New insights into the correlation structure of DSM-IV depression symptoms in the general population v. subsamples of depressed individuals

Abstract

First, caution is warranted when considering studies assessing dimensions of depression because general population-based studies and studies of depressed individuals generate different data that can lead to different conclusions. This problem likely generalises to other models based on the symptoms' inter-relationships such as network models. Second, whereas the overall severity aligns individuals on a continuum of disorder intensity that allows non-affected individuals to be distinguished from affected individuals, the clinical evaluation and treatment of depressed individuals should focus directly on each individual's symptom profile.

The proportion of non-depressed subjects in a study sample strongly affects the results of psychometric analyses of depression symptoms

“…The findings thereby imply that results of psychometric analyses vary substantially as a function of the proportion of non-depressed participants in a study's sample. This provides an explanation for the lack of reproducibility of previous psychometric studies [1]. In addition, these findings provide a potential explanation why psychometric results differed to such an extent when the baseline versus end-of-trial data of one and the same patient sample were compared in previous studies [2,3].…”

“…In recent years, a somewhat curious finding has emerged in studies on depression: both the strength and dimensionality of inter-relationships between depressive symptoms is influenced by the average level of depression severity observed in the study sample. For example, in two population samples of young adults from the USA and Switzerland: a) symptom correlations were strong in the general population, but surprisingly weak in those who were depressed; and b) the symptoms had a unidimensional factor structure in the general populations, but various multidimensional structures in those who were depressed [1]. In American and Dutch depression patients, Fried et al found that while the average depression severity in the samples decreased over time, the average symptom correlation increased and the factor structure of the correlations became simpler [2].…”

“…If these hypotheses hold, the implication is that dimensional, item response, and network models yield substantially different results, depending on sample composition. This would be a serious issue, since such analyses were based on rather different sample compositions [1][2][3][6][7][8][9][10][11][12][13][14][15][16]. Hence, these studies might actually be incomparable.…”

Chronic diseases and social risk factors in relation to specific symptoms of depression: Evidence from the U.S. national health and nutrition examination surveys

“…However, the current results also need to be considered with at least four limitations. First, the study was based on a population-based sample, and the symptom-specific associations might not be similar in clinical samples of depressed individuals (Foster and Mohler-Kuo, 2017). Second, depressive symptoms were assessed with a short self-reported rating scale, and the results might be different with clinical interviews or with other rating scales of depression, as different measures may provide more or less accurate tools to differentiate between specific symptoms (Fried, 2017).…”

“…High depression scores can therefore represent many different symptom combinations. Various subtypes of depression have been introduced to account for its heterogeneous nature (e.g., melancholic or atypical depression) but most of these subtypes have not received strong empirical support (Foster and Mohler-Kuo, 2017;Harald and Gordon, 2012;van Loo et al, 2012). For example, it remains unclear whether specific symptoms are differently related to environmental risk factors (Fried et al, 2014;Keller et al, 2007), biomarkers (Jokela et al, 2015;Lamers et al, 2017), and genetic liabilities (Kendler et al, 2013;Kendler and Aggen, 2017).…”