Benchmarking French regions according to their prevalence of healthcare-associated infections.
OBJECTIVE
To propose an original method of benchmarking regions based on their prevalence of healthcare-associated infections (HAIs) and to identify regions with unusual results.


METHODS
To study between-region variability with a three-level hierarchical logistic regression model and a Bayesian non-parametric method.


METHODS
French 2006 national HAIs point prevalence survey.


METHODS
A total of 336 858 patients from 2289 healthcare facilities in 27 regions. Patients with an imported HAI (1% of the data, 20.7% of infected patients), facilities with <5 patients and patients who had at least one missing value for the variables taken into account were excluded (5.0% of patients).


METHODS
Binary outcome variable indicates whether a given patient was infected.


RESULTS
Two clusters of regions were identified: one cluster of five regions had a lower adjusted prevalence than the other one of 22 regions, while no region with unusually high prevalence could be identified. Nevertheless, the degree of heterogeneity of odds ratios between facilities for facility-specific effects of use of invasive devices was more important in some regions than in others.


CONCLUSIONS
The adjusted regional prevalence of HAIs can serve as an adequate benchmark to identify regions with concerning results. Although no outlier regions were identified, the proposed approach could be applied to the data of the 2012 national survey to benchmark regional healthcare policies. The estimation of facility-specific effects of use of invasive devices may orient future regional action plans.
Applied Mathematics and Systems Department, École Centrale Paris, 92295 Châtenay-Malabry, France. yuting.chen@ecp.fr.
Y T
YT
Chen
M
M
Rabilloud
J M
JM
Thiolet
B
B
Coignard
M H
MH
Metzger
D000328:Adult; D000368:Aged; D019985:Benchmarking / Q000379:methods* / Q000706:statistics & numerical data; D003428:Cross Infection / Q000453:epidemiology*; D005260:Female; D005602:France / Q000453:epidemiology; D006801:Humans; D008297:Male; D008875:Middle Aged; D015995:Prevalence; D019984:Quality Indicators, Health Care / Q000706:statistics & numerical data; D011787:Quality of Health Care / Q000706:statistics & numerical data*
Bayesian method; benchmarking; cluster analysis; hierarchical logistic regression modelling; hospital infection