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Most studies on the impact of the COVID-19 pandemic on depression burden focused on the earlier pandemic phase specific to lockdowns, but the longer-term impact of the pandemic is less well studied. In this population-based cohort study with quasi-experimental design, we examined both the short-term and long-term impacts of COVID-19 on depression incidence and healthcare service use among patients with depression.
Methods
Using the territory-wide electronic medical records in Hong Kong, we identified patients with new diagnoses of depression from 2014 to 2022. An interrupted time-series (ITS) analysis examined changes in incidence of depression before and during the pandemic. We then divided patients into nine cohorts based on year of incidence and studied their initial and ongoing service use until December 2022. Generalized linear modeling compared the rates of healthcare service use in the year of diagnosis between patients newly diagnosed before and during the pandemic. A separate ITS analysis explored the pandemic impact on the ongoing service use among preexisting patients.
Results
There was an immediate increase in depression incidence (RR=1.21; 95% CI: 1.10, 1.33; p<0.001) in the population since the pandemic with a nonsignificant slope change, suggesting a sustained effect until the end of 2022. Subgroup analysis showed that increases in incidence were significant among adults and the older population, but not adolescents. Depression patients newly diagnosed during the pandemic used 11 percent fewer resources than the prepandemic patients in the first diagnosis year. Preexisting depression patients also had an immediate decrease of 16 percent in overall all-cause service use since the pandemic, with a positive slope change indicating a gradual rebound.
Conclusions
During the COVID-19 pandemic, service provision for depression was suboptimal in the face of greater demand generated by the increasing depression incidence. Our findings indicate the need to improve mental health resource planning preparedness for future public health crises.
We developed a real-world evidence (RWE) based Markov model to project the 10-year cost of care for patients with depression from the public payer’s perspective to inform early policy and resource planning in Hong Kong.
Methods
The model considered treatment-resistant depression (TRD) and development of comorbidities along the disease course. The outcomes included costs for all-cause and psychiatric care. From our territory-wide electronic medical records, we identified 25,190 patients with newly diagnosed depression during the period from 2014 to 2016, with follow-up until December 2020 for real-world time-to-event patterns. Costs and time varying transition inputs were derived using negative binomial and parametric survival modeling. The model is available as a closed cohort, which studies a fixed cohort of incident patients, or an open cohort that introduces new patients every year. Utilities values and the number of incident cases per year were derived from published sources.
Results
There were 9,217 new patients with depression in 2023. Our closed cohort model projected that the cumulative cost of all-cause and psychiatric care for these patients would reach USD309 million and USD58 million by 2032, respectively. In our open cohort model, 55,849 to 57,896 active prevalent cases would cost more than USD322 million and USD61 million annually in all-cause and psychiatric care, respectively. Although less than 20 percent of patients would develop TRD or its associated comorbidities, they contribute 31 to 54 percent of the costs. The key cost drivers were the number of annual incident cases and the probability of developing TRD and associated comorbidities and of becoming a low-intensity service user. These factors are relevant to early disease stages.
Conclusions
A small proportion of patients with depression develop TRD, but they contribute to a high proportion of the care costs. Our projection also demonstrates the application of RWE to model the long-term costs of care, which can aid policymakers in anticipating foreseeable burden and undertaking budget planning to prepare for future care needs.
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