Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

Electrosensory neural responses to natural electro-communication stimuli are distributed along a continuum

<div><p>Neural heterogeneities are seen ubiquitously within the brain and greatly complicate classification efforts. Here we tested whether the responses of an anatomically well-characterized sensory neuron population to natural stimuli could be used for functional classification. To do so, we recorded from pyramidal cells within the electrosensory lateral line lobe (ELL) of the weakly electric fish <i>Apteronotus leptorhynchus</i> in response to natural electro-communication stimuli as these cells can be anatomically classified into six different types. We then used two independent methodologies to functionally classify responses: one relies of reducing the dimensionality of a feature space while the other directly compares the responses themselves. Both methodologies gave rise to qualitatively similar results: while ON and OFF-type cells could easily be distinguished from one another, ELL pyramidal neuron responses are actually distributed along a continuum rather than forming distinct clusters due to heterogeneities. We discuss the implications of our results for neural coding and highlight some potential advantages.</p></div

Responses of LS pyramidal cells to the beat.

<p><b>A:</b> Peri-stimulus histograms (left) and cycle histograms (right) from six example On-type cells labeled according to phase of response to a 5Hz beat and baseline activity. Cells with higher baseline firing rates respond strongly to beats while those with lower baseline firing rates respond more weakly. Black arrows in the cycle histograms indicate the preferred phase and the length of the arrow gives the vector strength. Bin volume is indicated by values located at π/4 radians of each cycle histogram. Peak response magnitude values of example neurons are indicated by upward and downward pointing triangles on the colorbar (top) reflecting the logged stimulus driven firing rate. <b>B:</b> Same as in A but for six example Off-type neurons. <b>C:</b> Population distribution of response phase for all recordings in this study having a Z-stat ≥ 4. The histogram (bin size = π/6) reveals a bimodal distribution. Fitting the distribution with a Gaussian mixture model (black line) indicates an average on response at 1.08 radians and an average off response at 4.60 radians. The population (n = 74) is evenly divided into On- and Off-type neurons having mean vector strengths of 0.4175 ± SE 0.038 and 0.4226 ± SE 0.8664 respectively (panel inset). <b>D:</b> Linear regression models indicate a slight positive correlation of 0.445 exists between vector strength and baseline firing rate (p = 0.006) for On-type however no significant correlation exists for Off-type. <b>E:</b> No correlation exists between phase of response and baseline firing rate for either On-type or Off-type neurons as indicated by linear regression models. The rest is as in D.</p

Establishing a functional classification using naturalistic communication stimuli.

<p><b>A:</b> There are two types of pyramidal neurons, On- (blue) and Off- (red) type, which can be distinguished anatomically by the presence and absence of basilar dendrites, respectively (top). On- and Off-type pyramidal cells can furthermore be subdivided into six classes: On and Off-type superficial (S) intermediate (I) and deep (D) types which each exhibits different sized apical dendritic trees. There is a strong negative correlation between the size of the apical dendritic tree and the baseline (i.e., in the absence of stimulation) firing rate (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0175322#pone.0175322.s001" target="_blank">S1 Fig</a>). The baseline firing rate is indicated by colour saturation as per the colour bar above the circuit diagram. At the circuit level (bottom) within the pyramidal cell layer (orange boarder) all neurons receive input from sensory afferents encoding the animal’s self-generated electric field. On-type cells receive direct inputs from these afferents whereas Off-type cells receive indirect input via local inhibitory interneurons. All neuron classes project to the midbrain torus Semicircularis (not pictured here) while only deep neurons project to praeminentialis dorsalis (Pd) which provides different degrees of inhibitory feedback to superficial and intermediate pyramidal neurons via the eminentia granularis pars posterior (EGP). <b>B:</b> The four chirp stimuli featured in this study are shown in dark grey. A 25 ms response window following chirp onset is also indicated by a light grey window for two On-type chirps (3π/2, π) and the two Off-type chirps (π/2, 0). The 5 Hz beat stimulus is shown in black. <b>C:</b> A stimulus waveform is played to an awake and behaving animal while recordings are obtained from pyramidal cells within the lateral segment (LS) of the ELL. Example recordings from one On-type and one Off-type neuron are shown in response to a 5 Hz beat. Spike waveforms identified using spike sorting software are indicated for each cell (blue and red). The spike times were used to generate raster plots and peristimulus time histograms (as seen below the experimental setup). Example cells have peak stimulus driven firing rates of 136 Hz (On-type) and 123 Hz (Off-type) and their responses to the beat are in anti-phase. The color gradient in the color bar (bottom) is indicative of the response magnitude of recorded units (i.e. On- or Off-type). The transition from blue to red reflects an increase in response magnitude as the logarithm in base 10 of the stimulus driven peak-firing rate. <b>D:</b> A priori it is unclear whether ELL pyramidal cells can be functionally classified based on their responses to natural communication signals alone. There are two hypotheses: 1. Responses form distinct clusters This is schematized by a heatmap of response magnitude showing distinct response profiles. Directly beneath a hierarchical agglomerative clustering algorithm applied to a pairwise distance matrix representing the above heatmap results in a dendrogram (green) which is clearly divisible into distinct groups (dashed red line). 2. Responses do not form distinct clusters and instead form a continuum. The response heat map as in 1 thus gives rise to one clear transition between On- and Off-type cells. In this case a hierarchical agglomerative clustering algorithm applied to a pairwise distance matrix representing the above heatmap results in a dendrogram (green) that is only divisible into two groups (dashed red line), each of which constitutes a continuum.</p

Pyramidal cell responses to chirp stimuli form a continuum based on an unsupervised classification algorithm including dynamic time warping.

<p><b>A:</b> Optimally sorted dendrogram (green). The color code is the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0175322#pone.0175322.g004" target="_blank">Fig 4A</a>. <b>B:</b> Summarized concatenated PSTH response to the four chirp stimuli used in the study are presented for each neuron in the same order as the adjacent dendrogram. The color code is the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0175322#pone.0175322.g004" target="_blank">Fig 4B</a>.</p

Summary of steps taken in order to classify neuronal responses to naturalistic communication stimuli.

<p>The Common factor analysis technique (orange) aims to reduce dimensionality by developing a linear statistical model summarising in a low dimensional space the high dimensional response space of the data. Proximities within this space can then be used determine how responses are represented in the brain (green) (i.e. discrete clustered representations or a continuous representation). In contrast, the Dynamic time warping technique (blue) permits one to directly quantify the proximity between observations via a non-linear relation among responses abstracted as time series. Raster plots are transformed into time series (PSTHs). After defining a window of comparison to be permitted between PSTHs all pairwise comparisons between observations belonging to a population are made, yielding a pairwise distance matrix, which can then be used in the same way as for the Common Factor Analysis technique (green).</p

Pyramidal cell responses to chirp stimuli form a continuum based on an unsupervised classification algorithm including common factor analysis.

<p><b>A:</b> Optimally sorted dendrogram (green) tracing the path of a single linkage agglomerative clustering algorithm from the leaves (right) to the root (left) as it was applied to the pairwise distance matrix representing Euclidian distance for all pairwise comparisons between observations projected into eight dimensions using a factor analytic model. The baseline firing rate is also indicated using the same color code as previously (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0175322#pone.0175322.g001" target="_blank">Fig 1A</a>). <b>B:</b> Concatenated PSTH responses to the four chirp stimuli used in the study are presented for each neuron in the same order as the adjacent dendrogram. Responses are not normalized so that differences in response magnitude are readily apparent with the color gradient representing the logged stimulus driven firing rate allowing for a detailed visualization of each neuron’s stimulus preference. The baseline firing rate is also indicated using the same color code as previously.</p

Identifying response features suitable for building a factor model.

<p>18 measures were chosen to capture the variation observed across the entire population of ELL pyramidal cell neurons in response to a communication signal (i.e. “small chirp”) occurring at different phases (0 90 180 and 270 degrees) of a continuous beat cycle. Measures are described and their origin within the complete stimulus waveform are indicated. Beat cycles (1–2) precede chirp onset while beat cycles (2–4) proceed the 100ms chirp window following chirp onset. The “data type” refers to the distinct stages of preprocessing from which the 18 measures originate (spike times, cycle histogram or PSTH). The total number of measures considered “Number per neuron”, and of those, the ones that are normally distributed are tallied. The identities of the stimuli (i.e. chirp phase or beat) from which the normally distributed measures belong are indicated. Collinear relationships among this subset of measures are identified. From the collinear pairs of measures identified one was randomly chosen for removal to yield the final features used in CFA classification which are similarly tallied.</p

Responses of LS pyramidal cells to chirps.

<p><b>A:</b> Illustration of the methodology used to differentiate between the responses to the beat and to the chirp. The chirp stimuli of interest are shown in green and the corresponding responses from typical On-type (blue) and Off-type (red) neurons are also shown running the full extent of the stimulus. The response to a beat stimulus is then aligned in phase with the beat of the stimulus of interest both before and after the chirp. These two alignments are indicated by two separate dashed lines identified as the pre-chirp and post-chirp beat and run the full extent of the stimulus of interest. Directly beneath actual responses is a signal which can take on both positive and negative values as it was generated by subtracting the pre-chirp and post-chirp responses from the response to the stimulus of interest. The line running though or above this signal indicates a value of zero with positive values highlighted an appropriate color. The maximum value of this signal within the grey window (25 msec after chirp onset) is taken as the response of the neuron to the chirp. Responses to each of the four chirps are used to generate a 2 dimensional representation of the 4 dimensional response space known as a glyph. The correspondence between glyph dimensions and neural response to chirp phases are demonstrated for average On- and Off -type examples. Correspondence is indicated by highlighting the glyph axis associated with a given chirp phase on the glyph seen to the right of that chirp phase response. <b>B:</b> Peri-stimulus histograms from the same six example On-type cells used in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0175322#pone.0175322.g002" target="_blank">Fig 2</a>. Responses to the 4 different chirps were concatenated. Note that, while responses of superficial On-type cells to the beat are difficult to discern from the PSTH’s in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0175322#pone.0175322.g002" target="_blank">Fig 2</a>, their responses to chirps are quite clear. A glyph summarizing each example neuron’s location within the response space to these four chirps is located to the right of their PSTH and their logged peak firing rate response is indicated by a leftward or rightward pointing triangle on the adjacent colorbar. <b>C:</b> Same as in B but for 6 example Off-type neurons. <b>D:</b> Representation of the response space to 4 natural communication signals averaging over different populations. (Top left) Chirp responses of all On-type cells were averaged along each dimension of response space to generate an average “On glyph”. The same was done for all Off-type cells. Multidimensional scaling was used to project the response space into two dimensions and glyphs where plotted centered on their two coordinate representation. The visualization procedure was repeated but for more specific subpopulations by dividing On- and Off-type further into deep, intermediate, and superficial (bottom left). For comparison, the glyphs from individual neurons are also shown (right).</p