Revision of clinical case definitions: influenza-like illness and severe acute respiratory infection

Abstract in English, Arabic, Chinese, French, Russian, SpanishThe formulation of accurate clinical case definitions is an integral part of an effective process of public health surveillance. Although such definitions should, ideally, be based on a standardized and fixed collection of defining criteria, they often require revision to reflect new knowledge of the condition involved and improvements in diagnostic testing. Optimal case definitions also need to have a balance of sensitivity and specificity that reflects their intended use. After the 2009-2010 H1N1 influenza pandemic, the World Health Organization (WHO) initiated a technical consultation on global influenza surveillance. This prompted improvements in the sensitivity and specificity of the case definition for influenza - i.e. a respiratory disease that lacks uniquely defining symptomology. The revision process not only modified the definition of influenza-like illness, to include a simplified list of the criteria shown to be most predictive of influenza infection, but also clarified the language used for the definition, to enhance interpretability. To capture severe cases of influenza that required hospitalization, a new case definition was also developed for severe acute respiratory infection in all age groups. The new definitions have been found to capture more cases without compromising specificity. Despite the challenge still posed in the clinical separation of influenza from other respiratory infections, the global use of the new WHO case definitions should help determine global trends in the characteristics and transmission of influenza viruses and the associated disease burden.info:eu-repo/semantics/publishedVersio

Dot map cartograms for detection of infectious disease outbreaks: an application to Q fever, the Netherlands and pertussis, Germany.

Geographical mapping of infectious diseases is an important tool for detecting and characterising outbreaks. Two common mapping methods, dot maps and incidence maps, have important shortcomings. The former does not represent population density and can compromise case privacy, and the latter relies on pre-defined administrative boundaries. We propose a method that overcomes these limitations: dot map cartograms. These create a point pattern of cases while reshaping spatial units, such that spatial area becomes proportional to population size. We compared these dot map cartograms with standard dot maps and incidence maps on four criteria, using two example datasets. Dot map cartograms were able to illustrate both incidence and absolute numbers of cases (criterion 1): they revealed potential source locations (Q fever, the Netherlands) and clusters with high incidence (pertussis, Germany). Unlike incidence maps, they were insensitive to choices regarding spatial scale (criterion 2). Dot map cartograms ensured the privacy of cases (criterion 3) by spatial distortion; however, this occurred at the expense of recognition of locations (criterion 4). We demonstrate that dot map cartograms are a valuable method for detection and visualisation of infectious disease outbreaks, which facilitates informed and appropriate actions by public health professionals, to investigate and control outbreaks

Real-time Estimation of Epidemiologic Parameters from Contact Tracing Data During an Emerging Infectious Disease Outbreak.

Contact tracing can provide accurate information on relevant parameters of an ongoing emerging infectious disease outbreak. This is crucial to investigators seeking to control such an outbreak. However, crude contact tracing data are difficult to interpret and methods for analyzing these data are scarce. We present a method to estimate and visualize key outbreak parameters from contact tracing information in real time by taking into account data censoring

Echovirus type 6 transmission clusters and the role of environmental surveillance in early warning, the Netherlands, 2007 to 2016.

BackgroundIn the Netherlands, echovirus type 6 (E6) is identified through clinical and environmental enterovirus surveillance (CEVS and EEVS). AimWe aimed to identify E6 transmission clusters and to assess the role of EEVS in surveillance and early warning of E6. MethodsWe included all E6 strains from CEVS and EEVS from 2007 through 2016. CEVS samples were from patients with enterovirus illness. EEVS samples came from sewage water at pre-specified sampling points. E6 strains were defined by partial VP1 sequence, month and 4-digit postcode. Phylogenetic E6 clusters were detected using pairwise genetic distances. We identified transmission clusters using a combined pairwise distance in time, place and phylogeny dimensions. ResultsE6 was identified in 157 of 3,506 CEVS clinical episodes and 92 of 1,067 EEVS samples. Increased E6 circulation was observed in 2009 and from 2014 onwards. Eight phylogenetic clusters were identified; five included both CEVS and EEVS strains. Among these, identification in EEVS did not consistently precede CEVS. One phylogenetic cluster was dominant until 2014, but genetic diversity increased thereafter. Of 14 identified transmission clusters, six included both EEVS and CEVS; in two of them, EEVS identification preceded CEVS identification. Transmission clusters were consistent with phylogenetic clusters, and with previous outbreak reports. ConclusionAlgorithms using combined time-place-phylogeny data allowed identification of clusters not detected by any of these variables alone. EEVS identified strains circulating in the population, but EEVS samples did not systematically precede clinical case surveillance, limiting EEVS usefulness for early warning in a context where E6 is endemic

Visual tools to assess the plausibility of algorithm-identified infectious disease clusters: an application to mumps data from the Netherlands dating from January 2009 to June 2016.

IntroductionWith growing amounts of data available, identification of clusters of persons linked to each other by transmission of an infectious disease increasingly relies on automated algorithms. We propose cluster finding to be a two-step process: first, possible transmission clusters are identified using a cluster algorithm, second, the plausibility that the identified clusters represent genuine transmission clusters is evaluated.AimTo introduce visual tools to assess automatically identified clusters.MethodsWe developed tools to visualise: (i) clusters found in dimensions of time, geographical location and genetic data; (ii) nested sub-clusters within identified clusters; (iii) intra-cluster pairwise dissimilarities per dimension; (iv) intra-cluster correlation between dimensions. We applied our tools to notified mumps cases in the Netherlands with available disease onset date (January 2009 - June 2016), geographical information (location of residence), and pathogen sequence data (n = 112). We compared identified clusters to clusters reported by the Netherlands Early Warning Committee (NEWC).ResultsWe identified five mumps clusters. Three clusters were considered plausible. One was questionable because, in phylogenetic analysis, genetic sequences related to it segregated in two groups. One was implausible with no smaller nested clusters, high intra-cluster dissimilarities on all dimensions, and low intra-cluster correlation between dimensions. The NEWC reports concurred with our findings: the plausible/questionable clusters corresponded to reported outbreaks; the implausible cluster did not.ConclusionOur tools for assessing automatically identified clusters allow outbreak investigators to rapidly spot plausible transmission clusters for mumps and other human-to-human transmissible diseases. This fast information processing potentially reduces workload

Visual tools to assess the plausibility of algorithm-identified infectious disease clusters: an application to mumps data from the Netherlands dating from January 2009 to June 2016.

IntroductionWith growing amounts of data available, identification of clusters of persons linked to each other by transmission of an infectious disease increasingly relies on automated algorithms. We propose cluster finding to be a two-step process: first, possible transmission clusters are identified using a cluster algorithm, second, the plausibility that the identified clusters represent genuine transmission clusters is evaluated.AimTo introduce visual tools to assess automatically identified clusters.MethodsWe developed tools to visualise: (i) clusters found in dimensions of time, geographical location and genetic data; (ii) nested sub-clusters within identified clusters; (iii) intra-cluster pairwise dissimilarities per dimension; (iv) intra-cluster correlation between dimensions. We applied our tools to notified mumps cases in the Netherlands with available disease onset date (January 2009 - June 2016), geographical information (location of residence), and pathogen sequence data (n = 112). We compared identified clusters to clusters reported by the Netherlands Early Warning Committee (NEWC).ResultsWe identified five mumps clusters. Three clusters were considered plausible. One was questionable because, in phylogenetic analysis, genetic sequences related to it segregated in two groups. One was implausible with no smaller nested clusters, high intra-cluster dissimilarities on all dimensions, and low intra-cluster correlation between dimensions. The NEWC reports concurred with our findings: the plausible/questionable clusters corresponded to reported outbreaks; the implausible cluster did not.ConclusionOur tools for assessing automatically identified clusters allow outbreak investigators to rapidly spot plausible transmission clusters for mumps and other human-to-human transmissible diseases. This fast information processing potentially reduces workload

Echovirus type 6 transmission clusters and the role of environmental surveillance in early warning, the Netherlands, 2007 to 2016.

BackgroundIn the Netherlands, echovirus type 6 (E6) is identified through clinical and environmental enterovirus surveillance (CEVS and EEVS). AimWe aimed to identify E6 transmission clusters and to assess the role of EEVS in surveillance and early warning of E6. MethodsWe included all E6 strains from CEVS and EEVS from 2007 through 2016. CEVS samples were from patients with enterovirus illness. EEVS samples came from sewage water at pre-specified sampling points. E6 strains were defined by partial VP1 sequence, month and 4-digit postcode. Phylogenetic E6 clusters were detected using pairwise genetic distances. We identified transmission clusters using a combined pairwise distance in time, place and phylogeny dimensions. ResultsE6 was identified in 157 of 3,506 CEVS clinical episodes and 92 of 1,067 EEVS samples. Increased E6 circulation was observed in 2009 and from 2014 onwards. Eight phylogenetic clusters were identified; five included both CEVS and EEVS strains. Among these, identification in EEVS did not consistently precede CEVS. One phylogenetic cluster was dominant until 2014, but genetic diversity increased thereafter. Of 14 identified transmission clusters, six included both EEVS and CEVS; in two of them, EEVS identification preceded CEVS identification. Transmission clusters were consistent with phylogenetic clusters, and with previous outbreak reports. ConclusionAlgorithms using combined time-place-phylogeny data allowed identification of clusters not detected by any of these variables alone. EEVS identified strains circulating in the population, but EEVS samples did not systematically precede clinical case surveillance, limiting EEVS usefulness for early warning in a context where E6 is endemic

Adhering to a national surgical care bundle reduces the risk of surgical site infections

textabstractBackground: In 2008, a bundle of care to prevent Surgical Site Infections (SSIs) was introduced in the Netherlands. The bundle consisted of four elements: antibiotic prophylaxis according to local guidelines, no hair removal, normothermia and ‘hygiene discipline’ in the operating room (i.e. number of door movements). Dutch hospitals were advised to implement the bundle and to measure the outcome. This study’s goal was to assess how effective the bundle was in reducing SSI risk. Methods: Hospitals assessed whether their staff complied with each of the bundle elements and voluntary reported compliance data to the national SSI surveillance network (PREZIES). From PREZIES data, we selected data from 2009 to 2014 relating to 13 types of surgical procedures. We excluded surgeries with missing (non)compliance data, and calculated for each remaining surgery with reported (non)compliance data the level of compliance with the bundle (that is, being compliant with 0, 1, 2, 3, or 4 of the elements). Subsequently, we used this level of compliance to assess the effect of bundle compliance on the SSI risk, using multilevel logistic regression techniques. Results: 217 489 surgeries were included, of which 62 486 surgeries (29%) had complete bundle reporting. Within this group, the SSI risk was significantly lower for surgeries with complete bundle compliance compared to surgeries with lower compliance levels. Odds ratios ranged from 0.63 to 0.86 (risk reduction of 14% to 37%), while a 13% risk reduction was demonstrated for each point increase in compliance-level. Sensitivity analysis indicated that due to analysing reported bundles only, we probably underestimated the total effect of implementing the bundle. Conclusions: This study demonstrated that adhering to a surgical care bundle significantly reduced the risk of SSIs. Reporting of and compliance with the bundle compliance can, however, still be improved. Therefore an even greater effect might be achieved

Adhering to a national surgical care bundle reduces the risk of surgical site infections.

In 2008, a bundle of care to prevent Surgical Site Infections (SSIs) was introduced in the Netherlands. The bundle consisted of four elements: antibiotic prophylaxis according to local guidelines, no hair removal, normothermia and 'hygiene discipline' in the operating room (i.e. number of door movements). Dutch hospitals were advised to implement the bundle and to measure the outcome. This study's goal was to assess how effective the bundle was in reducing SSI risk