sj-docx-1-dhj-10.1177_20552076221084472 - Supplemental material for Are caregivers ready for digital? Caregiver preferences for health technology tools to monitor medication adherence among patients with serious mental illness

Supplemental material, sj-docx-1-dhj-10.1177_20552076221084472 for Are caregivers ready for digital? Caregiver preferences for health technology tools to monitor medication adherence among patients with serious mental illness by Felicia Forma, Kevin Chiu, Jason Shafrin, Dusica Hadzi Boskovic and S Phani Veeranki in Digital Health</p

sj-docx-2-dhj-10.1177_20552076221084472 - Supplemental material for Are caregivers ready for digital? Caregiver preferences for health technology tools to monitor medication adherence among patients with serious mental illness

Supplemental material, sj-docx-2-dhj-10.1177_20552076221084472 for Are caregivers ready for digital? Caregiver preferences for health technology tools to monitor medication adherence among patients with serious mental illness by Felicia Forma, Kevin Chiu, Jason Shafrin, Dusica Hadzi Boskovic and S Phani Veeranki in Digital Health</p

White Paper on the Value of Time Savings for Patients and Healthcare Providers of Breast Cancer Therapy: The Fixed-Dose Combination of Pertuzumab and Trastuzumab for Subcutaneous Injection as an Example

Article full textThe above infographic represents the opinions of the authors. For a full list of declarations, including funding and author disclosure statements, please see the full text online (see “read the peer-reviewed publication” opposite). © The authors, CC-BY-NC 2022. </div

Medication Adherence Patterns Among Patients with Multiple Serious Mental and Physical Illnesses

<p></p><p><b>Article full text</b></p> <p><br></p> <p>The full text of this article can be found here<b>. </b><a href="https://link.springer.com/article/10.1007/s12325-018-0700-6">https://link.springer.com/article/10.1007/s12325-018-0700-6</a></p><p></p><p></p><p> </p><p><br></p> <p><b>Provide enhanced content for this article</b></p> <p><br></p> <p>If you are an author of this publication and would like to provide additional enhanced content for your article then please contact <a href="http://www.medengine.com/Redeem/”mailto:[email protected]”"><b>[email protected]</b></a>.</p> <p><br></p> <p>The journal offers a range of additional features designed to increase visibility and readership. All features will be thoroughly peer reviewed to ensure the content is of the highest scientific standard and all features are marked as ‘peer reviewed’ to ensure readers are aware that the content has been reviewed to the same level as the articles they are being presented alongside. Moreover, all sponsorship and disclosure information is included to provide complete transparency and adherence to good publication practices. This ensures that however the content is reached the reader has a full understanding of its origin. No fees are charged for hosting additional open access content.</p> <p><br></p> <p>Other enhanced features include, but are not limited to:</p> <p><br></p> <p>• Slide decks</p> <p>• Videos and animations</p> <p>• Audio abstracts</p> <p>• Audio slides</p><br><p></p

Quantifying spillover benefits in value assessment: a case study of increased graft survival on the US kidney transplant waitlist

To quantify the wider impacts of increased graft survival on the size of the kidney transplant waitlist and health and economic outcomes. The analysis employed known steady-state solutions to a double-queueing system as well as simulations of this system. Baseline input parameters were sourced from the Organ Procurement and Transplant Network and the United States Renal Data System. Three increased graft survival scenarios were modeled: decreases in repeat transplant candidates joining the waitlist of 25%, 50%, and 100%. Under the three scenarios, we estimated that the US waitlist size would decrease from 91,822 to 85,461 (6.9% decrease), 80,073 (12.8% decrease), and 69,340 (24.4% decrease), respectively. Patient outcomes improved, with lifetime quality-adjusted life years (QALYs) for a 1-year cohort of transplant recipients increasing by 10,010, 16,888, and 43,345 over the three scenarios. Discounted lifetime costs for the cohort in the new steady state were lower by $1.6 billion,$2.3 billion, and $9.0 billion for each scenario, respectively. Spillover impacts (i.e. benefits that accrued beyond the patients who directly experienced increased graft survival) accounted for 41–48% of the QALY gains and ranged from cost increases of 3.3% to decreases of 5.5%. The model is a simplification of reality and does not account for the full degree of patient heterogeneity occurring in the real world. Health economic outcomes are extrapolated based on the assumption that the median patient is representative of the overall population. Increasing graft survival reduces demand from repeat transplants candidates, allowing additional candidates to receive transplants. These spillover impacts decrease waitlist size and shorten wait times, leading to improvements in graft and patient survival as well as quality-of-life. Cost-effectiveness analyses of treatments that increase kidney graft survival should incorporate spillover benefits that accrue beyond the direct recipient of an intervention.</p

Distribution of CHADS₂ scores among patients with undiagnosed atrial fibrillation.

Distribution of CHADS2 scores among patients with undiagnosed atrial fibrillation.</p

Baseline characteristics of patients with observed and unobserved AF.

<p>Baseline characteristics of patients with observed and unobserved AF.</p

Cohort diagram.

<p>Cohort diagram.</p

Modeling the impact of patient treatment preference on health outcomes in relapsing-remitting multiple sclerosis

Aims: Model how moving from current disease-modifying drug (DMD) prescribing patterns for relapsing-remitting multiple sclerosis (RRMS) observed in the United Kingdom (UK) to prescribing patterns based on patient preferences would impact health outcomes over time. Materials and methods: A cohort-based Markov model was used to measure the effect of DMDs on long-term health outcomes for individuals with RRMS. Data from a discrete choice experiment were used to estimate the market shares of DMDs based on patient preferences (i.e. preference shares). These preference shares and real-world UK market shares were used to calculate the effect of prescribing behavior on relapses, disability progression, and quality-adjusted life-years (QALYs). The incremental benefit of patient-centered prescribing over current practices for the UK RRMS population was then estimated; scenario and sensitivity analyses were also conducted. Results: Compared to current prescribing practices, when UK patients with RRMS were treated following patient preferences, health outcomes were improved. This population was expected to experience 501,690 relapses and gain 1,003,263 discounted QALYs over 50 years under patient-centered prescribing practices compared to 538,417 relapses and 958,792 discounted QALYs under current practices (−6.8% and +4.6%, respectively). Additionally, less disability progression was observed when prescribed treatment was based on patient preferences. In a scenario analysis where only oral treatments were considered, the results were similar, although the magnitude of benefit was smaller. Number of relapses was most sensitive to how the annualized relapse rate was modeled; disability progression was most sensitive to mortality rate assumptions. Limitations: Treatment efficacy estimates applied to various models in this study were based on data derived from clinical trials, rather than real-world data; the impact of patient-centered prescribing on treatment adherence and/or switching was not modeled. Conclusions: The population of UK RRMS patients may experience overall health gains if patient preferences are better incorporated into prescribing practices.</p

Baseline characteristics of patients with AF diagnosed before stroke versus after stroke.

<p>Baseline characteristics of patients with AF diagnosed before stroke versus after stroke.</p