Pitfalls of medication adherence approximation through EHR and pharmacy records: Definitions, data and computation

Investigating the Time-Varying Nature of Medication Adherence Predictors: An Experimental Approach Using Andersen's Behavioral Model of Health Services Use

Medication adherence is a crucial factor for managing chronic conditions, especially in aging adults. Previous studies have identified predictors of medication adherence. However, current methods fail to capture the time-varying nature of how risk factors can influence adherence behavior. This objective of this study was to implement multitrajectory group-based models to compare a time-varying to a time-fixed approach to identifying non-adherence risk factors. The study population comprised 11,068 Medicare beneficiaries aged 65 and older taking select medications for hypertension, high blood cholesterol, and oral diabetes medications, between 2008 and 2016. Time-fixed predictors (e.g., sex, education) were examined using generalized multinomial logistic regression, while time-varying predictors were explored through multitrajectory group-based modeling. Several predisposing, enabling, and need characteristics were identified as risk factors for following at least one non-adherence trajectory. Time-varying predictors displayed an alternative representation of those risk factors, especially depression symptoms. This study highlights the dynamic nature of medication adherence predictors and the utility of multitrajectory modeling. Findings suggest that targeted interventions can be developed by addressing the key time-varying factors affecting adherence.

Discordance and Performance of the ARC-HBR and PRECISE-DAPT High Bleeding Risk Definitions After Coronary Stenting

BACKGROUND

The aim of the ARC-HBR (Academic Research Consortium for High Bleeding Risk) and PRECISE-DAPT (Predicting Bleeding Complications in Patients Undergoing Stent Implantation and Subsequent Dual Antiplatelet Therapy) score definitions for high bleeding risk is to identify patients who would benefit from shorter or less intensive antiplatelet therapy after coronary stenting.

OBJECTIVES

The aim of this study was to assess the performance of the ARC-HBR and PRECISE-DAPT score definitions for high bleeding risk in routine clinical practice.

METHODS

Using nationwide registers, all patients in Stockholm, Sweden, who were discharged after coronary stenting with dual antiplatelet therapy (January 1, 2013, to July 1, 2018) were included. Patients were categorized as high bleeding risk according to the 2 risk tools, and risk for bleeding (BARC [Bleeding Academic Research Consortium] types 3-5 or TIMI major or minor) and ischemic events (myocardial infarction or ischemic stroke) within 1 year after discharge was assessed.

RESULTS

Of 7,562 patients, the proportions categorized as high bleeding risk were 27% (2,004 of 7,562) using the ARC-HBR definition and 38% (2,894 of 7,562) using the PRECISE-DAPT score; 22% (1,696 of 7,562) had discordant categorization of high bleeding risk comparing the 2 risk tools. Patients with vs without high bleeding risk according to the ARC-HBR definition had higher risk for BARC type 3 to 5 bleeding (1-year risk 7.1% vs 2.3%; HR: 3.21; 95% CI: 2.47-4.17) and ischemic events (7.8% vs 2.8%; HR: 2.96; 95% CI: 2.31-3.79). Patients with vs without high bleeding risk according to the PRECISE-DAPT score had higher risk for TIMI major or minor bleeding (4.4% vs 2.1%; HR: 2.17; 95% CI: 1.63-2.89) and ischemic events (6.2% vs 2.7%; HR: 2.38; 95% CI: 1.85-3.05). The PRECISE-DAPT score underestimated bleeding risk across almost all score levels (median absolute difference between observed and predicted 1-year risk 1.1%; Q1-Q3: 0.8%-1.4%).

CONCLUSIONS

There was substantial discordance in the categorization of high bleeding risk between the ARC-HBR definition and the PRECISE-DAPT score. Both tools identified patients at increased bleeding risk, but those patients also had increased ischemic risk. The PRECISE-DAPT score underestimated bleeding risk. Guideline-recommended high bleeding risk definitions may not be generalizable across patient populations, and refined scoring systems are needed.

Medication nonadherence - definition, measurement, prevalence, and causes: reflecting on the past 20 years and looking forwards

In 2003, Sabate's World Health Organisation report defined medication nonadherence as a phenomenon where individuals' behaviour does not correspond to prescribed treatment recommendations from their healthcare provider. This concept of nonadherence evolved beyond a categorisation of patients as adherent or nonadherent. Rather, nonadherence varies within the same individual and treatment over time, and between treatments and individuals. The type and patterns of nonadherence are key determinants of outcome with individuals with the same percentage nonadherence having different outcomes depending on their pattern of nonadherence. Often the poorest clinical outcomes occur in individuals who do not initiate medication or discontinue early, but much of the nonadherence literature remains focused on implementation. This paper provides a nuanced discussion of nonadherence which has been enabled in part by the growing availability of technologies such as electronic nonadherence monitors, new biomarkers for adherence and greater access to 'big data' (e.g., on prescription refills). These allow granular assessment of nonadherence that can be linked with biophysical markers captured using technologies such as wearables. More validated self-report measures have also become available to profile nonadherence in research and practice. Together, in-depth data on dosing and clinical measures provide an opportunity to explore complex interactions between medications, therapeutic effects and clinical outcomes. This variation in measurement and definition means that there is a more fine-grained understanding of the prevalence of nonadherence and a greater recognition of the prevalence of nonadherence, with growing evidence suggesting that approximately a fifth of patients do not initiate treatment, of those initiating treatment approximately 30%-50% of patients do not implement their treatment as prescribed and that, over long follow-up periods in some conditions 80%-100% of patients discontinue. There is potential too to better understand causes of nonadherence. New behavioural models synthesise determinants of nonadherence previously considered separately. Frameworks like the COM-B (considering individual capability, opportunity, and motivation factors) and MACO (focusing on Medication Adherence Contexts and Outcomes) emphasize the multifaceted nature of nonadherence determinants. Greater focus on dynamic processes with interplay between individual, social, and environmental influences is needed. Addressing these complexities could lead to more effective and personalised support for patients.

Resolution versus persistence of childhood idiopathic nephrotic syndrome-A population-based study

AIM

To determine the duration of relapsing childhood idiopathic nephrotic syndrome (INS).

METHODS

In this population-based study, we retrospectively analysed the computerised database of Israel's largest health maintenance organisation. Children (age 2-10 years) with a new INS diagnosis and a corticosteroid (CS) prescription between 2000 and 2010 were included. NS category was determined, according to CS and/or steroid-sparing agents (SSA) purchases.

RESULTS

Out of 1 669 977 eligible children, 608 fulfilled inclusion criteria. Patients in the fourth quartile of purchases (n = 132) had an older age at last relapse (17.9 ± 6.3 vs. 11.3 ± 5.9 years, p < 0.001) and more SSA use (78.8% vs. 20%, p < 0.001) compared to the remaining three quartiles. A single episode occurred in 84 patients. Of the remaining 524 patients (males 66%, diagnosis age: 4.8 ± 2.2 years, SSA prescribed: 35%) who were followed for 15.5 ± 5.1 years, 113 (21.6%) had a continuing disease at an age of 19.3 ± 6.3 years. The leftover 411 entered long-lasting treatment-free remission at age 11.2 ± 5.7 years.

CONCLUSION

In this multicentre study, we identified INS disease course by medication delivery. NS long-standing remission occurs at age 11.2 ± 5.7 years in most cases. However, the disease continues into adulthood in a fifth of the relapsing patients, implicating the need for proper transition to adult care.

Concordance between pharmacy dispensing and electronic monitoring data of direct oral anticoagulants - A secondary analysis of the MAAESTRO study

BACKGROUND

Direct oral anticoagulants are the preferred treatment for stroke patients with atrial fibrillation. Pharmacy dispensing data represent a practical method to identify suboptimal medication adherence.

OBJECTIVE

This study investigates whether pharmacy dispensing data are indicative of real-life adherence behavior, using data from 130 patients in the MAAESTRO study (2018-2022) in Basel, Switzerland.

METHODS

This secondary data analysis of the MAAESTRO study (Dietrich, 2024) included patients with electronic monitoring (EM) and dispensing data for 12 months. Patients with at least two refills were included in the analysis. We categorized refill series into three adherence patterns using the Delta T method (Baumgartner, 2022): all refills on time, erratic refills, end-gaps ≥10 days. EM-adherence was assessed through "taking adherence" and "missing days" (24h without intake). We analyzed: i) all dispensing data ("all refills"); ii) all data independently of the MAAESTRO phase ("all phases"); iii) the last two dispensing data ("last"), and iv) EM data from the MAAESTRO phase that match the date of the last refill ("matched"). Associations between refill patterns and adherence were examined using Spearman correlation and Fisher's exact test.

RESULTS

Data analyzed from 50 patients (mean age 76.4 ± 9.1 years, 56.0 % male) included 252 refills with a median of 4 refills per patient. Refill patterns were: all refills on time (40.0 %), erratic refills (36.0 %), and end-gaps >10 days (24.0 %). Mean taking adherence was 89.3 ± 13.7 %. EM data revealed missing days in 82.0 % of patients, with 61.0 % having irregular refill patterns. Matched taking adherence was moderately associated with Delta T over all refills (p = 0.034) and the last refill (p = 0.013).

CONCLUSIONS

Dispensing data processed with the Delta T method correlate moderately with EM data. The Delta T value for the last two refills shows promise for estimating irregular adherence, suggesting potential for targeted interventions in pharmacy practice.

Real world analysis of treatment change and response in adults with attention-deficit/hyperactivity disorder (ADHD) alone and with concomitant psychiatric comorbidities: results from an electronic health record database study is the United States

BACKGROUND

The objectives of this study were to examine the association of psychiatric comorbidities and patient characteristics with treatment change and response as well as to assess the association between treatment change and healthcare resource utilization (HCRU) among adult patients with attention-deficit/hyperactivity disorder (ADHD) and psychiatric comorbidities.

METHODS

De-identified electronic health records from the NeuroBlu Database (2002-2021) were used to select patients ≥ 18 years with ADHD who were prescribed ADHD-specific medication. The index date was set as the first prescription of ADHD medication. The outcomes were treatment change (discontinuation, switch, add-on, or drop) and HCRU (inpatient, outpatient, composite) within 12 months of follow-up. Cox proportional-hazard model was used to assess the association between clinical and demographic patient characteristics and treatment change, while generalized linear model with negative binomial distribution and log link function was used to assess the association between key risk factors linked to treatment change and HCRU rates.

RESULTS

A total of 3,387 patients with ADHD were included (ADHD only: 1,261; ADHD + major depressive disorder (MDD): 755; ADHD + anxiety disorder: 467; ADHD + mood disorder: 164). Nearly half (44.8%) of the study cohort experienced a treatment change within the 12-month follow-up period. Treatment switch and add-on were more common in patients with ADHD and comorbid MDD and anxiety disorder (switch: 18.9%; add-on: 20.5%) compared to other cohorts (range for switch: 8.5-13.6%; range for add-on: 8.9-12.1%) Survival analysis demonstrated that the probability of treatment change within 12 months from treatment initiation in the study cohort was estimated to be 42.4%. Outpatient visit rates statistically significantly increased from baseline (mean [SD] 1.03 [1.84] visits/month) to 3 months post-index (mean [SD] 1.62 [1.91] visits/month; p < 0.001), followed by a gradual decline up to 12 months post-index. Being prescribed both a stimulant and a non-stimulant at index date was statistically significantly associated with increased risk of treatment change (adjusted hazard ratio: 1.64; 95% CI: 1.13, 2.38; p = 0.01).

CONCLUSIONS

This real-world study found that treatment change was common among patients with ADHD and psychiatric comorbidities. These findings support the need for future studies to examine the unmet medical and treatment needs of this complex patient population.

Knowledge and perception of mHealth medication adherence applications among pharmacists and pharmacy students in Jazan, Kingdom of Saudi Arabia

The advances in digital health, including mobile healthcare (mHealth) medication adherence applications (MApps), have been demonstrated to support medication adherence and improve health outcomes. This study aims to evaluate the knowledge and perception of the MApps among pharmacists and pharmacy students. An online cross-sectional survey was conducted among 223 pharmacists and pharmacy students in the Jazan region of Saudi Arabia between 1st and 30th April 2023. The survey collected information about the participants' socio-demographics, knowledge, and perception of the MApps. Among the 223 participants included in the study, 105 (47.1%) were pharmacists and 118 (52.9%) were pharmacy students. Most participants were females (72.6%) and aged 18-30 (70.4%). About half of the participants had poor knowledge of the MApps [pharmacists (48.0%) and students (42.0%)] and mainly encountered Medisafe (18.1%) or Pills (17.0%) MApps, respectively. Pharmacy students showed significantly higher knowledge of MApps (p = 0.048), especially the Pills (p = 0.022) than pharmacists. However, the pharmacists had significantly higher knowledge of MyMeds (p = 0.001) than pharmacy students. Most participants had a positive perception of the usefulness of the MApps (pharmacists, 79.0%; students 80.0%). Notably, over 85% of the participants expressed willingness to know and provide guidance on MApps, with over 50% willing to recommend it to the patients. There was no significant difference in perception between the pharmacists and pharmacy students (p>0.05). In conclusion, the study demonstrates limited knowledge with a positive perception of mHealth medication adherence applications among pharmacists and pharmacy students. Integrating digital adherence tools like the MApps into pharmacy training could significantly improve professional practice mHealth competencies, and optimize healthcare delivery and patient outcomes.

'Under pressure': The role of therapeutic drug monitoring in the treatment of hypertension

Antihypertensive drugs do not qualify as optimal candidates for therapeutic drug monitoring (TDM), given their obvious physiological effect, the absence of a clear relationship between drug concentrations and pharmacodynamic outcomes and their wide therapeutic range. However, since non-adherence is a major challenge in hypertension management, using drug concentrations can be of value to identify non-adherence as a first step towards better blood pressure control. In this article we discuss the key challenges associated with measuring and interpreting antihypertensive drug concentrations that are important when TDM is used to improve non-adherence. Additionally, we elaborate on the role of TDM in optimizing antihypertensive drug treatment besides addressing non-adherence by highlighting its value in specific patient groups with altered pharmacokinetic parameters such as female vs. male or elderly patients.

Evaluation of adherence to antipsychotics: A real-world data study using four different dosing assumptions

AIMS

This study aimed to assess the frequency of dosing inconsistencies in prescription data and the effect of four dosing assumption strategies on adherence estimates for antipsychotic treatment.

METHODS

A retrospective cohort, which linked prescription and dispensing data of adult patients with ≥1 antipsychotic prescription between 2015-2016 and followed up until 2019, in Catalonia (Spain). Four strategies were proposed for selecting the recommended dosing in overlapping prescription periods for the same patient and antipsychotic drug: (i) the minimum dosing prescribed; (ii) the dose corresponding to the latest prescription issued; (iii) the highest dosing prescribed; and (iv) all doses included in the overlapped period. For each strategy, one treatment episode per patient was selected, and the Continuous Medication Availability measure was used to assess adherence. Descriptive statistics were used to describe results by strategy.

RESULTS

Of the 277 324 prescriptions included, 76% overlapped with other prescriptions (40% with different recommended dosing instructions). The number and characteristics of patients and treatment episodes (18 292, 18 303, 18 339 and 18 536, respectively per strategy) were similar across strategies. Mean adherence was similar between strategies, ranging from 57 to 60%. However, the proportion of patients with adherence ≥90% was lower when selecting all doses (28%) compared with the other strategies (35%).

CONCLUSION

Despite the high prevalence of overlapping prescriptions, the strategies proposed did not show a major effect on the adherence estimates for antipsychotic treatment. Taking into consideration the particularities of antipsychotic prescription practices, selecting the highest dose in the overlapped period seemed to provide a more accurate adherence estimate.

Long-Term Medical Treatment and Adherence in Patients With Left Ventricular Assist Devices: A Danish Nationwide Cohort Study

The use of a left ventricular assist device (LVAD) in treating advanced heart failure has increased. However, data regarding medical treatment and adherence following LVAD implantation is sparse, particularly whether socioeconomic factors (cohabitation status, educational level, employment status, and income) and multimorbidity influence these aspects, which are known to impact adherence in heart failure patients. We performed a nationwide cohort study of 119 patients with LVAD implanted between January 1, 2006, and December 31, 2018, who were discharged alive with LVAD therapy. We linked individual-level data from clinical LVAD databases, the Scandiatransplant Database, and Danish medical and administrative registers. Medical treatment 90-day pre-LVAD and 720-day post-LVAD were assessed using descriptive statistics in 90-day intervals. Medication adherence (proportion of days covered ≥80%) was assessed 181- to 720-day post-LVAD. The proportions of patients using angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (88.7%), beta-blockers (67.0%), mineralocorticoid receptor antagonists (62.9%), warfarin (87.6%), and aspirin (55.7%) within 90-day post-LVAD were higher than pre-LVAD and were stable during follow-up. Medication adherence ranged from 86.7% (aspirin) to 97.8% (warfarin). Socioeconomic factors and multimorbidity did not influence medical medication use and adherence. Among LVAD patients, medical treatment and adherence are at high levels, regardless of socioeconomic background and multimorbidity.

Longitudinal Trajectory Modeling to Assess Adherence to Sacubitril/Valsartan among Patients with Heart Failure

Medication adherence in chronic conditions is a long-term process. Modeling longitudinal trajectories using routinely collected prescription data is a promising method for describing adherence patterns and identifying at-risk groups. The study aimed to characterize distinct long-term sacubitril/valsartan adherence trajectories and factors associated with them in patients with heart failure (HF). Subjects with incident HF starting sac/val in 2017-2018 were identified from the Campania Regional Database for Medication Consumption. We estimated patients' continuous medication availability (CMA9; R package AdhereR) during a 12-month period. We selected groups with similar CMA9 trajectories (Calinski-Harabasz criterion; R package kml). We performed multinomial regression analysis, assessing the relationship between demographic and clinical factors and adherence trajectory groups. The cohort included 4455 subjects, 70% male. Group-based trajectory modeling identified four distinct adherence trajectories: high adherence (42.6% of subjects; CMA mean 0.91 ± 0.08), partial drop-off (19.6%; CMA 0.63 ± 0.13), moderate adherence (19.3%; CMA 0.54 ± 0.11), and low adherence (18.4%; CMA 0.17 ± 0.12). Polypharmacy was associated with partial drop-off adherence (OR 1.194, 95%CI 1.175-1.214), while the occurrence of ≥1 HF hospitalization (OR 1.165, 95%CI 1.151-1.179) or other hospitalizations (OR 1.481, 95%CI 1.459-1.503) were associated with low adherence. This study found that tailoring patient education, providing support, and ongoing monitoring can boost adherence within different groups, potentially improving health outcomes.

Methods to assess COPD medications adherence in healthcare databases: a systematic review

BACKGROUND

The Global Initiative for Chronic Obstructive Lung Disease 2023 report recommends medication adherence assessment in COPD as an action item. Healthcare databases provide opportunities for objective assessments; however, multiple methods exist. We aimed to systematically review the literature to describe existing methods to assess adherence in COPD in healthcare databases and to evaluate the reporting of influencing variables.

METHOD

We searched MEDLINE, Web of Science and Embase for peer-reviewed articles evaluating adherence to COPD medication in electronic databases, written in English, published up to 11 October 2022 (PROSPERO identifier CRD42022363449). Two reviewers independently conducted screening for inclusion and performed data extraction. Methods to assess initiation (dispensing of medication after prescribing), implementation (extent of use over a specific time period) and/or persistence (time from initiation to discontinuation) were listed descriptively. Each included study was evaluated for reporting variables with an impact on adherence assessment: inpatient stays, drug substitution, dose switching and early refills.

RESULTS

160 studies were included, of which four assessed initiation, 135 implementation and 45 persistence. Overall, one method was used to measure initiation, 43 methods for implementation and seven methods for persistence. Most of the included implementation studies reported medication possession ratio, proportion of days covered and/or an alteration of these methods. Only 11% of the included studies mentioned the potential impact of the evaluated variables.

CONCLUSION

Variations in adherence assessment methods are common. Attention to transparency, reporting of variables with an impact on adherence assessment and rationale for choosing an adherence cut-off or treatment gap is recommended.

Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review

BACKGROUND

Increasingly, patient medication adherence data are being consolidated from claims databases and electronic health records (EHRs). Such databases offer an indirect avenue to gauge medication adherence in our data-rich healthcare milieu. The surge in data accessibility, coupled with the pressing need for its conversion to actionable insights, has spotlighted data mining, with machine learning (ML) emerging as a pivotal technique. Nonadherence poses heightened health risks and escalates medical costs. This paper elucidates the synergistic interaction between medical database mining for medication adherence and the role of ML in fostering knowledge discovery.

METHODS

We conducted a comprehensive review of EHR applications in the realm of medication adherence, leveraging ML techniques. We expounded on the evolution and structure of medical databases pertinent to medication adherence and harnessed both supervised and unsupervised ML paradigms to delve into adherence and its ramifications.

RESULTS

Our study underscores the applications of medical databases and ML, encompassing both supervised and unsupervised learning, for medication adherence in clinical big data. Databases like SEER and NHANES, often underutilized due to their intricacies, have gained prominence. Employing ML to excavate patient medication logs from these databases facilitates adherence analysis. Such findings are pivotal for clinical decision-making, risk stratification, and scholarly pursuits, aiming to elevate healthcare quality.

CONCLUSION

Advanced data mining in the era of big data has revolutionized medication adherence research, thereby enhancing patient care. Emphasizing bespoke interventions and research could herald transformative shifts in therapeutic modalities.

Associations Between Sexual History Documentation in Electronic Health Records and Referral to Pre-Exposure Prophylaxis Navigator on Prescription of Pre-Exposure Prophylaxis at a Multi-Site Federally Qualified Health Center

This cross-sectional study examined the relationships between sexual history screening (SHS) and referrals to a pre-exposure prophylaxis (PrEP) navigator (non-clinical staff member who assists patients in overcoming structural barriers to PrEP) on the proportion of days covered by PrEP for adult patients at a federally qualified health center. Patients' sociodemographics, PrEP prescriptions, referral to a PrEP navigator, and SHS data were extracted from the electronic health record (EHR). The analytic sample was 214 adult patients who were human immunodeficiency virus (HIV) negative and taking PrEP to prevent infection from January 2016 to December 2019. Mixed-effects negative binomial models were conducted accounting for clustering by patients' primary care providers. Documentation of SHS was associated with a higher proportion of days covered by PrEP (incidence rate ratio = 1.44, 95% confidence interval: 1.17-1.77). There was no significant effect of having a referral to the PrEP navigator on the proportion of days covered by PrEP, nor did having a referral to the PrEP navigator moderate the relationship between having SHS documented in the EHR and the proportion of days covered by PrEP. This study is the first to investigate the relationship between having sexual history documented in the EHR, referrals to a PrEP navigator, and their combined effect on the proportion of days covered by PrEP. Results of this study provide foundational evidence for future studies examining SHS as an opportunity to improve PrEP access and adherence and indicate the need for additional research exploring the value of PrEP navigators as an implementation strategy to overcome social and structural barriers to care.

Estimating medication adherence from Electronic Health Records: comparing methods for mining and processing asthma treatment prescriptions

BACKGROUND

Medication adherence is usually defined as the extent of the agreement between the medication regimen agreed to by patients with their healthcare provider and the real-world implementation. Proactive identification of those with poor adherence may be useful to identify those with poor disease control and offers the opportunity for ameliorative action. Adherence can be estimated from Electronic Health Records (EHRs) by comparing medication dispensing records to the prescribed regimen. Several methods have been developed in the literature to infer adherence from EHRs, however there is no clear consensus on what should be considered the gold standard in each use case. Our objectives were to critically evaluate different measures of medication adherence in a large longitudinal Scottish EHR dataset. We used asthma, a chronic condition with high prevalence and high rates of non-adherence, as a case study.

METHODS

Over 1.6 million asthma controllers were prescribed for our cohort of 91,334 individuals, between January 2009 and March 2017. Eight adherence measures were calculated, and different approaches to estimating the amount of medication supply available at any time were compared.

RESULTS

Estimates from different measures of adherence varied substantially. Three of the main drivers of the differences between adherence measures were the expected duration (if taken as in accordance with the dose directions), whether there was overlapping supply between prescriptions, and whether treatment had been discontinued. However, there are also wider, study-related, factors which are crucial to consider when comparing the adherence measures.

CONCLUSIONS

We evaluated the limitations of various medication adherence measures, and highlight key considerations about the underlying data, condition, and population to guide researchers choose appropriate adherence measures. This guidance will enable researchers to make more informed decisions about the methodology they employ, ensuring that adherence is captured in the most meaningful way for their particular application needs.

Impact of rifaximin use following an initial overt hepatic encephalopathy hospitalization on rehospitalization and costs

AIM

To assess the impact of rifaximin (± lactulose) use following discharge of an initial overt hepatic encephalopathy (OHE) hospitalization on OHE rehospitalizations and healthcare costs in a real-world setting.

METHODS

Adults (18-64 years) with an OHE hospitalization were identified from MarketScan® Commercial claims (Q4'15-Q2'20), classified into two mutually exclusive treatment cohorts (i.e. rifaximin and no rifaximin treatment), and further stratified into four subgroups based on decreasing quality of care (QoC; i.e. Type 1 - rifaximin without delay post-discharge; Type 2 - rifaximin with delay post-discharge; Type 3 - lactulose only post-discharge; Type 4 - no rifaximin/lactulose treatment post-discharge). The impact of rifaximin use on 30-day and annualized OHE hospitalizations and healthcare costs were assessed between cohorts and by the QoC subgroup.

RESULTS

Characteristics were similar between the rifaximin (N = 1,452; Type 1: 1,138, Type 2: 314) and no rifaximin (N = 560; Type 3:337, Type 4: 223) treatment cohorts. The 30-day risk of OHE rehospitalization was lower for the rifaximin vs. no rifaximin treatment cohort (odds ratio 0.56, p < .01) and increased with decreasing QoC. The annual rate of OHE hospitalizations was 59% lower for the rifaximin treatment cohort (incidence rate ratio 0.41, p < .01) and increased with decreasing QoC. Compared to the no rifaximin treatment cohort, the rifaximin treatment cohort had higher pharmacy costs, lower medical costs, and no difference in total healthcare costs.

LIMITATIONS

This was a claims-based study subject to common data limitations such as billing inaccuracies or omissions in coded claims. Total healthcare costs were reported from a payer's perspective, which do not capture indirect costs associated with patient burden.

CONCLUSIONS

Initiation of rifaximin after an OHE hospitalization was associated with reduced OHE hospitalizations both in the 30-days following and annually. Further, reduced medical costs offset increased pharmacy costs, and no annual cost differences were observed between cohorts.

Real-life omalizumab exposure and discontinuation in a large nationwide population-based study of paediatric and adult asthma patients

BACKGROUND

This real-life study aimed to assess omalizumab treatment patterns in adult and paediatric asthma patients, and to describe asthma control and healthcare resource use (HCRU) at omalizumab initiation and discontinuation.

METHODS

The French healthcare database system (Système National des Données de Santé (SNDS)) was used to identify asthma patients aged ≥6 years who initiated omalizumab for at least 16 weeks from 2009 to 2019. We examined omalizumab treatment patterns using dispensation records.

RESULTS

We identified 16 750 adults and 2453 children initiating omalizumab. Median treatment persistence before discontinuation (TSTOP) was 51.2 (95% CI 49.3-53.4) months in adults and 53.7 (95% CI 50.6-56.4) months in children. At 2 years of omalizumab exposure, rate of hospitalisation for asthma decreased by 75% and use of oral corticosteroids (OCS) by 30%, in adults and children. Among adults who discontinued omalizumab while asthma was controlled, 70%, 39% and 24% remained controlled and did not resume omalizumab at 1, 2 and 3 years after discontinuation, respectively. These proportions were higher in children (76%, 44% and 33%, respectively). Over 2 years of follow-up after discontinuation, HCRU remained stable in adults and children, notably rate of hospitalisations for asthma (none before TSTOP, 1.3% and 0.6% at 2 years) and use of OCS (in adults and children, respectively: 20.0% and 20.2% before TSTOP, 33.3% and 24.6% at 2 years).

CONCLUSION

This is the first large-scale study describing omalizumab real-life exposure patterns in adult and paediatric asthma patients in France with >10 years of follow-up. We showed the long-term maintenance of low HCRU in adults and children who discontinued omalizumab while asthma was controlled, notably for OCS use and hospitalisations for asthma.

Adherence to medication by adolescents and young adults with childhood-onset systemic lupus erythematosus

Background: Approximately 20% of all cases systemic lupus erythematous (SLE) are juvenile onset. Children and adolescents with SLE usually present with more severe illness and have a higher mortality rate compared to adults with SLE. Adherence to medications in children and adolescents has a major impact on disease control as well as short- and long-term outcomes. Improved understanding of adherence rates, risk factors for non-adherence, and barriers to adherence are essential in order to increase patient adherence with medication regimens. The aim of our study was to evaluate adherence to medications among children and young adults with pediatric-onset SLE and identify barriers for non-adherence by utilizing several adherence evaluation methods.

Methods: Adherence to medications of patients aged 12–25, with childhood-onset SLE was assessed as follows: (1). The brief medication questionnaire (BMQ): self-report tool for screening adherence and barriers to adherence. (2). Mycophenolic acid (MPA) serum level. (3). Medication possession ratio (MPR): data assessing 90-day refills and dispense prior to patient’s enrollment was collected.

Results: Of the 38 patients who were enrolled in the study, 65% were found to be non-adherent according to at least 1 measurement method. Forty-four percent of patients were found to be non-adherent based on the self-reported questionnaire (BMQ). Of those taking MMF, 33% had an MPA level < 1 mcg/mL and were defined as non-adherent. Seventeen percent of patients were found to be non-adherent according to pharmacy refills rate. Forty-six percent of patients stated that their medications caused side effects, 33% of patients indicated difficulty remembering to take the medications, and 25% reported difficulty paying for medications. The disease activity index (SLEDAI) score of the “adherent group” at diagnosis was significantly lower compared to the “non-adherent” group. Patients with private insurance had more access barriers to obtaining medications compared to patients with public insurance.

Conclusion: Non-adherence to medications is highly prevalent among cSLE patients. Higher SLEDAI score is a risk factor for non-adherence. Adherence to medications should be routinely evaluated among adolescence and young adults with cSLE and barriers to adherence need to be addressed to decrease morbidity and improve patient outcomes.

Estimating Medication Adherence from Electronic Health Records Using Rolling Averages of Single Refill-based Estimates

Medication adherence is usually defined as the manner in which a patient takes their medication, in relation to the regimen agreed to with their healthcare provider. Electronic Health Records (EHRs) can be used to estimate adherence in a cost-effective and non-invasive manner across large-scale populations, although there is no universally agreed optimal approach to doing so. We sought to explore patterns of asthma ICS prescription refills in a large EHR dataset, and to evaluate the use of rolling-average based measures towards short-term adherence estimation. Over 1.6 million asthma controllers were prescribed for our cohort of 91,332 individuals, between January 2009 and March 2017. The Continuous Single interval measures of medication Availability (CSA) and Gaps (CSG) were calculated for individual prescriptions, as well as rolling-average adherence measures of the CSA over 3, 5, or 10 past prescription intervals. 16.7% of the study population had only a single prescription during their follow-up (a median duration of 7.1 years). 51% of prescriptions were refilled before (or on the day that) supply was exhausted, and for 19% of prescription refills, the amount of medication dispensed should have lasted at least twice as long as the duration before the next refill was filled. The rolling average measures had statistically strong associations (Spearman |R|>0.7) with the estimate for the subsequent prescription refill. Rolling averages of multiple individual refill-level adherence estimates provide a novel and simple way to crudely smoothen estimates from individual prescription refills, which are strongly influenced by common (and adherent) real-world behaviors, for more meaningful and effective trend detection. Clinical Relevance- This demonstrates a novel methodology for estimating medication adherence which can detect recent changes in trends.

Mobile Health Applications and Medication Adherence of Patients With Hypertension: A Systematic Review and Meta-Analysis

INTRODUCTION

Current evidence has revealed the beneficial effects of mobile health applications on systolic and diastolic blood pressure. However, there is still no solid evidence of the underlying factors for these outcomes, and hypertension treatment is performed mainly by medication intake. This study aims to analyze the impacts of health applications on medication adherence of patients with hypertension and understand the underlying factors.

METHODS

A systematic review and meta-analysis were conducted considering controlled clinical trials published, without year filter, through July 2020. The searches were performed in the electronic databases of Scopus, MEDLINE, and BVSalud. Study characteristics were extracted for qualitative synthesis. The meta-analysis examined medication-taking behavior outcomes using the generic inverse-variance method to combine multiple variables.

RESULTS

A total of 1,199 records were identified, of which 10 studies met the inclusion criteria for qualitative synthesis, and 9 met the criteria for meta-analysis with 1,495 participants. The analysis of mean changes revealed significant improvements in medication adherence (standardized mean difference=0.41, 95% CI=0.02, 0.79, I²=82%, p=0.04) as well as the analysis of the values measured after follow-up (standardized mean difference=0.60, 95% CI=0.30, 0.90, I²=77%, p<0.0001). Ancillary improvements were also identified, such as patients' perceived confidence, treatment self-efficacy and self-monitoring, acceptance of technology, and knowledge about the condition and how to deal with health issues.

DISCUSSION

There is evidence that mobile health applications can improve medication adherence in patients with hypertension, with broad heterogeneity between studies on the topic. The use of mobile health applications conceivably leads to ancillary improvements inherent to better medication adherence.

Developing real-world evidence from real-world data: Transforming raw data into analytical datasets

Development of evidence-based practice requires practice-based evidence, which can be acquired through analysis of real-world data from electronic health records (EHRs). The EHR contains volumes of information about patients-physical measurements, diagnoses, exposures, and markers of health behavior-that can be used to create algorithms for risk stratification or to gain insight into associations between exposures, interventions, and outcomes. But to transform real-world data into reliable real-world evidence, one must not only choose the correct analytical methods but also have an understanding of the quality, detail, provenance, and organization of the underlying source data and address the differences in these characteristics across sites when conducting analyses that span institutions. This manuscript explores the idiosyncrasies inherent in the capture, formatting, and standardization of EHR data and discusses the clinical domain and informatics competencies required to transform the raw clinical, real-world data into high-quality, fit-for-purpose analytical data sets used to generate real-world evidence.

Comparing the Predictive Effects of Patient Medication Adherence Indices in Electronic Health Record and Claims-Based Risk Stratification Models

Multiple indices are available to measure medication adherence behaviors. Medication adherence measures, however, have rarely been extracted from electronic health records (EHRs) for population-level risk predictions. This study assessed the value of medication adherence indices in improving predictive models of cost and hospitalization. This study included a 2-year retrospective cohort of patients younger than age 65 years with linked EHR and insurance claims data. Three medication adherence measures were calculated: medication regimen complexity index (MRCI), medication possession ratio (MPR), and prescription fill rate (PFR). The authors examined the effects of adding these measures to 3 predictive models of utilization: a demographics model, a conventional model (Charlson index), and an advanced diagnosis-based model. Models were trained using EHR and claims data. The study population had an overall MRCI, MPR, and PFR of 14.6 ± 17.8, .624 ± .310, and .810 ± .270, respectively. Adding MRCI and MPR to the demographic and the morbidity models using claims data improved forecasting of next-year hospitalization substantially (eg, AUC of the demographic model increased from .605 to .656 using MRCI). Nonetheless, such boosting effects were attenuated for the advanced diagnosis-based models. Although EHR models performed inferior to claims models, adding adherence indices improved EHR model performances at a larger scale (eg, adding MRCI increased AUC by 4.4% for the Charlson model using EHR data compared to 3.8% using claims). This study shows that medication adherence measures can modestly improve EHR- and claims-derived predictive models of cost and hospitalization in non-elderly patients; however, the improvements are minimal for advanced diagnosis-based models.

Prediction and pattern analysis of medication refill adherence through electronic health records and dispensation data

BACKGROUND AND PURPOSE

Low adherence to medication in chronic disease patients leads to increased morbidity, mortality, and healthcare costs. The widespread adoption of electronic prescription and dispensation records allows a more comprehensive overview of medication utilization. In combination with electronic health records (EHR), such data provides new opportunities for identifying patients at risk of nonadherence and provide more targeted and effective interventions. The purpose of this article is to study the predictability of medication adherence for a cohort of hypertensive patients, focusing on healthcare utilization factors under various predictive scenarios. Furthermore, we discover common proportion of days covered patterns (PDC-patterns) for patients with index prescriptions and simulate medication-taking behaviours that might explain observed patterns.

PROCEDURES

We predict refill adherence focusing on factors of healthcare utilization, such as visits, prescription information and demographics of patient and prescriber. We train models with machine learning algorithms, using four different data splits: stratified random, patient, temporal forward prediction with and without index patients. We extract frequent, two-year long PDC-patterns using K-means clustering and investigate five simple models of medication-taking that can generate such PDC-patterns.

FINDINGS

Model performance varies between data splits (AUC test set: 0.77-0.89). Including historical information increases the performance slightly in most cases (approx. 1-2% absolute AUC uplift). Models show low predictive performance (AUC test set: 0.56-0.66) on index-prescriptions and patients with sudden drops in PDC (Recall: 0.58-0.63). We find 21 distinct two-year PDC-patterns, ranging from good adherence to intermittent gaps and early discontinuation in the first or second year. Simulations show that observed PDC-patterns can only be explained by specific medication consumption behaviours.

CONCLUSIONS

Prediction models developed using EHR exhibit bias towards patients with high healthcare utilization. Even though actual medication-taking is not observable, consumption patterns may not be as arbitrary, provided that medication refilling and consumption is linked.