Can income-based co-payment rates improve disparity? The case of the choice between brand-name and generic drugs

Table 2 Estimation results for Top 5 drugs in terms of number of prescriptions

Dependent variable: Generic drug dispensed, Model: Logistic regression
	rebamipide 100 mg		amlodipine 5 mg		lansoprazole OD 15 mg		sennoside 12 mg		etizolam 0.5 mg
	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI
Copayment rate
10%	ref		ref		ref		ref		ref
30%	0.96^a	0.94–0.98	0.98^a	0.97–1.00	0.96^a	0.94–0.98	0.93^a	0.91–0.95	0.94^a	0.92–0.96
Sex
Male	ref		ref		ref		ref		ref
Female	0.85^a	0.84–0.87	0.80^a	0.78–0.82	0.85^a	0.83–0.87	0.98	0.96–1.00	0.80^a	0.77–0.82
Age, year
75–79	ref		ref		ref		ref		ref
80–84	0.89^a	0.88–0.91	0.86^a	0.83–0.88	0.93^a	0.90–0.96	0.99	0.96–1.02	0.93^a	0.90–0.96
85–89	0.81^a	0.79–0.83	0.82^a	0.80–0.85	0.96^a	0.93–0.99	1.06^a	1.03–1.10	0.90^a	0.86–0.93
90-	0.82^a	0.79–0.84	0.85^a	0.82–0.89	1.07^a	1.03–1.11	1.20^a	1.16–1.24	1.01	0.96–1.06

Notes: OR is the odds ratio estimate, and 95% CI is the associated 95% confidence interval. “ref” indicates the reference group. OD stands for orally disintegrating tablet. This table shows the results from Eq. (1) described in the main text: logistic regression of a binary variable for generic drug dispensed on a 30% copayment rate dummy adjusting for individual characteristics conducted separately for each drug. Adjusted characteristics include sex, age, prescribed amount, area of patient’s residence, monthly medical expenditure (excluding spending on drugs), and monthly spending on drugs besides the analyzed drug. Each spending is calculated as the sum of spending by the patient and the insurer. We show results for the top 5 prescribed drugs in terms of number of prescriptions in our data. ^a indicates significance at the 5% level

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