Publications
2024
Improving equitable healthcare resource use: developing a neighbourhood district nurse needs index for staffing allocation.
Filipe, L., Piroddi, R., Baker, W., Rafferty, J., Buchan, I., & Barr, B. (2024). Improving equitable healthcare resource use: developing a neighbourhood district nurse needs index for staffing allocation.. BMC health services research, 24(1), 1362. doi:10.1186/s12913-024-11832-0
System-wide health needs segmentation: innovating integrated care for complex needs households
Piroddi, R., Baker, W., Daras, K., Buchan, I., Rafferty, J., Astbury, A., & Barr, B. (2024). System-wide health needs segmentation: innovating integrated care for complex needs households. In European Journal of Public Health Vol. 34. Oxford University Press (OUP). doi:10.1093/eurpub/ckae144.036
OP01 Trends in inequalities in self-harm in young people over the COVID-19 pandemic period. A population-based linkage study of half a million people in Cheshire and Merseyside between 2018 and 2022
Piroddi, R., Astbury, A., Baker, W., Buchan, I., Daras, K., Garcia-Finana, M., . . . Barr, B. (2024). OP01 Trends in inequalities in self-harm in young people over the COVID-19 pandemic period. A population-based linkage study of half a million people in Cheshire and Merseyside between 2018 and 2022. In SSM Annual Scientific Meeting (pp. A1.1-A1). BMJ Publishing Group Ltd. doi:10.1136/jech-2024-ssmabstracts.1
2023
Identifying households with the most complex needs
Piroddi, R., Barr, B., & Daras, K. (2023). Identifying households with the most complex needs [Computer Software]. Internet - Git Hub repository: Git Hub. Retrieved from https://github.com/cipha-uk/complex_households
Can you tell we care? Identifying unpaid carers using local authority and GP data
Knight, H., Peytrignet, S., Alcock, B., Brownrigg, A., Davies, A., Chisambi, M., . . . Tallack, C. (2023). Can you tell we care? Identifying unpaid carers using local authority and GP data. The Health Foundation. Retrieved from https://www.health.org.uk/publications/long-reads/can-you-tell-we-care
Evaluating the impact of using mobile vaccination units to increase COVID-19 vaccination uptake in Cheshire and Merseyside, UK: a synthetic control analysis.
Zhang, X., Tulloch, J. S. P., Knott, S., Allison, R., Parvulescu, P., Buchan, I. E., . . . Barr, B. (2023). Evaluating the impact of using mobile vaccination units to increase COVID-19 vaccination uptake in Cheshire and Merseyside, UK: a synthetic control analysis.. BMJ open, 13(10), e071852. doi:10.1136/bmjopen-2023-071852
Effects on mortality of shielding clinically extremely vulnerable patients in Liverpool, UK, during the COVID-19 pandemic
Filipe, L., Barnett, L. A., Piroddi, R., Buchan, I., Duckworth, H., & Barr, B. (2023). Effects on mortality of shielding clinically extremely vulnerable patients in Liverpool, UK, during the COVID-19 pandemic. PUBLIC HEALTH, 222, 54-59. doi:10.1016/j.puhe.2023.06.037
2022
R code for NDL report: Improving children and young people's mental health services
Barr, B., O'Brien, J., & Piroddi, R. (2022). R code for NDL report: Improving children and young people's mental health services [Computer Software]. Retrieved from https://github.com/HFAnalyticsLab/NDL_CYPMH_Liverpool_Wirral
R code for NDL topic 2: Children and Young People Mental Health in Liverpool and Wirral
Barr, B., O'Brien, J., & Piroddi, R. (2022). R code for NDL topic 2: Children and Young People Mental Health in Liverpool and Wirral [Computer Software]. GitHub: The Health Foundation. Retrieved from https://github.com/HFAnalyticsLab/NDL_CYPMH_Liverpool_Wirral/tree/main/Report
Improving children and young people’s mental health services
Grimm, F., Alcock, B., Butler, J., Fernandez Crespo, R., Davies, A., Peytrignet, S., . . . Tallack, C. (2022). Improving children and young people’s mental health services. The Health Foundation. doi:10.37829/hf-2022-ndl1
Technical appendix: improving children and young people's mental health services
Grimm, F., Alcock, B., Butler, J., Davies, A., Fernandez Crespo, R., Peytrignet, S., . . . Tallack, C. (2022). Technical appendix: improving children and young people's mental health services. The Health Foundation. Retrieved from https://www.health.org.uk/sites/default/files/2022-07/technical_appendix_web.pdf
Children and young people mental health in Liverpool and Wirral between 2018 and 2021
Barr, B., O'Brien, J., & Piroddi, R. (n.d.). Children and young people mental health in Liverpool and Wirral between 2018 and 2021: Children and young people mental health in Liverpool and Wirral between 2018 and 2021 (NDL1_topic2_LW). GitHub. Retrieved from https://github.com/HFAnalyticsLab/NDL_CYPMH_Liverpool_Wirral/tree/main/Report
The impact of an integrated care intervention on mortality and unplanned hospital admissions in a disadvantaged community in England: a difference-in-differences study
Piroddi, R., Downing, J., Duckworth, H., & Barr, B. (2022). The impact of an integrated care intervention on mortality and unplanned hospital admissions in a disadvantaged community in England: A difference-in-differences study. HEALTH POLICY, 126(6), 549-557. doi:10.1016/j.healthpol.2022.03.009
SQL code to generically process secure MHSDS data
Fox, S., Piroddi, R., & Jones, K. (2022). SQL code to generically process secure MHSDS data [Computer Software]. GitHub. Retrieved from https://github.com/HFAnalyticsLab/MHSDS-cleaning-pipeline
2021
Excess mortality in Glasgow: further evidence of ‘political effects’ on population health
Schofield, L., Walsh, D., Bendel, N., & Piroddi, R. (2021). Excess mortality in Glasgow: further evidence of ‘political effects’ on population health. Public Health, 201, 61-68. doi:10.1016/j.puhe.2021.10.004
Computer code for Networked Data Lab: Hospital use for clinically extremely vulnerable population, the impact of the pandemic
Networked Data Lab partners, N. D. L. (2021). Computer code for Networked Data Lab: Hospital use for clinically extremely vulnerable population, the impact of the pandemic [Computer Software]. GitHub: The Health Foundation. Retrieved from https://github.com/HFAnalyticsLab/NDL_Output3_Hospital_care_CEV
Assessing the impact of COVID-19 on the clinically extremely vulnerable population
Hodgson, K., Butler, J. E., Davies, A., Houston, S., Marszalek, K., Peytrignet, S., . . . Deeny, S. (2021). Assessing the impact of COVID-19 on the clinically extremely vulnerable population. The Health Foundation. Retrieved from https://www.health.org.uk/
Assessing the impact of COVID-19 on the clinically extremely vulnerable population
Assessing the impact of COVID-19 on the clinically extremely vulnerable population (2021). . The Health Foundation. doi:10.37829/hf-2021-ndl01
Liverpool and Wirral antidepressant prescribing: January 2018 to February 2021
Chambers, S., Piroddi, R., & Barnett, L. (2021). Liverpool and Wirral antidepressant prescribing: January 2018 to February 2021: Liverpool and Wirral antidepressant prescribing: January 2018 to February 2021 (NDL1_Sat1). GitHub. Retrieved from https://github.com/
R code for NDL: The impact of Covid-19 on hospital utilisation by Clinically Extremely Vulnerable patients
Networked Data Lab partners, N. D. L. (2021). R code for NDL: The impact of Covid-19 on hospital utilisation by Clinically Extremely Vulnerable patients [Computer Software]. GitHub: The Health Foundation. Retrieved from https://github.com/HFAnalyticsLab/NDL_Output3_Hospital_care_CEV
How has hospital use by those clinically extremely vulnerable to Covid-19 been impacted by the pandemic?
Hodgson, K., Peytrignet, S., & Marszalek, K. (2021). How has hospital use by those clinically extremely vulnerable to Covid-19 been impacted by the pandemic?. The Health Foundation. Retrieved from https://www.health.org.uk/
R code for NDL: Comorbidities of CEV people from hospital admission records
Networked Data Lab, N. D. L. P. (2021). R code for NDL: Comorbidities of CEV people from hospital admission records [Computer Software]. GitHub: The Health Foundation. Retrieved from https://github.com/HFAnalyticsLab/NDL_Output2_Morbidity/tree/main/Analysis
Who was advised to shield from Covid-19?
Hodgson, K., & Peytrignet, S. (2021). Who was advised to shield from Covid-19?. The Health Foundation. Retrieved from https://www.health.org.uk/
Networked Data Lab: Demographic variations across Britain among those advised to shield from Covid-19
Networked Data Lab partners, N. D. L. (2021). Networked Data Lab: Demographic variations across Britain among those advised to shield from Covid-19 [Computer Software]. GitHub: The Health Foundation. Retrieved from https://github.com/HFAnalyticsLab/NDL_Output1_Demographics
2020
The Networked Data Lab: Statistical analysis plan for a descriptive analysis of clinically extremely vulnerable people during COVID-19
Networked Data Lab parteners including, N. D. L., & Piroddi, R. (2020). The Networked Data Lab: Statistical analysis plan for a descriptive analysis of clinically extremely vulnerable people during COVID-19: The Networked Data Lab:Statistical analysis planfor a descriptive analysisof clinically extremelyvulnerable peopleduring COVID-19 (NDL1_01_sap). On-line. Retrieved from https://www.health.org.uk/
Ethnicity and Outcomes from COVID-19: The ISARIC CCP-UK Prospective Observational Cohort Study of Hospitalised Patients
Harrison, E., Docherty, A., Barr, B., Buchan, I., Carson, G., Drake, T., . . . Investigators, I. (2020). Ethnicity and Outcomes from COVID-19: The ISARIC CCP-UK Prospective Observational Cohort Study of Hospitalised Patients. doi:10.2139/ssrn.3618215
Responding to COVID-19 in the Liverpool City Region. COVID-19: How Modelling is Contributing to the Merseyside Response.
Alexiou, A., Ashton, M., Barr, B., Buchan, I., O’Flaherty, M., Jewell, C., . . . Sheard, S. (n.d.). Responding to COVID-19 in the Liverpool City Region. COVID-19: How Modelling is Contributing to the Merseyside Response. (Policy Briefing 003). Heseltine Institute for Public Policy, Practice and Place.
2019
Using Manifold Embedding for Automatic Threat Detection: An Alternative Machine Learning Approach
Piroddi, R., Griffith, E., Goulermas, J. Y. I., Maskell, S., & Ralph, J. (2019, September 9). Using Manifold Embedding for Automatic Threat Detection: An Alternative Machine Learning Approach. In British Machine Vision Conference. Cardiff.
2018
Comparing interrelationships between features and embedding methods for multiple-view fusion
Piroddi, R., Goulermas, Y., Maskell, S., & Ralph, J. (2018). Comparing interrelationships between features and embedding methods for multiple-view fusion. In 2018 21ST INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) (pp. 1503-1510). Retrieved from https://www.webofscience.com/
Comparing Interrelationships Between Features and Embedding Methods for Multiple-View Fusion.
Piroddi, R., Goulermas, J. Y., Maskell, S., & Ralph, J. F. (2018). Comparing Interrelationships Between Features and Embedding Methods for Multiple-View Fusion.. In FUSION (pp. 1-5). IEEE. Retrieved from https://ieeexplore.ieee.org/xpl/conhome/8442112/proceeding
2017
METHOD AND APPARATUS FOR ORDERING IMAGE
Knee, M. J., & Piroddi, R. (2017, September 8). 15/699,758, METHOD AND APPARATUS FOR ORDERING IMAGE. US.
Image processing with segmentation using directionally-accumulated difference-image pixel values
Piroddi, R. (2014, May 28). US 9648339, Image processing with segmentation using directionally-accumulated difference-image pixel values. US.
2013
Method and apparatus for modifying a moving image sequence
Piroddi, R., Knee, M., & Brooks, D. (2007, February 13). US 8442318, Method and apparatus for modifying a moving image sequence. U.S.A..
2010
Networks of Concepts and Ideas
Petrou, M., Tabacchi, M. E., & Piroddi, R. (2010). Networks of Concepts and Ideas. COMPUTER JOURNAL, 53(10), 1738-1751. doi:10.1093/comjnl/bxp113
Aspect Ratio Problems in Television Today and Some New Solutions
Knee, M., & Piroddi, R. (2010). Aspect Ratio Problems in Television Today and Some New Solutions. SMPTE MOTION IMAGING JOURNAL, 119(1), 35-41. doi:10.5594/J14873
2008
Gradient-adaptive normalized convolution
Argyriou, V., Vlachos, T., & Piroddi, R. (2008). Gradient-adaptive normalized convolution. IEEE SIGNAL PROCESSING LETTERS, 15, 489-492. doi:10.1109/LSP.2008.919836
2006
A method for single-stimulus quality assessment of segmented video
Piroddi, R., & Vlachos, T. (2006). A method for single-stimulus quality assessment of segmented video. EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING. doi:10.1155/ASP/2006/39482
On the structure of the mind
Petrou, M., & Piroddi, R. (2006). On the structure of the mind. In Proceedings of AISB'06: Adaptation in Artificial and Biological Systems Vol. 2 (pp. 60-63).
A simple framework for spatio-temporal video segmentation and delayering using dense motion fields
Piroddi, R., & Vlachos, T. (2006). A simple framework for spatio-temporal video segmentation and delayering using dense motion fields. IEEE SIGNAL PROCESSING LETTERS, 13(7), 421-424. doi:10.1109/LSP.2006.873143
Texture recognition from sparsely and irregularly sampled data
Petrou, M., Piroddi, R., & Talebpour, A. (2006). Texture recognition from sparsely and irregularly sampled data. COMPUTER VISION AND IMAGE UNDERSTANDING, 102(1), 95-104. doi:10.1016/j.cviu.2005.11.003
2005
Integrating human and machine perception to reverse-engineer the human vision system
Piroddi, R., & Petrou, M. (2005). Integrating human and machine perception to reverse-engineer the human vision system. In HUMAN & MACHINE PERCEPTION: COMMUNICATION, INTERACTION, AND INTEGRATION (pp. 119-129). doi:10.1142/9789812703095_0010
Texture interpolation using ordinary Kriging
Chandra, S., Petrou, M., & Piroddi, R. (2005). Texture interpolation using ordinary Kriging. In PATTERN RECOGNITION AND IMAGE ANALYSIS, PT 2, PROCEEDINGS Vol. 3523 (pp. 183-190). Retrieved from https://www.webofscience.com/
2004
Irregularly sampled scenes
Petrou, M., Piroddi, R., & Chandra, S. (2004). Irregularly sampled scenes. In IMAGE AND SIGNAL PROCESSING FOR REMOTE SENSING X Vol. 5573 (pp. 319-333). doi:10.1117/12.579601
Analysis of irregularly sampled data: A review
Piroddi, R., & Petrou, M. (2004). Analysis of irregularly sampled data: A review. ADVANCES IN IMAGING AND ELECTRON PHYSICS, VOL 132, 132, 109-165. doi:10.1016/S1076-5670(04)32003-3
2002
Multiple-Feature Spatiotemporal Segmentation of Moving Sequences using a Rule-based Approach
Piroddi, R., & Vlachos, T. (2002). Multiple-Feature Spatiotemporal Segmentation of Moving Sequences using a Rule-based Approach. In Procedings of the British Machine Vision Conference 2002 (pp. 33.1-33.10). British Machine Vision Association. doi:10.5244/c.16.33
Multiple-Feature Spatiotemporal Segmentation of Moving Sequences using a Rule-based Approach.
Piroddi, R., & Vlachos, T. (2002). Multiple-Feature Spatiotemporal Segmentation of Moving Sequences using a Rule-based Approach.. In P. L. Rosin, & A. D. Marshall (Eds.), BMVC (pp. 1-10). British Machine Vision Association. Retrieved from http://www.informatik.uni-trier.de/~ley/db/conf/bmvc/bmvc2002.html
Object-based segmentation of moving sequences using multiple features
Piroddi, R., & Vlachos, T. (2002). Object-based segmentation of moving sequences using multiple features. In DSP 2002: 14TH INTERNATIONAL CONFERENCE ON DIGITAL SIGNAL PROCESSING PROCEEDINGS, VOLS 1 AND 2 (pp. 547-550). Retrieved from https://www.webofscience.com/