Download - Federal Public Health Surveillance
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Federal Public Health Surveillance
Session 4, Part 2
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Learning ObjectivesSession 4, Part 2
• List federal public health surveillance systems relevant to epidemiology programs
• Discuss the major components of surveillance data analysis
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OverviewSession 4, Part 2
• Role of the CDC in public health surveillance
• Examples of federal surveillance systems
• Basics of surveillance data analysis
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Role of CDC in Public Health Surveillance
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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.)
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Provisional cases of selected notifiable diseases, United States, weeks ending Dec 3, 2011, and Dec 4 2010 (48th week)
CDC Surveillance Data Reporting
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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
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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
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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
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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
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Examples
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Example: ArboNet
• A cooperative surveillance system maintained by CDC and 57 state and local health departments
• Detects and reports the occurrence of domestic arboviruses
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Arboviruses• Cache Valley • California serogroup
[unspecified]• Chikungunya• Colorado tick fever• Dengue• Eastern equine
encephalitis • Jamestown Canyon
• Japanese encephalitis • LaCrosse • Powassan • St Louis encephalitis • Venezuelan equine
encephalitis • Western equine
encephalitis • West Nile
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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
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Example: Dengue in Florida, 2011
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ArboNet: Non-human Data• Routine blood
donor screening results
• Veterinary (equine and other animals)
• Avian• Mosquito• Sentinel chickens
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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
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U.S. Influenza Surveillance 1. Viral strain surveillance
– WHO and National Respiratory and Enteric Virus Surveillance System
2. Outpatient illness surveillance– ILINet
3. Mortality surveillance– 22 Cities Mortality Reporting System– Influenza-associated Pediatric Mortality Surveillance
System
4. Hospitalization surveillance– FluSurv-NET
5. Summary of geographic spread– State and Territorial Epidemiologists Reports
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Influenza-like Illness (ILI)Case Definition
Fever of 100 degrees Fahrenheit or higher
AND
Cough and/or sore throat
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Sentinel Influenza Surveillance
Source: http://www.cdc.gov/flu/weekly/
40 (2008) 40 (2009) 40 (2010) 40 (2011)
Latepeak
Pandemic
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Basics of Surveillance Data Analysis
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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
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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
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Tuberculosis Cases: United States 1992 - 2010
Source: MMWR March 25, 2011 / 60(11);333-337Trends in Tuberculosis: http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6011a2.htm
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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
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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
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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)
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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
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Raw Numbers versus Rates
Source: MMWR March 25, 2011 / 60(11);333-337Trends in Tuberculosis: http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6011a2.htm
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Why Use Rates?
Rates provide frequency measures within the context of the population.
Raw Surveillance Data
Total Population
Crude Rate X 104
City A 10 1,000 .01 100 per 10,000
City B 10 1,000,000 .00001 .1 per 10,000
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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)
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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
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Number of HIV cases among IDUs and rate of IDU cases among all HIV cases, Estonia, 2000-2007
Source: EpiNorth.org. Kutsar K, Epshtein J. HIV infection Epidemiology in Estonia in 2000-2009. EpiNorth 2009; 10: 180-6.
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Raw Data MapNorth 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
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Choropleth MapNorth 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
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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
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Raw Data
Rates
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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?
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Example: Change in Case Definition
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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
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References and Resources • 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.
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References and Resources• 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.