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Medication nonadherence after hospital discharge impacts morbidity and mortality in patients with cardiovascular disease.[1] Primary nonadherence, part of the spectrum of medication underuse, occurs when a patient receives a prescription but does not fill it.[1] Prior studies utilizing retrospective administrative data have found a prevalence of postdischarge primary nonadherence between 24% and 28%,[1, 2] similar to findings in a variety of outpatient populations.[3, 4]
One strategy for reduction in nonadherence is discharge medication counseling, which has been associated with improved postdischarge outcomes.[1] We evaluated the prevalence and predictors of refractory primary nonadherence in a cohort of patients hospitalized for acute cardiovascular conditions who received pharmacist counseling prior to discharge to guide future adherence interventions.
METHODS
Setting and Participants
The present study represents a secondary analysis of data from the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL‐CVD) study. PILL‐CVD was a randomized controlled trial that evaluated the effect of a tailored intervention consisting of pharmacist‐assisted medication reconciliation, discharge counseling, low‐literacy adherence aids, and follow‐up phone calls in adults hospitalized for acute coronary syndromes or acute decompensated heart failure. Patients likely to be discharged home taking primary responsibility for their medication management were eligible. Full study methods and results, including inclusion and exclusion criteria, can be found elsewhere.[5] The institutional review boards of each site approved the study.
For the present analysis, patients were included if they had any new discharge prescriptions to fill and received the study intervention, including a postdischarge follow‐up phone call with questions about filling discharge prescriptions.
Baseline Measures
Baseline data were obtained from medical records and patient interviews, including demographic information as well as survey data for cognitive impairment (Mini‐Cog) and health literacy (Short Test of Functional Health Literacy in Adults).[6, 7]
Data were also collected related to medication use, including the number of scheduled and as‐needed medications listed at discharge, self‐reported preadmission adherence, medication understanding, and medication management practices (eg, use of a pillbox, refill reminders). Self‐reported medication adherence was measured with the 4‐item Morisky scale.[8] Medication understanding was assessed with a tool previously developed by Marvanova et al.[9]
Outcome Measures
The primary outcome was the percentage of patients who reported not filling at least 1 discharge prescription on a telephone call that was conducted 1 to 4 days postdischarge. Patients were asked a dichotomous question about whether or not they filled all of their discharge prescriptions. Further characterization of the class or number of medications not filled was not performed. Patients were asked to provide a reason for not filling the prescriptions.
Analysis
We evaluated the prevalence and possible predictors of primary nonadherence including age, gender, race, marital status, education and income levels, insurance type, health literacy, cognition, presence of a primary care physician, number of listed discharge medications, prehospital medication adherence, medication understanding, and medication management practices using Pearson 2, Fisher exact, or Wilcoxon rank sum tests as appropriate. Multiple logistic regression with backward elimination was performed to identify independent predictors, selected with P values<0.1. We also evaluated reasons that patients cited for not filling prescriptions. Two‐sided P values<0.05 were considered statistically significant. All analyses were conducted using Stata version 13.1 (StataCorp LP, College Station, TX).
RESULTS
Of 851 patients in the PILL‐CVD study, the present sample includes 341 patients who received the intervention, completed the postdischarge follow‐up call, and had new discharge prescriptions to be filled. This represents 85% of patients who received the intervention.
The mean age of participants was 61.3 years, and 59.5% were male (Table 1). The majority were white (75.1%), and 88% had at least a high school education. Married or cohabitating patients represented 54.3% of the group. Just over half of the patients (54%) had an income of $35K or greater. The primary source of insurance for 82.5% of patients was either Medicare or private insurance, and 7.4% of patients were self‐pay. Most patients (80%) had adequate health literacy. The median Mini‐Cog score was 4 out of 5 (interquartile range [IQR]=35), and 11% of patients had scores indicating cognitive impairment. Just less than one‐fourth of the patients (24.1%) had a Morisky score of 8, indicating high self‐reported adherence, and the median score of patients' understanding of medications (range of 03) was 2.5 (IQR=2.22.8), reflecting relatively high understanding. The median number of prescriptions on patients' discharge medications lists was 10 (IQR=813).
Variable | Overall 341 (100.0%) | Filled Prescription309 (90.6%) | Did Not Fill 32 (9.4%) | P Value |
---|---|---|---|---|
| ||||
Age, y, N (%) | 0.745a | |||
1849 | 69 | 63 (91.3) | 6 (8.7) | |
5064 | 128 | 114 (89.1) | 14 (10.9) | |
65+ | 144 | 132 (91.7) | 12 (8.3) | |
Gender, N (%) | 0.056a | |||
Male | 203 | 189 (93.1) | 14 (6.9) | |
Female | 138 | 120 (87.0) | 18 (13.0) | |
Race, N (%) | 0.712a | |||
White | 256 | 234 (91.4) | 22 (8.6) | |
African American | 60 | 54 (90.0) | 6 (10.0) | |
Other | 22 | 19 (86.4) | 3 (13.6) | |
Education, N (%) | 0.054a | |||
Less than high school | 40 | 32 (80.0) | 8 (20.0) | |
High school | 99 | 91 (91.9) | 8 (8.1) | |
1315 years | 93 | 83 (89.2) | 10 (10.8) | |
16 years | 109 | 103 (94.5) | 6 (5.5) | |
Marital status, N (%) | ||||
Separated/divorced/widowed/never married | 156 | 135 (86.5) | 21 (13.5) | 0.018a, b |
Married/cohabitating | 185 | 174 (94.1) | 11 (5.9) | |
Income, N (%) | 0.040a, b | |||
<10K<20K | 58 | 48 (82.8) | 10 (17.2) | |
20K35K | 86 | 76 (88.4) | 10 (11.6) | |
35K<50K | 40 | 36 (90.0) | 4 (10.0) | |
50K<75K | 46 | 43 (93.5) | 3 (6.5) | |
75K+ | 83 | 81 (97.6) | 2 (2.4) | |
Primary source of payment, N (%) | 0.272a | |||
Medicaid | 34 | 28 (82.4) | 6 (17.6) | |
Medicare | 145 | 131 (90.3) | 14 (9.7) | |
Private | 132 | 123 (93.2) | 9 (6.8) | |
Self‐pay | 25 | 22 (88.0) | 3 (12.0) | |
Primary care physician, N (%) | 1.000c | |||
None/do not know | 28 | 26 (92.9) | 2 (7.1) | |
Yes | 313 | 283 (90.4) | 30 (9.6) | |
Site, N (%) | 0.071a | |||
Nashville, TN | 172 | 151 (87.8) | 21 (12.2) | |
Boston, MA | 169 | 158 (93.5) | 11 (6.5) |
The prevalence of refractory primary nonadherence was 9.4%. In univariate analysis, single marital status, lower income, and having more than 10 total discharge medications were significantly associated with not filling medications (P=0.018, 0.04, 0.016, respectively; Table 1). In multivariable analysis, single marital status and having more than 10 total discharge medications maintained significance when controlling for other patient characteristics. Patients who were single had higher odds of failing to fill discharge prescriptions compared to married or cohabitating individuals (odds ratio [OR]: 2.2, 95% confidence interval [CI]: 1.014.8, P=0.047). Patients with more than 10 discharge medications also had higher odds of failing to fill compared with patients who had fewer total medications (OR: 2.3, 95% CI: 1.054.98, P=0.036).
Filling discharge prescriptions was not associated with health literacy, cognition, prehospital adherence, patients' medication understanding, or any of the surveyed medication management practices (Table 2). Patients' reasons for not filling included lack of time to go to the pharmacy, medications not being delivered or dispensed, or inability to afford prescriptions. Prescription cost was cited by 23.5% of patients who did not fill their prescriptions and provided a reason.
Variable | Overall 341 (100.0%) | Filled Prescription 309 (90.6%) | Did Not Fill 32 (9.4%) | P Value |
---|---|---|---|---|
| ||||
s‐TOFHLA score, range 036, N (%) | 0.443a | |||
Inadequate, 016 | 40 | 34 (85.0) | 6 (15.0) | |
Marginal, 1722 | 27 | 25 (92.6) | 2 (7.4) | |
Adequate, 2336 | 268 | 244 (91.0) | 24 (9.0) | |
MiniCog score, range 05, N (%) | 0.764b | |||
Not impaired, 35 | 304 | 276 (90.8) | 28 (9.2) | |
Impaired, 02 | 37 | 33 (89.2) | 4 (10.8) | |
Morisky score, range 48, N (%) | 0.517a | |||
Low/moderate self‐reported adherence, 47 | 249 | 224 (90.0) | 25 (10.0) | |
High self‐reported adherence, 8 | 79 | 73 (92.4) | 6 (7.6) | |
No. of discharge medications, range 126, N (%)c | 0.016a | |||
010 medications | 186 | 175 (94.1) | 11 (5.9) | |
11+medications | 155 | 134 (86.5) | 21 (13.5) | |
Patient responses to medication behavior questions | ||||
Patient associates medication taking time with daily events | 253 | 229 (90.5) | 24 (9.5) | 0.913a |
Patient uses a pillbox to organize medicine | 180 | 162 (90.0) | 18 (10.0) | 0.680a |
Friends of family help remind patient when it is time to take medicine | 89 | 79 (88.8) | 10 (11.2) | 0.486a |
Patient writes down instructions for when to take medicine | 60 | 55 (91.7) | 5 (8.3) | 0.758a |
Patient uses an alarm or a reminder that beeps when it is time to take medicine | 8 | 6 (75.0) | 2 (25.0) | 0.167a |
Patient marks refill date on calendar | 38 | 35 (92.1) | 3 (7.9) | 1.000b |
Pharmacy gives or sends patient a reminder when it is time to refill medicine | 94 | 84 (89.4) | 10 (10.6) | 0.624a |
Friends or family help patient to refill medicine | 60 | 53 (88.3) | 7 (11.7) | 0.504a |
DISCUSSION
Almost 1 in 10 patients hospitalized with cardiovascular disease demonstrated primary nonadherence refractory to an intervention including pharmacist discharge medication counseling. Being unmarried and having greater than 10 medications at discharge were significantly associated with higher primary nonadherence when controlling for other patient factors.
Patients with a cohabitant partner were significantly less likely to exhibit primary nonadherence, which may reflect higher levels of social support, including encouragement for disease self‐management and/or support with tasks such as picking up medications from the pharmacy. Previous research has demonstrated that social support mediates outpatient medication adherence for heart failure patients.[10]
Similar to Jackevicius et al., we found that patients with more medications at discharge were less likely to fill their prescriptions.[1] These findings may reflect the challenges that patients face in adhering to complex treatment plans, which are associated with increased coordination and cost. Conversely, some prior studies have found that patients with fewer prescriptions were less likely to fill.[11, 12] These patients were often younger, thus potentially less conditioned to fill prescriptions, and unlike our cohort, these populations had consistent prescription coverage. Interventions for polypharmacy, which have been shown to improve outcomes and decrease costs, especially in the geriatric population, may be of benefit for primary nonadherence as well.[13]
Additionally, patients with lower household incomes had higher rates of primary nonadherence, at least in univariate analysis. Medication cost and transportation limitations, which are more pronounced in lower‐income patients, likely play influential roles in this group. These findings build on prior literature that has found lower prescription cost to be associated with better medication adherence in a variety of settings.[3, 4, 14]
Because the prevalence of primary nonadherence in this cohort is less than half of historical rates, we suspect the intervention did reduce unintentional nonadherence. However, regimen cost and complexity, transportation challenges, and ingrained medication beliefs likely remained barriers. It may be that a postdischarge phone call is able address unintended primary nonadherence in many cases. Meds to beds programs, where a supply of medications is provided to patients prior to discharge, could assist patients with limited transportation. Prior studies have also found reduced primary nonadherence when e‐prescriptions are utilized.[3]
Establishing outpatient follow‐up at discharge provides additional opportunities to address unanticipated adherence barriers. Because the efficacy of any adherence intervention depends on individual patient barriers, we recommend combining medication counseling with a targeted approach for patient‐specific needs.
We note several limitations to our study. First, because we studied primary nonadherence that persisted despite an intervention, this cohort likely underestimates the prevalence of primary nonadherence and alters the associated patient characteristics found in routine practice (although counseling is becoming more common). Second, patient reporting is subject to biases that underestimate nonadherence, although this approach has been validated previously.[15] Third, our outcome measure was unable to capture the spectrum of non‐adherence that could provide a more nuanced look at predictors of postdischarge nonadherence. Fourth, we did not have patient copayment data to better characterize whether out of pocket costs or pharmacologic classes drove nonadherence. Finally, sample size may have limited the detection of other important factors, and the university setting may limit generalizability to cardiovascular patients in other practice environments. Future research should focus on intervention strategies that assess patients' individual adherence barriers for a targeted or multimodal approach to improve adherence.
In conclusion, we found a prevalence of primary nonadherence of almost 1 in 10 patients who received pharmacist counseling. Nonadherence was associated with being single and those discharged with longer medication lists. Our results support existing literature that primary nonadherence is a significant problem in the postdischarge setting and substantiate the need for ongoing efforts to study and implement interventions for adherence after hospital discharge.
Disclosures
This material is based on work supported by the Office of Academic Affiliations, Department of Veterans Affairs, Veterans Affairs National Quality Scholars Program, and with use of facilities at Veterans Affairs Tennessee Valley Healthcare System, Nashville, Tennessee (Dr. Wooldridge). The funding agency supported the work indirectly through provision of salary support and training for the primary author, but had no specific role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication. This work was also supported by R01 HL089755 (Dr. Kripalani) and in part by K23 HL077597 (Dr. Kripalani), K08 HL072806 (Dr. Schnipper), and the Center for Clinical Quality and Implementation Research at Vanderbilt University Medical Center. A preliminary version of this research was presented at the AcademyHealth Annual Research Meeting, June 16, 2015, Minneapolis, Minnesota. The authors report the following potential conflicts of interest: Jeffrey Schnipper: PI, investigator‐initiated study funded by Sanofi‐Aventis to develop, implement, and evaluate a multifaceted intervention to improve transitions of care in patients with diabetes mellitus discharged on insulin. Robert Dittus: passive co‐owner, Medical Decision Modeling, Inc.; Bayer HealthCare. One‐day consultation and panelist on educational video for population health (consultant fee); GlaxoSmithKline. One‐day consultant for population health, envisioning the future (consultant fee). Sunil Kripalani: Bioscape Digital, stock ownership
- Prevalence, predictors, and outcomes of primary nonadherence after acute myocardial infarction. Circulation. 2008;117(8):1028–1036. , , .
- Primary medication non‐adherence after discharge from a general internal medicine service. PloS One. 2013;8(5):e61735. , , , .
- Trouble getting started: predictors of primary medication nonadherence. Am J Med. 2011;124(11):1081.e9–22. , , , et al.
- The incidence and determinants of primary nonadherence with prescribed medication in primary care: a cohort study. Ann Intern Med. 2014;160(7):441–450. , , , , .
- Effect of a pharmacist intervention on clinically important medication errors after hospital discharge: a randomized trial. Ann Intern Med. 2012;157(1):1–10. , , , et al.
- Short Test of Functional Health Literacy in Adults. Snow Camp, NC: Peppercorn Books and Press; 1998. , , , .
- Simplifying detection of cognitive impairment: comparison of the Mini‐Cog and Mini‐Mental State Examination in a multiethnic sample. J Am Geriatr Soc. 2005;53(5):871–874. , , , , .
- Concurrent and predictive validity of a self‐reported measure of medication adherence. Med Care. 1986;24(1):67–74. , , .
- Health literacy and medication understanding among hospitalized adults. J Hosp Med. 2011;6(9):488–493. , , , , , .
- Medication adherence, social support, and event‐free survival in patients with heart failure. Health Psychol. 2013;32(6):637–646. , , , , , .
- Effect of patient comorbidities on filling of antihypertensive prescriptions. Am J Manag Care. 2009;15(1):24–30. , , , , , .
- Primary nonadherence to statin medications in a managed care organization. J Manag Care Pharm. 2013;19(5):367–373. , , , et al.
- Reducing cost by reducing polypharmacy: the polypharmacy outcomes project. J Am Med Dir Assoc. 2012;13(9):818.e811–815. , , , et al.
- The epidemiology of prescriptions abandoned at the pharmacy. Ann Intern Med. 2010;153(10):633–640. , , , et al.
- Can simple clinical measurements detect patient noncompliance? Hypertension. 1980;2(6):757–764. , , , , , .
Medication nonadherence after hospital discharge impacts morbidity and mortality in patients with cardiovascular disease.[1] Primary nonadherence, part of the spectrum of medication underuse, occurs when a patient receives a prescription but does not fill it.[1] Prior studies utilizing retrospective administrative data have found a prevalence of postdischarge primary nonadherence between 24% and 28%,[1, 2] similar to findings in a variety of outpatient populations.[3, 4]
One strategy for reduction in nonadherence is discharge medication counseling, which has been associated with improved postdischarge outcomes.[1] We evaluated the prevalence and predictors of refractory primary nonadherence in a cohort of patients hospitalized for acute cardiovascular conditions who received pharmacist counseling prior to discharge to guide future adherence interventions.
METHODS
Setting and Participants
The present study represents a secondary analysis of data from the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL‐CVD) study. PILL‐CVD was a randomized controlled trial that evaluated the effect of a tailored intervention consisting of pharmacist‐assisted medication reconciliation, discharge counseling, low‐literacy adherence aids, and follow‐up phone calls in adults hospitalized for acute coronary syndromes or acute decompensated heart failure. Patients likely to be discharged home taking primary responsibility for their medication management were eligible. Full study methods and results, including inclusion and exclusion criteria, can be found elsewhere.[5] The institutional review boards of each site approved the study.
For the present analysis, patients were included if they had any new discharge prescriptions to fill and received the study intervention, including a postdischarge follow‐up phone call with questions about filling discharge prescriptions.
Baseline Measures
Baseline data were obtained from medical records and patient interviews, including demographic information as well as survey data for cognitive impairment (Mini‐Cog) and health literacy (Short Test of Functional Health Literacy in Adults).[6, 7]
Data were also collected related to medication use, including the number of scheduled and as‐needed medications listed at discharge, self‐reported preadmission adherence, medication understanding, and medication management practices (eg, use of a pillbox, refill reminders). Self‐reported medication adherence was measured with the 4‐item Morisky scale.[8] Medication understanding was assessed with a tool previously developed by Marvanova et al.[9]
Outcome Measures
The primary outcome was the percentage of patients who reported not filling at least 1 discharge prescription on a telephone call that was conducted 1 to 4 days postdischarge. Patients were asked a dichotomous question about whether or not they filled all of their discharge prescriptions. Further characterization of the class or number of medications not filled was not performed. Patients were asked to provide a reason for not filling the prescriptions.
Analysis
We evaluated the prevalence and possible predictors of primary nonadherence including age, gender, race, marital status, education and income levels, insurance type, health literacy, cognition, presence of a primary care physician, number of listed discharge medications, prehospital medication adherence, medication understanding, and medication management practices using Pearson 2, Fisher exact, or Wilcoxon rank sum tests as appropriate. Multiple logistic regression with backward elimination was performed to identify independent predictors, selected with P values<0.1. We also evaluated reasons that patients cited for not filling prescriptions. Two‐sided P values<0.05 were considered statistically significant. All analyses were conducted using Stata version 13.1 (StataCorp LP, College Station, TX).
RESULTS
Of 851 patients in the PILL‐CVD study, the present sample includes 341 patients who received the intervention, completed the postdischarge follow‐up call, and had new discharge prescriptions to be filled. This represents 85% of patients who received the intervention.
The mean age of participants was 61.3 years, and 59.5% were male (Table 1). The majority were white (75.1%), and 88% had at least a high school education. Married or cohabitating patients represented 54.3% of the group. Just over half of the patients (54%) had an income of $35K or greater. The primary source of insurance for 82.5% of patients was either Medicare or private insurance, and 7.4% of patients were self‐pay. Most patients (80%) had adequate health literacy. The median Mini‐Cog score was 4 out of 5 (interquartile range [IQR]=35), and 11% of patients had scores indicating cognitive impairment. Just less than one‐fourth of the patients (24.1%) had a Morisky score of 8, indicating high self‐reported adherence, and the median score of patients' understanding of medications (range of 03) was 2.5 (IQR=2.22.8), reflecting relatively high understanding. The median number of prescriptions on patients' discharge medications lists was 10 (IQR=813).
Variable | Overall 341 (100.0%) | Filled Prescription309 (90.6%) | Did Not Fill 32 (9.4%) | P Value |
---|---|---|---|---|
| ||||
Age, y, N (%) | 0.745a | |||
1849 | 69 | 63 (91.3) | 6 (8.7) | |
5064 | 128 | 114 (89.1) | 14 (10.9) | |
65+ | 144 | 132 (91.7) | 12 (8.3) | |
Gender, N (%) | 0.056a | |||
Male | 203 | 189 (93.1) | 14 (6.9) | |
Female | 138 | 120 (87.0) | 18 (13.0) | |
Race, N (%) | 0.712a | |||
White | 256 | 234 (91.4) | 22 (8.6) | |
African American | 60 | 54 (90.0) | 6 (10.0) | |
Other | 22 | 19 (86.4) | 3 (13.6) | |
Education, N (%) | 0.054a | |||
Less than high school | 40 | 32 (80.0) | 8 (20.0) | |
High school | 99 | 91 (91.9) | 8 (8.1) | |
1315 years | 93 | 83 (89.2) | 10 (10.8) | |
16 years | 109 | 103 (94.5) | 6 (5.5) | |
Marital status, N (%) | ||||
Separated/divorced/widowed/never married | 156 | 135 (86.5) | 21 (13.5) | 0.018a, b |
Married/cohabitating | 185 | 174 (94.1) | 11 (5.9) | |
Income, N (%) | 0.040a, b | |||
<10K<20K | 58 | 48 (82.8) | 10 (17.2) | |
20K35K | 86 | 76 (88.4) | 10 (11.6) | |
35K<50K | 40 | 36 (90.0) | 4 (10.0) | |
50K<75K | 46 | 43 (93.5) | 3 (6.5) | |
75K+ | 83 | 81 (97.6) | 2 (2.4) | |
Primary source of payment, N (%) | 0.272a | |||
Medicaid | 34 | 28 (82.4) | 6 (17.6) | |
Medicare | 145 | 131 (90.3) | 14 (9.7) | |
Private | 132 | 123 (93.2) | 9 (6.8) | |
Self‐pay | 25 | 22 (88.0) | 3 (12.0) | |
Primary care physician, N (%) | 1.000c | |||
None/do not know | 28 | 26 (92.9) | 2 (7.1) | |
Yes | 313 | 283 (90.4) | 30 (9.6) | |
Site, N (%) | 0.071a | |||
Nashville, TN | 172 | 151 (87.8) | 21 (12.2) | |
Boston, MA | 169 | 158 (93.5) | 11 (6.5) |
The prevalence of refractory primary nonadherence was 9.4%. In univariate analysis, single marital status, lower income, and having more than 10 total discharge medications were significantly associated with not filling medications (P=0.018, 0.04, 0.016, respectively; Table 1). In multivariable analysis, single marital status and having more than 10 total discharge medications maintained significance when controlling for other patient characteristics. Patients who were single had higher odds of failing to fill discharge prescriptions compared to married or cohabitating individuals (odds ratio [OR]: 2.2, 95% confidence interval [CI]: 1.014.8, P=0.047). Patients with more than 10 discharge medications also had higher odds of failing to fill compared with patients who had fewer total medications (OR: 2.3, 95% CI: 1.054.98, P=0.036).
Filling discharge prescriptions was not associated with health literacy, cognition, prehospital adherence, patients' medication understanding, or any of the surveyed medication management practices (Table 2). Patients' reasons for not filling included lack of time to go to the pharmacy, medications not being delivered or dispensed, or inability to afford prescriptions. Prescription cost was cited by 23.5% of patients who did not fill their prescriptions and provided a reason.
Variable | Overall 341 (100.0%) | Filled Prescription 309 (90.6%) | Did Not Fill 32 (9.4%) | P Value |
---|---|---|---|---|
| ||||
s‐TOFHLA score, range 036, N (%) | 0.443a | |||
Inadequate, 016 | 40 | 34 (85.0) | 6 (15.0) | |
Marginal, 1722 | 27 | 25 (92.6) | 2 (7.4) | |
Adequate, 2336 | 268 | 244 (91.0) | 24 (9.0) | |
MiniCog score, range 05, N (%) | 0.764b | |||
Not impaired, 35 | 304 | 276 (90.8) | 28 (9.2) | |
Impaired, 02 | 37 | 33 (89.2) | 4 (10.8) | |
Morisky score, range 48, N (%) | 0.517a | |||
Low/moderate self‐reported adherence, 47 | 249 | 224 (90.0) | 25 (10.0) | |
High self‐reported adherence, 8 | 79 | 73 (92.4) | 6 (7.6) | |
No. of discharge medications, range 126, N (%)c | 0.016a | |||
010 medications | 186 | 175 (94.1) | 11 (5.9) | |
11+medications | 155 | 134 (86.5) | 21 (13.5) | |
Patient responses to medication behavior questions | ||||
Patient associates medication taking time with daily events | 253 | 229 (90.5) | 24 (9.5) | 0.913a |
Patient uses a pillbox to organize medicine | 180 | 162 (90.0) | 18 (10.0) | 0.680a |
Friends of family help remind patient when it is time to take medicine | 89 | 79 (88.8) | 10 (11.2) | 0.486a |
Patient writes down instructions for when to take medicine | 60 | 55 (91.7) | 5 (8.3) | 0.758a |
Patient uses an alarm or a reminder that beeps when it is time to take medicine | 8 | 6 (75.0) | 2 (25.0) | 0.167a |
Patient marks refill date on calendar | 38 | 35 (92.1) | 3 (7.9) | 1.000b |
Pharmacy gives or sends patient a reminder when it is time to refill medicine | 94 | 84 (89.4) | 10 (10.6) | 0.624a |
Friends or family help patient to refill medicine | 60 | 53 (88.3) | 7 (11.7) | 0.504a |
DISCUSSION
Almost 1 in 10 patients hospitalized with cardiovascular disease demonstrated primary nonadherence refractory to an intervention including pharmacist discharge medication counseling. Being unmarried and having greater than 10 medications at discharge were significantly associated with higher primary nonadherence when controlling for other patient factors.
Patients with a cohabitant partner were significantly less likely to exhibit primary nonadherence, which may reflect higher levels of social support, including encouragement for disease self‐management and/or support with tasks such as picking up medications from the pharmacy. Previous research has demonstrated that social support mediates outpatient medication adherence for heart failure patients.[10]
Similar to Jackevicius et al., we found that patients with more medications at discharge were less likely to fill their prescriptions.[1] These findings may reflect the challenges that patients face in adhering to complex treatment plans, which are associated with increased coordination and cost. Conversely, some prior studies have found that patients with fewer prescriptions were less likely to fill.[11, 12] These patients were often younger, thus potentially less conditioned to fill prescriptions, and unlike our cohort, these populations had consistent prescription coverage. Interventions for polypharmacy, which have been shown to improve outcomes and decrease costs, especially in the geriatric population, may be of benefit for primary nonadherence as well.[13]
Additionally, patients with lower household incomes had higher rates of primary nonadherence, at least in univariate analysis. Medication cost and transportation limitations, which are more pronounced in lower‐income patients, likely play influential roles in this group. These findings build on prior literature that has found lower prescription cost to be associated with better medication adherence in a variety of settings.[3, 4, 14]
Because the prevalence of primary nonadherence in this cohort is less than half of historical rates, we suspect the intervention did reduce unintentional nonadherence. However, regimen cost and complexity, transportation challenges, and ingrained medication beliefs likely remained barriers. It may be that a postdischarge phone call is able address unintended primary nonadherence in many cases. Meds to beds programs, where a supply of medications is provided to patients prior to discharge, could assist patients with limited transportation. Prior studies have also found reduced primary nonadherence when e‐prescriptions are utilized.[3]
Establishing outpatient follow‐up at discharge provides additional opportunities to address unanticipated adherence barriers. Because the efficacy of any adherence intervention depends on individual patient barriers, we recommend combining medication counseling with a targeted approach for patient‐specific needs.
We note several limitations to our study. First, because we studied primary nonadherence that persisted despite an intervention, this cohort likely underestimates the prevalence of primary nonadherence and alters the associated patient characteristics found in routine practice (although counseling is becoming more common). Second, patient reporting is subject to biases that underestimate nonadherence, although this approach has been validated previously.[15] Third, our outcome measure was unable to capture the spectrum of non‐adherence that could provide a more nuanced look at predictors of postdischarge nonadherence. Fourth, we did not have patient copayment data to better characterize whether out of pocket costs or pharmacologic classes drove nonadherence. Finally, sample size may have limited the detection of other important factors, and the university setting may limit generalizability to cardiovascular patients in other practice environments. Future research should focus on intervention strategies that assess patients' individual adherence barriers for a targeted or multimodal approach to improve adherence.
In conclusion, we found a prevalence of primary nonadherence of almost 1 in 10 patients who received pharmacist counseling. Nonadherence was associated with being single and those discharged with longer medication lists. Our results support existing literature that primary nonadherence is a significant problem in the postdischarge setting and substantiate the need for ongoing efforts to study and implement interventions for adherence after hospital discharge.
Disclosures
This material is based on work supported by the Office of Academic Affiliations, Department of Veterans Affairs, Veterans Affairs National Quality Scholars Program, and with use of facilities at Veterans Affairs Tennessee Valley Healthcare System, Nashville, Tennessee (Dr. Wooldridge). The funding agency supported the work indirectly through provision of salary support and training for the primary author, but had no specific role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication. This work was also supported by R01 HL089755 (Dr. Kripalani) and in part by K23 HL077597 (Dr. Kripalani), K08 HL072806 (Dr. Schnipper), and the Center for Clinical Quality and Implementation Research at Vanderbilt University Medical Center. A preliminary version of this research was presented at the AcademyHealth Annual Research Meeting, June 16, 2015, Minneapolis, Minnesota. The authors report the following potential conflicts of interest: Jeffrey Schnipper: PI, investigator‐initiated study funded by Sanofi‐Aventis to develop, implement, and evaluate a multifaceted intervention to improve transitions of care in patients with diabetes mellitus discharged on insulin. Robert Dittus: passive co‐owner, Medical Decision Modeling, Inc.; Bayer HealthCare. One‐day consultation and panelist on educational video for population health (consultant fee); GlaxoSmithKline. One‐day consultant for population health, envisioning the future (consultant fee). Sunil Kripalani: Bioscape Digital, stock ownership
Medication nonadherence after hospital discharge impacts morbidity and mortality in patients with cardiovascular disease.[1] Primary nonadherence, part of the spectrum of medication underuse, occurs when a patient receives a prescription but does not fill it.[1] Prior studies utilizing retrospective administrative data have found a prevalence of postdischarge primary nonadherence between 24% and 28%,[1, 2] similar to findings in a variety of outpatient populations.[3, 4]
One strategy for reduction in nonadherence is discharge medication counseling, which has been associated with improved postdischarge outcomes.[1] We evaluated the prevalence and predictors of refractory primary nonadherence in a cohort of patients hospitalized for acute cardiovascular conditions who received pharmacist counseling prior to discharge to guide future adherence interventions.
METHODS
Setting and Participants
The present study represents a secondary analysis of data from the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL‐CVD) study. PILL‐CVD was a randomized controlled trial that evaluated the effect of a tailored intervention consisting of pharmacist‐assisted medication reconciliation, discharge counseling, low‐literacy adherence aids, and follow‐up phone calls in adults hospitalized for acute coronary syndromes or acute decompensated heart failure. Patients likely to be discharged home taking primary responsibility for their medication management were eligible. Full study methods and results, including inclusion and exclusion criteria, can be found elsewhere.[5] The institutional review boards of each site approved the study.
For the present analysis, patients were included if they had any new discharge prescriptions to fill and received the study intervention, including a postdischarge follow‐up phone call with questions about filling discharge prescriptions.
Baseline Measures
Baseline data were obtained from medical records and patient interviews, including demographic information as well as survey data for cognitive impairment (Mini‐Cog) and health literacy (Short Test of Functional Health Literacy in Adults).[6, 7]
Data were also collected related to medication use, including the number of scheduled and as‐needed medications listed at discharge, self‐reported preadmission adherence, medication understanding, and medication management practices (eg, use of a pillbox, refill reminders). Self‐reported medication adherence was measured with the 4‐item Morisky scale.[8] Medication understanding was assessed with a tool previously developed by Marvanova et al.[9]
Outcome Measures
The primary outcome was the percentage of patients who reported not filling at least 1 discharge prescription on a telephone call that was conducted 1 to 4 days postdischarge. Patients were asked a dichotomous question about whether or not they filled all of their discharge prescriptions. Further characterization of the class or number of medications not filled was not performed. Patients were asked to provide a reason for not filling the prescriptions.
Analysis
We evaluated the prevalence and possible predictors of primary nonadherence including age, gender, race, marital status, education and income levels, insurance type, health literacy, cognition, presence of a primary care physician, number of listed discharge medications, prehospital medication adherence, medication understanding, and medication management practices using Pearson 2, Fisher exact, or Wilcoxon rank sum tests as appropriate. Multiple logistic regression with backward elimination was performed to identify independent predictors, selected with P values<0.1. We also evaluated reasons that patients cited for not filling prescriptions. Two‐sided P values<0.05 were considered statistically significant. All analyses were conducted using Stata version 13.1 (StataCorp LP, College Station, TX).
RESULTS
Of 851 patients in the PILL‐CVD study, the present sample includes 341 patients who received the intervention, completed the postdischarge follow‐up call, and had new discharge prescriptions to be filled. This represents 85% of patients who received the intervention.
The mean age of participants was 61.3 years, and 59.5% were male (Table 1). The majority were white (75.1%), and 88% had at least a high school education. Married or cohabitating patients represented 54.3% of the group. Just over half of the patients (54%) had an income of $35K or greater. The primary source of insurance for 82.5% of patients was either Medicare or private insurance, and 7.4% of patients were self‐pay. Most patients (80%) had adequate health literacy. The median Mini‐Cog score was 4 out of 5 (interquartile range [IQR]=35), and 11% of patients had scores indicating cognitive impairment. Just less than one‐fourth of the patients (24.1%) had a Morisky score of 8, indicating high self‐reported adherence, and the median score of patients' understanding of medications (range of 03) was 2.5 (IQR=2.22.8), reflecting relatively high understanding. The median number of prescriptions on patients' discharge medications lists was 10 (IQR=813).
Variable | Overall 341 (100.0%) | Filled Prescription309 (90.6%) | Did Not Fill 32 (9.4%) | P Value |
---|---|---|---|---|
| ||||
Age, y, N (%) | 0.745a | |||
1849 | 69 | 63 (91.3) | 6 (8.7) | |
5064 | 128 | 114 (89.1) | 14 (10.9) | |
65+ | 144 | 132 (91.7) | 12 (8.3) | |
Gender, N (%) | 0.056a | |||
Male | 203 | 189 (93.1) | 14 (6.9) | |
Female | 138 | 120 (87.0) | 18 (13.0) | |
Race, N (%) | 0.712a | |||
White | 256 | 234 (91.4) | 22 (8.6) | |
African American | 60 | 54 (90.0) | 6 (10.0) | |
Other | 22 | 19 (86.4) | 3 (13.6) | |
Education, N (%) | 0.054a | |||
Less than high school | 40 | 32 (80.0) | 8 (20.0) | |
High school | 99 | 91 (91.9) | 8 (8.1) | |
1315 years | 93 | 83 (89.2) | 10 (10.8) | |
16 years | 109 | 103 (94.5) | 6 (5.5) | |
Marital status, N (%) | ||||
Separated/divorced/widowed/never married | 156 | 135 (86.5) | 21 (13.5) | 0.018a, b |
Married/cohabitating | 185 | 174 (94.1) | 11 (5.9) | |
Income, N (%) | 0.040a, b | |||
<10K<20K | 58 | 48 (82.8) | 10 (17.2) | |
20K35K | 86 | 76 (88.4) | 10 (11.6) | |
35K<50K | 40 | 36 (90.0) | 4 (10.0) | |
50K<75K | 46 | 43 (93.5) | 3 (6.5) | |
75K+ | 83 | 81 (97.6) | 2 (2.4) | |
Primary source of payment, N (%) | 0.272a | |||
Medicaid | 34 | 28 (82.4) | 6 (17.6) | |
Medicare | 145 | 131 (90.3) | 14 (9.7) | |
Private | 132 | 123 (93.2) | 9 (6.8) | |
Self‐pay | 25 | 22 (88.0) | 3 (12.0) | |
Primary care physician, N (%) | 1.000c | |||
None/do not know | 28 | 26 (92.9) | 2 (7.1) | |
Yes | 313 | 283 (90.4) | 30 (9.6) | |
Site, N (%) | 0.071a | |||
Nashville, TN | 172 | 151 (87.8) | 21 (12.2) | |
Boston, MA | 169 | 158 (93.5) | 11 (6.5) |
The prevalence of refractory primary nonadherence was 9.4%. In univariate analysis, single marital status, lower income, and having more than 10 total discharge medications were significantly associated with not filling medications (P=0.018, 0.04, 0.016, respectively; Table 1). In multivariable analysis, single marital status and having more than 10 total discharge medications maintained significance when controlling for other patient characteristics. Patients who were single had higher odds of failing to fill discharge prescriptions compared to married or cohabitating individuals (odds ratio [OR]: 2.2, 95% confidence interval [CI]: 1.014.8, P=0.047). Patients with more than 10 discharge medications also had higher odds of failing to fill compared with patients who had fewer total medications (OR: 2.3, 95% CI: 1.054.98, P=0.036).
Filling discharge prescriptions was not associated with health literacy, cognition, prehospital adherence, patients' medication understanding, or any of the surveyed medication management practices (Table 2). Patients' reasons for not filling included lack of time to go to the pharmacy, medications not being delivered or dispensed, or inability to afford prescriptions. Prescription cost was cited by 23.5% of patients who did not fill their prescriptions and provided a reason.
Variable | Overall 341 (100.0%) | Filled Prescription 309 (90.6%) | Did Not Fill 32 (9.4%) | P Value |
---|---|---|---|---|
| ||||
s‐TOFHLA score, range 036, N (%) | 0.443a | |||
Inadequate, 016 | 40 | 34 (85.0) | 6 (15.0) | |
Marginal, 1722 | 27 | 25 (92.6) | 2 (7.4) | |
Adequate, 2336 | 268 | 244 (91.0) | 24 (9.0) | |
MiniCog score, range 05, N (%) | 0.764b | |||
Not impaired, 35 | 304 | 276 (90.8) | 28 (9.2) | |
Impaired, 02 | 37 | 33 (89.2) | 4 (10.8) | |
Morisky score, range 48, N (%) | 0.517a | |||
Low/moderate self‐reported adherence, 47 | 249 | 224 (90.0) | 25 (10.0) | |
High self‐reported adherence, 8 | 79 | 73 (92.4) | 6 (7.6) | |
No. of discharge medications, range 126, N (%)c | 0.016a | |||
010 medications | 186 | 175 (94.1) | 11 (5.9) | |
11+medications | 155 | 134 (86.5) | 21 (13.5) | |
Patient responses to medication behavior questions | ||||
Patient associates medication taking time with daily events | 253 | 229 (90.5) | 24 (9.5) | 0.913a |
Patient uses a pillbox to organize medicine | 180 | 162 (90.0) | 18 (10.0) | 0.680a |
Friends of family help remind patient when it is time to take medicine | 89 | 79 (88.8) | 10 (11.2) | 0.486a |
Patient writes down instructions for when to take medicine | 60 | 55 (91.7) | 5 (8.3) | 0.758a |
Patient uses an alarm or a reminder that beeps when it is time to take medicine | 8 | 6 (75.0) | 2 (25.0) | 0.167a |
Patient marks refill date on calendar | 38 | 35 (92.1) | 3 (7.9) | 1.000b |
Pharmacy gives or sends patient a reminder when it is time to refill medicine | 94 | 84 (89.4) | 10 (10.6) | 0.624a |
Friends or family help patient to refill medicine | 60 | 53 (88.3) | 7 (11.7) | 0.504a |
DISCUSSION
Almost 1 in 10 patients hospitalized with cardiovascular disease demonstrated primary nonadherence refractory to an intervention including pharmacist discharge medication counseling. Being unmarried and having greater than 10 medications at discharge were significantly associated with higher primary nonadherence when controlling for other patient factors.
Patients with a cohabitant partner were significantly less likely to exhibit primary nonadherence, which may reflect higher levels of social support, including encouragement for disease self‐management and/or support with tasks such as picking up medications from the pharmacy. Previous research has demonstrated that social support mediates outpatient medication adherence for heart failure patients.[10]
Similar to Jackevicius et al., we found that patients with more medications at discharge were less likely to fill their prescriptions.[1] These findings may reflect the challenges that patients face in adhering to complex treatment plans, which are associated with increased coordination and cost. Conversely, some prior studies have found that patients with fewer prescriptions were less likely to fill.[11, 12] These patients were often younger, thus potentially less conditioned to fill prescriptions, and unlike our cohort, these populations had consistent prescription coverage. Interventions for polypharmacy, which have been shown to improve outcomes and decrease costs, especially in the geriatric population, may be of benefit for primary nonadherence as well.[13]
Additionally, patients with lower household incomes had higher rates of primary nonadherence, at least in univariate analysis. Medication cost and transportation limitations, which are more pronounced in lower‐income patients, likely play influential roles in this group. These findings build on prior literature that has found lower prescription cost to be associated with better medication adherence in a variety of settings.[3, 4, 14]
Because the prevalence of primary nonadherence in this cohort is less than half of historical rates, we suspect the intervention did reduce unintentional nonadherence. However, regimen cost and complexity, transportation challenges, and ingrained medication beliefs likely remained barriers. It may be that a postdischarge phone call is able address unintended primary nonadherence in many cases. Meds to beds programs, where a supply of medications is provided to patients prior to discharge, could assist patients with limited transportation. Prior studies have also found reduced primary nonadherence when e‐prescriptions are utilized.[3]
Establishing outpatient follow‐up at discharge provides additional opportunities to address unanticipated adherence barriers. Because the efficacy of any adherence intervention depends on individual patient barriers, we recommend combining medication counseling with a targeted approach for patient‐specific needs.
We note several limitations to our study. First, because we studied primary nonadherence that persisted despite an intervention, this cohort likely underestimates the prevalence of primary nonadherence and alters the associated patient characteristics found in routine practice (although counseling is becoming more common). Second, patient reporting is subject to biases that underestimate nonadherence, although this approach has been validated previously.[15] Third, our outcome measure was unable to capture the spectrum of non‐adherence that could provide a more nuanced look at predictors of postdischarge nonadherence. Fourth, we did not have patient copayment data to better characterize whether out of pocket costs or pharmacologic classes drove nonadherence. Finally, sample size may have limited the detection of other important factors, and the university setting may limit generalizability to cardiovascular patients in other practice environments. Future research should focus on intervention strategies that assess patients' individual adherence barriers for a targeted or multimodal approach to improve adherence.
In conclusion, we found a prevalence of primary nonadherence of almost 1 in 10 patients who received pharmacist counseling. Nonadherence was associated with being single and those discharged with longer medication lists. Our results support existing literature that primary nonadherence is a significant problem in the postdischarge setting and substantiate the need for ongoing efforts to study and implement interventions for adherence after hospital discharge.
Disclosures
This material is based on work supported by the Office of Academic Affiliations, Department of Veterans Affairs, Veterans Affairs National Quality Scholars Program, and with use of facilities at Veterans Affairs Tennessee Valley Healthcare System, Nashville, Tennessee (Dr. Wooldridge). The funding agency supported the work indirectly through provision of salary support and training for the primary author, but had no specific role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication. This work was also supported by R01 HL089755 (Dr. Kripalani) and in part by K23 HL077597 (Dr. Kripalani), K08 HL072806 (Dr. Schnipper), and the Center for Clinical Quality and Implementation Research at Vanderbilt University Medical Center. A preliminary version of this research was presented at the AcademyHealth Annual Research Meeting, June 16, 2015, Minneapolis, Minnesota. The authors report the following potential conflicts of interest: Jeffrey Schnipper: PI, investigator‐initiated study funded by Sanofi‐Aventis to develop, implement, and evaluate a multifaceted intervention to improve transitions of care in patients with diabetes mellitus discharged on insulin. Robert Dittus: passive co‐owner, Medical Decision Modeling, Inc.; Bayer HealthCare. One‐day consultation and panelist on educational video for population health (consultant fee); GlaxoSmithKline. One‐day consultant for population health, envisioning the future (consultant fee). Sunil Kripalani: Bioscape Digital, stock ownership
- Prevalence, predictors, and outcomes of primary nonadherence after acute myocardial infarction. Circulation. 2008;117(8):1028–1036. , , .
- Primary medication non‐adherence after discharge from a general internal medicine service. PloS One. 2013;8(5):e61735. , , , .
- Trouble getting started: predictors of primary medication nonadherence. Am J Med. 2011;124(11):1081.e9–22. , , , et al.
- The incidence and determinants of primary nonadherence with prescribed medication in primary care: a cohort study. Ann Intern Med. 2014;160(7):441–450. , , , , .
- Effect of a pharmacist intervention on clinically important medication errors after hospital discharge: a randomized trial. Ann Intern Med. 2012;157(1):1–10. , , , et al.
- Short Test of Functional Health Literacy in Adults. Snow Camp, NC: Peppercorn Books and Press; 1998. , , , .
- Simplifying detection of cognitive impairment: comparison of the Mini‐Cog and Mini‐Mental State Examination in a multiethnic sample. J Am Geriatr Soc. 2005;53(5):871–874. , , , , .
- Concurrent and predictive validity of a self‐reported measure of medication adherence. Med Care. 1986;24(1):67–74. , , .
- Health literacy and medication understanding among hospitalized adults. J Hosp Med. 2011;6(9):488–493. , , , , , .
- Medication adherence, social support, and event‐free survival in patients with heart failure. Health Psychol. 2013;32(6):637–646. , , , , , .
- Effect of patient comorbidities on filling of antihypertensive prescriptions. Am J Manag Care. 2009;15(1):24–30. , , , , , .
- Primary nonadherence to statin medications in a managed care organization. J Manag Care Pharm. 2013;19(5):367–373. , , , et al.
- Reducing cost by reducing polypharmacy: the polypharmacy outcomes project. J Am Med Dir Assoc. 2012;13(9):818.e811–815. , , , et al.
- The epidemiology of prescriptions abandoned at the pharmacy. Ann Intern Med. 2010;153(10):633–640. , , , et al.
- Can simple clinical measurements detect patient noncompliance? Hypertension. 1980;2(6):757–764. , , , , , .
- Prevalence, predictors, and outcomes of primary nonadherence after acute myocardial infarction. Circulation. 2008;117(8):1028–1036. , , .
- Primary medication non‐adherence after discharge from a general internal medicine service. PloS One. 2013;8(5):e61735. , , , .
- Trouble getting started: predictors of primary medication nonadherence. Am J Med. 2011;124(11):1081.e9–22. , , , et al.
- The incidence and determinants of primary nonadherence with prescribed medication in primary care: a cohort study. Ann Intern Med. 2014;160(7):441–450. , , , , .
- Effect of a pharmacist intervention on clinically important medication errors after hospital discharge: a randomized trial. Ann Intern Med. 2012;157(1):1–10. , , , et al.
- Short Test of Functional Health Literacy in Adults. Snow Camp, NC: Peppercorn Books and Press; 1998. , , , .
- Simplifying detection of cognitive impairment: comparison of the Mini‐Cog and Mini‐Mental State Examination in a multiethnic sample. J Am Geriatr Soc. 2005;53(5):871–874. , , , , .
- Concurrent and predictive validity of a self‐reported measure of medication adherence. Med Care. 1986;24(1):67–74. , , .
- Health literacy and medication understanding among hospitalized adults. J Hosp Med. 2011;6(9):488–493. , , , , , .
- Medication adherence, social support, and event‐free survival in patients with heart failure. Health Psychol. 2013;32(6):637–646. , , , , , .
- Effect of patient comorbidities on filling of antihypertensive prescriptions. Am J Manag Care. 2009;15(1):24–30. , , , , , .
- Primary nonadherence to statin medications in a managed care organization. J Manag Care Pharm. 2013;19(5):367–373. , , , et al.
- Reducing cost by reducing polypharmacy: the polypharmacy outcomes project. J Am Med Dir Assoc. 2012;13(9):818.e811–815. , , , et al.
- The epidemiology of prescriptions abandoned at the pharmacy. Ann Intern Med. 2010;153(10):633–640. , , , et al.
- Can simple clinical measurements detect patient noncompliance? Hypertension. 1980;2(6):757–764. , , , , , .