A Comparison of Clinical Features of Depressed and Non-Depressed People Living with HIV/AIDS, in Nigeria, West Africa

Shittu, Rasaki O.; Issa, B. A.; Olanrewaju, G. T.; Mahmoud, Abdulraheem O; Aderibigbe, S.A.; Odeigah, Louis O.

doi:10.4236/ojmp.2014.31008

Cited by 3 publications

(2 citation statements)

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“…A total of 116 clinical and demographic variables typically available in medical claims databases were utilized as predictors associated with depression symptom severity. Variables were identified from (1) a literature search and review of publications, including studies related to causation or association of MDD or depression symptom severity (Amos et al., 2017; APA, 2013; Briggs et al, 2018; Carter et al., 2012; Chin et al., 2016; Gaynes, 2009; Gross et al., 2015; Hinz et al., 2016; Iosifescu et al., 2003; Katzelnick et al., 2011; Mulvahill et al., 2017; Raval et al., 2010; Rossom et al., 2016; Shittu et al., 2014; Wada et al., 2015; Waxmonsky et al., 2012), and (2) discussions with clinicians with expertise in treating patients with TRD. The potential predictors included demographic characteristics, treatment‐specific variables (e.g., site of care, nonpharmacologic treatment, number of prior MDD treatments, specific medications taken for MDD treatment), psychiatric comorbidities, medical comorbidities, measures of healthcare resource utilization, and others (see Table S2 for a full list of variables).…”

Section: Methodsmentioning

confidence: 99%

How well do clinical and demographic characteristics predict Patient Health Questionnaire‐9 scores among patients with treatment‐resistant major depressive disorder in a real‐world setting?

et al. 2021

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Objectives To create and validate a model to predict depression symptom severity among patients with treatment‐resistant depression (TRD) using commonly recorded variables within medical claims databases. Methods Adults with TRD (here defined as > 2 antidepressant treatments in an episode, suggestive of nonresponse) and ≥ 1 Patient Health Questionnaire (PHQ)‐9 record on or after the index TRD date were identified (2013–2018) in Decision Resource Group's Real World Data Repository, which links an electronic health record database to a medical claims database. A total of 116 clinical/demographic variables were utilized as predictors of the study outcome of depression symptom severity, which was measured by PHQ‐9 total score category (score: 0–9 = none to mild, 10–14 = moderate, 15–27 = moderately severe to severe). A random forest approach was applied to develop and validate the predictive model. Results Among 5,356 PHQ‐9 scores in the study population, the mean (standard deviation) PHQ‐9 score was 10.1 (7.2). The model yielded an accuracy of 62.7%. For each predicted depression symptom severity category, the mean observed scores (8.0, 12.2, and 16.2) fell within the appropriate range. Conclusions While there is room for improvement in its accuracy, the use of a machine learning tool that predicts depression symptom severity of patients with TRD can potentially have wide population‐level applications. Healthcare systems and payers can build upon this groundwork and use the variables identified and the predictive modeling approach to create an algorithm specific to their population.

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Section: Methodsmentioning

confidence: 99%

How well do clinical and demographic characteristics predict Patient Health Questionnaire‐9 scores among patients with treatment‐resistant major depressive disorder in a real‐world setting?

et al. 2021

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“…Depression further is associated with high morbidity and mortality, particularly due to impaired adherence to ART. 1,2 However, depression is under-recognized, under-diagnosed and undertreated, so screening for depression should be attempted regularly, especially at the time of diagnosis and before initiating HAART 3,4 Most western studies report the prevalence of depression in PLWHA as 2-3 times more than in the general population. Especially, studies after 1996, reported elevated rates of depression.…”

Section: Introductionmentioning

confidence: 99%