Impact of Upgraded Pediatric Operating Room on Surgical Volume in Africa: Evidence from Burkina Faso

INTRODUCTION: Lack of infrastructure drives a large unmet need in children's surgery in low- and middle-income countries (LMIC). This study examines the impact of upgrading existing pediatric operating rooms (ORs) on surgical volume in a hospital in Ouagadougou, Burkina Faso. METHOD(S): A nongovernmental organization upgraded 3 ORs in September 2019. Surgical volume before and after the upgrade were compared from January 2019 to January 2022 using interrupted time series analysis of a prospective data collection tool. Data from April to May 2020 were omitted owing to COVID-related lockdowns. RESULT(S): After the upgrade, patients were younger (5 vs 3.8 years, p < 0.001) and had lower rate of postoperative sepsis (2.4% vs 0.5%, p < 0.001) and surgical site infection (4.3% vs 2.2%;p = 0.005). Volume increased by 36 cases per month (p = 0.005) from a baseline of 161 cases per month. There was a positive trend of 1 case per month after upgrade and a significant difference between pre- and post- trends in volume (pre-upgrade -8 cases per month vs after upgrade of +1 case per month;p = 0.002). The mortality rate fell from 20% to 4% (p = 0.003) in the month after the upgrade, with a significant difference between pre- and postupgrade mortality rate trends (6.2% vs -1%, p < 0.001). CONCLUSION(S): There was significant improvement in volume and surgical outcomes after the OR upgrade This study supports the investment in surgical infrastructure to strengthen capacity in LMIC. Future work should include risk-adjusted mortality.

ABO Blood Groups Are Not Associated With COVID-19 Disease Incidence and Severity When Correcting for Ethnicity Differences in Blood Type.

OBJECTIVES: To determine if blood type is a risk factor for coronavirus disease 2019 (COVID-19) disease incidence and severity after correcting for ethnicity differences between novel infections and known ABO blood type frequency differences. METHODS: We performed a retrospective analysis on all severe acute respiratory system coronavirus 2 (SARS-CoV-2) infections and disease severity across two major testing sites in Colorado. We evaluated all individuals with a SARS-CoV-2 nucleic acid test (NAT) and a known blood type between March 1, 2020, and June 1, 2020. We then created a prediction algorithm based on the corrected blood types by ethnicity using data from the Colorado Department of Health and established blood types by ethnicity. We applied this prediction algorithm to all patients in our sample. RESULTS: Of 8,676 patients, 485 (5.6%) had a positive SARS-CoV-2 NAT test and 8,191 (94.4%) had a negative test. All patients had ABO blood types that mirrored the expected blood type distribution within the state of Colorado (P = .15, χ 2 statistic = 5.31). No differences in expected blood groups were present between ethnicity-adjusted SARS-CoV-2-negative and SARS-CoV-2-positive patients (χ 2 = 3.416313, P = .332). CONCLUSIONS: Blood type is not associated with COVID-19 disease incidence or severity after correcting for ethnicity differences in expected blood type frequencies.

Large-Scale Reality Modeling of a University Campus Using Combined UAV and Terrestrial Photogrammetry for Historical Preservation and Practical Use

Unmanned aerial vehicles (UAV) enable detailed historical preservation of large-scale infrastructure and contribute to cultural heritage preservation, improved maintenance, public relations, and development planning. Aerial and terrestrial photo data coupled with high accuracy GPS create hyper-realistic mesh and texture models, high resolution point clouds, orthophotos, and digital elevation models (DEMs) that preserve a snapshot of history. A case study is presented of the development of a hyper-realistic 3D model that spans the complex 1.7 km2 area of the Brigham Young University campus in Provo, Utah, USA and includes over 75 significant structures. The model leverages photos obtained during the historic COVID-19 pandemic during a mandatory and rare campus closure and details a large scale modeling workflow and best practice data acquisition and processing techniques. The model utilizes 80,384 images and high accuracy GPS surveying points to create a 1.65 trillion-pixel textured structure-from-motion (SfM) model with an average ground sampling distance (GSD) near structures of 0.5 cm and maximum of 4 cm. Separate model segments (31) taken from data gathered between April and August 2020 are combined into one cohesive final model with an average absolute error of 3.3 cm and a full model absolute error of <1 cm (relative accuracies from 0.25 cm to 1.03 cm). Optimized and automated UAV techniques complement the data acquisition of the large-scale model, and opportunities are explored to archive as-is building and campus information to enable historical building preservation, facility maintenance, campus planning, public outreach, 3D-printed miniatures, and the possibility of education through virtual reality (VR) and augmented reality (AR) tours.