Epidemiology 101 Exam 2

Descriptive Epidemiology: Patterns of Disease- Person, Place, Time
-unequal distributions of health and disease in populations
-to determine why health conditions vary throughout populations, one much answer the following questions:
1.who was affected?
2. where did the (health) event occur?
3. when did the (health) event occur?
Definition: Descriptive Epidemiology
the field of descriptive epidemiology classifies the occurrence of disease according to the following variables:
-person (who is affected)
-place (where the condition occurs)
-time (when and over what time period the condition has occurred)
Descriptive Epidemilogic Study
one that is “…concerned with characterizing the amount and distribution of health and disease within a population”
Descriptive Epidemiology
provides valuable information for the following activities:
-prevention of diease
-design of interventions
-conduct of additional research area that is emerging
Uses of Descriptive Epidemiologic Studies
-permit evaluation of trends in health and disease
-provide a basis for planning provision, and evaluation of health services
-identify problems to be studied by analytic methods and suggest areas that may be fruitful for investigation
Case Reports
-accounts of a single occurrence of a noteworthy health-related incident or of a small collection of such events
-example: adverse reactions due to cosmetic surgery in the United States
-something will pull a red flag, pull data and investigate inconsistency with physician
Case Series
-in comparison with a case report, it is a larger collection of cases of a disease, often grouped consecutively and listing common features, e.g. characteristics of affected patients
-example: reported cases of primary meningoencephalitis (121 cases reported between 1937 and 2007)
Cross-Sectional Studies
-a type of investigation “…that examines the relationship between diseases (or other health-related characterisitcs) and other variables of interest as they exist in a defined population at one particular time”
-a type of prevalence study
-more complex that case report and case series
-studied at the same time
-example: the Behavioral Risk Factor Surveillance System (BRFSS)
Epidemiologic Inferences from Descriptive Data
-Descriptive epidemiology and descriptive studies provide a basis for generating hypotheses
-descriptive epidemiologic studies connect intimately with the process of epidemiologic inference
Process of epidemiologic inference
1. Observation
2. Make Comparisons
3. Generate Hypotheses
4. Test Hypotheses
5. Epidemiologic Inference

-exposure and health outcomes
-actually have to test hypotheses
-have to take into effect biases or confounding factors

Age
perhaps the most important factor to consider when describing occurence of disease or illness
-age-specific disease rates usually show greater variation than rates defined by almost any other personal attribute
Examples of Age Associations
-the incidence of and mortality from chronic diseases increases with age
-some infections, e.g., mumps and chickenpox occure more commonly during childhood
-the leading cause of death among young adults is unintentional injuries
-maternal age is associated with rates of diabetes and related complications
Sex
-epidemiologic studies have shown sex differences in a wide scope of health phenomena including morbidity and mortality
-examples:
1. all cause age-specific mortality rates higher among males
2. differences in cancer rates, e.g., cancers of the genital system
3. men die younger regardless of cause
Race/ Ethnicity
-five major categories in Census 2000:
1.white
2.black or african american
3.american indian and alaska native
4.asian
5.native hawaiian and other pacific islander
-Census 2000 allowed respondents to check a multiracial category
-Alisha Jones (Quincy Jones’ daughter) is african american, welsh, and jewish
Ethnicity
people who identified with each other through a common culture
Nativity
a place of orgin, foreign born
Socioeconomic Status (SES)
-defined as a “Descriptive term for a person’s position in society…”
-more than a singular dimension
-often formulated as a composite measure of the following dimensions:
1. a person’s income level
2. education level
3. type of occupation
Socioeconomic Status (SES) continued
-the social class gradient
1. strong, inverse association of SES with levels of morbidity and mortality
-those in lowest SES positions are confronted with excesses of morbidity and mortality from numerous causes
-based upon access
-more than normal
Definition: Health Disparities
differences in the occurrence of diseases and adverse health conditions in the population
-example: cancer- “…adverse differences in cancer incidence, cancer prevalence, cancer death, cancer survivorship, and burden of cancer or related health conditions that exist among specific population groups in the United States”
-african americans (in comparison with other groups) have the highest age-adjusted overall cancer incidence and death rates
-cancer develop at different rates
International
-World Health Organization (WHO) studies: infectious and chronic diseases
-Factors affecting disease occurrence? climate, culture, diet, access to healthcare
-Variation in life expectancy (U.S. ranked number 47 in 2008) 78.9 in U.S.
1. war has impact
2. lifestyle has huge impact
National (Within-Country)
regional difference may affect the prevalence and incidence of disease.
-factors include:
1. climate
2. latitude
3. environmental pollution
Urban-Rural Differences
urban and rural sections of the United States show variations in morbidity and mortality related to environmental and lifestyle issues.
-urban example: elevated occurrence of lead poisoning among children who live in older buildings (housing managed by slum lords)
-rural example: pesticide exposure and farming injuries among agricultural workers (thousands die or permanently injured in farming accidents)
Localized Patterns of Disease
associated with specific environmental conditions that may exist in a particular geographic area
-examples:
1. cancer and radon gae
2. naturally occurring arsenic in water supply
3. presence of disease vectors: Dengue fever along the Texas- Mexico border caused by mosquito
Secular Trends
refer to gradual changes in the frequency of disease over long time periods
Cyclic (Seasonal) Trends
cyclic trends are increases and decreases in the frequency of a disease or other phenomenon over a period of several years or within a year
-example: mortality from pneumonia and influenza (peaks during February: comes from trend data)
Point Epidemics
the reponse 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
-food posioning
Clustering
“a closely grouped series of events or cases of a disease or other health-related phenomena with well-defined distribution patterns in relation to time or place or both..”
-aggregation of uncommon events- leukemia
-spatial clustering- aggregation of events in a geographic region (severe asthma)
-temporal clustering- occurrence of events related to time (P.P.D)
Variable Defined
“any quantity that varies. any attribute, phenomenon, or event that can have different values”
Association
-referes to a linkage between or among variables; variable that are associated with one another can be positively or negatively related
-positive association means that if the value of one variable increases, the value of the other variable increases as well
-negative association means that if the value of one variable increases, the value of the other variable decreases
Pearson Correlation Coefficient (r)
measure of associaton used with continuous variables
-continuous variable is a type of variable that can have an infinite number of values within a specified range. (Examples: height and weight)
1. varies from -1 to 0 to +1
2. the value of 0 means no association
3. as r appproaches either -1 or +1, the association between two variables becomes stronger
4. when r is negative, the association is inverse (opposite)
Type of Associations
some possible relationships between variable X (exposure factor) and variable Y (outcome):
-no association (X is unrelated to Y)
-associated (X is related to Y):
1. non-causally (X does not cause Y)
2. causally (X causes Y): have to have significant amount of evidence to say X causes Y
Hypothetical Examples of Association
sugar consumption (exposure variable) and type 2 diabetes (health outcome)
-possible association between exposure and outcome
1. no association (independence) between dietary sugar consumption and occurrences of diabetes
2. positive association between dietary sugar and diabetes
-causal: high diertary sugar intake “causes” diabetes
-non-causual: a third factor may be related to both preference for dietary sugar and occurrence of diabetes
3. negative association between dietary sugar consumption and diabetes
Scatter Plot (Diagram)
-plots two variables, one on the X axis (horizontal) and one on the Y axis (vertical)
-the measurements for each case are plotted as a single data point
-the closer the points lie with respect to the straight line of best fit through them (called the regression line), the stronger the association between variable X and variable Y
Dose-Response Curve
-a type of correlative association between an exposure and an effect
-threshold refers to the lowest dose at which a particular response occurs
-example: relationship between the number of cigarettes smoked daily and mortality from lung cancer, also robo trip and alcohol
Multimodal Curve
-has several peaks in the frequency of a condition
1. a mode is defined as the category in a frequency distribution that has the highest frequency of cases
-possible reasons for multimodal distributions of health outcomes:
1. changes in lifestyle and immune status of the host
2. latency effects (latency refers to the time period between initial exposure and a measurable response)
-host wouls be a person or animal
-latency: where disease is not active
Epidemic Curve
-“a graphic plotting of the distribution of cases by time of onset”
-aids in identifying the cause of a disease outbreak
-not going to be hills or valleys, just one
Contingency Table
-another method for demonstrating associations
-a type of table that tabulates data according to two dimensions
Generic Contingency Table
-A= exposure is present and disease is present
-B= exposure is present and disease is absent
-C= exposure is absent and disease is present
-D= exposure is absent and disease is absent
Hypothesis
-defined as “any conjecture (question) cast in a form that allow it to be tested and refuted
-one of the most common types is the null hypothesis
1. an example would be to hypothesize that there is no difference between smokers and nonsmokers in the occurrence of lung cancer
2. null hypothesis= no association
3. if there is a difference, reject the null
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
-example: differences in coronary heart disease rates between sedentary and non-sedentary workers
Method of Concomitant Variation
a type of association in which the frequency of an outcome increases with the frequency of exposure to a factor, the hypothesized cause of the outcome
-example: dose-response relationship between number of cigarettes smoked and mortality from lung cancer
Operationalization
refers to the process of defining measurement procedures for the variables used in a study
-example: in a study of the association between tobacco use and lung disease, the variables might be the number of cigarettes smoked and the occurrence of asthma
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 in the host
1. example: is there a causal relationship between viewing alcohol advertisements and binge drinking
-causality is a complex issue
-several criteria of causality must be satisfied in order to assert that a causal association exists
Hill’s Criteria of Causality
-strength
-consistency
-specificity
-temporality
-biological gradient
-plausibility
-coherence
-analogy
Strength
strong association give support to a causal relationship between factor and disease
Consistency
an association has been observed repeatedly
Speficity
association is constrained to a particular disease-exposure relationship
Temporality
the cause myst be observed before the effect
-example: texting caused accident, not accident caused texting
Biological gradient
-also known as a dose-reponse curve
-shows a linear trend in the association between exposure and disease
Plausibility
the association must be biologically plausible from the standpoint of contemporary biological knowledge
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…”
Analogy
Relates to the correspondence between known associations and one that is being evaluated for causality (e.g., thalidomide and rubella)
Smoking and Health, 1964 Surgeon General’s report
presented several criteria for evaluation of a causal association
Multifactorial (Multiple) Causality
-many types of causal relationships that are involved with the etiology of diseases involve more than one causal factor
-examples of multiple causal factors in the etiology of many chronic diseases include:
1. specific exposures
2. family history
3. lifestyle characteristics
4. environmental influences
Chance and Inference
-epidemiologists employ statistical procedures to assess the degree to which chance may have accounted for observed associations
-inference is “The process of passing from observations and axioms to generalizations.”
1. a goal of inference is to draw conclusions about a parent population from sample-based data
Point Estimate
-the value for a population is referred to as a parameter and the corresponding value for the sample is a statistic
-a single value (sample-based) chosen to represent the population parameter
Confidence Interval Estimate
a range of values that with a certain degree of probability contain the population parameter
-used as an alternative to point estimate
Power
-“…the ability of a study to demonstrate an association if one exists”
-related to sample size and effect size
1. effect size is related to the strength of the association that has been observed
-small sample size + large effect= not statistically significant
-large sample size + small effect= statistically significant
-not necessarily clinically significant
Importance of Analytic Studies
-lead to the prevention of diease
-assist in creation of quantitative evaluations of intervention programs
-aid in determining safety and efficiacy of new drugs and other procedures
Investigator Role in Analytical Studies
-observational design
1. does not have control over the exposure factor
2. usually is unable to assign subjects randomly to study conditions (natural environment)
-experimental design
1. controls who is exposed to a factor of interest
2. assigns subjects randomly to study groups
3. blind and double blind study
Characteristics of Study Designs
– who manipulates the exposure factor? (researcher)
-how many observations are made? (once, cohort more than once)
-what is the directionality of exposure? (exposure actually occurs)
– what are the methods of data collection? (type of study determined)
-what is the timing of data collection? (real time if possible)
-what is the unit of observation? (number of persons observed)
-how available are the study subjects? (disable, childern, inmates need clearance)
Ecologic Studies
-“… a study in which the unites of analysis are populations or groups of people rather than individuals”
-examples of groups are nations, states, census tracts, counties
-may be use when individual measurements are not available, but group-level data can be obtained
Ecologic comparison study
involves an assessment of the association between exposure rates and disease rates during the same time period
Ecologic correlation
an association between two variables (exposure and outcome) measured at the group level
Ecologic (Ecological) Fallacy
“An erroneous inference that may occur because an association observed between variables on an aggregate level does not necessarily represent or reflect the association that exists at an individual level;…”
Example of Ecologic Fallacy
-worldwide, richer cities have higher rates of coronary heart disease (CHD) than poorer cities
-it would be incorrect to infer that richer individuals have higher rates of CHD than poorer individuals
-in fact, in industrialized cities poorer people have higher CHD rates than richer ones
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
Disadvantages of Ecologic Studies
-ecologic fallacy
-imprecise measurement of exposure depending where you obtain data from
Case-Control Studies
-subjects are defined on the basis of the presence or absence of an outcome on interest
-cases are those individuals who have the outcome or disease of interest, whereas the controls do not
-cases already has disease but very similar characteristics to control group
Matched case-control study
one in which the cases and controls have been matched according to one or more criteria such as sex, age, race, or other variables
-matching aids in controlling confounding (researcher has to pay attention can throw off study)
Odds Ratio
a measure of the association between frequency of exposure and frequency of outcome used in case-control studies
– (AD)/ (BC)
Fourfold Table: Case-Control Study
-the odds in favor of exposure among the disease group (the cases)= A/C
-the odds in favor of exposure among the no-disease group (the controls)= B/D
-the OR is defined as (A/C) / (B/D)
-the OR can be expressed as AD/BC
Interpretation of an Odds Ration (OR)
if the observed OR is not due to chance (is statiscally significant), then
-an OR >1 suggest a positive association between exposure and disease
-an OR of 2.1 (about 2)suggests that the odds of disease are about two times higher among the exposed than among the nonexposed
-an OR
Protective Factor
related to development of disease
Advantages of Case-Control Studies
-can be used to study low-prevalence conditions
1. having a disease is a criterion for being selected as a case
– relatively quick and easy to complete
-usually inexpensive
-involve smaller number of subjects
Disadvantages of Case-Control Studies
-measurement of exposure may be inaccurate
-representativeness of cases and controls may be unknown (cannot generalize findings)
-provide indirect estimates of risk (just using Odds Ratio)
-the temporal relationship between exposure factor and outcome cannot always be ascertained (asking about recall)
Cohort Studies
defined as a population group, or subset thereof (distinguished by a common characteristic), that is followed over a period of time defined by researcher
-examples: birth or age, work cohort, school/ educational cohort
Prospective Cohort Study
subjects are classified according to their exposure to a factor of interest and then are observed over time to document the occurrence of new cases (incidence) of disease or other health events
Retrospective Cohort Study
-makes use of historical data to determine exposure level at some baseline in the past
-follow-up for subsequent occurrences of disease between baseline and present is performed
Historical Prospective Cohort Study
-combines retrospective and prospective approaches
Relative Risk (RR)
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 nonexposed group
Relative Risk Formular
Incidence rate in the exposed (risk 1)
—————————————————-
Incidence rate in the nonexposed (risk 2)
Relative Risk from the fourfold table
-total number of subjects in the exposure group (exposure status is Yes)= A+B
-total number of subjects in the nonexposed group (exposure status is No)= C+D
-Incidence of disease in the exposed group= A/(A+B)
-Incidence of disease in the nonexposed group = C/(C+D)
-The relative risk (RR)= [A/(A+B)]/[C/(C+D)]
Attributable risk
in a cohort study, refers to the difference between the incidence rate of a disease in the exposed group and the incidence rate in the nonexposed group
– [A/(A+B)*1000] – [C/(C+D)*1000]
Population Risk Difference
-incidence in the total population minus (-) incidence in the nonexposed segment
-example:
1. annual lung cancer incidence among men in the population is 79.4 per 100,000
2. annual lung cancer incidence among nonsmoking men is 28.0 per 100,000
3. 79.4-28.0= 51.4 per 100,000 men
Advantages of Cohort Studies
– permit direct observation of risk
-exposure factor is well defined (specific factor)
-can study exposures that are uncommon in the population
-the temporal relationship between factor and outcome in known (unimportant)
Disadvantages of Cohort Studies
-expensive and time consuming (start with healthy individuals)
-complicated and difficult to carry out
-subjects may be lost to follow-up during the course of the study (dropped out, die, moved away)
-exposures can be misclassified due to inaccurate records
Experimental Studies
– in epidemiology, these are implemented as intervention studies
Intervention Study
“an investigation involving intentional change in some aspect of the status of the subjects…”
Randomized controlled trial (RCT)
“… subjects in a population are randomly allocated into groups, usually called study and control groups, to receive or not to receive an experimental preventive or therapeutic procedure, maneuver, or intervention..”
-very rigorous
Prophylactic trial
designed to test preventive measures
Therapeutic trials
evaluate new treatment methods
Clinical trials
refers to “A research activity that involves the administration of a test regimen to humans to evaluate its efficacy and safety…”
Crossover design
participants may be switched between treatment groups
Community Intervention
an intervention designed for the purpose of educational and behavioral changes at the population level
Quasi-experimental study
a type of research in which the investigator manipulates the study factor but does not assign individual subjects randomly to the exposed and nonexposed groups
Program evaluation
used to determine whether the programs meets stated goals and is justified economically
External Validity
refers to one’s ability to generalize from the results of the study to an external population
-a convenience sample may not demonstrate external validity
-random samples are more likely to demonstrate external validity than convenience samples
Sampling Error
a type of error that arises when values (statistics) obtained for a sample differ from the values (parameters) of the parent population
Internal Validity
-refers to the degree to which the study has used methodologically sound procedures
Bias in Epidemiologic Studies
epidemiologic studies may be impacted by bias, which is “Systematic deviation of results or inferences from truth..”
-flawed set up of studies
-interperation of results
Hawthorne effect
participants’ behavioral changes as a result of their knowledge of being in a study
Recall bia
cases may remember an exposure more clearly than controls
Selection bias
“distortions that result from procedures used to select subjects and from factors that influence participation in the study…”
-health worker effect: the observation that employed populations tend to have a lower mortality experience than the general population”
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”
-example: age as a confounder