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

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Presentation on theme: "Time-varying and time-invariant covariates in a latent growth model of negative interactions and depression in widowhood Jason T. Newsom & David L. Morgan."— Presentation transcript:

1 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

2 Introduction 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

3 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

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

5 Depression Center for Epidemiologic StudiesDepression 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

6 Time-invariant Covariates Age at baseline Education Number of years

7 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 healthwould you say it is excellent, very good, good, fair, or poor? Possible range: 1-5

8 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

9 Model 1 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

10 Dep t 0 Dep t 1 Dep t 2 Intercept (Initial Depression) Slope (Rate of change of Depression) Figure 1 Growth curve model of depression at three time points

11 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

12 Figure 2 Time-invariant covariates in depression growth curve model Dep t 0 Dep t 1 Dep t 2 Intercept (Initial Depression) Slope (Rate of change of Depression) Educ Age

13 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

14 Figure 3 Time-varying and time-invariant covariates in depression growth curve model Dep t 0 Dep t 1 Dep t 2 Intercept (Initial Depression) Slope (Rate of change of Depression) Educ Age Health t 0 Negs t 0 Health t 1 Negs t 1 Health t 2 Negs t

15 Table 1 Sample characteristics of widows. Variable Mean Std. Dev. Age Education Income (thousands) Depression, T Depression, T Depression, T Perceived Health, T Perceived Health, T Perceived Health, T Negative Interactions, T Negative Interactions, T Negative Interactions, T

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

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

18 Table 4 Model including time-invariant covariates and time-varying covariates. Model fit statistics: N=311, 2 (15) =35.519, p =.0021, IFI =.950, SRMR =.050

19 Summary of Results 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

20 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)


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