Reducing inequalities using an unbiased machine learning approach to identify births with the highest risk of preventable neonatal deaths
We show that, by using publicly available administrative data sets and ML methods, it is possible to identify the births with the highest risk of preventable deaths with a high degree of accuracy. This is useful for policymakers as they can target health interventions to those who need them the most and where they can be effective without producing bias against disadvantaged populations. Overall, our approach can guide policymakers in reducing neonatal mortality rates and their health inequalities. Finally, it can be adapted for use in other developing countries.
Authors
- Marcus L. Nascimento (ORCID: https://orcid.org/0000-0002-9742-5683)
- Raphael de Freitas Saldanha (ORCID: https://orcid.org/0000-0003-0652-8466)
- Antonio Pedro Ramos (ORCID: https://orcid.org/0000-0002-6813-1379)
- Fábio Caldieraro (ORCID: https://orcid.org/0000-0002-1264-3279)
Institutions
- University of California, Los Angeles (US)
- Department for Work and Pensions (GB)
- Fundação de Estudos e Pesquisas Aquáticas (BR)
- Fundação Getulio Vargas (BR)
- Fundação Oswaldo Cruz (BR)
- United Nations Population Fund (US)
Publication Details
- Journal
- Population Health Metrics
- Published
- 2025-10-27
- DOI
- https://doi.org/10.1186/s12963-025-00420-x
- Primary Topic
- Global Maternal and Child Health
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Fundação Getulio Vargas
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
- Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro
- Escola Brasileira de Administração Pública e de Empresas
- National Institute of Child Health and Human Development
- California Center for Population Research, University of California, Los Angeles
- Eunice Kennedy Shriver National Institute of Child Health and Human Development