# Presentation: 704790

## Toward More Rational Risk Analysis:  Improving the ranking process in #152

**Tony Cox**
**Cox Associates**
**FDA Anti-Infective Drugs Advisory Committeee Meeting**
**1-9-03**
- _[www.cox-associates.com](http://www.cox-associates.com/)_

## What is necessary for rational decision-making?

- Identify risk management alternatives.
- E.g.,* *continue, ban, restrict, wait-and-see, etc
- Assess the probable human health consequences of each alternative decision option.
- Quantify probabilities of health consequences
- Choose the option giving the most desirable probable consequences.
- Choose options giving larger probabilities of preferred consequences

## CVM’s proposed qualitative approach does not support rational decisions

- Crucial quantitative information needed for rational decision-making is omitted.
- Quantitative *extent *of exposure is ignored, but is essential for determining probable human health outcomes
- Requires subjective probability rating of ambiguous events and undefined concepts
*Release:*  Pr(resistant bacteria present in animal “as a result of” the drug use) is not defined.  (Usually zero when the drug just selects for resistant bacteria that are already there!)
*Consequence:*  Pr(exposure “results in” adverse consequence) is not defined, especially when exposure is not quantified.

## Other key ambiguities

**Hazard:**  “illness *caused by* a specified resistant bacteria”, “*attributable to* a specified animal-derived food commodity” – italicized terms are undefined.
**Hazardous agent:**  “resistant bacteria *attributable to* a specified commodity” – “attributable to” is not defined.
**Risk **= Pr(human illness *caused by* a specified resistant bacteria *attributable to* a specified commodity, treated with drug of interest)
- For fluoroquinolones, CVM interpreted “attributable to” to mean “not attributed by us to anything else”.  No objective definition or data-based criteria used – purely subjective judgments.
- This definition of risk does *not *address probable consequences of risk management alternatives  No support for rational decisions!

## Performance problems

- The key aspect of rational decision-making is that it recommends the actions with the most preferred probable net benefits.
- Example:  “Worst first” problem ranking
- But CVM’s proposed approach recommends actions without regard for their net benefits (which depend on quantitative exposures)
- Ignores effects on non-resistant microbial load Ignores human health benefits of continued use Ignores probable extent of adverse effects

## A key disconnect

- Potential human health consequences of exposure can *not* be estimated from human medical importance of a drug.
*Example:*  Drug is very important but exposure does not reduce (or perhaps enhances) its performance.
- For decision-making purposes, it is not the health consequences of *exposures,* but health consequences of risk management *interventions *that must be assessed.

## CVM’s three factors do not predict risk

- The three factors of “Release”, “Exposure”, and “Consequence” (as interpreted by CVM) cannot logically or rationally be used to estimate or rank human health risks.
- The key questions – *how much harm* does exposure cause (if any) and *how would this change* if a ban or intervention were implemented – are not addressed by these factors.
- No expected correlation between “qualitative” risk ranking and “rational” (or quantitative) ranking

## Example:  Virginiamycin and Synercid

- CDER/CVM Criteria for ranking drugs based on “importance”:
- Factors related to drug efficacy
- 1) **sole therapy**/limited available therapies for treating humans
- 2) therapy of choice for human infection(s)
- 3) **spectrum of activity of particular importance**
- 4) importance as an oral (rather than parenteral) therapy
- 5) importance in treating food-borne infections
- 6) unique mechanism of antimicrobial action
- Factors related to development of antimicrobial resistance
- 7) **cross-resistance within drug class**
- 8) cross-resistance across drug classes
- 9) **ease of transmissibility of resistance determinants**
- 10) cross resistance between animal and human drugs

## Qualitative vs. Quantitative

- Based on the proposed criteria, it appears that the *qualitative* risk assessment procedure could assign a risk rating of “high” to virginiamycin (VM) – even if VM does not cause human harm.
- A *quantitative *risk assessment for VM and Synercid suggests that banning VM in 1Q-02 would have saved not more than 0.18 statistical lives in the whole US population over the next five years.  Such a perspective is (a)  Useful for decision-making; and (b)  Missing from CVM’s proposed qualitative approach.

## Quantitative vs. Qualitative:  What factors determining risk are considered?  

| Quantitative Factor | Mean | Key Data |
| --- | --- | --- |
| VRE cases x % that are vanA VREF | 9371 x 0.61  | Eliopoulos et al., 1998 
Clark et al., 1993 
Jones et al, 1995,
Rice 2001  |
| % not nosocomial | 0.17 | Bischoff et al., 1999; 
Austen et al., 1999; 
Thal et al., 1998  |
| % chicken-compatible | 0 to 0.124 | Willems et al.,  2000, 2001 |
| % prescribed QD | 0.92t | AMR, 2001; declining over time, Zyvox increasing |
| % QD-resistant | 0 to 0.01 | Eliopoulos et al., 1998 
Jones et al, 1999 |
| % Effective treatments | 0.72 | Moellering et al., 1999; Linden, 2002  |

## Quantitative vs. Qualitative

- A rough bounding quantitative assessment can be...
- Quicker, easier, and less costly than a qualitative assessment
- Produce clearer, more objective results
- Provide a more constructive framework for surfacing concerns and resolving conflicts
- Better able to support rational decision-making

## Key Steps in Health Risk Assessment

**0.**  	**Bound/scope** the analysis to support better decisions
*What *_*decisions*_* and *_*consequences*_* matter?*
**Hazard identification:**  *What are the source and effect?*
**Exposure assessment:**  *Who and how much?*
- Frequency/magnitude of individual exposures in the population.  Who is exposed to how much how often?
**Individual dose-response model:**  *How do exposure and other factors affect* *the frequency/severity of health effects?*
**Risk characterization:**  *How will decisions change* frequency of adverse health effects in population?
**Uncertainty analysis:**  *How confident are we in the results? What new information could change the recommendations?*

## Conclusions

- Risk is quantitative, not qualitative (at least as used in rational risk-management decision-making)
- Human health impacts of regulations are transmitted by *causal chains* that carry amounts of *physical quantities* (e.g., colony-forming units of susceptible and resistant bacteria) from animals to humans.
- Accurate risk assessment *must* estimate effects of  regulation on these quantities (exposure assessment) and on their probable adverse effects (dose-response modeling).
- Not doing so can randomize resource allocations and fail to protect human health. Doing so supports effective regulatory decisions.

## Recommendations

- Use quantitative risk assessment methods (e.g., upper-bounding analyses) to quantify or bound the probable human health consequences of proposed decisions.
- Clearly define key terms.  Provide objective interpretations.
- Use simulation to quantify/validate performance of the final recommended guidance

- Appendix: Details of VM Example

## Quantifying Risk

- Human treatment failures due to VM = Product of:
- Total vanA *E. faecium* (VREF) cases
- Proportion not of nosocomial origin
- Proportion with genogroup found in chickens
- Proportion that are prescribed QD
- Proportion that are QD-resistant
- Proportion that would have treatment success
*	**Each parameter can be estimated from available medical and genetic typing data.*

## Proportion of total VRE isolates that are vanA VREF 

- Focus is on vanA VREF because** **vanB VREF are susceptible to teicoplanin (Eliopoulos, 1998); QD is usually not prescribed to vanB VREF patients (Murray, 2000);  QD is not active against *Enterococcus faecali*s, and “Only *vanA* *E. faecium* can be linked to food animals.” (EAGAR, 2002)
- Parameter estimates based on data:
- USA:  E. faecium isolates submitted to the CDC from 1988 to 1992 were 83% vanA (Clark et al., 1993).  In a 1992 survey of 97 US laboratories, 79% of VREF isolates were vanA (Jones et al, 1995).

## Proportion of vanA VREFs Attributed to Chickens 

- Willems et al (2000) found four major AFLP genogroups among 255 vanA VREF strains.
- Among hospitalized patients: 4 had genogroup A strains (occurs in pigs, most community patients)
- 10 had genogroup B strains (found in 30 of 31 chickens, veal calves, community patients);
- 73 had genogroup C strains (found in hospitalized patients, veal calves, isolates from cats and dogs)
- Attributing *all *Group B strains (10 of 87 hospitalized patients) to chicken (conservative) gives a chicken-attributable fraction of Beta(11, 78), mean** **= **0.124**

## QD-Resistance Levels in Humans

- Eliopoulos et al., 1998 found that QD failed to inhibit **0.011** of the first isolates submitted by any single patient at  2g/ml.  Among 875 isolates from hospitals across the US (including multiple submissions) 29 (3.3%) had MIC was  4 g/ml
- The NCCLS has established the following QD resistance breakpoints for enterococci:  1 mg/L susceptible, 2 mg/L intermediate, and 4 mg/L resistant.

## VM-Attributable Risk Calculation

| Factor | Mean | Key Data |
| --- | --- | --- |
| VRE x % vanA VREF | 9371 x 0.61  | Eliopoulos et al., 1998 
Clark et al., 1993 
Jones et al, 1995,
Rice 2001  |
| % not nosocomial | 0.17 | Bischoff et al., 1999; 
Austen et al., 1999; 
Thal et al., 1998  |
| % chicken-compatible | 0 to 0.124 | Willems et al.,  2000, 2001 |
| % prescribed QD | 0.92t | AMR, 2001; declining over time, Zyvox increasing |
| % QD-resistant | 0 to 0.01 | Eliopoulos et al., 1998 
Jones et al, 1999 |
| % Effective treatments | 0.72 | Moellering et al., 1999; Linden, 2002  |

## Effects Model

- “Conservative Bayesian” (uniform prior, beta posterior) uncertainty models + Monte-Carlo uncertainty analysis.  (Bias means toward .5)
- Health consequences per treatment failure:
- Mortalities = 0.155 x treatment failures (Linden et al., 1997) (ignoring new treatment options such as Linezolid)
- Life-years = 22 (if same as healthy people)
- Quality-Adjusted Life Years = 0.04 (if treatment-days are as bad as life days lost)

## Main Results for US

- Banning VM now (1Q-02) could save up to:
- 0-1.85 cases of treatment failure
- 0-0.29 statistical lives
- 0-6.3 life-years
- for whole U.S. population over next 5 years.
- If ban is delayed until 2Q-04, expected impact declines to 0-0.09 statistical lives saved.

## Timing of Impacts

## What drives the numbers?

- Risk = product of small numbers.
- Key values come from available data.
- Risk = 0 if gene transfer from chickens to humans does not occur
- Upper-bounds risk estimates assume maximum attribution of human resistance to chickens.