Pyrethroid-resistant malaria vector Anopheles gambiae restored susceptibility after pre-exposure to piperonyl-butoxide: results from country-wide insecticide resistance monitoring in Tanzania, 2023.

Background: Effective vector control interventions, notably insecticide-treated nets (ITNs) and indoor residual spraying (IRS) are indispensable for malaria control in Tanzania and elsewhere. However, the emergence of widespread insecticide resistance threatens the efficacy of these interventions. Monitoring of insecticide resistance is, therefore, critical for the selection and assessment of the programmatic impact of insecticide-based interventions.

Return-to-School Experiences of Adolescents After Allogeneic Hematopoietic Cell Transplant: A Qualitative Interview Study of Transplant Recipients.

Background: Returning to school after allogeneic hematopoietic cell transplant (HCT) can improve quality of life and promote positive adjustment. However, this process may be challenging, and there is a limited understanding of school-aged children and adolescents' perspectives on this process.

Methods: We conducted semi-structured interviews over video with pediatric recipients of HCT (10-18 years of age at HCT; 1-7 years post HCT) who were treated at our institution and had returned to in-person school post HCT.

INteractive survivorship program to improve health care REsources [INSPIRE]: A study protocol testing a digital intervention with stepped care telehealth to improve outcomes for adolescent and young adult survivors.

Background: Adolescents and young adults with cancer (AYAs, ages 15-39 at the time of diagnosis) experience significant adverse health and psychosocial outcomes. AYAs live with emotional distress and health care demands that exceed those of their healthy peers but can have difficulty accessing care. Digitally delivered interventions are an attractive option for AYA survivors, a population that routinely utilizes online resources when seeking health information and support.

Raising awareness of potential biases in medical machine learning: Experience from a Datathon.

Objective: To challenge clinicians and informaticians to learn about potential sources of bias in medical machine learning models through investigation of data and predictions from an open-source severity of illness score.

Methods: Over a two-day period (total elapsed time approximately 28 hours), we conducted a datathon that challenged interdisciplinary teams to investigate potential sources of bias in the Global Open Source Severity of Illness Score. Teams were invited to develop hypotheses, to use tools of their choosing to identify potential sources of bias, and to provide a final report.