Revealing Environmental Causes of Preterm Births in a Quasi-Experimental Framework

Preterm birth (<37 weeks' gestation) is the leading cause of death in children under the age of 5 worldwide, accounting for 900k neonatal deaths a year.

Preterm birth rates vary widely within and between countries, with environmental causes of yet unclear aetiology suspected to be the main drivers.

The main goal of TinyTrend is to reveal the role of environmental risk factors and inform risk-mitigation policies for preterm births by evaluating environmental policy changes in a quasi-experimental setting.

This interdisciplinary project will devise a robust and novel methodology through embedding data science and artificial intelligence within an epidemiology framework to: 1) systematically identify policy changes targeting environmental factors, 2) evaluate their role on preterm birth rates in time and space (quasi-experimental framework), 3) draw causal inferences integrating original and previous evidence on the impact of these environmental factors on preterm births.

The Experienced Researcher (ER) will advance his skills in environmental science, public health, epidemiology and informatics through high-calibre external and in-house training from the host institution.

TinyTrend will leverage high-quality population-wide administrative health data from Lombardy (Italy), including 1M pregnancies for the 2010-2023 period, and generate a new FAIR dataset of policy changes.

For the first time, the ER will combine both data sources in an interrupted time series analysis with control series to generate robust evidence on new environmental causes and their critical time windows for preterm births.

TinyTrend will present a best practice template for other Italian regions to follow that is transferable to generate new insights into other diseases with a significant environmental component and elusive aetiology.