Recent advances in clinical prediction for diarrhoeal aetiology in low- and middle-income countries have revealed that the addition of weather data to clinical data improves predictive performance. However, the optimal source of weather data remains unclear. We aim to compare the use of model estimated satellite- and ground-based observational data with weather station directly observed data for the prediction of aetiology of diarrhoea. We used clinical and etiological data from a large multi-centre study of children with moderate to severe diarrhoea cases to compare their predictive performances. We show that the two sources of weather conditions perform similarly in most locations. We conclude that while model estimated data is a viable, scalable tool for public health interventions and disease prediction, given its ease of access, directly observed weather station data is likely adequate for the prediction of diarrhoeal aetiology in children in low- and middle-income countries.
Purpose: Individual-level state-transition microsimulations (iSTMs) have proliferated for economic evaluations in place of cohort state transition models (cSTMs). Probabilistic economic evaluations quantify decision uncertainty and value of information (VOI). Previous studies show that iSTMs provide unbiased estimates of expected incremental net monetary benefits (EINMB), but statistical properties of iSTM-produced estimates of decision uncertainty and VOI remain uncharacterized.
Despite the substantial expenditures on health care in the United States, persistent underperformance in health system metrics necessitates innovative approaches to address complex patient needs. The MedVantage Clinic in New Orleans, Louisiana, offers a regionally tailored, value-based primary care model targeting patients with high social and medical needs. This study provides an evaluation of the efficacy of the MedVantage Clinic in improving the cost of care and service utilization for this population.
Objective: Secondhand smoke (SHS) is a strong but comparatively controllable cardiometabolic risk factor. This study aims to assess the present and future burden of cardiometabolic diseases (CMDs) from SHS exposure.
Methods: Using the Global Burden of Disease (GBD) framework, we examined mortality and disability-adjusted life year (DALY) from CMDs attributable to SHS, by age, sex, and year, including cardiovascular disease [CVD, ischemic heart disease (IHD) and/or stroke], and/or Type 2 Diabetes Mellitus (T2DM) from 1990 to 2019.
The estimation of the pose of surgical instruments is important in Robot-assisted Minimally Invasive Surgery (RMIS) to assist surgical navigation and enable autonomous robotic task execution. The performance of current instrument pose estimation methods deteriorates significantly in the presence of partial tool visibility, occlusions, and changes in the surgical scene. In this work, a vision-based framework is proposed for markerless estimation of the 6DoF pose of surgical instruments.
Introduction: Diabetic peripheral neuropathy (DPN) represents a prevalent neurological complication affecting millions of patients globally. This clinical investigation evaluated the therapeutic efficacy and safety profile of a novel transdermal medical cannabis formulation (THC:CBD:CBN) in treating painful DPN of the lower extremities.
Methods: This phase III, double-blind, placebo-controlled, randomized clinical trial was conducted at Don Chan Hospital, Thailand, enrolling 100 participants over a 12-week intervention period.
