More than 50% of patients are non-adherent to medications, often without an easily identifiable reason to clinicians. No study has quantified the extent to which health behaviors like medication-taking are correlated within families using national or routinely collected data for a range of conditions.
To examine how an individual’s health behaviors are influenced by those of their family members, particularly in adherence to medications for chronic conditions.
Retrospective cohort study.
Using claims from a large nationwide insurer, we identified patients initiating medications for one of five chronic conditions with a family member who also recently filled one of these medications.
The primary exposure was whether family members were fully adherent (defined as a proportion of days covered ≥ 80%) before the patient’s date of initiation. The outcome of interest was whether patients were fully adherent in the 12 months after initiation. Baseline demographic and clinical characteristics were also measured before initiation. We used multivariable modified Poisson regression to examine the association between prior family adherence and subsequent patient adherence.
Among 254,144 patients, rates of full adherence among patients whose family members were and were not fully adherent were 37.3% and 26.9%, respectively (adjusted relative risk [aRR] 1.29, 95%CI 1.28–1.31). The association was stronger when both used cardiometabolic medications (aRR 1.35, 95%CI 1.32–1.37). Similarly, patients were also 38% more likely to be adherent if they and their family members used a medication for the same condition (aRR 1.38, 95%CI 1.35–1.40).
Adherence among family members appeared to be highly correlated, suggesting positive reinforcement by family or the sharing of unmeasured behaviors or characteristics associated with better adherence. Regardless, information about prior adherence among family members from routinely collected data could potentially inform adherence prediction or intervention efforts.
High deductible health plans (HDHP) are associated with high levels of patient cost-sharing and are becoming increasingly used in the United Status as a means of reducing healthcare utilization and spending. Our objective is to determine whether HDHP enrollment is associated with a change in adherence to evidence-based medications to treat cardiovascular risk factors and whether such changes vary based on race/ethnicity or socioeconomic status.
Methods and Results:
We conducted a retrospective cohort study using an interrupted time series with concurrent control group design among beneficiaries of Aetna—a national commercial insurer. We included 14 866 patients who filled prescriptions for medications to treat hypertension, high cholesterol, or diabetes mellitus between 2009 and 2014 and who switched from a traditional plan into an HDHP and 14 866 controls who did not switch to an HDHP matched based on calendar time, medication class, race/ethnicity, socioeconomic status, and propensity score. We were specifically interested in evaluating 4 prespecified subgroups based on race/ethnicity (white versus nonwhite) and socioeconomic status (higher versus lower). The main outcome was medication adherence as measured by proportion of days covered. The overall cohort had an average age of 53 years, and 44% were women. Baseline adherence was the lowest in the nonwhite patient group. Switching to an HDHP was associated with a decrease in the level of adherence of 5 percentage points across all 4 subgroups (change in level, −5.0%; 95% CI, −5.9% to −4.0%; P<0.0001).
HDHP enrollment was associated with a reduction in adherence to medications to treat cardiovascular risk factors. The magnitude of this effect did not vary based on race/ethnicity or socioeconomic status. Because racial/ethnic minorities have lower rates of medication adherence, future studies should evaluate whether HDHP-associated changes in adherence have greater clinical consequences for these patients.
Background Data on primary nonadherence remains sparse, due to a lack of data resources that combine information on medication prescribing and dispensing. In addition, previous work on primary nonadherence has used follow-up periods ranging from 30 days up to 18 months, making results difficult to compare.
Objective To evaluate the prevalence and predictors of primary nonadherence by measuring time until filling in a cohort of elderly patients.
Design Retrospective cohort study of new prescription episodes.
Patients Data comes from a linked database of electronic health records and claims for patients aged ≥ 65 years enrolled in Medicare Parts A, B, and D during 2007–2014. We identified patients receiving a new prescription for a chronic disease medication with continuous Medicare enrollment for 180 days prior to the index prescription order and no fills or orders for the medication during this period.
Main Measures Time until filling of the index prescription for up to 1 year.
Key Results In 32,586 new medication orders, the majority (75%; 95% confidence interval [CI] 74–75%) of new prescriptions were filled within 7 days, 81% (81–82%) were filled within 30 days, and 91% (91–92%) were filled within 1 year. The rate and timing of dispensing were similar across therapeutic areas. Timing of initial filling within 7 days or within 30 days could be predicted with moderate accuracy (C-statistics = 0.70–0.74). Patients with > 5 current medications on hand at the time of the index prescription and average out-of-pocket medication costs < $5 filled 89% of prescriptions within 7 days. Patients with no current medications and out-of-pocket costs > $50 filled only 25% of prescriptions within 7 days.
Conclusions Nearly 20% of patients do not fill a new chronic disease prescription within 30 days. Patients with fewer recent fills and higher out-of-pocket costs are at higher risk of primary nonadherence.
BACKGROUND: Medication nonadherence is a major public health problem. Identification of patients who are likely to be and not be adherent can guide targeted interventions and improve the design of comparative-effectiveness studies. OBJECTIVE: To evaluate multiple measures of patient previous medication adherence in light of predicting future statin adherence in a large U.S. administrative claims database. METHODS: We identified a cohort of patients newly initiating statins and measured their previous adherence to other chronic preventive medications during a 365-day baseline period, using metrics such as proportion of days covered (PDC), lack of second fills, and number of dispensations. We measured adherence to statins during the year after initiation, defining high adherence as PDC ≥ 80%. We built logistic regression models from different combinations of baseline variables and previous adherence measures to predict high adherence in a random 50% sample and tested their discrimination using concordance statistics (c-statistics) in the other 50%. We also assessed the association between previous adherence and subsequent statin high adherence by fitting a modified Poisson model from all relevant covariates plus previous mean PDC categorized as < 25%, 25%-79%, and ≥ 80%. RESULTS: Among 89,490 statin initiators identified, a prediction model including only demographic variables had a c-statistic of 0.578 (95% CI = 0.573-0.584). A model combining information on patient comorbidities, health care services utilization, and medication use resulted in a c-statistic of 0.665 (95% CI = 0.659-0.670). Models with each of the previous medication adherence measures as the only explanatory variable yielded c-statistics ranging between 0.533 (95% CI = 0.529-0.537) for lack of second fill and 0.666 (95% CI = 0.661-0.671) for maximum PDC. Adding mean PDC to the combined model yielded a c-statistic of 0.695 (95% CI = 0.690-0.700). Given a sensitivity of 75%, the predictor improved the specificity from 47.7% to 53.6%. Patients with previous mean PDC < 25% were half as likely to show high adherence to statins compared with those with previous mean PDC ≥ 80% (risk ratio = 0.49, 95% CI = 0.46-0.50). CONCLUSIONS: Including measures of previous medication adherence yields better prediction of future statin adherence than usual baseline clinical measures that are typically used in claims-based studies. DISCLOSURES: This study was funded by the Patient-Centered Outcomes Research Institute (ME-1309-06274). Kumamaru, Kohsaka, and Miyata are affiliated with the Department of Healthcare Quality Assessment at the University of Tokyo, which is a social collaboration department supported by National Clinical Database. The department was formerly supported by endowments from Johnson & Johnson K.K., Nipro, Teijin Pharma, Kaketsuken K.K., St. Jude Medical Japan, Novartis Pharma K.K., Taiho Pharmaceutical, W. L. Gore & Associates, Olympus Corporation, and Chugai Pharmaceutical. Gagne has received grants from Novartis Pharmaceuticals and Eli Lilly and Company to the Brigham and Women's Hospital for unrelated work. He is a consultant to Aetion, a software company, and to Optum. Choudhry has received grants from the National Heart, Lung, and Blood Institute, PhRMA Foundation, Merck, Sanofi, AstraZeneca, CVS, and MediSafe. Schneeweiss is consultant to WHISCON and Aetion, a software manufacturer of which he also owns equity. He is principal investigator of investigator-initiated grants to the Brigham and Women’s Hospital from Bayer, Genentech, and Boehringer Ingelheim unrelated to the topic of this study. He does not receive personal fees from biopharmaceutical companies. No potential conflict of interest was reported by the other authors.
BACKGROUND: Shortages of chronic medications are an increasingly common problem, yet little is known about their impact on drug utilization and clinical outcomes. We evaluated the population-level impact of metoprolol extended release shortage that occurred in the United States in 2009 to 2010.
METHODS AND RESULTS: We conducted a population-based, time series analysis of 38914 patients (mean age, 60 years; 69% men) discharged after hospitalization for myocardial infarction (MI) between January 2006 and November 2012 in a large commercial insurance database. The shortage period was defined as February 2009 to June 2010. Data before September 2008 was defined as preshortage period and data after June 2010 as postshortage period. Outcomes were proportion of patients who filled any long- or short-acting β-blocker within 30 days of discharge, adherence to β-blockers within the first year of therapy among patients who initiated β-blockers, and rates of 1-year rehospitalization for MI or unstable angina. Post-MI statin utilization and adherence were evaluated as control outcomes. During the preshortage period, 70% of patient filled a β-blocker, mean monthly adherence was 76%, and the average monthly rate of rehospitalization was 6.5 events per 100 person-years, as compared with β-blocker use of 62%, average adherence of 70%, and rehospitalization rate of 5.6 events per 100 person-years during the shortage. After accounting for the baseline (preshortage) trends, the shortage was associated with significant monthly reductions in postdischarge β-blocker use (−0.57% of patients [95% CI, −0.90 to −0.24] per month) and an immediate decrease in adherence (−4.58% days covered [95% CI, −6.12 to −3.04]). No negative impact on rates of rehospitalization, post-MI statin utilization, or statin adherence was observed. β-Blocker utilization began to increase after the resolution of the shortage.
CONCLUSIONS: The nationwide metoprolol extended release shortage in the United States was associated with fewer patients receiving any long- or short-acting β-blocker post-MI and lower adherence to β-blocker therapy for those who did receive it, but did not appear to appreciably affect clinical outcomes at the population level.
The influenza (‘flu’) vaccination is low cost1 and effective, typically reducing the likelihood of infection by 50–60%2 . It is recommended for nearly everyone older than 6 months of age3 ; yet, only 40% of Americans are immunized each year. Vaccination rates are higher among at-risk groups, such as those ≥65 years of age, but still only 6 in 10 receive it4. There have been numerous attempts to improve vaccination rates using strategies such as school-based programmes, financial incentives and reminders, but these have generally had limited success5–7 . Of the attempts that are successful, most are expensive—limiting scalability—and have not been evaluated in the elderly8. Conversely, lower-cost interventions, such as mailed information, hold promise for a scalable solution, but their limited effectiveness may result from how they have been designed. We randomly assigned 228,000 individuals ≥66 years of age to one of five versions of letters intended to motivate vaccination, including versions with an implementation intention prompt and an enhanced active choice implementation prompt. We found that a single mailed letter significantly increased influenza vaccination rates compared with no letter. However, there was no difference in vaccination rates across the four different letters tailored with behavioural science techniques.
OBJECTIVES: Preference weights derived from general population samples are often used for therapeutic decision making. In contrast, patients with cardiovascular disease may have different preferences concerning the benefits and risks of anticoagulant therapy. Using a discrete choice experiment, we compared preferences for anticoagulant treatment outcomes between the general population and patients with cardiovascular disease. METHODS: A sample of the general US population and a sample of patients with cardiovascular disease were selected from online panels. We used a discrete choice experiment questionnaire to elicit preferences in both populations concerning treatment benefits and risks. Seven attributes described hypothetical treatments: non-fatal stroke, non-fatal myocardial infarction, cardiovascular death, minor bleeding, major bleeding, fatal bleeding, and the need for monitoring. We measured preference weights and maximum acceptable risks in both populations. RESULTS: A total of 352 individuals from the general population and 341 patients completed the questionnaire. After propensity score matching, 284 from each group were included in the analysis. On average, the general population members valued a 1% increased risk of fatal bleeding as being the same as a 4.2% increase in a non-fatal myocardial infarction, a 2.8% increase in cardiovascular death, or a 14.1% increase in minor bleeding. Patients, in contrast, perceived a 1% increased risk of fatal bleeding as being the same as a 2.0% increase in a non-fatal myocardial infarction, a 3.2% increase in cardiovascular death, and a 16.7% increase in minor bleeding. CONCLUSIONS: The general population and patients with cardiovascular disease had slightly different preferences for treatment outcomes. The differences can potentially influence estimated benefits and risks and patient-centered treatment decisions.
Importance: Approximately half of patients with chronic conditions are nonadherent to prescribed medications, and interventions have been only modestly effective. Objective: To evaluate the effect of a remotely delivered multicomponent behaviorally tailored intervention on adherence to medications for hyperlipidemia, hypertension, and diabetes. Design, Setting, and Participants: Two-arm pragmatic cluster randomized controlled trial at a multispecialty group practice including participants 18 to 85 years old with suboptimal hyperlipidemia, hypertension, or diabetes disease control, and who were nonadherent to prescribed medications for these conditions. Interventions: Usual care or a multicomponent intervention using telephone-delivered behavioral interviewing by trained clinical pharmacists, text messaging, pillboxes, and mailed progress reports. The intervention was tailored to individual barriers and level of activation. Main Outcomes and Measures: The primary outcome was medication adherence from pharmacy claims data. Secondary outcomes were disease control based on achieved levels of low-density lipoprotein cholesterol, systolic blood pressure, and hemoglobin A1c from electronic health records, and health care resource use from claims data. Outcomes were evaluated using intention-to-treat principles and multiple imputation for missing values. Results: Fourteen practice sites with 4078 participants had a mean (SD) age of 59.8 (11.6) years; 45.1% were female. Seven sites were each randomized to intervention or usual care. The intervention resulted in a 4.7% (95% CI, 3.0%-6.4%) improvement in adherence vs usual care but no difference in the odds of achieving good disease control for at least 1 (odds ratio [OR], 1.10; 95% CI, 0.94-1.28) or all eligible conditions (OR, 1.05; 95% CI, 0.91-1.22), hospitalization (OR, 1.02; 95% CI, 0.78-1.34), or having a physician office visit (OR, 1.11; 95% CI, 0.91-1.36). However, intervention participants were significantly less likely to have an emergency department visit (OR, 0.62; 95% CI, 0.45-0.85). In as-treated analyses, the intervention was associated with a 10.4% (95% CI, 8.2%-12.5%) increase in adherence, a significant increase in patients achieving disease control for at least 1 eligible condition (OR, 1.24; 95% CI, 1.03-1.50), and nonsignificantly improved disease control for all eligible conditions (OR, 1.18; 95% CI, 0.99-1.41). Conclusions and Relevance: A remotely delivered multicomponent behaviorally tailored intervention resulted in a statistically significant increase in medication adherence but did not change clinical outcomes. Future work should focus on identifying which groups derive the most clinical benefit from adherence improvement efforts. Trial Registration: ClinicalTrials.gov identifier: NCT02512276.
BACKGROUND: Continuation of antiplatelet therapy beyond 12 months after a drug-eluting stent procedure reduced the risk of a major adverse cardiovascular and cerebrovascular event (MACCE) in the DAPT trial (Dual Antiplatelet Therapy). Observational studies have evaluated outcomes related to different durations of therapy but are susceptible to bias. METHODS AND RESULTS: Using deidentified claims from commercially insured and Medicare populations in the United States, we compared how increasingly stringent definitions of exposure affect associations between antiplatelet continuation versus discontinuation and MACCE, myocardial infarction, and intracerebral hemorrhage or gastrointestinal bleeding in patients meeting DAPT trial inclusion criteria between 2004 and 2013. Therapy continuation at 12 months was defined as (1) having antiplatelet supply on hand versus not (landmark time); (2) refilling within 30 days versus not among individuals with antiplatelet supply; (3) criteria 2 plus continuous prior antiplatelet use; and (4) criteria 2 and 3 plus a cardiologist visit in months 10 to 12. Propensity score-adjusted hazard ratios were compared. Cohort sizes were 53 679, 27 524, 16 971, and 7948, respectively, of which 20% were discontinuers on average. Increasing restriction led to progressively larger associations with continued treatment: cohort 1 MACCE hazard ratio, 0.79 (0.73, 0.87); myocardial infarction, 0.74 (0.65, 0.83); bleed, 1.03 (0.96, 1.11) versus cohort 4 MACCE hazard ratio, 0.66 (0.48, 0.91); myocardial infarction, 0.56 (0.37, 0.86); bleed, 1.24 (0.95, 1.61). Estimates trended toward DAPT trial estimates and were associated with reduced levels of exposure misclassification. CONCLUSIONS: In an example of long-term antiplatelet use, increasing restrictions on the definition of therapy continuation yielded results consistent with trial estimates by reducing exposure misclassification.
The writing of a prescription has long been one of the most expected steps to occur at the end of a physician-patient encounter. The subsequent events are assumed to follow a natural order: patients fill their prescriptions at a pharmacy and then continue to use their medications as prescribed. Unfortunately, these assumptions often do not hold. The challenges of consistent medication use, often called secondary adherence or persistence, have been well characterized by decades of research estimating that, on average, fewer than half of patients use their medications as prescribed over the long-term and many stop using their medications within months of beginning.1 Much less well appreciated has been primary nonadherence, where patients do not fill the initial prescriptions they are given.
BACKGROUND: Efforts at predicting long-term adherence to medications have been focused on patients filling typical month-long supplies of medication. However, prediction remains difficult for patients filling longer initial supplies, a practice that is becoming increasingly common as a method to enhance medication adherence. OBJECTIVES: To (a) extend methods involving short-term filling behaviors and (b) develop novel variables to predict adherence in a cohort of patients receiving longer initial prescriptions. METHODS: In this retrospective cohort study, we used claims from a large national insurer to identify patients initiating a 90-day supply of oral medications for diabetes, hypertension, and hyperlipidemia (i.e., statins). Patients were included in the cohort if they had continuous database enrollment in the 180 days before and 365 days after medication initiation. Adherence was measured in the subsequent 12 months using the proportion of days covered metric. In total, 125 demographic, clinical, and medication characteristics at baseline and in the first 30-120 days after initiation were used to predict adherence using logistic regression models. We used 10-fold cross-validation to assess predictive accuracy by discrimination (c-statistic) measures. RESULTS: In total, 32,249 patients met the inclusion criteria, including 14,930 patients initiating statins, 12,887 patients initiating antihypertensives, and 4,432 patients initiating oral hypoglycemics. Prediction using only baseline variables was relatively poor (cross-validated c-statistic = 0.644). Including indicators of acute clinical conditions, health resource utilization, and short-term medication filling in the first 120 days greatly improved predictive ability (0.823). A model that incorporated all baseline characteristics and predictors within the first 120 days after medication initiation more accurately predicted future adherence (0.832). The best performing model that included all 125 baseline and postbaseline characteristics had strong predictive ability (0.837), suggesting the utility of measuring these novel postbaseline variables in this population. CONCLUSIONS: We demonstrate that long-term, 12-month adherence in patients filling longer supplies of medication can be strongly predicted using a combination of clinical, health resource utilization, and medication filling characteristics before and after treatment initiation. DISCLOSURES: This work was supported by an unrestricted grant from CVS Health to Brigham and Women's Hospital. Shrank and Matlin were employees and shareholders at CVS Health at the time of this study; they report no financial interests in products or services that are related to this subject. Spettell is an employee of, and shareholder in, Aetna. This research was previously presented at the 2016 Annual Conference of the International Society for Pharmacoepidemiology; August 25-28, 2016; Dublin, Ireland.
Background Little is known about physicians’ approaches to adopting new cardiovascular drugs and how adoption varies between drugs of differing novelty. Methods Using data on dispensed prescriptions from IMS Health's Xponent™ database, we created a cohort of all primary care physicians (PCPs) and cardiologists in Pennsylvania who regularly prescribed anticoagulants, antihypertensives and statins from 2007 to 2011. We examined prescribing of three new cardiovascular drugs of differing novelty: dabigatran, aliskiren and pitavastatin. Outcomes were rapid adoption of each new drug, defined by early and sustained monthly prescribing detected by group-based trajectory models, by physicians within the first 15 months of marketplace introduction. Results 5953 physicians regularly prescribed each drug class. The majority of physicians (63.8%) adopted zero new drugs in the first 15 months, 35.0% rapidly adopted one or two, and 1.2% rapidly adopted all three. Physicians were more likely to rapidly adopt the most novel drug, dabigatran (27.3%), than aliskiren (10.5%) or pitavastatin (8.0%). Physician specialty and sex were the most consistent predictors of adoption. Compared to PCPs, cardiologists were more likely to rapidly adopt dabigatran (Adjusted Odds Ratio 8.90, 95% confidence interval 7.42–10.67; P<0.001) aliskerin (2.05, CI 1.56–2.69; P<0.001) and pitavastatin (3.44, CI 2.60–4.57; P<0.001). Female physicians were less likely to adopt dabigatran (0.71, CI 0.59–0.85; P <0.001) and aliskiren (0.64, CI 0.49–0.83; P <0.001). Conclusions Physicians vary in their prescribing of recently-introduced cardiovascular drugs. Though most physicians did not rapidly adopt any new cardiovascular drugs, drug novelty and cardiology training were associated with greater adoption.
Importance Medication nonadherence accounts for up to half of uncontrolled hypertension. Smartphone applications (apps) that aim to improve adherence are widely available but have not been rigorously evaluated.Objective To determine if the Medisafe smartphone app improves self-reported medication adherence and blood pressure control.Design, Setting, and Participants This was a 2-arm, randomized clinical trial (Medication Adherence Improvement Support App For Engagement—Blood Pressure [MedISAFE-BP]). Participants were recruited through an online platform and were mailed a home blood pressure cuff to confirm eligibility and to provide follow-up measurements. Of 5577 participants who were screened, 412 completed consent, met inclusion criteria (confirmed uncontrolled hypertension, taking 1 to 3 antihypertensive medications), and were randomized in a ratio of 1:1 to intervention or control.Interventions Intervention arm participants were instructed to download and use the Medisafe app, which includes reminder alerts, adherence reports, and optional peer support.Main Outcomes and Measures Co–primary outcomes were change from baseline to 12 weeks in self-reported medication adherence, measured by the Morisky medication adherence scale (MMAS) (range, 0-8, with lower scores indicating lower adherence), and change in systolic blood pressure.Results Participants (n = 411; 209 in the intervention group and 202 controls) had a mean age of 52.0 years and mean body mass index, calculated as weight in kilograms divided by height in meters squared, of 35.5; 247 (60%) were female, and 103 (25%) were black. After 12 weeks, the mean (SD) score on the MMAS improved by 0.4 (1.5) among intervention participants and remained unchanged among controls (between-group difference: 0.4; 95% CI, 0.1-0.7; P = .01). The mean (SD) systolic blood pressure at baseline was 151.4 (9.0) mm Hg and 151.3 (9.4) mm Hg, among intervention and control participants, respectively. After 12 weeks, the mean (SD) systolic blood pressure decreased by 10.6 (16.0) mm Hg among intervention participants and 10.1 (15.4) mm Hg among controls (between-group difference: −0.5; 95% CI, −3.7 to 2.7; P = .78).Conclusions and Relevance Among individuals with poorly controlled hypertension, patients randomized to use a smartphone app had a small improvement in self-reported medication adherence but no change in systolic blood pressure compared with controls.Trial Registration clinicaltrials.gov Identifier: NCT02727543
In the presence of heterogeneity of treatment effect (HTE), the average treatment effect from a randomized controlled trial (RCT) may not be applicable to different patients, such as those in observational settings. Our objective was to develop a novel approach that uses individual-level simulation to expand RCT results to target patient populations in the presence of HTE. For this purpose, we compared the results of the Randomized Evaluation of Long-Term Anticoagulation Therapy (RE-LY) trial, and two observational studies that compared benefits and risks of dabigatran to warfarin in patients with atrial fibrillation. We developed a simulation model that replicates the rates of ischemic stroke and major bleeding observed in RE-LY using published outcome risk models and participants' baseline characteristics. We used our validated simulation model to predict what the results of the RCT would have been had it been conducted in populations similar to those in the observational studies. This article is protected by copyright. All rights reserved.
Background Healthcare providers are increasingly encouraged to improve their patients' adherence to chronic disease medications. Prediction of adherence can identify patients in need of intervention, but most prediction efforts have focused on claims data, which may be unavailable to providers. Electronic health records (EHR) are readily available and may provide richer information with which to predict adherence than is currently available through claims. Methods In a linked database of complete Medicare Advantage claims and comprehensive EHR from a multi-specialty outpatient practice, we identified patients who filled a prescription for a statin, antihypertensive, or oral antidiabetic during 2011 to 2012. We followed patients to identify subsequent medication filling patterns and used group-based trajectory models to assign patients to adherence trajectories. We then identified potential predictors from both claims and EHR data and fit a series of models to evaluate the accuracy of each data source in predicting medication adherence. Results Claims were highly predictive of patients in the worst adherence trajectory (C=0.78), but EHR data also provided good predictions (C=0.72). Among claims predictors, presence of a prior gap in filling of at least 6 days was by far the most influential predictor. In contrast, good predictions from EHR data required complex models with many variables. Conclusion EHR data can provide good predictions of adherence trajectory and therefore may be useful for providers seeking to deploy resource-intensive interventions. However, prior adherence information derived from claims is most predictive, and can supplement EHR data when it is available.
Since 2010, four oral anticoagulants have been approved for marketing in addition to warfarin for treatment of thromboembolic disease. Limited head-to-head data exist comparing these treatments, leaving patients and clinicians with little guidance for selecting a strategy that balances recurrence reduction with bleeding risk. In the dabigatran, apixaban, rivaroxban, edoxaban and warfarin comparative effectiveness research study, we compare all five currently available oral anticoagulant agents for the extended treatment of deep venous thrombosis and pulmonary embolism, as well as no extended treatment, and evaluate whether results differ in specific sub-populations. As our population includes Medicare novel anticoagulant users and large numbers of commercially insured and Medicaid patients, our results will likely be transportable to the majority of US patients experiencing a DVT or pulmonary embolism. CLINICAL TRIALS REGISTRATION: NCT03271450.
Medication synchronization programs based in pharmacies simplify the refill process by enabling patients to pick up all of their medications on a single visit. This can be especially important for improving medication adherence in patients with complex chronic diseases. We evaluated the impact of two synchronization programs on adherence, cardiovascular events, and resource use among Medicare beneficiaries treated between 2011 and 2014 for two or more chronic conditions-at least one of which was hypertension, hyperlipidemia, or diabetes. Among nearly 23,000 patients matched by propensity score, the mean proportion of days covered (a measure of medication adherence) for the control group of patients without a synchronization program was 0.84 compared to 0.87 for synchronized patients-a gain of 3 percentage points. Adherence improvement in synchronized versus control patients was three times greater in patients with low baseline adherence, compared to those with higher baseline adherence. Rates of hospitalization and emergency department visits and rates of outpatient visits were 9 percent and 3 percent lower in the synchronized group compared to the control group, respectively, while cardiovascular event rates were similar. Synchronization programs were associated with improved adherence for patients with cardiovascular disease, especially those with low baseline adherence.