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This overview discusses the difficulties in measuring adherence to therapy for chronic illness using electronic databases and other methods. It explores the uses of reliable measures of adherence and various methods to assess adherence to long-term medications. Additionally, it examines demographic factors as correlates of adherence and the differences between subjective and objective methods. The article concludes with general recommendations for measuring long-term adherence.
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Overview of Issues in Measuring Adherence to Therapy for Chronic Illness Using Electronic Databases and Other Methods Dennis Ross-Degnan Harvard Medical School and Harvard Pilgrim Health Care
Adherence: Easy to Define but Difficult to Measure “the extent to which a person’s behavior – taking medication, following a diet, and/or executing lifestyle changes – corresponds with agreed recommendations from a health care provider” WHO Adherence to Long-Term Therapies Project
Uses of Reliable Measures of Adherence to Long-term Therapy • Clinical • Improve individual patient care • Target interventions to patients at risk • Behavioral research • Determinants of adherence • Barriers to improved adherence • Health services research • Relationships among adherence, clinical response, service use, and outcomes • Monitor program effectiveness
Methods to Assess Adherence to Long-Term Medications • Subjective measures • Physician chart entry or report • Patient or caretaker report or diary • Objective measures • Pill counts: counting remaining medicines, dispensing records • Physical tests: pharmacologic (serum or urine levels), physiologic (e.g., HbA1c) • Medication Event Monitoring Systems
Methods to Assess Treatment Adherence in 513 (314 Medication ) Studies Average adherence rates observed: 85.1% Objective Methods: 80.2% 72.9% 69.0% 72.6% Subjective Methods: 71.8% 71.8% 66,6% Source: DiMatteo, Medical Care 2004; 42(3): 200-209
Comparison of Adherence to Medications vs. Other Therapeutic Regimens Number of regimens: 328 9 13 88 57 25 Source: DiMatteo, Medical Care 2004; 42(3): 200-209
Average Adherence to Therapy in Studies of Selected Disease Conditions Number of studies: 8 22 42 65 9 17 129 19 34 20 41 23 16 Source: DiMatteo, Medical Care 2004; 42(3): 200-209
Demographic Factors as Correlates of Adherence • Age: Relationship varies widely • Children: higher in young vs. adolescents • Adult: Self-report negatively associated but other measures positively associated • Gender: In adults no relationship, in children, females more adherent • Education: Positively associated, mainly for chronic illness • Income/SES: Positively associated • Mostly studies using numeric income measures • Primarily in adult samples Source: DiMatteo, Medical Care 2004; 42(3): 200-209
Choosing Subjective vs. Objective Methods • No gold standard • Subjective • Can be direct, simple, inexpensive • Differences in respondent framing, interpretation • Potential bias (recall, social desirability, self-perception) but not inflated in meta-analysis • Objective • Difficult to measure key concepts of interest • Generally more expensive and complex • Potential for missing and unreliable data
Types of Information in Electronic Dispensing Records • Identification of drug • Name, unique code, strength • Indication or diagnosis (usually not available) * • Measures of amount • Quantity dispensed • Cost of medication • Indication of intended regimen • Dosage instructions * • Days’ supply dispensed * * Data are usually less reliable in most systems
Dispensing-based Adherence Measures: Positives and Negatives • Validity and Reliability • Can measure long-term patterns • Uncertain relation to actual use • No measure of actual timing of use • Missing data • Feasibility and Cost • Often derived from electronic data collected for other purposes • Requires sophisticated data handling
Types of Adherence Measures Derived From Dispensing Data • Continuous (e.g., % of expected dose taken) vs. dichotomous (e.g., if >80% of expected dose taken) • Single (2 fills) vs. multiple (many fills) intervals • Medication availability vs. gaps in availability during fixed measurement window Source: Steiner JF et al. J Clin Epidemiol 1997; 50(1), 105-116
Some Possible Measures of Adherence from Dispensing Data • Primary non-adherence • Does not fill a prescription in <#> days • Adherence • # days of medicine available divided by # of days in a period (but not > 1.0?) • Gap in treatment • # days between dispensings greater than # days supplied in first dispensing • Discontinuation • Treatment gap more than <#> days (30?)
Fill date = 0 30 75 130 210 Gap length 15 25 40 1.0 1.0 0.5 0.7 1.0 1.0 1.0 1.0 1.0 0.7 0.0 Adherence Discontinued Measuring Adherence Using Dispensing Data 0.5 Adapted from: Steiner JF et al. J Clin Epidemiol 1997; 50(1), 105-116
Problems in Interpreting Dispensing-Based Measures • Unreliable information biases measures • Dosage regimen or days supply • Unrecorded changes in regimen • Missing dispensings • Uncertainty about dynamics of observed consumption • Timing of doses • How distributed over time • Whether discontinued
General Recommendations in Measuring Long-term Adherence • Important to validate data reliability and completeness • Triangulate using both objective and subjective indicators • Medication consumption, OPD visits • Reported adherence behaviors • Reported reasons for non-adherence • Clinical correlates
Special Case: Measuring Adherence to ARVs • Key issues: • ART requires continuous tight adherence • Complex socio-behavioral context • Minimum indicators should include • Overall medication consumption • Dose consistency and timing • Reported reasons for inconsistent adherence • Frequency and severity of reported side effects • Consistency of clinical visits • Key physiological measures (CD4) • Patient, care provider, and clinician attitudes