Application of QMRA in waterborne pathogens

Transcription

1 FIRST INTERNATIONAL SYMPOSIUM ON FOOD SAFETY Application of QMRA in waterborne pathogens Aiko Adell Nakashima School of Veterinary Medicine Faculty of Ecology and Natural Resources Universidad Andres Bello

2 Quantitative Microbial Risk Assessment (QMRA) Methodology used to: - Estimate risk and consequences from an exposure of individuals or population to infectious organisms. - Identify situations that contribute to an increased risk of illness Allowing resources and efforts to be focused in prioritized ways to reduce risks

3 QMRA OBJECTIVES Aim of QMRA: Management and comunication. Help directors, managers, shareholders, public services, individuals, etc. to understand the risk and opportunities. Evaluate the available options to control those risks

4 QMRA: 4 STEPS 1) Hazard Identification 2) Dose-Response Assessment 3) Exposure Assessment: 4) Risk Characterization Step 1 Step 2 Step 3 =

5 QMRA TOOLS 1. SPREADSHEETS (Palisade) Crystal Ball (Oracle) ModelRisk (Vose)

6 FONDECYT INICIACION PROJECT: The impact of land use on the fecal sources of contamination of rivers and on human and animal health risks (Project ID: )

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8 1. Hazard Identification

9 1. Hazard Identification WHO 2015: 31 foodborne hazards have caused 600 million illness and 420,000 deaths. Diarrhea disease agents: 230,000 deaths. Chile Year N of Outbreaks Incidence rate Reference Prado et al., Prado et al., ,689 Olea et al, episode per person-year Thomas et al., 2011 WHO 2015, Prado, VJ, et al Rev. med. Chile. 130 (5): Olea, A et al Rev Chilena Infectol. 29(5): Thomas MK, et al Epidemiol. Infect. (2011): 139,

10 1. Hazard Identification Gastrointestinal Illness (GI) caused by parasites in Chile: Giardia duodenalis: prevalence % - principal cause of diarrhea in children (Tassara, 1999) : 1,392 children diarrhea = 183 (13,1%) positive samples = Blastocystus hominis, Giardia duodenalis and Cryptosporidium spp (ISP 2012). Water related Outbreaks: US: >50 cryptosporidiosis outbreaks in humans No treatment for Cryptosporidium Tassara R Pediatria. 1999; 70 (5): / ISP URL:

11 Protozoa: Cryptosporidium, Giardia Public Health Importance Adapter for waterborne transmission: Excreted in large quantities Oocysts (Cry) and cysts are immediately infective after being excreted Infection/illness: small doses (10 oocysts) Resistant to environmental conditions Chloride resistant

12 2. Exposure Assessment: Water Source: River MST Bacteroidales Cryptosporidium/ Giardia Fecal Coliforms + E. coli Dilution Factor Estimate contamination Viable (oo/cysts) Not Viable (oo/cysts) Human Exposure: Water Ingestion

13 2. Exposure Assessment: Estimation of oocysts / cysts ingested by humans: Protozoa Concentration in 10L* Own data Conversion from 10 L to 1 ml Dilution Factor: 1:1 1:30 Bacteroidales (%) Own data Protozoa viability 100% 0.1% Water ingestion (ml) Other studies *Adjusted for recovery % and detection limit

14 2) Dose-Response Assessment Exposition routes: Ingestion, inhalation, contact Final result: Infection or Illness Dose response Studies: Mild-virulent agents: healthy adult humans Cryposporium parvum: Okhuysen et al. 1999; Okhuysen et al. 2002, Chappell et al, 2006; etc Giardia: Rendtorff (1954), and Rendtorff and Holt (1954) Animal models

15 2) Dose-Response Assessment Independent action theory: One organism is capable of initiating illness But, as the probability of evading the host defenses and causing infection is low, More than one microorganism is required to cause infection QMRA supports this theory: to determine the probability that one microorganism cause disease: Models: Beta Exponencial Log probit

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18 2) Dose-Response Assessment Binomial Model d = oocysts and cysts dose given to an individual (it is not a median dose) r = probability that only one protozoa is able to cause infection The distribution of microorganism that survive (and cause infection) is binomial when each one of the ingested dose (d) has a probability of surviving (r) equal or identical (Haas, 2002)* *Haas, C.N., 2002, Conditional dose-response relationships for microorganisms: Development and application. Risk Anal 22,

19 1. Cryptosporidium (Teunis, 2009) 2) Dose-Response Assessment Dose response curve for humans: Okhuysen et al. 1999; Okhuysen et al. 2002, Chappell et al, 2006 ID50 = 35 oocysts II. Giardia (Rose and Gerba, 1991) Rendtorff (1954) and Rendtorff and Holt (1954) r =

20 4. Risk Characterization What is acceptable? EPA criteria for humans: 32 illness/1,000 individuals or 3.2% Percent of infections resulting in illness: 50% for Cryptosporidium and 45% for Giardia (Soller et al. (2010) Results: Humans: Probability of illness Soller, J.A., Schoen, M.E., Bartrand, T., Ravenscroft, J.E., Ashbolt, N.J., Water Res. 44, 4674e4691

21 5. Risk Characterization Assumptions Pathogens are randomly distributed in water Oocysts and cysts do not multiply in the environment Viability is not the same as infectivity A single pathogen can survive the host's defenses and reach the site where the infection develops The survival of a pathogen is independent of the presence of another pathogen Humans are healthy and immunocompetent individuals Human will be exposed once to the contaminated water Secondary infection won t occur

22 5. Risk Characterization

23 5. Risk Characterization Statistical Risk Stochastic models using MonteCarlos simulation Sensitivity Analysis: what variables in the model have a higher impact on the result

24 6. Results Sensitivity Analysis More Important Protozoa Concentration Bacteroidales (%) Less Important Water Ingested Swimming duration Sea otter body weight

25 Some applications of QMRA in Food Safety Search for a risk range when the exposition is known or assumed: X pathogen has been detected during the sampling of a specific finished food. What level of risk would the consumers have? Evaluation of HACCP systems Evaluate mitigation measures in the processing of a food: temperature, etc. Estimate the risk of a population that consumes a specific food containing X amount of the microorganism Y

26 Many thanks to: María Cristina Martínez ISP Woutrina Smith University of California, Davis