Transcript Document

Session 4, Part 2
Federal Public Health Surveillance
Learning Objectives
Session 4, Part 2
• List federal public health surveillance
systems relevant to epidemiology
programs
• Discuss the major components of
surveillance data analysis
Overview
Session 4, Part 2
• Role of the CDC in public health
surveillance
• Examples of federal surveillance systems
• Basics of surveillance data analysis
Role of CDC in Public Health
Surveillance
CDC’s Role in Surveillance
• Supports the states
– Facilitates development of definitions,
recommendations, and guidelines
– Provides training and consultation
– Distributes and oversees funding
• Receives, collates, analyzes, and reports data
• Suggests changes to be considered in public
health surveillance activities
• Reports to the World Health Organization
(WHO) as required (e.g. influenza, measles,
etc.)
CDC Surveillance Data
Reporting
Provisional cases of selected notifiable diseases, United States,
weeks ending Dec 3, 2011, and Dec 4 2010 (48th week)
Percentage* distribution of gestational ages at time of abortion,
by age of women --- selected states, United States, 2008
* Based on the total number of abortions reported with known weeks of gestation.
Source: CDC. Abortion Surveillance, United States – 2008.
http://www.cdc.gov/mmwr/preview/mmwrhtml/ss6015a1.htm?s_cid=ss601451_w
Federal Data Sources
• Surveillance systems collect data on infectious
and non-infectious conditions such as:
– Foodborne Diseases Active Surveillance Network
(FoodNet)
– National West Nile Virus Surveillance System
(ArboNet)
– Waterborne-Disease Outbreak Surveillance System
– Influenza Sentinel Physicians Surveillance Network
Federal Surveillance Resources
• CDC Morbidity and Mortality Weekly Report
(MMWR)
• CDC Division of Preparedness and Emerging
Infections
• CDC Office of Surveillance, Epidemiology, and
Laboratory Services
http://www.cdc.gov
Council of State and Territorial
Epidemiologists (CSTE)
• Collaborates with CDC to recommend changes
in surveillance, including what should be
reported / published in MMWR
• Develops case definitions
• Develops reporting procedures
http://www.cste.org
Examples
Example: ArboNet
• A cooperative surveillance system
maintained by CDC and 57 state and
local health departments
• Detects and reports the occurrence of
domestic arboviruses
Arboviruses
• Cache Valley
• California serogroup
[unspecified]
• Chikungunya
• Colorado tick fever
• Dengue
• Eastern equine
encephalitis
• Jamestown Canyon
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•
•
•
•
Japanese encephalitis
LaCrosse
Powassan
St Louis encephalitis
Venezuelan equine
encephalitis
• Western equine
encephalitis
• West Nile
ArboNet: Human Data
• Demographics
– Age, sex, county of
residence
• Clinical
– Date of onset
– Type of arbovirus
– Syndrome (encephalitis,
meningitis, fever)
• Hospitalization
• Outcome
• Medical risk factors
Example: Dengue in Florida, 2011
ArboNet: Non-human Data
• Routine blood
donor screening
results
• Veterinary (equine
and other animals)
• Avian
• Mosquito
• Sentinel chickens
ArboNet: Surveillance Issues
• “Real-time” reporting
– Novel occurrence of West Nile virus
– Web-based reporting (states)
– Still relies on paper-based reporting (local)
• Incorporates ecologic data
• NEDSS integrated
U.S. Influenza Surveillance
1.
Viral strain surveillance
–
2.
WHO and National Respiratory and Enteric Virus
Surveillance System
Outpatient illness surveillance
–
3.
ILINet
Mortality surveillance
–
–
4.
22 Cities Mortality Reporting System
Influenza-associated Pediatric Mortality Surveillance
System
Hospitalization surveillance
–
FluSurv-NET
5. Summary of geographic spread
–
State and Territorial Epidemiologists Reports
Influenza-like Illness (ILI)
Case Definition
Fever of 100 degrees Fahrenheit
or higher
AND
Cough and/or sore throat
Sentinel Influenza Surveillance
Pandemic
Late
peak
40 (2008)
40 (2009)
40 (2010)
Source: http://www.cdc.gov/flu/weekly/
40 (2011)
Basics of Surveillance Data
Analysis
Considerations
• Surveillance data describes patterns of
disease or injury
• Know the inherent strengths and
weaknesses of a data set
• Examine data from broad to narrow
Rely on Computers to:
• Generate descriptive statistics
– Tables of frequencies, proportions, rates
– Graphs (bar or line) of proportions, rates
– Maps of census tracts, counties, districts
• Aggregate or stratify rates
– State versus county
– Multiple weeks or months or years
– Entire population versus age, gender, or
race specific
Tuberculosis Cases: United States
1992 - 2010
Source: MMWR March 25, 2011 / 60(11);333-337
Trends in Tuberculosis: http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6011a2.htm
Rely on Public Health
Professionals to:
• Contact health care providers and
laboratories to obtain missing
data
• Interpret laboratory tests
• Make judgments about
epidemiological linkages
• Identify or correct mistakes in
data entry
• Determine if epidemics are in
progress
Descriptive Epidemiology
• Person
– What are the patterns
among different
populations?
• Place
– What are the patterns
in different geographic
locations?
• Time
– What are the patterns
at different times?
• Numbers
– Aggregate numbers
reported
• Ratios
– Proportions
• Rates
Ratios
• Definition
– A ratio is any fraction obtained by dividing one
quantity by another; the numerator and
denominator are distinct quantities, and
neither is a subset of the other.
• Ratio examples
– Odds
– Rates
– Proportions (special case)
Rates
• Measures the frequency of an event over
a period of time
• Numerator
– e.g., disease frequency for a period of time
• Denominator
– e.g., population size
Raw Numbers versus Rates
Source: MMWR March 25, 2011 / 60(11);333-337
Trends in Tuberculosis: http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6011a2.htm
Why Use Rates?
Raw
Surveillance
Data
Total
Crude
Population Rate
X 104
City A
10
1,000
.01
100 per
10,000
City B
10
1,000,000
.00001
.1 per
10,000
Rates provide frequency measures within the context of the population.
Crude versus Specific Rates
Crude Rate:
• Rate calculated for the total population
Specific Rate:
• Rate calculated for a sub-set of the
population (e.g., race, gender, age)
Sample Analyses
1. Graph of HIV cases over time (by year)
– Raw data
– Rates
2. Maps of Salmonella rates by county: North
Carolina, 2000
– Raw Data versus Rates
– Choropleth
Rate per 100 HIV Cases
Number of Cases among IDU
Number of HIV cases among IDUs and rate of IDU
cases among all HIV cases, Estonia, 2000-2007
Year
Source: EpiNorth.org. Kutsar K, Epshtein J. HIV infection Epidemiology in Estonia in 2000-2009. EpiNorth 2009; 10: 180-6.
Raw Data Map
North Carolina Salmonella Cases by County: 2000
Source: NC Communicable Disease Data by county for 2000, Communicable Disease
Branch, Epidemiology Section, North Carolina Division of Public Health
Choropleth Map
North Carolina Salmonella Cases by County: 2000
Source: NC Communicable Disease Data by county for 2000, Communicable Disease
Branch, Epidemiology Section, North Carolina Division of Public Health
Choropleth Map
North Carolina Salmonella Rates by County: 2000
Rate numerators: NC Communicable Disease Data for 2000
Rate denominators: U.S. Census population data, by county, for 2000
Raw Data
Rates
Data Interpretation:
Considerations
• Underreporting
• Inconsistent case definitions
• Has reporting protocol changed?
• Has the case definition changed?
• Have new providers or geographic regions
entered the surveillance system?
• Has a new intervention (e.g., screening or
vaccine) been introduced?
Example: Change in Case Definition
Summary
• Federal and state or local surveillance
– Collaborative, reciprocal pathway for data collection
and reporting
– Data collected is used for the practice of public health
• Analysis and interpretation of surveillance data
– Graph rates versus raw data
– Investigate broad, total population rates prior to
specific rates
References and Resources
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Disease Maps 2011 [Web page]. US Geological Survey. Available at:
http://diseasemaps.usgs.gov/. Accessed March 1, 2012.
Epidemiology Program Office [Web page]. Centers for Disease Control and
Prevention. Available at: http://www.cdc.gov/epo/. Accessed March 1, 2012.
Reportable Communicable Diseases – North Carolina. Raleigh: General
Communicable Disease Control Branch, Epidemiology Section, Division of
Public Health, North Carolina Department of Health and Human Services.
NC Communicable Disease Reports. Available at:
http://epi.publichealth.nc.gov/cd/figures.html#cds Accessed March 1, 2012.
Klein R, Schoenborn C. Age Adjustment Using the 2000 Projected U.S.
Population. National Center for Health Statistics / Centers for Disease
Control and Prevention; January 2001. Healthy People 2010 Statistical
Notes: No. 20. Available at:
http://www.cdc.gov/nchs/data/statnt/statnt20.pdf. Accessed March 1, 2012.
References and Resources
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Last JM. A Dictionary of Epidemiology. 2nd ed. New York, NY: Oxford
University Press; 1988.
Teutsch S, Churchill R. Principles and Practice of Public Health
Surveillance. New York, NY: Oxford University Press; 1994.
Background: West Nile Virus [Web page]. US Geological Survey; October
3, 2001. Available at: http://diseasemaps.usgs.gov/wnv_background.html.
Accessed March 1, 2012.
CDC Morbidity and Mortality Weekly Report (MMWR),
http://www.cdc.gov/mmwr.
CDC Division of Preparedness and Emerging Infections,
http://www.cdc.gov/ncezid/dpei/.
CDC Office of Surveillance, Epidemiology, and Laboratory Services,
http://www.cdc.gov/osels/.
Council of State and Territorial Epidemiologists, http://www.cste.org.