Number range CHAPTERState Age Sex Estimates

I. History

> A. Started in 70s - Broad Age Groups (BAG)
>
> 1\. Age groups 0-2, 3-4, 5-13, 14-24(SY), mostly 10-year groups up to
> 64, then 65+
>
> 2\. No sex detail, except age 64 (probably for use in county
> estimates)
>
> 3\. Method similar to current except school age migration RATE was
> used for each age group all the way from 0-64
>
> 4\. Medicare data used for 65+
>
> B. CHANGE IN EARLY 80s
>
> 1\. About 1983 went to all Cohort Component computing single years of
> age
>
> 2\. Change in use of School enrollment data
>
> From all rate to combination of rate and migration number(for 0-16)
>
> 3\. Only released, published Age Groups (0-4, 5-13, 14-17, 18-24,
> 25-34, ten yr groups to 85+)

Other aggregates available on request

> \[Try to get single years from cleverly constructed overlapping
> aggregates\]
>
> C. CHANGE TO CURRENT
>
> During the 1990 evaluation round, found evidence that on **average**
>
> Single years of age were no worse than aggregates.
>
> Produced Single Year file for general release.

1993 round - converted processing from FORTRAN to SAS. Very Large
programs, a basic straight conversion with no methodological changes.
\[Programs were getting long, taking time on Sun System - Edwin admits
when he ran them in FORTRAN they \"Brought the System to its knees\".\]

II\. CURRENT METHOD

Description not in order of computation. Will see need for items as they
arise.

> A. Computation in 3 parts, for civilian, by age:(First computed for
> total, then split to male and female using a sex-ratio (by age, year),
> for parts 1 and 2.
>
> 1\. 0-16 (No Military, Civilian=Resident)
>
> Cohort pop - cohort civilian deaths + single year mig
>
> 2\. 17-64 (Military)
>
> (Cohort pop - Cohort Resident Armed Forces)

\+ single year mig

\- cohort civilian deaths - net loss to mil.

> 3\. 65+ (No Military, Medicare Data used)
>
> B. Cohort Populations
>
> Uses Census Population, Births, depending on Age and Year
>
> .25\*Cen~A~ + .75\*Cen~A+1~
>
> .25\*Births + .75\*Cen

or

> .5\*Births~Y~ + .5\*Births~Y+1~
>
> C. Cohort Military
>
> 1\. Inputs
>
> a\. State Station strength, each year
>
> b\. US military by age, sex, race, Hispanic Origin, each year, World
> Wide and Resident (Inside)
>
> c\. US Controls (Deaths to Military, Deaths to Military Overseas)
>
> 2\. Results
>
> a\. Cohort Military Population
>
> b\. Cohort Military Deaths
>
> c\. net loss
>
> D. Cohort Deaths
>
> Use Calendar year deaths to make period cohort deaths (single year of
> age to 64)
>
> E. Single Year Migration Computation
>
> 1\. Migration Base
>
> (cohort pop - cohort AF)

\- (Cohort Civilian Deaths + net loss to Mil)/2

> 2\. Migration
>
> migration base \*
>
> (school age migration rate(one rate applied to all ages) + migration
> differential)
>
> 3\. School Age(SA) Migration Rate
>
> a\. SA Migration/SA Migration Base
>
> b\. SA migration= (SA Pop (Adjusted for Enrollment Rate)-Survived
> School Age Cohort)

\*(time adjustment)

> b1. SA Pop - From current School enrollment \*
>
> ratio of SA cohort to enrollment90
>
> (Controlled)
>
> b2. SSAC - SA Cohort - SA Cohort Deaths (for year)
>
> 4\. Migration Differential
>
> From census data on residence 5 years ago. Differential for each age
> group from school age.
>
> F. Medicare Estimates (65+)
>
> 1\. Split Age Groups
>
> a\. Medicare data comes in 2 year age groups for 65-74, 5-year age
> groups 75-84, and 85+
>
> b\. use 1990 census data to split to single years of age (by sex)
>
> 2\. Compute Estimate from Medicare Change
>
> POP~Y~ = POP~90~ + (Med~Y~-Med~90~)
>
> 3\. Control to US Totals
>
> G. Final Packaging
>
> 1\. Two-way rake (State Totals, National Age-Sex Totals - 51 by 172)
>
> 2\. Combine 3 age groups
>
> 3\. Sprinkle and Stuff
>
> 4\. Make Resident from Civilian and Military

III\. New Wrinkles and Issues

> A. New Wrinkles
>
> 1\. Control to State \<65, 65+ totals.
>
> 2\. Create 7/1/90 using method rather than interpolation.
>
> B. Issues for future consideration
>
> 1\. Micro Data Deaths
>
> Tally deaths by age and time of occurrence rather than splitting
> calendar year deaths into cohorts.
>
> Advantage - Uses \"Real\" data
>
> Disadvantage - Problems with data - coding age, time of death,
> completeness, etc.
>
> 2\. Migration modification, age 55-64
>
> While doing evaluation, we found that the worst ages were between 55
> and 64, with the degree of error increasing the closer we got to 65.
>
> Probably a product of using School Age Migration, when maybe this age
> group migrated more like the 65+ group, and thus Medicare migration
> would be more appropriate. Are considering a weighted scheme using
> more SA migration closer to 55 and more Medicare migration closer to
> 65.
>
> 3\. All New Methods

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

EARLY STATE TOTALS (during the 70s)Average of three methods:

> Method II
>
> Composite Component II (0-16)

Ratio Correlation (17-64)

Medicare (65+)

> Administrative Records

x:\\davis002\\wpdocs\\stagecoop