Bringing Clean Energy to the Base of the Pyramid: The Interplay of Business Models, Technology, and Local Context

Social enterprises are providing affordable energy and environmentally sustainable energy to a small but growing percentage of the four billion people living on less than $2,000/year. Santa Clara University’s Global Social Benefit Incubator (GSBITM) has worked with over 60 of these enterprises and profiled them on its Energy Map website. Based on this direct experience and associated research, the authors conclude that it is the interplay among innovative business models, quality technologies tailored to localized energy markets, and appropriate interfacing with local ecosystems that allows social enterprises to go to scale. This conclusion is supported by a review of prominent enterprises including Shindulai, Solar Sister, Angaza Design, Potential Energy, Selco, Husk Power Systems, and Practical Action

Solar Sister deliverables: Customer stories, Kiva profiles, and entrepreneur accounts

During a two month placement with Solar Sister in Uganda, the Santa Clara team was able to compile qualitative and quantitative data that will improve the organization\u27s awareness of their customers and employees. Three customers were interviewed and stories were written with short, medium, and long versions. Profiles were created for seven entrepreneurs that were deemed qualified enough to receive Kiva loans. Last, 50 Solar Sister Entrepreneurs were surveyed and important information was input into their Salesforce.com accounts. The team also compiled many photographs during Solar Sister site visits that will be transferred to Solar Sister online. Each customer story, Kiva profile, and Saleforce.com account has at least one photo attached to it. The following document contains samples of all the media and information we collected for Solar Sister

Market Evaluation: Viability of the SoLite3 in Uganda

This report was crafted with the intention of responding to the research question posed to the Santa Clara University Global Social Benefit Fellow (GSBF) team placed in Uganda during the summer of 2013. Angaza Design commissioned our GSBF team to take five units of its newest product, the SoLite3, to Uganda for a pilot using Solar Sister as a distribution channel. The purpose of this pilot was to evaluate whether Uganda is a viable market for the SoLite3

Akabot: 3d printing filament extruder

3D printing could usher in a new age of localized manufacturing in places like Uganda, where three of our senior design team members spent the summer of 2013. Motivated by a concept for our senior design project, one of our team members interned with Village Energy, a small electronics business in Kampala, Uganda, as it piloted the use of a 3D printer to manufacture enclosures for its solar lights. The need for our project arose when we realized that although the 3D printer proved a viable method of manufacturing enclosures, Village Energy could not afford to continue 3D printing with filament imported from abroad. The goal of our project is to provide companies like Village Energy with a solution to the problem of importing expensive filament. We aim to take plastic water bottles (in abundance inKampala but generally burned as trash) melt and extrude them as filament for a 3D printer. We present our filament maker, named the AkaBot. In this paper, we will discuss the AkaBot subsystems, design process, testing process, and results. This project has successfully built a machine that can melt and extrude plastic water bottle shreds, but the filament made from our machine still requires improved mechanical properties. We will also discuss related issues such as business plan, economics, social impact, environmental impact, ethics, health and safety, and sustainability

Trayendo energía limpia a la base de la pirámide: la interacción de los modelos de negocio, la tecnología y el contexto local

Social enterprises are providing affordable energy and environmentally sustainable energy to a small but growing percentage of the four billion people living on less than $2,000/year. Santa Clara University’s Global Social Benefit Incubator (GSBITM) has worked with over 60 of these enterprises and profiled them on its Energy Map website. Based on this direct experience and associated research, the authors conclude that it is the interplay among innovative business models, quality technologies tailored to localized energy markets, and appropriate interfacing with local ecosystems that allows social enterprises to go to scale. This conclusion is supported by a review of prominent enterprises including Shindulai, Solar Sister, Angaza Design, Potential Energy, Selco, Husk Power Systems, and Practical Action.Las empresas sociales están proporcionando energía asequible y sostenible medioambientalmente para un pequeño pero creciente porcen- taje de los cuatro mil millones de personas que viven con menos de US$ 2,000 / año. La Global Social Benefit Incubator de Santa Clara University (GSBITM) ha trabajado con más de 60 de estas empresas y ha creado sus perfiles en su sitio web Energy Map. En base a esta experiencia directa y la investigación asociada, los autores concluyen que esta interacción entre los modelos de negocio innovadores, las tecnologías de calidad adaptados a los mercados energéticos localizados, y las apropiadas interconexiones con los ecosistemas locales permite a las empresas sociales aumentar su tamaño. Esta conclusión se basa en una revisión de las empresas impor- tantes, incluyendo Shindulai, Solar Sister, Angaza Diseño, Potential Energy, Selco, Husk Power Systems y Practical Action

International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality

International audienceAbstract Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach

Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort studyResearch in Context

Summary: Background: Characterizing Post-Acute Sequelae of COVID (SARS-CoV-2 Infection), or PASC has been challenging due to the multitude of sub-phenotypes, temporal attributes, and definitions. Scalable characterization of PASC sub-phenotypes can enhance screening capacities, disease management, and treatment planning. Methods: We conducted a retrospective multi-centre observational cohort study, leveraging longitudinal electronic health record (EHR) data of 30,422 patients from three healthcare systems in the Consortium for the Clinical Characterization of COVID-19 by EHR (4CE). From the total cohort, we applied a deductive approach on 12,424 individuals with follow-up data and developed a distributed representation learning process for providing augmented definitions for PASC sub-phenotypes. Findings: Our framework characterized seven PASC sub-phenotypes. We estimated that on average 15.7% of the hospitalized COVID-19 patients were likely to suffer from at least one PASC symptom and almost 5.98%, on average, had multiple symptoms. Joint pain and dyspnea had the highest prevalence, with an average prevalence of 5.45% and 4.53%, respectively. Interpretation: We provided a scalable framework to every participating healthcare system for estimating PASC sub-phenotypes prevalence and temporal attributes, thus developing a unified model that characterizes augmented sub-phenotypes across the different systems. Funding: Authors are supported by National Institute of Allergy and Infectious Diseases, National Institute on Aging, National Center for Advancing Translational Sciences, National Medical Research Council, National Institute of Neurological Disorders and Stroke, European Union, National Institutes of Health, National Center for Advancing Translational Sciences

Clinical phenotypes and outcomes in children with multisystem inflammatory syndrome across SARS-CoV-2 variant eras: a multinational study from the 4CE consortiumResearch in context

Summary: Background: Multisystem inflammatory syndrome in children (MIS-C) is a severe complication of SARS-CoV-2 infection. It remains unclear how MIS-C phenotypes vary across SARS-CoV-2 variants. We aimed to investigate clinical characteristics and outcomes of MIS-C across SARS-CoV-2 eras. Methods: We performed a multicentre observational retrospective study including seven paediatric hospitals in four countries (France, Spain, U.K., and U.S.). All consecutive confirmed patients with MIS-C hospitalised between February 1st, 2020, and May 31st, 2022, were included. Electronic Health Records (EHR) data were used to calculate pooled risk differences (RD) and effect sizes (ES) at site level, using Alpha as reference. Meta-analysis was used to pool data across sites. Findings: Of 598 patients with MIS-C (61% male, 39% female; mean age 9.7 years [SD 4.5]), 383 (64%) were admitted in the Alpha era, 111 (19%) in the Delta era, and 104 (17%) in the Omicron era. Compared with patients admitted in the Alpha era, those admitted in the Delta era were younger (ES −1.18 years [95% CI −2.05, −0.32]), had fewer respiratory symptoms (RD −0.15 [95% CI −0.33, −0.04]), less frequent non-cardiogenic shock or systemic inflammatory response syndrome (SIRS) (RD −0.35 [95% CI −0.64, −0.07]), lower lymphocyte count (ES −0.16 × 109/uL [95% CI −0.30, −0.01]), lower C-reactive protein (ES −28.5 mg/L [95% CI −46.3, −10.7]), and lower troponin (ES −0.14 ng/mL [95% CI −0.26, −0.03]). Patients admitted in the Omicron versus Alpha eras were younger (ES −1.6 years [95% CI −2.5, −0.8]), had less frequent SIRS (RD −0.18 [95% CI −0.30, −0.05]), lower lymphocyte count (ES −0.39 × 109/uL [95% CI −0.52, −0.25]), lower troponin (ES −0.16 ng/mL [95% CI −0.30, −0.01]) and less frequently received anticoagulation therapy (RD −0.19 [95% CI −0.37, −0.04]). Length of hospitalization was shorter in the Delta versus Alpha eras (−1.3 days [95% CI −2.3, −0.4]). Interpretation: Our study suggested that MIS-C clinical phenotypes varied across SARS-CoV-2 eras, with patients in Delta and Omicron eras being younger and less sick. EHR data can be effectively leveraged to identify rare complications of pandemic diseases and their variation over time. Funding: None