1 / 78

Measuring the Frequency of Health Events

Measuring the Frequency of Health Events.

Télécharger la présentation

Measuring the Frequency of Health Events

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Measuring the Frequency of Health Events

  2. The mayor of your town was startled to learn that there are 3 people who were recently diagnosed as HIV+ in his neighborhood. He is concerned that this may just be the tip of the iceberg, and he is wondering if this signals an epidemic. He wants your help in assessing the magnitude of the problem. What information (data) do you need in order to assess: • How big the problem is in town, • Whether there is an epidemic starting. • How the problem in your town compares to that of neighboring towns.

  3. Population A group of people with some common characteristic (age, race, gender, place of residence). • Examples: • Residents of Boston • Members of Blue Cross/Blue Shield • Postmenopausal women in Massachusetts • Coal miners in Pennsylvania • Adolescents in U.S. Sample: Residents of Marshfield 19 who got hepatitis 38 who did not

  4. Dynamic & Fixed Populations • Dynamic Population: • Membership can be transient. Examples: • Homeless population of Boston • Residents of MA • Members of an HMO • Fixed population: • Membership is relatively permanent, and perhaps defined by some event. • Examples: • Responders to 911 terrorist attack • Attendees of a luncheon (Salmonella outbreak)

  5. Basic Concepts • Ratio • Proportion • Rate

  6. Women Women Men Men Ratio A number obtained by dividing one number by another. • Example:the ratio of women to men in a class • # women 120 • # men 15 • A ratio doesn’t have any dimensions or units. It just indicates the relative magnitude of the two entities. = =

  7. Proportion A type of ratio that relates a part to a whole; often expressed as a percentage (%) . • Example:proportion of women in a class • # women = 120 = 88.9% • total # students 135 Women Men Women

  8. Proportion A type of ratio that relates a part to a whole; often expressed as a percentage (%) . • Example:Theproportion of students who developed a respiratory infection during the semester. • # with colds 45 33.3% • total # students 135 = =

  9. Rate A type of ratio in which the denominator also takes into account the dimension of time. • Example: 120 miles in 2 hours • 120 miles = 60 miles per hr. • 2 hours Example: 60 gallons in 3 hours 60 gal. = 20 gal. per hr. 3 hours

  10. Rate A type of ratio in which the denominator also takes into account the dimension of time. • Example:the incidence rate of myocardial infarctions (heart attacks) in a group taking low dose aspirin. • 254.8 per 100,000 person-years

  11. If events aren’t recorded, there is no way to detect trends. Measures of Disease Frequency

  12. Causes of Death Lock jaw Abscess Scrofula Infected tonsils Croup England: Records of burials and christenings were kept intermittently from 1592-1603, then steadily from 1603. In 1839William Farr established a system for routinely recording cause of death in England and Wales.

  13. Counts of Disease Simple counts are essential to public health planners and policy makers by providing a direct measure of the need for resources for specific problems. Hepatitis A in our town 1998 5 1999 0 2000 2 2001 3 2002 1 2003 19 The simple count of hepatitis A cases provides the basis for significant discussions among city officials and health care providers.

  14. But just counting diseased people isn’t enough. Our Town 19 Next Town 5 Hepatitis Is hepatitis A more of a problem in our town? Converting to a standard population size (per 10,000) facilitates comparison.

  15. Measures of Disease Frequency Prevalence (a proportion) Incidence Cumulative incidence (a proportion) (Incidence rate) Estimates the burden of disease at a given point in time. An estimate of the rate at which new cases are added; an estimate of risk.

  16. The focus is on existing disease at a specific time, not the development of new cases. The proportion of a population that has disease at a giventime. Prevalence • Imagine you took a snapshot of a class and labeled those who had a COLD with a red “C” . C C C C C What proportion of the group has a cold at this point in time?

  17. “Point” Prevalence The proportion of a population that has disease at a “point” in time (although the ‘point’ might be broad, e.g. a year .) • The focus is on existing disease at a specific point in time, not the development of new cases. The “point” can be a specific calendar time, or... The “point” can be a lifetime “event” (e.g., birth, death, entry into the military).

  18. 310 had cataracts Eye exam survey of 2,477 people ‘Period’ Prevalence The proportion of a population that has disease during a given period of time. x x xx x x x xxx x x 1979 1980 1981 Prevalence = 310 (cataracts) 2,477 (total) = .125 = 12.5%

  19. Prevalence of HIV in MA in 2003 Time 2003 The ‘burden of disease’ on the population. 8,263 HIV+ Total MA population = 5.7 million in 2003 = 0.00145 = 0.145% = 145 per 100,000 • Need to know: • # HIV+ people living in MA in 2003 • Population size in 2003

  20. These Are All Equivalent 0.00145 per 1 person. 0.0145 per 10 persons 0.145 per 100 persons [%] 1.45 per 1,000 persons 14.5 per 10,000 persons 145 per 100,000 persons Each time you move the decimal to the right, you increase the number by a factor of 10. Express the result in a way that provides a reasonable number of people, not a fraction.

  21. Prevalence is the % with the condition at a point in time, but one can assess that repeatedly over time to get a sense of trends... www.cdc.gov

  22. Or differences based on personal characteristics such as gender and age. http://www.dizziness-and-balance.com/

  23. Differences in disease frequency: • over time or • from place to place or • among people with different personal characteristics Describe disease patterns so we can see trends, … … and invite us to speculate about explanationsfor differences in disease frequency.

  24. Incidence

  25. X X XX X XX X X X XX Incidence Frequency ofnewcases during aspan of timein people “at risk”. • Numerator: # new cases during a span of time. • Denominator: includes only people “at risk”. The focus is on measuring the probability of developing disease during a span of time.

  26. “At Risk” • Incidence should be assessed in people who are “at risk” of developing the outcome. Ideally, the denominator should NOT include: • Those who already have the disease. • Those who can’t get it, such as those who are immune or don’t have the organ [e.g. incidence of uterine cancer should be estimated in women who have not already had a hysterectomy].

  27. Prevalence versus Incidence 2006 2007 2008 2009 2010 2003 2004 2005 X X XX X XX X X X XX Prevalence in 2003 = 0.00145% Incidence: Frequency of new cases during a span of time in people at risk. Prevalence is the probability of having disease at a point in time. Incidence is the probability of developing disease during a span of time.

  28. Incidence Cumulative incidence (a proportion) Incidence rate (a true rate) • Both focus on # new cases of disease • (numerator) during a period of observation. • The difference is the way they handle time.

  29. x xxx x xx x xx xx x x x xx x x x x x x Cumulative Incidence Example: 60 colds in a class of 120 during fall semester. • A proportion • A fixed block of observation time • Assumes complete follow-up for all subjects. • You don’t know the precise “time at risk” for each person. • The time period is described in words(e.g. “… during fall semester” or “during calendar year 2008”). Sept. 2011 (120 students) Dec. 2011 (115 students) CI = 60/120 = 50% during fall semester

  30. Yes No This outbreak study involved a fixed population that was observed over the block of time when the outbreak took place. Here, it makes sense to calculate cumulative incidence. Got Giardiasis Cumulative Incidence Yes No 12.9% 16 108 124 Exposed to Kiddy Pool 3.9% 14 341 355 30 449 479 subjects

  31. If I wanted to estimate the incidence of TB in Boston during calendar year 2005, how would I do it?

  32. TB Incidence in Boston During 2005? In reality, people are moving in and out of Boston, and some will die (& no longer be members of the population). But there is no way to know the details of this. The best we can do is assume that the number of people in the population stays the same and they are always at risk.

  33. TB Incidence in Boston During 2005? We need to assume the population is fixed, i.e. all people were followed for the entire block of time. CI = # new cases 2005 est. pop. size Cumulative incidence (a proportion)

  34. Cumulative Incidence of AIDS in MA During 2004 CI = 523 new AIDS cases = 9.2/100,000 Population at risk: about 5.7 million from 1/1/04 to 1/31/04

  35. Cumulative incidence, the proportion of a population that develops a disease during a span of time, can also be assessed repeatedly (during serial ‘spans of time’) to get a feel for how ‘risk’ is changing over time.

  36. Stomach Cancer Cervical Cancer Lung Cancer Incidence Breast Cancer Prostate Cancer

  37. X o o X o o o o X o o X X X X X X X X X Cumulative Incidence Has Limitations Here, the outcome of interest is relief of pain (X). Which group has greater rate of relief? Which group has greater proportion of relief? New drug Old drug 1 2 3 4 5 6 7 8 9 10 Hours

  38. X o o X o o o o X o o X X X X X X X X X In a prospective cohort study, like Framingham Heart Study, you have detailed individual follow up, so you know whether they develop heart disease, and you also know when they develop it. Diabetics Non-diabetics 1 2 3 4 5 6 7 8 9 10 Years of Follow Up

  39. X = when they got disease Incidence Rate Time at Risk Subject A- B- C- D- E- F- G- H- I- J- K- L- 8.3 x 11.0 14.0 14.0 10.2 x 3.0 12.0 7.0 10.0 3.0 9.0 x 6.2 1980 1982 1984 1986 1988 1990 1992 1994 1996 Total =107.7 person-yrs CI = 3 12 over 16 yrs IR = 3 = 28 107.7 p-ys 1000 p-yrs

  40. Incidence Rate (Incidence Density) x x x x x x x x x x x x x # Subjects at Risk x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x o o o o o o o o o o o o o o o x x x x # Subjects at Risk x x x x x x x x x x x x x x x x x x Begin End Time

  41. Incidence Rate = Total # new cases Total amount of disease-free observation time for a group

  42. Incidence Rate of HIV Seropositivity in Prostitutes IR = 4 new AIDS cases = 0.15 = 15/100 P-Yrs 26 person-yrs Sum = 26 yrs

  43. X o o X o o o o X o o X X X X X X X X X CI versus IR? Which has greater incidence rate of heart attack? Which has greater cumulative incidence of heart attack? CI = 6/10 = 60% over 10 years IR = 6/49 p-yrs = 12.2/100 P-yrs Diabetics CI = 6/10 = 60% over 10 years IR = 6/85 p-yrs = 7/100 P-yrs Non- Diabetics 1 2 3 4 5 6 7 8 9 10

  44. Got Coronary Artery Disease Yes No Person-Years of Follow Up 30 - 54,308.7 Yes Postmenopausal Hormones Used 60 - 51,477.5 No The denominators are the total disease-free observation time in each group. This takes into account the number of people and how long they were known to be disease-free. Incidence Rate in treated group = 30 / 54,308.7 =55.2 / 100,000 P-Yrs in untreated group = 60 / 51,477.5=116.6 /100,000 P-Yrs

  45. Incidence rate can be used to estimate cumulative incidence. CI = IR x T CI = 1 - e(-IR x T), where 'e' = 2.71828 The approximation (CI = IR x T) doesn’t take into account the fact that the size of the population at risk declines over time.

  46. Suppose IR = 50/1000 person-years (0.050 per year), and population size is initially 1,000. CI = IR x T predicts 50 deaths per year x 5 = 250 deaths. However, the population is finite and is dwindling so # people at risk diminishes. • Year Pop. Deaths • 1000 50 • 950 48 • 905 45 • 860 43 • 817 41

  47. In addition to a diminishing population, the IR may not be constant. As people age, their risk of dying in an MVA changes. 4.7 x 15 = 70.5

  48. wgt kg hgt m2 <21 41 177,356 23.1 21-23 57 194,243 29.3 23-25 56 155,717 36.0 25-29 67 148,541 45.1 >29 85 99,573 85.4 Multiple Exposure Groups Association? Risk of Non-fatal Myocardial Infarction Obesity rate of MI per 100,000 P-Yrs (incidence) # MIs (non-fatal) person-years of observation BMI:

  49. Cumulative Incidence Make Sense Here Got Giardiasis Cumulative Incidence Yes No 12.9% 16 108 124 Yes Exposed to Kiddy Pool No 14 341 355 3.9% 30 449 479 subjects This was a fixed population studied for a brief period, so it makes sense to calculate cumulative incidence.

  50. Incidence provides a way of measuring the risk of becoming diseased. 2006 2007 2008 2009 2010 2003 2004 2005 X X XX X XX X X X XX Incidence: Frequency of new cases during a span of time in people at risk. Incidence is the probability of developing disease during a span of time.

More Related