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Year : 2019  |  Volume : 4  |  Issue : 1  |  Page : 50-51

Antifungal resistance: Implications for data policy and research

1 Scientific Director, Public Health Dynamics Laboratory, Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
2 Associate Director, Program Evaluation and Research Unit, University of Pittsburgh, Pittsburgh, PA, USA

Date of Web Publication20-Jun-2019

Correspondence Address:
Prof. Saumyadipta Pyne
Scientific Director, Public Health Dynamics Laboratory, Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/bjhs.bjhs_23_19

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How to cite this article:
Pyne S, Aruru M. Antifungal resistance: Implications for data policy and research. BLDE Univ J Health Sci 2019;4:50-1

How to cite this URL:
Pyne S, Aruru M. Antifungal resistance: Implications for data policy and research. BLDE Univ J Health Sci [serial online] 2019 [cited 2023 Jun 3];4:50-1. Available from: https://www.bldeujournalhs.in/text.asp?2019/4/1/50/260733


In an earlier issue of this journal,[1] we reviewed how analytical models of nosocomial infections could benefit from realistic representations of health-care facilities using a number of parameters that may determine the disease dynamics, for example, admission and discharge rate of patients in a given ward, antimicrobial usage, average patient length of stay in the ward, and contact rates between the health-care workers and patients. However, the potential of gaining any insight of actionable value might significantly diminish if, as noted in a recent collection of international media reports, a prevailing “culture of secrecy shields hospitals with outbreaks of drug-resistant infections” from providing the data necessary to calibrate such models.[2]

The reports describe an emerging concern for public health due to antifungal resistance. In particular, a globally increasing number of cases of nosocomial infections caused by the fungus Candida auris have been noted, including many in India [Figure 1].[3] Over the past decade, C. auris has attained global notoriety for being multidrug resistant (e.g., azoles), difficult to identify with standard laboratory methods, easy to colonize on skin and spread to almost every surface in patient care environments, present in different geographic clades, and most notably, responsible for hundreds of outbreaks in health-care settings. Nearly half of all patients who contract C. auris die within 90 days, according to the US Centers for Disease Control and Prevention. Yet, surprisingly few specifics on the characteristics and spread of C. auris outbreaks seem to be readily available from the online resources on public health surveillance, much less from the hospitals or the patients and their families. In its present form, it appears that the Global Antimicrobial Resistance Surveillance System of the WHO has precious little data to offer on this issue.
Figure 1: Global spread of Candida auris reported in a recent review (reproduced with permission)

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This brings us to the crux of the problem: the absence of reliable system-level data currently serve as a drawback for modeling C. auris disease dynamics or any possible intervention. While clinical infection control measures may include an array of steps ranging from placing a patient with C. auris in a single-patient room to disinfecting that environment and from adhering to hand hygiene to screening the contacts of new cases for C. auris colonization, it is also very important to ensure that such actions systematically generate space-time indexed entries in a database that could be used for monitoring. In addition, data on risk factors and comorbidities, including the patient's immunologic condition, results from microbiological analyses as well as details of the patient care environment including common surfaces and equipment such as catheters, and possibly even relevant social network data, must all be available – with the applicable privacy and security safeguards in place – for specification of models that can provide a thorough and precise understanding of this serious problem. Until concerted efforts from the stakeholders and policy-makers ensure accessibility and transparency of data in the spirit of sentinel surveillance, deadly C. auris may continue to remain “a mysterious infection, spanning the globe in a climate of secrecy.”[4]

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Conflicts of interest

There are no conflicts of interest.

  References Top

Lopez-Garcia M, Aruru M, Pyne S. Health analytics and disease modeling for better understanding of healthcare associated infections. BLDE J Health Sci 2018;3:69-74.  Back to cited text no. 1
Available from: http://www.nytimes.com/2019/04/08/health/candida-auris-hospitals-drug-resistant.html. [Last accessed on 2019 May 05].  Back to cited text no. 2
Cortegiani A, Misseri G, Fasciana T, Giammanco A, Giarratano A, Chowdhary A, et al. Epidemiology, clinical characteristics, resistance, and treatment of infections by Candida auris. J Intensive Care 2018;6:69.  Back to cited text no. 3
Available from: http://www.nytimes.com/2019/04/06/health/drug-resistant-candida-auris.html. [Last accessed on 2019 May 05].  Back to cited text no. 4


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