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ESTIMATING THE LIFETIME COST OF
CHILDHOOD OBESITY: WHAT HAVE
WE DONE, WHAT REMAINS TO BE
DONE
Kevin Balanda, IPH IRL
WP4 Leader
JANPA FINAL CONFERENCE
24 NOVEMBER 2017
Ministry for Solidarity and Health
Paris
JANPA COSTING MODEL
JANPA WP4 was very ambitious: first lifetime costing study that
developed and applied a standard methodology in more than one
country (8 EU countries)
Aims:
•Describe the lifetime cost of childhood obesity
•Assess the effect of reducing mean childhood BMI by 1% and 5%
Conducted within model principles; eg
•Societal economic perspective
•Transparency
•Maximising the use of resources
•Building capacity
HUMAN IMPACTS AND FINANCIAL COSTS
HUMAN IMPACTS FINANCIAL COSTS
ADULT
OBESITY/OVERWEIGHT
Prevalence Lifetime Income Losses
MORBIDITY Incidence
Prevalence
Years Lost due to
Disability (YLD)
Quality Adjusted Life
Years (QALY)
Direct healthcare costs
Productivity losses due
to absenteeism
MORTALITY Premature death
Years of Life Lost
(YLL)
Productivity losses due
to premature death
CONCEPTUAL FRAMEWORK
OBESE CHILD
Increased
morbidity in
adulthood
Increased morbidity
in childhood
Obese adult
HEALTH CARE
COSTS
Heightened
mortality
LIFETIME
INCOME
LOSSES
PRODUCTIVITY
LOSSES
(ABSENTEEISM)
PRODUCTIVITY
LOSSES
(PREMATURE DEATH)
Adaptation of Fernandes (2010)
5This presentation is part of the Joint Action JANPA (Grant agreement n°677063)
which has received funding from the European Union’s Health Programme (2014-2020)
Obesity-related treatment
& death
Obesity-related disease
RR
Deaths from other causes
Productivity loss:
•Premature death
•Absenteeism
Lifetime income loss
COHORT SIMULATIONS
USING COHORT SIMULATIONS
Can be used to desribe either:
1. Experiences of a population alive in some future year
2. Lifetime experiences of a population as they age
These
• Are conducted differently (births, immigration, emigration)
• Require different data (forecasts of population, disease
and mortality)
• Are reported differently (base population)
Second type of cohort simulations generate lifetime costs
EXCESS AND EFFECT METRICS
Excess costs attributable to childhood obesity/overweight
Cost (Obese/Overweight as child) – Cost (Healthy weight as child)
Effect of reductions in mean childhood BMI
Total cost (current BMI) – Total cost (reduced BMI)
SOFTWARE IMPLEMENTATION
WP4 Lead Team and UKHF modified UKHF’s existing software to
implement the JANPA costing model
Existing software:
• Mostly used for first type of cohort simulation
• Adult chronic disease model
• Direct healthcare costs
Substantial modifications were required
WORKFLOW
1. Data
collation
2. Pre-
simulation
data
processing
3.
Simulation
modelling
4. Post-
simulation
review
5.
Reporting
DATA REQUIREMENTS
Population Childhood population size
BMI Historical BMI distribution (all ages)
Disease parameters • Annual incidence rates
• Annual prevalence rates
• One-year survival probabilities
• Annual mortality rates
Direct healthcare costs Annual per case direct healthcare costs
Lifetime income losses
Productivity losses due to premature
mortality
Annual average income
Productivity losses due to absenteeism • Average number of days absent
• Social welfare payments
Other Life expectancies at birth, Minimum legal
working age, Retirement age
SOME DATA ISSUES (DISEASE)
Mismatch between disease definitions identified in evidence
reviews and those used in collated data
Mixture of “Bottom-up” and “Top-down” approaches used to
calculate disease parameters (prevalence, incidence, survival,
mortality). Approach varies with the country and disease
Extensive use of global, international & regional proxy data
Incidence and survival data often estimated from other disease
parameters (e.g. CHD, stroke, hypertension)
SOME DATA ISSUES (COSTS)
Direct healthcare cost areas (hospital, primary care and
pharmaceuticals) often not complete
Mixture of “Bottom-up” and “Top-down” methods used to calculate
annual per case costs. Approach varies with the country, disease and
healthcare cost area.
Discounting (at annual discount rate of 5% pa) is used on human
impacts (health outcomes) as well as financial costs
Extensive use of global, international & regional proxy data
CONCLUSIONS - REPUBLIC OF IRELAND (1)
• Total financial costs (€4,518.1M) account for 1.6% of GDP in 2015
• Lifetime financial cost is €16,036 per person
• Direct healthcare costs (€944.7M) account for 4.8% of public
health expenditure in 2015.
• Premature deaths (55,056) account for 1 in 10 of all premature
deaths
• Societal costs are larger than direct healthcare costs
• Premature death is a larger cause of productivity loss than
absenteeism (€2,795.4M vs €521.9M)
CONCLUSIONS - REPUBLIC OF IRELAND (2)
Gender differences:
• Male productivity losses due to prematue mortality and
lifetime income losses are higher
• Female direct healthcare costs and productivity losses due
to absenteeism are higher
Northern Ireland comparison highlights importance of context:
• Direct health care costs are relatively higher in Northern
Ireland
• Indirect (societal) costs are relatively higher in Republic of
Ireland
Large savings (€1,127M) with modest changes in childhood BM
BIGGEST DATA GAPS
Recommend greater co-ordination of information systems
across the EU:
• Obesity surveillance (particularly early years,
adolescence and later adult years)
• Surveillance of obesity-related diseases (particularly
incidence and survival)
• Healthcare costs (particularly primary care and
pharmaceutical costs)
• Approach to data use
BIGGEST RESEARCH GAPS
• Psychosocial impacts of childhood obesity and their
implications for human capital and the economy
• Sensitivity audit (uncertainty intervals and validation)
• Multi-morbidity
• Independent scientific review of JANPA costing model
and its development
• Longitudinal studies with long term follow-up
CONCLUSIONS
Very ambitious project. It is the first lifetime costing study of
childhood obesity/overweight that developed and applied
standard methodology to multiple countries (8 EU countries).
JANPAWP4 encountered unforeseen difficulties but established
that reliable estimates of lifetime cost of childhood
obesity/overweight could be obtained in more than one country
Estimates highlight the large cost and the large savings that could
follow from a modest change In childhood BMI.
If we deal with the unforeseen difficulties, the other valuable data
that has been collated can produce reliable and meaningful
estimates in the remaining countries
• Evidence paper and study protocols
• Local materials survey
• Data sources survey
• Data proposal template
• Data request form
• Model input form
• Model output tables template
JANPA COSTING MODEL TOOLBOX
PARTCIPATING COUNTRIES
ISAG
Thank you
This presentation is part of the Joint Action JANPA (Grant agreement n°677063) which has received funding from the European Union’s Health Programme (2014-2020).
The content of this presentation represents the views of the author only and is his/her sole responsibility; it cannot be considered to reflect the views of the European
Commission and/or the Consumers, Health, Agriculture and Food Executive Agency or any other body of the European Union. The European Commission and the Agency
do not accept any responsibility for use that may be made of the information it contains.
kevin.balanda@publichealth.ie
WP4 Leader

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ESTIMATING THE LIFETIME COST OF CHILDHOOD OBESITY: WHAT HAVE WE DONE, WHAT REMAINS TO BE DONE

  • 1. ESTIMATING THE LIFETIME COST OF CHILDHOOD OBESITY: WHAT HAVE WE DONE, WHAT REMAINS TO BE DONE Kevin Balanda, IPH IRL WP4 Leader JANPA FINAL CONFERENCE 24 NOVEMBER 2017 Ministry for Solidarity and Health Paris
  • 2. JANPA COSTING MODEL JANPA WP4 was very ambitious: first lifetime costing study that developed and applied a standard methodology in more than one country (8 EU countries) Aims: •Describe the lifetime cost of childhood obesity •Assess the effect of reducing mean childhood BMI by 1% and 5% Conducted within model principles; eg •Societal economic perspective •Transparency •Maximising the use of resources •Building capacity
  • 3. HUMAN IMPACTS AND FINANCIAL COSTS HUMAN IMPACTS FINANCIAL COSTS ADULT OBESITY/OVERWEIGHT Prevalence Lifetime Income Losses MORBIDITY Incidence Prevalence Years Lost due to Disability (YLD) Quality Adjusted Life Years (QALY) Direct healthcare costs Productivity losses due to absenteeism MORTALITY Premature death Years of Life Lost (YLL) Productivity losses due to premature death
  • 4. CONCEPTUAL FRAMEWORK OBESE CHILD Increased morbidity in adulthood Increased morbidity in childhood Obese adult HEALTH CARE COSTS Heightened mortality LIFETIME INCOME LOSSES PRODUCTIVITY LOSSES (ABSENTEEISM) PRODUCTIVITY LOSSES (PREMATURE DEATH) Adaptation of Fernandes (2010)
  • 5. 5This presentation is part of the Joint Action JANPA (Grant agreement n°677063) which has received funding from the European Union’s Health Programme (2014-2020) Obesity-related treatment & death Obesity-related disease RR Deaths from other causes Productivity loss: •Premature death •Absenteeism Lifetime income loss COHORT SIMULATIONS
  • 6. USING COHORT SIMULATIONS Can be used to desribe either: 1. Experiences of a population alive in some future year 2. Lifetime experiences of a population as they age These • Are conducted differently (births, immigration, emigration) • Require different data (forecasts of population, disease and mortality) • Are reported differently (base population) Second type of cohort simulations generate lifetime costs
  • 7. EXCESS AND EFFECT METRICS Excess costs attributable to childhood obesity/overweight Cost (Obese/Overweight as child) – Cost (Healthy weight as child) Effect of reductions in mean childhood BMI Total cost (current BMI) – Total cost (reduced BMI)
  • 8. SOFTWARE IMPLEMENTATION WP4 Lead Team and UKHF modified UKHF’s existing software to implement the JANPA costing model Existing software: • Mostly used for first type of cohort simulation • Adult chronic disease model • Direct healthcare costs Substantial modifications were required
  • 10. DATA REQUIREMENTS Population Childhood population size BMI Historical BMI distribution (all ages) Disease parameters • Annual incidence rates • Annual prevalence rates • One-year survival probabilities • Annual mortality rates Direct healthcare costs Annual per case direct healthcare costs Lifetime income losses Productivity losses due to premature mortality Annual average income Productivity losses due to absenteeism • Average number of days absent • Social welfare payments Other Life expectancies at birth, Minimum legal working age, Retirement age
  • 11. SOME DATA ISSUES (DISEASE) Mismatch between disease definitions identified in evidence reviews and those used in collated data Mixture of “Bottom-up” and “Top-down” approaches used to calculate disease parameters (prevalence, incidence, survival, mortality). Approach varies with the country and disease Extensive use of global, international & regional proxy data Incidence and survival data often estimated from other disease parameters (e.g. CHD, stroke, hypertension)
  • 12. SOME DATA ISSUES (COSTS) Direct healthcare cost areas (hospital, primary care and pharmaceuticals) often not complete Mixture of “Bottom-up” and “Top-down” methods used to calculate annual per case costs. Approach varies with the country, disease and healthcare cost area. Discounting (at annual discount rate of 5% pa) is used on human impacts (health outcomes) as well as financial costs Extensive use of global, international & regional proxy data
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  • 16. CONCLUSIONS - REPUBLIC OF IRELAND (1) • Total financial costs (€4,518.1M) account for 1.6% of GDP in 2015 • Lifetime financial cost is €16,036 per person • Direct healthcare costs (€944.7M) account for 4.8% of public health expenditure in 2015. • Premature deaths (55,056) account for 1 in 10 of all premature deaths • Societal costs are larger than direct healthcare costs • Premature death is a larger cause of productivity loss than absenteeism (€2,795.4M vs €521.9M)
  • 17. CONCLUSIONS - REPUBLIC OF IRELAND (2) Gender differences: • Male productivity losses due to prematue mortality and lifetime income losses are higher • Female direct healthcare costs and productivity losses due to absenteeism are higher Northern Ireland comparison highlights importance of context: • Direct health care costs are relatively higher in Northern Ireland • Indirect (societal) costs are relatively higher in Republic of Ireland Large savings (€1,127M) with modest changes in childhood BM
  • 18. BIGGEST DATA GAPS Recommend greater co-ordination of information systems across the EU: • Obesity surveillance (particularly early years, adolescence and later adult years) • Surveillance of obesity-related diseases (particularly incidence and survival) • Healthcare costs (particularly primary care and pharmaceutical costs) • Approach to data use
  • 19. BIGGEST RESEARCH GAPS • Psychosocial impacts of childhood obesity and their implications for human capital and the economy • Sensitivity audit (uncertainty intervals and validation) • Multi-morbidity • Independent scientific review of JANPA costing model and its development • Longitudinal studies with long term follow-up
  • 20. CONCLUSIONS Very ambitious project. It is the first lifetime costing study of childhood obesity/overweight that developed and applied standard methodology to multiple countries (8 EU countries). JANPAWP4 encountered unforeseen difficulties but established that reliable estimates of lifetime cost of childhood obesity/overweight could be obtained in more than one country Estimates highlight the large cost and the large savings that could follow from a modest change In childhood BMI. If we deal with the unforeseen difficulties, the other valuable data that has been collated can produce reliable and meaningful estimates in the remaining countries
  • 21. • Evidence paper and study protocols • Local materials survey • Data sources survey • Data proposal template • Data request form • Model input form • Model output tables template JANPA COSTING MODEL TOOLBOX
  • 23. ISAG
  • 24. Thank you This presentation is part of the Joint Action JANPA (Grant agreement n°677063) which has received funding from the European Union’s Health Programme (2014-2020). The content of this presentation represents the views of the author only and is his/her sole responsibility; it cannot be considered to reflect the views of the European Commission and/or the Consumers, Health, Agriculture and Food Executive Agency or any other body of the European Union. The European Commission and the Agency do not accept any responsibility for use that may be made of the information it contains. kevin.balanda@publichealth.ie WP4 Leader

Notes de l'éditeur

  1. SAY GENERAL CONCLUSIONS: GENERALISABILITY TO OTHER COUNTRIES CONCEPTUAL FRAMEWORK FINE ISSUES WITH DATA COLLECT MODELLING TOOLBOX