Considerable research suggests that adherence to medications is an important driver of clinical outcomes for patients with chronic diseases. 14,20–22
In some cases, greater adherence may even result in lower total medical expenditures.23
We extend the available literature by quantifying how price responsiveness varies by income for certain high-value chronic medications. Specifically, these analyses demonstrate that patients in low-income areas were less adherent to recommended medications than patients in higher-income areas. Moreover, medication adherence is more likely to decline when copayments increase for individuals in low-income areas. Our findings imply that increases in patient out-of-pocket expenditures for prescription drugs, a widespread cost containment mechanism, are likely to exacerbate health disparities.
Our study has several advantages over much of the existing literature. For example, inclusion of employer-fixed effects captures any unobserved, time-invariant differences across employers that may affect adherence and be correlated with copayment rates. Moreover, by using an employer-specific copay index as opposed to a measure of the copayment rate for chosen medications, we are able to estimate the effects of copayment rates without selection bias resulting from different types of patients selecting different types of medications. Additionally, by incorporating a measure of office copayment rate, we can control for the potentially confounding effects associated with the costs of physician visits faced by patients.
The magnitude of our elasticity findings, averaged over all income groups, is toward the low end reported in the available literature. Other studies that have used MPR as the outcome measure have shown elasticities ranging from −0.026 to −0.176.8,14,15,24–27
Our results are consistent with other studies that use other measures of effect size. For example, in a recent study of commercially insured patients with CHF, Cole et al. found that a $10 increase in copayments was associated with a 2.6% decrease in MPR for ACE inhibitors and a 1.8% decrease for beta-blockers.21
Adjusting our numbers to a $10.00 change yields comparable estimates of 2.9% and 1.9%, respectively. Two recent studies estimated the effects of cost sharing for patients on diabetes medications and found much stronger results than what we report (Roblin et al. showed that a greater than $10 increase in copayment for a 30-day supply of oral hypoglycemic medications resulted in an 18.5% reduction in daily adherence; Goldman et al. found that a 100% increase in copayment for oral “antidiabetics” [roughly $6–12 increase] resulted in a 25% reduction in utilization).8,33
There are several limitations to our work. First, we do not observe actual household income but instead follow other literature that uses ZIP code–based income measures8,15
. Krieger et al. report more than 50 existing studies that used ZIP code income to proxy for individual income.28
Existing literature examining income and health status gradients suggests that results using income at the ZIP code level are similar to those using income at the narrow census tract or census block level, but the magnitude of the findings may differ if actual household income were observed. For this reason, we interpret our findings as pertaining only to differences in income at the area level but recognize that a substantial portion of our findings may reflect the household level income effects (as opposed to the area-level effect).
Unfortunately, like other studies, we cannot separate area-level versus household-level income effects. Our estimates will also capture the effects of omitted-area traits if they are correlated with the responsiveness to price (as opposed to the level of adherence) but still present an accurate picture of how price changes affect individuals in low-income areas differentially than those in high-income areas. Moreover, because individuals in our sample work for large employers, they may have higher-than-average income in the low-income areas. If so, this may dampen our findings, and the effects reported for the lower-income areas may be underestimated. Nevertheless, these results demonstrate that price sensitivity is likely related to income and increasing copayments likely exacerbate disparities.
Second, we do not observe clinical outcomes. For this reason, we cannot project the clinical effects of the diminished adherence that we observed. Third, we examine only 2 clinical areas and only evaluate workers employed by relatively large firms participating in the MarketScan database, all of which have some form of prescription drug coverage. Because these data are not nationally representative, we cannot be certain whether these results are generalizable to other populations.
Finally, we assumed the same initial mix of medication use in high- and low-income areas, generating the same price index across areas. If individuals in low-income areas used more generics, their true price change would be lower than we assume (because generics had a slower price increase). Thus, the demand change we observe would reflect a response to a smaller price increase, and the elasticity would be higher than we report. People in high-income areas would face a more rapid increase in prices than we report, and so their true elasticity would be smaller than we report. Our conclusions of greater response in low-income areas would be even stronger if we constructed income-specific price indices.
From the provider perspective, the impact of rising cost sharing in low-income areas and probably on low-income individuals may be important. At a minimum, the practitioner needs to be cognizant of the amount of cost sharing faced by patients and the potential for increasing cost sharing to reduce adherence among lower-income patients. Adherence for those patients may need to be monitored more closely, and prescription choice may need to reflect the issues related to adherence.
The finding that increases in cost sharing may worsen socioeconomic health disparities illustrates the difficulty in balancing the desire to control costs with the desire to eliminate inequities. “Across the board” increases in copayment rates will have their greatest effect on lower-income individuals, not only because they will feel the greatest economic burden but also because worsening adherence may lead to relatively larger adverse clinical effects.
One approach to address this concern, referred to as value-based insurance design (VBID), has been proposed.29–31
VBID argues that copayments for high benefit services, such as medications essential for treatment of chronic disease, be kept low. Programs reducing patient copayments for chronic conditions have already been successfully introduced for individuals with DM and asthma.32
Such targeted copayment relief will help shield low-income patients from the deleterious clinical effects of rising copayment rates. As cost pressures continue, more research is needed to distinguish between high- and low-value services and to design systems that ensure that barriers to access, financial or nonfinancial, can be directed toward services of less clinical importance.