Developing sero-diagnostic tests to facilitate Plasmodium vivax Serological Test-and-Treat approaches: modeling the balance between public health impact and overtreatment.

BACKGROUND: Eliminating Plasmodium vivax will require targeting the hidden liver-stage reservoir of hypnozoites. This necessitates new interventions balancing the benefit of reducing vivax transmission against the risk of over-treating some individuals with drugs which may induce haemolysis. By measuring antibodies to a panel of vivax antigens, a strategy of serological-testing-and-treatment (PvSeroTAT) can identify individuals with recent blood-stage infections who are likely to carry hypnozoites and target them for radical cure. This provides a potential solution to selectively treat the vivax reservoir with 8-aminoquinolines. METHODS: PvSeroTAT can identify likely hypnozoite carriers with ~80% sensitivity and specificity. Diagnostic test sensitivities and specificities ranging 50-100% were incorporated into a mathematical model of vivax transmission to explore how they affect the risks and benefits of different PvSeroTAT strategies involving hypnozoiticidal regimens. Risk was measured as the rate of overtreatment and benefit as reduction of community-level vivax transmission. RESULTS: Across a wide range of combinations of diagnostic sensitivity and specificity, PvSeroTAT was substantially more effective than bloodstage mass screen and treat strategies and only marginally less effective than mass drug administration. The key test characteristic determining of the benefit of PvSeroTAT strategies is diagnostic sensitivity, with higher values leading to more hypnozoite carriers effectively treated and greater reductions in vivax transmission. The key determinant of risk is diagnostic specificity: higher specificity ensures that a lower proportion of uninfected individuals are unnecessarily treated with primaquine. These relationships are maintained in both moderate and low transmission settings (qPCR prevalence 10% and 2%). Increased treatment efficacy and adherence can partially compensate for lower test performance. Multiple rounds of PvSeroTAT with a lower performing test may lead to similar or higher reductions in vivax transmission than fewer rounds with a higher performing test, albeit with higher rate of overtreatment. CONCLUSIONS: At current performance, PvSeroTAT is predicted to be a safe and efficacious option for targeting the hypnozoite reservoir towards vivax elimination. P. vivax sero-diagnostic tests should aim for both high performance and ease of use in the field. The target product profiles informing such development should thus reflect the trade-offs between impact, overtreatment, and ease of programmatic implementation

La propagation des infections nosocomiales et des entérobactéries émergentes et multirésistantes au sein du réseau des hôpitaux : évaluation du rôle des transferts inter-établissement des patients sur le risque infectieux et les mesures de contrôle

La propagation des infections nosocomiales (IN), notamment liées aux bactéries multi-résistantes, au sein du réseau des hôpitaux, est un grand enjeu de santé publique. L’évaluation du rôle joué par les transferts inter-établissements des patients sur cette propagation pourrait permettre l’élaboration de nouvelles mesures de contrôle. L’identification de nouvelles mesures de contrôle est particulièrement importante pour les bactéries résistantes aux antibiotiques comme les entérobactéries productrices de carbapenemase (EPC) pour lesquelles les possibilités de traitement sont très limitées. L’utilisation des données de réseaux de contact inter-individus et de transferts inter-établissement dans la modélisation mathématique ont rendu ces modèles plus proches de la réalité. Toutefois, ces derniers restent limités à quelques milieux hospitaliers et quelques pathogènes. La thèse a eu pour objectifs de 1) mieux comprendre la structure des réseaux hospitaliers français et leur impact sur la propagation des IN ; et 2) évaluer le rôle des transferts sur la propagation des EPC.Les réseaux hospitaliers français sont caractérisés par des flux de patients vers des hubs et par deux niveaux de communautés des hôpitaux. La structure du réseau de transfert des patients présentant une IN n’est pas différente de celle du réseau général de transfert des patients. Au cours des dernières années, le nombre d’épisode d’EPC a augmenté en France et les prédictions prévoient une poursuite de cette augmentation, avec des pics de saisonnalité en octobre. Ce travail a également montré que, depuis 2012, les transferts de patients jouent avec les années un rôle de plus en plus important sur la diffusion des EPC en France. Des évènements de propagation multiple liée aux transferts sont également de plus en plus souvent observés.En conséquence, la structure du réseau des hôpitaux pourrait servir de base pour la proposition des nouvelles stratégies de contrôles des IN en général, et des EPC en particulier. Les hôpitaux très connectés des grandes métropoles et les flux des patients entre les communautés locale et régionale doivent être considérés pour le développement de mesures de contrôle coordonnées entre établissements de santé.The spread of healthcare-associated infections (HAIs) and multi-drug resistance in healthcare networks is a major public health issue. Evaluating the role of inter-facility patient transfers that form the structure of these networks may provide insights on novel infection control measures. Identifying novel infection control strategies is especially important for multi-drug resistant pathogens such as Carbapenemase-producing Enterobacteriaceae (CPE) due to limited treatment options. The increasing use of inter-individual contact and inter-facility transfer network data in mathematical modelling of HAI spread has helped these models become more realistic; however, they remain limited to a few settings and pathogens. The main objectives of this thesis were two-fold: 1) to better understand the structure of the healthcare networks of France and their impact on HAI spread dynamics; and 2) to assess the role of transfers on the spread of CPE in France during the 2012 to 2015 period. The French healthcare networks are characterized by centralized patient flows towards hubs hospitals and a two-tier community clustering structure. We also found that networks of patients with HAIs form the same underlying structure as that of the general patient population. The number of CPE episodes have increased over time in France and projections estimate that the number of monthly episodes could continue to increase with seasonal peaks in October. The general patient network was used to show that, since 2012, patient transfers have played an increasingly important role over time in the spread of CPE in France. Multiple spreading events of CPE linked to patient transfers were also observed. Despite subtle differences in the flows of patients with an HAI and the general patient population, the general patient network may best inform novel infection control measures for pathogen spread. The structure of healthcare networks may help serve as a basis for novel infection control strategies to tackle HAIs in general but also CPE in particular. Key healthcare hubs in large metropoles and key patient flows connecting hospital communities at the local and regional level should be considered in the development of coordinated regional strategies to control pathogen spread in healthcare systems

Assessing the role of a patient transfer network in the spread of carbapenemase-producing Enterobacteriaceae: The case of France between 2012 and 2015

International audienceIntroductionThe spread of carbapenemase-producing Enterobacteriaceae (CPE) is a major public health threat that has been associated with cross-border and local transfer of patients between healthcare facilities. However, the impact of healthcare transfer networks on CPE spread dynamics may vary in time and between countries. In this context, our study aimed to assess the contribution of the patient transfer network on CPE spread in France from 2012 to 2015.MethodsUsing the French healthcare network of 2.3 million patients, we extended a previously proposed statistical method and tested the ability of this network to support observed CPE incidence episodes. First, using 2237 CPE episodes that occurred from 2012 to 2015, we identified the most likely infector for the 1251 non-imported episodes using network-supported paths (NSPs). We then compared the observed NSP distances to those expected by chance, using random permutations of the CPE data. The impact of the assumed time window between infector episode and CPE episode was investigated in a sensitivity analysis.ResultsMore than half of all CPE episodes were linked, either as infectors or incident episodes. The percentage of episodes with identified potential infectors over the network increased with time, from 57% in 2012 to 66% in 2015. NSP distances from 2013 to 2015 were significantly shorter in the observed data than expected by chance, indicating a role of the transfer network in CPE spread dynamics in France. In 2012 however, this result was not found. Over the entire study period, linked episodes tended to occur in the same administrative department or within close geographic distances. The 3-to-4 weeks baseline window between infector and episode was supported by the sensitivity analysis, where the strongest evidence for network-supported CPE transmission was observed for 2014 and 2015 episodes.ConclusionsWe observed a transition in 2013 from an epidemic sustained by importation to local transmission events sustaining the epidemic. As a result of a growing contribution of transfers in CPE spread over time, coordinated prevention and infection control strategies in France should focus on at-risk patient transfers to reduce regional and inter-regional transmission of CPE

Spread of hospital-acquired infections: A comparison of healthcare networks

International audienceHospital-acquired infections (HAIs), including emerging multi-drug resistant organisms, threaten healthcare systems worldwide. Efficient containment measures of HAIs must mobilize the entire healthcare network. Thus, to best understand how to reduce the potential scale of HAI epidemic spread, we explore patient transfer patterns in the French healthcare system. Using an exhaustive database of all hospital discharge summaries in France in 2014, we construct and analyze three patient networks based on the following: transfers of patients with HAI (HAI-specific network); patients with suspected HAI (suspected-HAI network); and all patients (general network). All three networks have heterogeneous patient flow and demonstrate small-world and scale-free characteristics. Patient populations that comprise these networks are also heterogeneous in their movement patterns. Ranking of hospitals by centrality measures and comparing community clustering using community detection algorithms shows that despite the differences in patient population, the HAI-specific and suspected-HAI networks rely on the same underlying structure as that of the general network. As a result, the general network may be more reliable in studying potential spread of HAIs. Finally, we identify transfer patterns at both the French regional and departmental (county) levels that are important in the identification of key hospital centers, patient flow trajectories, and regional clusters that may serve as a basis for novel wide-scale infection control strategies

Modeling Carbapenemase-producing Enterobacteriaceae episodes' evolution in France over 2010-2020

International audienceBackgroundIncidence of Carbapenemase-Producing Enterobacteriaceae (CPE) episodes within hospitals is rising at an alarming rate and threaten health systems and patient safety worldwide. Their number is growing in France since 2009 associated with inter-regional dissemination and importation of international cases. This study aimed at describing the dynamics of CPE episodes in France over 2010-2016 and forecasting their evolution for 2017-2020.MethodsSurveillance data of CPE episodes (imported and non-imported) from August 2010 to November 2016 were issued from the French national Healthcare-Associated Infections Early Warning and Response System. Impact of seasonality on the number of CPE episodes was analyzed using seasonal-to-irregular ratios. Seven models issued from time series analysis and three ensemble stacking models (average, convex and linear stacking) were used to describe and forecast CPE episodes. The model with the best forecasting’s quality was then trained on all available data (2010-2016) and used to predict CPE episodes over 2017-2020.ResultsOver 2010-2016, 3,559 CPE episodes were observed in France. Compared to the average yearly trend, we observed a 30% increase in the number of CPE episodes in September and October. On the opposite, a decrease of 20% was noticed in February compared to other months. We also noticed a 1-month lagged seasonality of non-imported episodes compared to imported ones. The number of non-imported episodes appeared to grow faster than imported ones starting from 2014. Average stacking gave the best forecasts and predicted an increase over 2017-2020 with a peak up to 345 CPE episodes (95% PI [124-1,158], 80% PI [171-742]) in September 2020.ConclusionsThe number of CPE episodes is predicted to rise in the next years in France because of non-imported episodes. These results could help public health authorities in the definition and evaluation of new containment strategies

Assessing the role of inter-facility patient transfer in the spread of carbapenemase-producing Enterobacteriaceae: the case of France between 2012 and 2015

International audienceThe spread of carbapenemase-producing Enterobacteriaceae (CPE) in healthcare settings is a major public health threat that has been associated with cross-border and local patient transfers between healthcare facilities. Since the impact of transfers on spread may vary, our study aimed to assess the contribution of a patient transfer network on CPE incidence and spread at a countrywide level, with a case study of France from 2012 to 2015. Our results suggest a transition in 2013 from a CPE epidemic sustained by internationally imported episodes to an epidemic sustained by local transmission events through patient transfers. Incident episodes tend to occur within close spatial distance of their potential infector. We also observe an increasing frequency of multiple spreading events, originating from a limited number of regional hubs. Consequently, coordinated prevention and infection control strategies should focus on transfers of carriers of CPE to reduce regional and inter-regional transmission

Networks characteristics of the French healthcare networks.

<p>Networks characteristics of the French healthcare networks.</p

The intercommunity networks of patient transfers.

<p>(a) The intercommunity network from the 18 detected general patient network Greedy-based communities named based on the French metropolitan regions they encompass. Edge size and color indicate the source community and number of patients discharged. (b) The intercommunity network from 113 Map Equation communities detected in the general network. The nodes of the networks represent the geographical center of hospitals within the shared community.</p

Regional clustering of communities detected with greedy algorithm.

<p>Network hospitals and patient trajectories of the healthcare network in France of (a) the general healthcare network, (b) the suspected-HAI healthcare network, and (c) the HAI-specific healthcare network. In the general healthcare network, 18 communities were detected by the community clustering algorithm. Four of the 18 communities identified by the algorithm combine hospitals from two regions each, such that the 22 geographical regions are mapped into 18 communities. The original 22 French metropolitan regions before they were reformed to 13 regions implemented in 2016 are shown to correspond to the 2014 data. For the HAI-specific and suspected-HAI networks, the algorithm detected a higher number of communities (36 and 21 communities respectively). The communities, which overlapped the same regional communities in the general network, were given the same color and the newly detected communities were given different colors.</p