Transcript
Page 1: 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.

Rat

e pe

r 100

HIV

Cas

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Num

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f Cas

es a

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Year

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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.


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