Resources

This is a simple, Excel-based tool for assessing and comparing costs of rotavirus vaccination programs with each rotavirus vaccine product available in the global market. It aims to help national-level policymakers specifically in countries eligible for support from Gavi, the Vaccine Alliance, compare products and estimate vaccination program costs for different rotavirus vaccines, exploring up to four different vaccine options at a time.The tool calculates cold chain volume and costs annually and for a total period of five years. Cost estimates are composed of vaccine cost (i.e., vaccine and supplies procurement and international shipping) and vaccination program costs (i.e., vaccine cost and cost of delivery). Cost estimates are provided separately for the country perspective and the combined country and Gavi perspective.It is important to note that cost is only one consideration when selecting a rotavirus vaccine product, and users involved in decision-making around new vaccine introduction/switch or product selection should always consider other dimensions as well. This model is meant to provide insights into the potential costs of alternative product choices and should not replace detailed budget planning once a product has been selected.A user guide is included in the file on a tab labeled “READ ME FIRST” which provides detailed instructions on how to use the tool and interpret the results. The tool is available to download in multiple languages. Please be sure to check out this seven-minute tutorial video, which is in English, with French and Spanish subtitled versions also available. If you have additional questions or need support, contact PATH’s Health Economics & Outcomes Research team: HEOR@path.org.NOTE: Please refer to this web page for the most up-to-date version of the calculator, which is periodically updated to reflect the evolving vaccine landscape.

PATH, in collaboration with the Maharashtra Department of Health and Family Welfare, conducted a biomedical equipment survey in health facilities across the state in January 2024. This report analyzes data collected through facility assessments to provide a snapshot of the medical oxygen ecosystem of 34 health facilities in 27 of Maharashtra’s 36 districts. The purpose of this work is to quantify existing oxygen delivery and production equipment, consumables for administering oxygen therapy, bed capacity, and facility infrastructure characteristics.

PATH, in collaboration with the Uttar Pradesh Department of Health and Family Welfare, conducted a biomedical equipment survey in health facilities across the state in January 2024. This report analyzes data collected through facility assessments to provide a snapshot of the medical oxygen ecosystem of 149 health facilities in 65 districts across Uttar Pradesh, India. The purpose of this work is to quantify existing oxygen delivery and production equipment, consumables for administering oxygen therapy, bed capacity, and facility infrastructure characteristics.

This report summarizes the proceedings from an international meeting organized by PATH and partners and held in Bangkok, Thailand, from September 3 to 4, 2025 for manufacturers and regulatory testing laboratories on polio vaccine quality control assays. Presenters provided updates on evolving guidance for the implementation of quality control assays for polio vaccines. This included the dissemination of results from a recent World Health Organization (WHO) collaborative study supporting the replacement of animal models with high-throughput sequencing for routine vaccine lot release. Other topics included the latest WHO collaborative study results on the suitability of the Sabin inactivated polio vaccine International Standard for in vivo rat potency testing; the availability of WHO universal reagents for polio vaccine potency assays; and the importance of harmonizing in-house assay standards and reagents with international standards and the WHO guidance. Finally, presenters discussed the potential of next-generation products, such as S19 virus-based IPV, that can be developed currently without biocontainment requirements.

The integration of artificial intelligence (AI) technologies into clinical practice stands as a defining challenge and opportunity for global health in the coming decade. While use cases such as clinical documentation and diagnostic support are becoming increasingly common in high-resource settings, where over half of clinicians now report using AI tools, much less is known about frontline clinicians’ experiences, barriers, and real-world needs in low- and middle-income countries (LMICs).In LMIC contexts, large language model (LLM)-enabled clinical decision support systems (CDSSs) offer the potential to extend high-quality decision support to settings with limited human and technical resources. Yet they also carry significant risks, including language bias, inequitable access, limited local capacity for oversight, and the possibility of recommendations that are misaligned with local medical guidelines that undermine patient safety or trust.Over the past two years, rapid advances in generative AI have outpaced the establishment of evidence, governance, and capacity frameworks required for responsible use. Health systems and policymakers are now faced with urgent questions:How can AI tools be evaluated for clinical safety and contextual relevance?What data infrastructures and regulatory mechanisms are needed for equitable deployment?And how can local researchers and health institutions lead, not just participate, in AI innovation?These questions form the basis of this CDSS Learning Agenda, which aims to generate collective insight and actionable evidence to guide safe, effective, and equitable integration of AI into health systems. The agenda identifies six thematic priorities—localization and language equity; evaluation and real-world evidence; voice-enabled and multimodal tools; capacity and local ownership; governance and trust; and infrastructure and enabling environments—that collectively define the ecosystem required for sustainable AI adoption in LMICs.