Results from the Canadian Nosocomial Infection Surveillance Program on Carbapenemase-Producing Enterobacteriaceae, 2010 to 2014.

Carbapenemase-producing Enterobacteriaceae (CPE) are increasing globally; here we report on the investigation of CPE in Canada over a 5-year period. Participating acute care facilities across Canada submitted carbapenem-nonsusceptible Enterobacteriaceae from 1 January 2010 to 31 December 2014 to the National Microbiology Laboratory. All CPE were characterized by antimicrobial susceptibilities, pulsed-field gel electrophoresis, multilocus sequence typing, and plasmid restriction fragment length polymorphism analysis and had patient data collected using a standard questionnaire. The 5-year incidence rate of CPE was 0.09 per 10,000 patient days and 0.07 per 1,000 admissions. There were a total of 261 CPE isolated from 238 patients in 58 hospitals during the study period. blaKPC-3 (64.8%) and blaNDM-1 (17.6%) represented the highest proportion of carbapenemase genes detected in Canadian isolates. Patients who had a history of medical attention during international travel accounted for 21% of CPE cases. The hospital 30-day all-cause mortality rate for the 5-year surveillance period was 17.1 per 100 CPE cases. No significant increase in the occurrence of CPE was observed from 2010 to 2014. Nosocomial transmission of CPE, as well as international health care, is driving its persistence within Canada.

Understanding Canadians' knowledge, attitudes and practices related to antimicrobial resistance and antibiotic use: Results from public opinion research.

Background: Antimicrobial resistance is a current and pressing issue in Canada. Population-level antibiotic consumption is a key driver. The Public Health Agency of Canada undertook a comprehensive assessment of the Canadian public's knowledge, attitudes and practices in relation to antimicrobial resistance and antibiotic use, to help inform the implementation of public awareness and knowledge mobilization. Methods: Data were collected in three phases: 1) six in-person focus groups (53 participants) to help frame the survey; 2) nationwide survey administration to 1,515 Canadians 18 years and older via cell phone and landline; and 3) 12 online focus groups to analyze survey responses. Survey data is descriptive. Results: A third (33.9%) of survey respondents reported using antibiotics at least once in the previous 12 months, 15.8% more than twice and 4.6% more than five times. Antibiotic use was reported more among 1) those with a household income below $60,000, 2) those with a medical condition, 3) those without a university education and 4) among the youngest adults (18-24 years of age) and (25-34 years of age). Misinformation about antibiotics was common: 32.5% said antibiotics "can kill viruses"; 27.9% said they are "effective against colds and flu"; and 45.8% said they are "effective in treating fungal infections". Inaccurate information was reported more often by those 1) aged 18-24 years, 2) with a high school degree or less and 3) with a household income below $60,000. In focus groups, the time/money trade-offs involved in accessing medical care were reported to contribute to pushing for a prescription or using unprescribed antibiotics, particularly in more remote contexts, while the cost of a prescription contributed to sharing and using old antibiotics. A large majority, across all demographic groups, followed the advice of medical professionals in making health decisions. Conclusion: High trust in medical professionals presents an important opportunity for knowledge mobilization. Delayed prescriptions may alleviate concerns about the time/money constraints of accessing future care. Consideration should be given to prioritizing access to appropriate diagnostic and other technology for northern and/or remote communities and/or medical settings serving many young children to alleviate concerns of needing a prescription or of needing to return later.

The development of respondent-driven sampling (RDS) inference: A systematic review of the population mean and variance estimates.

BACKGROUND: Respondent-driven sampling (RDS) is a successful data collection method used in hard-to-reach populations, like those experiencing or at high risk of drug dependence. Since its introduction in 1997, identifying appropriate methods for estimating population means and sampling variances has been challenging and numerous approaches have been developed for making inferences about these quantities. To guide researchers and practitioners in deciding which approach to use, this article reviews the literature on these methodological developments. METHODS: A systematic review using four electronic databases was conducted in order to summarize the progress of RDS inference over the last 20 years and to provide insight to researchers on using the appropriate estimators in analyzing RDS data. Two independent reviewers selected the relevant abstracts and articles; thirty-two studies were included. The content of the studies was further categorized into developing and evaluating RDS mean and variance estimators. RESULTS: The population mean estimator RDSIEGO and the sampling variance estimators associated with tree boot strapping were identified as promising methods as the most robust population mean and variance estimate, respectively; as these estimators rely on a fewer assumptions. CONCLUSIONS: RDS holds substantial promise as a sampling method for understanding populations at high risk. The varied approaches to inference with RDS data each rely on different assumptions, but some require fewer assumptions than others and provide more robust and accurate inferences, when their corresponding assumptions are met.

Antimicrobial use among adult inpatients at hospital sites within the Canadian Nosocomial Infection Surveillance Program: 2009 to 2016.

BACKGROUND: Antimicrobial resistance is a growing threat to the world's ability to prevent and treat infections. Links between quantitative antibiotic use and the emergence of bacterial resistance are well documented. This study presents benchmark antimicrobial use (AMU) rates for inpatient adult populations in acute-care hospitals across Canada. METHODS: In this retrospective surveillance study, acute-care adult hospitals participating in the Canadian Nosocomial Infection Surveillance Program (CNISP) submitted annual AMU data on all systemic antimicrobials from 2009 to 2016. Information specific to intensive care units (ICUs) and non-ICU wards were available for 2014-2016. Data were analyzed using defined daily doses (DDD) per 1000 patient days (DDD/1000pd). RESULTS: Between 2009 and 2016, 16-18 CNISP adult hospitals participated each year and provided their AMU data (22 hospitals participated in ≥1 year of surveillance; 11 in all years). From 2009 to 2016, there was a significant reduction in use (12%) (from 654 to 573 DDD/1000pd, p = 0.03). Fluoroquinolones accounted for the majority of this decrease (47% reduction in combined oral and intravenous use, from 129 to 68 DDD/1000pd, p < 0.002). The top five antimicrobials used in 2016 were cefazolin (78 DDD/1000pd), piperacillin-tazobactam (53 DDD/1000pd), ceftriaxone (49 DDD/1000pd), vancomycin (combined oral and intravenous use was 44 DDD/1000pd; 7% of vancomycin use was oral), and ciprofloxacin (combined oral and intravenous use: 42 DDD/1000pd). Among the top 10 antimicrobials used in 2016, ciprofloxacin and metronidazole use decreased significantly between 2009 and 2016 by 46% (p = 0.002) and 26% (p = 0.002) respectively. Ceftriaxone (85% increase, p = 0.0008) and oral amoxicillin-clavulanate (140% increase, p < 0.0001) use increased significantly but contributed only a small component (8.6 and 5.0%, respectively) of overall use. CONCLUSIONS: This study represents the largest collection of dispensed antimicrobial use data among inpatients in Canada to date. Between 2009 and 2016, there was a significant 12% decrease in AMU, driven primarily by a 47% decrease in fluoroquinolone use. Modest absolute increases in parenteral ceftriaxone and oral amoxicillin-clavulanate use were noted but contributed a small amount of total AMU. Ongoing national surveillance is crucial for establishing benchmarks and antimicrobial stewardship guidelines.

At-a-glance - Opioid surveillance: monitoring and responding to the evolving crisis. / Aperçu - Crise des opioïdes : assurer le suivi et faire évoluer les données de surveillance.

The incidence of opioid-related overdoses is increasing at an alarming pace, largely driven by the increased use of fentanyl and its analogues. The need for sound and reliable sources of data on opioid use is crucial in order to make decisions on implementing efficient interventions, and develop appropriate policies and guidelines to mitigate the burden of opioid use. This article highlights initiatives undertaken by federal partners to address the opioid crisis in Canada. The need for novel surveillance approaches that improve the collection and harmonization of drug-related data is also discussed.

RÉSUMÉ: Le nombre de cas de surdoses liées à la consommation d'opioïdes augmente à un rythme alarmant, principalement en raison de l'usage accru du fentanyl et de ses analogues. Des sources de données fiables et solides sur l'utilisation des opioïdes sont cruciales pour la planification d'interventions efficaces, de même que pour l'élaboration de politiques et de directives appropriées en vue de réduire le fardeau des opioïdes. Cet article porte sur les mesures entreprises par les partenaires fédéraux pour s'attaquer à la crise des opioïdes au Canada. Il traite aussi de la nécessité de nouvelles méthodes de surveillance destinées à améliorer la collecte et l'harmonisation des données sur les drogues.

NEW SURVEY QUESTIONS AND ESTIMATORS FOR NETWORK CLUSTERING WITH RESPONDENT-DRIVEN SAMPLING DATA.

Respondent-driven sampling (RDS) is a popular method for sampling hard-to-survey populations that leverages social network connections through peer recruitment. While RDS is most frequently applied to estimate the prevalence of infections and risk behaviors of interest to public health, such as HIV/AIDS or condom use, it is rarely used to draw inferences about the structural properties of social networks among such populations because it does not typically collect the necessary data. Drawing on recent advances in computer science, we introduce a set of data collection instruments and RDS estimators for network clustering, an important topological property that has been linked to a network's potential for diffusion of information, disease, and health behaviors. We use simulations to explore how these estimators, originally developed for random walk samples of computer networks, perform when applied to RDS samples with characteristics encountered in realistic field settings that depart from random walks. In particular, we explore the effects of multiple seeds, without replacement versus with replacement, branching chains, imperfect response rates, preferential recruitment, and misreporting of ties. We find that clustering coefficient estimators retain desirable properties in RDS samples. This paper takes an important step toward calculating network characteristics using nontraditional sampling methods, and it expands the potential of RDS to tell researchers more about hidden populations and the social factors driving disease prevalence.