A large literature is devoted to the investigation of race/ethnic
health disparities relying on data analysis. While the minority
population is growing (U.S. Census Bureau 2008; Arias 2010), quite often
past and current data have small samples of Mexican Americans, Latinos
including Mexican Americans, blacks, and others, especially when
stratified by basic demographic characteristics, such as age and gender
(Dunlop et al. 2002). Representativeness of the participating
respondents of the population of interest and nonrandom loss from the
study are one of the biggest concerns leading to bias estimates and
nongeneralizability of some results (Hausman and Wise 1979; Diehr and
Johnson 2005). While representativeness of the sample is difficult to
test due to lack of information about nonparticipants, attrition has
been analyzed and addressed in some studies on health disparities.
However, none of the studies have compared models of attrition across
different ethnic and racial groups of aging populations. Using the
Health and Retirement Study (HRS) 1992-2008, this study compares models
of attrition for Latinos including Mexican American, Mexican American
separately, black, and white elderly Americans. The main hypothesis is
whether variables describing foreign birth, health insurance, and health
status are associated with attrition and whether these associations
differ across racial/ethnic groups of elderly.
This study falls into the strand of literature about determinants
of attrition among elderly individuals (Mihelic and Crimmins 1997; Van
Beijsterveldt et al. 2002; Chatfield et al. 2005; Kapteyn et al. 2006;
Tyas et al. 2006; Stimpson and Ray 2010). A recent literature review of
attrition among elderly found that age, cognitive impairment, and poor
health were associated with high attrition rates (Chatfield et al.
2005). However, none of the studies provided a comparison of
determinants of attrition across different races and ethnicities. Such
attrition is essential to the assessment of race/ethnic health
disparities. Kapteyn et al. (2006) analyzed attrition in the HRS and
found that being born outside of the United States, being Hispanic,
being in poor/fair health, or having the onset of a health condition was
associated with a higher probability of attrition than remaining in the
HRS, depending on the type of model estimated. However, their analysis
did not address the question of whether Hispanics and blacks with
certain characteristics, for example, being in poor health, were more
likely to attrite compared to similar whites. Rather than estimating a
separate model for each race/ethnicity or interacting race/ ethnicity
with other covariates, the authors estimated a multinomial logit model
for the pooled sample of whites, blacks, and Hispanics while introducing
binary variables for each race/ethnicity category. This approach imposes
a "common-effect" assumption when effects of other covariates
on attrition are the same across different groups of elderly, which is
relaxed in our study (Jha et al. 2007). Documenting differences in the
attrition by race/ethnicity is also relevant to the literature on the
"Hispanic paradox," a concept that describes a mortality
advantage among Hispanics compared to similar whites and African
Americans (Markides and Coreil 1986). Among other explanations, some
researchers have argued that this phenomenon is a result of the
selective return migration to the country of origin by older people in
poor health, the so-called salmon bias hypothesis (Markides and Eschbach
2005). However, we cannot test this hypothesis due to lack of data on
migration.
A competing risks model was estimated using a multinomial logit
model when respondents of the study faced competing types of risks, such
as dying, being lost from the study, and nonresponding in some years
(Kapteyn et al. 2006). That is, when respondents entered the HRS in
1992, some of the participants were still present in 2008, the latest
year available. However, some of the respondents died, others were in
and out of the study during this period, while the rest of the
respondents were lost from the study. Only one event/failure, such as
death, nonresponse, or loss, can take place exclusively to the others
with some probability, which defines a "competing risks"
situation. The main assumption is the independence of risks of each
event/failure type conditional on explanatory variables. The key
explanatory variables are foreign birth, health insurance, and health
status.
Given the substantial financial and human resources dedicated to
the issue of racial/ethnic health disparities, this study may assist in
developing future studies on health disparities in aging populations.
Moreover, differential attrition by race/ethnicity in panel surveys has
significant negative implications for health research in general due to
growing ethnical diversity of the U.S. population.
METHODS
Data Source and Sample
This study analyzed data from the HRS. The HRS is the most current,
comprehensive, longitudinal study of aging (Juster and Suzman 1995). The
HRS is a multistage clustered area probability sampled study of 51- to
61-year-old individuals that started in 1992 by collecting information
about 12,600 people born between 1931 and 1941 and their spouses. These
data were supplemented with individuals born before 1923 as part of the
Study of Asset and Health Dynamics among the Oldest Old (AHEAD) in 1993
and combined with younger cohorts in 1998 and 2004. All individuals were
re-interviewed every 2 years with a response rate of more than 80
percent across all waves Ouster and Suzman 1995; Institute for Social
Research 2010). The HRS contains detailed information on demographics,
health status, and health care utilization that is uniform across all
ethnic/racial groups and perfectly suited for the analysis of
race/ethnic differential in attrition.
To maximize the Latino sample, our analysis includes all
individuals from the HRS who were observed at age 51 or older resulting
in a sample of 1,492 Mexican Americans, 2,487 Latinos including Mexican
Americans, 4,367 blacks, and 21,845 whites. Table 1 reports the
distribution of the birth cohort in the sample. Samples of whites,
Mexican Americans, Latinos, and blacks have 1.5, 2.5, 2.2, and 2.4
percent of observations with missing values for one or more variables
respectively and were excluded from the analysis.
Measures
The outcome variable is the vital status of each HRS respondent in
2006. The status of each respondent can fall in four exclusive
categories. We labeled "always in" those respondents that
responded in each wave up to 2006; "died" those respondents
who were reported dead between the first wave they were interviewed and
2006; "ever out" those respondents who did not respond in some
years; and "attritor" those who entered the study at some
point but were continuously absent from the study up until 2008. Since
2008 data are already available, we used the status in 2008 to determine
whether nonresponding individuals in 2006 or earlier were attritors or
if they were back in the study in 2008.
The set of explanatory variables included continuous variables such
as age, age squared, and binary variables for being female, married, for
not completing high school, completing high school, having some college
or higher, being foreign born, being born before 1931, and being born
after 1941. We also controlled for lack of health insurance and being on
Medicaid. Health status was described by the number of self-reported
health conditions (high blood pressure, diabetes, cancer, heart
problems, stroke, lung problems, and arthritis) and number of activities
of daily living (ADLs) (dressing, walking, bathing, eating, getting in
and out of bed, and using the toilet). Since severity of health
conditions may vary significantly across individuals and ethnicities, we
controlled for self-reported health with a binary variable for reporting
poor or fair health status. Controls for financial well-being were
measured by the log of household income in the HRS.
Analytic Approach
We estimated a multinomial logit model of the status of each
respondent by 2008 as a function of baseline
characteristics--characteristics when observed for the first time--of
those who are 51 and older across all groups of elderly combined and
later by race/ethnicity to determine whether predictive effects of
covariates vary across race/ethnicity. We also tested our model to
insure that it satisfies the Independence of Irrelevant Alternatives
assumption using the Hausman test (Hausman 1978; Long and Freese 2006).
Since the sample of Mexican Americans in the HRS includes only 168
attritors total across seven waves, we did not estimate wave-by-wave
determinants of transition across race/ethnicity due to significant loss
of power.
Table 1 reports summary statistics and sample sizes. Table 2
reports relative-risk ratios and 95 percent confidence intervals by
race/ethnicity, while Table 3 reports predicted probabilities for
selected variables based on race/ ethnic-specific models.
RESULTS
Table 1 provides summary statistics of the key variables, sample
size, and percent of people who died, ever were out, or were lost by
race and ethnicity from the HRS between 1992 and 2006. Compared to
whites and blacks, Latinos were younger, with fewer years of schooling,
more likely to be foreign born, and less likely to have health
insurance. Blacks and Latinos were more likely to be on Medicaid
compared to whites. Blacks had a greater number of health conditions,
while the number of ADLs was most prevalent among blacks and especially
Mexican Americans despite their younger age. According to Table 1, 22
percent of whites, 24 percent of blacks, and only 17 percent of Mexican
Americans died by 2006. Surprisingly, Mexican Americans had the lowest
percent of respondents being lost from the study.
Table 2 presents estimates for the multinomial logit model
predicting the relative-risk ratios for being dead, ever out, or being
an attritor by 2006 compared to being "always in" as a
function of baseline characteristics. The first column reports the
result for the combined sample of white, black, Latino, and other race
elderly populations. Consistent with the well-documented "Hispanic
paradox" literature, Hispanics faced a smaller probability of dying
compared to whites. Foreign born were also less likely to be attrited
due to death. As we would expect, lack of health insurance, greater
number of health conditions, and ADLs were associated with greater
probability of dying, while higher income protected from greater
mortality. Blacks, Latinos, those who were foreign born, and those
without health insurance were more likely to nonrespond in some waves;
that is, to be "ever out." Surprisingly, estimates of the
combined sample shows that Mexican Americans were less likely to attrite
from the study. On the other hand, foreign born were more likely to
attrite. As it was previously documented, those in poor health were more
likely to attrite than to remain in the study, although respondents
having an additional health condition or reporting being in fair/poor
health were more likely to die than to attrite.
Columns 2 through 5 report estimates of a similar model by
race/ethnicity. Foreign-born Latinos had a smaller probability of dying,
while lack of health insurance was associated with greater probability
of dying for whites and blacks but not for Mexican Americans and Latinos
in general. While the number of health conditions and self-reported
poor/fair health was associated with higher probability of dying across
all groups, the number of ADLs was significant only for whites and
African Americans. The number of health conditions and ADLs was a
significant predictor of being an attritor across all groups of aging
populations as well. Surprisingly, being foreign born was not
significant for Mexican Americans, while it was significant for others.
However, Mexican Americans with no health insurance had a greater
probability of dropping out of the study.
Interestingly, separate models demonstrated that chances of
attrition associated with health conditions were greater for Mexican
Americans than for white counterparts. That is, according to estimates,
Mexican Americans having an additional health condition had almost
similar chances of dying and dropping out of the study. However, white
elderly having an additional health condition had greater chances of
dying than being lost from the study. Different formatting (boldface,
italics, underlining) indicates whether relative-risk ratios for
becoming an attritor were statistically different from the relative-risk
ratios of dying. For whites and African Americans these ratios are
significantly different, while for Mexican Americans and Latinos in
general these ratios are statistically the same.
To facilitate interpretation of the results, Table 3 presents
predicted probabilities for selected variables based on
race/ethnic-specific model estimates that were applied to the whole
sample while setting a variable of interest to a particular value. Thus,
a foreign-born Latino had a 19.3 percent probability of being an
attritor and 15.5 percent probability of being dead, while for a
non-Latino white the corresponding probabilities were 15.9 and 20.4
percent. Across all groups of elderly, foreign birth was associated with
greater probability of attrition than death. Lack of health insurance
was associated with about 25 percent probability of death for whites and
blacks, while for Mexican Americans and Latinos these probabilities were
smaller. However, a Mexican American without health insurance had a 19
percent probability of becoming an attritor; for similar non-Latino
whites this probability was only 11.6 percent. Medicaid status was
associated with lower probability of attrition for all groups of
elderly. Having two health conditions led to significantly greater
probability of death relative to probability of attrition for non-Latino
whites. For Latinos and Mexican Americans these probabilities
statistically were the same.
DISCUSSION
This study compares models of attrition across different
racial/ethnic groups to demonstrate some implications of growing
diversity of the U.S. population for health-related research and assist
in planning of future studies of health disparities in aging
populations. Variables describing immigration status, insurance, and
health status differed in their prediction of attrition across minority
groups of elderly that may lead to biased estimates of the relationships
under investigation. Our findings suggest that work examining
racial/ethnic disparities in mortality, health decline, and disability
onset using the HRS was likely to underestimate health disparities due
to higher relative attrition of Latinos in poor health measured by the
number of health conditions and ADLs (Sudano and Baker 2006; Dunlop et
al. 2007). Sudano and Baker (2006) found that lack of health insurance
was responsible for small fraction of disparities in health outcomes
that could be explained by the lack of attrition adjustment in their
work. Similarly, our findings suggest that work debating about the
relationship between lack of health insurance and mortality is likely to
be sensitive to attrition process by health insurance status,
particularly for Mexican Americans (McWilliams et al. 2004; Kronick
2009). McWilliams et al. (2004) found that while the lack of health
insurance was associated with significantly higher mortality rate among
whites, there was no mortality differential for Hispanics referring to
Hispanic paradox as an explanation. Ethnic differences in the attrition
processes by health status and health insurance could explain their
finding as well. Due to growing ethnic diversity of the U.S. population,
researchers would be advised to pay a closer attention to potential
ethnic/ racial attrition differentials in health-related research.
This study has several limitations. The sample of Latinos in the
HRS was still small, forcing us to combine several cohorts to conduct
analysis. Due to the small sample of Latinos, we were unable to test
interactions of covariates and ethnic/racial binary variables to test
statistical significance of differences in the effects of covariates
across races/ethnicities. The small sample size problem may also have
been responsible for some of the nonsignificant results for Latinos. As
a share of minority population becomes more prevalent, findings of our
study can be validated in the future. Larger representation of the
minority population in surveys will allow additional testing of our
model.
To summarize, there are important differences in the attrition
process across different groups of aging minorities. Our study
indentified respondent characteristics that may improve attrition
adjustment in health-related research and guide future effort to retain
minority population in panel surveys.
ACKNOWLEDGMENTS
Joint Acknowledgment~Disclosure Statement: Natalia A Zhivan and
Alfonso Ang are supported by the Network for Multicultural Research on
Health and Healthcare, Department of Family Medicine, David Geffen
School of Medicine, UCLA, funded by the Robert Wood Johnson Foundation.
No conflict of interest exists. Each author has made substantive
intellectual contributions to the study.
Disclosures: None.
Disclaimers: None.
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SUPPORTING INFORMATION
Additional supporting information may be found in the online
version of this article:
Appendix SA1: Author Matrix.
Please note: Wiley-Blackwell is not responsible for the content or
functionality of any supporting materials supplied by the authors. Any
queries (other than missing material) should be directed to the
corresponding author for the article.
[Corrections made after online publication 9/23/2011: Some
highlighting that was in the submitted version of Table 2 of the article
did not appear in the originally published version. These highlights are
now included in the forms of bold, italic, and underlined text.] Address
correspondence to Natalia A. Zhivan, Ph.D., Department of Global Health
Systems and Development, School of Public Health and Tropical Medicine,
Tulane University, 1440 Canal Street, Suite 1900, New Orleans, LA 70112;
e-mail: nzhivan@tulane.edu. Alfonso Ang, Ph.D., is with the Department
of General Internal Medicine, University of California, Los Angeles, CA.
Hortensia Amaro, Ph.D., is with the Institute on Urban Health Research,
Bouve College of Health Sciences, Northeastern University, Boston, MA.
William A. Vega, Ph.D., is with the School of Social Work, University of
Southern California, Los Angeles, CA. Kyriakos S. Markides, Ph.D., is
with the Department of Preventive Medicine and Community Health,
University of Texas Medical Branch, Galveston, TX.
DOI: 10.1111/j.1475-6773.2011.01322.x
Table 1: Sample Size and Means for All the Variables by Race and
Ethnic Groups When the First Time Observed, Health and Retirement
Study (HRS) 1992-2004
Variable Whites Blacks
Age 64 62
Female (%) 55 60
Married (%) 72 50
Born outside the U.S. (%) 5 5
Less than high school (%) 24 47
Some college plus (%) 41 26
No insurance (%) 7 15
Medicaid (%) 3 15
Household income (U.S. $) 69,077 39,859
Household wealth (U.S.$) 329,79.5 91,819
No. of health conditions 1.14 1.43
No. of ADLs 0.23 0.42
Birth cohort (%)
Born before 1931 44 34
Born 1931-1941 34 41
Born 1941-19,54 22 25
Final status (%)
Always in 56 49
Died 22 24
Ever out 8 14
Attritors 13 13
No. of observations 21,845 4,367
Variable Mex. Anger. Latinos, All
Age 60 60
Female (%) 54 56
Married (%) 72 67
Born outside the U.S. (%) 44 56
Less than high school (%) 73 65
Some college plus (%) 13 18
No insurance (%) 32 28
Medicaid (%) 16 17
Household income (U.S. $) 42,471 41,751
Household wealth (U.S.$) 114,910 116,693
No. of health conditions 1.03 1.04
No. of ADLs 0.45 0.42
Birth cohort (%)
Born before 1931 28 30
Born 1931-1941 39 39
Born 1941-19,54 33 31
Final status (%)
Always in 57 55
Died 17 15
Ever out 15 16
Attritors 11 14
No. of observations 1,492 2,487
Notes. About 70 percent of respondents entered the HRS in Waves 1 and
2 across all ethnic and racial groups, the rest of the respondents
were allowed to enter between Waves 3 and 7 to maxi mize the size of
the minority population. Samples of whites, Mexican Americans,
Latinos, and blacks have 1.5, 2..5, 2.2, and 2.4 percent of
observations with missing values for one or more vari ables,
respectively, and were excluded from the further analysis. ADL,
activities of daily living.
Table 2: Multinomial Lop-it Model, Base Outcome "Always in," Health
and Retirement Study (HRS) All 51+
All Whites
Variable RRR/CI RRR/CI
Died
Black 1.076 --
[0.972,1.1901 --
Mexican American 0.801 ** --
[0.666,0.964] --
Latino, other 0.700 *** --
[0.550,0.889] --
Born outside the U.S. 0.855 ** 0.932
[0.741,0.9861 [0.771,1.126]
No insurance 1.270 *** 1.296 ***
[1.105,1.459] [1.084,1.550]
Medicaid 1.078 1.053
[0.936,1.242] [0.850,1.304]
Log of household 0.932 *** 0.898 ***
income [0.906,0.959] [0.864,0.932]
No. of health 1.313 *** 1.301 ***
conditions [1.271,1.357] [1.253,1.352]
Number of ADLs 1.139 *** 1.157 ***
[1.092,1.187] [1.096,1.221]
Poor/fair self- 1.825 *** 1.972 ***
reported health [1.676,1.988] [1.782,2.181]
Ever out
Black 1.748 *** --
[1.564,1.953] --
Mexican American 1.382 *** --
[1.150,1.661] --
Latino, other 1.647 *** --
[1.328,2.042] --
Born outside the U.S. 1.325 *** 1.395 ***
[1.145,1.533] [1.119,1.738]
No insurance 1.281 *** 1.324 ***
[1.125,1.457] [1.108,1.583]
Medicaid 0.900 1.287
[0.741,1.092] [0.948,1.749]
Log of household 0.961 ** 0.952 **
income [0.932,0.991] [0.907,0.998]
No. of health 0.991 0.978
conditions [0.948,1.035] [0.926,1.033]
Number of ADLs 0.976 0.983
[0.915,1.041] [0.897,1.076]
Poor/fair self- 1.117 * 1.225 ***
reported health [0.997,1.250] [1.056,1.421]
Attritors
Black 0.996 --
[0.891,1.1141 --
Mexican American 0.731 *** --
[0.601,0.891] --
Latino, other 1.119# --
[0.902,1.388]# --
Born outside the U.S. 1.401# *** 1.257# **
[1.221,1.608]# [1.036,1.524]#
No insurance 1.004# 0.913#
[0.875,1.153]# [0.760,1.097]#
Medicaid 0.758# *** 0.938
[0.639,0.898]# [0.732,1.201]
Log of household 0.967~ ** 0.948@ **
income [0.938,0.998]~ (0.909,0.988]@
No. of health 1.188# *** 1.178# ***
conditions [1.145,1.233]# [1.128,1.229]#
Number of ADLs 1.139# *** 1.144 ***
[1.087,1.194] [1.077,1.214]#
Poor/fair self- 1.312 *** 1.459# ***
reported health [1.191,1.446] [1.299,1.638]
Pseudo R2 0.1413 0.1433
No. of Obs. 28,523 21,286
Blacks Mex. Amer.
Variable RRR/CI RRR/CI
Died
Black -- --
-- --
Mexican American -- --
-- --
Latino, other -- --
-- --
Born outside the U.S. 0.806 0.647 **
[0.508,1.278] [0.449,0.931]
No insurance 1.485 *** 1.095
[1.113,1.982] [0.685,1.751]
Medicaid 1.460 *** 0.829
[1.135,1.877] [0.520,1.3201
Log of household 0.976 0.974
income [0.915,1.040] [0.879,1.079]
No. of health 1.304 *** 1.422 ***
conditions [1.201,1.415] [1.208,1.673]
Number of ADLs 1.163 *** 1.149 *
[1.066,1.269] [0.982,1.345]
Poor/fair self- 1.497 *** 1.548 **
reported health [1.219,1.839] [1.055,2.271]
Ever out
Black -- --
-- --
Mexican American -- --
-- --
Latino, other -- --
-- --
Born outside the U.S. 1.518 ** 1.242
[1.041,2.214] [0.878,1.756]
No insurance 1.431 *** 1.148
[1.091,1.878] [0.787,1.675]
Medicaid 1.100 0.429 ***
[0.788,1.536] [0.233,0.791]
Log of household 1.038 0.918 **
income [0.964,1.118] [0.855,0.986]
No. of health 0.963 1.198 **
conditions [0.872,1.062] [1.007,1.427]
Number of ADLs 0.98.5 0.936
[0.869,1.117] [0.763,1.148]
Poor/fair self- 0.841 1.423 *
reported health [0.661,1.071] [0.988,2.049]
Attritors
Black -- --
-- --
Mexican American -- --
-- --
Latino, other -- --
-- --
Born outside the U.S. 2.129# *** 1.306#
[1.434,3.160]# [0.881,1.935]#
No insurance 1.079~ 1.688 **
[0.769,1.514]~ [1.065,2.674]
Medicaid 0.833# 0.765
[0.600,1.157]# [0.431,1.3.57]
Log of household 1.043 0.923 *
income [0.957,1.136] [0.849,1.002]
No. of health 1.179 *** 1.449 ***
conditions [1.069,1.301] [1.205,1.742]
Number of ADLs 1.174 *** 1.216 **
[1.060,1.301]# [1.018,1.452]#
Poor/fair self- 1.064# 0.820#
reported health [0.832,1.360] [0.534,1.257]
Pseudo R2 0.1357 0.1.553
No. of Obs. 4,225 1,427
Latinos, All
Variable RRR/CI
Died
Black --
--
Mexican American --
--
Latino, other --
--
Born outside the U.S. 0.764 *
[0.582,1.002]
No insurance 0.976
[0.655,1.455]
Medicaid 0.971
[0.681,1.386]
Log of household 1.001
income [0.923,1.085]
No. of health 1.408 ***
conditions [1.235,1.604]
Number of ADLs 1.079
[0.954,1.221]
Poor/fair self- 1.483 **
reported health [1.095,2.009]
Ever out
Black --
--
Mexican American --
--
Latino, other --
--
Born outside the U.S. 1.237 *
[0.966,1.584]
No insurance 1.023
[0.764,1.369]
Medicaid 0.567 ***
[0.369,0.869]
Log of household 0.947 *
income [0.896,1.001]
No. of health 1.092
conditions [0.955,1.250]
Number of ADLs 0.974
[0.842,1.127]
Poor/fair self- 1.188
reported health [0.895,1.576]
Attritors
Black --
--
Mexican American --
--
Latino, other --
--
Born outside the U.S. 1.610# ***
[1.230,2.107]#
No insurance 1.291
[0.931,1.790]
Medicaid 0.559 ***
[0.370,0.844]
Log of household 0.998
income [0.934,1.065]
No. of health 1.304 ***
conditions [1.140,1.493]
Number of ADLs 1.152 **
[1.012,1.310]#
Poor/fair self- 0.854#
reported health [0.630,1.158]
Pseudo R2 0.1313
No. of Obs. 2,388
Note: Estimates are not reported for female, age, age squared,
married, educational attainment variables, cohort variables, and
variable for "other" race. Formatting indicates whether relative-risk
ratios for being an attritor are statistically significantly different
from the relative-risk ratio of dying: boldface: statistically
significant at 0.01 level; italic: statistically significant at 0.05
level; underlining: statistically significant at 0.1 level. Hausman
test does not reject the IIA for the samples of Mexican Americans,
Latinos including Mexican Americans, blacks, and for the sample of
white and pooled sample for the "Dead" and "Ever out" categories and
rejects hypothesis for the "Attritors" category for the pooled and
white samples.
*** Statistically significant at 0.01 level;
** Statistically significant at 0.05 level;
* Statistically significant at 0.1 level.
ADL, activities of daily living.
Note:
Boldface: statistically
significant at 0.01 level is indicated with #.
Italic: statistically significant at 0.05
level is indicated with @.
Underlining: statistically significant at 0.1 level
is indicated with ~.
Table 3: Predicted Probabilities for Selected Variables Based on Race/
Ethnic-Specific Multinomial Logit Models
Race/Ethnicity Always in (%) Died (%)
Born outside the U.S.
White non-Latino 52.4 20.4
African Americans 43.1 15.6
Mexican Americans 52.1 13.6
Latinos, all 48.6 15.5
No insurance
White non-Latino 52.2 25.7
African Americans 44.0 25.4
Mexican Americans 48.3 17.9
Latinos, all 49.7 18.9
Medicaid
White non-Latino 53.9 22.8
African Americans 47.4 27.0
Mexican Americans 61.0 19.3
Latinos, all 58.4 22.8
Having two health conditions
White non-Latino 52.6 24.6
African Americans 47.6 24.0
Mexican Americans 48.3 20.8
Latinos, all 47.9 22.5
Poor/fair self-reported health
White non-Latino 48.2 28.0
African Americans 48.0 26.0
Mexican Americans 49.6 22.4
Latinos, all 49.2 23.9
Race/Ethnicity Ever Out (%) Attritors (%)
Born outside the U.S.
White non-Latino 11.3 15.9 ***
African Americans 18.8 22.5 ***
Mexican Americans 17.0 17.3 ***
Latinos, all 16.6 19.3 ***
No insurance
White non-Latino 10.5 11.6 ***
African Americans 18.1 12.5 *
Mexican Americans 14.9 19.0
Latinos, all 14.8 16.6
Medicaid
White non-Latino 10.6 12.7
African Americans 15.1 10.5 ***
Mexican Americans 7.6 12.2
Latinos, all 10.0 8.8 **
Having two health conditions
White non-Latino 8.3 14.6 ***
African Americans 13.8 14.5
Mexican Americans 15.0 15.9
Latinos, all 14.6 15.0
Poor/fair self-reported health
White non-Latino 8.9 14.9 ***
African Americans 12.7 13.3 ***
Mexican Americans 17.0 10.9 ***
Latinos, all 15.8 11.2 ***
Notes. Statistical significance indicates whether particular variable
is a significant predictor of being an attritor compared to being dead
and is based on Table 2. To obtain predicted probabilities, estimates
from race/ethnic specific multinomial logit models were sequentially
applied to the entire sample of 28,.523 individuals while the variable
of interest was set to one (for born outside the U.S., no insurance,
Medicaid, and poor/fair health) or two (no. of health conditions).
*** statistically significant at 0.01 level;
** statistically significant at 0.05 level;
* statistically significant at 0.1 level.
ADL, activities of daily living.