1. SMRs, PMRs and Survival Measures Principles of Epidemiology Lecture 3 Dona SchneiderDona Schneider, PhD, MPH, FACE

2. Epidemiology (Schneider) REVIEW: Adjusted Rates are Created Through Standardization Standardization: The process by which you derive a summary figure to compare health outcomes of groups The process can be used for mortality, natality, or morbidity data

3. Epidemiology (Schneider) Standardization Examples Direct Method requires Age-specific rates in the sample population The age of each case The population-at-risk for each age group in the sample Age structure (percentage of cases in each age group) of a standard population Summary figure is an AGE-ADJUSTED RATE

4. Epidemiology (Schneider) Standardization: Age Adjustment (cont.) Indirect method requires Age structure of the sample population at risk Total cases in the sample population (not ages of cases) Age-specific rates for a standard population Summary figure is a STANDARDIZED MORTALITY RATIO (SMR)

5. Epidemiology (Schneider) Indirect Standardization Instead of a standard population structure, you utilize a standard rate to adjust your sample Indirect standardization does not require that you know the stratum-specific rates of your cases The summary measure is the SMR or standardized mortality/morbidity ratio SMR = Observed X 100 Expected

6. Epidemiology (Schneider) Indirect Standardization (cont.) An SMR of 100 or 100% means no difference between the number of outcomes in the sample population and that which would be expected in the standard population

7. Epidemiology (Schneider) Total expected deaths per year: 2,083 (3) = (1) X (2)(2)(1) 1,27522,95355,56555-64 5648,21268,68745-54 1742,86860,83835-44 591,59437,03025-34 111,3837,98920-24 Expected Number of Deaths for Farmers and Farm Managers per 1,000,000 Standard Death Rates per 1,000,000 (All Causes of Death) Number of Farmers and Farm Managers (Census, 1951) Age Group Example: SMR for Male Farmers, England and Wales, 1951 Total observed deaths per year: 1,464 SMR = 1,464 X 100 = 70.3% 2,083

8. In 1951, male farmers in England and Wales had a mortality rate 30 percent lower than the comparably-aged general population.

9. Epidemiology (Schneider) SMR = Observed / Expected X 100 SMR (for 20–59 yr olds) = 436 / 181.09 X 100 = 241% 436 181.09Totals 11231.9675.2342,49455-59 (4)(3) = (1) X (2)(2)(1) 17458.3256.82102,649 45-54 9850.5533.96148,87035-44 2217.4121.5480,84530-34 2013.7116.1285,07725-29 109.1412.2674,59820-24 Observed Deaths from TBC in White Miners Expected Deaths From TBC in White Miners if They Had the Same Risk as the General Population Death Rate (per 100,000) for TBC in Males in the General Population Estimated Population of White Miners Age (yr) SMR for Tuberculosis for White Miners Ages 20 to 59 Years, United States, 1950

10. Epidemiology (Schneider) In the United States in 1950, white miners ages 20 to 59 years died of tuberculosis almost 2.5 times as often as comparably-aged males in the general population

11. Epidemiology (Schneider) Individuals in a cohort may contribute different amounts of risk due to length of exposure (person-years) Calculation of stratum or age-specific and total SMRs SMR = O/E X100 = 179/88.15 X 100 = 203% 88.15 24.38 46.50 14.27 3.00 (4) = (2) X (3) Exp Study Cohort 2.03 179 Total 1.9725.097548 70-79 2.1112.43,75098 60-69 1.896.12,3402750-59 2.002.51,200640-49 (1) / (4) (3)(2)(1) SMR = Reference Population Rate per 1,000 Person- Years in TOTAL cohort Number or outdomes of interest (Obs) Age (yr)

12. Epidemiology (Schneider) Workers in this cohort were twice as likely to have the outcome of interest as the general population Those ages 60-69 had the highest age-specific SMR Those ages 50-59 had the lowest age-specific SMR

13. Epidemiology (Schneider) SMR’s (con’t) Sometimes exposures change over time and individuals may have different amounts of exposure when they are in a cohort over multiple years Example: Over a period of years, the manufacturing process of product X changed. The occupational cohort involved in the processes had 58 deaths (we do not know their ages). Was this more or less than would be expected in the general population? Stratify the cohort by known exposure periods

14. Epidemiology (Schneider) 9.5450.92,09855-64 6.7432.01,55255-64 4.7409.41,14455-64 0.111.254415-24.0617.53,70225-34 1.944.24,38235-44 4.7157.72,96845-54 1958-1963 0.010.3415-24 0.418.82,20625-34 2.246.34,73735-44 6.8164.14,11445-54 42.9 TOTAL SMR = observed/expected x 100% = 58 / 42.9 x 100% = 135% 1953-1957 1948-1952 3.1150.82,02845-54 1.544.53,27535-44 0.617.73,42325-34 0.19.91,25015-24 Exp. Cancer Deaths US White Male CA Deaths (per 100,000) Person-years in Cohort Age Group

15. Epidemiology (Schneider) Persons in this cohort had the outcome 35% more often than would be expected in the general population. We could not calculate age-specific SMRs without the ages of the cases. If we have the ages of cases:

16. Epidemiology (Schneider) SMR =  Obs /  Exp X 100 = 15 / 12.9 X 100 = 116%  Exp = 12.92.60.9 30-34 1.52.31.725-29 0.30.91.8Age 20-24 Expected deaths = population rates x person-years / 1000 1.71.81.930-34 1.5 1.725-29 1.61.8 Age 20-24 Population rates(per 1,000)  Obs = 1521030-34 24325-29 012Age 20-24 Observed Deaths 1500500 30-34 10001500100025-29 2005001000 Age 20-24 1980-841975-791970-74 Person-years

17. Epidemiology (Schneider) From these data you can compute A total SMR (116%) Age-specific SMRs (age 20-25, SMR = 100%) Time period SMRs (1970-1974, SMR = 114%) Age-specific and time period SMRs (age 20-24, 1970-74, SMR = 111%)

18. Epidemiology (Schneider) SMRs Expect a Healthy worker effect Occupational studies should have SMRs < 100 Workers tend to be healthier than the general population which comprises both healthy and unhealthy individuals You cannot compare SMRs between studies -- only to the standard population

19. Epidemiology (Schneider) Comparison of Rates Hides subgroup differencesPermits group comparison Magnitude depends on population standard Controls confounders Fictional rateProvides a summary figure Adjusted No summary figureProvides detailed information Cumbersome if there are many subgroups Controls for homogeneous subgroups Specific Readily calculable Difficult to interpret because of differences in population structures Actual Summary rates Crude DisadvantagesAdvantages

20. Epidemiology (Schneider) In Summary: One type of rate is not necessarily more important than another. Which you choose depends on the information sought. Crude rates are often used to estimate the burden of disease and to plan health services. To compare rates among subpopulations or for various causes, specific rates are preferred. To compare the health of entire populations, adjusted rates are preferred because they allow for comparison of populations with different demographic structures.

21. Epidemiology (Schneider) CDC Wonder http://wonder.cdc.gov/

22. Epidemiology (Schneider) Additional Outcome Measures Proportionate Mortality Ratio Proportionate Mortality Rate Case Fatality Rate Years of Potential Life Lost Measures of Survival

23. Epidemiology (Schneider) Additional Outcome Measures Proportionate Mortality Ratio The ratio of observed/expected deaths (in terms of proportions of deaths in the standard population) x 100 PMRs are explained similarly to SMRs 100% = no difference between groups

24. Epidemiology (Schneider) Computing a PMR All Deaths 1950-541955-591960-64 20-241052 25-29 101510 30-345515 Cancer Deaths 20-24210 25-29342 observed 30-34012  =15 Population Proportion of Cancer Deaths 20-240.07 25-290.090.10 30-340.110.12 Expected deaths due to cancer = Population proportion x all deaths in sample 20-24 0.7 0.4 0.1 25-29 0.91.5 1.0 expected 30-340.6 1.8  =7.6 PMR = Observed/Expected x 100 = (15/7.6) x 100 = 197%

25. Epidemiology (Schneider) PMR = 197% The study population has twice the proportion of cancer deaths as the standard population.

26. Epidemiology (Schneider) CHD Proportionate Mortality Rate

27. Epidemiology (Schneider) 2.71.52,211Diabetes mellitus9 5.43.0 4,449 Chronic liver disease and cirrhosis 7 4.1 2.33,343 Cerebrovascular diseases 8 14.9 8.312,281Suicide6 100 147,750 All causes 2.71.52,203 Pneumonia and influenza 10 15.0 8.412,372 Homicide and legal intervention 5 19.210.715,822 Diseases of the heart 4 26.414.721,747HIV infection3 27.015.022,228 Malignant neoplasms 2 32.218.026,526 Accidents and adverse effects 1 Cause-specific death rate per 100,000 Proportionate mortality rate (%) Number Cause of Death Rank Order Ten Leading Causes of Death, 25-44 Years, All Races, Both Sexes, United States, 1991 (Population 82,438,000)

28. Epidemiology (Schneider) Comparing Mortality and Case-Fatality Rates Assume a 1995 population of 100,000 people where 20 contract disease X and 18 people die from the disease. One remains stricken and one recovers. What is the mortality rate and what is the case-fatality rate for disease X? Mortality rate from disease X 18 / 100,000 =.00018 =.018% Case-fatality rate from disease X 18 / 20 =.9 = 90%

29. Epidemiology (Schneider) Years of Potential Life Lost Death occurring in a particular individual at an early age results in a greater loss of that individual’s productivity than if that same individual lived to an average life span. By convention, YPLL (or PYLL) is based on a life expectancy of 75 years YPLL can be calculated for individual or group data

30. Epidemiology (Schneider) Example: Individual data method A person who died at age 20 would contribute 55 potential years of life lost (75- 20=55 YPLL) Deaths in individuals 75 years or older are excluded The rate is obtained by dividing total potential years of life lost by the total population less than 75 years of age.

31. Epidemiology (Schneider) *excluded YPLL from Disease X = 169.5 / 4 = 42.4 per person 169.5xxxSum 15605 xx85*4 60153 20552 74.56 months1 YPLL Contributed (75-age) Age at Death (Years) Individual

32. Epidemiology (Schneider) Example: Age Group Method In a population of 12,975,615, what is the rate of YPLL for 2000? 1.Obtain the ages at the time of death for each case (column 1) Exclude those over age 75 2.Calculate the mean age for each age group (column 2) 3.Subtract the mean age from 75 (column 3) 4.Calculate stratum-specific YPLL by multiplying column 1 by column 3 5.Sum the stratum-specific YPLL 6.Divide by the total population for the ages selected

33. Epidemiology (Schneider) Rate of YPLL per 1,000 persons = 93,234.0/12,975,615 = 7.2 per 1,000 in 2000 6412.537.5 17135-39 xxx 2.5 7.5 12.5 17.5 22.5 27.5 32.5 42.5 47.5 52.5 57.5 62.5 67.5 72.0 74.5 Age 75-mean (3) 93,234.0 xxx 175.072.57070-74 480.067.56465-69 1075.062.58660-64 1487.557.58555-59 1912.552.58550-54 3190.047.511645-49 4257.542.513140-44 10327.532.524330-34 14630.027.530825-29 21525.022.541020-24 18112.517.531515-19 4000.012.56410-14 3510.07.5525-9 2016.03.0281-4 298.00.54<1 YPPL (1)x(3) Mean Age at Death (2) # Deaths (1) Age

34. Epidemiology (Schneider) Measuring Survival Five-year survival Not a magical number May be subject to LEAD TIME BIAS Cannot evaluate new therapies

35. Epidemiology (Schneider) Measuring Survival (cont.) Life Tables (assume no change in treatment over the time of observation) Used to calculate probability of surviving fixed segments of time Allow each case to contribute to data analysis regardless of the time segment in which they are enrolled The probability of surviving 5 years is the product of surviving each year (p.89)

36. Epidemiology (Schneider) Measuring Survival (cont.) Kaplan-Meier Time periods are not predetermined but are set by the death or diagnosis of a case Withdrawls and those lost to follow-up are removed from the analysis Typically used for small numbers of cases

37. Epidemiology (Schneider) Measuring Survival (cont.) Median Survival The time that half the population survives Not effected by outliers like the mean Can calculate the median survival time when half rather than all the cases die

38. Epidemiology (Schneider) Measuring Survival (cont.) Relative survival rate Compares survival from a given disease to a comparable group who do not have the disease Relative Survival Rate (%) = Observed/Expected x 100