Quantitative microbial risk assessment to estimate the public health risk from exposure to enterotoxigenic E. coli in drinking water in the rural area of Villapinzon, Colombia

2021 ◽  
pp. 100173
Author(s):  
J.L. Moncada Barragán ◽  
Lucumí D.I. Cuesta ◽  
M.S. Rodriguez Susa
2010 ◽  
Vol 73 (2) ◽  
pp. 274-285 ◽  
Author(s):  
E. FRANZ ◽  
S. O. TROMP ◽  
H. RIJGERSBERG ◽  
H. J. van der FELS-KLERX

Fresh vegetables are increasingly recognized as a source of foodborne outbreaks in many parts of the world. The purpose of this study was to conduct a quantitative microbial risk assessment for Escherichia coli O157:H7, Salmonella, and Listeria monocytogenes infection from consumption of leafy green vegetables in salad from salad bars in The Netherlands. Pathogen growth was modeled in Aladin (Agro Logistics Analysis and Design Instrument) using time-temperature profiles in the chilled supply chain and one particular restaurant with a salad bar. A second-order Monte Carlo risk assessment model was constructed (using @Risk) to estimate the public health effects. The temperature in the studied cold chain was well controlled below 5°C. Growth of E. coli O157:H7 and Salmonella was minimal (17 and 15%, respectively). Growth of L. monocytogenes was considerably greater (194%). Based on first-order Monte Carlo simulations, the average number of cases per year in The Netherlands associated the consumption leafy greens in salads from salad bars was 166, 187, and 0.3 for E. coli O157:H7, Salmonella, and L. monocytogenes, respectively. The ranges of the average number of annual cases as estimated by second-order Monte Carlo simulation (with prevalence and number of visitors as uncertain variables) were 42 to 551 for E. coli O157:H7, 81 to 281 for Salmonella, and 0.1 to 0.9 for L. monocytogenes. This study included an integration of modeling pathogen growth in the supply chain of fresh leafy vegetables destined for restaurant salad bars using software designed to model and design logistics and modeling the public health effects using probabilistic risk assessment software.


2020 ◽  
Vol 18 (3) ◽  
pp. 292-305
Author(s):  
M. M. Majedul Islam ◽  
Md. Atikul Islam

Abstract A Quantitative Microbial Risk Assessment (QMRA) technique was applied to assess the public health risk from exposure to infectious microorganisms at bathing areas of three rivers in Bangladesh. The QMRA assessed the probability of illness due to the accidental ingestion of river water impacted by untreated sewage. The simplified QMRA was based on average concentrations of four reference pathogens Escherichia coli (E. coli) O157:H7, Cryptosporidium spp, norovirus and rotavirus relative to indicator bacterium E. coli. Public health risk was estimated as the probability of infection and illness from a single exposure of bathers. The risks of illness were ranged from 7 to 10% for E. coli O157:H7, 13 to 19% for Cryptosporidium, 7 to 10% for norovirus and 12 to 17% for rotavirus. The overall risk of illness at the rivers was slightly higher in children (9–19%) compared to adults (7–16%). The risks of illness in individuals exposed to the river bathing were unacceptably high, exceeding the USEPA acceptable risk of 3–6 illnesses per hundred bathing events. This study gives a basis for reducing the burden of disease in the population by applying appropriate risk management. Findings and methods of this study will be helpful for other countries with similar socio-economic and geographic settings.


Author(s):  
Chukwuemeka Kingsley John ◽  
Jaan H. Pu ◽  
Rodrigo Moruzzi ◽  
Manish Pandey

Abstract This paper presents a study to assess the roof-harvested rainwater (RHRW) in the Ikorodu area of Lagos state, Nigeria, and recommends guidance to minimise the health risk for its households. The types, design and use of rainwater harvesting systems have been evaluated in the study area to inspect the human risk of exposure to Escherichia coli (E. coli). To achieve these objectives, a detailed survey involving 125 households has been conducted which showed that 25% of them drink RHRW. Quantitative microbial risk assessment (QMRA) analysis has been used to quantify the risk of exposure to harmful E. coli from RHRW utilised as potable water, based on the ingestion of 2 L of rainwater per day per capita. Results have revealed that the maximum E. coli exposure risk from the consumption of RHRW, without application of any household water treatment technique (HHTTs) and with application of alum only, were 100 and 96 respectively, for the estimated number of infection risk per 10,000 exposed households per year. This estimation has been done based on 7% of E. coli as viable and harmful. Conclusively, it is necessary that a form of disinfectant be applied to the RHRW before use.


LWT ◽  
2021 ◽  
Vol 144 ◽  
pp. 111201 ◽  
Author(s):  
Prez Verónica Emilse ◽  
Victoria Matías ◽  
Martínez Laura Cecilia ◽  
Giordano Miguel Oscar ◽  
Masachessi Gisela ◽  
...  

Author(s):  
Annalaura Carducci ◽  
Gabriele Donzelli ◽  
Lorenzo Cioni ◽  
Ileana Federigi ◽  
Roberto Lombardi ◽  
...  

Biological risk assessment in occupational settings currently is based on either qualitative or semiquantitative analysis. In this study, a quantitative microbial risk assessment (QMRA) has been applied to estimate the human adenovirus (HAdV) health risk due to bioaerosol exposure in a wastewater treatment plant (WWTP). A stochastic QMRA model was developed considering HAdV as the index pathogen, using its concentrations in different areas and published dose–response relationship for inhalation. A sensitivity analysis was employed to examine the impact of input parameters on health risk. The QMRA estimated a higher average risk in sewage influent and biological oxidation tanks (15.64% and 12.73% for an exposure of 3 min). Sensitivity analysis indicated HAdV concentration as a predominant factor in the estimated risk. QMRA results were used to calculate the exposure limits considering four different risk levels (one illness case per 100, 1.000, 10.000, and 100.000 workers): for 3 min exposures, we obtained 565, 170, 54, and 6 GC/m3 of HAdV. We also calculated the maximum time of exposure for each level for different areas. Our findings can be useful to better define the effectiveness of control measures, which would thus reduce the virus concentration or the exposure time.


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