MARATTO

dataset · Harvard Dataverse

Replication Data for: Cost-Effectiveness and Budget Impact Analyses of Strategies to Improve Measles Vaccination Coverage in Kenya

2026Open accessUniversity of Zambia

Abstract

This dataset includes the excel-based Markov Model that was developed for the cost-effectiveness of the Measles Vaccination Strategies. Within it contains all the parameters used in the analysis that are also reported in the manuscript. The parameters were collected from different secondary sources that are also detailed in the manuscript. Current Vaccine coverage estimates were sources from the Kenya National Vaccine Immunization Program while target coverage were either based on historic trends or assumptions. Epidemiological estimates including reporting rates, probability of infection, vaccine efficacy estimates, and case-fatality rates were sourced from different literature sources that are reported in detail in the manuscript. Cost-estimates were from different sources including UNICEF reports, literature sources for delivery costs (Levin et al 2023), Ministry of Health costing data and a cost of illness study for measles in Kenya. Disability adjusted life years were obtained from global burden of disease studies. These data was then used to develop the transition probabilities across the different health states, the Markov model for the different strategies and subsequently, the Cost-Effectiveness Analysis Planes and Cost-Effectiveness Acceptability Curves, all of which are available in the excel-based Markov Model.

Read the original research

This page summarises published work. The authoritative version sits with the publisher.

DOI: 10.7910/dvn/kexazf

Is something wrong with this record? Report it or request removal.

Discussion

Discuss this research

Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.

No discussion yet. Open the first thread.