Epidemiological Concepts

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• Epidemiological Concepts
• D. Charles Hunt, MPH
• Kansas Department of Health and Environment

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Acknowledgements

• Much (but not all!) of the following material was
  shamelessly borrowed from the following programs
• Evidence Based Public Health A Course in
  Chronic Disease Prevention
• - St. Louis University School of Public Health
• Health Agency Training Program
• -University of Oklahoma Health Sciences
  Center

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How we view the world..

• Pessimist The glass is half empty.
• Optimist The glass is half full.
• Epidemiologist As compared to what?

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• The glass is too big!
• -George Carlin

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What is Epidemiology?

• The study of the distribution and determinants
  of health-related states and events in specified
  populations and the application of this study to
  the control of health problems.

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Epidemiology is a Quantitative Discipline

• Measures of frequency
• Counts and rates
• Measures of association
• Relative risk
• Odds ratio
• Statistical inference
• P-value
• Confidence limits

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So, what does it mean?

• Epidemiology is the basic science of public
  health
• Epidemiology provides insight regarding the
  nature, causes, and extent of health and disease
  states
• Epidemiology provides the information needed to
  plan and target resources appropriately

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Descriptive Epidemiology

• Morbidity Refers to the presence of disease in
  a population
• Mortality Refers to the occurrence of death in a
  population

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Needs Assessment

• Key Questions
• What is the frequency of disease in the
  population?
• Which subgroups have the highest (or lowest)
  disease rates?
• type I evidence vs. target population

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Number of Diabetes Deaths by Year and Race
Interpretation?
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Descriptive Epidemiology

• How do we determine disease frequency for a
  population?
• Rate Frequency of defined events in specified
  population for given time period
• Rates allow comparisons between two or more
  populations of different sizes or of a population
  over time

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Compute Disease Rate

• Number of persons at risk 5,595,211
• Number of persons with disease 17,382
• Rate 17,382 persons with heart
  disease 5,595,211 persons
• .003107 heart disease / resident / year

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Descriptive Epidemiology

• Rates are usually expressed as integers and
  decimals for populations at risk during specified
  periods to make comparisons easier.
• .003107 heart disease / resident / year x
  100,000
• 310.7 heart disease / 100,000 residents / year

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Number and Rate per 100,000 of Diabetes Deaths,
by Year and Race
Interpretation?
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Descriptive Epidemiology

• Prevalence vs. Incidence
• Prevalence is the number of existing cases of
  disease in the population during a defined
  period.
• Incidence is the number of new cases of disease
  that develop in the population during a defined
  period.

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Incidence

• Incidence rate is a measure of the probability of
  the event among persons at risk.

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Incidence Rates

• Population denominator
• IR new cases during time period X K
• specified population at risk

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Example (Incidence Rate)

• In 2000, there were 6,057 cases of Chlamydia
  trachomatis infections reported to KDHE (1,083
  males and 4,974 females). What was the incidence
  rate per 100,000 population of this disease for
  Kansas in 2000?

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Example (Incidence Rate)

• During a six-month time period, a total of 53
  nosocomial infections were recorded by an
  infection control nurse at a community hospital.
  During this time, there were 832 patients with a
  total of 1,290 patient days. What is the rate of
  nosocomial infections per 100 patient days?

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Give it a try?

• In 2000, there were 10 deaths due to lung cancer
  in Finney County. The population of Finney
  County in 2000 was 40,595 persons. What was the
  mortality rate per 100,000 population for lung
  cancer?

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Answer

• Crude death rate per 100,000 population due to
  lung cancer in Finney County in 2000

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Descriptive Epidemiology

• Question
• Which data are better for estimating disease
  rates
• Incidence or mortality data?

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Mortality Rates

• A special type of incidence rate
• Number of deaths occurring in a specified
  population in a given time period

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Descriptive Epidemiology

• Mortality rates are used to estimate disease
  frequency when

• incidence data are not available,
• case-fatality rates are high,
• goal is to reduce mortality among screened or
  targeted populations

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Mortality Rates Examples

• Crude mortality death rate in an entire
  population
• Rates can also be calculated for sub-groups
  within the population
• Cause-specific mortality rate at which deaths
  occur for a specific cause

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Mortality Rates Examples

• Case-fatality Rate at which deaths occur from a
  disease among those with the disease
• Maternal mortality Ratio of death from
  childbearing for a given time period per number
  of live births during same time period

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Mortality Rates Examples

• Infant mortality Rate of death for children less
  than 1 year per number of live births
• Neonatal mortality Rate of death for children
  less than 28 days of age per number of live births

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Prevalence

• Prevalence Existing cases in a specified
  population during a specified time period (both
  new and ongoing cases)
• Prevalence is a measure of burden of disease or
  health problem in a population

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Prevalence

• Prevalence The number of existing cases in the
  population during a given time period.
• PR existing cases during time period
• population at same point in time
• Prevalence rates are often expressed as a
  percentage.
• Examples?

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Discussion of Incidence Prevalence

• Community diabetes coalition decides to develop
  a physical activity program for persons with
  diabetes. Which measure should be used to plan
  resources and interventions appropriately?

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Discussion of Incidence and Prevalence

• Local SAFE Kids Coalition wants to develop a
  program to reduce head injuries from bicycle
  crashes. Which measure should be used to
  evaluate program effectiveness?

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Descriptive Epidemiology

• Question Are we measuring prevalence or
  incidence?
• The number of new employees who test positive for
  exposure to tuberculosis among all new employees
  who receive the TB skin test during 2002.
• The number of deaths due to SIDS among infants lt1
  year of age during a 1-year period after
  launching the Back-To-Sleep program.

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Group Exercise 1
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Descriptive Epidemiology

• Intermediate outcomes may be used
• when it is not feasible to wait years to see the
  effects of a new public health program,
• and
• there is sufficient type I evidence supporting
  the relationship between behavior changes and
  disease reduction.

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Descriptive Epidemiology

• Long-term outcomes
• cardiovascular disease
• lung cancer
• breast cancer mortality
• arthritis

• Intermediate outcomes
• -- obesity, physical activity
• -- cigarette smoking
• -- mammography screening
• -- Examples?

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Prevalence Data
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Prevalence Data
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Prevalence Data
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Breast Cancer Screening Among Women gt 40 Years of
Age
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A word of caution.

• Previously, we calculated the lung cancer
  mortality rate for Finney County in 2000 to be
  24.6 per 100,000.
• In 1999, the rate was only 12.3 per 100,000
• What caused this dramatic increase in lung cancer
  deaths?
• Shouldnt somebody do something about this?

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The Achilles Heel of Epidemiology

• Estimating Rates for Smaller Populations
• Remember that our mortality rate calculation for
  Finney County was based on only 10 deaths
• There were only 5 deaths in 1999
• Rates are not considered reliable if fewer than
  20 cases in the numerator

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Surveillance
relative standard error
numerator size
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Dealing with Small Numbers

• Expand the study period (combine several years of
  data together)
• Expand the population (combine geographic areas)
• Caution!

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What factor is most likely to influence death
rates in a population?

• Sooner or later, all epidemiologists must accept
  the fact that life is essentially a
  sexually-transmitted condition with a 100
  case-fatality rate.

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Confounding

• A factor that is related both to the risk factor
  being studied as well as the outcome

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Age-adjusted Rates

• Used for comparing rates between two or more
  populations
• Age-adjustment removes confounding affects of
  differences in age distributions between
  comparison populations

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Example of Age-adjusted Rates
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Selection of Standard Population Matters!
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Basic Measures of Association

• We often need to know the relationship between an
  outcome and certain factors (e.g., age, sex,
  race, smoking status, etc.)
• Used to guide planning and intervention strategies

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2 x 2 Table for Calculation of Measures of
Association
Note Exposure is a broad term that represents
any factor that may be related to an outcome.
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Relative Risk

• Ratio of the incidence rates between two groups
• Can only be calculated from prospective studies
  (cohort studies)
• Interpretation
• RR gt 1 Increased risk of outcome among
  exposed group
• RR lt 1 Decreased risk, or protective effects,
  among exposed group
• RR 1 No association between exposure and
  outcome

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Calculation of Relative Risk

• incidence rate among exposed
• RR
• incidence rate among non-exposed

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Calculation of Relative Risk
Relative Risk
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Relative Risk Case Study

• Smoking and low birth weight

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Answers to Relative Risk Case Study

• 1. Incidence of LBW among smokers

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Answers to Relative Risk Case Study

• 2. Incidence of LBW among non-smokers

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Answers to Relative Risk Case Study

• Relative risk for having a LBW baby among smokers
  versus non-smokers

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Understanding Probability and Odds

• A probability is the chance or risk of an event
  occurring (a proportion)
• The odds in favor of an event is actually a ratio
  of the probability of an event occurring to the
  probability of an event not occurring
• Odds P/(1-P)

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Odds Ratio

• The odds ratio (OR) is a ratio of two odds.
• The OR can be calculated for all three study
  designs cross-sectional, case-control, and
  cohort.

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Odds Ratio

• For cohort studies, the OR is a ratio of the odds
  of the outcome in exposed persons to the odds of
  the outcome in non-exposed persons.
• For case-control studies, the OR is a ratio of
  the odds of exposure in cases to the odds of
  exposure in controls.
• Provides an estimate of the relative risk when
  the outcome is rare

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Interpretation of Odds Ratio

• OR gt 1 Increased odds of exposure among those
  with outcome
• OR lt 1 Decreased odds, or protective effects,
  among those with outcome
• OR 1 No association between exposure and
  outcome

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Calculation of Odds Ratio
Odds Ratio
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Keeping the Terms Straight

• Risk ratio relative risk
• Relative odds odds ratio
• Remember the key is recognizing the terms
  risk and odds

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Odds Ratio Case Study

• Class Exercise

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Odds Ratio Case Study
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Appropriateness of Measures

• Remember that the relative risk can only be
  calculated in prospective studies
• Odds ratio can be calculated for any design
• Cohort / prospective
• Case-control
• Cross-sectional

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Inference

• The relative risk and odds ratio provide the
  magnitude of difference between some factor and
  an outcome
• How do we know if the magnitude statistically
  significant?

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Confidence Intervals

• A confidence interval is a range of values that
  is likely (e.g., 95) to contain the true value
  in the underlying population
• If the confidence interval around a relative risk
  or an odds ratio contains 1, the result is not
  considered statistically significant

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Example

• Relationship between breastfeeding and asthma in
  childhood
• Examined odds of being breast fed 9 months or
  less among children with asthma and children with
  wheeze
• OR 99 CI
• Asthma 2.39 (0.95-6.03)
• Wheeze 1.54 (1.04-2.29)
• Dell S, To T. Breastfeeding and asthma in young
  children Findings from a population-based
  study. Arch Pediatr Adolesc Med.
  20011551261-1265.

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Confidence Intervals

• Confidence intervals may also be calculated for
  percentages reported from survey data
• Percentage of Adults who are Obese
• 95 CI
• Total Population 21.6 20.2 23.0
• Diabetics 49.1 42.5 55.8
• Non-diabetics 19.9 18.5 21.3