economic analysis for different levels of decision making
DESCRIPTION
I was invited to give a keynote presentation for the German languaged Epidemiology meeting which was held last week in Zurich, Switzerland. My presentation gave an overview of the decision problem in animal health and gives some examples of economic analyses that have been made at different levels of decision making. Specific items were: dry cow therapy, Q fever and BSETRANSCRIPT
Economic analysis for different levels of decision making
Henk Hogeveen
Who am I
Born on a dairy farm (1966)
Animal science at Wageningen University
●Epidemiology (simulation model of management regarding cystic ovaries)
●Economics (long term effects of herd health management programs)
PhD at Fac. Veterinary Medicine (AI to diagnose mastitis)
Professor in Animal health managementIn between Wageningen University and Faculty of Vet. Med. (since 2001)
@henkhogeveen
animal-health-management.blogspot.com
Outline
Decision making on animal health
●The decision problem
●The levels of decision making
Some examples of analyses
●Dry cow therapy
●Q fever outbreak
●Slaughterhouse measures to reduce the BSE load
Final words
Economic effects of animal disease
Output
MilkMeatEggsDraft power…….
After: McInerney, 1996
Human benefit (utility)
Input
LandLabourCapital
The field: Economic effects of animal disease
Output
MilkMeatEggsDraft power…….
Disease
1. Lower efficiency
2. Lower suitability for consumption
3. Lower human well-being
After: McInerney, 1996
Human benefit (utility)
Input
LandLabourCapital
1.
2. 3.
Most economic work
Types of animal diseases
Production diseases
●On-farm optimization
●Externalities
●E.g., mastitis, lameness, APP
Endemic contagious diseases
●On-farm control decision
● Interaction between farms
●E.g., BVD, Aujeszky’s disease
Notifiable contagious diseases
●Regional control decisions (eradication)
●Surveillance
●E.g., FMD, AI, rabies, BSE
The management problem
Consequences animal health
Epidemiological consequences
Veterinary knowledge of diseases
The management problem
Consequences animal welfare
Consequences human health
Consequences animal health
Epidemiological consequences
Knowledge about externalities
The management problem
Consequences animal welfare
Consequences human health
Costs of intervention
Consequences animal health
Epidemiological consequences
Decisons become increasingly complex
Decision maker
ObjectivesAvailable resources
Consequences animal welfare
Consequences human health
Costs of intervention
Consequences animal health
Epidemiological consequences
Outline
Decision making on animal health
●The decision problem
●The levels of decision making
Some examples of analyses
●Dry cow therapy
●Q fever outbreak
●Slaughterhouse measures to reduce the BSE load
Final words
Levels of decision making
Individual animals
● Treatment
● Culling
● Interaction
Groups of animals (herd/farm)
● Prevention
● Eradication
Sector
● Control
● Eradication
Region
● Control
● Eradication
Levels of decision making
Individual animals
● Treatment
● Culling
● Interaction
Groups of animals (herd/farm)
● Prevention
● Eradication
Sector
● Control
● Eradication
Region
● Control
● Eradication
Production diseases& Endemic contagious diseases
Type of disease
Contagious nofiable diseases
Levels of decision making
Individual animals
● Treatment
● Culling
● Interaction
Groups of animals (herd/farm)
● Prevention
● Eradication
Sector
● Control
● Eradication
Region
● Control
● Eradication
Farmer, supported by advisor
Farmer’s organisationProcessors
Government
Decision maker
Basic approach
Normative modelling
●Relate costs of interventionwith animal health andepidemiological consequences
●Cost-benefit analysis (alternative: cost effective or cost utility analysis)
●Assuming profit maximising behaviour of farmers
●Basis for on-farm decision support tools
Empirical modelling
●Use data to compare farms/animals/groups of animals with and without intervention
●Experiments or existing datasets (accountancy data)
Challenges Handle multiple objectives
Handle multiple objectives
●Internal (farmer)
●External (societal, chain)
●On-farm decision support models
●Capturing complexity
●Useful for farm-specific modelling
Outline
Decision making on animal health
●The decision problem
●The levels of decision making
Some examples of analyses
●Dry cow therapy
●Q fever outbreak
●Slaughterhouse measures to reduce the BSE load
Final words
Dry cow therapy
Individual cow decision
Two modes of action:
●Cure of existing (chronic) intramammary infections
●Prevention of new infections during dry period
Often herd decision (blanket dry cow therapy)
Debate on selective vs blanket dry cow therapy
Stochastic model (Huijps et al., 2007)
Cow as basic unit
Dynamic around dry period
Results summarized for whole herd
Accounting for differences between pathogens
Dutch circumstances
Selective dry cow treatment cheapest
Blanket Selective No
IMIdo (%) 15 (7.7, 23.1) 15 (7.7, 23.1) 15 (7.7, 23.1)
Treatment (%) 100 35 (23, 46) 0
IMI at calving 7.5 (3.1, 12.3) 12.3 (6.2, 20) 19.3 (12.3, 27.7)
Clinical mastitis (%) 1.8 (0, 4.6) 3.2 (0, 7.7) 5.1 (1.5, 10.8)
Treatment costs (€/cow) 10.1 (10.1, 10.1) 3.5 (2.3, 4.7) 0
Production losses (€/cow) 1.3 (0.5, 2.2) 2.1 (1.0, 3.4) 3.3 (2.0, 4.7)
Clinical mastitis (€/cow) 4.2 (0, 14.6) 8.1 (0, 22.9) 14.7 (2.0, 38.5)
Total costs (€/cow) 15.6 (10.6, 26.6) 13.7 (4.9, 29.4) 18.0 (4.1, 42.6)
New discussion onantibiotic resistance
Resistance of mastitis pathogens
●Self-interest
●No increase seen (Hogan, IDF-factsheet)
Antibiotic resistance in humans
●Externality
●Dairy cattle has very minor contribution (Oliver et al., 2011)
Decision of government
In the Netherlands (self) regulation
●Maximum amount of antibiotics to be used (< 50 %)
Optimizing: linear programming (Maas, 2014, MSc thesis)
Farm level
Cows with high SCC are treated
●Primiparous > 150.000 cells/ml
●Multiparous > 250.000 cells/ml
Other cows selective
Categorized at SCC level
Optimization to minimize total costs of treatment and mastitis around dry period
Based on: Maas, 2014, MSc thesis, in
preparation
We’re also interested in amount of AB
Constraining antibiotic use has economic effects
100%
95%90%85%80%75%70%65%60%55%50%45%40%35%30%25%20%15%10% €39
€41
€43
€45
€47
€49
€51
€53
Average farmLow BTSCC farmHigh BTSCC farm
Percentage allowed antibiotics (%)
Costs
per
lo w
SC
C c
ow
Outline
Decision making on animal health
●The decision problem
●The levels of decision making
Some examples of analyses
●Dry cow therapy
●Q fever outbreak
●Slaughterhouse measures to reduce the BSE load
Final words
Q fever outbreak
In 2005 Coxiella burnetii diagnosed in the Netherlands as cause of abortion problems on a dairy goat farm
In 2007 the first Q fever outbreak in humans was diagnosed
Since then thousands of people got infected, which reached a climax in 2009
Year and week of notification
Source: www.eurosurveillance.org
Roest et al., 2011
Government involved
Control measures
• Vaccination programme
• Culling of (pregnant) goats from infected farms
• Animal movement restrictions
• Breeding ban
• Bulk milk monitoring -> no good confirmation
• Extra hygiene programmes
Around 62,500 dairy goats were culled significant drop in milk production
Economic impact (Gonggrijp et al., 2014)
How large was the negative economic impact for affected farmers?
Were other actors of the industry also negatively affected by the control measures?
Were the relations of the actors and their behaviour in the industry still the same?
Objective:
Study the impact of Q fever control measures on the Dutch dairy goat industry with the use of a quantified value chain analysis
Value chain analysis
Mapping the value chain
Governance in the value chain
Upgrading in the value chain
Distribution of value in the value chain
Value chain analysis
Information on the structure, the trade flows and all the relations between the involved actors of a livestock sector
Often qualitative and descriptive
In this value chain analysis focus on quantification
Preliminary map of the value chain
Final map of the value chain
The distribution of goat milk and milk equivalents 2009
Goat farmers Milk collectors Prim. dairy processors
Second. dairy processors
(Feed) suppliers Meat processing Retail Total0
10,000,000
20,000,000
30,000,000
40,000,000
50,000,000
60,000,000
70,000,000
80,000,000
90,000,000
100,000,000
20092010
Eur
o (€
) x 1
0⁶
Gross margins of the Dutch dairy goat industry in 2009 - 2010
Gross margin results
Decrease of gross margins in 2010 of the total industry of -12% and -23% for farmers compared to 2009
Enormous difference in decrease between affected farmers (-53%) and non-affected farmers (-12%)
Primary dairy processors, meat processing and retail not negatively affected
Outline
Decision making on animal health
●The decision problem
●The levels of decision making
Some examples of analyses
●Dry cow therapy
●Q fever outbreak
●Slaughterhouse measures to reduce the BSE load
Final words
BSE
1986 first described 1996 -> link with Creutzveldt Jacobs Disease (vCJD) Since August 1989 measures against BSE in the
Netherlands●Since 1990 feed ban (no animal protein)●Since 2000 dead cattle older than 30 m tested●Since 2001 slaughtered cattle older than 30 m
tested●Disposal of BSE risk materials●Culling of cohort of detected animal
Incidence of BSE is decreasing
Are preventive measures cost-effective?(Benedictus et al., 2009)
Simulation modelling
●Static
●Stochastic
●Simulation
Monte carlo model
●1 iteration = 1 year
●Baseline: no intervention
●Alternative: one or more interventions
Model
3 types of BSE
●Clinically affected
●Test detectable
●Non detectable (3 for every detectable)
Per BSE type of BSE load (from different organs) of the food supply was calculated
Based on Infectious doses, risk of vCJD
Prevented case of vCJD -> life years saved (most likely 51)
Comparison: do nothing vs intervention
Costs
Removal of specific risk material (~60 kg): €/kg slaughtered weight
Transport of specific risk material
Post mortem testing: € 90 per head
Costs of cohort culling
Results - retrospective
Year 2002 2005
Number of BSE cases (total, at slaughter) 24, 12 3, 2
BSE load of the food supply Mean 5th – 95th Mean 5th – 95th.
Baseline scenario 34,857 30,213-39,602 5,502 3,592-7,620
SRM removal 2,330 2,020-2,648 368 240-509
Post-mortem testing (PMT) 7,455 4,846-10,306 939 198-2,091
PMT and cohort culling 7,059 4,505-9,865 939 197-2088
SRM removal and PMT 498 324-689 63 13-140
SRM removal and PMT and cohort culling 472 301-659 63 13-139
Food risk (life years lost) Mean 5th – 95tb Mean 5th – 95th pct.
Baseline scenario 16.98 8.66-26.70 2.69 1.25-4.61
SRM removal 1.14 0.58-1.79 0.18 0.08-0.31
Post-mortem testing (PMT) 3.63 1.67-6.27 0.46 0.08-1.11
PMT and cohort culling 3.44 1.56-5.94 0.46 0.08-1.11
SRM removal and PMT 0.24 0.11-0.42 0.03 0.005-0.07
SRM removal and PMT and cohort culling 0.23 0.10-0.40 0.03 0.005-0.07
Costs (mln €)
Year2002 2003 2004 2005
SRM removal19.22 18.27 19.29 19.82
Post-mortem testing38.16 29.56 26.57 21.12
Cohort culling6.97 4.80 3.41 2.43
Total costs64.34 52.64 49.27 43.37
Cost-effectiveness
Cost-effectiveness 2002-2005
Outline
Decision making on animal health
●The decision problem
●The levels of decision making
Some examples of analyses
●Dry cow therapy
●Q fever outbreak
●Slaughterhouse measures to reduce the BSE load
Final words
Take home message
Animal health management decisions are taken daily
Economics are useful/necessary to support decisions
A first step are “cost of disease” studies
●General interest
●Supporting stakeholders (negotiations)
●Start for “economics of intervention” studiesCost-effectivity, cost-utility and cost-benefit
Choose appropriate method for level of decision making
More importantly: choose appriate approach in model:animal vs farm vs sector vs society
Combine economic modelling knowledge with domain knowledge
Thank you for your attention