Alexis A. Krumme

Summary: To describe the epidemiology, treatment patterns, overall survival (OS) and healthcare resource use among patients with warm autoimmune haemolytic anaemia (wAIHA) in Taiwan, adult patients with ≥1 diagnosis code for autoimmune haemolytic anaemia (AIHA) with record of wAIHA‐related treatments between 2017 and 2022 were retrospectively identified from Taiwan's National Health Insurance Research Database linked with the National Death Registry and Taiwan Cancer Registry. The wAIHA incidence was 1.27 per 100 000 person‐years, and the 2022 prevalence was 4.58 per 100 000 persons. Corticosteroid monotherapy was the most common treatment across all lines, with 30.2% of patients remaining on corticosteroids 2 years after diagnosis. Blood transfusions were common in the first year following diagnosis, with 52.5% of patients receiving at least one emergency transfusion. The 5‐year OS probability, estimated using Kaplan–Meier analysis, was 67.1%. The median annualised medical cost was NT$ (New Taiwan dollar) 106 464 (interquartile range [IQR]: 49 088–285 856) or US$ (US dollar) 3327 (IQR: 1534–8933). Intensive care unit admissions were the primary cost driver (adjusted exponentiated coefficient: 2.76 [95% confidence interval: 2.26–3.36]), determined using a generalised linear model with a gamma distribution and log link function. Overall, significant unmet medical needs remain for wAIHA management in Taiwan, including addressing prolonged reliance on corticosteroids, sub‐optimal OS outcomes and high medical service use.

Background When golimumab (GLM) was approved for the treatment of moderate to severe ulcerative colitis (UC) in 2013, a post-authorization safety study was conducted. Objective Our objective was to examine whether exposure to GLM was associated with an increased incidence of all-cause total colectomy, colorectal cancer, and hepatosplenic T-cell lymphoma in Denmark and Sweden. Methods We conducted a new-user, active comparator cohort study of patients with UC in 2013–2021. Exposure to GLM, other anti-tumor necrosis factor (TNF) agents (infliximab and adalimumab) and thiopurines was a time-varying variable. Therapies were based on prescription redemptions and hospital-based administration of medications from national prescription and hospital registers. The association between exposure to study therapies and outcomes was evaluated using Poisson regression of incidence rates (IRs), presented as IR ratios (IRRs) and 95% confidence intervals (CIs). Results A total of 5177 and 7469 patients were included in Denmark and Sweden, respectively. The IR of all-cause total colectomy per 1000 person-years was higher in Denmark (IR 42.6; 95% CI 38.9–46.2) than in Sweden (IR 16.1; 95% CI 14.2–18.0). No significant difference was observed in all-cause total colectomy between GLM and other anti-TNF agents (Denmark: adjusted IRR [aIRR] 1.28; 95% CI 0.98–1.66; Sweden: aIRR 1.17; 95% CI 0.72–1.90). A significant difference was observed between GLM and thiopurines (Denmark: aIRR 13.62; 95% CI 8.73–21.26; Sweden: aIRR 4.52; 2.75–7.41). Privacy regulations prevented analysis of a few colorectal cancer events. No hepatosplenic T-cell lymphoma events were identified. Conclusion The IR of all-cause total colectomy with GLM was similar to that with other anti-TNF agents but was much higher than with thiopurines, probably related to confounding by indication.

Background Administrative healthcare claims databases are used in drug safety research but are limited for investigating the impacts of prenatal exposures on neonatal and pediatric outcomes without mother-infant pair identification. Objective We developed a mother-infant linkage algorithm and evaluated it in two, large US commercially insured populations. Study Design We used two US commercial health insurance claims databases during the years 2000 to 2021. Mother-infant links were constructed where persons of female sex 12-55 years of age with a pregnancy episode ending in live birth were associated with a person who was 0 years of age at database entry, who shared a common insurance plan ID, had overlapping insurance coverage time, and whose date of birth was within ±60-days of the mother’s pregnancy episode live birth date. We compared the characteristics of linked vs non-linked mothers and infants to assess similarity. Results The algorithm linked 3,477,960 mothers to 4,160,284 infants in the two databases. Linked mothers and linked infants comprised 73.6% of all mothers and 49.1% of all-infants, respectively. 94.9% of linked infants’ dates of birth were within ±30-days of the associated mother’s pregnancy episode end dates. Linked mothers were older, had longer pregnancy episodes, and had greater post-pregnancy observation time than mothers with live births who did not meet linkage algorithm criteria. Linked infants had less observation time and greater healthcare utilization than non-linked infants. Other characteristics were similar in linked vs non-linked mothers and infants. Conclusion We developed a mother-infant linkage algorithm and applied it to two US commercial healthcare claims databases that achieved a high linkage proportion and demonstrated that linked and non-linked mother and infant cohorts were similar. Transparent, reusable algorithms applied to large databases enables large-scale research on exposures during pregnancy and pediatric outcomes with relevance to drug safety. These features suggest that prenatal exposure causal risk assessment that uses this algorithm can produce valid and generalizable evidence to inform clinical, policy, and regulatory decisions. Key points A. Why was this study conducted? -This study establishes reliable mother-infant links in two US commercial healthcare databases to facilitate research on prenatal exposures and infant health outcomes B. What are the key findings? -Linked mothers with live births comprise 73.6% of all mothers with live births and linked infants comprise 49.1% of all infants -Linked vs. non-linked mother and infant cohorts have similar demographic and clinical profiles -Substantial linked coverage and linked vs non-linked characteristic similarity suggests that prenatal exposure causal risk assessment using the linked cohorts will produce valid and generalizable evidence C. What does this study add to what is already known? -This study created large mother-infant linked cohorts to enable research on rare exposures and outcomes available in healthcare claims databases -Linked mother and infant coverage is similar to that reported in previous linkage studies -Descriptive comparisons between linked vs. non-linked mother and infant cohorts increases confidence that results from research on linked cohorts also apply to mother and infant populations that do not meet linkage algorithm criteria -This mother-infant linkage algorithm is publicly available and easily implemented in databases converted to a common data model

Purpose: We sought to determine whether an association study using information contained in clinical notes could identify known and potentially novel risk factors for nonadherence to antihypertensive medications.Methods: We conducted a retrospective concept‐wide association study (CWAS) using clinical notes to identify potential risk factors for medication nonadherence, adjusting for age, sex, race, baseline blood pressure, estimated glomerular filtration rate, and a combined comorbidity score. Participants included Medicare beneficiaries 65 years and older receiving care at the Harvard Vanguard Medical Associates network from 2010‐2012 and enrolled in a Medicare Advantage program. Concepts were extracted from clinical notes in the year prior to the index prescription date for each patient. We tested associations with the outcome for 5013 concepts extracted from clinical notes in a derivation cohort (4382 patients) and accounted for multiple hypothesis testing by using a false discovery rate threshold of less than 5% (q < .05). We then confirmed the associations in a validation cohort (3836 patients). Medication nonadherence was defined using a proportion of days covered (PDC) threshold less than 0.8 using pharmacy claims data.Results: We found 415 concepts associated with nonadherence, which we organized into 11 clusters using a hierarchical clustering approach. Volume depletion and overload, assessment of needs at the point of discharge, mood disorders, neurological disorders, complex coordination of care, and documentation of noncompliance were some of the factors associated with nonadherence.Conclusions: This approach was successful in identifying previously described and potentially new risk factors for antihypertensive nonadherence using the clinical narrative.

Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Adherence to evidence-based therapies such as medications and exercise remains poor among patients after myocardial infarction (MI). Text message reminders have been shown to improve rates of regular medication taking and exercise but existing studies have been of short duration. Objective: Two single-center randomized controlled pilot trials were conducted to evaluate the impact of text message reminders over twelve months on adherence to cardiac medications and exercise among patients receiving cardiac rehabilitation after hospitalization for MI. Methods: In the medication adherence trial, 34 patients were randomized to receive usual care alone or usual care plus daily text message reminders delivered at the time of day at which medications were to be taken. In the exercise adherence trial, 50 patients were randomized to receive usual care alone or usual care plus four daily text messages reminding them to exercise as directed. Results: Text messages led to a mean 14.2 percentage point improvement in self-reported medication adherence over usual care (95% CI 7 – 21). In the exercise trial, text messages resulted in 4.2 (95% CI 1.9 – 6.4) more days and 4.0 hours (95% CI 2.4 – 5.6) of exercise per month over usual care, and a non-significant increase of 1.2 metabolic equivalents or METS (P=0.06) in exercise capacity, as assessed by a BRUCE protocol at 12 months. Conclusions: Text message reminders significantly increased adherence to medication and exercise among post-MI patients receiving care in a structured cardiac rehabilitation program. This technology represents a simple and scalable method to ensure consistent use of evidence-based cardiovascular therapies. Clinical Trial: NCT02783287 at clinicaltrials.gov

Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.

BACKGROUND: Attempts to predict who is at risk of future nonadherence have largely focused on predictions at the time of therapy initiation; however, these users are only a small proportion of all patients on therapy at any point in time. Methods to predict nonadherence for established medication users, which have not been previously described in the literature, would be helpful to guide efforts to enhance the use of evidence-based therapies. OBJECTIVE: To test approaches for adherence prediction among prevalent statin users, namely the use of short-term filling behavior, investigator-specified predictors from medical and pharmacy administrative claims, and the empirical selection of potential predictors using the high-dimensional propensity score variable selection algorithm. METHODS: Medical and prescription claims data from a large national health insurer were used to create a cohort of patients who filled statin medication prescriptions in January 2012. We defined 6 groups of adherence predictors and estimated 10 main models to predict medication adherence in the full cohort. The same was done for the population stratified based on the days supply of the index statin prescription (≤ 30 days vs. > 30 days). RESULTS: The study cohort consisted of 93,777 individuals, 58.4% of which were adherent to statins during follow-up. The use of 3 pre-index adherence predictors alone achieved a c-statistic of 0.70. Investigator-specified and empirically selected pharmacy, medical, and demographic variables did substantially worse (0.57-0.60). The use of 3 indicators of post-index adherence achieved a higher c-statistic than the best-performing model using pre-index information (0.74 vs. 0.72). The addition of 3 pre-index adherence predictors further improved discrimination (0.78). CONCLUSIONS: This analysis demonstrated the ability to predict adherence among medication users using filling behavior before and immediately after an index prescription fill. DISCLOSURES: This work was supported by an unrestricted grant from CVS Health to Brigham and Women’s Hospital. Shrank, Brennan, and Matlin were employees and shareholders at CVS Health at the time of this manuscript preparation; they report no financial interests in products or services that are related to the subject of the manuscript. Franklin has received consulting fees from Aetion. Chourdry has received grants from the National Heart, Lung, and Blood Institute, PhRMA Foundation, Merck, Sanofi, AstraZeneca, and MediSafe. Spettell is an employee of, and shareholder in, Aetna. The other authors have nothing to disclose. Krumme, Choudhry, Tong, and Franklin contributed to the study design, interpretation of results, and manuscript drafting. Tong prepared and analyzed the data. Isaman, Spettell, Shrank, Brennan, and Matlin provided interpretation of results and critical manuscript revisions.

The burden of visiting pharmacies to fill medications is a central contributor to nonadherence to maintenance medications. Recently, pharmacies have begun offering services that align prescription fill dates to allow patients to pick up all medications on a single visit. We evaluated the prevalence and structure of synchronization programs and evidence of their impact on adherence and clinical outcomes. Mixed-methods approach consisting of semi-structured interviews, data from surveillance activities, and a systematic literature review. We conducted interviews with opinion leaders from nonprofit advocacy organizations and exemplary synchronization programs. Program prevalence was determined using data from regular surveillance efforts. A literature review included Medline, EMBASE, Google Scholar, and general Internet searches. Synchronization programs exist in approximately 10% of independent, 6% of stand-alone chain, and 11% of retail store pharmacies. The majority of programs include a monthly pharmacist appointment and reminder communication. Programs reported the importance of pharmacist buy-in, technology to track and recruit patients, links to other healthcare services, and flexible solutions for managing costs and communication preferences. Although existing peer-reviewed literature suggests that synchronization improves adherence, more evidence is needed to evaluate its impact on patient-centered outcomes. As medication synchronization programs shift directions and compete for patients and payer resources, it will be more important than ever to rigorously evaluate their ability to improve clinical outcomes while also providing the growing number of patients managing multiple chronic conditions with the highest level of patient engagement and consumer choice.

To evaluate the ability of initial medication dispensings to predict long-term patterns of adherence. A retrospective cohort study of statin initiators enrolled in a Medicare Part D drug plan from CVS Caremark from 2005 to 2008. We used group-based trajectory models to classify patients into 6 adherence trajectories based on patterns of statin filling over the year following therapy initiation. Baseline clinical characteristics and indicators of statin filling during the first 2 to 4 months following initiation were used to predict adherence trajectory in logistic regression models, separately within strata of the days' supply of the initial statin dispensing. Cross-validation was used to measure predictive accuracy of models in data not used for model estimation. Among 77,703 statin initiators, prediction using baseline variables only was poor (cross-validated C statistic ≤ 0.61). When using 3 months of initial adherence to predict trajectory, prediction was greatly improved among patients with an index supply ≤30 days (0.62 ≤ C ≤ 0.91). With 4 months of initial adherence in the model, prediction was strong for all patients (C ≥ 0.72), especially for the best and worst trajectories (C = 0.90 and 0.94, respectively, in patients with an index supply ≤ 30 days; and C = 0.83 and 0.90, respectively, in patients with an index supply > 30 days). Initial filling behavior strongly predicted future adherence trajectory. Predicting adherence trajectories may facilitate better targeting of interventions to patients most likely to benefit.