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One size fits some: the impact of patient treatment attitudes on the cost-effectiveness of a depression primary-care intervention.

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Title: One size fits some: the impact of patient treatment attitudes on the cost-effectiveness of a depression primary-care intervention.
Authors: Pyne JM (AUTHOR), Rost KM (AUTHOR), Farahati F (AUTHOR), Tripathi SP (AUTHOR), Smith J (AUTHOR), Williams DK (AUTHOR), Fortney J (AUTHOR), Coyne JC (AUTHOR)
Source: Psychological Medicine. Jul2005, Vol. 35 Issue 6, p839-854. 16p.
Abstract: BACKGROUND: Despite their impact on outcomes, the effect of patient treatment attitudes on the cost-effectiveness of health-care interventions is not widely studied. This study estimated the impact of patient receptivity to antidepressant medication on the cost-effectiveness of an evidence-based primary-care depression intervention. METHOD: Twelve community primary-care practices were stratified and then randomized to enhanced (intervention) or usual care. Subjects included 211 patients beginning a new treatment episode for major depression. At baseline, 111 (52.6%) and 145 (68.7%) reported receptivity to antidepressant medication and counseling respectively. The intervention trained the primary-care teams to assess, educate, and monitor depressed patients. Twelve-month incremental (enhanced minus usual care) total costs and quality-adjusted life years (QALYs) were calculated. RESULTS: Among patients receptive to antidepressants, the mean incremental cost-effectiveness ratio was dollar 5,864 per QALY (sensitivity analyses up to dollar 14,689 per QALY). For patients not receptive to antidepressants, the mean incremental QALY score was negative (for both main and sensitivity analyses), or the intervention was at least no more effective than usual care. CONCLUSIONS: These findings suggest a re-thinking of the 'one size fits all' depression intervention, given that half of depressed primary-care patients may be non-receptive to antidepressant medication treatment. A brief assessment of treatment receptivity should occur early in the treatment process to identify patients most likely to benefit from primary-care quality improvement efforts for depression treatment. Patient treatment preferences are also important for the development, design, and analysis of depression interventions. [ABSTRACT FROM AUTHOR]
Copyright of Psychological Medicine is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: One size fits some: the impact of patient treatment attitudes on the cost-effectiveness of a depression primary-care intervention.
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  Data: <searchLink fieldCode="AR" term="%22Pyne+JM%22">Pyne JM</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rost+KM%22">Rost KM</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Farahati+F%22">Farahati F</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tripathi+SP%22">Tripathi SP</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Smith+J%22">Smith J</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Williams+DK%22">Williams DK</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fortney+J%22">Fortney J</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Coyne+JC%22">Coyne JC</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Psychological+Medicine%22">Psychological Medicine</searchLink>. Jul2005, Vol. 35 Issue 6, p839-854. 16p.
– Name: Abstract
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  Group: Ab
  Data: BACKGROUND: Despite their impact on outcomes, the effect of patient treatment attitudes on the cost-effectiveness of health-care interventions is not widely studied. This study estimated the impact of patient receptivity to antidepressant medication on the cost-effectiveness of an evidence-based primary-care depression intervention. METHOD: Twelve community primary-care practices were stratified and then randomized to enhanced (intervention) or usual care. Subjects included 211 patients beginning a new treatment episode for major depression. At baseline, 111 (52.6%) and 145 (68.7%) reported receptivity to antidepressant medication and counseling respectively. The intervention trained the primary-care teams to assess, educate, and monitor depressed patients. Twelve-month incremental (enhanced minus usual care) total costs and quality-adjusted life years (QALYs) were calculated. RESULTS: Among patients receptive to antidepressants, the mean incremental cost-effectiveness ratio was dollar 5,864 per QALY (sensitivity analyses up to dollar 14,689 per QALY). For patients not receptive to antidepressants, the mean incremental QALY score was negative (for both main and sensitivity analyses), or the intervention was at least no more effective than usual care. CONCLUSIONS: These findings suggest a re-thinking of the 'one size fits all' depression intervention, given that half of depressed primary-care patients may be non-receptive to antidepressant medication treatment. A brief assessment of treatment receptivity should occur early in the treatment process to identify patients most likely to benefit from primary-care quality improvement efforts for depression treatment. Patient treatment preferences are also important for the development, design, and analysis of depression interventions. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Psychological Medicine is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Jul2005
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