Shih-Fan Lin 1, DrPH. Brian K. Finch 1, Ph.D. Audrey N. Beck 1, Ph.D. Robert A. Hummer 2, Ph.D. Ryan...
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Transcript of Shih-Fan Lin 1, DrPH. Brian K. Finch 1, Ph.D. Audrey N. Beck 1, Ph.D. Robert A. Hummer 2, Ph.D. Ryan...
Black-White Disparity in Late-Life Disability: Exploring the Effects of Age, Period, and Cohort
Shih-Fan Lin1, DrPH.Brian K. Finch1, Ph.D.
Audrey N. Beck1, Ph.D.Robert A. Hummer2, Ph.D.
Ryan K. Masters3, Ph.D.
1San Diego State University; 2University of Texas at Austin; 3Columbia University
BACKGROUND: ObjectivesElucidate how U.S. disability prevalence
changed among older adults age 70+ using the Age-Period-Cohort (A-P-C) models. Compare (a) unadjusted and (b) socio-
demographics and A-P-C adjusted trends.Examine the black-white disparity trend in
late-life disability using the A-P-C modelsSocio-demographics and A-P-C adjusted
disparity trends.Stratify by gender.
BACKGROUND: Outcome DefinitionOutcome: Late-life disability
ADL Disability: limitations on activities of daily living such as bathing, ambulating, and toileting.
IADL Disability: limitations on instrumental activities of daily living such as shopping, writing a check, and cooking.
BACKGROUND: The Age-Period-Cohort (APC) ModelIn the realm of demography, sociology, and
epidemiology, time can be captured by three unique temporal dimensions: Age (A), Period (P), and Cohort (C).
Each aspect of A-P-C has a unique contribution to the study of population health including disability.
BACKGROUND: Age EffectAge is a proxy for biological processes that
ultimately lead to disease, disability, and/or death.
Age may also be associated with changes in status, social roles, and social position (Yang and Land 2007).
Individuals’ aging processes can have differential impacts on population sub-groups (e.g. racial groups) over time.
BACKGROUND: Period EffectPeriod or survey year, reflects changes in
socio-cultural, economic, technological, medical, and environmental factors that may affect the entire population at a given time simultaneously, but perhaps not equally.
For example, a drought may lead to increased food prices, which may impose greater impacts on those with lower incomes than the more well-off.
BACKGROUND: Cohort EffectCohort describes a unique set of individuals who
are both born into a social system during a similar time period and experience similar formative social experiences over their life course.
Successive cohorts that experience different historical and social conditions differ in their exposure to socioeconomic, behavioral, and environmental risk factors.
The colloquial concept of generational difference is an attempt to capture the unique characteristics of distinct cohorts.
METHODS: DataIntegrated Health Interview Series (IHIS), 1982-2009
Harmonizes National Health Interview Survey (NHIS) variables to allow consistent coding across each survey to facilitate temporal analysis.
The NHIS is a repeated cross-sectional survey.Purpose: to investigate and monitor the prevalence of
important health outcomes (including disability) of the civilian non-institutionalized U.S. population.
Inclusion criteria: Older adults aged 70 and over. Age of 70 is the youngest common age cut point for which
the disability items were inquired between the 1982-2009 survey periods.
1982 is the first NHIS survey year that the disability status was inquired.
METHODS: Tackling the Identification ProblemIdentification problems occur when the predicting
variables in a regression are linearly dependent.There is an exact linear dependence between age,
period, and birth cohort.
To break the linear dependence, we group cohorts into 5-year bands.
For example, individuals who were born between 1898-1902 were collapsed into the 1900 cohort (mid-point of 1898 and 1902).
Period Age Cohort
METHODS: AnalysisUnadjusted ADL/IADL disability trends:
6 separate logistic regressions in which ADL/IADL disability (dichotomous) was regressed on A-P-C separately (e.g. ADL Disability = β0 + β1 age).
Adjusted ADL/IADL disability trends:ADL/IADL disability (dichotomous) was regressed on
A-P-C simultaneously with the addition of socio-demographic variables (e.g. Adjusted ADL disability trend: ADL Disability = β0 + β1 age + β2 period + β3 cohort + β4 race + β5 education +……… βn income).
Age was entered linearly while periods were entered as single-year dummies and cohorts were entered as 5-year bands. Omitted category for period: 1982 Omitted category for cohort: 1885
METHODS: AnalysisAdjusted disparity trends in ADL/IADL disability.
ADL/IADL disability was regressed on A-P-C simultaneously while interacting race (black/white) with each A-P-C and controlling for socio-demographic variables (e.g. Disparity trend by cohort: ADL Disability = β0 + β1 race + β2 age + β3 period + β4 cohort + β5 race × cohort
+ β6 education + ………βn income).Age was entered linearly and as a squared term
while periods were entered as single-year dummies and cohorts were entered as 5-year bands. Omitted category for period: 1982 Omitted category for cohort: 1895
RESULTS: ADL/IADL Disability by AgeUnadjusted Trends Adjusted Trends
70 71 72 73 74 75 76 77 78 79 80 81 82 83 840.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
ADL Disability IADL Disability
Age
Fit
ted P
robabil
ity
70 71 72 73 74 75 76 77 78 79 80 81 82 83 840.00
0.05
0.10
0.15
0.20
0.25
0.30
ADL Disability IADL Disability
Age
Fit
ted P
robabil
ity
RESULTS: ADL/IADL Disability by PeriodUnadjusted Trends Adjusted Trends
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
0.00
0.05
0.10
0.15
0.20
0.25
ADL Disability IADL Disability
Period
Fit
ted P
robabil
ity
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
0
0.05
0.1
0.15
0.2
0.25
ADL Disability IADL Disability
Period
Fit
ted P
robabil
ity
RESULTS: ADL/IADL Disability by CohortUnadjusted Trends Adjusted Trends
1885
1890
1895
1900
1905
1910
1915
1920
1925
1930
1935
1940
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
ADL Disability IADL Disability
Cohort
Fit
ted P
robabil
ity
1885
1890
1895
1900
1905
1910
1915
1920
1925
1930
1935
1940
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
ADL Disability IADL Disability
Cohort
Fit
ted P
robabil
ity
KEY FINDINGS: Disability Trends for Age and PeriodThe unadjusted predicted probabilities of
ADL and IADL disability increase substantially with age. The age effects remain strong after adjusting for period and cohort effects and socio-demographic variables.
The unadjusted and adjusted periods trends show similar results – there was a substantial decline in IADL disability between 1982 and 2009 while ADL disability remained stable over the last 3 decades.
KEY FINDINGS: Disability Trends for CohortThe unadjusted cohort trends for both
outcomes also showed continual declines across each successive cohort; however, increasing cohort trends were evidenced in the adjusted model.
More recent cohorts of U.S. older adults are becoming more disabled, net of age effect and net of changes in socio-cultural, technological, medical, economic and environmental factors captured by period effects.
RESULTS: Adjusted ADL and IADL Disparity Trends by Age and Gender
70 71 72 73 74 75 76 77 78 79 80 81 82 83 840.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
ADL Racial Disparity: Men
ADL Racial Disparity: Women
Age
Diff
ere
nce i
n F
itte
d P
rob
ab
il-
ity
(Bla
ck
- W
hit
e)
RESULTS: Adjusted ADL and IADL Disparity Trends by Period and Gender
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
0.12
ADL Racial Disparity: MenADL Racial Disparity: Women
Period
Diff
ere
nce i
n F
itte
d P
rob
ab
ilit
y(B
lack
- W
hit
e)
RESULTS: Adjusted ADL and IADL Disparity Trends by Cohort and Gender
1895
1900
1905
1910
1915
1920
1925
1930
1935
1940
-0.04
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
0.12
ADL Racial Disparity: MenADL Racial Disparity: Women
Cohort
Diff
ere
nce i
n F
itte
d P
rob
ab
ilit
y(B
lack
- W
hit
e)
KEY FINDINGS: General Disparity Trends in DisabilityBlacks are more likely to experience
disability than whites. Women tend to have greater black-white
disparities than men with respect to each age, period, and cohort trend and each disability outcome.
The black-white disparities in IADL disability tend to be greater than the disparities in ADL disability.
KEY FINDINGS: Disparity Trends in Disability by Age & GenderFor both men and women, there is a
persistent increase of disparity in ADL and IADL disabilities across age.
This supports the “double jeopardy hypothesis” which suggests that both minority status and aging together contribute to double disadvantages in health (disability).
The double jeopardy effect seems to be more pronounced for black women than black men, especially for the ADL disability.
KEY FINDINGS: Disparity Trends in Disability by Period & GenderAlthough a decreasing trend of IADL
disability and a steady ADL trend were observed across each period (slide #13), there is not a consistent period-based disparity trend for ADL or IADL disability.
Fluctuations of disparity are greater for IADL than ADL disability.
There are several dips (1984, 1995, 2000, and 2004) where blacks actually had advantages over whites on both types of disability; however, the race × period interactions for these years were not significant.
KEY FINDINGS: Disparity Trends in Disability by Cohort & GenderExcept for a few cohort variations, the ADL
disparity for both men and women remained quite stable between 1905 and 1930 cohorts.
The cohort-based IADL disparity trend for men follow closely to the cohort-based ADL disparity trend.
However, there is a persistent increase (except the two most recent cohorts) of IADL disparity across each successive cohort among women. This is consistent with the general increase of IADL disability across cohorts (slide #14).
ACKNOWLEDGEMENTSThis project was supported
by Award Number R01MD004025 from the National Institute on Minority Health and Health Disparities (NIMHD).
The content of this presentation is solely the responsibility of the authors and does not necessarily represent the official views of the NIMHD or the National Institute of Health.