Federal Public Health Surveillance

Session 4, Part 2

Learning ObjectivesSession 4, Part 2

• List federal public health surveillance systems relevant to epidemiology programs

• Discuss the major components of surveillance data analysis

OverviewSession 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.)

Provisional cases of selected notifiable diseases, United States, weeks ending Dec 3, 2011, and Dec 4 2010 (48th week)

CDC Surveillance Data Reporting

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

• 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

– 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

Influenza-like Illness (ILI)Case Definition

Fever of 100 degrees Fahrenheit or higher

AND

Cough and/or sore throat

Sentinel Influenza Surveillance

Source: http://www.cdc.gov/flu/weekly/

40 (2008) 40 (2009) 40 (2010) 40 (2011)

Latepeak

Pandemic

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);333-337Trends 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);333-337Trends in Tuberculosis: http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6011a2.htm

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

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

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

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 • 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• 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.