The Cholera Fountain
Environment and disease
The Black Death
Use of mortality counts
Smallpox vaccination
Use of natural experiments
William Farr
Identification of specific agents of disease
The 1918 influenza pandemic

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The Cholera Fountain
Dresden, Germany
Dresden, Germany, was spared from a deadly cholera epidemic during 19th Century.
Mid 1800s–Residents constructed a Cholera Fountain to express their gratitude for escaping the cholera epidemic that threatened the city.

The Environment
Hippocrates wrote On Airs, Waters, and Places in 400 BC.
He suggested that disease might be associated with the physical environment.
This represented a movement away from supernatural explanations of disease causation.

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The Black Death
Occurred between 1346-1352.
Claimed one-quarter to one-third of population of Europe.

Use of Mortality Counts
John Graunt, in 1662, published Natural and Political Observations Made upon the Bills of Mortality.

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John Graunt’s Contributions
Recorded seasonal variations in births and deaths
Showed excess male over female differences in mortality
Known as the “Columbus” of biostatistics
See Yearly Mortality Bill for 1632: The 10 Leading Causes of Mortality in Graunt’s Time.

Edward Jenner
Jenner conducted an experiment to create a smallpox vaccine.
He developed a method for smallpox vaccination.
In 1978 smallpox was finally eliminated worldwide.
Since 1972, routine vaccination of the nonmilitary population of the U.S. has been discontinued.

Use of Natural Experiments
John Snow was an English physician and anesthesiologist.
He investigated a cholera outbreak that occurred during the mid-19th century in Broad Street, Golden Square, London.

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Snow’s Contributions
Linked the cholera epidemic to contaminated water supplies
Used a spot map of cases and tabulation of fatal attacks and deaths

Snow’s Natural Experiment
Two different water companies supplied water from the Thames River to houses in the same area.
The Lambeth Company moved its source of water to a less polluted portion of the river.
Snow noted that during the next cholera outbreak those served by the Lambeth Company had fewer cases of cholera.

Natural Experiment
Refers to “naturally occurring circumstances in which subsets of the population have different levels of exposure to a supposed causal factor in a situation resembling an actual experiment, where human subjects would be randomly allocated to groups. The presence of persons in a particular group is typically nonrandom.”

Ignaz Semmelweis
Mid-19th century, Viennese hospital
Clinical assistant in obstetrics and gynecology
Observed higher mortality rate among the women on the teaching wards for medical students and physicians than on the teaching wards for midwives
Postulated that medical students and physicians had contaminated their hands during autopsies
Introduced the practice of hand washing

William Farr
Appointed compiler of abstracts in England, 1839
Provided foundation for classification of diseases (ICD system)
Used data such as census reports to study occupational mortality in England
Examined linkage between mortality rates and population density

Koch’s Postulates
Microorganism must be observed in every case of the disease
Microorganism must be isolated and grown in pure culture
Pure culture must, when inoculated into a susceptible animal, reproduce the disease
Microorganism must be observed in, and recovered from, diseased animal

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The 1918 Influenza Pandemic
“The Mother of All Pandemics” and Spanish Flu
Occurred between 1918 and 1919
Killed 50- to 100 million persons worldwide
2.5% case-fatality rate versus 0.1% for other influenza pandemics
Deaths most frequent among 20- to 40-year-olds

Other Historical Developments
Alexander Fleming discovered the antimicrobial properties of the mold: led to the discovery of the antibiotic penicillin.
Alexander Langmuir established CDC’s Epidemic Intelligence Service.
Wade Hampton Frost was the first professor of epidemiology in the U.S.
Joseph Goldberger discovered the cure for pellagra.

Recent Applications of Epidemiology
The Framingham Heart Study (ongoing since 1948) investigates coronary heart disease risk factors.
Smoking and lung cancer; e.g., Doll and Peto’s study of British doctors’ smoking
AIDS, chemical spills, breast cancer screening, second-hand cigarette smoke
Association between HPV and cervical cancer

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Additional Applications of Epidemiology
Infectious diseases
SARS, pandemic influenza 2009 H1N1, Avian influenza
Environmental health
Chronic diseases
Lifestyle and health promotion
Psychological and social epidemiology
Molecular and genetic epidemiology

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Chapter 4

Descriptive Epidemiology: Person, Place, Time

Learning Objectives
State primary objectives of descriptive epidemiology
Provide examples of descriptive studies
List characteristics of person, place, and time
Characterize the differences between descriptive and analytic epidemiology

Descriptive vs. Analytic Epidemiology
Descriptive studies–used to identify a health problem that may exist. Characterize the amount and distribution of disease
Analytic studies–follow descriptive studies, and are used to identify the cause of the health problem

Objectives of Descriptive Epidemiology
To evaluate and compare trends in health and disease
To provide a basis for planning, provision, and evaluation of health services
To identify problems for analytic studies (creation of hypotheses)

Descriptive Studies and Epidemiologic Hypotheses
Hypotheses–theories tested by gathering facts that lead to their acceptance or rejection
Three types:
Positive declaration (research hypothesis)
Negative declaration (null hypothesis)
Implicit question (e.g., to study association between infant mortality and region)

Mill’s Canons of Inductive Reasoning
The method of difference–all the factors in two or more places are the same except for a single factor.
The method of agreement–a single factor is common to a variety of settings. Example: air pollution.

Mill’s Canons (cont’d)
The method of concomitant variation–the frequency of disease varies according to the potency of a factor.
The method of residues–involves subtracting potential causal factors to determine which factor(s) has the greatest impact.

Method of Analogy
(MacMahon and Pugh)
The mode of transmission and symptoms of a disease of unknown etiology bear a pattern similar to that of a known disease.
This information suggests similar etiologies for both diseases.

Three Approaches to Descriptive Epidemiology
Case reports–simplest category of descriptive epidemiology
Case series
Cross-sectional studies

Case Reports and Case Series
Case reports–astute clinical observations of unusual cases of disease
Example: a single occurrence of methylene chloride poisoning
Case series–a summary of the characteristics of a consecutive listing of patients from one or more major clinical
Example: five cases of hantavirus pulmonary syndrome

Cross-sectional Studies
Surveys of the population to estimate the prevalence of a disease or exposure
Example: National Health Interview Survey

Characteristics of Persons Covered in Chapter 4
Age
Sex
Marital Status
Race and ethnicity
Nativity and migration
Religion
Socioeconomic status

Age
One of the most important factors to consider when describing the occurrence of any disease or illness

Trends by Age Subgroup
Childhood to early adolescence
Leading cause of death, ages 1-14 years—unintentional injuries
Infants—mortality from developmental problems, e.g., congenital birth defects
Childhood—occurrence of infectious diseases such as meningococcal disease

Trends by Age Subgroup (cont’d)
Teenage years
Leading causes of death—unintentional injuries, homicide, and suicide
Other issues—unplanned pregnancy, tobacco use, substance abuse

Trends by Age Subgroup (cont’d)
Adults—leading causes of death
Unintentional injuries
Cancer
Heart disease
Older adults—deaths from chronic diseases (e.g., cancer and heart disease) dominate.
Elderly—deaths from chronic diseases and limitations in activities of daily living

Age Trends in Cancer Incidence
Age-specific rates of cancer incidence increase with age with apparent declines late in life.

Reasons for Age Associations
Validity of diagnoses across the life span
Multimodality of trends
Latency effects
Action of the “human biologic clock”
Life cycle and behavioral phenomena

Validity of Diagnoses
Classification errors
Age-specific incidence rates among older groups
Exact cause of death can be inaccurate due multiple sources of morbidity that affect elderly.

Age-Specific Distributions of Disease Incidence
Age-specific distributions of disease incidence can be linear or multimodal.
Linear trend—incidence of cancer
Multimodal (having several peaks in incidence)
Tuberculosis—peaks at ages 0 to 4 and ages 20-29
Meningococcal disease—peaks among infants younger than age 1 year and teenagers about 18 years old

Latency Effects
Age effects on mortality may reflect the long latency period between environmental exposures and subsequent development of disease.

Biologic Clock Phenomenon
Waning of the immune system may result in increased susceptibility to disease, or aging may trigger appearance of conditions believed to have genetic basis.
Example: Alzheimer’s disease

Sex Differences: Males
All-cause age-specific mortality rates is higher for men than for women.
May be due to social factors
May have biological basis
Men often develop severe forms of chronic disease.
Generally, death rates for both sexes are declining.

Sex Differences: Female Paradox
Reports from the 1970s indicated female age-standardized morbidity rates for many acute and chronic conditions were higher than rates for males, even though mortality was higher among males.
Higher female rates for:
Pain
Asthma
Some lung difficulties

Cancer
Cancer of the lung and bronchus is leading cause of cancer death for both men and women in the U.S.
Increases among women are related to changes in lifestyle and risk behavior, e.g., smoking.

CHD among Women
Coronary heart disease (CHD) is the leading cause of mortality among women (and also men).
Women may not be alert for symptoms of CHD and fail to seek needed treatment.

Minority Women in Economically Disadvantaged U.S. Areas
In Los Angeles County, some have higher rates of diabetes and hypertension than men.
A large percentage are physically inactive.
High rates of obesity among Latinas and African Americans.

Marital Status
Categories
Single or non-married (e.g., never married, divorced, widowed)
Married
Living with a partner

Marital Status (cont’d)
In general, married people tend to have lower rates of morbidity and mortality.
Examples: chronic and infectious diseases, suicides, and accidents.
Never married adults (especially men) less likely to be overweight

Marital Status (cont’d)
Marriage may operate as a protective or selective factor.
Protective hypothesis: marriage provides an environment conducive to health.
Selective hypothesis: people who marry are healthier than people who never marry.

Marital Status (cont’d)
Widowed persons
Suicide rates
Elevated among young white males who were widowed
Depression
Elevated rates among widowed persons

General Comments About Race
U.S. is becoming increasingly more diverse.
Race is an ambiguous concept that overlaps with other dimensions.
Some scientists propose that race is primarily a social and cultural construct.

Measurement of Race
Census 2000 changed the race category by allowing respondents to choose one or more race categories.
Census 2000 used five categories of race.
Census 2010 continued with this classification scheme (Refer to Exhibit 4-1 in text).

Race/Ethnicity Categories Discussed in Chapter 4
African American
American Indian
Asian
Hispanic/Latino

African Americans
In a classic study of differential mortality in U.S., they had the highest rate of mortality of all groups studied.
Higher blood pressure levels
Possible influence of stress or diet.
Higher rates of hypertensive heart disease.
In 2007, age-adjusted death rate for African Americans was 1.3 times rate for whites.
Differences in life expectancy

American Indians/Alaska Natives
High rates of chronic diseases, adverse birth outcomes, and some infectious diseases
Pima Indians (1975-1984 data):
High mortality, e.g., male death rate (ages 25 to 34) was 6.6 times that for all races in U.S.
Infectious diseases were the 10th leading cause of death.

Asians
Japanese Americans have lower mortality rates than whites.
Lower rates of CHD and cancer.
Low CHD rates attributed to low-fat diet and institutionalized stress-reducing strategies.
Some Asian groups, e.g., Cambodian Americans, have high smoking rates.
TB rates are highest among Asian/Pacific Islander group.

Acculturation
Defined as modifications that individuals or groups undergo when they come in contact with another country
Provides evidence of the influence of environmental and behavioral factors on chronic disease
Example: Japanese migrants experience a shift in rates of chronic disease toward those of the host country.

Hispanics/Latinos
Hispanic Health and Nutrition Examination Survey (HHANES).
Examined health and nutrition status of major Hispanic/Latino populations in the U.S.
San Antonio Heart Study
Found high rates of obesity and diabetes among Mexican Americans
Hispanic mortality paradox (text box)

Nativity and Migration
Nativity–Place of origin of the individual
Categories are foreign born and native born.
Nativity and migration are related.

Impact of Migration
Importation of “Third World” disease by immigrants from developing countries
Leprosy during 1980s
Programmatic needs resulting from migration:
Specialized screening programs (tuberculosis and nutrition)
Familiarization with formerly uncommon (in U.S.) tropical diseases

Healthy Migrant Effect
Observation that healthier, younger persons usually form the majority of migrants
Often difficult to separate environmental influences in the host country from selective factors operative among those who choose to migrate

Religion
Certain religions prescribe lifestyles that may influence rates of morbidity and mortality.
Example: Seventh Day Adventists
Follow vegetarian diet and abstain from alcohol and tobacco use
Have lower rates of CHD, reduced cancer risk, and lower blood pressure
Similar findings for Mormons

Socioeconomic Status
Low social class is related to excess mortality, morbidity, and disability rates.
Factors include:
Poor housing
Crowded conditions
Racial disadvantage
Low income
Poor education
Unemployment

Measurement of Social Class
Variables include:
Prestige of occupation or social position
Educational attainment
Income
Combined indices of two or more of the above variables

Hollingshead and Redlich
Studied association of socioeconomic status and mental illness
Classified New Haven, Connecticut, into five social classes based on occupational prestige, education, and address

Hollingshead and Redlich Findings
Strong inverse association between social class and likelihood of being a mental patient under treatment.
As social class increased, severity of mental illness decreased.
Type of treatment varied by social class.

Mental Health and Social Class
In the U.S., the highest incidence of severe mental illness occurs among the lowest social classes.

Mental Health and Social Class: Two Hypotheses
Social causation explanation (breeder hypothesis)—conditions associated with lower social class produce mental illness.
Downward drift hypothesis—Persons with severe mental disorders move to impoverished areas.

Other Correlates of Low Social Class
Higher rate of infectious disease
Higher infant mortality rate and overall mortality rates
Lower life expectancy
Larger proportion of cancers with poor prognosis
May be due to delay in seeking health care
Low self-perceived health status

Characteristics of Place
Types of place comparisons:
International
Geographic (within-country) variations
Urban/rural differences
Localized occurrence of disease

International Comparisons of Disease Frequency
World Health Organization (WHO) tracks international variations in rates of disease.
Infectious and chronic diseases show great variation across countries.
Variations are attributable to climate, cultural factors, dietary habits, and health care access.
The U.S. fell in the bottom half of OECD countries for both male and female life expectancy; Japan was highest.

Within-Country Variations in Rates of Disease
Due to variations in climate, geology, latitude, pollution, and ethnic and racial concentrations
In U.S., comparisons can be made by region, state, and/or county.
Examples include: higher rates of leukemia in Midwest; state by state variations in infectious, vector-borne, parasitic diseases

Urban/Rural Differences in Disease Rates
Urban
Diseases and mortality associated with crowding, pollution, and poverty
Example: lead poisoning in inner cities
Homicide in central cities
Rural
Mortality (among all age groups) increases with decreasing urbanization.
Health risk behaviors higher in rural South

Standard Metropolitan Statistical Areas (SMSAs)
Established by the U.S. Bureau of the Census to make regional and urban/rural comparisons in disease rates

Metropolitan Statistical Areas (MSAs)
Provide a distinction between metropolitan and nonmetropolitan areas by type of residence, industrial concentration, and population concentration

Definition of MSA
Used to distinguish between metropolitan and nonmetropolitan areas
Metropolitan area—large population nucleus together with adjacent communities
Six urban-classification levels used by the National Center for Health Statistics (refer to text.)

Census Tracts
Small geographic subdivisions of cities, counties, and adjacent areas
Each tract contains about 4,000 residents.
Are designed to provide a degree of uniformity of population economic status and living conditions in each tract

Localized Place Comparisons
Disease patterns are due to unique environmental or social conditions found in particular area of interest. Examples include:
Fluorosis: associated with naturally occurring fluoride deposits in water.
Goiter: iodine deficiency formerly found in land-locked areas of U.S.

Geographic Information Systems (GIS)
A method to provide a spatial perspective on the geographic distribution of health conditions
A GIS produces a choroplath map that shows variations in disease rates by different degrees of shading.

Reasons for Place Variation in Disease
Gene/environment interaction
Examples: sickle-cell gene; Tay-Sachs disease.
Influence of climate
Examples: yaws, Hansen’s disease
Environmental factors
Example: chemical agents linked to cancer

Characteristics of Time
Cyclic fluctuations
Point epidemics
Secular time trends
Clustering
Temporal
Spatial

Cyclic Fluctuations
Periodic changes in the frequency of diseases and health conditions over time
Examples:
Birth rates
Higher heart disease mortality in winter
Influenza
Unintentional injuries
Meningococcal disease
Rotavirus infections

Cyclic Fluctuations (cont’d)
Related to changes in lifestyle of the host, seasonal climatic changes, and virulence of the infectious agent

Common Source Epidemic
Outbreak due to exposure of a group of persons to a noxious influence that is common to the individuals in the group
Types: point epidemic; continuous common source epidemic
Refer to Figure 4-22 for an example an influenza outbreak in a residential facility.

Point Epidemics
The response of a group of people circumscribed in place and time to a common source of infection, contamination, or other etiologic factor to which they were exposed almost simultaneously.
Examples: foodborne illness; responses to toxic substances; infectious diseases.

Influenza-Related Illness at a Residential Facility

Secular Time Trends
Refer to gradual changes in the frequency of a disease over long time periods.
Example is the decline of heart disease mortality in the U.S.
May reflect impact of public health programs, dietary improvements, better treatment, or unknown factors.

Clustering
Case clustering–refers to an unusual aggregation of health events grouped together in space and time
Temporal clustering: e.g., post-vaccination reactions, postpartum depression
Spatial clustering: concentration of disease in a specific geographic area, e.g., Hodgkin’s disease

Chapter 3

Measures of Morbidity and Mortality Used in Epidemiology

Learning Objectives
Define and distinguish among ratios, proportions, and rates
Explain the term population at risk
Identify and calculate commonly used rates for morbidity, mortality, and natality
State the meanings and applications of incidence rates and prevalence

Learning Objectives (cont’d)
Discuss limitations of crude rates and alternative measures for crude rates
Apply direct and indirect methods to adjust rates
List situations where direct and indirect adjustment should be used

Overview of Epidemiologic Measures

Count
The simplest and most frequently performed quantitative measure in epidemiology.
Refers to the number of cases of a disease or other health phenomenon being studied.

Examples of Counts
Cases of influenza reported in Westchester County, New York, during January of a particular year.
Traffic fatalities in Manhattan in a 24-hour time period
College dorm students who had mono
Foreign-born stomach cancer patients

Ratio
The value obtained by dividing one quantity by another.
Consists of a numerator and a denominator.
The most general form has no specified relationship between numerator and denominator.
Rates, proportions, and percentages are also ratios.

Example of a
Simple Sex Ratio Calculation
A ratio may be expressed at = X/Y
Simple sex ratio (data from textbook)
Of 1,000 motorcycle fatalities, 950 victims are men and 50 are women.

Number of male cases 950
Number of female cases 50

19:1 male to female
=
=

Example of a
Demographic Sex Ratio Calculation
This ratio refers to the number of males per 100 females. In the U.S., the sex ratio in 2010 for the entire population was 96.7, indicating more females than males.

Number of male cases 151,781,326
Number of female cases 156,964,212

96.7
X 100 =
=
X 100

Example of a
Sex Ratio at Birth Calculation
The sex ratio at birth is defined as: (the number of male births divided by the number of female births) multiplied by 1,000.

Number of male births
Number of female births
X 1,000

Definition of Proportion
A measure that states a count relative to the size of the group.
A ratio in which the numerator is part of the denominator.
May be expressed as a percentage.

Uses of Proportions
Can demonstrate the magnitude of a problem.
Example: 10 dormitory students develop hepatitis. How important is this problem?
If only 20 students live in the dorm, 50% are ill.
If 500 students live in the dorm, 2% are ill.

Example of a Proportion
Calculate the proportion of African-American male deaths among African-American and white boys aged 5 to 14 years.

Rate
Definition: a ratio that consists of a numerator and a denominator and in which time forms part of the denominator.
Contains the following elements:
disease frequency
unit size of population
time period during which an event occurs

Crude death rate =
Number of deaths in a given year
Reference population
(during the midpoint of the year
X 100,000
Example:
Number of deaths in the United States during 2007 = 2,423,712
Population of the U.S. as of July 1, 2007 = 301,621,157
2,423,712
301,621,157
Crude death rate =
= 803.6 per 100,000
Example of Rate Calculation

Definition of Prevalence
The number of existing cases of a disease or health condition in a population at some designated time.

Figure 3-1: Analogy of prevalence and incidence. The water flowing down the waterfall symbolizes incidence and water collecting in the pool at the base symbolizes prevalence. Source: Robert Friis.

Interpretation of Prevalence
Provides an indication of the extent of a health problem.
Example 1: Prevalence of diarrhea in a children’s camp on July 13 was 15.
Example 2: prevalence of obesity among women aged 55-69 years was 367 per 1,000.

Uses of Prevalence
Describing the burden of a health problem in a population.
Estimating the frequency of an exposure.
Determining allocation of health resources such as facilities and personnel.

Point Prevalence
Point Prevalence =
Number of persons ill
Total number in the group
at point in time
Example:
Total number of smokers in the group = 6,234
Total number in the group 41,837
or 14.9%
= 149.0 per 1,000

Period Prevalence =
during a time period
Period Prevalence
Number of persons ill
Average population
Example:
Persons ever diagnosed with cancer = 2,293
Average population 41,837
= 5.5%

Definition of Incidence
The number of new cases of a disease that occur in a group during a certain time period.

Incidence Rate (Cumulative Incidence)
Describes the rate of development of a disease in a group over a certain time period.
Contains three elements:
Numerator = the number of new cases.
Denominator = the population at risk.
Time = the period during which the cases occur.

Example of Incidence Data
Number of new cases of HIV infection diagnosed in a population in a given year: a total of 164 HIV diagnoses were reported among American Indians or Alaska natives in the U.S. during 2009.

Incidence Rate Calculation (IWHS Data)
Incidence rate =
Number of new cases
over a time period
Total population at risk
during the same time period
X multiplier (e.g., 100,000)
Number of new cases = 1,085
Population at risk = 37,105
Incidence rate =
1,085
37,105
= 0.02924/8 = 0.003655 x 100,000
= 365.5 cases per 100,000 women per year

Attack Rate (AR)
Alternative form of incidence rate.
Used for diseases observed in a population for a short time period.
Not a true rate because time dimension often uncertain.
Example: Salmonella gastroenteritis outbreak
Formula:
Ill
Ill + Well
AR =
x 100 (during a time period)

Incidence Density
An incidence measure used when members of a population or study group are under observation for different lengths of time.

Number of new cases during the time period
Total person-time of observation
Incidence density =
Number of new cases during the time period
Total person-years of observation
Incidence density =
If period of observation is measured in years, formula becomes:
Formulas for Incidence Density

Incidence Density, Example

Interrelationship Between Prevalence and Incidence

Interrelationship Between Prevalence and Incidence (cont’d)
If duration of disease is short and incidence is high, prevalence becomes similar to incidence.
Short duration–cases recover rapidly or are fatal.
Example: common cold

Interrelationship Between Prevalence and Incidence (cont’d)
If duration of disease is long and incidence is low, prevalence increases greatly relative to incidence.
Example: HIV/AIDS prevalence

‹#›

Crude Rates, Measures of Natality
Crude birth rate
Fertility rate
General
Total
Infant mortality rate
Fetal death rate
Neonatal mortality rate
Postneonatal mortality rate
Perinatal mortality rate
Maternal mortality rate

Crude Birth Rate
Crude Birth Rate =
Number of live births
within a given period
Population size at the
middle of that period
X 1,000 population
Sample calculation: 4,130,665 babies were born in the U.S. during 2009, when the U.S. population was 307,006,550. The birth rate was
4,130,665/307,006,550 = 13.5 per 1,000.
Used to project population changes; it is affected by the number and age composition of women of childbearing age

General Fertility Rate
General
fertility rate
=
# of live births
within a year
# of women
aged 15-44 yrs.
during the midpoint
of the year
X
1,000 women
aged 15-44
Sample calculation: During 2009, there were 61,948,144 women aged 15 to 44 in the U.S. There were 4,130,665 live births. The general fertility rate was 4,130,665/61,948,144 = 66.7 per 1,000 women aged 15 to 44.
Used for comparisons of fertility among age, racial, and
socioeconomic groups.

Total Fertility Rate
This rate is “[t]he average number of children that would be born if all women lived to the end of their childbearing years and bore children according to a given set of age-specific fertility rates.”
In the United States, the total fertility rate was 2.06 in 2012. This rate is close to
The replacement fertility rate is 2.1.

Fetal Death Rate
Used to estimate the risk of death of the fetus associated with the stages of gestation.

Fetal Death Ratio
Refers to the number of fetal deaths after gestation of 20 weeks or more divided by the number of live births during a year.
Fetal Death Ratio =
Number of fetal deaths after 20 weeks or more gestation
Number of live births
X 1,000
(during a year)

Infant Mortality Rate
Used for international comparisons; a high rate indicates unmet health needs and poor environmental conditions.

Neonatal Mortality Rate
Reflects events happening after birth, primarily:
Congenital malformations
Prematurity (birth before gestation week 28)

Neonatal Mortality Rate Formula

Postneonatal Mortality Rate
Measures risk of dying among older infants during a given year.

Perinatal Mortality Rate
Reflects environmental events that occur during pregnancy and after birth; it combines mortality during the prenatal and postnatal periods.

Perinatal Mortality Ratio

Maternal Mortality Rate
Reflects health care access and socioeconomic factors; it includes maternal deaths resulting from causes associated with puerperium (period after childbirth), eclampsia, and hemorrhage.

Crude Rates
Use crude rates with caution when comparing disease frequencies between populations.
Observed differences in crude rates may be the result of systematic factors (e.g., sex or age distributions) within the population rather than true variation in rates.

Specific Rates
Specific rates refer to a particular subgroup of the population defined in terms of race, age, sex, or single cause of death or illness.

Cause-Specific Rate
Cause-specific mortality rate (age group 25-34) due to HIV in 2003 = 1,588/39,872,598 = 4.0 per 100,000
Example:

Proportional Mortality Ratio (PMR) %

PMR (%) for HIV among the 25- to 34-year-old group = 1,588/41,300 = 3.8%
Indicates relative importance of a specific cause of death; not a measure of the risk of dying of a particular cause.
Example:

Age-Specific Rate (Ri)

Method for Calculation of Age-Specific Death Rates

Adjusted Rates
Summary measures of the rate of morbidity and mortality in a population in which statistical procedures have been applied to remove the effect of differences in composition of various populations.

Direct Method
The direct method may be used if age-specific death rates in a population to be standardized are known and a suitable standard population is available.

New Standard Population
Year 2000 population
Replaces the standard based on 1940 population
Results in age-adjusted death rates that are much larger
Affects trends in age-adjusted rates for certain causes of death
Narrows race differentials in age-adjusted death rates
Reduces the three different standards into one acceptable standard

Direct Method for Adjustment of Rates

Weighted Method for Direct Rate Adjustment

Indirect Method
Indirect method may be used if age-specific death rates of the population for standardization are unknown or unstable, for example, because the rates to be standardized are based on a small population.
The standardized mortality ratio (SMR) can be used to evaluate the results of the indirect method.

Standardized Mortality Ratio
(SMR)

Interpretation of SMR
If the observed and expected numbers are the same, the SMR would be 1.0, indicating that observed mortality is not unusual.
An SMR of 2.0 means that the death rate in the study population is two times greater than expected.

Indirect Age Adjustment (cont’d)
From previous table, SMR is (502/987.9) X 100 = 50.8%.

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