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

Disease Interventions
Are we doing as good as we know?
2016 CEVA Pre-AASV Seminar
Kent Schwartz, Veterinary Diagnostician
Iowa State University Veterinary Diagnostic Laboratory

Disease Interventions
Are 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 Bohemia
Sir Arthur Conan Doyle
Trying to achieve EVIDENCE-BASED-decisions
Diagnosis and Control of DISEASE
in endemic and/or vaccinated populations

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 Bohemia
Sir Arthur Conan Doyle
Trying to achieve EVIDENCE-BASED-decisions
Diagnosis and Control of DISEASE
in 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

• 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 isolated
1900-1930: Microbes! SD, CSF, SIV
1920-1980: More bugs! Koch’s
postulates: 1 bug1 disease
1935-1985: 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 feeding
Nostrums: Lye soaked oats and arsenic
Immune stimulation
Controlled exposure / immunity (HCV)
Vaccination (erysipelas, lepto, PPV, PRV
Improve nutrition/micronutrients,
Antibiotics, technology, less labor, nostrums
Biosecurity principals: external and internal
Age segregation, elimination, SPF, transport
Environmental controls, autogenous
Economics drives tweaking: more nostrums;
old, new & autogenous vaccines; # doses,
adjuvants, “technologies”; regulations
controlled 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 isolated
1900-1930: Bacteria and viruses
dysentery, influenza, CSF
1920-1970: Koch’s postulates
(1 bug1 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; PEDV
management by numbers
Small groups, forage feeding
Immune stimulation
Better nutrition/micronutrients
Antibiotics
More nostrums
Age segregation
Environmental control
Tweaking with more nostrums; old, new and
autogenous vaccines with more doses, adjuvants,
“technologies”; more nostrums
controlled exposure, antibiotics
Natural immunity (controlled exposure) can work; has pitfalls
Agent persists in the population
Agent can transmit to other populations (Biosecurity)
Biological cost = cost of immunity + cost of disease

Salmonella isolated
(1 bug1 disease)
Immune stimulation
Antibiotics
More nostrums
Age segregation
Vaccines can work but vary in efficacy; depends!
Pathophysiology of agent: each is unique
Pathogenesis: damage and duration
Location (systemic/mucosal)
Immune mechanisms (antibody/CMI)
Human tinkering / cutting corners

Salmonella isolated
(1 bug1 disease)
Immune stimulation
Antibiotics
More nostrums
Age segregation
Eradication and “high health”
Works well if never exposed and never will be exposed

Salmonella isolated
(1 bug1 disease)
Immune stimulation
Antibiotics
More nostrums
Age segregation
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-omics
Sequencing (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!!!

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 Causation
What happens when bureaucrats and politicians do not understand?
Emotion-driven decisions
Brazil  Glyphosate causes microencephaly, not Zika virus

“Potential
Pathogens”
“immunity, nutrition, genetics”
“facilities, management”
“infectious diseases”
Dose x Virulence
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 Total
SIV 23% 26% 27% 27% 27% 6537
PRRSV 29% 26% 23% 24% 22% 6264
P. multocida 9% 8% 7% 8% 8% 2039
S.suis 7% 9% 8% 7% 9% 1987
M. hyopneumoniae 9% 8% 7% 7% 7% 1954
Mixed 6% 6% 7% 7% 7% 1644
Bacterial 4% 4% 6% 6% 5% 1206
Actinobacillus sp. 4% 4% 5% 4% 3% 1013
H. parasuis 3% 2% 2% 3% 3% 672
Interstitial 1% 1% 1% 4% 1% 387
B. bronchiseptica 2% 2% 1% 1% 2% 352
T. pyogenes 2% 1% 1% 1% 1% 331
Viral 0% 1% 2% 2% 2% 295
PCV 1% 1% 1% 1% 1% 236
All other 1% 1% 1% 1% 1% 274
Grand Total 5285 5703 4821 4711 4662 25182
% of ”tissue cases” with respiratory disease a component (ISU VDL)

Agent 2011 2012 2013 2014 2015 Grand Total
SIV 23% 26% 27% 27% 27% 6537
PRRSV 29% 26% 23% 24% 22% 6264
P. multocida 9% 8% 7% 8% 8% 2039
S.suis 7% 9% 8% 7% 9% 1987
M. hyopneumoniae 9% 8% 7% 7% 7% 1954
Mixed 6% 6% 7% 7% 7% 1644
Bacterial 4% 4% 6% 6% 5% 1206
Actinobacillus sp. 4% 4% 5% 4% 3% 1013
H. parasuis 3% 2% 2% 3% 3% 672
Interstitial 1% 1% 1% 4% 1% 387
B. bronchiseptica 2% 2% 1% 1% 2% 352
T. pyogenes 2% 1% 1% 1% 1% 331
Viral 0% 1% 2% 2% 2% 295
PCV 1% 1% 1% 1% 1% 236
All other 1% 1% 1% 1% 1% 274
Grand 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 agents
are 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 Lame
effect 70% E. coli PCVAD SALM PCVAD Adhesions
from 60% Rotavirus SALM SIV SIV PCVAD
infectious 50% Rotavirus SALM SIV SIV Bacteria
disease 40% SIV E. coli MHYO MHYO SIV
30% PRRSV Rotavirus LAWSONIA LAWSONIA MHYO
20% PRRSV PRRSV PRRSV LAWSONIA LAWSONIA
10% 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 important
Diseases distribute over large populations of individuals-overlaps
With coinfections, individual diseases last longer
Cumulative Effects

from 60% Rotavirus SALM SIV SIV PCVAD
3 7 14 18 24

from 60% Rotavirus Bacteria Bacteria SIV PCVAD
3 7 14 18 24

from 60% Rotavirus Bacteria Bacteria SIV PCVAD
3 7 14 18 24
Endemic Disease DIAGNOSIS and RELATIVE IMPACT
Not 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” happens
Clinically detectable level
(tipping point)
Distribution of an attribute: Variation
Average: doesn’t tell the whole storyAttributeofapopulation:
pen/barn/site/flow/sysetm/nationalherd!
What is Disease
Impact?
How incremental changes can go unnoticed

Red line: Something “bad” happens
Clinically detectable level
(tipping point)
Average: doesn’t tell the whole storyAttributeofapopulation:
pen/barn/site/flow/sysetm/nationalherd!
What is Disease
Impact?
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 DECISIONS
Identify Opportunity
Continuous Improvement
DIAGNOSTIC PROCESS
Think, Analyze, Research
DIAGNOSTIC “ALIGNMENT”
Deductive Reasoning
from fact to theory/diagnosis
Awareness of types sources of bias
Confirmatory bias
Motivation

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 context
Histopathologic 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 ACCURACY
DIAGNOSTIC PROCESS

Look at the pigs!
factors
Collect Information
DIAGNOSTIC ACCURACY
DIAGNOSTIC PROCESS
The attending veterinarian
Makes the final diagnosis
NOT the laboratory

Look at the pigs!
factors
Collect Information
DIAGNOSTIC ACCURACY
DIAGNOSTIC PROCESS
A systematic and “iterative” process
WHY?
Refine current diagnosis
Unintended consequences of previous decision
Or … on to next issue

Look at the pigs!
factors
Collect Information
DIAGNOSTIC ACCURACY
DIAGNOSTIC PROCESS
WHY?
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?

Look at the pigs!
factors
Collect Information
DIAGNOSTIC ACCURACY
DIAGNOSTIC PROCESS
WHY?
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 50011
515-294-1950 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)
Weekspost-weaning
Type ofProject
Objective oftesting:
Start Date End Date
Specimen types
Premises ID
Farm / Site:
#PDNS pigs(skin
lesions):
Age: % "A" pigs(normal):
EXPECTED NEGATIVE: NOTESTING 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
XXXXXXXXXXX
XXXXXXXXXXX
XXXXXXXXXXX
XXXXXXXXXXX
XXXXXXX
XXXXXXXXXXX
% "B/C" =fallbacksin
general population
XXXXXX
XXXX
XXXXPhone/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
PRRS
MHP
Vaccinationsgiven 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 pig
Animals 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 process
Mess 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 process
Mess this up and nobody can fix it!!
Am I (or is our workforce) trained or am I (or they) educated?
Trained:
Can do this task
Educated:
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

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
PCR determines if present (colonized) but not disease state
Combination 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 Ball
The 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
[Type (PCV2)]
[Co-infections]
[Dose]
[Macrophage activation]
[Virulence]
[Innate and Acquired Immunity]
Variation in Disease Expression  All PCVAD

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 10
8 17 17
9 1 20 21
10 17 17
11 26 26
12 3 21 24
13 2 21 23
14 2 28 30
15 4 18 22
16 3 18 21
17 7 17 24
18 5 19 24
19 11 19 30
20 21 22 43
22 11 5 16
23 16 5 21
24 12 8 20
25 13 3 16
26 12 5 17
27 9 5 14
28 19 3 22
29 21 3 24
30 25 7 32
31 28 1 29
32 23 23
33 34 3 37
34 33 4 37
35 33 3 36
Neg 539 26 565
Grand
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 10
8 17 17
9 1 20 21
10 17 17
11 26 26
12 3 21 24
13 2 21 23
14 2 28 30
15 4 18 22
16 3 18 21
17 7 17 24
18 5 19 24
19 11 19 30
20 21 22 43
22 11 5 16
23 16 5 21
24 12 8 20
25 13 3 16
26 12 5 17
27 9 5 14
28 19 3 22
29 21 3 24
30 25 7 32
31 28 1 29
32 23 23
33 34 3 37
34 33 4 37
35 33 3 36
Neg 539 26 565
Grand
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/serum
IHC inherent sensitivity
IHC inherent specificity
IHC 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 year
2003 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

22669-29
22625-3422669-36
22625-3622669-30
HQ395032-PCV2B-09HEB
HM038017-PCV2-BDH-ORF2-N
HQ395061-PCV2B-V
10BJ-3
JX535296-22625-33
JX535297-22669-35
99
AY181946-PCV2D-TJ
99
AF055394-PCV2B-ORF2-FRANNCE-N
AY181945-PCV2B-GD-TS
AY874163-PCV2B-W
B-H-1
JQ955679-PCV2B-CC1-NEW
GU799576-2B-NMB-USA-ORF2-N
JQ692110-PCV2B-06-06274
HQ713495-PCV2-05-55004-7-USA-ORF2-N.SEQ
9499
JQ806749-2A-10JS-2
EF524532-PCV2E-GX0601
99
AF055392-PCV2A-ORF2-CANADA
H
Q
395054-2A-10G
D
DQ397521-PCV2-USA-ORF2-NJQ994269-PCV2A-FMV-07-0039
FR823451-2A-SOUTHKOREA
99
99
EU148503-PCV2C-DENMARK
PCV2c
PCV2a
PCV2b
PCV2d/mPCV2b
Molecular testing is “sexy”
Almost always find differences
“What does it mean?”
If we don’t know, we speculate
Speculation can become “fact”

22669-29
22625-3422669-36
22625-3622669-30
HQ395032-PCV2B-09HEB
HM038017-PCV2-BDH-ORF2-N
HQ395061-PCV2B-V
10BJ-3
JX535296-22625-33
JX535297-22669-35
99
AY181946-PCV2D-TJ
99
AF055394-PCV2B-ORF2-FRANNCE-N
AY181945-PCV2B-GD-TS
AY874163-PCV2B-W
B-H-1
JQ955679-PCV2B-CC1-NEW
GU799576-2B-NMB-USA-ORF2-N
JQ692110-PCV2B-06-06274
HQ713495-PCV2-05-55004-7-USA-ORF2-N.SEQ
9499
JQ806749-2A-10JS-2
EF524532-PCV2E-GX0601
99
AF055392-PCV2A-ORF2-CANADA
H
Q
395054-2A-10G
D
DQ397521-PCV2-USA-ORF2-NJQ994269-PCV2A-FMV-07-0039
FR823451-2A-SOUTHKOREA
99
99
EU148503-PCV2C-DENMARK
PCV2c
PCV2a
PCV2b
PCV2d/mPCV2b
SEQUENCE DATA
Demonstrates inevitable biological diversity
Useful for epidemiology or evolutionary biology (relatedness)
SEQUENCE DATA does NOT
Accurately 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 Comment
Atrophic 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, PRV
Effective vaccine and unstable agent:  SIV/IAV
Evolution/rate of change is unique to each agent… Evolution happens
Agents can rapidly move between continents, populations
What 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 PCV2b
mPCV2b =
PCV2d
Grand
Total
2013 10 30 18 58
2014 5 11 37 53
2015 26 13 136 175
Grand 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” happening
Metric reaches “tipping point”
“Average” does not acknowledge tails of distributions
Attribute of
a population:
pen/barn
site/flow
System
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

• Brain: Does it make sense vs analysis paralysis; SPC concepts
• PCR
Inductive
Conclusion
What does it mean?

• Brain: Does it make sense vs analysis paralysis; SPC concepts
• PCR
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)

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

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

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