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Every year, 435,000 people worldwide die from Malaria, mainly in Africa and Asia. However, malaria is a curable and preventable disease. Most countries are developing malaria elimination plans to meet sustainable development goal three, target 3.3, which includes ending the epidemic of malaria by 2030. Rwanda, through the malaria strategic plan 2012-2018 set a target to reduce malaria incidence by 42% from 2012 to 2018. Assessing the health policy and taking a decision using the incidence rate approach is becoming more challenging. We are proposing suitable statistical methods that handle spatial structure and uncertainty on the relative risk that is relevant to National Malaria Control Program. We used a spatio-temporal model to estimate the excess probability for decision making at a small area on evaluating reduction of incidence. SIR and BYM models were developed using routine data from Health facilities for the period from 2012 to 2018 in Rwanda. The fitted model was used to generate relative risk (RR) estimates comparing the risk with the malaria risk in 2012, and to assess the probability of attaining the set target goal per area. The results showed an overall increase in malaria in 2013 to 2018 as compared to 2012. Ofall sectors in Rwanda, 47.36% failed to meet targeted reduction in incidence from 2012 to 2018. Our approach of using excess probability method to evaluate attainment of target or identifying threshold is a relevant statistical method, which will enable the Rwandan Government to sustain malaria control and monitor the effectiveness of targeted interventions.

Introduction Africa was threatened by the coronavirus disease 2019 (COVID-19) due to the limited health care infrastructure. Rwanda has consistently used non-pharmaceutical strategies, such as lockdown, curfew, and enforcement of prevention measures to control the spread of COVID-19. Despite the mitigation measures taken, the country has faced a series of outbreaks in 2020 and 2021. In this paper, we investigate the nature of epidemic phenomena in Rwanda and the impact of imported cases on the spread of COVID-19 using endemic-epidemic spatio-temporal models. Our study provides a framework for understanding the dynamics of the epidemic in Rwanda and monitoring its phenomena to inform public health decision-makers for timely and targeted interventions. Results The findings provide insights into the effects of lockdown and imported infections in Rwanda’s COVID-19 outbreaks. The findings showed that imported infections are dominated by locally transmitted cases. The high incidence was predominant in urban areas and at the borders of Rwanda with its neighboring countries. The inter-district spread of COVID-19 was very limited due to mitigation measures taken in Rwanda. Conclusion The study recommends using evidence-based decisions in the management of epidemics and integrating statistical models in the analytics component of the health information system.

A Rift Valley fever epizootic affected livestock in Rwanda during March-October 2022. We confirmed 3,112 infections with the virus, including 1,342 cases, 1,254 abortions, and 516 deaths among cattle, goats, and sheep. We recommend a One Health strategy for investigations and response to protect animal and human health.

Coverage Evaluation Surveys (CES) are tools used by NTD control programs to assess drug coverage after mass drug administration (MDA), typically conducted at the district level. However, little research has explored how this district-level approach may mask uneven drug coverage within districts. Such uniform reporting may obscure gaps at smaller administrative levels, potentially limiting the effectiveness of MDA efforts. This study examines CES findings from a 2021 survey in Mozambique investigating coverage at the sub-district level to better understand heterogeneity in drug coverage, while also exploring other actionable survey data, like reasons for not taking drugs, to better understand why coverage gaps and discrepancies with reported coverage may have occurred. A CES was conducted in May of 2021 to assess district level drug coverage following schistosomiasis MDA in Nov-Dec 2020 in 10 districts in northern Mozambique. Following data collection, administrative post-level classifications were added retroactively by linking GPS data from the survey to spatial shapefiles, enhancing the analysis resolution. Programmatic coverage estimates were then calculated as the proportion of eligible individuals who reported taking praziquantel in each administrative unit. Finally, an ANOVA model was applied to examine the variance in drug coverage across province, district, and administrative post level, with cross tabulation exploring sub-district level variation. Effective programmatic coverage (≥75%) was achieved with 95% confidence in five districts (Macossa, Majune, Mandiba, Maua, and Nipepe). Coverage discrepancies between surveyed and reported data were observed in nine out of ten districts, highlighting potential inconsistencies in routine reporting. ANOVA modeling showed that 49% of the observed variation in drug coverage could be explained by provincial, district, and administrative post-level differences. The ANOVA results, along with cross tabulation of coverage among administrative posts together highlight the likelihood of drug coverage variability within districts. Analysis of non-participation revealed that absence from the community at the time of MDA and lack of awareness of the campaign were the leading reasons for not taking the drug, particularly in urban districts. This study highlights how CES can be used to detect heterogeneity of coverage within districts while simultaneously identifying behavioral and operational barriers to participation. Integrating sub-district level spatial analysis with contextual data on reasons for non-uptake can support in the identification of local treatment gaps, which may be integral to tailor approaches to improve MDA participation in future campaigns as national schistosomiasis programs are increasingly urged to conduct MDA at a sub-district level. These findings suggest that more granular data collection within CES could better inform program adjustments and resource allocation.

Background Cataract is the leading cause of avoidable blindness worldwide, and limited surgical access continues to affect elimination efforts, especially in low- and middle-income countries. In line with this burden, Rwanda implemented two complementary models for cataract surgery services: The Mobile Teams Outreach Cataract Surgery Model (MTO-CSM) and the Continuous Hospital-Based Cataract Surgery Model (CHB-CSM). This study compared the performance, quality adherence, efficiency, and cost-effectiveness of the two models. Methods A retrospective, cross-sectional study was conducted across 10 health facilities (6 implementing MTO-CSM and 4 implementing CHB-CSM) between October 2022 and May 2023, covering data from October 2021 to September 2022. Data sources included hospital surgery registers, the Health Management Information System (HMIS), facility assessments, staff interviews, and financial records. Key indicators included the number of cataract surgeries (CS) performed, adherence to quality standards, total surgery time (in person-minutes), and the cost per CS. Results A total of 10,584 CS were performed. District hospitals contributed 2,337 surgeries, mainly under MTO-CSM. Kabgayi District Hospital and Rwanda Charity Eye Hospital, both implementing CHB-CSM, conducted 4,297 and 3,668 surgeries, respectively, accounting for 74% of the total national CS output. Quality compliance was higher at CHB-CSM facilities (an average of 88%, with 100% and 94% at Kabgayi and Rwanda Charity, respectively) compared to 58% in MTO-CSM sites. The average time per surgery was 801 person-minutes for CHB-CSM versus 1,950 person-minutes for MTO-CSM. The cost per surgery was RWF 207,224 (USD 193.7) for CHB-CSM and RWF 349,566 (USD 326.7) for MTO-CSM, making the outreach model 69% more expensive. Conclusions The CHB-CSM had a higher surgical volume, better quality adherence, greater time efficiency, and a lower cost per case. In contrast, the MTO-CSM, although more expensive, played an essential role in expanding access to remote areas. A hybrid strategy, integrating the efficiency of continuous services with the outreach model’s equity focus, is recommended to achieve Rwanda’s national cataract surgery targets and the WHO’s 2030 goal for effective cataract surgical coverage (eCSC).

Background Currently, there is limited evidence on the effectiveness of second-line antiretroviral therapy (ART) in sub-Saharan Africa. To address this challenge, outcomes of second-line protease inhibitor (PI) based ART in Rwanda were assessed. Methods A two-stage cluster sampling design was undertaken. 49 of 340 health facilities linked to the open-source electronic medical record (EMR) system of Rwanda were randomly sampled. Data sampling criteria included adult HIV positive patients with documented change from first to second-line ART regimen. Retention in care and treatment failure (viral load above 1000 copies/mL) were evaluated using multivariable Cox proportional hazards and logistic regression models. Results A total of 1688 patients (60% females) initiated second-line ART PI-based regimen by 31st December 2016 with a median follow-up time of 26 months (IQR 24–36). Overall, 92.5% of patients were retained in care; 83% achieved VL ≤ 1000 copies/ml, 2.8% were lost to care and 2.2% died. Defaulting from care was associated with more recent initiation of ART- PI based regimen, CD4 cell count ≤500 cells/mm 3 at initiation of second line ART and viral load > 1000 copies/ml at last measurement. Viral failure was associated with younger age, WHO stage III&IV at ART initiation, CD4 cell count ≤500 cells/mm 3 at switch, atazanavir based second-line ART and receiving care at a health center compared to hospital settings. Conclusions A high proportion of patients on second-line ART are doing relatively well in Rwanda and retained in care with low viral failure rates. However, enhanced understandings of adherence and adherence interventions for less healthy individuals are required. Routine viral load measurement and tracing of loss to follow-up is fundamental in resource limited settings, especially among less healthy patients.

ObjectiveTo evaluate the use of antigen-based rapid diagnostic tests (Ag-RDTs) alongside a digital tool to deliver household-level COVID-19 testing by community health workers (CHWs), in line with Rwanda’s ambition to decentralise COVID-19 testing.DesignThis was an operational pilot study to evaluate the impact and operational characteristics of using the digital e-ASCov tool combined with Ag-RDTs to support COVID-19 symptom screening and rapid testing by CHWs across eight districts in Rwanda. A total of 800 CHWs selected from both rural and urban areas were trained in delivering Ag-RDTs for COVID-19 testing and using the e-ASCOV application for data capture on a smartphone. Laboratory technicians repeated a subset of Ag-RDTs to assess the concordance of results obtained by CHWs. The study also assessed CHWs’ experience of the intervention using a mixed-methods approach.SettingEight rural, urban and semiurban districts in Rwanda.ParticipantsA total of 19 544 individuals were enrolled and screened for signs and symptoms of COVID-19.InterventionsCommunity-based screening for COVID-19 by CHWs using the digital tool e-ASCov combined with rapid testing using Ag-RDTs.Main outcome measuresNumber of participants screened and tested; concordance of Ag-RDT results between CHWs and laboratory technicians; feasibility of study procedures by CHWs and CHWs perceptions of the digital tool and Ag-RDT testing.ResultsFrom February to May 2022, CHWs screened 19 544 participants, of whom 4575 (23.4%) had COVID-19-related symptoms or a history of exposure to the infection. Among them, 86 (1.9%) were positive on Ag-RDTs. Concordance of Ag-RDT results between CHWs and laboratory technicians was 100%. Of the 800 trained CHWs, 746 (93.3%) were independently able to conduct household-based COVID-19 screening, perform the Ag-RDTs and send data to the central server. Most CHWs (>80%) found Ag-RDTs and e-ASCOV easy to use.ConclusionsThis study demonstrated the feasibility of deploying a digital tool and Ag-RDTs for household-level SARS-CoV-2 detection in Rwanda. The findings support a broader roll-out of digitally supported rapid testing by CHWs to broaden access to testing for priority diseases.

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.

Background Digital health platforms are transforming primary care delivery in low- and middle-income countries. Babyl provided Rwanda’s first nationwide telemedicine service from 2019 to September 2023, integrating nurse-led triage with physician oversight, e-prescriptions, and national health insurance. Despite processing 3.9 million consultations, evidence on population-level impacts of scaled telemedicine services like Babyl in sub-Saharan Africa remains limited. This study aimed to quantify the association between national-scale telemedicine implementation and facility-based healthcare utilization for common primary care conditions in Rwanda, using interrupted time series analysis to estimate immediate and sustained effects across introduction and discontinuation periods. Methods We integrated deidentified administrative data from Babyl ( n  = 3,899,788 consultations), Rwanda’s Health Management Information System (2015–2024). We employed two analytical approaches: (1) descriptive analysis of user demographics, insurance coverage, and clinical characteristics; (2) segmented regression with interrupted time series modeling using ordinary least squares with Newey-West heteroskedasticity- and autocorrelation-consistent standard errors to quantify level and slope changes across pre-intervention, intervention, and post-discontinuation periods for gastritis, diarrhea, urinary tract infections, malaria, and respiratory infections. Results The platform recorded 3.90 million consultations (2019–2023), with 75.4% covered by community-based health insurance and 54.7% among female patients. Task-shifting was substantial: triage nurses managed 44.2% of consultations, senior nurses 25.6%, and general practitioners 30.2%. Interrupted time series analysis revealed immediate reductions in facility-based cases following Babyl’s introduction: respiratory infections decreased by 1055 cases (95% CI -1098 to -1011; P  < .001), malaria by 246 cases (95% CI -258 to -234; P  < .001), gastritis by 137 cases (95% CI -146 to -127; P  < .001), and urinary tract infections by 114 cases (95% CI -124 to -105; P  < .001). Post-discontinuation, monthly increases ranged from 1 case (gastritis, diarrhea, urinary tract infections) to 10 cases (respiratory infections), suggesting demand reversal to facilities. Conclusions National-scale telemedicine implementation was associated with substantial reductions in facility-based consultations for common conditions and successful task-shifting to nurses. The post-discontinuation reversal patterns underscore telemedicine’s role in healthcare access. Future digital health initiatives should prioritize sustainable financing, system interoperability, and regulatory frameworks to maintain service continuity.

As a result of the COVID-19 pandemic, providing health care while maintaining social distancing has resulted in the need to provide care remotely, support quarantined or isolated individuals, monitor infected individuals and their close contacts, as well as disseminate accurate information regarding COVID-19 to the public. This has led to an unprecedented rapid expansion of digital tools to provide digitized virtual care globally, especially mobile phone-facilitated health interventions, called mHealth. To help keep abreast of different mHealth and virtual care technologies being used internationally to facilitate patient care and public health during the COVID-19 pandemic, we carried out a rapid investigation of solutions being deployed and considered in 4 countries. The aim of this paper was to describe mHealth and the digital and contact tracing technologies being used in the health care management of the COVID-19 pandemic among 2 high-income and 2 low-middle income countries. We compared virtual care interventions used for COVID-19 management among 2 high-income countries (the United Kingdom and Canada) and 2 low-middle income (Kenya and Rwanda) countries. We focused on interventions used to facilitate patient care and public health. Information regarding specific virtual care technologies was procured from a variety of resources including gray literature, government and health organization websites, and coauthors' personal experiences as implementers of COVID-19 virtual care strategies. Search engine queries were performed to find health information that would be easily accessible to the general public, with keywords including \"COVID-19,\" \"contact-tracing,\" \"tool-kit,\" \"telehealth,\" and \"virtual care,\" in conjunction with corresponding national health authorities. We identified a variety of technologies in Canada, the United Kingdom, Rwanda, and Kenya being used for patient care and public health. These countries are using both video and text message-based platforms to facilitate communication with health care providers (eg, WelTel and Zoom). Nationally developed contact tracing apps are provided free to the public, with most of them using Bluetooth-based technology. We identified that often multiple complimentary technologies are being utilized for different aspects of patient care and public health with the common purpose to disseminate information safely. There was a negligible difference among the types of technologies used in both high-income and low-middle income countries, although the latter implemented virtual care interventions earlier during the pandemic's first wave, which may account for their effective response. Virtual care and mHealth technologies have evolved rapidly as a tool for health care support for both patient care and public health. It is evident that, on an international level, a variety of mHealth and virtual care interventions, often in combination, are required to be able to address patient care and public health concerns during the COVID-19 pandemic, independent of a country's economic standing.