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177 Cards in this Set
- Front
- Back
Core Functions of Public Health
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Assessment
Policy Development Assurance |
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Assessment
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To regularly and systematically collect,
assemble, analyze, and make available information on the health of the community, including statistics on health status, community health needs, and epidemiologic and other studies of health problems |
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Policy Development
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To serve the public interest in the development
of comprehensive public health policies by promoting the use of the scientific knowledge base in decision-making about public health and by leading in the developing public health policy |
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Assurance
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To assure their constituents that services
necessary to achieve agreed upon goals are provided, either by encouraging actions by other entities (private or public), by requiring such action through regulation, or by providing services directly |
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Primary Prevention
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Strategies, tactics, and procedures
that prevent the occurrence of disease in the first place Examples: safe drinking water, vaccinations |
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Secondary Prevention
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Strategies, tactics, and procedures,
including screening tests, that detect disease as early as possible so that its progress can be arrested and, if possible, the disease eradicated Examples: Pap test for cervical cancer, HIV test |
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Tertiary Prevention
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Strategies, tactics, and procedures,
including interventions, that aim to arrest the progress of established disease Examples: chemotherapy for colorectal cancer, stroke rehabilitation program |
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What is Epidemiology?
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The STUDY of the DISTRIBUTION and DETERMINANTS
of HEALTH-RELATED STATES OR EVENTS in specified populations, and the application of this study to control health problems |
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“Study” includes:
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surveillance
observation hypothesis testing analytic research experiments |
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"Distribution” refers to analysis by:
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• time
• place • characteristics of persons affected |
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“Determinants” refer to:
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• physical factors
• biological factors • social factors • cultural factors • behavioral factors that influence health |
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“Health-related states or events” include:
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• diseases
• causes of death • behaviors (e.g., tobacco use) • reactions to preventive regimens • provision and use of health services |
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Epidemiology as a Tool
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Premised upon the assumptions…
• Patterns of disease result from specific causes, and • Through an orderly sequence of reasoning those disease patterns can be elucidated, THEN, once elucidated, this information can be used to prevent disease and promote health. |
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Epidemiology as a Liberal Art
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• Because epidemiology is useful for taking a first
look at a new problem, it is applicable to a broad range of interesting phenomena. • Epidemiology illustrates the approaches to problems and the kinds of thinking that a liberal education should cultivate: - the scientific method - analogic thinking - deductive reasoning - problem solving within constraints |
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Objectives of Epidemiology
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1. To identify the etiology or cause of a disease and
the relevant risk factors 2. To determine the extent of disease occurring in the community 3. To study the natural history and prognosis of disease 4. To evaluate both existing and new preventive and therapeutic measures and modes of health care delivery 5. To provide the foundation for developing policy regarding disease prevention and health promotion |
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10 leading causes of death and
frequency of deaths: US, 2008 |
1. Diseases of heart
2. Malignant neoplasms (i.e., cancer) 3. Chronic lower respiratory diseases 4. Cerebrovascular diseases (i.e., stroke) 5. Unintentional injuries 6. Alzheimer's disease 7. Diabetes mellitus 8. Influenza and pneumonia 9. Nephritis, nephrotic syndrome, and nephrosis (i.e., kidney disease) 10. Suicide |
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Defining a Population
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Person (Who is getting the disease?)
e.g., age, sex, race/ethnicity, job, SES Place (Where is it occurring?) e.g., geopolitical (country, state), natural geographic features, environment (physical, biologic, social) Time (How is it changing over time? e.g., calendar year, time in the life course, can range from hours to decades |
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Why is the population is important?
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Variations in disease by person, place, and
time provide useful information! Understand health status of population Formulate hypotheses regarding disease Plan, implement, evaluation public health programs to control and prevent disease |
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Definition of population
is related to question at hand |
Political entities – e.g., country, state, city
Geographic areas – e.g., specific neighborhoods, living within 10 miles of a power plant Health service catchment population – e.g., Medicare enrollees, patients at a federally qualified health center (FQHC) Occupational group – e.g., firefighters Exposure group – e.g., persons taking Vioxx |
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Population “At Risk”
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• Some measures in epidemiology focus on new
cases of a disease, so denominators for these measures should exclude people who cannot develop the disease. • In those cases, the denominator should only consist of people who are “at risk” for the disease. |
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Those who are not at risk include…
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– people who currently have the disease
– people who lack the organ defining the disease – people who are immune to the disease |
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Dynamic (i.e., open) population
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– membership is based on a condition and is transitory
– e.g., Population of the US in 2012 |
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Fixed (i.e., closed) population
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– membership based on an event and is permanent
– e.g. Persons living in Chernobyl on April 25, 1986; persons on a cruise ship during an outbreak of norovirus |
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Estimating the size of
the population: dynamic population |
Dynamic population: You usually assume “steady
state” conditions apply for short intervals; the number leaving are balanced by the number entering, e.g., use midpoint estimate |
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Estimating the size of
the population: fixed population |
You take account of “persontime” (e.g., person-years) under observation
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Person-Time” at Risk
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In studies of closed (i.e., fixed)
populations, it is often important to know not only who was at risk, but also how long each person was at risk. |
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Heterogeneity within a population
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A population is a group of people who share a
common characteristic In reality, every population is heterogeneous with regard to other characteristics We often specify subgroups of a population and treat each subgroup as being homogeneous |
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Subgroups of a population
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However, even subgroups are heterogeneous
and made up of further subgroups How far we go in sub-grouping depends on… –Scientific justification –Demographic, social, political issues –Size of subgroups –Availability of data |
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comparing populations
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Inherently, statistics about populations
are weighted averages over all of the subgroups making up the population, and this become especially important when comparing populations |
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Crude rates
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refer to rates calculated for the
entire population of interest without regard to different subgroups of the population |
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Specific rates
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are those calculated for
designated subgroups or strata of the population |
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Populations differ by characteristics
that affect morbidity and mortality |
• Differences in disease rates may be due to
differences in distribution of characteristics • Populations at two time periods may differ in distribution of these characteristics • Adjustment techniques equalize distributions when comparing different populations |
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"Secular trends” or long-term variation
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Changes in disease occurrence over a
period longer than a year |
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Cyclic changes or periodic fluctuations
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Recurrent alterations in the frequency of
disease (e.g., seasonal variation) |
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Characterization of relative disease burden
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Endemic vs. Epidemic vs. Pandemic
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Seasonal Variation
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Many disease cycles occur annually and represent
seasonal variation in disease occurrence Seasonal variation is a well-known characteristic for many infectious diseases and is usually based on… - characteristics of the infectious agent itself, - the life pattern of the vector or other animal hosts, and/or - changes in the likelihood of person-to-person spread. |
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Endemic
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The constant presence of a disease within a
given geographical area or population group May also refer to the usual prevalence of a given disease within such an area or group |
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Epidemic
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The occurrence in a community or region of
cases of an illness, specific health-related behavior, or other health-related events clearly in excess of normal expectancy Relative to usual frequency of the disease |
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Pandemic
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An epidemic occurring worldwide, or
over a very wide area, crossing international boundaries, and usually affecting a large number of people |
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Demographic Concepts
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• Population census
• Demographic equation • Epidemiologic transition model • Demographic transition model • Population pyramid |
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Uses of Censuses in the US
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Government and administrative matters
- Apportionment of Representatives to states - Drawing of Congressional districts - Allocation of funding Science - Demographic trends (e.g., population growth, urbanization, changes in population structure) - Current and future trends in occupations, standard of living, education, economic and business cycles - Sampling frames |
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Demographic Equation
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Natural change in population size =
Births - Deaths ± Migration |
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The Epidemiologic Transition
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Theory focuses on…
• The complex change in patterns of health and disease • The interactions between these patterns and their demographic, economic and sociologic determinants and consequences Parallels demographic and technologic transitions observed in developed and developing countries |
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Ages in the Epidemiologic Transition
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The Age of Pestilence and Famine
The Age of Receding Pandemics The Age of Degenerative and Man-Made Diseases |
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The Age of Pestilence and Famine
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Mortality is high
and fluctuating, low and variable life expectancy (range 20-40 years) |
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The Age of Receding Pandemics
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Mortality declines
with fewer epidemics, life expectancy increases (from 30 to 50 years), population growth is sustained and begins to be exponential |
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The Age of Degenerative and Man-Made Diseases
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Mortality continues to decline and approaches
stability at low level, chronic diseases replace infectious diseases as the primary causes of death |
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Determinants of Transitions
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• Complex balance between disease agents, the
level of hostility in the environment and the resistance of the host. • Socioeconomic, political and cultural determinants include standards of living, health habits and hygiene and nutrition. • Medical and public health determinants are specific preventive and curative measures used to combat disease; they include improved public sanitation, immunization and the development of decisive therapies. |
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Leading Causes of Death in the US,
1900 & 2008 |
1900
1. Pneumonia & influenza 2. Tuberculosis 3. Diarrhea 4. Heart disease 5. Vascular lesions of CNS 6. Chronic nephritis 7. Accidents 8. Malignant neoplasms 9. Diseases of early infancy 10. Diphtheria 2008 1. Heart disease 2. Malignant neoplasms 3. Chronic lower resp. diseases 4. Cerebrovascular disease 5. Injuries 6. Alzheimer’s disease 7. Diabetes 8. Pneumonia & Influenza 9. Chronic nephritis 10. Suicide |
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Infectious versus Chronic Diseases
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Infectious disease > communicable
• Microorganism (e.g., virus, bacteria) • Examples: tuberculosis malaria measles Chronic disease > non-communicable • Usually no organism • Examples: cancer heart disease |
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The Demographic Transition Model
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Pre-industrial → industrialized economic system
Shift from high to low levels of mortality and fertility Age structure changes younger → older Population pyramid changes shape triangular → more rectangular |
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The Population Pyramid
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Graphical summary of the age and sex
composition of a population Useful tool – Important source of clues for • Health status • Causes of mortality Broad base and narrow apex indicates a high fertility population |
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Numerators and Denominators
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Numerators
– How do we find / identify “cases” of disease and death in the population? (Surveillance, vital registration, surveys, hospital records) Denominators – How do we measure the population at risk over time? (Census, samples of the population, surveys) |
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Types of Calculations to Measure
Morbidity & Mortality |
Rate
Proportion Ratio |
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Rate
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How fast is disease occurring?
Events (e.g., cases)/ Population-Time |
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Proportion
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What fraction of the population is affected by disease?
Number Affected/ Total Population |
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Ratio
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How do two quantities compare?
Estimate in Ratio population #1: Estimate in population #2 Note: Ratios are unitless (when the two estimates are in the same units) |
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Incidence as a concept
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The number of new cases of a disease that occur
during a specified period of time in a population at risk for developing the disease Incidence = Number of NEW cases of a disease occurring in the population during a specified period of time / Number of persons who are AT RISK of developing the disease during that same period of time |
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Cumulative Incidence formula
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Cumulative Incidence (%) =
Number of new cases of a disease occurring in the population during a specified period of time / Number of persons at risk for the disease at the beginning of the period Note: All individuals are observed for entire period |
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Cumulative Incidence
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Cumulative incidence is a proportion
Cumulative incidence is a measure of risk Risk is the probability that an event will occur during a specified time Similar to attack “rates” in outbreak investigations “Ever” questions on surveys (i.e., Have you ever been diagnosed with asthma?) |
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Second “flavor” - Incidence Rate
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Incidence expressed as a rate in units of
person-time “at risk” Note: Some individuals are not observed for entire period (i.e., different people are observed for different lengths of time) # new cases/Person-time at risk example: 5/11.67 person-yr = 42.8 per 100 person- |
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Prevalence
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The proportion (%) of the population affected by
the disease at a given time (i.e., point in time or period in time); not a measure of risk Prevalence= (Number of existing cases of disease present in the population at a specified time/ Number of persons in the population at that specified time) x 100 Range for prevalence: [0,1] or [0%, 100%] Prevalence = Incidence x Duration If there is a steady-state situation (i.e., rates are not changing and in-migration equals out-migration) |
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Factors Influencing Observed Prevalence
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Increased by:
• Longer duration of the disease • Prolongation of life of patient without cure • Increase in new cases • In-migration of cases • Out-migration of healthy people • Improvements in diagnosis (or better reporting) Decreased by: • Shorter duration of disease • High case-fatality rate from disease • Decrease in new cases • In-migration of healthy people • Out-migration of cases • Improved cure rate of cases |
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Annual All-cause Mortality Rate
(per 1,000 population) |
Annual All-cause Mortality Rate per 1,000 population=
(Total number of deaths from all causes in 1 year/ Number of persons in the population at mid-year) x1,000 |
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Cause-specific Mortality Rate
(per 1,000 population) |
AnnualMortality rate from “Disease A” per 1,000 population= Number of deaths from “Disease A” in 1 year/ Number of persons in the population at mid-year) x1,000
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Proportionate Mortality
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What proportion of all deaths in a given
year are due to a particular disease? # deaths due to particular disease/ Total # deaths Sometimes this is (incorrectly) called the Proportionate Mortality Rate. |
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Case-Fatality Rate (CFR) (%)
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# deaths from Disease Y/ # people with Disease Y
Note: Although usually called a “rate”, the CFR is really a proportion. CFR is a measure of the severity of a disease. |
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When is a mortality rate a good index
of an incidence rate? |
When case-fatality rate is high (e.g., untreated rabies)
When the duration of the disease (i.e., survival) is short (e.g., pancreatic cancer) |
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Adjustment
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A procedure for adjusting rates (e.g.,
death rates), designed to minimize the effects of differences in population composition when comparing rates for different populations |
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Two methods of adjustment
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Direct - weighted average of specific rates
• Uses rates of sub-groups of populations being compared • Need to know sub-group distributions of events in both populations Indirect - compares observed number of events to expected number • Used when sub-group distribution of events in population of interest is not known |
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Direct age-adjustment
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A standard population is used in order to
eliminate the effects of any difference in age between the populations being compared |
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Direct age-adjustment
Steps |
• Step 1 – Calculate rates for each population
overall and by age category – Divide the number of events by the population • Step 2 - Identify the standard population – Most often, add populations of interest by age categories – Can select appropriate population with age categories (e.g., 2010 US population) • Step 3 – Apply all rates to standard population by age category to obtain expected number of events for each population • Step 4 – Sum the expected number of events for each population • Step 5 – For each population, divide the sum of expected number of events by the total standard population to obtain the age-adjusted rate |
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Interpreting differences between
crude and age-adjusted rates I |
When conducting direct age-adjustment,
the standard population has a different age composition from that of population being compared. Another way to say this… the standard population has different “weights” of age categories than those of the population being compared. |
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Interpreting differences between
crude and age-adjusted rates II |
Assuming the rate of interest is related to
disease that increases with older age… If the population of interest has a higher proportion of older ages (i.e., it’s weighted with older ages) than the standard population, then the age-adjusted rate for the population of interest will be lower than the crude rate for that population. |
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Interpreting differences between
crude and age-adjusted rates III |
Assuming the rate of interest is related to
disease that increases with older age… If the population of interest has a lower proportion of older ages (i.e., it’s weighted with younger ages) than the standard population, then the age-adjusted rate for the population of interest will be higher than the crude rate for that population. |
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Indirect age-adjustment
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Comparison of expected versus observed events is
utilized in order to eliminate the effects of any difference in age between the populations being compared Employed when numbers of events for each agespecific stratum are not available |
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Indirect age-adjustment
Steps |
• Step 1 – Identify total observed events in
population of interest • Step 2 – Identify age-specific event rates for known population of comparison • Step 3 – Apply age-specific event rates (from comparison population) to age-specific strata of population of interest to obtain expected number of deaths for each age category • Step 4 – Sum the expected number of events for the population of interest • Step 5 – Divide the number of observed events by the number of expected events to obtain the standardized mortality ratio (SMR) |
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Standardized mortality ratio
(SMR) |
Observed number of deaths/ Expected number of deaths
Note: Multiplication by 100 is often done to yield results without decimals |
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Things to remember regarding
adjustment |
• Always examine data before adjusting
• Adjusted rates are hypothetical |
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Measures of Prognosis
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5-year survival rate
Observed survival rates – person-time – life tables – Kaplan-Meier method Median survival time Relative survival rate |
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5-Year Survival Rate
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Proportion of persons in a specified group
alive at the beginning of the 5 years who survive to the end of the 5 years |
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Observed survival rates
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Person-time
Life tables |
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Two Assumptions Made
In Using Life Tables |
No changes have occurred in survivorship over
calendar time. Those lost to follow-up experience the same survivorship as those who are followed. |
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Kaplan-Meier Method
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requires date last observed or date outcome occurred on each individual (end of study can be the last date observed) The essence of the Kaplan-Meier (KM) method is having the date each outcome in the cohort occurred.
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CALCULATING SURVIVAL USING THE
KAPLAN-MEIER METHOD |
Columns
(1)Times to deaths from starting treatment (months) (2) Number alive at each time (3) Number who died at each time (4) Proportion who died at that time Column(3)/ Column(2) (5) Proportion who survived at that time 1-Column(4) (6) Cumulative proportion who survived to that time (Cumulative Survival) |
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Median Survival Time
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Length of time that half of the study
population survives Why median rather than mean? – Less affected by extreme values – Only need to observe deaths of half of the study group rather than the entire group |
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Relative Survival Rate
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Observed Survival Rate/
Expected Survival Rate |
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Investigating an Outbreak I
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0. Determine existence of epidemic
1. Case definition 2. Identify cases and population at risk 3. Obtain information on time, place, and person 4. Collect specimens for lab analysis 5. Analyze data 6. Develop, test, and refine hypotheses 7. Implement control measures 8. Prepare report and disseminate findings |
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Determine existence of epidemic
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Info from local health officials
-observed versus expected Beware of changes! -local reporting practices, interest in diseases, “new kid on the block,” diagnostic methods May need to acquire additional data |
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Case definition
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Clinical features and known causes
What tests to confirm diagnosis? Start with a simple case definition, and then refine as you go -What happens when you change the case definition? |
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Identify cases and population at risk
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Identify sources for finding cases
May need more intensive efforts Collect info likely to provide clues to determine population at risk -Natural history of epidemic (disease transmission) Characteristics of the ill (opportunities for exposure) |
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Obtain information
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Time of occurrence
Place Characteristics of persons affected -Demographics -Potential exposures |
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Collect specimens for lab analysis
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Collect from people, vectors, fomites
Serologic tests X-rays Etc |
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Analyzing Data
in Outbreak Investigations |
1. Determine whether the observed number
of cases clearly exceeds the expected number 2. Calculate attack rates – Rate defined as the cumulative incidence of infection in a group observed during an epidemic – Note, time is implicit 3. Epidemic curve - A graph of the frequency of occurrence of cases by time of onset Patterns: Point source epidemic Common source epidemic Propagated epidemic 4. Spot map - A detailed map identifying the location of cases in a discrete area |
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How to Calculate an Attack Rate
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Number of people at risk in whom a certain illness develops / Total number of people at risk
• AKA cumulative incidence • Can be designated for a given exposure, e.g., food-specific • Time is implicit |
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Characteristic Patterns
of Epidemic Curves |
• Point source outbreak
• Common source outbreak • Propagated outbreak |
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Point Source Outbreaks
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Population is exposed briefly /
simultaneously / at a single point in time Cases occur suddenly, and, after a brief peak period of time, fall off in a logarithmic fashion One incubation period |
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Common Source Outbreak
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Exposure is not brief / simultaneous, rather it
occurs over time Cases arise suddenly and continue to arise as more individuals continue to be exposed to the source Peaks in epidemic curve may represent two or more incubation periods |
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Propagated Outbreak
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Exposure can be brief or over time
Person-to-person transmission of disease Two or more peaks in epidemic curve as secondary cases occur from person-toperson spread |
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Determinants of Propagated Outbreaks
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Balance between proportion of susceptible
and immune individuals in the population -Immunity may be: Innate (genetic) or Acquired (previous illness, immunization) Effective contact between agent and susceptible host Virulence of the organism |
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Herd Immunity
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The relative resistance of a group to an
outbreak of a disease when a critical proportion, but not all, of the group is immune. This occurs when the disease is spread by person-to-person contact. The greater the proportion of immune people in the group, the less likely it is that an infected person will encounter a susceptible person. If such encounters are sufficiently rare, the disease will not propagate in the group. The critical proportion of immune people varies with the virulence of the disease, e.g. for measles it is about 94% |
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Develop, test, and refine hypotheses
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Examine distribution of cases by time and place
Look for time-place interactions Examine possible interactions among potential exposures Incorporate existing knowledge, if any, about disease Refine hypotheses and collect additional data that may be needed |
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Cross-tabulation of Attack Rates
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Risk Difference = (ARexposed) – (ARnon-exposed)
Risk Ratio = (ARexposed) / (ARnon-exposed) |
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Incubation Period
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The time interval from infection
(exposure) to appearance of first sign or symptoms of the disease |
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Median Incubation Period
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• 50th percentile
• observation at the middle of the distribution Example: Time of symptom onset was known for 30 people (N=30) Median = (30+1)/2 = 15.5 observation |
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Dose-response Relationship
|
A gradient in the attack rate in relation to the
amount of the exposure Provides strong evidence that the exposure is the cause of the disease |
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Institute control measures
|
Control of current outbreak
Prevention of future similar outbreaks Directed at source -modify environment, quarantine, treat infected persons/animals, etc Directed at susceptibles -modify behavior to reduce risk to self and/or others, administer post-exposure prophylaxis, etc |
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Prepare report and disseminate findings
|
Document for action, record of performance,
document for medical/legal issues, enhance outbreak investigations, instrument for teaching epi Share with… Public health personnel involved in policy development and implementation General public Scientific community |
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(EIS)
|
Epidemic Intelligence Service
- Featured in the movie Contagion |
|
Public Health Surveillance
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Continuous, systematic collection, analysis and
interpretation of health-related data needed for the planning, implementation, and evaluation of public health practice. Passive – Routine reporting of disease cases seen in health care facilities Active - Special search to find disease cases Sentinel – Disease-specific reporting systems in defined catchment areas |
|
Public health surveillance can…
|
• Serve as an early warning system for
impending public health emergencies • Document the impact of an intervention, or track progress towards specified goals • Monitor and clarify the epidemiology of health problems, to allow priorities to be set and to inform public health policy and strategies |
|
Data Sources
|
• Mortality data
• Morbidity data • Laboratory data • Individual case reports • Epidemic reporting • Sentinel systems • Knowledge of vertebrate and arthropod vector species • Surveys of general population, special databases • Demographic and environmental factors |
|
Real-time Surveillance
|
• Alert public health care practitioners in early
phases of outbreak • Promptly institute case finding and control measures • Improve access to treatment with the goal of reducing morbidity and mortality |
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Syndromic Surveillance
|
Adapted CDC definition:
Surveillance using already existing healthrelated data that precede diagnosis and signal a sufficient probability of a case or an outbreak to warrant further public health response Supplements existing surveillance methods |
|
Syndromic Surveillance
Clinical Data Sources |
Emergency Department (ED) data, e.g., chief
complaint, total patient volume Emergency medical system call type, e.g., 911 Poison control center call Unexplained deaths Insurance claims or billing data Clinical laboratory or radiology ordering volume |
|
Syndromic Surveillance
Alternative Data Sources |
School absenteeism
Work absenteeism Over-the-counter medication sales Health-care provider database searches Internet-based health searches by public Animal illness or deaths |
|
Analysis of Web Data
|
• Best used for surveillance of epidemics and
disease with high prevalence • Better suited for use in developed countries with large populations of web search users • Pitfall - Disease publicity may increase use of disease related search terms |
|
Social Media Use in the US
|
• Rapid uptake of internet use and social media
(e.g., social networking, blogging, etc) • Internet access- disparities exist by race/ethnicity and health status – Though, among people with access, these characteristics do not affect social media use • Growth of social media is not uniformly distributed across age groups |
|
Desirable Characteristics
of Real-time Data in Any Setting |
• Authentic users
• Ability to tease apart “real traffic” for disease versus chatter – Media coverage on disease – Sales at pharmacies • Protection of privacy (both real and perceived privacy concerns) |
|
mHealth
|
mHealth is the use of mobile
hand-held devices, especially text and web-enabled cell phones, for the delivery of health information and messaging mHealth Cell phone market penetration in the developed world is around 90 percent, and in the developing world about one-third of that, and growing Android operating system is open-source |
|
Uses of mHealth
|
Mobile phones can provide reminders about
healthy activities, sources for disease-specific information |
|
Promise of mHealth
|
Mobile phones are also being explored as a
means of… - Tracking patient wellbeing or compliance with treatments - Data collection for public health purposes - Warning of disasters or emergencies (e.g., tsunami, JHU text alerts) |
|
Challenges of mHealth
|
Evaluation is difficult
How to quantify effectiveness as part of an intervention? How to transition from pilot projects to scaledup programs? Sustainability No standards for mHealth platform integration |
|
Magpi
|
• Creates web-based data gathering forms that
can be filled out via cell phone from the field • Omits data collection on paper and input into electronic databases for sharing and analysis • GPS enabled phones allow data to be tagged with geo-coordinates |
|
Desirable Attributes
of a Health Measurement |
• Relevant
• Valid • Precise • Quantitative • Safe • Acceptable • Practical • Inexpensive |
|
Validity
|
An expression of the
degree to which a measurement measures what it purports to measure |
|
Sensitivity
|
The measurement ability of the
test to identify correctly those who have the disease |
|
Specificity
|
The measurement ability of the
test to identify correctly those who do not have the disease |
|
Determining the Sensitivity and Specificity
of a New Measurement |
“Gold standard” or reference standard measurement
100% accurate, or best measurement available Often invasive or expensive New measurement -Less expensive and/or easier to administer Compare the performance of the new measurement to the reference standard measurement |
|
Sensitivity formula
|
= # positive among those with disease/ total # with disease
= Pr ( + | Disease) |
|
Specificity formula
|
# negative among those with no disease/ total # with no disease
= Pr ( - | No Disease) |
|
Type 2 Diabetes Mellitus
|
High prevalence in the U.S. and increasing,
especially obese populations Reference standard test for diagnosis is the oral glucose tolerance test; requires drinking a high glucose drink and multiple blood draws over several hours Screening test is a fasting plasma glucose level |
|
Where to Draw the Cutpoint?
(Criterion of Positivity) |
Minimize the number of false positives (FP) if the
gold standard test is expensive or invasive → Increases specificity Minimize the number of false negatives (FN) if the penalty for missing cases is high (e.g., disease is serious but treatment exists, disease easily spreads, screening and diagnostic tests are cheap and have low risk) → Increases sensitivity Balance severity of FP and FN |
|
Use of Multiple Tests
|
Two-Stage Screening (Sequential)
Use a less expensive, less invasive, or less uncomfortable test first Rescreen those who screened positive with a more expensive, more invasive, or more uncomfortable test (or the same one) |
|
Two-Stage Sequential Screening
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People are identified/defined as having the
disease only if they test positive twice Purpose of re-testing those who screened positive is to have a second chance to rule out the false positives Ruling out more false positives increases specificity Undesirable consequence is that true positives may become false negatives; this decreases sensitivity No need to re-test those who initially screened negative, as they won’t meet definition of disease |
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Simultaneous Screening
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Purpose of re-testing everyone is to have a
second chance to rule out the false negatives (i.e., identify more positives) Ruling out more false negatives increases sensitivity Undesirable consequence is that true negatives may be mistakenly labeled as false positives; this decreases specificity Those who tested positive the first time really do not need to be re-tested as they already meet the definition of disease, but this is done for convenience with little added cost |
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Simultaneous Screening – Net Sensitivity
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Net sensitivity = test positive on both or either test /
total # with disease Test positive on both tests = test A sensitivity x test B sensitivity x total # with disease Test positive on just one test = correctly tested positive for that test minus overlap |
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Simultaneous Screening
Net Sensitivity Formula |
= sensitivity test A + sensitivity test B – (sensitivity
test A x sensitivity test B) |
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Simultaneous Screening – Net Specificity
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Net specificity = test negative both tests / total # with no disease
Test negative on both tests = test A specificity x test B specificity x total # with no disease |
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Simultaneous Screening
Net Specificity Formula |
= test A specificity x test B specificity
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Comparison of Simultaneous
and Sequential Testing Versus Using Either Test Alone |
Sequential testing
– “Positive” = positive result on both tests – Net loss in sensitivity – Net gain in specificity Simultaneous testing – “Positive” = positive result on either test – Net gain in sensitivity – Net loss in specificity |
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Positive Predictive Value
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Of those who test positive, the proportion who actually
have the disease Pr ( Disease | +) = # tested positive among those with disease / total # tested positive synonyms: predictive value positive, PVP, PPV, PV+ PPV is not a fixed characteristic of the test |
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Negative Predictive Value
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Of those who test negative, the proportion of
patients who are actually free of the disease Pr ( No Disease | -) = # tested negative among those with no disease / total # tested negative synonyms: predictive value negative, PVN, NPV, PV NPV is not a fixed characteristic of the test |
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Predictive Value of a Test
Depends on |
• The prevalence of the disease in the
population tested, and • The specificity of that test (if the disease prevalence is low) Recall that the PV is not a fixed characteristic of the test. |
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Evaluating Measurements
Reliability |
Can the result of the test be replicated
if the test is repeated? i.e., consistency of results under repeated examination (by the same people, or similarly trained people, under the same/similar conditions) Synonyms: repeatability, precision |
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Percent agreement
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Overall percent agreement (concordance)
for test with dichotomous outcomes: Percent agreement = (a + d)/total x 100 |
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Correcting Overall Agreement
for Chance Agreement |
Two observers - some portion of agreement may
be due to chance alone, especially when a large proportion of test results fall into one category (e.g., most tests are negative) Example: Pick 2 people in the class who have no experience reading x-rays Each ‘reads’ some as positive but most as negative By chance alone, they agree on many |
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Kappa Statistic
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((Observed Agreement(%))-(Agreement Expected By Chance Alone (%))) / (100% - (Agreement Expected By Chance Alone (%)))
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Kappa Statistic Numerator:
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How much better is the observers’ agreement than by chance alone?
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Kappa Statistic Denominator:
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What is the maximum
the observers could possibly improve agreement over chance alone? |
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Interpreting Values of Kappa
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Value of Kappa vs. Strength of Agreement
0.0 -No agreement better than chance alone < 0.20 -Poor 0.41 - 0.60 -Fair 0.61 - 0.80 -Moderate 0.81 - 1.00 -Good 0.21 - 0.40 -Very Good |
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Predictive Value of a Test
Depends on Prevalence |
• The prevalence of the disease in the
population affects the PV of a given test. • Recall that the PV is not a fixed characteristic of the test. |
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Descriptive Epidemiology
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Epidemiology =
The study of the occurrence and distribution of health-related states or events in specified populations, including the study of determinants influencing such states, and the application of this knowledge to control the health problems. |
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Descriptive Epidemiology: Why?
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Public health planning: How many people are
affected? –How important is a health problem is to individuals and to society • Does the problem merit resources and risks and benefits of intervention? How do the patterns of occurrence of the public health problem vary by different characteristics? –Helps to generate hypotheses about the causes of the problem (descriptive >> analytical) |
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Analytical Epidemiology: Why?
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• Determining risk factors and causes of disease
- Is childhood obesity associated with increased incidence of Type 2 diabetes? • Evaluating preventive and therapeutic interventions that alter the course of disease - Does beginning HIV treatment earlier (i.e., at higher CD4 levels) lead to better health outcomes (e.g., non-detectable viral load)? |
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Experimental Trial
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• Studies prevention and treatment of disease
• Investigator actively manipulates which groups receive the exposure/intervention |
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Observational
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• Studies causes, prevention and treatment for diseases
• Investigator passively watches as nature takes its course |
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Cohort
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• Examines multiple health effects of an exposure
• Subjects defined by exposure levels and follow for disease occurrence |
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Case-Control
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• Typically examines multiple exposures in relation to a
disease • Subjects are defined as cases and controls and exposure histories compared |
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Cross-sectional
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• Examines relationship between exposure and disease
prevalence in a defined population at one point in time |
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Ecologic
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• Examines relationship between exposure and disease
with population-level data rather than individual-level data |
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Goals of analytic studies
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• Determine risk factors and causes of disease
• Evaluate preventive and therapeutic interventions that alter the course of disease |
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Populations in the context of study designs:
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Target – The population about which inferences
are desired Source – The source of subjects for a particular study, subset of target population, can be enumerated Study – Subset of source population, the subjects who actually participate in study |
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Key Parameters of Study Design
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Unit of Observation
Allocation of Exposure Under Study Timing of Observations |
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Unit of Observation
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1. Group (of individuals) as unit
– Examples: Lecture hall of students, city, country – Ecologic study 2. Individual as unit – Examples: Person or event (e.g., pregnancy) |
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Allocation of Exposure Under Study
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1. Experimental
– Performed by researcher – May or may not be random – Example: Clinical trial 2. Non-experimental (i.e., observational) – By nature, self-selection, imposed by others, etc – Researcher does not want to interfere with exposure allocation |
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Timing of Observations
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1. Longitudinal recording of exposures and
outcomes over time (i.e., data collection at least two time points) 2. Cross-sectional - Information on exposures, outcomes, other factors collected at the same time (i.e., data collection at only one time point) - Prevalence data - No attempt to reconstruct exposure history |
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Relevant Etiologic Time Window
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Appropriate time window must be defined
based upon: • Available knowledge of disease • Hypothesized effect of exposure |
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Sampling of Source Population
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1. By exposure status
– Exposed versus unexposed – Cohort studies 2. By outcome status – Disease (i.e., case) versus no disease (i.e., control) – Case control studies |
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Type of Data
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1. Primary data – Collected for the purpose of
a study – Example: Questionnaires – Large studies may have various exposures and outcomes 2. Secondary data – Collected primarily for other purposes – Example: Medical records – Use with caution! |
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Ecologic Study
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Association based on sample of
groups, rather than sample of individuals Distinguished by the fact that groups are the “unit of observation” |
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Ecologic Studies
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Examine rates of disease in relation to a
population-level exposure factor -Summaries of individual population members, environmental measures, global measures (e.g., health care system) Populations/groups may be defined by time or place or both Measure of association is often the correlation coefficient (r) - not an estimate of risk |
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Advantages in Ecologic Studies
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Can usually be completed relatively quickly if
based on available data Inexpensive if data are available Good for hypothesis generation Can examine wide range of exposure levels |
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Limitations in Ecologic Studies
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Individuals who are exposed may not be
the same as those with relevant outcome Cannot adjust for other extraneous factors (i.e., lack of information on important variables) Can be difficult to interpret, as data are crude by nature Cannot establish temporality or causation |
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Key Limitation: Ecologic Fallacy
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The bias that may occur because an
association observed between variables on the aggregate/group level does not necessarily represent the association that exists at the individual level. Therefore, we cannot ascribe group characteristics to individual members of the group. |
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Cross-sectional Study
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Observational / non-experimental study
Individual is unit of observation Data on exposures, outcomes, other factors collected from defined population at the same time, and at a single point in time Participants are often selected without regard to exposure or outcome status Advantages: - Generate inferences and hypotheses - Quick - Low cost - Highly generalizable, when based on sample of general population Often used as basis for public health policy and programming decisions Example: NHANES (National Health and Nutrition Examination Survey) |
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Limitations of Cross-sectional studies
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Cannot establish temporality
Cannot determine causation May miss prevalent cases of short duration Healthy worker effect |