Testing interventions to reduce clinical inertia in the treatment of hypertension: rationale and design of a pragmatic randomized controlled trial.

BACKGROUND: Clinical inertia, or failure to intensify treatment when indicated, leads to suboptimal blood pressure control. Interventions to overcome inertia and increase antihypertensive prescribing have been modestly successful in part because their effectiveness varies based on characteristics of the provider, the patient, or the provider-patient interaction. Understanding for whom each intervention is most effective could help target interventions and thus increase their impact. METHODS: This three-arm, randomized trial tests the effectiveness of 2 interventions to reduce clinical inertia in hypertension prescribing compared to usual care. Forty five primary care providers (PCPs) caring for patients with hypertension in need of treatment intensification completed baseline surveys that assessed behavioral traits and were randomized to one of three arms: 1) Pharmacist e-consult, in which a clinical pharmacist provided patient-specific recommendations for hypertension medication management to PCPs in advance of upcoming visits, 2) Social norming dashboards that displayed PCP's hypertension control rates compared to those of their peers, or 3) Usual care (no intervention). The primary outcome was the rate of intensification of hypertension treatment. We will compare this outcome between study arms and then evaluate the association between characteristics of providers, patients, their clinical interactions, and intervention responsiveness. RESULTS: Forty-five primary care providers were enrolled and randomized: 16 providers and 173 patients in the social norming dashboards arm, 15 providers and 143 patients in the pharmacist e-consult arm, and 14 providers and 150 patients in the usual care arm. On average, the mean patient age was 64 years, 47% were female, and 73% were white. Baseline demographic and clinical characteristics of patients were similar across arms, with the exception of more Hispanic patients in the usual care arm and fewest in the pharmacist e-consult arm. CONCLUSIONS: This study can help identify interventions to reduce inertia in hypertension care and potentially identify the characteristics of patients, providers, or patient-provider interactions to understand for whom each intervention would be most beneficial. TRIAL REGISTRATION: Clinicaltrials.gov (NCT, Registered: NCT04603560).

"How" Versus "Why" Messaging to Increase Uptake of Booster Vaccination Against COVID-19: Results of a Pragmatic Randomized Trial.

BACKGROUND: Messages aimed at increasing uptake of vaccines have been modestly successful, perhaps in part because they often focus on why individuals should receive a vaccine. Construal Level Theory posits that messages emphasizing "how" to get a vaccine may be more effective at encouraging vaccination than emphasizing "why." This message framing may be particularly important for COVID-19 booster acceptance. OBJECTIVE: To determine if pre-visit patient portal messages designed using Construal Level Theory increase rates of COVID-19 booster vaccination. DESIGN AND INTERVENTIONS: This 3-arm randomized trial was conducted across three large, diverse primary care clinics in Massachusetts between February and May 2022, testing the impact of "how" versus "why" framed pre-visit messages versus no messages ("usual care"). Messages were sent by patient portal two business days before a visit. PARTICIPANTS: Adults with upcoming primary care visits who had electronic health record evidence of receiving their initial COVID-19 vaccination series but not a booster dose. MAIN MEASURES: Receipt of a COVID-19 booster vaccination after the message was sent through the visit date (primary outcome) or 6 weeks (secondary outcome). KEY RESULTS: A total of 3665 patients were randomized (mean age: 53.5 years (SD: 17.3), 59% female, 65.2% White, 26.6% Hispanic), with 1249 to "how" 1199 to "why," and 1217 to usual care arms. Except for clinic and preferred language, characteristics were well balanced across arms. Rates of COVID-19 booster were 13.6% (usual care), 11.7% ("how") (odds ratio (OR) "how" vs usual care: 0.87, 95%CI: 0.67-1.14), and 13.7% ("why") ("why" vs usual care: OR: 1.01, 95%CI: 0.81-1.28). At 6 weeks, "why" outperformed "how" for vaccination (OR: 1.26, 95%CI: 1.06-1.49), with no difference versus usual care. CONCLUSIONS: We found no differences on visit booster receipt after single pre-visit portal messages designed using Construal Level Theory. Further studies to identify effective messaging interventions are needed, especially as additional doses are recommended. CLINICAL TRIAL REGISTRATION: This trial is registered at clinicaltrials.gov, ID: NCT04871776 . Initial release occurred 04/30/2021.

Disentangling drug contributions: anticholinergic burden in older adults linked to individual medications: a cross-sectional population-based study.

BACKGROUND: Medications with potent anticholinergic properties have well-documented adverse effects. A high cumulative anticholinergic burden may arise from the concurrent use of multiple medications with weaker anticholinergic effects. We sought to identify patterns of high anticholinergic burden and associated patient characteristics. METHODS: We identified patients aged ≥ 65 who filled ≥ 1 medication with anticholinergic adverse effects in 2019 and had a cumulative Anticholinergic Burden score (ACB) ≥ 4 (i.e., high anticholinergic burden) in a large US health insurer. We classified patients based on how they attained high burden, as follows: 1) only filling strong or moderate anticholinergic medications (i.e., ACB = 2 or 3, "moderate/strong"), 2) only filling lightly anticholinergic medications (i.e., ACB = 1, "light/possible"), and 3) filling any combination ("mix"). We used multinomial logistic regression to assess the association between measured patient characteristics and membership in the three anticholinergic burden classifications, using the moderate/strong group as the referent. RESULTS: In total, 83,286 eligible patients with high anticholinergic burden were identified (mean age: 74.3 years (SD:7.1), 72.9% female). Of these, 4.5% filled only strong/moderate anticholinergics, 4.3% filled only light/possible anticholinergics, and the rest filled a mix (91.2%). Within patients in the mixed group, 64.3% of medication fills were for light/possible anticholinergics, while 35.7% were for moderate/strong anticholinergics. Compared with patients in the moderate/strong anticholinergics group, patients filling only light/possible anticholinergics were more likely to be older (adjusted Odds Ratio [aOR] per 1-unit of age: 1.06, 95%CI: 1.05-1.07), less likely to be female (aOR: 0.56, 95%CI: 0.50-0.62 vs. male), more likely to have comorbidities (e.g., heart failure aOR: 3.18, 95%CI: 2.70-3.74 or depression aOR: 1.20, 95%CI: 1.09-1.33 vs. no comorbidity), and visited fewer physicians (aOR per 1-unit of change: 0.98, 95%CI: 0.97-0.98). Patients in the mixed group were older (aOR per 1-unit of age: 1.02, 95%CI: 1.02-1.03) and less likely to be female (aOR: 0.89, 95%CI: 0.82-0.97 vs. male) compared with those filling moderate/strong anticholinergics. CONCLUSION: Most older adults accumulated high anticholinergic burden through a combination of light/possible and moderate/strong anticholinergics rather than moderate/strong anticholinergics, with light/possible anticholinergics being the major drivers of overall anticholinergic burden. These insights may inform interventions to improve prescribing in older adults.

Disagreement between pharmacy claims and direct interview to identify patients with non-adherence to chronic cardiometabolic medications.

BACKGROUND: Accurate methods of identifying patients with suboptimal adherence to cardiometabolic medications are needed, and each approach has benefits and tradeoffs. METHODS: We used data from a large trial of patients with poorly controlled cardiometabolic disease and evidence of medication non-adherence measured using pharmacy claims data whose adherence was subsequently assessed during a telephone consultation with a clinical pharmacist. We then evaluated if the pharmacist assessment agreed with the non-adherence measured using claims. When pharmacist and claims assessments disagreed, we identified reasons why claims were insufficient and used multivariable modified Poisson regression to identify patient characteristics associated with disagreement. RESULTS: Of 1,069 patients identified as non-adherent using claims (proportion of days covered [PDC] <80%), 646 (60.4%) were confirmed as non-adherent on pharmacist interview. For the 423 patients (39.6%) where the interview disagreed with the claims, the most common reasons were paying cash or using an alternate insurance (36.6%), medication discontinuation or regimen change (32.8%), and recently becoming adherent (26.7%). Compared to patients whose claims and interview both showed non-adherence, patients whose interview disagreed with claims were less likely to miss outpatient office visits (RR:0.91, 95%CI:0.85-0.97) and more likely to have a baseline PDC above the median (RR:1.35, 95%CI:1.10-1.64). CONCLUSIONS: Among patients identified as non-adherent by claims, 39.6% were observed to be adherent when assessed during pharmacist consultation. This discrepancy was largely driven by paying out-of-pocket, using alternative insurance, or medication discontinuation or change. These findings have important implications for using pharmacy claims to identify and intervene upon medication non-adherence.

Reducing ethnic and racial disparities by improving undertreatment, control, and engagement in blood pressure management with health information technology (REDUCE-BP) hybrid effectiveness-implementation pragmatic trial: Rationale and design.

BACKGROUND: While racial/ethnic disparities in blood pressure control are documented, few interventions have successfully reduced these gaps. Under-prescribing, lack of treatment intensification, and suboptimal follow-up care are thought to be central contributors. Electronic health record (EHR) tools may help address these barriers and may be enhanced with behavioral science techniques. OBJECTIVE: To evaluate the impact of a multicomponent behaviorally-informed EHR-based intervention on blood pressure control. TRIAL DESIGN: Reducing Ethnic and racial Disparities by improving Undertreatment, Control, and Engagement in Blood Pressure management with health information technology (REDUCE-BP) (NCT05030467) is a two-arm cluster-randomized hybrid type 1 pragmatic trial in a large multi-ethnic health care system. Twenty-four clinics (>350 primary care providers [PCPs] and >10,000 eligible patients) are assigned to either multi-component EHR-based intervention or usual care. Intervention clinic PCPs will receive several EHR tools designed to reduce disparities delivered at different points, including a: (1) dashboard of all patients visible upon logging on to the EHR displaying blood pressure control by race/ethnicity compared to their PCP peers and (2) set of tools in an individual patient's chart containing decision support to encourage treatment intensification, ordering home blood pressure measurement, interventions to address health-related social needs, default text for note documentation, and enhanced patient education materials. The primary outcome is patient-level change in systolic blood pressure over 12 months between arms; secondary outcomes include changes in disparities and other clinical outcomes. CONCLUSION: REDUCE-BP will provide important insights into whether an EHR-based intervention designed using behavioral science can improve hypertension control and reduce disparities.

Association Between Cost-Saving Prescription Policy Changes and Adherence to Chronic Disease Medications: an Observational Study.

BACKGROUND: Pharmacy benefit design is one tool for improving access and adherence to medications for the management of chronic disease. OBJECTIVE: We assessed the effects of pharmacy benefit design programs, including a change in pharmacy benefit manager (PBM), institution of a prescription out-of-pocket maximum, and a mandated switch to 90 days' medication supply, on adherence to chronic disease medications over time. DESIGN: We used a difference-in-differences design to assess changes in adherence to chronic disease medications after the transition to new prescription policies. SUBJECTS: We utilized claims data from adults aged 18-64, on ≥ 1 medication for chronic disease, whose insurer instituted the prescription policies (intervention group) and a propensity score-matched comparison group from the same region. MAIN MEASURES: The outcome of interest was adherence to chronic disease medications measured by proportion of days covered (PDC) using pharmacy claims. KEY RESULTS: There were 13,798 individuals in each group after propensity score matching. Compared to the matched control group, adherence in the intervention group decreased in the first quarter of 2015 and then increased back to pre-intervention trends. Specifically, the change in adherence compared to the last quarter of 2014 in the intervention group versus controls was - 3.6 percentage points (pp) in 2015 Q1 (p < 0.001), 0.65 pp in Q2 (p = 0.024), 1.1 pp in Q3 (p < 0.001), and 1.4 pp in Q4 (p < 0.001). CONCLUSIONS: In this cohort of commercially insured adults on medications for chronic disease, a change in PBM accompanied by a prescription out-of-pocket maximum and change to 90 days' supply was associated with short-term disruptions in adherence followed by return to pre-intervention trends. A small improvement in adherence over the year of follow-up may not be clinically significant. These findings have important implications for employers, insurers, or health systems wishing to utilize pharmacy benefit design to improve management of chronic disease.

Prescribing decision making by medical residents on night shifts: A qualitative study.

INTRODUCTION: Prescribing of medications with well-known adverse effects, like antipsychotics or benzodiazepines, during hospitalisation is extremely common despite guideline recommendations against their use. Barriers to optimal prescribing, including perceived pressure from allied health professionals and fatigue, may be particularly pronounced for less experienced medical residents, especially during night shifts when these medications are often prescribed. Under these circumstances, physicians may be more likely to use 'quick', often referred to as System 1 choices, rather than 'considered' System 2 strategies for decisions. Understanding how medical residents use, these different cognitive approaches could help develop interventions to improve prescribing. METHODS: To understand decision-making and contextual contributors that influence suboptimal prescribing during night coverage by medical residents, we conducted semi-structured qualitative interviews with residents in general medicine inpatient settings. The interviews elicited perspectives on shift routines, stressful situations, factors influencing prescribing decision making and hypothetical measures that could improve prescribing. Interviews were audio-recorded and transcribed. Data were analysed using codes developed by the team to generate themes using immersion/crystallisation approaches. RESULTS: We conducted interviews with 21 medical residents; 47% were female, 43% were White, and 43% were Asian. We identified five key themes: (i) time pressures affecting prescribing decisions, (ii) fears of judgement by senior physicians and peers and being responsible for patient outcomes, (iii) perceived pressure from nursing staff, amplified by nurses' greater experience, (iv) clinical acuity as a key factor influencing prescribing, and (v) strategies to improve communication between members of the care team, like ensuring adequate hand-off by day teams. CONCLUSION: Medical residents highlighted numerous contextual factors that promote quick thinking rather than slower thinking when prescribing on night shifts, particularly time constraints, perceived pressure and patient clinical acuity. Interventions aimed at reducing prescribing should address how to manage stress and perceived pressure in decision making.

Patient factors associated with new prescribing of potentially inappropriate medications in multimorbid US older adults using multiple medications.

BACKGROUND: The use of potentially inappropriate medications (PIMs) is common in older adults and is associated with potential negative consequences, such as falls and cognitive decline. Our objective was to investigate measurable patient factors associated with new outpatient prescribing of potentially inappropriate medications in older multimorbid adults already using multiple medications. METHODS: In this retrospective US cohort study, we used linked Medicare pharmacy and medical claims and electronic health record data from a large healthcare system in Massachusetts between 2007 and 2014. We identified patients aged ≥65 years with an office visit who had not been prescribed or used a PIM in the prior 180 days. PIMs were defined using 2019 Beers criteria of the American Geriatrics Society. To specifically evaluate factors in patients with polypharmacy and multimorbidity, we selected those who filled medications for ≥90 days (i.e., chronic use) from ≥5 pharmaceutical classes in the prior 180 days and had ≥2 chronic conditions. Multivariable Cox regression analysis was used to estimate the association between baseline demographic and clinical characteristics on the probability of being prescribed a PIM in the 90-day follow-up period. RESULTS: In total, we identified 17,912 patients aged ≥65 years with multimorbidity and polypharmacy who were naïve to a PIM in the prior 180 days. Of those, 10,497 (58.6%) were female, and mean age was 78 (SD = 7.5). On average, patients had 5.1 (SD = 2.3) chronic conditions and previously filled 6.1 (SD = 1.4) chronic medications. In total, 447 patients (2.5%) were prescribed a PIM during the 90-day follow-up. Male sex (adjusted hazard ratio (HR) = 1.29; 95%CI: 1.06-1.57), age (≥85 years: HR = 0.75, 95%CI: 0.56-0.99, 75-84 years: HR = 0.87, 95%CI: 0.71-1.07; reference: 65-74 years), ambulatory visits (18-29 visits: HR = 1.42, 95%CI: 1.06-1.92; ≥30 visits: HR = 2.12, 95%CI: 1.53-2.95; reference: ≤9 visits), number of prescribing orders (HR = 1.02, 95%CI: 1.01-1.02 per 1-unit increase), and heart failure (HR = 1.38, 95%CI: 1.07-1.78) were independently associated with being newly prescribed a PIM. CONCLUSION: Several demographic and clinical characteristics, including factors suggesting lack of care coordination and increased clinical complexity, were found to be associated with the new prescribing of potentially inappropriate medications. This knowledge could inform the design of interventions and policies to optimize pharmacotherapy for these patients.

Preferences for mHealth Technology and Text Messaging Communication in Patients With Type 2 Diabetes: Qualitative Interview Study.

BACKGROUND: Individuals with diabetes need regular support to help them manage their diabetes on their own, ideally delivered via mechanisms that they already use, such as their mobile phones. One reason for the modest effectiveness of prior technology-based interventions may be that the patient perspective has been insufficiently incorporated. OBJECTIVE: This study aims to understand patients' preferences for mobile health (mHealth) technology and how that technology can be integrated into patients' routines, especially with regard to medication use. METHODS: We conducted semistructured qualitative individual interviews with patients with type 2 diabetes from an urban health care system to elicit and explore their perspectives on diabetes medication-taking behaviors, daily patterns of using mobile technology, use of mHealth technology for diabetes care, acceptability of text messages to support medication adherence, and preferred framing of information within text messages to support diabetes care. The interviews were digitally recorded and transcribed. The data were analyzed using codes developed by the study team to generate themes, with representative quotations selected as illustrations. RESULTS: We conducted interviews with 20 participants, of whom 12 (60%) were female and 9 (45%) were White; in addition, the participants' mean glycated hemoglobin A1c control was 7.8 (SD 1.1). Overall, 5 key themes were identified: patients try to incorporate cues into their routines to help them with consistent medication taking; many patients leverage some form of technology as a cue to support adherence to medication taking and diabetes self-management behaviors; patients value simplicity and integration of technology solutions used for diabetes care, managing medications, and communicating with health care providers; some patients express reluctance to rely on mobile technology for these diabetes care behaviors; and patients believe they prefer positively framed communication, but communication preferences are highly individualized. CONCLUSIONS: The participants expressed some hesitation about using mobile technology in supporting diabetes self-management but have largely incorporated it or are open to incorporating it as a cue to make medication taking more automatic and less burdensome. When using technology to support diabetes self-management, participants exhibited individualized preferences, but overall, they preferred simple and positively framed communication. mHealth interventions may be improved by focusing on integrating them easily into daily routines and increasing the customization of content.

The accuracy of self-reported blood pressure in the Medication adherence Improvement Support App For Engagement-Blood Pressure (MedISAFE-BP) trial: Implications for pragmatic trials.

Self-report of health conditions and behaviors is one potential strategy to increase the pace of enrollment into pragmatic clinical trials. In this study, we assessed the accuracy of self-reported poorly controlled hypertension among adults in the community who were screened for participation in the MedISAFE-BP trial. Of individuals who self-reported poorly controlled hypertension using the online trial enrollment platform, 64% had a systolic blood pressure less than 140 mm Hg when measured at home. Although we identified several characteristics associated with accurate self-report including older age (odds ratio [OR] 1.02 per year, 95% CI 1.01-1.03), diabetes (OR 1.59, 95% CI 1.17-2.14), and low health activation (OR 1.56 95% CI 1.17-2.07), we were unable to identify patients for whom self-reported hypertension would be a reliable method for their inclusion in a pragmatic trial.

Comparison of a new 3-item self-reported measure of adherence to medication with pharmacy claims data in patients with cardiometabolic disease.

BACKGROUND: Less than half of patients with cardiometabolic disease consistently take prescribed medications. While health insurers and some delivery organizations use claims to measure adherence, most clinicians do not have access during routine interactions. Self-reported scales exist, but their practical utility is often limited by length or cost. By contrast, the accuracy of a new 3-item self-reported measure has been demonstrated in individuals with HIV. We evaluated its concordance with claims-based adherence measures in cardiometabolic disease. METHODS: We used data from a recently-completed pragmatic trial of patients with cardiometabolic conditions. After 12 months of follow-up, intervention subjects were mailed a survey with the 3-item measure that queries about medication use in the prior 30 days. Responses were linearly transformed and averaged. Adherence was also measured in claims in month 12 and months 1-12 of the trial using proportion of days covered (PDC) metrics. We compared validation metrics for non-adherence for self-report (average <0.80) compared with claims (PDC <0.80). RESULTS: Of 459 patients returning the survey (response rate: 43.5%), 50.1% were non-adherent in claims in month 12 while 20.9% were non-adherent based on the survey. Specificity of the 3-item metric for non-adherence was high (month 12: 0.83). Sensitivity was relatively poor (month 12: 0.25). Month 12 positive and negative predictive values were 0.59 and 0.52, respectively. CONCLUSIONS: A 3-item self-reported measure has high specificity but poor sensitivity for non-adherence versus claims in cardiometabolic disease. Despite this, the tool could help target those needing adherence support, particularly in the absence of claims data.

Not there yet: using data-driven methods to predict who becomes costly among low-cost patients with type 2 diabetes.

BACKGROUND: Diabetes is a leading cause of Medicare spending; predicting which individuals are likely to be costly is essential for targeting interventions. Current approaches generally focus on composite measures, short time-horizons, or patients who are already high utilizers, whose costs may be harder to modify. Thus, we used data-driven methods to classify unique clusters in Medicare claims who were initially low utilizers by their diabetes spending patterns in subsequent years and used machine learning to predict these patterns. METHODS: We identified beneficiaries with type 2 diabetes whose spending was in the bottom 90% of diabetes care spending in a one-year baseline period in Medicare fee-for-service data. We used group-based trajectory modeling to classify unique clusters of patients by diabetes-related spending patterns over a two-year follow-up. Prediction models were estimated with generalized boosted regression, a machine learning method, using sets of all baseline predictors, diabetes predictors, and predictors that are potentially-modifiable through interventions. Each model was evaluated through C-statistics and 5-fold cross-validation. RESULTS: Among 33,789 beneficiaries (baseline median diabetes spending: $4153), we identified 5 distinct spending patterns that could largely be predicted; of these, 68.1% of patients had consistent spending, 25.3% had spending that rose quickly, and 6.6% of patients had spending that rose progressively. The ability to predict these groups was moderate (validated C-statistics: 0.63 to 0.87). The most influential factors for those with progressively rising spending were age, generosity of coverage, prior spending, and medication adherence. CONCLUSIONS: Patients with type 2 diabetes who were initially low spenders exhibit distinct subsequent long-term patterns of diabetes spending; membership in these patterns can be largely predicted with data-driven methods. These findings as well as applications of the overall approach could potentially inform the design and timing of diabetes or cost-containment interventions, such as medication adherence or interventions that enhance access to care, among patients with type 2 diabetes.

The lock-in loophole: Using mixed methods to explain patient circumvention of a Medicaid opioid restriction program.

BACKGROUND: Lock-in programs are proliferating among private and public payers to restrict access to controlled substance prescriptions and enhance care coordination for patients exhibiting high-risk use of, primarily, opioids. Patients enrolled in lock-in programs are required to seek opioids from a designated provider and pharmacy for insurance coverage of their opioid and benzodiazepine prescriptions. Lock-in program restrictions are often circumvented by patients through out-of-pocket cash purchases of opioid prescriptions, undermining the program's intended function. This study sought to construct and explain trajectories of Medicaid-covered and cash pay opioid prescription fills among adults enrolled in an opioid lock-in program. Methods: We used sequential explanatory mixed methods, which involved a quantitative retrospective cohort analysis of opioid fill trajectories using North Carolina Medicaid administrative claims data linked with state prescription drug monitoring program data, followed by qualitative semi-structured interviews with North Carolina pharmacists. The quantitative component included adults enrolled in the North Carolina Medicaid lock-in program between 10/1/2010-3/31/2012. The qualitative component included a maximum variation sample of community pharmacists in North Carolina delivering care to lock-in patients. Quantitative outcomes included group-based trajectories of monthly Medicaid-covered and cash pay opioid prescription fills six months before and after LIP enrollment, and qualitative analyses generated themes explaining observed trajectories. Results: Two-thirds of subjects exhibited reduced Medicaid-covered opioid prescription fills and no increase in cash pay fills after lock-in enrollment, with one-third exhibiting increased cash pay fills after lock-in. Pharmacists attributed increases in cash pay fills primarily to illicit behaviors, while some cash pay behavior likely reflected new unintended barriers to care. Conclusions: Lock-in programs appear to reduce prescription opioid use for most enrolled patients. However, lock-in programs may have limited capacity to deter illicit behaviors among patients intent on abusing, misusing, or diverting these medications and may introduce new access barriers to necessary care for some.

Quantifying Social Reinforcement Among Family Members on Adherence to Medications for Chronic Conditions: a US-Based Retrospective Cohort Study.

BACKGROUND: 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. OBJECTIVE: To examine how an individual's health behaviors are influenced by those of their family members, particularly in adherence to medications for chronic conditions. DESIGN: Retrospective cohort study. PATIENTS: 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. MAIN MEASURES: 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. KEY RESULTS: 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). CONCLUSIONS: 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.

Association Between Patient-Centered Medical Homes and Adherence to Chronic Disease Medications: A Cohort Study.

BACKGROUND: Despite the widespread adoption of patient-centered medical homes into primary care practice, the evidence supporting their effect on health care outcomes has come primarily from geographically localized and well-integrated health systems. OBJECTIVE: To assess the association between medication adherence and medical homes in a national patient and provider population, given the strong ties between adherence to chronic disease medications and health care quality and spending. DESIGN: Retrospective cohort study. SETTING: Claims from a large national health insurer. PATIENTS: Patients initiating therapy with common medications for chronic diseases (diabetes, hypertension, and hyperlipidemia) between 2011 and 2013. MEASUREMENTS: Medication adherence in the 12 months after treatment initiation was compared among patients cared for by providers practicing in National Committee for Quality Assurance-recognized patient-centered medical homes and propensity score-matched control practices in the same Primary Care Service Areas. Linear mixed models were used to examine the association between medical homes and adherence. RESULTS: Of 313 765 patients meeting study criteria, 18 611 (5.9%) received care in patient-centered medical homes. Mean rates of adherence were 64% among medical home patients and 59% among control patients. Among 4660 matched control and medical home practices, medication adherence was significantly higher in medical homes (2.2% [95% CI, 1.5% to 2.9%]). The association between medical homes and better adherence did not differ significantly by disease state (diabetes, 3.0% [CI, 1.5% to 4.6%]; hypertension, 3.2% [CI, 2.2% to 4.2%]; hyperlipidemia, 1.5% [CI, 0.6% to 2.5%]). LIMITATION: Clinical outcomes related to medication adherence were not assessed. CONCLUSION: Receipt of care in a patient-centered medical home is associated with better adherence, a vital measure of health care quality, among patients initiating treatment with medications for common high-cost chronic diseases. PRIMARY FUNDING SOURCE: CVS Health.

Longitudinal Patterns of Spending Enhance the Ability to Predict Costly Patients: A Novel Approach to Identify Patients for Cost Containment.

BACKGROUND: With rising health spending, predicting costs is essential to identify patients for interventions. Many of the existing approaches have moderate predictive ability, which may result, in part, from not considering potentially meaningful changes in spending over time. Group-based trajectory modeling could be used to classify patients into dynamic long-term spending patterns. OBJECTIVES: To classify patients by their spending patterns over a 1-year period and to assess the ability of models to predict patients in the highest spending trajectory and the top 5% of annual spending using prior-year predictors. SUBJECTS: We identified all fully insured adult members enrolled in a large US nationwide insurer and used medical and prescription data from 2009 to 2011. RESEARCH DESIGN: Group-based trajectory modeling was used to classify patients by their spending patterns over a 1-year period. We assessed the predictive ability of models that categorized patients in the top fifth percentile of annual spending and in the highest spending trajectory, using logistic regression and split-sample validation. Models were estimated using investigator-specified variables and a proprietary risk-adjustment method. RESULTS: Among 998,651 patients, in the best-performing model, prediction was strong for patients in the highest trajectory group (C-statistic: 0.86; R: 0.47). The C-statistic of being in the top fifth percentile of spending in the best-performing model was 0.82 (R: 0.26). Approaches using nonproprietary investigator-specified methods performed almost as well as other risk-adjustment methods (C-statistic: 0.81 vs. 0.82). CONCLUSIONS: Trajectory modeling may be a useful way to predict costly patients that could be implementable by payers to improve cost-containment efforts.

Effect of Combination Therapy on Adherence Among US Patients Initiating Therapy for Hypertension: a Cohort Study.

BACKGROUND: New trial evidence suggests that many patients may require more aggressive pharmacologic management to achieve lower blood pressure goals. Especially when first initiating anti-hypertensive treatment, it is unknown whether starting patients on multiple medications may be better for long-term adherence and persistence compared with starting one medication. OBJECTIVE: To examine contemporary patterns of anti-hypertensive therapy initiation and compare long-term adherence and persistence among patients initiating fixed-dose combinations and single anti-hypertensive therapies. DESIGN: Retrospective cohort study. PATIENTS: Using claims from a large nationwide insurer, we identified all patients initiating oral hypertension treatment from 2009 to 2013. We categorized patients into three categories based on the number and type of anti-hypertensive medications they initiated: a fixed-dose combination, a multi-pill combination or a single therapy. MAIN MEASURES: The primary outcome was persistence to any anti-hypertensive medication, either the initiated medication or other anti-hypertensive, 12 months after initiation in administrative claims. We also measured adherence to at least one anti-hypertensive in the 12 months after initiation and refilling at least one anti-hypertensive medication as outcomes. Full adherence was defined as having ≥80% of potential days covered with medication. Multivariable modified Poisson regression models were used to examine the association between initiating a fixed-dose combination anti-hypertensive and medication outcomes. KEY RESULTS: Of the 484,493 patients who initiated oral anti-hypertensives, 78,958 patients initiated fixed-dose combinations, 383,269 initiated a single therapy, and 22,266 initiated multi-pill combinations. Patients initiating fixed-dose combinations were 9% more likely to be persistent (relative risk [RR]: 1.09, 95% CI: 1.08-1.10) and 13% more likely to be adherent (RR: 1.13 95% CI: 1.11-1.14) than those who started on a single anti-hypertensive therapy. Refill rates were also slightly higher among fixed-dose combination initiators. CONCLUSIONS: Fixed-dose combination pills appear to enhance adherence and persistence to anti-hypertensive medications among commercially insured patients starting treatment compared with single therapy.

Rationale and design of the Study of a Tele-pharmacy Intervention for Chronic diseases to Improve Treatment adherence (STIC2IT): A cluster-randomized pragmatic trial.

BACKGROUND: Approximately half of patients with chronic cardiometabolic conditions are nonadherent with their prescribed medications. Interventions to improve adherence have been only modestly effective because they often address single barriers to adherence, intervene at single points in time, or are imprecisely targeted to patients who may not need adherence assistance. OBJECTIVE: To evaluate the effect of a multicomponent, behaviorally tailored pharmacist-based intervention to improve adherence to medications for diabetes, hypertension, and hyperlipidemia. TRIAL DESIGN: The STIC2IT trial is a cluster-randomized pragmatic trial testing the impact of a pharmacist-led multicomponent intervention that uses behavioral interviewing, text messaging, mailed progress reports, and video visits. Targeted patients are those who are nonadherent to glucose-lowering, antihypertensive, or statin medications and who also have evidence of poor disease control. The intervention is tailored to patients' individual health barriers and their level of health activation. We cluster-randomized 14 practice sites of a large multispecialty group practice to receive either the pharmacist-based intervention or usual care. STIC2IT has enrolled 4,076 patients who will be followed up for 12months after randomization. The trial's primary outcome is medication adherence, assessed using pharmacy claims data. Secondary outcomes are disease control and health care resource utilization. CONCLUSION: This trial will determine whether a technologically enabled, behaviorally targeted pharmacist-based intervention results in improved adherence and disease control. If effective, this strategy could be a scalable method of offering tailored adherence support to those with the greatest clinical need.