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ERROR: type should be string, got "https://doi.org/10.1007/s10764-021-00220-8\nInternational Journal of Primatology (2021) 42:589–599 https://doi.org/10.1007/s10764-021-00220-8\nInternational Journal of Primatology (2021) 42:589–599 Handling Editor: Joanna Setchell. 1\nDepartment of Biosciences, Singleton Park Campus, Swansea University, Swansea, UK The Relationship Between GPS Sampling Interval\nand Estimated Daily Travel Distances in Chacma\nBaboons (Papio ursinus) R. McCann1 & A. M. Bracken1 & C. Christensen1 & I. Fürtbauer1 & A. J. King1 Received: 9 December 2020 /Accepted: 14 April 2021/\n# The Author(s) 2021\nPublished online: 31 May 2021 * R. McCann\nrebecca.mccann55@hotmail.co.uk * A. J. King\na.j.king@swansea.ac.uk Keywords Daily travel distance . Day path length . GPS . Movement ecology . Papio\nursinus Introduction Understanding how animals interact with and move through their environment\nenables researchers to better understand animal behavior, physiology, and ecology\n(Getz and Saltz 2008; Nathan et al. 2008). Modern studies of animal movement\nuse the Global Positioning System (GPS) to estimate animals’ travel distance over\na given time period. Researchers record GPS fixes at intervals along the journey of\na focal animal or group— either using a handheld GPS (Santhosh et al. 2015;\nSchreier and Grove 2010), or by attaching a GPS logger to a focal animal\n(Hampson et al. 2010a,b; Ren et al. 2008)—and sum the distances traveled\nbetween GPS fixes. More refined estimates of distance traveled are also possible;\nfor example, modeling movement as a continuous-time stochastic process mini-\nmizes the effects of position and velocity autocorrelation that are inherent in such\ndata (Calabrese et al. 2016). Recording of GPS at intervals in time (rather than continuously) is common because\nit saves battery life and allows researchers to increase the time over which data are\ncollected (Mitchell et al. 2019; Ryan et al. 2004; Sahraei et al. 2017). However, this\npractice underestimates travel distance (McGavin et al. 2018; Sennhenn-Reulen et al. 2017). For example, a study of Guinea baboons (Papio papio) (Sennhenn-Reulen et al. 2017) examined differences in travel distance estimates from 2-h periods by subsam-\npling GPS data collected at one fix per second, finding that travel distances were\nsignificantly shorter if less frequent GPS fixes were used in calculations. Indeed,\nextensive theoretical and empirical work has shown that the temporal resolution of\nGPS fixes needs to match those of the behavior of interest; otherwise estimates are\nlikely to be inappropriate (Borger et al. 2006; de Weerd et al. 2015; Ganskopp and\nJohnson 2007; Johnson and Ganskopp 2008; McGavin et al. 2018; Mills et al. 2006;\nMitchell et al. 2019; Noonan et al. 2019; Postlethwaite and Dennis 2013; Rowcliffe\net al. 2012; Sennhenn-Reulen et al. 2017; Swain et al. 2008; Tanferna et al. 2012). Here, we estimate daily travel distances for chacma baboons (Papio ursinus) using\nGPS data collected at one fix per second synchronously for 12 adult individuals over\n11–12 days. By sampling different temporal resolutions from this high-frequency GPS\ndata set, we investigate the relationship between estimated travel distances and GPS\nsampling frequency (Sennhenn-Reulen et al. 2017). Abstract Modern studies of animal movement use the Global Positioning System (GPS) to\nestimate animals’ distance traveled. The temporal resolution of GPS fixes recorded\nshould match those of the behavior of interest; otherwise estimates are likely to be\ninappropriate. Here, we investigate how different GPS sampling intervals affect esti-\nmated daily travel distances for wild chacma baboons (Papio ursinus). By subsampling\nGPS data collected at one fix per second for 143 daily travel distances (12 baboons over\n11–12 days), we found that less frequent GPS fixes result in smaller estimated travel\ndistances. Moving from a GPS frequency of one fix every second to one fix every 30 s\nresulted in a 33% reduction in estimated daily travel distance, while using hourly GPS\nfixes resulted in a 66% reduction. We then use the relationship we find between\nestimated travel distance and GPS sampling interval to recalculate published baboon\ndaily travel distances and find that accounting for the predicted effect of sampling\ninterval does not affect conclusions of previous comparative analyses. However, if\nshort-interval or continuous GPS data—which are becoming more common in studies\nof primate movement ecology—are compared with historical (longer interval) GPS\ndata in future work, controlling for sampling interval is necessary. Keywords Daily travel distance . Day path length . GPS . Movement ecology . Papio 1\nDepartment of Biosciences, Singleton Park Campus, Swansea University, Swansea, UK McCann R. et al. 590 Introduction Then, we use the quantified\nrelationship between estimated travel distance and GPS sampling interval to recalculate\npublished baboon daily travel distances (e.g., Dunbar 1992; Johnson et al. 2015) and\nsee how estimates alter when accounting for the relationship between estimated\ndistance and GPS sampling interval found in our own data set. Study System We studied wild adult chacma baboons in the Da Gama group in Cape Town,\nSouth Africa (34.1617° S, 18.4054° E). The group’s home range includes urban\nareas comprising residential suburbs and natural areas that fall mostly within\nTable Mountain National Park which are dominated by indigenous fynbos The Relationship Between GPS Sampling Interval and Estimated Daily... 591 vegetation with smaller patches of exotic vegetation (Hoffmann and O’Riain\n2012). The Mediterranean climate of the Cape Peninsula is characterized by hot\ndry summers and mild winters with moderate–high rainfall (Hoffman and O’Riain\n2012), and in this study we use GPS data collected during winter (August) of a\nfield season lasting from July to November 2018. The Da Gama group comprised\n2 adult males, 19 adult females, and ca. 30 subadults, juveniles, and infants. Movement Data During the field season, we recorded GPS data for 13 individuals (2 males, 11 females)\nfor a mean ± SD of 42.77 ± 9.92 days, range = 21–54 days (Bracken et al. in press)\nusing in-house assembled SHOALgroup collars (F2HKv3) containing GiPSy 5 GPS\nloggers (TechnoSmArt, Italy) recording GPS fixes at 1-s sampling intervals between\n06:00:00 and 18:00:00 UTC (Bracken et al. in press). Here we use a subset of these\nGPS data that provide continuous data for 12 baboons (2 males, 10 females) for 11–12\ndays in August 2018, representing 143 daily travel distances. Before calculating daily travel distances (below), we removed erroneous GPS fixes\noutside the study area, or successive GPS fixes between which it would have been\nimpossible for the baboons to travel (Bracken et al. in press). These fixes represented a\nmedian 0.01% of GPS fixes per collar (range 0.00%–0.01%) and the remaining missing\nor removed fixes that lasted a time period of less than or equal to 10 s, were interpolated\nusing the fixLocNA function in the swaRm package (Garnier 2016) following O'Bryan\net al. (2019) and Bracken et al. (in press). This resulted in a median 0.01% of each\nbaboon’s tracks being interpolated (range 0.00%–0.01%). Remaining missing fixes\nlasting >10 s represented a median 0.56% per collar (range 0.00%–1.61%). Quantifying the Reduction in Daily Travel Distance We compared estimated daily travel distance using one fix per second GPS data to\ndifferent GPS sampling intervals to quantify the reduction in estimated distance when\nusing less frequent sampling intervals and expressed this value as a proportion. We\nfound the reduction in estimated distance traveled was proportional to GPS sampling\ninterval and was best modeled by a logarithmic function. Using this model, we\nrecalculated travel distances for 38 baboon groups (provided by Johnson et al. 2015)\nthat provide information on GPS sampling intervals. Daily Travel Distances To investigate the effect of GPS sampling interval on estimated daily travel\ndistance, we subsampled the high-frequency GPS data and calculated travel\ndistances for each baboon, for each day, using GPS fixes set at 1 s, 30 s, 60 s,\n300 s, 1200 s, 3600 s, and 7200 s. We estimated daily distance by summing\ndistances between GPS fixes and used fixed time intervals from the 1 s data set,\nsince we wanted to simulate different programmed sampling intervals used by on-\nanimal GPS loggers. Because travel distance estimates made using short GPS sampling intervals will\nbe more sensitive to measurement error than estimates made using longer GPS\nsampling intervals, we also calculated daily travel distances using 1 s smoothed\ndata in an attempt to reduce high-frequency noise (Noonan et al. 2019). To\nsmooth data, we used the function TrajSmoothSG from the trajr package in\nRstudio (version 1.3.0), which uses a Savitzky–Golay ethod (McLean and\nSkowron Volponi 2018). We applied a filter order of 2 and a filter length of 7,\nwhich approximately corresponds to our maximum level of GPS error and was\nthus expected to reduce potential noise while retaining track characteristics\n(McLean and Skowron Volponi 2018). We performed ad hoc checks of the GPS\ndata using known landmarks at the field site in South Africa, and in Swansea, UK\nand these indicated positional accuracy always to be within 5 m. McCann R. et al. 592 GPS Sampling Interval and Daily Travel Distances We investigated how GPS sampling interval affected daily travel distance estimates by\nfitting a linear mixed-effect model in RStudio using the lme4 package (Bates et al. 2015). We fitted daily travel distances (N = 1144) as our response variable and\nsampling interval (1 s, 1 s [smoothed], 30 s, 60 s, 300 s, 1200 s, 3600 s, and 7200 s)\nas a fixed categorical effect. We fitted baboon identity as a random effect to control for\npotential interindividual differences in travel distance, checked model residuals, and\nused the emmeans package (version 1.4.8; Lenth 2020) for post hoc (Tukey method)\ntests for each combination of sampling interval. Ethical Note To fit collars, a veterinarian anesthetized baboons using Ketamine (dose adjusted for\nbody mass) after cage trapping conducted by service providers in accordance with local\nprotocols (described by Fehlmann et al. 2017a). Collars were approved by Swansea\nUniversity's Ethics Committee (IP-1314-5), weighed mean 2.2% baboon body mass\n(range 1.2%–2.6%), and were fitted with a drop-off mechanism (version CR-7,\nTelonics, Inc.) to avoid the need for recapture (ESM Fig. S1). The authors declare that\nthere are no conflicts of interests. Data Availability The dataset generated and analyzed during is available in the Elec-\ntronic Supplementary Material (ESM 3). Results The mean estimated daily travel distance across all days and baboons was 10.86 km\nwhen calculated using a 1 fix per second sampling interval and 2.71 km when using a\n7200 s sampling interval. The estimated daily travel distance becomes progressively\nshorter with less frequent GPS sampling because fewer GPS fixes do not properly\ncapture the animal’s movement path (Fig. 1; ESM Video S1). As a result, less frequent\nGPS fixes result in a significant reduction in calculated daily travel distances (Fig. 2a;\nESM Table S1; Video S1), and this reduction changes with GPS sampling interval\naccording to a logarithmic function (proportion distance captured = 0.081ln(sampling\ninterval) + 0.9682; r2 = 0.99; Fig. 2b and c). The Relationship Between GPS Sampling Interval and Estimated Daily... 593 y travel distances (Fig. 3a), we found\none fix per second sampling interval\nma baboon between 06:18 and 18:00 UTC on\nng a GPS sampling interval of (a) 1 s, (b) 1 s\nd (h) 7200 s. In (b)–(h) an additional green line\nshown for comparison. 593\nDaily... Applying our model to published baboon\ntravel distances were ≥50% farther when us\nFig. 1 Path traveled (black line) by one adult female\nAugust 4th, 2018 in Cape Town, South Africa, estimate\nsmoothed, (c) 30 s, (d) 60 s, (e) 300 s, (f) 1200 s, (g) 3600\nrepresenting the path estimated using 1-s sampling inter ly travel distances (Fig. 3a), we found\nma baboon between 06:18 and 18:00 UTC on\ning a GPS sampling interval of (a) 1 s, (b) 1 s\nnd (h) 7200 s. In (b)–(h) an additional green line\nshown for comparison. Fig. 1 Path traveled (black line) by one adult female chacma baboon between 06:18 and 18:00 UTC on\nAugust 4th, 2018 in Cape Town, South Africa, estimated using a GPS sampling interval of (a) 1 s, (b) 1 s\nsmoothed, (c) 30 s, (d) 60 s, (e) 300 s, (f) 1200 s, (g) 3600 s, and (h) 7200 s. In (b)–(h) an additional green line\nrepresenting the path estimated using 1-s sampling interval is shown for comparison. Fig. Results 2 (a) Kernel probability density of daily travel distances by 12 chacma baboons over 11–12 days,\nCape Town, South Africa, measured using GPS sampling intervals ranging one fix per second to one fix p 7200 s; smoothed 1-s data (1S) are also shown. (b) Comparison of the estimated distance calculated with o\nfix per second GPS compared to less frequent GPS sampling intervals, expressed as a proportion. Comparison of the estimated distance calculated with one fix per second GPS compared to less freque\nGPS sampling intervals (log scale). For (b) and (c) individual baboon data (N = 12) are modeled by color\nlines, and the fitted logarithmic function across all data is given by the black line. The vertical axis in (b) a\n(c) is reversed to aid interpretation. (Fig. 3b). We found that the range of GPS sampling intervals used in the published\nwork is small (300–3600 s; Fig. 3a), and the proportion of distance captured did not get\nlarger or smaller for groups that travel farther (Fig. 3b and c). Discussion Using less frequent GPS sampling intervals to estimate chacma baboon daily travel\ndistances reduces the opportunity to measure an animal’s deviation from a linear path,\nresulting in smaller estimated daily travel distances. The reduction in estimated travel Fig. 3 (a) Comparison of the estimated distance calculated with one fix per second GPS (filled circle)\ncompared to less frequent GPS sampling intervals, expressed as a proportion. The dashed box indicates the\nrange of GPS sampling interval (300–3600 s) used in 38 published groups’ daily travel distances (Johnson\net al. 2015). (b) Estimation of the proportion of distance captured for 38 published group daily travel distances\n(data points given by open circles inside the dashed box) based on their reported GPS sampling intervals,\nusing the relationship modeled in (a). One fix per second GPS data used in the current study is shown by the\nfilled circle data point. (c) Predicted daily distance traveled for 38 published groups (Johnson et al. 2015),\nbased on the reported groups’ daily travel distances and their GPS sampling interval, using the model shown in\n(a). One fix per second GPS data (current study) is shown by the filled circle that falls on a 1:1 line. Fig. 3 (a) Comparison of the estimated distance calculated with one fix per second GPS (filled circle)\ncompared to less frequent GPS sampling intervals, expressed as a proportion. The dashed box indicates the\nrange of GPS sampling interval (300–3600 s) used in 38 published groups’ daily travel distances (Johnson\net al. 2015). (b) Estimation of the proportion of distance captured for 38 published group daily travel distances\n(data points given by open circles inside the dashed box) based on their reported GPS sampling intervals,\nusing the relationship modeled in (a). One fix per second GPS data used in the current study is shown by the\nfilled circle data point. (c) Predicted daily distance traveled for 38 published groups (Johnson et al. 2015),\nbased on the reported groups’ daily travel distances and their GPS sampling interval, using the model shown in\n(a). One fix per second GPS data (current study) is shown by the filled circle that falls on a 1:1 line. Fig. 3 (a) Comparison of the estimated distance calculated with one fix per second GPS (filled circle)\ncompared to less frequent GPS sampling intervals, expressed as a proportion. Results 1 Path traveled (black line) by one adult female chacma baboon between 06:18 and 18:00 UTC o\nAugust 4th, 2018 in Cape Town, South Africa, estimated using a GPS sampling interval of (a) 1 s, (b) 1\nsmoothed, (c) 30 s, (d) 60 s, (e) 300 s, (f) 1200 s, (g) 3600 s, and (h) 7200 s. In (b)–(h) an additional green lin\nrepresenting the path estimated using 1-s sampling interval is shown for comparison. Applying our model to published baboon daily travel distances (Fig. 3a), we found\ntravel distances were ≥50% farther when using one fix per second sampling interval Applying our model to published baboon daily travel distances (Fig. 3a), we found\ntravel distances were ≥50% farther when using one fix per second sampling interval McCann R. et al. 594 Fig. 2 (a) Kernel probability density of daily travel distances by 12 chacma baboons over 11–12 days, in\nCape Town, South Africa, measured using GPS sampling intervals ranging one fix per second to one fix per\n7200 s; smoothed 1-s data (1S) are also shown. (b) Comparison of the estimated distance calculated with one\nfix per second GPS compared to less frequent GPS sampling intervals, expressed as a proportion. (c)\nComparison of the estimated distance calculated with one fix per second GPS compared to less frequent\nGPS sampling intervals (log scale). For (b) and (c) individual baboon data (N = 12) are modeled by colored\nlines, and the fitted logarithmic function across all data is given by the black line. The vertical axis in (b) and\n(c) is reversed to aid interpretation. Fig. 2 (a) Kernel probability density of daily travel distances by 12 chacma baboons over 11–12 days, in\nCape Town, South Africa, measured using GPS sampling intervals ranging one fix per second to one fix per\n7200 s; smoothed 1-s data (1S) are also shown. (b) Comparison of the estimated distance calculated with one\nfix per second GPS compared to less frequent GPS sampling intervals, expressed as a proportion. (c)\nComparison of the estimated distance calculated with one fix per second GPS compared to less frequent\nGPS sampling intervals (log scale). For (b) and (c) individual baboon data (N = 12) are modeled by colored\nlines, and the fitted logarithmic function across all data is given by the black line. The vertical axis in (b) and\n(c) is reversed to aid interpretation. Fig. Discussion The dashed box indicates the\nrange of GPS sampling interval (300–3600 s) used in 38 published groups’ daily travel distances (Johnson\net al. 2015). (b) Estimation of the proportion of distance captured for 38 published group daily travel distances\n(data points given by open circles inside the dashed box) based on their reported GPS sampling intervals,\nusing the relationship modeled in (a). One fix per second GPS data used in the current study is shown by the\nfilled circle data point. (c) Predicted daily distance traveled for 38 published groups (Johnson et al. 2015),\nbased on the reported groups’ daily travel distances and their GPS sampling interval, using the model shown in\n(a). One fix per second GPS data (current study) is shown by the filled circle that falls on a 1:1 line. The Relationship Between GPS Sampling Interval and Estimated Daily... 595 distance seen with increasing GPS sampling interval (here, the difference between\nestimates at one fix per second and other intervals) can be modeled by a logarithmic\nfunction. Our findings therefore support empirical and theoretical work showing that\nthe interval at which GPS fixes are taken can systematically change movement\ndistances calculated (Borger et al. 2006; de Weerd et al. 2015; Ganskopp and Johnson\n2007; Johnson and Ganskopp 2008; McGavin et al. 2018; Mills et al. 2006; Mitchell\net al. 2019; Noonan et al. 2019; Postlethwaite and Dennis 2013; Rowcliffe et al. 2012;\nSennhenn-Reulen et al. 2017; Swain et al. 2008; Tanferna et al. 2012) and affirm\nresearch with Guinea baboons reporting similar findings when estimating travel dis-\ntances over a shorter time frame (2-h blocks) and with fewer baboons (N = 4)\n(Sennhenn-Reulen et al. 2017). (\n)\nMiscalculation of travel distances can have important implications for studies of\nmovement ecology (Hebblewhite and Haydon 2010; Patterson et al. 2008; Schick et al. 2008), disease dynamics (Dougherty et al. 2018; White et al. 2018) and designation of\nconservation spaces (Cristescu et al. 2013; Darnell et al. 2014; Douglas-Hamilton et al. 2005). For example, distances traveled calculated from GPS data have been used to\nestimate the energy cost coefficients of locomotion (e.g., Brosh et al. 2010) and these\nwill alter substantially if the relationship between estimated distances and sampling\ninterval that we report is typical across species and contexts. Discussion Estimated travel distances using high-frequency GPS data therefore cannot\nbe compared to published distance estimates (that use less frequent sampling intervals)\nwithout properly controlling for differences in sampling regimes. p\np\ny\ng\np\ng\ng\nOur case study also highlights an understudied aspect of high-resolution GPS data in\nanimal movement studies: positional accuracy. Because GPS positional error is Gauss-\nian in nature, this error will not tend to systematically alter estimates of interindividual\ndistances (Haddadi et al. 2011; King et al. 2012) or interaction with features of the\nenvironment (Fehlmann et al. 2017a; Strandburg-Peshkin et al. 2017), or conspecifics\n(Farine et al. 2016, 2017; Strandburg-Peshkin et al. 2015), and therefore does not\nnormally need to be accounted for in such contexts. However, calculated distance\ntraveled estimates are sensitive to positional measurement error (McGavin et al. 2018;\nNoonan et al. 2019), and these errors are pronounced at short GPS sampling intervals\nwhich will affect the estimated travel path. We therefore smoothed our 1-s GPS data in\nan attempt to reduce the impact of such high-frequency noise, and this resulted in\nsignificantly shorter distance estimates (ESM Table SI). Further work is now needed to\nexplore if such smoothing is required because GPS loggers have on-board smoothing\nalgorithms (which typically cannot be accessed by the end-user). These algorithms\nminimize “jitter” or “drift” when the logger is slow-moving or stationary (see ESM Fig. S2 for an example from our data) making it challenging to determine if post hoc\nsmoothing removes “real movement,” “noise,” or both. Combining aerial video footage\nand GPS data of moving animals in the wild (e.g., on a beach where tracks are left)\nwould be one way to investigate the relationship between true movement and GPS\nmeasured movement. Another would be to match GPS data to acceleration data to\ndistinguish between active and nonactive time periods (Fehlmann et al. 2017b). Finally, our findings highlight the need to choose an appropriate GPS sampling\ninterval. The smaller the sampling interval, the higher the number of GPS fixes\ntaken within a given time frame and the higher the accuracy of any subsequent\ndistance estimate. But this comes at the cost of shorter battery life, and hence a\nshorter data collection period. This makes high-resolution GPS sampling less\npractical for longer-term studies in primate spatial ecology because collars need\nto increase in size and weight to accommodate larger batteries. Discussion Indeed, our baboon case\nstudy suggests that moving from a GPS frequency of one fix every second to one fix\nevery 30 s results in a 33% reduction in estimated daily travel distance, while using\nhourly GPS fixes results in a 66% reduction in estimated daily travel distance. Future studies should consider the impact of GPS sampling intervals on distance\nestimates. Assuming that estimated distances change with GPS sampling interval\naccording to a logarithmic function may be informative, but other factors will also\nneed to be considered. In the context of baboon behavior, for example, 1) the tortuosity\nof the travel path and 2) the speed of travel will affect how much a path is\nunderestimated (Sennhenn-Reulen et al. 2017), because while slower movement de-\ncreases travel distance, more tortuous movement increases travel distance (Johnson\net al. 2015). Therefore, while the logarithmic relationship we describe could be a\ngeneral phenomenon, the effect size (exponent) will change with a myriad of social and\necological factors (Dunbar 1992; Johnson et al. 2015). Where high-accuracy estimates\nof travel distance are needed, researchers should therefore consider continuous-time\nstochastic process models (Calabrese et al. 2016) to minimize confounding effects of\nposition and velocity autocorrelation. Comparative investigations of daily travel distances between species and popula-\ntions rely on estimates of travel distances, typically from GPS data (Carbone et al. 2005; Dunbar 1992; Johnson et al. 2015). Given the significant differences in estimated\ndistances according to GPS sampling interval, this could result in flawed comparisons. Using the relationship described for our data, we calculated daily travel distance for 38\nbaboon groups (Johnson et al. 2015) as if they had used a GPS sampling interval of one\nfix per second. Published travel distances captured a minimum 50% of the distance\npredicted if a 1-s sampling interval was used, but because the range of GPS sampling\nintervals used by baboon researchers to date is small (300–3600 s) the model predicted\ndistances did not systematically vary across groups/sampling intervals. Previous com-\nparisons of daily travel distances in baboons are therefore sound. However, if high-\nresolution GPS data (as used in the present study) were to be included in such 596 McCann R. et al. comparisons in future, this would introduce pronounced differences in travel distance\nestimates. Discussion However, this\nissue can be overcome if collars use solar cells with rechargeable batteries and\ndynamically switch between different sampling rates depending on the animal’s\nactivity (e.g., Wilson et al. 2018). Given these tradeoffs, studies will likely\ncontinue to use different GPS sampling regimes, and so our case study provides\nuseful rule-of-thumb for the magnitude of change expected when estimated travel\ndistances with different GPS sampling intervals. Supplementary Information\nThe online version contains supplementary material available at https://doi. org/10.1007/s10764-021-00220-8. Supplementary Information\nThe online version contains supplementary material available at https://doi.\norg/10.1007/s10764-021-00220-8. Acknowledgments\nData were collected during fieldwork approved by the Baboon Technical Team (BTT)\nin the Cape Peninsula and by Research Agreement with South African National Parks (SANParks). Thanks to\nHuman Wildlife Solutions and veterinarian Dorothy Breed for organizing baboon cage trapping, and to Justin\nO’Riain, Gary Buhrman, Esme Beamish, and Human Wildlife Solutions for their assistance. We thank Carlo\nCatoni (TechnoSmArt), Gwenda Kesans (Ride and Drive Equestrian), Gaelle Fehlmann, Mark Holton, and References Bates, D., Machler, M., Bolker, B. M., & Walker, S. C. (2015). Fitting linear mixed-effects models usin\nlme4. Journal of Statistical Software, 67(1), 1–48. Borger, L., Franconi, N., De Michele, G., Gantz, A., Meschi, F., et al (2006). Effects of sampling regime on\nthe mean and variance of home range size estimates. Journal of Animal Ecology, 75(6), 1393–1405. Bracken, A. M., Christensen, C., O’Riain, M. J., Fehlmann, G., Holton, M. D., et al (in press). Socioecology\nexplains individual variation in urban space-use in response to management in Cape chacma baboons\n(Papio ursinus). International Journal of Primatology. Brosh, A., Henkin, Z., Ungar, E. D., Dolev, A., Shabtay, A., et al (2010). Energy cost of activities and\nlocomotion of grazing cows: A repeated study in larger plots. Journal of Animal Science, 88(1), 315–323. Calabrese, J. M., Fleming, C. 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J., Spiegel, O., & Getz, W. M. (2018). Going through the motion\nIncorporating movement analyses into disease research. Ecology Letters, 21(4), 588–604. Douglas-Hamilton, I., Krink, T., & Vollrath, F. (2005). Movements and corridors of African elephants in\nrelation to protected areas. Naturwissenschaften, 92(4), 158–163. Dunbar, R. I. M. (1992). Time: A hidden constraint on the behavioural ecology of baboons. Behavioral\nEcology and Sociobiology, 31(1), 35–49. Farine, D. R., Strandburg-Peshkin, A., Berger-Wolf, T., Ziebart, B., Brugere, I., et al (2016). Both nearest\nneighbours and long-term affiliates predict individual locations during collective movement in wild\nbaboons. Scientific Reports, 6, 27704. Farine, D. R., Strandburg-Peshkin, A., Couzin, I. The Relationship Between GPS Sampling Interval and Estimated Daily... 597 Phil Hopkins for assistance with collar design and build. AB thanks Alexis Malagnino for advice with GPS\nprocessing. AJK and IF thank Layla King for support. AB and CC were supported by College of Science/\nSwansea University PhD scholarships. Research was supported by grants from Swansea University’s College\nof Science and the Association for the Study of Animal Behaviour (ASAB). We are grateful to Editor-in-Chief\nJo Setchell and two anonymous reviewers for excellent and constructive feedback during peer review. Phil Hopkins for assistance with collar design and build. AB thanks Alexis Malagnino for advice with GPS\nprocessing. AJK and IF thank Layla King for support. AB and CC were supported by College of Science/\nSwansea University PhD scholarships. Research was supported by grants from Swansea University’s College\nof Science and the Association for the Study of Animal Behaviour (ASAB). We are grateful to Editor-in-Chief\nJo Setchell and two anonymous reviewers for excellent and constructive feedback during peer review. Author Contributions\nAJK and IF conceived the study. AB and CC constructed the tracking collars and\ncollected data in the field. AB processed the data. RMcC analyzed the data and conducted statistical analyses\nwith input from AJK, IF, and AB. RMcC wrote the first draft of the manuscript, which was revised by AJK\nwith input from all authors, who read and approved the final manuscript. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which\npermits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give\nappropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and\nindicate if changes were made. The images or other third party material in this article are included in the\narticle's Creative Commons licence, unless indicated otherwise in a credit line to the material. 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ERROR: type should be string, got "https://doi.org/10.1007/s12098-019-03123-y\nThe Indian Journal of Pediatrics (February 2020) 87(2):125–131 https://doi.org/10.1007/s12098-019-03123-y\nThe Indian Journal of Pediatrics (February 2020) 87(2):125–131 https://doi.org/10.1007/s12098-019-03123-y\nThe Indian Journal of Pediatrics (February 2020) 87(2):125–131 REVIEW ARTICLE Abstract Bloodstream infections (BSI) due to multidrug-resistant organisms, especially from pediatric intensive care units (PICU), are\nbeing increasingly reported across the world. Since BSI is associated with high mortality, it is essential to treat these infections\nearly with appropriate antibiotics. Surveillance of etiology and emerging antimicrobial resistance (AMR) is considered an\nimportant step in the formulation of antibiotic policy for early treatment and judicious use of antibiotics. In this review on\netiology and its antibiogram in community acquired BSI, S. typhi followed by S. paratyphi A were the major bacterial isolates. Quinolone resistance of more than 90% in Salmonella is now reported from all over India. Ceftriaxone remains the drug of choice\nfor enteric fever due to its 100% susceptibility. In PICU there is an emergence of candidemia due to non-albicans candida which\nare now predominant isolates at few centers. BSI due to gram-negative bacteria, mostly by Klebseilla pneumoniae and gram-\npositive cocci (S. aureus) are the other major pathogens commonly observed in BSI from PICU. There is a high prevalence of\nantimicrobial resistance to commonly used antibiotics like ampicillin (94.9%–90.7%), cefotaxime (92.4%–71.4%), piperacillin-\ntazobactum (31.2%–27.5%) and levofloxacin (42.4%–39.8%). Resistance to carbapenems, primarily due to blaNDM is seen in all\nthe centers and the rate varies between 1%- 79% with K. pneumoniae and A. baumannii showing the maximum resistance. This\nreview highlights the magnitude of the AMR in the pediatric population and calls for the urgent implementation of antimicrobial\nstewardship programs to save the remaining antimicrobials. Keywords Pediatric blood culture . PICU . AMR . Etiology . Surveillance Keywords Pediatric blood culture . PICU . AMR . Etiology . Surveillance Introduction population include immature innate and adaptive immunity\nwhich further affects the severity and duration of infections\n[4, 5]. Infectious Diseases Society of America (IDSA) guide-\nlines on Antimicrobial stewardship program (AMSP) recom-\nmends the provision of institute specific etiology and\nantibiogram for various HAIs for formulating appropriate an-\ntibiotic policy for effective treatment within first few hours to\ndecrease the mortality with BSI [6]. In spite of the above fact,\nthere is scarce data available from India on the etiology of BSI\nand its susceptibility pattern in pediatric population. Multi-drug-resistant (MDR) infections in the pediatric age\ngroup, especially due to gram-negative bacteria (GNB) are\nincreasing world over with higher mortality [1]. Overall mor-\ntality in Indian PICU due to hospital-acquired infections\n(HAIs) has been estimated to be 26%. Bloodstream infections\n(BSI) are considered as the most serious infections in pediatric\nintensive care units (PICU) and carry the highest mortality\nwith an estimated attributable mortality of 3% and crude mor-\ntality of 18% [2, 3]. Many risk factors, especially in PICU, for\nHAIs are common for adults and children which include ex-\nposure to invasive devices including intravascular catheters,\nintubation, hyper-alimentation, and other comorbidities like\nimmune-suppression. Additional risk factors in the pediatric Pediatric Blood Cultures and Antibiotic Resistance: An Overview Chand Wattal1 & Neeraj Goel1 Received: 5 November 2019 /Accepted: 6 November 2019\n# The Author(s) 2019\n/Published online: 21 December 2019 1\nDepartment of Clinical Microbiology & Immunology, Sir Ganga\nRam Hospital, Rajinder Nagar, New Delhi, India * Chand Wattal\nchandwattal@gmail.com Blood Culture Positivity The positivity rate of blood culture in pediatrics from India\nvaries from 7.2% to 88.5% with median of 35.4% [11]. The\nwide variation in the positivity of blood culture can be attrib-\nuted to many variables like the etiology of BSI, prior intake of\nantibiotics, volume and methods of blood culture practices. BSI due to endocarditis, meningitis and septic shock, are as-\nsociated with high organism load as compared to other BSI,\ntherefore such patients have high positivity as compared to\nBSI due to other reasons [12]. S. typhi (6.3%) compared to S. paratyphi A (1.98%) (Fig. 1). MDR in Salmonella is estimated to vary between 1.9% to\n4.1% [20–22]. Multiple reports from all over India have also\nshown improved susceptibility to ampicillin, co-trimoxazole,\nand chloramphenicol (ACCo) [20, 22]. Similarly, at authors’\ncenter too they noted a low ACCo resistance of 2.3% [22]. The decline in the ACCo resistance could be attributed to\nlimited use of these antibiotics due to the availability of better\nalternatives like 3rd generation cephalosporins. On the other\nhand, there has been an increasing trend of resistance to quin-\nolones in Salmonella and now almost 90–100% quinolone\nresistance in S. typhi & Paratyphi A has been reported espe-\ncially after British Society of Antimicrobial & Chemotherapy\n(BSAC) revised its breakpoint guidelines in the year 2011\n[20]. At authors’ center, they have also observed a high resis-\ntance to quinolones (96%) and nil resistance to ceftriaxone in\nthe pediatric population during the years 2014–2018. However, few cases of ceftriaxone resistance have been re-\nported from Bangladesh, Nepal, United Arab Emirates and\nGermany [23–26]; therefore a strict vigilance on the emer-\ngence of ceftriaxone resistance in salmonella needs to be\nmaintained. Azithromycin is recommended as an alternative\ntherapy to ceftriaxone in the case of resistance to quinolones The volume of blood is an important determinant in the\npositivity of blood cultures. Clinical & Laboratory Standard\nInstitute (CLSI) recommends 10 ml (adults) and 3 ml\n(Pediatrics) blood in two sets of two bottles each with one of\nthe bottles in the set as an anerobic one amounting to 40 ml\nblood in adults and 12 ml blood in pediatrics being subjected\nto culture. This can detect 90–95% of bacteremia [13, 14]. Etiology of BSI/Blood Culture Isolates Enteric fever in India affects children and adolescents of al-\nmost all age groups but the highest numbers of cases are\nreported in school going children between 5 and 15 y of age\nfollowed by preschool children (2–5 y) [19]. Enteric fever due\nto S. typhi and Paratyphi A remains the most common cause\nof community-acquired BSI in the pediatric age group at au-\nthors’ hospital (Fig. 1). In a study by Iyer et al., the prevalence of enteric fever at a\npediatric tertiary care hospital from South India during the ten-\nyear study period from 2007 to 2016, was found to be 0.5%\nand 0.1% for S. typhi and S. paratyphi A, respectively [20]. On\nthe other hand, in a systematic review and meta-analysis in\nadults and children in India, a prevalence of 9.7% of S. typhi\nand 0.9% of S. paratyphi A was observed [21]. At authors’\ncenter too they have observed a similar higher prevalence of\nS. typhi (6.3%) compared to S. paratyphi A (1.98%) (Fig. 1). Surveillance of HAIs Therefore as\nof now, we have patchy institution-specific data available for\nassessing the etiology and AMR data. The authors here have\ndone the review of isolates from pediatrics patients from the\nyear 2014 through 2018. This document will review the liter-\nature from other centers from India, as well. In this review,\nonly pediatric blood culture isolates will be discussed, since\nthis is the sample that remains the most sacrosanct among the\nbacteriological samples sent for culture and sensitivity and\nremains most representative of the BSI. central line. This practice also aids in differentiating coagulase\nnegative Staphylococcus (CONS) as colonizers or pathogens\nas per CDC guidelines [17]. Most modern laboratories nowadays utilize automated in-\ncubation and detection system that has higher efficiency and\nlower contamination rate. Most of such automated systems\nhave a shorter incubation time to positivity as compared to\nthe non-automated conventional systems. Addition of resins\nand charcoal help in improving the positivity of blood cultures\nprovided by automated systems by absorbing/neutralizing the\npresence of antibiotics [18]. Surveillance of HAIs Surveillance in a pediatric healthcare facility is usually depen-\ndent on the national, regional or institutional health require-\nments, along with the commitment and resources available. Information technology (IT) support is recognized as an es-\nsential important resource to generate reliable HAI surveil-\nlance data [6]. The patchy data on the incidence of HAIs in\ndeveloping countries is mostly due to lack of national or * Chand Wattal\nchandwattal@gmail.com Indian J Pediatr (February 2020) 87(2):125–131 126 regional AMR surveillance network, compared to the western\ncountries [4]. In India AMR data is plagued by the absence of\nmajor National AMR surveillance network. Earlier initiatives\nincluded Indian Clinical Epidemiological Network (INCLEN)\n& Indian Network for Surveillance of Antimicrobial\nResistance (INSAR) [7, 8]. More recently, the Indian\nCouncil of Medical Research (ICMR) has launched a national\nlevel the Anti-Microbial Resistance Surveillance and\nResearch Network (AMRSN) across the country in 2013 for\ngenerating data on HAIs and AMR, but has not stratified the\ndata as per the age [9]. However, resistance patterns can sig-\nnificantly differ in adults and children [10], therefore this data\ncannot be extrapolated to the pediatric age group. Therefore as\nof now, we have patchy institution-specific data available for\nassessing the etiology and AMR data. The authors here have\ndone the review of isolates from pediatrics patients from the\nyear 2014 through 2018. This document will review the liter-\nature from other centers from India, as well. In this review,\nonly pediatric blood culture isolates will be discussed, since\nthis is the sample that remains the most sacrosanct among the\nbacteriological samples sent for culture and sensitivity and\nremains most representative of the BSI. regional AMR surveillance network, compared to the western\ncountries [4]. In India AMR data is plagued by the absence of\nmajor National AMR surveillance network. Earlier initiatives\nincluded Indian Clinical Epidemiological Network (INCLEN)\n& Indian Network for Surveillance of Antimicrobial\nResistance (INSAR) [7, 8]. More recently, the Indian\nCouncil of Medical Research (ICMR) has launched a national\nlevel the Anti-Microbial Resistance Surveillance and\nResearch Network (AMRSN) across the country in 2013 for\ngenerating data on HAIs and AMR, but has not stratified the\ndata as per the age [9]. However, resistance patterns can sig-\nnificantly differ in adults and children [10], therefore this data\ncannot be extrapolated to the pediatric age group. Blood Culture Positivity Studies suggest that of the multiple 20 ml blood cultures\ndrawn in 24 h in adults, approximately 70% will have positive\ncultures after the first draw, 85% at second and 97% after 3rd\nand 99% after fourth [15, 16]. In-spite of the above CLSI\nguidelines, it is a common practice in India to obtain only\none blood culture bottle (5 ml) for diagnosing BSI. The au-\nthors presume increased cost maybe the hindrance in the im-\nplementation of CLSI guidelines. At authors’ institute, they\nhave the policy to collect at least 1 set of blood cultures (2\nblood cultures bottles of 5 ml each) from 2 different peripheral\nsites and an additional 1 blood culture bottle (5 ml) if there is a 127 Indian J Pediatr (February 2020) 87(2):125–131 Indian J Pediatr (February 2020) 87(2):125–131 0\n50\n100\n150\n200\n250\n300\n350\n400\n358\n132\n112\n15\n14\n13\n11\n10\n45\nSamples: 5634\nIsolates: 710\nFig. 1 Etiology of BSI in\npediatric age group in OPD\nsamples (2014–2018) Fig. 1 Etiology of BSI in\npediatric age group in OPD\nsamples (2014–2018) as per WHO guidelines and the current scarce literature on\nazithromycin shows little resistance to this drug [20, 27]. Therefore, nowadays, ceftriaxone and azithromycin remain\nthe drugs of choice for the empiric treatment of enteric fever\nalthough treatment may be modified based on susceptibility\nreport of the isolates. considered as skin contaminants but can be a cause of BSI in\ncases of indwelling central lines, supported by two positive\ncultures from two different sites accompanied by specific clin-\nical signs and symptoms of BSI [17]. In authors’ case, they did\nnot have adequate data to differentiate between the two. In one\nstudy CONS from a single positive culture are considered as\ncontaminant in 75% to 95% [14]. An important finding in\npresent study was the emergence of Candida spp. as the 2nd\nmost common cause of BSI in PICU. Observed candidemia\nrates in authors’setting were probably due to the greater use of\nbroad-spectrum antibacterial agents, invasive devices, more\nextensive surgical procedures and the use of advanced life\nsupport in various critical and immunosuppressed patients as\nhas been shown in a previous studies [30, 31]. Similar to\nauthors’ findings, HAIs due to Candida spp. are increasingly\nreported worldwide and are considered as major pathogens\namong immunosuppressed and critically ill patients [4, 22,\n29, 31]. Fig. 2 Isolates of BSI in Pediatric\nICU (2014–2018) Blood Culture Positivity The etiology of BSI in PICU has been changing over the\nlast few years of authors’ surveillance. Instead of GNBs being\ncommonly reported as the predominant bacterial isolates in\nmany studies [4, 11, 28], now Candida spp. have become\nthe predominant pathogens in the pediatric ICUs. In a 5 y\nstudy period on the etiology of BSI in PICU at Sir Ganga\nRam Hospital, authors processed 4307 blood samples, out of\nwhich 408 (9.5%) isolates were obtained. CONS (25.5%),\nwere the commonest bacteria isolated, followed by Candida\nspp. (13.5%), Klebseilla pneumoniae (10.8%),\nStaphylococcus aureus (6.4%), Acinetobacter baumannii\n(4.6%), Pseudomonas aeruginosa (18%) and E. coli (15%)\n(Fig. 2). Few other studies have also shown CONS as a com-\nmon organism isolated in BSI [28, 29]. CONS are normally Additionally, there was an emergence of non albicans\ncandidemia at authors’ centre. C. albicans constituted just 0\n20\n40\n60\n80\n100\n120\n104\n55\n44\n26\n19\n18\n18\n15\n15\n13\n11\n70\nSamples:4307\nIsolates: 408\nIsolates\nFig. 2 Isolates of BSI in Pediatric\nICU (2014–2018) Fig. 2 Isolates of BSI in Pediatric\nICU (2014–2018) 128 Indian J Pediatr (February 2020) 87(2):125–131 other predominant isolates in this study [28]. Dharmapalan\net al., reviewed the published data of Indian Neonatal and\npediatric population during 2000–2015 from India. After an\nextensive electronic search, 89 papers were reviewed; this 15-\nyear data reported bacteremia caused by GNB to be around\n53.3% whereas gram-positive organisms caused infection in\n30.9% of the pediatric population [11]. 12.7% of the total candidemia. Most common Candida spp. isolated were Candida tropicalis (38.2%), followed by\nCandida pelliculosa (16.4%) and Candida albicans (12.7%)\n(Fig. 3). Similar to authors’ data, in an another study by\nLakshmi et al., on BSI in PICU, there was a shift to non-\nCandida albicans species with the majority (77.8%) of them\nbeing C. tropicalis [28]. The emergence of non albicans can-\ndida at authors’ centre has been shown to correlate with in-\ncreasing use of fluconazole in authors’ previous study [31]. The data from developing countries is in contrast to the\nwestern countries, where GPCs are the predominant isolates\nfrom nosocomial infections in PICU. In a National Healthcare\nSafety Network (NHSN) by Centers for Disease Control and\nPrevention from 2011 to 2014 in 1003 hospitals, 20,390 pe-\ndiatric HAIs were reported. Blood Culture Positivity Staphylococcus aureus (17%),\nfollowed by CONS (17%), Escherichia coli (11%),\nKlebsiella pneumoniae (9%), and Enterococcus faecalis\n(8%) were the commonest organisms isolated [34]. K. pneumoniae was the commonest GNB isolated; the pos-\nsible source could be respiratory tract infections as it was the\nmost common isolate in pediatric pneumonia in PICU. The\nfinding of S. aureus as the commonest pathogen in gram-\npositive cocci (GPCs) makes empirical treatment of BSI in\npediatric ICU difficult as it would involve treatment against\ncandida, GNBs and GPCs as per the etiology of authors’\nPICU. One important observation in authors’ study was the\npresence of central lines as a major risk factor for candidemia\nin the PICU, and in the absence of central lines, GNB was the\ncommonest bacterial isolate. Fig. 3 Various species of Candida\nisolated in BSI from PICU (2014–\n2018) Emerging Antimicrobial Resistance Table 1\nPercentage susceptibility of GNBs isolated from PICU from blood\nGNBs\nNo. of isolates\nAmpicillin\nCefuroxime\nCeftriaxone\nCeftazidime\nCefepime\nPiperacillin+Tazobactum\nCefoperazone+Sulbactum\nE. coli\n15\n0\n0\n0\n–\n6\n44\n44\nKlebseilla pneumoniae\n44\n3\n5\n7\n–\n16\n27\n33\nPseudomonas aeruginosa\n18\n–\n–\n–\n67\n67\n67\n67\nAcinetobacter baumannii\n19\n–\n0\n5\n–\n16\n16\n17\nEnterobacter spp. 15\n0\n13\n33\n–\n40\n53\n53\nGNBs\nQuinolones\nGentamicin\nAmikacin\nNetilmicin\nErtapenem\nImipenem/\nMeropenem\nColisti\nE. coli\n40\n60\n88\n60\n60\n71\n100\nKlebseilla pneumoniae\n24\n31\n47\n33\n36\n42\n98\nPseudomonas aeruginosa\n64\n67\n67\n69\n–\n73\n100\nAcinetobacter baumannii\n13\n16\n21\n17\n–\n21\n100\nEnterobacter spp. 47\n53\n73\n70\n53\n67\n96 In GNBs, authors’ observed a very low susceptibility of 3rd\ngeneration cephalosporins in GNBs ranging from 7% to 33%\n(Table 1). Such a high level of resistance to cephalosporins\nrenders them ineffective for the empirical treatment of BSIs. Similarly, they observed a low susceptibility to beta lactam-\nbeta lactamase inhibitors (BL-BLI) like piperacillin/\ntazobactum and cefoperazone/sulbactum (16% to 67%) and\nquinolones (13% to 64%). What is most worrisome is that\neven the carbapenems, considered as the last resort drugs,\nshow low susceptibility in A. baumannii (21%),\nK. pneumoniae (42%), while E. coli (71%), and\nP. aeruginosa (67%) had relatively higher susceptibility. Fortunately, the authors have not seen much resistance to co-\nlistin in GNBs except in K. pneumoniae (2%) and\nEnterobacter spp. (4%). Similar to authors’ data, other centers are also reporting\nhigh resistance in GNBs from PICU. Dharmapalan et al., ob-\nserved more than 90% resistance for ampicillin in GNBs,\nwhereas resistance to amikacin ranged from 22.4% to 50%. Similarly, high resistance to cephalosporins of 62.6% in\nK. pneumoniae and 47.5% in E.coli was observed. High rates\nof resistance in the GNBs were also noted for piperacillin-\ntazobactum varying between 16.7% to 42% [11]. Other stud-\nies have also reported high resistance to commonly used first-\nline antibiotics, ampicillin (94.9%–90.6%%), cefotaxime\n(92.4%–71.4%), piperacillin-tazobactum (31.2%–27.5%)\nand levofloxacin (42.4%–39.8%) [35]. Since cephalosporins\nare the first-line antibiotics recommended in India in pediat-\nrics practice for enteric fever, meningitis, and pneumonia [11],\nits high rate of resistance is worrisome on the efficacy of these\nantibiotics. Resistance to carbapenems too is now commonly\nreported from different centers in India. Emerging Antimicrobial Resistance The authors studied the antibiogram of BSI of the important\nbacteria during the last five years at PICU of their tertiary care\ncentre. Methicillin-resistant Staphylococcus aureus (MRSA)\nprevalence of 46% and clindamycin resistance of 23% was\nobserved in 26 isolates of S. aureus. Similar to authors’ data,\nin one of the largest data from Indian Network for\nSurveillance of Antimicrobial Resistance (INSAR) group on\nthe prevalence of MRSA across 15 centres from India in a\nmixed population of pediatrics and adults, an overall MRSA\nprevalence of 41% was found from a total of 26,310 S. aureus\nisolates [8]. Although the majority of the S. aureus isolates\nwere from skin and soft tissue infections followed by BSI. There was significantly higher rates of resistance in MRSA\n(erythromycin: 70.8%, clindamycin: 46.6%, gentamicin:\n58.3%) as compared to methicillin-susceptible\nStaphylococcus aureus (MSSA) (erythromycin: 26.3%, Other studies from Indian PICU have shown higher rates of\nBSI due to GNBs as compared to GPCs and Candida spp. [11,\n29, 32]. In a retrospective study of nosocomial infections in\nPICU between 1994 and 2003, Singhi et al. observed GNB as\nthe predominant isolates, common being Klebsiella\npneumoniae (20.1%) Enterobacter spp. (16.6%) and\nAcinetobacter spp. (8.6%) [32]. In a study by Thacker et al.,\nit was shown that overall enterobacteriaceae isolates consti-\ntuted more than half of the GNBs in pediatric BSI and twice\nthat of Pseudomonas spp. [33]. In another Indian study on 285 children admitted in PICU,\nthe incidence of BSI was observed as 31.2 episodes/ 1000\npatient days with the mean age of BSI as 3.7 ± 3.5 y. GNBs\nwere the major isolates (53.5%) with Klebsiella pneumoniae\nbeing the most prevalent (24.4%), followed by S. aureus\n(20.9%). CONS (8.1%) and Candida spp. (10.5%) were the 0.0\n5.0\n10.0\n15.0\n20.0\n25.0\n30.0\n35.0\n40.0\n38.2\n16.4\n12.7\n5.5\n5.5\n3.6\n3.6\n1.8\n12.7\nCandida spp. (n=55)\n% \nFig. 3 Various species of Candida\nisolated in BSI from PICU (2014–\n2018) Candida spp. (n=55) Indian J Pediatr (February 2020) 87(2):125–131 129 clindamycin: 14.7%, gentamicin: 17.4%). There was no doc-\numented resistance to vancomycin or teicoplanin and linezo-\nlid. High rates of MRSA (50%) among the pediatric popula-\ntion has also been reported from another study as well [11]. Due to the high prevalence of MRSA in PICU, it appears that\nvancomycin should be used for suspected GPC infection\npending susceptibility reports. References 1. Falagas ME, Tansarli GS, Karageorgopoulos DE, Vardakas KZ. Deaths attributable to carbapenem-resistant Enterobacteriaceae in-\nfections. Emerg Infect Dis. 2014;20:1170–5. 2. Gray JW. A 7-year study of bloodstream infections in an English\nchildren's hospital. Eur J Pediatr. 2004;163:530–5. 2. Gray JW. A 7-year study of bloodstream infections in an English\nchildren's hospital. Eur J Pediatr. 2004;163:530–5. 3. Stoll BJ, Hansen N, Fanaroff AA, et al. Late-onset sepsis in very\nlow birth weight neonates: the experience of the NICHD Neonatal\nResearch Network. Pediatrics. 2002;110:285–91. 3. Stoll BJ, Hansen N, Fanaroff AA, et al. Late-onset sepsis in very\nlow birth weight neonates: the experience of the NICHD Neonatal\nResearch Network. Pediatrics. 2002;110:285–91. 4. Posfay-Barbe KM, Zerr DM, Pittet D. Infection control in paediat-\nrics. Lancet Infect Dis. 2008;8:19–31. 4. Posfay-Barbe KM, Zerr DM, Pittet D. Infection control in paediat-\nrics. Lancet Infect Dis. 2008;8:19–31. 5. Wattal C, Oberoi J. Infections in pediatric intensive care units\n(PICU). Indian J Pediatr. 2012;79:647–9. 5. Wattal C, Oberoi J. Infections in pediatric intensive care units\n(PICU). Indian J Pediatr. 2012;79:647–9. Emergence of non-albicans Candida spp. at authors’ center\nhas resulted in increased resistance to the first-line anti-fungal\ndrug, fluconazole. Low fluconazole susceptibility of 90.5% and\n47.6% was noted for C. tropicalis and C. pelliculosa, respec-\ntively from authors’ centre [39]. Even C. albicans which was\nshowing 100% susceptibility to fluconazole in the year 2012 is\nnow showing reduced susceptibility of 88.9% [39]. This re-\nduced susceptibility was partly due to revision in the break\npoints of fluconazole for C. albicans in 2012 [40]. C. auris is\nan emerging multi-drug resistant candida spp. in ICU settings\nand has shown 0%, 7.6% and 7.6%, 79.5% susceptibility to\nfluconazole, amphotericin, voriconazole and caspofungin, re-\nspectively, which make these difficult to treat PDR bugs [39]. 6. Barlam TF, Cosgrove SE, Abbo LM, et al. Implementing an anti-\nbiotic stewardship program: guidelines by the Infectious Diseases\nSociety of America and the Society for Healthcare Epidemiology of\nAmerica. Clin Infect Dis. 2016;62:e51–77. 7. India clen. The INCLEN India Infectious Disease Initiative (IIDI)\nUSAID/ INCLEN Final Report, 2014. Available at: http://www. inclentrust.org/inclen/ uploadedbyfck/file/publication/the%\n20inclen/USAID%20IIDI-final%20version.pdf. Accessed 26\nAugust 2015. 8. Indian Network for Surveillance of Antimicrobial Resistance\n(INSAR) group, India. Methicillin-resistant Staphylococcus aureus\n(MRSA) in India: prevalence & susceptibility pattern. Indian J Med\nRes. 2013;137:363–9. 9. Walia K, Ohri VC, Mathai D. Antimicrobial Stewardship\nProgramme of ICMR. Antimicrobial stewardship programme\n(AMSP) practices in India. Emerging Antimicrobial Resistance In an analysis of 82\npublished literature from different Indian NICU and PICU, a\nmedian carbapenem resistance of 1% in K. pneumoniae, 9% in\nE. coli, 16.7% in P. aeruginosa and 11.5% in A. baumannii\nwas seen [11]. A similar resistance of 11.1% to imipenem was\nseen in enterobacteriaceae from tertiary care centers from\nSouth-India from years 2012 to 2014 [28]. Higher resistance\nto carbapenems in PICU at authors’ centre could be due to the\nfact that it is a tertiary care center where many critical cases are\nreferred with high case mix index (CMI) [36], who are either\nalready colonized with multidrug resistant organisms\n(MDROs) or require higher prescription of antibiotics which\nmay contribute to the emergence of MDROs [37]. There is little data from India on the type of carbapenemase\nprevalence in carbapenem resistant enterobacteriaceae (CRE). There is little data from India on the type of carbapenemase\nprevalence in carbapenem resistant enterobacteriaceae (CRE). 130 Indian J Pediatr (February 2020) 87(2):125–131 approach. Azithromycin can be considered as an alternative to\nceftriaxone while ACCo sensitivity has returned. On the other\nhand, BSI related to Indian PICU is a complex scenario. The\netiology can be ranging from candida to GNBs and GPCs. In\nthe presence of central lines, it would be prudent to give anti-\nfungals empirically for the treatment of BSI, otherwise, treat-\nment with carbapenem or combination of carbapenem and\ncolistin is advisable due to high resistance to the first-line\ntherapy. In the absence of a national network of AMR surveil-\nlance for pediatrics infections, institute specific hospital-based\nsurveillance of AMR is essential for formulating antibiotic\npolicy for the judicious use of the antibiotics. The authors could only find one study from South India,\nVellore on the type of carbapenemases in BSI from PICU. In\nthis study bla NDM (72.7%) was shown to be the predominant\nplasmid in CRE, followed by bla OXA (9.1%), and a combina-\ntion of bla NDM/bla OXA (9.1%) [37]. Therefore, bla KPC has\ngot replaced by the above two plasmids. The treatment of BSI\nwith CRE is challenging as it is accompanied by up to 90%\nresistance to other drugs like amikacin [37]. Often, colistin is\nused as the last resort drug to treat CRE infections. But this is\nfurther complicated by the lack of robust data on\npharmacokinetics/pharmacodynamics of this drug. Conflict of interest\nNone. Conflict of interest\nNone. Open Access This article is distributed under the terms of the Creative\nCommons Attribution 4.0 International License (http://\ncreativecommons.org/licenses/by/4.0/), which permits unrestricted use,\ndistribution, and reproduction in any medium, provided you give appro-\npriate credit to the original author(s) and the source, provide a link to the\nCreative Commons license, and indicate if changes were made. Emerging Antimicrobial Resistance Further,\nquestions on the efficacy of colistin monotherapy or combi-\nnation therapy are still not resolved. Therefore clinicians are\nforced to often use unproven therapies, including colistin, in\ncases of CRE which is associated with high mortality of up to\n52% [37]. Expectedly, the rampant use of colistin has led to\nthe emergence of its resistance in GNBs. A median resistance\nto colistin resistance from different pediatric centers across\nIndia has been shown to be as: E.coli (8.8%),\nK. pneumoniae (3.8%), A. baumaanii (0%), and\nP. aeruginosa (0%) [11]. Similarly, in adult ICUs at authors’\nhospital, they observed colistin resistance only in\nK. pneumoniae BSI, albeit at a much higher rate of 17%\n[22]; therefore it remains a grave concern for its potential\nspread to PICU too. The recent increase in the resistance to\ncolistin in GNBs across the world has been associated with the\nemergence of plasmid-mediated gene mcr-1 in the year 2016\n[38]. Colistin resistance was previously associated with only\nchromosomal mutations but now it is feared that mcr-1 may\nemulate NDM-1 in its rapid global widespread. Colistin resis-\ntance in GNBs has resulted in the emergence of pan drug-\nresistant (PDR) bugs with no antibiotics left for the treatment. Although not so common in PICUs, PDR bugs are now a\ncommon scenario in adult ICUs [22]. Therefore it would be\nprudent to implement AMSP on an urgent basis in the PICUs\nto salvage whatever little is left of the remaining antibiotics. References Indian J Med Res. 2015;142:130–8. To conclude, this review of BSI in the Indian pediatric\npopulation highlights that Salmonella continues to be the ma-\njor etiology of community-acquired BSI. In such cases, em-\npirical treatment with ceftriaxone remains the most pragmatic To conclude, this review of BSI in the Indian pediatric\npopulation highlights that Salmonella continues to be the ma-\njor etiology of community-acquired BSI. In such cases, em-\npirical treatment with ceftriaxone remains the most pragmatic 10. Bielicki JA, Lundin R, Sharland M. Antibiotic resistance preva-\nlence in routine bloodstream isolates from children’s hospitals\nvaries substantially from adult surveillance data in Europe. Pediatr\nInfect Dis J. 2015;34:734–41. 131 Indian J Pediatr (February 2020) 87(2):125–131 26. Pfeifer Y, Matten J, Rabsch W. 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