1206 infection control & hospital epidemiology october 2015, vol. 36, no. 10


published. 20 Also, survival analyses are based on a hospital discharge cohort. A hypothesis had to be made to control this bias owing to the retrospective hospital cohort where patients without hospitalization during the observation period are con- sidered as lost to follow-up or informative censoring; the time- to-event and lost to follow-up definition are based on hospital discharge databases. However, only 10 patients were lost to follow-up directly after the arthroplasty hospital stay. This bias of “lost to follow-up” was then minimized. Concurrent events needing outpatient medical care were not checked, but PJI are usually known as requiring hospital management. Moreover, the absence of systematic coding for several interesting comor- bidities (such as tobacco or drug abuse), surgical sequences (prophylaxis type, length of intervention), or ASA score could represent a misinterpretation of the cofactors studied. Diabetes or cancers are exhaustively reported instead, owing to financial valuation. However, the results of this cohort study, compared with the English analysis from their National Joint Registry, are superposable, similar for the confounding factors (liver diseases, cancer, chronic renal failure)14 and could provide an easy and inexpensive way to survey arthroplasty. Another bias of the study could be represented by the choice to study both hip and knee arthroplasties and not to separate the different types of hip replacement. One next step will be to study singly national total knee and total hip arthroplasty. Differences in morbidity and mortality are significantly associated with comorbidities and they would be emphasized by separating the different profiles of patients according to the indication of joint replacement and its location.


Conclusion


The evolving demographic and environmental contexts, as well as the progression of the medical practice and findings, influence the development and outcome of diseases and con- sequently their epidemiologic surveillance. Using the medical information systems that cover the entire population health services allows data analysis and production of indicators, then benchmarking.41 This approach allows the opportunity to understand the burden of comorbidities and other impact factors in the occurrence of diseases.


acknowledgments


Wethank all the medical doctors and infection control teams around the world that allow surveillance networks to exist. We thank the CRIM (collège régional de l’information médicale), study group of medical doctors specialized in medical information systems in the region Centre, for help and comment on the building of algorithms and for coordinating the relations with the authors. We thank J. Druon, MD, in orthopedics, and G. Gras, MD, in infectious disease, for constructive comments and availability. Financial support. None reported. Potential conflicts of interest. All authors report no conflicts of interest


relevant to this article. Disclaimer: This study was approved by the Commission nationale de l'informatique et des libertés, Paris, France, on March 15, 2013 (n° 131024).


Address correspondence to Leslie Grammatico-Guillon, MD, PhD,


SIMEES, CHRU de Tours, Hôpital Bretonneau, 2 BD Tonnellé, 37000 Tours France (Leslie.guillon@univ-tours.fr).


supplementary material


To view supplementary material for this article, please visit http://dx.doi.org/10.1017/ice.2015.148


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