Disparities in Patient Portal Engagement Among Patients With Hypertension Treated in Primary Care.

Importance: Patient portals are increasingly used for patient-clinician communication and to introduce interventions aimed at improving blood pressure control. Objective: To characterize patient portal use among patients with hypertension managed in primary care. Design, Settings, and Participants: This retrospective cohort study used electronic health records linked with patient portal log file data from a large, diverse Midwestern health care system. Patients with hypertension who had a primary care visit from January 1, 2021, to December 31, 2021, were included. The first visit in 2021 was considered the baseline visit; patient portal engagement was evaluated during the following year. Multivariate logistic regressions, presented as odds ratios (ORs) and 95% CIs, were used to evaluate associations between patient characteristics and patient portal engagement, adjusting for potential confounders. Exposures: Primary exposures included 4 sociodemographic factors routinely collected in the electronic health record: race and ethnicity, insurance, preferred language, and smoking status. Main Outcomes and Measures: Indicators of patient engagement with the patient portal included accessing the patient portal at least once, accessing the portal within 7 days of at least 50.0% of primary care physician (PCP) visits, frequent logins (<28 vs ≥28), messaging (<2 vs ≥2), and sharing home blood pressure readings. Results: Among 366 871 patients (mean [SD], 63.5 [12.6] years), 52.8% were female, 3.4% were Asian, 7.8% were Hispanic, 19.7% were non-Hispanic Black, 66.9% were non-Hispanic White, and 2.3% were of other race or ethnicity. During the 1-year study period starting in 2021, 70.5% accessed the patient portal at least once, 60.2% accessed around the time of their PCP visits, 35.7% accessed the portal frequently, 28.9% engaged in messaging, and 8.7% shared home blood pressure readings. Compared with White patients, non-Hispanic Black and Hispanic patients had lower odds of any access (Black: OR, 0.53; 95% CI, 0.52-0.54; Hispanic: OR, 0.66; 95% CI, 0.64-0.68), access around PCP visit time (Black: OR, 0.49; 95% CI, 0.48-0.50; Hispanic: OR, 0.62; 95% CI, 0.60-0.64), frequent access (Black: OR, 0.56; 95% CI, 0.55-0.57; Hispanic: OR, 0.71; 95% CI, 0.69-0.73), and messaging (Black: OR, 0.63; 95% CI, 0.61-0.64); Hispanic: OR, 0.71; 95% CI, 0.69-0.73). Conclusions and Relevance: This cohort study of patients with hypertension found clear sociodemographic disparities in patient portal engagement among those treated in primary care. Without special efforts to engage patients with portals, interventions that use patient portals to target hypertension may exacerbate disparities.

Costs of Prescription Drugs for Children and Parental Adherence to Long-Term Medications.

Importance: The adverse effects of prescription drug costs on medication adherence and health have been well described for individuals. Because many families share financial resources, high medication costs for one could lead to cost-related nonadherence in another; however, these family-level spillover effects have not been explored. Objective: To evaluate whether the cost of a child's newly initiated medication was associated with changes in their parent's adherence to their own medications and whether that differed by likely duration of treatment. Design, Setting, and Participants: This cohort study used interrupted time-series analysis with a propensity score-matched control group from a large national US health insurer database (2010-2020) and included children initiating medication and their linked presumed parents using long-term medications. Exposure: The cost of the child's initiated medication. Child medication cost was classified based on highest (≥90th) or lowest (<10th) decile from out-of-pocket medication spending, stratified by whether the medication was intended for short- or long-term use. Children initiating high-cost medications (based on the highest decile) were propensity-score matched with children initiating low-cost medications. Main Outcome and Measures: The child's parent's adherence to long-term medication assessed by the widely used proportion of days covered metric in 30-day increments before and after the child's first fill date. Parent demographic characteristics, baseline adherence, and length of treatment, and family unit size and out-of-pocket medication spending were key subgroups. Results: Across 47 154 included pairs, the parents' mean (SD) age was 42.8 (7.7) years. Compared with a low-cost medication, initiating a high-cost, long-term medication was associated with an immediate 1.9% (95% CI, -3.8% to -0.9%) reduction in parental adherence sustained over time (0.2%; 95% CI, -0.1% to 0.4%). Similar results were observed for short-term medications (0.6% immediate change; 95% CI, -1.3% to -0.01%). Previously adherent parents, parents using treatment for longer periods, and families who spent more out-of-pocket on medications were more sensitive to high costs, with immediate adherence reductions of 2.8% (95% CI, -4.9% to -0.6%), 2.7% (95% CI, -4.7% to -0.7%), and -3.8% (95% CI, -7.2% to -0.5%), respectively, after long-term medication initiation. Conclusions and Relevance: In this cohort study small reductions in adherence across parents with higher child drug costs were observed. Health care systems should consider child-level or even household-level spending in adherence interventions or prescription policy design.

Deficits and Disparities in Early Uptake of Glucagon-Like Peptide 1 Receptor Agonists and SGLT2i Among Medicare-Insured Adults Following a New Diagnosis of Cardiovascular Disease or Heart Failure.

OBJECTIVE: To examine the association of race/ethnicity and socioeconomic deprivation with initiation of guideline-recommended diabetes medications with cardiovascular benefit (glucagon-like peptide 1 receptor agonists [GLP1-RA] and sodium-glucose cotransporter 2 inhibitors [SGLT2i]) among older adults with type 2 diabetes (T2D) and either incident atherosclerotic cardiovascular disease (ASCVD) or congestive heart failure (CHF). RESEARCH DESIGN AND METHODS: Using Medicare data (2016-2019), we identified 4,057,725 individuals age >65 years with T2D and either incident ASCVD or CHF. We estimated incidence rates and hazard ratios (HR) of GLP1-RA or SGLT2i initiation within 180 days by race/ethnicity and zip code-level Social Deprivation Index (SDI) using adjusted Cox proportional hazards models. RESULTS: Incidence rates of GLP1-RA or SGLT2i initiation increased over time but remained low (<0.6 initiations per 100 person-months) in all years studied. Medication initiation was less common among those of Black or other race/ethnicity (HR 0.81 [95% CI 0.79-0.84] and HR 0.84 [95% CI 0.75-0.95], respectively) and decreased with increasing SDI (HR 0.96 [95% CI 0.96-0.97]). Initiation was higher in ASCVD than CHF (0.35 vs. 0.135 initiations per 100 person-months). Moderate (e.g., nephropathy, nonalcoholic fatty liver disease) but not severe (e.g., advanced chronic kidney disease, cirrhosis) comorbidities were associated with higher probability of medication initiation. CONCLUSIONS: Among older adults with T2D and either ASCVD or CHF, initiation of GLP1-RA or SGLT2i was low, suggesting a substantial deficit in delivery of guideline-recommended care or treatment barriers. Individuals of Black and other race/ethnicity and those with higher area-level socioeconomic deprivation were less likely to initiate these medications.

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.

Using Data-Driven Approaches to Classify and Predict Health Care Spending in Patients With Gout Using Urate-Lowering Therapy.

OBJECTIVE: Despite increasing overall health care spending over the past several decades, little is known about long-term patterns of spending among US patients with gout. Current approaches to assessing spending typically focus on composite measures or patients agnostic to disease state; in contrast, examining spending using longitudinal measures may better discriminate patients and target interventions to those in need. We used a data-driven approach to classify and predict spending patterns in patients with gout. METHODS: Using insurance claims data from 2017-2019, we used group-based trajectory modeling to classify patients ages 40 years or older diagnosed with gout and treated with urate-lowering therapy (ULT) by their total health care spending over 2 years. We assessed the ability to predict membership in each spending group using logistic and generalized boosted regression with split-sample validation. Models were estimated using different sets of predictors and evaluated using C statistics. RESULTS: In 57,980 patients, the mean ± SD age was 71.0 ± 10.5 years, and 17,194 patients (29.7%) were female. The best-fitting model included the following groups: minimal spending (13.2%), moderate spending (37.4%), and high spending (49.4%). The ability to predict groups was high overall (e.g., boosted C statistics with all predictors: minimal spending [0.89], moderate spending [0.78], and high spending [0.90]). Although average adherence was relatively high in the population, for the high-spending group, the most influential predictors were greater gout medication adherence and diabetes melllitus diagnosis. CONCLUSION: We identified distinct long-term health care spending patterns in patients with gout using ULT with high accuracy. Several clinical predictors could be key areas for intervention, such as gout medication use or diabetes melllitus.

Sacubitril/valsartan use patterns among older adults with heart failure in clinical practice: a population-based cohort study of >25 000 Medicare beneficiaries.

AIMS: Sacubitril/valsartan is strongly supported in guidelines for the management of heart failure, but suboptimal adherence and treatment non-persistence may limit the population-level benefit that this therapy might otherwise offer. METHODS AND RESULTS: We identified a cohort of Medicare beneficiaries (2014-2017) initiating sacubitril/valsartan after ≥6 months of continuous enrolment. We assessed adherence as the proportion of days covered (PDC) and proportion of patients non-persistent (having no prescription available) at 180 days after initiation. We fit a multivariable negative binomial model with a count of adherent days to evaluate independent factors associated with of sacubitril/valsartan adherence. Among 27 063 new sacubitril/valsartan users, most (n = 17 663, 65%) were prescribed low-dose at 24 mg/26 mg and most (n = 19 984, 74%) were switched from prior angiotensin-converting enzyme inhibitor/angiotensin receptor blocker (ACEi/ARB) rather than being RASi treatment naïve. Median 180-day PDC was 86% (25th-75th percentiles 58-98%). Black patients, those with high comorbid disease burden (≥8 comorbidities), and patients with recent hospitalization within 30 days had fewer adherent days, while those treated with preceding ACEi/ARB had more adherent days. Thirty-four percent of patients did not have an active sacubitril/valsartan prescription at day 180. Among these, few had preceding dose down-titrations (6% among patients on 49 mg/51 mg and 9% among patients on 97 mg/103 mg) and 68% did not have a subsequent ACEi/ARB prescription. Among patients who remained persistent, dose up-titrations occurred in 29% of patients who started on 24 mg/26 mg and 27% of patients on 49 mg/51 mg. CONCLUSIONS: Overall adherence to sacubitril/valsartan among Medicare beneficiaries is acceptable, but is lower in Black patients, those with higher comorbidities or those who started therapy after recent hospitalization. While broad implementation of guideline-directed medical therapy is a key priority, additional focused efforts to improve adherence early after hospitalization and among at-risk patients are needed in parallel.

Contemporary Trends in the Use of and Expenditures on Digoxin in the United States.

BACKGROUND: Digoxin is indicated for the management of heart failure with reduced ejection fraction and atrial fibrillation. Despite stronger guideline recommendations for other pharmacologic and device therapies, digoxin retains a role in select patients unable to tolerate or refractory to standard therapies. Contemporary utilization of and costs related to digoxin in the United States of America (USA) remain uncharacterized. The objective of this study was to estimate trends in digoxin use and expenditures across the USA from 2010 to 2017. METHODS: We utilized the Medical Expenditure Panel Survey to estimate trends in digoxin use and expenditures across the USA from 2010 to 2017. The Medical Expenditure Panel Survey is an overlapping panel survey that interviews households in the USA to ascertain their healthcare utilization and expenditures. Complex sampling procedures allow for nationally representative estimates of utilization and expenditures. We report the number of digoxin users and expenditures across key subgroups in 2-year increments from 2010 to 2017. RESULTS: The number of digoxin users in the USA declined by 47% from 766 users per 100,000 adults in 2010-11 to 402 users per 100,000 adults in 2016-17. While digoxin use declined among women and self-identified White adults, adults living at or below the federal poverty level and those who self-identified as Asian or Hispanic represent an increasing proportion of overall digoxin users. While nationwide digoxin expenditures declined by 26% from 2010-11 to 2012-13, they peaked at $260.3 million in 2014-15 and remained elevated at $188.7 million in 2016-17. CONCLUSIONS: Despite a nationwide trend towards declining use, digoxin remains prevalent amongst people of Asian and Hispanic descent in the USA. After a spike in cost in 2013, digoxin prices have yet to return to pre-spike levels. The role of digoxin in contemporary heart failure and arrhythmia management will continue to evolve as additional randomized and observational analyses become available.

Three Sides to the Story: Adherence Trajectories During the First Year of SGLT2 Inhibitor Therapy Among Medicare Beneficiaries.

OBJECTIVE: We aimed to understand the factors associated with sodium-glucose cotransporter 2 inhibitor (SGLT2i) adherence and longitudinal adherence trajectories in older adults with type 2 diabetes. RESEARCH DESIGN AND METHODS: Using Medicare claims data (April 2013-December 2017), we identified 83,675 new SGLT2i users ≥66 years old with type 2 diabetes. We measured SGLT2i adherence as the proportion of days covered (PDC) during the first year of SGLT2i therapy. We used linear regression to assess the association between baseline covariates and PDC. Then we used group-based trajectory modeling to identify distinct longitudinal SGLT2i adherence groups and used a multivariable logistic regression model to examine the association between baseline covariates and membership in these adherence groups. RESULTS: Unadjusted mean PDC was 63%. Previous adherence to statins had the strongest positive association with PDC (regression coefficient 6.00% [95% CI 5.50, 6.50]), whereas female sex (-5.51% [-6.02, -5.00]), and Black race/ethnicity (-5.06% [-6.03, -4.09]) had the strongest negative association. We identified three adherence trajectory groups: low (23% of patients, mean PDC 17%), moderate (32%, mean PDC 50%), and high (45%, mean PDC 96%) adherence. More patients in the high adherence group were previously adherent to statins (odds ratio 1.43 [95% CI 1.39, 1.48]), and more women (1.28 [1.23, 1.32]) and Black patients (1.31 [1.23, 1.40]) were in the low adherence group. CONCLUSIONS: In a large population of older patients with type 2 diabetes, 45% were highly adherent during the first year of SGLT2i treatment. Female sex and Black race/ethnicity were most strongly associated with low adherence.

Comparison of measures of medication adherence from pharmacy dispensing and insurer claims data.

OBJECTIVE: Medication nonadherence is linked to worsened clinical outcomes and increased costs. Existing system-level adherence interventions rely on insurer claims for patient identification and outcome measurement, yet suffer from incomplete capture and lags in data acquisition. Data from pharmacies regarding prescription filling, captured in retail dispensing, may be more efficient. DATA SOURCES: Pharmacy fill and insurer claims data. STUDY DESIGN: We compared adherence measured using pharmacy fill data to adherence using insurer claims data, expressed as proportion of days covered (PDC) over 12 months. Agreement was evaluated using correlation/validation metrics. We also explored the relationship between adherence in both sources and disease control using prediction modeling. DATA EXTRACTION METHODS: Large pragmatic trial of cardiometabolic disease in an integrated delivery network. PRINCIPAL FINDINGS: Among 1113 patients, adherence was higher in pharmacy fill (mean = 50.0%) versus claims data (mean = 47.4%), although they had moderately high correlation (R = 0.57, 95% CI: 0.53-0.61) with most patients (86.9%) being similarly classified as adherent or nonadherent. Sensitivity and specificity of pharmacy fill versus claims data were high (0.89, 95% CI: 0.86-0.91 and 0.80, 95% CI: 0.75-0.85). Pharmacy fill-based PDC predicted better disease control slightly more than claims-based PDC, although the difference was nonsignificant. CONCLUSIONS: Pharmacy fill data may be an alternative to insurer claims for adherence measurement.

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.

Impact of implementing electronic prior authorization on medication filling in an electronic health record system in a large healthcare system.

OBJECTIVE: Medications frequently require prior authorization from payers before filling is authorized. Obtaining prior authorization can create delays in filling prescriptions and ultimately reduce patient adherence to medication. Electronic prior authorization (ePA), embedded in the electronic health record (EHR), could remove some barriers but has not been rigorously evaluated. We sought to evaluate the impact of implementing an ePA system on prescription filling. MATERIALS AND METHODS: ePA was implemented in 2 phases in September and November 2018 in a large US healthcare system. This staggered implementation enabled the later-implementing sites to be controls. Using EHR data from all prescriptions written and linked information on whether prescriptions were filled at pharmacies, we 1:1 matched ePA prescriptions with non-ePA prescriptions for the same insurance plan, medication, and site, before and after ePA implementation, to evaluate primary adherence, or the proportion of prescriptions filled within 30 days, using generalized estimating equations. We also conducted concurrent analyses across sites during the peri-implementation period (Sept-Oct 2018). RESULTS: Of 74 546 eligible ePA prescriptions, 38 851 were matched with preimplementation controls. In total, 24 930 (64.2%) ePA prescriptions were filled compared with 26 731 (68.8%) control prescriptions (Adjusted Relative Risk [aRR]: 0.92, 95%CI: 0.91-0.93). Concurrent analyses revealed similar findings (64.7% for ePA vs 62.3% control prescriptions, aRR: 1.03, 95%CI: 0.98-1.09). DISCUSSION: Challenges with implementation, such as misfiring and insurance fragmentation, could have undermined its effectiveness, providing implications for other health informatics interventions deployed in outpatient care. CONCLUSION: Despite increasing interest in implementing ePA to improve prescription filling, adoption did not change medication adherence.

Race/Ethnic Differences in Atherosclerotic Cardiovascular Disease Risk Factors Among Patients With Hypertension: Analysis From 143 Primary Care Clinics.

BACKGROUND: While it is known that sex and race/ethnic disparities persist for atherosclerotic cardiovascular disease (ASCVD), disparities in risk factor control have not been well-described in primary care where ASCVD can be prevented. METHODS: Adult patients with a hypertension diagnosis without ASCVD were included in this analysis of electronic health records from a large US healthcare system from 2018. Patients were categorized based on risk factor control defined as blood pressure (BP) <130/80 mm Hg; statin prescription among patients with indications, HbA1c of <7%, and not smoking. Multivariable Poisson regressions were developed to explore associations with race/ethnicity. Results are presented as relative risk (RR), 95% confidence intervals (CIs). RESULTS: Among 5,227 patients, 55.8% women and 60.0% men had uncontrolled BP, 47.3% women and 46.4% men with statin therapy indication did not have a prescription, 34.9% women and 40.9% men had uncontrolled HbA1c values, and 9.3% women and 13.7% men were smokers. African Americans were more likely to have uncontrolled BP (women: RR 1.18, 95% CI 1.07-1.30; men: RR 1.20, 95% CI 1.05-1.34) and more likely to lack a statin prescription (women: RR 1.23, 95% CI 1.05-1.45; men: RR 1.25, 95% CI 1.03-1.51) compared to Caucasians. Differences in HbA1c control were not statistically significant among Hispanic/Latino compared to Caucasians (women: RR 1.28, 95% CI 0.86-1.90; men: RR 1.20, 95% CI 0.72-1.97). CONCLUSIONS: Disparities in controlling ASCVD risk factors in primary care persist and were not fully explained by demographic or clinical characteristics. Monitoring changes in disparities is important to ensure equity as interventions to prevent ASCVD in primary care are developed and implemented.

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.

Utilization and Spending on Potentially Inappropriate Medications by US Older Adults with Multiple Chronic Conditions using Multiple Medications.

BACKGROUND: The utilization of potentially inappropriate medications (PIMs) in older adults can lead to adverse events and increased healthcare costs. Polypharmacy, the concurrent utilization of multiple medications, is common in older adults with multiple chronic conditions. OBJECTIVE: To investigate the utilization and costs of PIMs in multimorbid older adults with polypharmacy over time. METHODS: This retrospective cross-sectional study used linked Medicare claims and electronic health records from seven hospitals/medical centers in Massachusetts (2007-2014). Participants were ≥65 years old, had ≥2 chronic conditions (to define multimorbidity), and used drugs from ≥5 pharmaceutical classes for ≥90 days (to define polypharmacy). Chronic conditions were defined using the Chronic Conditions Indicator from the Agency for Health Research and Quality. PIMs were defined using the American Geriatrics Society 2019 version of the Beers criteria. We calculated the percentage of patients with ≥1 PIMs and the percentages of patients using different types of PIMs. We used logistic regression analyses to test the odds of taking ≥1 PIMs. We calculated mean costs spent on PIMs by dividing the costs spent on PIMs by the total medication cost. RESULTS: ≥69% of patients used ≥1 PIM. After adjusting for healthcare utilization, chronic conditions, medication intake, and demographic factors, female sex (2014: Odds ratio (OR)=1.27, 95%CI 1.25-1.30), age (2014: OR=0.92, 95%CI 0.90-0.93), and Hispanic ethnicity (2014: OR=1.41, 95%CI 1.27-1.56) were associated with PIM use. Gastrointestinal drugs and central nervous system drugs were the most commonly-used PIMs. In patients using ≥1 PIM, >10% of medication costs were spent on PIMs. CONCLUSION: The utilization of PIMs in US older adults with multimorbidity and polypharmacy is high.

Use of Data-Driven Methods to Predict Long-term Patterns of Health Care Spending for Medicare Patients.

Importance: Current approaches to predicting health care costs generally rely on a single composite value of spending and focus on short time horizons. By contrast, examining patients' spending patterns using dynamic measures applied over longer periods may better identify patients with different spending and help target interventions to those with the greatest need. Objective: To classify patients by their long-term, dynamic health care spending patterns using a data-driven approach and assess the ability to predict spending patterns, particularly using characteristics that are potentially modifiable through intervention. Design, Setting, and Participants: This cohort study used a retrospective cohort design from a random nationwide sample of Medicare fee-for-service administrative claims data to identify beneficiaries aged 65 years or older with continuous eligibility from 2011 to 2013. Statistical analysis was performed from August 2018 to December 2019. Main Outcomes and Measures: Group-based trajectory modeling was applied to the claims data to classify the Medicare beneficiaries by their total health care spending patterns over a 2-year period. The ability to predict membership in each trajectory spending group was assessed using generalized boosted regression, a data mining approach to model building and prediction, with split-sample validation. Models were estimated using (1) prior-year predictors and (2) prior-year predictors potentially modifiable through intervention measured in the claims data. These models were evaluated using validated C-statistics. The relative influence of individual predictors in the models was evaluated. Results: Among the 329 476 beneficiaries, the mean (SD) age was 76.0 (7.2) years and 190 346 (57.8%) were female. This final 5-group model included a minimal-user group (group 1, 37 572 individuals [11.4%]), a low-cost group (group 2, 48 575 individuals [14.7%]), a rising-cost group (group 3, 24 736 individuals [7.5%]), a moderate-cost group (group 4, 83 338 individuals [25.3%]), and a high-cost group (group 5, 135 255 individuals [41.2%]). Potentially modifiable characteristics strongly predicted these patterns (C-statistics range: 0.68-0.94). For groups with progressively increasing spending in particular, the most influential factors were number of medications (relative influence: 29.2), number of office visits (relative influence: 30.3), and mean medication adherence (relative influence: 33.6). Conclusions and Relevance: Using a data-driven approach, distinct spending patterns were identified with high accuracy. The potentially modifiable predictors of membership in the rising-cost group represent important levers for early interventions that may prevent later spending increases. This approach could be adapted by organizations to target quality improvement interventions, particularly because numerous health care organizations are increasingly using these routinely collected 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.

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.

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.