New Zealand 2006 - 2016
MANATÜ HAUORA

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 Standardised framework for measuring health loss
(‘nonfinancial health accounts’)
 Health loss estimated for a comprehensive set of disease, injury and risk factor causes
 Fatal and nonfatal health losses integrated via the disabilityadjusted life-year (DALY) metric
 Comprehensive and internally consistent

 Estimates and projections of level and distribution of unmet need for health services (health need analysis)
 Estimates of effectiveness for cost effectiveness analysis
(priority setting)
 Epidemiological estimates and projections for other studies
 Identification of gaps in health information system

 Methodology developed early 1990s by Chris Murray (World
Bank)
 First Global Burden of Disease Study (GBDS 1990) published 1996, second in 2004 (GBDS 2000), third currently in progress (GBDS 2005)
 More than 50 national BDS completed or in progress, including two Australian BDS, most recently 2003 (published
2007)
 Previous NZBDS (1996 data, published 2001) – relied heavily on ABDS

 Time based health gap measure
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Gap between population’s current state of health and that of an idealised population in which everyone lives long lives free from disability
 Integrates fatal with nonfatal health outcomes - combines years of life lost to untimely death (YLL) with equivalent years of healthy life lost because of time lived in health states less than full health (YLD)
 Preference for nonfatal health states expressed on a 0
– 1 scale where 0 = full health and 1 = dead,
 So YLD is commensurate with YLL and the two can be summed:
DALY = YLL + YLD
 1 DALY = 1 year of healthy life lost
 DALY is a standardised, inverted QALY

 Number of deaths from each cause in each subpopulation in reference year
 Multiplied by weight which varies with age but nothing else
 Variation with age reflects preference to live long life
 Weights are remaining life expectancies at each age taken from a reference lifetable
 Reference lifetable constructed by taking lowest mortality rate at each age already achieved by any national population
 Formula: YLL = Σ (d x w) where d = count of deaths from specified cause at specified age w = life expectancy remaining at that age (from reference lifetable)

 Incidence perspective captures only the nonfatal burden resulting from incident cases in reference year (cohort approach)
YLD = I x D x DW where I = incident cases of condition in cell, D = duration (survival before recovery or death), DW = disability weight (preference for health state)
 Prevalence perspective captures the nonfatal burden arising from all prevalent cases alive in reference year (irrespective of when they were incident) (cross sectional approach)
YLD = P x DW where P = prevalent cases in cell
 Provided duration is short, both yield similar YLD values ( since P = I x D if D is short)
 Incidence perspective better suited to CEA, prevalence perspective better suited to health need analysis
 Condition may involve multiple health states - YLD calculated for each separately, then summed

State 1
(eg not diseased) i
{
State 2
(eg diseased)
(duration) r m
 m
Dead

 Preference for being in health state – ‘disability severity’ metric
 Measurement strategies: standard gamble, person trade-off (PTO), time trade-off (TTO) 0 – 1 scale (full health =0, dead = 1)
 Stable over duration of health state (separate DW for each health state)
 Current values derived from GBDS 2004 (mainly based on Dutch focus group study, using PTO) – may vary by age and sex but nothing else
 Will substitute values from current GBDS when available (mainly based on internet survey of 50 000 people, plus 6 country surveys)

 People often experience more than one condition simultaneously
 Disability will typically be less than sum of disabilities from each condition
 Simple model is applied to adjust DW downward:
DWc = 1 Π(1- DWi)
 Note independence assumption – we have shown this is reasonable

 Condition model (outcome tree) – ‘sequelae’
 Prevalence (or incidence and duration) by 5 year age group, sex, ethnicity – may need to use DISMOD
 Distribution of cases across sequelae
 Severity distribution by sequela
 DWs for each severity level of each sequela (= health state)
 Consistent thresholds and case definitions for all nonfatal health states and their DWs

Risk factor exposure
• Current distribution
(2006)
• Counterfactual distribution (TMRED)
Risk factor – disease relationship
(Hazard ratio or relative risk)
Potential impact fraction
Total disease burden
Attributable burden in 2006

Potential impact fraction = proportional reduction in burden that would occur if exposure to a risk factor were reduced to the theoretical minimum risk exposure distribution
Attributable burden = total burden of linked condition x PIF
PIF
 i
 n

1
P RR i i
 i
 n

1 i
 n

1
P RR i i
P i

RR i

Distributional transitions towards the theoretical minimum - a counterfactual or alternative exposure scenario

Continuous associations between nutritional risks and disease

 MRG report 2010: more needs-driven, better priority setting
 Approved by ELT and Minister of Health mid 2010
 Work commenced September 2010
 Two year time frame
 Small core team (with 1 person seconded from ACC)
 Project Advisory Group
 Clinical advisors (including advisors from ACC)
 Link to BODE

 Generate an internally consistent set of epidemiological estimates for a comprehensive set of ~220 conditions
 Using this database, estimate the burden of disease and injury (in DALYs) in 2006, for Maori and non-Maori
 Project the burden (at condition group level) to 2011 and
2016
 Estimate the burden attributable to major biological and behavioural risk factors in 2006 and project to 2016

 Condition selection and classification: 16 Level I categories including Injury, 217 Level II conditions, ~600 sequelae (health states)
 Reference year is 2006 (in principle)
 YLL: redistribution of implausible and inadequately specified causes of death (5 different redistribution algorithms)
 YLD: data from NZ Health Tracker where possible, other data sources where necessary
(disease registers, unlinked administrative data, health survey & related surveys, research studies)
 Prevalence approach (I x D used where D < 1 year)
 DISMOD for incidence to prevalence modelling and consistency checking
 DWs from GBDS 2004 provisionally, adjusted for independent comorbidity
 Uncertainty quantified by simulation, sensitivity analysis added where necessary
 Projections (trend extrapolation for YLL, fixed relational model for YLD based on compression assumption)

 Risk factor selection and classification: metabolic and physiological (SBP, TBC, FPG, BMI,
25(OH)D, osteoporosis); dietary (vegetable, fruit, sodium, saturated fat); behavioural (physical activity, tobacco use, alcohol use, illicit drug use, unsafe sex); adverse health care events; diseases as risk factors (diabetes, depression etc
); injury ‘E’ codes as risk factors (eg transport)
 Counterfactual modelling (continuous or discrete risk factor distributions); direct estimation; mixed model
 Exposure distributions from Adult Nutrition Survey 2008/09 or other data sources (depending on the risk factor)
 TMREDs and HRs from GBDS or other sources (eg APCSC)
 Standard formula for joint risk factor distributions: PIFc = 1 -
Π(1- PIFi)
 Uncertainty quantification by simulation, plus sensitivty analysis where required
 Projections mainly demographic (trend analysis for tobacco, TBC, BMI)

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 First to include non-hospitalised injury
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First to report injury burden from both a ‘cause of injury’ (E code) and a ‘nature of injury’ (N code) perspective
 First to include sleep disorders
 First to include chronic pain syndromes
 First to include stilbirths (albeit intrapartum only)
 First to include adverse health care events (albeit in-hospital only)
 First to make systematic use of data linkage to estimate epi parameters (mainly prevalence)
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Improved analysis of infant conditions and birth defects, attributing health losses to ‘correct’ age groups
 Transparent assumptions underlying methods for GC redistribution, comorbidity adjustment, uncertainty quantification and burden projection

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Reliance on ‘medical model’ –fails to capture wider welfare impacts
 Does not capture clinical workload impact or health sector costs
 Modelling sometimes heavily assumption driven
 Not all conditions (or health states) and only selected risk factors included
 Not all epi data of high quality (eg 1.6% of injury deaths were ‘unknown intent’)
 Not all DWs as robust as we would like (new GBDS weights may be better)
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Simulation plus sensitivity analysis may not fully capture ‘model uncertainty’ (but our method conservative)
 Projections based on overly simplistic assumptions (but short-term only)

 Understand relative importance of injury / disease as a cause of health loss
 Guide needs-based investment decisions for injury prevention (E lens) and trauma care (N lens) – both wrt services and research
 And similarly for different diseases – CRA (prevention), BDS (treatment)
 Understand equity issues in relation to injury / disease outcomes – and design policy accordingly
 Monitor progress over time – and take corrective action accordingly

 DALYs do not correlate strongly with clinical workload – not very useful for workforce planning
 DALYs do not correlate strongly with costs to the health care system – not very useful for short-term budgeting
 Burden of disease & injury studies are only one tool for analysing need (or demand) as an input into evidenceinformed policy – not a panacea

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Complete analytic phase, including substitution of ‘new’ for ‘old’ DWs
 Implement report writing / KT phase
 Extend to regional / district level
 Extend to other ethnic groups
 Extend to analysis by NZDep
 Extend to time series

rom initial study to ongoing information system
 Regular updating (and progressive improvement - including comprehensiveness of risk factors, conditions and sequelae, extension to ethnic minorities, deprivation quintiles and DHBs, time series analysis)
 Substitution of Tracker indicators for other data sources to the fullest extent possible (necessary to make updating cost effective)
 Stabilisation of data sources, condition selection and classification, thresholds, case definitions, condition models, modelling and statistical methods, outputs
 Regular production of outputs (including updated epi tables and burden analyses) and application to surveillance, needs assessment, monitoring of unmet need / scope for health gain, monitoring of inequalities, inputting to CEA
(priority setting - BODE) and resource allocation more genearally

 MOH: Martin Tobias, Maria Turley, Terry Quirke, Ann Sears, Nadia Bartholomew, Michelle
Liu, Li-Chia Yeh, Craig Wright, Anna Davies, Mishra Suryaprakash, Jane Wang, Jo Wall,
Robert Templeton, Stephen Salzano, Kylie Mason, Deepa Weerasekera, Lisa Lankshear
 ACC: Zhi-Ling Zhang, John Wren, Zeeman Van Der Merwe, Charlotte Kieft
 Collaborators: Don Schopflocher (University of Alberta), Barbara Hjalsted (NIPH,
Denmark), Tony Blakely (BODE)
 More than 30 clinical / epidemiological advisors
 NZBDS Steering Group (project advisors)
 GBDS 2005 Steering Group (Chris Murray, Alan Lopez, Theo Vos, Majid Ezzati, Colin
Mathers, Gretchen Stephens)