1. Evidence-Based Public Health: A Course in Chronic Disease Prevention MODULE 3: Quantifying the Issue Anjali Deshpande March 2013

2. Learning Objectives 1.To measure and characterize disease frequency in defined populations 2.To find and use disease surveillance data presently available on the Internet 2

3. 1999 Obesity Trends* Among U.S. Adults BRFSS, 1990, 1999, 2008 (*BMI 30, or about 30 lbs. overweight for 5’4” person) 2008 1990 No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30% 3

4. Percent obese high school students, USA, 2009 4

5. Descriptive Epidemiology Define disease Define population at risk Select time frame acute myocardial infarction (AMI) CO residents 2009 5 How do we determine disease frequency for a population?

6. Descriptive Epidemiology How do we determine disease frequency for a population? Compute disease rate for year 2007 number with AMI = 1,204 number at risk of having heart disease = 4,842,770 6

7. Descriptive Epidemiology How do we determine disease frequency for a population? Compute disease rate for year 2007 1,205 Colorado residents with AMI 4,842,770 Colorado residents =.000249 AMI / Coloradoan/ year 7 =

8. Rates are usually expressed as whole numbers for populations at risk during specified periods:.000249 AMI / Coloradoan/ year x 100,000 = 24.9 AMI / 100,000 Coloradoans/ year Question: Can we follow every Coloradoan at risk of developing AMI to identify those who develop AMI during a one-year period? Descriptive Epidemiology 8

9. Problems with estimating the population at risk It is difficult to follow each person in a dynamic population for long periods A more precise way to deal with persons moving in or out of a dynamic population during the study period is to estimate “person-time” 9

10. Descriptive Epidemiology 10 A l O1.00 B l X 0.75 C l + 0.25 D l O0.75 E l X0.25 3.00 JFMAMJJASOND Actual “Person-years” PY l = enters studyO / + = leaves studyX = develops disease

11. Descriptive Epidemiology 11 Computing “person-time” allows for … persons who enter the population after the study period begins, persons who are “lost” during the study period, and persons who develop the disease during the study period and are no longer at risk of developing the disease

12. Descriptive Epidemiology Person-time can be computed by either... counting the “person-time” contributed by each person in the population during the study period, or multiplying the average size of the population at the mid-point of the study period times the duration of the study period. 12

13. Descriptive Epidemiology 13 A l O1.00 B l X 0.75 C l + 0.25 D l O0.75 E l X0.25 3.00 JFMAMJJASOND Actual “Person-years” PY l = enters studyO / + = leaves studyX = develops disease

14. Descriptive Epidemiology 14 A l O1.00 B l X 0.75 C l + 0.25 D l O0.75 E l X0.25 3.00 l = enters studyO / + = leaves study X = develops disease JFMAMJJASOND Estimated “Person-years” PY 3 persons x 1 year = 3 person-years

15. Question: Does the heart disease rate for Coloradoans distinguish between existing and new cases of AMI for this population? Descriptive Epidemiology 15

16. Prevalence vs. Incidence Prevalence is the number of existing cases of disease in the population during a defined period Incidence is the number of new cases of disease that develop in the population during a defined period Descriptive Epidemiology 16

17. Descriptive Epidemiology Question: Are we measuring prevalence or incidence? The number of persons living with HIV in your community as of December 31, 2008 The number of persons diagnosed with breast cancer in your community during 2010 17

18. Descriptive Epidemiology Question: Which data are better for estimating disease rates? incidence or mortality data

19. Descriptive Epidemiology Mortality rates are used to estimate disease frequency when… 19 incidence data are not available, case-fatality rates are high, goal is to reduce mortality among screened or targeted populations

20. Descriptive Epidemiology Intermediate outcomes may be used… when it is not feasible to wait years to see the effects of a new public health program, and there is sufficient type I evidence supporting the relationship between modifiable risk factors and disease reduction. 20

21. Descriptive Epidemiology Long-term outcomes cardiovascular disease lung cancer breast cancer mortality arthritis Intermediate outcomes obesity, physical activity cigarette smoking mammography screening ? 21

22. 22

23. Descriptive Epidemiology Estimating Rates often available for national and state-wide populations not always available for smaller geographically or demographically defined populations 23

24. Descriptive Epidemiology Estimating Rates for Smaller Populations simple solution is to expand the study period or other parameters, e.g., single vs. multiple counties, for the population at risk rates are not reliable if fewer than 20 cases in the numerator 24

25. Surveillance 25 numerator size relative standard error* *RSE = 1 / cases

26. Descriptive Epidemiology Disease Rates crudeor, unadjusted category-specificor, stratified adjustedor, standardized 26

27. Descriptive Epidemiology Crude (or unadjusted) rates estimate the actual disease frequency for a population can be used to provide data for allocation of health resources and public health planning can be misleading if compared over time or across populations 27

28. Descriptive Epidemiology Category-specific (or stratified) rates: are “crude rates” for subgroups of the total population Example: gender-specific AMI death rates for all Coloradoans during 2007 males = 28.1AMI deaths / 100,000 / year females = 21.6 AMI deaths / 100,000 / year 28

29. Descriptive Epidemiology Category-specific (or stratified) rates: provide more detailed information than crude rates about patterns of disease frequency in the population can be used for valid comparison of populations can be cumbersome if there is a large number of categories to compare 29

30. Acute myocardial infarction death rates per 100,000 U.S. residents, 1999-2007 30 Age Group199920002001200220032004200520062007 < 1 year1.7 **N/A1.5** 0.3**1.5**N/A 0.5** 25-34 years1.2**0.3**1.0**0.7**0.3**1.0**0.4 ** 2.5** 35-44 years4.14.53.83.53.74.62.2**3.913.2 45-54 years17.718.114.613.71312.914.913.833.2 55-64 years65.951.344.150.637.542.634.932.179.2 65-74 years151.5145.5127.6111106.988.595.192.8222.2 75-84 years400.4375.5379.1337.3297.9288266.8205.4527.4 85+ years994.7886.7844.6874875.3753.8599.761624.9 Total40.937.435.23431.830.427.825.931.8 ** Rates are unreliable due to small number of cases

31. Category-specific rates can provide general characteristics of the frequency of disease in a population, particularly by... person place time Descriptive Epidemiology 31

32. age gender race / ethnicity Descriptive Epidemiology 32 education income health insurance status Person: Who has the lowest / highest disease rates in the population?

33. Gender- and age-specific AMI death rates, CO, 2007 33 FemaleMaleTotal Age Group Crude Rate 25-34 years0.9 (Unreliable)3.2 (Unreliable)0.05 35-44 years1.7 (Unreliable)21.92.5 45-54 years4.6 (Unreliable)49.213.2 55-64 years17.5112.833.2 65-74 years49.3271.479.2 75-84 years186.8660222.2 85+ years463.428.1527.4 Total21.624.9

34. geographic unit – state – county – census tract Descriptive Epidemiology 34 population density migration Place: Where are the lowest / highest disease rates for a population?

35. AMI Death Rates, Colorado, 2007 35 per 100,000 residents

36. AMI Death Rates, Colorado, 2007 36 CountyCrude RateCountyCrude RateCountyCrude Rate Adams County, CO 23 Jefferson County, CO 28.4 Larimer County, CO 16.7 Arapahoe County, CO 24.7 La Plata County, CO 12.1 (Unreliable) Mesa County, CO 28.8 Boulder County, CO 11.1 Larimer County, CO 16.7 Montezuma County, CO 79.4 Costilla County, CO 212.6 (Unreliable) Mesa County, CO 28.8 Pueblo County, CO 31.7 Denver County, CO 25.2 Montezuma County, CO 79.4 Weld County, CO 21.4

37. short-term trends long-term or secular trends Descriptive Epidemiology 37 cyclic trends age, period, and birth cohort effects Time: Are the disease rates changing over time for a population?

38. Gender-specific AMI death rates, CA, 1999-2007 38 FemaleMaleBoth Sexes YearDeathsCrude RateDeathsCrude RateDeathsCrude Rate 1999829249.4895253.61724451.5 2000788346.4866051.31654348.8 2001781245.2828948.21610146.7 2002779844.6821247.11601045.9 2003739041.8791544.91530543.3 2004684938.4743241.81428140.1 2005645736706539.41352237.7 2006622234.4691438.31313636.4 2007564031631634.71195632.9 Total6434340.66975544.213409842.4 Rates per 100,000

39. Gender-specific AMI death rates, CA, 1999-2007 39

40. Rate per 100,000 Age Age-specific lung cancer mortality rates in 1970 Descriptive Epidemiology 40

41. Descriptive Epidemiology Rate per 100,000 Age Age-specific lung cancer mortality rates in 1970 1910 1900 1890 1880 Birth cohort-specific lung cancer mortality rates over many years 41

42. Descriptive Epidemiology Rate per 100,000 Age Age-specific lung cancer mortality rates in 1970 1910 1900 1890 1880 Birth cohort-specific lung cancer mortality rates over many years 42

43. Descriptive Epidemiology 43 FemaleMale Age GroupDeathsCrude RateDeathsCrude Rate 25-34 years4 0.5 (Unreliable)13 1.4 (Unreliable) 35-44 years19 2.1 (Unreliable)9210 45-54 years1291432535.5 55-64 years2653954785.2 65-74 years36685697195 75-84 years823257861410 85+ years12627737261038 Total286944613047.8 Rates per 100,000 AMI death rates by age and gender, IL Residents, 1999-2007

44. Descriptive Epidemiology 44 FemaleMale Age GroupDeathsCrude RateDeathsCrude Rate 25-34 years40.5 (Unreliable)13 1.4 (Unreliable) 35-44 years192.1 (Unreliable)9210 45-54 years1291432535.5 55-64 years2653954785.2 65-74 years36685697195 75-84 years823257861410 85+ years12627737261038 Age-Adjusted Total36,3675137,73447.8 Rates per 100,000

45. Colorado Population Estimates, 2010 45

46. Descriptive Epidemiology Adjusted (or standardized) rates: are computed in order to remove the effect of age (or other factors) from crude rates to allow meaningful comparisons across populations when age distributions are different for the populations being compared 46

47. Two methods can be used when comparing disease rates across populations Descriptive Epidemiology 47 compare category-specific rates among the populations that are being compared, or adjust crude rates for the populations that are being compared.

48. Group A Group B AgeDeathsPersonsRate*DeathsPersonsRate* <2911001020 1,00020 30-59255005050500100 >601001,00010020100200 Total1261,60079901,60056 * per 1,000 population per year Descriptive Epidemiology 48

49. Group A Group B AgeDeathsPersonsRate*DeathsPersons Rate* <2911001020 1,000 10020 30-59255005050500 500100 >601001,00010020100 1,000200 Total126799056 * per 1,000 population per year Descriptive Epidemiology 49

50. Group A Group B (reference population) (comparison population) AgeDeaths PersonsRate Persons Rate Exp* <291100 10 /1000100 x 20 /1000 = 2 30-5925500 50 /1000500 x 100 /1000 = 50 >601001,000 100 /10001,000 x 200 /1000 =200 Total126 252 *exp. number deaths Descriptive Epidemiology 50

51. “Age-adjusted” mortality rate for group B = (expected number of deaths / total population at risk) x 10 n = (252 deaths / 1,600 persons / year) x 1,000 = 158 deaths / 1,000 persons / year Mortality rate for group A = 79 deaths / 1,000 persons / year Descriptive Epidemiology 51

52. Public health surveillance is the ongoing collection and timely analysis, interpretation, and communication of health information for public health action. Public health surveillance systems are important tools for collecting and disseminating descriptive epidemiologic data. Surveillance 52

53. Surveillance Method population-based representative sample convenience sample Example vital statistics BRFSS survey at local mall 53 Different surveillance collection methods provide varying levels of confidence in the data

54. Surveillance Vital Statistics births deaths Reportable Diseases childhood Food-borne infectious sexually transmitted 54

55. Surveillance Registries cancers birth defects other diseases Surveys NHIS NHANES BRFSS 55

56. Public Health Surveillance Loop 56 Data Program Interpretation Evaluation Data Information Program Analysis Dissemination Implementation Data Program Collection Planning

57. Exercise 57

58. Surveillance CDC WONDER BRFSS WISQARS MICA http://wonder.cdc.gov http://www.cdc.gov/brfss http://www.cdc.gov/ncipc/wisqars http://www.dhss.mo.gov/MICA 58 Examples of data sources on the Internet

59. 59

60. 60

61. 61

62. 62

63. 63

64. 64

65. 65

66. 66

67. 67

68. 68

69. 69