1150 infection control & hospital epidemiology october 2015, vol. 36, no. 10
LTACH. To account for the burn-in time, the first 20% of the iterations were not used for calculation of parameter estimates. Plots of convergence of the parameter estimates were inspected visually, and the Geweke diagnostic was calculated for all chains. To calculate summary measures for all LTACHs, a meta-analysis using a random-effects model was performed.30,31 A natural logarithm transformation was per- formed for α and β to normalize distributions. To investigate the effects of cohorting, numbers of acquisitions per 1,000
A total of 1,000,000 iterations of the algorithmwere run per
patient days at risk were calculated for non-cohort and cohort floors. In addition, absolute numbers of acquisitions per LTACH were estimated. These numbers were then converted to numbers of acquisitions per 1,000 patient days to facilitate comparisons between LTACHs and the effects of different cohorting strategies. A weighted least-squares regression analysis was performed to relate differences in study protocol adherence to numbers of acquisitions. Effects on parameter estimates of clinical cultures that yielded KPC were evaluated in a sensitivity analysis. We also analyzed the data using the assumption from the original study that patients remained colonized for the remainder of the study duration once they had tested positive. To that end, we created a new dataset in which positive cultures were added on the day of admission for patients who had previously tested positive. The algorithm for the parameter estimates was written in
positive patients on cohort floors or in single rooms) were 88% in LTACH A, 97% in LTACH B, 91% in LTACH C, and 99% in LTACH D.
MCMC Model
Pooled and individual parameter value estimates from the model are presented in Table 2 and Online Figure S1. For individual LTACHs, 95% credible intervals largely overlap. Trace plots of all chains were inspected and seemed stable. All chains had a Geweke diagnostic (z-value) below 2 and were considered converged. The estimated sensitivity of the screening process was 81%.
C++, the meta-analysis was done in Microsoft Office Excel 2010, convergence diagnostics were done in R, and the regression was performed in SPSS 21 (IBM, Armonk, NY).
results
The current analysis included 95,982 patient days and 3,257 admissions of 2,575 unique patients, with comparable patient mix among LTACHs (Table 1). A total of 7,250 cultures were obtained (6,757 surveillance
cultures and 493 clinical cultures). The median number of cultures per admission was 2 (range, 0–28), and KPC was detected in 761 cultures (11.3%). Of positive isolates, 90% were K. pneumoniae, 5% were E. coli, 3% were Enterobacter aerogenes, and 2% were other Enterobacteriaceae species. Rates of adherence to cohorting (represented as the proportion of
No. of patient admissions No. of unique patients Patient days
No. of distinct units Mean census per day
Median length of stay, d (IQR) Mean age of patients, y (SD) Sex, % male
table 1. Patient and Admission Characteristics Total 3,257
2,575
95,982 247
24 (14–37) 64 (16) 56.2%
The per-admission reproduction number RA, which is the average number of KPC transmissions caused by 1 KPC- positive patient during a single admission,32,33 can be approximated by multiplying the transmission parameter β (0.0136) by the mean length of stay (29.5 days), which yields a value of 0.40. The relative importance of patient-to-patient transmission compared to background transmission can be calculated as β * mean prevalence/(β * mean prevalence + α). With an overall mean prevalence of 29.3% as calculated by the model, we estimate that 60.9% of the acquisitions resulted from patient-to-patient transmission and that the remaining 39.1% resulted from background transmission. Calculated acquisitions per 1,000 patient days at risk,
absolute numbers of acquisitions per month, and acquisitions per 1,000 patient days are depicted in Table 3. When com- paring these to the numbers reported in the original study, the model predicts more acquisitions than were observed.24 LTACHs B andDhad the lowest number of acquisitions per
1,000 patient days, while LTACHs A and C had higher rates. The strategies adopted at LTACHs B (single-room isolation) and LTACH D (a strict cohort floor for colonized patients) or the implementation of the infection control bundle at these locations were more effective in reducing KPC cross trans- mission than the strategies implemented at LTACHs A and C. The weighted least-squares regression analysis showed a
negative association between adherence to cohorting (mean percentage of KPC-positive patients on cohort floors or in single rooms) and the number of acquisitions per 1,000 patient days at risk. Adherence to collection of admission swabs,
LTACH A 768
595 19,840
22 (13–33) 65 (16) 52.8%
LTACH B 730
570
20 5555 52
20,322 53
23 (15–36) 63 (16) 57.7%
NOTE. LTACH, long-term acute care hospital; IQR, interquartile range; SD, standard deviation.
39,070 99
25 (14–41) 61 (16) 57.3%
LTACH C 1,187
937
LTACH D 572
473
16,750 44
26 (16–39) 68 (14) 56.4%
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