Ethics statement

De-identified patient data were obtained from the Infections in Oxfordshire Research Database (IORD) which has generic Research Ethics Committee, Health Research Authority and Confidentiality Advisory Group approvals (19/SC/0403, 19/CAG/0144) as a de-identified electronic research database.

We describe an approach for combining data from electronic patient records on hospital ward movements and whole-genome sequencing data from sampled cases to investigate transmission of healthcare-associated infection. We reconstruct the timing and source of infection events, which we denote the transmission tree, and determine key parameters which underlie the transmission process. Hospital admission and discharge times and the specific ward stays within each admission are assumed to be available for all patients (cases and non-cases). For patients diagnosed with an infection, we assume the time the diagnostic sample was obtained is known and a whole-genome sequence of the resulting isolate is available. We apply our methods to investigate the transmission of C. difficile, but applications to other healthcare-associated infections would also be possible.

Transmission model

Transmission is represented by an SIR (susceptible, infectious, recovered) epidemiological model, using discrete time in whole days (Fig 1). The use of discrete time reflects the precision of available data on ward admission and discharge times and patient sampling times. For the purposes of the model, once infected, patients were assumed to recover and then be protected from a repeat infection with the same sequence type, as only 41/1223 (3%) of patients in a previous study using the same dataset had evidence of the same infection persisting or recurring beyond 90 days, and many of these can be allowed for by modelling recovery with a sufficiently long-tailed distribution [2]. Patients with several genetically distinct infections are included in the dataset once for each infection with a different multi-locus sequence type (MLST). This duplication of patients had minimal impact on the numbers of patients overall, as the number of non-cases was considerably greater than the number of cases, there were 1223 cases overall and 291,669 non-cases, which is broadly similar to estimates of around 2 cases to 1000 non-cases in previous studies in other settings [16].

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Fig 1. Transmission model states.

Both boxes shaded blue represent an infectious patient.

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Patients can start the study period susceptible or infected (with probability α). We assume transmission occurs as a density dependent process, i.e. proportional to the number of infectious individuals present. (As ward occupancy in our setting is near 100%, this is very similar to the alternative frequency dependent formulation where the force of transmission depends on the proportion of all patients present who are infectious.) Susceptible patients can be infected by several sources while in hospital: infectious patients on the same ward (at a rate β_w per infectious patient per day); infectious patients in the same hospital, but on a different ward (β_h); ward-based environmental contamination left by previous patients on the same ward (detailed below); and a background rate representing infection from unsampled sources, for example unsampled asymptomatic patients (β_bh). Patients can also become infected in the community, at a community-specific background rate (β_bc).

As bed-level admission data and detailed hospital layout data are not available, mixing of patients at the level of a ward is assumed to be homogenous such that any infectious patient present on the ward on a given day is equally likely to be the source of a new infection. In support of this, most patients in the dataset studied did not have individual patient rooms, and commodes and bathroom facilities were typically shared at a ward level. Mixing is also assumed to be homogenous at a hospital level as well when considering infections transmitted from patients on other wards, e.g. via visits to common areas such as radiology or via contact with the same healthcare workers.

Patients are only considered infectious while in hospital, from the time interval after they become infected, until the time interval before they recover. While infectious, patients can infect other patients on any day during their hospital stay, including the day of admission and day of discharge.

We allow for persisting contamination of the hospital environment by C. difficile spores after a patient has left a ward or recovered. Patients are assumed to leave environmental contamination on any ward they are admitted to while infectious. The environmental contamination starts in the time interval after a ward discharge, or the time interval in which a patient recovers. The force of infection from environmental contamination is additive over all contributing patients. Per patient, the initial force of infection from environmental contamination is the same irrespective of the duration of ward stay while infectious and is determined by the product of ϕ_c (constrained to be between 0 and 1, representing the relative infectiousness of environmental contamination compared to an infectious patient on the same ward) and β_w. The level of environmental contamination from each source also decays over time following a geometric distribution (analogous to an exponential distribution in continuous time), governed by a parameter, ω, the proportion of the force of infection lost after each time step.

The time interval between a patient becoming infected and being sampled is assumed to follow a negative binomial distribution (analogous to a gamma distribution in continuous time), with parameters λ_mean and λ_size. Patients are assumed to be infectious during this period, representing a combination of possible transmission from i) pre-symptomatic colonised patients who will go on to display symptoms and ii) from symptomatic patients prior to diagnosis. Weakly informative priors were chosen such that most intervals were expected to be <100 days. Explicitly modelling this interval is adopted to allow the duration of time between acquisition and diagnosis in patients infected by other patients (where the source is sampled and therefore the time of acquisition is relatively constrained by ward overlaps) to inform on the likely time of acquisition in patients infected by background sources in the hospital and community (where the source is not sampled nor the time of transmission otherwise constrained). If this interval is estimated to be long this will favour acquisition from community-based background sources, conversely if it is short, hospital-based background sources are more likely.

Similarly, the time interval between a patient being sampled and recovering, i.e. ceasing to be infectious, is also assumed to follow a negative binomial distribution, with parameters γ_mean and γ_size. The timing of sampling is assumed to determine the start of recovery as only patients diagnosed with an infection are considered and these patients receive treatment following sampling. Weakly informative priors were chosen to set the median recovery time to around 12 weeks (in keeping with previous empirical observations [17]), but allowing for shorter times, and times up to a year.

For computational tractability, patients who are not sampled and patients with only negative tests for infection are assumed not to be cases, i.e. the diagnostic test is assumed to be perfectly sensitive. Allowing for this simplifying assumption, as diagnostic testing for C. difficile is in fact imperfectly sensitive, transmission events arising from any unsampled or falsely negative patients, as well as those asymptomatically colonised without ever developing infection, are attributed to the hospital background source. The formulation of the model also means that there will be patients who are asymptomatically colonised with C. difficile, but who do not go on to become infected, who are labelled susceptible, as the model only considers acquisition that leads to a subsequent infection.

Transmission model parameters are summarised in Table 1.

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Table 1. Transmission model parameters and priors.

For parameters constrained to be positive, normal priors were truncated at zero. In addition to the listed parameters, augmented data included the unobserved transmission tree, consisting of infection times and sources denoted T and the set of unobserved recovery times denoted R.

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Inference overview

We use a similar representation to Worby et al [4] for the likelihood of the observed data for a given set of transmission parameters. We extend this approach to include recovery from infection and multiple routes of transmission in hospital as above. Additionally, we present an alternative formulation for the genetic component of the likelihood.

We denote the observed set of sample collection dates E and the observed sequencing data S. The augmented unobserved transmission tree, consisting of infection times and sources is denoted T and the set of unobserved recovery times R. The likelihood of these observed and augmented data given our parameters, θ, can be factorized as follows:

As the sequencing data, S, does not depend on the recovery times, R, this simplifies to:

Taking each element of the likelihood in turn, Pr(E, T, R|θ) represents the transmission model, which is described in more detail below. Pr(S|E, T, θ) is the likelihood of the genetic data for a given transmission tree and sampling dates.

Transmission model likelihood

To determine Pr(E, T, R|θ), we first consider a single patient, i, who is not infected at the start of the study (with probability (1−α)), who becomes infected at . The likelihood of their infection time, , source of infection, sampling time, , and recovery time, , given the parameters θ, is the product of: the probability of avoiding infection until ; the probability of being infected at ; the probability of the specific source of their infection given infection at ; the probability of being sampled at given ; and the probability of recovering at given .

The probability of avoiding infection until is given by: where is the total force of infection experienced by a particular patient, i, at time t.

Patients experience a constant background rate of infection from unsampled sources. When a patient is located in the hospital this is given by β_bh and when their location is the community by β_bc. For a patient, i, admitted to hospital the force of infection at time t is determined by the ward within the hospital they are admitted to, denoted a. is the number of infectious patients on ward a at time t, is the number of infectious patients on other wards in the same hospital (multiple hospitals are present in the dataset), and is the level of environmental contamination on ward a, giving:

ϕ_c denotes the relative infectiousness of ward contamination compared to an infectious individual on the same ward (constrained to between 0 and 1). The level of ward contamination at time t, , is determined by the τ(t) patients who have been previously discharged from the same ward, a, while still infectious or have recovered on the ward. The contribution of each such patient, p, to the total environmental contamination at time t declines geometrically with increasing time between , the time of discharge or recovery of patient p and t:

ω is a geometric parameter determining the rate of decline of ward contamination.

The probability of patient i being infected by any source at , conditional on being uninfected at is:

When we come to evaluate the genetic support for a transmission event a specific source of transmission needs to be proposed. Therefore, we include individual sources of infection in the transmission model. We define the probability, ζ_i, of patient i being infected by a specific source patient or background source, given patient i became infected at , based on the route of transmission as in Table 2.

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Table 2. Probability of infection by a given source patient or background source.

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For a patient who is never infected, the probability of avoiding infection is calculated as above, but now summing over all time considered (t = 0 to t = t^max).

The probability of a patient i being sampled at is defined relative to their infection time, , such that the probability of being sampled days after infection is given by the probability mass function, f, for the negative binomial distribution with parameters λ_mean and λ_shape, denoted κ. Therefore, for all n infected patients,

Similarly, the probability of recovering at is defined relative to the sampling time, . The probability of recovering days after sampling is given by the probability mass function, g, for the negative binomial distribution with parameters γ_mean and γ_shape, denoted ψ. Therefore,

Overall the likelihood Pr(E, T, R|θ), including those not infected, is the product of these terms above, for the set M of m patients who remain uninfected at t^max and the set N of n infected patients, q of whom start infected:

Where is an indicator variable taking the value 1 where the specified condition is true, and zero otherwise.

Genetic model and likelihood

Assessing the likelihood of a collection of genomes conditional on the underlying transmission tree is very challenging [4–7], the more so given the presence of recombination [19,20]. To make progress, we adopted an approximate approach that sought to exploit the valuable information about transmission while achieving computational efficiency and avoiding artefacts caused by recent recombination.

Conceptually, we evaluated the joint likelihood of the observed genomes given the transmission events and other parameters. In practice, we defined the approximate likelihood so that it depended only on an n×n pairwise genetic distance matrix d, distances which we inferred by estimating a recombination-corrected maximum likelihood phylogeny using PhyML[21] and ClonalFrameML [19].

The structure of our approximate likelihood was a composite or quasi-likelihood of the form

Here we consider the likelihood of each sequence, S_i, given all other sequences, denoted S_k. We approximated each term in the likelihood depending on whether the source of infection was another case versus background.

Infection from another case

When the source of infection of case i was patient j, we approximated the conditional likelihood by considering only the integer genetic distance between the two genomes, d_ij. This distance is determined by the extent of with-host diversity in the transmission source, the rate of mutation (μ per unit time), and time t between their samples. Based on coalescent theory, and assuming mutations are rare with respect to the length of the genome (known as the infinitely many sites assumption), the pairwise likelihood depends only on the genetic distance between the two genomes. This can be described as the sum of two Poisson processes, firstly the variation that arose in the time, t, between the samples being taken, and secondly the variation that has arisen in the time, u, between the time of the most recent common ancestor of the two samples and the time the first sequence was obtained, t₀ (Fig 2).

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Fig 2. Coalescent-based model.

Each box represents an infected patient (blue: patient j, yellow: patient i). Time runs from the bottom of the plot to the top. Samples are shown as circles. t₀ denotes the time of the first sample, t₁ the time of the second sample. t is the time between the samples, and u the time between the first sample and the common ancestor of the samples.

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We make the simplifying assumption that we can ignore the genetic bottleneck that founded the infection in the source patient j and assume the source had bacterial effective population size N_e. If u were known then, by the infinitely many sites assumption, we would model d_ij using a Poisson distribution with mean equal to the mutation rate μ multiplied by the total time for mutations to occur (t+2u):

Since u is not known we integrate over its possible values. We approximate by assuming a standard coalescent process with effective population size N_e, which implies that:

The probability of a given SNP distance conditional on u is, from the Poisson distribution:

The probability density of u is, from the exponential distribution:

Integrating over all possible values of u gives: where is an incomplete gamma function.

Therefore we assume where N_e and μ are contained within θ and t depends on the sampling times (E), and i and j are drawn from the transmission tree (T).

Infection from background

The other possible scenario is where a case is inferred to have been acquired from a hospital or community background source, i.e. the immediate source has not been sampled. We approximated the conditional likelihood by considering only the integer genetic distance d_ij between the genome of case i and another case j, assumed to be sampled at random from the population. For a random member of the population, not related by recent transmission, the coalescence time is likely to be much longer, to the extent that time differences between samples are assumed to be negligible compared to the age of the population as a whole. The distribution of coalescence times was parameterized by N_pop, the effective size of the background population.

If the time, v, to most recent common ancestor of a pair of samples i and j were known then, by the infinitely many sites assumption, we would model their genetic distance, d_ij, with a Poisson distribution with mean equal to the total time for mutations to occur, 2v, multiplied by the mutation rate μ:

Since v is not known we integrate over its possible value. We approximate by assuming a standard coalescent process with effective population size N_pop, which implies that:

Integrating over v gives: which is a geometric distribution with parameter , probability and mean 2N_popμ, which is the expected diversity of randomly sampled sequences from the population.

Since case j was chosen at random, we next averaged over the possible identity of case j. Formally, we adopted a quasi-likelihood of the form:

Similar to above, N_pop and μ on which p depends are contained within θ and the pool of n cases from i and j are drawn is specified by the transmission tree (T). We took a geometric mean rather than an arithmetic mean because this gave superior performance in preliminary simulations. While our genetic likelihoods were highly approximate, we subjected the method to rigorous testing to establish satisfactory performance in detailed simulations as detailed in the first section of the Results.

Table 3 provides a summary of the genetic model parameters.

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Table 3. Genetic model parameters and priors.

For parameters constrained to be positive, normal priors were truncated at zero.

https://doi.org/10.1371/journal.pcbi.1008417.t003

Markov chain Monte Carlo algorithm and data augmentation

Samples are drawn from π(θ|E, S) using MCMC, also integrating over all possible values of the augmented variables T and R, as by Bayes theorem:

Initial infection times and recovery times are set by randomly sampling values relative to the sampling time from two negative binomial distributions, determined by the starting values of λ_mean and λ_shape and γ_mean and γ_shape. Initially the source of all infections is set to be background, with a community or hospital route depending on the patient’s location at the sampled infection time. Where the sampled infection time is before t = 0, the patient is initially set to have started infected.

The MCMC algorithm proceeds in three stages per iteration, 1) parameter updates, 2) one-at-a-time infection time, source and recovery time updates, 3) block updates to infection times and sources (summarised in S1 Fig).

The parameters are updated one at a time, using a univariate normally distributed proposal centred on the current value. If the proposed parameter value is negative, except for logistic transformed parameters, the proposal is rejected. The variance of the proposal distribution is adapted during the initial part of the chain to target a proportion of accepted proposals between 0.20 and 0.40 and the adaptive phase discarded as part of the burn-in period.

For each iteration, following proposals/updates for each of the parameters, 10% of cases are selected at random. For each selected case, updated infection times are proposed one at a time from a normal distribution centred on the current value. The standard deviation of the distribution can take either a small value (5 days) or large value (25 days) with 50% probability to allow for moves of different sizes. If a proposed infection time is not compatible with the existing transmission tree the move is rejected, i.e. because the new infection time is after an onward transmission, or after a ward discharge that has left contamination leading to onward transmission. Based on the proposed infection time, a source of the infection is drawn. The relative probability of each source is determined by the product of their relative infectiousness (see Table 2) and their pairwise genetic likelihood. The proposed new infection time and source together are then compared to the current state. As the change in infection time is symmetrically proposed, with equal probabilities of large and small moves, this does not itself contribute to the Hastings ratio, which is calculated as the probability of choosing the current source, given the current infection time divided by the probability of choosing the proposed source, given the proposed infection time.

Updates to recovery times are made in a similar way, one-at-a-time for cases in the 10% chosen, proposing an updated time from a normal distribution centred on the current time (standard deviation 25 days), rejecting moves not compatible with the transmission tree, i.e. moving the recovery time before an onward transmission, moving the recovery time before a ward discharge that leaves contamination that leads to onward transmission, or moving the recovery time later such that an existing transmission via contamination can no longer occur.

Finally, at each iteration, one block update of infection times and sources is proposed. This is to allow reversal of the direction of transmission, which is only possible when the sources of two or more infections are updated simultaneously. A case, z, is selected at random, all cases that have z as their direct or indirect source are identified following the edges of the transmission tree, until a maximum limit is reached of 10 cases. For case z and the subsequent cases together, the set of cases , all transmission events leading to these cases are removed from the list of constraints on updated infection times. New infection times are proposed for each of the cases in . Then working forwards in time, new sources are sampled as above for each case, each conferring a component to the overall Hastings ratio. The acceptance ratio is then calculated for the new infection times and sources as a whole.

Parameter priors, π(θ), are given in Tables 1 and 3. Default values assume the rate of mutation and extent of within host genetic diversity are previously well described, hence the relatively tight priors which match previous findings [2].

For each analysis at least 3 separate chains of at least 50000 iterations were run and evaluated to confirm convergence. Chains were visually inspected to set an appropriate burn-in of ≥20%. Convergence was confirmed using Gelman-Rubin-Brooks plots, generated using the R coda library, and requiring scale reduction factors of ≤1.1 to be achieved for each parameter. After discarding the burn-in period, all chains were combined, including sufficient iterations to achieve an effective sample size of ≥200 for all parameters.

C++ code implementing our model is available at https://github.com/davideyre/transmission_inference.

Simulations

We used simulation to assess the performance of our model by comparing model inferences based on simulated ward movements, sample dates and genomic data, with the known underlying simulated transmission events and parameters. Fifty simulations, 10 from each of 5 scenarios were generated using a simulated community of individuals admitted to a hospital at random, using the transmission model described above. The 5 scenarios varied in complexity (Table 4), the simplest included transmission in hospital from infected patients on the same ward and from other sources in the hospital at a constant rate, denoted hospital background above. The most complex also allowed for spore-based transmission arising from persistent ward contamination after discharge or recovery of a case, transmission across different hospital wards, and a community-based background rate of transmission. Performance under different durations of spore persistence was tested (“short” and “long” duration spores). Genetic data were simulated using the multi-species coalescent as described previously [22] with a narrow simulated bottleneck at transmission. Simulation parameters were chosen to represent a population at relatively high risk of admission to hospital and contracting C. difficile, i.e. typical of a cohort of patients that might be at risk of admission to an acute medical ward. The intervals between acquisition and sampling and sampling and recovery were chosen to be similar to the priors provided above, i.e. consistent with previous literature. Transmission parameters were chosen such that the number of infections acquired by each route of transmission simulated typically exceeded 10–20 cases.

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Table 4. Simulation scenarios.

Simulated scenarios for a hospital with 4 wards, serving a population of 6000 patients at risk of admission with a daily probability of admission of 0.002 and a mean length of stay of 5 days. Simulation run for 365 days. Sampling distribution parameters, μ = 10, size = 5; Recovery distribution parameters, μ = 90, size = 3. Assuming 1 mutation per genome per year, N_pop = 20000 and N_e = 22.5. All patients assumed to start susceptible.

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We compared the performance of our model to two alternatives. The first was to fit the same transmission model but without genomic data to investigate the added value of sequencing data. The second was a heuristic algorithm for assigning the source of infection similar to one we have used in previous descriptive studies [2,17]: initially all prior cases within ≤2 SNPs of a case were considered potential sources and ranked as follows, accounting for epidemiological links: potential sources with shared time on the same ward as the case, following the source’s diagnosis and prior to the case’s, were ranked highest. Cases admitted to the same hospital ward, within ≤28 days following the discharge of an infected source, were ranked next, and finally cases sharing time in the same hospital, but not in the same ward were ranked last. Within each class of transmission route, the most plausible source was chosen as the source diagnosed closest in time to the diagnosis of the case. Similar to C. difficile surveillance recommendations [23], cases >2 SNPs from any previous case, or without an epidemiological link to another case ≤2 SNPs, were classified as acquired from hospital background if diagnosed within 12 weeks of a hospital stay, and community acquired if >12 weeks since the last hospital exposure (or if no previous hospital exposure).

We conducted additional simulations to test if our model was robust to variation in the transmission parameters by setting the daily probability of hospital admission to 0.02 to increase the number of patients in hospital, but reducing β_bh, β_w, β_h to 2e-4, 2e-4 and 3e-5 respectively and setting β_bc to zero and ω and ϕ_c to 0.2 and 0.8. We also undertook simulations to assess the impact of reducing the sample size, and therefore power, setting β_bh, β_w, β_h to 0.002, 0.005 and 0.001 respectively and setting β_bc to zero and ω and ϕ_c to 0.2 and 0.8, we generated 10 simulations each for population sizes of 6000, 5000, 4000, 3000, 2000, and 1000 individuals.

C. difficile transmission in Oxfordshire

We applied our model to a previously published whole-genome sequencing study of consecutive C. difficile infections in Oxfordshire, UK, from 2007–2011 [2]. Sequences were available from 1223 CDI cases, together with dates of sampling. Data on ward admissions and discharges for all patients, cases and non-cases, were available for the study period. Separate analyses were performed for infections from each of the 10 most prevalent subtypes of C. difficile, based on MLSTs determined previously [2]. This approach allowed differences in transmission properties of different sequence types (STs) to be investigated and also allowed for separate estimates of N_pop per ST, reflecting the differing levels of genetic diversity present within each ST. To generate aggregate findings across all STs, 10,000 samples were obtained from each ST’s final combined MCMC chain. For each iteration counts of the transmission events of each type were combined across STs, and the posterior mean and highest posterior density (HPD) interval calculated. We used previous estimates of C. difficile mutation rates and the extent of within-host diversity [2] to set relatively tight priors for μ and N_e, rather than expect our model to estimate these parameters from the data.

Simulations demonstrate our model is accurate and well calibrated

We used simulated data to assess the accuracy of inferences made using our transmission model. Fifty simulations were generated, 10 from each of 5 transmission route scenarios. In the simplest scenario, in which transmission could arise from other cases present on the same ward or at a constant background rate for patients admitted to the hospital (reflecting unmeasured sources of in-hospital transmission), the median (range) percentage of true source types (ward vs. background) correctly estimated as the most likely was 92.4% (83.3–96.6%), and the percentage of correct individual sources was 86.5% (65.0–94.9%).

In an alternative scenario allowing for transmission via one of five routes (hospital background, community background, ward, spore and hospital-wide transmission) the percentages were 83.9% (80.0–92.3%) and 84.4% (76.6–91.3%) respectively. The higher value for the correct individual source reflects that the correct source patient could be identified but with the wrong transmission route, e.g. ward vs. spore-based transmission.

Results from these and other scenarios are shown in Fig 2. Uncertainty in the estimates was well calibrated, with the 95% highest posterior probability sets (95% credibility intervals, CI) capturing the true source type >95% of the time in 49/50 (98%) simulations and true individual sources >95% of the time in 48/50 (96%) of simulations (Fig 3A and 3B).

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Fig 3. Source type and individual source attribution in simulated data.

The left-hand columns (panels A and C) display the accuracy of inferences about the source type, i.e. distinguishing between hospital background, community background, ward, spore and hospital-wide transmission. The right-hand columns (panels B and D) display the accuracy of inferences about the specific patient source of each infection (for background source types this is an unknown source). Panels A and B display inferences from the full transmission model. Panels C and D display the accuracy of predictions running an inference model without using genetic data (circles) or based on a heuristic rule (squares). CI, credibility interval.

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Similar proportions of simulated source types and individual sources were correctly identified using a heuristic transmission rule that assigned the single most likely source of each case based on genetic similarity and temporospatial proximately of cases, in the simplest scenario 95% (88.5–98.4%) and 86.3% (78.8–98.4%), and in the scenario with five routes 83.4% (77.1–88.5%) and 84.5% (77.3–91.3%) respectively (Fig 3C and 3D). In contrast, the performance of the transmission model without genetic data was substantially worse (Fig 3C and 3D). However, estimates from the heuristic transmission rule showed consistent biases that were not present in estimates from the transmission model using genetic and epidemiological data (Fig 4). The heuristic rule contains a hierarchy of transmission events, with ward-based transmission favoured over transmission via spores left behind after discharge/recovery or hospital-wide transmission. This resulted in consistent over-estimates of the extent of transmission via direct contact on the same ward, but under-estimates of the extent of transmission via spores or across hospital wards.

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Fig 4. Comparison of source type attribution using a stochastic transmission model and heuristic rules.

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Further details of the precision of source type attribution within each scenario are provided in S2–S6 Figs. Details of the accuracy of inferred infection times and parameter estimates are provided in S7 and S8 Figs. We also undertook a sensitivity analysis of the performance of our model with lower rates of transmission, which demonstrated model estimates remained accurate and uncertainty estimates well-calibrated (S9 Fig). We also simulated the effect of reduced sample size on the power of our model to estimate transmission parameters (S10 Fig, S1 Table), which suggested that around 40–70 cases were sufficient to estimate the main transmission parameters.

Oxfordshire C. difficile infection–inference demonstrates strain-specific differences in transmission

Isolates from 1223 of 1250 consecutive hospital and community onset CDI cases between 01 September 2007 and 31 March 2011 in Oxfordshire, UK, underwent whole-genome sequencing as previously described [2]. Linked epidemiological data on all patient admissions and ward movements, for cases and non-cases, at the county’s four hospitals were available. All isolates in the study also underwent MLST, the incidence of each ST in the study and temporal trends in STs are shown in S11 Fig. The most notable change was in ST1, which was the most common ST overall, but declined markedly during the study, such that only 2 ST1 CDIs were seen in the final year of the study, i.e. after 31 March 2010. S12 Fig shows a maximum likelihood phylogeny of the 1223 C. difficile genomes studied. C. difficile follows a conserved clade structure, with 5 major clades, and within these clades the large majority of genomes from different STs form conserved sub-clades. Therefore, to account for differences in the transmission of different C. difficile lineages and allow comparisons between lineages in a way compatible with previously published literature, 11 separate models were fitted for the 10 most common STs, and the remaining STs as a single group. Although the group of “Other” STs is genetically diverse, the fact that these STs are relatively less frequent reflects that they share in common more limited healthcare-associated transmission. We therefore analysed these lineages as a single group as we lacked sufficient numbers of cases to analyse each separately. The extent of genetic diversity within each ST is summarised in S13 Fig.

Routes of acquisition

Summarising the findings using posterior means, of the 1223 cases, 262 (21% [95% CI 20–22%]) were estimated to have been acquired from another known case: 121 (10% [9–11%]) via contact during overlapping ward stays, 96 (8% [6–9%]) by spores left behind after discharge or recovery, and 46 (4% [3–5%]) via contact during overlapping stays in different wards. The remaining 950 (78% [77–79%]) cases were estimated to be acquired from other unmeasured sources of transmission. Based on the assumption that the time between acquisition and sampling was similar in hospital to the community, then 819 (67% [66–68%]) were attributed to hospital-background and 131 (11% [10–11%]) to the community. However, given there are several possible reasons that the time interval between acquisition and sampling may vary by patient location, this breakdown by hospital versus community should be interpreted with caution and the headline figure of 78% [77–79%] of cases attributed to unmeasured sources regarded as the main result.

There was heterogeneity by ST in the proportion of cases acquired from a known case, which was highest in ST1 (most common ribotype equivalent: 027), 63% (131.2/214 cases, 95%CI 58–67%), ST42 (ribotype-106), 43% (19.6/45 cases, 40–44%), and ST3 (ribotype-001), 36% (22.7/63 cases, 30–41%), but <10% in most other STs (Fig 5A and 5B). All three ribotypes (027, 106, and 001) have been associated with healthcare-associated outbreaks [24], with ribotype-027 in particular regarded as an epidemic strain [25].

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Fig 5. Proportion of cases acquired from each transmission route, by sequence type.

Panels A to C show the estimated proportion of cases acquired from each of the 3 major source types, known patients (A, blue), hospital background sources (B, green) and the community (C, gold). Panels D to F subdivide the cases shown in panel A, into those acquired by direct ward contact (D), ward-based spores (E) and hospital-wide transmission (F). Note the y-axis scale differs between the upper panels and lower panels, to allow for comparison of the relative contribution of ward, spore and hospital-wide routes to transmission from other cases, and hospital vs. community for background transmission.

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The proportion of cases acquired in the community were similar by ST (Fig 5C), but highest for ST10/44 (ribotype-015) and ST11 (ribotype-078). Sequence types associated with higher rates of transmission from known cases (ST1, ST3 and ST42) as well as having the greatest proportion of cases acquired by direct-ward contact, had higher proportions of cases arising from spores, i.e. residual ward contamination left after discharge or recovery of a case, 31% (23–37%), 11% (5–16%), 19% (11–27%) respectively (Fig 5D–5F).

Parameter estimates, by ST

Parameter estimates by ST are shown in the Supplementary Materials (S14 Fig shows a example set of MCMC traces, S15–S24 Figs provide summary plots for all parameters and distributions for the transmission parameters). The point estimates for the transmission rate (per 10,000 person bed-days) from other cases present on the same ward, β_w, were highest for ST1 (2.5 [95%CI 1.6–3.5]), ST3 (2.6 [1.1–4.1]), and ST42 (3.1 [1.4–5.1]). The rate was significantly higher in ST1 than ST6 (1.2 [0.3–2.2], p = 0.03), ST8 (1.1 [0.4–1.8], p = 0.01), ST44 (0.7 [0.05–1.5], p = 0.005), and the other ST group (1.1 [0.6–1.7], p = 0.004), however the relatively wide credibility intervals shown in S15 Fig reflect the limited power of the study to differentiate between STs.

S20–S22 Figs show the parameters governing the persistence and relative infectiousness of spores. Spores from ST1 persisted significantly longer than those from all other STs other than ST3, ST42 and the other group (all p<0.048) (also see S25 Fig). The mean (95%CI) spore persistence time for ST1 was 39.0 (22.7–66.8) days, compared to 40.2 (5.6–113.9) days for ST3, 22.5 (10.5–45.3) days for ST42, 17.5 (1.2–42.6) days for the other ST group and between 2.5 and 12.5 days for the remaining STs. There was no clear information in the dataset on the relative infectiousness of spores compared to direct ward contact with a case, all estimates were similar to the broad prior (S10 Fig). S23 Fig shows estimated within-host diversity and mutation rates followed the priors set based on a previous study [2] and estimates of within ST diversity reflected the observed distributions of pairwise SNP differences (S13 Fig).

S24 Fig shows the distribution of the parameters governing the time delay between infection and sampling, and sampling and recovery. Across the different STs, the mean time between infection and sampling was estimated at around 10–20 days, but associated with a distribution with a long tail, such that in some individuals there could be a much longer interval (see S25 Fig for examples). Given that most patients leave the hospital before they recover, the distribution of recovery times followed the prior distribution (S24 and S25 Figs).

Transmission rates by specialty

Inferred rates of inpatient acquisition of C. difficile varied by specialty. Of 1092 infections estimated to have occurred in the hospital, 504 were acquired while patients were cared for by the acute medicine and geratology service and 212 on general surgery units. The overall rate of inpatient acquisition was 6.7 per 10,000 bed-days, with the highest rates seen on the renal and transplant, 18.1 (95%CI 17.2–18.8), gastroenterology 17.1 (14.9–19.1) and acute medicine / geratology wards 11.0 (10.8–11.2) (Fig 6). Rates of acquisition from a known case per 10,000 bed-days were also greatest in the same three specialties, 6.6 (5.6–7.4), 4.3 (2.9–5.8) and 3.0 (2.8–3.26) respectively.

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Fig 6. Inpatient C. difficile acquisition by specialty.

The numbers at the bottom of each bar indicate the total number of CDI cases in each category.

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Between hospital comparisons

Four hospitals serve the county of Oxfordshire, hospital A (cancer centre, specialist medicine and renal services), hospital B (general district hospital), hospital C (providing acute medicine and surgery, specialist surgery, maternity and children’s services), and hospital D (elective orthopaedics and rheumatology). Overall rates of inpatient C. difficile acquisition were 6.1 (95%CI 6.0–6.2), 8.5 (8.2–8.8), 8.7 (8.5–8.9), and 2.6 (2.3–2.8) per 10000 bed-days respectively. However, these differences were largely explained by case mix given the different services based in each hospital (see Fig 5). Incidence rates in acute medicine and geratology were marginally higher at the hospital B, and rates in general surgery higher at hospital C (Fig 7).

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Fig 7. Inpatient C. difficile acquisition by hospital.

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Temporal patterns in C. difficile acquisition

Overall rates of inpatient C. difficile fell over the course of the study from 12.6 (95%CI 11.3–13.5) per 10000 bed-days in the third quarter of 2007 to 3.6 (3.3–3.8) in the first quarter of 2011. Perhaps even more strikingly by the end of the study period inpatient acquisition from a known case had been almost completely controlled, rates falling from 4.3 (3.3–5.2) to 0.3 (0.1–0.5) respectively (Fig 8).

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Fig 8. Temporal trends in C. difficile inpatient acquisition.

Q, quarter; CDI, C. difficile infection.

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