Introduction to Public Health Surveillance

1. Introduction to Public Health Surveillance

2. Goals • Define surveillance, explain surveillance systems • Describe basic surveillance techniques by person, place, time • Touch on importance of standardization • Provide overview of how to present surveillance data

3. Surveillance • For persons who need to carry out surveillance activities but have little prior experience or training • Also helpful for people who would like to better understand the process and reasoning behind surveillance methods and interpretation

4. What Is Surveillance? • Centers for Disease Control and Prevention (CDC): epidemiologic surveillance is “ongoing systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice, closely integrated with the timely dissemination of these data to those who need to know.”

5. Why Is Surveillance Important? • Collecting data is merely one step • Critical goal is to control and/or prevent diseases • Any data collected must be organized and carefully examined • Any results need to be communicated to public health and medical communities

6. Why Is Surveillance Important? • Vital to communicate results • During potential outbreak so public health and medical communities can help with disease prevention and control efforts • During non-outbreak times to provide information about baseline levels of disease • Baseline provides information to public health officials monitoring health at community level, serves as reference in future outbreaks

7. Surveillance Systems • Classified as passive or active • Passive surveillance: local and state health departments rely on health care providers or laboratories to report cases of disease • Primary advantage is efficiency: simple and requires relatively few resources • Disadvantage is possibility of incomplete data due to underreporting • Majority of public health surveillance systems are passive

8. Surveillance Systems • Active surveillance: health department contacts health care providers or laboratories requesting information about conditions or diseases to identify possible cases • Requires more resources than passive surveillance • Useful when important to identify all cases • Example: between 2002 and 2005, active surveillance used to detect adverse events associated with smallpox vaccine. (2)

9. Why Is Surveillance Important? • Surveillance information has many uses: • Monitoring disease trends • Describing natural history of diseases • Identifying epidemics or new syndromes • Monitoring changes in infectious agents • Identifying areas for research • Evaluating hypotheses • Planning public health policy • Evaluating public health policy/interventions

10. Why Is Surveillance Important? • Examples of uses of surveillance data: • Evaluating impact of national vaccination campaigns • Identifying AIDS when unknown syndrome • Estimating impact of AIDS on US health care system in 1990s (using mathematical models based on surveillance data) • Identifying outbreaks of rubella and congenital rubella among Amish and Mennonite communities in 6 states in 1990 and 1991 (3) • Monitoring obesity, physical activity, other indicators for chronic diseases

11. How to Conduct Surveillance • Surveillance data allow description and comparison of patterns of disease by person, place, and time • Several ways to describe and compare patterns, from straightforward presentations to statistically complex analyses • Will concentrate on simple techniques

12. How to Conduct Surveillance: Person • When available, demographic characteristics such as gender, age, race/ethnicity, occupation, education level, socio-economic status, sexual orientation, immunization status can reveal disease trends • Example: looking at Streptococcus pneumoniae, a common cause of community-acquired pneumonia and bacterial meningitis, examining distribution of cases by race provides important information about burden of disease in different populations

13. How to Conduct Surveillance: Person – Numbers and Rates • Table 1 shows data collected on Streptococcus pneumoniae from CDC Emerging Infections Program Network, a surveillance program that collects data from multiple counties in 10 US states (4)

14. How to Conduct Surveillance: Person – Numbers and Rates • Data show majority of cases reported among whites • Can draw only limited conclusions because race not recorded for 684 cases (15%) • Shows only number of reported cases, not rate • Total number of individuals by race needed to determine if there is a disproportionate burden of disease among races

15. How to Conduct Surveillance: Person – Numbers and Rates • Table 2 shows same data with 2006 population estimates of total number of persons in each racial category used to calculate disease rates (4)

16. How to Conduct Surveillance: Person – Numbers and Rates • While Table 1 showed that whites had the highest number of cases, Table 2 indicates that the rate of disease was highest among blacks • Using rates, stratifying by race provides information about disease burden in different populations that would not be apparent from total case numbers

17. More on Rates • Rates—A rate is “an expression of the frequency with which an event occurs in a defined population” • Using rates rather than raw numbers is essential to compare different classes of persons or populations at different times or places. (5) Rate = number of events in a specified period average population during the period

18. How to Conduct Surveillance: Place • Best to characterize cases by place of exposure rather than by place at which cases reported • The two may differ and place of exposure is more relevant to epidemiology of a disease • Example: travelers on a cruise ship exposed to a disease just prior to disembarking but become symptomatic and are diagnosed after return to various home locations • Example: person exposed to disease in small rural town but referred to tertiary care center 100 miles away where disease is diagnosed and reported

19. How to Conduct Surveillance: Place – Presenting Data • Data by geographic location can be presented in a table • Also helpful to use maps to facilitate recognition of spatial associations in data • See FOCUS Volume 5, Issue 2: Mapping for Surveillance and Outbreak Investigation for discussion of maps and visual presentation of information • Inferential analysis can also be done using multilevel modeling, other statistical methods

20. How to Conduct Surveillance: Place – Modeling Resources • Modeling of surveillance data by place is beyond scope of this issue • Resources for further information: • Centers for Disease Control and Prevention. Resources for creating public health maps. http://www.cdc.gov/epiinfo/maps.htm. Updated August 14, 2008. Accessed August 22, 2008. • Clarke KC, McLafferty SL, Tempalski BJ. On epidemiology and geographic information systems: A review and discussion of future directions. Emerg Infect Dis. 1996; 2(2):85-92.

21. How to Conduct Surveillance: Place – Spot Maps • Spot maps: maps on which a dot or symbol marks a case of disease • Made by indicating exposure locations of reported cases of disease on hard copy map with pins or colored pen • Or with geographic information systems (GIS) • Computer programs designed for storing, manipulating, analyzing, and displaying data in a geographic context • Very useful for mapping surveillance data by place • Epi Map (part of Epi Info™) can be downloaded for free at http://www.cdc.gov/epiinfo to assist with map making

22. How to Conduct Surveillance: Place – Spot Maps • Example: spot map used to show geographic spread of cases in 1995 outbreak of toxoplasmosis thought to be associated with a municipal water system in British Columbia, Canada (5) • Spot maps show geographic distribution of cases but not population size at each location, so should not be used to assess disease risk

23. How to Conduct Surveillance: Time • Compare number of cases reported in time period of interest (weeks, months, years) to number of cases reported during similar historical period • Usually a delay (sometimes months to years) between disease onset and date when disease is reported, so preferable to use date of onset, if available, rather than date of report

24. How to Conduct Surveillance: Time – Line Graphs • Especially helpful for examining data not likely to have much short term variation • Example: there is limited variation in number of AIDS cases reported each month • Provide valuable qualitative information; disease outbreaks often obvious from visual inspection of data, may not require a quantitative analysis

25. How to Conduct Surveillance: Time – Line Graphs • Example of line graph using fabricated data: reported cases of Salmonella typhimurium for 2-year time intervals from 1974 to 2002 • Spike in 1994 indicating outbreak of S. typhimurium obvious without quantitative analysis

26. How to Conduct Surveillance: Time – Incidence Rates • May use line graph to plot incidence rates • Incidence rate is number of new cases that occur during a specified time interval in a population at risk for developing the disease • Number of new cases may be used as a proxy for overall disease occurrence • Value often multiplied by 1,000 or 100,000 to improve interpretability • Reporting incidence rates rather than numbers particularly important if population has changed in size or characteristics • Example: addition of towns to a surveillance region has increased population size, or influx of migrant workers has significantly changed the demographics

27. Standardization • Rate made up of numerator and denominator • Surveillance data often numerator data (number of cases reported in time period) • Utility of these raw numbers is limited because do not take into account size of population or distribution of demographic factors such as age or gender • Rates allow more meaningful comparisons over time within a population, among subpopulations, or between populations • Rates take into account size of the population and time period involved (3)

28. Standardization • Crude rates often calculated using surveillance data • Number of events of interest (such as reported cases of disease) for a specific period of time for the entire population • Only appropriate to compare crude rates if populations are similar with respect to factors related to disease of interest, such as age, gender, race • Example: would be inappropriate to compare rate of prostate cancer in population with high proportion of elderly men to rate in another population with mostly young men, since risk of prostate cancer increases with age

29. Standardization • Standardization used to remove effects of differences in confounding variables such as age when comparing two or more populations • Results in adjusted rates • Is particularly useful when comparing rates in different populations (e.g., comparing state data to national data) when comparison of crude rates may be misleading if populations differ on key variables  • Most common technique uses weighted average rates specific to potential confounding variables, based on specified distribution of the variables (5)

30. Data Presentation • Surveillance data must be presented in way that is easy to understand and interpret • Many ways to display surveillance data: (3) • Line graphs for displaying data by time • Maps for presenting data in geographic context • Graphical displays such as histograms, frequency polygons, box plots, scatter diagrams, bar charts, pie charts, or stem-and-leaf displays • Spot or chloropleth maps • Single/multivariable tables

31. Data Presentation • The choice of a particular graph or table depends on type of data, but presentation should be simple and easy to follow • Should provide all information necessary to interpret the figure without referring to text • Include concise title that describes subject or disease, time, place (when relevant) • Define any abbreviations or symbols • Note any data exclusions (3)

32. Data Presentation • Additional display guidelines for tables and graphs

33. Conclusion • Surveillance is valuable epidemiologic tool that can serve many purposes • When surveillance data is collected, analyzed, interpreted, reported appropriately, these data can provide important information about disease patterns to inform public health practice and policy

34. References • Thacker SB, Berkelman RL. Public health surveillance in the United States. Epidemiol Rev. 1988;10:164-190. • Thomas TN, Reef S, Neff L, Sniadack MM, Mootrey GT. A review of the smallpox vaccine adverse events active surveillance system. Clin Infect Dis. 2008;46 Suppl 3:S212-S220. • Janes GR, Hutwanger L, Cates Jr W, Stroup DF, Williamson GD. Descriptive Epidemiology: Analyzing and Interpreting Surveillance Data. In: Teutsch SM, Churchill RE, eds. Principles and Practice of Public Health Surveillance. New York, NY: Oxford University Press, inc, 2000:112-167.

35. References • Centers for Disease Control and Prevention. Active Bacterial Core Surveillance Report (ABCs), Emerging Infections Program Network, Streptococcus pneumoniae, 2006. http://www.cdc.gov/ncidod/dbmd/abcs/survreports/spneu06.pdf2007. Published 2007. Accessed August 21, 2008. • Last JM, ed. A Dictionary of Epidemiology. 3rd ed. New York, NY: Oxford University Press, Inc, 1995. • Eng SB, Werker DH, King AS, et al. Computer-generated dot maps as an epidemiologic tool: Investigating an outbreak of toxoplasmosis. Emerg Infect Dis. 1999;5(6):815-819.