- 64 Views
- Uploaded on
- Presentation posted in: General

Measuring the Occurrence of Disease 2

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.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Sue Lindsay, Ph.D., MSW, MPH

Division of Epidemiology and Biostatistics

Institute for Public Health

San Diego State University

- Annual Mortality Rate
- Case Fatality Rate
- Proportionate Mortality
- Years of Potential Life Lost

- Crude Rates
- Specific Rates
- Adjusted Rates

Total No. of Deaths From

All Causes in 1 Year

Annual Rate

Per 1,000

=

No. of Persons in the

Population at Midyear

Total No. of Deaths From

All Causes in 1 Year In Children

Younger Than 10 Years

Annual Rate

Per 1,000 in

Children

< 10 yrs.

=

No. of Children in the Population

Younger Than 10 Years at Midyear

Total No. of Deaths From

Lung Cancer in 1 Year

Annual Rate

From Lung

Cancer

Per 1,000

=

No. of Persons in the

Population at Midyear

Total No. of Deaths From

Leukemiain 1 Year In Children

Younger Than 10 Years

Annual Rate

From

Leukemia

Per 1,000 in

Children

< 10 yrs.

=

No. of Children in the Population

Younger Than 10 Years at Midyear

- 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

No. of Persons Dying Of

Disease After Disease

Onset

Case-

Fatality

Rate

=

No. of Persons With The Disease

During a Specified Period of Time

- Measures the severity of disease
- Most commonly used in infectious diseases
- The denominator is limited to persons who already have the disease

No. of Deaths From a

Specific Cause in 1 Year

=

PM

Total Deaths in the

Population in 1 Year

- 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

- 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

- 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 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

- 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)

Crude Mortality Rates in Alaska and Florida

Florida

Alaska

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

Source: Vital Statistics of the U.S. (1991)

Percentage of Total

Age

- 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.

U.S.

Population

(standard)

Florida

Age-Specific

Death Rate/

100,000

Age

Group

Calculation of

Expected Deaths

Expected

Deaths

<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

245,700,000

1,996,463

1,996,463

Age Adjusted Death Rate =

=812.6 per 100,000

245,700,000

Alaska

Age-Specific

Death Rate/

100,000

Age

Group

U.S.

Population

(standard)

Calculation of

Expected Deaths

Expected

Deaths

<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

245,700,000

1,880,695

1,880,695

Age Adjusted Death Rate =

= 765.4 per 100,000

245,700,000

Florida

Alaska

Crude Mortality Rate

Age Adjusted Mortality Rate

1,062.4/100,000

812.6/100,000

393.9/100,000

765.4/100,000

- 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)

Total Observed Deaths in the Population

Total Expected Deaths in the Population

SMR =

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.

U.S.

Death Rate/

100,000

(standard)

Expected

Deaths

Age

Group

Calculation of

Expected Deaths

Florida

Population

<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

144,539

Observed

131,044

SMR =

=

= 0.91

Expected

144,539

U.S.

Death Rate/

100,000

(standard)

Expected

Deaths

Calculation of

Expected Deaths

Alaska

Population

Age

Group

<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

2,295

Observed

2,064

SMR =

=

= 0.90

Expected

2,295

Florida

Alaska

Crude Mortality Rate

Standardized Mortality Ratio

1,062.4/100,000

0.91

393.9/100,000

0.90

- 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.

- 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”

- 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!

- Self-report
- Historical documentation (medical records)
- Direct observation
- Direct examination
- Specimen collection and measurement

- 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

- Demographics
- History: symptoms, diagnosis, exposures
- Disease state: physical exam, imaging, autopsy
- Analysis of body fluids
- Body composition: BMI, DEXA scan, MRI

- 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

- 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.

Validity is the degree to which the variable you are measuring actually accurately measures the phenomenon you are interested in.

- 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?

- How well does the measure represent the aspects of the phenomenon you are interested in?

- 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?

- 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?

- 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

- Low density lipoprotein cholesterol has been validated to measure the risk of atherosclerosis by:

- 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.

Random - + - +

Systematic+ - - +

error

error

- 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.