Adherence: Easy to Define but Difficult to Measure

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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?)

15. 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

16. 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

17. 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

18. 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