Prioritisation of Informed Health Choices (IHC) key concepts to be included in lower secondary school resources: A consensus study

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Assessment of the socioeconomic impact of COVID-19 in Rwanda:Findings from a country-wide community survey, preliminary analysis to inform further global research

Rationale: The COVID-19 pandemic along with its devastating impact on human lives has disrupted the socioeconomic situation worldwide. Rwanda has adopted lockdowns and other measures to prevent the spread of the COVID-19 pandemic. Recent studies documented the macro-level socio-economic pandemic impact but the impact on a household’s daily life has been scarcely documented especially in low-and-middle-income countries.Objective: This work describes the interplay between multiple factors to assess the socio-economic impact of COVID-19 on the Rwandan population at the micro-level (household).Methods: Data from a country-wide community survey conducted in Rwanda between December 2021 and March 2022 were used. A total of 26,412 response forms were received from around 4400 participants surveyed in 6 recurrent bi-weekly phases where participants were randomly selected. The Multivariable Logistic regression model was fitted to data with a backward stepwise elimination algorithm to assess the socioeconomic impact of COVID-19 on households’ income. Factors considered in this study are gender, age group, residence, level of education, occupation, change in employment status, socioeconomic status, and marital status.Results: The multivariable logistic regression model provided the factors associated with the decline in income due to COVID-19. The results show that people living without a partner are more likely to experience income decline due to COVID-19 than people living with their partner. It is seen that the higher the number of children in a household, the higher the risk of experiencing a decrease in income. Taking into consideration the education level and comparing people with post-secondary and university level vis-a-vis people who did not attend school, the latter are 27 times more likely to experience a decrease in their income, those who attended primary school are 5 times more likely to experience a decrease in income, and those who attended secondary school are almost 2 times more likely to experience a decrease in income.Conclusions: The findings from this research will be used by policymakers and other stakeholders to design and implement preventive and responsive measures for future pandemics that should be multifactorial and tailored to transversal parameters like gender and residence

Assessment of the socioeconomic impact of COVID-19 in Rwanda:Findings from a country-wide community survey, preliminary analysis to inform further global research

Rationale: The COVID-19 pandemic along with its devastating impact on human lives has disrupted the socioeconomic situation worldwide. Rwanda has adopted lockdowns and other measures to prevent the spread of the COVID-19 pandemic. Recent studies documented the macro-level socio-economic pandemic impact but the impact on a household’s daily life has been scarcely documented especially in low-and-middle-income countries.Objective: This work describes the interplay between multiple factors to assess the socio-economic impact of COVID-19 on the Rwandan population at the micro-level (household).Methods: Data from a country-wide community survey conducted in Rwanda between December 2021 and March 2022 were used. A total of 26,412 response forms were received from around 4400 participants surveyed in 6 recurrent bi-weekly phases where participants were randomly selected. The Multivariable Logistic regression model was fitted to data with a backward stepwise elimination algorithm to assess the socioeconomic impact of COVID-19 on households’ income. Factors considered in this study are gender, age group, residence, level of education, occupation, change in employment status, socioeconomic status, and marital status.Results: The multivariable logistic regression model provided the factors associated with the decline in income due to COVID-19. The results show that people living without a partner are more likely to experience income decline due to COVID-19 than people living with their partner. It is seen that the higher the number of children in a household, the higher the risk of experiencing a decrease in income. Taking into consideration the education level and comparing people with post-secondary and university level vis-a-vis people who did not attend school, the latter are 27 times more likely to experience a decrease in their income, those who attended primary school are 5 times more likely to experience a decrease in income, and those who attended secondary school are almost 2 times more likely to experience a decrease in income.Conclusions: The findings from this research will be used by policymakers and other stakeholders to design and implement preventive and responsive measures for future pandemics that should be multifactorial and tailored to transversal parameters like gender and residence

Leveraging artificial intelligence and data science techniques in harmonizing, sharing, accessing and analyzing SARS-COV-2/COVID-19 data in Rwanda (LAISDAR Project):study design and rationale

Background: Since the outbreak of COVID-19 pandemic in Rwanda, a vast amount of SARS-COV-2/COVID-19-related data have been collected including COVID-19 testing and hospital routine care data. Unfortunately, those data are fragmented in silos with different data structures or formats and cannot be used to improve understanding of the disease, monitor its progress, and generate evidence to guide prevention measures. The objective of this project is to leverage the artificial intelligence (AI) and data science techniques in harmonizing datasets to support Rwandan government needs in monitoring and predicting the COVID-19 burden, including the hospital admissions and overall infection rates.Methods: The project will gather the existing data including hospital electronic health records (EHRs), the COVID-19 testing data and will link with longitudinal data from community surveys. The open-source tools from Observational Health Data Sciences and Informatics (OHDSI) will be used to harmonize hospital EHRs through the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The project will also leverage other OHDSI tools for data analytics and network integration, as well as R Studio and Python. The network will include up to 15 health facilities in Rwanda, whose EHR data will be harmonized to OMOP CDM.Expected results: This study will yield a technical infrastructure where the 15 participating hospitals and health centres will have EHR data in OMOP CDM format on a local Mac Mini (“data node”), together with a set of OHDSI open-source tools. A central server, or portal, will contain a data catalogue of participating sites, as well as the OHDSI tools that are used to define and manage distributed studies. The central server will also integrate the information from the national Covid-19 registry, as well as the results of the community surveys. The ultimate project outcome is the dynamic prediction modelling for COVID-19 pandemic in Rwanda.Discussion: The project is the first on the African continent leveraging AI and implementation of an OMOP CDM based federated data network for data harmonization. Such infrastructure is scalable for other pandemics monitoring, outcomes predictions, and tailored response planning

Leveraging artificial intelligence and data science techniques in harmonizing, sharing, accessing and analyzing SARS-COV-2/COVID-19 data in Rwanda (LAISDAR Project):study design and rationale

Background: Since the outbreak of COVID-19 pandemic in Rwanda, a vast amount of SARS-COV-2/COVID-19-related data have been collected including COVID-19 testing and hospital routine care data. Unfortunately, those data are fragmented in silos with different data structures or formats and cannot be used to improve understanding of the disease, monitor its progress, and generate evidence to guide prevention measures. The objective of this project is to leverage the artificial intelligence (AI) and data science techniques in harmonizing datasets to support Rwandan government needs in monitoring and predicting the COVID-19 burden, including the hospital admissions and overall infection rates.Methods: The project will gather the existing data including hospital electronic health records (EHRs), the COVID-19 testing data and will link with longitudinal data from community surveys. The open-source tools from Observational Health Data Sciences and Informatics (OHDSI) will be used to harmonize hospital EHRs through the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The project will also leverage other OHDSI tools for data analytics and network integration, as well as R Studio and Python. The network will include up to 15 health facilities in Rwanda, whose EHR data will be harmonized to OMOP CDM.Expected results: This study will yield a technical infrastructure where the 15 participating hospitals and health centres will have EHR data in OMOP CDM format on a local Mac Mini (“data node”), together with a set of OHDSI open-source tools. A central server, or portal, will contain a data catalogue of participating sites, as well as the OHDSI tools that are used to define and manage distributed studies. The central server will also integrate the information from the national Covid-19 registry, as well as the results of the community surveys. The ultimate project outcome is the dynamic prediction modelling for COVID-19 pandemic in Rwanda.Discussion: The project is the first on the African continent leveraging AI and implementation of an OMOP CDM based federated data network for data harmonization. Such infrastructure is scalable for other pandemics monitoring, outcomes predictions, and tailored response planning

Marburg virus disease in Rwanda, 2024 – public health and clinical responses

Background On September 27, 2024, Rwanda reported an outbreak of Marburg virus disease (MVD), after a cluster of cases of viral hemorrhagic fever was detected at two urban hospitals. Methods We report key aspects of the epidemiology, clinical manifestations, and treatment of MVD during this outbreak, as well as the overall response to the outbreak. We performed a retrospective epidemiologic and clinical analysis of data compiled across all pillars of the outbreak response and a case-series analysis to characterize clinical features, disease progression, and outcomes among patients who received supportive care and investigational therapeutic agents.Results Among the 6340 patients with suspected MVD who underwent testing, 66 had laboratory-confirmed MVD, 51 (77%) of whom were health care workers. The median estimated incubation period was 10 days (interquartile range, 8 to 13), and symptom onset occurred a median of 2 days (interquartile range, 1 to 3) before hospital admission. The results of epidemiologic investigations were highly suggestive of a zoonotic origin of the outbreak: an index patient was identified who had been exposed to Egyptian fruit bats at a mining site. The case fatality rate in the outbreak was 23% (15 deaths among 66 patients). Remdesivir and the monoclonal antibody MBP091 were used under expanded access and clinical trial protocols. In addition, 1710 frontline workers and high-risk contacts received the chimpanzee adenovirus 3–vectored vaccine ChAd3-MARV under emergency use authorization in a phase 2 clinical trial. Conclusions Implementation of containment measures, advanced supportive care, and access to investigational countermeasures may have contributed to reduced mortality from MVD in this outbreak. Enhancing surveillance, improving infection prevention and control in health care settings, and ensuring timely deployment of medical countermeasures will be critical for mitigating the effects of future filovirus disease outbreaks

Capacity of community advisory boards for effective engagement in clinical research: a mixed methods study

Abstract Background Community engagement is a key component in health research. One of the ways health researchers ensure community engagement is through Community Advisory Boards (CABs). The capacity of CABs to properly perform their role in clinical research has not been well described in many resource limited settings. In this study, we assessed the capacity of CABs for effective community engagement in Uganda. Methods We conducted a cross sectional study with mixed methods. We used structured questionnaires and key informant interviews (KII) to collect data from CAB members, trial investigators, and community liaison officers. For quantitative data, we used descriptive statistics while for qualitative data we used content analysis. Results Seventy three CAB members were interviewed using structured questionnaires; 58.9% males, median age 49 years (IQR 24–70), 71.2% had attained tertiary education, 42.5% never attended any research ethics training, only 26% had a training in human subject protection, 30.1% had training in health research, 50.7% never attended any training about the role of CABs, and 72.6% had no guidelines for their operation. On the qualitative aspect, 24 KIIs cited CAB members to have some skills and ability to understand and review study documents, offer guidance on community norms and expectations and give valuable feedback to the investigators. However, challenges like limited resources, lack of independence and guidelines, and knowledge gaps about research ethics were cited as hindrances of CABs capacity. Conclusion Though CABs have some capacity to perform their role in the Ugandan setting, their functionality is limited by lack of resources to facilitate their work, lack of independence, lack of guidelines for their operations and limited knowledge regarding issues of research ethics and protection of the rights of trial participants. </jats:sec

Capacity of Community Advisory Boards for Effective Engagement in Clinical Research; a Mixed Methods Study

Abstract Background: Community engagement is a key component in health research. One of the ways health researchers ensure community engagement is through Community Advisory Boards (CABs). The capacity of CABs to properly perform their role in clinical research has not been well described in many resource limited settings. In this study, we assessed the capacity of CABs for effective community engagement in Uganda.Methods: We conducted a cross sectional study with mixed methods. We used structured questionnaires and key informant interviews (KII) to collect data from CAB members, trial investigators, and community liaison officers. For quantitative data, we used descriptive statistics while for qualitative data we used content analysis. Results: Seventy three CAB members were interviewed using structured questionnaires; 58.9% males, median age 49 years (IQR: 24-70), 71.2% had attained tertiary education, 42.5% never attended any research ethics training, only 26% had a training in human subject protection, 30.1% had training in health research, 50.7% never attended any training about the role of CABs, and 72.6% had no guidelines for their operation. On the qualitative aspect, 24 KIIs cited CAB members to have some skills and ability to understand and review study documents, offer guidance on community norms and expectations and give valuable feedback to the investigators. However, challenges like limited resources, lack of independence and guidelines, and knowledge gaps about research ethics were cited as hindrances of CABs capacity.Conclusion: Though CABs have some capacity to perform their role in the Ugandan setting, their functionality is limited by lack of resources to facilitate their work, independence, guidelines for their operations and limited knowledge.</jats:p

Additional file 1 of Capacity of community advisory boards for effective engagement in clinical research: a mixed methods study

Additional file 1. Dataset

Additional file 2 of Capacity of community advisory boards for effective engagement in clinical research: a mixed methods study

Additional file 2. Data collection tools