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      The Economic Burden of Adults with Major Depressive Disorder in the United States (2010 and 2018)

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          Abstract

          Background

          The incremental economic burden of US adults with major depressive disorder (MDD) was estimated at $US210.5 billion in 2010 (year 2012 values).

          Objective

          Following a similar methodology, this study updates the previous findings with more recent data to report the economic burden of adults with MDD in 2018.

          Method

          This study used a framework for evaluating the incremental economic burden of adults with MDD in the USA that combined original and literature-based estimates, focusing on key changes between 2010 and 2018. The prevalence rates of MDD by sex, age, employment, and treatment status over time were estimated based on the National Survey on Drug Use and Health (NSDUH). The incremental direct and workplace costs per individual with MDD were primarily derived from administrative claims data and NSDUH data using comparative analyses of individuals with and without MDD. Societal direct and workplace costs were extrapolated by multiplying NSDUH estimates of the number of people with MDD by the direct and workplace cost estimates per patient. The suicide-related costs were estimated using a human capital method.

          Results

          The number of US adults with MDD increased by 12.9%, from 15.5 to 17.5 million, between 2010 and 2018, whereas the proportion of adults with MDD aged 18–34 years increased from 34.6 to 47.5%. Over this period, the incremental economic burden of adults with MDD increased by 37.9% from $US236.6 billion to 326.2 billion (year 2020 values). All components of the incremental economic burden increased (i.e., direct costs, suicide-related costs, and workplace costs), with the largest growth observed in workplace costs, at 73.2%. Consequently, the composition of 2018 costs changed meaningfully, with 35% attributable to direct costs (47% in 2010), 4% to suicide-related costs (5% in 2010), and 61% to workplace costs (48% in 2010). This increase in the workplace cost share was consistent with more favorable employment conditions for those with MDD. Finally, the proportion of total costs attributable to MDD itself as opposed to comorbid conditions remained stable at 37% (38% in 2010).

          Conclusion

          Workplace costs accounted for the largest portion of the growing economic burden of MDD as this population trended younger and was increasingly likely to be employed. Although the total number of adults with MDD increased from 2010 to 2018, the incremental direct cost per individual declined. At the same time, the proportion of adults with MDD who received treatment remained stable over the past decade, suggesting that substantial unmet treatment needs remain in this population. Further research is warranted into the availability, composition, and quality of MDD treatment services.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s40273-021-01019-4.

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          Most cited references26

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          A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation

          The objective of this study was to develop a prospectively applicable method for classifying comorbid conditions which might alter the risk of mortality for use in longitudinal studies. A weighted index that takes into account the number and the seriousness of comorbid disease was developed in a cohort of 559 medical patients. The 1-yr mortality rates for the different scores were: "0", 12% (181); "1-2", 26% (225); "3-4", 52% (71); and "greater than or equal to 5", 85% (82). The index was tested for its ability to predict risk of death from comorbid disease in the second cohort of 685 patients during a 10-yr follow-up. The percent of patients who died of comorbid disease for the different scores were: "0", 8% (588); "1", 25% (54); "2", 48% (25); "greater than or equal to 3", 59% (18). With each increased level of the comorbidity index, there were stepwise increases in the cumulative mortality attributable to comorbid disease (log rank chi 2 = 165; p less than 0.0001). In this longer follow-up, age was also a predictor of mortality (p less than 0.001). The new index performed similarly to a previous system devised by Kaplan and Feinstein. The method of classifying comorbidity provides a simple, readily applicable and valid method of estimating risk of death from comorbid disease for use in longitudinal studies. Further work in larger populations is still required to refine the approach because the number of patients with any given condition in this study was relatively small.
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            Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.

            Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms. ICD-10 coding algorithms were developed by "translation" of the ICD-9-CM codes constituting Deyo's (for Charlson comorbidities) and Elixhauser's coding algorithms and by physicians' assessment of the face-validity of selected ICD-10 codes. The process of carefully developing ICD-10 algorithms also produced modified and enhanced ICD-9-CM coding algorithms for the Charlson and Elixhauser comorbidities. We then used data on in-patients aged 18 years and older in ICD-9-CM and ICD-10 administrative hospital discharge data from a Canadian health region to assess the comorbidity frequencies and mortality prediction achieved by the original ICD-9-CM algorithms, the enhanced ICD-9-CM algorithms, and the new ICD-10 coding algorithms. Among 56,585 patients in the ICD-9-CM data and 58,805 patients in the ICD-10 data, frequencies of the 17 Charlson comorbidities and the 30 Elixhauser comorbidities remained generally similar across algorithms. The new ICD-10 and enhanced ICD-9-CM coding algorithms either matched or outperformed the original Deyo and Elixhauser ICD-9-CM coding algorithms in predicting in-hospital mortality. The C-statistic was 0.842 for Deyo's ICD-9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and 0.859 for the enhanced ICD-9-CM coding algorithm, 0.868 for the original Elixhauser ICD-9-CM coding algorithm, 0.870 for the ICD-10 coding algorithm and 0.878 for the enhanced ICD-9-CM coding algorithm. These newly developed ICD-10 and ICD-9-CM comorbidity coding algorithms produce similar estimates of comorbidity prevalence in administrative data, and may outperform existing ICD-9-CM coding algorithms.
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              Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries.

              With advances in the effectiveness of treatment and disease management, the contribution of chronic comorbid diseases (comorbidities) found within the Charlson comorbidity index to mortality is likely to have changed since development of the index in 1984. The authors reevaluated the Charlson index and reassigned weights to each condition by identifying and following patients to observe mortality within 1 year after hospital discharge. They applied the updated index and weights to hospital discharge data from 6 countries and tested for their ability to predict in-hospital mortality. Compared with the original Charlson weights, weights generated from the Calgary, Alberta, Canada, data (2004) were 0 for 5 comorbidities, decreased for 3 comorbidities, increased for 4 comorbidities, and did not change for 5 comorbidities. The C statistics for discriminating in-hospital mortality between the new score generated from the 12 comorbidities and the Charlson score were 0.825 (new) and 0.808 (old), respectively, in Australian data (2008), 0.828 and 0.825 in Canadian data (2008), 0.878 and 0.882 in French data (2004), 0.727 and 0.723 in Japanese data (2008), 0.831 and 0.836 in New Zealand data (2008), and 0.869 and 0.876 in Swiss data (2008). The updated index of 12 comorbidities showed good-to-excellent discrimination in predicting in-hospital mortality in data from 6 countries and may be more appropriate for use with more recent administrative data. © The Author 2011. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved.
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                Author and article information

                Contributors
                Andree-Anne.Fournier@analysisgroup.com
                Journal
                Pharmacoeconomics
                Pharmacoeconomics
                Pharmacoeconomics
                Springer International Publishing (Cham )
                1170-7690
                1179-2027
                5 May 2021
                5 May 2021
                : 1-13
                Affiliations
                [1 ]GRID grid.417986.5, ISNI 0000 0004 4660 9516, Analysis Group, Inc, ; 111 Huntington Ave., 14th Floor, Boston, MA 02199 USA
                [2 ]GRID grid.38142.3c, ISNI 000000041936754X, Harvard Medical School, ; Boston, MA USA
                Author information
                http://orcid.org/0000-0002-1601-0858
                Article
                1019
                10.1007/s40273-021-01019-4
                8097130
                33950419
                57dccd10-c552-404e-8b39-688a5a18c339
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 11 March 2021
                Categories
                Original Research Article

                Economics of health & social care
                Economics of health & social care

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