The American Journal of Managed Care February 2011
Effects of Nonadherence With Prescription Drugs Among Older Adults
Two sets of results are described. “Time to the next ED visit” effects of nonadherence were estimated as the hazard of using an ED after a period of nonadherence (Table 2, Table 3). We also examined the number of annual ED visits relative to the number
of days of adherence using count regressions (Table 4, Table 5). The count regressions modeled the effects of prior years of nonadherence on the current annual use of the ED. The final estimates converted the correlation between nonadherence and ED visits to an equivalent number of additional years of age.
Cox Hazard Rates for Nonadherence
The hazard function measured the instantaneous rate of transition to an ED visit. Consistent with the prior literature,8-11,30-41 we hypothesized that nonadherence is negatively correlated with health and hence is a positive coefficient in the hazard function: when elderly patients are nonadherent with their medications, the associated duration until their next ED is shorter. We partitioned these Cox regression analyses by chronic condition in Table 3 and reported the estimated factors of proportionality there. Missed refills were associated with a higher hazard of visiting an ED, except for patients with COPD. Our results for COPD patients agree with prior estimates.41
The correlation between nonadherence and an ED visit can be expressed in terms of the correlation between an additional year of age and the probability of an ED visit. The correlation between an additional year of age for our older sample and ED use was empirically equivalent to the correlation between being nonadherent for 12 to 18 months and ED use. For example, missing medications for CHD for 12 months had a cumulative impact on the hazard rate of 0.168 (12 × .014), while the effect of growing 1 year older was 0.165. There was no significant change in the results when a covariate was inserted to control for a patient taking multiple medications.
Count Regression Estimates of Nonadherence Effects
The count regression (Poisson regression) measured whether the fraction of nonadherent days was correlated with subsequent ED visits. The analysis was limited to the years 2002-2004 to allow for the lagged covariates (1999-2001). Only persons enrolled in AHCCCS throughout the period were included, to permit the use of fixed-effect models. The year 2005 was omitted because this model’s estimation of N.Adher.Ratio relies on future years to indicate nonadherence.
The descriptive statistics for the nonadherence ratio dummies in Table 4 indicate that the majority of persons in our sample adhered to their medication. The current year nonadherence ratios for hypertension indicated that a slight majority of the subjects were in adherence with their medication all year round: 53.32% (100%–12.71%–9.04%–9.95%–14.98%). The 2 largest nonadherent categories among hypertensive patients in the current year were those who were nonadherent less than 25% of the time (N.Adher.Ratio.1 = 12.71%) and those who were nonadherent 75% or more of the time (N.Adher.Ratio.4 = 14.98%).
The positive coefficients in Table 5 imply that nonadherence was correlated with more ED visits, confirming the Cox regression correlations. In every case, there was a strong relationship between nonadherence and increased ED visits in both current and future years.
For example, consider those who were least adherent with their hypertension medications—those in the N.Adher.Ratio.4 category (about 15% of those with hypertension prescriptions; see Table 4). The patients with hypertension who were least adherent also had 68% [exp(0.5195) = 1.681] more visits to the ED than those who were fully adherent; this difference is statistically significant at better than the .0001 level. The least adherent patients in lagged year 1—regardless of their level of adherence in the current year—had 42% more ED visits than those who were fully adherent in lagged year 1. Regardless of current and prior year adherence, those who were nonadherent 2 years before the current year also had 23% more ED visits in the current year, although again, this increase may not be casual because of time-variant omitted factors. Similar results were obtained for patients taking medication for diabetes or high cholesterol.
While CHD showed the weakest long-term effects (ie, lagged effects) of nonadherence on ED visits, we note that in lagged 1 year, the coefficient for N.Adher.Ratio.3 was both positive and significant and that there were no negatively significant coefficients for this or any other condition (with the exception of the N.Adher.Ratio.1 for the diabetes coefficient in lagged year 2).
Though the Cox hazard-rate model indicated that nonadherence may not significantly impact the number of ED visits for patients with COPD, the results in Table 5 indicated both a strong long-term (lagged 1 year) and current-year effect for COPD.
Adherence to regimens of care is believed to be correlated with adherence to healthy lifestyles. Where, as in our results, healthy lifestyles were unobservable, there was uncertainty concerning the measured effects of adherence. We partially overcame the problem by measuring changes over time for the same individuals (through fixed-effects models) rather than measuring differences among different individuals at a point in time. Together, our models suggest that risks were associated with nonadherence for any of the chronic conditions studied. We believe our models provide somewhat stronger evidence of relationship between medication nonadherence and increased ED visits in our panel than in the prior literature, as time-invariant effects for each individual were controlled for in the analyses. But as is true for all studies in this area, unobservable time-variant changes (changes in the provider-patient relationship, the patient’s cognitive ability, or depression) might have biased our estimates. Further, our estimates for the elderly in Arizona might not generalize to other regions of the country.
Since no prior studies have attempted to estimate the longer term correlation between nonadherence and health as we did with our count regression models, these results show that for many conditions, this omission may actually lead to an underestimation of the costs of nonadherence. Moreover, the very conservative nature of our definition of nonadherence was likely to understate the long-term effects of not maintaining a consistent medication regimen.
Some forms of nonadherence could not be estimated from our data. It has been estimated that as many as one-fifth of all patients never fill their prescriptions and others stop taking the medication before the end of the prescribed period.17 Therefore, our estimates, although more complete than those in studies of shorter durations, might have understated the effects of nonadherence.
Nonadherence both reduces patients’ health and increases healthcare expenditures by reducing the effectiveness of prescription drugs. Increases in adherence to prescribed regimens of care would increase the quality of care while reducing expenditures. Nowhere in the population is the opportunity greater than among older adults with chronic conditions. They are the most intensive users of prescription drugs and their numbers will increase dramatically in the next decade.
If healthcare costs for this portion of the population are to be reduced, it is important that future healthcare systems be structured to increase medication adherence for all patients with the long-term chronic health conditions of hypertension, CHD, COPD, diabetes, and hypercholesterolemia, especially for Medicaid patients such as those examined here. Any decrease in adherence for these conditions can be expected to be associated with an increased number of preventable ED visits.
We acknowledge the Arizona Health Care Cost Containment System (AHCCCS) director and staff for permission to use the data. Besides AHCCCS, helpful comments were also provided by Ryan Rapp, BA, Nathan Kleinman, PhD, and Kent Davis, MD. The authors are solely responsible for the conclusions and analysis.
Author Affiliations: From the Department of Economics (RJB, TKD), Brigham Young University, Provo, UT; Department of Biomedical Informatics and Center for Health Information & Research (WGJ), Arizona State University Biomedicine, Tempe, AZ; and Human Capital Management Services (HHG), Cheyenne, WY.
Funding Source: This research was made possible by a grant from the Gerontology Committee of the School of Family Life at Brigham Young University, and by computing support from the Center for Health Information and Research, Arizona State University.
Author Disclosures: The authors (RJB, TKD, WGJ, HHG) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (RJB, TKD, WGJ, HHG); acquisition of data (WGJ); analysis and interpretation of data (RJB, TKD, WGJ); drafting of the manuscript (RJB, TKD, WGJ); critical revision of the manuscript for important intellectual content (RJB); statistical analysis (RJB, TKD); obtaining funding (HHG); and administrative, technical, or logistic support (HHG).
Address correspondence to: Richard J. Butler, PhD, Brigham Young University, 183 FOB, Provo, UT 84602. E-mail: email@example.com.
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