Projecting the 10-year costs of care and mortality burden of depression until 2032: a Markov modelling study developed from real-world data

Vivien Kin Yi Chan; Man Yee Mallory Leung; Sandra Sau Man Chan; Deliang Yang; Martin Knapp; Hao Luo; Dawn Craig; Yingyao Chen; David Makram Bishai; Gloria Hoi Yan Wong; Terry Yat Sang Lum; Esther Wai Yin Chan; Ian Chi Kei Wong; Xue Li

doi:10.1016/j.lanwpc.2024.101026

Projecting the 10-year costs of care and mortality burden of depression until 2032: a Markov modelling study developed from real-world data

Lancet Reg Health West Pac. 2024 Feb 6:45:101026. doi: 10.1016/j.lanwpc.2024.101026. eCollection 2024 Apr.

Authors

Vivien Kin Yi Chan¹, Man Yee Mallory Leung^{2

3}, Sandra Sau Man Chan⁴, Deliang Yang², Martin Knapp^{5

6}, Hao Luo⁶, Dawn Craig⁷, Yingyao Chen⁸, David Makram Bishai⁹, Gloria Hoi Yan Wong⁶, Terry Yat Sang Lum⁶, Esther Wai Yin Chan^{1

10}, Ian Chi Kei Wong^{1

10

11}, Xue Li^{1

2

10}

Affiliations

¹ Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
² Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
³ Faculty of Business and Economics, The University of Hong Kong, Hong Kong SAR, China.
⁴ Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China.
⁵ Department of Health Policy, London School of Economics and Political Science, United Kingdom.
⁶ Department of Social Work and Social Administration, Faculty of Social Sciences, The University of Hong Kong, Hong Kong SAR, China.
⁷ Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, United Kingdom.
⁸ National Health Commission Key Laboratory of Health Technology Assessment, Fudan University, China.
⁹ Division of Health Economics, Policy and Management, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
¹⁰ Laboratory of Data Discovery for Health (D4H), Hong Kong SAR, China.
¹¹ Research Department of Policy and Practice, University College London School of Pharmacy, London, United Kingdom.

Abstract

Background: Based on real-world data, we developed a 10-year prediction model to estimate the burden among patients with depression from the public healthcare system payer's perspective to inform early resource planning in Hong Kong.

Methods: We developed a Markov cohort model with yearly cycles specifically capturing the pathway of treatment-resistant depression (TRD) and comorbidity development along the disease course. Projected from 2023 to 2032, primary outcomes included costs of all-cause and psychiatric care, and secondary outcomes were all-cause deaths, years of life lived, and quality-adjusted life-years. Using the territory-wide electronic medical records, we identified 25,190 patients aged ≥10 years with newly diagnosed depression from 2014 to 2016 with follow-up until 2020 to observe the real-world time-to-event pattern, based on which costs and time-varying transition inputs were derived using negative binomial modelling and parametric survival analysis. We applied the model as both closed cohort, which studied a fixed cohort of incident patients in 2023, and open cohort, which introduced incident patients by year from 2014 to 2032. Utilities and annual new patients were from published sources.

Findings: With 9217 new patients in 2023, our closed cohort model projected the 10-year cumulative costs of all-cause and psychiatric care to reach US$309.0 million and US$58.3 million, respectively, with 899 deaths (case fatality rate: 9.8%) by 2032. In our open cohort model, 55,849-57,896 active prevalent cases would cost more than US$322.3 million and US$60.7 million, respectively, with more than 943 deaths annually from 2023 to 2032. Fewer than 20% of cases would live with TRD or comorbidities but contribute 31-54% of the costs. The greatest collective burden would occur in women aged above 40, but men aged above 65 and below 25 with medical history would have the highest costs per patient-year. The key cost drivers were relevant to the early disease stages.

Interpretation: A limited proportion of patients would develop TRD and comorbidities but contribute to a high proportion of costs, which necessitates appropriate attention and resource allocation. Our projection also demonstrates the application of real-world data to model long-term costs and mortality, which aid policymakers anticipate foreseeable burden and undertake budget planning to prepare for the care need in alternative scenarios.

Funding: Research Impact Fund from the University Grants Committee, Research Grants Council with matching fund from the Hong Kong Association of Pharmaceutical Industry (R7007-22).

Keywords: Burden projection; Comorbidities; Cost; Depression; Health policy; Mortality; Real-world data; Real-world evidence; Time-varying Markov model; Treatment-resistant depression.