Characteristics of the Study Population
Beneficiaries with depression were more likely to spend up to the coverage-gap threshold as their drug coverage improved. For example, among seniors with depression, 43.1% in the no-coverage, 69.2% in the generic-only, and 72.2% in the LIS group reached the gap threshold (P-value <0.05).
reports each group’s characteristics after the propensity score adjustment. After the adjustment, all characteristics used in calculating propensity scores were comparable (p-value>0.05) between each study group and the comparison group. On average, aged beneficiaries with depression have over 4 other coexisting medical conditions such as hypertension, heart failure and diabetes. In particular, in the study groups, about 60% also had hypertension, over 32% had heart failure, and 31% of the no-coverage group and 37% of the generic-only group had diabetes.
Characteristics of Elderly Beneficiaries with Depression Who Entered the Coverage Gap But Did not Go Through it in 2007 after the Propensity Score Weighting*
The mean length of time spent in the gap by the no-coverage group was 112 days, so the length in the pre-gap period was 365–112=253 days. The mean length of time spent in the gap by the LIS group was 128 days and that by the generic-only group was 136 days.
Effects of the Coverage Gap on Medication Use
– present the effects of the coverage gap on the use of antidepressant, heart failure, and oral anti-diabetic medications. These results were estimated from the difference-indifference model with the propensity-score weighting. There are four main findings:
The Impact of the Coverage Gap on Antidepressant Use among Elderly Beneficiaries Diagnosed with Depression in 2007
The Impact of Coverage Gap on Oral Anti-diabetic Medication Use and Spending among Elderly Beneficiaries Diagnosed with Depression in 2007
First, in examining the use of “all antidepressant medications”, having a gap in drug coverage was associated with a significant reduction in all measures of use in both the no-coverage and the generic-only groups.
Second, with the exception of the outcome “using any generic medication”, those with no coverage reduced their use of medications more than those with generic-only coverage. For example, the no-coverage group reduced the number of monthly prescriptions for antidepressants by 12.1% (95% CI 9.9%–14.3%) while the generic-only group reduced it by 6.9% (95% CI 4.8%–9.1%).
Third, most of the reduction in antidepressant use came from the reduction in the number of brand-name drugs. Relative to the comparison group, the no-coverage group reduced the average number of monthly prescriptions for antidepressants by 0.09 (95% CI 0.07–0.11), or by 12.1% (95% CI 9.9%–12.1%), from the pre-gap level. About 77% of this reduction (0.07 out of 0.09) was attributed to lower use of brand-name antidepressants and 23% to lower use of generic antidepressant use. The findings for those with generic coverage were more pronounced: about 86 percent of the reduction in the use of overall antidepressants was attributed to the lower use of brand-name drugs.
Fourth, the pattern of the decrease in the use of heart failure and anti-diabetic drugs in the gap was similar to that for the use of antidepressants. Relative to the comparison group, those with no coverage reduced their monthly number of antidepressants by 12.1% (95% CI 9.9%–14.3%), while they reduced heart failure and anti-diabetic drug usage by 12.9% (95% CI 11.2%–14.7%) and 13.4% (95% CI 8.2%–18.6%) respectively.
Effects of the Coverage Gap on Non-drug Medical Spending
presents results on the effect of the coverage gap on non-drug medical spending, broken-down by physician, outpatient and inpatient spending. The discontinuation in drug use in the gap did not lead to the increase in the non-drug medical use. The probability of hospitalization was lower in the within-gap period compared to the pre-gap period in all three groups, because on average the within-gap period was shorter than the pre-gap period. However, rates of hospitalizations fell more in the no-coverage and generic-only groups, relative to the LIS group. Physician spending also declined in the within-gap compared to the pre-gap period. Thus, after controlling for the comparison group, the gap was not associated with an increase in medical spending.
The Impact of the Coverage Gap on Non-drug Medical Use and Spending among Elderly Beneficiaries Diagnosed with Depression in 2007