The development and validation of an algorithm to predict future depression onset in unselected youth

Cohen, Joseph R.; Thakur, Hena; Young, Jami F.; Hankin, Benjamin L.

doi:10.1017/s0033291719002691

Cited by 14 publications

(8 citation statements)

References 58 publications

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“…Existing literature, however, indicates that both negative affect and positive affect play a role in predicting relapse of depression [ 58 , 59 ] and future treatment outcome [ 60 , 61 ]. Additionally, negative affect was found to be predictive of depression onset in youth [ 62 ]. There are a very limited number of prognostic studies available, focusing on older adults [ 63 ].…”

Section: Discussionmentioning

confidence: 99%

Screening for Depression in Daily Life: Development and External Validation of a Prediction Model Based on Actigraphy and Experience Sampling Method

Minaeva¹,

Riese²,

Lamers³

et al. 2020

J Med Internet Res

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Background In many countries, depressed individuals often first visit primary care settings for consultation, but a considerable number of clinically depressed patients remain unidentified. Introducing additional screening tools may facilitate the diagnostic process. Objective This study aimed to examine whether experience sampling method (ESM)-based measures of depressive affect and behaviors can discriminate depressed from nondepressed individuals. In addition, the added value of actigraphy-based measures was examined. Methods We used data from 2 samples to develop and validate prediction models. The development data set included 14 days of ESM and continuous actigraphy of currently depressed (n=43) and nondepressed individuals (n=82). The validation data set included 30 days of ESM and continuous actigraphy of currently depressed (n=27) and nondepressed individuals (n=27). Backward stepwise logistic regression analysis was applied to build the prediction models. Performance of the models was assessed with goodness-of-fit indices, calibration curves, and discriminative ability (area under the receiver operating characteristic curve [AUC]). Results In the development data set, the discriminative ability was good for the actigraphy model (AUC=0.790) and excellent for both the ESM (AUC=0.991) and the combined-domains model (AUC=0.993). In the validation data set, the discriminative ability was reasonable for the actigraphy model (AUC=0.648) and excellent for both the ESM (AUC=0.891) and the combined-domains model (AUC=0.892). Conclusions ESM is a good diagnostic predictor and is easy to calculate, and it therefore holds promise for implementation in clinical practice. Actigraphy shows no added value to ESM as a diagnostic predictor but might still be useful when ESM use is restricted.

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Section: Discussionmentioning

confidence: 99%

Screening for Depression in Daily Life: Development and External Validation of a Prediction Model Based on Actigraphy and Experience Sampling Method

Minaeva¹,

Riese²,

Lamers³

et al. 2020

J Med Internet Res

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“…In other words, it is possible that depressed youth are reporting on the behavioral impairment stemming from depression, whereas parents are reporting on behavioral risk factors. In line with these findings, recent research suggests systematic investigations of risk, impairment, and distress may be necessary for emerging depression screening protocols (Cohen et al, 2019).…”

Section: Discussionmentioning

confidence: 79%

“…With regard to prospective outcomes, we expect a more complex algorithm to emerge. As parent-reported symptoms have been shown to be incrementally valid for prospective depression (Cohen, Thakur, Young, & Hankin, 2019), we hypothesized that a combination of parent-reported behaviorally oriented internalizing symptoms (i.e., withdrawn, somatic subscales) and self-reported cognitively oriented internalizing symptoms (i.e., Anxious/Depressed subscale) would best forecast prospective depression. Further, as externalizing symptoms predict prospective depression (Petty et al, 2008) and parents tend to accurately report on these symptoms (Smith, 2007), we predicted that parent-reported externalizing symptoms would also predict patterns of prospective depression.…”

Section: The Present Studymentioning

confidence: 99%

Depression screening in youth: Multi-informant algorithms for the child welfare setting.

Thakur¹,

Cohen²

2019

Psychological Assessment

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The Achenbach System of Empirically Based Assessment (ASEBA) represents the most widely used protocol for mental and behavioral health screening in the Child Welfare System (CWS). However, because past studies have (a) relied on the self-or parent report, (b) focused on the internalizing subscales, (c) focused solely on current or prospective depression, and (d) not assessed incremental validity, it is difficult to use the ASEBA to address recommended universal depression screening initiatives in the CWS. In response, the present study used an evidence-based medicine (EBM) framework to identify the best combination of subscales that predict adolescent depression outcomes within the CWS. Overall, we found that a combination of self-reported internalizing symptoms, and to a lesser extent, self-reported attention problems, delinquent behavior, and parent-reported social problems best forecasted concurrent depression status. Meanwhile, self-reported anxious/depressed and externalizing symptoms, in addition to parent-reported somatic complaints and withdrawn symptoms, were necessary to adequately forecast prospective depression outcomes. Using these algorithms, we were able to differentiate and classify youth at minimal, moderate, or substantial risk for current and future depression symptoms. Findings are contextualized with past research on the Achenbach scales and clinical implications for more targeted depression screening are discussed. Public Significance StatementThe present study demonstrates that assessing the incremental validity of multi-informant and multisymptom inventories can help to create more unbiased and targeted referral processes for children at risk for current and/or future depressive symptoms in the Child Welfare System.

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“…Existing literature, however, indicates that both negative affect and positive affect play a role in predicting relapse of depression [58,59] and future treatment outcome [60,61]. Additionally, negative affect was found to be predictive of depression onset in youth [62]. There are a very limited number of prognostic studies available, focusing on older adults [63].…”

Section: Comparison With Prior Workmentioning

confidence: 99%

Screening for Depression in Daily Life: Development and External Validation of a Prediction Model Based on Actigraphy and Experience Sampling Method (Preprint)

Minaeva¹,

Riese²,

Lamers³

et al. 2020

Preprint

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BACKGROUND In many countries, depressed individuals often first visit primary care settings for consultation, but a considerable number of clinically depressed patients remains unidentified. Introducing additional screening tools may facilitate the diagnostic process. We aimed to examine whether Experience Sampling Method (ESM)-based measures of depressive affect and behaviors can discriminate depressed from non-depressed individuals. In addition, the added value of actigraphy-based measures was examined. OBJECTIVE The aim of our study was to examine whether ESM-assessed depression-related affect and behavior can discriminate between depressed and non-depressed individuals, whether actigraphy data can discriminate between depressed and non-depressed individuals on its own, and whether it has added value with respect to the use of ESM. METHODS We used data from two samples to develop and validate prediction models. The development dataset included 14 days of ESM and continuous actigraphy of currently depressed (n=43) and non-depressed individuals (n=82). The validation dataset included 30 days of ESM and continuous actigraphy of currently depressed (n=27) and non-depressed individuals (n=27). Backward stepwise logistic regression analyses were applied to build the prediction models. Performance of the models was assessed with goodness of fit indices, calibration curves, and discriminative ability (AUC - the area under the receiver operating characteristic curve). RESULTS In the development dataset, the discriminative ability was good for the actigraphy model (AUC=.790) and excellent for the ESM (AUC=.991) and combined-domains model (AUC=.993). In the validation dataset, the discriminative ability was reasonable for the actigraphy model (AUC=.648) and excellent for the ESM (AUC=.891) and combined-domains model (AUC=.892). CONCLUSIONS ESM was a good diagnostic predictor and is easy to calculate, and it therefore holds promise for implementation in clinical practice. Actigraphy showed no added value to ESM as a diagnostic predictor, but might still be useful when ESM use is restricted.

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The development and validation of an algorithm to predict future depression onset in unselected youth

Cited by 14 publications

References 58 publications

Screening for Depression in Daily Life: Development and External Validation of a Prediction Model Based on Actigraphy and Experience Sampling Method

Screening for Depression in Daily Life: Development and External Validation of a Prediction Model Based on Actigraphy and Experience Sampling Method

Depression screening in youth: Multi-informant algorithms for the child welfare setting.

Screening for Depression in Daily Life: Development and External Validation of a Prediction Model Based on Actigraphy and Experience Sampling Method (Preprint)

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