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## CPH EXAM REVIEW– EPIDEMIOLOGY

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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**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 www.nbphe.org**Outline**• Overview • Terminology • Study design • Causation and validity • Screening**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)**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**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**Ratio**• One number (x) divided by another (y): • Range: zero (0) to infinity (∞) • (x) and (y) may be related or completely independent • Sex of children • Attending a clinic**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**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?**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)**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)**Prevalence proportion**Where: A = number of existing cases B = number of non-cases N = total population**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)**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**Where: a= number of new onset cases (events) N = population-at-risk at beginning**Incidence Proportion**• Appropriate for fixed (closed) populations and short follow-up • Must specify time period of observationbecause 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**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.**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**Incidence Rate**Where: a = number of new onset cases T = person-time at risk during study period (follow-up)**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**Person-time**T = person-time at risk during study period = 1+5+5+3+2 = 16 person-years**Person-time Assumption**• 100 persons followed 10 years = 1000 person years • 1000 persons followed for 1 year = 1000 person years**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**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**Mortality**• Measures the occurrence death • Can be measured as a proportion or a rate • Can measure disease severity or effectiveness of treatment**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**Mortality Rate**Where: d = number of deaths N = total population at mid-point of time period T = follow-up time (usually one year)**Cancer Death Rates*, for Men, US, 1930-2003***Age-adjusted to the 2000 US standard population. Source: US Mortality Public Use Data Tapes 1960-1999, US Mortality Volumes 1930-1959, National Center for Health Statistics, Centers for Disease Control and Prevention, 2002.**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?**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**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 2013x 1,000 Total deaths in the U.S. for 2013**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**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**Mortality Rates (Year = 2000)**Panama Sweden Why do you think Sweden has almost a 2x higher mortality rate? 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**Differences in Mortality Rates**Crude mortality rates do not take into account differences between populations such as age**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**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)**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?**Study Design**• Experimental studies (Clinical Trial, Randomized Controlled Trial) • Observational studies • Cohort • Case-control • Cross-sectional • Ecological**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?**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.**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)**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)**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)