Transcript Next steps - Centre for Public Health Research

The Burden of Disease, Injury
and Risk Factors
New Zealand 2006 - 2016
MANATÜ HAUORA
Overview

Context

NZBDS: objectives and methods

NZBDS: key results (provisional)

Next steps
CONTEXT
Outline

Background to burden of disease studies (BDS)

The DALY metric

Comparative risk assessment (CRA)
What is a BDS?

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
What can you learn from a BDS?

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
History of BDS

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
The DALY (disability-adjusted lifeyear) metric

Time based health gap measure

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
YLL

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)
YLD: incidence vs prevalence perspective

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
DISMOD epi software
State 1
(eg not diseased)
m
Dead
i
r
m
{
State 2
(eg diseased)
(duration)

Disability weights

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)
Comorbidity adjustment (YLDs)

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
The art of YLD measurement

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
CRA: counterfactual modelling approach
Risk factor exposure
Risk factor – disease relationship
• Current distribution
(2006)
• Counterfactual
distribution (TMRED)
(Hazard ratio or relative risk)
Potential impact
fraction
Total disease burden
Attributable burden in 2006
CRA: PIF model
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
n
PIF 
P
i 1
i
n
RRi   Pi  RRi
i 1
n
P
i 1
i
RRi
CRA: TMRED
Distributional transitions towards the theoretical minimum - a
counterfactual or alternative exposure scenario
CRA: Hazard ratio
Continuous associations between nutritional risks and disease
NZBDS: Objectives
and Methods
Provenance of the NZBDS 2006

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)
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Link to BODE
Specific objectives of NZBDS

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
NZBDS methodology: BDS

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)
NZBDS methodology: CRA

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)
KEY RESULTS
(PROVISIONAL)
NEXT STEPS
Outline

Strengths and weaknesses of study

Policy and research applications

Next steps
Strengths of NZBDS

First to include non-hospitalised injury

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)

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
Weaknesses of NZBDS

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)

Simulation plus sensitivity analysis may not fully capture ‘model uncertainty’ (but
our method conservative)

Projections based on overly simplistic assumptions (but short-term only)
Potential policy applications for ACC and MOH
– what DALYs tell us

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
What DALYs don’t tell us

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
Next steps

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
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Extend to analysis by NZDep

Extend to time series
Institutionalising the NZBDS:
from 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
Acknowledgements

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
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NZBDS Steering Group (project advisors)

GBDS 2005 Steering Group (Chris Murray, Alan Lopez, Theo Vos, Majid Ezzati, Colin
Mathers, Gretchen Stephens)