PMRs SMRs, and Survival Measures
Principles of Epidemiology Lecture 3
Dona Schneider, PhD, MPH, FACE
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
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
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)
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 Expected
Epidemiology (Schneider)
X
100
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
Epidemiology (Schneider)
Example: SMR for Male Farmers,
England and Wales, 1951
Number of Farmers and Farm Managers Age Group 20-24 25-34 35-44 45-54 55-64 (Census, 1951) (1) 7,989 37,030 60,838 68,687 55,565 Standard Death Rates per 1,000,000 (All Causes of Death) (2) 1,383 1,594 2,868 8,212 22,953 Expected Number of Deaths for Farmers and Farm Managers per 1,000,000 (3) = (1) X (2) 11 59 174 564 1,275
Total expected deaths per year: 2,083
SMR = 1,464 X 100 = 70.3%
Total observed deaths per year: 1,464
Epidemiology (Schneider)
2,083
In 1951, male farmers in England and Wales had a mortality rate 30 percent lower than the comparably-aged general population.
SMR for Tuberculosis for White Miners Ages 20 to 59 Years, United States, 1950
Estimated Population of White Miners
(1)
Age (yr)
Expected Deaths Death Rate (per From TBC in White 100,000) for TBC in Miners if They Had the Males in the General Same Risk as the Population General Population
(2) (3) = (1) X (2)
Observed Deaths from TBC in White Miners
(4)
20-24 25-29 30-34 35-44
45-54
55-59 Totals
74,598 85,077 80,845 148,870 102,649 42,494
12.26 16.12 21.54 33.96 56.82 75.23
9.14 13.71 17.41 50.55 58.32 31.96 181.09
10 20 22 98 174 112 436
SMR = Observed / Expected X 100
SMR (for 2059 yr olds) = 436 / 181.09 X 100 = 241%
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
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%
Study Cohort
Number or Personoutdomes Years in of interest TOTAL (Obs) cohort Age
(yr)
Reference Population Rate per 1,000 (3) 2.5 6.1 12.4 25.0
Exp
SMR =
(1) / (4) 2.00 1.89 2.11 1.97 2.03
40-49 50-59 60-69 70-79 Total
(1) 6 27 98 48 179
(2) 1,200 2,340 3,750 975
(4) = (2) X (3) 3.00 14.27 46.50 24.38 88.15
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
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
Epidemiology (Schneider)
Age Group
1948-1952
Person-years US White Male CA in Cohort Deaths (per 100,000)
1,250 3,423 3,275 2,028 1,144 544 3,702 4,382 2,968 1,552 4 2,206 4,737 4,114 2,098 9.9 17.7 44.5 150.8 409.4 11.2 17.5 44.2 157.7 432.0 10.3 18.8 46.3 164.1 450.9
Exp. Cancer Deaths
0.1 0.6 1.5 3.1 4.7 0.1 .06 1.9 4.7 6.7 0.0 0.4 2.2 6.8 9.5
15-24 25-34 35-44 45-54 55-64
1953-1957
15-24 25-34 35-44 45-54 55-64
1958-1963
15-24 25-34 35-44 45-54 55-64 TOTAL
42.9
SMR = observed/expected x 100% = 58 / 42.9 x 100% = 135%
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:
Epidemiology (Schneider)
Person-years
Age 20-24
1970-74
1000 1000 500
1975-79
500 1500 500 1 4 1
1980-84
200 1000 1500 0 2 2
25-29 30-34 Observed Deaths
Age 20-24 2 25-29 3 30-34 0 Population rates(per 1,000) Age 20-24 1.8 25-29 1.7 30-34 1.9 Expected deaths = population rates Age 20-24 25-29 30-34
Epidemiology (Schneider)
Obs = 15
1.8 1.6 1.5 1.5 1.8 1.7 x person-years / 1000 0.9 2.3 0.9 0.3 1.5 2.6
1.8 1.7 0.9
SMR = Obs / Exp X 100 = 15 / 12.9 X 100 = 116%
Exp = 12.9
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%)
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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
Epidemiology (Schneider)
Comparison of Rates
Advantages Crude
Actual Summary rates Readily calculable
Disadvantages
Difficult to interpret because of differences in population structures Cumbersome if there are many subgroups No summary figure
Specific Controls for homogeneous
subgroups Provides detailed information
Adjusted Provides a summary figure
Controls confounders Permits group comparison
Epidemiology rate (Schneider)
Fictional Magnitude depends on population standard Hides subgroup differences
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.
Epidemiology (Schneider)
CDC Wonder
http://wonder.cdc.gov/
Epidemiology (Schneider)
Additional Outcome Measures
Proportionate Mortality Ratio Proportionate Mortality Rate Case Fatality Rate Years of Potential Life Lost Measures of Survival
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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
Epidemiology (Schneider)
Computing a PMR
All Deaths
20-24
25-29
30-34
Cancer Deaths
1950-54 10 10 5
1955-59 5 15 5
1960-64 2 10 15
20-24 2 1 0 observed 25-29 3 4 2 30-34 0 1 2 =15 Population Proportion of Cancer Deaths 20-24 0.07 0.07 0.07 25-29 0.09 0.10 0.10 30-34 0.11 0.12 0.12 Expected deaths due to cancer = Population proportion x all deaths in sample 20-24 0.4 0.7 0.1 expected 25-29 1.0 0.9 1.5 30-34 0.6 0.6 1.8 =7.6
PMR = Observed/Expected x 100 = (15/7.6) x 100 = 197%
Epidemiology (Schneider)
PMR = 197%
The study population has twice the proportion of cancer deaths as the standard population.
Epidemiology (Schneider)
CHD Proportionate Mortality Rate
Figure 3-19. Deaths from heart disease as a percent of deaths from all causes, by age group, United States, 1986. 100%
Percent of All Deaths
80%
60%
40%
20%
0% All Ages <1 1-4 5-14 15-24 25-34 35-44 45-54 55-64 65-74 75-84 85+
Heart Disease
All Other Causes
Epidemiology (Schneider)
Ten Leading Causes of Death, 25-44 Years, All Races, Both Sexes, United States, 1991 (Population 82,438,000)
Rank Order
Cause of Death
Cause-specific Proportionate death rate per Number mortality rate (%) 100,000 18.0 15.0 14.7 10.7 8.4 8.3 3.0 2.3 1.5 1.5 32.2 27.0 26.4 19.2 15.0 14.9 5.4 4.1 2.7 2.7
1 Accidents and adverse effects 26,526 Malignant neoplasms 2 22,228 3 HIV infection 21,747 4 Diseases of the heart 15,822 Homicide and legal 5 12,372 intervention 6 Suicide 12,281 Chronic liver disease 7 4,449 and cirrhosis 8 Cerebrovascular diseases 3,343 9 Diabetes mellitus 2,211 Pneumonia and influenza 10 2,203
Epidemiology (Schneider)
All causes
147,750
100
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%
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
Epidemiology (Schneider)
Example: Individual data method
A person who died at age 20 would contribute 55 potential years of life lost (7520=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.
Epidemiol...