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Using epidemiological data to model efficiency in reducing the burden of depression*
Gavin Andrews1, Kristy Sanderson, Justine Corry
1UNSW Psychiatry at St. Vincent's Hospital, 299 Forbes Street, Darlinghurst, NSW 2010, Australia, gavina@crufad.unsw.edu.au
The Journal of Mental Health Policy and Economics
|April 23, 2002
Summary
Optimizing depression treatment in Australia could significantly reduce disability burden. Current care is inefficient, with only 13% of the burden averted, while optimal care offers greater health gains at lower costs.
Area of Science:
- Public Health
- Mental Health Research
- Health Economics
Background:
- Mental disorders, particularly depression, represent a leading global cause of disability.
- This study focuses on depression, analyzing current and optimal treatment efficiency in Australia.
Purpose of the Study:
- To model the burden of depression averted by current care.
- To estimate the burden potentially avertable with optimal care.
- To assess the cost-effectiveness of current versus optimal depression services.
Main Methods:
- Calculated effectiveness and efficiency using disability-adjusted life years (DALYs) averted.
- Utilized data from the Australian National Survey of Mental Health and Wellbeing.
- Incorporated treatment efficacy from meta-analyses and cost data from published sources.
Main Results:
- Only 32% of depression cases received effective treatment, averting limited disability.
- Optimal care models demonstrated increased treatment contacts and better outcomes.
- Optimal care averted more DALYs (28,632) at a lower cost (AUD295 million) compared to current care (19,297 DALYs, AUD720 million).
Conclusions:
- Current depression treatment in Australia has low effective coverage, averting only 13% of the total burden.
- Optimal care significantly outperforms current services in both effectiveness and cost-efficiency.
- There is a critical need for improved public and clinician education on effective depression treatments and the development of more powerful interventions.