Jason T. Newsom & David L. Morgan Portland State University Time-varying and time-invariant covariates in a latent growth model of negative interactions and depression in widowhood   Jason T. Newsom & David L. Morgan Institute on Aging  Portland State University

Introduction Steady recovery over 1-2 years common Increase in depressive symptoms following a loss common Steady recovery over 1-2 years common Depression commonly approaches normal levels at end of this period These patterns are common but not universal (Wortman & Silverman, 1989) Increased attention to individual differences in process of recovery and variables that may affect them Growth curve analysis ideal for examining individual differences in initial depression levels and rate of recovery

Methods Sample 376 widows recruited using death certificates Ages 59 through 85 Followed over 18 months Initial interview 3-6 months after loss Face-to-face interviews every 6 months 311 with complete data Attrition No Significant Differences Income Education Depression Perceived health Negative social interactions Significant Differences Age: nonrespondents 1.46 yrs older

Measures Dependent Measure Depression Time-invariant Covariates Age Education Time-varying Covariates Negative Social Interactions Perceived Health

Depression Center for Epidemiologic Studies—Depression scale (CES-D; Radloff, 1977) 20 items (e.g., bothered by things that do not usually bother you, felt depressed, enjoyed life) Frequency of symptom in last week 0 = None of the time (< 1 day) 1 = A little of the time (1-2 days) 2 = A moderate amount of the time (3-4 days) 3= Most of the time (5-7 days) Possible range: 0-60

Time-invariant Covariates Age at baseline Education Number of years

Time-varying Covariates Negative Social Interactions Number of network members who were source of negative interactions Three domains: emotional, instrumental, informational Average of number network members in the three domains Perceived Health “Compared to others your age, how do you rate your overall health—would you say it is excellent, very good, good, fair, or poor?” Possible range: 1-5

Latent Growth Curve Analyses Mplus 2.0 (Muthen & Muthen, 2001) Maximum Likelihood Three Models Model 1: Basic growth model Depression at three time points Model 2: Adds time-invariant covariates Age, education Model 3: Adds time-varying covariates Perceived health, negative interactions

Basic Latent Growth Curve Model Mean Structure Provides Information About: Average rate of decline in depression (mean slope) Average initial depression (mean intercept) Latent Variances Provide Information About: Variability of initial depression levels Variability of rate of decline in depression Correlation of initial level and decline

Growth curve model of depression at three time points Figure 1 Growth curve model of depression at three time points Slope (Rate of change of Depression) Intercept (Initial Depression) 1 1 1 2 1 Dep t0 Dep t1 Dep t2

Adding Time-Invariant Covariates Model 2 Adding Time-Invariant Covariates Age and education added as predictors of intercepts and slopes Structural means become intercept values Initial level and rate of decline adjusted for covariates Age and education are “centered” to facilitate interpretation Variability of initial levels and decline become residual variances, representing unaccounted for variance

Time-invariant covariates in depression growth curve model Figure 2 Time-invariant covariates in depression growth curve model Age Educ Intercept (Initial Depression) Slope (Rate of change of Depression) 1 1 1 2 1 Dep t0 Dep t1 Dep t2

Adding Time-varying Covariates Model 3 Adding Time-varying Covariates Perceived Health and Negative Interactions Added Both variables measured at 3 time points Included as predictors of depression at each time point Initial level and decline in depression adjusted for health and negative interactions at each time point

Figure 3 Time-varying and time-invariant covariates in depression growth curve model Age Educ Intercept (Initial Depression) Slope (Rate of change of Depression) 1 1 1 1 2 Dep t0 Dep t1 Dep t2 Health t0 Negs t0 Health t1 Negs t1 Health t2 Negs t2

Table 1 Sample characteristics of widows. Variable Mean Std. Dev. Age 71.0 6.2 Education 13.0 2.0 Income (thousands) 23.7 16.3 Depression, T0 12.4 9.4 Depression, T1 11.5 9.5 Depression, T2 8.5 8.2 Perceived Health, T0 3.7 1.0 Perceived Health, T1 3.6 1.0 Perceived Health, T2 3.7 1.0 Negative Interactions, T0 .74 .98 Negative Interactions, T1 .73 .85 Negative Interactions, T2 .58 .84

Basic model growth curve results Table 2 Basic model growth curve results   Model fit statistics: N=311, c2 (1) = 7.168, p = .0074, IFI = .979, SRMR = .033.

Table 3 Model including time-invariant covariates. Model fit statistics: N= 311, c2 (3) = 10.538, p = .0144, IFI = .975 , SRMR = .026.  

Table 4 Model including time-invariant covariates and time-varying covariates. Model fit statistics: N=311, c2 (15) =35.519 , p = .0021, IFI = .950, SRMR = .050

Summary of Results Steady recovery over 18-month period High levels of depression following loss Steady recovery over 18-month period Significant differences in initial levels of depression and in recovery rates Age and education do not predict initial levels or recovery rate Age and education do not account for variability in initial levels or recovery rate Change in depression over time becomes nonsignificant once variation due to changes in perceived health and negative interactions is removed Variability in recovery rates becomes nonsignificant once variation due to changes in perceived health and negative interactions is removed

Conclusions and Limitations Unique approach to examining individual differences in initial depression and rate of recovery among widows Change in negative interactions or health appear to explain recovery rates of older widows More explicit causal models can be tested (e.g., using growth factors for health or negative interactions as predictors) Despite longitudinal data, causal directionality unclear (e.g., do negative interactions predict depression or does depression predict negative interactions) Health may play a role as a moderator (e.g., those in poor health may have slower recovery rates)