Variable impact of an antimicrobial stewardship programme in three intensive care units: time-series analysis of 2012-2017 surveillance data.

BACKGROUND: Preprescription authorization (PPA) and postprescription review with feedback (PPRF) were successively implemented in 2012 and 2016 in our 1500-bed hospital. AIM: The impact of PPA and PPRF on carbapenems use and resistance levels of Pseudomonas aeruginosa was assessed in three intensive care units (ICUs). METHODS: Carbapenems use (in DDDs/1000 occupied bed-days) and resistance of P. aeruginosa (percentage of non-susceptible (I+R) isolates to imipenem and/or meropenem) were analysed using a controlled interrupted time-series method. Two periods were compared: 2012-2015 (PPA) and 2016-2017 (PPA+PPRF). Models were adjusted on the annual incidence of extended-spectrum ß-lactamase-producing enterobacteriacae. FINDINGS: Carbapenem use was stable over the PPA period in all ICUs, with a significant change of slope over the PPA+PPRF period only in ICU1 (ß2 = -12.8, 95% confidence interval (CI) = -19.5 to -6.1). There was a switch from imipenem to meropenem during the PPA period in all three units. Resistances of P. aeruginosa were stable over the study period in ICU1 and ICU2, and significantly decreased over the PPA+PPRF period in ICU3 (ß2 = -0.18, CI = -0.3 to -0.03). CONCLUSION: In real-life conditions and with the same antimicrobial stewardship programme (AMSP) led by a single team, the impact of PPRF was heterogeneous between ICUs. Factors driving the impact of AMSPs should be further assessed in comparable settings through real-life data, to target where they could prove cost-effective.

Commentary: resolving pertussis resurgence and vaccine immunity using mathematical transmission models.

The epidemiology of pertussis-a vaccine-preventable respiratory infection typically caused by the bacterium Bordetella pertussis-remains puzzling. Indeed, the disease seems nowhere close to eradication and has even re-emerged in certain countries-such as the US-that have maintained high vaccination coverage. Because the dynamics of pertussis are shaped by past vaccination and natural infection rates, with the relevant timescale spanning decades, the interpretation of such unexpected trends is not straightforward. In this commentary, we propose that mathematical transmission models play an essential role in helping to interpret the data and in closing knowledge gaps in pertussis epidemiology. We submit that recent advances in statistical inference methods now allow us to estimate key parameters, such as the nature and duration of vaccinal immunity, which have to date been difficult to quantify. We illustrate these points with the results of a recent study based on data from Massachusetts (Domenech de Cellès, Magpantay, King, and Rohani, Sci. Transl. Med. 2018;10: eaaj1748. doi:10.1126/scitranslmed.aaj1748), in which we used such methods to elucidate the mechanisms underlying the ongoing resurgence of pertussis. In addition, we list a number of safety checks that can be used to critically assess mathematical models. Finally, we discuss the remaining uncertainties surrounding pertussis vaccines, in particular the acellular vaccines used for teenage booster immunizations.

Seasonality of urinary tract infections in the United Kingdom in different age groups: longitudinal analysis of The Health Improvement Network (THIN).

Evidence regarding the seasonality of urinary tract infection (UTI) consultations in primary care is conflicting and methodologically poor. To our knowledge, this is the first study to determine whether this seasonality exists in the UK, identify the peak months and describe seasonality by age. The monthly number of UTI consultations (N = 992 803) and nitrofurantoin and trimethoprim prescriptions (N = 1 719 416) during 2008-2015 was extracted from The Health Improvement Network (THIN), a large nationally representative UK dataset of electronic patient records. Negative binomial regression models were fitted to these data to investigate seasonal fluctuations by age group (14-17, 18-24, 25-45, 46-69, 70-84, 85+) and by sex, accounting for a change in the rate of UTI over the study period. A September to November peak in UTI consultation incidence was observed for ages 14-69. This seasonality progressively faded in older age groups and no seasonality was found in individuals aged 85+, in whom UTIs were most common. UTIs were rare in males but followed a similar seasonal pattern than in females. We show strong evidence of an autumnal seasonality for UTIs in individuals under 70 years of age and a lack of seasonality in the very old. These findings should provide helpful information when interpreting surveillance reports and the results of interventions against UTI.

Pertussis immunity and epidemiology: mode and duration of vaccine-induced immunity.

The resurgence of pertussis in some countries that maintain high vaccination coverage has drawn attention to gaps in our understanding of the epidemiological effects of pertussis vaccines. In particular, major questions surround the nature, degree and durability of vaccine protection. To address these questions, we used mechanistic transmission models to examine regional time series incidence data from Italy in the period immediately following the introduction of acellular pertussis (aP) vaccine. Our results concur with recent animal-challenge experiments wherein infections in aP-vaccinated individuals proved as transmissible as those in naive individuals but much less symptomatic. On the other hand, the data provide evidence for vaccine-driven reduction in susceptibility, which we quantify via a synthetic measure of vaccine impact. As to the precise nature of vaccine failure, the data do not allow us to distinguish between leakiness and waning of vaccine immunity, or some combination of these. Across the range of well-supported models, the nature and duration of vaccine protection, the age profile of incidence and the range of projected epidemiological futures differ substantially, underscoring the importance of the remaining unknowns. We identify key data gaps: sources of data that can supply the information needed to eliminate these remaining uncertainties.