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Define Epidemiology (Gordis)

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THE STUDY OF HOW DISEASE IS DISTRIBUTED IN POPULATIONS (descriptive) AND THE FACTORS THAT DETERMINE THAT DISTRIBUTION (analytic).

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Define Epidemiology (Porta)

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THE STUDY OF THE OCCURRENCE AND DISTRIBUTION OF HEALTH RELATED STATES AND EVENTS IN SPECIFIED POPULATIONS, INCLUDING THE STUDY OF THE DETERMINANTS INFLUENCING SUCH STATES, AND THE APPLICATION OF THIS KNOWLEDGE TO CONTROL HEALTH PROBLEMS (Interventional)

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Unique skill of Epidemiologists?

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Measuring disease frequency

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Components of Measuring Disease Frequency

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1) Classification of Disease 2) Constitution of Case 3) Source for ascertaining cases 4) Define Population at Risk 5) Define period of Time of Risk 6) Obtain permission to study people 7) Measure disease frequency 8) Relate cases back to population and time at risk

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Descriptive Epidemiology

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Examining the distribution of a disease in a population, and observing the basic features of its distribution in terms of time, place, and person.

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Analytic Epidemiology

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Testing a specific hypothesis about the relationship of a disease to a putative cause, by conducting an epidemiologic study that relates the exposure of interest to the disease of interest

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Triad of Epidemiology

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Time, Place, and Person

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Examples of Time

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Examples: Changing or Stable? Seasonal Variations? Clustered (epidemic) or evenly distributed (endemic)? Point-source or propogated?

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Examples of Place

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Examples: Geographically restricted or widespread (pandemic)? Relation to water or food supply, or other source of environmental contamination? Multiple clusters or one?

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Examples of Person

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Examples: Age SES Gender Ethnicity/Race Behavior

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Relationship between Descriptive and Analytic Epidemiology

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-Descriptive epidemiology is a necessary antecedent of analytic epidemiology -To undertake an analytic epidemiologic study you must first understand what is known of the descriptive epidemiology of a condition. -Most national surveys (NHANES, NHIS) are examples of important descriptive epidemiology

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Define Outbreak

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-small, localized clusters of disease

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Define Epidemic

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-broader, more widespread increases in disease frequency

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Define Endemic

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-a disease which is present at a higher frequency in some area at all times. -Ex. In areas of iodine deficiency, goiter and cretinism are endemic.

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Define Pandemic

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-a disease which exists in epidemic proportions (i.e. a major increase over expectation) over much of the world. -Ex. Influenza each winter

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Eight Concepts in Outbreak Investigation

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1) Case Definition 2) Portal of Entry 3) Shape of Epidemic Curve 4) Secondary cases 5) Attack Rate 6) Incubation period 7) Spot Map 8) Herd immunity

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Case Definition

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-a decision must made as to what constitutes a "case" of disease, and how it should be operationally defined. -Must be usable in the field, but not require extensive investigation that cannot be applied under outbreak conditions.

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Portals of Entry

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Respiratory tract: inhalation (e.g. influenza, lung cancer) Gastrointestinal tract: ingestion (e.g. cholera) Blood: needlestick injury (e.g. hepatitis B,C) Urogenital tract: sexual contact (e.g. syphilis) manual contact (e.g. puerperal sepsis) - Skin and mucous membranes open wound or punctures (tetanus) mosquito bite (malaria) Eyes: gonorrheal conjunctivitis, vaccinia autoinoculation Placenta: congenital malaria

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Shape of Epidemic Curve

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-Point source/common source: most people affected are exposed to the cause at one point in time. -Propagated: the agent is transmitted person-to-person, and cases continue to emerge for some time later

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Secondary Cases

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-In propagated epidemics, the people first affected transmit the disease to other people, creating secondary cases --> if the curve continues to be propogated, tertiary cases and further are often identified. -In point-source epidemics, by definition, there are few secondary cases.

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Attack Rate

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-The proportion of a group EXPOSED to a presumed cause of an outbreak that experiences the outcome under study. -Ex. Imagine that 100 people attended a church supper and gastro-intestinal illness was reported among attendees. The investigators used two case definitions: Self-reported severe diarrhea (15 cases) Severe diarrhea + positive Salmonella stool culture (4 cases) Attack rates are 15% and 4% for the two definitions

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Incubation Period

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-The period of time from the moment the infectious agent enters the body until the first sign of sickness appears. -The moment of entry is often not known, so exposure to what appears to be the causal event or circumstance (e.g. the time of the church supper or the tick bite or drinking from the contaminated water fountain) is taken as the start of the incubation period.

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Spot Map

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It is sometimes useful to plot the location of cases on a map to see if any pattern emerges.

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Herd Immunity

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-The level of immunity of an entire population. Herd immunity reflects the proportion of the population who are immune. -If a high enough proportion of the population is immune, while individual case of disease may occur, successful transmission of disease is limited, and epidemics will not occur. -Vaccination can provide herd immunity if some 80% (differs for different agents) of the population is immunized, thus protecting the unimmunized to some extent also. -The likelihood of an outbreak is a function of herd immunity. Measles immunity has dropped sufficiently in the US for outbreaks to be possible in some places.

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Triad of Analytic Epidemiology

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Host, Agent, Environment

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Host Factors

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-Genetic endowment -Immunologic state -Age -Personal behavior

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Types of Agents

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-Nutrients -Poisons -Allergens -Radiation -Physical trauma -Microbes -Psychological experiences

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Environment

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-Crowding -Atmosphere -Herd Immunity -Forces that bring agent and host together

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Modes of Communication Explained

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-Epidemiologists recognize that all disease processes can be thought of as having three components - an agent of disease, a susceptible host, and an environment in which the two meet. To understand disease fully you must understand the ways in which the host and the agent are brought together by forces in the environment. In AIDS, for example, the agent is the HIV virus, and the susceptible host is virtually any human. But this is not enough. We must understand the mode of communication, which turns out to be patterns of sexual behavior, drug use and medical care practices.

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Modes of Communication Definitions

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-The sets of processes that bring host and agent together in the real world. -Risk factors for disease operate at different ecological levels - societal, neighborhood, individual, cellular. -The connections between risk factors for disease operating at different ecological levels. -Population-level mechanisms of disease.

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Vehicle Definition (Modes of Communication)

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-An inanimate object which serves to communicate disease. For example, a glass of water containing microbes, or a dirty rag, etc

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Vector Definition (Modes of Communication)

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-A live organism that serves to communicate disease. For example, mosquitoes and other arthropods.

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Reservoir Definition (Modes of Communication)

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-A location that serves as a continuing source of disease - for example, a water tower (common in legionella infections), the soil for tetanus, etc.)

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Host Definition (Modes of Communication)

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-A living animal or human that sustains the development of some stage of an infective agent. Term usually used for parasites.

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Definitive Host Definition (Modes of Communication)

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-The animal in which sexual stage or maturation of the parasite occurs (the mosquito in malaria)

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Intermediate Host Definition (Modes of Communication)

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-The animal in which the asexual stage of the parasite develops (the human in malaria)

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Why are Modes of Communication important?

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-Because only work in the field can uncover the way in which an agent links to a host in the real world outside of the laboratory -In disease prevention, knowing the mode of communication is generally more important than identifying the specific agent. -The MOC of AIDS (sexually transmitted and blood-borne) was known before HIV was discovered, permitting prevention activities, which were extremely important.

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Great Vector Revolution

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-1878 - 1911 -extended the findings of bacteriologists and provided information essential to disease control.

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Discoveries of Modes of Transmission and their Discoverers

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1) Direct Contact - 1848 - Semmelweis discovered that puerperal sepsis is transmitted manually from the autopsy room to the delivery room by doctors. 2) Fecal-Oral route - Water as Vehicle - 1849. John Snow published evidence that cholera is transmitted by the fecal oral route and by the water supply. William Budd discovered similar track for Typhoid Fever. 3) Asymptomatic Carrier State: Four diseases were discovered (between 1893 and 1905) to have an asymptomatic carrier state. -Diphtheria -Typhoid Fever -Meningococcus -Polio 4) Arthropod Vectors: Filariasis - mosquito Malaria * - mosquito Yellow Fever - mosquito (aedes sp.) Sleeping Sickness - tse-tse fly Plague - fleas Chagas disease - kissing bugs Typhus* - louse

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Great Impacts of Modes of Communication Discoveries (19th/20th Centuries)

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-Virtual elimination of puerperal sepsis via hand rinsing by doctors with chloride of lime. -Greatly lowered death rates from typhoid fever and cholera by 1900 in cities without sewage intake to water supplies. End of cholera epidemics in US and UK by 1860's, but not in Germany. -Reduction of yellow fever deaths from 1,500 to zero in one year in Havana following mosquito control measures. Mosquito eradication permitted construction of the Panama Canal by control of yellow fever and malaria -Understanding the role of lice permitted control of typhus, the great disease of armies, in WW I.

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Great Impacts of Modes of Communication Discoveries (since 1950's)

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-Smoking control measures reduce both lung cancer and heart disease (specific agent of lung cancer in smoking remains unknown) -Understanding of sexual behavior and blood borne transmission allows control of AIDS prior to virus identification -Tampon use identified as vehicle for toxic-shock syndrome before bacterial agent identified Positioning babies on their backs for sleep position reduces SIDS rates. -Changes in dietary patterns and behaviors have been associated with multiple health improvements

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Health States of Public Health Importance

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1) DEATH - Not whether, but when. Time is critical in epidemiology. 2) DISEASE - A cluster of signs, symptoms and laboratory findings linked by a common patho-physiologic sequence, and that cause human distress. 3) DISABILITY and HANDICAP - Difficulty in performing expected functions, especially those to ordinary human life, i.e. interfering with work, activities of daily living, etc. 4) DESTITUTION - The economic burden imposed by a health state. 5. DYSFUNCTION - Poor or subnormal performance on a test of some single or complex biological function. 6. DISCOMFORT - Uncomfortable symptoms such as dizziness, nausea, fatigue, but short of a specific disease.

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Disease Definition

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-Cluster of signs, symptoms and laboratory findings linked by a common patho-physiologic sequence. Most epidemiology is about disease.

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Illness Definition

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-Subjective state of the individual who feels aware of not being well. The ill individual may or may not be suffering from disease.

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Sickness Definition

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-Social role assumed by an individual suffering from an illness.

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Syndrome Defintion

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-When the signs and symptoms have not yet clearly been placed in a common pathophysiologic sequence it is referred to as a SYNDROME.

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Condition Definition

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-Diseases of a chronic nature are sometimes called CONDITIONS, rather than disease, especially if they are present since birth.

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Spectrum of Disease

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-The point at which disease comes to medical attention may be anywhere along a SPECTRUM. -Characteristically, population-based studies see a BROADER SPECTRUM OF DISEASE than do studies based on cases diagnosed in medical settings.

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TEMPORAL Spectrum of Disease

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1. EXPOSURE 2. SUBCLINICAL PATHOLOGIC CHANGES sub-clinical infection with antibodies some cancer screening (colon) Atheroma formation in arteries 3. FIRST SYMPTOMS OR SIGNS/MILD DISEASE some cancer screening (prostate, breast) Outpatient management 4. MORE SEVERE DISEASE In-patient management, especially in referral center 5. DEATH OR RECOVERY

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Spectrum of Disease SEVERITY

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-LARGELY INAPPARENT: Most people don't get sick. Often (not always) the setting for a carrier state. Examples: polio, chickenpox, tuberculosis, West Nile virus, some early cancers, hypertension -LARGELY CLINICAL: Most people who are exposed will get sick. Examples: measles, plague, heart attack, stroke. -LARGELY FATAL: Very Rare. Examples include rabies, possibly Lhasa fever.

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Classifications of Disease are usually...

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-Clinical manifestations (signs/symptoms linked to a common cause) - more useful for treatment and management. -Etiological (presumed cause) - more useful for prevention.

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Impairment Definition

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-Physical abnormality (e.g., arthritis)

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Disability Definition

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-Inability to perform a human function (activity limitation) (e.g., inability to use fingers for fine tasks)

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Handicap Definition

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-Inability to perform a social role (participation restriction) (e.g., cannot work as a piano player)

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Because epidemiology is interested in CASES OF DISEASE, it does not (usually) study the epidemiology of :

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-symptom inventories -abnormal laboratory tests -health behaviors -medical care **All of these can of course be exposures to examine in relation to disease. Major exceptions: where the test or behavior is closely linked to the disease, or a major risk factor for disease. Ex.'s: -SEROEPIDEMIOLOGY - the study of the prevalence of antibodies to a specific infectious agent. -Hypertension, smoking, hypercholesterolemia

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Because epidemiology is interested in cases of illness...

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-it is generally more interested in TAILS OF THE DISTRIBUTION than in the entire distribution. -Disease states are linked to the tails of the distribution of some normally distributed phenomena.

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Classifications of the Causes of Death

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IMMEDIATE -The last thing that went wrong UNDERLYING -The main disease process that led to death CONTRIBUTORY -Other things that might have contributed to the death

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Frequency - Numerator and Denominator

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Numerator: # of cases Denominator: N of population at risk -With very few exceptions, the numerator (cases of disease) for measures of disease frequency comes from the denominator; it is a part of the denominator.

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Frequency - Ratio

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-A fraction in which the numerator is not part of the denominator. -Ratios should be used sparingly, since they violate the general rule that the numerator should arise from the denominator.

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Frequency - proportion

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-A fraction in which the numerator is part of the denominator. e.g. Fetal death rate: Fetal deaths/all births **Synonyms for proportions are: a risk and, (if expressed per 100) a percentage.

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Frequency - Rate

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-A rate should refer to a proportion in which change over time is considered. -BUT, in public health practice, the term is used interchangeably with any kind of proportion, without reference to time, (as I did previously for fetal and infant death rates).

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Prevalence Definition

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-Current burden of disease in population of interest.

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Incidence Definition

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-Rate at which disease is entering a population of interest.

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Point Prevalence Definition and Example

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-The proportion of individuals in a specified population at risk who have the disease of interest at a given point in time. -Example : Assume we have a registry of everyone known to have tuberculosis in the City of Lansing. When anyone is diagnosed with TB, they are listed on the register. If they are cured, or die, they are taken off. -On September 25, 2015: The registry lists 115 people with TB. The population of Lansing is 115,000 The point prevalence rate of known TB on that date was 115/115,000 = 1 case per /1,000 population.

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Period Prevalence Definition and Examples

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-The proportion of individuals in a specified population at risk who have the disease of interest over a specified period of time. -For example: -annual prevalence rate. This counts anyone who had the disease of interest during the year of interest -lifetime prevalence rate. This counts all episodes of disease during the life of an individual to that point **(When the type of prevalence rate is not specified it is usually point prevalence, or its closest practical approximation)

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Annual Prevalence Rate Example

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-Between Jan 1, 2014 and December 31 2014, the Lansing TB register: Had 100 people who were on the register the entire year Had 12 newly diagnosed people with TB added during the year 6 people with TB who were removed because they died during the year 20 people with TB who were removed because they were cured during the year. -The annual prevalence rate of TB in Lansing was 100 + 12 + 6 + 20 = 138/115,000 = 1.2 cases/1,000 population. This figure is higher than point prevalence, because all people who had disease during any point in the year are counted

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Example of Lifetime Prevalence Rate

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On September 15, 2015, a random-sample telephone survey of adults is performed in Lansing (every adult in Lansing with a phone is at equal risk of being called) The question is asked: Have you ever been diagnosed with tuberculosis in your lifetime? Of 1,000 people responding, three answered yes. One had it as a child, the second in adolescence, and both recovered with treatment. The third was diagnosed in his seventies, and is now being treated. His doctors told him he had probably been infected with the TB bacillus for decades. The lifetime prevalence of TB in Lansing, based on this survey, is 3/1,000. Lifetime prevalence questions are commonly asked of diseases characterized by short attacks with recovery, such as asthma or epilepsy, where point prevalence would be a severe undercount of the burden of disease.

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Comparing Types of Prevalence Rates

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-In general, if a disease is stable in incidence and mortality, the prevalence increases as one goes from point prevalence, to annual prevalence, to life-time prevalence. -One important exception: If the disease has a high mortality, lifetime prevalence may be low. (REMEMBER cumulative incidence)

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Cumulative Incidence Rate Definition and Example

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-Number of NEW cases of disease occurring over a specified period of time per number of individuals at risk at the beginning of the interval. -Ex. If we count all new cases of influenza occurring in MSU undergraduates from September 1, 2013 - August 31, 2014, and we take as the denominator all undergraduates enrolled in September 1, 2013, we would be describing the cumulative incidence rate of influenza.

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Incidence Density Definition

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-The number of NEW cases of disease occurring over a specified period of time per number of individuals at risk over the ENTIRE interval.

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Comparing Types of Incidence Rates

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-The numerator does not differ between the two types of incidence, but the denominator can. -The denominator can differ in incidence density from cumulative incidence because only incidence density takes account of: -Students who left school during the year -Students who died -Students who had influenza once and will not have it again the same season, and thus are no longer at risk of getting influenza. -Students who entered school later in the year -Incidence density requires us, in calculating the size of the denominator, to add up the period of time each individual was present in the population, and was at risk of becoming a new case of disease, and remove people during the time they were not at risk of becoming a new case. -The cumulative incidence rate, by contrast, ignores all these changes and relies only on the population present at the beginning of the interval of observation. -Incidence density characteristically uses as the denominator person-years at risk. (Time period can be person-months, days, or even hours, depending on the disease process being studied.)

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Comparing Types of Incidence Rates 2

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-Both forms of incidence include the concept of time, but expressed differently. In incidence density, time at risk is estimated for each individual, instead of assuming a constant risk of exposure for all individuals as in cumulative incidence. -Incidence Density gives best estimate of risk AT ANY TIME. -Cumulative Incidence gives best estimate of HOW MANY PPL WILL EVENTUALLY GET DISEASE.

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Difference between lifetime incidence and lifetime prevalence

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-The difference is that cumulative incidence begins its observation in the beginning, whereas lifetime prevalence typically starts at the end and works back. Anyone who died or was otherwise missing from the survey is unknown in lifetime prevalence. (Weakness = lifetime prevalence)

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Relationship between Incidence and Prevalence

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-In a STEADY STATE (i.e. if incidence is not changing, and the population is stable), the following formula holds Prevalence rate = incidence rate times the duration of disease -(P = I x D)

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Six Differences between Incidence and Prevalence

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1) Incidence is generally used for acutely acquired diseases, while prevalence is used for more permanent states, conditions or attributes of ill-health. 2) Incidence is more important when thinking of etiology of the disorder, prevalence when thinking of societal burden of the disorder including the costs and resources consumed as a result of the disorder. 3) Incidence always requires a duration of time, prevalence may or may not. 4) In incidence, the unit of analysis is the event. Thus incidence may exceed 100% (e.g. annual incidence of colds) unless a convention is adopted to count only first episodes of an illness that can occur more than once. 5) Prevalence can never exceed 100%, because, unlike incidence, the unit of analysis is the person. 6) Incidence generally requires an initial disease-free interval before counting starts, because incidence is measured only in those at-risk of disease.

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Nigel's Final Thoughts on Incidence and Prevalence

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-Prevalence estimates the BURDEN of surviving disease in the population -Incidence estimates the FORCE of disease entering the population -Our perception of disease frequency is more often more attuned to prevalence than to incidence -As survival from a disease increases, we see more people with the disease and may mistakenly assume that the force of disease has increased -We tend to be more conscious of prevalence than incidence, so duration of disease can affect our perception of how frequent the disease is. -Prior to the first chemotherapy for childhood leukemia (1947), survival averaged only a few weeks from diagnosis. Five year survival is now about 90%, so we see many more children with leukemia, even though incidence has changed little or not at all.

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All-cause/Crude Mortality Rate

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-Total deaths in a year/Estimate of N of people alive during that year* *Often referred to as the mid-point population -Usually denominatored to 1,000 -Numerator is usually from death certificates -Denominator is usually from census

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Cause-specific Mortality Rate

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-Annual Deaths from a specific cause/mid-point of population of interest -Usually denominatored to 100,000 because individual causes of death are rarer than all causes together

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Stratum-Specific Mortality Rate

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-A mortality rate in a specific segment of the population, such as 55-60 year olds (age-specific), or in men (sex-specific) or in a population group (e.g. hispanic mortality rates) any other stratum of the population.

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Case-fatality Rate

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-Deaths from a specific disease/# of cases of that disease. -Note that time is undefined, because this measure is generally used when mortality occurs only during a fixed period of time, as with acute infections. *This mortality rate is not a strict incidence rate

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Proportionate Mortality Rate

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Deaths from a Specific Cause/Deaths from ALL CAUSES -Note that this is a potentially misleading rate. Use with care, if at all. -Almost all autopsy series base conclusions on proportionate mortality rate. -As a proportion, with deaths in both the numerator and the denominator, and no source population in the denominator, the proportionate mortality rate is neither an incidence rate nor a prevalence rate.

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Survival Rate (5-year survival rate)

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Number of people alive after 5 years/Number of people alive at beginning of the interval -Commonly used in chronic diseases such as cancer, where mortality may be spread out over several years. -Usually disease-specific. -Any interval can be used, 10 years also fairly common.

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Standardized/Adjusted Rate

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-A rate which differs from a crude rate in having been standardized to a different population (usually to a standard population) to remove the influence of some extraneous variable, most commonly age.

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Years of Potential Life Lost (YPLL)

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-The number of deaths due to a cause of disease multiplied by the difference in years between age at death and a certain target age. -For example, if the target age is 70, then: 100 deaths at age 60 = 1,000 YPLL 50 deaths at age 50 = 1,000 YPLL 20 deaths at age 20 = 1,000 YPLL YPLL's prioritize age at death, counting younger deaths more heavily. Thus, YPLL's are very sensitive to infant mortality

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Disability Adjusted Life Years (DALYs)

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-DALY adds a measure of disability to YPLL. If we know the incidence of disabilities, and can score their severity, we can roughly quantify the amount of years lost, not just to death, but to disabling conditions as well. -For example, a person surviving after a stroke with paralysis and aphasia (unable to speak) might have a quality of life of 0.5 (death = 0, perfect health = 1). The surviving years of the individual with the disabling condition are counted as half as many as if they had been completely healthy.

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Calculating DALYs

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If the target age is 70, then: - 100 deaths at age 60 with each year counted as 1 DALY lost = 100 deaths x 10 years= 1,000 DALY's -20 people with stroke at age 60 who did not die, with each post-stroke year counted as ½ DALY lost = 20 people x 10 (½ years) = 100 DALYS -This population has experienced 1,100 DALYS

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Risk vs. Odds

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Risk: The chances of something happening/the chances of ALL THINGS happening Odds: The chances of something happening/the chances of THAT SOMETHING not happening *Odds is special type of ratio in that numerator and denominator sum up to 1.

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Relative Risk (Risk Ratio)

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-The relative risk is a ratio of two risks. -Assume that among the 100 people at risk, 50 are men and 50 women. If 15 men and 5 women develop influenza, then the relative risk of developing influenza in men, as compared with women, is: Risk in men = 15/50 divided by Risk in women = 5/50 Relative risk is 15/50 ÷ 5/50 = 3.0

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Odds Ratio

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-The odds ratio is a ratio of two odds -The odds in men = 15/35 divided by -The odds in women = 5/45 -The odds ratio is 15/35 ÷ 5/45 = 3.9 -We conclude that the odds of men getting influenza over the year are 3.9 times higher than the odds of women getting influenza.

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Rare Disease Assumption

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-We often say in epidemiology that if the incidence of disease in the population we are studying is less than 20%, the "rare disease" assumption holds, and the OR is then a pretty good approximation of the RR. -Why do we need such an approximation? -Because the world tends to think in terms of risks, not odds, and can be confused by odds, as I will show you shortly

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When calculating Relative Risks, be sure that...

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the incidences expressed in the question are the SAME! (e.g., cumulative incidence rates OR incidence densities)

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How do you express Incidences as Odds?

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-Express the incidence as cases/non-cases rather than cases/(cases + non-cases) -We can term cases/non-cases as the odds of disease, as contrasted to the risk of disease

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In Cohort and Cross-sectional studies...

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-Either OR's or RR's are calculable (RR's are traditionally used.)

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In Case-control studies...

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-which begin from cases, and where exposure is thus not available on the total population, only an OR can be calculated. -I.e., Definition of the Odds Ratio: OR = Oe (odds of disease in the exposed) ---------------------------------------------- Ou (odds of disease in the unexposed)

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Concept of Exposure

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-When we think about comparing risks or rates, we are usually comparing risks of disease in two groups. In the previous slides the two groups were men and women. -More commonly, we are interested in some factor that we think may elevate risk of disease. -We refer to these factors as "exposures", and we are generally comparing "exposed" and "unexposed" groups of people. -Examples of exposures - smoking, high blood pressure, having contact with an Ebola patient

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Attributable Risk (AR) (Etiologic Fraction)

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-The incidence of disease in the exposed population whose disease can be attributed to the exposure. AR = Ie - Iu -For example, if smokers have a risk of ischemic heart disease death of 180/100,000, and the non-smoker rate is 100/100,000, then we might be able to attribute (if the association is fully causal) 80 deaths/100,000 to smoking in smokers

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Attributable Risk Fraction (ARF)

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-The proportion of disease in the exposed population whose disease can be attributed to the exposure. -ARF = (Ie - Iu)/Ie -In the previous example, the ARF would be: 180 - 100 ----------- = 44% 180 This ARF indicates, that if the relationship between smoking and ischemic heart disease is causal, and these mortality rates were accurate, that 44% of all IHD deaths in smokers are because of their smoking.

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Population Attributable Risk (PAR)

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-The incidence of disease in the total population whose disease can be attributed to the exposure. -PAR = Ip - Iu For example, if the total population has risk of ischemic heart disease death of 125/100,000, and the non-smoker rate is 100/100,000, then we might be able to attribute (if the association is fully causal) 25 deaths/100,000 to smoking in the population.

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Population Attributable Risk Fraction (PARF)

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-The proportion of disease in the total population whose disease can be attributed to the exposure. -PARF = (Ip - Iu)/Ip In the previous example, the ARF would be: 125 - 100 ------------- = 20% 125 This ARF indicates, that if the relationship between smoking and ischemic heart disease is causal, and these mortality rates are accurate, that 20% of all IHD deaths in the population are because of smoking.

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Inferring an ARF from a Relative Risk

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-Note that Ie = Iu times the relative risk (RR) -So substituting Iu x RR for Ie in the equation for attributable risk fraction: -(Ie - Iu)/Ie -If you divide this through by Iu, you get ARF = RR - 1 --------- RR

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Standardization Definition

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-The major method for adjusting for a confounding variable (age, usually) in use in public health.

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Purpose of Standardization Explained

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-Standardization (synonym: adjustment) is used to remove the effect of an unwanted variable, most often age, from a comparison between two populations. -To do this, the variable to be standardized is made the same for the two populations being compared. -This is done by making use of a standard population. -The standard population provides either its age distribution or its mortality rates by age stratum (referred to as the age-specific mortality rates) to serve as the basis for standardization.

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Direct Standardization Use

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-uses the age distribution of the standard population, AND, -is used whenever stable stratum-specific rates are available

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Indirect Standardization Use

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-uses the age-specific mortality rates of the standard population, AND, -is used when stable stratum-specific rates are not available or are not stable (because of small sample sizes)

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C=

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-crude rate for population being standardized

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Ci=

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-stratum-specific rate for the population being standardized.

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Cs=

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-crude rate for the standard population.

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Csi=

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-stratum-specific rate for the standard population.

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Pi=

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-Stratum-specific proportion in the population being standardized.

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Psi=

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-Stratum-specific proportion in the standard population

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Directly Standardized Mortality Rate Definition

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-The sum of the product of stratum-specific mortality rates in a specific population being standardized and the proportional representation of those strata in a standard population. -Formula: i CDIRECT = Sigma (Ci x Psi) 0

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Indirectly Standardized Mortality Rate Definition

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-The sum of the product of stratum-specific mortality rates in a standard population and the proportional representation of those strata in the population being standardized is used to produce expected deaths. -The actual deaths in the population being standardized are divided by the expected deaths to produce the standardized mortality ratio. Formula: Step 1: i ED = Sigma (Csi x Pi ) x 1,000 0 Step 2: Divide the actual deaths by the expected deaths (ED) to obtain the standardized mortality ratio (SMR). SMR = actual deaths/expected deaths

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Reliability

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-how well an observer classifies the same individual under different circumstances.

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Validity

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-how well a given test reflects another test of known greater accuracy.

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Difference between Reliability and Validity

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Reliability includes: -assessments of the same observer at different times - INTRA-OBSERVER RELIABILITY -assessments of different observers at the same time - INTER-OBSERVER RELIABILITY **Reliability assumes that all tests or observers are equal. Validity, in contrast, assumes that that they are unequal, that there is a gold standard to which a test or observer should be compared.

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Assessing Reliability Methods

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-One way is to look simply at PERCENT AGREEMENT. -For example, we might want to see what percent of all diagnoses are classified the same way by two doctors.

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Cohen's Kappa

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-is a widely used test of inter- or intra-observer agreement (or reliability) which corrects for chance agreement.

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Kappa values explained

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+ 1 means that the two observers are perfectly reliable. They classify everyone exactly the same way. 0 means there is no relationship at all between the two observer's classifications, above the agreement that would be expected by chance. - 1 means the two observers classify exactly the opposite of each other. If one observer says yes, the other always says no.

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Good and Bad Kappas

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-Kappa > .80 is considered excellent -Kappa .60 - .79 is considered good -Kappa .41 - .60 is considered fair -Kappa < .40 is considered poor

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All EPI data describing risk of disease or mortality over a period of time in people of varying ages represent one or more of three effects...

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1) age 2) period 3) cohort

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Age Effects

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-Patterns of disease frequency which are due to the aging of the population. E.g., - Increasing rates of coronary artery disease by age.

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Period Effects

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-Patterns of disease frequency which are due to the period of time in which the people live. E.g., - Seasonal: elevated death rates from pneumonia and influenza in the winter Episodic: elevated death rates during epidemics, hurricanes, wars, famines, etc.

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Cohort Effects

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-Patterns of disease frequency due to an exposure occurring to a group of people at about the same time in their lives (and who are therefore of the same age, and, who, as they age, represent a cohort) E.g., - -Birth cohort - all people born in 1900 -Veteran cohort - all men (of similar ages)who served in the military together -Employment cohort - all individuals (of similar ages) hired by a company at about the same time **Cohort effects are the most subtle and easily overlooked of the three effects.

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Detecting Cohort Effects

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-Cohort effects are a special interaction of age and period. The three effects (age, period, cohort) are interrelated. - -ONCE TWO ARE SPECIFIED THE THIRD IS DETERMINED BY THE OTHER TWO -Cohort effects are often hard to detect because analysis is usually by age and period. -Cohort effects are usually seen as an elevated risk which CHANGES WITH AGE OVER TIME.

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Disease with Cohort Effects

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-Peptic Ulcers -TB -Congenital Rubella

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