Instrumental Activities of Daily Living (IADL), Religiosity and
Depressive Symptoms among elderly Mexican Americans
Veronika N. Stiles
Biostatistics 524
Data Analytic Project
University of Michigan Clinical Research Design and Statistical Analysis Program
May 10, 2012
INTRODUCTION
Depression is a major health problem for older adults.1 An extensive body of research suggests that
Mexican-Americans report greater depressive symptomotology compared to non-Hispanic Whites and
African Americans.2,3,4 Literature review has provided the number of demographic and social factors
identified consistently in previous research as predictors of depressive symptoms in Mexican- Americans
including but not limited to low formal education, low income, immigration status/country of birth, and
religiosity. 5,6 Specifically in regards to religiosity, patients who frequently use religion to cope with
stress have the lowest level of cognitive symptoms of depression.
7
Health-related identified predictors for elevated depressive symptoms include diabetes mellitus,
hypertension, hip fracture, pulmonary disease, significant breathing problems, cancer, arthritis,
stomach ulcers, kidney disease, urinary and bladder incontinence.
8,9,10,11
All of these health-related
predictors significantly impact the instrumental activities of daily living (IADL), which for the purposes of
this project defined as driving, preparing meals, doing housework, shopping, managing finances,
managing medication, and using the telephone.
Primarily, this data analytic project aims to explore the association between instrumental activities of
daily living, religiosity and depression among elderly Mexican-Americans and to identify the factors that
might compound this association. Secondarily, whether or not the effect of IADL based on gender or
nativity will be explored in this project.
METHODS
Sample
The sample consists of 1,682 elderly Mexican-American subjects.
Measures
Dependent variable- Depressive symptoms were measured with Center for Epidemiological Studies
Depression Scale (CES-D), the most widely used depression scale in studies with older adults. Higher
scores indicate greater levels of depressive symptoms. The CES-D scores range form 0-60. Previous
research studies have used score greater or equal to 16 as a dichotomous indication of high depressive
symptoms. 2 However, for the purposes of this project CES-D scores will be predicted by the regression
models as a continuous variable.
Independent variables- The main independent variables of interest are the Instrumental Activities of
Daily Living (IADL) with potential range of 0-10; higher scores indicating a greater number of IADL
problems) and religiosity (1=very religious; 2=moderately religious; 3=neutral; 4=not religious at all).
Covariates—To account for potential confounding variables, the following health-related variables that
might be associated with depressive symptoms were controlled for : hypertension, as well as scores on
Mini Mental Status Exam (MMSE) and scores for Activities of Daily Living (ADL).
Demographics-Demographic characteristic variables that were controlled for included gender (1=male;
0=female); nativity (US born; 1=born in the US; 0=not born in the US); highest grade completed, and
marital status.
Statistical Analyses
All analyses were performed using SAS 9.3 (SAS Institute, Cary, N.C.) to adjust for complex sample
design. Descriptive statistics and identification of outliers and normality of each variable distribution
were performed before bivariate and multiple linear regression analyses. Upon data examination using
histograms and quantile-quantile plots, the variables of grade, IADL, ADL were skewed to the right, and
the variable MMSE was skewed to the left. A major outlier of 999 was identified for the outcome
variable of CES-D total. These variables were converted to their natural logarithmic values to normalize
the skewed distributions and to address the issue of outliers.
Upon univariate data analysis, it was noted that the following data was missing: missing data for grade
(n=25), missing data for number of living sons/daughters (n=9), missing data for high blood pressure
(n=14), missing data for diabetes (n=3), missing data for hip fracture (n=2), missing data for high
cholesterol (n=45), missing data for mini mental status exam (MMSE) (n=93), missing data for CES-D
score (n=120), missing data for IADL (n=10), missing data for activities of daily living (ADL) (n=17),
missing data for life satisfaction (n=161), missing data for religiosity (n=153), and missing data if
respondent is bedridden (n=108). A comparison of participants with missing data to participants with
deleted missing values (Table 1, 2) showed no meaningful differences with respect to the following
continuous variables: grade, age, number of living children, health, MMSE, CES-D score, IADL, ADL, and
religiosity. Missing data for the categorical variables of hypertension equals to 0.83% of all
observations; diabetes equals to 0.18% of all observations; hip fracture equals to 0.12% of all
observations; high cholesterol equals to 2.68% of all observations; and missing data if the patient is
bedridden equals to 6.42% of all observations. The latter is the only significant percentage of missing
data from the group. This fact should be noted in the limitations section of the paper if the investigators
decided to omit all missing values. By default, SAS procedures handle missing values by omitting missing
data. Another way of approaching missing value issue is by imputing missing values using best subset
regression. However, this approach is beyond the scope of current project.
In this project, the following associations were tested:
1) IADL and CES-D controlling for age, subject’s nativity, marital status, grade, gender; MMSE, ADL; and
HBP, diabetes and hip fracture;
2) Religiosity and CES-D controlling for for age, subject’s nativity, marital status, grade, gender; MMSE,
ADL; and HBP, diabetes and hip fracture;
3) IADL, religiosity and CES-D controlling for subject’s nativity, marital status, grade, gender; MMSE, ADL;
and HBP;
4) Interaction effect of MMSE and religiosity on CES-D (rationale: previous research suggests that
depressive symptoms have been reported as a risk factor for cognitive decline and church attendance
has been associated with fewer depressive symptoms.) 12
In bivariate analyses of the continuous covariates, only age (P=0.0019), total score on MMSE, IADL, ADL
(transformed; P<.0001) were significantly associated with CES-D scores (transformed). Highest grade
completed was not significantly associated with CES-D (P=0.2739).
Simple linear regression was performed to evaluate for any significant associations between CES-D score
and subject’s nativity, marital status, gender, religiosity; diagnosis of high blood pressure, diabetes, and
hip fracture. Subject nativity (P=0.0092), marital status (P<0.0001), male (P<0.0001), religiosity
(P=0.0008), HBP (P<0.0001), diabetes (P=0.0153) were significantly associated with CES-D scores. Hip
fracture was not significantly associated with CES-D (P=0.1618).
In a multiple regression analysis, depressive symptoms (CES-D) were regressed on IADL (Table 3, Model
1) and religiosity (Table 4, Model 2) separately, adjusting for age, subject’s nativity, marital status, grade,
gender; MMSE, ADL; and HBP, diabetes and hip fracture. Model 3 (Table 5) included both IADL and
religiosity as predictors, adjusting for relevant risk factors. Model 4 (Table 6) presented MLR parameter
estimates of CES-D scores as a function of MMSE, religiosity and their interaction term.
Using an automated model building method of stepwise selection, final fully adjusted model is obtained.
Model diagnostics is performed consequently to check for model assumptions.
Assumptions for multiple linear regression models include:
1) Residuals follow the Normal distribution. Analysis of residuals by regressors indicates that
distribution is skewed to the right for the log_totmmse4 regressor only. Hypertension, gender, marital
status and nativity are categorical variables.
2) Constant variance assumption. There is a slight fan-shape noted amongst the residuals of the
log_totmmse regressor.
3) True relationship is linear. The assumption appears to hold true. Results of this analysis are
presented in Table 8.
4) To check for the presence of multicollinearity, the variance inflation factors (VIFs) were measured and
all of them were less than 2, well below the threshold (VIF=10) that would indicate a problem of
multicollinearity in a multivariate regression model. Therefore, the assumption of independence of
each observation from all others holds true for the purposes of this analysis.
When testing whether or not the effect of the log_ IADL on log_CESD varies by gender or nativity,
multiple linear regression techniques were used.
Results
In an unadjusted model predicting CES-D by IADL, the estimated change in mean log CES-D for a 1 unit
increase in IADL is 0.36 (P<.0001, SE=0.02731; 95% CI=0.31134; 0.41849).In an unadjusted model
predicting CES-D by the religiosity, the estimated change in mean log CES-D for a 1 unit increase in
religiosity is 0.15 (P=0.0008, SE=0.04576; 95% CI=0.06346; 0.24299). Unadjusted models are not shown
in this analysis.
Regression Model 1 (Table 3) presents beta coefficients for the prediction of continuous log_CES-D
scores by log_IADL, adjusting for age, nativity, marital status, grade completed, gender, MMSE scores,
ADL scores, hypertension, diabetes, and hip fracture.
Similarly, Model 2 indicates a significant
association between religiosity and log_CES-D scores with adjustments for relevant individual factors. In
Model 3 with both transformed IADL and religiosity included in the model, there was an estimated
change in mean log_CES-D score for a 1 unit increase in log_IADL of 0.27 (SE=0.35; P<0.0001; 95% CI
(0.19791; 0.33252). There was an estimated change in mean log_CES-D score for 1 a unit increase in
religiosity of 0.19 (SE=0.04; P<.0001; 95% CI (0.10496; 0.27324). Additional analysis results for testing
the interaction effect between log_totalMMSE and religiosity as an additional predictor variable
reported in Model 4 indicates a non-significant association (P =0.9729). Model 5 is the summary of the
stepwise selection automated model building method. It is, however, the same model that was
obtained by the multiple linear regression model reported as Model 3, excluding the non-significant
findings. Variables age (P=0.2361), diabetes (P=0.9845) and hip fracture (P=0.8865) were excluded from
further analysis. Final Multiple Regression Model for the continuous log_CES-D scores with 95%
confidence intervals is reported in Table 9.
Overall, R2 for the final fully adjusted model is 0.1718. The adjusted R2 for the model is 0.1665. In this
model, only 16.65% of the variation in log_CES-D scores that is collectively explained by the log_IADL
scores and the level of religiosity, adjusting for the relevant individual factors.
To answer the secondary question of whether or not the effect of log_IADL varies by gender or nativity
(adjusting for relevant individual factors), multiple linear regression technique was used to perform this
analysis. In the analysis of gender (1=male, 0=female), “male 1” was used as a reference category. As a
result, there was significant difference between males and females (P=0.0023, SE=0.06969, 95% CI
(0.075919; 0.34936). In the analysis of nativity (0=not born in the US, 1=born in the US), the difference
was also significant (P=0.0055, SE=0.0621; 95% CI (0.050986375; 0.294739). In this case, “US born 1”
was used as a reference category.
Conclusion
The fully adjusted model from this data analytic project generally echoed the findings from the previous
research. Having one or more IADL limitations was significantly associated with depressive symptoms in
previous research. Church attendance, on the other hand, has been reported to be associated with
fewer depressive symptoms.12 Surprisingly, diabetes was not a part of the final model, even though
previous literature reports it as a highly prevalent disease in Mexican Americans and has been found to
be associated with increased rates of depressive symptomotology.2 In conclusion, an increased
understanding of depressive symptoms as a function of IADL limitations and level of religiosity can
improve clinical practices and public health policies for Mexican American populations. However, the
practical application and the clinical significance of estimated changes in the mean log_CES-D scores as a
function of log_IADL scores and the level of religiosity should be questioned and examined further in
future studies.
Table 1. Characteristics of Participants with Missing Data Variables (N=1,682)
Variable
Label
N
Mean
Std Dev
GRADE
GRADE
1657
4.9185275
3.8973856
USBORN
USBORN
1682
0.5760999
0.4943218
AGE4
AGE4
1682
79.1159334
5.7347886
MARSTAT4
MARSTAT4
1682
2.5891795
1.5155594
NKIDS4
NKIDS4
1673
4.7866109
3.3231841
HEALTH4
HEALTH4
1682
2.7110583
0.8433434
KHYPER41
KHYPER41
1668
0.5137890
0.4999597
MDIAB41
MDIAB41
1679
0.2858845
0.4519692
NFRAC41
NFRAC41
1680
0.0333333
0.1795589
U43S
U43S
1637
0.2700061
0.4440983
TOTMMSE4
TOTMMSE4
1589
20.9351794
7.0774913
CESDTOT4
CESDTOT4
1562
7.7564981
26.2136258
TOTIADL4
TOTIADL4
1672
2.6519139
3.4573068
TOTADL4
TOTADL4
1665
0.9615616
2.0119146
CC43
CC43
1521
1.7343853
0.7410393
EE46
EE46
1529
1.8757358
0.7210330
HHA4
HHA4
1574
0.0349428
0.1836934
OO49LANG
OO49LANG
1682
0.1545779
0.3616093
MALE
MALE
1682
0.3846611
0.4866598
Table 2 Characteristics of Participants with Deleted Missing Data (N=1,420)
Variable
GRADE
AGE4
NKIDS4
HEALTH4
TOTMMSE4
Label
N
Mean
Std Dev
GRADE
1420
5.0147887
3.9178212
AGE4
1420
78.5176056
5.2796737
NKIDS4
1420
HEALTH4
1420
TOTMMSE4
1420
4.7802817
2.6464789
3.3192211
0.8193108
22.0500000
5.5818246
CESDTOT4
TOTIADL4
TOTADL4
CC43
EE46
CESDTOT4
TOTIADL4
TOTADL4
CC43
EE46
1420
7.4521127
27.3298284
1420
2.0971831
2.9983069
1420
0.6521127
1.6047287
1420
1.7204225
0.7337662
1420
1.8732394
0.7188115
Table 3 Regression Model 1 predicting continuous CES-D scores by IADL adjusting for relevant risk factors (N=1420)
Parameter Estimates
Variable
Label
Intercept
Intercept
log_totIADL4
DF Parameter Standard t Value Pr > |t|
Estimate
Error
1
2.89260
0.59010
4.90 <.0001
1
0.26871
0.03527
7.62 <.0001
AGE4
AGE4
1
-0.00783
0.00633
-1.24 0.2164
USBORN
USBORN
1
-0.17585
0.06267
-2.81 0.0051
MARSTAT4
MARSTAT4
1
0.05009
0.02244
2.23 0.0258
1
0.13376
0.03147
4.25 <.0001
1
-0.15154
0.06921
-2.19 0.0287
log_totmmse4
1
-0.39993
0.08180
-4.89 <.0001
log_totadl4
1
0.09470
0.04848
1.95 0.0510
log_grade
MALE
MALE
KHYPER41
KHYPER41
1
0.26904
0.06267
4.29 <.0001
MDIAB41
MDIAB41
1
0.01336
0.07073
0.19 0.8502
NFRAC41
NFRAC41
1
0.00556
0.18771
0.03 0.9764
Table 4 Regression model 2 predicting continuous CES-D scores by religiosity adjusting for relevant risk factors (N=1,420)
Variable
Label
Intercept
Intercept
1
2.29349
0.60490
3.79 0.0002
religiosity
EE46
1
0.18241
0.04387
4.16 <.0001
AGE4
AGE4
1 0.00076258
0.00633
0.12 0.9042
USBORN
USBORN
1
-0.20783
0.06341
-3.28 0.0011
MARSTAT4
MARSTAT4
1
0.05767
0.02273
2.54 0.0113
1
0.11624
0.03193
3.64 0.0003
1
-0.26431
0.07096
-3.72 0.0002
log_totmmse4
1
-0.47590
0.08213
-5.79 <.0001
log_totadl4
1
0.29553
0.04163
7.10 <.0001
log_grade
MALE
MALE
DF Parameter Standard t Value Pr > |t|
Estimate
Error
KHYPER41
KHYPER41
1
0.28213
0.06355
4.44 <.0001
MDIAB41
MDIAB41
1
0.06793
0.07124
0.95 0.3405
NFRAC41
NFRAC41
1
0.08553
0.19028
0.45 0.6531
Table 5 Regression model 3 predicting continuous CES-D scores by IADL and religiosity adjusting for related risk factors (N=1420)
ri
Parameter Estimates
Variable
Label
Intercept
Intercept
log_totiIADL4
DF Parameter Standard t Value Pr > |t|
Estimate
Error
1
2.49831
0.59321
4.21 <.0001
1
0.27136
0.03505
7.74 <.0001
religiosity
EE46
1
0.18816
0.04299
4.38 <.0001
AGE4
AGE4
1
-0.00746
0.00630
-1.19 0.2361
USBORN
USBORN
1
-0.17567
0.06227
-2.82 0.0048
MARSTAT4
MARSTAT4
1
0.04926
0.02229
2.21 0.0273
1
0.12630
0.03131
4.03 <.0001
1
-0.20680
0.06992
-2.96 0.0032
log_totmmse4
1
-0.38120
0.08139
-4.68 <.0001
log_totadl4
1
0.09704
0.04818
2.01 0.0442
log_grade
MALE
MALE
KHYPER41
KHYPER41
1
0.25241
0.06238
4.05 <.0001
MDIAB41
MDIAB41
1
-0.00136
0.07036
-0.02 0.9845
NFRAC41
NFRAC41
1
0.02663
0.18657
0.14 0.8865
Table 6 Regression Model 4 parameter estimates of CES-D scores as a function of MMSE, religiosity and their interaction
Parameter Estimates
Variable
Label
Intercept
Intercept
DF Parameter Standard t Value Pr > |t|
Estimate
Error
1
1.89882
0.68741
2.76 0.0058
1
-0.37428
0.21943
-1.71 0.0883
1
0.17825
0.32223
0.55 0.5802
MMSE*religiosity
1
0.00353
0.10395
0.03 0.9729
log_totiadl4
1
0.26517
0.03436
7.72 <.0001
log_totmmse4
religiosity
EE46
USBORN
USBORN
1
-0.17274
0.06226
-2.77 0.0056
MARSTAT4
MARSTAT4
1
0.04416
0.02189
2.02 0.0438
1
0.12854
0.03120
4.12 <.0001
1
-0.21271
0.06975
-3.05 0.0023
1
0.09444
0.04796
1.97 0.0491
1
0.25742
0.06147
4.19 <.0001
log_grade
MALE
MALE
log_totadl4
KHYPER41
KHYPER41
Table 7 Summary of Stepwise Selection: Final Model 5. Multiple Regression Parameter Estimates of log_CES-D as a Function of the Included
Predictors (N=1,420)
Step Variable
Entered
Variable
Label
Removed
Number Partial
Model
C(p)
F Value Pr > F
Vars In
R-Square R-Square
1
0.1118
0.1118
96.0271 178.52 <.0001
2
0.0133
0.1252
75.3278 21.60
<.0001
1
log_totiadl4
2
KHYPER41
3
log_totmmse4
3
0.0117
0.1369
57.3781 19.22
<.0001
4
log_grade
4
0.0093
0.1462
43.5287 15.43
<.0001
5
EE46
EE46
5
0.0082
0.1543
31.6255 13.66
0.0002
6
MALE
MALE
6
0.0085
0.1629
19.0905 14.41
0.0002
7
USBORN
USBORN
7
0.0042
0.1671
13.9193 7.14
0.0076
8
MARSTAT4
MARSTAT4 8
0.0024
0.1695
11.8829 4.03
0.0449
9
log_totadl4
9
0.0023
0.1718
10.0000 3.88
0.0490
KHYPER41
Table 8 Partial Regressor Residuals as a Diagnostic Tool to Check Linearity (Functional Form)
Partial Regression Residual Plot
Table 8 Partial Regressor Residuals as a Diagnostic Tool to Check Linearity (Functional Form)
Table 9 Final Multiple Regression Model with 95% Confidence Intervals Reported
Variable
Label
Intercept
Intercept
log_totiadl4
DF Parameter Standard
t
Estimate
Error Value
Pr >
|t|
95% Confidence
Limits
1
1.87747
0.27832
6.75 <.0001
1.33151 2.42344
1
0.26522
0.03431
7.73 <.0001
0.19791 0.33252
EE46
EE46
1
0.18910
0.04289
4.41 <.0001
0.10496 0.27324
USBORN
USBORN
1
-0.17286
0.06213
-2.78 0.0055
-0.29474 -0.0509
MARSTAT4
MARSTAT4
1
0.04418
0.02187
2.02 0.0436
0.00127 0.08708
1
0.12851
0.03117
4.12 <.0001
0.06735 0.18966
1
-0.21264
0.06970
-3.05 0.0023
-0.34936 -0.0759
log_totmmse4
1
-0.36734
0.08038
-4.57 <.0001
-0.52502 -0.2096
log_totadl4
1
0.09436
0.04788
1.97 0.0490 0.0004236 0.18829
1
0.25746
0.06144
4.19 <.0001
log_grade
MALE
KHYPER41
MALE
KHYPER41
0.13695 0.37798
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