Basic Epidemiologic Concepts and Principles Chapter 1 Basic Epidemiologic Concepts and Principles

Epidemiology… Study of something that affects a population Study of factors that determine the occurrence & distribution of disease in a population One of the ways in which disease, injury and clinical practice are studied

Classical Vs. Clinical Classical Clinical Population oriented studies of the community origins of health problems. Clinical Use of research designs and statistical tools to study patient in health care setting Main differences between clinical and classical epidemiology are the place of the investigation and the population being studied

Stages of Disease Pre-disease Stage: No disease present Latent Stage: Asymptomatic; disease is in progress Symptomatic Stage: Manifestation of disease The way a disease progresses in the absence of medical or public health intervention is often called the natural history of the disease. During the predisease stage (before the pathologic process begins), early intervention may prevent exposure to the agent of disease (e.g., lead, trans-fatty acids, or microbes), preventing the disease process from starting; this is called primary prevention. During the latent stage (when the disease process has begun, but is still asymptomatic), screening and appropriate treatment may prevent progression to symptomatic disease; this is called secondary prevention. During the symptomatic stage (when disease manifestations are evident), intervention may slow, arrest, or reverse theprogression of disease; this is called tertiary prevention. These concepts are discussed in more detail in Chapter 14 and subsequent chapters.

VECTORS Defined: Examples: Insects, arthropods and animals which aid in the spread of disease by themselves being a host capable of transmitting disease causing organisms to the host they are living on Examples: Mosquitoes = West Nile Virus Tick = Lyme Disease Rats = Bubonic Plague Mosquitoes = Canine Heartworm Snail = Haemonchus contortus (nematode) The causes of a disease often are considered in terms of a triad of factors: the host, the agent, and the environment. For many diseases, it is useful to add a fourth factor, the vector (Fig. 1-1). Vectors of disease commonly include insects (e.g., mosquitoes associated with the spread of malaria), arthropods (e.g., ticks associated with Lyme disease), and animals (e.g., raccoons associated with rabies in the eastern US).

Risk Factors for Diseases Biological/Behavioral Environmental Immunological (Herd Immunity) Nutritional Genetics (Genetic Susceptibility) Services, Social Factors, Spiritual Factors Herd immunity results when a vaccine reduces an immunized person’s ability to spread a disease, leading to reduced disease transmission. It is well established that the genetic inheritance of individuals interacts with diet and environment in complex ways to promote or protect against a variety of illnesses, including heart disease and cancer. Genetic epidemiology is a growing field of research. Population genetics and genetic epidemiology are concerned with, among other things, the distribution of normal and abnormal genes in the population and whether or not these are in equilibrium.

Genetic Susceptibility Evaluation of which of the following potentially preventable causes of disease is most likely to raise ethical concerns? (A) Dietary intake (B) Genetic susceptibility (C) Immunization status (D) Smoking history (E) Social support networks Social support networks, immunization status, dietary intake, and smoking status all are factors that can be modified to prevent disease. Currently available technology permits the identification of genetic susceptibility to some diseases (e.g., breast cancer). Because the ability to modify genetic risk is not nearly as great as the ability to recognize it, however, ethical concerns have been raised. Little good may come from informing people about a risk that they cannot readily modify, whereas harm, such as anxiety or increased difficulty and expense involved in obtaining medical insurance, might result.

Epidemiologic Data Sources and Measurements Chapter 2 Epidemiologic Data Sources and Measurements

Types of Data Used in Health Studies Numerator Data A definition of the events or conditions of concern Example: Lung Cancer Denominator Data A definition of the population at risk Example: Smokers at risk for lung cancer When discussing denominator and numerator data; what is the correct configuration of these in terms of definitions? Event/population Population/date of event Date of event/population None of the above

Epidemiological Measurements Frequency – deals with numbers Incidence – number of occurrences (well: ill or live: dead) Prevalence – number of cases within a population at a given time Risk – deals with proportion of people unaffected at the beginning of a study Gave us discussion between the differences of incidence and prevalence The total number of cases of an epidemic disease reported over time is the cumulative incidence. According to the CDC, the cumulative incidence of AIDS cases in the US through December 31, 1991, was 206,392, and the number known to have died was 133,232. The introduction of the term cohort is introducted in this section. It is defined as a risk event may be death, disease, or injury,and the persons at risk for the event at the beginning ofthe study period are called a cohort.

Epidemiological Measurements Rates: Frequency of events that occur in/during a defined period of time. Generally the following rules are in play Numerator: rate of the frequency of event Denominator: number of people at risk during period being studied/considered Constant multiplier: usually 100 in order to get a percentage; 1000, 10,000 or 100,000 used for numbers less than “1”. Not used a lot.

Methods of Data Collection United States Census Takes place every 10 years Also collects continuously data on births and deaths through the Dept. of Vital Statistics U.S Vital Statistics Basically deals in birth and death of the population Local and state officials collect information Federal government collates the information after it is collected

Maternal and Fetal Associated Rates Live birth is the delivery of a product of conception that shows any sign of life after complete removal from the mother. Infant mortality rates: death of infants born alive Neonatal and Post-neonatal Mortality Rates: death of infants a) during first 28 days of life (neo=new) and b) from 28th day to 1 year of life (post=after event). Perinatal mortality rates: deaths that occur around the time of birth (i.e. stillborn) Maternal mortality rates: death of a pregnant woman as a result of pregnancy related health issue Incidence Rate Prevalence Rate Crude Rates Specific Rates Death Rates

Epidemiologic Surveillance & Outbreak Investigation Chapter 3 Epidemiologic Surveillance & Outbreak Investigation

Types of Surveillance Passive Surveillance Active Surveillance Routine data collection Physicians Clinics Laboratories Hospitals Example: Infant Mortality rate in Duval County, Florida in January 2008 Active Surveillance Periodic reports By phone or visit Labor intensive Health departments Example: The insuring that TB patients are taking their medications as directed In passive surveillance the persons responsible for reporting data or ……… Widely publicized fatalities associated with an emerging disease such as Hantavirus may be an example of active surveillance

Things to Know About Outbreaks Epidemic: Occurrence of disease at an unusual frequency Syndromic Surveillance: Surveillance which is looking for unlikely symptoms that may identify possible bio-terrorist activity using biologicals.

The data in this figure are presented in the form of a semilogarithmic graph, with a logarithmic scale used for the vertical, y-axis and an arithmetic scale for the horizontal, x-axis. Used to monitor long and secular trends

Elementary my dear Watson… Investigating an Epidemic: Establish a common diagnosis Establish a case definition (criteria of the disease) Establish a given number of diagnosed cases This is somewhat predicated by the number of cases of the disease normally seen in the area at the same time of the year Establish Time, Place and Person index case—the case that introduced the organism into the population.

Causation in Epidemiologic Investigation & Research Chapter 4 Causation in Epidemiologic Investigation & Research

The first and most basic requirement for a causal relationship to exist is that there must be an association between the outcome of interest (e.g., a disease or death) and the presumed cause. Causal/Non-causal Associations: the association between the disease/death and the presumed cause of that action. One must investigate associations in order to correctly determine that there is actually a viable cause and effect in play in terms of an epidemiological episode.

Pitfalls to Causal Research There are many things that can slow or interfere with validating research into cause and effect Bias (differential error): Most dangerous as it is a deviation or distortion of data or interpretation that goes in one direction. Random Error: Something that happens randomly and unexpectedly for no reason, and appears as unexpected highs or lows in the statistical analysis Confounding: Confusion of two variables in such a way as to not be able to discern which is which, causing the data to be discarded A confounder is a third variable that is associated with the exposure variable and the outcome variable in question.

More Still… Synergism: an interaction of two variables that produces an effect that is greater together then than the separate effect of the variables Effect Modification: Appearance of an unexpected third variable which affects the performance of the two accounted for variables

Common Research Designs of Epidemiology Chapter 5 Common Research Designs of Epidemiology

To generate a workable hypothesis To test the hypothesis Identify variables that may cause an effect to happen Identify risk factors Minimize aforementioned pitfalls from occurring (i.e. bias, confounding, etc.)

Research Design Generating Hypothesis Cross sectional surveys (interviews) Cross sectional ecological surveys (behavior) Longitudinal ecologic studies (on-going surveillance) Hypothesis generation is the process of developing a list of possible candidates for the “causes” of the disease and obtaining initial evidence that supports one or more of these candidates.

Hypothesis Testing Prospective cohort studies: The identification of a group of people/individuals on whom baseline (initial) data is collected after which the same data is collected over a long period of time Ex: Framingham Heart Study – a study that has been on-going since 1950. Can be expensive and time-consuming Retrospective Cohort Study Choosing a group of people in which something has already occurred and reviewing what their lives were like after the event. Ex: Study of people exposed to radiation after Chenobyl incident in Russia The defining difference between a prospective and retrospective cohort study is time

More Hypothesis Testing Case-Control Studies Randomized Controlled Clinical Trials (therapeutic in nature) Randomized Controlled Field Trials (preventive in nature)

Assessment of Risk and Benefit in Epidemiologic Studies Chapter 6 Assessment of Risk and Benefit in Epidemiologic Studies Epidemiologic research usually is designed to permit one or more primary contrasts in risk, rate, or odds of disease or exposure. The most straightforward of these measures are the risk difference (the attributable risk) and the rate difference, which show in absolute terms how much the risk of one group (usually the group that is exposed to a risk factor or a preventive factor) differs from that of another group. This contrast also can be expressed as a ratio of risks, rates, or odds; the greater this ratio, the greater the difference the exposure makes. The impact of the risk factor on the total disease burden can be measured in terms of an attributable risk percent for the exposed group or for the population

Attributable Risk (AR) Defined: An estimate of the amount of risk which is attributable to the risk factor Formula: AR = [a/(a+b)] – [c/(c+d)]

Relative Risk RR = [a/(a+b)]/[c/(c+d)] Defined: This is somewhat of a comparison of the ratio of risk in an exposed group to the ratio of risk in the unexposed group. Formula: RR = [a/(a+b)]/[c/(c+d)] Hint: Notice that we are dividing the two sets of numbers not subtracting them as we did with AR

Ratio OR = (a/c) / (b/d) Defined: An estimate of a odds ratio Formula: HINT: Do not use the step in the book that instructs you to convert the above formula to OR = ad/bc. The reason is because the numbers sometimes become too large to work with and muddy the waters.

Understanding the Quality of Data in Clinical Medicine Chapter 7 Understanding the Quality of Data in Clinical Medicine Epidemiologic research usually is designed to permit one or more primary contrasts in risk, rate, or odds of disease or exposure. The most straightforward of these measures are the risk difference (the attributable risk) and the rate difference, which show in absolute terms how much the risk of one group (usually the group that is exposed to a risk factor or a preventive factor) differs from that of another group. This contrast also can be expressed as a ratio of risks, rates, or odds; the greater this ratio, the greater the difference the exposure makes. The impact of the risk factor on the total disease burden can be measured in terms of an attributable risk percent for the exposed group or for the population

Key Concepts Accuracy: Ability of a measurement to be correct on the average Precision: Ability of a measurement to give the same results with repeated measurements of the same thing Both of these are necessary in statistics and neither takes a back seat to the other Precision Is reproducibility, reliability

False is False and True is True Or is it? Type I Error Also known as a false-positive error or Alpha error The error is in the fact that a positive reading is registered when the results are actually negative One way in a which a researcher can judge how useful a screening or testing procedure is involves the evaluation of the number of correct test results

Continued… Type II Error Also known as a false-negative error or a beta error The error is in the fact that a negative reading is registered when the results are actually positive

Sensitive Vs. Specific Sensitivity – Ability of a test to detect the disease when present Specificity – Ability of a test to indicate non-disease status when no disease is present The difference between sensitivity and specificity is the presence or non presence of a disease.

Improving Decisions in Clinical Medicine Chapter 8 Improving Decisions in Clinical Medicine Epidemiologic research usually is designed to permit one or more primary contrasts in risk, rate, or odds of disease or exposure. The most straightforward of these measures are the risk difference (the attributable risk) and the rate difference, which show in absolute terms how much the risk of one group (usually the group that is exposed to a risk factor or a preventive factor) differs from that of another group. This contrast also can be expressed as a ratio of risks, rates, or odds; the greater this ratio, the greater the difference the exposure makes. The impact of the risk factor on the total disease burden can be measured in terms of an attributable risk percent for the exposed group or for the population

Bayes Theorum A somewhat convoluted theory that is used in the mathematical exercise of determining probability. If a patient has symptom A what is the probability that the symptom is caused by a certain disease?

Evidence Based Medicine A form of medical practice that: Bases a diagnosis on symptoms + prevalence Relies heavily on the use of Bayes Theorum Must perform tests to rule out diseases as part of the diagnostic process Uses a sequential approach to tests Begin with most sensitive test for fastest and most accurate results Continues with other tests in a sequence until a satisfactory answer is arrived at (i.e. diagnosis)

Decision Trees A method of improving decision making in times of uncertainly A way of outlining ones thoughts and rationale concerning a medical dilemma Create a decision tree Identify/set limits to the problem Diagram options Obtain information on each option Compare the values Perform sensitivity analysis to arrive at an answer How many steps are included in the creating of a decision tree? 5 The decision node is a point where clinicians have to make a decision When developing a decision tree which comes first.