SAS_AE16_Shootout_Team39

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Copyr ight © 2016, SAS Institute Inc. All rights reser ved.
Baby Boomers and the Aging Population
Team 39: Yaotong Cai, Chunla He,
Wenhao Pan, Richard Ross , Shiyu Ye
Advisor: Dr. Dan Hall

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Problem Summary
 Oldest Baby Boomers Reaching Retirement Age
 Aging population will carry new challenges
 How will the incidence of common diseases shift
 Labor force age demographics
 Social Security solvency
 Potential Impacts
 Vaccines
 Policy Changes

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 Results
 Workflow
 Age Handling
 Modeling - Disease and Labor Force
 Uncertainty
Outline

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 Cancer is expected to continue to decline in incidence
 Asthma is expected to increase in incidence for females
through 2060, but has already peaked for males (2002)
 Alzheimer’s Disease & Dementia, Osteoarthritis, and
Septicemia are predicted to increase over the next 45 years
without reaching a peak
Results – Hospitalization Rates

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Disease Trajectories

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Results – Labor Force
 Dominating Age Groups:
 Management, professional and related: 25-34 and 35-44 will make up more
than half of this industry by 2045
 Natural Resources, construction, and maintenance: 45-54 age group will hold
a majority of this industry by 2050.
 Production, transportation and material moving: 20-24 and 25-34 age group
will hold a majority of this industry by 2050.
 Sales and Office: 25-34, 55-64, and 65 years and over age group will hold
over 70% of this industry by 2050.
 Service: 20-24 and 25-34 age group will hold a majority of this industry by
2045.

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 Industry level: (1) Natural Resources, Construction, and Maintenance,
(2) Production, Transportation and Material Moving, and
(3) Sales and Office
were most affected by economic recession.
 Age Group level: younger workers were hit hardest by
recession

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Health Care Cost Reduction: Vaccine
 Suppose Septicemia Vaccines developed in 2025
 Hospitalizations decreased by 20%, costs per hospitalization
decreased by 25%
 Savings Projections:
 Septicemia Rate projected to increase greatly
 $40B savings in 2025
 $2.5 T savings in 2060

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 Projected Revenue and Expenditures – Current System
Estimate of payment: 117,000 × 12.4%
Estimate of cost: $ 1503 × 12 for over 67 age population
Result: Society Security Payments (inflow) will be less than expenditures
(outflow) after 2020
Social Security system will reach insolvency by 2035, consistent with
the Social Security Administration's current estimates
Forecasted $8 trillion deficit by 2060
Forecast on Sustainability of Social Security
& Tax System

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 Projected Revenue and Expenditures – Policy Change Strategies
1) Increase the retirement age to 70
Balanced until 2030, solvent through 2060
2) Reduce the benefit amount to $1300/month
3) Increase the tax rate from 12.4% to 14.5%
Forecast on Sustainability of Social Security
& Tax System

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Workflow: Analysis Process
EDA &
Feature
Engineering
Aggregation &
Matching of Age
Group:
(1). Simple
Combination
(2). Age Mapping
Disease Prevalence
Study
--Poisson GLMM
--2D P-spline Models
Labor Force Demand
Study
--Multinomial Logistic
GLMM with P-splines
Projected population
in industries;
Solutions for Social
Security & Tax
sustainability
Projected trends of
disease incidence;
Implications of
Vaccines
Missing Data
Handling

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Age Categories in Three Data Sets

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 Simple combination
 Arithmetic aggregation or disaggregation for some age groups
 Smoothing
 Reconcile the remaining Age groups
based Rizzi et al. (2015)’s work to
disaggregate and re-aggregate age
in one-year bins, while adding cohort
effects (smoothing age and cohort)
Overall Strategy – Matching age groups

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Smoothing Results

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 Labor force and disease modeling problems have similar
elements:
 Stratified annual count data for multiple age groups over relatively
short time spans, with goal of forecasting far into future.
 Wanted to impose little structure on data (smoothing) and borrow
strength across age strata (mixed models).
 Use Generalized linear mixed models (GLMMs) to implement
P-spline smoothing (PROC GLIMMIX) to model observed
trends and make forecasts.
 Prediction intervals important. Must recognize huge uncertainty.
 Framework accommodates missing data easily.
Overall Modeling Strategy:

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 Primary analysis based on Poisson GLMMs with log link and P-
splines for time effects.
 Models included age stratum-specific intercepts and slopes on time but
smoothing parameters common to all age groups.
 For strata with anomalous behavior/small sample size, model
constraints used to link adjacent age strata and avoid unrealistic results.
 Secondary analysis based on 2-D P splines (Currie et al. 2006,
MortalitySmooth package in R).
 Smoothing over time and age (counts disaggregated by age).
 Results similar to primary analysis
Disease Models:

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 Natural framework is multinomial logit model (each worker in one of five
industries or other).
 Again, P-splines to smooth effects of time, with age-specific intercepts and
slopes on time → multinomial logit GLMM.
 By Begg and Gray, 1984, if large prevalence “other” category used as
reference level, binomial error distributions can be used to fit separately
for each industry w/ little efficiency loss. Therefore used binomial logit
GLMMs in PROC GLIMMIX.
 Models included log-time effect truncated at year 2006 to allow effects of
economic recession on labor force to dissipate. Avoids short term effects
of recession having undue effect on long term forecasts.
Labor Force Models

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 Uncertainty in modeling diseases data
1. Predicting rates several decades into the future based on 10-
15 years data.
2. Training data covers a time period with a major anomalous
event
3. Age matching and missing data
Limitations

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SAS_AE16_Shootout_Team39

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