As an NHS England statistician, Stefano contributes to the planning, design, analysis, interpretation and reporting of impact evaluations of health-care improvement programmes rolled out by the NHS in England.
Stefano collaborated on impact assessments of notably the Elective Hubs programme, of COVID Oximetry @home and of selected NHS New Care Models. His background lies in methodological and applied bio-statistics. His research interests span Bayesian inference, statistical meta-modelling, Probabilistic Sensitivity Analysis, Value of Information, Multi-Parameter Evidence Synthesis and causal inference.
Before joining the Improvement Analytics Unit, Stefano was a statistician for Public Health England and a visiting researcher at the MRC Biostatistics Unit. He previously held research statistician posts at the University of Sheffield and the University of York. Stefano holds a PhD and BSc (from La Sapienza University, IT) and an MSc (from Carnegie Mellon University, USA), all in Statistics. He is passionate about cycling, basketball, yoga, bakeries (!) and assorted nerdy stuff.
Tracey F., Ge X., Conti S., Briggs T.W.R., Bechberger E., Kennedy N., Renfrew J., Gray W.K., Clarke G.M. (2024): Elective Surgical Hubs for Helping the Recovery of Elective Services A National Survey in England. The Bulletin of the Royal College of Surgeons of England, 106(7):427-433; DOI: 10.1308/rcsbull.2024.135
Seamer P., Lloyd T., Conti S., O'Neill S. (2023): The Long-Term Impacts of an Integrated Care Programme on Hospital Utilisation among Older Adults in the South of England: A Synthetic Control Study. International Journal of Integrated Care, 23(3):1-11; DOI: 10.5334/ijic.6475
Clarke G. M., Conti S., Wolters A., Steventon A. (2019): Evaluating the Impact of Healthcare Interventions Using Routine Data. The BMJ Education; DOI: 10.1136/bmj.l2239. An introduction effectiveness evaluations of a health-care improvement programmes, with guidelines on their design, analysis, implementation and interpretation.
Jackson C.H., Presanis A.M., Conti S., De Angelis D. (2019): Value of Information: Sensitivity Analysis and Research Design in Bayesian Evidence Synthesis. Journal of the American Statistical Association, 114(528):1436-1449; DOI: 10.1080/01621459.2018.1562932
Lloyd T., Conti S., Santos F., Steventon A. (2019): Effect on Secondary Care of Providing Enhanced Support to Residential and Nursing Home Residents: A Subgroup Analysis of a Retrospective Matched Cohort Study. BMJ Quality & Safety, 28(7):534-546; DOI: 10.1136/bmjqs-2018-009130
Farah M., Birrell P. J., Conti S., De Angelis D. (2014): Bayesian Emulation and Calibration of a Dynamic Epidemic Model for H1N1 Influenza. The Journal of the American Statistical Association, 109(508):1398-1411; DOI: 10.1080/01621459.2014.934453. On the development of a statistical modelling framework for a computer simulator of the 2009 A/H1N1 influenza epidemic in London.
De Angelis D., Presanis A.M., Conti S., Ades A.E. (2014): Estimation of HIV Burden through Bayesian Evidence Synthesis. Statistical Science, 29(1):9-17; DOI: 10.1214/13-STS428
Conti S., Presanis A. M., van Veen M. G., Xiridou M., Donoghoe M. C., Rinder Stengaard A., De Angelis D. (2011): Modeling of the HIV Infection Epidemic in The Netherlands: A Multi-Parameter Evidence Synthesis Approach. The Annals of Applied Statistics, 5(4):2359-2384; DOI: 10.1214/11-AOAS488. On the development of a statistical modelling framework for synthesising, assessing and reconciling separate sources of evidence on HIV prevalence in The Netherlands in 2007.
Conti S., Claxton K. P. (2009): Dimensions of Design Space: A Decision-Theoretic Approach to Optimal Research Design. Medical Decision Making, 29(6):643-660; DOI: 10.1177/0272989X09336142. Illustration of a Value of Information-based approach to the determination of the sample sizes of a ensemble of medical studies for cost-effectiveness analysis purposes, with an application to a NICE-sponsored HTA of antibiotic treatments for adult seasonal flu.