Citizen surveillance for environmental monitoring: combining the efforts of citizen science and crowdsourcing in a quantitative data framework

Marijke Welvaert
Peter Caley
Citizen surveillance for environmental monitoring: combining the efforts of citizen science and crowdsourcing in a quantitative data framework
10.1186/s40064-016-3583-5
BackgroundOver the last decade, citizen science has become popular in many scientific areas. Involving citizens in scientific research has benefits for both parties. Scientists get access to a wealth of data that would not have been attainable by individual researchers, while citizens get the satisfaction of being involved in science and being part of a community project. In addition, crowdsourcing offers another data stream in which the general public collects or processes data that are used for scientific studies. However, citizen science and crowdsourcing seem to live parallel lives. While for citizen science the focus isAbstractCitizen science and crowdsourcing have been emerging as methods to collect data for surveillance and/or monitoring activities. They could be gathered under the overarching term citizen surveillance. The discipline, however, still struggles to be widely accepted in the scientific community, mainly because these activities are not embedded in a quantitative framework. This results in an ongoing discussion on how to analyze and make useful inference from these data. When considering the data collection process, we illustrate how citizen surveillance can be classified according to the nature of the underlying observation process measured in two dimensions-the degree of observer reporting intention and the control in observer detection effort. By classifying the observation process in these dimensions we distinguish between crowdsourcing, unstructured citizen science and structured citizen science. This classification helps the determine data processing and statistical treatment of these data for making inference. Using our framework, it is apparent that published studies are overwhelmingly associated with structured citizen science, and there are well developed statistical methods for the resulting data. In contrast, methods for making useful inference from purely crowd-sourced data remain under development, with the challenges of accounting for the unknown observation process considerable. Our quantitative framework for citizen surveillance calls for an integration of citizen science and crowdsourcing and provides a way forward to solve the statistical challenges inherent to citizen-sourced data. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

on citizens doing science (e.g. data collection, conducting experiments, etc.), in crowdsourcing the volunteers contribute resources (e.g. computing power, money, data, etc.) to science. Figure 1 demonstrates that both disciplines have attracted increasing interest from the scientific community. Searching for publications relating to citizen science and crowdsourcing using the Web of Science database clearly shows that the number of publications has risen exponentially during the past 5 years. Interestingly, Fig. 1c indicates that citizen science and crowdsourcing has become increasingly popular for monitoring and surveillance activities. Furthermore, according to Roy et al. (2012), many citizen science projects are making use of crowdsourced data. Therefore, it seems natural to consider both disciplines as potential data sources for monitoring and surveillance activities.

In this paper, we propose a quantitative data-focused framework for characterizing citizen surveillance. We define citizen surveillance as any type of activity conducted by volunteers, recruited or not, that results in monitoring or surveillance data. Natural application domains for citizen surveillance are environmental surveillance, biodiversity monitoring and biosecurity surveillance, and both citizen science and crowdsourcing are logical subdisciplines of citizen surveillance in this context. Fig. 1 Annual Web of Science publication hits for queries related to a citizen science, b crowdsourcing, and c combining citizen science or crowdsourcing with surveillance or monitoring Other reviews (for example Bonney et al. 2009) have mainly focused on how to develop citizen science projects and the benefits in terms of science knowledge and scientific literacy for the communities involved. Although these frameworks include data analysis as an important aspect of the citizen science project, data validation and analysis is still a little studied part of the possibilities of citizen science (Butt et al. 2013). Citizen science, and in this paper we will extend this to citizen surveillance, still struggles to be accepted as a valid scientific method (Bonney et al. 2014) and is often associated with some skepticism with respect to the quality of the data generated. Therefore, not surprisingly, a lot of effort has gone in assessing whether volunteer collected data are of similar quality as data collected by professionals or experts. Recently, quite a number of studies have revealed a strong agreement between volunteer-collected data and empirical or expertcollected data (e.g. Davies et al. 2012;Hoyer et al. 2012;Gollan et al. 2012). However, Snall et al. (2011) warn to be cautious when using citizen science data because these data should be handled with great care. When using citizen science data for environmental surveillance purposes, one needs to be aware of and account for the limitations of those data. For example, Gardiner et al. (2012) demonstrated an overestimation of species richness and diversity values in lady beetles based on citizen science data compared to verified data. This was attributed to under-reporting of common species combined with over-reporting of rare native species (including false positives). Accounting for such findings can clearly improve the analysis of citizen surveillance data.

By focusing on the quantitative aspects of citizen science and crowdsourcing in relation to the reporting and detection process, we hope to provide a way forward to a unified search for statistical solution for citizen-sourced data. We discuss the trade-off between data quality and data quantity in citizen surveillance data, which datasets can be expected from the different types of citizen surveillance and we highlight the analytic challenges which go hand in hand with citizen surveillance data. We hope that by focusing on the quantitative data aspects of citizen surveillance, users will be guided in how to choose an appropriate type of citizen surveillance for their environmental research questions or monitoring goals.

The remainder of this paper is organized as follows: "The emergence of citizen science" section provides a brief overview of the history of citizen-sourced data and presents the results of a extensive literature analysis demonstrating the lack of studies investigating statistical methods for analysing citizen-sourced data. "Citizen surveillance data: a tradeoff between data quality and data quantity" section goes into the details of the observation process of citizen surveillance and proposes a new framework that differentiates between different types of citizen surveillance based on common elements in the observation and reporting process. This framework is used to assess the statistical challenges for citizen surveillance. Finally, in "Conclusion" section we provide some recommendations for maximizing the information retrieved from citizen surveillance data and urge for an integrated approach in developing statistical methods for citizen-sourced data.

The emergence of citizen science

Citizen science may be a rather new term, but in fact its practice dates back several centuries. Before the twentieth century, scientific work was most often conducted by gentleman scientists such as Isaac Newton, Abraham Franklin and Charles Darwin. It is only since the mid-twentieth century that science has become almost completely professionalized with scientists employed by universities and research organizations. While this is still mostly the case, over recent decades, several projects have emerged that have successfully engaged volunteers without specific scientific training into posing research questions, and collecting and analyzing data (e.g. Trumbull et al. 2000).

Citizen science has been defined as "the systematic collection and analysis of data; development of technology; testing of natural phenomena; and the dissemination of these activities by researchers on a primarily avocational basis" (OpenScientist 2011). In short, citizen science is commonly known as "public participation in scientific research" (e.g. Hand 2010) and is sometimes referred to as crowd science, crowd-sourced science, civic science, or networked science. The term "citizen science" has become commonly used by both scientists (see Fig. 1) and the community from 2009 onward (see Bonney et al. 2009 for a definition of the fiels). It is believed that citizen science is a cost-effective means to provide solutions to research questions, because, in general the data is cheaper than that gathered by trained professionals, while similar in quality and quantity after intelligent analyzing and processing (Barbier et al. 2012). In particular, citizen science can be helpful when researchers attempt to answer questions on a large spatial or temporal scale ) that cannot be sampled otherwise due to limited funding.

Citizen science projects are increasing in popularity (see Theobald et al. 2015;See et al. 2016; Fig. 1). In order to assess whether this is also the case in the scientific literature, we conducted a literature search using the Web Of Science database, indexing scientific publications going back as far as 1864, and the query "citizen science". The search resulted in 379 publication hits (results as of December 5, 2013). After inspection, 33 of these (8.7%) were considered irrelevant because they were not related to citizen science as we defined above, another 5 publications (1.3%) were disregarded because they had no assigned author and therefore their geographical source could not be traced. After removing these results, 342 publications remained that all discussed citizen science, although in many different forms.

A content-based analysis of the database revealed that the most common publication type related to citizen science were applications (i.e. 45.6%). This included reporting on the analysis and interpretation of citizen science data (e.g. Wilson et al. 2013); the development of educational projects using citizen science was discussed (e.g. Zoellick et al. 2012); the implementation of a new citizen science project was described (e.g. Arvanitidis et al. 2011); or reporting the discovery of new species as a result of citizen science (e.g. Collins et al. 2011;Cohen et al. 2011).

Only 5.8% of the publications discuss specific analysis challenges and methods to analyze citizen science data, while another 12.2% present studies that tried to validate the citizen science data by, for example, comparing the quality of the data gathered by volunteers on the one hand and by expert professionals on the other. This illustrates that data validation and analysis is indeed understudied. Interestingly, more than half of the citizen science publications found here were published in an environmental related field, namely Environmental Sciences & Ecology, Biodiversity & Conservation, Zoology, and Entomology. These fields carry a tradition of large monitoring surveys and projects aimed to assist the conservation of species and it is obvious that citizen science is already well embedded. However, we will demonstrate that not all types of citizen surveillance will be the optimal choice to gather data for conservation monitoring.

Citizen surveillance data: a trade-off between data quality and data quantity

Citizen science projects can be classified into different types depending on several factors. For example, Roy et al. (2012) derived different clusters of citizen science projects based on how they scored on two dimensions. The first dimension accounted for the degree of mass participation (i.e. mass participation projects versus local monitoring projects), while the second dimension was interpreted as the degree of investment (i.e. investment of project managers, investment of background information and supporting material, and the time-investment of the participants). In this paper, we follow a similar thinking process in terms of classifying different types of citizen surveillance into a twodimensional system. However, we specifically focus on the quantitative characteristics of the citizen-sourced data, because they are more relevant with respect to choosing an analysis method.

Structuring citizen surveillance based on observation and reporting

Citizen-sourced surveillance/monitoring data, in particular from the natural environment, will depend jointly on the processes of species detection/observation and reporting. Factors relating to detection and reporting can be used to characterize the data observation process, with consequences for data preprocessing, appropriate statistical analysis and ultimately the amount of useful information that can be retrieved from the data. To our knowledge, thus far this distinction between the two processes has not been made. Here we argue, differentiating between these processes is crucial for making inference from these data. Figure 2 illustrates how three broad types of citizen surveillance can be situated in the quadrants of a detection-reporting axis system. On the detection axis, a distinction is made between controlled and opportunistic detections. Controlled detection implies that the time and place of the detection is part of a sampling design, while opportunistic detections are side-products from other day-to-day activities (e.g. day hiking) and therefore there is less control on where and when these detections occur. On the reporting axis, we differentiate between unintentional and intentional reporting. This dimension mainly refers to the platform to which the citizen detections are being reported. Intentional reporting is typically made on a dedicated citizen science platform (e.g. website), while an unintentional reporting is made on a medium that is not directly related to citizen science (e.g. social media).

Within this two-dimension system, we note that three types of citizen science can be differentiated: (1) Structured citizen science projects are projects that are typically organized by a scientist (or using scientific principles) and in which strong control is exercised on the observation and reporting process. These projects typically offer some form of training to the volunteers, which are purposefully recruited, and provide questionnaires and checklists. In some cases even, an experimental design is adopted. (2) In unstructured citizen science, the reporting is still done on purpose, but the observation process is opportunistic. Observations are typically spontaneous and a result of trips planned or undertaken independently with respect to the species reported. There is typically no information available on the time spent in the field, nor information on other species recorded on the same visit to a given site. This type of citizen science typically needs to be enabled by a web-platform on which volunteers can report their detections.

(3) A last category is crowdsourcing. This term captures information that is observed in an opportunistic way and reported unknowingly. For example, Tweets reporting something, sometimes supported by photographic material (see Fig. 3). Crowdsourcing is often not regarded as citizen science because the volunteers are not necessarily involved in the scientific research (Miller-Rushing et al. 2012). However, within the context of our framework, this data stream could be a natural part of citizen surveillance, keeping in mind that it will have its own statistical challenges and does not necessarily provide the same benefits to the community (e.g. scientific literacy).

The literature analysis described in "The emergence of citizen science" section showed that the scientific literature is dominated by the first type, structured citizen science (Fig. 1). The close involvement of scientists in these types of projects is the most probable explanation for this over-representation. As mentioned before, crowdsourcing is often not regarded as citizen science, and therefore, is most likely under-represented in the database presented in "The emergence of citizen science" section. Figure 3 also illustrates one of the data quality issues inherent to citizen science (i.e. misidentification), whereby a tweet contains both "plague" and "locust"-seemingly referring to an outbreaking species that may cause extensive crop damage. The subject of the tweet (as revealed by a photo) is a harmless species. One of the benefits of the framework shown in Fig. 2 is that we can clearly identify a trade-off along the diagonal axis between data quality and data quantity.  Fig. 2 Schematic representation of the citizen surveillance framework. The different types of citizen surveillance are distinguished within a two-dimensional system representing the detection process and the reporting process. Examples of well known data sources for the different types are represented in green. The blue circles represent the number of publications for each type in our literature review database Fig. 3 Example of crowdsourcing from Twitter. The photographic material makes it possible to correct the misidentification in the tweet. In this case it is a cicada, unrelated to locusts

Table 1 Evaluation of some typical characteristics of citizen surveillance data

Choosing a type of citizen surveillance will typically be a trade-off between data quality and data quantity Data quality issues have been heavily investigated in the assessment of crowd-sourced data for mapping purposes (Antoniou and Skopeliti 2015;See et al. 2013;Jokar Arsanjani et al. 2015). While a lot of the issues related to this field are inherently embedded in citizen surveillance for environmental monitoring, we do believe that the data quality problems go even further due to the differences in the data generating process. For example, disaster detection like earthquakes based on georeferenced information relies on the fact that the public will be reporting about the disaster using media that are georeferenced, e.g. Twitter. This works quite well because people immediately recognise the disaster (the observation) and it affects them in such manner that they feel inclined to Tweet about it (the reporting). However, in environmental monitoring, both of these processes are challenged. The observation will be subject to bias and false positives because some species, especially rare/exotic species and/or small species, are hard to detect and it can be very difficult to differentiate between the species to the untrained eye. In addition, the mere observation of a species will not always trigger a reporting response from the observer, unless they have a vested interest in reporting that species or are highly motivated to do so.

Because of its underlying scientific principles, structured citizen science has the major advantage that there is relative control over the observation process. This might be the reason why this type is so present in the scientific literature. On the other hand, unstructured citizen science and crowdsourcing are subject to many uncontrolled processes, most of them unknown. The challenge in analyzing the data from these types of citizen science will be accounting for unknown observation processes and coming up with ways to incorporate the increased uncertainty. In addition, the differences in data type will trigger different analysis strategies. While structured citizen science most likely will provide counts or presence-absence data, unstructured citizen science and crowdsourcing will mainly provide presence-only data. This further complicates the inference that can be made from these data and is amongst statisticians a topic of ongoing debate (Royle et al. 2012;Hastie and Fithian 2013).

Another important aspect is data quantity. Typically, the more data that are available, the more likely it is that uncertainty in the data can be accounted for (i.e. partition signal from noise). However, in citizen science data this is not necessarily true. Data quantity is linked to the signal-to-noise ratio. For example, crowdsourcing provides huge datasets but the signal of interest in these data is only a small fraction and can be totally buried in the noise. Analysis of these data will require a lot of preprocessing and will be extremely hard to validate. There might be less data from structured citizen science, but the signalto-noise ratio will be a lot higher, meaning that the information in the data is more easily accessible.

Temporal and spatial coverage are important characteristics for environmental surveillance in particular. Rather than having data for one location in a specific time period, environmental monitoring and biodiversity monitoring can benefit immensely from data that cover a lot of space and that are observed frequently. Because structured citizen science is mostly, but not always, an organized activity that brings together a lot of people at the same time at the same place, it is expected to have less spatial and temporal coverage compared to unstructured citizen science and crowdsourcing. The joint effort in these types of citizen surveillance will most likely cover more ground and time, but are also prone to spatial and temporal biases due to opportunistic sampling (see "Challenges" section).

Challenges

Sampling design and analysis

We have already demonstrated that citizen surveillance can vary from highly structured to completely unstructured. Structured surveillance can be found in avian monitoring, which has a long tradition of citizen science and, not surprisingly, has also been the origin of improved sampling designs for large monitoring studies (e.g. Heezik and Seddon 2012;MacLeod et al. 2012). In contrast, for unstructured citizen surveillance, a statistically sound sampling design is normally lacking and most data are collected based on convenience sampling (Boakes et al. 2010). This means that sampling locations are often near roads or other easily accessible locations, and sampling is most likely on weekends and during daylight hours. In addition, both inter-and intra-observer sampling effort is highly variable and will result in heterogeneity of detection probabilities. The previously mentioned analysis methods, while potentially useful for structured citizen science projects, do not apply to the unstructured part of citizen surveillance. In these cases, induced heterogeneity will have to be accounted for in the analysis of the data.

Developing validated methods for correcting sampling bias for such data is an active area of research in the species distribution modelling field (e.g. Fithian et al. 2015), including the use of high-quality data from planned surveys to leverage information from presence-only data (Dorazio 2014). Other bias correction mechanisms have a logical basis (e.g. using the density of roads as a covariate in the case of citizen-sighted koalas), though are applied without knowing how well the correction is performing (see Sequeira et al. 2014).

In some situations, however, no amount of correction can remedy observer reporting (c.f. sampling) bias inherent in unstructured citizen science. For example, Wild Dog Scan (https://www.feralscan.org.au/wilddogscan/) enables landholders in Australia to report sightings of dingoes (Canis lupus dingo) and their hybrids. Such sightings are of particular interest to the sheep pastoral industry, as dingoes may cause severe economic losses. The resulting dingo sightings map is dominated by sightings within the sheep pastoral zone, despite dingoes being rare in this region as a result of intensive control, in comparison with areas to the north outside the dingo barrier fence, where dingoes are comparatively abundant (Fig. 4). Recognising that these data are unstructured should alert anyone using them to research the observation process before undertaking any analysis-failure to do so in this case would result in the incorrect inference that dingo abundance is highest inside the dingo fence. In this situation the sightings probability being somewhat perversely inversely related to abundance is driven by sightings being of much greater economic importance to graziers inside the dingo fence than out. This is not to say that the reporting App and the data it generates aren't of use to users within the dingo fence.

In the case of crowdsourcing involving unintentional reporting in conjunction with opportunistic detection, there has been little development of validated methodology for retrieving stable surveillance information these type of data.

Observer effects

Because citizen surveillance often relies so heavily on inexperienced volunteers, observer effects due to heterogeneity in skills, age, background, etc. can be non-trivial and should not be ignored. Not only do they affect species detectability, they also will influence the adopted sampling design and ultimately the error probability. In the case of citizen science, probably most forms of bias in the data can be traced back to the observers, either through their observing skills, or their choice of sampling locations and time. However, reducing these effects through, for example, standardization may not be sufficient to eliminate the heterogeneity as there are other variables that will influence species detectability (Yoccoz et al. 2001). Sauer et al. (1994) illustrate that long-term trends in observer quality can substantially bias estimates of population trend.

In general, it is known that novices and non-experts are more likely to over-report rare species that are easy to detect (Sullivan et al. 2009). On the other hand, self-assessed experts may suffer more from overconfidence (Larrick et al. 2007), making them more prone to false-positive errors (Moore and Healy 2008).

However, these observer effects can be either controlled through training with structured citizen science, or accounted for provided information is available on, for example, survey effort, experience, etc.

A way forward

Merely pointing out the challenges arising from using citizen surveillance data is obviously not enough. When making inference from those data, sampling bias and observer Fig. 4 Sightings and evidence of activity of wild dogs (dingoes, domestic dogs and their hybrids) from inland southeastern Australia uploaded to Wild Dog Scan (https://www.feralscan.org.au/wilddogscan/; Accessed 12 September, 2016). The orange line marks the position of the dingo barrier fence that separates areas of high dingo density to the north of the fence (shaded grey) from areas of very low dingo density to the south within the sheep pastoral zone effects will have to be accounted for. In our proposed framework (Fig. 2) we made a clear distinction between the observation and reporting process. Most of the challenges discussed above were looked at in the light of the observation process simply because little is known on how the reporting process is affected. We can speculate that there will be similarities in the types of bias arising in both processes, but there might also be differences. From a statistical perspective, we would like to account for as much bias as possible to ensure valid inference and it would be particularly useful if we could even distinguish between the different sources of potential bias.

Given the rise of web-platforms enabling unstructured citizen science, we believe that effort should be targeted to developing statistical methods for analysing these data. Because of their less controlled observation and reporting process, compared to structured citizen science, sampling and reporting bias will play a major role (as illustrated by the Wild Dog example discussed earlier) and will need to be accounted for. However, this will only be possible, if information around sampling effort or knowledge around what drives the reporting is available. Only then statisticians can try to develop methods that incorporate such information to improve inference from unstructured citizen science.

Conclusion

In this paper, we advocate an integrated view on citizen surveillance that combines the existing efforts of citizen science and crowdsourcing. Based on the characteristics of the observation process, both disciplines can be naturally combined in one quantitative framework that allows for an integrated approach to solve the statistical challenges inherent to citizen-sourced data. See et al. (2016) also present a two-dimensional framework to distinguish types of crowdsourced geographic information based on who collects the data (government agencies versus general public) and the contribution characteristic of the data (active versus passive). Their framework no doubt contributes to structuring the thinking around crowdsourced geographic information, however, it does not cover off on the specifics of the data collection process as practiced in citizen surveillance for environmental monitoring. In particular, no distinction is being made between the actual observation process of a species versus the reporting process of a species. In this review, we have highlighted the importance of this distinction as knowledge of those processes, or the absence of such knowledge, will have to inform the inference methods applied to these data.

Our framework distinguished between three major types of citizen surveillance:

Structured citizen science

Currently the most published form of citizen surveillance and also the type that aligns most with the current understanding of citizen science. The use of scientific principles, the potential for training of participants and the overall high level of involvement of the volunteers creates a data stream that has the potential to provide high-quality data that are in a format (e.g. counts) that can be analyzed with readily available tools. However, because of its reliance on funding, the temporal and spatial scope can be limited.

Unstructured citizen science

Enabled by online platforms like iNaturalist (http://www.inaturalist.org), unstructured citizen science provides access to an audience that is intrinsically motivated to report observations. These datasets are more prone to sampling and observer bias, but additional data fields in the recording process can allow for the collection of meta-data that can be incorporated in the analysis process. However, statistical techniques to make useful inference of these data are not readily available at this point in time and more effort will have to be targeted to the validation of these techniqes if we want to rely more on unstrictured citizen science for environmental monitoring.

Crowdsourcing

Crowdsourcing offers an untapped resource of surveillance data with contributions to, for example, social media increasing constantly. Successful examples include, but are not limited too, the tracking of human disease (Culotta 2010) and resource allocation after disasters (Zhu et al. 2011). However, analyzing these data for environmental surveillance purposes will be a huge challenge because the information of interest can be buried amongst numerous irrelevant posts. Methods to reliably retrieve this information are still being developed. In addition, both unstructured citizen science and crowdsourcing will most commonly provide presence-only data and the analysis of these data is still an active topic of discussion amongst statisticians (Royle et al. 2012;Hastie and Fithian 2013).

This framework that classifies citizen surveillance data is a way forward to achieve the full potential of those data. Indeed, such classification models are a necessity because they point to the importance of describing the sampling process and the observation process (Bird et al. 2014). This information is crucial during the analysis process as it can be used to improve statistical models for citizen surveillance data. Initiatives like eBird (http://www.ebird.org) make it possible to collect environmental data world-wide. While current efforts go mainly in engaging volunteers, data storage and data sharing, there is still a major role for statisticians to guide citizen surveillance projects with better ways to collect their data and to improve statistical models for citizen surveillance data.

Symbol legend: ++ Good; + Moderate; −/+ Variable; − Low; −− Bad

Table 1
1summarizes some of the advantages and disadvantages for the different types of citizen science.CONTROLLED

DETECTION

OPPORTUNISTIC

DETECTION

UNINTENTIONAL

REPORTING

INTENTIONAL

REPORTING

STRUCTURED

CITIZEN SCIENCE

UNSTRUCTURED

CITIZEN SCIENCE

CROWDSOURCING

93 %

6 %
1 %

eBird/Audubon Christmas

Bird Count/iNaturalist

Atlas of Living Australia

iNaturalist/eBird

Twitter/Facebook

Instagram

AcknowledgementsThis work was supported by the Australian Government's Cooperative Research Centres Program (Project CRC 1029, Plant Biosecurity CRC). Many thanks to Sorada Tapsuwan, Ross Sparks, Rieks Van Klinken and an anonymous reviewer for their insightful comments on earlier drafts of this paper. We would also like to thank Roger Pech, Peter West and Jennyffer Cruz-Bernall for their input on the topic of citizen surveillance.Competing interestsBoth authors declare that they have no competing interests.Received: 24 November 2015 Accepted: 19 October 2016Authors' contributionsMW collected the data, designed the study and wrote the manuscript. PC participated in the design of the study and helped draft the manuscript. Both authors read and approved the final manuscript.Author details
Measures and indicators of VGI quality: an overview. In: ISPRS annals of the photogrammetry, remote sensing and spatial information sciences. V Antoniou, A Skopeliti, 10.5194/isprannals-II-3-W5-345-2015doi:10.5194/ isprannals-II-3-W5-345-2015La Grande Motte, FranceAntoniou V, Skopeliti A (2015) Measures and indicators of VGI quality: an overview. In: ISPRS annals of the photogramme- try, remote sensing and spatial information sciences, La Grande Motte, France, vol II-3/W5, pp 345-351. doi:10.5194/ isprannals-II-3-W5-345-2015

Quality assessment of the contributed land use information from OpenStreetMap versus authorative datasets. J J Arsanjani, P Mooney, A Zipf, A Schauss, OpenStreetMap in GIScience. Lecture notes in geoinformation and cartography. Arsanjani JJ, Zipf A, Mooney P, Helbich MCham, ZwitserlandSpringer International PublishingArsanjani JJ, Mooney P, Zipf A, Schauss A (2015) Quality assessment of the contributed land use information from OpenStreetMap versus authorative datasets. In: Arsanjani JJ, Zipf A, Mooney P, Helbich M (eds) OpenStreetMap in GIScience. Lecture notes in geoinformation and cartography. Springer International Publishing, Cham, Zwitserland, pp 37-58

Engaging the broader community in biodiversity research: the concept of the comber pilot project for divers in vibrant. C Arvanitidis, S Faulwetter, G Chatzigeorgiou, L Penev, O Banki, T Dailianis, E Pafilis, M Kouratoras, E Chatzinikolaou, L Fanini, A Vasileiadou, C Pavloudi, P Vavilis, P Koulouri, C Dounas, 10.3897/zookeys.150.2149doi:10.3897/ zookeys.150.2149ZooKeys I. 150Arvanitidis C, Faulwetter S, Chatzigeorgiou G, Penev L, Banki O, Dailianis T, Pafilis E, Kouratoras M, Chatzinikolaou E, Fanini L, Vasileiadou A, Pavloudi C, Vavilis P, Koulouri P, Dounas C (2011) Engaging the broader community in biodiver- sity research: the concept of the comber pilot project for divers in vibrant. ZooKeys I(150):211-229. doi:10.3897/ zookeys.150.2149

Maximizing benefits from crowdsourced data. G Barbier, R Zafarani, H J Gao, G Fung, H Liu, 10.1007/s10588-012-9121-2Comput Math Organ Theory. 183Barbier G, Zafarani R, Gao HJ, Fung G, Liu H (2012) Maximizing benefits from crowdsourced data. Comput Math Organ Theory 18(3):257-279. doi:10.1007/s10588-012-9121-2

Statistical solutions for error and bias in global citizen science datasets. T J Bird, A E Bates, J S Lefcheck, N A Hill, R J Thomson, G J Edgar, R D Stuart-Smith, S Wotherspoon, M Krkosek, J F Stuart-Smith, G T Pecl, N Barrett, S Frusher, Biol Conserv. 173Bird TJ, Bates AE, Lefcheck JS, Hill NA, Thomson RJ, Edgar GJ, Stuart-Smith RD, Wotherspoon S, Krkosek M, Stuart-Smith JF, Pecl GT, Barrett N, Frusher S (2014) Statistical solutions for error and bias in global citizen science datasets. Biol Conserv 173:144-154

Distorted views of biodiversity: spatial and temporal bias in species occurrence data. E H Boakes, Pjk Mcgowan, R A Fuller, C Q Ding, N E Clark, O' Connor, K Mace, G M , 10.1371/journal.pbio.1000385PLoS Biol. 8611Boakes EH, McGowan PJK, Fuller RA, Ding CQ, Clark NE, O'Connor K, Mace GM (2010) Distorted views of biodiversity: spatial and temporal bias in species occurrence data. PLoS Biol 8(6):11. doi:10.1371/journal.pbio.1000385

Citizen science: a developing tool for expanding science knowledge and scientific literacy. R Bonney, C B Cooper, J Dickinson, S Kelling, T Phillips, K V Rosenberg, J Shirk, 10.1525/bio.2009.59.11.9Bioscience. 5911Bonney R, Cooper CB, Dickinson J, Kelling S, Phillips T, Rosenberg KV, Shirk J (2009) Citizen science: a developing tool for expanding science knowledge and scientific literacy. Bioscience 59(11):977-984. doi:10.1525/bio.2009.59.11.9

Next steps for citizen science. R Bonney, J L Shirk, T B Phillips, A Wiggins, H L Ballard, A J Miller-Rushing, J K Parrish, Science. 343Bonney R, Shirk JL, Phillips TB, Wiggins A, Ballard HL, Miller-Rushing AJ, Parrish JK (2014) Next steps for citizen science. Science 343:1436-1437

Quantifying the sampling error in tree census measurements by volunteers and its effect on carbon stock estimates. N Butt, E Slade, J Thompson, Y Malhi, T Riutta, Ecol Appl. 234Butt N, Slade E, Thompson J, Malhi Y, Riutta T (2013) Quantifying the sampling error in tree census measurements by volunteers and its effect on carbon stock estimates. Ecol Appl 23(4):936-943

Discovery and significance of the colonial tunicate Didemnum Vexillum in Alaska. C S Cohen, L Mccann, T Davis, L Shaw, G Ruiz, 10.3391/ai.2011.6.3.03Aquat Invasions. 63Cohen CS, McCann L, Davis T, Shaw L, Ruiz G (2011) Discovery and significance of the colonial tunicate Didemnum Vexil- lum in Alaska. Aquat Invasions 6(3):263-271. doi:10.3391/ai.2011.6.3.03

Naming the Bonaire banded box jelly, Tamoya ohboya, n. sp. (Cnidaria: Cubozoa: Carybdeida: Tamoyidae). A G Collins, B Bentlage, W B Gillan, T H Lynn, A C Morandini, A C Marques, Zootaxa. 2753Collins AG, Bentlage B, Gillan WB, Lynn TH, Morandini AC, Marques AC (2011) Naming the Bonaire banded box jelly, Tamoya ohboya, n. sp. (Cnidaria: Cubozoa: Carybdeida: Tamoyidae). Zootaxa 2753:53-68

Towards detecting influenza epidemics by analyzing twitter messages. A Culotta, 10.1145/1964858.1964874Proceedings of the first workshop on social media analytics. the first workshop on social media analyticsNew York, NY, USA, SOMA '10ACMCulotta A (2010) Towards detecting influenza epidemics by analyzing twitter messages. In: Proceedings of the first work- shop on social media analytics, ACM, New York, NY, USA, SOMA '10, pp 115-122. doi:10.1145/1964858.1964874

Can citizen science monitor whale-shark aggregations? Investigating bias in mark-recapture modelling using identification photographs sourced from the public. T K Davies, G Stevens, M G Meekan, J Struve, J M Rowcliffe, 10.1071/WR12092Wildl Res. 398Davies TK, Stevens G, Meekan MG, Struve J, Rowcliffe JM (2012) Can citizen science monitor whale-shark aggregations? Investigating bias in mark-recapture modelling using identification photographs sourced from the public. Wildl Res 39(8):696-704. doi:10.1071/WR12092

Accounting for imperfect detection and survey bias in statistical analysis of presence-only data. R M Dorazio, 10.1111/geb.12216Glob Ecol Biogeogr. 2312Dorazio RM (2014) Accounting for imperfect detection and survey bias in statistical analysis of presence-only data. Glob Ecol Biogeogr 23(12):1472-1484. doi:10.1111/geb.12216

Bias correction in species distribution models: pooling survey and collection data for multiple species. W Fithian, J Elith, T Hastie, D A Keith, 10.1111/2041-210x.12242Methods Ecol Evol. 6Fithian W, Elith J, Hastie T, Keith DA (2015) Bias correction in species distribution models: pooling survey and collection data for multiple species. Methods Ecol Evol 6:424-438. doi:10.1111/2041-210x.12242

Lessons from lady beetles: accuracy of monitoring data from US and UK citizen-science programs. M M Gardiner, L L Allee, Pmj Brown, J E Losey, H E Roy, R R Smyth, 10.1890/110185Front Ecol Environ. 109Gardiner MM, Allee LL, Brown PMJ, Losey JE, Roy HE, Smyth RR (2012) Lessons from lady beetles: accuracy of monitoring data from US and UK citizen-science programs. Front Ecol Environ 10(9):471-476. doi:10.1890/110185

Can volunteers collect data that are comparable to professional scientists? A study of variables used in monitoring the outcomes of ecosystem rehabilitation. J Gollan, L L De Bruyn, N Reid, L Wilkie, 10.1007/s00267-012-9924-4Environ Manag. 505Gollan J, de Bruyn LL, Reid N, Wilkie L (2012) Can volunteers collect data that are comparable to professional scientists? A study of variables used in monitoring the outcomes of ecosystem rehabilitation. Environ Manag 50(5):969-978. doi:10.1007/s00267-012-9924-4

People power. E Hand, 10.1038/466685aNature. 4667307Hand E (2010) People power. Nature 466(7307):685-687. doi:10.1038/466685a

Inference from presence-only data: the ongoing controversy. T Hastie, W Fithian, 10.1111/j.1600-0587.2013.00321.xEcography. 368Hastie T, Fithian W (2013) Inference from presence-only data: the ongoing controversy. Ecography 36(8):864-867. doi:10.1111/j.1600-0587.2013.00321.x

A comparison between professionally (Florida Department of Environmental Protection) and volunteer (Florida LAKEWATCH) collected trophic state chemistry data in Florida. M V Hoyer, N Wellendorf, R Frydenborg, D Bartlett, D E CanfieldJr, 10.1080/07438141.2012.736016Lake Reserv Manag. 284Hoyer MV, Wellendorf N, Frydenborg R, Bartlett D, Canfield DE Jr (2012) A comparison between professionally (Florida Department of Environmental Protection) and volunteer (Florida LAKEWATCH) collected trophic state chemistry data in Florida. Lake Reserv Manag 28(4):277-281. doi:10.1080/07438141.2012.736016

Social comparison and confidence: when thinking you're better than average predicts overconfidence (and when it does not). R P Larrick, K A Burson, J B Soll, 10.1016/j.obhdp.2006.10.002doi:10.1016/j. obhdp.2006.10.002Organ Behav Hum Decis Process. 1021Larrick RP, Burson KA, Soll JB (2007) Social comparison and confidence: when thinking you're better than average predicts overconfidence (and when it does not). Organ Behav Hum Decis Process 102(1):76-94. doi:10.1016/j. obhdp.2006.10.002

Measuring occupancy for an iconic bird species in urban parks. C J Macleod, G Tinkler, A M Gormley, E B Spurr, N Z J Ecol. 36MacLeod CJ, Tinkler G, Gormley AM, Spurr EB (2012) Measuring occupancy for an iconic bird species in urban parks. N Z J Ecol 36(3, SI):398-407

The history of public participation in ecological research. A Miller-Rushing, R Primack, R Bonney, 10.1890/110278Front Ecol Environ. 106Miller-Rushing A, Primack R, Bonney R (2012) The history of public participation in ecological research. Front Ecol Environ 10(6):285-290. doi:10.1890/110278

The trouble with overconfidence. D A Moore, P J Healy, 10.1037/0033-295X.115.2.502Psychol Rev. 1152Moore DA, Healy PJ (2008) The trouble with overconfidence. Psychol Rev 115(2):502-517. doi:10.1037/0033-295X.115.2.502

Finalizing a definition of "Citizen Science" and. Openscientist, Citizen Scientist. 17OpenScientist (2011) Finalizing a definition of "Citizen Science" and "Citizen Scientist". http://www.openscientist. org/2011/09/finalizing-definition-of-citizen.html. Accessed 17 Dec 2013

Understanding citizen science and environmental monitoring. Final report on behalf of UK environmental observation framework. H Roy, M Pocock, C Preston, D Roy, J Savage, J Tweddle, L Robinson, Roy H, Pocock M, Preston C, Roy D, Savage J, Tweddle J, Robinson L (2012) Understanding citizen science and environ- mental monitoring. Final report on behalf of UK environmental observation framework, Nov 1-67

Likelihood analysis of species occurrence probability from presenceonly data for modelling species distributions. J A Royle, R B Chandler, C Yackulic, J D Nichols, 10.1111/j.2041-210X.2011.00182.xMethods Ecol Evol. 33Royle JA, Chandler RB, Yackulic C, Nichols JD (2012) Likelihood analysis of species occurrence probability from presence- only data for modelling species distributions. Methods Ecol Evol 3(3):545-554. doi:10.1111/j.2041-210X.2011.00182.x

Observer differences in the North American breeding bird survey. J R Sauer, B G Peterjohn, W A Link, Auk. 1111Sauer JR, Peterjohn BG, Link WA (1994) Observer differences in the North American breeding bird survey. Auk 111(1):50-62

Comparing the quality of crowdsourced data contributed by expert and non-experts. L See, A Comber, C Salk, S Fritz, M Van Der Velde, C Perger, C Schill, I Mccallum, F Kraxner, M Obersteiner, 10.1371/journal.pone.0069958PLoS ONE. 8711See L, Comber A, Salk C, Fritz S, van der Velde M, Perger C, Schill C, McCallum I, Kraxner F, Obersteiner M (2013) Compar- ing the quality of crowdsourced data contributed by expert and non-experts. PLoS ONE 8(7):11. doi:10.1371/journal. pone.0069958

Crowdsourcing, citizen science or volunteered geographic information? The current state of crowdsourced geographic information. L See, P Mooney, G Foody, L Bastin, A Comber, J Estima, S Fritz, N Kerle, B Jiang, M Laakso, H Y Liu, G Milcȋnski, M Niksȋc̆, M Painho, A Pődör, A M Olteanu-Raimond, M Rutsinger, 10.3390/ijgi5050055doi:10.3390/ ijgi5050055Int J Geoinf. See L, Mooney P, Foody G, Bastin L, Comber A, Estima J, Fritz S, Kerle N, Jiang B, Laakso M, Liu HY, Milcȋnski G, Niksȋc̆ M, Painho M, Pődör A, Olteanu-Raimond AM, Rutsinger M, (2016) Crowdsourcing, citizen science or volunteered geographic information? The current state of crowdsourced geographic information. Int J Geoinf. doi:10.3390/ ijgi5050055

Distribution models for koalas in South Australia using citizen science-collected data. A M Sequeira, P E Roetman, C B Daniels, A K Baker, C J Bradshaw, Ecol Evol. 411Sequeira AM, Roetman PE, Daniels CB, Baker AK, Bradshaw CJ (2014) Distribution models for koalas in South Australia using citizen science-collected data. Ecol Evol 4(11):2103-2114

Evaluating citizen-based presence data for bird monitoring. T Snall, O Kindvall, J Nilsson, T Part, 10.1016/j.biocon.2010.11.010Biol Conserv. 1442Snall T, Kindvall O, Nilsson J, Part T (2011) Evaluating citizen-based presence data for bird monitoring. Biol Conserv 144(2):804-810. doi:10.1016/j.biocon.2010.11.010

eBird: a citizen-based bird observation network in the biological sciences. B L Sullivan, C L Wood, M J Iliff, R E Bonney, D Fink, S Kelling, 10.1016/j.biocon.2009.05.006Biol Conserv. 14210Sullivan BL, Wood CL, Iliff MJ, Bonney RE, Fink D, Kelling S (2009) eBird: a citizen-based bird observation network in the biological sciences. Biol Conserv 142(10):2282-2292. doi:10.1016/j.biocon.2009.05.006

Global change and local solutions: tapping the unrealized potential of citizen science for biodiversity research. E J Theobald, A K Ettinger, H K Burgess, L B Debey, N R Schmidt, H E Froehlich, C Wagner, J Hillerislambers, J Tewksbury, M A Harsch, J K Parrish, 10.1016/j.biocon.2014.10.021Biol Conserv. 181Theobald EJ, Ettinger AK, Burgess HK, DeBey LB, Schmidt NR, Froehlich HE, Wagner C, HilleRisLambers J, Tewksbury J, Harsch MA, Parrish JK (2015) Global change and local solutions: tapping the unrealized potential of citizen science for biodiversity research. Biol Conserv 181:236-244. doi:10.1016/j.biocon.2014.10.021

Thinking scientifically during participation in a citizen-science project. D J Trumbull, R Bonney, D Bascom, A Cabral, 10.1002/%28SICI%291098-237X%28200003%2984:2%3C265::AID-SCE7%3E3.0.CO;2-5Sci Educ. 842Trumbull DJ, Bonney R, Bascom D, Cabral A (2000) Thinking scientifically during participation in a citizen-science project. Sci Educ 84(2):265-275. doi:10.1002/(SICI)1098-237X(200003)84:2<265::AID-SCE7>3.0.CO;2-5

Accounting for detectability when estimating avian abundance in an urban area. Y Van Heezik, P J Seddon, N Z J Ecol. 363van Heezik Y, Seddon PJ (2012) Accounting for detectability when estimating avian abundance in an urban area. N Z J Ecol 36(3):391-397

Citizen science reveals an extensive shift in the winter distribution of migratory western grebes. S Wilson, E M Anderson, Asg Wilson, D F Bertram, P Arcese, 10.1371/journal.pone.0065408PLoS ONE. Wilson S, Anderson EM, Wilson ASG, Bertram DF, Arcese P (2013) Citizen science reveals an extensive shift in the winter distribution of migratory western grebes. PLoS ONE. doi:10.1371/journal.pone.0065408

Monitoring of biological diversity in space and time. N Yoccoz, J Nichols, T Boulinier, 10.1016/S0169-5347(01)02205-4Trends Ecol Evol. 168Yoccoz N, Nichols J, Boulinier T (2001) Monitoring of biological diversity in space and time. Trends Ecol Evol 16(8):446- 453. doi:10.1016/S0169-5347(01)02205-4

Statistically modeling the effectiveness of disaster information in social media. J Zhu, F Xiong, D Piao, Y Liu, Y Zhang, 10.1109/GHTC.2011.48Global humanitarian technology conference (GHTC). Zhu J, Xiong F, Piao D, Liu Y, Zhang Y (2011) Statistically modeling the effectiveness of disaster information in social media. In: Global humanitarian technology conference (GHTC), 2011 IEEE, pp 431-436. doi:10.1109/GHTC.2011.48

Participatory science and education: bringing both views into focus. B Zoellick, S J Nelson, M Schauffler, 10.1890/110277Front Ecol Environ. 106Zoellick B, Nelson SJ, Schauffler M (2012) Participatory science and education: bringing both views into focus. Front Ecol Environ 10(6, SI):310-313. doi:10.1890/110277


Address: 1 Section of Adult Hematology and Hematopoietic, Stem Cell Transplant

Khalid A Al-Anazi
Asma M Al-Jasser
Section of Microbiology
Department of Pathology
Armed Forces Hospital
Box: X-966P.O. Box: 789711159RiyadhSaudi Arabia

Hazza A Al-Zahrani email@example.com
Naem Chaudhri email@example.com
Fahad I Al-Mohareb email@example.com

King Faisal Specialist Hospital and Research Centre
P.O. Box: 334511211RiyadhSaudi Arabia

Address: 1 Section of Adult Hematology and Hematopoietic, Stem Cell Transplant
10.1186/1757-1626-1-160BioMed Central Page 1 of 4 (page number not for citation purposes) Case Report * Corresponding author
Background: Klebsiella oxytoca can cause various infectious complications in healthy as well as in immunocompromised individuals.Case Presentations: Case 1: A 49 year old female with multiple myeloma received an autologous hematopoietic stem cell transplant in October 2005. Eight days following her autograft she developed septic shock caused by Klebsiella oxytoca bacteremia which was successfully treated with intravenous meropenem and gentamicin. Case 2: A 29 year old female with sickle cell anemia and severe aplastic anemia underwent an allogeneic hematopoietic stem cell transplant in July 2005. Seven months following her unsuccessful allograft, she developed septic shock due to Klebsiella oxytoca bacteremia caused by a urinary tract infection. The septic episode was successfully managed with intravenous meropenem and gentamicin. Both patients were treated at King Faisal Specialist Hospital and Research Centre in Riyadh, Saudi Arabia. To our knowledge, they are the first reports of Klebsiella oxytoca bacteremias and septic shocks in hematopoietic stem cell transplant recipients.Conclusion:Klebsiella oxytoca should be considered as a possible cause of severe infections in recipients of various forms of hematopoietic stem cell transplantation. However, these infections may be complicated by bacteremias, septic shocks, systemic dysfunctions and even deaths if not managed promptly and appropriately.

Introduction

Klebsiella species are the second most frequent cause of gram-negative bacteremia [1]. Since the early 1980s, Klebsiella oxytoca (K. oxytoca) isolates have been recognized as clinically significant [2]. The isolation of this organism from clinical specimens is an indication for therapy [2]. Extended spectrum beta lactamase (ESBL) production by K. oxytoca causes bacterial resistance to B-lactam antibiotics and contributes to therapeutic problems [2,3]. The organism is usually resistant to the oxyimino B-lactams such as cefotaxime, ceftazidime and the monobactam aztreonam [2]. In patients with Klebsiella bacterermia, monotherapy with an antibiotic that is active in-vitro against Klebsiella eg a beta-lactam or an aminoglycoside is a sufficient treatment for hemodynamically stable patients but in severely ill patients having hypotension, an antibiotic combination therapy with a beta-lactam and an aminoglycoside is usually preferred [1].

Case presentations

Case 1

A 49 year old Saudi female was diagnosed to have multiple myeloma (MM), IgG kappa, at King Faisal Specialist Hospital and Research Centre (KFSH&RC) in Riyadh in April 2005. She presented with: fatigue; bony pains; anemia; hypercalcemia; high total protein, Ig G level, beta-2 microglobulin and erythrocytic sedimentation rate; 40% plasma cells in the bone marrow and diffuse lytic lesions. After receiving 4 courses of VAD chemotherapy (vincristine, doxorubicin and dexamethasone), her disease became under control. In September 2005, she underwent an autologous peripheral blood stem cell collection in preparation for an autologous hematopoietic stem cell transplant (HSCT). On admission to the HSCT unit, the patient was asymptomatic and her physical examination did not reveal any abnormality. Investigations showed: CBC: WBC: 4.4 × 10 9 /L (neutrophilis: 2.64), Hb 92 g/L and PLT: 419 × 10 9 /L. The renal, hepatic and coagulation profiles were all within normal limits and a repeat bone marrow biopsy showed 4% plasma cells. The patient was conditioned with high dose IV melphalan and she received acyclovir, fluconazole and trimethoprim-sulphamethoxazole (TMP/SMZ) as infection prophylaxis. On 5/10/2005, the patient received her autograft without any complications. In the early post-HSCT period, she developed: grade II mucositis treated with IV morphine infusion and Clostridiun difficile colitis treated with metronidazole. On 13/10/2005; the patient became febrile so septic screens were taken and she was empirically commenced IV piperacillin-tazobactam 4.5 grams thrice daily and IV gentamicin 2 mg/kg twice daily. On 14/10/05, she started to have high fever and rigors and her BP dropped to 85/40 mm Hg. However, she had no other complaints and physical examination did not reveal any focus of infection. The septic screens were repeated and the IV tazobactam-piperacillin was replaced by IV meropenem 1 gram thrice daily. Few hours later, the patient started to improve clinically and her BP became stable. The central blood cultures taken on 13/10/05 grew K. oxytoca sensitive to augmentin, cefazolin, ceftiaxone, gentamicin and meropenem. The peripheral blood cultures as well as the urine, the throat and the stool cultures were negative. As the patient maintained her hemodynamic stability, she was continued on meropenem and gentamicin. She engrafted her leucocytes on day +14 and her platelets on day + 12 HSCT. After the recovery of her blood counts and the control of her septic episode, the IV antibiotics were replaced by oral cefuroxime. On 23/10/2005, the patient was having no complaints and her repeated physical examination revealed no abnormality. The blood indices, the renal and hepatic profiles were normal. She was sent home on: cefuroxime 500 mg orally twice daily for 4 days in addition to prophylactic TMP/SMZ, acyclovir and fluconazole till day +30 HSCT. Thereafter the patient had regular follow up at the HSCT outpatient clinic and no major complication was encountered.

Case 2

A 29 year old Saudi female with clinically mild sickle cell disease, requiring infrequent blood transfusions and mild analgesia but no iron chelating agents, was diagnosed to have severe aplastic anemia (SAA) at KFSH&RC in Riyadh in late December 2005. She presented with mucosal bleeding, anemic manifestation, pancytopenia and severely hypocellular bone marrow without blasts or abnormal cells. Screens for fanconi anemia and paroxysmal nocturnal hemoglobinuria were negative. Hepatitis serology and viral screens including parvovirus B-19 were also negative. After failing to respond to a course of antithymocyte globulin, cyclosporine-A and prednisone and finding a healthy and an HLA-identical sibling donor, the patient was planned for an allogeneic HSCT. She was conditioned with cyclophosphmide and fludarabine. She received methotrexate and cyclosporine-A as graft versus host disease (GVHD) prophylaxis and fluconazole, acyclovir and penicillin as infection prophylaxis. She received her allograft on 11/7/2005. Unfortunately she had no engraftment so she remained pancytopenic and she was continued on G-CSF in addition to platelet and packed red cell transfusions. She was given various antimicrobials for the repeated infections encountered. On 15/2/2006, the patient was readmitted with fever, rigors, dysuria, dizziness and fatigue for 2 days. Physical examination showed: temperature: 39.4°C, BP: 80/40 mmHg and pulse rate 112/minute. She was looking unwell and pale. Her chest was clear. There was bilateral loin tenderness but no palpable abdominal organomegaly. The cardiovascular and neurological examinations were normal. After taking blood cultures and septic screens, the patient was commenced on: IV fluids at 150 cc/hour, IV meropenem: 1 gram thrice daily and IV gentamicin 2 mg/kg twice daily in addition to GCSF and blood product transfusion support. Few hours later, the patient started to improve; the pyrexia and the rigors subsided and the BP started to stabilize. Thereafter the patient maintained her hemodynamic stability. The central and the peripheral blood cultures as well as the urine cultures taken on 15/2/2006 grew: K. oxytoca sensitive to: augmentin, cefazolin, gentamicin and meropenem but resistant to amoxycillin. On 16/9/2006, the patient was asymptomatic, afebrile, her BP was normal and her repeated physical assessment showed no more loin tenderness. Six days after the septic shock, the IV gentamicin was stopped but the IV meropenem was continued for a total duration of 2 weeks. Later on, the patient remained pancytopenic but clinically and hemodynamically stable. On 22/2/2006, the patient received a booster dose of peripheral blood stem cells from the same donor, which did not have any positive impact on her bone marrow function. On 5/3/2006, the patient was discharged on: prophylactic ciprofloxacin, fluconazole and acyclovir in addition to pentamidine nebulizer 300 mg once/month. Thereafter, she continued to be pancytopenic and to have regular follow up at the HSCT outpatient clinic.

Discussion

Infections caused by K. oxytoca occur in healthy individuals, newborn babies and immunocompromised hosts such as patients with diabetes mellitus, solid tumours and leukemia [4][5][6][7][8][9][10][11][12][13]. These infections may be encountered in patients having: neurosurgical procedures, prostatectomies, colonoscopies, intravascular catheters, platelet transfusions, urinary tract infections and pre-existing viral or antibiotic induced colitis [4][5][6][7]. K. oxytoca can cause systemic infections such as meningitis, adrenal hemorrhage, hemorrhagic colitis and septic shock [4][5][6][7][8]. Outbreaks of K. oxytoca infections have been reported in: newborn babies following colonization of their digestive tracts, oncology patients following contamination of intravenous fluids and in cardiac patients following contamination of invasive blood pressure monitoring devices [11][12][13].

Bacteremia caused by K. oxytoca can be nosocomial or community-acquired [4][5][6][8][9][10][11][12][13][14]. It is usually associated with polymicrobial bacteremias and occurs in patients with certain underlying conditions eg hepatobiliary or pancreatic disorders, neoplastic disorders and diabetes mellitus [14,15]. The clinical manifestations of K. oxytoca infections are very variable and may include: biliary tract infections, primary bacteremias, intravascular deviceassociated infections, urinary tract infections, skin and soft tissue infections and peritonitis [14,15]. These infections may be complicated by: bacteremia and septic shock, disseminated intravascular coagulation and death [5,14,15]. The mortality rates range between 9 and 24% [14,15]. Poor prognosis and fatal outcomes are associated with: septic shock, deterioration of mental status, polymicrobial bacteremias and having certain underlying conditions such as solid tumours [14,15]. In K. oxytoca infections, surgical intervention, which may occasionally be required, has been shown to have a beneficial protective role [14]. Previous antibiotic therapy is strongly associated with resistance to the extended-spectrum cephalosporins [14]. In patients with K. oxytoca bacteremia, 86% of the strains have been found to be susceptible to cefazolin and almost all the strains have been found to be susceptible to: ampicillin-sulbactam, aminoglycosides, cefmetazole, quinolones and carbapenems [15]. ESBLs are extremely broad spectrum B-lactamase enzymes that are found in a variety of enterobacteriaceae. When producing these enzymes, the organisms become highly effective at inactivating various B-lactam antibiotics. The most common ESBL producing organisms are: E. coli, K. pneumoniae and K. oxytoca [3]. ESBL producing bacteriae are frequently resistant to many classes of antibiotics including aminoglycosides and flouroquinolones thus resulting in infections that are difficult to treat [2,3]. Piperacillin-tazobactam and cefepime should not be routinely administered for the treatment of ESBL producers. However, carbapenems are considered the treatment of choice for patients infected with ESBL producing pathogens [3].

The first patient presented had MM and she received an autologous HSCT after the control of her disease in an attempt to cure the myeloma. Despite giving IV gentamicin and piperacillin-tazobactam to control her febrile neutropenic episode, she developed the septic shock one day later. There was no clinical focus for the infection although it coincided with an episode of colitis treated with metronidazole. Few hours after shifting to IV meropenem, her septic shock became under control. The second patient had recurrent infections as she remained pancytopenic following the failure of her allogeneic HSCT. The urinary tract infection was the cause of her K. oxytoca bacteremia. As the patient presented to the HSCT clinic with clinical evidence of septicemia (fever, rigors and hypotension), she was immediately commenced on IV meropenem and gentamicin which controlled her septic shock within few hours. In both patients, the organisms were not ESBL producers and the septic episodes were successfully managed in the HSCT unit without resorting to higher levels of care or even inotropic support.

Conclusion

K. oxytoca can cause serious infections, bacteremias and septic shocks in immunocompromised individuals including recipients of various forms of HSCT. Careful clinical assessment, taking enough investigations and administration of appropriate antimicrobial therapy are essential not only to control these infections but also to prevent further complications.
 (page number not for citation purposes)
AcknowledgementsWe are grateful to all medical, nursing and technical staff, at King Faisal Specialist Hospital and Research Centre in Riyadh, Saudi Arabia, who participated in the management of the patients presented.Competing interestsThe authors declare that they have no competing interests.Authors' contributionsAll authors participated, from clinical and laboratory points of view, in the management of the patients presented.ConsentWritten consents were obtained from both patients for publication of these case reports.
Prospective observational study of Klebsiella bacteremia in 230 patients: outcome for antibiotic combinations versus monotherapy. J A Korvick, C S Bryan, B Farber, T R Beam, L Schenfeld, R R Muder, D Weinbaum, R Lumish, D N Gerding, M M Wagener, Antimicrob Agents Chemother. 3612Korvick JA, Bryan CS, Farber B, Beam TR, Schenfeld L, Muder RR, Weinbaum D, Lumish R, Gerding DN, Wagener MM: Prospective observational study of Klebsiella bacteremia in 230 patients: outcome for antibiotic combinations versus monotherapy. Antimicrob Agents Chemother 1992, 36(12):2639-2644.

Characterization of Klebsiella oxytoca septicemia isolates resistant to aztreonam and cefuroxime. S W Wu, K Dornbusch, E Goransson, U Ransjo, G Kornvall, J Antimicrob Chemother. 283Wu SW, Dornbusch K, Goransson E, Ransjo U, Kornvall G: Charac- terization of Klebsiella oxytoca septicemia isolates resistant to aztreonam and cefuroxime. J Antimicrob Chemother 1991, 28(3):389-397.

Extended-spectrum Blactamases: epidemiology, detection and treatment. S Nathisuwan, D S Burgess, J S Lewis, Ii, Pharmacotherapy. 218Nathisuwan S, Burgess DS, Lewis JS II: Extended-spectrum B- lactamases: epidemiology, detection and treatment. Pharma- cotherapy 2001, 21(8):920-928.

Klebsiella oxytoca meningitis: frequent association with neurosurgical procedures. L M Tang, S T Chen, Infection. 233Tang LM, Chen ST: Klebsiella oxytoca meningitis: frequent association with neurosurgical procedures. Infection 1995, 23(3):163-167.

Adrenal hemorrhage associated with Klebsiella oxytoca bacteremia. K Hori, H Yasoshima, A Yamada, K Sakurai, E Ohkubo, A Kubota, K Uematsu, H Sasio, Y Mizokami, T Shimoyama, Intern Med. 3711Hori K, Yasoshima H, Yamada A, Sakurai K, Ohkubo E, Kubota A, Uematsu K, Sasio H, Mizokami Y, Shimoyama T: Adrenal hemor- rhage associated with Klebsiella oxytoca bacteremia. Intern Med 1998, 37(11):990-994.

Septic shock caused by Klebsiella oxytoca after colonoscopy. J Haddad, N Marcato, P Cassan, Haddad J, Marcato N, Cassan P: Septic shock caused by Klebsiella oxytoca after colonoscopy.

. Gastroenterol Clin Biol. 182Gastroenterol Clin Biol 1994, 18(2):181-182.

Klebsiella oxytoca septicemia complicating rota virus-associated acute diarrhea. Y Kashiwagi, S Sato, M Nakamura, S Kuboshima, H Numabe, H Kawashima, K Takekuma, A Hoshika, T Matsumoto, Pediatr Infect Dis J. 262Kashiwagi Y, Sato S, Nakamura M, Kuboshima S, Numabe H, Kawashima H, Takekuma K, Hoshika A, Matsumoto T: Klebsiella oxytoca septicemia complicating rota virus-associated acute diarrhea. Pediatr Infect Dis J 2007, 26(2):191-192.

Klebsiella oxytoca septicemia following platelet transfusion. D Boyeldieu, H Vu-Thien, C Dollfus, M Bounetta, P Ballerini, I Gerota, D Moissenet, J Landman, B Tourniaire, G Leverger, Pathol Biol. 475Boyeldieu D, Vu-Thien H, Dollfus C, Bounetta M, Ballerini P, Gerota I, Moissenet D, Landman J, Tourniaire B, Leverger G: Klebsiella oxy- toca septicemia following platelet transfusion. Pathol Biol (Paris) 1999, 47(5):405-407.

A cluster of nosocomial Klebsiella oxytoca bloodstream infection in a university hospital. Y C Sardan, P Zarakolu, B Altun, A Yildirim, G Yildirim, G Hascelik, O Uzuno, Infect Control Hosp Epidemiol. 2510Sardan YC, Zarakolu P, Altun B, Yildirim A, Yildirim G, Hascelik G, Uzuno O: A cluster of nosocomial Klebsiella oxytoca blood- stream infection in a university hospital. Infect Control Hosp Epi- demiol 2004, 25(10):878-882.

Klebsiella oxytoca as a causative organism of antibiotic-associated hemorrhagic colitis. C Hogenauer, C Langer, C Beuber, I T Lippe, R Schicho, G Gorkiewicz, R Krause, N Gerstgrasser, G J Krejs, T A Hinterleitner, N Engl J Med. 35523Hogenauer C, Langer C, Beuber C, Lippe IT, Schicho R, Gorkiewicz G, Krause R, Gerstgrasser N, Krejs GJ, Hinterleitner TA: Klebsiella oxytoca as a causative organism of antibiotic-associated hemorrhagic colitis. N Engl J Med 2006, 355(23):2418-2426.

Nosocomial colonization of premature babies with Klebsiella oxytoca. Probable role of enteral feeding procedure in transmission and control of the outbreak with the use of gloves. P Berthelot, F Grattard, H Patural, Ros A Jelassi-Saoudin, H Pozzetto, B Teyssier, G Lucht, F , Infect Control Hosp Epidemiol. 223Berthelot P, Grattard F, Patural H, Ros A, Jelassi-Saoudin H, Pozzetto B, Teyssier G, Lucht F: Nosocomial colonization of premature babies with Klebsiella oxytoca. Probable role of enteral feed- ing procedure in transmission and control of the outbreak with the use of gloves. Infect Control Hosp Epidemiol 2001, 22(3):148-151.

An outbreak of Klebsiella oxytoca septicemia associated with the use of invasive pressure monitoring equipment. U Ransjo, Z Good, K Jalakas, I Kuhn, I Siggelkow, B Aberg, E Anjou, Acta Anaesthesiol Scand. 363Ransjo U, GooD Z, Jalakas K, Kuhn I, Siggelkow I, Aberg B, Anjou E: An outbreak of Klebsiella oxytoca septicemia associated with the use of invasive pressure monitoring equipment. Acta Anaesthesiol Scand 1992, 36(3):289-291.

Outbreak of catheter associated Klebsiella oxytoca and Enterobacter cloacae bloodstream infections in an oncology chemotherapy center. J T Watson, R C Jones, A M Siston, J R Fernandez, K Martin, E Beck, S Sokalski, B J Jensen, M J Arduino, A Srinivasan, S I Gerber, Arch Intern Med. 16522Watson JT, Jones RC, Siston AM, Fernandez JR, Martin K, Beck E, Sokalski S, Jensen BJ, Arduino MJ, Srinivasan A, Gerber SI: Outbreak of catheter associated Klebsiella oxytoca and Enterobacter cloacae bloodstream infections in an oncology chemother- apy center. Arch Intern Med 2005, 165(22):2639-2643.

Retrospective analysis of clinical and microbiological aspects of Klebsiella oxytoca bacteremia over a 10 year period. B N Kim, J Ryu, Y S Kim, J H Woo, Eur J Clin Microbial Infect Dis. 216Kim BN, Ryu J, Kim YS, Woo JH: Retrospective analysis of clini- cal and microbiological aspects of Klebsiella oxytoca bacter- emia over a 10 year period. Eur J Clin Microbial Infect Dis 2002, 21(6):419-426.

Bacteremia due to Klebsiella oxytoca: clinical features of patients and antimicrobial susceptibilities of the isolates. R D Lin, P R Hsueh, S C Chang, Y C Chen, W C Hsieh, K T Luh, Clin Infect Dis. 246Lin RD, Hsueh PR, Chang SC, Chen YC, Hsieh WC, Luh KT: Bacter- emia due to Klebsiella oxytoca: clinical features of patients and antimicrobial susceptibilities of the isolates. Clin Infect Dis 1997, 24(6):1217-1222.