Quantitative and qualitative prediction of human health risks from animal antibiotics
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Quantitative and Qualitative Prediction of Human Health Risks from Animal Antibiotics. Tony Cox and Douglas Popken Cox Associates Denver, Colorado www.cox-associates.com. Motivation: Uncertain Science. Does animal antibiotic use (AAU) really increase human treatment failures?

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Quantitative and Qualitative Prediction of Human Health Risks from Animal Antibiotics

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Quantitative and Qualitative Prediction of Human Health Risks from Animal Antibiotics

Tony Cox and Douglas Popken

Cox Associates

Denver, Colorado


Motivation: Uncertain Science

  • Does animal antibiotic use (AAU) really increase human treatment failures?

    • Has it in the past?

    • Does it now?

    • Will it in the future?

    • How much, how soon, how probable?

  • Do human treatment failures really increase adverse clinical outcomes?

Proposed RM Principles

  • Precautionary: “When in doubt, go without!”

    • Not linked to causing desired outcomes

    • “Compound that may impact on human drug efficacy should be terminated” ; MAFF Bukai meeting 10-02

  • Decision Analysis: “When in doubt, go find out”… and/or make your best bet now!

    • Good risk assessment informs good decisions

    • Value of information is based on changing decisions

  • Regulatory: When in doubt, make conservative guesses. Let evidence/judgment trigger actions.

    • Triggered action may do more harm than good

Trying to make things better can potentially make them worse…

Enrofloxacin example: Risk model outputs

Q: How can this be?

  • A: Banning enrofloxacin increases the prevalence of airsacculitis…

    • Even assuming farmer’s try other drugs

  • Which increases the variance in bird sizes and weights at processing…

  • Which increases fecal contamination and variance/right tail of microbial load.

    • Data: Russell, 2003

  • Which may increase human health risk

  • Lesson: System-wide view is crucial!

Using Risk Assessment (RA) to Improve RM Decisions

The promise of quantitative risk assessment:

  • Science-based decisions, driven by facts and data.

    • Bridging gaps in the science and data: Assume vs. condition, bound, approximate, decompose…

  • RA lets science help achieve better (preferred) human health outcomes and make better bets

  • Can be more authoritative, participatory, open, and interpretable/less judgmental than “qualitative” RA

    • Tiered approach (qualitative/quantitative) often desirable

  • Quantitative risk assessment can be quicker, easier, cheaper, and more useful than qualitative!

Barriers to RA benefits

  • Lack of clarity on what to do and how to do it.

    • Q: Which methods/data are sound and useful?

    • A: Those that quantify and validate causal relations between decisions and their probable consequences that (should) drive evaluation and choice

  • Lack of willingness/resources to do needed work.

    • Is there a simpler way?

    • What is the least work needed for good answers?

  • Lack of credibility/value of results

    • Perceived or actual

    • Results too driven by uncertain assumptions (?)

Health Risk Analysis Basics

Common-sense foundations:

  • A causal chain links risk management acts to their probable human health consequences:

    Decision/act Exposures  Illnesses  Consequences

     

    behaviors  susceptibility  treatment

  • Risk assessment quantifies the causal input-output relation for each link, pastes them together

    Good RA supports good RM

  • Risk management seeks acts/coordinating policies that make preferred consequences more likely.

Scoping a Risk Assessment

  • What acts are to be assessed?

    • Change drug use, HACCP, cooking, prescriptions…

  • What consequences matter?

    • Human infections, illnesses, durations, fatalities

    • Resistance in animals at slaughter and retail?

    • How about human health benefits from AAU?

  • What human subpopulations are to be considered?

  • What transmission paths are included?

    • Drugs, bugs, food products, preparation practices/venues

  • Time frames of consequences: Past, present, future

    • Static (consequences per year) vs. dynamic/transient


  • Risk estimates without decisions/goals/scope

  • Models without data

  • Attribution without proof/evidence

  • Attribution with epidemiological data

  • Causal conclusions without causal analysis

  • Recommendations based on data only

    • Situation-action triggers

    • Recommendations without decision analysis

  • Circular citations and popularity contests

Risk Assessment Steps

  • Scope the assessment

    • Decisions (e.g., AAU), strains (e.g., resistant and susceptible, bacteria), foods, populations, health effects

  • Validate causal chain (= hazard identification)

    • Use  Exposure Illnesses  Consequences

  • Estimate the links from data

    • Ratios (e.g., Illnesses / Exposure)

    • Conditional probabilities, e.g., Pr(illness | exposure)

  • Multiply ratios or compose link relations via Monte Carlo simulation. Pr(c | x) = xPr(c | r)Pr(r | x)

  • Sum estimates and uncertaintiesover paths, populations, impacts in scope to get total risk.

Guidance #152

  • Basic idea:Risk is high (H) if release potential, exposure potential, or consequence (misinterpreted as “importance in human medicine”) are high.

    • Replace discussion of uncertain, perhaps disputed, facts with consensus-building on labels

    • Flexible, judgment-driven use of much information

    • Avoids need to estimate health consequences of acts

  • Limitations:

    • Labels (H, M, L) are ambiguous, not objective, may be difficult/expensive to build consensus on

    • Decisions informed by labels may not be good decisions

Rapid Risk Rating Technique (RRRT)

  • Goals: Fast, accurate, simple risk ratings and uncertainty analysis. Easy to carry out.

  • Use numbers instead of labels.

  • Focus on evaluating decisions instead of evaluating/labeling situations.

  • Compatible with full quantitative assessment.

  • Results are estimated human health impacts per year for different AAU decisions.

    • Changes in cases, illness-days, QALYs, fatalities

Goals of Rating

What should an ideal qualitative rating system do?

  • Minimize expectederrors in qualitative rating (compared to true quantitative risk)?

    • Result: To minimize classification errors, ignore ratings from very uncertain estimates of factors.

  • Minimize expected cost of errors?

  • Reliable screening/identification of non-problems?

  • Maximize probability of picking worst problems?

    • Then why not pick them all? (Current #152 is close)

  • Maximize risk management productivity (value of problems addressed per unit time)?

    The new framework can do all of these.

RRRT Framework: Basics

  • “Rapid Risk Rating Technique”

    • Designed to be simple, correct, flexible/realistic

    • Work needed: estimating and documenting risk factors and uncertainty factors

      Basic idea: Estimate population risk as:

       Population Risk (e.g., illnesses/year) =

      ( use) * ( exposure/ use)*( illnesses/ exposure) * ( health impacts/  illness)

    • Adjust for multiple groups, outcomes, uncertainties

RRRT Key Data Elements

  • Use = fraction of animals treated

  • Exposure = contaminated servings ingested per year

  • Preventable fraction = fraction of exposure that would be prevented (or caused) by change in AAU

    • Not to be confused with “attributable risk”

  • Illnesses = campylobacteriosis, salmonellosis, etc.

    • Resistant vs. susceptible are distinguished

  • Impacts = illness-days by severity class (e.g., mild, moderate, severe, fatal), QALYs lost per year, etc.

    • Reflect treatment-seeking, prescription practices, outcome probabilities

RRRT Calculations

  • Calculate preventable illnesses per year from AAU

    • total illnesses per year * fraction from food * fraction of food-borne illnesses from specific commodity * fractional change in contaminated servings from AAU

  • Sum over all paths: resistant and susceptible bacteria, drugs and bacteria of direct and indirect (co-selected, cross-resistant, etc.) interest

  • Sum over years and populations of interest

  • Weight by consequences (illness-days, fatalities, QALYs lost, etc. by severity class) per illness

  • Result: Expected human health impacts/yr.

RRRT Lessons

  • Proof-of-concept applications: Streptogramins, macrolides, tetracyclines, fluoroquinolones

  • Lesson #1: Human health risks are often much smaller than might be expected (<< 0.1 case per year caused by AAU)

  • Lesson # 2: Potential net human health benefits from AAU may be significant (>> 100 cases prevented per year) when quantified

  • Lesson # 3: Key data need is relation between AAU and microbial loads in processed food

VM-Attributable Risk Calculation

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