Katia Iskandar1,2,3, Christine Roques4,5, Souheil Hallit6,7, Rola Husni-Samaha8,9, Natalia Dirani10, Rana Rizk6,11, Rachel Abdo6,12, Yasmina Yared13, Matta Matta14, Inas Mostafa15, Roula Matta16, Pascale Salameh6,16,12, Laurent Molinier17. 1. Department of Mathématiques Informatique et Télécommunications, Université Toulouse III, Paul Sabatier, INSERM, UMR 1295, F-31000, Toulouse, France. katia_iskandar@hotmail.com. 2. INSPECT-LB: Institut National de Santé Publique, d'Épidémiologie Clinique et de Toxicologie-Liban, Beirut, Lebanon. katia_iskandar@hotmail.com. 3. Department of Pharmacy, Lebanese University, Mount Lebanon, Beirut, Lebanon. katia_iskandar@hotmail.com. 4. Department of Bioprocédés et Systèmes Microbiens, Laboratoire de Génie Chimique, Université Paul Sabatier Toulouse III, UMR 5503, Toulouse, France. 5. Department of Bactériologie-Hygiène, Centre Hospitalier Universitaire, Toulouse, Hôpital Purpan, Toulouse, France. 6. INSPECT-LB: Institut National de Santé Publique, d'Épidémiologie Clinique et de Toxicologie-Liban, Beirut, Lebanon. 7. Faculty of Medicine and Medical Sciences, Holy Spirit University of Kaslik (USEK), Jounieh, Lebanon. 8. Department of Medicine, Lebanese American University, Byblos, Lebanon. 9. Department of Infection Control, Lebanese American University Medical Center, Beirut, Lebanon. 10. Department of Infectious Diseases, Dar El Amal University Hospital, Baalbeck, Lebanon. 11. Department of Health Services Research, School CAPHRI, Care and Public Health Research Institute, Maastricht University, 6200, MD, Maastricht, The Netherlands. 12. Medical School, University of Nicosia, Nicosia, Cyprus. 13. Department of Clinical Pharmacy, Geitaoui Hospital, Beirut, Lebanon. 14. Department of Medicine, St Joseph University, Beirut, Lebanon. 15. Department of Quality and Safety, Nabatieh Governmental Hospital, Nabatieh, Lebanon. 16. Department of Pharmacy, Lebanese University, Mount Lebanon, Beirut, Lebanon. 17. Department of Medical Information, Centre Hospitalier Universitaire, INSERM, UMR 1027, Université Paul Sabatier Toulouse III, F-31000, Toulouse, France.
Abstract
BACKGROUND: Our aim was to examine whether the length of stay, hospital charges and in-hospital mortality attributable to healthcare- and community-associated infections due to antimicrobial-resistant bacteria were higher compared with those due to susceptible bacteria in the Lebanese healthcare settings using different methodology of analysis from the payer perspective . METHODS: We performed a multi-centre prospective cohort study in ten hospitals across Lebanon. The sample size consisted of 1289 patients with documented healthcare-associated infection (HAI) or community-associated infection (CAI). We conducted three separate analysis to adjust for confounders and time-dependent bias: (1) Post-HAIs in which we included the excess LOS and hospital charges incurred after infection and (2) Matched cohort, in which we matched the patients based on propensity score estimates (3) The conventional method, in which we considered the entire hospital stay and allocated charges attributable to CAI. The linear regression models accounted for multiple confounders. RESULTS: HAIs and CAIs with resistant versus susceptible bacteria were associated with a significant excess length of hospital stay (2.69 days [95% CI,1.5-3.9]; p < 0.001) and (2.2 days [95% CI,1.2-3.3]; p < 0.001) and resulted in additional hospital charges ($1807 [95% CI, 1046-2569]; p < 0.001) and ($889 [95% CI, 378-1400]; p = 0.001) respectively. Compared with the post-HAIs analysis, the matched cohort method showed a reduction by 26 and 13% in hospital charges and LOS estimates respectively. Infections with resistant bacteria did not decrease the time to in-hospital mortality, for both healthcare- or community-associated infections. Resistant cases in the post-HAIs analysis showed a significantly higher risk of in-hospital mortality (odds ratio, 0.517 [95% CI, 0.327-0.820]; p = 0.05). CONCLUSION: This is the first nationwide study that quantifies the healthcare costs of antimicrobial resistance in Lebanon. For cases with HAIs, matched cohort analysis showed more conservative estimates compared with post-HAIs method. The differences in estimates highlight the need for a unified methodology to estimate the burden of antimicrobial resistance in order to accurately advise health policy makers and prioritize resources expenditure.
BACKGROUND: Our aim was to examine whether the length of stay, hospital charges and in-hospital mortality attributable to healthcare- and community-associated infections due to antimicrobial-resistant bacteria were higher compared with those due to susceptible bacteria in the Lebanese healthcare settings using different methodology of analysis from the payer perspective . METHODS: We performed a multi-centre prospective cohort study in ten hospitals across Lebanon. The sample size consisted of 1289 patients with documented healthcare-associated infection (HAI) or community-associated infection (CAI). We conducted three separate analysis to adjust for confounders and time-dependent bias: (1) Post-HAIs in which we included the excess LOS and hospital charges incurred after infection and (2) Matched cohort, in which we matched the patients based on propensity score estimates (3) The conventional method, in which we considered the entire hospital stay and allocated charges attributable to CAI. The linear regression models accounted for multiple confounders. RESULTS:HAIs and CAIs with resistant versus susceptible bacteria were associated with a significant excess length of hospital stay (2.69 days [95% CI,1.5-3.9]; p < 0.001) and (2.2 days [95% CI,1.2-3.3]; p < 0.001) and resulted in additional hospital charges ($1807 [95% CI, 1046-2569]; p < 0.001) and ($889 [95% CI, 378-1400]; p = 0.001) respectively. Compared with the post-HAIs analysis, the matched cohort method showed a reduction by 26 and 13% in hospital charges and LOS estimates respectively. Infections with resistant bacteria did not decrease the time to in-hospital mortality, for both healthcare- or community-associated infections. Resistant cases in the post-HAIs analysis showed a significantly higher risk of in-hospital mortality (odds ratio, 0.517 [95% CI, 0.327-0.820]; p = 0.05). CONCLUSION: This is the first nationwide study that quantifies the healthcare costs of antimicrobial resistance in Lebanon. For cases with HAIs, matched cohort analysis showed more conservative estimates compared with post-HAIs method. The differences in estimates highlight the need for a unified methodology to estimate the burden of antimicrobial resistance in order to accurately advise health policy makers and prioritize resources expenditure.
Entities:
Keywords:
Antimicrobial resistance; Healthcare cost; In-hospital mortality; Length of stay
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