Biology subjects

Cooper, B. S.

Publications and source records attributed to Cooper, B. S..

6 recordsLinked to original sources

bioRxiv2018

Transmission dynamics and between-species interactions of multidrug-resistant Enterobacteriaceae

Widespread resistance to antibiotics is among the gravest threats to modern medicine, and controlling the spread of multi-drug resistant Enterobacteriaceae has been given priority status by the World Health Organization. Interventions to reduce transmission within hospital wards may be informed by modifiable patient-level risk factors for becoming colonised, however understanding of factors that influence a patients risk of acquisition is limited. We analyse data from a one year prospective carriage study in a neonatal intensive care unit in Cambodia using Bayesian hierarchical models to estimate the daily probability of acquiring multi-drug resistant organisms, while accounting for patient-level time-varying covariates, including interactions between species, and interval-censoring of transmission events. We estimate the baseline daily probability for becoming colonised with third generation cephalosporin resistant (3GC-R) Klebsiella pneumoniae as 0.142 (95% credible interval [CrI] 0.066, 0.27), nearly ten times higher than the daily probability of acquiring 3GC-R Escherichia coli (0.016 [95% CrI 0.0038, 0.049]). Prior colonization with 3GC-R K. pneumoniae was associated with a greatly increased risk of a patient acquiring 3GC-R E. coli (odds ratio [OR] 6.4 [95% CrI 2.8, 20.9]). Breast feeding was associated with a reduced risk of colonization with both 3GC-R K. pneumoniae (OR 0.73 [95% CrI 0.38, 1.5]) and E. coli (OR 0.62 [95% CrI 0.28, 1.6]). The use of an oral probiotic (Lactobacillus acidophilus) did not show clear evidence of protection against colonization with either 3GC-R K. pneumoniae (OR 0.83 [95% CrI 0.51, 1.3]) or 3GC-R E. coli (OR 1.3 [95% CrI 0.77, 2.1]). Antibiotic consumption within the past 48 hours did not strongly influence the risk of acquiring 3GC-R K. pneumoniae. For 3GC-R E. coli, ceftriaxone showed the strongest effect for increasing the risk of acquisition (OR 2.2 [95% CrI 0.66, 6.2]) and imipenem was associated with a decreased risk (OR 0.31 [95% CrI 0.099, 0.76). Using 317 whole-genome assemblies of K. pneumoniae, we determined putatively related clusters and used a range of models to infer transmission rates. Model comparison strongly favored models with a time-varying force of infection term that increased in proportion with the number of colonized patients, providing evidence of patient-to-patient transmission, including among a cluster of Klebsiella quasipneumoniae. Our findings provide support for the hypothesis that K. pneumoniae can be spread person-to-person within ward settings. Subsequent horizontal gene transfer within patients from K. pneumoniae provides the most parsimonious explanation for the strong association between colonization with 3GC-R K. pneumoniae and acquisition of 3GC-R E. coli.

epidemiology↗

bioRxiv2018

Using machine learning to guide targeted and locally-tailored empiric antibiotic prescribing in a children’s hospital in Cambodia

BackgroundEarly and appropriate empiric antibiotic treatment of patients suspected of having sepsis is associated with reduced mortality. The increasing prevalence of antimicrobial resistance risks eroding the benefits of such empiric therapy. This problem is particularly severe for children in developing country settings. We hypothesized that by applying machine learning approaches to readily collected patient data, it would be possible to obtain actionable and patient-specific predictions for antibiotic-susceptibility. If sufficient discriminatory power can be achieved, such predictions could lead to substantial improvements in the chances of choosing an appropriate antibiotic for empiric therapy, while minimizing the risk of increased selection for resistance due to use of antibiotics usually held in reserve.\n\nMethods and FindingsWe analyzed blood culture data collected from a 100-bed childrens hospital in North-West Cambodia between February 2013 and January 2016. Clinical, demographic and living condition information for each child was captured with 35 independent variables. Using these variables, we used a suite of machine learning algorithms to predict Gram stains and whether bacterial pathogens could be treated with standard empiric antibiotic therapies: i) ampicillin and gentamicin; ii) ceftriaxone; iii) at least one of the above.\n\n243 cases of bloodstream infection were available for analysis. We used 195 (80%) to train the algorithms, and 48 (20%) for evaluation. We found that the random forest method had the best predictive performance overall as assessed by the area under the receiver operating characteristic curve (AUC), though support vector machine with radial kernel had similar performance for predicting Gram stain and ceftriaxone susceptibility. Predictive performance of logistic regression, simple and boosted decision trees and k-nearest neighbors were poor in comparison. The random forest method gave an AUC of 0.91 (95%CI 0.81-1.00) for predicting susceptibility to ceftriaxone, 0.75 (0.60-0.90) for susceptibility to ampicillin and gentamicin, 0.76 (0.59-0.93) for susceptibility to neither, and 0.69 (0.53-0.85) for Gram stain result. The most important variables for predicting susceptibility were time from admission to blood culture, patient age, hospital versus community-acquired infection, and age-adjusted weight score.\n\nConclusionsApplying machine learning algorithms to patient data that are readily available even in resource-limited hospital settings can provide highly informative predictions on susceptibilities of pathogens to guide appropriate empiric antibiotic therapy. Used as a decision support tool, such approaches have the potential to lead to better targeting of empiric therapy, improve patient outcomes and reduce the burden of antimicrobial resistance.\n\nAuthor summaryO_LSTWhy was this study done?C_LSTO_LIEarly and appropriate antibiotic treatment of patients with life-threatening bacterial infections is thought to reduce the risk of mortality.\nC_LIO_LIIn hospitals that have a microbiology laboratory, it takes 3-4 days to get results which indicate which antibiotics are likely to be effective; before this information is available antibiotics have to be prescribed empirically i.e. without knowledge of the causative organism.\nC_LIO_LIIncreasing resistance to antibiotics amongst bacteria makes finding an appropriate antibiotic to use empirically difficult; this problem is particularly severe for children in developing country settings.\nC_LIO_LIIf we could predict which antibiotics were likely to be effective at the time of starting antibiotic therapy, we might be able to improve patient outcomes and reduce resistance.\nC_LI\n\nO_LSTWhat Did the Researchers Do and Find?C_LSTO_LIWe evaluated the ability of a number of different algorithms (i.e. sets of step-by-step instructions) to predict susceptibility to commonly-used antibiotics using routinely available patient data from a childrens hospital in Cambodia.\nC_LIO_LIWe found that an algorithm called random forests enabled surprisingly accurate predictions, particularly for predicting whether the infection was likely to be treatable with ceftriaxone, the most commonly used empiric antibiotic at the study hospital.\nC_LIO_LIUsing this approach it would be possible to correctly predict when a different antibiotic would be needed for empiric treatment over 80% of the time, while recommending a different antibiotic when ceftriaxone would suffice less than 20% of the time.\nC_LI\n\nO_LSTWhat Do These Findings Mean?C_LSTO_LIUsing readily available patient information, sophisticated algorithms can enable good predictions of whether antibiotics are likely to be effective several days before laboratory tests are available.\nC_LIO_LIAlgorithms would need to be trained with local hospital data, but our study shows that even with relatively limited data from a small hospital, good predictions can be obtained.\nC_LIO_LIUsed as part of a decision support system such algorithms could help choose appropriate antibiotics for empiric therapy; this would be expected to translate into better patient outcomes and may help to reduce resistance.\nC_LIO_LISuch as a decision support system would have very low costs and be easy to implement in low- and middle-income countries.\nC_LI

epidemiology↗

bioRxiv2017

Transmission patterns of hyper-endemic multi-drug resistant Klebsiella pneumoniae in a Cambodian neonatal unit: a longitudinal study with whole genome sequencing

BackgroundKlebsiella pneumoniae is an important and increasing cause of life-threatening disease in hospitalised neonates. Third generation cephalosporin resistance (3GC-R) is frequently a marker of multi-drug resistance, and can complicate management of infections. 3GC-R K. pneumoniae is hyper-endemic in many developing country settings, but its epidemiology is poorly understood and prospective studies of endemic transmission are lacking. We aimed to determine the transmission dynamics of 3GC-R K. pneumoniae in a newly opened neonatal unit (NU) in Cambodia.\n\nMethodsWe performed a prospective longitudinal study between September and November 2013. Rectal swabs from 37 consented patients were collected upon NU admission and every three days thereafter. Morphologically different colonies from swabs growing cefpodoxime-resistant K. pneumoniae were selected for whole-genome sequencing (WGS).\n\nResults32/37 (86%) patients screened positive for 3GC-R K. pneumoniae and 93 colonies from 119 swabs were sequenced. Isolates were resistant to a median of six (range 3-9) antimicrobials. WGS revealed high diversity; pairwise distances between isolates from the same patient were either 0-1 SNV or >1,000 SNVs; 19/32 colonized patients harboured K. pneumoniae colonies differing by >1000 SNVs. Diverse lineages accounted for 18 probable importations to the NU and nine probable transmission clusters involving 19/37 (51%) of screened patients. Median cluster size was 5 patients (range 3-9).\n\nConclusionsThe epidemiology of 3GC-R K. pneumoniae was characterised by multiple introductions and a dense network of cross-infection, with half of screened neonates part of a transmission cluster. Efforts to reduce the 3GC-R K. pneumoniae disease burden should consider targeting both processes.

epidemiology↗

bioRxiv2017

Reactive and pre-emptive vaccination strategies to control hepatitis E infection in emergency and refugee settings: a modelling study.

BackgroundHepatitis E Virus (HEV) is an important cause of morbidity and mortality in emergency and refugee camp settings. Symptomatic infection is associated with case fatality rates of ~20% in pregnant women. However, its epidemiology is poorly understood and the potential impact of immunisation in outbreak settings uncertain. We aimed to estimate key epidemiological parameters for HEV and to evaluate the potential impact of both reactive vaccination (initiated in response to an epidemic) and pre-emptive vaccination.\n\nMethodsWe analysed data from one of the worlds largest recorded HEV epidemics, which occurred in refugee camps in Uganda (2007-2009), using transmission dynamic models to estimate epidemiological parameters and assess the potential impact of reactive and pre-emptive vaccination strategies.\n\nResultsUnder baseline assumptions we estimated the basic reproduction number of HEV to range from 3.9 (95% CrI 2.8, 5.4) to 8.9 (5.4, 14.2). Mean latent and infectious periods were estimated to be 34 (28, 39) and 40 (23, 71) days respectively.\n\nReactive two-dose vaccination of those aged 16-65 years excluding pregnant women (for whom vaccine is not licensed), if initiated after 50 reported cases, led to mean camp-specific reductions in mortality of 10 to 29%. Pre-emptive vaccination with two doses reduced mortality by 35 to 65%. Both strategies were more effective if coverage was extended to groups for whom the vaccine is not currently licensed. For example, two dose pre-emptive vaccination, if extended to include pregnant women, led to mean reductions in mortality of 66 to 82%.\n\nConclusionsHEV has a high transmission potential in refugee camp settings. Substantial reductions in mortality through vaccination are expected, even if used reactively. There is potential for greater impact if vaccine safety and effectiveness can be established in pregnant women.\n\nFundingWellcome Trust (106491/Z/14/Z and 089275/Z/09/Z). BC: MRC/DfID (MR/K006924/1).

epidemiology↗

bioRxiv2017

Enumerating the Economic Cost of Antimicrobial Resistance Per Antibiotic Consumed to Inform the Evaluation of Interventions Affecting their Use

Background- Antimicrobial resistance (AMR) poses a colossal threat to global health and incurs high economic costs to society. Economic evaluations of antimicrobials and interventions such as diagnostics and vaccines that affect their consumption rarely include the costs of AMR, resulting in sub-optimal policy recommendations. We estimate the economic cost of AMR per antibiotic consumed, stratified by drug class and national income level.\n\nMethods- The model is comprised of three components: correlation coefficients between human antibiotic consumption and subsequent resistance; the economic costs of AMR for five key pathogens; and consumption data for antibiotic classes driving resistance in these organisms. These were used to calculate the economic cost of AMR per antibiotic consumed for different drug classes, using data from Thailand and the United States (US) to represent low/middle and high-income countries.\n\nResults- The correlation coefficients between consumption of antibiotics that drive resistance in S. aureus, E. coli, K. pneumoniae, A. baumanii, and P. aeruginosa and resistance rates were 0.37, 0.27, 0.35, 0.45, and 0.52, respectively. The total economic cost of AMR due to resistance in these five pathogens was $0.5 billion and $2.8 billion in Thailand and the US, respectively. The cost of AMR associated with the consumption of one standard unit (SU) of antibiotics ranged from $0.1 for macrolides to $0.7 for quinolones, cephalosporins and broad-spectrum penicillins in the Thai context. In the US context, the cost of AMR per SU of antibiotic consumed ranged from $0.1 for carbapenems to $0.6 for quinolones, cephalosporins and broad spectrum penicillins.\n\nConclusion- The economic costs of AMR per antibiotic consumed were considerable, often exceeding their purchase cost. Differences between Thailand and the US were apparent, corresponding with variation in the overall burden of AMR and relative prevalence of different pathogens. Notwithstanding their limitations, use of these estimates in economic evaluations can make better-informed policy recommendations regarding interventions that affect antimicrobial consumption and those aimed specifically at reducing the burden of AMR.

epidemiology↗

bioRxiv2017

A Maladaptive Combination Of Traits Contributes To The Maintenance Of A Stable Hybrid Zone Between Two Divergent Species Of Drosophila

Geographical areas where two species come into contact and hybridize serve as natural laboratories for assessing mechanisms that limit gene flow between species. The ranges of about half of all closely related Drosophila species overlap, and the genomes of several pairs reveal signatures of past introgression. However, only two contemporary hybrid zones have been characterized in the genus, and both are recently diverged sister species (D. simulans-D. sechellia, Ks = 0.05; D. yakuba-D. santomea, Ks = 0.048). Here we present evidence of a new hybrid zone, and the ecological mechanisms that maintain it, between two highly divergent Drosophila species (Ks = 0.11). On the island of Bioko in west Africa, D. teissieri occupies mostly forests, D. yakuba occupies mostly open agricultural areas, and recently, we discovered that hybrids between these species occur near the interface of these habitats. Genome sequencing revealed that all field-sampled hybrids are F1 progeny of D. yakuba females and D. teissieri males. We found no evidence for either advanced-generation hybrids or F1 hybrids produced by D. teissieri females and D.yakuba males. The lack of advanced-generation hybrids on Bioko is consistent with mark-recapture and laboratory experiments that we conducted, which indicate hybrids have a maladaptive combination of traits. Like D. yakuba, hybrids behaviorally prefer open habitat that is relatively warm and dry, but like D. teissieri, hybrids have low desiccation tolerance, which we predict leaves them physiologically ill-equipped to cope with their preferred habitat. These observations are consistent with recent findings of limited introgression in the D. yakuba clade and identify an ecological mechanism for limiting gene flow between D. yakuba and D. teissieri; namely, selection against hybrids that we have documented, in combination with hybrid male sterility, contributes to the maintenance of this narrow (~30m), stable hybrid zone centered on the forest-open habitat ecotone. Our results show how a deleterious combination of parental traits can result in unfit or maladapted hybrids.

evolutionary biology↗