Epidemiology Exam 2

  1. Descriptive Epidemiology
    Epidemiologic studies that are concerned with characterizing the amount and distribution of health and disease within a population.
  2. Variables of Descriptive Epidemiology
    • Time
    • Place
    • Person
  3. Person varibles
    • Age
    • Sex
    • Race
    • Socioeconomic Status
  4. Time Variables
    • Cyclic fluctuation
    • Point epidemic
    • Secular trends
    • Temporal Clustering
  5. Place Variables
    • International
    • Within Country
    • Localized/spatial clustering
    • Urban-rural
  6. Age
    Perhaps the most important factor to consider when describing occurrence of a disease or illness.
  7. Age and Cancer
    • 2.5 per 100,000 cases ages 5-14
    • 1637 per 100,000 cases over age 85
  8. Age and Vehicle accidents
    Highest ages 15-24 and over age 75
  9. First Year of Life
    Risk of death from all causes is higher in the first year of life than any othe rage until age 55
  10. Cervical carcinoma and age
    Peak 25-29 sharp decline after 50
  11. Sex
    Epidemiologic studies have shown sex differences in a wide scope of health phenomena including morbidity and mortality.
  12. Females vs. Males
    • More males are born each year and more males die every year.
    • Females outlive males 5-7 years.
    • Risk of cancer in males is greater.
  13. Race/Ethnicity
    • Somewhat amibiguous classifications
    • Tends to overlap with nativity and religion
    • Some scientists propose that it is a social construct rather than a biological construct
    • Used to track various health outcomes.
  14. Race/Ethnic major categories used in Census
    • White
    • Black or African American
    • American Indian and Alaska Native
    • Asian
    • Native Hawaiian and other Pacific Islander
  15. Nativity
    Place of origin of the individual or his or her relatives.
  16. Socioeconomic status
    Descriptive term for a person's position in society
  17. SES is a composite measure of the following dimensions:
    • A person's income level
    • Education level
    • Type of occupation
  18. International Variables
    Climate, cultural factors, national dietary habits, and access to health care.
  19. National (within country)
    Regional differences may affect the prevalence and incidence of disease.
  20. Urban-Rural
    Urban and rural sections of the US show variations in morbidity and mortality related to environmental and lifestyle isues.
  21. Localized patterns of disease
    Associated with specific environmental conditions that may exist in a particular geographic area.
  22. Cyclic fluctation
    An increase or decrease in the frequence of a disease or health condition in a population over a period of years or within each year
  23. Point Epidemic
    Response of a group of people circumscribed in place to a common source of infection, contamination, or other etiologic factor to which they were exposed almost simultaneously.
  24. Secular Trends
    Gradual changes in disease frequency over long time periods
  25. Temporal Clustering
    Association between common exposure to an etiologic agent at the same time and the development of mrobidity or mortality in a group or population.
  26. Case report
    Accounts of a single occurrence of a note-worthy health related incident or of a small collection of such events.
  27. Case Series
    A larger collection of cases of a disease, often grouped consecutively and listing common features.
  28. Cross-sectional studies
    A type of prevalence study that examines the relationship between diseases and other variables of interest as they exist in a defined population at one particular time.
  29. Uses of descriptive epidmiology
    • 1. prevention of disease
    • 2. Design of interventions
    • 3. conduct of additional research
  30. Noncausal Associations
    X does not cause y
  31. Causal Association
    X causes Y
  32. Positive Association
    If the value of one variable increases, the value of the other variable increases as well.
  33. Negative Assocation
    If the value of one variable increases, the value of the other variable decreases.
  34. Pearson Correlation Coefficient (r)
    • Measure of assocation used with continuous variables
    • Varies from -1 to +1
    • As r approaches either -1 or +1 the association is stronger
  35. Continuous variable
    One that can have an infinite number of values within a specified range (ht and wt)
  36. Types of Associations
    • No association
    • Causally Associated
    • Noncausally associated
  37. Dose-Response Curve
    The plot of a dose-response relationship, which is a type of correlative association between an exposure and an effect.
  38. Threshold
    The lowest dose at which a particular response occurs
  39. Muiltimodal Curve
    One that has several peaks in the frequency of a condition
  40. Epidemic Curve
    • A graphic plotting of the distribution of cases by time of onset.
    • Aids in identifying the cause of a disease outbreak.
  41. Contingency Table
    • Another method for demonstrating associations
    • A type of table that tabulates data according to two dimensions
  42. Hypotheses: Where They Come From
    • Method of difference
    • Method of concomitant variation
  43. Method of Difference
    All of the factors in two or more domains are the same except for a single factor, which is hypothesized to be the "cause" of a disease.
  44. Method of Concomitant Variation
    A type of association in which the frequency o fan outcome increases with the frequency of exposure to a factor, the hypothesized cause of the outcome
  45. Operationalization
    Refers to the process of defining measurement procedures for the variables used in a study
  46. Causality in Epidemiologic Studies
    • One of the central concerns of epidemiology is to be able to assert that a causal association exists between an exposure factor and disease.
    • Complex issue
    • Several criteria must be satisfied in order to asser that a causal association exists.
  47. Hill's Criteria of Causality
    • 1. Strength
    • 2. Consistency
    • 3. Specificity
    • 4. Temporality
    • 5. Biological gradient
    • 6. Plausibility
    • 7. Coherence
    • 8. Analogy
  48. Strength
    Strong associations give support to a causal relationsihp between factor and disease
  49. Consistency
    An association has been observed repeatedly
  50. Specificity
    Association is constrained to a particular disease-exposure relationship
  51. Temporality
    The cause must be observed before the effect
  52. Biological gradient
    Also known as dose-response curve, shows a linear trend in the association between exposure and disease
  53. Plausibility
    The association must be biologically plausible from the standpoint of ceontemporary biological knowledge
  54. Coherence
    The cause-and-effect interpretation of our data should not seriously conflict with the generally known facts of the natural history and biology of the disease
  55. Analogy
    Relates to the correspondence between known associations and one tha tis being evaluated for causality
  56. Case-Control
    • Subjects are defined on the basis of the presence of absence of an outcome of interest.
    • Cases are those individuals who have the outcome or disease of interest, whereas controls do not.
  57. Matched Case-Control Study
    Is one in which the cases and controls have been matched according to one o rmore criteria such as sex, age, race, or other variables.
  58. Cohort
    Defined as a population group, or subset thereof, tha tis followed over a period of time
  59. Types of Cohort Studies
    • Prospective
    • Retrospective
    • Historical Prospective
  60. Ecologic studies
    A study in which the units of analysis are populations or groups rathe rthan individuals
  61. Ecologic comparison study
    Involves an assessment of th eassociation between expousre rates and disease rates during the same time period
  62. Ecologic correlation
    An association betwen two variables measure at the group level.
  63. Intervention study
    An investiagion involving intentional change in some aspect of the status of the subject
  64. Observational Design
    • Does not have control over the exposure factor
    • Usually is unable to assign subjects randomly to study conditions
  65. Experimental Design
    • Controls who is exposed to a factor of interest
    • Assigns subjects randomly to study groups
  66. Prospective Study
    Subjects ar eclassified according to their exposure to a factor of interest and then are observed over time to document the occurrence of new cases of disease or other health events
  67. Retrospective Study
    • Makes use of historical data to determine exposure level at some baseline in the past
    • Follow-up for a subsequent occurrences of disease between baseline and present is performed
  68. Historical Prospective Study
    Combines retrospective and prospective approaches
  69. Ecologic Fallacy
    An erroneous inference that may occur because an association observed between variables on an aggregate level doe snot necessarily represent or reflect the assocation that exists at an individual level
  70. Advantages of Ecologic Studies
    • May provide information about the context of health
    • Can be performed when individual level measurements are not available
    • Can be conducted rapidly and with minimal resources
  71. Disadvantages of Ecologic Studies
    • Ecologic fallacy
    • Imprecise measurement of exposure
  72. Odds Ratio
    • A measure of the association between frequency of exposure and frequency of outcome used in case-control study.
    • AD/BC
  73. Advantages of Case Control Study
    • Can be used to study low-prevalence conditions
    • relatively quick and easy to complete
    • Usually inexpensive
    • Involve smaller number of subjects
  74. Disadvantages of Case Control Study
    • Measurement of exposure may be inaccurate
    • representativeness of cases and controls may be unknown
    • Provide indirect estiamates of risk
    • The temporal relationship between expousre factor and outcome cannot always be ascertained
  75. Relative Risk
    The ratio of the incidence rate of a disease or health outcome in an exposed group to the incidence rate of the disease or condition in a non-exposed group
  76. Population Risk Difference
    Inidence in the total population- incidence in the nonexposed segment
  77. Advantages of Cohort Studies
    • Permit direct observation of risk
    • Exposure factor is well defined
    • Can study expousres that are uncommon in the population
    • The temporal relationship between factor and outcome is known
  78. Disadvantages of Cohort Studies
    • Expensive and time consuming
    • Complicated and difficult to carry out
    • Subjects may be lost to follow-up during the course of the study
    • Exposures can be mis classified
  79. Randomized Control Trial
    Subjects in a population are randomly allocated into groups, usualy called study and control groups, to receive or not to receive an experimental preventative or therapeutic procedure, maneuver, or intervention
  80. Bias
    Systematic devation of results or inferences from truth
  81. Types of Bias
    • Hawthorne effect
    • Recall bias
    • Selection bias
    • Healthy worker effect
    • Confounding
  82. Hawthorne effect
    Participant's behavioral changes as a result of their knowledge of being in a study
  83. Recall Bias
    Cases may remember an exposure more clearly than controls
  84. Selection bias
    Distortions that result from procedures used to select subjects and from factors that influence participation in the study
  85. healthy worker effect
    the observation that employed populations tend to have a lower mortality experience than the general population
  86. Confounding
    the distortion of a measure of the effect of an exposure on an outcome due to the association of the exposure with other factors that influence the occurrence of the outcome
  87. Which of the Hill's criteria doesn't really belong?
  88. Direct causal relationship
    • X causes Y
    • Example: Delta F508 causes Cystic Fibrosis
  89. Indirect Causal relationship
    • X causes Y causes Z
    • High cholesterol causes artery thickening which caueses hemostatic factors which can lead to MI
  90. Sufficient but not necessary
    • Factor can produce disease, but is not necessary
    • Radiation exposure and leukemia
  91. Necessary but not sufficient
    • Multiple factors, including main factor, required
    • Example: Development of TB and immunosuppression to cause disase
    • Bacteria still necessary, but not sufficient to cause disease.
  92. Necessary and Sufficient
    • Without factor, disease doesn't develop
    • HIV
  93. Neither sufficient nor necessary
    • Complex models of disease etiology
    • High fat diet and heart disease
  94. Four Types of Causal Factors
    • Necessary and Sufficient
    • Necessary but not sufficient
    • Sufficient but not necessary
    • Neither sufficient nor necessary
  95. Web of Causation
    Model of multifactorial causality
Card Set
Epidemiology Exam 2
Epidemiology Exam 2