Dr. Kent Schwartz - Disease Interventions: Are We Doing as Good as We Know?

Disease InterventionsAre we doing as good as we know?

2016 CEVA Pre-AASV Seminar

Kent Schwartz, Veterinary DiagnosticianIowa State University Veterinary Diagnostic Laboratory

Disease InterventionsAre we doing as good as we know?• Disease, biology, ecology: Science

vs Practice?• Megatrends, agent characterization and

ecology• Koch, causation and other distractions • Diagnosis: PCV2 and MHP as examples• Assessing of endemic agents• Interventions and unintended

consequences

• Implementing best practices?• Air, water, nutrition, animal comfort, • Biosecurity, transportation and

commingling • Diagnosis and analysis in context• Vaccinology and immunology

Dr. Watson“This note is indeed a mystery. What do you imagine that it means?”

Sherlock Holmes “I have no data yet. It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.”From (1887): A Scandal in BohemiaSir Arthur Conan Doyle

Trying to achieve EVIDENCE-BASED-decisions

Diagnosis and Control of DISEASEin endemic and/or vaccinated populations

http://images.google.com/imgres?imgurl=http://www.portsmouthcc.gov.uk/images/sherlocksilhouette.jpg&imgrefurl=http://www.portsmouthcc.gov.uk/visiting/10195.htm&usg=__TEY6YqcSjryyOjAxJDPMOnFsc-s=&h=150&w=200&sz=8&hl=en&start=1&tbnid=kk5dDWBBfnnQeM:&tbnh=78&tbnw=104&prev=/images?q=Sherlock+Holmes+silouette&gbv=2&hl=en

Dr. Watson“This note is indeed a mystery. What do you imagine that it means?”

Sherlock Holmes “I have no data yet. It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.”From (1887): A Scandal in BohemiaSir Arthur Conan Doyle

Trying to achieve EVIDENCE-BASED-decisions

Diagnosis and Control of DISEASEin endemic and/or vaccinated populations

Bias skews my perspective• Point of view of diagnostician (What is “wrong”)

• Opportunity to access information

• Not a decision maker or responsible for business viability

• Many more…including opinions in absence of supporting data

http://images.google.com/imgres?imgurl=http://www.portsmouthcc.gov.uk/images/sherlocksilhouette.jpg&imgrefurl=http://www.portsmouthcc.gov.uk/visiting/10195.htm&usg=__TEY6YqcSjryyOjAxJDPMOnFsc-s=&h=150&w=200&sz=8&hl=en&start=1&tbnid=kk5dDWBBfnnQeM:&tbnh=78&tbnw=104&prev=/images?q=Sherlock+Holmes+silouette&gbv=2&hl=en

• WHAT? WHERE? WHEN? Epidemiology• Good tools: Serum and oral fluids serology / PCR

• HOW? WHY? Science and “ologies”• Extrapolations or fact? More than one “right” answer?

• IMPACT? So what?• Context, perspective, risk, $, welfare, data, externalities

• INTERVENTIONS? Management, nostrums• Need to tinker? Reality or Illusion of “control”• Unintended consequences

• ASSESSMENT, OUTCOMES and CREDIT / BLAME? • Change with time, context, new information• Stay “nimble”

Questions:What do we know with “certainty”?

“Megatrends” and “Disease solutions” 1880-1900: Germ theory, Pasteur Salmonella, Mycoplasma isolated1900-1930: Microbes! SD, CSF, SIV 1920-1980: More bugs! Koch’s postulates: 1 bug1 disease1935-1985: Confinement; larger groups, population densities

1980-2000: Integration, systems, contracts, genetics, populations, age segregation, transportation, PRRSV

2000+: sequencing, metagenomics big data, externalities: PRRS, PCV, PED; healthcare by the number$

Confirms infinite biodiversity and ability to find “new” strains/agents

Small groups, forage / garbage feedingNostrums: Lye soaked oats and arsenicImmune stimulation Controlled exposure / immunity (HCV) Vaccination (erysipelas, lepto, PPV, PRVImprove nutrition/micronutrients, Antibiotics, technology, less labor, nostrums

Biosecurity principals: external and internalAge segregation, elimination, SPF, transportEnvironmental controls, autogenous

Economics drives tweaking: more nostrums; old, new & autogenous vaccines; # doses, adjuvants, “technologies”; regulationscontrolled exposure, antibiotics; “natural”

Need clinical and pathological context

Paul Ehrlich (and John Wayne) are long dead, but…

….we’re still on the quest for the “Magic Bullets”

“Megatrends” and “Solutions” 1880-1900: Germ theory, Pasteur, Salmonella isolated1900-1930: Bacteria and viruses dysentery, influenza, CSF 1920-1970: Koch’s postulates

(1 bug1 disease)1935-1985: Confinement/nutrition; larger farms/populations

1980-2000: Large systems, integration and contracts,

larger populations, age segregation, transportation

(and PRRSV after 1990)2000+: metagenomics and big data

got lucky with PCV2; PEDVmanagement by numbers

Small groups, forage feedingNostrums: Lye soaked oats and arsenic

Immune stimulation Controlled exposure / immunity (HCV) Vaccination (erysipelas, lepto, PPV, PRV

Better nutrition/micronutrientsAntibioticsMore nostrums

Biosecurity principals: external and internal Age segregationEnvironmental control

Tweaking with more nostrums; old, new and autogenous vaccines with more doses, adjuvants, “technologies”; more nostrumscontrolled exposure, antibiotics

Natural immunity (controlled exposure) can work; has pitfallsAgent persists in the populationAgent can transmit to other populations (Biosecurity)Biological cost = cost of immunity + cost of disease












Vaccines can work but vary in efficacy; depends!Pathophysiology of agent: each is unique

Pathogenesis: damage and durationLocation (systemic/mucosal)

Immune mechanisms (antibody/CMI)Human tinkering / cutting corners













Eradication and “high health” Works well if never exposed and never will be exposed
















Eradication and “high health” Works well if never exposed and never will be exposed





Antibiotics and other nostrums are “aids”Often “work” but are doomed to be misused and/or fail over time

So perhaps we shouldn’t be talking about Big Data making decisions better, but about Diverse Data connecting the dots using new technologies, processes, and skills. We need to connect the dots or we risk drowning in Big Data.

So perhaps we shouldn’t be talking about Big Data making decisions better, but about Diverse Data connecting the dots using new technologies, processes, and skills. We need to connect the dots or we risk drowning in Big Data.

Metagenomics, metabolomics and IM-baffled-omicsSequencing (evolutionary biology)can other applications be over-interpreted?

Deep sequencing…to infinity-and BEYOND!

Of course there will be a difference found…IT IS BIOLOGY!!!

http://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0ahUKEwiE1bWNzYzLAhWLkIMKHXztDTEQjRwIBw&url=http://stealthrecruitment.com.au/blog/2015/january/outer-space-mining.aspx&psig=AFQjCNH1fQtGn88T7AaQEjt3MgHoCP3C3Q&ust=1456272808999268

What is the cause of an infectious disease? “Koch’s postulates” (one bug one disease) ignores complexity:• Complexity of microflora and potential pathogens• Variation in susceptibility of different pig ages and populations • Multifactorial nature of diseases and risk factors

What is causation? • Cause = agents + risk factors for expression• Necessary and sufficient? (PEDV, PRV, CSF, ASF, Bacillus anthracis)• Necessary not sufficient? (most endemic bacteria, MHP, PCV2)• Not all disease or “sources of variation at close-out” are infectious• What we find with a test today may not be the ultimate cause• Injuries, social hierarchy, competition, toxins, deficiencies

What is the “WHAT”?And how do we know it?

Association versus CausationWhat happens when bureaucrats and politicians do not

understand?Emotion-driven decisions Brazil Glyphosate causes microencephaly, not Zika virus

“PotentialPathogens”

“immunity, nutrition, genetics”

“facilities, management”“infectious diseases”

Dose x Virulence

MHPPasteurella

Streptococcus

Haemophilus

Salmonella

Lawsonia

PCV2Rotaviruses

Coccidia

These don’t

HAVE TO

cause disease!

CONCEPT:Many swine “pathogens” are ENDEMIC

Accuracy of diagnosis?Do we find what we look for (confirmation bias)?Do we seek simple answers by ignoring complexity and confounding

• Proximate cause(s)• Human nature want to blame one thing• Often, “diagnose” (blame) first thing we find that fits our bias• Extrapolate individual animal affliction to the whole population• May ignore:

• Cumulative insults• Additive, synergistic or multifactorial insults• Impact of distributions, populations and changes over time

• Ultimate causes? Risk factors? Sufficient, not necessary? Longer-term consequences• Confinement, large populations, commingling, transportation of animals or products

risk factors or “perfect storms” decisions based on short term economics/gain versus long term consequences

Agent 2011 2012 2013 2014 2015 Grand TotalSIV 23% 26% 27% 27% 27% 6537

PRRSV 29% 26% 23% 24% 22% 6264P. multocida 9% 8% 7% 8% 8% 2039

S.suis 7% 9% 8% 7% 9% 1987M. hyopneumoniae 9% 8% 7% 7% 7% 1954

Mixed 6% 6% 7% 7% 7% 1644Bacterial 4% 4% 6% 6% 5% 1206

Actinobacillus sp. 4% 4% 5% 4% 3% 1013H. parasuis 3% 2% 2% 3% 3% 672Interstitial 1% 1% 1% 4% 1% 387

B. bronchiseptica 2% 2% 1% 1% 2% 352T. pyogenes 2% 1% 1% 1% 1% 331

Viral 0% 1% 2% 2% 2% 295PCV 1% 1% 1% 1% 1% 236

All other 1% 1% 1% 1% 1% 274Grand Total 5285 5703 4821 4711 4662 25182

% of ”tissue cases” with respiratory disease a component (ISU VDL)

Agent 2011 2012 2013 2014 2015 Grand TotalSIV 23% 26% 27% 27% 27% 6537

PRRSV 29% 26% 23% 24% 22% 6264P. multocida 9% 8% 7% 8% 8% 2039

S.suis 7% 9% 8% 7% 9% 1987M. hyopneumoniae 9% 8% 7% 7% 7% 1954

Mixed 6% 6% 7% 7% 7% 1644Bacterial 4% 4% 6% 6% 5% 1206

Actinobacillus sp. 4% 4% 5% 4% 3% 1013H. parasuis 3% 2% 2% 3% 3% 672Interstitial 1% 1% 1% 4% 1% 387

B. bronchiseptica 2% 2% 1% 1% 2% 352T. pyogenes 2% 1% 1% 1% 1% 331

Viral 0% 1% 2% 2% 2% 295PCV 1% 1% 1% 1% 1% 236

All other 1% 1% 1% 1% 1% 274Grand Total 5285 5703 4821 4711 4662 25182

% of ”tissue cases” with respiratory disease a component

SIV frequency has increased over the last 8 years

Most systemic/respiratory agentsare endemic in most herds…opportunists

No real trends in diagnostic frequency: not prevalence

(Are these proximate and / or ultimate “causes”?)

EXAMPLE of dynamics, impact of infectious disease pressures (and cummulative effects)

100% Unknown Unknown Unknown Unknown Unknown% of 90% S. suis S. suis Adhesions Lame Lame

observed 80% Bordetella Hps PCVAD Adhesions Lameeffect 70% E. coli PCVAD SALM PCVAD Adhesionsfrom 60% Rotavirus SALM SIV SIV PCVAD

infectious 50% Rotavirus SALM SIV SIV Bacteriadisease 40% SIV E. coli MHYO MHYO SIV

30% PRRSV Rotavirus LAWSONIA LAWSONIA MHYO20% PRRSV PRRSV PRRSV LAWSONIA LAWSONIA10% PRRSV PRRSV PRRSV PRRSV PRRSV

3 7 14 18 24

EACH POPULATION / GROUP / SITE / FLOW is UNIQUE

Timeline in weeks of age

Order or sequence of insults is probably importantDiseases distribute over large populations of individuals-overlaps

With coinfections, individual diseases last longer

Cumulative Effects



observed 80% Bordetella Hps PCVAD Adhesions Lameeffect 70% E. coli PCVAD SALM PCVAD Adhesionsfrom 60% Rotavirus SALM SIV SIV PCVAD



3 7 14 18 24







observed 80% Bordetella Hps PCVAD Adhesions Lameeffect 70% E. coli PCVAD SALM PCVAD Adhesionsfrom 60% Rotavirus Bacteria Bacteria SIV PCVAD



3 7 14 18 24










3 7 14 18 24










3 7 14 18 24










3 7 14 18 24










3 7 14 18 24





Endemic Disease DIAGNOSIS and RELATIVE IMPACTNot a matter of IF… more a WHEN and HOW BAD?

CUMULATIVE INSULTS Must look across time and systematically

to determine disease order and magnitude of impact

Modern diagnostic investigation requires a PROTOCOL

Simplify only AFTER study of complexity

Time can be days, weeks, months, years

Red line: Something “bad” happensClinically detectable level

(tipping point)

Distribution of an attribute: Variation

Average: doesn’t tell the whole storyAtt

ribut

e of

a p

opul

ation

: pe

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ite/fl

ow/s

yset

m/n

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What is DiseaseImpact?

How incremental changes can go unnoticed


Red line: Something “bad” happensClinically detectable level

(tipping point)


Average: doesn’t tell the whole storyAtt

ribut

e of

a p

opul

ation

: pe

n/ba

rn/s

ite/fl

ow/s

yset

m/n

ation

al h

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What is DiseaseImpact?

Is “mortality” a disease or an outcome?(mortality increased kill some pigs to see why they are dying)

Need to get beyond mortality as THE measure of health

How incremental changes can go unnoticed

Collect Information

DIAGNOSTIC ACCURACY Does it “make sense”?

INTERVENTION DECISIONSIdentify Opportunity

Continuous Improvement

DIAGNOSTIC PROCESS

Think, Analyze, Research

DIAGNOSTIC “ALIGNMENT”

Deductive Reasoningfrom fact to theory/diagnosis

Awareness of types sources of biasConfirmatory biasMotivation

Issue / Complaint what?History and signalment who, where, when?Clinical Observations: prioritize observations

Look at the pigs!Gross Lesions infectious / noninfectious?Diagnosis A need for laboratory testing? Laboratory Testing Interpretations

Purpose? What is the diagnostic question What decision Impacted?

Laboratory results interpret in contextHistopathologic Lesions Compatible or Not?

“truth filter” … What else could it be?Diagnosis: Prioritize cause(s)

Proximate cause(s): what is the status today?Ultimate causes(s): primary initiators and risk factors

Collect Information




DIAGNOSTIC PROCESS









Collect Information




DIAGNOSTIC PROCESS



The attending veterinarianMakes the final diagnosis

NOT the laboratory







Collect Information




DIAGNOSTIC PROCESS



A systematic and “iterative” process

WHY?Refine current diagnosis

Unintended consequences of previous decision

Or … on to next issue







Collect Information




DIAGNOSTIC PROCESS






Or … on to next issue / continuous improvement

The DX execution is altered depending on your question(s):• What is affecting THIS pig?• What is affecting this group?• What has greatest impact in this group?• What has the greatest impact in this flow/system?







Collect Information




DIAGNOSTIC PROCESS






The DX execution is altered depending on your question(s):• What is affecting THIS pig?• What is affecting this group?• What has greatest impact in this group?• What has the greatest impact in this flow/system?

Think through a protocol for each diagnostic investigation

Veterinary Diagnostic Laboratory Laboratory Use Only

Iowa State University Case no.

1600 S. 16th St Ames, IA 50011515-294-1950 Fax 515-294-6961 www.vdpam.iastate.edu VDL Vet

Veterinarian VDL Contact:

VDL Contact:

Address Owner

City, State, Zip

Business Phone

Cell Phone Email: REFERENCE: Secondary Contact

Sample Collection Date:

Species

% D pigs (chronics/sick pen)

Weeks post-weaning

Type of Project

Objective of testing:

Start Date End Date

Specimen types

Premises ID

Farm / Site:

# PDNS pigs (skin lesions):

Age: % "A" pigs (normal):

EXPECTED NEGATIVE: NO TESTING REQUIRED

My SPECIAL STUDY NAME

VDL Project Worksheet and Submission Form

Three oral fluids collected per site. Tissue from 4 pigs

KJS coordinator; POD can process-push through

Kent Schwartz for questions

XXXXXXXXXXX

XXXXXXXXXXXXXXXXXXXXXX

XXXXXXXXXXXXXXXXXXXXXX

XXXXXXX

XXXXXXXXXXX

% "B/C" = fallbacks in general population

XXXXXX

XXXXXXXXPhone/email: Billing Party:

XXXXXXX

PORCINE

2 ml dose

10-Jan-14 10-Feb-14

Case Series: expect to have at least 10 cases submitted for this protocol

Fresh and fixed from 4+ pigs: Pig A=normal pig; Pig B and C =fall back pigs; Pig D=sick/chronic pig

Evaluate role of MHP in respiratory morbidity in Iowa finishers

1 ml dose Comments

PRRSMHP

Vaccinations given since weaning:

PCV2

% lung consolidation

Pig A

Pig B

Pig C

Pig D

Pig E (optional)

Pig F (optional)

Submission of fresh and fixed tissues from 4 pigs: Pig A=normal pig; Pig B and C =fall back pigs; Pig D=sick pig

ALL submissions will be tested as follows:

Morphologic diagnosis on individual pig tissues with relevant lesions (A, B, C, D).Lesions will be scored and presented in table format

_____5. Freeze back lung tissue individually; pathologist discretion on remainder

a. Rule in/out a role for Mhyo; b. pursue other etiologies if gross/microscopic evidence merits (or requested by submitter below)

Additional testing per written requests on submission form below by the submitting veterinarian.

_____1: MHP and SIV by PCR on oral fluids_____2: Histopathology individually reported on pigs (A, B, C, D) - emphasis on MHP

_____3: Individual IHC and/or PCR on suspected lesions per pathologist's discretion_____4: Bacteriology only on lesions with bacterial suspected; ID only / no antibiotic sensitivities

_____6: Pathologist discretion to pursue relevant lesions / suspicions; there are two objectives

Gross Lesions per SUBMITTER: (or include copy of site report)

Fresh and fixed from 4+ pigs: Pig A=normal pig; Pig B and C =fall back pigs; Pig D=sick/chronic pigAnimals will be selected by veterinarians Testing per instructions on back

Timeline varies with agent and circumstances: Considerable variation with MHP: “hits and stays”• D0: locates on cilia (from 10 days of age to adult)• D10 (to 60+): proliferation, attracts lymphocytes, compromises

cilia function, clinical signs• D15 (to 120+): clinical signs in some not all; atelectasis,

pneumonia, mild-to-severe• D20 (to 120+): seroconversion• D60 (to 210+): lesion resolution; clearance of MHP by immune

sterilization

• Diagnosis of DISEASE (sample a few pigs well) vs PRESENCE (PCR/EPI)• Often delegated Should it be? Sampling is more than an SOP!• Good criteria exist: are they followed?

• Who to sample? • Antemortem or post mortem sampling• Choosing representative animals with typical clinical signs and lesions

• What to sample?• Which tissues, what part of the tissue?• Swabs? • Common example: “CNS signs” would imply need brain!

• How to collect and preserve?• Freeze: PCR and chemistry• Refrigerate (immediately): Bacteriology• Formalin immediately (no freezing): tissues for histopathology

SAMPLING: A very important step ANY DX processMess this up and nobody can fix it!!

• Diagnosis of DISEASE (sample a few pigs well) vs PRESENCE (PCR/EPI)• Often delegated Should it be? It is more than SOP!• Good criteria exist: are they followed?

• Who to sample? • Antemortem or post mortem sampling• Choosing representative animals with typical clinical signs and lesions

• What to sample?• Which tissues, what part of the tissue?• Swabs? • “CNS” would imply need brain!

• How to collect and preserve?• Freeze: PCR and chemistry• Refrigerate: Bacteriology• Formalin (no freezing): tissues for histopathology

SAMPLING: A very important step ANY DX processMess this up and nobody can fix it!!

Am I (or is our workforce) trained or am I (or they) educated?Trained:

Can do this taskEducated:

Understand the what, why, when, where, who, how AND can assess outcomes objectively and broadly for continuous improvement

Why do we not have the time to do it right?Is doing less better an option?

Diagnosis of M. hyopneumoniae (and PRDC)



Finding MHP: “Test” sensitivity and specificity for diagnosis of DISEASE state vs colonized

• Clinical signs: subjective: good sensitivity but poor specificity there are clinical MHP nuances

• Gross lesions: subjective: good sensitivity but poor specificity Cranioventral bronchopneumonia with clear demarcation

• Histologic lesions: subjective: low specificity; good disease sensitivity Lymphocytic cuffs/follicles IHC is very specific but low sensitivity and sample-dependent

• PCR: objective test: very sensitive and specific; location, location, location Sample-dependent (MHP not shed in high numbers) Not consistent in oral fluids: Not “like” PRRSV, IAV, PCV2

• Serology: objective test: positive generally means colonized Maternal/passive antibody vs active Colostrum for monitoring sow herds

Finding MHP: “Test” sensitivity and specificity for diagnosis of DISEASE state vs colonized

• Clinical signs: subjective: good sensitivity but poor specificity there are MHP nuances

• Gross lesions: subjective: good sensitivity but poor specificity Cranioventral bronchopneumonia with clear demarcation

• Histologic lesions: subjective: low specificity; good disease sensitivity Lymphocytic cuffs/follicles IHC is very specific but low sensitivity and sample-dependent

• PCR: objective test: very sensitive and specific; location, location, location Sample-dependent (MHP not shed in high numbers) Not consistent in oral fluids: Not “like” PRRSV, IAV, PCV2

• Serology: objective test: positive generally means colonized Maternal/passive antibody vs active Colostrum for monitoring sow herds

PCR determines if present (colonized) but not disease stateCombination of clinical signs, gross and microscopic lesions

+Absence / presence of notable confounders determines

whether important to this pig

Importance to herd? Systematic approach using case series and data collection tools

• Problem in “MHP negative” populations… yes, here MHP may be acting alone• Acclimation of naïve gilts in to positive farms• Negative sow farms that go positive• Finishers in Iowa• Vaccine alone is often not sufficient to prevent disease in naïve pigs

• Problem in “PRDC” (PRRSV, IAV, bacteria, MHP)• All agents are more severe when combined infections• Tweaking brings MHP under control

• Vaccination practices: age, timing, doses, maternal considerations

• VDL Perspective: Little evidence to support “vaccine escape”• Antigenic diversity and genetic diversity have not translated to “vaccine escape” (yet)

COMMENT: Rarely find MHP acting alone“problems” depend on point of view / case access

“Welcome to the Masquerade BallThe Many Faces of PCV2” (B. Arruda)

APES

IHC POSITIVE

ENTEROCOLITIS

REPRODUCTIVE

PNEUMONIA TBLN

EDEMA

WASTING

HEPATITIS

PDNS

VARIATION !!!

PCV2 + Risk Factors Spectrum (distribution) of disease

Virus factors

Host/Environment factors

[Type (PCV2)]

[Co-infections]

[Dose]

[Macrophage activation]

[Virulence]

[Innate and Acquired Immunity]

Variation in Disease Expression All PCVAD

INFECTION Subclinical Disease Clinical disease SEVERE disease (PMWS)

Finding PCV2 vs PCVAD• Criteria for PMWS = clinical + lesions + IHC positive (Sorden)

• PCV2 will circulate and infect in vaccinated populations• Finding the virus is not a diagnosis of disease

• However, there sublethal and subclinical PCV2 infections• Very little IHC staining expected in a properly vaccinated healthy pigs

• Subjective test!!! VARIES by tissue examined and by pig• Many pigs with sublethal infections are negative by IHC• Will still be positive with PCR (lower end of Ct range)• Some will have cleared the virus … lesions suggestive but not pathognomonic

• Diagnosis in individual: compatible lesion + substantial PCV2 presence• IHC or PCR• Additional samples to support

• Final diagnosis affected by motivation and bias to assign a role for PCV2 (or others)?

False negative IHC? 44/289 (15%) pigs with PCR <20 were IHC

neg(wrong tissue, not the same pig?)

IHC PCR Ct NEG POS Total

7 10 108 17 179 1 20 21

10 17 1711 26 2612 3 21 2413 2 21 2314 2 28 3015 4 18 2216 3 18 2117 7 17 2418 5 19 2419 11 19 3020 21 22 4322 11 5 1623 16 5 2124 12 8 2025 13 3 1626 12 5 1727 9 5 1428 19 3 2229 21 3 2430 25 7 3231 28 1 2932 23 2333 34 3 3734 33 4 3735 33 3 36

Neg 539 26 565Grand Total 887 354 1241

False positive IHC?

26 of 519 pigs (5%) had IHC positive with PCR negative

IHC looses sensitivity and predictably around Ct=20

Lesion and IHC location not predictable or “standardized”

diagnostic samples research samples

Are laboratory tests (or pathologists) infallible?Cases where both PCR and IHC were applied to tissues

False negative IHC? 44/289 (15%) pigs with PCR <20 were IHC

neg(wrong tissue, not the same pig?)

IHC PCR Ct NEG POS Total

7 10 108 17 179 1 20 21

10 17 1711 26 2612 3 21 2413 2 21 2314 2 28 3015 4 18 2216 3 18 2117 7 17 2418 5 19 2419 11 19 3020 21 22 4322 11 5 1623 16 5 2124 12 8 2025 13 3 1626 12 5 1727 9 5 1428 19 3 2229 21 3 2430 25 7 3231 28 1 2932 23 2333 34 3 3734 33 4 3735 33 3 36

Neg 539 26 565Grand Total 887 354 1241

False positive IHC?

26 of 519 pigs (5%) had IHC positive with PCR negative

IHC looses sensitivity and predictably around Ct=20

Lesion and IHC location not predictable or “standardized”

diagnostic samples research samples

Are laboratory tests (or pathologists) infallible?Cases where both PCR and IHC were applied to tissues

SOURCES OF ERROR?Sample variation: IHC looks at a couple tissues PCR may be pooled/serumIHC inherent sensitivityIHC inherent specificityIHC is subjective test pathologist opinion staining variation

PCVAD: No seasonality; recent flat trend in cases(percent of all porcine cases with histopathology)

Year Total PCVAD cases Total All Cases % cases with PCVAD % of all PCVAD by year2003 562 10615 5.29% 6.72%2004 483 10775 4.48% 5.78%2005 625 12109 5.16% 7.48%2006 2125 14932 14.23% 25.42%2007 1782 15152 11.76% 21.32%2008 793 12890 6.15% 9.49%2009 364 10829 3.36% 4.35%2010 249 10741 2.32% 2.98%2011 259 11183 2.32% 3.10%2012 226 11678 1.94% 2.70%2013 242 12970 1.87% 2.89%2014 308 13531 2.28% 3.68%2015 342 13892 2.46% 4.09%

8360

2% of tissues cases with PCVAD for the last 6 years

JX5 35 297 - 2 266 9-3 5

99

EU

148503-PCV

2C-D

EN

MA

RK0.01

PCV2cPCV2a

PCV2b

PCV2d/mPCV2b

Molecular testing is “sexy”Almost always find differences

“What does it mean?”

If we don’t know, we speculateSpeculation can become “fact”

JX53 52 97 - 2 266 9-3 5

99

EU

148503-PCV

2C-D

EN

MA

RK0.01

PCV2cPCV2a

PCV2b

PCV2d/mPCV2b

SEQUENCE DATA Demonstrates inevitable biological diversity

Useful for epidemiology or evolutionary biology (relatedness)

SEQUENCE DATA does NOTAccurately predict virulence

Accurately predict cross protection / immunity

Should we sequence more? Probably.DEFINITELY sequence if suspect vaccine failure

or virus escape.

Invoke necessary mechanisms(systemic, mucosal, CMI, antibody)• Vaccines don’t mimic natural exposure exposure causes disease

How measured?• Antibody? CMI? Leukocyte stimulation? Animal model study?• Ultimate measure Field trials in individual production systems

Vaccination and protection is unique to each agent/host• Stimulate immune mechanisms relevant to pathogen entry/pathogenesis• Harness anamnestic response Priming + booster with sufficient interim period

• Repeated dosing of killed? Repeated doses of MLV?

Adjuvants: persistence of antigen + inflammation

Antigenic mass is very important• Repeated MLV… neutralized before stimulating anamnestic• Killed partial dosing is not the same as MLV partial dosing

Avoid maternal interference and respect variation

Concepts of immunity: One size does not fit all

Vaccine efficacy

Agent / Disease EXPECTATION CommentAtrophic rhinitis No crooked snouts Perception of vaccine failure fairly common

PRV No clinical signs Very effective; failures rare

E.coli in piglets No watery diarrhea Very effective vaccine when husbandry present

PRRSV: Repro Less abortions than previous Virus variation/mediocre protection-low expectations

PRRSV: Resp less severe clinical signs Virus variation keeps expectations low

SIV No signs of flu Vaccine failure fairly common dt virus variation

MHP No cough Protection from colonization not expected

PCV2 No disease, no virus by IHC Individual pigs afflicted; difficulty assessing impact

Lawsonia No disease Inadequate protection with administration issues

Effective vaccine and stable agent Erysipelas, PRVEffective vaccine and unstable agent: SIV/IAVEvolution/rate of change is unique to each agent… Evolution happens

Agents can rapidly move between continents, populationsWhat is “vaccination (or immunization)” failure”Based on expectations? Semantics? Need consistent measures

Common human factors compromising vaccine efficacy

• Timing (pig age) for convenience rather than maximum efficacy.

• Off-label usage: • Reduced- or partial-dose • Single dose of vaccine when two are recommended

• Method, site and execution of administration (some pigs get “missed”)

• Vaccine handling (outdated, poor storage, refrigeration, handling)

• Noncompliance by vaccine administrators (per label or actually doing it)

• Vaccinating sick or stressed animals (infectious, metabolic, nutritional)

• Unrealistic expectations

• Inaccurate conclusions from data available • misuse of diagnostic tests inappropriate samples, misinterpretation• Extrapolation of a few to many

PCV2a PCV2bmPCV2b =

PCV2dGrand Total

2013 10 30 18 582014 5 11 37 532015 26 13 136 175Grand Total 41 54 191 286

ISU data from TISSUE CASES

Research and Biopharma removed

This is NOT prevalence data

PCV2d (mPCV2) is likely becoming predominant strain

• mPCV2 is becoming predominant strain so we find it more often but maybe the rate of immunization failure really hasn’t changed?• Actual difference in mPCV2 magnitude or duration of viremia? Virulence?• DOI is less for whatever reason?• More antigenic diversity? “Antigen drift”? MHC? Antigen presentation? • Variation in level of cross-protective? Immunity is not simple!• Sweet spot / window for effective immunization is smaller• the window between maternal Ab and start of virus circulation

• One more virus strain increases chances of “decoy antigen” interfering with induction of effective immunity• Others….

Why might vaccine be perceived as less protective for different strain (e.g. mPCV2 or PCV2d?)

• mPCV2 is becoming predominant strain so we find it more often but maybe the rate of immunization failure really hasn’t changed?• Actual difference in mPCV2 magnitude or duration of viremia? Virulence?• DOI is less for whatever reason?• More antigenic diversity? “Antigen drift”? MHC? Antigen presentation? • Variation in level of cross-protective? Immunity is not simple!• Sweet spot / window for effective immunization is smaller• the window between maternal Ab and start of virus circulation

• One more virus strain increases chances of “decoy antigen” interfering with induction of effective immunity• Others….

Why might vaccine be perceived as less protective for different strain (e.g. mPCV2 or PCV2d?)

No evidence that current vaccines are not cross-protective for all PCV2 types

Vigilance is warranted… the day will come

The pigs will likely tell us

Molecular testing is not predictive for cross-protection or virulence

• How is protection measured? • Antibody? CMS? Shedding? Viremia? Lesions? • Clinical disease expression? Impact on growth or carcass performance?

• How much data is enough?• Research setting? High health (excellent management) setting? • Should “real-world” studies be expected? • As a commodity business, economics pushes health to the brink of disaster

• Field trials: each farm is different field trials for and by skeptics is warranted• Customer-specific field trials?• Agent / isolate-specific experimental challenge trials for efficacy?

What is protection and how is it measured?

• Classic Statistical Analysis: p values are not “absolutes”• Confidence, interpretation and inferences anchoring belief

• Derived from point in time studies• Derived from studies with specified and controlled conditions• External validity may be overestimated

• Studies are something to think from, not to chisel in stone

• Statistical process control (SPC)• Stochastics: biology is more random that we want to believe• Bayesian mentality: interpretation/answers are probabilities which change with new information

and over time• Black Swan (Taleb) awareness: pitfalls of predictions• Let the process inform you!!

Does science answers questions in biology? “It depends” context matters

Root cause analysis: Analyzing processes (manufacturing) Be able to think in “Bayesian”: time changes underlying assumptions


Red line: Something “bad” happeningMetric reaches “tipping point”


“Average” does not acknowledge tails of distributions

Attribute of a population:

pen/barnsite/flowSystem

national herd!

What is Impact of Each Disease and how would you measure it ?

BAD

Good “Average”

Sample these!

Tools: Outcomes depend on how the tools are wielded• Brain: Does it make sense? vs analysis paralysis? SPC concepts

• Sources of variation, error; distributions• Infection, immunity; ecology, disease expression

• “Tests”: subjective with bias of experience and opinion• Objective clinical examination

• Production records, SPC, trial and error• Gross lesions: “mortality” is not a disease

• Necropsy a many pigs as possible; photos; categorize• Microscopic lesions: a filter for adding confidence

• IHC (immunohistochemistry): • “Tests”: objective with biases

• PCR• Genetic Sequencing• Antibody Detection

• Tools to seek and understand context

Conclusions Deductive

Inductive

Conclusion

Tools: Outcomes depend on how the tools are wielded• Brain: Does it make sense vs analysis paralysis; SPC concepts









Inductive

Conclusion

What does it mean?

Tools: Outcomes depend on how the tools are wielded• Brain: Does it make sense vs analysis paralysis; SPC concepts








Field Trials: get good at them


Inductive

Conclusion

What does it mean?

Confirmation bias Tendency to search/interpret information

that supports one’s pre-existing belief

Selection bias in collecting evidence

And is a systematic error of inductive reasoning

• True scientific merit versus quackery and pseudoscience • Internet science, soundbite education and attention spans, desperation, gullibility, quick profit• Scientific method with skepticism: Skeptical empiricist (The Black Swan) • What are blinded, randomized, controlled trials? What part of that is not important

• Balance between regulatory/economic oversight & safety vs regulatory suppression• Regulatory suppression or corporate economic constraints stifle innovation? • Compromise timeliness, nimbleness, flexibility in reacting to biological changes and biological threats• Compromises economics of bringing innovation to the market• Litigation avoidance, building bureaucracies

• Science is leading to novel biological interventions and engineering• Genetic and epigenetic manipulations and many more examples

• PRRSV resistant pigs• In utero and epigenetic influences

Ideas and obstacles going forward?

Vaccinology: Each organism is different, requiring specific science!• Immune Modulation

• Immunomodulators: Zelnate, levamisole, TNF, MANY (in vitro vs in vivo)• Adjuvants: new and re-examine existing products and formulations; cytokine

modulation specific for mechanism/type of immune response• Nanotechnologies, whatever they are

• Delivery systems that are both safe and effective • Aerosol, IN, intraocular, intracutaneous, intramammary, fetal, neonate, respository

polymers with one or more agents represented• Refining immunization targets and agent selection

• Reverse engineering of epitopes or histocompatibility• Subunit platforms for antigen expression• MLV/ALV: Bacteria or viruses; each developed on own merit and variability

• controlled exposure, immunity and competitive exclusion• Examples primarily bacterial: Salmonella, Lawsonia; many more conceivable• IN influenza or polio in humans


Vaccinology• Autogenous” products and/or customized vaccines

• How agents are selected: isolated from lesions vs nonpathogens• Methods to predict virulence capability

• Vaccines construction (whole cell, subunit, reverse engineered)• How products are tested: constraints?

• Safety and potency only?• Efficacy? Small challenge systems in vivo or in vitro?

• Vaccine production and regulation / consumer confidence• Science vs practice vs economics vs unintended consequences (because we

can doesn’t mean we should)• Large system on-site vaccine production: QA, efficacy, liability….?• BioPharma: large and small vs startups: American capitalism• Regulatory constraints


• Accurately measuring impact of endemic agents on production is daunting• Use tools that acknowledge multiple agents and cumulative effects• Summarize and analyze be rational, don’t rationalize

• Harnessing immune response requires healthy pigs be properly vaccinated• “Vaccination failures” are sometimes deserved

• Vaccine escapes with PCV2 or MHP are not well-documented• Vigilance is warranted• Vigilance for “vaccine escape” includes “listening to the pigs”

• PCV2 can, has and does change over time; however, genetic change does not usually predict virulence change or immunologic change (cross-protection)

• MHP has considerable genetic diversity and variability in epitopes: so what?• Impact on immune response for protection or immune clearance not known• As always, “more study is needed” as we know it is imperfect vaccine

Food for thought: disease and interventions

• Measures of vaccine efficacy Expectations• Scientific studies in challenge models with healthy pigs• Confounders in field settings hamper interpretation• Anecdotes vs randomized, blinded controlled field trials• Get good at field trials

• There are no magic bullets and very few secrets to produce healthy pigs• Short-term gain vs long term impacts (pig health, risks and sustainability)

• Large populations, commingling, transportation• Least cost nutrition may have long term consequences• Cutting corners on vaccine application• Many examples of how humans foil health programs

• Get good, then better at field trials


• Evolution happens – augmented by human influences and unintended consequences• Better technologies for measuring (evolutionary biology): So what? • What does it mean and can human nature or technology respond?• Is the 10-20 year lag in adoption still our reality? Are we recycling old nostrums?

• (Re)Emergence of virulence (or vaccine escape) likely to happen someday• We cannot predict if now, 3 years, 10 years or 100 years but it will change• In general, we cannot predict with accuracy – but we can be vigilant and wary


• The only thing constant is change• What are motivations to change?

• Economics• Competition and drive for bigger, better, more• Fear or reality of externalities: regulation, disease, consumerism, Black Swans

• Antimicrobial resistance, animal welfare, …. It’s always something

• With more infectious pressures, are more vaccinations the only answer?• John Harding (IPVS 2014): Accountabilities … do we have “systemic problem”?

• What could / should we stop doing?• What could / should we start doing?• Who’s first?


“If I have seen further, it is by standing on ye, on the sholders(sic) of Giants”

(Letter from Isaac Newton to Robert Hooke)

Health & Medicine

Dr. Kent Schwartz - Disease Interventions: Are We Doing as Good as We Know?