ObjectiveAs the multiple sclerosis (MS) disease-modifying drug (DMD) treatment options have expanded to include oral therapies, it is important to understand whether route of administration is associated with DMD adherence. The objective of this study was to compare adherence to DMDs in patients with MS newly initiating treatment with a self-injectable versus an oral DMD.MethodsThis retrospective database study used IMS Health Real World Data Adjudicated Claims – US data between July 1, 2010 and June 30, 2014. Adherence was measured by medication possession ratio (MPR), calculated as the total number of treated days divided by the total number of days from the first treated day until the end of 12-month follow-up. A binary measure representing adherence (MPR ≥0.8) versus nonadherence (MPR <0.8) to therapy was used. Logistic regression evaluated the likelihood of adherence to index DMD type (self-injectable vs oral). Covariates included patient baseline characteristics (ie, age, sex, comorbidities) and index DMD type.ResultsThe analysis included 7,207 self-injectable and 1,175 oral DMD-treated patients with MS. In unadjusted analyses, the proportion of patients adherent to therapy (MPR ≥0.8) did not differ significantly between the self-injectable (54.1%) and the oral DMD cohorts (53.0%; P=0.5075). After controlling for covariates, index DMD type was not a significant predictor of adherence (odds ratio [OR] 1.062; 95% confidence interval [CI]: 0.937–1.202; P=0.3473). Higher likelihood of adherence was associated with male sex (OR 1.20; 95% CI: 1.085–1.335; P=0.0005) and age groups older than 18–34 years (ORs 1.220–1.331; P<0.01). Depression was associated with a lower likelihood of adherence (OR 0.618; 95% CI: 0.511–0.747; P<0.0001).ConclusionMale sex and age older than 18–34 years were significantly associated with a higher likelihood of adherence, while depression was associated with a lower likelihood of adherence. Index DMD type, stratified by the route of administration (self-injectable vs oral DMD), was not a significant predictor of DMD adherence.

BackgroundLimited data exist on the economic implications of stroke among patients with atrial fibrillation (AF). This study assesses the impact of AF on healthcare costs associated with ischemic stroke (IS), hemorrhagic stroke (HS), or transient ischemic attack (TIA).Methods and ResultsA retrospective analysis of MarketScan claims data (2005‐2011) for AF patients ≥18 years old with ≥1 inpatient claim for stroke, or ≥1 ED or inpatient claim for TIA as identified by ICD‐9‐CM codes who had ≥12 months continuous enrollment prior to initial stroke. Initial event‐ and stroke‐related costs 12 months post‐index were compared among patients with AF and without AF. Adjusted costs were estimated, controlling for demographics, comorbidities, anticoagulant use, and baseline resource use. Data from 23 807 AF patients and 136 649 patients without AF were analyzed. Unadjusted mean cost of the index event was $20 933 for IS, $59 054 for HS, $8616 for TIA hospitalization, and $3395 for TIA ED visit. After controlling for potential confounders, adjusted mean incremental costs (index plus 12‐month post‐index) for AF patients were higher than those for non‐AF patients by: $4726, $7824, and $1890 for index IS, HS, TIA (identified by hospitalization), respectively, and $1700 for TIA (identified by ED) (all P<0.01). In multivariate regression analysis, AF was associated with a 20% (IS), 13% (HS), and 18% (TIA) increase in total stroke‐related costs.ConclusionStroke‐related care for IS, HS, and TIA is costly, especially among individuals with AF. Reducing the risk of AF‐related stroke is important from both clinical and economic standpoints.

ObjectiveTo assess the trends in the prevalence of comorbidities in US patients with multiple sclerosis (MS), and the association of demographic characteristics with the presence of comorbidities.Study designA retrospective analysis was conducted from a sample of 5 million patients from the IMS Health Real World Data Adjudicated Claims – US database.MethodsComorbidity in patients with MS was assessed by year (2006–2014), and logistic regression models evaluated the association of age, sex, and region with select comorbidities.ResultsThe most common comorbidities from 2006 to 2014 were hyperlipidemia and hypertension (25.9%–29.7% of patients within an individual year), followed by gastrointestinal disease (18.4%–21.2% of patients) and thyroid disease (12.9%–17.1% of patients). The proportion with a claim for hyperlipidemia increased from 2006 to 2009, was stable from 2009 to 2011, and then declined from 2011 to 2014. The proportion with a claim for hypertension generally increased from 2006 to 2013, then declined from 2013 to 2014. The proportion with a claim for gastrointestinal disease, thyroid disease, and anxiety generally increased from 2006 to 2014. Claims for comorbidities were statistically significantly more likely among older age groups (p<0.05), with the exception of anxiety and alcohol abuse, which were statistically significantly less likely among older age groups. Claims for gastrointestinal disease (OR=0.75), thyroid disease (OR=0.36), chronic lung disease (OR=0.76), arthritis (OR=0.71), anxiety (OR=0.63), and depression (OR=0.69) were statistically significantly less likely among males versus females (all p<0.05). Claims for hyperlipidemia (OR=1.39), hypertension (OR=1.25), diabetes (OR=1.31), and alcohol abuse (OR=2.41) were significantly more likely among males (p<0.05). Many comorbidity claims were statistically significantly more likely in the Northeast and South compared with the Midwest and West.ConclusionThis study provides select comorbidity claims estimates in US patients with MS, and thus highlights the importance of comprehensive patient care approaches.

Background For stroke, traumatic brain injury (TBI), and other neurologic conditions associated with speech-language disorders, speech and language therapy is the standard of care for promoting recovery. However, barriers such as clinician time constraints and insurance reimbursement can inhibit a patient’s ability to receive the support needed to optimize functional gain. Although digital rehabilitation has the potential to increase access to therapy by allowing patients to practice at home, the clinical and demographic characteristics that impact a patient’s level of engagement with technology-based therapy are currently unknown. Objective This study aimed to evaluate whether the level of engagement with digital therapy differs by various patient characteristics, including age, gender, diagnosis, time from disease onset, and geographic location (urban vs rural). Methods Data for patients with stroke or TBI that initiated the use of Constant Therapy, a remotely delivered, cloud-based rehabilitation program for patients with speech-language disorders, were retrospectively analyzed. Only data from therapeutic sessions completed at home were included. The following three activity metrics were evaluated: (1) the number of active weeks of therapy, (2) the average number of active therapy days per week, and (3) the total number of therapeutic sessions completed during the first 20 weeks of program access. An active day or week was defined as having at least one completed therapeutic session. Separate multiple linear regression models were performed with each activity measure as the dependent variable and all available patient demographics as model covariates. Results Data for 2850 patients with stroke or TBI were analyzed, with the average patient completing 8.6 weeks of therapy at a frequency of 1.5 days per week. Contrary to known barriers to technological adoption, older patients were more active during their first 20 weeks of program access, with those aged 51 to 70 years completing 5.01 more sessions than patients aged 50 years or younger (P=.04). Similarly, patients living in a rural area, who face greater barriers to clinic access, were more digitally engaged than their urban counterparts, with rural patients completing 11.54 more (P=.001) sessions during their first 20 weeks of access, after controlling for other model covariates. Conclusions An evaluation of real-world data demonstrated that patients with stroke and TBI use digital therapy frequently for cognitive and language rehabilitation at home. Usage was higher in areas with limited access to clinical services and was unaffected by typical barriers to technological adoption, such as age. These findings will help guide the direction of future research in digital rehabilitation therapy, including the impact of demographics on recovery outcomes and the design of large, randomized controlled trials.

The ACC/AHA blood cholesterol treatment guidelines recommend statin therapy for all patients after experiencing an acute cardiovascular event. Previous analyses have shown that physicians have been slow to adopt guidelines, and many patients remain untreated or undertreated with statins after a cardiovascular event. However, reasons for this remain unknown. This analysis used electronic medical records and patient chart data from Reliant Medical Group (Worcester, Massachusetts) to evaluate physician adherence to the 2013 ACC/AHA blood cholesterol guidelines when treating patients with evidence of acute atherosclerotic cardiovascular disease and the reasons for the observed treatment decisions. Less than 50% of acute atherosclerotic cardiovascular disease patients were treated according to the ACC/AHA guidelines. Nearly 42% of patients not treated according to guidelines received a lower statin intensity than recommended. The most common reason cited by 41.8% of physicians for treating with a statin intensity below the recommended intensity was low-density lipoprotein cholesterol stable or at goal, despite ACC/AHA guidelines recommending specific statin intensities rather than specific low-density lipoprotein cholesterol levels. In conclusion, physician and patient education on the importance of maximizing lipidlowering therapy in this high-risk patient population should be emphasized.

Linaclotide was found to be a less costly option vs lubiprostone for the treatment of adult patients with IBS-C.

The economic impact of psychiatric relapse and recidivism among patients with schizophrenia is substantial from the state government perspective. This general model can be made state-specific by utilizing local criminal justice data sources.

Objectives Access to safe and nutritious food is essential for good health. However, food can become unsafe due to contamination with pathogens, chemicals or toxins, or mislabeling of allergens. Illness resulting from the consumption of unsafe foods is a global health problem. Here, we develop a machine learning approach for detecting reports of unsafe food products in consumer product reviews from Amazon.com. Materials and Methods We linked Amazon.com food product reviews to Food and Drug Administration (FDA) food recalls from 2012 to 2014 using text matching approaches in a PostGres relational database. We applied machine learning methods and over- and under-sampling methods to the linked data to automate the detection of reports of unsafe food products. Results Our data consisted of 1 297 156 product reviews from Amazon.com. Only 5149 (0.4%) were linked to recalled food products. Bidirectional Encoder Representation from Transformations performed best in identifying unsafe food reviews, achieving an F1 score, precision and recall of 0.74, 0.78, and 0.71, respectively. We also identified synonyms for terms associated with FDA recalls in more than 20 000 reviews, most of which were associated with nonrecalled products. This might suggest that many more products should have been recalled or investigated. Discussion and Conclusion Challenges to improving food safety include, urbanization which has led to a longer food chain, underreporting of illness and difficulty in linking contaminated food to illness. Our approach can improve food safety by enabling early identification of unsafe foods which can lead to timely recall thereby limiting the health and economic impact on the public.