Transmission dynamics and control of multidrug-resistant Klebsiella pneumoniae in neonates in a developing country

Abstract

Multidrug-resistant Klebsiella pneumoniae is an increasing cause of infant mortality in developing countries. We aimed to develop a quantitative understanding of the drivers of this epidemic by estimating the effects of antibiotics on nosocomial transmission risk, comparing competing hypotheses about mechanisms of spread, and quantifying the impact of potential interventions. Using a sequence of dynamic models, we analysed data from a one-year prospective carriage study in a Cambodian neonatal intensive care unit with hyperendemic third-generation cephalosporin-resistant K. pneumoniae. All widely-used antibiotics except imipenem were associated with an increased daily acquisition risk, with an odds ratio for the most common combination (ampicillin + gentamicin) of 1.96 (95% CrI 1.18, 3.36). Models incorporating genomic data found that colonisation pressure was associated with a higher transmission risk, indicated sequence type heterogeneity in transmissibility, and showed that within-ward transmission was insufficient to maintain endemicity. Simulations indicated that increasing the nurse-patient ratio could be an effective intervention.

Keywords: Klebsiella pneumoniae; South East Asia; antibiotic resistance; cohort study; epidemiology; global health; infectious disease; microbiology; neonates; pathogen genomics.

Figure 1.. Descriptive epidemiological data from a cohort of 333 infants admitted to a neonatal unit in a Children’s Hospital in Cambodia from September 2013 to September 2014.

Daily counts of neonates colonised with third generation cephalosporin-resistant (3GC-R) Klebsiella pneumoniae sensu lato over the study period are shown in panel A, where colour reflects uncolonised, imported or acquired cases, according to case definitions. The total height of the peaks shows the ward occupancy on that day. The results from rectal swabs among the 191 infants uncolonised at entry for 3GC-R K. pneumoniae s.l. are shown in panel B, with the window highlighting the swab outcomes from the first thirty five infants uncolonised at entry. Each row represents a patient and each coloured block represents a swab interval, where the width is the number of days in the interval (i.e. time between swabs). Outcomes are shown up to the first swab positive for 3GC-R K. pneumoniae s.l., after which time the patient is assumed to be colonised until discharge. The length of stay distribution for infants in the neonatal unit is shown as a histogram in panel C, where the bin width is two days. An infant’s length of stay is the total time in the neonatal unit during the study period, including re-admissions. The 333 infants were present in the neonatal unit for a total of 3417 study days. The proportion of study days when infants took the six most common antibiotic combinations, or other antibiotics, or none are shown in panel D.

Figure 2.. Posterior distributions for risk factors for the daily probability of acquiring third-generation cephalosporin-resistant (3GC-R) Klebsiella pneumoniae sensu lato among 191 susceptible neonates.

Odds ratios for the daily risk of colonisation are shown in panel A. The daily risk of colonisation per patient day is shown in panel B. Note that the 864 patient days have been thinned by a factor of five for visualisation. The cumulative risk for different patient scenarios is explored in panel C; a four day old girl, born full term, without severe conditions, breast milk fed and not taking antibiotics or probiotics over eight days in the neonatal unit is shown in blue. The red line shows the same infant, however ampicillin + gentamicin is taken from day three onwards. The lines and points in both cases show the cumulative probability posterior median, and the shaded area shows the 80% credible interval (CrI). In panel D, we took the probability of colonisation for each of the 400 swab interval and binned them into five quantiles. We then compared the expected number of colonisation events predicted by the model with the observed number of colonisation events (squares) in the swab intervals by quantile. In panels A, B and D points represent posterior medians, thick blue/purple lines represents the 80% CrI and thinner black lines represent the 95% CrI.

Figure 2—figure supplement 1.. Posterior chains from Hamiltonian Markov chain Monte Carlo fitting using Stan for risk factor model A (see Table 2).

The parameters are the intercept [alpha] and 14 slopes for covariates [betas]. The Gelman-Rubin diagnostic (${\displaystyle \hat{R}}$) is <1.01 for all parameters. The tail effective sample size (ESS_tail) ranges from 1948 to 2499 and the bulk effective sample size (ESS_bulk) ranges from 1808 to 2350.

Figure 2—figure supplement 2.. Estimates from risk factor model A with variable priors.

The intercept (α) prior is varied from normal (μ=0, σ=10) to normal (μ=0, σ=2). This changes the median posterior probability from 0.23 to 0.26 (panels A and B). Covariate prior distributions were varied from normal (μ=0, σ=5) to normal (μ=0, σ=1.5). The median odds ratio for the effect of breast feeding on the risk of acquisition/detection of resistant Klebsiella pneumoniae changes from 0.68 to 0.69 (C–D), and the effect of taking ampicillin + gentamicin within the past 48 hours changes from 2.0 to 1.9 (E–F). Note that priors are fitted on the log-odds scale and that the prior and posterior distributions shown in this figure have been logit-transformed (A–B) or exponentiated (C–F).

Figure 3.. Population structure of third-generation cephalosporin-resistant (3GC-R) Klebsiella and force of infection by sequence type (ST).

An unrooted phylogeny of 317 3GC-R Klebsiella isolates cultured from rectal and environmental swabs over a four month period in a neonatal unit in a children’s hospital in Cambodia is shown in panel A, where the branch lengths correspond to the mash distance (a measure of k-mer similarity) between whole-genome assemblies. The four largest STs are labelled as well as the population subdivisions by Klebsiella species. The frequency distribution of STs is shown in panel B, with the four largest STs shown in colour. Results from a transmission model estimating the force of infection by ST are shown in panel C, where the force of infection scales linearly with the number of colonised infants with that ST. The largest four STs have again been highlighted. Horizontal jitter has been applied to prevent overplotting of points. The uncertainty around the transmission parameter estimates are shown in panel D for the four most common STs, where the posterior mean is shown with a dotted line. The daily incidence of new colonisation events with the four most frequent STs are shown between the 1st January to the 15th March 2014 in panel E, along with the estimated force of infection over the same period in panel F using parameter estimates of β from transmission model 4 (Table 3).

Figure 3—figure supplement 1.. Posterior chains from Hamiltonian Markov chain Monte Carlo fitting for Klebsiella transmission models (see Table 3).

(A) Transmission model 1; intercept [alpha] ${\displaystyle \hat{R}}$=1.001, ESS_bulk = 1359, ESS_tail = 1171. (B) Transmission model 2; alpha and beta; ${\displaystyle \hat{R}}$ ranges from 1.001 to 1.002, ESS_bulk ranges from 1144 to 1298, ESS_tail ranges from 1247 to 1282. (C) Transmission model 3; alpha, beta, gamma and lambda. R̂ ranges from 1.000 to 1.003, ESS_bulk ranges from 1274 to 1396, ESS_tail ranges from 1180 to 1285. (D) Transmission model 4. alpha and beta hyper-parameters scale/shape 1, location/shape two and transmission parameters for each ST (62, not shown), ${\displaystyle \hat{R}}$ ranges from 0.999 to 1.003, ESS_bulk ranges from 1687 to 2887 and ESS_tail ranges from 2105 to 2796.

Figure 3—figure supplement 2.. Estimation of λ from transmission model 3 under different prior assumptions.

The more informative prior, half-normal(µ=1, σ=2), gave a median parameter estimate of 4.7 (panel A) corresponding to an environmental half life of 3.6 hours (panel C). The less informative prior, half-normal(µ=0, σ=5), gave a median parameter estimate of 6.7 (panel B) corresponding to an environmental half life of 2.5 hours (panel C). The only published estimate of resistant Klebsiella pneumoniae half life on surfaces is 12 hours (λ=1.39, see Methods), therefore the informative prior gives more weight to biologically plausible values of λ. The qualitative interpretation of the model (that environmental contamination with K. pneumoniae decays quickly) is unchanged with either prior, as is WAIC.

Figure 4.. Simulation results from dynamic agent-based models using parameter estimates on acquisition of third generation cephalosporin-resistant (3GC-R) Klebsiella pneumoniae sensu lato among neonates in a Children’s Hospital in Cambodia.

The distribution of ward reproduction number (R_A) values shown in panel A was obtained by taking 2000 samples from the force of infection posterior distribution, and for each sample running the agent-based simulation 100 times and taking the mean value. The results from simulating counterfactual scenarios with a dynamic agent-based model are shown in panels B, C and D. In B, the proportion of infants taking a probiotic (Lactobacillus acidophilus) on entry to the ward was varied between 0, -.5 and 1 in setting with a high proportion of imported cases (0.4) and a lower proportion of imported cases (0.05). In panel C, the proportion of infants that were breast milk fed was varied was varied between 0.25, 0.5 and 0.9in settings with a high proportion of imported cases (0.4) and a lower proportion of imported cases (0.05). In panel D, the infant nurse ratio was varied between 3:1, 2:1 and 1:1 in settings with a high proportion of imported cases (0.4) and a lower proportion of imported cases (0.05). The outcome measure in all simulations is the proportion of infants susceptible on entry that remained uncolonised with 3GC-R K. pneumoniae s.l. on discharge. The simulated outcomes are displayed as density plots, with dashed lines showing the median value.