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The American Journal of Managed Care February 2011
Effect of Multiple Chronic Conditions Among Working-Age Adults
James M. Naessens, ScD; Robert J. Stroebel, MD; Dawn M. Finnie, MPA; Nilay D. Shah, PhD; Amy E. Wagie, BA; William J. Litchy, MD; Patrick J. F. Killinger, MA; Thomas J. D. O'Byrne, BS; Douglas L. Wood, MD; and Robert E. Nesse, MD
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Karen M. Keogh, PhD; Susan M. Smith, MD; Patricia White, PhD; Sinead McGilloway, PhD; Alan Kelly, PhD; James Gibney, MD; and Tom O'Dowd, MD
Abolishing Coinsurance for Oral Antihyperglycemic Agents: Effects on Social Insurance Budgets
Kostas Athanasakis, MSc; Anastasis G. Skroumpelos, MSc; Vassiliki Tsiantou, MSc; Katerina Milona, MSc; and John Kyriopoulos, PhD
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Effects of Nonadherence With Prescription Drugs Among Older Adults
Richard J. Butler, PhD; Taylor K. Davis, BA; William G. Johnson, PhD; and Harold H. Gardner, MD
Timing of Follow-up After Abnormal Screening and Diagnostic Mammograms
Karen J. Wernli, PhD; Erin J. Aiello Bowles, MPH; Sebastien Haneuse, PhD; Joanne G. Elmore, MD, MPH; and Diana S.M. Buist, PhD, MPH
Excess Hospitalization Days in an Academic Medical Center: Perceptions of Hospitalists and Discharge Planners
Christopher S. Kim, MD, MBA; Anita L. Hart, MD; Robert F. Paretti, MD; Latoya Kuhn, MPH; Ann E. Dowling, BSN, RN; Judy L. Benkeser, BSN, RN; and David A. Spahlinger, MD
Health Insurance in India: Need for Managed Care Expertise
Thomas K. Thomas, MBA
Outpatient Wait Time and Diabetes Care Quality Improvement
Julia C. Prentice, PhD; B. Graeme Fincke, MD; Donald R. Miller, ScD; and Steven D. Pizer, PhD

Effects of Nonadherence With Prescription Drugs Among Older Adults

Richard J. Butler, PhD; Taylor K. Davis, BA; William G. Johnson, PhD; and Harold H. Gardner, MD
Prescription nonadherence in older patients with chronic health conditions resulted in more emergency department use.

Objective: To determine the association between prescription nonadherence and the health of older adults with 1 or more of 5 chronic conditions.


Study Design: Analysis of the correlation between prescription nonadherence and the health of older adults.


Methods: Data were from a 7-year panel with information on healthcare encounters and prescriptions. We used fixed-effects Cox duration regression models and fixed-effects Poisson count regression models to control for time-invariant factors specific to each subject when examining the impact of nonadherence on time to an emergency department (ED) visit (Cox regression) or number of ED visits (Poisson count regression).


Results: Nonadherence was associated with subsequent ED visits for hypertension, chronic heart disease, diabetes, and hypercholesterolemia. No significant short-term effects of nonadherence on chronic obstructive pulmonary disease (COPD) were detected. However, long-term effects of nonadherence (using the count regression model with lagged counts) were detected for COPD, as well as for hypertension, diabetes, and hypercholesterolemia.


Conclusions: Medicinal adherence was important for all 5 conditions analyzed here. A year of nonadherence had the same correlation with ED use as making an older adult 1 year older.


(Am J Manag Care. 2011;17(2):153-160)

The correlation between emergency department use and adherence with prescription medicine in older adults with chronic conditions (hypertension, chronic heart disease, diabetes, chronic obstructive pulmonary disease, and hypercholesterolemia) was analyzed.


  • Older adults with hypertension and high cholesterol had poor outcomes up to 3 years after their period of nonadherence.


  • A year of nonadherence had the same correlation with ED use as making an older adult 1 year older.
Approximately 88% of persons aged >60 years in the United States take prescription drugs, usually on a regular basis.1 The effectiveness of many prescription drugs is seriously impaired by the failure of patients to follow the recommended regimen of medication. Nonadherence rates are as high as 40% to 86% for some conditions and/or patient groups, resulting in  preventable visits to emergency departments (EDs) and inpatient stays.2-6 It is estimated that between 4% and 11.4% of all  hospitalizations and 7.6% of all ED visits are related to nonadherence.7-10 These estimates suggest that increased adherence  could significantly improve health and reduce healthcare costs,6,11 especially among older adults.12

The incidence of ED visits among adults increases with age. As adults age, they are more likely to arrive at an ED by ambulance,13-16 to have longer ED visits,14-16 and to be admitted from the ED to inpatient care.13,15,17,18 Those who are admitted to inpatient care are more likely than younger adults to require care in an intensive care unit.17

In this article we describe a retrospective, longitudinal study of adherence to prescription drugs in the years 1999-2005 among persons aged >60 years who were insured by Medicaid (Arizona Health Care Cost Containment System [AHCCCS]). Each subject had 1 or more of 5 chronic conditions. We compared ED visits among adherent older adults with visits by older adults who were nonadherent.

The benefits of increased adherence appear large in current studies, but these benefits may be understated because they are based on periods of less than 2 years, omitting the longer-term benefits among chronically ill patients. This study contributes information on the longer-term benefits.


Subject Population

The subjects include all AHCCCS-insured persons aged >60 years at some time during the study period (January 1, 1999–December 31, 2005) with 1 or more of 5 chronic conditions: hypertension, chronic obstructive pulmonary disease (COPD), chronic heart disease (CHD), diabetes, or hypercholesterolemia (high blood cholesterol). The subjects lived in Maricopa County, Arizona, which includes Phoenix, the fifth largest city in the United States. Approximately 127,916 persons met the selection criteria. The AHCCCS data include details of all healthcare encounters; all filled prescriptions; Current Procedural  Terminology, International Classification of Diseases, Ninth Revision, and National Drug Code (NDC) codes; and patient demographics. Patient characteristics are shown in Table 1.

The number of records in Table 1 exceeded 127,916 because individuals had multiple conditions. Elimination of records with inconsistent or missing values reduced the number of cases that were used in the estimation (row 2 vs row 1 in Table 1).

Measuring Nonadherence

Nonadherence existed when 8 or more days elapsed between the time a prescription was consumed and the time it was refilled, after which we assumed the patient remained nonadherent until the prescription was refilled. We excluded prescriptions that were not refilled within 365 days because we could not know whether this patient was taken off the medicine  by a physician, a change in medication was made, or there was simply a long period of nonadherence.

Estimation of Average Number of Pills per Day. The drugs normally prescribed for each condition were identified from the published results of the national Medical Expenditure Panel Survey19 and associated with their respective NDC. The number of pills consumed per day was estimated by dividing the number of pills refilled by the number of days from the original prescription.

The intended prescription length was estimated by dividing the quantity of pills prescribed by the number of pills consumed per day. The majority of the prescriptions were intended to last 30 days, with a median of 33 days. We assumed that the intended length for estimates from 25 to 38 days was 30 days and reestimated intended lengths. The estimates seemed to be a reasonable approximation of the number of pills per day. Only 5.3% of the 1692 NDCs had estimated values that were not exact multiples of 0.5 or 0.333.

Measuring Emergency Department Visits and Other Variables

We could not identify the primary reason for an ED visit by older persons with chronic conditions. We assumed that our estimates were reasonable for comparing adherent with nonadherent patients since injuries or acute conditions should be distributed more or less equally between adherent and nonadherent patients.

Statistical Analysis

Randomization of treatment and control groups is the gold standard for drug studies, but it is impossible to randomize when analyzing adherence to medications for chronic conditions. There is the moral problem of randomly halting medication for some elderly persons but not for others, as well as forcing control group members to take their medications when they may choose to do otherwise. The inability to randomize makes it difficult for cross-sectional studies to separate the effects of adherence from  individual-specific healthy lifestyles and other unobservable personal characteristics.20 This sample selection problem may  result in upward bias of the adherence effects, which appear large in current cross-sectional studies. This bias may be partially offset by the omission of longer term effects in the cross-sectional studies, which typically used sample periods of less than 2 years.

We partially corrected for the problem of unobserved characteristics by using fixed-effects models that identified nonadherence effects for those individuals who changed adherence over time, rather than identifying the effect of nonadherence by using variation between patients at a point in time. While reasons for nonadherence among older persons are not all well known, clear relationships have been found with dose, insurance coverage, complexity of treatment, race, and number of medications.21-26 Race is of course time invariant, dosage and medications were included in the data, and the persons in the sample had AHCCCS coverage throughout the period and were not subject to copays or deductibles. However, the complexity of treatment was unobserved and changes in complexity could either increase or decrease adherence.

There are other important time-variant but unobservable influences on adherence, including changes in providerpatient relationships, depression, and changes in cognitive impairment,21,22,27-29 which was a limitation of our study and biased our attempt to estimate the effects of nonadherence. However, regressions of nonadherence on patient and treatment characteristics for AHCCCS patients suggest that our biases might have been relatively small. Medicinal adherence is higher for persons over 65 years of age than younger age groups, suggesting that age-related cognitive impairments are probably not  an important driver of nonadherence for our sample. Treating physician specialty—which also may change over time for a given patient—is statistically insignificant in explaining adherence. Moreover, comorbidities, which are correlates of the complexity of  care, were generally statistically insignificant in explaining adherence. In particular, for AHCCCS patients, depression exhibited either a statistically insignificant or quantitatively small effect on adherence. Our fixed-effects models improved on previous estimates but could not control for all potential time-variant but unobservable influences. Our results must be interpreted within those constraints.

We used 2 models—a Cox proportional hazard model and an unconstrained Poisson maximum likelihood model (ie, a count regression model), both with fixed effects for each individual in our sample.

Cox Proportional Hazard Model. The Cox model estimated the correlation between nonadherence and the propensity to use the ED, given the time elapsed since the last ED visit. Each observation consisted of the length of time beginning 7 days after a prescription was estimated to be exhausted until the next ED visit. Also included were a dummy variable recording whether or not the given prescription was filled on time, an age variable, and a censoring variable indicating (before an ED visit) nonadherence durations still in process (ie, not yet completed) when the sample was taken.

(1) h[t, Noncomp, Age]i = hi(t,ai) × exp(B1Noncomp B2 Age)

The hi(t,ai) function is the baseline hazard function for each individual, including an ai-effect representing all time-invariant covariates. Each individual had his or her own baseline time between ED visits. The baseline duration was assumed to be proportionally shifted by the “exp(B1Noncomp B2Age)” term. Because of repeated spells on the same patients, the Cox regression partialed out the hi(t,ai) term for each individual, controlling for their baseline duration and time-invariant factors (including race, sex, family size, genetic predisposition, motivational and ability factors, and the duration of medication use before each person was initially observed in the sample.

Count Regression Estimates of Nonadherence Effects. The count regression measured the correlation between the number of adherent days and the number of subsequent ED visits. The mean number of ED visits for the subjects was higher (between 2 and 3 visits per year) than the median number.

The count regression model estimated longer term trends by including nonadherent outcomes in prior years. A nonadherent ratio (N.Adher.Ratio) for each year in which a patient was continuously enrolled in AHCCCS was calculated as follows:

(2) N.Adher.Ratio = (number of nonadherence days)

                                          (total days in year)

Four dummy variables measured variations in ED use with respect to different levels of nonadherence to capture possible nonlinear effects between number of ED visits and levels of nonadherence:

N.Adher.Ratio.1 = 1 if 0 <N.Adher.Ratio .25

2 = 1 if .25 <N.Adher.Ratio .5

3 = 1 if .5 <N.Adher.Ratio .75

4 = 1 if .75 <N.Adher.Ratio 1

The fixed-effects, unconditional Poisson count regression, estimated for each condition by maximum likelihood, was:

(3) E(ED) = exp(Ø ai μt NADH0xt,1 NADH1xt–1,i NADH2x2i NADH3x3i )

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