Medication non-adherence has been widely recognized as limiting the effectiveness of prescription treatment. However, assessment of adherence and interventions to improve adherence have been largely conditional on patients filling their initial prescriptions, and have failed to accurately account for primary non-adherence. The costs for pharmacies and insurers from patients not picking up medications has been recognized and defined in the industry as “leakage” of prescriptions, but this phenomenon has not been quantified in the medical literature.28
The potential of e-prescribing for accurate measurement of primary non-adherence should create opportunities to improve medication adherence. In this study we found that 22% of e-prescriptions were never filled, with an increase to 28% never filled when we restricted the sample to newly prescribed medications. Several factors were associated with lower primary adherence.
To date there has been a limited body of research addressing primary adherence. Beardon and colleagues tracked over 20,000 prescriptions written in 1989 for patients in general practice in the UK and found 5.2% primary non-adherence,21
while Matsui and colleagues tracked 1,014 prescriptions written in a pediatric ED in 1997 and found 7.3% primary non-adherence.8
Getting copies of actual prescriptions is very cumbersome, so most other studies of primary adherence relied on surveys, finding non-adherence rates ranging from 4% to 21.6%.23,24
The increasing use of e-prescribing creates the opportunity to measure primary non-adherence in larger populations, but very few such studies have been published to date. Ekedahl and Mansson evaluated over 91,000 e-prescriptions in a Swedish population with universal drug coverage and found a non-adherence rate of 2.4%.22
Two recent studies done at the Geisinger clinic in Pennsylvania used electronic data to study the care of patients with single conditions. One found that patients being started on treatment for hypertension failed to fill new prescriptions 17% of the time27
and another found that those being started on treatment for diabetes failed to fill new prescriptions 15% of the time.26
Our study found slightly higher rates of primary non-adherence. We evaluated patients treated in mostly smaller practices using stand-alone e-prescribing systems while the Geisinger clinic is a large integrated system with a full electronic health record. In addition, our data evaluated the first year of a new e-prescribing program during which e-prescribing use was increasing slowly, only approaching 50% of prescriptions at the end of the study period.29
We cannot say for certain if those differences in study settings account for the higher rate of non-adherence in our study. In either case, non-adherence in US studies was higher than in other countries; it is not clear whether this is due to differences between populations and health care systems or is due to differences in the ability to ascertain prescription filling in the United States; additional studies will be needed to verify these results.
From a policy perspective, the high rate of non-adherence to medications for hypertension, diabetes, and hyperlipidemia is an especially alarming finding; these three conditions contribute greatly to the burden of mortality and morbidity from cardiovascular disease in the United States. It has previously been shown that many patients with these conditions either lack awareness of their diagnoses or are not receiving treatment.30,31
If over one-quarter of such patients do not fill the medications prescribed by their physicians, the potential health gains from these medications will not be realized. In a study of patients being discharged after myocardial infarction, Jackevicius and colleagues found that lower adherence with discharge medications was associated with increased 1-year mortality.32
Whether these findings would hold in outpatient treatment has not yet been established. Interventions aimed at improving the quality of chronic disease management must address the needs and challenges of patients who are first initiated on essential medications.
Interventions to reduce primary non-adherence will be most effective if targeted towards those who are least likely to fill prescriptions. Higher patient cost-sharing has been shown to delay initiation of treatment for chronic conditions.33
Correspondingly, prescription of medications with lower copayments appeared to be associated with better primary adherence in two recent studies of single drug classes.26,27
These results highlight the importance of prescribing in compliance with a patient’s formulary, which was previously shown to affect refill adherence.34
We had limited patient-specific information, but did have a borderline significant finding that women were less likely than men to fill prescriptions, as found in an earlier study of hypertension medications,27
but not in a study of diabetes.26
Prescriptions from primary care clinicians were more likely to be filled. While some physician characteristics (female gender, young age) were associated with lower rates of primary adherence, our ability to explain non-adherence in this population is relatively limited. Some physicians may be more likely to prescribe lower-tier medications from patients′ formularies.35
Unmeasured patient and physician factors, such as the extent of physician–patient communication and education about prescriptions are likely to influence adherence. Further research will be needed to confirm and understand the causes of these findings and to develop interventions to improve primary adherence. The potential to identify primary non-adherence rapidly and accurately may allow for such interventions to be targeted specifically towards patients likely to benefit from education about the importance of their prescribed medications.
There are limitations that must be considered when interpreting these results. Our population is drawn from early adopters of e-prescribing and e-prescribing was not used for all prescriptions issued,29
so the physicians and patients may be atypical, limiting the generalizability of our findings. We have limited data on patient and physician characteristics, which limits our ability to explain the causes of non-adherence. Since we do not have data on the number of prescriptions written for patients who did not get e-prescriptions, we cannot extrapolate these findings to more general populations. Actions taken by patients, prescribers, or pharmacists that were not captured in our data may lead us to underestimate the rate of adherence. For example, if patients pay cash for inexpensive prescriptions (e.g. $4 generics),36
or fill medications at another location (e.g. VA) we would misclassify the prescription as unfilled. Any other data errors in the process of matching e-prescribing data to filled claims data could lead to a patient to being falsely labeled as non-adherent–this type of misclassification of the outcome could lead us to overestimate non-adherence. There are not standard approaches for performing these types of matches, so there is no benchmark against which we can compare our matching results; the methods that we employed will need to be evaluated further in other settings.
Telephone contacts between the pharmacy and the prescriber that lead to verbal changes in the medication prescribed would also cause us to misclassify prescriptions as unfilled. Our claims data ended on March 31, 2005, so we may have misclassified some e-prescriptions written in the final month but filled later. Non-adherence was only 4% higher for the final month and excluding it did not change any of the effect estimates in our models, so we did not exclude those data. Prescriptions given for possible but not definite use (e.g. antibiotics prescribed to be filled only if culture results are positive) would lead us to underestimate primary adherence. Although these errors may occur, we suspect they would not account for a large portion of the e-prescriptions.
These findings highlight the need to reprioritize efforts when trying to improve the quality of prescription drug care in the United States. We must continue to support patients who have already filled prescriptions for essential medication to enhance their persistence. However, our results suggest that relying solely on such a strategy to improve medication use will lead us to miss the potentially large number of patients who received a prescription but never initiate therapy. Our ability to link e-prescribing data to filled claims allows us to measure primary non-adherence, which was not possible previously. As more physicians adopt e-prescribing, the methods we describe here can be used to understand the predictors of non-adherence, explore barriers to appropriate medication use, and potentially provide adherence information back to prescribers. This research will be instrumental in developing interventions that encourage patients to initiate medications when prescribed, and to adhere to a full course of chronic therapy.