Cristal Salatas
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Doctoral Candidate, Liggins Institute, The University of Auckland, NZ
Cristal Salatas is a PhD candidate at the University of Auckland, specialising in Perinatal Sciences. Her research utilises Geographic Information Systems (GIS) to analyse the spatial, nutritional, environmental, and socioeconomic determinants of preterm birth risk across New Zealand. By integrating large-scale health and environmental datasets, she develops predictive models that inform targeted, location-based maternal health interventions.
Cristal’s research bridges spatial epidemiology, AI-driven risk modelling, and public health policy, ensuring that geospatial insights translate into actionable healthcare solutions. She collaborates with key stakeholders, including midwifery organisations and policymakers, to enhance equitable access to maternal healthcare. An advocate for interdisciplinary research, she has presented her work at multiple forums, including the Global Young Scientists Summit, earning recognition for her innovative approach to maternal health solutions. |
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Mapping Preterm Birth Risk in Aotearoa: Integrating NZ Data and GIS to Inform Equity-Driven Maternal Health – Opportunities and Gaps
Geographic Information Systems (GIS) is a powerful tool to visualise and analyse health outcomes across space and time. In this presentation, I will explore how national-level administrative data can be harnessed to understand the spatial distribution of preterm birth (PTB) across New Zealand. By integrating data from the Integrated Data Infrastructure (IDI), including socioeconomic, nutritional, maternal, and environmental indicators, we used spatiotemporal modelling to identify geographic disparities and high-risk regions for a 20-year period, particularly in rural areas with limited access to healthy food environments.
We highlight how existing New Zealand data sources can be combined to generate meaningful insights for policy and practice, particularly around maternal health and equity. Importantly, we also discuss key limitations and data gaps encountered throughout the study, such as incomplete dietary intake reporting, limited granularity in supplementation data, and restricted availability of high-resolution geospatial layers relevant to maternal health.
This session is especially relevant to those interested in using GIS for applied health research and policy, including researchers, planners, and decision-makers from government and health sectors. With government agencies around the world increasingly turning to data-informed approaches, this presentation shows both the promise and the challenges of bringing disparate data sources together to inform equity-driven maternal health interventions.
We highlight how existing New Zealand data sources can be combined to generate meaningful insights for policy and practice, particularly around maternal health and equity. Importantly, we also discuss key limitations and data gaps encountered throughout the study, such as incomplete dietary intake reporting, limited granularity in supplementation data, and restricted availability of high-resolution geospatial layers relevant to maternal health.
This session is especially relevant to those interested in using GIS for applied health research and policy, including researchers, planners, and decision-makers from government and health sectors. With government agencies around the world increasingly turning to data-informed approaches, this presentation shows both the promise and the challenges of bringing disparate data sources together to inform equity-driven maternal health interventions.
