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

Quantitative and Qualitative Prediction of Human Health Risks from Animal Antibiotics

Tony Cox and Douglas Popken

Cox Associates

Denver, Colorado


Motivation uncertain science

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

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

Trying to make things better can potentially make them worse…

Enrofloxacin example: Risk model outputs

Q how can this be

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

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

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

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

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

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

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

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

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

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

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

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

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

VM-Attributable Risk Calculation

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