Introduction to Epidemiology

• Disease isn't random; patterns reflect underlying factors.
• Epidemiology studies disease patterns to understand causes and prevention.

Today’s Objectives

• Understand basic epidemiologic concepts
• Learn the historical foundations of epidemiology
• Study the changes in human morbidity and mortality over time
• Learn methods of epidemiological research including study design
• Use 2 X 2 tables to calculate risk and in measuring screening test accuracy
• Understand the terms Reliability and Validity
• Establish a causal relationship
• Establish a vision for future career in the discipline

Lecture Outline

• Epidemiology Defined
• Historical Considerations
• Descriptive and Analytic Epidemiology
• Measurement of Morbidity and Mortality
• Screening and Prevention
• Study Designs and Measures of Association
• Causal Relationships and Measuring Evidence
• What Does the Future Hold?

Epidemiology Defined

• The classical definition of Greek origin: Epi means upon, Domos means the people, and Ology means the study of, therefore it means "the study of epidemics"
• Seven Uses of Epidemiology:
• Study the history of the health of the population
• Diagnose the health of the community
• Study the working of health services
• Estimate individual risks from group experience
• Identify syndromes
• Complete the clinical picture of chronic disease
• Search for causes

Epidemiology and Medicine

• Epidemiology looks at the population.
• Medicine looks at the individual.
• Epidemiology is becoming more "medicalized"
• Medicine is increasingly looking to epidemiologic principles of study design and population-based focus.

Historical Considerations

• Prior to the Industrial Revolution (400 BC – 1600), Hippocrates dispelled demons as the cause of disease and injury.
• During the age of Enlightenment and the Industrial Revolution:
• John Graunt counted births, deaths, and people.
• Graunt designed the first life table for residents surviving at a certain age
• John Snow (father of epidemiology) proposed the Waterborne Theory to postulate why people were getting sick from a specific well in London.
• Snow's methods represent modern foundations of epidemiological study;
  • Comparing cholera rates by neighborhood (ecological studies)
  • Comparing disease rates in exposed and unexposed persons (cohort studies)
  • Comparing water sources in infected and uninfected persons (case/control studies)

The Transition of Epidemiology and the 20th Century

• Health departments in the 1700s US responded to cholera outbreaks.
• The 20th century brought a refocus of causes of mortality from acute and contagious to chronic and lifestyle-related morbidity.
• In 1900 people died from TB, influenza, diarrhea and cholera.
• In 1990 people succumbed to heart failure, COPD, malignancies and stroke.
• Public Health Initiatives caused the transition in Mortality:
• Antibiotics – Probiotics
• Birth Control
• Prenatal and Neonatal care
• Improved nutrition – especially newborn nutrition
• Sanitation
• Improved standard of living

Descriptive and Analytical Concepts

• Descriptive: Who, what, when, and where.
• Who: age, gender, sociodemographics, occupation
• What: disease, injury, death
• When: time, seasonality, secular trends
• Where: place, neighborhood, city, county, census tract
• Distribution: frequency of the event and pattern of the frequency.
• Analytical: determinants of disease; understand factors and ‘how’ and ‘why’ aspects.

Analytical Concepts

• The epidemiologist formulates a hypothesis, e.g. the salmonella contamination originated from fecal contamination
• Mill's Canons of inductive reasoning are:
• The method of difference: 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 different settings.
• The method of concomitant variation: disease frequency varies according to the potency of a factor and the linked suggest that it is the causative agent for the disease.
• The method of residues: subtracting causal factors to determine which individual factor or set makes the greatest impact on a dependent variable.

Measures of Morbidity and Mortality

• Objectives:
• Examine host - disease relationships and disease transmission modes
• Systematically investigate an epidemic outbreak
• Define and differentiate incidence and prevalence
• Measure key indices of morbidity and mortality
• Epidemiologic Triad of Disease consists of:
• Agent (Plasmodium vivax)
• Host (humans)
• Vector (mosquito)
• Environment (swamps, standing water)

Public Health Surveillance

• Applications
• Estimate the magnitude of the problem
• Determine the geographic distribution of illness
• Portray the nature history of disease
• Detect epidemics / define a problem
• Generate hypothesis – stimulate research
• Evaluate control measures
• Monitor changes in infectious agents
• Detect changes in health practice
• Facilitate planning

Outbreak Investigation

• Steps to define the Epidemic/Outbreak:
• Numerator (cases) What is the disease, Serological characteristics, Known causes
• Denominator (population at risk)
• Calculate Incubation period
• Calculate attack rates
• Examine distribution: Time and place
• Identify relevant variables or their combination
• Develop hypothesis
• Test hypothesis
• Recommend control measures

Key Terms

• Attack Rate = exposed persons who ate spinach and got sick divided by exposed persons who ate spinach and did not get sick
• Incidence Rate = # of new cases of disease occurring during a specific period divided by # of persons at risk of developing the disease during that same period
• Prevalence Rate = # of cases of disease occurring during a specific period divided by # of persons in the population at that same period
• Prevalence = Incidence X Duration of Disease

Concepts of Mortality

• Selected Mortality Indicators:
• Crude death rate
• Cause-specific mortality rate
• Infant mortality - typically a benchmark of the health of a country
• Neonatal mortality – used by hospitals to compare their OB service
• Case Fatality Rate
• Crude death Rate (annual mortality rate, all causes) = number of deaths X 1000 divided by number of persons at mid-year

Case Fatality Rate

• Case Fatality Rate = # of deaths from a specific disease (mortality) / # of persons with a specific disease (morbidity)
• For example: 600 people have skin cancer; 9 of them die from the cancer
• Case fatality rate = (9/600) X 100% = 1.5%

Screening & Prevention Objectives

• Define screening and levels of prevention
• Measure reliability
• Measure validity

Screening & Prevention

• Process of classifying people by the likelihood that they have a disease.
• Primary prevention of disease is the best approach.
• Several levels of prevention:
  • Primary: prevent new cases of a disease from developing in the population (no smoking campaigns, sun blocks, prophylactics for STDs).
  • Secondary: reduce the number of existing cases of a disease including cancer screenings – mammography, colonoscopy)
  • Tertiary: limit the disability resulting from disease and improve functioning (cardiac rehab, PT, OT)

Levels of Prevention

• Prevention Strategy:
  • Primary Prevention:
  • Secondary Prevention:
  • Tertiary Prevention
• Population's Disease Status:
  • Susceptible
  • Asymptomatic
  • Symptomatic
• Effects:
  • Reduced Disease incidence
  • Reduced Prevalence/consequence
  • Reduced Complications disability

Screening Tests

• When are screening tests appropriate (BEFORE symptoms develop):
• The disease is an important cause of morbidity and mortality
• Treatment is available
• The impact of the disease can be minimized before symptoms develop
• Prevalence of preclinical disease is high
• What is a good screening test?
• Easy to administer (CRP, ABI, BNP)
• Results readily available (automated lab reporting)
• Test is inexpensive (ABI < $40)
• Test imposes minimal discomfort (finger stick vs phlebotomy)

Screening Tests – Key Factors

• Screening tests must be:
• Reliable – consistently gives the same results
• Valid - able to distinguish between who has and who does not have the disease
• Validity considerations:
  • Sensitivity – correctly ID those with the disease
  • Specificity - correctly ID those who do not have the disease
  • Positive predictive value – proportion of individuals screened who actually have the condition.
  • Negative predictive value – portion of individuals screened without the disease.
• Check the validity by using how are screening tests “tested" via the 2 X 2 table.

2 X 2 Table

• The 2 X 2 table’s components are:
• True positive
• False positive
• False negative
• True negative
• It allows the calculation of:
• sensitivity: a/(a+c)
• specificity: d/(b+d)
• (+) predictive value of a positive test: a/(a+b)
• (-) predictive value of a negative test: d/(c+d)

Study Design & Measures of Association Objectives

• Examine different epidemiologic study designs
• Evaluate measures of association using different designs

Study Design

• Epidemiologists conduct studies by observational and experimental approaches.
• Observational = researcher observes association between exposure and outcome without controlling conditions.
• Experimental = researcher controls research conditions including:
• Who gets exposed
• Randomization of subjects (exposed, not exposed)
• Evaluation and follow-up
• Example: statins and lipid disease

Descriptive Study Design

• Touched on descriptive (who, what, when) vs. analytic epidemiology (determinants).
• Descriptive study designs generate a hypothesis:
• Case reports
• Case series
• Cross - sectional studies
• Ecologic studies
• Case - control studies
• Retrospective studies
• Two types of analytical studies test a hypothesis:
• Observational Cohort (Framingham Heart), Case control
• Experimental Randomized controlled trial Community interventions

Study Design Validity

• Experimental ones are the most valid.
• Validity from most to least:
  • Experimental studies (controlled/randomized trials and community trials)
  • Prospective Cohort study
  • Retrospective Cohort study
  • Case - control study
  • Time series study
  • Cross-sectional study
  • Ecologic study
  • Case (observational) study
  • Anecdotal

Goal of Trials

• Goal is to measure risk of an event or exposure on a defined group of individuals What is the risk of hemorrhagic stroke from exposure to diet supplements
• Risk is the probability of an event occurring
• Absolute risk equals incidence of disease
• Excess risk = increase in incidence due to exposure (brain damage due to lead paint)
• Attributable risk: the amount of incidence due to exposure (to lead paint)
• Measure risk by:
  • Cohort study: risk ratio (relative risk) incidence of exposed / incidence of non exposed Using the 2 X 2 table, a/(a+b) / c/(c+d)
  • Case / control study: odds ratio Using the 2 X 2 table: (a/c) / (b/d)
  • If RR < 1: protection > risk, > 1: risk in exposed > but is this causal?, =1: no association
  • If OR < 1: protective effect, > 1: exposure to cases > than controls, = 1: no association

Study Flaws

• Potential flaws in study design that affect results:
  • Type I and II errors: relates to the acceptance or failure to reject the null due to chance.
  • Random error: deviation of results and inferences from the truth, occurring only as a result from the operation of chance.
  • Confounding: a unique feature of the subjects has not been recognized and measured in results
  • Bias (systematic):
• Recall bias
• Interview bias
• Selection bias
• Family bias
• Halo effect

Study Designs

• Study designs are classified as:
• Descriptive: communicates & documents experience.
• Case reports, Case series, Ecological studies
• Experimental: Investigator controls allocation, evaluates efficacy of therapy or a process
• Randomized Clinical trials
• Observational: Investigator observes nature, seeks causes, predictors
• Cross-sectional studies, Case-control studies, Cohort studies

Causal Relationships

• Objectives:
• Evaluate the effect relationship between a risk factor & disease
• Explore a tool to evaluate interventions
• Criteria for evaluating the effect relationship, by Austin Bradford Hill in 1965:
• Temporal relationship - studying lipid disease requires time for lipid deposits to form
• Strength of association - the stronger the association the less likely the error
• Dose response relationship - the longer the exposure to radiation, the higher the risk of cancer
• Replication of the findings - has the association been observed by other researchers
• Biologic plausibility - given the knowledge of the day, is the conclusion valid
• Experiment - does a natural experiment support the relationship
• Specificity of the association - the more specific the association, the tighter the conclusion
• Consistency with other knowledge - is the cause effect relationship consistent with other studies.

The Future of the Field

• The Bureau of Labor Statistics estimates a 34 % growth rate for epidemiologists thru 2014.
• Worldwide disease outbreaks are expected to increase due to urban density, poverty and human – animal cohabitation.
• Aging in the United States results in significant societal changes.
• The Census Department estimates a net 7.6% drop in age cohorts 0 – 44 while cohorts >65 have + 56 million more people
• Hospitals and traditional healthcare delivery models look to public health for solutions to chronic care management in disparate populations.