Herd health investigation 2

1
Herd health investigation 2

• Mark Stevenson
• EpiCentre, IVABS, Massey University, Palmerston
  North
• M.Stevenson_at_massey.ac.nz

2
Herd health

• What this series of lectures will cover
• Lecture 1
• multifactorial nature of disease
• investigating problems and implementing
  interventions
• Lectures 2 and 3
• causation
• measures of health
• Lecure 4
• case studies
• tools for herd health investigations

3
The multifactorial nature of disease
Host
Agent
Environment
4
Investigating problems

• What is the problem?
• Is there a true excess of disease?
• Establish a case definition
• Enhance surveillance
• Describe problem in terms of animal, place and
  time
• Generate and test hypotheses
• Implement interventions

5
Roadmap

• Causation
• Measures of health

6
Causation

• Cause
• something that brings about a result especially a
  person or thing that is the agent of bringing
  something about (Merriam-Webster Dictionary)
• an event, condition, or characteristic without
  which the disease would not have occurred
  (Rothman)

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Causation

• Cause
• Must precede the effect
• Can be either host, agent or environmental
  factors (e.g. characteristics, conditions,
  actions of individuals, events, natural, social
  or economic phenomena)
• Can be either positive (presence of a causative
  exposure) or negative (lack of a preventive
  exposure)

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Causation

• Cause
• the component-cause model is based on the
  concepts of two types of cause necessary and
  sufficient

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Causation

• Necessary cause
• the factor must be present for disease to occur
• for example, foodborne disease after eating
  chicken salad has been shown to be due to
  Salmonella spp.
• Salmonella spp. is a necessary cause of diarrhoea

10
Causation

• Sufficient causes
• not usually a single factor, often several
• a set of causes without any one of which the
  disease would not have occurred (the whole pie)
• a set of sufficient causes must include a
  necessary cause

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Causation
This illustration shows a disease that has 3 sets
of sufficient causes. A is a necessary cause
since it appears as a member of each sufficient
cause. B, C, and F are not necessary causes
since they fail to appear in all 3 sets of
sufficient causes.
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Causation

• Sufficient cause(s) the whole pie
• Necessary cause the most important piece of the
  pie

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Causation

• Causes operate in different ways
• predispose age, sex, previous illness
• enable low income, poor nutrition, bad housing,
  inadequate medical care getting to the edge
• precipitate exposure to a specific disease agent
  tipping you over
• reinforce repeated exposure (e.g. repeated hard
  work) may aggrevate an established disease or
  state
• interact the effect of two or more causes acting
  together is often greater than would be expected
  on the basis of summing the individual effects
  (e.g. smoking and exposure to asbestos)

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Causation

• Causal inference is the term used for the process
  of working out whether observed associations are
  likely to be causal

15
Age adjusted mortality rates (deaths per 100,000)
as a function of particulate air matter
concentration for 100 capital cities throughout
the world.
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Causation

• Does air pollution cause high rates of mortality?
• maybe
• more likely that air pollution is a marker for
  other variables that are more closely associated
  with mortality (e.g. poor health care
  infrastructure)
• would be more correct to say that air pollution
  is associated with high rates of mortality

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Causation

• A systematic process required to work out causal
  mechanisms behind observed associations
• US Surgeon General (1964) used this approach to
  establish that cigarette smoking caused lung
  cancer
• This approach further elaborated by Bradford Hill
  (1965) in a set of guidelines for causation

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Causation

• Hills criteria for causation
• Strength of association
• Consistency
• Specificity
• Temporality
• Dose-response relationship
• Plausibility and coherence
• Experimental evidence
• Analogy

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Hills criteria (1)

• Strength of association
• strong associations are more likely to be causal
• indicated by risk ratio or rate ratio of greater
  than 2.0
• relative risk of lung cancer in smokers vs
  non-smokers 9
• relative risk of CHD in smokers vs non-smokers
  2
• cannot infer that weak association is not causal

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Hills criteria (2)

• Consistency
• has the cause and effect relationship been
  identified by a number of different researchers?
• smoking has been associated with lung cancer in
  at least 29 retrospective and 7 prospective
  studies
• sometimes there are good reasons why study
  results differ, for example, one study may have
  looked at low level exposures while another
  looked at high level exposures

21
Relative risks (and their 95 confidence
interval) from six trials comparing the effect of
CIDR treatment with untreated controls on
submission rate.
Meta-analysis
22
Relative risks (and their 95 confidence
interval) from 12 trials comparing the effect of
post insemination CIDR treatment with untreated
controls on conception rate.
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Hills criteria (3)

• Specificity
• a single exposure should cause a single disease
• this is a hold-over from the concepts of
  causation that were developed for infectious
  diseases
• many exceptions
• smoking is associated with lung cancer as well as
  many other diseases
• when present, specificity does provide evidence
  of causality, but its absence does not preclude
  causation

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Hills criteria (4)

• Temporality
• cause must precede effect
• if B comes after C, then B did not cause C
• can be difficult to establish
• long induction periods
• long latent (sub-clinical) phase

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Frequency of seat belt use and injury occurrence
in the United Kingdom 1982 1983.
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Hills criteria (5)

• Dose-response relationship
• as the level of exposure is increased, the rate
  of disease also increases
• be aware that there may be also non-linear effects

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Age adjusted death rates for lung cancer as a
function of approximate number of cigarettes
smoked per day.
28
Annual mortality (per 1000 men) from ischaemic
heart disease.
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Hills criteria (6)

• Plausibility and coherence
• does a causal interpretation fit with known facts
  of natural history and biology of disease,
  including distribution in time and space and
  laboratory experiments?
• that is, does the association make biological
  sense?
• more willing to accept the case for a
  relationship that is consistent with current
  knowledge/belief
• not objective
• readier to accept arguments similar to others
  that we accept

30
John Snow (1813 - 1858)

• Anaesthetist
• developed the first vapouriser
• Epidemiologist
• believed that drinking water was responsible for
  the spread cholera this theory was at odds with
  conventional wisdom about the disease
• convincing evidence for waterborne spread
  provided by mapping cholera cases in Golden
  Square in the 1850s
• Identified high numbers of cholera cases around a
  communal water pump (supplied by one particular
  water company)

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John Snow (1813 - 1858)
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John Snow (1813 - 1858)

• What does an epidemiologist do when visiting
  London?

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Hills criteria (7)

• Experimental evidence
• investigator-initiated interventions that modify
  exposure through prevention, treatment, or
  removal should result in less disease
• study designs, in order of usefulness
• randomised, controlled trials
• cohort studies some opportunity to minimise
  bias
• case-control studies subject to bias
• cross sectional studies not useful because they
  provide no direct evidence of the time sequence
  of events

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Hills criteria (8)

• Analogy
• has a similar relationship been observed with
  another exposure and/ or disease?
• BSE and scrapie/TME

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Hills criteria

• Judging the evidence
• none of my viewpoints can bring indisputable
  evidence for or against the cause and effect
  hypothesis and none can be regarded as sine qua
  non (Hill 1965)
• causal inference less certain than logical
  deductions
• no set of criteria replaces judgement in causal
  inference
• sine qua non an essential condition or element

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Causation

• Scientific knowledge
• always incomplete, whether it is observational or
  experimental
• liable to be upset or modified by advancing
  knowledge
• Dont sit around and wait for complete scientific
  knowledge before making (what might be very
  important) decisions!
• e.g. John Wilesmith, epidemiologist for the UK
  state veterinary service in January 1988

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Roadmap

• Causation
• Measures of health

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Measures of health

• One of the fundamental tasks in epidemiological
  research is to quantify the occurrence of disease
• Why?
• compare level of disease with other populations
• assess the need for interventions
• monitor responses to control efforts

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Indonesian feedlot.
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Measures of health

• Cumulative incidence of deaths and salvages at
  XYZ feedlot, 10 November 2002 to 6 April 2003.

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Measures of health

• To compare levels of disease among groups of
  individuals, time frames or locations we need to
  consider counts of cases in context of the size
  of the population from which those cases arose
• I had 10 calves die of respiratory disease last
  week
• If there are 20 calves in the group Thats
  terrible!
• If there are 40,000 calves in the group
  Congratulations!

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Measures of health

• The term morbidity is used to refer to the extent
  of disease or disease frequency within a
  population
• Two measures of morbidity are
• prevalence
• incidence
• Need to use these terms correctly!

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Measures of health

• Prevalence
• the number of individuals in a population who are
  in the diseased state at a specified period of
  time
• prevalence is a proportion obtained by dividing
  the count of existing (prevalent) cases by the
  population size
• can be interpreted as the probability of an
  individual from a population having a disease at
  a given point in time

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Measures of health

• Incidence
• measures how frequently susceptible individuals
  become disease cases as they are observed over
  time
• an incident case occurs when an individual
  changes from being susceptible to being diseased
• the count of incident cases is the number of such
  events that occur in a defined population during
  a specified time period

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Measures of health

• There are two ways to express incidence
• incidence risk (? cumulative incidence)
• incidence rate (? incidence density)

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Measures of health

• Incidence risk
• the proportion of initially susceptible
  individuals in a population who become cases
  during a defined time period
• also sometimes called cumulative incidence
• example for the period 1986 - 1997, the
  incidence risk of BSE in Great Britain was 1.10
  cases per 100 cattle

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Measures of health

• Incidence rate
• the number of new cases of disease that occur per
  unit of individual time at risk, over a defined
  period
• also called incidence density
• example for the 2002 milking season the
  incidence risk of udder disorders was was 17
  cases per 100 cow-years at risk

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Measures of health

• Incidence rate
• accounts for individuals that enter and leave the
  population throughout the period of study ('open'
  populations)
• also accounts for multiple disease events in the
  same individual
• lameness
• mastitis
• what is the incidence rate of boredom in
  epidemiology lectures?

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Measures of health
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Individuals
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Minutes at risk
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Measures of health

• Incidence rate of boredom in epidemiology
  lectures
• numerator 7 cases
• denominator (25 40 30 20 35 40 30
  55 20) 295 minutes
• time period todays lecture
• the incidence rate of boredom for todays
  lecture 7 cases per 295 minutes at risk (1.30
  cases per 55 lecture-minutes at risk)
• how does this compare to surgery lectures?

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Other measures of health

• Secondary attack rates
• used to describe infectiousness
• the number of cases at the end of the study
  period less the number of initial (primary) cases
  divided by the size of the population that were
  initially at risk
• the assumption is that there is spread of an
  agent within an aggregation of individuals (e.g.
  a herd or a family) and that not all cases are a
  result of a common-source exposure

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Other measures of health

• Mortality rate
• the incidence of fatal cases of a disease in the
  population at risk of death from the disease
• the denominator includes both prevalent cases of
  the disease (that is, the individuals that
  haven't died yet) as well as individuals who are
  at risk of developing the disease
• Fatality rate
• incidence of death among individuals who develop
  the disease

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Other measures of health

• Proportional mortality
• the proportion of all deaths that are due to a
  particular cause for a specified population and
  time period

54
Proportional removal rates retrospective
culling study in four University-owned dairy
herds 1986 1992.
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Summary

• Causation
• Measures of health