measuring the occurrence of disease 2 sue lindsay, ph.d., msw, mph division of epidemiology and...
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Measuring the Occurrence of Disease 2
Sue Lindsay, Ph.D., MSW, MPH
Division of Epidemiology and Biostatistics
Institute for Public Health
San Diego State University
Measures of Mortality
• Annual Mortality Rate
• Case Fatality Rate
• Proportionate Mortality
• Years of Potential Life Lost
Annual All Cause Mortality Rate
Annual Rate
Per 1,000=
Total No. of Deaths From
All Causes in 1 Year
No. of Persons in the
Population at Midyear
Age-Specific Mortality Rate
Annual Rate
Per 1,000 in
Children
< 10 yrs.
=
Total No. of Deaths From
All Causes in 1 Year In Children
Younger Than 10 Years
No. of Children in the Population
Younger Than 10 Years at Midyear
Disease-Specific Mortality Rate
Annual Rate
From Lung
Cancer
Per 1,000
=
Total No. of Deaths From
Lung Cancer in 1 Year
No. of Persons in the
Population at Midyear
Annual Rate
From
Leukemia
Per 1,000 in
Children
< 10 yrs.
=
Total No. of Deaths From
Leukemia in 1 Year In Children
Younger Than 10 Years
No. of Children in the Population
Younger Than 10 Years at Midyear
Disease and age-specific mortality rate
Disease Specific Mortality Rates: Important Concepts
• The denominator equals the number of people at risk of dying. Any person counted in the denominator must be at risk of becoming a death in the numerator
• The time period is arbitrary but must be specified (most often annual)
• A good index of disease severity
• Can be used as a measure of the risk of disease and can approximate incidence rates
• When the case fatality rate is high
• Duration of the disease is short
The Case-Fatality Rate
Case-
Fatality
Rate
=
No. of Persons Dying Of
Disease After Disease
Onset
No. of Persons With The Disease
During a Specified Period of Time
Case-Fatality Rate: Important Concepts
• Measures the severity of disease
• Most commonly used in infectious diseases
• The denominator is limited to persons who already have the disease
Proportionate Mortality
PM =
No. of Deaths From a
Specific Cause in 1 Year
Total Deaths in the
Population in 1 Year
Proportionate Mortality: Important Concepts
• Usually expressed as a percentage
• Percentage of deaths from heart disease
• Provides a quick look at major causes of death
• Does not yield the risk of dying - Mortality rates provide this
Years of Potential Life Lost (YPLL)
• Death at younger ages is associated with greater loss of future productive years of life
• Used as an alternative measure of the burden of disease
Problems With Mortality Data
• Information from death certificates may not be accurate
• Quality varies
• Primary and secondary causes of death
• Changes in disease coding and definition will impact mortality rates
• Validity may be disease specific
Age Adjustment
• Age is one of the main determinants of disease onset and mortality
• The age distribution of a population will influence the total mortality rate and often influence the incidence rate of disease
• Age adjusted mortality rates correct for differences in age distribution in a population
Age Adjustment Methods
• Direct Age Adjustment• Uses the age-specific mortality rates of each population of
interest and the age distribution of a “standard” population
• Indirect Age Adjustment• Uses the age-specific mortality rate of a “standard”
population and calculates a Standardized Mortality Ratio (SMR)
Age-Adjustment Example
Crude Mortality Rates in Alaska and Florida
Number of Deaths
Total Population
Crude Mortality Rate
131,044
12,335,000
1,062.4 per
100,000
2,064
524,000
393.9 per
100,000
Florida Alaska
Source: Vital Statistics of the U.S. (1991)
Percentage Distribution ofAge Groups in Florida andAlaska Populations, 1988
0
5
10
15
20
25
30
35
40
45
<5 5-19 20-44 45-64 65+
Florida
AlaskaP
erce
nta
ge
of
To
tal
Age
Age Adjustment: Direct Method
• Select a standard population (choice is arbitrary)
• U.S. 1988 Total US Population
• Apply the age-specific mortality rates of both Florida and Alaska to the standard population distribution to calculate the expected number of deaths that would occur in each age group in the standard population
• Sum the expected number of deaths over all age groups. Calculate the overall age-adjusted mortality rate for both Florida and Alaska.
Florida: Direct Method
<5
5-19
20-44
45-64
>65
284
57
198
815
4425
18,300,000
52,900,000
98,100,000
46,000,000
30,400,000
.00284X18,300,000=
.00057X52,900,000=
.00198X98,100,000=
.00815X46,000,000=
.04425X30,400,000=
51,972
30,153
194,238
374,900
1,345,200
Age
Group
Florida
Age-Specific
Death Rate/
100,000
U.S.
Population
(standard)
Calculation of
Expected Deaths
Expected
Deaths
245,700,000 1,996,463
Age Adjusted Death Rate = 1,996,463
245,700,000= 812.6 per 100,000
Alaska: Direct Method
<5
5-19
20-44
45-64
>65
274
65
188
629
4350
18,300,000
52,900,000
98,100,000
46,000,000
30,400,000
.00274X18,300,000=
.00065X52,900,000=
.00188X98,100,000=
.00629X46,000,000=
.04350X30,400,000=
50,142
34,385
184,428
289,340
1,322,400
Age
Group
Alaska
Age-Specific
Death Rate/
100,000
U.S.
Population
(standard)
Calculation of
Expected Deaths
Expected
Deaths
245,700,000 1,880,695
Age Adjusted Death Rate = 1,880,695
245,700,000= 765.4 per 100,000
Age-Adjustment Example: Direct Method
Crude Mortality Rate
Age Adjusted Mortality Rate
1,062.4/100,000
812.6/100,000
393.9/100,000
765.4/100,000
Florida Alaska
Age Adjustment: Indirect Method
• Select a standard population (choice is arbitrary)
• U.S. 1988 Total US Population
• Apply the age-specific mortality rates of the standard population to the age distributions of Alaska and Florida to calculate the total expected deaths in each age group if they were subjected to the mortality experience of the standard population. Sum expected deaths over all age groups.
• Calculate Standardized Mortality Ratio (SMR)
Standardized Mortality Ratio (SMR)
Total Observed Deaths in the Population
Total Expected Deaths in the Population
IF SMR=1: Observed mortality is the same as expected mortality
If SMR >1: Mortality is higher than expected.
IF SMR<1: Mortality is lower than expected.
SMR =
Florida: Indirect Method
<5
5-19
20-44
45-64
>65
251.1
47.2
161.8
841.9
5,104.8
850,000
2,280,000
4,410,000
2,600,000
2,200,000
.00251X850,000=
.000472X2,280,000=
.001618X4,410,000=
.008419X2,600,000=
.051048X2,200,000=
2,134
1,076
7,135
21,889
112,305
Age
Group
U.S.
Death Rate/
100,000
(standard)
Florida
Population
Calculation of
Expected Deaths
Expected
Deaths
144,539SMR =
131,044
144,539= 0.91
Observed
Expected=
Alaska: Indirect Method
<5
5-19
20-44
45-64
>65
251.1
47.2
161.8
841.9
5,104.8
60,000
130,000
240,000
80,000
20,000
.00251X60,000=
.000472X130,000=
.001618X240,000=
.008419X80,000=
.051048X20,000=
151
61
388
674
1,021
Age
Group
U.S.
Death Rate/
100,000
(standard)
Alaska
Population
Calculation of
Expected Deaths
Expected
Deaths
2,295SMR = 2,064
2,295 = 0.90
Observed
Expected=
Age-Adjustment Example: Indirect Direct
Crude Mortality Rate
Standardized Mortality Ratio
1,062.4/100,000
0.91
393.9/100,000
0.90
Florida Alaska
Age-Adjustment: Important Concepts
• Both methods depend on the choice of the standard population
• Standard populations can be:
• Independent of either study population, a combination of the two populations, the larger of the two populations, etc.
• Age-adjusted rates (direct method) are not “real”. It is important to know the population that was used as the standard.
• SMR (indirect method) is a ratio not a rate. It gives only relative information and does not describe the mortality of the population.
Age-Adjustment: Important Concepts
• Direct method uses age-specific death rates. Requires that this detailed information be known
• Indirect methods are used if age-specific rates are unstable or unknown
• Both methods can be used for other types of rates: i.e. incidence
• Do not confuse with multivariate “adjustment for age”
Measurement
• Quality of measurement is often the weakest and least considered area of study design
• Don’t make this mistake!
• Poorly designed or executed measures can effect the interpretation of your study!
• You can get the wrong answer!
How do we measure?
• Self-report
• Historical documentation (medical records)
• Direct observation
• Direct examination
• Specimen collection and measurement
Why do we measure?
• Characterize patients at baseline
• Determine eligibility for the study
• To stratify or randomize
• To assess similarity of comparison groups
• To assess risk factors, protective factors and outcomes
What can be measured?
• Demographics
• History: symptoms, diagnosis, exposures
• Disease state: physical exam, imaging, autopsy
• Analysis of body fluids
• Body composition: BMI, DEXA scan, MRI
What can be measured?
• Movements of fluids and molecules (cardiac output)
• Electrophysiology (ECG, EEG, nerve conduction
• Psychometry (cognition, emotional status etc.)
• Behaviors
• Subjective outcomes such as quality of life and satisfaction with care
Types of Measurement Scales
• Qualitative:
• Nominal, unordered, categorical, dichotomous or polychotomous
• Ordinal or semi-quantitative (Likert scale)
• Quantitative:
• Ordered discrete with intervals that are integers: number of cigarettes smoked
• Continuous: ordered continuous intervals: weight, BP, etc.
Quality of Measurement: Validity
Validity is the degree to which the variable you are measuring actually accurately measures the phenomenon you are interested in.
Validity
• Face Validity
• How well the measure works based on intuitive judgment? Does it make sense that it should be measured this way?
• Sampling validity
• How well does the measure represent the aspects of the phenomenon you are interested in?
• Is time-to-run one mile a good measure of cardiovascular health?
Construct Validity
• How well does the measure conform to our current theoretical models or concepts of the phenomenon?
• Which biomarker is the best estimator of level of smoking?
• Which lab test best reflects vulnerability to opportunistic infections?
• Which lab test best reflects our current understanding of the biology of stress?
• Levels of what hormone indicate that a patient is in menopause?
Criterion-Related Validity
• Correlational validity. Does your measure correlate well to a widely accepted criterion or “gold standard”
• Does your self-report stress scale correlate with other known measures of stress?
• Can your assay for viruses produce the correct results when you use it to test a known amount of virus?
• Predictive validity. A variable’s ability to predict outcomes.
• Does your depression index predict suicide?
• Do levels of a serum tumor maker predict cancer recurrence?
Convergent Validity
• Acceptance of the variable is based on multiple lines of evidence• Low density lipoprotein cholesterol has been validated to measure the
risk of atherosclerosis by:• Histopathologic studies of diseased tissues
• Epidemiologic studies of populations and families
• Multiple animal models
• Interventional studies with lipid lowering agents
Quality of Measurement: Accuracy and Precision
• Accuracy: The degree to which a variable agrees with a reference or “gold” standard or is free of systematic error (bias).
• Precision (reliability): The degree to which the variable is reproducible, or is free from random error.
Relationship of Precision and Accuracy:The target shooting analogy
Random - + - +
Systematic + - - +
error
error
Selecting Measures for Your Study
• Find standardized measures of established validity, if available, but use a technique only if it captures the phenomenon you are interested in (construct validity).
• Consult with experienced experts about applying an established measure or designing a new measure for your specific purpose.
• Pilot the measure for practice and assessment of validity, accuracy, and precision.