| Full text | |
| Author(s): |
Vilches, T. N.
[1]
;
Bonesso, M. F.
[2]
;
Guerra, H. M.
[2]
;
Fortaleza, C. M. C. B.
[2]
;
Park, A. W.
[3, 4]
;
Ferreira, C. P.
[1]
Total Authors: 6
|
| Affiliation: | [1] Sao Paulo State Univ UNESP, Dept Biostat, Inst Biosci, BR-18618689 Botucatu, SP - Brazil
[2] Univ Estadual Paulista, Fac Med Botucatu, Dept Doencas Trop, Botucatu, SP - Brazil
[3] Univ Georgia, Odum Sch Ecol, Athens, GA 30602 - USA
[4] Univ Georgia, Dept Infect Dis, Athens, GA 30602 - USA
Total Affiliations: 4
|
| Document type: | Journal article |
| Source: | EPIDEMICS; v. 26, p. 104-115, MAR 2019. |
| Web of Science Citations: | 1 |
| Abstract | |
Healthcare-associated infections cause significant patient morbidity and mortality, and contribute to growing healthcare costs, whose effects may be felt most strongly in developing countries. Active surveillance systems, hospital staff compliance, including hand hygiene, and a rational use of antimicrobials are among the important measures to mitigate the spread of healthcare-associated infection within and between hospitals. Klebsiella pneumoniae is an important human pathogen that can spread in hospital settings, with some forms exhibiting drug resistance, including resistance to the carbapenem class of antibiotics, the drugs of last resort for such infections. Focusing on the role of patient movement within and between hospitals on the transmission and incidence of enterobacteria producing the K. pneumoniae Carbapenemase (KPC, an enzyme that inactivates several antimicrobials), we developed a metapopulation model where the connections among hospitals are made using a theoretical hospital network based on Brazilian hospital sizes and locations. The pathogen reproductive number, R(0 )that measures the average number of new infections caused by a single infectious individual, was calculated in different scenarios defined by both the links between hospital environments (regular wards and intensive care units) and between different hospitals (patient transfer). Numerical simulation was used to illustrate the infection dynamics in this set of scenarios. The sensitivity of R-0 to model input parameters, such as hospital connectivity and patient-hospital staff contact rates was also established, highlighting the differential importance of factors amenable to change on pathogen transmission and control. (AU) | |
| FAPESP's process: | 16/23738-3 - Construction and analysis of epidemiological models |
| Grantee: | Cláudia Pio Ferreira |
| Support type: | Scholarships abroad - Research |