| Literature DB >> 29865180 |
Richard Bentham1, Harriet Whiley2.
Abstract
Quantitative microbial risk assessment (QMRA) is a relatively new approach in identifying health risks associated with the ubiquitous presence of pathogens and opportunists in the human environment. The methodology builds on experimental and meta-analytical data to identify measurable factors that contribute to, and can quantify, the likely extent of disease given a particular exposure. Early modelling was particularly focused on food-borne disease, and subsequently water-borne disease, with the emphasis focused on ingestion and its role in enteric disease. More recently, there has been a focus on translating these principles to opportunist waterborne infections (OWI) with primary focus on Legionella spp. Whereas dose and susceptibility are well documented via the ingestion route of exposure there is considerably less certainty regarding both factors when understanding Legionella spp. and other OWI. Many OWI can arise through numerous routes of transmission with greatly differing disease presentations. Routes of Legionella spp. infection do not include ingestion, but rather aspiration and inhalation of contaminated water are the routes of exposure. The susceptible population for OWI is a vulnerable sub-set of the population unlike those associated with enteric disease pathogens. These variabilities in dose, exposure and susceptibility call in to question whether QMRA can be a useful tool in managing risks associated with OWI. Consideration of Legionella spp. as a well-documented subject of research calls into question whether QMRA of OWI is likely to be a useful tool in developing risk management strategies.Entities:
Keywords: Mycobacterium avium; Pseudomonas; QMRA; drinking water; manufactured water systems; non-tuberculous mycobacteria; opportunistic pathogens; public health; risk assessment
Mesh:
Year: 2018 PMID: 29865180 PMCID: PMC6025005 DOI: 10.3390/ijerph15061150
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
Figure 1World Health Organization accepted framework for water related QMRA [4].
Summary of the uncertainties associated with Legionella Quantitative Microbial Risk Assessment (QMRA).
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| What hazards? | |
| Which exposure pathways? | Inhalation and/or aspiration of |
| Which Health outcomes? | Legionnaire’s disease in susceptible populations, Pontiac fever in general populations. |
| What certainty is needed for risk management? | This has not been quantified. |
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| Pathogen reduction | Achieved by barriers/control measures and managing re-contamination risks–well established control measures for most sources have been developed and applied. |
| Source concentration | Not defined as there are multiple sources. The relationship between source concentration and exposure has not been quantified due to the diversity of exposure scenarios. A robust and broadly applicable relationship between source concentration and dose has not been quantified. |
| Magnitude and frequency of exposure | Not quantifiable. Intermittent or occasional use of variable sources by different demographic groups. Many and variable exposure scenarios have been identified. |
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| Dose-response | Not quantified in humans. Susceptibility is dependent on the host immune status. Two principle routes of dose delivery exist. |
| Illness and sequalae | In many cases none. Disease may be self-limited, profound, or fatal. Sequalae range from minor to severe, prolonged and debilitating. Secondary transmission is extremely rare. |
| Impact on disease burden | DALY * for Years Lived with Disability (YLD) is low (8%). DALY for Years of Life Lost is high (92%). |
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| Quantification of risk | Reported cases range from 0.5 to 5+: 100,000. Prevalence of disease is much higher in susceptible populations but not quantified. Disease is probably considerably under-reported |
| Variability and uncertainty analysis | Not calculable for the range of infecting organisms, sources and exposure routes on available evidence. |
| Sensitivity analysis | Insufficient uncertainty data |
* Disability Adjusted Life Years