BACKGROUND: Attrition, or dropping out of treatment, remains a major issue in the care of depressed outpatients. Whether different factors are associated with attrition for different socioeconomic groups is not known. This report assessed whether attrition rates and predictors of attrition differed among depressed outpatients with different income levels. METHODS: Outpatients with nonpsychotic major depressive disorder treated for up to 14 weeks with citalopram in the first step of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study were divided by household incomes of <$20,000, $20,000-<$40,000, and >or=$40,000. Attrition rates and sociodemographic and clinical correlates of attrition were identified for each group. RESULTS: Regardless of income level, remission rates were lower for participants who dropped out of treatment. Attrition rates increased as income decreased. For all income levels, younger age was independently associated with attrition. For the lowest income level, less education, better mental health functioning, being on public insurance, and having more concurrent Axis I conditions were associated with a greater likelihood of attrition. For the middle income group, less education, better mental health functioning, being Black or of another non-White race, and treatment in a psychiatric versus primary-care setting predicted greater attrition. For the highest income group, being Hispanic, having a family history of drug abuse, and melancholic features predicted attrition. Atypical symptom features (middle income group) and recurrent depression (highest income group) were associated with retention. CONCLUSIONS: Efforts to retain patients in antidepressant treatment should focus especially on less educated patients with lower household incomes and younger patients.
BACKGROUND: Attrition, or dropping out of treatment, remains a major issue in the care of depressed outpatients. Whether different factors are associated with attrition for different socioeconomic groups is not known. This report assessed whether attrition rates and predictors of attrition differed among depressed outpatients with different income levels. METHODS: Outpatients with nonpsychotic major depressive disorder treated for up to 14 weeks with citalopram in the first step of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study were divided by household incomes of <$20,000, $20,000-<$40,000, and >or=$40,000. Attrition rates and sociodemographic and clinical correlates of attrition were identified for each group. RESULTS: Regardless of income level, remission rates were lower for participants who dropped out of treatment. Attrition rates increased as income decreased. For all income levels, younger age was independently associated with attrition. For the lowest income level, less education, better mental health functioning, being on public insurance, and having more concurrent Axis I conditions were associated with a greater likelihood of attrition. For the middle income group, less education, better mental health functioning, being Black or of another non-White race, and treatment in a psychiatric versus primary-care setting predicted greater attrition. For the highest income group, being Hispanic, having a family history of drug abuse, and melancholic features predicted attrition. Atypical symptom features (middle income group) and recurrent depression (highest income group) were associated with retention. CONCLUSIONS: Efforts to retain patients in antidepressant treatment should focus especially on less educated patients with lower household incomes and younger patients.
Authors: Allison J Applebaum; Wendy G Lichtenthal; Hayley A Pessin; Julia N Radomski; N Simay Gökbayrak; Aviva M Katz; Barry Rosenfeld; William Breitbart Journal: Psychooncology Date: 2011-07-12 Impact factor: 3.894
Authors: W W IsHak; J Mirocha; D James; G Tobia; J Vilhauer; H Fakhry; S Pi; E Hanson; R Nashawati; E D Peselow; R M Cohen Journal: Acta Psychiatr Scand Date: 2014-06-23 Impact factor: 6.392
Authors: Daniel Fernández; Daniel Vigo; Nancy A Sampson; Irving Hwang; Sergio Aguilar-Gaxiola; Ali O Al-Hamzawi; Jordi Alonso; Laura Helena Andrade; Evelyn J Bromet; Giovanni de Girolamo; Peter de Jonge; Silvia Florescu; Oye Gureje; Hristo Hinkov; Chiyi Hu; Elie G Karam; Georges Karam; Norito Kawakami; Andrzej Kiejna; Viviane Kovess-Masfety; Maria E Medina-Mora; Fernando Navarro-Mateu; Akin Ojagbemi; Siobhan O'Neill; Marina Piazza; Jose Posada-Villa; Charlene Rapsey; David R Williams; Miguel Xavier; Yuval Ziv; Ronald C Kessler; Josep M Haro Journal: Psychol Med Date: 2020-04-28 Impact factor: 7.723
Authors: Stephen E Gilman; Garrett M Fitzmaurice; Martha L Bruce; Thomas Ten Have; M Maria Glymour; Hannah Carliner; George S Alexopoulos; Benoit H Mulsant; Charles F Reynolds; Alex Cohen Journal: Epidemiology Date: 2013-01 Impact factor: 4.822
Authors: A Kathleen Burlew; Jerren C Weekes; La'Trice Montgomery; Daniel J Feaster; Michael S Robbins; Carmen L Rosa; Lesia M Ruglass; Kamilla L Venner; Li-Tzy Wu Journal: Am J Drug Alcohol Abuse Date: 2011-09 Impact factor: 3.829