Implications of non-response of older women to a short form of the Center for Epidemiologic Studies Depression Scale

The Center for Epidemiologic Studies Depression Scale (CES-D) is frequently used in epidemiological surveys to screen for depression, especially among older adults. This article addresses the problem of non-completion of a short form of the CES-D (CESD-10) in a mailed survey of 73- to 78-year-old women enrolled in the Australian Longitudinal Study on Women's Health. Completers of the CESD-10 had more education, found it easier to manage on available income and reported better physical and mental health. The Medical Outcomes Study Short Form Health Survey (SF-36) scores for non-completers were intermediate between those for women classified as depressed and not depressed using the CESD-10. Indicators of depression had an inverted U-shaped relationship with the number of missing CESD- 10 items and were most frequent for women with two to seven items missing. Future research should pay particular attention to the level of missing data in depression scales and report its potential impact on estimates of depression.

Multiple imputation for body mass index: lessons from the Australian Longitudinal Study on Women's Health

In large epidemiological studies missing data can be a problem, especially if information is sought on a sensitive topic or when a composite measure is calculated from several variables each affected by missing values. Multiple imputation is the method of choice for 'filling in' missing data based on associations among variables. Using an example about body mass index from the Australian Longitudinal Study on Women's Health, we identify a subset of variables that are particularly useful for imputing values for the target variables. Then we illustrate two uses of multiple imputation. The first is to examine and correct for bias when data are not missing completely at random. The second is to impute missing values for an important covariate; in this case omission from the imputation process of variables to be used in the analysis may introduce bias. We conclude with several recommendations for handling issues of missing data. Copyright (C) 2004 John Wiley Sons, Ltd.