Peter Waiswa

Education is a powerful social determinant of health, with extensive evidence linking educational attainment to better health across the life course [1,2]. Yet, access to inclusive and equitable qu...

Unacceptable disparities in global progress in child health remain between low- and high-income countries. The quality of healthcare in low-income countries is often limited by high patient volumes...

Reducing perinatal deaths is a global health priority to reach the target of less than 12 deaths per 1,000 births by 2030 [1,2]. Each year, 1.9 million babies die before birth and 2.4 million die d...

Neonatal mortality poses a significant burden on global health, particularly in low- and middle-income countries (LMICs). Neonatal deaths do not only have an emotional and psychological effects on families but can also have broader societal implications, straining healthcare systems, impeding economic development, and exacerbating inequalities. There were an estimated 2.3 million neonatal deaths globally in […]

Timely and consistent digital payment of health workers is crucial for improving the effectiveness of immunization campaigns and achieving the polio eradication goals by 2026. However, evidence on the enablers and barriers to digital payments in sub-Saharan Africa (SSA) is limited. To explore the enablers and barriers to digital payments for immunization campaign health workers in eight selected SSA countries. An exploratory case study using qualitative methods was conducted in eight SSA countries: four English-speaking (Uganda, Kenya, Nigeria, Ghana) and four French-speaking (Senegal, Ivory Coast, Cameroon, Democratic Republic of the Congo). A scoping review and in-depth interviews were conducted between March and May 2022 in each country. Data were analyzed thematically. Digital payments are rapidly expanding in SSA, including during large-scale immunization campaigns. Key enablers included supportive regulatory frameworks, increasing mobile phone and digital platform coverage, and the benefits of digital payments. Barriers included inadequate telecom infrastructure, cybercrime, challenges with customer registration, higher transaction costs, and payment delays. This study identifies key enablers of digital payments in eight SSA countries, including supportive regulations, growing mobile phone ownership, and expanding digital platforms. Persistent challenges - such as limited infrastructure, verification constraints, and payment delays - affect implementation. Findings, while context-specific, offer valuable insights for policymakers to strengthen digital payment systems, improve verification, and enhance coordination to optimize health worker payments during immunization and public health campaigns.

Background: Malaria remains one of the most significant global health threats, and its control depends on timely, accurate entomological surveillance. In Uganda, routine vector surveillance relies heavily on microscope-based mosquito identification by a limited number of trained entomologists, which can delay reporting. VectorCam is an AI-enabled digital tool to support real-time mosquito identification and can be operated by community health workers, supporting rapid data reporting. After co-designing the implementation package using a human-centered design approach with Uganda’s Ministry of Health, we are implementing a surveillance strategy that incorporates VectorCam into routine work. Objective: This study aims to evaluate acceptability, fidelity, feasibility, cost, and preliminary effectiveness of VectorCam, the intervention, along with supporting implementation strategies as it is integrated into Uganda’s national malaria control program. Methods: This mixed methods hybrid type III implementation-effectiveness study uses district-level restricted randomization to assign 12 districts to four sequential rollout waves following a 2-district pilot phase. The implementation package will be introduced over a one-year period across malaria-endemic districts in Northern and Eastern Uganda. Implementation outcomes, including acceptability, feasibility, fidelity, and cost, will be assessed using qualitative interviews, system usage logs, field observations, and program documentation. Effectiveness outcomes include technical performance of mosquito species identification, time efficiency, and operational use of surveillance data for decision-making. Accuracy will be evaluated through comparison with molecular identification methods, alongside additional classification performance metrics. Data will be analyzed using a mixed methods approach, with triangulation of qualitative and quantitative data and longitudinal analyses across districts and rollout waves. An adaptive learning approach will guide iterative refinement of the implementation package. Results: Fourteen districts were selected by the Ministry of Health in September 2025 for VectorCam introduction. A pilot phase conducted between December 2025 and January 2026 in two districts was used to refine implementation strategies and research tools through stakeholder workshops. Ethical approval was obtained from Johns Hopkins in January 2026 and Ugandan ethics committees in September 2025. Implementation began in February 2026 and will continue through March 2027 with concurrent data collection and analysis throughout the implementation timeline. Conclusions: This study will evaluate a task-shifting approach to entomological surveillance by integrating an AI-enabled tool into routine workflows of community health workers through a co-designed implementation package. By examining how the VectorCam intervention and supporting implementation strategies function together within real-world health system contexts, this study will generate evidence on the sustainability of digitally enabled surveillance. Findings will provide insight into how task shifting and digital tools can expand surveillance capacity, improve timeliness of data reporting, and strengthen the use of entomological data for decision-making. These results will inform national scale-up efforts in Uganda and offer a model for other malaria-endemic settings seeking to modernize vector surveillance.

BACKGROUND Malaria remains one of the most significant global health threats, and its control depends on timely, accurate entomological surveillance. In Uganda, routine vector surveillance relies heavily on microscope-based mosquito identification by a limited number of trained entomologists, which can delay reporting. VectorCam is an artificial intelligence (AI) enabled digital tool to support real-time mosquito identification and can be operated by community health workers, supporting rapid data reporting. After co-designing the implementation package using a human-centered design approach with Uganda’s Ministry of Health, we are implementing a surveillance strategy that incorporates VectorCam into routine work. OBJECTIVE This study aims to evaluate acceptability, fidelity, feasibility, cost, and preliminary effectiveness of VectorCam, the intervention, along with supporting implementation strategies as it is integrated into Uganda’s national malaria control program. METHODS This mixed-methods hybrid type III implementation-effectiveness study uses district-level restricted randomization to assign 12 districts to four sequential rollout waves following a two-district pilot phase. The implementation package will be introduced over a one-year period across malaria-endemic districts in Northern and Eastern Uganda. Implementation outcomes, including acceptability, feasibility, fidelity, and cost, will be assessed using qualitative interviews, system usage logs, field observations, and program documentation. Effectiveness outcomes include technical performance of mosquito species identification, time efficiency, and operational use of surveillance data for decision-making. Accuracy will be evaluated through comparison with molecular identification methods, alongside additional classification performance metrics. Data will be analyzed using a mixed-methods approach, with triangulation of qualitative and quantitative data and longitudinal analyses across districts and rollout waves. An adaptive learning approach will guide iterative refinement of the implementation package. RESULTS Fourteen districts were selected by the Ministry of Health in September 2025 for VectorCam introduction. A pilot phase conducted between December 2025 and January 2026 in two districts was used to refine implementation strategies and research tools through stakeholder workshops. Ethical approval was obtained from Johns Hopkins in January 2026 and Ugandan ethics committees in September 2025. Implementation began in February 2026 and will continue through March 2027 with concurrent data collection and analysis throughout the implementation timeline. CONCLUSIONS This study will evaluate a task-shifting approach to entomological surveillance by integrating an AI–enabled tool into routine workflows of community health workers through a co-designed implementation package. By examining how the VectorCam intervention and supporting implementation strategies function together within real-world health system contexts, this study will generate evidence on the sustainability of digitally enabled surveillance. Findings will provide insight into how task shifting and digital tools can expand surveillance capacity, improve timeliness of data reporting, and strengthen the use of entomological data for decision-making. These results will inform national scale-up efforts in Uganda and offer a model for other malaria-endemic settings seeking to modernize vector surveillance.

Family planning (FP) is a key public health intervention with broad health, social, and economic benefits. However, access and sustained use remain limited

According to the latest estimates of 2022, 2.3 million newborns died globally, accounting for 47% of all child deaths under the age of five [1]. Sub-Saharan Africa recorded one of the highest neonatal mortality rates in 2022, at 27.1 deaths per 1000 live births and 21.1 stillbirths per 1000 total births [1–4]. The majority of […]

Every year, an estimated 2.3 million newborns die during the first 28 days of life, with around 1.9 million of these deaths being stillbirths, and nearly all (89%) occurring in low- and middle-income countries (LMICs) [1–3]. The sub-Saharan African region has the highest neonatal mortality and stillbirth rate in the world, with 27 neonatal deaths […]