Item Bias in the Center for Epidemiologic Studies Depression Scale: Effects of Physical Disorders and Disability in an Elderly Community Sample

Journal of Gerontology: PSYCHOLOGICAL SCIENCES 2000, Vol. 55B, No. 5, P273 P282 Copyright 2000 by The Gerontological Society of America Item Bias in the Center for Epidemiologic Studies Depression Scale: Effects of Physical Disorders and Disability in an Elderly Community Sample D. A. Grayson, 1 A. Mackinnon, 2 A. F. Jorm, 3 H. Creasey, 1 and G. A. Broe 1 1 Centre for Education & Research on Ageing of the University of Sydney, at RGH Concord, Sydney, Australia. 2 Mental Health Research Institute of Victoria and Department of Psychological Medicine, Monash University, Melbourne, Australia. 3 Psychiatric Epidemiology Research Centre at the Australian National University, Canberra, Australia. The Center for Epidemiologic Studies Depression Scale (CES-D) is frequently used in studies of elderly individuals. One controversy regarding its use turns on the issue of whether the effect of physical disorder on the CES-D total score reflects genuine effects on depression or item-level artifacts. The present article addresses this issue using medical examination data from 506 community-dwelling individuals aged 75 or older. A form of structural equation modeling, the MIMIC model, is used, enabling the effect of a physical disorder on CES-D total score to be partitioned into bias and genuine depression components. The results show substantial physical disorderrelated artifacts with the CES-D total score. Caution is required in the use of CES-D (and possibly other) depression scales in groups in which physical disorders are present, such as in elderly individuals. T HE Center for Epidemiologic Studies Depression Scale (CES-D; Radloff, 1977) has become a standard measure of depressive symptomatology in elderly persons. The CES-D has demonstrated good internal consistency, test retest reliability, concurrent validity on clinical and self-report criteria, and construct validity. It correlates well with clinical ratings of depression (Roberts & Vernon, 1983; Weissman, Sholomskas, Pottenger, Prusoff, & Locke, 1977). A large number of studies using the CES-D have attempted to define the trajectory of depressive symptomatology over the adult lifespan. Recent reviews of studies using the CES-D, other inventories, and diagnostic classifications have reached opposing conclusions: Bodies of evidence are available to support increasing, decreasing, and stable levels of depression as a function of age (Jorm, 2000). Studies using the CES-D that exemplify this diversity include those finding no change in the prevalence of high CES-D scores as function of age (Eaton & Kessler, 1981), those finding decreasing prevalence of high scores with age (Comstock & Helsing, 1976; Frerichs, Aneshensel, & Clark, 1981), and those reporting a curvilinear relationship, with lowest scores in middle-aged individuals and higher scores in younger and older adults (Gatz & Hurwicz, 1990; Mirowsky & Ross, 1992). A similar curvilinear function was derived using pooled data from several studies covering parts of the adult life span (Newmann, 1989). Before valid comparisons can be made between groups that differ in age or any other attribute, it must be established that members of each group respond to the items in the inventory in a comparable manner. The same holds for longitudinal studies in which there must be confidence that the items are responded to in comparable ways on different measurement occasions. Expressed formally, this requires that persons who are at the same location on the underlying trait or attribute measured by the inventory respond to an item in the same way, regardless of any other characteristic they may have. Items that do not have this property are said to be biased or to exhibit differential item functioning (DIF). The identification of DIF in tests of intellectual ability has long been recognized as an important task in constructing and refining instruments. Surprisingly little recognition of the potential importance of DIF is found in the construction of measures of personality, attitudes, and symptomatology. There is, however, growing evidence of possible bias associated with increasing age and physical disorder in selfreport measures of depressive symptomatology such as the CES-D. Debate has centered on whether the inclusion of somatic symptoms may artifactually raise depression scores because of higher rates of physical disorder and effects of medications among older persons (Berry, Storandt, & Coyne, 1984; Blumenthal, 1975; Downes, Davies, & Copeland, 1988; Simons et al., 1992; Steuer, Olsen, & Jarvik, 1980). Naïve attempts to identify DIF in items involve the comparison of item-response distributions between groups. This is inappropriate, because there may be an actual association between the construct or latent variable measured and the purported biasing variables. Evidence of this type of effect arose from a study showing that physical disabilities were associated with all items in the CES-D, not just the somatic items, and that age was associated with few depressive symptoms except insofar as the older person was disabled (Berkman et al., 1986). A formal model is needed of the effects of bias variables on individual items and on the latent variable they measure. Without such a model, it is impossible to disaggregate the effects of bias variables on an item into components be- P273

P274 GRAYSON ET AL. cause of their relationship with depression and bias. Item response theory (IRT) provides one such model (Hambleton & Swaminathan, 1985). This approach was used in comparing item parameters of a depression inventory in a sample of elderly individuals with those obtained in a younger group (Mackinnon et al., 1994). These researchers found substantial differences in both the discriminating power of items and also the degree of severity of depression at which the item functioned. A limitation of approaches to DIF on the basis of IRT parameters is that estimates of parameters in each group of interest are required. This may involve arbitrary divisions of continuous variables such as age. The comparison of more than two groups is difficult, and the investigation of multiple, correlated sources of bias is impractical. A particular form of a structural equation model the Multiple Indicators, Multiple Causes (MIMIC) Model can be used to investigate item bias (Muthén, 1988). The model, as applied to the CES-D, is illustrated in Figure 1. The core of the model consists of a measurement model for the CES-D, with all 20 items loading on a single factor representing the latent depression variable. The demographic, disability, and physical disorder covariates that are investigated as potential sources of bias in this study are included in the model as predictors of the latent variable. This part of the model may be viewed as a multiple regression of the latent variable against the covariates. In addition to estimating the effect of the covariates on the latent variable ( ), the model allows the effects of the covariates on each of the CES-D items to be assessed directly. The differential effects of each covariate (e.g., physical disorder) can be assessed by a direct path from the covariate to the item ( ). If, for example, respondents with higher levels of a physical disorder are more likely to report the depression symptom everything is an effort than are healthy respondents with the same level of depression, a significant positive coefficient on the path from the physical disorder to everything is an effort will be observed. The effect could be detected even if, overall, there was a decline in depression with increased physical infirmity. The latter effect should be manifested with a negative coefficient ( ) on the path from physical disorder level to the depression factor. A significant coefficient indicates the relationship between the predictor concerned and the latent variable, whereas a significant coefficient indicates DIF or item bias. A problem in DIF analyses, shared by many forms of statistical analysis, is determining the magnitude of the effect on scale scores: Although bias may be statistically significant, it may result in only negligible effects on scale scores. Considering a score derived by the addition of unweighted item responses (the usual CES-D total score), the effect of a predictor in the model shown in Figure 1 can be partitioned into a bias effect and an effect due to the actual association between genuine depression and the predictor. The bias effect on the total score is the sum of all the direct loadings from that predictor to the 20 items ( i in Figure 1); whereas the genuine effect, arising from the effect of the predictor on the latent depression variable, is the sum of the loadings from the factor depression to the 20 items, multiplied by the loading from the predictor to the depression factor ( i in Figure 1). Figure 1. Path diagram of MIMIC model incorporating a single CES-D factor with a single predictor variable. Res residual for CES-D factor; r1 residual for item 1, etc.; SCF single common factor; 1 differential effect of the predictor on item 1; effect of predictor on SCF depression; 1 factor loading from SCF depression to item 1. An advantage of the MIMIC model is that effects of the covariates on the latent variable can be measured simultaneously, allowing the effects of physical disorder on depression to be assessed while also estimating the effects of other covariates such as age. The direct effect of age on each of the items can also be assessed simultaneously, thereby estimating the biasing effects of physical disorder while controlling for the effects of age, gender, and other variables. A variant of this approach has been applied to DSM-III-R depression symptoms (Gallo, Anthony, & Muthén, 1994) and to two inventories of depression and anxiety (Christensen et al., 1999). Both studies found evidence of DIF associated with age: Controlling for level of depression, older people were more likely to report sleep difficulty, tiredness, and thinking about death (Gallo et al., 1994). In contrast, older people were less likely to report dysphoria, weight change, and agitation (Gallo et al., 1994). DIF has also been found in sleep- and energy-related items, with similar effects for items related to thoughts about the future (Christensen et al., 1999). These analyses suggest the importance of identifying DIF in other depression measures such as the CES-D. In applying the MIMIC model to CES-D items, a measurement model must first be developed. Early research showed that the CES-D had four correlated factors in some populations: depressive affect, somatic symptoms, well-being,

BIAS IN THE CES-D IN THE ELDERLY P275 and interpersonal relations (Berkman et al., 1986; Radloff, 1977). However, other studies have found that a single second-order depression factor fitted correlations between the four first-order factors (Herzog, Van Alstine, Ugala, Hultsh, & Dixons, 1990). Fitting a variation of this single secondorder factor, it was demonstrated that CES-D can be viewed as predominantly driven by a substantial single common factor, with unique components corresponding to the four firstorder factors accounting for only a very small proportion of the variance (Mackinnon, McCallum, Andrews, & Anderson, 1998; McCallum, Mackinnon, Simons, & Simons, 1995). The focus on a single CES-D factor accords with Radloff s (1977) recommendation that the total score should be used. The aim of this article is to determine the extent to which variations in responses to the CES-D with physical health reflect an actual association with depression and the extent to which they reflect item-level artifacts. The data analyzed were collected from a random probability sample of elderly persons living in the community. An important feature of this study was that participants underwent a physical examination by a physician experienced in geriatric medicine. Unlike selfreported health status, which has been used in many other studies, the ratings of health used in these analyses are independent of the psychological status of the individual. Furthermore, specific medical diagnoses give more information about the sources of any item bias than do global health ratings. METHODS Sample Participants in this analysis were part of the Sydney Older Persons Study (Broe et al., 1998; Waite et al., 1997). This study recruited 630 participants aged 75 or older, living in the community (not in hostels or nursing homes) in the inner western suburbs of Sydney, Australia. The analyses reported were undertaken on the 506 individuals for whom complete relevant data were available. A small number of cases had missing data imputed within each module. Half the participants were selected with an area-based random probability sample, whereas the other half were randomly drawn using Department of Veteran Affairs lists of entitled veterans living in the study area (including women veterans and war widows). Procedure Sociodemographic data on all 630 participants were obtained by a social scientist interviewer at the respondent s home. Education status was assessed on a 10-point scale, ranging from 1 (no schooling) to 10 (completed tertiary). Marital status was measured with a four-category variable, from which dichotomous variables were derived indicating being married and widowed, after pooling never married and divorced/separated variables to form a single reference category. Responses to the 20-item CES-D (Radloff, 1977) were obtained from 604 participants at this interview. The CES-D was filled in by the participants, or administered orally to those who were visually impaired. Each depression item was scored 0 3, with 3 indexing higher depression. A second interview was conducted in the participant s home by a physician experienced in geriatric medicine. At this interview, data were obtained from 527 individuals on disability: mobility (5-point scale, between 0 and 1 for complete immobility 0, 1/4, 1/2, 3/4, 1); incontinence (4-point scale, 0 1 indexing bladder and bowel incontinence); selfcare (activities of daily living [ADL]; 10-point scale, scored 0 to 3, reflecting maximal disability with bathing, feeding, and dressing); and instrumental activities of daily living (IADL; 7-point scale, scored 0 to 3, reflecting maximal disability with cooking, house-cleaning, and shopping; Ahktar et al., 1973). The interviewing physicians ratings (4-point scale, scored well [absent], mild, moderate, or severe 0, 1/3, 2/3, 1) on a range of chronic diseases common among elderly individuals (see Table 1) were obtained on 522 individuals. Analysis Structural analysis of the CES-D involved three phases. First, the acceptability of a single common factor model in representing the scale was evaluated using confirmatory factor analysis. Next, the model in Figure 1 was fitted to the CES-D items using (serially) individual predictor variables. This was essentially a screening exercise aimed at reducing the number of predictors in the full model, although the results are of use in comparing the present with those from other studies. Finally, a multivariate model including all significant predictors was fit to the data. Maximum likelihood parameter estimates for all models were obtained using AMOS 3.6.1 (Arbuckle, 1997). Good- Table 1. Variables Used in Analyses: Total Center for Epidemiologic Studies Depression Scale Score (CES-D) and Sociodemographic, Disability, and Disease Predictors Variable M SD Skew % CES-D total 8.76 7.90 1.68 Sociodemographic Age (years) 80.86 4.17 1.14 Education level (1 10) 4.61 2.05 0.99 Gender (% Male) 52 Marital status a Married/De facto 27 Widowed 58 Never married 9 Divorced/Separated 6 Disability Mobility 0.12 0.16 1.35 Incontinence 0.06 0.15 3.31 ADL 0.04 0.22 7.23 IADL 0.56 0.75 0.75 Physical disorders Heart disease 0.17 0.21 0.91 Stroke 0.06 0.15 2.65 Peripheral vascular disease 0.05 0.14 3.08 Chronic lung disease 0.08 0.19 2.50 Bone and joint disease 0.29 0.22 0.31 Other systemic disease 0.14 0.20 1.16 Gait instability 0.20 0.23 0.86 Gait slowing 0.11 0.25 2.48 Obesity 0.05 0.14 2.67 Cognitive impairment 0.28 0.36 1.11 Note: ADL activities of daily living; IADL instrumental activities of daily living. a Dichotomous indicators were constructed for the widowed and married variables.

P276 GRAYSON ET AL. ness of fit was assessed using the goodness of fit index (GFI; Arbuckle, 1997), the Tucker Lewis index (TLI; Bentler & Bonnett, 1980), and the root mean square error of approximation (RMSEA; Browne & Cudeck, 1992). GFI and TLI values in excess of 0.90 are indicative of well-fitting models (Marsh, Balla, & McDonald, 1988), whereas RMSEA values less than 0.08 indicate a reasonable fit, with those below 0.04 indicating close fit of a model (Browne & Cudeck, 1992). Because maximum likelihood estimation assumes multivariate normality, there was concern that standard errors produced under this method may not be accurate. Therefore, confidence intervals reported here were obtained by bootstrapping, using 500 resamplings from the original sample (Yung & Bentler, 1996). A bias loading parameter estimate was deemed to be statistically significantly different from 0 if its magnitude exceeded twice its standard error (a critical ratio of 2, corresponding to a z score of 2). Loadings to the CES-D Depression factor, however, only required a more liberal 1.5 to be deemed significant. Thus, a more stringent criterion was applied in concluding item bias than in concluding an effect on genuine depression. In all models examining item bias, Item 6 ( felt depressed ) was used as a reference item for which the direct paths from predictor variables were set to 0. A constraint of this form is necessary to achieve identification. Thus, bias in other items was assessed relative to this reference. We return to this point in the Discussion section. RESULTS The average age of participants was 80.9 years (SD 4.17, range 75 97.8), 52% were men. The mean CES-D score was 8.76 (see Table 1), which is comparable to the means summarized by Newmann (1989) for American community samples aged in their 70s, but lower than the mean of 11.86 reported by Murrell, Himmelfarb, and Wright (1983) for the 75 age group. Table 1 also gives sample characteristics on the sociodemographic, disability, and physical disorder variables. The mean values for incontinence, mobility, and ADL disability are quite low, consistent with the fact that individuals in nursing homes and hostels were excluded from the sample. Confirmatory Factor Analysis A single common factor model was fitted to the 20 CES-D items. This model provided an adequate fit to the data (GFI 0.908, TLI 0.836, RMSEA 0.062). Cronbach s alpha for the 20 items on this sample was 0.85, and there was a correlation of 0.995 between the score obtained by weighting each item with its loading on the single common factor score and the usual CES-D total score, providing strong support for the contention that the latent variable defined by this model is essentially equivalent to the score derived in applications of the CES-D. We also fitted the Scmid-Leiman multifactor model (see McCallum et al., 1995), which posits a single Depression factor loading, to all 20 items, and four residual factors which correspond to the original four CES-D factors Somatic, Well-being, Interpersonal, and Affective. This model fit the data better (GFI 0.949, TLI 0.929, RMSEA 0.041), although at the cost of substantially reduced parsimony. The single Depression factor accounted for 93% of the common factor variance on the CES-D total score. Thus the MIMIC models below were based on the single factor model of the CES-D (CES-D Depression factor). MIMIC Model Univariate models. Table 2 presents the results of univariate models addressing item bias. As in Figure 1, each predictor, analyzed individually, was allowed to have an indirect effect on individual items by means of the CES-D factor as well as direct effects to the items. These bias effects are relative to Item 6 ( felt depressed ), which was constrained for identification purposes to have zero bias. Of the 20 analyses using single predictors, only the results for 17 analyses are presented, as Education, Divorced, and Obesity did not yield estimates with critical ratios exceeding either 1.5 on the predictor loading to the CES-D Depression factor or 2.0 for any of the 19 bias loadings. These three predictors are not examined further. For all 17 models reported in Table 2, the GFIs ranged between 0.911 and 0.915, the TLIs between 0.817 and 0.833, and the RMSEAs between 0.060 and 0.062, all indicating satisfactory fit. None of the sociodemographic variables was significantly associated with the CES-D Depression factor, although numerous item-specific effects were found: Older participants reported being more bothered by things and less hopeful about the future; men found things less of an effort, were less fearful, slept better, and reported crying less. Being widowed was associated with feeling at least as good as others and with more fear and loneliness. None of these effects were associated with elevated depression. Of the disability variables, the increases on the depression factor were associated with disability of any sort other than ADL. Mobility, ADL, and IADL also had direct effects on several items, in particular poor appetite, finding everything an effort, restless sleep, and inability to get going. Physical disorder variables also influenced the CES-D Depression factor and items directly. Heart disease, stroke, any other systemic disease, gait instability, and cognitive impairment were all associated with a genuine rise in depression, although they all show other effects on the CES-D. The items good as others, talked less, people unfriendly, enjoyed life, crying spells, felt sad, and people dislike me all showed significant negative direct loadings, indicating that individuals with accompanying physical disorders underreport on these items for reasons unassociated with depression. As with disability, items poor appetite, everything an effort, and inability to get going had higher endorsement levels in individuals with particular diseases for reasons other than a disease-related elevation in depression. A problem in DIF analyses, shared by many forms of statistical analysis, is determining the magnitude of the effect on scale scores: Although bias may be statistically significant, it may result in only negligible effects on scale scores. Considering a score derived by the addition of unweighted item responses, as discussed above, the effect of a predictor in the model in Figure 1 can be partitioned into a bias effect ( i in Figure 1) and an effect due to the actual association between depression and the predictor ( i in Figure 1).

BIAS IN THE CES-D IN THE ELDERLY P277 Table 2. Univariate Loadings of Predictors on the Center for Epidemiologic Studies Depression Scale (CES-D) Depression Factor and Directly on Items (Bias) Sociodemographic a Physical Disorders a Disability Gender Cognitive Predictor Age (male) Widowed Mobility Incontinence ADL IADL Heart CLD Bone Stroke Visual PVD Other Instability Slowing impairment CES-D Depression factor 0.39 0.47 0.14 0.27 0.44 0.29 0.25 0.22 Bothered by things 0.02 Poor appetite 0.95 0.16 0.51 0.56 0.43 Felt blue Good as others 0.15 0.51 Trouble concentrating 0.12 0.62 0.29 Felt depressed b Everything an effort 0.17 1.07 0.56 0.29 0.45 0.65 0.68 0.40 0.52 Hopeful about future 0.04 0.19 0.49 0.37 Life a failure Felt fearful 0.23 0.18 0.08 Sleep restless 0.23 0.74 0.64 I was happy Talked less 0.41 Felt lonely 0.31 0.51 People unfriendly 0.32 0.21 0.24 Enjoyed life 0.50 Crying spells 0.11 0.24 Felt sad 0.36 People dislike me 0.26 Could not get going 0.95 0.19 0.37 0.55 Note: ADL activities of daily living; IADL instrumental activities of daily living; CLD chronic lung disease; PVD peripheral vascular disease. a No significant paths were found for education, being divorced, or obesity. b Loadings set to 0 for identification purposes.

P278 GRAYSON ET AL. Table 3 shows these contributions to the CES-D total score for each predictor. For example, the regression of age on CES-D total score yields a beta weight of 0.09: Each increase of 1 year in age is associated with an increase of 0.09 in CES-D total. In the next columns, the model described in Figure 1 is fitted but, unlike the data in Table 2, all effects, significant or otherwise, are kept and accumulated as described above, yielding a bias contribution to all 20 items of 0.14 and a genuine depression contribution of 0.05. Partitioning the total association of age with CES-D score gives the following: 0.09 0.14 ( 0.05). The final two columns of Table 3 show the same accumulations of bias and genuine effects but only for the significant loadings shown in Table 2. Thus, bias effects of age on CES-D of 0.06 arise from the only significant loadings, which are on the items bothered by things (0.02) and hopeful about the future (0.04); whereas age has no loading on the Depression factor. In interpreting Table 3, some comments about the issue of statistical significance are pertinent. The regression beta estimates in Column 1 (0.09, etc.) are reported irrespective of their p values arising from the regressions yielding these estimates. The true population parameter value for the entry in Column 1 may well be 0, yet biases and common factor components nonzero, adding to 0 in a compensatory manner. Thus, it is possible that a regression coefficient relating a predictor to the CES-D total might be nonsignificant, whereas significant effects relate that predictor to some individual items and/or the common factor. The same comment applies to the bias and factor loading entries in the middle columns although all these estimates are presented irrespective of statistical significance, the combined estimates are estimating the same parameters as the regression betas, and the partition of these regression betas is informative. The main purpose of these entries is to demonstrate the partition of the manifest regression coefficient into bias and genuine components. However, even without the filter of statistical significance, all these estimates are estimating their underlying parameters (which may or may not be 0 in value), and in the present context inspection of the relative magnitudes of the bias and genuine components may be informative, albeit in a speculative manner, warranting further research. Given the emphasis of the present article on item-level effects, the bias and factor loading entries in the rightmost columns of Table 3 represent an appropriate locus for applying the filter of statistical significance, and for the points made below, recall that bias loadings had to achieve a critical ratio of 2, whereas loadings to the CES-D Depression factor only required a more liberal 1.5. The results in Table 3 are quite striking. The bias effects range from negligible (widowed) to over seven times the magnitude (bone and joint disease) of the effects on the Depression factor (averaging 157%) and are frequently in the opposite direction. The situation does not markedly improve when the conservative principle of statistical significance is adopted to distill the relative contributions: Of 17 predictors, 9 show only nondepression effects on the CES-D, and only 1 (incontinence) supports the use of the CES-D as an unbiased measure of depression; with the remaining 7 predictors showing joint contributions, the bias component ranges from 4% (heart disease) to 64% (mobility) of the magnitude of the genuine depression component. Multivariate models. The results in Table 2 show the effects of each predictor fitted separately. It is possible that effects for a given predictor reflect other associations with depression. For instance, an association with age may be causally determined by a physical disorder variable that increases with age. Table 4 presents the results of a model in which all predictors with significant loadings in the univariate models were fitted simultaneously. For identification purposes, Item 6 ( felt depressed ) was again constrained to have no bias. This model fitted the data well, 2 (N 506,170) 802.95, p 0.0001, GFI 0.916, TLI Table 3. Univariate Effects of Predictors on the Center for Epidemiologic Studies Depression Scale (CES-D), Partitioned Into Effects by Means of the CES-D Depression Factor, and on Specific Item Factors (Bias) All Effects Significant Effects Predictor variable Regression Bias CES-D Factor Bias CES-D Factor Age (years) 0.09 0.14 0.05 0.06 Gender (male) 1.35 1.75 0.42 0.74 Widowed 1.22 0.04 1.17 0.34 Mobility 11.78 6.00 5.77 3.72 5.77 Incontinence 8.59 1.56 7.06 7.06 ADL 3.39 4.09 0.69 1.53 IADL 3.59 1.54 2.05 1.04 2.05 Heart disease 5.09 1.03 4.07 0.17 4.07 Chronic lung disease 3.15 0.81 3.97 0.50 Bone and joint disease 2.24 2.58 0.36 1.54 Stroke 5.66 0.99 6.63 1.31 6.63 Visual loss 1.37 0.49 0.87 0.36 Peripheral vascular disease 4.11 0.64 4.73 0.76 Other systemic disease 3.52 0.87 4.38 0.48 4.38 Gait instability 5.97 2.22 3.75 1.45 3.75 Gait slowing 5.67 4.42 1.22 2.01 Cognitive impairment 3.62 0.32 3.29 0.66 3.29 Note: ADL activities of daily living; IADL instrumental activities of daily living.

BIAS IN THE CES-D IN THE ELDERLY P279 0.869, RMSEA 0.04, P r (RMSEA 0.05) 1.00. The dashes in Table 4 denote those univariate loadings from Table 2 that no longer remained significant. The effects which remain are essentially the same magnitude and direction as those in Table 2, although many effects lose significance in the multivariate model particularly among the disabilities; in particular incontinence, ADL, and gait immobility no longer have any significant loadings and are dropped from further consideration. Table 5 is analogous to Table 3 above, but here the regression betas refer to a single multiple regression run involving those predictors that have at least one significant multivariate loading in Table 4. The middle panel describes the partition of these estimates of the multiple regression of CES-D onto the predictors, and again statistical significance is ignored. To obtain this partition, a model was estimated with every loading included: from the predictors to depression and from the predictors to the items (excepting identification constraints to Item 6), as well as the factor loadings from depression to the items. The rightmost panel in Table 5 describes the partition when only those significant loadings shown in Table 4 are used, with critical ratios of 2 and 1.5 for bias and genuine depression loadings, respectively. Again, in this multivariate context, the CES-D total score is polluted with contributions unrelated to depression, although some partial predictors (IADL, heart disease, and other systemic disease) seem to manifest their contribution to CES-D total score mainly through genuine effects by means of the depression factor. Nonetheless, the remaining 11 significant yet smaller contributors to CES-D appear to involve no genuine effects on depression. DISCUSSION The use of a MIMIC model to investigate the effects of sociodemographic, disability, and physical disorder variables on responses to the CES-D has revealed a number of important effects. Being older, being female, and being widowed have all been shown to be significantly and independently related to an increase in the CES-D total score, not because of an elevation in depression, but because of itemspecific effects that will accumulate over specific items. These item biases are present when age-related disabilities and physical disorders are controlled by inclusion in a multivariate model. Most of the disabilities and physical disorders influencing the CES-D do so because of item bias effects. Only IADL, heart disease, and other systemic disease have independent impacts on the depression factor. These three predictors also have bias effects on the CES-D total score. Mobility disability, chronic lung disease, bone and joint disease, stroke, visual impairment, peripheral vascular disease, gait instability, and cognitive impairment all have effects on CES-D items in the absence of a genuine effect on depression. Although these bias effects are not large, research that attributes to depression per se all of the change in CES-D total score associated with a particular disability or physical disorder in elderly individuals is clearly questionable. By definition, felt depressed had no bias. The data seemed to support this (conceptually arbitrary) choice, in that other affective items felt blue, life a failure, and I was happy also showed no bias in these analyses. It may be fruitful to view these four items as a core subscale for depression in elderly individuals. Individuals with a disability, bone and joint disease, and stroke were more likely to report that everything is an effort above and beyond levels of depression, and again, so it may be. The items good as others, talked less, people unfriendly, enjoyed life, crying spells, felt sad, and people dislike me had only negative associations with particular disorders. That is, participants with more severe physical disorder respond to these items in a less extreme manner than expected for given levels of depression. This result is unexpected, as discussions about bias have usually focussed on somatic items with attendant concern about artifactually high depression scores. Any explanation of this effect must be speculative; however, it suggests that depression, which is essentially an internal state may not be projected into the interpersonal arena to the same extent as in physically well persons. This is analogous to the suggestion elsewhere that items that involve assessments about the future are responded to differentially by older individuals (Christensen et al., 1999). Alternatively, it may be that physical disorders might lead to more care and contact from others, so the sick person gets relatively more conversation, social contact, and attention. The cause of the observed association can be resolved only by further investigation. Compared with the work of others (Christensen et al., 1999; Gallo et al., 1994), few items had significant bias associated with age in the multivariate analysis. To some extent this is probably due to the narrower age range of this study. However, the overall pattern of results suggests that, within the elderly age group, bias in depression items might not be due to age per se, but possibly to physical infirmity associated with age. However, because the present study is based only on individuals aged at least 75 or older, it cannot bear directly on this speculation. Nevertheless, even in this group physical disorders and disability common among elderly individuals accounted for much item bias, whereas age, albeit restricted in variance, had few such effects. Thus, in future studies involving a wider age span seeking to investigate item bias with depression items, we recommend the inclusion of some measures of age-related disability and disorder to exclude the possibility of age acting as a proxy for these age-related conditions in such item effects. A caveat, applicable to all investigations of DIF, is appropriate. The detail of the reported results turns very much on the choice for identification purposes of the item felt depressed as unbiased. The factor of depression then becomes that for which this item is unbiased, and biases on other items are in relation to this particular factor. The choice was made on the grounds of face validity and gains support by the empirical plausibility of the detailed results which followed; for instance, in Table 4 the physical disorders bone and stroke have no effect on genuine depression but cause overendorsement on the symptom everything an effort. This makes empirical sense. However, had we chosen instead everything an effort to be unbiased, the corresponding factor of depression would have absorbed these effects, showing positive associations between these physical disorders and the (new) factor of depression,

P280 GRAYSON ET AL. Table 4. Multivariate Loadings of Predictors on the Center for Epidemiologic Studies Depression Scale (CES-D) Depression Factor and Directly on Items (Bias) Predictor Age Sociodemographic a Physical Disorders a Disability Gender (male) Widowed Mobility Incontinence ADL IADL Heart Lung Bone Stroke Visual PVD Other Instability Slowing Cognitive impairment CES-D Depression factor 0.17 0.31 0.22 Bothered by things 0.02 Poor appetite 0.55 Felt blue Good as others 0.49 Trouble concentrating 0.61 0.27 Felt depressed b Everything an effort 0.18 0.45 0.67 Hopeful about future 0.04 0.33 Life a failure Felt fearful 0.14 0.14 Sleep restless 0.19 I was happy Talked less 0.49 Felt lonely 0.36 People unfriendly 0.26 0.24 Enjoyed life 0.48 Crying spells 0.08 0.20 Felt sad 0.39 People dislike me 0.25 Could not get going 0.80 Notes: ADL activities of daily living; IADL instrumental activities of daily living; CLD chronic lung disease; PVD peripheral vascular disease. Dashes indicate significant univariate loadings (as shown in Table 2) that are no longer significant. a No significant paths were found for education, being divorced, or obesity. b Loadings set to 0 for identification purposes.

BIAS IN THE CES-D IN THE ELDERLY P281 Table 5. Multivariate Effects of Predictors on the Center for Epidemiologic Studies Depression Scale (CES-D), Partitioned into Effects by Means of the CES-D Depression Factor, and on Specific Item Factors (Bias) Predictor variable a Regression All effects Bias CES-D factor Significant effects Bias CES-D factor Age (years) 0.16 0.03 0.19 0.05 Gender (male) 0.83 1.86 1.05 0.41 Widowed 0.78 1.09 1.89 0.50 Mobility 0.87 1.82 0.91 0.80 IADL 3.20 1.47 1.71 0.18 2.61 Heart disease 3.53 0.27 3.27 0.75 4.63 Chronic lung disease 0.94 1.16 2.12 0.48 Bone and joint disease 1.13 0.70 1.84 0.45 Stroke 0.23 2.14 2.36 1.28 Visual loss 0.57 0.09 0.49 0.39 Peripheral vascular disease 1.80 0.24 1.55 0.75 Other systemic disease 3.18 0.83 3.99 0.44 3.25 Gait instability 1.79 0.29 1.53 0.55 Cognitive impairment 0.58 1.23 1.81 0.60 Note: IADL instrumental activities of daily living. a Incontinence, ADL, and gait immobility omitted, as no multivariate loadings were significant. whereas the symptom felt depressed would now show biases indicating underreporting. But even with the unavoidable arbitrariness introduced by this identification issue, the presence per se of substantial item biases in the CES-D is unambiguous. The multitude of bias entries in Table 4 would have to have appeared among other CES-D items, albeit with different values, whichever other item we chose to assume unbiased. As discussed above, some such choice has to be made for purposes of identification. We chose felt depressed on the grounds of its face validity and semantic simplicity. The choice of reference item is definitional: The construct of depression on which the effect of various disability/disorder predictors is evaluated is defined in this study to be that for which the item felt depressed has no bias with respect to these predictors. This choice leaves unaddressed the issue of whether elderly individuals underreport on this item (see Gallo et al., 1994, for relevant discussion of this possibility). It should be emphasized that such a choice is implicitly made by researchers who analyze the CES-D total score: They are implicitly assuming either that all items are unbiased or that biases present compensate exactly, fortuitously cancelling each other out in the CES-D total score. Such a choice is in a real sense definitional: The construct of depression that we are defining, and on which we evaluate the effect of various disability/disorder predictors, is defined in this study to be that for which the item felt depressed has no bias (with respect to these predictors). The analyses presented in this article suggest that physical disorder-related artifacts exist in the CES-D and are of a magnitude to warrant concern in research involving depression in elderly individuals. Persons with particular disorders are likely to respond to items on the CES-D in a manner determined by the disorder rather than solely according to their underlying depressive state. As would be expected, greater endorsement was observed for items relating to effort, sleep, or anergia. However, some disorders were associated with lower than expected item endorsement for given levels of depression. Because few studies involve the comprehensive medical evaluation undertaken in this study, this is the first to determine the contribution of specific physical disorders to bias in the CES-D. There is a clear need to develop refined instruments for the measurement of depressive symptomatology, not only so valid comparisons can be made across the lifespan, but in order that subpopulations of elderly persons who may differ in physical health and in other characteristics may be compared meaningfully on this important construct. In studies of elderly individuals in which the population involved includes participants varying on the aspects shown above to induce bias, it would seem wise to measure depression with a core affective subscale similar to that already discussed felt depressed, felt blue, life a failure, and I was happy. Conversely, in a study aimed at evaluating a depression intervention among elderly individuals, it would seem sensible to control on the variables above, either by selection of participants (free of the diseases/disabilities discussed) or by including their status on these aspects in the analysis. When studying the trajectory of depression across the lifespan, the present results indicate that the biasing effect of age-related disorders/disabilities must be dealt with appropriately before comparing CES-D totals of relatively younger and older individuals. 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