1. is for Epi Epidemiology basics for non-epidemiologists

2. Session III Part I Descriptive and Analytic Epidemiology

3. Session Overview 1.Define descriptive epidemiology 2.Define incidence and prevalence 3.Discuss examples of the use of descriptive data 4.Define analytic epidemiology 5.Discuss different study designs 6.Discuss measures of association 7.Discuss tests of significance

4. Today’s Learning Objectives Understand the distinction between descriptive and analytic epidemiology, and their utility in surveillance and outbreak investigations Recognize descriptive and analytic measures used in the epidemiological literature Know how to interpret data for measures of association and common statistical tests

5. Descriptive Epidemiology Prevalence and Incidence

6. What is Epidemiology? Study of the distribution and determinants of states or events in specified populations, and the application of this study to the control of health problems –Study risk associated with exposures –Identify and control epidemics –Monitor population rates of disease and exposure

7. What is Epidemiology? Looking to answer the questions: –Who? –What? –When? –Where? –Why? –How?

8. Descriptive vs. Analytic Epidemiology Descriptive epidemiology deals with the questions: Who, What, When, and Where Analytic epidemiology deals with the remaining questions: Why and How

9. Descriptive Epidemiology Provides a systematic method for characterizing a health problem Ensures understanding of the basic dimensions of a health problem Helps identify populations at higher risk for the health problem Provides information used for allocation of resources Enables development of testable hypotheses

10. Case Definition A set of standard diagnostic criteria that must be fulfilled in order to identify a person as a case of a particular disease Ensures that all persons who are counted as cases actually have the same disease Typically includes clinical criteria (lab results, symptoms, signs) and sometimes restrictions on person, place, and time

11. Descriptive Epidemiology What? Addresses the question “How much?” Most basic is a simple count of cases –Good for looking at the burden of disease –Not useful for comparing to other groups or populations Race# of Salmonella cases Black119 White497 http://www.vdh.virginia.gov/epi/Data/race03t.pdf Pop. size 1,450,675 5,342,532

12. Prevalence The number of affected persons present in the population divided by the number of people in the population # of cases Prevalence = ----------------------------------------- # of people in the population

13. Prevalence Example In 1999, a US state reported an estimated 253,040 residents over 20 years of age with diabetes. The US Census Bureau estimated that the 1999 population over 20 in that state was 5,008,863. 253,040 Prevalence= = 0.051 5,008,863 In 1999, the prevalence of diabetes was 5.1% –Can also be expressed as 51 cases per 1,000 residents over 20 years of age

14. Prevalence Useful for assessing the burden of disease within a population Valuable for planning Not useful for determining what caused disease

15. Incidence The number of new cases of a disease that occur during a specified period of time divided by the number of persons at risk of developing the disease during that period of time # of new cases of disease over a specific period of time Incidence = # of persons at risk of disease over that specific period of time

16. Incidence Example A study in 2002 examined depression among persons with dementia. The study recruited 201 adults with dementia admitted to a long-term care facility. Of the 201, 91 had a prior diagnosis of depression. Over the first year, 7 adults developed depression. 7 Incidence = = 0.064 110 The one year incidence of depression among adults with dementia is 6.4% –Can also be expressed as 64 cases per 1,000 persons with dementia

17. Incidence High incidence represents diseases with high occurrence; low incidence represents diseases with low occurrence Can be used to help determine the causes of disease Can be used to determine the likelihood of developing disease

18. Prevalence and Incidence Prevalence is a function of the incidence of disease and the duration of disease

19. Prevalence and Incidence Prevalence = prevalent cases

20. Prevalence and Incidence Old (baseline) prevalence = prevalent cases= incident cases New prevalence Incidence No cases die or recover

21. Prevalence and Incidence = prevalent cases= incident cases= deaths or recoveries

22. Practice Scenario A town has a population of 3600. In 2003, 400 residents of the town are diagnosed with a disease. In 2004, 200 additional residents of the town are diagnosed with the same disease. The disease is lifelong but it is not fatal. How would you calculate the prevalence in 2003? In 2004? How would you to calculate the incidence in 2004?

23. Practice Scenario Answers Population : 3600 2003: 400 diagnosed with a disease 2004: 200 additional diagnosed with the disease No death, no recovery Numerator Denominator Prevalence (2003) 400 3600 11.1% Prevalence (2004) 600 3600 16.7% Incidence (2004) 200 3200 6.3%

24. Descriptive Epidemiology Person, Place, Time

25. Descriptive Epidemiology Who? When? Where? Related to Person, Place, and Time Person –May be characterized by age, race, sex, education, occupation, or other personal characteristics Place –May include information on home, workplace, school Time –May look at time of illness onset, when exposure to risk factors occurred

26. Person Data Age and Sex are almost always used in looking at data –Age data are usually grouped – intervals will depend on what type of disease / event is being looked at May be shown in tables or graphs May look at more than one type of person data at once

27. Overweight and obesity by age: United States, 1960-2002 SOURCES: Centers for Disease Control and Prevention, National Center for Health Statistics, National Health Examination Survey and National Health and Nutrition Examination Survey. Overweight including obese, 20-74 years Overweight, 6-11 years Overweight, 12-19 years Overweight, but not obese, 20-74 years Obese, 20-74 years 1960- 62 1963- 65 1966 -70 1971- 74 1976- 80 1988- 94 1999- 2002 Year Data Characterized by Person

28. Data Characterized by Person Primary and Secondary Syphilis, US, 1996-2000 Age White, Non- Hispanic Black, Non- HispanicHispanic Asian/Pacific Islander American Indian/Alaska Native Group MaleFemaleMaleFemaleMaleFemaleMaleFemaleMaleFemale 10-140.13.00.56.90.21.80.00.20.00.3 15-197.067.618.399.36.033.50.53.40.64.0 20-2412.155.823.081.08.734.70.83.60.63.6 25-295.316.411.126.44.715.90.51.60.31.5 30-342.55.95.69.42.26.90.30.90.20.7 35-391.62.63.14.31.02.80.20.50.10.4 40-440.91.21.51.70.51.10.10.20.10.2 45-540.7 1.10.90.30.70.1 0.00.1 55-640.20.10.2 0.00.10.0 65+0.10.20.10.20.00.10.0 TOTAL 30.5153.864.9231.023.897.92.510.42.010.9 http://www.cdc.gov/std/stats00/Tables/2000Table32A.htmhttp://www.cdc.gov/std/stats00/Tables/2000Table32A.htm Data shown are /1,000

29. Data Characterized by Person

30. Data Characterized by Person Emergency Room Visits for Consumer-product Related Injuries among the Elderly (65 years and older), 2002

31. Time Data Usually shown as a graph –Number / rate of cases on vertical (y) axis –Time periods on horizontal (x) axis Time period will depend on what is being described Used to show trends, seasonality, day of week / time of day, epidemic period

32. Data Characterized by Time http://www.dhhs.state.nc.us/docs/ecoli.htm

33. Data Characterized by Time http://www.hivclearinghouse.org/0Surveillance%203rd%20Quarter%20Report.pdf

34. Data Characterized by Time http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5153a1.htm

35. Data Characterized by Time http://www.health.qld.gov.au/phs/Documents/cdu/12776.pdf

36. Place Data Can be shown in a table; usually better presented pictorially in a map Two main types of maps used: choropleth and spot –Choropleth maps use different shadings/colors to indicate the count / rate of cases in an area –Spot maps show location of individual cases

37. Children aged 10 µg/dL by state and year— selected U.S. sites, 1997–2001 http://www.cdc.gov/mmwr/preview/mmwrhtml/ss5210a1.htm

38. Data Characterized by Place

39. http://www.cdc.gov/ncidod/EID/vol9no7/02- 0653-G1.htm Spot map of men who tested positive for HIV at time of entry into the Royal Thai Army, Thailand, November 1991–May 2000.

40. Data Characterized by Place Source: Olsen, S.J. et al. N Engl J Med. 2003 Dec 18; 349(25):2381-2.