HLSC 4613 Principles of Epidemiology

HLSC 4613 Principles of Epidemiology
Instructor: Ches Jones, PhD University of Arkansas

Contents Unit One-Introduction and Definitions
Unit Two-Rates and Measurements Unit Three-Descriptive Epidemiology Unit Four-Analytic Epidemiology Unit Five-Screening and Surveillance

Introduction and Definitions
Unit One Introduction and Definitions

Epidemiology-Definition
Branch of medicine dealing with a combination of knowledge and research methods concerned with the distribution and determinants of health and illness in populations, and with contributors to health and control of health problems.

Main Components of Epi An analytic, descriptive component termed classical epidemiology, and A diagnosis, management of illness, and critical review of literature termed clinical epidemiology.

Evolution of Modern Epidemiology
3 Eras Miasma (Sanitary statistics) Disease due to bad air. Prior to 1850 Infectious Disease (Germ theory) Chronic Disease (Black box) 1930-present

Infectious Disease (Acute)
Cause  DiagnosisTreatment  Severity Disease of short duration Affects mainly the young

Chronic Disease Cause  Diagnosis  Treatment (cure)
Disease of long induction period Time allows multiple causes to develop Affects mainly the old

Public Health Approach
Implementation How to do it? Intervention Evaluation What works? Risk Factor Identification What’s the cause? Surveillance What’s the problem? Problem Response

3 Levels of Prevention Primary- prevention of the development of disease Secondary- early detection and treatment of disease Tertiary-rehabilitation and/or restoration of effective functioning after treatment of disease

Epidemiologic Surveillance
Definition The ongoing process and systematic collection, analysis, and interpretation of health data in the process of describing and investigating the health status of a population.

Epidemiological Surveillance
Two types: Passive-Disease frequency data collected Periodically. Current results not available Active-Disease status is updated constantly. Usually as the result of an outbreak or other identified epidemic. Is more costly than passive surveillance.

Current Uses of Epidemiology
Identifying the etiology and cause of a new epidemic or syndrome. Examples: Carpal Tunnel Syndrome Toxic Shock Syndrome Post Traumatic Stress Syndrome

Investigating the risk associated with a harmful exposure Examples: Health risks associated with: Radon exposure Lead Environmental tobacco smoke Dioxin

Determine if a treatment is effective. Results from a study showing survival rates following segmental and total mastectomies.

Study and identify health service utilization needs and trends. Examples: Effect of health insurance coverage on health services used by poor and near-poor populations. Impact of youth violence on emergency room services and utilization

To provide rationalization and justification for health policy planning. Examples: Smoking bans Gun-control bans Drunk-driving laws Hazardous waste regulations

Aims of Epidemiology Study occurrence, distribution, and progression of diseases and to describe the health status of a population. Provide data that will contribute to the understanding of the etiology of health and disease Promote utilization of epidemiological concepts to the management of health services.

Types of Epi Strategies Used
Descriptive Analytic (retrospective (case-control), prospective (longitudinal or cohort), and cross-sectional) Experimental (cause and effect)

Limitations of Epidemiology
Difficult to assess risk from epidemiology data because: 1) Research studies on humans are sometimes unethical, expensive, and difficult to obtain. 2) Chronic disease situations often finds very low risk.

Limitations of Epidemiology (Continued)
3) The number of persons with the disease or exposure is very small. 4) Latency period between exposure and disease status are sometimes many years apart. 5) Humans may be exposed to multiple chemical, biological, and physical hazards.

Epidemiological Models
Traditional Model Health Field Concept Other Models

Traditional Model Agent Host Environment

Health Field Concept Biology/Heredity Lifestyle Environment
Health Care System

Health Field Concept Lifestyle Environment Leisure
Consumption patterns Employment/occupational risks Environment Physical Social Psychological

Health Field Concept Human Biology Medical Care System
Genetic Inheritance maturation and aging Medical Care System Preventive Restorative Curative

Use of HFC in Epi Selection of diseases that are of high risk and contribute to mortality and morbidity. Allocate resources proportionally to disease occurrences. Allocate total health expenditures to the four elements of the epidemiology model.

Web of Causation Shows multiple factors Antecedents of risk factors
Time Illustrates complication of disease etiology Identifies intervention points

Concept of Risk With multiple causes and chronic diseases, epidemiologists like to refer to the concept of causality based on the odds (risks, chances) of the occurrence of disease or health status as associated with the occurrence of a specific exposure (risk/protective factor).

Criteria for determining causality
(more applicable to single cause/single effect) Temporal relationship: a causes b, then a comes first Specificity: a cause leads to a single effect Strength or intensity (strong relationship between findings) Consistency (same association is found study after study) Coherence (does it make sense?)

Criteria for a Risk factor
Risk increases with increased exposure Time sequence Risk Factor Disease Limited or no error involved

Chronic Disease Risk Factors

Epidemiological Measurement
Unit Two Epidemiological Measurement

Mortality Rates Morbidity Rates

Where to get data? Mortality/Vital Statistics Morbidity/Hospital/Clinic Records Health Assessments/Behavior Surveys Surveillance Systems

Measures of Mortality Crude Mortality Rate Infant Mortality Rate
Specific Mortality Rate (age, sex, race, and cause) Case Fatality Rate Proportionate Mortality Ration (PMR)

Epidemiological Measurements
General Formula Number of events (cases, deaths, services) In a specified time period Population at risk of experiencing the event X 10n Some base of ten: 1,000 10,000 100,000

Rates and Risks Ecological Fallacy (generalizing)
Reasons to Use Caution When Interpreting Rates and Risks Ecological Fallacy (generalizing) Variations in Base (what base is used) False Association (rates apply to pop’n) Variance of Rates (differences based on rates)

Crude Mortality Rate All deaths during a calendar year
Population at mid-year X 1,000 = deaths per 1,000

X 1,000 (common rate) Infant Mortality Rate
Most widely accepted measure for estimating the health status of a population Number of infant deaths * (less than 1 year of age) Number of live births *excludes fetal deaths X 1,000 (common rate)

Specific Mortality Rates
Before the experiences of two populations can be compared, account must be taken for differences in age, sex, race, or cause. Rates are adjusted in order to remove the effect of a confounding variable, such as age, sex, or race.

Specific Mortality Rates
Examples Mortality Rates Specific For: Age Specific MR: by age group Gender Specific MR: for males, for females Race/Ethnic Group: for white, blacks, etc.

Cause Specific-Mortality
Deaths assigned to the specified disease during a calendar year Population at mid-year X 100,000 =deaths per 100,000 population per year

Case Fatality Ratio Number of deaths due to the disease in a specific time period number of cases of the disease in the same time period X 100 Express as %

Case Fatality Ratio This measure represents the probability of death among diagnosed cases, or the killing power of a disease.

Proportionate Mortality Ratio
Deaths assigned to the disease in a certain year Total deaths in the population in the same year X 100 Express as %

Proportionate Mortality Ratio
Used to describe the proportion of the overall mortality that is ascribed to a specific cause.

Morbidity Rates Attack Rate Incidence Prevalence
Years of Potential Life Lost (YPLL)

Attack Rate An incidence rate used to describe the occurrence of food borne illnesses, infectious diseases, and other acute, short time period diseases. ill ill + well X 100 (%)

Attack Rate (example) ill = 10 not ill = 3 Total = 13 10 13

Incidence and Prevalence
The two main measures of disease frequency (morbidity). Incidence = NEW cases of a certain disease Prevalence = ALL cases of a certain disease

Incidence and Prevalence
Incidence Recover Death Prevalence Pot

Incidence Incidence rates are designed to measure the rate at which people without a disease develop the disease during a specific period of time. Number of new cases of a disease over a period of time population at risk of the disease in the time period Incidence rate =

Incidence Example Gonorrhea in Arkansas 1987 1996
8898 new cases 2,342,699 = 381/100,000 5027 new cases 2,509,793 = 200/100,000

Prevalence Prevalence rates measure the number of people in a population who have the disease at a given point of time. Prevalence rate = Total number of cases of a disease at a given time Total population at a given time

Prevalence Types Annual (yearly) Lifetime (overall prevalence)
Period (specific period of time) Point (right now!)

Years of Potential Life Lost
Indicates how diseases compare in reducing life expectancy. Calculated for ages up to 65. Example: A person killed at the age of 25 has lost 40 years of potential life. (25-65=40)

Years of Potential Life Lost
Application: In 1988, an estimated 1,198,887 years of potential life lost (YPLL) before age 65 were attributed to smoking. Source: CDC. Smoking-attributable mortality and years of potential life lost -- United States, MMWR 1991;40:62-3,69-71.

AIDS in Arkansas County Benton Carroll Pulaski Wash
Pop’n 105, , , ,737 AIDS 9/95-6/96 AIDS Total

Rate Adjustment (Standardized Rates)
Adjustment for differences in population Composition (age, gender, ethnicity, etc.) -Direct Adjustment -Indirect Adjustment

Direct Method of Adjustment
Application of population composition specific rates to determine the expected number of events in a standard population. Uses Standard Populations

Indirect Method of Adjustment
Standard rates applied to populations being compared in order to calculate the expected number of events, and the compared with the observed number of events. Uses Standard Rates

Standardized Mortality/Morbidity Ratio (SMR)
A rate used for comparing the standardized mortality rates. Observed Deaths Expected Deaths

Descriptive Epidemiology
Unit Three Descriptive Epidemiology

Descriptive and Analytic Epidemiology
Descriptive Epidemiology- amount and distribution of disease within a population by person, place, and time Analytic Epidemiology-more focused study on the determinants of disease or reason for relatively high or low frequency in specific groups.

Ask these questions: Who (Person) – D
What (Type of Disease, illness, disability)-D When (Time) –D Where (Place) – D How (Etiology or cause of event) – A D= Descriptive A= Analytic

Case Definition Standard criteria used to assess whether a person has a particular disease or health condition. Ensures that every case is diagnosed using the same criteria. Comparisons with time, place, and populations can be conducted.

Foodborne Illness Outbreak Case Definition
An incident in which two or more persons experience a similar illness after ingestion of a common food, and epidemiologic analysis implicates the food as the source of the illness

Descriptive Person 3 main characteristics: Age Gender Ethnic

Age Age- is the most important determinant among the person variables. Mortality and morbidity rates of conditions show some relation to age. Infectious disease-younger Chronic disease- older

Gender Mortality- higher among males Morbidity- higher among females

Gender Mortality- linked with inheritance, hormonal balance, environment, or habit pattern Morbidity- women have higher rates of illness and more physician contacts than men. Possible reasons: 1)Women seek medical care more freely and perhaps at an earlier stage of disease and, 2) The same disease will tend to have a less lethal dose in women than in men

Ethnic Group Classifying people by ethnic group is difficult but important in field of epidemiology. Why: 1.) Many diseases differ in frequency, severity, or both in different racial groups, and, 2.) Statistics by race are helpful for identifying health problems.

Other Person Variables
Social Class Occupation Marital Status Family Variables Family Size Birth Order Personality traits Maternal Age Parental Deprivation Blood Type Environmental Exposure

Place considerations Frequency of disease can be related to place of occurrence by: Natural Boundaries (more useful) (such as river, deserts, mountains) Political Subdivisions (more convenient)

Place considerations Mapping environmental factors
Urban-Rural differences International comparisons

Time considerations 3 major time measurements:
Secular trends (long-term variations) Cyclic (recurrent alterations in the frequency of disease) Short-term fluctuations

Analytic Study Designs
Unit Four Analytic Study Designs

Criteria to Evaluate Study
1.) Study size—was it large enough 2.) How were subjects selected? 3.) Bias prone? 4.) Confounding prone? 5.) Adequate analysis? 6.) Were limitations discussed?

Study Design Definitions
Confounding- illusory associated between two variables. Association caused by 3rd factor, “confounder” Example: link between coffee and colon cancer may actually be caused by smoking.

Bias- Subjects chosen for study are unrepresentive of the population. Types of bias include: (over 57 types) Healthy Worker bias Information Volunteer Recall Researcher

Control for Confounding
Prevention Randomization Matching Restriction Analysis Stratification Multivariate techniques

Reducing Bias Case Definition High Participation Rates
Ensure Representation Use Standardized Forms Training of Research Personnel Blind Participants and Researchers

Chance- making assumptions and inferences of the measure of disease frequency concerning the experience of a population based on an evaluation of only a sample. Because of chance variation, for any two sample in a population to be identical is highly unlikely.

Chance-example via checkerboard

Eliminate Chance Findings
P-value Confidence Intervals Reduce Errors

Causation is not measured by the P-value
P-value only reflects that results are a consequence of chance (random error). Not: Result of bias- (systematic error) Attributable to confounding Study is reflecting causal relationship Study design is correct

Types of Study Designs Case Report or Case Series
Descriptive (Population-based) Analytic (Individual-based) Follow-Up (Cohort) Case-Control

Follow-Up Studies (Cohort)
Retrospective Prospective

Retrospective Investigates the association between a disease and past exposure to a risk factor among a cohort.

Retrospective PAST PRESENT Look for past exposure Select cohort
in population Select cohort

Retrospective Strengths 1.) Less expensive 2.) Faster to do study
Limitations 1.) Impossible to control for confounding factors 2.) Bias prone

Prospective Study starts with a group (cohort) of people who are free of disease, but who vary according to exposure to probable disease factor.

Prospective Present Future Follow-up to Select cohort and
Classify as to Exposure to factor Follow-up to see frequency with which disease develops

Prospective Strengths 1.) Temporal sequence is clear
2.) Bias and confounding are relatively easy to control 3.) Absolute measure of occurrence are available (incidence, mortality, etc.) 4.) Provides information on many factors

Prospective Limitations 1.) Very expensive and time consuming
2.) May not provide significant findings until after 5-10 years 3.) Inappropriate for rare diseases 4.) Problems with following up on subjects 5.) Extremely inefficient

Case Control Study People diagnosed as having a disease (cases) are compared with persons who do not have the disease (controls) with relation to various risk factors.

Case Control Study PAST PRESENT Select individuals with the
disease(cases) Look for past exposure to factor in cases and controls Select individuals without the disease (controls)

Case-Control Study Dominate form of epidemiologic study (>80%)
Difficult but rewarding design to use Case-control studies have been used in other areas besides causation-preventive services and health services research

Case-Control Study Strengths Appropriate for rare diseases
Appropriate for disease with long induction time. Economical and done rapidly Allow evaluation of multiple hypotheses Extremely efficient Large amount of information on small amount of subjects

Case-Control Study Limitations People don’t understand it (abused)
Study is poor when exposure of interests is rare Only relative measures are available Bias prone

Case-Control Study Design questions Where to get cases?
Population based (expensive) Selected Population Where to get controls? General population (ideal but unrealistic) Hospital controls

Measure of Risk Absolute Risk Relative Risk Attributable Risk

Caution with Risks 1.) All those exposed to the disease factor will not develop the disease or illness but just have a probability of doing so. 2.) Some people not exposed to disease factor will develop the disease.

Absolute Risk Synonymous with incidence and means the rate of occurrence of the disease.

Relative Risk and Attributable Risk
Epidemiologic measures of the association between exposure to a particular factor and risk of a certain outcome.

Relative Ratio (Odds Ratio)
Incidence rate among exposed Incidence rate among non-exposed

I (exposed) – I (non-exposed)
Attributable Risk I (exposed) – I (non-exposed)

Case-Control Analysis
Disease Status CA-Yes CO-No b M1 Yes a Exposure Status No c d M0 N1 N0

Exposure Rates Case exposure = Control exposure = a N1 b N0

Odds Ratio (RR) Odds Ratio = (a x d)
Among people who (risk factor), the incidence of (disease) is (OR) greater or lower than those who don’t (risk factor). (b X c)

Attributable Incident Rate
AIE %= (OR-1)/OR Among people who (risk factor), % of (disease) is attributable to (risk factor). AIT %=(AIE %) (CAE) If nobody (risk factor), I of (disease) would go down by % in the population.

Confidence Intervals Confidence intervals are calculations of the best estimate of the OR. Researchers are stating that they are (%) confident that their true range is between the lower and upper limits of the confidence interval.

Screening and Surveillance
Unit Five Screening and Surveillance

Screening Purpose To identify people who have an enhanced probability of receiving a disease and have no signs or symptoms of disease. A screening test is not intended to be diagnostic.

Screening Characteristics of a good screening program:
Targeted at appropriate disease Uses a good test Has good compliance from targeted population Follow-up on those tested positive. Assist them in accessing medical care services

Screening Problems with screening Creates anxiety in people
False sense of reassurance Produces morbidity through test itself (screening devices and equipment) Excess morbidity

No Screening B 20 40 50 55 60 Exposure period Symptoms diagnosis Cells
exfoliate Age Death Cancer begins

Screening B 20 40 50 55 60 Exposure period Symptoms diagnosis Cells
exfoliate Age Death Screening Detection Cancer begins

Screening Current Situation Care for Chronic Disease Self-Referral
Diagnosis Surveillance Recovery

Projection for the Future
Screening Projection for the Future Care for Chronic Disease Self-Referral Diagnosis Surveillance Recovery

Three Phases of Disease
Age Pre-Clinical Phase (PCP)- begins when cancer begins. Pre-clinical Phase ends at symptom diagnosis Detectable Pre-Clinical Phase (DPCP) begins at first possible detection of cancer. Detectable Pre-Clinical Phase ends when symptoms appear Clinical Phase 30 55 45 55

Screening Characteristics of disease that makes it suitable for screening: Serious disease Early therapy better than late therapy The detectable pre-clinical phase is high There is treatment available for disease

Screening Analysis True Diagnosis Test Results Diseased Not Diseased
Positive a b a+b Negative c d c+d a+c b+d a+b+c+d

Measures of a Screening Test
True Positive Rate, Sensitivity – a/(a+c) Capacity of a test to give a positive finding when the person tested truly has the disease. True Negative Rate, Specificity- d/(b+d) Capacity of a test to give a negative finding when the person tested is truly free of disease.

Measures of Screening Test
False Negative Rate- c/(a+c) Percent measure of a test to give a negative finding when the person tested truly has the disease. False Positive Rate-b/(b+d) Percent measure of a test to give a positive finding when the person tested does not have the disease.

Screening Example True Diagnosis Test Results Diseased Not Diseased
Positive Negative , ,010 , ,150

Screening Example Sensitivity (a/a+c) = 80%
False Negative Rate (c/a+c) = 20% Specificity (d/b+d) = 91% False Positive Rate (b/b+d) = 9%

Evaluating Screening Program
Three primary methods 1.) Process measures Number of people screened Number of times people were screened Total cost of program Cost per case detected

Evaluating a Screening Program
2.) Special process measure Predicted Value Positive (PVP) is useful in measuring the proportion of positive tests that are truly positive. PVP= a/ (a+b) PVP= 40/ 140 = 29% A high PV signifies a satisfactory test, but alone it does not provide any information on the tests validity

2.) Special process measure Predicted Value Negative (PVN) is useful in measuring the proportion of negative tests that are truly negative. PVP = d/ (c+d) PVP= 1000/1010 = 99% A high PV signifies a satisfactory test, but alone it does not provide any information on the tests validity.

3.) Outcome measures Mortality of screened disease Case Fatality Rate of screened disease

Problems with Screening Evaluation
Lead Time Bias- belief that screening program has given more years of life to individual who was positively screened for disease. Length Time Bias- belief that screen detected cases have a better prognosis than symptoms-detected cases. Patient Self-Selection Bias

Epidemiologic Surveillance
Definition The ongoing process and systematic collection, analysis and interpretation of health data in the process of describing and investigating the health status of a population.

Characteristics of System
Public health importance of the health event/problem Describe the surveillance system Usefulness of system Evaluate according to 7 attributes Resources used to operate system Conclusions and Recommendations

Public Health Importance
Number of cases Incidence Prevalence Case fatality Index of severity Preventability Hospital and medical costs

Describe System Objectives of surveillance system
Describe the health events (case definition of each health event) Flow chart of the system Components and operation of system Population Time of data collection Information collected Who provides data How is information stored, transferred How is data analyzed, how often How are reports distributed and to whom

Usefulness of System A surveillance system is useful if it contributes to the prevention and control of a health problem. It may also indicate other health events or problems as being serious.

Evaluation Evaluate system based on following attributes:
Simplicity- How simple is the surveillance system to use Flexibility- is surveillance system flexible to adapt to changing information needs and operating conditions Acceptability- are health care and public health agencies willing to participate in the surveillance system Sensitivity- how efficient is system in detecting cases of disease or adverse health conditions.

Evaluation Predictive value positive (PVP)-the proportion of persons identified as having cases who actually have the disease. Representativeness-does surveillance system accurately describe. The occurrence of a health problem Its distribution in the population by place, time, and person Timeliness-the time of reporting cases within each step of the surveillance system.

Resources to Operate System
Personnel requirements Other resources Travel Supplies Equipment, etc.

Conclusions and Recommendations
One of the main purposes of a surveillance system is to provide feedback and information to prevent and control disease. After disease has been monitored, suggestions and recommendations are provided in order to facilitate the control of disease and prevent future outbreaks and occurrences of health events.

Classification Systems
Definition An orderly arrangement of data that serves a specific purpose. Should meet 3 criteria: Classes used must be mutually exclusive It should be exhaustive It should have a reasonable number of classes and a reasonable frequency of cases in each class.

Examples of Classification Systems
International Classification of Disease E-codes National Ambulatory Medical Care Survey Utilization Behavior Diagnosis-Related Groups (DRG)

International Classification of Diseases (ICD-9 codes)
Primarily used to code mortality and morbidity cases to obtain statistical summaries and analysis. Many other classification systems base their surveillance mechanisms on this system. Most popular and used system. Classes = 17 (main sections)

National Ambulatory Medical Care Survey
Survey gives information concerning ambulatory patients’ visits to primary care physician (PCP). It also provides measure of the magnitude and nature of complaints by those who visit PCP. Classes = 13 (refers to anatomic site or system)

National Ambulatory Medical Care Survey
Example: Estimating the impact of a national health insurance plan on health care utilization that is based on pilot plans or studies.

Classification by Utilization Behavior
Classification system used to group diseases into classes most likely to result in similar medical care usage. Classes = 10 (disease and non-disease groups)

Classification by Utilization Behavior
Example Useful in linking medical care usage to health conditions. The types and amount of medical care services could be assessed. Can provide information on the value of various interventions or services.

Diagnosis-Related Groups (DRGs)
Classification system of hospitalized cases that is used for reimbursing hospitals prospectively on a cont-per-case bases for the care of Medicare patients. Based on length of stay (LOS) and severity of illness. Classes = 23 major diagnostic categories

Diagnosis-Related Groups (DRGs)
Example: The Hospital Efficiency and Effectiveness Analysis Identifies health care facilities, or areas within a facility, which have utilization habits and/or pricing policies inconsistent with the local market and those facilities which have superior or adverse outcomes as compared with the nations.

HLSC 4613 Principles of Epidemiology

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