Measures of disease occurrence

1. Measures of disease occurrence October 5 2004 Epidemiology 511 W. A. Kukull

2. Defining disease (health events) • What disease features do cases have in common? • What disease features make cases different from non-cases? • How can we observe disease features • Interview • Exam • Lab test or autopsy

3. Observing onset • Clinical diagnosis: hx, signs, symptoms • Pathological diagnosis: examination of biological specimens, e.g., biopsy, labs • Insidious onset • Abrupt onset • Recurrent: many “onsets” possible • Persistent/Chronic

4. Defining a Population • What characteristics do members of the chosen population have? • How are member characteristics different from non-members? • Geography: residents of County • Individual features: 75 – 79 y.o. men • Time period: 1929 - 1938

5. Population and time • Closed population: once defined, no new persons may enter. • Disease occurrence and death reduce pool • Airline passengers on a non-stop • Open population: new members may be added, loss may occur • Non-diseased persons may be lost • Boeing machinists employed 2000 - 2003

6. Who is “at-risk” ? • Susceptible: the probability you could get the disease is NOT zero. • Does not mean you are especially likely to get the disease, or suffer the health event. • Non-Susceptible: the probability you could get the disease IS Zero. • Persons who have had their appendix removed are non-susceptible to future appendicitis

7. Goals • Define disease (or health event) • Define population • Find all cases in the population • Existing cases • New cases • Create measures of case frequency per population

8. Counts: “Numerator data” • Number of people with the disease • “ We report 5 cases of Parkinson’s disease in 20-30 year olds” • Numerator data: often hard to interpret without knowing the size of the population giving rise to the cases • Very rare or unusual occurrences

9. Cases per year

10. Problems determining disease • Diagnostic criteria • Poor recognition • Survey errors • respondents • interviewers • Hospital data not meant for research

11. Creating a frequency measure:Critical questions • Count cases in relation to the population at-risk (per time) • If each of the cases had not developed disease, would they have been in the population (denominator)? • If each of the non-cases in the population had developed disease would they have been included as a case? • The answers should be “yes”

12. Mortality for selected causesper 100,000 population (hypothetical data)

13. PrevalenceHow common is the disease today? • EXISTING CASES at a specified time / persons in defined population at that time • “47% of persons over 85 years old, in East Boston were demented, in 1990.” • A “snapshot” view of the disease at a single point in time (a.k.a. point prevalence) • NOT a measure of risk and NOT a Rate

14. Incidence: counting the new cases that occur with time • Cumulative Incidence (a “risk”) • NEW CASES / initial pop-at-risk • The incidence of nasal papilloma in Seattle was 6 per million population in 1984” • Incidence rate (a “rate”) • NEW CASES / at-risk time • Stroke incidence is 5 per 100,000 person-years

15. Prevalent case biasLonger disease duration increases chance of selection Cross-sectional Sample Time

16. Mortality: an incidence-like measure • [Deaths from disease X in 19xx] divided by [midyear population] • “the annual CHD mortality rate dropped from 370 per 100,000 in 1968 to 270 per 100,000 in 1975 • Risk of dying from disease X, during the time interval, for someone in the population

17. Disease Frequency Relationships • P = I * D • prevalence = incidence times average duration of the diseased state • Robust when I and D are stable and P is <10% • M = I * C • Mortality = incidence times Case Fatality Rate • this holds when I and C are approximately stable over time

18. Example: Prevalence, incidence and duration Where is disease risk highest?

19. Comparing measures(“Rate” used in a broad sense) • Crude Rates • overall, summary rate for a population of comparison group • may differ between populations due to other factors e.g., age distribution • usually not used for inter-population comparisons • Specific Rates: can “always” be compared

20. Standardized Rates • Alternative to Crude rate when a single summary rate is needed for comparison • example: when age distributions are different and disease is age related • “ficticious” summary rates are computed reflecting state “if the populations had the same age distributions”

21. Example (Direct)(After Jekel, Katz & Elmore, 2001)

22. Example (Direct)Standard Population

23. Direct Standardization(there will be an exercise in homework) • Choose a “standard population” • Multiply (age)-specific rates from pop#1 by standard pop age groups; repeat for pop#2 • Sum the pop#1 numbers and divide by total standard population; repeat for pop #2 • Compare! • This adjusts for the confounding effect of age

24. Indirect Standardization • An alternative method of standardization • when you know the total deaths and you know your age distribution but you don’t know age-specific rates • Apply (age)-specific rates from a standard population to compute “expected” deaths • [Observed deaths] / [expected deaths] *100 = SMR (standardized mortality ratio)

25. Direct and Indirect Standardization

26. Summary rates • Magnitude depends on choice of standard population • Give “what if” comparison between groups • Specific rates are usually preferable (and are compare-able)

27. Proportional Mortality • [# Deaths from a specific cause] divided by [all cause deaths] for a given time period • Example: The proportion of all deaths (in NYC males 15-25) that were due to homicide in 1998 • This is not a risk nor rate; the denominator is all deaths.

28. Proportions of all death due to specific causes (hypothetical data)

29. Proportionate Mortality Ratio • PMR= [observed deaths in population A] / [expected deaths based on the proportion in the population B] • Sometimes seen in occupational studies

30. Proportional mortality and PMR • Often used when you don’t know the number of persons in the population • Frequently used in Occupational Studies • Can be Misleading • if all cause death rate differs; cause specific rates can differ greatly but proportionate mortality may stay the same

31. PMR • In Bantu laborers in South Africa, 91% of cancer deaths were due to liver cancer • Usually liver cancer accounts for about 1% of cancer deaths • Therefore Bantus have an unusually high liver cancer death rate

32. Example: Mortality per 100,000 in 19xx (After MacMahon&Trichopoulos) PMR overstated excess of liver Ca in Bantu and did not reveal great difference at other sites

33. Sources of Morbidity Data • Disease registries • Insurance Plans • State L&I • Medicare/ HCFA; VA, armed forces • CDC web sites, MMWR • Hospitals • Industries, Schools • Surveys and specific studies

34. Sources of Mortality Data • US Vital Statistics • State Vital Statistics • Individual death certificates • Disease registries • Health maintenance organizations • cdc.wonder.gov

35. Causes of death seen on death certificates (after Gordis) • A mother died in infancy • Deceased had never been fatally sick • Died suddenly, nothing serious • Went to bed feeling well, but woke up dead • Died suddenly without the aid of a physician • Cardio-Respiratory arrest

36. Rate confusion • “Rates” loosely used includes: proportions, ratios, risk and instantaneous rate (D D/Dt) • Proportions include the numerator in the denominator (e.g., prevalence is a proportion but not a risk nor a rate) • Ratios: numerator and denominator may be different groups e.g, male/female ratio

37. Rates and Risks • Rate: • denominator in person-time; time must be part of the measure • average population during the observation time • Risk: • result of rates that prevailed over a period • denominator: persons at-risk at beginning; a closed population followed over time • time is not a dimension but used descriptively to specify period of observation

38. Incidence Density and Cumulative Incidence • ID = [new cases] / [person-years] • technically the rate • CI = [new cases] / [initial pop-at-risk] • the cumulative effect of the ID on pop-at-risk over a specified time period • technically a risk • CIt = 1 - e -IDD(t) • to estimate the cumulative effect of a rate [ID] on a population after “t” years ( units of time)

39. Example Calculation CIt = 1 - e -IDD(t) Where: e =2.71828… base of natural logs (or just push the ‘e’ button on your calculator) ID = incidence density rate (=124.7 per 1000) Dt = years of observation (2, 5, 10 or 20) So, e is raised to the “power” [ -(.1247)(2)] Then subtracted from 1 to yield CI

40. Example: Constant mortality rate of 124.7 per 1000 person-years (ID). What is cumulative risk (CI) at 2, 5, 10 and 20 years [CIt = 1 - e -IDD(t) ]