PH 302 Epidemiology Lecture on "Age Adjustment"

10/14/2010

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Age Adjustment(U.S. Practices)

Elena Yu, Ph.D.

PH302: Epidemiology of Communicable and Infectious Diseases

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Review: Epidemiology

• Study of the distribution and determinants of disease frequency in human populationshuman populations

• Distribution and patterns of disease and death are analyzed by characteristics of person, place, and time.

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Disease rates categorized by…

• Person: Who has the disease?male vs. females, young vs. old, black vs. white

• Place: Where is the disease more or less common?common? Different scales of geography: regions of earth, countries, states, counties, cities, neighborhoods

• Time: Is the disease rate changing over time?Different scales of time: decades to seasons to days

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Descriptive statistics: Where?

• Routinely collected data– mortality and natality from vital records

– reportable diseases from surveillance programs

– other health-related events from national surveys

• National Center of Health Statistics

http://www.cdc.gov/nchs/

• More available through other sites

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Descriptive statistics are useful for:

1. Providing clues about disease causation and prevention that are usually investigated further in formal studies

2 Assessing the health status of a2. Assessing the health status of a population (e.g. Healthy People 2010)

3. Allocating resources efficiently and targeting populations for education or preventive programs

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If morbidity or mortality from a given disease changes over time, you can infer:

• It may be real - Some causes of the disease must also be changing

• Or it could be "artifactual“ (spurious)

For example, there are differences in disease definition, diagnosis, or reporting over time.

Or there are changes in enumerating the population denominator of the rate.

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Crude Rates

• A summary measure • The numerator is the total number

of cases or deaths in the populationof cases or deaths in the population• The denominator is the total

number of individuals in that population at a specified time period

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Example: Afghanistan and the U.S2005 Estimates

Afghanistan United States

# of Deaths,

in thousands 485 2,449

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,

Midyear Population, in thousands

25,538 295,753

Crude Mortality Rate (per 1,000) 19 8

Different Age Distributions

Not a good idea to compare the crude death rates

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When the age distribution is so different between the two populations

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Different Years, Same Country

The U.S. population is aging!

Are disease rates going up only because of population aging?

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Problems Comparing Crude Rates

• Groups differ with respect to underlying characteristics that affect overall rate of disease (especially age, sex, and race)– So you may be making an unfair comparisonSo you ay be a g a u a co pa so

• Even within the same country, comparison of data from different time periods is problematic because we don’t know if the change is real or due to population aging

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• Useful to compare age-specific death rates between the groups

• Choose rates specific to some

Category‐Specific Death Rates

Choose rates specific to some particular sub-population:

• age-specific: compare two groups age for age

• race-specific

• sex-specific

• Income-specific

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Category‐Specific Rates

• With category‐specific rates, we don’t have a summary measure.

• Reading out each category‐specific rate is cumbersomecumbersome.

– The table is “very busy”

• How do we get a summary measure that would take into account the different age structure of populations being compared?

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Communities Differ in Age Structure

• A community made up of more families with young children will have a higher rate of bicycle injuries than a community with fewer young childrenyoung children.

• A community with a larger number of older individuals will have higher rates of cancer than one with younger individuals.

– This is because cancer is an age‐associated disease

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Confounding by Age

• Even if the individuals in two communities have the same risk of developing cancer, the one with proportionally more older people will show a higher cancer ratewill show a higher cancer rate.

• Epidemiologists refer to this as “confounding”.

– Confounding happens when the measurement of the association between the exposure and the disease is mixed up with the effects of some extraneous factor (a confounding variable).

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Age Adjustment “Removes” Age

• Age adjustment is a statistical way to remove confounding caused by age.

• To use the example on cancer, age adjustment removes the effect of cancer increasing due toremoves the effect of cancer increasing due to population aging, so that we can compare two communities.

– The comparison then allows us to conclude if cancer rates are truly higher in one community than in another.

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Changes in Mortality RatesMassachusetts: 2000 and 2007

CauseRate

% Change2000 2007Cancer 206.1 179.0 * 13%

Heart Disease 216.7 166.0 * 23%

Stroke 50.9 35.0 * 31%

Rates are per 100,000 population. Age-adjusted to the 2000 US standard population. * Statistically different than 2000 rate (p<0.05)

Chronic Lower Respiratory Disease 41.8 31.5 * 25%All Injuries 35.9 42.5 * 18%Alzheimer’s Disease 19.5 20.9 7%Nephritis 17.6 17.9 2%

Diabetes 19.6 16.5 * 16%

All Diabetes-related 61.5 52.9 * 14%

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Age‐Adjusted Rate: Meaning?

• Age-adjustment was done by the direct method

• It is a summary rate that accounts for age difference between populations.

• Any differences between rates cannot be attributed to age.

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Age‐Adjustment:A Statistical Procedure

• Commonly used in comparing mortality rates across time for the whole country or for state‐level data and uses a standard population for– Health outcomes

– Risk factors

– Health services data

• Also used in small‐area study or clinical sample, but a conventional “standard population” is lacking in these type of studies

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What is a Standard Population?

• It is a population age distribution that is “agreed upon” by convention to be the choice for age adjustment

• WH O uses a “world standard population” to• W.H.O. uses a world standard population to compare morbidity rates between countries

• United States uses the (real) year 2000 population age distribution as the standard for computing mortality and morbidity rates

27Source: http://www.naphsis.org/NAPHSIS/files/ccLibraryFiles/Filename/000000000957/Mortality_AgeAdj%20Final_Lois.pdf

∑ = 1.0

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A Weighted Average

• The age‐adjusted rate can be considered an average of each of the individual age‐specific rates, but rather than being a simple average, it is a weighted average with each age‐it is a weighted average with each agespecific rate weighted by the proportion of people in the same age group in the standard population.

• The weight is the % of population in each age group, expressed as decimals. Must: ∑ = 1.0

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Direct Age Adjustment: How?

• To apply direct age‐adjustment to a set of rates, the age‐specific rate for each age group in the study population is multiplied by the appropriate “weight” (i e population size orappropriate weight (i.e., population size or proportion) in each corresponding age group of the standard population.

• The sum of these products is the directly age‐adjusted, or age‐standardized rate

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Example of Age Adjustment

Comparing State of New Mexico

with Sierra County in New Mexico

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Crude Rate Age-Adjusted32

Footnotes to the Table

33Crude Rate Age-Adjusted 34

Death Rate for Diabetes Mellitus

State of New Mexico

• Crude Rate per 100,000

32.754

• Based on crude rate we

Sierra County in N.M.

• Crude Rate per 100,000

53.73

• Based on age‐adjusted• Based on crude rate, we would conclude that death rate for New Mexico state is lower.

• Age‐adjusted rate

33.54

• Based on age‐adjusted death rate, however, our conclusion is the reverse!

• Age‐Adjusted Rate

27.01

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Age‐adjustment: Direct Method

Step‐by‐step illustration

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Age‐Adjusted Death Rate for Diabetes Mellitus, State of New Mexico, 2003‐2005

A B C

Age # of Deaths New Mexico Diabetes Death U.S. 2000 Expected

Group For Diabetes Population Rate Per 100,000 Standard Population Death Rate

Under 1 0 84,952 0

1 to 4 0 325,508 0

5 to 14 2 828,663 0.24135264

15 to 24 2 893,809 0.22376145

25 to 34 19 718 484

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25 to 34 19 718,484

35 to 44 61 810,632

45 to 54 160 833,948

55 to 64 297 602,768

65 to 74 443 381,451

75 to 84 546 235,030

85 & older 369 82,660

Total 1899 5,797,905


A B C


Group For Diabetes Population Rate Per 100,000 Standard Population Death Rate

Under 1 0 84,952 0

1 to 4 0 325,508 0

5 to 14 2 828,663 0.24135264

15 to 24 2 893,809 0.22376145

25 to 34 19 718 484 2 64445694

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25 to 34 19 718,484 2.64445694

35 to 44 61 810,632 7.52499285

45 to 54 160 833,948 19.1858485

55 to 64 297 602,768 49.2726887

65 to 74 443 381,451 116.135493

75 to 84 546 235,030 232.310769

85 & older 369 82,660 446.406968

Total 1899 5,797,905 32.75321


A B C

Age # of Deaths New Mexico Diabetes Death U.S. 2000 ExpectedGroup For Diabetes Population Rate per 100,000 Standard Population Death Rate

Under 1 0 84,952 0 0.013818

1 to 4 0 325,508 0 0.055317

5 to 14 2 828,663 0.24135264 0.145565

15 to 24 2 893,809 0.22376145 0.138646

25 to 34 19 718 484 2 64445694 0 135573

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25 to 34 19 718,484 2.64445694 0.135573

35 to 44 61 810,632 7.52499285 0.162613

45 to 54 160 833,948 19.1858485 0.134834

55 to 64 297 602,768 49.2726887 0.087247

65 to 74 443 381,451 116.135493 0.066037

75 to 84 546 235,030 232.310769 0.044842

85 & older 369 82,660 446.406968 0.015508

Total 1899 5,797,905 32.75321 1

The population size in each age group is expressed as a proportion of the total population


A B C

Age # of Deaths New Mexico Diabetes Death U.S. 2000 ExpectedGroup For Diabetes Population Rate per 100,000 Standard Population Death Rate

Under 1 0 84,952 0 0.013818 0

1 to 4 0 325,508 0 0.055317 0

5 to 14 2 828,663 0.24135264 0.145565 0.035132

15 to 24 2 893,809 0.22376145 0.138646 0.031024

25 to 34 19 718 484 2 64445694 0 135573

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25 to 34 19 718,484 2.64445694 0.135573

35 to 44 61 810,632 7.52499285 0.162613

45 to 54 160 833,948 19.1858485 0.134834

55 to 64 297 602,768 49.2726887 0.087247

65 to 74 443 381,451 116.135493 0.066037

75 to 84 546 235,030 232.310769 0.044842

85 & older 369 82,660 446.406968 0.015508

Total 1899 5,797,905 32.75321 1

The death rate for each age group is weighted by the proportion represented by the age group


A B C


Group For Diabetes Population Rate per 100,000 Standard Population Death Rate

Under 1 0 84,952 0 0.013818 0

1 to 4 0 325,508 0 0.055317 0

5 to 14 2 828,663 0.24135264 0.145565 0.035132

15 to 24 2 893,809 0.22376145 0.138646 0.031024

25 to 34 19 718 484 2 64445694 0 135573 0 358517

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25 to 34 19 718,484 2.64445694 0.135573 0.358517

35 to 44 61 810,632 7.52499285 0.162613 1.223662

45 to 54 160 833,948 19.1858485 0.134834 2.586905

55 to 64 297 602,768 49.2726887 0.087247 4.298894

65 to 74 443 381,451 116.135493 0.066037 7.669240

75 to 84 546 235,030 232.310769 0.044842 10.417279

85 & older 369 82,660 446.406968 0.015508 6.922879

Total 1899 5,797,905 32.75321 1 33.543532

The sum is the age-adjusted (expected) death rate for the whole population

Good Practices (1)

• When reporting age‐adjusted rates, always report the standard population used

• Comparisons can only be made between rates calculated using thebetween rates calculated using the same standard population

• The age‐adjusted rate is hypothetical

– Useful only for comparing populations, either over time, by geographic area, by sex or by racial/ethnic group

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Good Practices (2)

• Although age‐adjustment may be used with broad population age groups, such as adults (e.g., age 18+), depending on the outcome being studied it may not be necessary (orbeing studied, it may not be necessary (or meaningful) to age‐adjust data for smaller age groups.

• Age‐adjustment is not appropriate if there is a cross‐over of specific rates between the two groups under comparison.

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Good Practices (3)

• Do not do age adjustment if death rates among younger persons are increasing over time, but death rates among older persons are decreasing over timedecreasing over time.

– The trends are not consistent. So, you cannot use one number to represent the population.

– A summary measure, such as an age‐adjusted rate, would refer distort (or mis‐represent) the reality.

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Age specific cancer death rates among females, 1970 to 1995

1200

1400

1600

1995

0

200

400

600

800

1000

<1 1-4 5-14 15-25 25-34 35-44 45-54 55-64 65-74 75-84 85+

1995

1970

Source: National Vital Statistics System, CDC, NCHS.46

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Female cancer death rates, by age adjustment standard

175

195

215Crude rate

75

95

115

135

155

1970 1980 1985 1990 1995

1940 standard population

2000 standard population

Source: National Vital Statistics System, CDC, NCHS.48

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Diabetes prevalence by race/ethnicity (Obj. 5‐3), 1999

4

5

6

7

en

t

0

1

2

3

4

pe

rce

Source: National Health Interview Survey (NHIS), CDC, NCHS.Source: National Health Interview Survey (NHIS), CDC, NCHS.49

Diabetes age specific rates, 1999

20

25

Hispanic not-Hispanic White

0

5

10

15

20

<18 18-44 45-64 65-74 75+

perc

ent


2000 Census age distribution

40

50

Hispanic not-Hispanic White

0

10

20

30

<18 18-44 45-64 65-74 75+

perc

ent

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Diabetes prevalence (Obj. 5‐3), 1999

6

7

8

9 Overall

American Indian/Alaska Native

Asian/ Pacific

0

1

2

3

4

5

Crude

pe

rce

nt Asian/ Pacific

Islander

Hispanic

Not-HispanicWhite

Not-HispanicAfrican American


Diabetes prevalence (Obj. 5‐3), 1999

6

7

8

9 Overall

American Indian/Alaska Native

Asian/ Pacific

0

1

2

3

4

5

Crude Age-adjusted

pe

rce

nt Asian/ Pacific

Islander

Hispanic

Not-HispanicWhite

Not-HispanicAfrican American


Caution

• NCHS in the past (before 2000) used

– 1940 age distribution as the standard for mortality

– Mostly 1970 and 1980 age distribution as the standard for survey datastandard for survey data

• NCHS now uses year 2000 U.S. resident population as the “standard population” for all

• The 1940 U.S. standard population is younger

• The 2000 U.S. standard population is older

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55

800

1000

1200

Crude and age adjusted death rates based on year 1940 and 2000 standard populations: United States, 1979‐2000

2000 standard

crude rate

0

200

400

600

1979 1982 1985 1988 1991 1994 1997 2000

1940 standard

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NCHS Practices

• Between the spreadsheet and the “manual method” of calculating age adjustment, NCHS uses the manual method.

– Healthy People 2010 rate calculations are– Healthy People 2010 rate calculations are rounded/truncated to one decimal place.

– Weights are rounded/truncated to six places.

• Question: You are focusing on death rates for female breast cancer. Do you still use the 2000 standard population (for both sexes)?

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Age‐adjustment inHealthy People 2010

• Main purposes:– Observe trends in populations over time

– Monitor disparity between populations both at a point in time and over timepoint in time and over time

• Several different age groupings are used to age-adjust data from different sources

• Some data sources use fewer age groupings to stabilize the rates of less common events and smaller subpopulations (e.g. age groups for chronic disease)

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Healthy People 2010 age-adjusted measures and age adjustment groups

Data Standard Source Distribution**NVSS-M # 1 <1, 1-4, 5-14, 15-24, 25-34, 35-44, 45-54, 55-64, 65-74, 75-84, 85+NHIS # 3 <18, 18-44, 45-54, 55-64, 65-74, 75+NHDS # 4 <18, 18-44, 45-64, 65-74, 75+CSFII # 5 2-5, 6-11, 12-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80+NHIS # 9 18-24, 25-34, 35-44, 45-64, 65+NHANES # 11 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80+NHANES # 12 20-39, 40-59, 60+

Age Adjustment Groups

NHIS # 15 40-49, 50-64, 65+NHIS # 16 45-49, 50-64, 65+NHIS # 17 50-64, 65+NHDS # 18 65-74, 75+NHIS # 19 0-4, 5-11, 12-17NHDS # 21 5-17, 18-44, 45-64NHIS # 22 18-24, 25-34, 35-44, 45-64

** Healthy People Statistical Notes, no. 20. January 2001.Education group breakouts begin age groups at age 25.

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Healthy People 2010 age-adjusted measures and age adjustment groups

Data Standard Source Distribution**NVSS-M # 1 <1, 1-4, 5-14, 15-24, 25-34, 35-44, 45-54, 55-64, 65-74, 75-84, 85+NHIS # 3 <18, 18-44, 45-54, 55-64, 65-74, 75+NHDS # 4 <18, 18-44, 45-64, 65-74, 75+

NHDS/NHIS # 4 (revised)1 0-44, 45-64, 65-74, 75+CSFII # 5 2-5, 6-11, 12-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80+

NHIS # 6 (revised)1 2-44, 45-54, 55-64, 65-74, 75+

NHIS # 8/9(revised) 1,2 18-44, 45-64, 65-74, 75+NHIS # 9 18-24, 25-34, 35-44, 45-64, 65+

NHIS # 9 (revised) 1 18-44, 45-64, 65+

NHANES # 10 (revised)3 18-49, 50-59, 60-69, 70-79, 80+NHANES # 11 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80+

NHANES # 11 (revised)3 20-49 50-59 60-69 70-79 80+

Age Adjustment Groups

NHANES # 11 (revised) 20-49, 50-59, 60-69, 70-79, 80+NHANES # 12 20-39, 40-59, 60+NHIS # 15 40-49, 50-64, 65+NHIS # 16 45-49, 50-64, 65+NHIS # 17 50-64, 65+NHDS # 18 65-74, 75+NHIS # 19 0-4, 5-11, 12-17NHDS # 21 5-17, 18-44, 45-64NHIS # 22 18-24, 25-34, 35-44, 45-64

NHIS # 22 (revised)1 18-44, 45-64

* For datalines where denominator is people with chronic conditions.** Healthy People Statistical Notes, no. 20. January 2001.1 <45 age groups aggregated (denominator is people with chronic conditions).2 65+ age group diaggregated (workgroup's request).3 <50 age groups aggregated (denominator is people with chronic conditions). Education group breakouts begin age groups at age 25.

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Age Groupings

There are detailed age groupings of the year 2000 Standard Population

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Confidence Intervals

• In order to determine the reliability and the chance variation of a death rate (especially those based on small numbers of events) as well as to determine significant changes overwell as to determine significant changes over time, or significant differences when comparing rates, it is highly recommended that a standard error or confidence interval (usually 95%) be calculated and shown for the rates.

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Exceptions

Data that are not age adjusted

• Infant mortality

• Maternal mortalityUses LIVE BIRTHS as the denominator

• National Household Survey on Drug Abuse

• Occupational Injury and Death

– Fatality Analysis and Reporting System

– Census of Fatal Occupational Injuries

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Indirect Age Adjustment

Used when population size is small or age‐specific events are few

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Indirect Standardization

• The method applies the age‐specific rates found in the standard population to the age distribution of the smaller area or sub‐population “Expected number”.p p p

• The number of observed deaths (in the population of interest) is divided by the number of expected deaths, multiplied by 100, to obtain a standardized mortality (or morbidity) ratio, called SMR for short.

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SMR for Small # of Events

• If the total number of events is 25 or less, calculate SMR.

• SMRs within the same population can be compared with each othercompared with each other. – Example: SMRs for Prostate cancer, breast cancer, lung cancer, skin

cancer within Chinese‐American population can be compared with each other.

– We are using the age‐specific death rates for prostate cancer, breast cancer, lung cancer, and skin cancer in the mainstream population (usually white population is used), and multiplying them by the population size of Chinese Americans in each age group.

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SMR is a Ratio

O (Observed number of deaths)__

E (Expected number of deaths)SMR =

SMR > 1 means that there were “excess deaths” compared to what was expected.

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SMR = 1 means that the observed # deaths = Expected # of deaths.

So, the statistical test of significance for the SMR is whether the ratio is Significantly different from 1.0.

To gauge statistical significance of SMR, we must calculate the 95% confidenceInterval. If the 95% C.I. excludes 1.0, it may be considered statistically significant.

The 95% C.I. is equal to 1.96 times the standard error of the estimate.

SMR is a Relative Index

• The ratio obtained from dividing the observed by the expected number of deaths is usually multiplied by 100 SMR.

• As with any age adjusted rates indirectly age• As with any age‐adjusted rates, indirectly age standardized rates should be viewed as relative indexes.

• They are not actual measures of mortality risk, and do not convey the magnitude of the problem.

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Caution

• Two indirectly standardized rates, from two different (small) populations, cannot be compared with each other.

• For example: the SMR for Latinos cannot beFor example: the SMR for Latinos cannot be compared to the SMR for Asians.

• SMRs from different populations cannot be compared, because they have different population age structure.– SMR for prostate cancer among Latinos cannot be compared with SMR

for prostate cancer among Chinese Americans.

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Documents

PH 302 Epidemiology Lecture on "Age Adjustment"