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EE191 Projecting costs of care burden of depression between 2023 and 2032 in Hong Kong: a time-inhomogeneous cohort Markov model using real-world evidence

Chan, V, Leung, MY, Yang, D, Knapp, Martin ORCID: 0000-0003-1427-0215, Chan, SSM, Lou, H, Craig, D, Chan, EWY and Wong, ICK (2023) EE191 Projecting costs of care burden of depression between 2023 and 2032 in Hong Kong: a time-inhomogeneous cohort Markov model using real-world evidence. Value in Health, 26 (12). S87 - S88. ISSN 1098-3015

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Identification Number: 10.1016/j.jval.2023.09.460

Abstract

Objectives We developed an RWE-based prediction model to estimate the costs of care among patients with depression from 2023 to 2032 from the public healthcare perspective in Hong Kong to inform policy planning. Methods We used a cohort Markov model with yearly cycles, to capture the pathway of treatment-resistant depression (TRD) and co-morbidities development along the disease course. Outcomes included costs of all-cause and psychiatric care. Time-inhomogeneous transition probabilities and cost inputs were derived from our territory-wide electronic medical records. We identified 25,190 patients aged ≥10 years with depression diagnosed between 2014-2016; with a follow-up of seven years maximum until 2020 for their real-world drug use, diagnoses, attendance and deaths. We applied the model as closed cohort, which studied a fixed cohort of incident patients, and open cohort, which introduced new incident patients on a yearly basis derived from the mean depression incidence between 2014-2018 and the published population projections from 2023 onwards. Results With 9,217 new patients with depression in 2023, our closed cohort model projected their cumulative costs of all-cause and psychiatric care would reach US$309 million and US$58 million by 2032. In our open cohort model, 55,849–57,896 active prevalent cases would cost more than US$322 million and US$61 million in all-cause and psychiatric care annually. Despite below 20% of cases living with TRD or co-morbidities, these two states substantially contributed to 31–54% of costs. Sensitivity analysis indicated that the number of new patients, probabilities from the non-TRD state to co-morbidities, low-intensity service users and TRD states were the key cost drivers, which were relevant to the early stages of disease. Conclusions The results of our projection model can aid policy makers to anticipate resource allocation and undertake appropriate budget planning for the preparedness of care need. Our model also forms the basis for further economic evaluation for innovative medicines.

Item Type: Article
Official URL: https://www.valueinhealthjournal.com/
Divisions: Care Policy and Evaluation Centre
Health Policy
Subjects: R Medicine > RA Public aspects of medicine
Date Deposited: 08 Jul 2024 16:06
Last Modified: 14 Sep 2024 10:10
URI: http://eprints.lse.ac.uk/id/eprint/124133

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