Aim

Harnessing the power of real-world health data and evidence synthesis from pre-conception to childhood to inform health improvements at population-level.

Leads – Professor Laila Tata and Dr Lisa Szatkowski

Professor Laila Tata

Laila’s research in applied epidemiology includes drug safety, healthcare service use, consequences of chronic illness, risk prediction and health inequalities mainly through the use of real-world health data that are routinely collected on large populations: Linked general practice databases (Clinical Practice Research Datalink (CPRD), The Health Improvement Network (THIN) database, QResearch); national level Hospital Episode Statistics (HES and Maternity HES); national disease registry data linked across multiple sources; routine US CDC and Swedish Registry Data. Her perinatal research has a particular focus on pregnancy, maternal and child health.

Dr Lisa Szatkowski

Associate Professor in Medical Statistics, Lisa has a background in human geography, demography, epidemiology and medical statistics. Her work utilises expertise in managing and analysing quantitative data, ranging from relatively small local survey datasets to extremely large datasets of routinely collected primary and secondary care electronic patient record data. Her research interests lie in using these data sources to study the care of preterm infants and their immediate and longer-term health outcomes. She teaches epidemiology and statistics to undergraduate medical students and postgraduate students on the University of Nottingham’s Master of Public Health course.

Research Focus

1. Pre-conception and pregnancy. Through linkage across large national-level primary care and secondary care health records and evidence synthesis, we assess how risk factors before and during pregnancy may modify maternal and child health outcomes, such as congenital anomalies and adverse events at birth. We develop large population-based cohorts of linked maternal-child records to assess healthcare use across pregnancy, including the consequences of acute and chronic illnesses, socioeconomic and lifestyle factors, and medication use in pregnancy.
   
   
2. Newborn and early life. We are harnessing the power of large datasets of electronic patient record data and novel methods of analysis to understand how best to care for newborn infants, particularly those who require neonatal intensive care, and how health and development in early life, and into childhood and beyond, are influenced by experiences in-utero and as a newborn. Interests include pharmacoepidemiology and drug safety, neonatal nutrition, respiratory health and using artificial intelligence and machine learning to predict adverse outcomes.