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We use the method of coarsened exact matching (CEM) and general practice patient-record data from New Zealand to estimate the impact of mood disorders on medical care costs. The CEM model exploits a discretisation of the data to identify for each patient with a mood disorder a perfect statistical twin, which allows us to control for comorbidity-related variations in medical care costs. CEM with perfect balancing of covariates yields lower annual cost estimates of mood disorders per patient (NZ$366) than regression or conventional matching methods (between $NZ372 and $413). National government expenditures on managing mood disorders are estimated to lie between 0.06 per cent and 0.10 per cent of GDP.

This paper is the first to use the method of coarsened exact matching (CEM) to estimate the impact of mood disorders on medical care costs in order to address the endogeneity of mood disorders. Models are estimated using restricted-use, general practice patient records data from New Zealand for 2009-2012. The CEM model, which exploits a discretization of the data to identify for each patient with a mood disorder a perfect statistical twin, yields estimates of the impact of mood disorders on medical costs that are lower than the estimates obtained from random effects models or conventional matching methods. For example, mood disorders lead to NZ$366 higher annual medical costs (in 2012 dollars) when perfect balancing of covariates is achieved, while minimal and conventional balancing yield estimated costs of over NZ$465 and NZ$400, respectively. The national government expenditures on managing mood disorders is estimated to be 13.4% of total general practice funding (NZ$123 Million) based on CEM.