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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

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
1)
2)
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)
– 1850-1930

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
Current Uses of Epidemiology
Investigating the risk associated with a
harmful exposure
 Examples: Health risks associated with:

– Radon exposure
– Lead
– Environmental tobacco smoke
– Dioxin
Current Uses of Epidemiology
• Determine if a treatment is effective.

Results from a study
showing survival
rates following
segmental and total
mastectomies. 100
90
80
70
0
1
2
3
4
5
Years after Surgery
Total Maste ctomy
Segmental Mastectomy
Current Uses of Epidemiology
Study and identify health service
utilization needs and trends.
Examples:

– Effect of health insurance coverage on
health services used by poor and nearpoor populations.
– Impact of youth violence on emergency
room services and utilization
Current Uses of Epidemiology
To provide rationalization and justification
for health policy planning.
Examples:
– Smoking bans
– Gun-control bans
– Drunk-driving laws
– Hazardous waste regulations
Aims of Epidemiology
1)
2)
3)
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
1)
Descriptive
2)
Analytic (retrospective (case-control),
prospective (longitudinal or cohort),
and cross-sectional)
3)
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
– Leisure
– Consumption patterns
– Employment/occupational risks

Environment
– Physical
– Social Psychological
Health Field Concept

Human Biology
– Genetic Inheritance maturation and aging

Medical Care System
– Preventive
– Restorative
– Curative
Use of HFC in Epi
1)
2)
3)
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

Diabetes
Diet
Smoking
Lack of Exercise
High Sugar
Weight Loss
Stress
Addiction
Snacks
Poor diet
Job
High School
Child
No time
Family
No Equipment
Job
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
1)
Risk increases with increased
exposure
2)
Time sequence
Risk Factor
3)
Disease
Limited or no error involved
Chronic Disease Risk Factors
Unit Two
Epidemiological Measurement
Epidemiological Measurement
Mortality Rates
Morbidity Rates
Epidemiological Measurement

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
n
10
Some base of ten:
1,000
10,000
100,000
Rates and Risks
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
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
X 100,000
Population at mid-year
=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
Express as %
X 100
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
X 100 = 76%
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
Incidence
=
rate
population at risk of the
disease in the time period
Incidence Example
Gonorrhea in Arkansas
1987
8898 new cases
2,342,699
= 381/100,000
1996
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.
Total number of cases of a
Prevalence rate =
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. (2565=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, 1988. MMWR 1991;40:623,69-71.
AIDS in Arkansas
County Benton Carroll Pulaski Wash
Pop’n 105,588 19,505 353,394 142,737
AIDS
8
9/95-6/96
0
60
17
AIDS
Total
23
687
161
68
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
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
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.
Study Design Definitions
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

Study Design Definitions
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
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
Select cohort and
Classify as to
Exposure to factor
Future
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
Look for past
exposure
to factor in cases
and controls
PRESENT
Select individuals
with the
disease(cases)
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 causationpreventive services and health services
research

Case-Control Study
Strengths
1) Appropriate for rare diseases
2) Appropriate for disease with long induction
time.
3) Economical and done rapidly
4) Allow evaluation of multiple hypotheses
5) Extremely efficient
6) Large amount of information on small
amount of subjects
Case-Control Study
Limitations
1) People don’t understand it (abused)
2) Study is poor when exposure of
interests is rare
3) Only relative measures are available
4) Bias prone
Case-Control Study
Design questions
1) Where to get cases?
•
•
Population based (expensive)
Selected Population
Where to get controls?
2)
•
•
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
Attributable Risk
I (exposed) – I (non-exposed)
Case-Control Analysis
Disease Status
CA-Yes
CO-No
Yes
a
b
M1
No
c
d
M0
N1
N0
Exposure
Status
Exposure Rates
a
Case exposure =
N1
Control exposure =
b
N0
Odds Ratio (RR)
Odds Ratio = (a x d)
(b X c)
Among people who (risk factor), the
incidence of (disease) is (OR) greater or
lower than those who don’t (risk factor).
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.
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
1) Creates anxiety in people
2) False sense of reassurance
3) Produces morbidity through test itself
(screening devices and equipment)
4)
Excess morbidity
No Screening
Age
B
Exposure
period
Cells
exfoliate
20
40
Symptoms
diagnosis
50
55
60
Death
Cancer
begins
Screening
Age
B
Exposure
period
Cells
exfoliate
20
40
Symptoms
diagnosis
50
55
60
Death
Cancer
begins
Screening
Detection
Screening
Current Situation
Care for Chronic
Disease
Self-Referral
Diagnosis
Surveillance
Recovery
Screening
Projection for the Future
Care for Chronic
Disease
Self-Referral
Diagnosis
Surveillance
Recovery
Three Phases of Disease





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
Age
30
55
45
55
Screening
Characteristics of disease that makes it
suitable for screening:
1) Serious disease
2) Early therapy better than late therapy
3) The detectable pre-clinical phase is
high
4) There is treatment available for
disease
Screening Analysis
Test Results
Positive
Negative
True Diagnosis
Diseased
Not
Diseased
a
b
c
d
a+c
b+d
a+b
c+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
Test Results
Positive
Negative
True Diagnosis
Diseased
Not
Diseased
40
100
10
1,000
50
1,100
140
1,010
1,150
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
Evaluating a Screening
Program
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.
Evaluating a Screening
Program
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
1.
2.
3.
4.
5.
6.
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.
1) The occurrence of a health problem
2) 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:
1) Classes used must be mutually exclusive
2) It should be exhaustive
3) 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-percase 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.