Grand Challenges is a family of initiatives fostering innovation to solve key global health and development problems. Each initiative is an experiment in the use of challenges to focus innovation on making an impact. Individual challenges address some of the same problems, but from differing perspectives.
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Vinicius de Araujo Oliveira of Fiocruz in Brazil will develop a framework for the re-use of large clinical and administrative datasets to enable comparative analysis of COVID-19 vaccine safety and effectiveness in Brazil and in Pakistan, with colleagues at Shaukat Khanum Memorial Cancer Hospital and Research Centre there, to improve pandemic responses and promote data-driven evidence generation in the Global South. Monitoring vaccinations across different settings is crucial for containing pandemics. However, comparative analysis of large health datasets in different scenarios is challenging due to concerns around safety and reproducibility and the loss of the context in which the data was collected, which can affect research results. They will adapt data science standards and tools to different local health system scenarios and run individual and joint vaccine effectiveness analyses for the two countries to assess compatibility and reproducibility of the findings. They will also build a public data visualization dashboard for health managers and policymakers to monitor the pandemic, particularly in vulnerable populations.
Vincent Cubaka of Partners In Health in the U.S. will build robust data governance structures to enable the utilization of electronic medical records from multiple countries for research purposes to improve health. So-called FAIR (Findable, Accessible, Interoperable, Reusable) data principles enhance the value of personal medical records for research, and CARE principles were developed to protect the owners of these data. However, the rigidity of these principles can create conflicts, which can make it difficult, for example, to open access to datasets across different countries. To address this, for their current project studying the impact of COVID-19 on chronic care patients across four low- and middle-income countries, they will develop data governance structures and set-up a multi-country community oversight committee to enable full access by researchers to appropriately de-identified individual-level data on a suitable platform.
Haroon Hafeez of Shaukat Khanum Memorial Cancer Hospital and Research Centre in Pakistan will develop a framework for the re-use of large clinical and administrative datasets to enable comparative analysis of COVID-19 vaccine safety and effectiveness in Pakistan and in Brazil, with colleagues at Fiocruz there, to improve pandemic responses and promote data-driven evidence generation in the Global South. Monitoring vaccinations across different settings is crucial for containing pandemics. However, comparative analysis of large health datasets in different scenarios is challenging due to concerns around safety and reproducibility and the loss of the context in which the data was collected, which can affect research results. They will adapt data science standards and tools to different local health system scenarios and run individual and joint vaccine effectiveness analyses for the two countries to assess compatibility and reproducibility of the findings. They will also build a public data visualization dashboard for health managers and policymakers to monitor the pandemic, particularly in vulnerable populations.
Luc Samison of Centre d'Infectiologie Charles Mérieux - University of Antananarivo in Madagascar will support more responsive and resilient antimicrobial resistance (AMR) surveillance systems in Madagascar and Burkina Faso by building a data science center for the electronic collection, analysis and dissemination of data. They will develop and refine data collection tools and sharing processes to promote multi-disciplinary collaborations and strengthen data governance and standards. These will be applied to detecting multi-drug resistant Escherichia coli and Enterobacteriaceae in several settings including pregnant women, hospitalized patients, chickens and surface water. They will also develop new tools and processes to provide stakeholders with strategic AMR indicators in real-time to support decision-making. This project will also support data-centered health research on AMR surveillance and can be applied to a range of pathogen surveillance settings in other low- and middle-income countries.
Damazo Kadengye of the African Population and Health Research Center in Kenya will establish a functional Learning Health System to promote the exploration of population health data from multiple sources to improve public health responses to infectious diseases in sub-Saharan Africa. Utilizing the data revolution to generate new knowledge is crucial for achieving global health targets, but there is a lack of suitable tools and limited access to data from different sources. They will integrate multiple large HIV/AIDS datasets from 11 longitudinal population cohorts in East Africa and develop an organizational architecture that enhances data discoverability and promotes responsible open data sharing, supporting collaborations between healthcare professionals, policy makers and researchers. They will also train scientists to produce data-based evidence using data science tools and predictive statistical models and to work with policy makers at local and national levels.
Andrew Boulle and colleagues at the Western Cape Government Health Department and the University of Cape Town in South Africa will use a data science approach applied to anonymized COVID-19 health data from the government health department including over one million tests and 60,000 hospital admissions, to study the clinical epidemiology and evolution of a new variant of SARS-CoV-2 that emerged in South Africa and the impact on patients with existing health conditions. They will conduct a case-control study to determine the clinical severity of the variant and use a cross-sectional design to explore the evolution of viral load. They will also analyze the impact of COVID-19 on pregnancy by evaluating birth weight and other birth outcomes, such as still births, and use death registries to determine mortality rates in patients with HIV, TB, and diabetes.
Xiaofan Liu at the City University of Hong Kong in China and colleagues will reconstruct COVID-19 transmission chains between individuals in communities and households using statistical methods applied to existing datasets to more reliably estimate COVID-19 transmission characteristics, such as reproduction rates, that are critical for planning effective control measures. Currently, transmission characteristics are estimated using aggregated-level data, which leads to inaccuracies. Ideally, data on how COVID-19 is transmitted between individuals are needed. They will curate an existing collection of datasets containing over 40,000 COVID-19 cases in five Asian countries with person-to-person transmission evidence to reconstruct transmission chains. They will then apply statistical tests and an analytical methodology called regression analysis to identify the most important transmission risk factors, which may include virus strain, transmission media, population density, and climate conditions.
Luis Felipe Reyes at the Universidad de La Sabana in Colombia and colleagues will develop a standardized strategy for researchers to better utilize the ISARIC-COVID-19 dataset, which consists of over 520,000 hospitalized patients from more than 62 countries, and identify the causes and health impacts of severe complications. The dataset is particularly valuable because it covers varying standards-of-care around the world and could be used to study the geographic and time-based variability of the disease. The team will develop a standardized strategy to reformat and clean the ISARIC-COVID-19 dataset by producing data descriptors and reference codes and use this strategy to identify the risk factors and clinical characteristics of COVID-19 complications, such as cardiovascular complications, which are a major contributor to long-term morbidity and mortality, in order that vulnerable patients can be better treated.
Fernando Bozza at Fiocruz in Brazil and colleagues will quantify the real-world value of COVID-19 vaccines in Brazil for protecting individuals from severe disease and for protecting the entire population from being infected. Knowing how effective vaccination is, and how durable the response in the real world is, particularly in low- and middle-income countries, it is critical for ending the pandemic. They will determine the effectiveness of the vaccine for protecting individuals using an approach called test-negative design together with statistical and machine learning approaches to compare the severity of respiratory disease in COVID-19 patients from 43 hospitals. At the population level, they will perform an ecological study, and use regression analysis accounting for inequities to vaccine access, to measure the effect of vaccinations on COVID-19 cases, hospitalizations, and deaths.
Maria Yury Ichihara and colleagues at the Centre for Data and Knowledge Integration for Health (Cidacs) at Fiocruz in Brazil will create a social disparities index to measure inequalities relevant to the COVID-19 pandemic, such as unequal access to healthcare, to identify regions that are more vulnerable to infection and to better focus prevention efforts. In Brazil, markers of inequality are associated with COVID-19 morbidity and mortality. They will develop the index of available COVID-19 surveillance data, hosted on the Cidacs platform, and build a public data visualization dashboard to share the index and patterns of COVID-19 incidence and mortality with the broader community. This will enable health managers and policymakers to monitor the pandemic situation in the most vulnerable populations and target social and health interventions.