Hospitalisations with infections related to antimicrobial-resistant bacteria from the French nationwide hospital discharge database, 2016

Abstract

Massive use of antibiotics has led to increased bacterial resistance to these drugs, making infections more difficult to treat. Few studies have assessed the overall antimicrobial resistance (AMR) burden, and there is a paucity of comprehensive data to inform health policies. This study aims to assess the overall annual incident number of hospitalised patients with AMR infection in France, using the National Hospital Discharge database. All incident hospitalisations with acute infections in 2016 were extracted. Infections which could be linked with an infecting microorganism were first analysed. Then, an extrapolation of bacterial species and resistance status was performed, according to age class, gender and infection site to estimate the total number of AMR cases. Resistant bacteria caused 139 105 (95% CI 127 920–150 289) infections, resulting in a 12.3% (95% CI 11.3–13.2) resistance rate. ESBL-producing Enterobacteriaceae and methicillin-resistant Staphylococcus aureus were the most common resistant bacteria (>50%), causing respectively 49 692 (95% CI 47 223–52 142) and 19 493 (95% CI 15 237–23 747) infections. Although assumptions are needed to provide national estimates, information from PMSI is comprehensive, covering all acute bacterial infections and a wide variety of microorganisms.

Costs and Outcomes of 1-year post-discharge care trajectories of patients admitted with infection due to antibiotic-resistant bacteria

“…As reported in a previous study, 9 numerous hospitalizations with an infection PD are not associated with an identified infecting microorganism code. Two groups were then defined, one with at least one microorganism recorded and a second without.…”

“…(2019; Supplementary Material: Table 1 ). 9 In case of multiple infections or multiple organisms or resistance status, the infection cannot be properly identified since the database does not allow linking an infection with the responsible bacteria or bacteria with their resistance status, except for some specific combined codes (such as methicillin-resistant S.aureus , MRSA). To reduce the number of stays excluded because of unrecorded microorganism/resistance pair, a previously elaborated algorithm 9 was used to retain some of such hospitalizations in the selected population.…”

“…9 In case of multiple infections or multiple organisms or resistance status, the infection cannot be properly identified since the database does not allow linking an infection with the responsible bacteria or bacteria with their resistance status, except for some specific combined codes (such as methicillin-resistant S.aureus , MRSA). To reduce the number of stays excluded because of unrecorded microorganism/resistance pair, a previously elaborated algorithm 9 was used to retain some of such hospitalizations in the selected population. In the situation where an infection has several identified organisms, resistance was attributed to the most relevant bacteria identified: methicillin resistance was assigned to S. aureus , extended spectrum β-lactamases (ESBL) resistance to Enterobacteriaceae or other Gram-negative bacteria and vancomycin resistance to Enterococcus .…”

“…Six infections sites were retained, including: (1) Urinary and genital tract, (2) Skin and soft tissue, (3) Lower respiratory tract, (4) Gastrointestinal and abdominal, (5) Bone, joint and Device-Associated, (6) Heart and mediastinum. 9 Follow-up data of the selected population were described with mean ( ± standard deviation, SD) cumulative lengths of stay (c-LOS), including patient admission in day-care center ( < 1 day) considered as full day hospitalization; when necessary, length of stay was censored at 1 year from the day of discharge from the index hospitalization.…”

“…Due to the lack of recorded microorganisms for some stays, an extrapolation was performed assuming that these stays were comparable to the selected population with microbiologically documented infection regarding sex, age and infection sites. 9 Thus AMR distribution and repartition of care sequence patterns was extrapolated to the number of patients in each care trajectory groups as well as patient characteristics. 9 Data management was performed using SAS Enterprise Guide v7.13 and sequence analyses using Rstudio 1.1.456 with package TramineR.…”