ABSTRACT
BACKGROUND: Drastic increases in the rates of maternal depression and anxiety have been reported since the COVID-19 pandemic began. Most programs aim to improve maternal mental health or parenting skills separately, despite it being more effective to target both concurrently. The Building Emotional Awareness and Mental health (BEAM) program was developed to address this gap. BEAM is a mobile health program aiming to mitigate the impacts of pandemic stress on family well-being. Since many family agencies lack infrastructure and personnel to adequately treat maternal mental health concerns, a partnership will occur with Family Dynamics (a local family agency) to address this unmet need. The study's objective is to examine the feasibility of the BEAM program when delivered with a community partner to inform a larger randomized controlled trial (RCT). METHODS: A pilot RCT will be conducted with mothers who have depression and/or anxiety with a child 6-18 months old living in Manitoba, Canada. Mothers will be randomized to the 10 weeks of the BEAM program or a standard of care (i.e., MoodMission). Back-end App data (collected via Google Analytics and Firebase) will be used to examine feasibility, engagement, and accessibility of the BEAM program; cost-effectiveness will also be examined. Implementation elements (e.g., maternal depression [Patient Health Questionnaire-9] and anxiety [Generalized Anxiety Disorder-7]) will be piloted to estimate the effect size and variance for future sample size calculations. DISCUSSION: In partnership with a local family agency, BEAM holds the potential to promote maternal-child health via a cost-effective and an easily accessible program designed to scale. Results will provide insight into the feasibility of the BEAM program and will inform future RCTs. TRIAL REGISTRATION {2A}: This trial was retrospectively registered with ClinicalTrial.gov ( NCT05398107 ) on May 31st, 2022.
ABSTRACT
IntroductionHealth technology reassessment (HTR) is a structured evidence-based assessment of an existing technology in comparison to its alternatives. The process results in the following four outputs: (i) increased use;(ii) decreased use;(iii) no change;or (iv) de-adoption. However, implementing these outputs remains a challenge. Knowledge translation (KT) can be applied to implement findings from the HTR process. This study sought to identify which characteristics of KT theories, models, and frameworks (TMFs) could be useful, specifically for decreasing the use of or de-adopting a technology.MethodsA qualitative descriptive approach was used to ascertain the perspectives of international KT and HTR experts on the characteristics of KT TMFs for decreasing the use of or de-adopting a technology. One-to-one semi-structured interviews were conducted. Interviews were audio recorded and transcribed verbatim. Themes and sub-themes were deduced from the data through framework analysis using the following five distinctive steps: familiarization;identifying an analytic framework;indexing;charting;and mapping and interpretation. Themes and sub-themes were also mapped to existing KT TMFs.ResultsThirteen experts participated. The following three themes emerged as ideal characteristics of a KT TMF: (i) principles foundational for HTR: evidence-based, high usability, patient-centered, and ability to apply to micro, meso, and macro levels;(ii) levers of change: characterized as positive, neutral, or negative influences for changing behavior;and (iii) steps for knowledge to action: build the case for HTR, adapt research knowledge, assess context, select, tailor, and implement interventions, and assess impact. The Consolidated Framework for Implementation Research had the greatest number of ideal characteristics.ConclusionsApplication of KT TMFs to the HTR process has not been clearly established. This is the first study to provide an understanding of characteristics within KT TMFs that could be considered by users undertaking projects to decrease or de-adopt technologies. Characteristics to be considered within a KT TMF for implementing HTR outputs were identified. Consideration of these characteristics may guide users in choosing which KT TMF(s) to use when undertaking HTR projects.
ABSTRACT
Despite COVID-19's significant morbidity and mortality, considering cost-effectiveness of pharmacologic treatment strategies for hospitalized patients remains critical to support healthcare resource decisions within budgetary constraints. As such, we calculated the cost-effectiveness of using remdesivir and dexamethasone for moderate to severe COVID-19 respiratory infections using the United States health care system as a representative model. A decision analytic model modelled a base case scenario of a 60-year-old patient admitted to hospital with COVID-19. Patients requiring oxygen were considered moderate severity, and patients with severe COVID-19 required intubation with intensive care. Strategies modelled included giving remdesivir to all patients, remdesivir in only moderate and only severe infections, dexamethasone to all patients, dexamethasone in severe infections, remdesivir in moderate/dexamethasone in severe infections, and best supportive care. Data for the model came from the published literature. The time horizon was 1 year; no discounting was performed due to the short duration. The perspective was of the payer in the United States health care system. Supportive care for moderate/severe COVID-19 cost $11,112.98 with 0.7155 quality adjusted life-year (QALY) obtained. Using dexamethasone for all patients was the most-cost effective with an incremental cost-effectiveness ratio of $980.84/QALY; all remdesivir strategies were more costly and less effective. Probabilistic sensitivity analyses showed dexamethasone for all patients was most cost-effective in 98.3% of scenarios. Dexamethasone for moderate-severe COVID-19 infections was the most cost-effective strategy and would have minimal budget impact. Based on current data, remdesivir is unlikely to be a cost-effective treatment for COVID-19.