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**CPH EXAM REVIEW– EPIDEMIOLOGY**

Lina Lander, Sc.D. Associate Professor Department of Epidemiology, College of Public Health University of Nebraska Medical Center January 24, 2014

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**Review of basic topics covered in the epidemiology section of the exam**

Materials covered cannot replace basic epidemiology course This review will be archived on the NBPHE website under Study Resources

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**Outline Overview Terminology Study design Causation and validity**

Screening

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**Populations Group of people with a common characteristic**

Place of residence, age, gender, religion People who live in Omaha, Nebraska in January, 2014 Occurrence of a life event (undergoing cancer treatment, giving birth) This is different from the study of disease in individuals, and populations are at the heart of epidemiology. What you will find in epidemiology is that everything that involves measurement must be defined as precisely as possible. This includes populations. In general, a population is a group of people with a common characteristic. This common characteristic could be as broad as place of residence or age or gender, or as specific as people who live along the Missouri River, or within one mile of a nuclear power plant. Populations can also be defined by occurrence of a life event, such as a medical procedure, giving birth, or entering college. So, for example, you might be interested in evaluating a treatment in men who have been diagnosed with cardiac arrhythmia. The important aspect of defining the population is to identify men that have been diagnosed with cardiac arrhythmia. That is the population in this example.

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**Populations Membership can be permanent or transient**

Population with permanent membership is referred to as “Fixed” or “Closed” People present at Hiroshima Passengers on an airplane Population with transient membership is referred to as “Dynamic” or “Open” Population of Omaha Because populations can be defined by just about anything, a unifying concept for identifying a population is whether the membership can be permanent or transient. When a population has a permanent membership, we call it a fixed or closed population. An example of this is the Japanese who were present at Hiroshima when the atomic bomb was dropped. No one else can ever be added to this population because this was an historical event. Populations with transient membership are referred to as dynamic or open populations. An example of this is the population of Omaha, where people are migrating in, immigrating out, and people are being born and dying. So new members can be added. Key concepts in the transient population is migration, birth and death.

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Measures of Morbidity

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**Measures of Frequency “Count” - the most basic epidemiologic measure**

Expressed as integers (1, 2, 3, …) Answers the question, “How many people have this disease?” Often the numerator of many measures Important to distinguish between incident (new) and prevalent (existing) cases Counts is the most basic epidemiologic measure. It is never negative and can reach infinity. This measure answers the question “How many people have this disease”? And constitutes the numerator of most epidemiologic measures.

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**Forecast of cancer deaths**

This graph shows the number of cancer deaths in the United States from 1900 to 2000, and you can tell that the number of deaths are increasing monotonically over time. Again, at first glance, you may think that life in the US has become more dangerous and that more people are dying every day. What is missing from this information?

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**Ratio One number (x) divided by another (y): 𝑥 𝑦**

Range: zero (0) to infinity (∞) (x) and (y) may be related or completely independent Sex of children Attending a clinic A ratio is simply one number divided by another. So, x divided by y. You can see that since most epidemiology measures are of this form, most epidemiologic measures are RATIOS. Interestingly, though, only a few epidemiologic measures are actually CALLED ratios. Two examples of ratios are shown at the bottom of the slide. For example, of all children coming into the clinic, what is the ratio of females to males? The numerator and denominator are clearly not related in this measure as they are in the second measure, which is the number of females divided by all the children coming into the clinic. What is this measure? Yes, this is a proportion. So you can see that a proportion is always a ratio, but a ratio is not always a proportion.

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**Which of the following terms is expressed as a ratio (as distinguished from a proportion)?**

(A) Male Births / Male + Female Births (B) Female Births / Male + Female Births (C) Male Births / Female Births (D) Stillbirths / Male + Female Births

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Proportion Ratio in which the numerator (x) is included in the denominator (x+y): Range: zero (0) to one (1) Often expressed as percentage ( e.g., Among all children who attended a clinic, what proportion was female? We saw proportion in the last slide, but here is the example again. And you can see that it is different from a ratio in two ways. One – the upper range stops at 1 or 100% if you are expressing it as a percentage, and the numerator is included in the denominator.

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Rate Can be expressed as (a/T) where (a) = cases and (T) involves a component of time Range: zero (0) to infinity (∞) Measures speed at which things happen Rate of at-risk females coming to a clinic (time) Rate is the most misused measure of frequency. This is likely due to the use of the term in many science disciplines. Rate is expressed as the number of cases divided by a unit of time, rather than a number of people in a population. The often confusing aspect of rate is that a population of risk is used in the denominator, but it is multiplied by the amount of time the population is being followed. Rate measures how quickly things happen.

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**Prevalence Proportion**

Not a rate – no time component in the calculation Measures proportion of existing disease in the population at a given time “Snapshot” Dimensionless, positive number (0 to 1)

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**Prevalence proportion**

Where: A = number of existing cases B = number of non-cases N = total population

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Incidence Measures the occurrence of new cases in a population at risk over time Can be measured as a proportion or a rate The most fundamental epidemiologic indicator Measures force of morbidity (as a rate) Measures conversion of health status (proportion /rate) In addition to the form of the measure, we also need to distinguish whether we are talking about incident or prevalent disease. Incidence measures the occurrence of new cases in a population over a specified period of time. Incidence is usually considered the most fundamental epidemiologic indicator because it measures the conversion of health state. As a rate, incidence measures the force of morbidity (or mortality if we’re measuring death) over time.

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**Incidence proportion Synonyms: incidence, cumulative incidence, risk**

Measures probability (risk) of developing disease during period of observation Dimensionless, positive number (0 to 1) Incidence proportion, then, measures the probability of developing a disease during a period of observation. This is what we measure when we talk about RISK of disease. Risk is the only measure that can be interpreted on an individual level. So, when you go to the doctor, you could actually ask, what is my risk of getting this disease.

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**Incidence Proportion Where: a = number of new onset cases (events)**

The basic measure of incidence is incidence proportion, which is simply the number of new cases of disease divided by the Number of people in a population at risk at the beginning of a time period. Where: a = number of new onset cases (events) N = population-at-risk at beginning

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Incidence Proportion Appropriate for fixed (closed) populations and short follow-up Must specify time period of observation because risk changes with time Not appropriate for long-term follow-up due to potential loss of subjects Assuming: complete follow-up, same risk over time

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Follow 2000 newborns at monthly intervals to measure development of respiratory infection in the first year Suppose 50 infants develop respiratory infection in first year of life The risk (probability) of developing a respiratory infection in the first year of life is ~ 2.5% 25 of 1000 infants in this population or 1 in 40 will develop infection in the first year of life.

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Incidence Rate Measures how rapidly new cases develop during specified time period Cases per person-time Synonyms: incidence, incidence density, rate Follow-up may be incomplete Risk period not the same for all subjects

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**Incidence Rate Where: a = number of new onset cases**

T = person-time at risk during study period (follow-up)

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Person-time Accounts for all the time each person is in the population at risk The length of time for each person is called person-time Sum of person-times is called the total person-time at risk for the population

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**Person-time 1 2 3 4 5 Died T = person-time at risk during study period**

= = 16 person-years

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**Person-time Assumption**

100 persons followed 10 years = 1000 person years 1000 persons followed for 1 year = 1000 person years

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Follow 2000 newborns at monthly intervals to measure development of respiratory infection in the first year 50 infants develop respiratory infection 1900 complete the first year disease free 25 complete 3 months (0.25 years) before infection 25 complete 6 months (0.5 years) before infection Calculate incidence rate: = 2.6 per 100 person-years

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**Incidence, Prevalence, Duration**

Prevalence increases as new cases added to the existing cases (i.e., incidence) Prevalence decreases as people are cured or die Prevalence = Incidence * Duration

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Measures of Mortality

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**Mortality Measures the occurrence death**

Can be measured as a proportion or a rate Can measure disease severity or effectiveness of treatment

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Mortality Rate Measures rate of death in the population over a specified amount of time Positive number (0 to ∞) Can be a measure of incidence rate (risk) when disease is severe and fatal, e.g. pancreatic cancer Synonym: fatality rate

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**Mortality Rate Where: d = number of deaths**

N = total population at mid-point of time period T = follow-up time (usually one year)

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**Cancer Death Rates*, for Men, US, 1930-2003**

*Age-adjusted to the 2000 US standard population. Source: US Mortality Public Use Data Tapes , US Mortality Volumes , National Center for Health Statistics, Centers for Disease Control and Prevention, 2002.

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**Case Fatality Rate This is not a rate, this is a proportion**

Proportion of deaths from a specific illness Case Fatality Rate Where: a = Number of deaths from an illness N = Number of people with that illness What percentage of people diagnosed as having a disease die within a certain time after diagnosis?

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Case-fatality rate Case-fatality – a measure of the severity of the disease Case-fatality – can be used to measure benefits of a new therapy As therapy improves - the case-fatality rate would be expected to decline e.g. AIDS deaths with the invention of ARVs

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**Proportionate Mortality**

Of all deaths, the proportion caused by a certain disease Can determine the leading causes of death Proportion of cause-specific death is dependent on all other causes of death This does not tell us the risk of dying from a disease Proportionate mortality from Cardiovascular Disease in the U.S, in 2013 = # of U.S deaths from cardiovascular diseases in 2013 x 1,000 Total deaths in the U.S. for 2013

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Which measure of mortality would you calculate to determine the proportion of all deaths that is caused by heart disease? (A) Case fatality (B) Cause-specific mortality rate (C) Crude mortality rate (D) Proportionate mortality ratio (E) Potential years of life lost

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**Other Mortality Rates Crude Mortality Rate**

Includes all deaths, total population, in a time period Cause-Specific Mortality Rate Includes deaths from a specific cause, total population, in a time period Age-Specific Mortality Rate Includes all deaths in specific age group, population in the specific age group, in a time period

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**Age-Specific Mortality Rates**

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**Mortality Rates (Year = 2000)**

Panama Sweden Population = 2,899,513 Deaths = 13,483 Mortality Rate = 4.65 per 1000 per year Population = 8,923,569 Deaths = 93,430 Mortality Rate = 10.47 per 1000 per year Why do you think Sweden has almost a 2x higher mortality rate?

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**Differences in Mortality Rates**

Crude mortality rates do not take into account differences between populations such as age

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**Can we remove this confounding by age?**

Separate (stratify) the population into age groups and calculate rates for each age Compare age-specific mortality rates If two different populations, adjust (standardize) the mortality rates of the two populations, taking into account the age structures Results in comparable rates between populations or in the same population over time

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**Direct Standardization**

If the age composition of the populations were the same, would there be any differences in mortality rates? Direct age adjustment is used to remove the effects of age structure on mortality rates in two different populations Apply actual age-specific rates to a standard population (US population 2000)

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**Indirect Standardization**

When age-specific rates are not available – use age-specific mortality rates from the general population to calculate expected number of deaths Standardized mortality ratios (SMR) = observed deaths/ expected deaths If the age composition of the populations were the same, would there be any differences in mortality rates?

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Study Design Experimental studies (Clinical Trial, Randomized Controlled Trial) Observational studies Cohort Case-control Cross-sectional Ecological

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**Experimental studies are characterized by:**

The population under study: who is eligible for study entry? The intervention(s) being used or compared: what treatment(s) are being used? (Therapeutic (e.g., drug) or preventive (e.g., education) The method of treatment assignment: how are subjects assigned to intervention(s)? The outcomes of interest: how will success be measured?

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**Randomized Controlled Trials**

A randomized controlled trial is a type of experimental research design for comparing different treatments, in which the assignment of treatments to patients is made by a random mechanism. Customary to present table of patient characteristics to show that the randomization resulted in a balance in patient characteristics.

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**Randomized Controlled Trials**

Time

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**Steps in carrying out a clinical trial**

Select a sample from the population Ethical considerations Measure baseline variables Randomize Apply interventions Follow up the cohorts Measure outcome variables (blindly, if possible)

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Use of “Blinding” Important when knowing treatment could influence the interpretation of results Especially important when outcomes are subjective (pain, functional status) and/or when placebo is employed (either alone or to mask actual treatment) Placebo- ensure control and treatment group have same “experience” May not be necessary if the outcome is an object measure (death, blood glucose)

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Treat Blinding of the participants to which treatment was used ensures that bias is avoided Single-blind: patient does not know what treatment they are receiving Double–blind: patient and investigator do not know what treatment (cannot be used for some treatments, e.g. surgery) Once we have comparable groups, we treat in a double-blind fashion and according to standard practice.

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It All Comes Down to… Obtaining groups that are comparable for everything except the treatment… So that differences in outcome can fairly be ascribed only to the difference between the groups (i.e., to the treatment).

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Cohort Studies Definition: groups, defined on the basis of some characteristics (often exposure and non-exposure) are typically prospectively followed to see whether an outcome of interest occurs (may also be retrospective) Comparison of interest: Compare the proportion of persons with the disease in the exposed group to the proportion with the disease in the unexposed group. Motivation: If the exposure is associated with the disease, we expect that the proportion of persons with the disease in the exposed group will be greater than the proportion with disease in the unexposed group.

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**Cohort Studies Not Exposed Exposed Not diseased Not diseased Diseased**

Prospective Retrospective past now Not Exposed Diseased Not diseased Exposed Diseased Not diseased future now

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**Prospective cohort studies**

Define sample free of the disease/outcome of interest, measure the exposure and classify to exposed vs unexposed at time zero, then follow up at fixed time point to ascertain outcome Measure the ratio of outcome between the exposed and unexposed (Relative Risk)

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**Retrospective cohort studies**

Synonyms: historical cohort study, historical prospective study, non-concurrent prospective study Do not design retrospective cohort studies a priori – question always in retrospect Exposures and Outcomes have already occurred - data on the relevant exposures and outcomes must have been collected

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**Cohort study strengths**

May be used to define incidence / natural history Known temporal sequence Efficient in investigating rare exposures Permits study of multiple exposures AND outcomes Fewer biases or bias can be limited or evaluated Homogenous sample population Accurate measurement of important variables No bias in ascertainment of outcome

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**Cohort study limitations**

Expensive and inefficient – especially for rare diseases or outcomes (large sample size, long-term follow up) Associations may be due to confounding Can adjust statistically for any measured potential confounders Must exclude subjects with outcome present at onset Disease with long pre-clinical phase may not be detected Sensitive to follow-up bias (loss of diseased subjects)

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Case-control Studies Definition: compare various characteristics (past exposure) for cases (subjects with disease) to those of controls (subjects without the disease) Comparison of interest: Compare the proportion with the exposure in the cases to the proportion with the exposure in the control group. Motivation: If the exposure is associated with the disease, we expect that the proportion of persons with the exposure in the cases will be greater than the proportion with the exposure in the control group.

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**Case-control Studies Cases with disease Exposed in past**

Not Exposed in past Controls without disease Exposed in past Not Exposed in past

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**Case-Control Studies Efficient for rare diseases**

Efficient for diseases with long latency Can evaluate multiple exposures Lower costs of exposure assessment relative to cohort studies Incident cases preferable for causal research Challenges of control selection Challenges of retrospective exposure assessment

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**Case-control studies are among the best observational designs to study diseases of:**

(A) High prevalence (B) High validity (C) Low case fatality (D) Low prevalence

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**Nested Case-Control Studies**

Case-Cohort Study: A case control study nested in a cohort study Controls selected either at baseline (case-cohort) or at the time the case occurs (nested) Advantage Data on exposure are obtained before disease develops Possibility of recall bias is thus eliminated. Less expensive than expanding the analysis to include the entire cohort Here the OR is a statistically unbiased estimate of relative risk

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**Example Is smoking associated with coronary heart disease (CHD)?**

3000 smokers and 5000 nonsmokers were followed to see if they developed CHD Case control or cohort study? Exposure Developed CHD No CHD Total Smoker Non-smoker 84 87 171 2916 4913 7829 3000 5000 8000

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**Cross-Sectional Studies**

Prevalence studies All measurements of exposure and outcome are made simultaneously (snapshot) Disease proportions are determined and compared among those with or without the exposure or at varying level of the exposure Examine association – determination of associations with outcomes; generates hypotheses that are the basis for further studies Most appropriate for studying the associations between chronic diseases and and chronic exposure Sometimes useful for common acute diseases of short duration

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**Cross-Sectional Studies**

Time Defined Population Gather Data on Exposure (Cause) and Disease (Effect / Outcome) T0 Exposed: Have Disease Exposed: No Disease Not Exposed: Have Disease Not Exposed: No disease

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**Ecological The unit of observation is the population or community**

Disease rates and exposures are measured in each of a series of populations Disease and exposure information may be abstracted from published statistics and therefore does not require expensive or time consuming data collection

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**Study Design Category Type Subtype Characteristics Analytic**

Experimental Investigates prevention and treatment Observational Investigates causes, prevention and treatment Cohort Investigates health effects of exposure Cross-Sectional Examines exposure-disease associations at a point in time Case-Control Investigates risk factors for disease Ecological Examines exposure-disease association at the population level Descriptive Describes health of populations

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Probabilities Denote the probability of an event by p, where p ranges from 0 to 1. Notation: p = probability that event occurred 1-p = probability that event did not occur

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**Example What is the probability of CHD? 171/8000 = 0.02 Exposure**

Developed CHD No CHD Total Smoker Non-smoker 84 87 171 2916 4913 7829 3000 5000 8000

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**Relative Risk RR = incidence among exposed incidence among unexposed**

Approximates how much more likely it is for the outcome to be present among a certain group of subjects than another group RR = 1 implies that the risk is the same in the two groups RR < 1 implies that the risk is higher in the unexposed RR > 1 implies that the risk is higher in the exposed

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**Example Sleeping Position and Crib Death Crib Death**

Usual sleeping position YES NO TOTAL Prone Other 9 6 837 1755 846 1761 Total 15 2592 2607 1-year cumulative incidence prone = 9/846 = per 1000 1-year cumulative incidence other = 6/1761 = per 1000 Risk Ratio = per = 3.1 3.41 per 1000

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Odds Ratios Relative risk requires an estimate of the incidence of the disease For case control studies, we do not know the incidence of disease because we determine the number of cases and controls when the study is designed For case control studies, use the odds ratio (OR)

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**Odds Odds are another way of representing a probability**

The odds is the ratio of probability that the event of interest occurs to the probability that it does not. The odds are often estimated by the ratio of the number of times that the event occurs to the number of times that it does not.

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Odds Ratio

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**Odds Ratio Example CHD Cases Controls Total Smokers 112 176 288**

Nonsmokers 88 224 312 Total 200 400 600 Case control study of 200 CHD cases and 400 controls to examine association of smoking with CHD (Note: now we are examining the probability of exposure) What is the probability of smoking among CHD cases? p = 112/200=0.56 What is the odds of smoking among CHD cases? p/(1-p) = 0.56/0.44 = 1.27

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**Odds Ratio Example CHD Cases Controls Total Smokers 112 176 288**

Nonsmokers 88 224 312 Total 200 400 600 What is the probability of smoking among controls? p = 176/400=0.44 What is the odds of smoking among controls? p/(1-p) = 0.44/0.56 = 0.79 The odds ratio is / 0.79 = 1.62 Interpretation: The odds of smoking is 1.62 times higher for CHD cases compared with controls

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**Odds Ratio vs. Relative Risk**

Disease No Disease Total Exposed a b a+b Not Explosed c d c+d a+c b+d a+b+c+d

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**Odds ratio Odds ratio = odds of exposure in case**

odds of exposure in controls OR=1 exposure is not associated with the disease OR>1 exposure is positively associated with the disease OR<1 exposure is negatively associated with the disease

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**Odds Ratio vs. Relative Risk**

Both compare the likelihood of an event between two groups OR compares the relative odds of an event in each group RR compares the probability of an event in each group More ‘natural’ interpretation because risk measured in terms of probability Cannot always be computed Can lead to ambiguous interpretations

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A case-control study comparing ovarian cancer cases with community controls found an odds ratio of 2.0 in relation to exposure to radiation. Which is the correct interpretation of the measure of association? Women exposed to radiation had 2.0 times the risk of ovarian cancer when compared to women not exposed to radiation (B) Women exposed to radiation had 2.0 times the risk of ovarian cancer when compared to women without ovarian cancer (C) Ovarian cancer cases had 2.0 times the odds of exposure to radiation when compared to controls (D) Ovarian cancer cases had 2.0 times the odds of exposure to radiation when compared to women with other cancers

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**Odds Ratios vs. Relative Risks**

In general, odds ratios summarize associations from case-control studies and cross-sectional studies, and relative risks can be used to summarize associations in cohort studies. Odds ratio can be used to estimate the relative risk when in a case control study when: 1. Cases are representative of people with the disease in the population with respect to history of exposure AND The controls are representative of people without the disease in the population with respect to history of exposure AND The disease is rare

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**Odds ratio estimates relative risk when disease is rare**

When the disease is rare, the number of people with the disease (a and c) is small so that a+b≈b and c+d≈d

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**Odds Ratios for matched case control studies**

Often, cases are matched with a control based on age, sex, etc. For a matched study, describe the results for each pair Concordant pairs: both case and control exposed or both not exposed Discordant pairs: Case exposed/control unexposed or case unexposed/control exposed

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**Odds Ratios for matched case control studies**

Exposed Unexposed a b c d Cases OR is based on the discordant pairs: OR = b/c

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Cohort study is to risk ratio as: (A) Ecologic fallacy is to cross-sectional study (B) Case-control study is to odds ratio (C) Genetics is to environment (D) Rate ratio is to ecologic study

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**Measures of Effect and Association**

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**Risk RR and OR measure strength of the association**

How much of the disease can be attributed to the exposure? How much of the CHD risk experienced by smokers can be attributed to smoking? OR and RR do not address this.

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**Measures of Association**

Contrast measure of occurrence in two populations Cancer incidence rates in males and females in Canada Incidence rate of dental caries in children within a community before and after fluoridation Both of these are measures of association

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**Absolute Measures Causal rate difference Incidence rate difference**

Incidence density difference Rate difference Attributable rate Causal risk difference Incidence proportion difference Cumulative incidence difference Risk difference Excess risk Attributable risk

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**Risk Difference Most often referred to as “attributable risk”**

Refers to the amount of risk attributable to the exposure of interest For example, in the birth cohort analysis, where exposure = prenatal care in the first 5 months RD = R1 – R0 = Excess risk of preterm birth attributable to prenatal care

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**Absolute Excess Measures**

Incidence proportion (or rate) Absolute Excess Measures Incidence due to exposure Excess risk (or rate) in the exposed Incidence not due to exposure Background risk – incidence rate in unexposed Unexposed Exposed If E is thought to cause D: Among persons exposed to E, what amount of the incidence of D is E responsible for?

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**Example Sleeping Position and Crib Death Crib Death**

Usual sleeping position YES NO TOTAL Prone Other 9 6 837 1755 846 1761 Total 15 2592 2607 1-year cumulative incidence prone = 9/846 = per 1000 1-year cumulative incidence other = 6/1761 = per 1000 Risk difference = per 1000 – 3.41 per 1000 = 7.23 per 1000 Added risk due to exposure

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**Attributable Risk Percent**

(Risk difference / Risk in Exposed) x 100 What proportion of occurrence of disease in exposed persons is due to the exposure?

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**Example Sleeping Position and Crib Death Usual sleeping position YES**

NO TOTAL Prone Other 9 6 837 1755 846 1761 Total 15 2592 2607 1-year cumulative incidence prone = 9/846 = per 1000 1-year cumulative incidence other = 6/1761 = per 1000 Risk difference = per 1000 – 3.41 per 1000 = 7.23 per 1000 Attributable risk percent = per 1000 – 3.41 per 1000 x = 68.0% 10.64 per 1000

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**Population Attributable Risk**

What is the excess risk in the population caused by exposure E?

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**Population Attributable Risk Percent**

What proportion of occurrence of disease in the population is due to the exposure?

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**Population Attributable Risk**

Incidence proportion (or rate) Unexposed Exposed Population Should resources be allocated to controlling E or, instead, to exposures causing greater health problems in the population

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**Example Sleeping Position and Crib Death Crib Death**

Usual sleeping position YES NO TOTAL Prone Other 9 6 837 1755 846 1761 Total 15 2592 2607 1-year cumulative incidence total = 15/2607 = 5.75 per 1000 1-year cumulative incidence other = 6/1761 = per 1000 Population attributable risk (PAR) = 5.75 per 1000 – 3.41 per 1000 = = 2.35 per 1000

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**Example Sleeping Position and Crib Death Crib Death**

Usual sleeping position YES NO TOTAL Prone Other 9 6 837 1755 846 1761 Total 15 2592 2607 1-year cumulative incidence total = 15/2607 = 5.75 per 1000 1-year cumulative incidence other = 6/1761 = per 1000 Population attributable risk percent (PAR) = = 5.75 per 1000 – 3.41 per 1000 x 100 = 40.8% 5.75 per 1000

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**Population Attributable Risk Percent**

Affected by the prevalence of exposure in the population and the relative risk

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**Absolute Measures Measure Abbrev. Formula Helps answer the question**

Risk difference (attributable risk to the exposed) RD AR I1 – I0 If E is thought to cause D: Among persons exposed to E, what amount of the incidence of D is E responsible for? Attributable risk percent AR% [(I1 – I0)/I1] X 100 What proportion of occurrence of disease in exposed persons was due to the exposure? Attributable risk to the population PAR IT – I0 Should resources be allocated to controlling E or, instead, to exposures causing greater health problems in the population? Attributable risk to the population (%) PAR% [(IT – I0)/IT] X 100 What portion of D in the population is caused by E? Should resources allocated for D be directed toward etiologic research or E?

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Summary of Measures Absolute measures address questions about public health impact of an exposure Excess risk in the exposed or population attributable to the exposure Relative measures address questions about etiology and relations between exposure and outcome Relative difference in risk between exposed and unexposed populations

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Causal Inference

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Definition of a cause “That which produces an effect, result or consequence or the one such as a person, event or condition that is responsible for an action or result” American Heritage Dictionary Implies reason and occasion

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**Key Characteristic of a Cause**

Essential attributes: association, time order and direction Causes include: host and environmental factors active agents and static conditions Causes may be either positive (presence induces disease) or negative (absence induces disease)

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**The Epidemiologic Triad**

HOST ENVIRONMENT AGENT

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**Factors involved in the Natural History of Disease**

Agent Vector In the case of many communicable diseases, such as malaria, the agent can only reach the host via a third party, called the vector. The vector is animate. For example, the vector for malaria is the female anopheles mosquito. She can convey the malaria parasite to a susceptible host when she consumes a blood meal. Environment Host

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Risk factors vs. causes Risk factors often used in epidemiology instead of causes A cautious way of making causal inference Risk factors are not direct causes of disease Serve to identify proximate causes

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Causal Inference During 1950s -1960s epidemiologists developed a set of postulates for causal inferences regarding non-infectious diseases of unknown etiology Response to the discovery of association between smoking and lung cancer Debates by many epidemiologists yielded 5 criteria in the 1964 Report of the Advisory committees to the US Surgeon general on Smoking and Health Sir Austin Hill came up with the best known criteria or guidelines in 1965 In 1976 Rothman presented a view of causations now known as the “Sufficient-Component Theory of Causation”

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**Hill Criteria 1. Strength of Association 2. Consistency**

3. Specificity of the Association 4. Temporal relationship 5. Biological gradient 6. Biologic plausibility 7. Coherence 8. Experiment 9. Analogy

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**Sufficient-Component Cause Model**

Sufficient cause is a complete causal mechanism that inevitably produces disease Sufficient cause is not a single factor but rather a minimal set of factors that inevitably produce disease Sufficient cause for AIDS may include: exposure - HIV infection, susceptibility, lack of preventive exposures-absence of ARVs Each participating factor in a sufficient cause is termed a component cause

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**Disease Causation – 2 components**

Sufficient Cause precedes the disease if the cause is present, the disease always occurs Necessary Cause if the cause is absent, the disease cannot occur 1. Using these 2 components of Causation, we can produce a unified framework of causation that will encompass all Disease Processes

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**Disease causation: Types of causal relationships**

Necessary and sufficient: Without that factor, the disease never develops, and in the presence of that factor, the disease always develops Necessary but not sufficient: Without that factor, the disease never develops but need other factors as well Sufficient but not necessary: The factor can produce the disease, but so can other factor. Neither sufficient not necessary: The factor itself cannot cause the disease but plays a role—multiple factors interact to cause the disease

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**Sufficient-Component Cause Model - attributes**

Blocking the action of a single component stops the completion of the sufficient cause, thus prevents the disease from occurring by that pathway Completion of a sufficient cause is synonymous with the biologic onset of disease Component causes may be distant causes and others may be proximate causes many causal components remain unknown

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**From Study to Causation**

Associations between ‘exposures’ and outcomes identified in observational studies may or may not be ‘causal’ There is need to pay attention to valid assessment of exposure and outcome in order to think about causality Reliability Validity External validity Internal validity – three concepts are considered Bias Confounding Chance (Random error)

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Validity Implies that a measure purports to measure what it is expected to measure: Appropriate Accurate (has same numerical value as the phenomenon being investigated, i.e. free of systemic error or bias) Precise (minimal variations are only because of chance or random error) Validity of a study implies that the findings are the “truth” The degree to which a measurement or study reaches a correct conclusion Two types of validity: Internal validity, External validity

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**External validity: generalizabilty**

The extent to which the results of a study are applicable to the general population Do the study results apply to other patients? A representative sample is drawn from the population (usually randomly) Individuals have equal chance to participate in the study Usually involves a sampling frame Inference is made back to the population

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Internal validity Is the extent to which the results of the study accurately reflect the true situation of the study population Is influenced by: Chance The probability that an observation occurred unpredictability without discernible human intention or observable cause Bias Any systemic error (not random or due to chance) in a study which leads to an incorrect estimate of the association between exposure and disease Confounding The influence of other variables in a study which leads to an incorrect estimate of the association between exposure and disease

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Random error Chance “That part of our experience that we cannot predict” (Rothman and Greenland) Usually most easily conceptualized as sampling variability and can be influenced by sample size The term “random error” is widely employed yet less widely understood. Rothman and Greenland (Modern epidemiology, p78) define it as “that part of our experience that we cannot predict” (some might say the part that we do not attempt to predict). The concept can easily be demonstrated in the form of sampling variability, where we can readily show that a series of samples from a single population will in general differ from one another and from the population, at least slightly.

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**Random error can be problematic, but . . .**

Influence can be reduced – increase sample size – change design of sampling – improve precision of instrument Probability of some types of influence can be quantified (e.g., confidence interval width) Random error can certainly be problematic, but it is generally not as problematic as systematic error. For one, we know a great deal about random error and how to reduce it. We can, for one, increase sample size. It may also be possible to reduce random error by changing the design of our sampling plan or using more precise measurement instruments. Moreover, the probability of various degrees of influence from random error can be quantified, so that we can use confidence intervals to express the uncertainty inherent in our estimates due to sampling variability. The precision of an estimate is defined as the reciprocal of the standard error of the estimate, but the width of the confidence interval is often used in epidemiology. The difference between the upper and lower 95% confidence limits for a rate or proportion is approximately four (2 x 1.96) times the standard error of the point estimate. The standard errors for many ratio measures (e.g., , risk ratio, rate ratio, odds ratio) are obtained with a log transformation, so the width of their confidence intervals is quantified as the ratio of the upper to the lower confidence limit (called the confidence interval ratio, or CLR). The natural log of the CLR is 2 x 1.96 times the log of the standard error.

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I. Bias - Definition Any systemic error (not random or due to chance) in a study which leads to an incorrect estimate of the association between exposure and disease or outcome Therefore: Bias is a systematic error that results in an incorrect (invalid) estimate of the measure of association

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I. Bias - Definition Can create spurious association when there is none (bias away from the null) Can mask an association when there is one (bias towards the null) Bias is primarily introduced by the investigator or study participants Bias does not mean that the investigator is “prejudiced” Can occur in all study types: experimental, cohort, case-control Occurs in the design and conduct of a study Bias can be evaluated but not “fixed” in the analysis phase Two main types are selection and observation bias

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Direction of bias Bias towards the null – observed value is closer to 1.0 than is the true value Bias away from the null – observed value is farther from 1.0 than is the true value Observed True Null True Observed 2 Null Observed 1

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**Types of bias Selection bias Refusals, exclusions, non-participants**

Failure to enumerate the entire population Loss to follow up Observation/Information bias Diagnostic (lead time) surveillance bias Interviewer bias Recall bias Classification of exposure and outcome Misclassification bias (is part of information bias) Non-differential Differential

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II. Selection bias Any systematic error that occurs in the process of identifying study populations The error that occurs whenever the identification and selection of individual subjects for inclusion into study is not independent of outcome (cohort) or exposure (case-control) Error due to systematic difference between those selected for study versus those not selected for the study

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II. Selection Bias Results from procedures used to select subjects into a study that lead to a result different from what would have been obtained from the entire population targeted for the study Most likely to occur in case-control or retrospective cohort because exposure and outcome have occurred at the time of study selection Selection bias can also occur in prospective cohort and experimental studies form differential loss to follow-up - impacts which subjects are “selected” for the analysis

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II. Selection bias Occurs when there is a systematic difference between those selected for study versus those who were not Refusers, non-participants, non-response, exclusions Failure to enumerate the entire population Those lost to follow up if related to exposure or outcome Differential selection of exposed/unexposed groups, or cases and controls Volunteers Healthy workers - example

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**II. Selection bias- cohort study**

Selection bias occurs when selection of exposed and unexposed subjects is not independent of the outcome (so this type can only occur in retrospective cohort study) Examples: A retrospective study of an occupational exposure to asbestos and lung disease in a factory setting The exposed and unexposed groups are enrolled on the basis of prior employment records The records are old and many are lost, so the complete cohort working in the plant is not available for study. If people who did not develop the disease and were exposed were more likely to have their records lost, then there will be an overestimate of association between the exposure and disease

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**II. Selection bias- cohort study**

Solutions: Increase participation Get relevant information on refusers Develop follow-up mechanisms Use comparable populations Valid assessment of outcome in prospective cohort studies (for example: blinding)

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**II. Selection bias: case-control study**

Sources of selection bias Decisions about selecting incident or prevalent (survival) cases When controls do not reflect the population that gave rise to the cases The selection of cases and controls must be independent of the exposure status Do controls in the study have higher or lower prevalence of exposure than controls not selected for the study? Cases and controls should have the same exposure opportunities (e.g., welders and general population)

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**II. Selection bias: case-control study**

Occurs when controls or cases are more or less likely to be included in a study if they have been exposed – inclusion in the study is not independent of exposure Results: relationship between exposure and disease observed among study participants is different from relationship between exposure and disease in eligible individuals who were not included The odds ratio from a study that suffers from selection bias will incorrectly represent the relationship between exposure and disease in the overall study population

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**II. Selection bias: cross-sectional study**

Selection bias can occur when Sampling frame does not represent the true underlying population of interest Voter registration lists Driver’s license records Telephone lists

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**II. Selection bias: cross-sectional study**

Estimates of association do not take into account the sampling structure There are sufficient numbers of refusers that the underlying sampling structure is compromised When the “sample” is a convenience sample (sampling fraction)

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**II. Selection Bias: solutions?**

Little or nothing can be done to fix this bias once it has occurred in cross-sectional studies Need to avoid it during design and implementation: using the same criteria for selecting cases and controls obtaining all relevant subject records obtaining high participation rates taking in account diagnostic patterns of disease

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**III. Observation/information bias**

An error that arises from systematic differences in the way information on exposure or disease is obtained from the study groups Results in participants who are incorrectly classified as either exposed or unexposed or as diseased or not diseased Occurs after the subjects have entered the study Several types of observation bias: recall bias, interviewer bias, and differential and non-differential misclassification

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**III. Observation/Information bias**

Recall bias People with disease remember or report exposures differently (more/less accurately) than those without disease Differential ability of subject to remember previous activities and exposures, e.g. in serious diseases Cases search their memory to understand their illness E.g., birth defects Can result in over-or under-estimation of measure of association

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**III. Observation/Information bias**

Recall bias Solutions: Use controls who are themselves sick Use standardized questionnaires that obtain complete information Mask subjects to study hypothesis

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**III. Observation/Information bias**

Interviewer bias Systematic difference in soliciting, recording, interpreting information Can occur whenever exposure information is sought when outcome is known (as in case-control) or when outcome information is sought when exposure is known (as in cohort study)

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**III. Observation/Information bias**

Interviewer bias The way interviewer asks questions, and there is possibility of probing e.g. in-person interviews and telephone interviews, especially where the outcome has already occurred (case-control, and retrospective cohort studies) Solutions: Mask interviewers to study hypothesis and disease or exposure status of subjects Use standardized questionnaires, or standardized methods of outcome or exposure ascertainment Use biomarkers to compare when possible Surrogates tend to underreport exposures

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**III. Observation/Information bias**

Classification of exposure and outcome Leads to misclassification bias If exposure status is known in cohort studies, or outcome status is known in case-control studies Solution- blinding of data collectors to exposure/outcome status

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**III. Observation/Information bias – Misclassification bias**

A type of information bias Error arising from inaccurate measurement or classification of study subjects or variables Subject’s exposure or disease status is erroneously classified Happens at the assessment of exposure or outcome in both cohort and case-control studies Two types: non-differential and differential

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**A. Non-differential misclassification**

Inaccuracies with respect to disease classification are independent of exposure Inaccuracies with respect to exposure are independent of disease status The probability of exposure (or of outcome) misclassification is the same for cases and controls (or in study/comparison groups) Bias results towards the null - if the exposure has two categories, will make groups more similar (Type II error) Solution: Use multiple measurements, most accurate sources of information

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**B. Differential Misclassification**

Probability of misclassification of disease or exposure status differs for exposed and unexposed persons (cohort) or presence of absence of exposure (case-control) Probability of misclassification is different for cases and controls or for levels of exposure within cases and controls Direction of bias is unknown, i.e. overestimation or underestimation of the true risk Know that the observed OR deviates from truth, but direction is unknown

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**B. Differential Misclassification**

Also known as systematic misclassification: The probability of misclassification of disease or exposure status is correlated with presence or absence of characteristic in the study or control group. Thus, misclassification of presence or absence of disease differs for exposed and unexposed persons, or of presence or absence of exposure in cases and controls: it is differential

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Confounding

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Definition and Impact An alternate explanation for the observed association between exposure and disease “A mixing of effects”: the association between exposure and disease is distorted because it is mixed with the effects of another factor that is associated with the disease Result of confounding is to distort the true association toward the null (negative confounding) or away from the null (positive confounding)

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Confounder A confounder is associated with the exposure and independently of that exposure is a risk factor for the disease A confounder has effect on the outcome which can be: overestimating (positive), or underestimating (negative) or even change the direction of the observed effect, a spurious relationship

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**Criteria for a variable to be a confounder**

The third variable must not be an intermediate link in the causal chain between exposure and outcome (i.e., is not an intermediate or intervening variable) The third variable must cause the outcome event (i.e., must be an independent predictor of disease with or without exposure) The third variable must be associated (correlated) with exposure (but not caused by the exposure)

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Example: smoking is a confounder of effect of occupational exposures (to dyes) on bladder cancer age is confounder of effect of DDT pesticide exposure and breast cancer

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**Opportunities for confounding**

In an experimental designs: Participation differs in study and control groups There is no evidence for randomization There is evidence of residual confounding In cohort and case-control studies: When selection of comparison group differs by subject characteristics When risk factors other than the exposure are distributed differently between the exposed and unexposed groups

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**Controlling for confounding**

In the design phase: Goal is to eliminate or reduce variation in the level of the confounding factor between compared groups Remember, a variable can only be a confounder if it is different between compared groups.

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**Control for confounding- design phase**

Randomization With sufficient sample size, randomization is likely to control for both known and unknown confounders- but not guaranteed Restriction Restrict admissibility criteria for study subjects and limit entrance to individuals who fall within a specified category of the confounder Matching Select study subjects so that the potential confounders are distributed in an identical manner among the exposed and unexposed groups (cohort study) or among the cases and controls (case-control study)

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**Control for confounding- analysis phase**

Standardization: by age, race, gender, or calendar time in order to make fair comparisons between populations Stratified analysis: test for homogeneity between strata to check if it is confounding or effect modification, only pool for summary measure if evidence of homogeneity Matched analysis: implement analysis of matched design Restriction: Restrict during data analysis Multivariate analysis: To enable controlling for several potential confounders simultaneously

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**Effect modification Interaction**

The strength of the association between an exposure and disease differs according to the level of another variable. Modification of the relationship between exposure and a disease by a third variable. If the association changes according to the level of the third variable, then effect modification is present

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Measurement Error

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Measurement Measurement of exposure, outcome, and other relevant characteristics are a key part of epidemiologic studies Almost all tests and measures are imperfect! Knowledge of how well a measure performs helps to: Choose alternative measures Interpret results of studies using a specific measure

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Reliability How closely do duplicate measurements of the same characteristic agree with each other Examples: Test-retest reliability: agreement between responses on a questionnaire that is administered two (or more) times to the same person Intra-observer: agreement of a given interpreter with him/herself Inter-observer: agreement among different interpreters Reliability is usually higher with more standardized or automated measurement procedures, lower when more complex judgments are required by human observers (e.g., x-ray reading)

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Validity The degree to which an instrument measures what it sets out to measure For many epidemiologic applications, the underlying characteristic being measured is dichotomous (e.g., diseased vs. non diseased; exposed vs. not exposed). Then, validity can be regarded as having two components: Sensitivity Specificity

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**Results of Screening Test**

Sensitivity The probability of testing positive if the disease is truly present Sensitivity = a / (a + c) True Disease Status + - Results of Screening Test a b c d

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**Results of Screening Test**

Specificity The probability of screening negative if the disease is truly absent Specificity= d / (b + d) True Disease Status + - Results of Screening Test a b c d

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**Test Disease PRESENT (+) ABSENT (-) Test positive (+) TP FP**

Test negative (-) FN TN Test Sens= TP /(TP+FN) Spec= TN /(TN+FP) PPV= TP /(TP+FP) NPV= TN /(TN+FN)

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**Relationship between Sensitivity and Specificity**

Lowering the criterion of positivity results in an increased sensitivity, but at the expense of decreased specificity Making the criterion of positivity more stringent increases the specificity, but at the expense of decreased sensitivity The goal is to have a high sensitivity and high specificity, but this is often not possible or feasible

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**Relationship between Sensitivity and Specificity**

The decision for the cut-point involves weighing the consequences of leaving cases undetected (false negatives) against erroneously classifying healthy persons as diseased (false positives) Sensitivity should be increased when the penalty associated with missing a case is high When the disease can be spread When subsequent diagnostic evaluations are associated with minimal cost and risk Specificity should be increased when the costs or risks associated with further diagnostic techniques are substantial (minimize false positives) Example: positive screen requires that a biopsy be performed

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A screening test is used in the same way in two similar populations, but the proportion of false-positive results among those who test positive in population B is higher than that among those who test positive in population A. What is the most likely explanation for this finding? (A) The specificity of the test is higher in population A (B) The specificity of the test is lower in population A (C) The prevalence of disease is higher in population A (D) The prevalence of disease is lower in population A

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**Performance Yield Predictive Value Positive (PV+)**

Individuals with a positive screening test results will also test positive on the diagnostic test Predictive Value Negative (PV-) Individuals with a negative screening test results are actually free of disease

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**Results of Screening Test**

Performance Yield Predictive Value Positive (PV+) The probability that a person actually has a disease given that he/she tests positive PV+ = a / (a + b) Predictive Value Negative (PV- ) The probability that a person is truly disease free given that he/she tests negative PV- = d / (c + d) True Disease Status + - Results of Screening Test a b c d

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**Performance Yield Factors that influence PV+ and PV-**

The more specific the test, the higher the PV+ The higher the prevalence of preclinical disease in the screened population, the higher the PV+ The more sensitive the test, the higher the PV-

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Sampling errors Reality Treatments not different Ho true Treatments are different Ho false Conclude treatments are not different Fail to reject Ho Correct decision Type II error β Conclude treatments are different Reject Ho Type I error α Power Probability =1- β Decision Correct : Reject the null hypothesis when it is false Do not reject the null hypothesis when it is true Errors: Reject the null hypothesis when it is true ( Type I error =a) Do not reject the null hypothesis when it is false( Type II error =ß) Power = probability of detecting a difference if one truly exists, i.e. probability that a study will find a statistically significant difference, when a difference of a given magnitude truly exists. Power = 1- ß, where beta is the probability of declaring a difference not statistically significant, when a difference truly exists.

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Outbreak Endemic: Habitual presence of disease within a given geographic area Epidemic: Occurrence in a community of a group of illnesses of similar nature in excess of what would normally be expected. Amount of disease depends on number susceptible (at risk) and number not susceptible by way of immunization, or genetics (immune). Pandemic: worldwide epidemic

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**Outbreak Types Common source: group of persons exposed to common agent**

Point-exposed over a brief period of time Intermittent-exposed over a long period of time Propagated: spreads gradually from person to person Mixed epidemic: common source and from person to person

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**How do you find outbreaks?**

Surveillance Track disease/injury rates over time Only for reported diseases Time delay: primary purpose is to examine trends over time Laboratory reports Healthcare institutions Public health office Observant healthcare personnel Key Points: Surveillance is what you use to collect the data. Lab reports are a good method of identifying an outbreak. Laboratories can test for resistance factors. If there is an outbreak at another nearby healthcare institution, it might be a good idea to review your own situation and make sure that everything is as it should be. Public Health officials can help to identify an outbreak by contributing lab resources. It is usually an astute clinician or infection preventionist that identifies the outbreak.

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**Epidemic Investigation**

Establish the presence of an epidemic – case definition Communicate/Control Analyze the outbreak Form a hypothesis Test the hypothesis Complete the investigation

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BIOSTATISTICS Lecture 2. The role of Biostatisticians Biostatisticians play essential roles in designing studies, analyzing data and creating methods.

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