Difference between revisions of "Dose response assessment"

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(Available Dose Response Models)
(Available Dose Response Models)
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| [[Dose response models for Vibrio cholera|''Vibrio cholera'' - Cholera]]
| [[Dose response models for Vibrio cholera|''Vibrio cholera'' - Cholera]]
|Beta-Poisson, α = 0.250, N<sub>50</sub> = 243, ID<sub>50</sub> = 243, Human, Inaba 569B strain, oral with NaHCO3, Infection
|Beta-Poisson, α = 0.250, N<sub>50</sub> = 243, ID<sub>50</sub> = 243, Human, Inaba 569B strain, oral with NaHCO<sub>3</sub>, Infection
|Hornick et al. 1971  
|Hornick et al. 1971  

Revision as of 22:49, 16 November 2011

Dose Response

In the QMRA framework, the dose response assessment phase is the quantitative yardstick for the risk estimate, as this phase estimates a risk of response (infection, illness or death) with respect to a known dose of a pathogen. The basis of the dose response phase is the dose response models, which are mathematical functions derived to describe the dose response relationship for specific pathogens. Therefore, for a particular endpoint (response), a specific pathogen and exposure route there is a unique dose response relationship and consequently a dose response model. Dose response models are necessary as it is not possible to perform a direct study (even with animals) to assess dose corresponding to an acceptably low risk.

Dose Response Models

To be plausible a model should consider the discrete (particulate) nature of organisms, which has a high variability at low dose. It should also be based on the concept of infection from one or more “survivors” of initial dose. Therefore dose response models for QMRA need to be physiologically plausible and be derived from what is known of the general infection process. There are two models which are derived based on these needs for the QMRA dose response relationship, the exponential and beta Poisson models.

Types of Models

Exponential Dose Response Model


  • Poisson distribution of organisms among replicated doses (mean number in dose=d).
  • One organism is capable of producing an infection if it arrives at an appropriate site.
  • Organisms have independent and identical probability (k) of surviving to reach and infect at an appropriate site. Some sources use the letter 'r' instead of 'k' (equation 1). Here we define r=1/k, so the alternative form for equation 1 can be given as P(response) = 1- exp(-dose/r)

Beta-Poisson Model

Assumptions same as the exponential model except:

  • Nonconstant survival and infection probabilities
  • Survival probabilities (k) are given by the beta distribution

The slope of the beta-Poisson dose response curve is more shallow than the exponential. The exponential model is the same as the beta-Poisson model when alpha approaches infinity. The parameters are alpha and N50. N50 is the dose at which 50% of the population is expected to be affected. The beta-Poisson model is sometimes expressed with a beta parameter instead of an N50 parameter; N50=beta*[2^(1/alpha)-1]. Both the alpha and the beta parameters derive from the use of the beta distribution to model nonconstant pathogen survival probabilities.

The exact form of the beta-Poisson model uses the confluent hypergeometric function, which can be difficult to optimize. However since both the exact and approximate form of the beta Poisson dose response models demonstrate linearity in the low dose range, and there is not a substantial difference between the forms in fitting dose response data, there is not reason to not use the more intuitive form of the beta Poisson. Equation 2 shows the approximate form of the beta Poisson using the N50 parameter, which can be directly optimized using dose response data or estimated using the conversion in equation 4.

Revised exponential.png
Beta poisson with beta beta conversion.png

Utility of Using Dose Response Models

An optimized dose response model allows for greater flexibility and a wider range of understanding in the estimated risk. Rather than having a median infectious or lethal dose for a pathogen a model that can describe the full range of probability of response beyond just the median and one that is still accurate at low doses.

Available Dose Response Models

Microbial Group Pathogen Recommended Model* Dose Response Data Reference
Bacteria Bacillus anthracis - Anthrax Exponential, k = 1.62 E-05, LD50 = 42,924, Guinea pig, Inhalation, Death Druett et al., (1953)
Burkholderia - Glanders Exponential, k = 1.0 E-04, ID50 = 6,919, C57BL6 mice/KHW, Intranasal, Infection Brett and Woods, (2000)
Campylobacter - Campylobacteriosis Beta-Poisson, α = 0.14, N50 = 890.38 , ID50 = 890.38, Human, A3249 strain, oral (in milk), Infection Black et al., 1988; Medema et al., 1996
Coxiella burnetii - Q-fever Beta-Poisson, α = 0.36, N50 = 4.93E+08, LD50 = 4.93E+08, mice/ phase I Ohio strain, interperitoneal, Death Williams et al., (1982)
Escherichia coli Beta-Poisson, α = 0.16, N50 = 2.11E+06, ID50 = 2.11E+06, Human, EIEC 1624, oral with milk, Infection DuPont et al., 1971
enterohemorrhagic Escherichia coli (EHEC) Exponential, k = 2.18E-04, ID50 = 3.18E+03, 3 month old pigs, EHEC O157:H7, strain 86-24, oral (in food), Infection Cornick and Helgerson, 2004
Francisella tularensis - Tularemia Exponential, k = 0.047, LD50 = 14.65, monkeys / SCHU S-4, inhalation, Death Day and Berendt, (1972)
Legionella pneumophila - Legionella Exponential, k = 0.06, ID50 = 11.57, Guinea pigs / Philadelphia 1 strain, Inhalation, Infection Muller et al (1983)
Rickettsia rickettsi Beta-Poisson , α = 0.14, N50 = 50.13, LD50 = 50.13, Rhesus Macaques, Aerosol, Death Saslaw and Carlisle (1966)
Salmonella - Salmonellosis
Shigella Beta-Poisson, α = 0.27, N50 = 1.48E+03, ID50 = 1.48E+03, Human, S. flexneri 2a (strain 2457T), oral with milk, Infection DuPont et al. 1972
Vibrio cholera - Cholera Beta-Poisson, α = 0.250, N50 = 243, ID50 = 243, Human, Inaba 569B strain, oral with NaHCO3, Infection Hornick et al. 1971
Yersinia pestis - Plague Exponential, k = 1.63E-03, LD50 = 426.08, Mouse/ CO92, Intranasal, Death Lathem et al, (2005)
Virus Adenovirus Exponential, k = 0.61, ID50 = 1.14, Humans/type 4, Inhalation, Infection Couch et al., 1966
Echovirus Beta-Poisson, α = 1.06, N50 = 921.94, LD50 = 921.94, humans/ echovirus-12 strain, oral, Infection Schiff et al., (1984)
Enteroviruses Exponential, k = 3.75E-03, ID50 = 185.10, pigs/ Porcine enterovirus type 7, oral, Infection Cliver, (1981)
Influenza Beta-Poisson, α = 0.58, N50 = 9.45E+05, ID50 = 9.45E+05, Human, intranasal, Infection Murphy et al., (1984), Murphy et al., (1985)
Lassa virus - Hemmorhagic fevers Exponential, k = 2.95, LD50 = 0.24, guinea pig/ Josiah strain, Subcutaneous, Death Jahrling et al., (1982)
Rhinovirus - Common cold Beta-Poisson, α = 0.20, N50 = 9.22, ID50 = 9.22, humans/ rhinovirus type 14, strain SF 765, oral, Infection Hendley et al., (1972)
SARS Exponential, k = 2.46E-03, LD50 = 281.97, mice/rSARS-CoV and Mice/MHV-1, intranasal, Death DeDiego et al., (2008), De Albuquerque et al., (2006)
Protozoa Cryptosporidium - Cryptosporidiosis
Entamoeba Beta-Poisson, α = 0.10, N50 = 340.78, ID50 = 340.78, Humans, Ingestion, Infection Rendtorff, (1954)
Giardia - Giardiasis
Prion Prion Beta-Poisson, α = 1.76 N50 = 1.04E+05, LD50 = 1.04E+05, hamsters/scrapie strain 263K, oral, Death Diringer et al., (1998)
Amoeba Naegleria Exponential, k = 3.42E-07, LD50 = 2.03E+06, mice/Naegleria fowleri LEE strain, intravenous, Death Adams et al. (1976), Haggerty and John (1978)

Criteria for choosing dose response models

We prefer dose response models with the following criteria, in rough order of importance:

  1. Statistically acceptable fit (fail to reject goodness of fit, p > 0.05)
  2. Human subjects, or animal models that mimic human pathophysiology well
  3. Infection as the response, rather than disease, symptoms, or death
  4. Exposure route similar/identical to the exposure route of natural infection
  5. Pathogen strain is similar to strains causing natural infection
  6. Pooled model using data from 2 or more experiments, provided the data sets are statistically similar (fail to reject that datasets are from the same distribution, p > 0.05)
  7. Low ID50/LD50 (to obtain a conservative risk estimate)

We generally recommend a single dose response model, and we justify the decision in terms of the above criteria. This decision is somewhat subjective, since dose response datasets seldom meet all of these criteria. If all available models are unsatisfactory, we choose a single model to ‘recommend with reservations’. Our recommended model will seldom (if ever) be the best model for all applications. The user should carefully choose the model that is most appropriate for their particular problem.

Besides dose response assessment, the other major components of microbial risk assessment are hazard identification, exposure assessment, and risk characterization.