Background: Pneumonia is the leading cause of death in children globally. In low- and middle-income countries the diagnosis of pneumonia relies heavily on an accurate assessment of respiratory rate, which can be unreliable in nurses and clinicians with less advanced training. In order to inform more accurate measurements, we investigate the repeatability of the RRate app used by nurses in district hospitals in Uganda. Methods: This planned secondary analysis included 3679 children aged 0-5 years. The dataset had two sequential measurements of respiratory rate using the RRate app. We measured the agreement between respiratory rate observations and clustering around fixed thresholds defined by WHO for fast breathing, which are 60 breaths per minute (bpm) for under two months (Age-1), 50 bpm for two to 12 months (Age-2), and 40 bpm for 12.1 to 60 months (Age-3). We then assessed the repeatability of the paired measurements using the Intraclass Correlation Coefficient (ICC). Results: The respiratory rate measurement took less than 15 seconds for 7,277 (98.9%) of the measurements. Despite respiratory rates clustering around the WHO fast-breathing thresholds, the breathing classification based on the thresholds was changed in only 12.6% of children. The mean (SD) respiratory rate by age group was 60 (13.1) bpm for Age-1, 49 (11.9) bpm for Age-2, and 38 (10.1) for Age-3, and the bias (Limits of Agreements) were 0.3 (-10.8 – 11.3), 0.4 (-8.5 – 9.3), and 0.1 (-6.8, 7.0) for Age-1, Age-2, and Age-3 respectively. Most importantly, the repeatability of the two respiratory rate measurements for the 3,679 children was high, with an ICC value (95% CI) of 0.95 (0.94 – 0.95). Discussion: The RRate measurements were both efficient and repeatable. The simplicity, repeatability, and efficiency of the RRate app used by healthcare workers in LMICs supports more widespread adoption for clinical use. NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at sepsiscolab@bcchr.ca or visit our website.

This dataset tracks annual graduation rate from 2013 to 2023 for Platte County R-III School District vs. Missouri

Visceral Leishmaniasis is a very dangerous form of leishmaniasis and, shorn of appropriate diagnosis and handling, it leads to death and physical disability. Depicting the spatiotemporal pattern of disease is important for disease regulator and deterrence strategies. Spatiotemporal modeling has distended broad veneration in recent years. Spatial and spatiotemporal disease modeling is extensively used for the analysis of registry data and usually articulated in a hierarchical Bayesian framework. In this study, we have developed the hierarchical spatiotemporal Bayesian modeling of the infected rate of Visceral leishmaniasis in Human (VLH). We applied the Stochastics Partial Differential Equation (SPDE) approach for a spatiotemporal hierarchical model for Visceral leishmaniasis in human (VLH) that involves a GF and a state process is associated with an autoregressive order one temporal dynamics and the spatially correlated error term, along with the effect of land shield, metrological, demographic, socio-demographic and geographical covariates in an endemic area of Amhara regional state, Ethiopia. The model encompasses a Gaussian Field (GF), affected by an error term, and a state process described by a first-order autoregressive dynamic model and spatially correlated innovations. A hierarchical model including spatially and temporally correlated errors was fit to the infected rate of Visceral leishmaniasis in human (VLH) weekly data from January 2015 to December 2017 using the R package R-INLA, which allows for Bayesian modeling using the stochastic partial differential equation (SPDE) approach. We found that the mean weekly temperature had a significant positive association with infected rate of VLH. Moreover, net migration rate, clean water coverage, average number of households, population density per square kilometer, average number of persons per household unit, education coverage, health facility coverage, mortality rate, and sex ratio had a significant association with the infected rate of visceral leishmaniasis (VLH) in the region. In this study, we investigated the dynamic spatiotemporal modeling of Visceral leishmaniasis in Human (VLH) through a stochastic partial differential equation approach (SPDE) using integrated nested Laplace approximation (INLA). Our study had confirmed both metrological, demographic, sociodemographic and geographic covariates had a significant association with the infected rate of visceral leishmaniasis (VLH) in the region.

This dataset tracks annual graduation rate from 2012 to 2022 for Oran R-III School District vs. Missouri

This dataset tracks annual graduation rate from 2012 to 2022 for North Park School District R-1 vs. Colorado

This dataset tracks annual graduation rate from 2012 to 2022 for La Plata R-II School District vs. Missouri

This dataset tracks annual graduation rate from 2012 to 2022 for Naylor R-II School District vs. Missouri

This dataset tracks annual graduation rate from 2013 to 2023 for Jackson R-II School District vs. Missouri

This dataset tracks annual graduation rate from 2012 to 2022 for Ballard R-II School District vs. Missouri

This dataset tracks annual graduation rate from 2013 to 2023 for Chaffee R-II School District vs. Missouri

This dataset tracks annual graduation rate from 2013 to 2023 for Verona R-VII School District vs. Missouri

This dataset tracks annual graduation rate from 2012 to 2022 for Pattonsburg R-II School District vs. Missouri

This dataset tracks annual graduation rate from 2012 to 2022 for Appleton City R-II School District vs. Missouri

This dataset tracks annual graduation rate from 2012 to 2022 for Willow Springs R-IV School District vs. Missouri

This dataset tracks annual graduation rate from 2013 to 2023 for Concordia R-II School District vs. Missouri

This dataset tracks annual graduation rate from 2011 to 2022 for Hi-Plains R-23 School District vs. Colorado

This dataset tracks annual graduation rate from 2013 to 2023 for Harrisonville R-IX School District vs. Missouri

This dataset tracks annual graduation rate from 2012 to 2022 for Brunswick R-II School District vs. Missouri

This dataset tracks annual graduation rate from 2012 to 2022 for Elsberry R-II School District vs. Missouri

This dataset tracks annual graduation rate from 2012 to 2022 for Risco R-II School District vs. Missouri