E-contact tracing of hospital infections – validation of SIRSZ automatic risk assessment
DOI:
https://doi.org/10.15503/emet2020.51.70Keywords:
e-health, hospital infections, tele-medine, epidemiological modelling, support decision system, epidemiological intelligenceAbstract
Aim. Contact networks play a crucial role in infectious disease propagation and position in the network mediate risk of acquiring or sending infections. We studied the spread of hospital-associated infections through computer simulations and validated our ‘computer assisted’ risk assessment with ‘human’ risk assessment in a prospective study.
Concept. We collected time-varying structure of contacts and covariates reconstructed from Polish Hospitals:
1. The organisational structure is mapped by a set of questionnaires, CAD maps integration, functional paths annotation and local vision. It is done mostly by surveys within medical staff through an interactive web application.
2. The Cohabitation layer processes data from the registry of patient admissions and discharges from each hospital unit (wards, clinics, etc.) and medical shift register. With simulated infection paths, we were able to compute network centrality measures for patients. We obtained the risk of getting infected, based on the patient’s incoming connections, and the risk of spreading infections resulting from outgoing connections. We compare various standard centrality measures – position of patients and staff in contact networks (‘computer assisted’ risk assessment) of both contacts and paths networks, with a predictor of ‘human’ risk perception (based on 190 patients).
Results. We showed that the best predictor of HAI risk is Adjusted Rage Rank on paths (r= 0.42, p < 0.01). However, surprisingly good predictive power in risk assessment was found in the betweenness centrality of the underlying network of contacts (r = 0.30, p < 0.01).
Conclusion: We conclude that epidemiology of a given pathogen in a given place and time could be explained only with the contact network only to a large extent. However, further possibility of the collection, processing and storage of the data on individual persons, translated to mathematical modelling could lead in future to satisfactory improvement in risk assessment.
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References
Adlassnig, K-P., et al. (2012). Artifi cial-Intelligence-Based Hospital-Acquired Infection Control digitalized data concerning the patient’s medical history, In R.G. Bushko (Ed.), Strategy for the Future of Health (pp. 103-110). IOS Press.
Ahmed, N., et al. (2020). A survey of COVID-19 contact tracing apps. IEEE access, 8, 134577-134601. http://doi.org/10.1109/ACCESS.2020.3010226
Barrat, A., Cattuto, C., Colizza, V., Isella, L., Rizzo, C., Tozzi, A.E. & Van den Broeck, W. (2010). Wearable sensor networks for measuring face-to-face contact patterns in healthcare settings. In International Conference on Electronic Healthcare (pp. 192-195). Springer. http://doi.org/10.1007/978-3-642-23635-8_24
Becker, A. D., Grantz, K. H., Hegde, S. T., Bérubé, S., Cummings, D. A., & Wesolowski, A. (2021). Develo pment and dissemination of infectious disease dynamic transmission models during the COVID-19 pandemic: what can we learn from other pathogens and how can we move forward?, The Lancet Digital Health, 3(1), e41-e50. http://doi.org/10.1016/S2589-7500(20)30268-5
Belik, V., Geisel T., & Brockmann, D. (2011). Natural human mobility patterns and
spatial spread of infectious diseases, Physical Review X, 1(1), 011001. https://doi.org/10.1103/PhysRevX.1.011001
Belik, V., Hovel P., & Mikolajczyk R. (2016). Control of Epidemics on Hospital Networks. In Control of Self-Organizing Nonlinear Systems (pp. 431-440). Springer. https://doi.org/10.1007/978-3-319-28028-8_22
Belik, V., Karch, A., Hövel, P., & Mikolajczyk, R. (2017). Leveraging Topological and Temporal Structure of Hospital Referral Networks for Epidemic Control. In Temporal Network Epidemiology (pp. 199-214). Springer. https://doi.org/10.1007/978-981-10-5287-3_9
Cassini, A., et al. (2016). Burden of six Healthcare-associated infections on european population health: estimating incidence-based disability-adjusted life years through a population prevalence-based modelling study. PLoS medicine, 13(10), e1002150. https://doi.org/10.1371/journal.pmed.1002150
Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. International Journal of Complex Systems, 1695(5), 1-9.
Curtis, D. E., Hlady, C. S., Pemmaraju, S.V., Polgreen, P. M., & Segre, A. M. (2010). Modeling and estimating the spatial distribution of healthcare workers. In Proceedings of the 1st ACM International Health Informatics Symposium, (pp. 287-296). ACM. https://doi.org/10.1145/1882992.1883034
Czekaj, Ł., Domaszewicz, J., Jarynowski, A., Kitłowski, R. & Doboszyńska, A. (2021). Validation and usability of AIDMED - telemedical system for cardiological and pulmonary disease. [Manuscript accepted for publication in e-methodology]
de Bruin, J.S., Seeling, W., & Schuh, C. (2014). Data use and effectiveness in electronic surveillance of healthcare associated infections in the 21st century: a systematic review. Journal of the American Medical Informatics Association, 21(5), 942-951. https://doi.org/10.1136/amiajnl-2013-002089
Deptuła, A., Trejnowska, E., Ozorowski, T., & Hryniewicz, W. (2015). Risk factors for healthcare-associated infection in light of two years of experience with the ECDC point prevalence survey of healthcare-associated infection and antimicrobial use in Poland.
Journal of Hospital Infection, 90(4), 310-315. https://doi.org/10.1016/j.jhin.2015.03.005
Donker, T., Wallinga, K., & Grundmann, H. (2010). Patient referral patterns and the spread of hospital-acquired infections through national health care networks. PLoS Comput Biol, 6(3); e1000715. https://doi.org/10.1371/journal.pcbi.1000715
Donker, T., et al. (2021). Navigating hospitals safely through the COVID-19 epidemic tide: predicting case load for adjusting bed capacity. Infection Control & Hospital Epidemiology, 42(6), 653-658. https://doi.org/10.1017/ice.2020.464
ECDC. (2020). Contact tracing: Public health management of persons, including healthcare workers, having had contact with COVID-19 cases in the European Union-second update.
Eggo, R.M., Cauchemez, S., & Ferguson, N.M. (2011). Spatial dynamics of the 1918 influenza pandemic in England, Wales and the United States. Journal of The Royal Society Interface, 8(55), 233-243. https://doi.org/10.1017/10.1098/rsif.2010.0216
EMerGe-Net. (2017). Effectiveness of infection control strategies against intra- and interhospital transmission of MultidruG-resistant Enterobacteriaceae. Retrieved August 21, 2021, from https://www.medizin.uni-halle.de/index.php?id=7761.
Furuya-Kanamori, L., Doi SA., Smith, P.N., Bagheri, N., Clements, A.C., & Sedrakyan A. (2017). Hospital effect on infections after four major surgical procedures: outlier and volume and outcome analysis using all-inclusive state data. Journal of Hospital Infection. 97(2), 115-121. https://doi.org/10.1016/j.jhin.2017.05.021
Grabowski, A., & Rosińska, M. (2012). The relationship between human behavior and the process of epidemic spreading in a real social network. The European Physical Journal B, 85(7), 248. https://doi.org/10.1140/epjb/e2012-20250-1
Grabowski A., & Jarynowski, A. (2016). Rumor propagation in temporal contact network from Polish polls, In 2016 Third European Network Intelligence Conference (ENIC) (pp. 85-89). IEEE. https://doi.org/10.1109/ENIC.2016.020
Grabska, E., Łachwa, A., & Ślusarczyk, G. (2012). New visual languages supporting design of multi-storey buildings. Advanced Engineering Informatics, 26(4), 681-690.
Grundmann H., & Hellriegel B. (2006). Mathematical modelling: a tool for hospital infection control. The Lancet infectious diseases, 6(1), 39-45. https://doi.org/10.1016/j.aei.2012.03.009
health-i-care. (2017). Innovations for safer healthcare. Retrieved August 30, 2019, from http://www.health-i-care.eu.
Heslop D. J., Chughtai A. A., Bui C. M., & MacIntyre C. R. (2017). Publicly available software tools for decision-makers during an emergent epidemic-Systematic evaluation of utility and usability. Epidemics, 12, 1-2. https://doi.org/10.1016/j.epidem.2017.04.002
Jaramillo C., Taboada M., Epelde F., Rexachs D., & Luque E. (2015). Agent based model and simulation of MRSA transmission in emergency departments. Procedia Computer Science, 51, 443-452. https://doi.org/10.1016/j.procs.2015.05.267
Jarynowski, A. (2014). Obliczeniowe Nauki Społeczne w Praktyce. Wydawnictwo Niezależne.
Jarynowski, A., & Liljeros, F. (2015). Contact networks and the spread of MRSA in Stockholm hospitals. In Second European Network Intelligence Conference (ENIC), (pp. 150-154), IEEE. https://doi.org/10.1109/ENIC.2015.30.
Jarynowski, A., & Grabowski, A. (2015). Modelowanie epidemiologiczne dedykowane Polsce [Mathematical modelling dedicated to Poland], „Portal CZM” (Centrum Zastosowań Matematyki), ISBN 978-83-942807-9-6
Jarynowski, A., Marchewka, D., & Grabowski, A. (2017). SIRSZ: System Informatyczny Redukcji Szpitalnych Zakażeń Raport z Badania. IBI.
Jarynowski, A. & Grabowski, A. (2018). Infl uence of Temporal Aspects and AgeCorrelations on the Process of Opinion Formation Based on Polish Contact Survey. In International Conference on Social Informatics (pp. 118-128). Springer. https://doi.org/10.1007/978-3-030-01159-8_11
Jarynowski, A., Marchewka, D., & Buda A. (2018). Internet-assisted risk assessment of infectious diseases in women sexual and reproductive health. E-methodology, 4, 135-144. https://doi.org/
Jarynowski, A. & Grabowski, A. (2019). Niewykorzystany potencjał systemów informatycznych w epidemiologii zakażeń szpitalnych w Polsce. Collegium of Economic Analysis Annals, 56, 261-273.
Kontakt. (2020). Introducing Enterprise ‘COVID-19 Contact Tracer’ Webapp to Prevent
Infections Spread in Corporate. Retrieved August 21, 2021, from https://kontakt.io/
blog/introducing-enterprise-covid-19-contact-tracer-webapp-to-prevent-infectionsspread-in-corporate-real-estate/
Kitsak, M., Gallos, L.K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H.E., & Makse, H.A. (2010). Identifi cation of infl uential spreaders in complex networks. Nature physics; 6(11), 888. https://doi.org/10.1038/nphys1746
Knight, G. M., Davies, N. G., Colijn, C., Coll, F., Donker, T., Gifford, D. R., ... & Atkins, K.
E. (2019). Mathematical modelling for antibiotic resistance control policy: do we know
enough?. BMC infectious diseases, 19(1), 1-9. https://doi.org/10.1186/s12879-019-4630-y
Landherr, A., Bettina, F., & Heidemann, J. (2010). A critical review of centrality measures in social networks. Business and Information Systems Engineering, 2(6), 371-385. https://doi.org/10.1007/s12599-010-0127-3
Leibovici, L., Kariv, G., & Paul, M. (2013). Long-term survival in patients included
in a randomized controlled trial of TREAT, a decision support system for antibiotic treatment. Journal of Antimicrobial Chemotherapy, 68(11), 2664-2666. https://doi.org/10.1093/jac/dkt222
Lenz, H, et al. (2016). Disease spread through animal movements: a static and temporal
network analysis of pig trade in Germany. PloS one, 11(5), e0155196. https://doi.org/10.1371/journal.pone.0155196
Liljeros, F., Giesecke, L., & Holme, P. (2007). The contact network of inpatients in a regional healthcare system. A longitudinal case study. Mathematical Population Studies,14(4), 269-284. https://doi.org/10.1080/08898480701612899
Obadia, T., et al. (2015). I-Bird Study Group. Detailed contact data and the dissemination of Staphylococcus aureus in hospitals. PLoS Comput Biol, 11(3), e1004170. https://doi.org/10.1371/journal.pcbi.1004170
Ohst. J., et al. (2014). The network positions of methicillin resistant Staphylococcus
aureus affected units in a regional healthcare system. EPJ Data Science, 3, 1-15. https://doi.org/10.1140/epjds/s13688-014-0029-6
O’Neill, J. (2016). The review on antimicrobial resistance. Tackling drug-resistant infections globally: fi nal report and recommendations. Welcome Trust.
Paradowski, M. B., Jarynowski, A., Czopek, K., & Jelińska, M. (2021). Peer Interactions
and Second Language Learning: The Contributions of Social Network Analysis in
Study Abroad versus At-Home Environments. In: R. Mitchell & H. Tyne (Eds.), Language, Mobility and Study Abroad in the Contemporary European Context. Taylor & Francis. https://doi.org/10.4324/9781003087953-8
Piszczek, R., Karbowski, A., Skawina, I. R., Wróbel, E., Jabłońska, Z., Kołodziej, J., & Wróbel, M. (2021). Rola zakażeń układu moczowego ZUM w aspekcie opieki nad osobami starszymi [The role of urinary tract infections UTI in the aspect of care for the elderly]. In: J. Mesjasz & I. Skawina (Eds.), Współczesne Wyzwania Gerontologii Człowiek w pełni – podejście holistyczne [Contemporary Challenges of Gerontology A fully human being - a holistic approach]. SAN.
Mikolajczyk, R.T., Akmatov, M. K., Rastin, S., & Kretzschmar, M. (2008). Social contacts
of school children and the transmission of respiratory-spread pathogens. Epidemiology Infection. 136(6), 813-822. https://doi.org/10.1017/S0950268807009181
Muscatello, DJ., Chughtai, AA., Heywood, A., Gardner, LJ., Heslop, DJ., Macintyre, CR. (2017). Translating real-time infectious disease modeling into routine public health practice, Emerging Infectious Diseases, 23(5), e161720. https://doi.org/10.3201/eid2305.161720
Rocha, L. E., Singh, V., Esch, M., Lenaerts, T., Liljeros, F., & Thorson, A. (2020). Dynamic
contact networks of patients and MRSA spread in hospitals. Scientifi c reports, 10(1), 1-10. https://doi.org/10.1038/s41598-020-66270-9
Rodríguez-Rojas, A., Makarova, O., & Rolff, J. (2014). Antimicrobials, stress and mutagenesis. PLoS pathogens, 10(10), e1004445. https://doi.org/ 10.1371/journal.ppat.1004445
Różańska, A., Jarynowski, A., Kopeć-Godlewska, K., Wójkowska-Mach, J., MisiewskaKaczur, A., Lech, M., ... & Domańska, J. (2018). Does surgical site infection after Caesarean section in Polish hospitals refl ect high-quality patient care or poor postdischarge
surveillance? Results from a 3-year multicenter study. American journal of infection control, 46(1), 20-25. https://doi.org/10.1016/j.ajic.2017.07.025
Rustam, A. H. (2006). Epidemic Network and Centrality. Masteroppgave, University of Oslo.
RPL „Rządowy Proces Legislacyjny RP - Konsultacje Społeczne”. (2017). Projekt ustawy o zmianie ustawy o zapobieganiu oraz zwalczaniu zakażeń i chorób zakaźnych u ludzi [Draft act amending the act on preventing and combating infections and infectious diseases in humans ], UD90, 30.10.2017.
Sadkowska-Todys, M., Zieliński, A., Czarkowski, M. P. (2017). Infectious diseases in Poland in 2015. Przegl Epidemiol, 69, 329-334.
Sadsad, R., Sintchenko, V., McDonnell, GD., Gilbert, GL. (2013). Effectiveness of hospital-wide methicillin-resistant Staphylococcus aureus (MRSA) infection control policies differs by ward specialty. PloS one, 8(12), e83099. https://doi.org/10.1371/journal.pone.0083099
Sadsad, R., O’Sullivan, M., Sintchenko, V., & Gilbert, G. (2016). Genotyping and genome sequencing for hospital outbreak surveillance and investigation. Infection, Disease Health. 21(3), 128. https://doi.org/10.1016/j.idh.2016.09.054
Seweryn, M. (2018). Zakażenia szpitalne w polskich placówkach. [Healthcare Associated infection in Polish hospitals]. Menedżer Zdrowia, 2018(6), 20-21.
Smieszek, T., Burri, E. U., Scherzinger, R., & Scholz, R. W. (2012). Collecting close-contact social mixing data with contact diaries: reporting errors and biases. Epidemiology Infection, 140(4), 744-52. https://doi.org/10.1017/S0950268811001130
Stenhem, M, et al. (2006). Epidemiology of methicillin-resistant Staphylococcus aureus
(MRSA) in Sweden 2000-2003, increasing incidence and regional differences. Bmc Infectious Diseases, 6(1), 1-7.
Tahir, H., López-Cortés, L. E., Kola, A., Yahav, D., Karch, A., Xia, H., ... & Kretzschmar,
M. E. (2021). Relevance of intra-hospital patient movements for the spread of healthcare-associated infections within hospitals-a mathematical modeling study. PLoS computational biology, 17(2), e1008600. https://doi.org/10.1371/journal.pcbi.1008600
Teixeira, H., Norton, P., Azevedo, A., & Pina, M. F. (2015). Indoor geographical information system: an innovative approach to analyze health events inside a hospital. Poster at GEOMED 2015, Florence.
Ueno, T., & Masuda, N. (2008). Controlling nosocomial infection based on structure of hospital social networks. Journal of theoretical biology, 254(3), 655-666. https://doi.org/10.1016/j.jtbi.2008.07.001
Valdano, E., Poletto, C., Giovannini, A., Palma, D., Savini, L., & Colizza, V. (2015). Predicting epidemic risk from past temporal contact data. PLoS Comput Biol, 11(3), e1004152. https://doi.org/10.1371/journal.pcbi.1004152
Vitabile, S., et al. (2019). Medical data processing and analysis for remote health and
activities monitoring. In High-Performance Modelling and Simulation for Big Data Applications (pp. 186-220). Springer. https://doi.org/10.1007/978-3-030-16272-6_7
Vanhems, P., et al. (2013). Estimating potential infection transmission routes in hospital wards using wearable proximity sensors. PloS one, 8(9), e73970. https://doi.org/10.1371/journal.pone.0073970
Wasilewski, P. (2018). Zakażenia szpitalne-nierozwiązany problem w lecznictwie zamkniętym. [Hospital infections - unresolved problem in inpatient treatment] Kontrola Państwowa, 63(6), 75-88.
WHO. (2020). Digital tools for COVID-19 contact tracing: annex: contact tracing in the context of COVID-19. World Health Organization.
Wójkowska-Mach, J., et al. (2016). Zakażenia szpitalne w jednostkach opieki zdrowotnej,
[Hospital infections in healthcare setup]. PZWL.
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