David N Fisman

University of Toronto, Toronto, Ontario, Canada

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Publications (150)801.5 Total impact

  • Source
    Expert Review of Vaccines 07/2014; · 4.22 Impact Factor
  • Kevin A Brown, David N Fisman, Nick Daneman
    07/2014; 35(7):911-912.
  • Marija Vasilevska, Jennifer Ku, David N Fisman
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    ABSTRACT: Background and objective. Healthcare workers experience occupational risk of infection and may transmit infections to patients. Vaccination provides an efficient means of protecting workers and patients, but uptake may be low. We sought to identify factors influencing vaccine acceptance by healthcare workers in order to obtain insights leading to more effective vaccination programs in this population. Design. Systematic review and meta-analysis. Methods. We searched Medline, Embase, and CINAHL databases to identify studies published up to May 2012. Factors influencing vaccination acceptance were devised a priori. Random-effects meta-analysis was performed to generate summary estimates of effect. Heterogeneity and publication bias were explored using statistical tools. Results. Thirty-seven studies evaluating a variety of vaccines (against influenza, pertussis, smallpox, anthrax, and hepatitis B) were included. Homogeneous effects on vaccine acceptance were identified with desire for self-protection (odds ratio [OR], 3.42 [95% confidence interval (CI), 2.42-4.82]) and desire to protect family and friends (OR, 3.28 [95% CI, 1.10-9.75]). Concern that vaccine transmits the illness it was meant to prevent decreased acceptance (OR, 0.42 [95% CI, 0.30-0.58]). Differences in physician and nurse acceptance of immunization were seen between Asian and non-Asian studies. Conclusions. Consideration of self-protection (rather than absolute disease risk or protection of patients) appears the strongest and most consistent driver of healthcare workers' decisions to accept vaccination, though other factors may also be impactful, and reasons for between-study divergence in effects is an important area for future research. This finding has important implications for the design of programs to enhance healthcare worker vaccine uptake.
    Infection Control and Hospital Epidemiology 06/2014; 35(6):699-708. · 4.02 Impact Factor
  • David N Fisman, Gabriel M Leung, Marc Lipsitch
    The Lancet 01/2014; 383(9913):189-90. · 39.06 Impact Factor
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    ABSTRACT: In the 19th century, there were several major cholera pandemics in the Indian subcontinent, Europe, and North America. The causes of these outbreaks and the genomic strain identities remain a mystery. We used targeted high-throughput sequencing to reconstruct the Vibrio cholerae genome from the preserved intestine of a victim of the 1849 cholera outbreak in Philadelphia, part of the second cholera pandemic. This O1 biotype strain has 95 to 97% similarity with the classical O395 genome, differing by 203 single-nucleotide polymorphisms (SNPs), lacking three genomic islands, and probably having one or more tandem cholera toxin prophage (CTX) arrays, which potentially affected its virulence. This result highlights archived medical remains as a potential resource for investigations into the genomic origins of past pandemics.
    New England Journal of Medicine 01/2014; 370:334-340. · 51.66 Impact Factor
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    Ashleigh R Tuite, Ann N Burchell, David N Fisman
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    ABSTRACT: Syphilis co-infection risk has increased substantially among HIV-infected men who have sex with men (MSM). Frequent screening for syphilis and treatment of men who test positive might be a practical means of controlling the risk of infection and disease sequelae in this population.
    PLoS ONE 01/2014; 9(7):e101240. · 3.73 Impact Factor
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    ABSTRACT: Antibiotic therapy is the principal risk factor for Clostridium difficile infection (CDI), but little is known about how risks cumulate over the course of therapy and abate after cessation. We prospectively identified CDI cases among adults hospitalized at a tertiary hospital between June 2010 and May 2012. Poisson regression models included covariates for time since admission, age, hospitalization history, disease pressure, and intensive care unit stay. Impacts of antibiotic use through time were modeled using 4 measures: current antibiotic receipt, time since most recent receipt, time since first receipt during a hospitalization, and duration of receipt. Over the 24-month study period, we identified 127 patients with new onset nosocomial CDI (incidence rate per 10,000 patient days [IR] = 5.86). Of the 4 measures, time since most recent receipt was the strongest independent predictor of CDI incidence. Relative to patients with no prior receipt of antibiotics in the last 30 days (IR = 2.95), the incidence rate of CDI was 2.41 times higher (95% confidence interval [CI] 1.41, 4.13) during antibiotic receipt and 2.16 times higher when patients had receipt in the prior 1-5 days (CI 1.17, 4.00). The incidence rates of CDI following 1-3, 4-6 and 7-11 days of antibiotic exposure were 1.60 (CI 0.85, 3.03), 2.27 (CI 1.24, 4.16) and 2.10 (CI 1.12, 3.94) times higher compared to no prior receipt. These findings are consistent with studies showing higher risk associated with longer antibiotic use in hospitalized patients, but suggest that the duration of increased risk is shorter than previously thought.
    PLoS ONE 01/2014; 9(8):e105454. · 3.73 Impact Factor
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    ABSTRACT: Haiti has been in the midst of a cholera epidemic since October 2010. Rainfall is thought to be associated with cholera here, but this relationship has only begun to be quantitatively examined. In this paper, we quantitatively examine the link between rainfall and cholera in Haiti for several different settings (including urban, rural, and displaced person camps) and spatial scales, using a combination of statistical and dynamic models. Statistical analysis of the lagged relationship between rainfall and cholera incidence was conducted using case crossover analysis and distributed lag nonlinear models. Dynamic models consisted of compartmental differential equation models including direct (fast) and indirect (delayed) disease transmission, where indirect transmission was forced by empirical rainfall data. Data sources include cholera case and hospitalization time series from the Haitian Ministry of Public Health, the United Nations Water, Sanitation and Health Cluster, International Organization for Migration, and Hôpital Albert Schweitzer. Rainfall data was obtained from rain gauges from the U.S. Geological Survey and Haiti Regeneration Initiative, and remote sensing rainfall data from the National Aeronautics and Space Administration Tropical Rainfall Measuring Mission. A strong relationship between rainfall and cholera was found for all spatial scales and locations examined. Increased rainfall was significantly correlated with increased cholera incidence 4-7 days later. Forcing the dynamic models with rainfall data resulted in good fits to the cholera case data, and rainfall-based predictions from the dynamic models closely matched observed cholera cases. These models provide a tool for planning and managing the epidemic as it continues.
    Epidemics. 12/2013; 5(4):197-207.
  • Source
    David N Fisman, Ashleigh R Tuite
    The Lancet Infectious Diseases 11/2013; · 19.97 Impact Factor
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    ABSTRACT: In Canada, tuberculosis (TB) rates are at a historic low, with the remaining risk concentrated in a few vulnerable population subgroups. To describe the epidemiology of TB in the Canadian province of Ontario and to characterise risk factors associated with transmission events, identified using genetic typing techniques. Retrospective analysis of 2186 culture-positive TB cases between August 2007 and December 2011. Temporal trends and risk of spatiotemporal and genotypic clustering were evaluated using Poisson and logistic regression models. Being in a spatiotemporal cluster was associated with Aboriginal status (odds ratio [OR] 3.63, 95% confidence interval [CI] 1.23-10.71). Cases in genotypic clusters were more likely to report homelessness as a risk factor (adjusted OR [aOR] 2.92, 95%CI 1.74-4.90) or be male (aOR 1.35, 95%CI 1.09-1.68), and were less likely to be aged ≥65 years (aOR 0.63, 95%CI 0.49-0.82), foreign-born (aOR 0.32, 95%CI 0.24-0.43) or Aboriginal (aOR 0.40, 95%CI 0.16-0.99). The Beijing lineage had an annual rate of increase of almost 10% (P = 0.047), and was associated with genotypic clustering (aOR 2.84, 95%CI 2.19-3.67). Genotypic data suggest that disease clusters are smaller, but far more common, than would be estimated using spatiotemporal clustering.
    The International Journal of Tuberculosis and Lung Disease 10/2013; 17(10):1322-7. · 2.76 Impact Factor
  • Antimicrobial Agents and Chemotherapy 08/2013; 57(8):4094. · 4.57 Impact Factor
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    ABSTRACT: The 2009 influenza A (H1N1) pandemic was mild by historical standards, but was more severe in isolated Canadian Indigenous communities. Oseltamivir was used aggressively for outbreak control in an isolated northern Ontario First Nations community. We used mathematical modeling to quantify the impact of antiviral therapy on the course of this outbreak. We used both a Richards growth model and a compartmental model to evaluate the characteristics of the outbreak based on both respiratory visits and influenza-like illness counts. Estimates of best-fit model parameters, including basic reproductive number (R0 ) and antiviral efficacy, and simulations, were used to estimate the impact of antiviral drugs compared to social distancing interventions alone. Using both approaches, we found that a rapidly growing outbreak slowed markedly with aggressive antiviral therapy. Richards model turning points occurred within 24 hours of antiviral implementation. Compartmental models estimated antiviral efficacy at 70-95%. Plausible estimates of R from both modeling approaches ranged from 4·0 to 15·8, higher than published estimates for southern Canada; utilization of aggressive antiviral therapy in this community prevented 962-1757 cases of symptomatic influenza and as many as 114 medical evacuations in this community. Although not advocated in other settings in Canada, aggressive antiviral therapy markedly reduced the impact of a pandemic-related influenza A (H1N1) outbreak in an isolated Canadian First Nations community in northern Ontario, Canada. The differential risk experienced by such communities makes tailored interventions that consider risk and lack of access to medical services, appropriate.
    Influenza and Other Respiratory Viruses 07/2013; · 1.47 Impact Factor
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    ABSTRACT: Syphilis incidence among men who have sex with men (MSM) continues to rise despite attempts to increase screening and treatment uptake. We examined the marginal effect of increased frequency versus increased coverage of screening on syphilis incidence in Toronto, Canada. We developed an agent-based, network model of syphilis transmission, representing a core population of 2,000 high-risk MSM. Epidemiological and biological parameters were drawn from regional surveillance data and literature-derived estimates. The pre-intervention period of the model was calibrated using surveillance data to identify 1000 credible simulations per strategy. Evaluated strategies included: annual syphilis screening at baseline coverage, increased screening frequency at baseline coverage, and increased coverage of annual screening. Intervention impact was measured as annual prevalence of detected infectious cases and syphilis incidence per year over 10 years. Of the strategies evaluated, increasing the frequency of syphilis screening to every three months was most effective in reducing reported and incident syphilis infections. Increasing the fraction of individuals tested, without increasing test frequency, resulted a smaller decline in incidence, because reductions in infectious syphilis via treatment were counterbalanced by increased incident syphilis among individuals with prior latent syphilis. For an equivalent number of additional tests performed annually, increased test frequency was consistently more effective than improved coverage. Strategies that focus on higher frequency of testing in smaller fractions of the population were more effective in reducing syphilis incidence in a simulated MSM population. The findings highlight how treatment-induced loss of immunity can create unexpected results in screening-based control strategies.
    BMC Public Health 06/2013; 13(1):606. · 2.08 Impact Factor
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    ABSTRACT: Seasonal variations in the incidence of pneumonia and influenza are associated with nosocomial Clostridium difficile infection (CDI) incidence, but the reasons why remain unclear. Our objective was to consider the impact of pneumonia and influenza timing and severity on CDI incidence. We conducted a retrospective cohort study using the US National Hospital Discharge Survey sample. Hospitalized patients with a diagnosis of CDI or pneumonia and influenza between 1993 and 2008 were identified from the National Hospital Discharge Survey data set. Poisson regression models of monthly CDI incidence were used to measure 1) the time lag between the annual pneumonia and influenza prevalence peak and the annual CDI incidence peak and 2) the lagged effect of pneumonia and influenza prevalence on CDI incidence. CDI was identified in 18,465 discharges (8.52 per 1,000 discharges). Peak pneumonia prevalence preceded peak CDI incidence by 9.14 weeks (95% confidence interval: 4.61, 13.67). A 1% increase in pneumonia prevalence was associated with a cumulative effect of 11.3% over a 6-month lag period (relative risk = 1.113, 95% confidence interval: 1.073, 1.153). Future research could seek to understand which mediating pathways, including changes in broad-spectrum antibiotic prescribing and hospital crowding, are most responsible for the associated changes in incidence.
    American journal of epidemiology 05/2013; · 5.59 Impact Factor
  • David N Fisman, Victoria Lee, Monika Naus
    Vaccine 04/2013; · 3.77 Impact Factor
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    ABSTRACT: The rising incidence of Clostridium difficile infection (CDI) could be reduced by lowering exposures to high risk antibiotics. The objective of this study was to determine the association between antibiotic class and the risk of CDI in the community setting. EMBASE and PubMed were queried without restriction to date or language. Comparative observational studies and RCTs considering the impact of antibiotic exposures on CDI risk among non-hospitalized populations were considered. We estimated pooled odds ratios (OR) for antibiotic classes using random effects meta-analysis. Our search criteria identified 465 articles, of which 7 met inclusion criteria; all were observational studies. Five studies considered antibiotic risk relative to no antibiotic exposure: clindamycin (OR=16.80, 95% confidence interval [CI]: 7.48-37.76), fluoroquinolones (OR=5.50, 95% CI: 4.26-7.11) and cephalosporins, monobactams and carbapenems (CMCs, OR=5.68, 95% CI: 2.12-15.23) had the largest effects, while macrolides (OR=2.65, 95% CI: 1.92-3.64), sulfonamides and trimethoprim (OR=1.81, 95% CI: 1.34-2.43) and penicillins (OR=2.71, 95% CI: 1.75-4.21) had lesser associations with CDI. We noted no effect of tetracyclines on CDI risk (OR=0.92, 95% CI: 0.61-1.40). In the community setting, there is substantial variation in risk of CDI associated with different antimicrobial classes. Avoidance of high risk antibiotics (such as clindamycin, CMCs and fluoroquinolones) in favor of lower risk antibiotics (such as penicillins, macrolides and tetracyclines) may help reduce the incidence of CDI.
    Antimicrobial Agents and Chemotherapy 03/2013; · 4.57 Impact Factor
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    Critical care (London, England) 01/2013; 17(1):107. · 4.72 Impact Factor
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    ABSTRACT: Despite highly successful vaccination programs and high vaccine uptake, both endemic pertussis and periodic pertussis outbreaks continue to occur. The under-recognized role of adolescents and adults in disease transmission, due to waning immunity following natural infection and vaccination, and reduced likelihood of correct diagnosis, may contribute to pertussis persistence. We constructed a mathematical model to describe the transmission of pertussis in Southern Ontario in both pre-vaccine and vaccine eras, to estimate the underlying burden of pertussis in the population. The model was well calibrated using the best available data on pertussis in the pre-vaccination (1880-1929) and vaccination (1993-2004) eras in Ontario. Pertussis under-reporting by age group was estimated by comparing model-projected incidence to reported laboratory-confirmed cases for Greater Toronto. Best-fit model estimates gave a basic reproductive number of approximately 10.6, (seasonal range 9.9 to 11.5). Under-reporting increased with age, and approximately >95% of infections in children were caused by infections in persons with waning immunity to pertussis following prior infection or vaccination. A well-calibrated model suggests that under-recognized cases of pertussis in older individuals are likely to be an important driver of ongoing pertussis outbreaks in children. Model projections strongly support enhancement of booster vaccination efforts in adults.
    PLoS ONE 01/2013; 8(12):e83850. · 3.73 Impact Factor
  • Ashleigh R Tuite, Amy L Greer, David N Fisman
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    ABSTRACT: Tick-borne illnesses represent an important class of emerging zoonoses, with climate change projected to increase the geographic range within which tick-borne zoonoses might become endemic. We evaluated the impact of latitude on the rate of change in the incidence of Lyme disease in the United States, using publicly available data.
    CMAJ open. 01/2013; 1(1):E43-7.
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    ABSTRACT: Haiti has been in the midst of a cholera epidemic since October 2010. Rainfall is thought to be associated with cholera here, but this relationship has only begun to be quantitatively examined. In this paper, we quantitatively examine the link between rainfall and cholera in Haiti for several different settings (including urban, rural, and displaced person camps) and spatial scales, using a combination of statistical and dynamic models. Statistical analysis of the lagged relationship between rainfall and cholera incidence was conducted using case crossover analysis and distributed lag nonlinear models. Dynamic models consisted of compartmental differential equation models including direct (fast) and indirect (delayed) disease transmission, where indirect transmission was forced by empirical rainfall data. Data sources include cholera case and hospitalization time series from the Haitian Ministry of Public Health, the United Nations Water, Sanitation and Health Cluster, International Organization for Migration, and Hôpital Albert Schweitzer. Rainfall data was obtained from rain gauges from the U.S. Geological Survey and Haiti Regeneration Initiative, and remote sensing rainfall data from the National Aeronautics and Space Administration Tropical Rainfall Measuring Mission. A strong relationship between rainfall and cholera was found for all spatial scales and locations examined. Increased rainfall was significantly correlated with increased cholera incidence 4–7 days later. Forcing the dynamic models with rainfall data resulted in good fits to the cholera case data, and rainfall-based predictions from the dynamic models closely matched observed cholera cases. These models provide a tool for planning and managing the epidemic as it continues.
    Epidemics 01/2013; 5(4):197–207. · 2.26 Impact Factor

Publication Stats

3k Citations
801.50 Total Impact Points

Institutions

  • 2003–2014
    • University of Toronto
      • • Dalla Lana School of Public Health
      • • Department of Laboratory Medicine and Pathobiology
      • • Division of Neurology
      • • Division of Neurosurgery
      Toronto, Ontario, Canada
  • 2013
    • The University of Western Ontario
      • Department of Applied Mathematics
      London, Ontario, Canada
  • 2011
    • Mount Sinai Hospital, Toronto
      • Department of Microbiology
      Toronto, Ontario, Canada
  • 2009–2011
    • National Research Council Canada
      • Institute for Biodiagnostics (IBD)
      Ottawa, Ontario, Canada
    • Public Health Ontario
      Toronto, Ontario, Canada
    • The University of Calgary
      • Department of Pathology and Laboratory Medicine
      Calgary, Alberta, Canada
    • CUNY Graduate Center
      New York City, New York, United States
  • 2010
    • China Medical University Hospital
      臺中市, Taiwan, Taiwan
    • Baycrest
      Toronto, Ontario, Canada
    • Health Sciences Centre Winnipeg
      Winnipeg, Manitoba, Canada
  • 2007–2010
    • SickKids
      Toronto, Ontario, Canada
    • University of Pennsylvania
      Philadelphia, Pennsylvania, United States
  • 2008
    • Ontario Ministry Of Health And Long-Term Care
      Toronto, Ontario, Canada
    • Sunnybrook Health Sciences Centre
      • Department of Evaluative Clinical Sciences
      Toronto, Ontario, Canada
  • 2003–2007
    • Drexel University
      • School of Public Health
      Philadelphia, Pennsylvania, United States
  • 2006
    • Princeton University
      • Center for Health and Wellbeing
      Princeton, NJ, United States
  • 2002–2004
    • McMaster University
      • Department of Clinical Epidemiology and Biostatistics
      Hamilton, Ontario, Canada
  • 2001
    • Harvard Medical School
      Boston, Massachusetts, United States
  • 2000–2001
    • Beth Israel Deaconess Medical Center
      • Division of Infectious Diseases
      Boston, MA, United States
    • Harvard University
      • Center for Risk Analysis
      Boston, MA, United States