Transcript Document

Session 3, Part 1
Descriptive Epidemiology
Learning Objectives
Session 3, Part 1
• Define descriptive epidemiology
• Calculate incidence and prevalence
• List examples of the use of descriptive data
Overview
Session 3, Part 1
• Prevalence and incidence
• Person, place, and time
Prevalence and Incidence
What is Epidemiology?
Study of distribution and determinants of
states or events in specified populations,
and the application of this study to the
control of health problems
Purposes:
• Study risk associated with exposures
• Identify and control epidemics
• Monitor population rates of disease and
exposure
Epidemiologic Investigation
• To answer the questions:
– Who?
– What?
– When?
– Where?
– Why?
– How?
Descriptive vs. Analytic
Epidemiology
Descriptive epidemiology
Analytic epidemiology
• Who
• Why
• What
• How
• When
• Where
Descriptive Epidemiology
• Provides a systematic method for characterizing
a health problem
• Ensures understanding of the basic dimensions
of a health problem
• Helps identify populations at higher risk for the
health problem
• Provides information used for allocation of
resources
• Enables development of testable hypotheses
Case Definition
• Standard diagnostic criteria that must be
fulfilled to identify a person as a case of a
particular disease
• Ensures that all persons who are counted
as cases actually have the same disease
• Typically includes clinical criteria and
restrictions on person, place, and time
Example Case Definition:
Cyclosporiasis
• Probable
– A case that meets the clinical description and
that is epidemiologically linked to a confirmed
case
• Confirmed
– A case that meets the clinical description and
at least one of the criteria for laboratory
confirmation as described above
Descriptive Epidemiology
What is the problem?
• Most basic: a simple count of cases
– Useful for looking at the burden of disease
– Not useful for comparing to other groups or
populations
County
# of Salmonella cases
Pop. size
A
120
1,500,000
B
500
5,300,000
Prevalence
• The number of affected persons present in
the population divided by the number of
people in the population
Prevalence =
# of cases
# of people in the population
Prevalence Example
• In 2010, a US state reported an estimated 253,040
residents over 20 years of age with diabetes. The
US Census Bureau estimated that the 2010
population over 20 in that state was 5,008,863.
Prevalence =
253,040
5,008,863
Prevalence Example
• In 2010, a US state reported an estimated 253,040
residents over 20 years of age with diabetes. The
US Census Bureau estimated that the 2010
population over 20 in that state was 5,008,863.
Prevalence =
253,040
5,008,863
= 0.051
• In 2010, the prevalence of diabetes was 5.1%
– Can also be expressed as 51 cases per 1,000
residents over 20 years of age
Prevalence
• Useful for assessing the
burden of disease within
a population
• Valuable for planning
• Not useful for
determining what causes
disease
Incidence
• The number of new cases of a disease that
occur during a specified period of time divided
by the number of persons at risk of developing
the disease during that period of time
# of new cases of disease
over a specific period of time
Incidence =
# of persons at risk of disease
over the specified period of time
Incidence Example
A study is examining factors related to non-small cell lung
cancer (NSCLC) in community-dwelling adults. During the
study period, 77,719 adults aged 50-76 were followed, and
612 developed NSCLC.
Incidence =
612
77,719
Source: Slatore et al. BMC Cancer 2011, 11:22
Incidence Example
A study is examining factors related to non-small cell lung
cancer (NSCLC) in community-dwelling adults. During the
study period, 77,719 adults aged 50-76 were followed, and
612 developed NSCLC.
Incidence =
612
=
0.0079
77,719
• The one year incidence of non-small cell lung cancer
among adults aged 50-76 is 0.79%
– Can also be expressed as 79 cases per 10,000 persons
aged 50-76
Source: Slatore et al. BMC Cancer 2011, 11:22
Incidence
• High incidence represents
diseases with high occurrence;
low incidence represents
diseases with low occurrence
• Can be used to help determine
the causes of disease
• Can be used to determine the
likelihood of developing
disease
Prevalence and Incidence
• Prevalence is a function of the incidence
of disease and the duration of the disease
Prevalence and Incidence
Prevalence
= prevalent cases
Prevalence and Incidence
New prevalence
Incidence
Old (baseline)
prevalence
No cases die
or recover
= prevalent cases
= incident cases
Prevalence and Incidence
= prevalent cases
= incident cases
= deaths or recoveries
Practice Scenario
A town has a population of 3600. In 2010, 400 residents
of the town are diagnosed with a disease. In 2011, 200
additional residents of the town are diagnosed with the
same disease. The disease is lifelong but it is not fatal.
• How would you calculate the prevalence in 2010? In 2011?
• How would you calculate the incidence in 2011?
Practice Scenario Answers
•
•
•
•
Population: 3600
2010: 400 diagnosed with a disease
2011: 200 additional diagnosed with the disease
No death, no recovery
Prevalence Prevalence Incidence
(2010)
(2011)
(2011)
Numerator
400
600
?
Denominator
3600
3600
?
11.1%
16.7%
?
Practice Scenario Answers
•
•
•
•
Population: 3600
2010: 400 diagnosed with a disease
2011: 200 additional diagnosed with the disease
No death, no recovery
Prevalence Prevalence Incidence
(2010)
(2011)
(2011)
Numerator
400
600
200
Denominator
3600
3600
3200
11.1%
16.7%
6.3%
Descriptive Epidemiology
Person, Place, Time
Who? Where? When?
• Person
– May be characterized by age, race, sex,
education, occupation, or other personal
characteristics
• Place
– May include information on home, workplace,
school
• Time
– May look at time of illness onset, when exposure
to risk factors occurred
Person Data
• Age and sex are almost always
used
– Age data are usually grouped –
intervals depend on type of
disease / event
• May be shown in tables or
graphs
• May look at more than one type
of person data at once
Person Data: Race/Ethnicity
Prevalence of obesity among men aged 20 years and over by race/ethnicity,
United States, 1988-1994 and 2007-2008
SOURCE: Centers for Disease Control and Prevention, National Center for Health Statistics, National Health
Examination Survey and National Health and Nutrition Examination Survey III 1988-1994 and 2007-2008
Person Data: Age
Reported abortions, by known age group and year --- selected
states,* United States, 2005--2007
Age group (yrs)
2005
2006
2007
<15
1.3
1.2
1.2
15--19
14.9
15.1
14.8
20--24
29.5
30.4
30.0
25--29
21.9
22.6
22.0
30--34
13.5
13.9
13.7
35--39
7.7
8.0
7.9
≥40
2.6
2.7
2.7
Abortion rate†
SOURCE: MMWR Surveillance Summaries. http://www.cdc.gov/mmwr/preview/mmwrhtml/ss6001a1.htm
Person Data: Age and Sex
Age-specific cancer incidence rates, by sex
SOURCE: Wisconsin Cancer Incidence and Mortality Report, 1996, p. 26
http://s3.amazonaws.com/zanran_storage/dhs.wisconsin.gov/ContentPages/3730888.pdf
Person Data Limited by Age
Emergency Room Visits for Consumer-product Related Injuries
among the Elderly (65 years and older), 2002
Packaging and
containers,
35,020
Home
workshop
tools, 38,210
Bathrooms,
85,630
Yard / garden
equipment,
41,780
Housewares,
52,990
Personal use
items, 58,220
Sports,
57,120
SOURCE: http://www.cpsc.gov/library/foia/foia05/os/older.pdf
Time Data
• Usually shown as a graph
– Number / rate of cases on vertical
(y) axis
– Time periods on horizontal (x)
axis
• Time period will depend on
what is being described
• Used to show trends,
seasonality, day of week / time
of day, epidemic period
Time Data: Day
Epi Curve for E.Coli Outbreak, n=108
12
Number of cases
10
8
6
4
2
0
10/11 10/14 10/17 10/20 10/23 10/26 10/29 11/1
11/4
11/7 11/10
Date of onset
SOURCE: http://www.dhhs.state.nc.us/docs/ecoli.htm
Time Data: Year
SOURCE: Broome County, NY: http://www.gobroomecounty.com/clinics/lyme-disease
Time Data: Year
SOURCE: http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5153a1.htm
Time Data: Week
SOURCE: http://www.cdc.gov/flu/weekly/weeklyarchives2010-2011/weekly34.htm
Place Data
• Can be shown in a table;
usually better presented
pictorially in a map
• Two main types of maps used:
choropleth and spot
– Choropleth maps use different shadings/colors
to indicate the count / rate of cases in an area
– Spot maps show location of individual cases
Place Data: State
2010 Obesity by State
% State
%
28.2 Montana
23.0
29.6 Nebraska
26.9
28.4 Nevada
22.4
29.4 New Hampshire 25.0
State
Alabama
Alaska
Arizona
Arkansas
%
32.2
24.5
24.3
30.1
State
Illinois
Indiana
Iowa
Kansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Hawaii
Idaho
24.0
21.0
22.5
28.0
22.2
Kentucky
Louisiana
Maine
Maryland
Massachusetts
31.3
31.0
26.8
27.1
23.0
New Jersey
New Mexico
New York
North Carolina
North Dakota
23.8
25.1
23.9
27.8
27.2
26.6
29.6
22.7
26.5
Michigan
Minnesota
Mississippi
Missouri
30.9
24.8
34.0
30.5
Ohio
Oklahoma
Oregon
Pennsylvania
29.2 West Virginia
30.4 Wisconsin
26.8 Wyoming
28.6
SOURCE: CDC http://www.cdc.gov/obesity/data/trends.html
State
Rhode Island
South Carolina
South Dakota
Tennessee
%
25.5
31.5
27.3
30.8
Texas
Utah
Vermont
Virginia
Washington
31.0
22.5
23.2
26.0
25.5
32.5
26.3
25.1
Place Data: State
SOURCE: http://www.cdc.gov/obesity/data/trends.html
Place Data:
Individual Cases
Spot map of men who tested
positive for HIV at time of
entry into the Royal Thai
Army, Thailand, November
1991–May 2000.
SOURCE: http://www.cdc.gov/ncidod/EID/vol9no7/02-0653-G1.htm
Place Data: Airplane Seat
SOURCE: Olsen, S.J. et al. N Engl J Med. 2003 Dec 18; 349(25):2381-2.
Summary
• Descriptive epidemiology describes:
– What happened
– The population it happened in
– When it happened
• Descriptive epidemiology identifies
populations at high risk, helps with
allocation of resources, and provides a
foundation for developing hypotheses
Summary
• Commonly used measures in descriptive
epidemiology are prevalence and
incidence
• The main characteristics of descriptive
epidemiologic data are person, place and
time
References and Resources
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Centers for Disease Control and Prevention. Principles of Epidemiology.
3rd ed. Atlanta, Ga: Epidemiology Program Office, Public Health Practice
Program Office; 1992.
Gordis L. Epidemiology. 2nd ed. Philadelphia, Pa: WB Saunders Company;
2000.
Gregg MB, ed. Field Epidemiology. 2nd ed. New York, NY: Oxford
University Press; 2002.
Hennekens CH, Buring JE. Epidemiology in Medicine. Philadelphia, Pa:
Lippincott Williams & Wilkins; 1987.
Last JM. A Dictionary of Epidemiology. 4th ed. New York, NY: Oxford
University Press; 2001.
McNeill A. Measuring the Occurrence of Disease: Prevalence and
Incidence. EPID 160 Lecture Series. Department of Epidemiology,
University of North Carolina at Chapel Hill School of Public Health; January
2002.
References and Resources
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Morton RF, Hebel JR, McCarter RJ. A Study Guide to Epidemiology and
Biostatistics. 5th ed. Gaithersburg, Md: Aspen Publishers Inc; 2001.
Incidence vs. Prevalence. ERIC Notebook [serial online]. 1999:1(2).
Department of Epidemiology, University of North Carolina at Chapel Hill
School of Public Health / Epidemiologic Research & Information Center,
Veterans Administration Medical Center. Available at:
http://cphp.sph.unc.edu/trainingpackages/ERIC/issue2.htm. Accessed
March 1, 2012.
Wisconsin Cancer Incidence and Mortality, 1996. Wisconsin Department of
Health and Family Services; October 1998. Available at:
http://s3.amazonaws.com/zanran_storage/dhs.wisconsin.gov/ContentPages
/3730888.pdf. Accessed March 1, 2012.
Slatore CG, Gould MK, Au DH, Deffebach ME, White E. Lung cancer stage
at diagnosis: Individual associations in the prospective VITamins and
lifestyle (VITAL) cohort. BMC Cancer. 2011;11:228.
References and Resources
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Ogden CL, Carroll DL. Prevalence of Overweight, Obesity, and Extreme
Obesity Among Adults: United States, Trends 1960-1962 Through 20072008. Centers for Disease Control and Prevention / National Center for
Health Statistics, Division of Health and Nutrition Examination Surveys;
June 2010. Available at:
http://www.cdc.gov/NCHS/data/hestat/obesity_adult_07_08/obesity_adult_0
7_08.pdf. Accessed March 1, 2012.
Abortion Surveillance --- United States, 2007. MMWR Surveillance
Summaries. 2011;60(ss01):1-39. Available at:
http://www.cdc.gov/mmwr/preview/mmwrhtml/ss6001a1.htm. Accessed
March 1, 2012.
Torugsa K, Anderson S, Thongsen N, et al. HIV Epidemic among Young
Thai Men, 1991-2000. Emerg Infect Dis [serial online]. 2003;9(7).
http://www.cdc.gov/ncidod/EID/vol9no7/02-0653-G1.htm. Accessed March
1, 2012.
Olsen SJ, Chang HL, Cheung TYY, et al. Transmission of the severe acute
respiratory syndrome on aircraft. N Engl J Med. 2003;349:2381-2382.